Vehicle and Automotive Engineering 4: Select Proceedings of the 4th VAE2022, Miskolc, Hungary (Lecture Notes in Mechanical Engineering) 3031152107, 9783031152108
113 28 177MB
English Pages [1046]
Preface
Acknowledgements
Contents
About the Editors
Autonomous Vehicles and Safety
An Approach to Implementation of Autoencoders in Intelligent Vehicles
1 Introduction
2 How to Implement the Autoencoders?
2.1 Structure of the Model
2.2 Pre-processing Phase
2.3 Post-processing
3 Conclusion
References
Case Study of a Computer-Controlled RC Car Based on RF Wireless Communication
1 Introduction
1.1 Stat of the Art of RF Wireless Communication Protocol
1.2 Transmission of the Information in the Wireless Communication System
2 Materials and Methods
2.1 Description of the Vehicles
2.2 Measurement Tools
3 Results and Discussion
3.1 Identifying the Used Carrier Frequencies
3.2 Monitoring the Used Carrier Frequencies
3.3 Analyzing the Captured Data
4 Conclusion
References
Nonlinear Model Predictive Control for Autonomous Quadrotor Trajectory Tracking
1 Introduction
2 Materials and Methods
2.1 Tello Quadrotor Dynamic Mathematical Model
2.2 Control Strategy for Nonlinear Dynamic Model
2.3 Open-Loop Simulation
2.4 Nonlinear Model Predictive Control
2.5 Design Nonlinear MPC for Tello Quadrotor System in Simulink
3 Results and Discussion
3.1 Open-Loop Simulation Results
3.2 Closed-Loop Nonlinear MPC Simulation Results
4 Conclusion
References
Effect of Non-conventional Seating Position on Driver Injuries in the Case of a Self-driving Car
1 Introduction
2 Seating Position Scenarios
3 Honda Accord 2008 EUNCAP Crash Test
4 Results of the Simulations
4.1 The Seat Rotated 30°/60°/90°
4.2 Seat Rotated 135°/180°
5 Conclusion
References
Implementation of a System for Signaling the Approach of Emergency Vehicles Within Other Vehicles
1 Introduction
2 Vehicle Communication Technologies
2.1 D2D (Device to Device)
2.2 IoT (Internet of Things)
2.3 VND (Vehicular Neighbour Discovery)
2.4 VANET
2.5 Connected Vehicles-V2X Communication
3 Practical Implementation
3.1 First Milestone
3.2 Second Milestone
3.3 Third Milestone/Complete System
4 Further Development Opportunities
5 Summary
References
Safe In and Out of the Car
1 Elements of Traffic
1.1 Is the Individual the Only One Responsible for Safe Traffic?
1.2 Vehicles as the Tools of Traffic
1.3 Weather Conditions
1.4 Quality of Roads and Infrastructure
2 3E System
3 Education for Safe Traffic
4 Campaign-Like Educational Programs
4.1 Safe Nursery School Program (Biztonságos Óvoda Program)
4.2 Mobile-Kids a Mercedes-Benz Initiative
4.3 Bicaj-Ricsaj Cycling Traffic Team Competition
5 Conclusion
References
Possibilities of Using of Online Vehicle Diagnostics in the Future
1 History of Analysis and Predictive/Preventive Maintenance Based on Online Data Collection (Flight Applications)
2 Real-Time Monitoring in China (Beijing, Shanghai) Road Traffic Application
3 Audi’s Pilot Project for Online Data Collecting from Vehicles
4 The Possibility of Online Analysis in Vehicle Manufacturing
5 Development Opportunities for Online Diagnostics in the Future
5.1 On-Site but yet not Online Vehicle Diagnostic with a Scan Tool (in Use Already)
5.2 Partially Online but not Real-Time Vehicle Diagnostic (in Use Already)
5.3 Online but no Real-Time Data Collection (Already in Use): Statistical Function in the Vehicle MMI (Men Machine Interface)
5.4 Real-Time Online Vehicle Diagnostics (Partially Used in the Meantime)
5.5 Fully Real-Time Online Vehicle Diagnostics (Possible Future):
6 Possible Future: Vehicle Repair and Diagnostic Without a Generic Scan Tool
7 Summary
References
Security and Safety Systems on Modern Vehicles
1 Introduction
2 Automotive Safety
2.1 Passive Safety Solutions
2.2 Active Safety Solutions
2.3 Safety Systems of the Future
3 Automotive Safety
3.1 Accident-Free Future
4 Summary
References
Design and Powertrains
Contact Ratio of Spiral Bevel Gears
1 Introduction
2 Calculation of Profile Contact Ratio
3 Calculation of the Overlap Ratio on Pitch Plane of Imaginary Crown Gear
4 Calculation of the Overlap Ratio Based on the Virtual Cylindrical Gear
5 Calculation of the Overlap Ratio According to AGMA Standard
6 A General Interpretation of the Contact Ratio
7 The Path of Contact and Contact Ratio
8 Examples
9 Summary
References
Stability Analysis and Optimization of Vehicle Active Motion Control System with Feedback Time Delay
1 Introduction
2 Modelling of Vehicle Lateral System
2.1 Equation of Handling Motions
2.2 Rotational Wheel Dynamics
2.3 Modelling of the Tyre Characteristics
3 Dynamic Properties of Vehicle Lateral Motion System
3.1 Vehicle Handling Analysis
3.2 Phase Plane Analysis for Steering Characteristics
3.3 Active Stability Controller
4 Stability Analysis of the Delayed System
4.1 Stability Charts in the Presence of Time Delay
4.2 Variation of the Stable Domains in Changing Conditions
5 Dynamics of the Delayed System in Time Domain
5.1 Time Histories of the Delayed System
5.2 Gain Optimization for the Fastest Settling
6 Conclusion
References
Cogging Torque Analysis of Toyota Prius 2004 IPMSM Motor with the Digital-Twin-Distiller
1 Introduction
2 Materials and Methods
2.1 Modelling with Digital-Twin-Distiller
2.2 Validation
2.3 Resolving the Conflicting Constraints
3 Torque Analysis
3.1 Range of Parameters and Model Creation
3.2 Cogging Torque Analysis
4 Conclusion
5 Data Availability Statement
References
Effect of the Radial Constrain for the Impact Energy-Absorbing Behaviour of the Closed-Cell Metal Foam
1 Introduction
2 Material and Experimental Methods
2.1 Experimental Methods and Technology
3 Interpretation of the Stress-Strain Diagram
4 Specimen Preparation
5 Results of the Tests
5.1 Compression Test Without Constrain
5.2 Compression Test with Radial-Constrain
6 Comparison of the Results
7 Conclusion
References
Investigation of Tilting Table with Parallel Kinematic
1 Introduction
2 Presentation of the Structure
3 Determination of the Degree of Freedom of the Structure by Analytical Calculation
3.1 Chebyshev – Grübler – Kutzbach Formula
4 Verification of the Degree of Freedom of the Structure with Parametric Software
5 Summary
References
Influence of Speed to Rolling Resistance Factor in Case of Autobus
1 Introduction
2 Elaborating an Experimental Plan to Determine the Rolling Resistance
2.1 Choosing the Right Stretch of Road for Measurements
2.2 Elaborating the Aspects of Measurement
2.3 Process of Experiment
2.4 Parameters of Autobus Used for the Experiments
3 Experiment
3.1 Environmental Conditions of the Measurement
3.2 Evaluation Process of Measurement
3.3 Measurement Results
4 Conclusion
References
New Adder and Distribution Gearbox Used in the Transmission of the Heavy Truck
1 Introduction
2 Critical Analysis of the Adder Box
3 Experimental Setup
4 Results
5 Conclusions and Discussions
References
Length of Contacting Generating Lines Interpreted in the Regular Rectangular Contact Zone of Helical Gears and Zone Dependence of Their Change
1 Characteristics of the Regular Rectangular Contact Zone by Cylindrical Helical Gears
2 The Original Interpretation of the Contact Ratio
3 Determining the Length of Contacting Generating Lines in Regular Rectangular Zone Components and the Complete Contact Zone
3.1 Determining the Phase Boundaries of Meshing
3.2 The Total Length of the Contacting Generating Lines Interpreted in the Subzones
3.3 The Constant Part of the Total Length of the Contacting Generating Lines
3.4 The Variable Part of the Total Length of the Contacting Generating Lines
3.5 The Total Length of the Contacting Generating Lines for the Entire Contact Zone
4 Conclusions, Results
References
Diagnostic and Prognostic Strategies for Monitoring of Diesel Engines’ Technical Conditions
1 Introduction
2 Fuzzy Expert Systems
3 Machines’ Faults Prognosis
4 Brief Description of Marine Diesel Engines’ Different Malfunctions
4.1 Some Operating Malfunctions and their Possible Causes
5 Diagnosing Parameters Selection
6 The Conception of a Newly Elaborated Diagnosis System for Marine Engines
7 Conclusions
References
Development of a Knowledge-Based System for Diagnosing of Diesel Engines
1 Introduction
2 Expert SYSTem’s Structure and Development Stages
3 Uncertain Information Analysis
3.1 Uncertainty Management Approaches
4 Software Tools Used for Expert Systems
4.1 C Language Integrated Production Systems (CLIPS)
5 Development of a Knowledge-Based System for Diagnosing of Marine Diesel Engines – Theoretical Background
6 Introduction of the Newly Developed Diagnosis System for Marine Diesel Engines
6.1 Structure of the new Diagnosis System
6.2 Case Study for the Operation of the New DIAGNOSIS System – Diagnosis of the Marine Engines’ Air System
7 Conclusions
References
Examination of Bolt Connection with Finite Element Method
1 Introduction
2 Calculation of Tightening Torque, the Problem of Tightening Sequence
3 Create the CAD Model of the Screw Connection
4 Development of a FEM Model for Bolt Connection, Simulations
5 Evaluation of the Simulation Results, Possibilities of Use
References
Calculation Methods and Measurement of the Heating of Small Plastic Gears
1 Introduction
2 The Calculation Methods of the VDI Guidelines
2.1 The VDI 2545 Guideline
2.2 The Modified VDI 2545 Method by Licharz
2.3 The Calculation Method of the VDI 2736 Guideline
3 Correlation Between the Methods in the VDI Guidelines
4 The Method of the JIS B 1759 Standard
5 Other Calculation Methods
6 Calculations Based on the VDI Methods
7 The Former Equipment for Measuring Heating Caused by Reactive Loads
8 Applications of the Previously Used Drive Units and the Results Achieved
9 Problems with the Previously Used Drive Units and Expectations for the New Equipment
10 The New Drive Units Made for the New Tests
11 Summary
References
External Tyre Loading Predictions from Inner Tyre Deformation Measurements
1 Introduction
2 Tyre-road Interaction
3 Finite Element Tyre Models
4 Load Predictions
5 Conclusions
References
Design Study of the Low-Cost Advance Rider Assistance System
1 Introduction
2 Literature Review
3 Research Methodology
3.1 Original Study
3.2 Component Testing
3.3 Improving of the Mechanical Design of the Sensory and the Display Unit
4 Results
4.1 Image Processing
4.2 Speed Measurement and the Proximity Warning
4.3 Improved Design of the Sensory Unit
4.4 Improved Design of the Display Unit
5 Conclusion
References
Load Testing of Alternating Current Hydraulic Drive
1 Introduction
2 Load Angle of Alternating Current Hydraulic Drives
3 Measurement of the Load Angle
4 Summary
References
A Review on HCNG/Diesel Tri Fuel Engine Performance
1 Introduction
2 Engine Combustion Performance Characteristics
2.1 In-Cylinder Pressure
2.2 Heat Release Rate
2.3 Brake Thermal Efficiency (BTE)
2.4 Brake Specific Fuel Consumption (BSFC)
2.5 Brake Power (BP)
2.6 Brake Torque (BT)
2.7 Summary of Engine Performance
3 Conclusions
References
Electric and Thermal
Investigations on the Effects of Capacitive Couplings in an Automotive Phase-Shifted Full-Bridge Power Supply Used in Electric Vehicles
1 Introduction
2 The Phase-Shifted Full-Bridge
3 Electromagnetic Compatibility
3.1 Conducted Emissions Measurement
4 EMC Issues of the PSFB
5 Simulation Results
5.1 Interwinding Capacitance
5.2 Switch-Node Capacitances
6 Conclusions
References
A Literature Review of a Dual-Purpose Solar Collector
1 Introduction
2 Overview of Different Design Approaches
2.1 A Standalone DPSC
2.2 Building- Integrated DPSC
2.3 New Trend
3 Mathematical Model of DPSC
4 Conclusion and Future Work
References
Overview of the Market of Electric Cars by Multilogistic Curves
1 Introduction
2 Approximation of Product Lifecycle Curves as Sigmoid Curves
3 The Market of Electric Cars
4 Conclusions
References
Electromobility: The Spreading of Electric Cars Versus Internal Combustion Engine Vehicles
1 Introduction
2 Materials and Methods
2.1 Background Data for the Modelling
2.2 Exponential Model to Forecast the Spreading of Electric Cars
2.3 Different Scenarios to Forecast the Spreading
3 Results
3.1 Realistic Spreading Scenario
3.2 Rapid Spreading Scenario
3.3 Slow Spreading Scenario
3.4 Comparison of the Scenarios
4 Conclusion
References
Investigation of the Effect of a Coolant Inlet Duct on the Thermal Performance of Car Radiators
1 Foreword
2 The Cooling System
3 The Investigated Radiator
4 Results and Discussion
References
Comparison of Thermal Insulation Performance of Different Materials Used for Aircrafts
1 Introduction
2 Materials and Methods
2.1 The Tested Materials
2.2 Thermal Conduction
2.3 Test Procedure
3 Results
4 Conclusions
References
ANN Modeling for Thermal Load Estimation in a Cabin Vehicle
1 Introduction
2 Methodology
2.1 The Model Selected
2.2 The Cooling Load Calculation
3 Artificial Neural Networks
3.1 Structure of ANNs
3.2 Neural Network Performance
4 Results and Discussion
5 Conclusions
References
A Critical Review of Multiple Impingement Jet Mechanisms for Flow Characteristics and Heat Transfer Augmentation
1 Introduction
1.1 Steady Twin Impingement Jets
1.2 Pulsating Twin Jets Impingement
2 Discussion
3 Conclusion
4 Future Recommendations
References
Logistics and Sustainability
Process-Based Selection of Handling Equipment in the Automotive Production
1 Introduction
2 Material Handling Processes
2.1 Role of the Material Handling Processes
2.2 Handling Processes in the Automotive Industry
3 Planning of Handling Processes
3.1 Planning of Material Handling
3.2 Equipment Selection Principles for Handling Tasks
3.3 Planning of Handling Processes in the Automotive Industry
4 Scenario for the Equipment Selection
4.1 Handling Equipment Selection Based on the Workplace Parameters
4.2 Handling Equipment Selection Based on the Handling Relations
4.3 Handling Equipment Selection Based on the Handling Processes
4.4 Handling Equipment Selection Based on the Handling System
4.5 Comparison of the Handling Solutions
5 Summary
References
Evolution of Startups in Automotive Supply Chain
1 Introduction
2 Industry Overview
2.1 Automotive Product Development and Risk Management
2.2 Startup Product Development and Risk Management
3 Startups in the Automotive Supply Chain
3.1 Risks and Opportunities for Cooperation
4 Conclusion
References
Investigation the Effect of the Data Frequency on the Driving Cycle of an Urban Bus Route
1 Introduction
1.1 Background of the Research
1.2 Short Introduction of the Dynamic Model
2 Methods of Creating Driving Cycles
3 Creating the Driving Cycles of a Bus Route of Debrecen
3.1 Micro-trip Method
3.2 Markov Chain Method
3.3 Simulation Results
4 Conclusion
References
Logistics and Mechatronics Related Research in Mobile Robot-Based Material Handling
1 Introduction
2 Systematic Literature Review
2.1 Methodology of the Systematic Literature Review
2.2 Descriptive Analysis
2.3 Content Analysis
2.4 Conclusions of the Systematic Literature Review
3 Mechatronics Related Aspects of Mobile Robot Systems
4 Optimisation of Mobile Robot-Based Material Handling
5 Conclusions
References
New Generation Hydrogen – How to Package Pastous Hydrogen for Mobility Applications
1 Introduction
2 Methodology
3 Results
3.1 Safety Requirements
3.2 Logistics Requirements
3.3 Application Requirements
3.4 Recycling Requirements
4 Outlook and Critical Appraisal
References
Transformation of Conventional Manufacturing and Service Systems into a Cyber-Physical Environment: Review of Potential Solutions
1 Introduction
2 Systematic Literature Review
2.1 Methodology of the Systematic Literature Review
2.2 Descriptive Analysis
2.3 Content Analysis
2.4 Conclusions of the Systematic Literature Review
3 Efficiency Improvement Through Matrix Production in Automotive Manufacturing Systems
4 Hyper-Connected Collection and Distribution Systems in City Logistics
5 Switch Pool Packaging Logistics in Industry 4.0 era
6 Conclusions
References
A Compendium Analysis on the Possible Usage of Advanced Biofuels in the Transport Sector from a European Perspective
1 Foreword
2 Advanced Biofuels
3 Share of RES in the Transport Sector of the EU
4 Recent Developments in Advanced Biofuels for the Transport Sector
4.1 Bioethanol (C2H2OH) from Lignocellulosic Biomass
4.2 Microalgal Biofuel
5 Techno-Economic Comparative Analysis- Identifying Challenges and Opportunities in Biofuel Production
6 SWOT Analysis- Biofuels in the Transport Sector
7 Conclusion
References
The Application of CFD Software for Modelling the Dispersion of Hydrogen Gas at Renewable Energy Fueling Stations
1 Introduction and Motivation
2 Previous Studies
3 Hydrogen Fueling Station by CFD Simulation Study
3.1 Effect of Airflow with 2 m/s
3.2 Effect of Airflow with 11 m/s
4 Conclusions
References
Materials, Technology and Education
The Current Situation of the Rare-Earth Material Usage in the Field of Electromobility
1 Introduction
2 Situation Analysis of the Rare Earth Elements Usage and Supply Chain
2.1 Ecological Effect of Rare Earth Ores and Magnet Production
2.2 The Geopolitical Situation Regarding Rare Earth Usage
3 Rare Earth Magnet Usage in the Field of Electric Mobility
3.1 Summary of Cause-and-Effect Relations in the Frame of eMobility Needs
3.2 The Importance of the 0 km Footprint
3.3 Tendencies in the Field of Electric Mobility
4 Conclusion
References
A New Approach to Steel Grade Selection for Automotive Parts
1 Introduction
2 Current Practice in Material Selection
2.1 Task Clarification and Activity Planning
2.2 Conceptual Design
2.3 Embodiment Design
2.4 Detail Design
2.5 Associated Processes to Design Process
2.6 Summary of Current Practices in Material Selection
3 Characteristics of Rolled Steel Products
4 Frustrations with the Current Material Selection Method in the Case of Rolled Steel Products
5 Proposal for a New Approach to the Selection of Materials in the Steel Industry
6 Conclusions
References
Iron Oxide and Tungsten Trioxide Nanofluids to Enhance Automotive Cooling Radiators: Experimental Analysis
1 Introduction
2 Experimental Methods
2.1 Nanofluid Preparation
2.2 System Description
2.3 Data Reduction
3 Validation of Experimental
4 Results and Discussion
4.1 Effect of Temperature with Volume Concentration on Friction Factor and Reynolds Number
4.2 Heat Transfer Coefficient
4.3 Heat Transfer Rate
4.4 Nusselt Number (Nu)
5 Conclusion
References
Investigation of Shape Correctness of Thermally Tested Alternator Stators
1 Introduction
2 Experimental Investigations
2.1 The Applied Experimental Design Method
2.2 The Testing Processes
2.3 Measuring of Roundness
2.4 Analysis of Roundness Parameters
3 Results
4 Conclusions
References
Optical Investigation of the Strain Distribution with Different Orientations on Aerospace Composite Material
1 Introduction
2 Materials and Methods
2.1 Specimen Preparation
2.2 Tensile Testing
2.3 Data Evaluation
3 Results and Discussion
4 Conclusion
References
Multilayered Aluminum Clinch Joints: An Experimental and Numerical Investigation of the Manufacturing Process
1 Introduction
2 Experimental Investigation
3 Finite Element Analysis of the Joint Forming Process
4 Summary
References
Extending an Industrial Robot with Image Processing System
1 Introduction
1.1 Fanuc iRVision
2 Communication
3 A Possible Solution
3.1 The Image Processing Unit
3.2 Client Module on the Robot Controller
3.3 Positioning the Camera
4 Summary
References
Investigation of Turbocharger Compressor Wheel Damage Due to Collision with Condensed Water Droplets
1 Introduction
2 Investigations on Turbocharger Component Testbench
3 Results
4 Conclusions
References
Investigation of the Applicability of Topological Methods
1 Introduction
2 The Basis of the Case Study
3 Design of the Components of the Landing Gear
4 The Generative Design Process in iCAD System
4.1 Determination of Design Space
4.2 Definition of Loads and Constraints
4.3 Definition of Design Objective Functions
4.4 Evaluation and Selection of Solutions
5 Summary
References
CFD Analyses of the Pressure Distribution in Hydrostatic Journal Bearings with Different Recess Shapes
1 Introduction
2 Governing Equation
2.1 Basic Equations
2.2 Limit Speed Calculation
3 Model Setup
3.1 Generating the Volume Model
3.2 Meshing and Boundary Conditions
4 Results of the CFD Analysis
4.1 Reference Bearing
4.2 Different Recess Configurations
5 Conclusion
References
Investigation of the Tribological Behaviour of Advanced TiAlN and CrAlN Hard Coatings Deposited on X153CrMoV12 Cold Work Tool Steel
1 Introduction
2 Investigated Materials
2.1 Substrate Material
2.2 Coatings
3 Tests and Evaluation
3.1 Progressive Loading Scratch Tests
3.2 Constant Load Scratch Test
3.3 Ball-on-Disc Tests
4 Summary
References
A Comparative Study on the Tribological Performance of a Monolayer TiBN and Multilayered TiBN/DLC Coating on X210Cr12 Tool Steel
1 Introduction
2 Experimental Work
2.1 Substrate Material and Samples
2.2 Investigated Coatings
2.3 Test Procedures
3 Results and Discussion
3.1 Hardness Test
3.2 Rockwell C Adhesion Test
3.3 Ball Cratering Test
3.4 Increasing Loading Scratch Tests
3.5 Ball-on-Disc Tests
4 Summary
References
The Appropriate Way of Sharing Project Information: A Student Approach
1 Introduction
1.1 Changing Toolset of Project Communication
1.2 Forms of Project Communication
1.3 Communication Within the Project Team
2 Research Design
2.1 Research Goals and Methods
2.2 Research Sample and Limitations
3 Results
3.1 The Vision of Company Utilization and Evaluation of the Effectiveness
3.2 Patterns of Opinions
4 Discussion and Conclusion
References
Surface Residual Stresses in High-Feed Face Milling of Carbon Steel
1 Introduction
2 Methods and Experimental Conditions
3 Results and Discussion
4 Conclusions
References
Investigation on 3D Printing Parameters of PLA Polymers for Gear Applications
1 Introduction
2 Material Testing Methods
2.1 Ball-on-Disk Test
2.2 Compression Test
3 Results and Discussion
4 Conclusion
References
Optimization
Fitness Landscape Analysis of Population-Based Heuristics in Solving a Complex Vehicle Routing Problem
1 Introduction
2 Vehicle Routing Problem
3 Population-Based Optimization Algorithms
3.1 Ant System
3.2 Elitist Strategy of Ant System
3.3 Firefly Algorithm
3.4 Genetic Algorithm
4 Test Results
4.1 Ant System
4.2 Elitist Strategy of Ant System
4.3 Firefly Algorithm
4.4 Genetic Algorithm
4.5 Summary
5 Conclusions
References
Analysis of the Multi-Objective Optimisation Techniques in Solving a Complex Vehicle Routing Problem
1 Introduction
2 Complex Multi-Echelon Vehicle Routing Problem
3 Applied Heuristic Algorithm
3.1 Ant Colony System
3.2 Firefly Algorithm
3.3 Genetic Algorithm
3.4 Simulated Annealing
3.5 Tabu Search
4 Multi-Objective Optimisation
4.1 Normalisation of the Objective Function
4.2 Methods with a Priori Articulation of Preferences
4.3 Population-Based Techniques - Global Optimisation Techniques
5 Test Results
6 Conclusions
References
Vehicle Routing for Municipal Waste Collection Systems: Analysis, Comparison and Application of Heuristic Methods
1 Introduction and Theoretical Background
2 Optimization Algorithms Description
2.1 Genetic Algorithm (GA)
2.2 Particle Swarm Algorithm (PSO)
2.3 Simulated Annealing (SA)
3 Optimization Algorithms Benchmarks
4 TSP Application in Miskolc
4.1 Case Study Data
4.2 Case Study Results
4.3 Conclusions
5 Summary
References
Comparative Study of Different Metaheuristics on CEC 2020 Benchmarks
1 Introduction
2 Cec 2020
3 The Metaheuristics
3.1 Fertilisation Optimisation Algorithm
3.2 Dynamic Differential Annealed Optimisation
3.3 Artificial Bee Colony
3.4 Ant Lion Optimisation
3.5 Firefly Algorithm
3.6 Particle Swarm Optimisation
3.7 Whale Optimisation Algorithm
3.8 Sine Cosine Optimisation Algorithm
3.9 Harris Hawks Optimisation
3.10 Grey Wolf Optimisation
4 Statistical Results
5 Discussion on FO Algorithm
6 Conclusion
References s
Weight Optimization of All-Composite Sandwich Structures for Automotive Applications
1 Introduction
2 Analytical Model of the Investigated Sandwich Structure
3 Weight Optimization of the FRP Composite Sandwich Structure
3.1 Design Variables
3.2 Weight Objective Function
3.3 Design Constraints
4 Numerical Model
5 Results and Discussion
6 Conclusion
References
Optimum Design for the Bottom Panel of a Heavy-Duty Truck by Using a Composite Sandwich Structure
1 Introduction
2 Structural Design of Bottom Panel in a Heavy-Duty Truck
3 Structure of the Investigated Bottom Panel
4 Structural Optimisation
4.1 Design Variables
4.2 Weight Objective Function
4.3 Design Constraints
5 Numerical Validation Model
6 Results
7 Conclusions
References
Advanced Methods to Solve Multi-project Scheduling Problems Taking into Account Multiple Objective Functions
1 Introduction and the Scope of Our Research
2 Multi-objective Multi-project Scheduling Problems
2.1 The Initial Model of Our Research
2.2 Mathematical Model of RCPSP
2.3 An Extended Mathematical Model
3 An Advanced Solver
3.1 A Short Review of Related Research
3.2 Compositional Structure
3.3 Solver Behavioural Model
3.4 Injection of Search Variables into Scheduling Generation Scheme
4 Experimental Results
4.1 Implemented Generation Scheme
4.2 Implemented Search Solvers
4.3 Numerical Results
5 Conclusion
References
Combination of GPU Programming and FEM Analysis in Structural Optimisation
1 Introduction
2 Differential Evolution
3 Finite Element Model of Truss Structure
4 The Optimisation Problem
5 Parallelisation with CUDA and MATLAB
6 Comparison of Sequential and Parallel Optimisation
7 Conclusion
References
Global and Local Cost Calculations at Welded Structures
1 Introduction
2 The Local Approach Cost Elements
2.1 The Cost of Material
2.2 The Cost of Manufacturing in General
2.3 Fabrication Times for Welding
2.4 Calculation of Preparation, Assembly, and Tacking Times
2.5 Actual Welding Time Estimation
2.6 Calculation of Additional Fabrication Actions Time
3 Thermal and Waterjet Cutting
3.1 Plate Cutting and Edge Grinding Times
4 Surface Preparation Time
5 Painting Time
6 Total Cost (Local Approach)
7 Global Cost Approach
7.1 Life Cycle Assessment (LCA)
7.2 Main Environmental Impacts to Be Considered
7.3 Calculation of Potential Environmental Impacts
7.4 Global Warming Potential (GWP)
8 Ozone Depletion Potential (ODP)
9 Acidification Potential (AP)
10 Eutrophication Potential (EP)
11 Photochemical Ozone Creation Potential (POCP)
12 Abiotic Depletion Potential
13 Illustrative Example
14 Optimisation of Stiffened Plates
15 Optimisation Method and Results for the Local Approach
16 Optimisation Method and Results for the Global Approach
17 Conclusion
References
Vibration and Noise
Vehicle Dynamics Modelling of the Mercedes-Benz REFORM 501 LE Urban Bus by Using AVL Cruise Software
1 Introduction
2 Model Structure
3 Summary
References
An Approach for Hierarchical Clustering of Road Vehicle Vibration Spectrums
1 Introduction
2 Methods
2.1 Source Data
2.2 On the Use of Dendrograms
2.3 The Distribution of D
2.4 A Benefit-to-Cost Ratio
3 Results
3.1 Replications
4 Discussion
5 Conclusion
Nomenclature
References
Evaluation of a CUSUM-Type Changepoint Detector Applied in the Time-Frequency Domain of Synthetic Road Vehicle Vibrations
1 Introduction
2 Methods
2.1 Test Samples
2.2 The Detector
2.3 Operational Surface
2.4 Receiver Operating Characteristic
2.5 Segment Length Distribution
3 Results
4 Discussion
5 Conclusion
Appendix
Nomenclature
References
Investigation of the Vibrational Behavior of a Quarter-Car Model
1 Introduction
2 Model of the Quarter Car
2.1 Displacement Formulation of the Problem
2.2 Momentum Formulation
3 Numerical Analysis
4 Controlling the Vibration with the Use of Feedback
5 Conclusion
References
Influence of Kinematic Excitation on the Dynamic Load of Rotary Machines Bearings Mounted on a Rail Vehicle
1 Introduction
2 Environment, Object, Goals, Description and Methodology of Vibration Measurement
2.1 Measurement Environment, Conditions and Subject of Measurements
2.2 Objectives, Equipment and Measuring Points
3 Vibration Severity of the Radial Piston Pumps as an Indicator of Dynamic Load
3.1 Vibration Severity
3.2 Vibration Severity and Dynamic Load
4 Discussion and Conclusion
References
Malfunction or Normal Operation? Evaluation of the Subjectivity of Noise and Vibration Phenomena Accompanying the Operation of Motor Vehicles
1 Introduction
2 Psychoacoustics
3 Problem Description and Measurements
3.1 Problem 1: Impact/hit Noise of a Manual Gearbox
3.2 Problem 2: Whistling Noise of an Automatic Gearbox
4 Analysis
4.1 Analysis of Impact/hit Noise
4.2 Analysis of Whistling Noise
5 Conclusions
References
Vibroacoustic Investigation of Automotive Turbochargers Focusing on the Effect of Lubricant Temperature and Bearing Conditions
1 Introduction
2 Measurement Methodology
2.1 Test Environment and Turbocharger Testing Circumstances
2.2 Vibroacoustic Test Environment
2.3 Evaluation Techniques
3 Results
3.1 The Effect of Lubricant Temperature on Subsynchronous Vibrations
3.2 The Effect of Bearing Wear on Subsynchronous Vibration
4 Conclusions
5 Future Opportunities
References
Comparative Testing of Vibrations in Vehicles Driven by Electric Motor and Internal Combustion Engine (ICE)
1 Introduction
2 Test Rig and the Tested Vehicle Used for Analysis
3 Tools and Instruments Used for Analysis
4 Measurements Carried Out During the Test
5 Results Obtained
6 Conclusions
References
Welding
Design and Manufacture Requirements of Welded Car Bodies and Components for Innovative Railway Vehicles
1 Introduction
2 The Carbody Structures of Railway Vehicles
3 Design Requirements
4 Defining the Weld Performance Class of the Joints
5 Welding Technology
6 Further Possibilities for Development
7 Summary
References
The Effect of Multiple Flame Straightening on High-Strength Steels Applied in Vehicle Industry
1 Introduction
2 Review on Flame Straightening Experimental Research
3 Applied Materials and Methods
3.1 Chemical Compositions and Mechanical Properties
3.2 Temperature Profiles for the Physical Simulations
3.3 Experimental Circumstances
4 Results and Discussion
4.1 Optical Microscopic and Hardness Tests
4.2 Instrumented Charpy V-notch Pendulum Impact Tests
5 Conclusions
References
Welding
Design and Manufacture Requirements of Welded Car Bodies and Components for Innovative Railway Vehicles
1 Introduction
2 The Carbody Structures of Railway Vehicles
3 Design Requirements
4 Defining the Weld Performance Class of the Joints
5 Welding Technology
6 Further Possibilities for Development
7 Summary
References
The Effect of Multiple Flame Straightening on High-Strength Steels Applied in Vehicle Industry
1 Introduction
2 Review on Flame Straightening Experimental Research
3 Applied Materials and Methods
3.1 Chemical Compositions and Mechanical Properties
3.2 Temperature Profiles for the Physical Simulations
3.3 Experimental Circumstances
4 Results and Discussion
4.1 Optical Microscopic and Hardness Tests
4.2 Instrumented Charpy V-notch Pendulum Impact Tests
5 Conclusions
References
Bending Fatigue Characteristics of Butt Joints by Laser-Arc Hybrid Welding for Steel Bridge Members
1 Introduction
2 Fabrication of Welded Joints
2.1 Material and Specimen
2.2 Fabrication of Welded Joints
3 Vickers Hardness Test and Bead Shape
3.1 Vickers Hardness Test
3.2 Bead Shape
4 Fatigue Test
4.1 Four-Point Bending Fatigue Test
4.2 Test Results
4.3 Investigation of Differences in Crack Propagation
5 Conclusions
References
An Experimental Study of the Gas Metal Arc Welding Ultraviolet Effect as a Function of the Distance
1 Introduction
1.1 Ultraviolet Radiation (UV)
1.2 Health Effects of the Ultraviolet Radiation
1.3 The Permissible Level of UV Radiation
2 Methods
2.1 Measurement of the Welder Arc Emitted UV Radiation
3 Results and Discussion
4 Conclusion
References
Numerical Solution of the Aluminium Plate Welding Process by Friction Stir Welding
1 Introduction
2 Modelling of Welding of Two Aluminium Plates Using FSW
2.1 Thermo-fluid Numerical Simulation
2.2 Thermo - Mechanical Simulation
3 Conclusion
References
Numerical Simulation of Laser Beam Welding of Stainless Steel and Copper Butt Joint
1 Introduction
2 Experimental Procedure
2.1 Materials
2.2 Calculations of Thermal and Structural Properties
2.3 Definition of Heat Source
2.4 Creating Geometrical Model
2.5 Thermal Transient
2.6 Power Density
2.7 Initial Conditions
2.8 Welding Parameters Set for Simulation.
3 Results and Discussion
3.1 Interpretation of Simulation Results
3.2 Conclusion
References
Electron Beam Welding of Overlapped Joints Copper - Stainless Steel
1 Introduction
2 Experiment
2.1 Experimental Material
2.2 Electron Beam Welding Equipment and Welding Parameters
2.3 Preparation of Overlapped Welded Joints
3 Results
3.1 Macroscopic and Microscopic Analysis
3.2 Microhardness Test
3.3 Mechanical Test of Overlapped Welded Joints
4 Conclusion
References
Laser Beam Welding of Overlapped Joints Copper - Stainless Steel
1 Introduction
2 Experiment
2.1 Experimental Material
3 Conclusion
References
Design and Fabrication Factors for the Fatigue Assessment of Welded Structures
1 Introduction
2 Fatigue Parameters
2.1 Loading
2.2 Global Structure
2.3 Structural Details and Imperfections
2.4 Weld Geometry
2.5 Weld Imperfections and Cracks
2.6 Residual Stress
2.7 Crack Initiation and Growth
3 The Case Structure – A Heavy Working Machine
4 Conclusions
References
Investigation of Resistance Spot Welded Joints Made on Ultra-high-Strength Steel Sheets
1 Introduction
2 Material and Method
2.1 Samples Preparation
2.2 Macroscopic and Microhardness Tests
2.3 Shear-Tensile Test
3 Results and Discussion
3.1 Shear-Tensile Test
3.2 Microstructural Evaluation and Hardness Profile
4 Conclusion
References
Influence of Filler Metals on Microstructure and Mechanical Properties of Gas Metal Arc Welded High Strength Steel
1 Introduction
2 Materials and Methods
3 Material Tests
4 Conclusion
References
Examination of Absorbed Specific Fracture Energy and Notch Opening Displacement on S960QL Steel and Its Welded Joints
1 Introduction
2 Theoretical Backgrounds and Previous Applications
2.1 Specific Fracture Energy (ASPEF)
2.2 Notch Opening Displacement (NOD)
3 Investigations and Their Results
3.1 Preparation and Basic Investigations of Welded Joints
3.2 Basic Investigation of Welded Joints
3.3 Notch Opening Displacement and Specific Fracture Energy Investigations
4 Summary and Conclusions
References
Comparison of Fatigue Life of K Joints with and Without Overlap Using 3D Fatigue FEM Analysis
1 Introduction
2 FE Welding Analysis Procedure of K-type Joint
2.1 FE Welding Analysis Model (K-type Joint)
2.2 Welding Temperature and Thermal Elastoplastic Analysis
3 Fatigue FE Analysis for K-type Joint
3.1 Load and boundary condition of 3D Fatigue FEA
4 Results and Discussion: Comparison of Fatigue FEA Results in Overlapping and Non-overlapping Joints
5 Conclusion
References
Author Index
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Lecture Notes in Mechanical Engineering
Károly Jármai Ákos Cservenák Editors
Vehicle and Automotive Engineering 4 Select Proceedings of the 4th VAE2022, Miskolc, Hungary
Lecture Notes in Mechanical Engineering Series Editors Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia Francesco Gherardini , Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy Vitalii Ivanov, Department of Manufacturing Engineering, Machines and Tools, Sumy State University, Sumy, Ukraine Editorial Board Members Francisco Cavas-Martínez , Departamento de Estructuras, Construcción y Expresión Gráfica Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain Francesca di Mare, Institute of Energy Technology, Ruhr-Universität Bochum, Bochum, Nordrhein-Westfalen, Germany Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia Young W. Kwon, Department of Manufacturing Engineering and Aerospace Engineering, Graduate School of Engineering and Applied Science, Monterey, CA, USA Justyna Trojanowska, Poznan University of Technology, Poznan, Poland
Lecture Notes in Mechanical Engineering (LNME) publishes the latest developments in Mechanical Engineering—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNME. Volumes published in LNME embrace all aspects, subfields and new challenges of mechanical engineering. Topics in the series include: • • • • • • • • • • • • • • • • •
Engineering Design Machinery and Machine Elements Mechanical Structures and Stress Analysis Automotive Engineering Engine Technology Aerospace Technology and Astronautics Nanotechnology and Microengineering Control, Robotics, Mechatronics MEMS Theoretical and Applied Mechanics Dynamical Systems, Control Fluid Mechanics Engineering Thermodynamics, Heat and Mass Transfer Manufacturing Precision Engineering, Instrumentation, Measurement Materials Engineering Tribology and Surface Technology
To submit a proposal or request further information, please contact the Springer Editor of your location: China: Ms. Ella Zhang at [email protected] India: Priya Vyas at [email protected] Rest of Asia, Australia, New Zealand: Swati Meherishi at [email protected] All other countries: Dr. Leontina Di Cecco at [email protected] To submit a proposal for a monograph, please check our Springer Tracts in Mechanical Engineering at https://link.springer.com/bookseries/11693 or contact [email protected] Indexed by SCOPUS. All books published in the series are submitted for consideration in Web of Science.
More information about this series at https://link.springer.com/bookseries/11236
Károly Jármai Ákos Cservenák •
Editors
Vehicle and Automotive Engineering 4 Select Proceedings of the 4th VAE2022, Miskolc, Hungary
123
Editors Károly Jármai Faculty of Mechanical Engineering and Informatics, Institute of Energy Engineering and Chemical Machinery University of Miskolc Miskolc, Hungary
Ákos Cservenák Faculty of Mechanical Engineering and Informatics, Institute of Logistics University of Miskolc Miskolc, Hungary
ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-3-031-15210-8 ISBN 978-3-031-15211-5 (eBook) https://doi.org/10.1007/978-3-031-15211-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
After the 3rd International Conference on Vehicle and Automotive Engineering, there was a need to organize the next one. There were over 318 thousand downloads of the previous proceedings from the Springer Website. Unfortunately, the COVID-19 crisis made it more difficult. Finally, we decided to organize an online conference to avoid any danger from the COVID-19 virus. The production of the automotive and vehicle industry and its component suppliers, manufacturers of machines and equipment and the connected mechanical engineering and process engineering industry have significantly increased in the last decades. The quick transportation of persons and goods is more and more important, as also the quick production of the vehicles. People would like to reach their destination as quickly as possible. This is the case in Hungary, where the improvement of the car and vehicle industry has been significant in the last decades. Great car producers settled here like Mercedes Benz, Audi, Suzuki and Opel, and small and medium enterprises connected to car element production have significantly developed. The aim of the 4th International Conference on Vehicle and Automotive Engineering at the University of Miskolc, Hungary, is to provide a good opportunity to discuss professional topics in this field both for academic and industrial experts. The online version of the conference makes it possible to avoid any danger of COVID-19 virus. The main requirements for cars and car elements are safety, manufacturability and economy. Safety against different loads such as permanent and variable actions is guaranteed by design constraints on stresses, deformations, stability, fatigue, eigen frequency and noise, while manufacturability is considered by fabrication constraints. The economy is achieved by minimization of the cost. The main topics of the conference are as follows: • Autonomous vehicles and safety Hybrid vehicles, electric vehicles, fuel cell vehicles, autonomous and connected vehicles, artificial intelligence, Internet of Things (IoT), applications in smart cities, future trends and emerging technologies
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Preface
• Design and powertrains Geometric modelling, design and reconstruction of vehicle structures and surfaces, evaluation and correction of vehicle surfaces, computer, graphics and image processing in visualization and design, 3D printing and prototyping in vehicle development • Electrical and thermal aspects • Logistics/sustainability Standards and regulations, design for environment, virtual design and testing, Inspection and maintenance, life cycle assessment, recycling, supply chain and logistics. Sustainability aspects • Materials and manufacturing Advanced materials and innovations in manufacturing, metal parts forming, joining and casting technologies, coating, wear, corrosion protection and surface engineering, fatigue, fracture, failure and testing of materials and structural parts, prototype building, flexible processes • Optimization Topology optimization, shape optimization, sizing, optimization methods, cost calculation • Vibration and noise Engine vibration, engine noise, tyre noise, other sources of noise, measurement techniques, simulation and analysis • Welding Different welding technologies, application of ultra-high-strength steels, application of welding in the vehicle industry. It is a great pleasure to organize this conference and to give participants an opportunity to show and discuss the new research results online in a friendly atmosphere. The organizers wish all participants successful online meetings to collect new ideas and make new acquaintances. May 2022
Károly Jármai Ákos Cservenák
Acknowledgements
The editors would like to acknowledge the cooperation and help of the following organizations • • • • •
Ministry of Technology and Industry, (TIM), International Institute of Welding (IIW), Hungarian Welding Association (MAHEG), Hungarian Steel Structure Association (MAGÉSZ), Hungarian Welding Technology and Material Testing Association (MHtE),
and last but not least University of Miskolc, Hungary, which hosts the conference. The editors would like to acknowledge the help of the following person: László Kota, assistant professor, software developer, and the reviewers: Al Ali, Mohamad; Al-Fatlawi Deli, Alaa Abdulzahra; Alyasiri, Qudama; Árvai-Homolya, Szilvia; Ausserhoffer, Norbert; Azizi, Mahdi; Azuma, Koji; Babcsánné Kiss, Judit; Baghalzadeh, Milad; Baksáné Varga, Erika; Bánlaki, Pál; Bányai, Tamás; Barabás, István; Barna, Imre Ferenc; Behrendt, Fabian; Bencs, Péter; Benotsmane, Rabab; Beri, Bence; Beronská, Naďa; Bihari, Zoltán; Brezinová, Janette; Cecić, Mojmil; Czégé, Levente; Dabboura, Eyad; Dadvandipour, Samad; Deák, Krisztián; Divya, Shalini; Dobosy, Ádám; Dömötör, Ferenc; Dudás, László; Fábián, Enikő Réka; Felhő, Csaba; Ficzere, Péter; Fowzi, Mohammed; Gáspár, Marcell; Gatial, Martin; Ghica, Valeriu Gabriel; Grozav, Sorin Dumitru; Günther, Frank; Hegedűs, György; Hencz, Csaba; Hirohata, Mikihito; Hodúlová, Erika; Horváth, László István; Iclodean, Călin Doru; Imran, Ahmed Abdulnabi; Jalghaf, Humam Kareem; Jálics, Károly; Jármai, Károly; Javorek, Lubomir; Kaczmar, Ireneusz; Kamel, Mohammed Saad; Kamondi, László; Kattea, Wisam Abed; Keitel, Steffen; Kelemen, Michal; Kollár, László; Košťál, Peter; Kota, László; Kuzsella, László; Lakatos, István; Lepine, Julien; Lerez, Christoph; Magdolen, Ľubomír; Major, Zoltán; Mannheim, Viktória; Máté, Márton;
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Acknowledgements
Matijošius, Jonas; Mátyás, László; Menyhárt, József; Milović, Ljubica; Móger, Róbert; Murariu, Alin; Németh, Alexandra Kitti; Orosz, Tamás; Palotás, Béla; Pástor, Miroslav; Pere, Balázs; Petrik, Máté; Piros, Attila; Polák, Peter; Popa-Müller, Izolda; Pruncu, Catalin; Qiao, Viktor; Rácz, Pál; Rassõlkin, Anton; Rodríguez, Miguel A.; Rohde-Brandenburger, Jan; Samet, Akar; Sarka, Ferenc; Šebo, Juraj; Ségľa, Štefan; Selisteanu, Dan; Seyedzavvar, Mirsadegh; Simon-Koncsik, Zsuzsanna; Soltész, László; Šraml, Matjaž; Stranieri, Silvia; Szabó, Ferenc; Szabó, Tamás; Száva, János; Széll, Attila; Szepesi, Gábor L.; Szűcs, Máté; Takács, Ágnes; Tar, József; Telek, Péter; Thalmaier, György; Tímár, Imre; Tóth, Álmos; Tóth, Balázs; Török, Ádám; Trník, Anton; Trojahn, Sebastian; Tutak, Wojciech Kazimierz; Varga, Gyula; Varga, Tibor; Viňáš, Ján; Virág, Zoltán; Wersényi, György; Wilhelm, Gerald; Zelinko, Andrii; Zohn, Jozef; Zöldy, Máté; Zsótér, Brigitta. Károly Jármai Ákos Cservenák
Contents
Autonomous Vehicles and Safety An Approach to Implementation of Autoencoders in Intelligent Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Samad Dadvandipour and Aadil Gani Ganie
3
Case Study of a Computer-Controlled RC Car Based on RF Wireless Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Attila Trohak and Rabab Benotsmane
11
Nonlinear Model Predictive Control for Autonomous Quadrotor Trajectory Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rabab Benotsmane and József Vásárhelyi
24
Effect of Non-conventional Seating Position on Driver Injuries in the Case of a Self-driving Car . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laszlo Porkolab and Istvan Lakatos
35
Implementation of a System for Signaling the Approach of Emergency Vehicles Within Other Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dávid Makó and Ákos Cservenák
50
Safe In and Out of the Car . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agnes Takacs
63
Possibilities of Using of Online Vehicle Diagnostics in the Future . . . . . Jozsef Nagy and Istvan Lakatos
71
Security and Safety Systems on Modern Vehicles . . . . . . . . . . . . . . . . . József Répás and Lajos Berek
84
Design and Powertrains Contact Ratio of Spiral Bevel Gears . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Miklós Gábor Várkuli, Gabriella Vadászné Bognár, and József Szente
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Stability Analysis and Optimization of Vehicle Active Motion Control System with Feedback Time Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Hangyu Lu, Jianwei Lu, Gabor Stepan, and Takacs Denes Cogging Torque Analysis of Toyota Prius 2004 IPMSM Motor with the Digital-Twin-Distiller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Mihály Katona, Péter Kiss, Krisztián Gadó, and Tamás Orosz Effect of the Radial Constrain for the Impact Energy-Absorbing Behaviour of the Closed-Cell Metal Foam . . . . . . . . . . . . . . . . . . . . . . . 139 József Kertész and Tünde Anna Kovács Investigation of Tilting Table with Parallel Kinematic . . . . . . . . . . . . . . 151 István Tüske and György Hegedűs Influence of Speed to Rolling Resistance Factor in Case of Autobus . . . 157 Sándor Pálinkás New Adder and Distribution Gearbox Used in the Transmission of the Heavy Truck . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Száva Ioan, Vlase Sorin, Gheorghe Vasile, Száva Renata Ildiko, Gálfi Botond Pál, and Popa Gabriel Length of Contacting Generating Lines Interpreted in the Regular Rectangular Contact Zone of Helical Gears and Zone Dependence of Their Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Zsuzsa Drágár and László Kamondi Diagnostic and Prognostic Strategies for Monitoring of Diesel Engines’ Technical Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 Hla Gharib and György Kovács Development of a Knowledge-Based System for Diagnosing of Diesel Engines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 Hla Gharib and György Kovács Examination of Bolt Connection with Finite Element Method . . . . . . . . 212 Ferenc Sarka Calculation Methods and Measurement of the Heating of Small Plastic Gears . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Imre Marada and János Bihari External Tyre Loading Predictions from Inner Tyre Deformation Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 R. Gast, P. S. Els, D. N. Wilke, S. Kok, and T. R. Botha Design Study of the Low-Cost Advance Rider Assistance System . . . . . 248 Václav Mašek and Roman Čermák
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Load Testing of Alternating Current Hydraulic Drive . . . . . . . . . . . . . . 261 Tamás Fekete A Review on HCNG/Diesel Tri Fuel Engine Performance . . . . . . . . . . . 268 Hassan Sadah Muhssen, Ákos Bereczky, and Máté Zöldy Electric and Thermal Investigations on the Effects of Capacitive Couplings in an Automotive Phase-Shifted Full-Bridge Power Supply Used in Electric Vehicles . . . . 291 Róbert Orvai and Márk Csörnyei A Literature Review of a Dual-Purpose Solar Collector . . . . . . . . . . . . . 302 Mustafa M. Hasan and Krisztián Hriczó Overview of the Market of Electric Cars by Multilogistic Curves . . . . . 322 Ferenc János Szabó Electromobility: The Spreading of Electric Cars Versus Internal Combustion Engine Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330 Dénes Kocsis, Judit T. Kiss, Gábor Bellér, and István Árpád Investigation of the Effect of a Coolant Inlet Duct on the Thermal Performance of Car Radiators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Máté Petrik and Gábor L. Szepesi Comparison of Thermal Insulation Performance of Different Materials Used for Aircrafts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346 Ákos Lakatos and Alagba Henry Eze ANN Modeling for Thermal Load Estimation in a Cabin Vehicle . . . . . 357 Ali Habeeb Askar, Endre Kovács, and Betti Bolló A Critical Review of Multiple Impingement Jet Mechanisms for Flow Characteristics and Heat Transfer Augmentation . . . . . . . . . . . . . . . . . 374 Mahir Faris Abdullah, Humam Kareem Jalghaf, and Rozli Zulkifli Logistics and Sustainability Process-Based Selection of Handling Equipment in the Automotive Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Péter Telek Evolution of Startups in Automotive Supply Chain . . . . . . . . . . . . . . . . 412 Tamás Bence Venczel, László Berényi, and Krisztián Hriczó Investigation the Effect of the Data Frequency on the Driving Cycle of an Urban Bus Route . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 Attila Vámosi, Dániel Nemes, Levente Czégé, and Imre Kocsis
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Logistics and Mechatronics Related Research in Mobile Robot-Based Material Handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428 Tamás Bányai and Ákos Cservenák New Generation Hydrogen – How to Package Pastous Hydrogen for Mobility Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 Julius Brinken, Björn Könecke, Malte Kania, and Tom Assmann Transformation of Conventional Manufacturing and Service Systems into a Cyber-Physical Environment: Review of Potential Solutions . . . . 456 Tamás Bányai A Compendium Analysis on the Possible Usage of Advanced Biofuels in the Transport Sector from a European Perspective . . . . . . . . . . . . . . 472 Baibhaw Kumar, Gábor L. Szepesi, and Zoltán Szamosi The Application of CFD Software for Modelling the Dispersion of Hydrogen Gas at Renewable Energy Fueling Stations . . . . . . . . . . . . . . 483 Levente Tugyi, Zoltán Siménfalvi, and Gábor L. Szepesi Materials, Technology and Education The Current Situation of the Rare-Earth Material Usage in the Field of Electromobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 Csongor Horváth A New Approach to Steel Grade Selection for Automotive Parts . . . . . . 505 Béla Kondás and Zoltán Péter Kovács Iron Oxide and Tungsten Trioxide Nanofluids to Enhance Automotive Cooling Radiators: Experimental Analysis . . . . . . . . . . . . . . . . . . . . . . . 521 Mohammed Alktranee, Mohammed A. Shehab, Zoltán Németh, and Péter Bencs Investigation of Shape Correctness of Thermally Tested Alternator Stators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 538 Viktoria Ferencsik and Gyula Varga Optical Investigation of the Strain Distribution with Different Orientations on Aerospace Composite Material . . . . . . . . . . . . . . . . . . . 549 Máté File, Imre Kállai, Dávid Huri, and Tamás Mankovits Multilayered Aluminum Clinch Joints: An Experimental and Numerical Investigation of the Manufacturing Process . . . . . . . . . . . . . 558 Szabolcs Jónás and Péter Zoltán Kovács Extending an Industrial Robot with Image Processing System . . . . . . . . 568 József Lénárt
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Investigation of Turbocharger Compressor Wheel Damage Due to Collision with Condensed Water Droplets . . . . . . . . . . . . . . . . . . . . . . . 575 Richárd Takács, András Lajos Nagy, and Ibolya Zsoldos Investigation of the Applicability of Topological Methods . . . . . . . . . . . 582 Kristóf Szabó CFD Analyses of the Pressure Distribution in Hydrostatic Journal Bearings with Different Recess Shapes . . . . . . . . . . . . . . . . . . . . . . . . . . 592 Sándor Gergő Tóth Investigation of the Tribological Behaviour of Advanced TiAlN and CrAlN Hard Coatings Deposited on X153CrMoV12 Cold Work Tool Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604 Pusta Jalalova, Fruzsina Fülöp, and Maria Berkes Maros A Comparative Study on the Tribological Performance of a Monolayer TiBN and Multilayered TiBN/DLC Coating on X210Cr12 Tool Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 620 Fruzsina Fülöp and Maria Berkes Maros The Appropriate Way of Sharing Project Information: A Student Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 636 László Soltész and László Berényi Surface Residual Stresses in High-Feed Face Milling of Carbon Steel . . . 648 Dmytro Borysenko, János Kundrák, Bernhard Karpuschewski, Florian Welzel, Zsolt Maros, and Csaba Felhő Investigation on 3D Printing Parameters of PLA Polymers for Gear Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654 Ziya Mehdiyev, Csaba Felhő, and Kovács Péter Zoltán Optimization Fitness Landscape Analysis of Population-Based Heuristics in Solving a Complex Vehicle Routing Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . 667 Anita Agárdi Analysis of the Multi-Objective Optimisation Techniques in Solving a Complex Vehicle Routing Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 678 Anita Agárdi Vehicle Routing for Municipal Waste Collection Systems: Analysis, Comparison and Application of Heuristic Methods . . . . . . . . . . . . . . . . 694 Mohammad Zaher Akkad, Yaman Rajab, and Tamás Bányai Comparative Study of Different Metaheuristics on CEC 2020 Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 709 Shaymaa Alsamia, Hazim Albedran, and Károly Jármai
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Weight Optimization of All-Composite Sandwich Structures for Automotive Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720 Mortda Mohammed Sahib, György Kovács, and Szabolcs Szávai Optimum Design for the Bottom Panel of a Heavy-Duty Truck by Using a Composite Sandwich Structure . . . . . . . . . . . . . . . . . . . . . . . . . 734 Mortda Mohammed Sahib, György Kovács, and Szabolcs Szávai Advanced Methods to Solve Multi-project Scheduling Problems Taking into Account Multiple Objective Functions . . . . . . . . . . . . . . . . 747 Krisztián Mihály, Mónika Kulcsárné-Forrai, and Gyula Kulcsár Combination of GPU Programming and FEM Analysis in Structural Optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 756 Szilárd Nagy, Károly Jármai, and Attila Baksa Global and Local Cost Calculations at Welded Structures . . . . . . . . . . . 768 Károly Jármai Vibration and Noise Vehicle Dynamics Modelling of the Mercedes-Benz REFORM 501 LE Urban Bus by Using AVL Cruise Software . . . . . . . . . . . . . . . . . . . . . . 793 Dániel Nemes and Sándor Hajdú An Approach for Hierarchical Clustering of Road Vehicle Vibration Spectrums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 799 László Róbert Hári and Péter Földesi Evaluation of a CUSUM-Type Changepoint Detector Applied in the Time-Frequency Domain of Synthetic Road Vehicle Vibrations . . . . . . . 812 László Róbert Hári and Péter Földesi Investigation of the Vibrational Behavior of a Quarter-Car Model . . . . 824 László Rónai Influence of Kinematic Excitation on the Dynamic Load of Rotary Machines Bearings Mounted on a Rail Vehicle . . . . . . . . . . . . . . . . . . . 835 Stanislav Žiaran, Ondrej Chlebo, and Ľubomír Šooš Malfunction or Normal Operation? Evaluation of the Subjectivity of Noise and Vibration Phenomena Accompanying the Operation of Motor Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 848 Balázs J. Kriston and Károly Jálics Vibroacoustic Investigation of Automotive Turbochargers Focusing on the Effect of Lubricant Temperature and Bearing Conditions . . . . . 861 Márk Pesthy, Richárd Takács, Jan Rohde-Brandenburger, and Csaba Tóth-Nagy
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Comparative Testing of Vibrations in Vehicles Driven by Electric Motor and Internal Combustion Engine (ICE) . . . . . . . . . . . . . . . . . . . . 871 József Zoltán Szabó and Ferenc Dömötör Welding Design and Manufacture Requirements of Welded Car Bodies and Components for Innovative Railway Vehicles . . . . . . . . . . . . . . . . . . . . . 883 István Borhy and László Belső The Effect of Multiple Flame Straightening on High-Strength Steels Applied in Vehicle Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893 Marcell Gáspár, László Gyura, and Raghawendra P. S. Sisodia Bending Fatigue Characteristics of Butt Joints by Laser-Arc Hybrid Welding for Steel Bridge Members . . . . . . . . . . . . . . . . . . . . . . . . . . . . 904 Gang Chen, Natsumi Sakai, Mikihito Hirohata, Kengo Hyoma, Naoyuki Matsumoto, and Koutarou Inose An Experimental Study of the Gas Metal Arc Welding Ultraviolet Effect as a Function of the Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 917 Márton Schramkó, Abdallah Kafi, László Gyura, and Tünde Anna Kovács Numerical Solution of the Aluminium Plate Welding Process by Friction Stir Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 925 Roland Jančo, Ladislav Écsi, and Pavel Élesztős Numerical Simulation of Laser Beam Welding of Stainless Steel and Copper Butt Joint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 933 Martin Hnilica, Erika Hodúlová, Miroslav Sahul, Pavel Kovačócy, Beáta Šimeková, and Ingrid Kovaříková Electron Beam Welding of Overlapped Joints Copper - Stainless Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 946 Beáta Šimeková, Pavel Kovačócy, Miroslav Sahul, Ingrid Kovaříková, Maroš Martinkovič, and Erika Hodúlová Laser Beam Welding of Overlapped Joints Copper - Stainless Steel . . . 957 Ingrid Kovaříková, Pavel Kovačócy, Miroslav Sahul, Beáta Šimeková, Maroš Martinkovič, and Erika Hodúlová Design and Fabrication Factors for the Fatigue Assessment of Welded Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 966 Tuomas Skriko, Antti Ahola, and Timo Björk Investigation of Resistance Spot Welded Joints Made on Ultra-highStrength Steel Sheets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 981 Sahm alden Abd al al and Ákos Meilinger
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Influence of Filler Metals on Microstructure and Mechanical Properties of Gas Metal Arc Welded High Strength Steel . . . . . . . . . . . 995 Judit Kovács and János Lukács Examination of Absorbed Specific Fracture Energy and Notch Opening Displacement on S960QL Steel and Its Welded Joints . . . . . . . 1006 Illés Sas and János Lukács Comparison of Fatigue Life of K Joints with and Without Overlap Using 3D Fatigue FEM Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1022 Shazia Muzaffer, Kyong-Ho Chang, and Zhen-Ming Wang Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1037
About the Editors
Dr. Károly Jármai is a professor at the Faculty of Mechanical Engineering at the University of Miskolc, where he graduated as a mechanical engineer and received his doctorate (dr.univ.) in 1979. He teaches the design of steel structures, welded structures, composite structures and optimization in Hungarian and in the English language for foreign students. His research interest includes structural optimization, mathematical programming techniques and expert systems. He wrote his C.Sc. (Ph.D.) dissertation at the Hungarian Academy of Science in 1988, became a European Engineer (Eur. Ing. FEANI, Paris) in 1990 and got his habilitation (dr.habil.) at Miskolc in 1995. Having successfully defended his doctor of technical science thesis (D.Sc.) in 1995, he received awards from the Engineering for Peace Foundation in 1997 and a scholarship as Széchenyi professor between the years 1997 and 2000. He is the co-author of five books in English Analysis and Optimum Design of Metal Structures, Economic Design of Metal Structures, Design and optimization of metal structures, Optimization for robot modelling with MATLAB and three monographs in Hungarian and has published over 750 professional papers, lecture notes, textbook chapters and conference papers. He has about 967 independent citations. He is a founding member of International Society for Structural and Multidisciplinary Optimization (ISSMO), a Hungarian delegate, vice chairman of commission XV and a subcommission chairman XV-F of International Institute of Welding (IIW). He has held several leading positions in Hungarian Scientific Society of Mechanical Engineers (GTE) and has been the president of this society at the University of Miskolc between 1991 and 2006. He was a visiting researcher at the Chalmers University of Technology in Sweden in 1991, visiting professor at Osaka University from 1996 to 97, at the National University of Singapore in 1998 and at the University of Pretoria several times between 2000 and 2005. He was the vice-rector of the university between 2013 and 2017 in the field of strategy and research. He is member of the editorial board of several national and international journals. He is an honorary doctor of the Technical University of Kosice, Slovakia.
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Dr. Ákos Cservenák is a senior lecturer at the Institute of Logistics at the Faculty of Mechanical Engineering and Informatics at the University of Miskolc. He was born in Miskolc in 1991. He got MSc degree with honours in mechatronical engineering at the University of Miskolc in February 2016. He was teacher of engineering between February and August 2016 at the Robert Bosch Department of Mechatronics. He was honoured with numbers of awards, e.g. Scholarship of Hungarian Republic, Medallion for Studying Hard, New National Excellence Program. He started the doctoral programme in September 2016 at the Robert Bosch Department of Mechatronics. He earned a second MSc degree in vehicle engineering at the Széchenyi István University in Győr in June 2018. In September 2021, he successfully defended his PhD dissertation, and in February 2022, he got the PhD diploma. He was an assistant research fellow between December 2019 and November 2021, and now, he is a senior lecturer since December 2021 in the Institute of Logistics. His languages skills are Hungarian (native), English (technical proficiency (C1)) and German (general, intermediate (B2)). He specialized in mechatronics, robots, vehicles.
Autonomous Vehicles and Safety
An Approach to Implementation of Autoencoders in Intelligent Vehicles Samad Dadvandipour(B)
and Aadil Gani Ganie
Institute of Information Science, The University of Miskolc, Miskolc, Hungary [email protected]
Abstract. We can see a rise in the number of smart vehicles in the last past few years. These types of cars are usually or, in other words, they are physically work as intelligent as robots. Intelligent vehicles have become an important part as they are equipped with intelligent agents that give services to human beings. It is approximated that over 1 billion cars travel the streets and roads of the world today. With such traffic, it is apparent that there are many situations where the driver has to react quickly. As Intelligent vehicles are connected to a large amount of data, these data may be dimensionally decreased and kept as latent data. Then, when needed, they can be reconstructed and used. The aim of the current paper is an approach to the implementation of an unsupervised autoencoder technique in intelligent vehicles. The autoencoders have significant importance as they detect and recognize unknown data. In this case, we can say the autoencoders may replace labelled supervised neural networks if they learn effective encoding (data representation). Keywords: Autoencoders · Implementation · Intelligent vehicles
1 Introduction To build and train the model, we have used an image matrix of unknown data. They are limited to small amounts of data. The image data are the drivers’ behaviour of any kind like being sleepy or lighting a cigarette or listening to a piece of music, etc. Intelligent vehicles [7, 8] during these situations can send a signal to draw the drivers’ reaction and attention very quickly. We have to remind that the input images are the numbers used to apply unsupervised autoencoder implementation. This set of numbers may represent a part of a drivers’ behaviour [9] while driving the vehicle. The autoencoders have significant importance as they detect and recognize unknown data. In this case, we can say the autoencoders may replace labelled supervised neural networks if they learn effective encoding (data representation). This may be done by training the relevant networks using big and updated data. And it can be much more effective if we train the networks to pass over disturbing noise (signals), denoising the images and image compression [10], and generate image data [5].
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 3–10, 2023. https://doi.org/10.1007/978-3-031-15211-5_1
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2 How to Implement the Autoencoders? Following the case study, autoencoders have two main parts encoding and decoding. Furthermore, Autoencoders are Unsupervised Deep Learning Techniques [1–4, 6]. First, they encode the inputs and compress them to get the latent space, then reconstruct the output using the presented matrix achieved from the compressed latent space. As a result, the output is very similar to or close to the given input. Therefore, we have shown how the autoencoders implement the encoders and decoders. 2.1 Structure of the Model Let’s consider an input image matrix of 4 × 4. We suppose it is a convolution layer with its features. These features are the parts that represent the behaviour of a driver or driver without considering any events like accidents mentioned in the introduction part of this paper. The input matrix will be converted and compressed by applying the pooling method. The stride length is one with kernel size (2 × 2). Thus, we can have the following image matrix (Fig. 1).
Fig. 1. Input data
import numpy as np data = [[4,3,8,6], [1,1,2,2], [24,18,32,24], [6,6,8,8]]
2.2 Pre-processing Phase Given the pooling process on the convolutional layer, we convert the image matrix into a compressed arrangement with suitable extracted features.
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Fig. 2. Pooling the 4 × 4 convolution matrix into four 2 × 2 matrices only with a stride of length one
Def pooling(data): div = int(len(data)/2) blocks = [ ] b1,b2,b3,b4 =[ ],[ ],[ ],[ ] for I in range(0, div): for j in range(0, div): b1.append(data[I, j]) b2.append(data[I, j+2]) b3.append(data[i+2, j]) b4.append(data[i+2, j+2])
b1 = b1.reshape(2,2) b2 = b2.reshape(2,2) b3 = b3.reshape(2,2) b4 = b4.reshape(2,2) blocks.append([b1,b2,b3,b4]) blocks = np.array(blocks) return(blocks) data = np.array(data) pool_data = pooling(data) pool_data = pool_data[0] pool_data
b1 = np.array(b1) b2 = np.array(b2) b3 = np.array(b3) b4 = np.array(b4)
The highlighted images (Fig. 2) are the appropriate features that we can take from the convolution layer and build the compressed matrix of 2 × 2 image resulting from 4 × 4 matrix, illustrated in the following (Fig. 3):
Fig. 3. Compressed or latent matrix obtained from pooling method considering the minimum numbers from each matrix
We could build an encoding portion of the autoencoding neural network in the mentioned compressed image. Now let’s start with decompress processing or decoding process. First, we use the feature map of a 2 × 2 image to build an un-pooling process (Fig. 4):
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Fig. 4. Feature map matrix with a length of one stride
pool = pool_data[0] pool array([[1, 2], [6, 8]]) pool data array([[[ 4, 3], [ 1, 1]], [[ 8, 6], [ 2, 2]], [[24, 18], [ 6, 6]], [[32, 24],[ 8, 8]]])
2.3 Post-processing Let us see the the un-pooling process in the following figure (Figs. 5, 6 and 7): pool = pool_data[0] pool x = pool x =np.pad(x, [(0, 2), (0, 2)], mode='constant', constant_values=0) x
Feature map
Compressed feature
Multiplication
Fig. 5. The feature map (left) and latent matrix (middle), and the output (right) elements sequentially
Summing up all the un-pooled matrix results, we get the final decoding (decompressed) 4 × 4 image, which copies the given input image matrix Fig. 8.
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Fig. 6. Un-pooling illustration using the multiplication of the feature map and latent matrix
Fig. 7. Summing up all the reconstructed matrices
def reconstruction(compressed_one, kernel): reconstructed_data = [ ] for i in range(0,2): for j in range(0, 2): reconstructed_data.append(compressed_one[i, j]*kernel) reconstructed_data = np.array(reconstructed_data) blocks = [ ] b1,b2,b3,b4 =[ ],[ ],[ ],[ ] b1 = x =np.pad(reconstructed_data[0], [(0, 2), (0, 2)], mode='constant',constant _values= 0) b2 = x =np.pad(reconstructed_data[1], [(0, 2), (2, 0)], mode='constant', constant_values=0) b3 = x =np.pad(reconstructed_data[2], [(2, 0), (0, 2)], mode='constant', constant_values=0) b4 = x =np.pad(reconstructed_data[3], [(2, 0), (2, 0)], mode='constant', constant_values=0) return (b1+b2+b3+b4)
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Encoding
Latent matrix
Fig. 8. The final result of encoding and decoding
compressed_one array([[1, 2],[6, 8]]) kernel array([[4, 3],[1, 1]]) data array([[ 4, 3, 8, 6],[ 1, 1, 2, 2],[24, 18, 32, 24],[ 6, 6, 8, 8]]) final = reconstruction(compressed_one, kernal) final array([[ 4, 3, 8, 6],[ 1, 1, 2, 2],[24, 18, 32, 24],[ 6, 6, 8, 8]])
In Figure 8, we can see the result of the autoencoder process as a network of deep learning. First, we build it by adjusting the digits, which we did previous to latent codes extraction using error and trial, to get the appropriate answer. Loss Function and generation of images Our input data is a matrix (4, 4): Data = array([[ 4, 3, 8, 6],[ 1, 1, 2, 2],[24, 18, 32, 24],[ 6, 6, 8, 8]])
To load the above data (4, 4 matrices) into the autoencoder model, we first reshape the data as data. Then, reshape (4, 4, 1) to convert our matrix into a greyscale image which is the accepted data format to our model. Then, we run the autoencoder model using this data, and the loss function obtained is given below. Please see the conclusion respectively for more information (Fig. 9). Loss Import matplotlib.pyplot as plt plt.plot(model_history.history["loss"]) plt.title("Loss vs. Epoch") plt.ylabel("Loss") plt.xlabel("Epoch") plt.grid(True)
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Fig. 9. Loss function
3 Conclusion The main goal was an approach to show the autoencoder applications in intelligent vehicles—the given input results from an optional image of 4x4 output matrix for this purpose. The given data is a matrix of number images representing the different behaviour of the driver or drivers. These data are taken as number images, which may present any unknown behaviour due to an event or events. Furthermore, using the numbers could reduce the complexity of applying the autoencoder application approach, which is the main aim of this paper. They do not belong to specific data types, like only sleepy futures or distraction and interferences. We applied a convolutional neural network matrix using the pooling method with one stride length. The latent matrix for the encoder part is calculated based on the minimum numbers taken from each pooling matrix. The 2 × 2 image matrix of the stride length of one is the feature map. Furthermore, we used the un-pooling method after the presentation matrix (latent/ compressed matrix), which is the input for the decoder parts. Having multiplication the elements of the feature map and the compressed matrix sequentially, we created the decoder part. The loss function was plotted with the slightest error, as the numbers were taken as digital ones and compared with some reshaped images recognized by the program.
References 1. Bank, D., Koenigstein, N., Giryes, R.: Autoencoders arXiv: 2003.05991v2 [cs.LG], 3 April 2021 2. Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: Guyon, I., Dror, G., Lemaire, V., Taylor, G., Silver, D. (eds.) JMLR: Workshop and Conference Proceedings, vol. 27, pp. 37–50 (2012) 3. http://proceedings.mlr.press/v27/baldi12a/baldi12a.pdf 4. https://en.wikipedia.org/wiki/Autoencoder 5. https://www.toptal.com/machine-learning/generative-adversarial-networks 6. Da Cruz, S.D., Taetz, B., Wasenmüller, O., Stifter, T., Stricker, D.: Autoencoder based intervehicle generalization for in-cabin occupant classification. In: 2021 IEEE Intelligent Vehicles Symposium (IV), pp. 1296–1303 (2021). https://doi.org/10.1109/IV48863.2021.9575641
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7. Eskandarian, A.: Introduction to intelligent vehicles. In: Eskandarian, A. (ed.) Handbook of Intelligent Vehicles, pp. 1–13. Springer, London (2012). https://doi.org/10.1007/978-085729-085-4_1 8. Broggi, A., Zelinsky, A., Özgüner, Ü., Laugier, C.: Intelligent vehicles. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics. Springer Handbooks, pp. 1627–1656. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32552-1_62 9. Muhammad, K., Ullah, A., Lloret, J., Del Ser, J., de Albuquerque, V.H.C.: Deep learning for safe autonomous driving: current challenges and future directions. IEEE Trans. Intell. Transp. Syst. 22(7), 4316–4336 (2020) 10. Cheng, Z., Sun, H., Takeuchi, M., Katto, J.: Deep convolutional autoencoder-based lossy image compression. In 2018 Picture Coding Symposium (PCS), pp. 253–257. IEEE, June 2018
Case Study of a Computer-Controlled RC Car Based on RF Wireless Communication Attila Trohak(B)
and Rabab Benotsmane(B)
University of Miskolc, Miskolc-Egyetemváros 3515, Hungary {attila.trohak,iitrabab}@uni-miskolc.hu
Abstract. At the industrial level, great progress has been achieved since the deployment of communication systems. These technologies have succeeded in recent decades, allowing the emergence of growing user needs in terms of accessibility, speed, amount of data and energy consumption. Thanks to communication systems, the intelligence embedded in objects could ensure their connectivity and respond to a need for control or monitoring. The main aim is not only to improve user connectivity but also to connect billions of objects between them. These connected objects are physical elements, autonomous electronic/digital devices, capable of communicating with each other, thus enabling a revolution in technology that brings more ambitious innovations in different areas of application such as medicine, industry, environment or security. Since the appearance of the Industry 4.0 concept, wireless communication became a significant tool to shift to this paradigm, where the machines and robots started to be controlled remotely. Until now, wireless digital data transmissions are based mainly on the modulation of electromagnetic waves (in a very wide range of frequencies and according to various methods). For example, in the industry, we can directly transmit data by radio waves using a radio modem in association with an adequate communication protocol or in association with certain network technology, which adapts to it. Among these different possibilities, we can distinguish the WiFi and ZigBee technologies, all the technologies used by the cellular networks of mobile telephony, and technologies exploited to communicate with the satellites. We can also consider RFID technology (Radio-Frequency IDentification) which is essentially used as a proximity interface for the purpose of identification and traceability. Apart from industrial applications, we can find Bluetooth networks, the radio local loop (BLR), Wimax and WUSB (Wireless USB). This paper presents a case study on the RF communication of remote-control toy vehicles. The paper will be divided into three parts. The first part highlights a literature review of RF communications and their use in the remote control. The second part mainly describes our measurement system and the used vehicles. Finally, we will show the results of our measurements and show the direction to substitute the remote controllers of the vehicles for building a centralized control for multiple vehicles. Keywords: Wireless communication · Remote control · RC car · RF
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 11–23, 2023. https://doi.org/10.1007/978-3-031-15211-5_2
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A. Trohak and R. Benotsmane
1 Introduction Nowadays, technological solutions and technical innovations have enabled the massive deployment of connected objects known as the Internet of Things concept, especially at the industrial level, where great progress has been achieved since the appearance of communication protocols between the different systems [1]. These technologies presented as a new way of communication succeeded in fulfiling user needs in terms of accessibility, speed, amount of data and energy consumption. Thanks to communication systems, the intelligence embedded in objects could ensure their connectivity and respond to a need for control or monitoring [2]. The main aim is not only to improve user connectivity but also to connect billions of objects between them. These connected objects are physical elements, autonomous electronic/digital devices, capable of communicating with each other [3], thus enabling a revolution in technology that brings more ambitious innovations in different areas of application such as medicine, industry, environment, or security. Since the appearance of the Industry 4.0 concept [4], in the communication field, wireless communication became a significant tool to shift to this paradigm, where machines, robots, and plenty of devices have started to be controlled remotely. Until now, wireless digital data transmissions are based mainly on the modulation of electromagnetic waves (in a very wide range of frequencies and according to various methods) [5]. For example, in the industry, we can directly transmit data by radio waves using a radio modem in association with an adequate communication protocol or in association with certain network technology, which adapts to it [6]. Among these different possibilities, we can distinguish the WiFi and ZigBee technologies [7], all the technologies used by the cellular networks of mobile telephony, and technologies exploited to communicate with the satellites. We can also consider RFID technology (Radio-Frequency IDentification) which is essentially used as a proximity interface for the purpose of identification and traceability. Apart from industrial applications, we can find Bluetooth networks, the radio local loop (BLR), Wimax (Worldwide Interoperability for Microwave Access), and WUSB (Wireless USB). In this paper, our focus is directed to dealing with RF communication technology, where RF refers to radio frequencies within the electromagnetic spectrum associated with the propagation of radio waves. RF current creates electromagnetic fields when applied to an antenna, propagating the applied signal through space [8]. Communications based on electromagnetic waves have been used for many decades, especially for wireless voice communications and data communications. The frequency of the RF signal is inversely proportional to the wavelength of the field. The rate of oscillation of radio frequencies is between approximately 30 kHz and 300 GHz. RF technology can fulfil many advantages in communication field. The RF systems developed are based on the possibility of being mobile, embedded and/or capable of adapting to the environment in which they operate [9]. The RFM team focuses on wireless communication technologies to provide a connection that is as optimal as possible, taking into account constraints such as the cost of devices, energy consumption, and performance speed [10]. In this paper, we present a case study of a computer controlled remote control toy, where the toy is presented as an RC vehicle tele-operated with the computer using RF wireless communication protocol, the aim of the paper is to create a process that comports different set of vehicles, the control of these vehicles is substituted with the same remote control, which is a joystick remote control based
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on RF communication of 2.4 GHz. The paper will be divided in three parts, the first part highlight a literature review of RF communication, and its use in remote control field, this literature facilitates the comprehension of how it works the embedded system that we have, where the second part is mainly based on describing our system and its architecture, finally, we will show the results of the case study obtained and how we can build from the computer a remote control for other devices. 1.1 Stat of the Art of RF Wireless Communication Protocol Radio frequencies are electromagnetic waves, also called waves, radio electric or Hertzian frequencies, where electromagnetic waves result from the combination of an electric wave and a magnetic wave, which propagate in space at the speed of light. Each wave is associated with a particle (photon) whose own energy depends on the frequency. The frequency (in Hertz, Hz) corresponds to the number of oscillations per second; it is inversely proportional to the wavelength λ (in meters), representing the distance separating two oscillation points. The range of radiation extends from zero frequency corresponding to static electric and magnetic fields to infinity, as described in Fig. 1.
Fig. 1. Electromagnetic spectrum
Electromagnetic waves, unlike mechanical waves, do not need physical support to travel. Depending on their length and frequency, electromagnetic waves fall into different categories. Those that can be visible since they are perceptible, and others, which are invisible and cannot be perceived by the eye. The wavelengths of electromagnetic rays vary between 0.001 nm and 100 m, where different types exist according to the wavelength and the range of the frequency, as shown in Fig. 2: 1.2 Transmission of the Information in the Wireless Communication System The purpose of digital communication is to convey information in forms between a source and one or more recipients according to radio frequency propagation [11]. The signals transported can be either direction of the digital origin or analogue (speech, image, text…), but converted into digital form. The task of the transmission system is to transmit information from the source to the recipient as reliable as possible. The characteristics of the transmission environment are very important and directly affect the design of communication systems and their functions. If the message generated by the
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Fig. 2. Electromagnetic spectrum types
source is of the analog type, it is converted into a sequence of bits by successive stages of sampling, quantization, and binary coding [12]. In the ideal case, this sequence should be the shortest possible. Therefore, a source encoder compresses data by eliminating insignificant bits in an effort to increase transmission efficiency and optimize system resource usage. During transmission, the signal is tainted by noise and distorted by interference, generating errors at the receiver. In order to increase the reliability of the transmission, a channel coder was introduced to perfectly control redundancy in the sequence of information. This coding is known as the detective and error-correcting coding since the receiver knows the law coding used and can detect and possibly correct the data’s wrong binaries. However, this improvement in the quality of the message comes at the expense of the overall transmission rate, and if we also refer to the work conducted by Shannon on information theory, channel coding is only feasible if the source bit rate binary is less than the capacity of the transmission channel [13]. At the output of the channel encoder, the binary information sequence is injected into a modulator, which is the process of preparing the signal to be suitable for transmission. This is achieved by superimposing our original information onto a high frequency signal known as a carrier wave (or sinusoidal signal). In the modulation process, a parameter of the carrier wave (such as amplitude, frequency, or phase) is varied in accordance with the modulating signal. This variation acts as a code for data transmission. The transmitter then transmits the final signal called the modulated signal; the receiver demodulates the received modulated signal and gets the original information signal back. Figure 3 shows the synoptic structure of the transmission chain in wireless communication, where the basic functions are defined as follows: • The source: It sends a digital message in the form of a sequence of binary elements. • The encoder: can possibly delete insignificant elements (data compression or source coding), or on the contrary add redundancy in the information in order to protect it against noise and disturbances present on the transmission channel (channel coding). The channel coding is only possible if the source bit rate is lower than the capacity of the transmission channel (the probability of error, in this case, tends towards a zero value according to the work of Shannon [10, 31, and 32]). • The digital modulator: the digital message being a series of elements binary will be associated with a physical representation in the form of a signal. Modulation consists in associating with each element or group of elements, a waveform according to a
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Fig. 3. The synoptic structure of the transmission chain in wireless communication
modulation law, the whole then forming a signal likely to be sent in the baseband channel. Each waveform associated with a group of bits is called a “symbol”. • The propagation channel: describes the physical support used to transmit the information of the transmitter to the receiver, in our case is, the air. • The demodulator: the signal is re-amplified and demodulated, i.e. making a new frequency transposition in order to obtain the baseband signal. • The decoder: consists of performing the reverse operations of the encoder in order to find the initial binary information. Therefore, the transmission system’s performance depends on many parameters such as the characteristics of the channel, the transmission power, and the choice of coding or even the waveform used. As we described earlier that the modulation is an essential step in the transmission of the information, where two types exist: - analog modulation and digital modulation. Actually, the digital modulation is more used in web science, where it provides greater capacity information compared to analog modulation, compatibility with digital data services, and a level of higher data security, higher quality communications, and faster system availability. The radio frequency spectrum (Radio Frequency RF) must be shared between the different users, or the request for the services of communication increases daily. This operation consists of modifying one or more parameters of a carrier wave of the form sinusoidal of the general expression: S(t) = Acos(ωt + ϕ) where: A is the amplitude, ω is the angular frequency, ϕ is the phase. In general, there are three families of digital modulation using the amplitude, the frequency, and the phase of the modulated signal: • Amplitude Shift Keying ASK: (Amplitude Shift Keying ASK) it consists of varying the amplitude of the carrier signal. • Frequency shift keying FSK: it is the instantaneous frequency, the derivative of the instantaneous phase, which can take several values associated with the possible states. • Phase shift keying PSK: the only parameter likely to vary is the phase of the carrier wave.
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2 Materials and Methods The objective of this paper is to present our research results in the field of RF communication of RC vehicles. We did our research to get familiar with the operation of communication to find out how it would be possible to substitute the remote controllers with a central radio. As each vehicle has its own controller, therefore the target is to substitute more controllers in one. The communication between the controller and the vehicle is RF transmission. In the following, a deep description is presented to understand the communication used by the different vehicles. 2.1 Description of the Vehicles In our research first, we examined three kinds of construction vehicles: Dump truck – Excavator – Front loader from Jamara toy company. Later we extended our research with a car with omnidirectional wheels. The four vehicles are controlled with their remote controller, as shown in Fig. 4.
Fig. 4. The used construction vehicles
Based on the texts printed on the vehicles and controllers, we knew they use a 2.4 GHz communication, but nothing more. So, we opened one of the controllers of the construction vehicles. We have analyzed the PCB (Printed Circuit Board), and we could identify the used RF MCU by searching for 24GTX3. In the found documentation, we could get closer to the frequency range they should use. It was 2.41–2.475 GHz. We
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continued our research to make measurements in this range to have more knowledge regarding their communication. 2.2 Measurement Tools As we aimed to understand the RF communication between the controller and the controlled vehicle, we started to work with a Rohde & Schwarz FSH8 handheld spectrum analyzer. We used it to make measurements at the frequency range we identified earlier in the documentation. With its help, we were able to exactly identify the frequencies used by the different vehicles. The measurement results will be introduced in Sect. 3. Figure 5 describes FSH8 and the USRP devices.
Fig. 5. The used measurement systems
As we planned to capture data at the measured frequencies, we started to deal with NI USRP (Universal Software Radio Peripheral) devices (NI USRP 2921, NI USRP 2952R 40 MHz BW). The choice of these devices was based on their ability to receive and transmit RF data based on custom settings. As the NI USRP 2921 is only able to handle one carrier frequency even in MIMO mode, we decided to go forward with the 2952R which is able to work with two radio interfaces with different carrier frequency settings. For handling the devices, we used the NI LabView software environment. We programmed and parametrized them to capture RF data sent by the remote controllers when we pushed their buttons to be able to analyze it more deeply. The captured data was exported to Excel to be able to handle them with custom algorithms. It was necessary to write custom algorithms to slice the data into smaller units, as we had 500 000 measurement points for every button on a controller for every frequency they use. For this slicing procedure, we developed a function and a subroutine
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in VBA (Visual Basic for Applications). It made it possible to easily visualize and compare the different cases and parts of the RF communication.
3 Results and Discussion 3.1 Identifying the Used Carrier Frequencies As a first and simple measurement, we used the FSH8 spectrum analyzer. We tried to do our measurements in an RF free environment to be able to clearly see the purely used frequencies of the remote controllers. As Fig. 6 shows, we set the start and stop frequencies to that we found in the documentation (2.41–2.475 GHz). We applied settings to get the results as it is shown. This measurement was made with a Jamara controller, and it shows it is communicating on four frequencies described by the four highest peaks.
Fig. 6. The used frequencies of a Jamara controller
As a next measurement, we went forward with the car with omnidirectional wheels. As for this vehicle, we could not identify the used RF chip after opening its controller, and we started our above-mentioned measurement with a wider frequency range. As a result, we could finally identify two frequencies used by this controller, as shown in Fig. 7. In the figure, we marked the found frequencies with markers (M1 = 2.42439 GHz, M2 = 2.6001 GHz). After knowing the frequencies where the information is carried, we could start to prepare for measuring with the USRP devices.
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Fig. 7. The used frequencies of the car’s controller
3.2 Monitoring the Used Carrier Frequencies As a next step, we monitored one of the carrier frequencies of the car’s controller to see what was in the air. Figure 8 shows us on an intensity chart in LabView that something is transferred at the carrier frequency we identified earlier.
Fig. 8. Intensity chart in LabView
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At this point, we configured the NI USRP 2952R 40 MHz BW to monitor and capture data from the two carrier frequencies of the car’s controller simultaneously. After a detailed inspection of the RF waves, we applied the following settings: • CH0 (white): 2.425 GHz as the carrier frequency, • CH1 (green): 2.46 GHz as the carrier frequency. And the common settings for both channels are: • IQ Rate [S/sec] is 2.4M, • Gain is 30 as we measured without mounted antennas to cancel disturbing frequencies, • The number of samples per fetch is 500 000. Figure 9 shows the samples captured from two carrier frequencies.
Fig. 9. Samples captured from two carrier frequencies
We found that four “packets” are transferred on every carrier frequency periodically. Next, we wanted to know the “content” of these packages to be able to determine if they are similar or different. For this purpose, we tried to zoom in on them. Figure 10 shows four packets.
Fig. 10. Samples of four packets at one carrier frequency
When we zoomed in on one packet, we found that it will be impossible to compare and analyze more packets with “print screening” them. We decided to export the captured
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data to handle it in Excel. Finally, with the car’s remote controller, we took captures of 10 button states and the idle state, when we also experienced communication. It led to having 22 pieces of captures with 500 000 samples in each as we saved the different channels into different files. 3.3 Analyzing the Captured Data In Excel, we started the analyzing process to understand the captured data and try to find out the content and the roles of the different parts when we pushed a button on our controller. This step is essential to be able to build a centralized system in the future which is able to substitute the controllers of the vehicles to transmit RF signals by an algorithm instead of using their buttons. When we started to analyze the captured data in Excel, we found that a packet is around 400 samples long. At this point, we are faced with that problem, it is impossible to scroll and select data from 500 000 samples to copy and to try to plot in charts. Usually, a capture contained six times four packets. This situation inspired us to develop a custom algorithm to slice the 500 000 samples into packets. Figure 11 shows a packet sliced with our algorithm from the captured data of pushing the right button on the car’s controller.
Right Buon CH0 0.003 0.002 0.001
-0.001
1 17 33 49 65 81 97 113 129 145 161 177 193 209 225 241 257 273 289 305 321 337 353 369 385 401
0
-0.002 -0.003
Fig. 11. Samples of one packet at one carrier frequency when pushing the right button
When we saw the packets on a big screen and started to analyze them, we found interesting things. First, we tried the define scenarios and cases for comparison of packets. As we have already mentioned in one capture in the 500 000 samples, we had six segments each with four packets, which means 24 packets for one button state at one carrier frequency. We tried to find out if the segments were the same, or similar, but after a while, they were repeating! For this, we should plot the right packets of the investigated segments to identify if they match. If the signal is a bit noisy and because of
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the large number of cases, it is hard to be done visually. If we also would like to identify differences when using the different buttons of the controller, that will generate new cases for comparison. In Fig. 12, we plotted the first packets of the first two segments of the captured data to compare them. It also highlights the well-spread solutions used in RF communication at this frequency range, the FSK (Frequency Shift Keying) and PSK (Phase Shift Keying), which also describes our packets. From the figure we can assume that the first half (until around sample no. 200) of the packet is the same. In the second half, we assume that they are also the same if we apply PSK at the right positions.
Fig. 12. The first packets of the first two segments when pushing the right button
But finally, if we also found that when we tried to compare packets of different button cases, they can be completely different. Because of that, the order of the segments is shifted in the different captures. To find the right and fitting pairs of packets is impossible to do with 24 * 22 cases visually.
4 Conclusion This paper describes our research results in RF communication of RC vehicles at 2.4 GHz, where the main aim is to find out how it would be possible to substitute the remote controller with another RF device, which can be centralized and programmable to control multiple vehicles. We applied different hardware and software tools to measure, capture and analyze the communication to go forward. At this point of our research, we could successfully capture and analyze the data. Based on the obtained results, we would highlight two directions on how to build the centralized system. One way can be that, when we apply intelligent data processing methods to identify the similarities in the captured data to try to find rules and details about the used communication protocol. The second way can be that when we don’t try to understand the communication just want to record and replay it.
References 1. Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38, 393–422 (2002). https://doi.org/10.1016/S1389-1286(01)00302-4
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2. Artusio-Glimpse, A., Simons, M.T., Rvger, I., et al.: Measurement of radio-frequency radiation pressure. In: CPEM 2018 - Conference on Precision Electromagnetic Measurements (2018). https://doi.org/10.48550/arxiv.1802.05551 3. Bai, K.S.N.: (1AD) A Review on Wireless Communication Protocol and Security Privacy: Connectivity - UDP Protocols, 11 August 2017. https://services.igi-global.com/resolv edoi/resolve.aspx?doi=104018/IJWNBT2019070102.. https://doi.org/10.4018/IJWNBT.201 9070102 4. Benotsmane, R., Kovács, G., Dudás, L.: Economic, social impacts and operation of smart factories in Industry 4.0 focusing on simulation and artificial intelligence of collaborating robots. Soc. Sci. 8, 143 (2019). https://doi.org/10.3390/SOCSCI8050143 5. Gallager, R.G.: Principles of Digital Communication. Cambridge University Press, Cambridge (2008) 6. Jazdi, N.: Cyber physical systems in the context of Industry 4.0. In: Proceedings of 2014 IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR 2014. IEEE Computer Society (2014) 7. Kountchev, R., Mahanti, A., Chong, S., Patnaik, S., Favorskaya, M. (eds.): Advances in Wireless Communications and Applications. SIST, vol. 191. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5879-5 8. Li, S., Da, X., Zhao, S.: 5G Internet of things: a survey. J. Ind. Inf. Integr. 10, 1–9 (2018). https://doi.org/10.1016/J.JII.2018.01.005 9. Rákay, R., Galajdová, A., Šeminský, J., Cviti´c, I.: Selected wireless communication protocols and their properties for use in IoT systems. Res. Pap. Fac. Mater. Sci. Technol. Slovak Univ. Technol. 27, 26–32 (2019). https://doi.org/10.2478/RPUT-2019-0022 10. Saad, C., Mostafa, B., Cheikh, E.A., Abderrahmane, H.: Comparative performance analysis of wireless communication protocols for intelligent sensors and their applications. Int. J. Adv. Comput. Sci. Appl. 5 (2014) https://doi.org/10.14569/IJACSA.2014.050413 11. Sampei, S.: Applications of Digital Wireless Technologies to Global Wireless Communications. Prentice Hall PTR, Hoboken (1997) 12. Verdü, S.: Fifty years of Shannon theory. IEEE Trans. Inf. Theory 44, 2057–2078 (1998). https://doi.org/10.1109/18.720531 13. Security in WLAN, WPAN, WSN and WMAN through Wi-FiTM, BluetoothTM, ZigBeeTM and WiMAXTM. In: Labiod, H., Afifi, H., Santis, C.D. (eds.) WI-FI TM, BLUETOOTH TM, ZIGBEE TM AND WIMAX TM, pp. 153–220. Springer, Dordrecht (2007). https://doi.org/ 10.1007/978-1-4020-5397-9_6
Nonlinear Model Predictive Control for Autonomous Quadrotor Trajectory Tracking Rabab Benotsmane(B)
and József Vásárhelyi(B)
University of Miskolc, Miskolc-Egyetemváros 3515, Hungary {iitrabab,vajo}@uni-miskolc.hu
Abstract. It is well known that all the physical devices that surround us, in our daily life are nonlinear systems, the meaning in which the change of the output is not proportional to the change of the input, which results from changes in variables over time, to model such system with the aim to control it, was always an issue for the researchers, where from the literature review, many methods and solutions were created in order to facilitate the task. One of the most efficient approximations was linearizing the system and using linear or nonlinear controllers, so the theoretical part can be more friendly to handle it, the model still is far from reality, therefore it can contain errors and not reliably responds as the real environment. The paper is based on the nonlinear controller system. The controller is embedded with the nonlinear model predictive tool with the aim to track the path executed by the quadrotor. The paper is divided into three sections, the first section highlights a literature review about the efficiency of the linear and nonlinear controllers in the automation and control field, focusing on the nonlinear controllers. In the second section a case study of the nonlinear predictive control application for Tello quadrotor is presented. Finally, the simulation results are discussed, and a comparison is made to understand the potential of using a nonlinear controller instead of a linear one. Keywords: Nonlinear MPC · Controllers · Dynamic system · Linear system · Model predictive control
1 Introduction Drones are autonomous or semi-autonomous flying machines capable of being selfpiloted or remote-controlled. The abbreviation known UAV, which refers to “Unmanned Aerial Vehicles”. For decades, unmanned aerial vehicles or drones have been designed for the military purpose, where the objective was to replace the human being when the task can be harmful or repetitive, especially in a hostile environment where the pilot’s safety was no longer be guaranteed. Nowadays, the interest for the scientific community in UAV is growing rapidly [1, 2], where their uses now extend to civilian areas such as the management of infrastructure (dams, high voltage lines), meteorological services (measurement of the temperature and humidity), telecommunications (telecommunications relay), as well as aerial photography (press, film production). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 24–34, 2023. https://doi.org/10.1007/978-3-031-15211-5_3
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Today there is a wide variety of drones, where the classification can be done according to several criteria, including size (length, wingspan), airfoil (fixed, rotating, flapping), altitude or stamina [3]. In this paper, we are particularly interested in the quadrotor type from the Tello brand market, which is a flexible and lightweight aircraft having four rotors that tilt up to provide lift for takeoff and can then be tilted forward for propulsion while in flight. The four rotors have propellers, one pair of four propellers rotates clockwise compared to the other pair, and the flight path is controlled by the speed of each rotor. The main target is to deal with the control part for such a system, to design a control law to follow trajectories designated by the user. This is because this type of drone presents some challenges, not only is it sensitive to disturbances due to the strong coupling between the state variables and the control inputs, but in addition, its dynamics are under-actuated, i.e., the number of inputs which is 4° is less than the number of 6 degrees of freedom. Therefore, the control technique to adopt must overcome these obstacles. The design of control law is based on the knowledge of the model of the system, in particular, its physical parameters. A variation of these is likely to degrade the response of the closed-loop system or even cause instability [4]. In these conditions, it would be necessary to reconfigure the controller to adapt it to the new model, which considers the modification of the parameters. Often this requires work additional for the recalculation of control gains. Therefore, the modelling part presents an important step before continue dealing with the control part, where one should avoid any mistake in building a nonlinear mathematical model nearly equal to the real one. From the literature review, researchers have already designed the kinematic and dynamic models for the drone-based on Newton Euler equations or Lagrangian derivatives [5, 6]. So only the physical parameters can be changed to one model to another, also depending on the disturbance taking into consideration, and the different flight conditions (stationary, in translation, or in rotation). The mathematical model of the drone is always nonlinear, this is due to the considerable coupling between the state variables of the system and its control inputs, which makes the system more interesting to deal with the control theory of its model [7–9] and [10]. Many scientists dealed with this topic by proposing plenty solutions, the most used linearize the original system, so the model became linear and easy to handle with the classic control methods, others suggest to go forward with the original nonlinear model and build controllers that handle the situation as it is [11–13] and [14]. In this paper, we focus on the nonlinearity concept for the quadrotor system, which presents a big deal for the engineers since a long time, the main target of the paper is building a nonlinear controller system which fulfils the requirements of our Tello drone. The first section highlights a literature review about the efficiency of the linear and nonlinear controllers in the automation and control field, focusing on the nonlinear controllers and which up-to-date tools are used for drone controlling, where in the second section a case study of the nonlinear predictive control application for Tello quadrotor is presented. Then in the last section the simulation results are discussed, and a comparison is made to understand the potential of using nonlinear controller instead of linear one.
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2 Materials and Methods The aim of this paper is to present a methodology for simulating the behaviour of UAV quadrotor based on a nonlinear MPC controller; the implementation of the simulation scenarios is based on real parameters, where we used Tello EDU quadrotor as a plant model, this type of drone is useful for education and research purposes, the main characteristics for continuing the research development with this drone model is the lightweight and the embedded system, where the flight controller can be upgraded by using more effective controller techniques. 2.1 Tello Quadrotor Dynamic Mathematical Model To success in tracking trajectory for the aircraft, or to control its commands and avoid obstacles in its environment, we should first understand what is happening in the surrounding; this leads us to understand how our system is moving, which kind of forces and torques are applied, how many degrees of freedom we have compared to the inputs of the system, which velocities are required to have different flight modes, these definitions are presenting as kinematics and dynamics equations of the system. The modelling of UAV is a delicate task since the dynamics of the system is strongly nonlinear and fully coupled. Many literature reviews explained the methodology for modelling such systems, in this paper is presented only the nonlinear dynamic model for and the main parameters for Tello drone. In order to better understand the model dynamic developed below, here are the different working hypotheses: • The structure of the quadrotor is assumed rigid and symmetrical, which induces that the matrix of inertia will be assumed diagonal, • The propellers are assumed rigid in order to be able to neglect the effect of their deformation during of rotation. • The center of mass and the origin of the frame linked to the structure coincide. • The lift and drag forces are proportional to the squares of the speed of the rotation of the rotors, which is a very close approximation of the behaviour aerodynamic. • To evaluate the mathematical model of the quadrotor we use two frames, an inertial fixed frame to the earth Rf and the second mobile frame fixed in the quadrotor Rm . The passage between the body frame and the inertial frame is given by the transformation matrix T , which contains the orientation and the position mobile reference relative to the fixed reference. Figure 1 shows the axis convention token for Tello drone to model the mathematical dynamic, where the forces, inertial moments, and angular velocities are described. From Fig. 1 the motion of the quadrotor can be expressed with 6 DOF, including three T translations and three rotations x y z φ θ ψ , so the dynamic structure is as follows: the four rotors (numbered from 1 to 4) create forces called thrust forces, where: Fi = b · ωi2 , i = 1 . . . 4 the number of rotors, ω is the angular velocity of the rotor, and b is the thrust constant of the quadrotor. The differential thrust forces between F2 and F4 generates the rotation around X axis called roll motion, where the differential thrust forces between F1 and F3 generates the
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Fig. 1. Modelling frame assignments for Tello quadrotor
rotation around Y axis called pitch motion, and differential torques between clockwise and anticlockwise rotors, i.e., (τ1 − τ2 + τ3 − τ4 ) generates the rotation around Z axis called yaw motion, other force is taking into consideration, known as the drag force, which is expressed by Fd = d · ω2 , where d the drag constant, the thrust and the drag forces are the main forces responsible of the creation of the three inertial moments around X , Y , Z, so we have: ⎧ ⎨ Mx = l(F4 − F2 ) = lbω42 − ω22 2 2 (1) M = l(F 32− F12) = lb2 ω3 −2 ω1 ⎩ y Mz = d ω1 − ω2 + ω3 − ω4 where: l is the length of the arm between the rotor and the center of gravity of the quadrotor. Other inertial moments as Gyroscopic effect and aerodynamic friction can be taking into consideration as disturbances, in our nonlinear model, we assume that these disturbances are neglected. To summarize the system has 4 inputs U and 6 outputs Y expressed as follow: ⎤ ⎡ ⎡ ⎤ ⎡ ⎤⎡ 2 ⎤ Ft b b b b u1 ω1 ⎢ u2 ⎥ ⎢ Mx ⎥ ⎢ 0 −lb 0 lb ⎥⎢ ω2 ⎥ ⎥ ⎢ ⎥ ⎢ ⎥⎢ 2 ⎥ (2) Inputs: ⎢ ⎣ u3 ⎦ = ⎣ My ⎦ = ⎣ −lb 0 lb 0 ⎦⎣ ω2 ⎦ u4
−d d −d Outputs: x y z φ θ ψ T Mz
d
3
ω42
(3)
Using Newton – Euler equations, the dynamics of the quadrotor under external forces and moments in the mobile frame is described with two equations: • Dynamic equation of translation motion m˙vinertial =
Fexternal
(4)
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where m is the masse of the rigid body and vinertial and Fexternal are the linear velocity and the forces projected in the inertial frame respectively, by the simplification we get: ⎧ 4 ⎪ x¨ = m1 (CφSθ Cψ + SφSψ) Fi ⎪ i=1 ⎪ ⎨ 4 F (5) y¨ = m1 (CφSθ Sψ − SφCψ) i i=1 ⎪ ⎪ ⎪ 4 1 ⎩ z¨ = (CφCθ ) i=1 Fi − g m • Dynamic equation of rotation motion ˙ + × J = J
Mexternal
(6)
where in this equation represent the rotation velocity presented in the inertial axes, J is the inertial tensor of the symmetric rigid body around its center of mass. Mexternal represent the sum of moments acting on the vehicle, ⎡
⎤ Jxx 0 0 J = ⎣ 0 Jyy 0 ⎦ 0 0 Jzz
(7)
where Jxx , Jyy , Jzz are calculated according to the mass ditribution, by doing the simplification, we get: ⎧ 2 2 ˙˙ ⎪ ¨ = −(Jzz −Jyy )θ ψ+lb ω4 −ω2 ⎪ φ ⎪ J xx ⎨ θ¨ =
⎪ ⎪ ⎪ ⎩ ψ¨ =
2 ˙ −(Jzz −Jxx )φ˙ ψ+lb ω3 −ω12 Jyy ˙ −(Jyy −Jxx )φ˙ θ+d ω12 −ω22 +ω32 −ω42 Jzz
(8)
2.2 Control Strategy for Nonlinear Dynamic Model The obtained dynamic model shown above is purely nonlinear, to go forward with the control field, first, we identify the states of the system as follows, where we can have plenty suggestions, in our paper we will go forward with this notation: T X = x y z φ θ ψ x˙ y˙ z˙ φ˙ θ˙ ψ˙
(9)
− → − → → X˙ = f ( X , − u)
(10)
where X denotes the state variable, and u is the input control force. From the literature review, to study the control behaviour of the system, it is always better to linearize the dynamic model around an equilibrium point X_e, in order to facilitate the calculation part; this process requires the identification of one state of the quadrotor, mostly we
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choose the hover position, where only the thrust force is applied against the weight force mg, to take off the quadrotor. This means that we have the following: T Xe = xe ye ze 0 0 0 0 0 0 0 0 0
(11)
T ue = mg 0 0 0
(12)
By replacing these vectors and applying Taylor series expansion, one can get the linear model that facilitates the control part, so one can build different linear controllers mentioned earlier, this can be a good solution but not a robust one, because the linear model is not a universal model, which create the uncertainties that make the controller incapable to cancel them. Therefore, in the paper a controller is presented using the original “nonlinear dynamic model” for the quadrotor. The required controller for this task is a nonlinear type in order to achieve better performance, where the linear controller type is incapable of calculating the exact inputs that guarantee the desired trajectory. Model predictive controller can be a good choice for such system, this system is a closed-loop implementation of optimal control, where the control strategy is based on the prediction that simulates the dynamic model in real time. The controller architecture is built from the dynamic model of the plant. So, it is able to anticipate the behaviour of the system in the future, this function is known as prediction horizon, using a cost function. The controller starts to optimize the problem based on the injected inputs, which are the state variables of the system (the input vectors of the system: the force and the three inertial moments around the three axes; the desired trajectory). The model predictive controller has different versions “linear and nonlinear version, or hybrid one”, in the following a deep explanation of nonlinear MPC for the dynamic model of Tello drone and the methodology to design the right architecture for such controller type is presented. 2.3 Open-Loop Simulation After getting the dynamic mathematical model for Tello quadrotor, the Simulink blocks for the plant were built; this helped to observe the behaviour of the drone regarding the mathematical model. The open-loop simulation is based on giving desired inputs, so we can observe the change of the outputs. Figure 2 describes the Simulink blocks of the open-loop system for the Tello quadrotor.
Fig. 2. Open-loop Simulink blocks for Tello quadrotor
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The inputs of the block Actuators present the four voltage values for each rotor, where block Actuator calculates the angular velocity for each rotor, so the four angular velocity values will be the inputs of the block Nonlinear dynamic model plant that contains the mathematical model function, using another function we could calculate from the angular velocity values, the main vector input that includes u1 , u2 , u3 , u4 , using scoop block, we can visualize the simulation result of the position and orientation states. The results of this section are presented in Sect. 3. 2.4 Nonlinear Model Predictive Control This version of the controller is useful if we have a nonlinear model plant that cannot be approximated by a linear model or we have nonlinear constraints and a cost function, then the traditional linear MPC will not be sufficient to control the system, instead we can use a nonlinear MPC which works similar to traditional MPC in the sense that it uses a prediction model and solves an optimization problem to compute the control actions, the key difference between the nonlinear and linear MPC is that the nonlinear MPC uses a nonlinear prediction model presented as the model of the plant, and a nonlinear constraint, and a non-quadratic cost function. Figure 3 shows how the actual control action presented as the measured output is calculated by solving, at each sampling time, a finite prediction horizon open-loop optimal control problem, this is by taking the actual state of the plant as the initial state; the optimization yields an optimal control sequence, and the first control in this sequence is applied to the plant [15–17] and [18].
Fig. 3. Nonlinear MPC concept
2.5 Design Nonlinear MPC for Tello Quadrotor System in Simulink The nonlinear dynamic model was designed starting from the previous open-loop model adding the MPC controller. Then one had to reconfigure the internal structure according to the system and tuning the main parameters to achieve the high performance. Regarding to our system, the controller should receive different input signals: the reference trajectory – 12 state variables –last 4 manipulated variables that present the four control inputs variables u1 , u2 , u3 , u4 as presented in Fig. 4. In this case was neglected the gyroscopic effect and the other disturbances applied on the system.
Nonlinear Model Predictive Control for Autonomous Quadrotor Trajectory Tracking
31
Fig. 4. Nonlinear MPC controller inputs – outputs
It is well known that nonlinear controllers require a huge time simulation, therefore, to reduce this process and to guarantee a high performance for the system, it is always better to calculate the Jacobian function of the quadrotor, this function will fast the simulation efficiency. Table 1 identifies the necessary parameters for the nonlinear MPC that fit Tello quadrotor. The weight parameter in the controller refers to the priority of the variables to track; usually in the quadrotor, we set higher priority for the position variables than the orientation variables, in our case we are looking for the best performance so we set higher priority for the all six variables. Figure 5 presents the block diagram of the nonlinear model predictive controller for the Tello quadrotor, where the block trajectory generator presents a function that generates the desired position and orientation for the quadrotor plant. Table 1. The required parameters of nonlinear MPC for Tello quadrotor Nonlinear MPC parameters Sample time (Ts )
0.1 s
Prediction Horizon (P)
18 s
Control Horizon (M)
2s
Nonlinear MPC constraints Boundaries of u1
[0–12] N.m
Boundaries of u2
[0–10] N.m
Boundaries of u3
[0–10] N.m
Boundaries of u4
[0–10] N.m
Nonlinear MPC weights Translation motion along x
1
Translation motion along y
1
Translation motion along z
1
Pitch motion along φ
1
Roll motion along θ
1
Yaw motion along ψ
1
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R. Benotsmane and J. Vásárhelyi
Fig. 5. Block Simulink of Tello plant model with nonlinear MPC
3 Results and Discussion 3.1 Open-Loop Simulation Results By giving different voltage values to the main system, we could visualize the following behaviour for the position and orientation variables, as shown in Fig. 6.
Fig. 6. Simulation results of the open-loop system
From Fig. 6, we observe that the system behaves in the same as in the real environment. By generating the same value of voltage for the four inputs, means that the four angular velocities are equal, so we will have an increased value in X, Y, Z positions, where no rotation around axis can be done,
Nonlinear Model Predictive Control for Autonomous Quadrotor Trajectory Tracking
33
3.2 Closed-Loop Nonlinear MPC Simulation Results To check the performance of the nonlinear controller, the following desired trajectory function was set to the nonlinear controller, so we can track the behaviour of the outputs x y z φ θ ψ : ⎧ 6 · sin 3t ⎨x = t t sin(t) · cos y = − 6 · sin 3 3 ⎩ z = 6 · cos 3t
where t = [0 − 20 s]
(13)
Figure 7 presents the tracking trajectory compared to the reference one according to the three axes, respectively X, Y and Z and their orientations φ, θ, ψ. Figure 7 visualizes that the controller tracks the desired trajectory with high performance, where for the three axes of the position X, Y, and Z the three actual diagrams match their reference diagrams, for the three angles of the orientation states are driven to the neighbourhood of zeros within 4[s].
Fig. 7. Simulation results of the outputs variables regarding nonlinear MPC
4 Conclusion In this paper, a case study was presented about how to control a nonlinear dynamic model for the quadrotor, which presents an unstable system regarding to its lightweight mass. The case study prepares a new research program for adaptive computing. The closedloop control is based on using a nonlinear strategy with the application of Nonlinear Model Predictive Control controller. It was presented the effectiveness results of the designed controller, where the desired trajectory is well tracked by the computation of the high performed control inputs. The disturbances, in this case, were neglected, so only the internal forces were taken into consideration. The Jacobian function is an important
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tool to guarantee a smooth track and fast simulation, where the setting of prediction horizon and the control is always depending on the system parameters. For the future, we are looking to import this control model to a real embedded system, in order to see the real behaviour of our Tello drone.
References 1. Zhou, Q.L., Zhang, Y., Rabbath, C.A., Theilliol, D.: Design of feedback linearization control and reconfigurable control allocation with application to a quadrotor UAV. In: Conference on Control and Fault-Tolerant Systems, SysTol 2010 - Final Program and Book of Abstracts, pp. 371–376 (2010) 2. Elmeseiry, N., Alshaer, N., Ismail, T.: A detailed survey and future directions of unmanned aerial vehicles (UAVs) with potential applications. Aerospace 8(12), 363 (2021) 3. Custers, B.: Drones here, there and everywhere introduction and overview. In: Custers, B. (ed.) The Future of Drone Use. Information Technology and Law Series, vol. 27, pp. 3–20. T.M.C. Asser Press, The Hague (2016). https://doi.org/10.1007/978-94-6265-132-6_1 4. Ducard, G.J.J., Allenspach, M.: Review of designs and flight control techniques of hybrid and convertible VTOL UAVs. Aerosp. Sci. Technol. 118, 107035 (2021) 5. Zhang, X., Li, X., Wang, K., Lu, Y.: A survey of modelling and identification of quadrotor robot. Abstr. Appl. Anal. 2014 (2014) 6. Chovancová, A., Fico, T., Chovanec, E., Hubinský, P.: Mathematical modelling and parameter identification of quadrotor (a survey). Procedia Eng. 96, 172–181 (2014) 7. Mokhtari, M.R., Cherki, B.: A new robust control for minirotorcraft unmanned aerial vehicles. ISA Trans. 56, 86–101 (2015) 8. Barbaraci, G.: Modeling and control of a quadrotor with variable geometry arms. J. Unmanned Veh. Syst. 3(2), 35–57 (2015) 9. Tahir, Z., Jamil, M., Liaqat, S.A., Mubarak, L., Tahir, W., Gilani, S.O.: State space system modeling of a quad copter UAV. Indian J. Sci. Technol. 9(27) (2016) 10. Koszewnik, A.: The parrot UAV controlled by PID controllers. Acta Mechanica et Automatica 8(2), 65–69 (2014) 11. Raffo, G.V., Ortega, M.G., Rubio, F.R.: Robust nonlinear control for path tracking of a quadrotor helicopter. Asian J. Control 17(1), 142–156 (2015) 12. García, C.E., Prett, D.M., Morari, M.: Model predictive control: theory and practice—a survey. Automatica 25(3), 335–348 (1989) 13. Lungu, M., Lungu, R.: Adaptive backstepping flight control for a mini-UAV. Int. J. Adapt. Control Signal Process. 27(8), 635–650 (2013) 14. Herrera, M., Chamorro, W., Gómez, A.P., Camacho, O.: Sliding mode control: an approach to control a quadrotor. In: Proceedings - 2015 Asia-Pacific Conference on Computer-Aided System Engineering, APCASE 2015, pp. 314–319, October 2015 15. Qin, S.J., Badgwell, T.A.: An overview of nonlinear model predictive control applications. Nonlinear Model Predict. Control, 369–392 (2000) 16. Rawlings, J.B., Meadows, E.S., Muske, A.D.K.R.: Nonlinear model predictive control: a tutorial and survey. IFAC Proc. Vol. 27(2), 185–197 (1994) 17. Bodó, Z., Lantos, B.: Modeling and control of outdoor quadrotor UAVs. In: SISY 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics, Proceedings, pp. 111–116, November 2018 18. Bodó, Z., Lantos, B.: Integrating backstepping control of outdoor quadrotor UAVs. Periodica Polytechnica Electr. Eng. Comput. Sci. 63(2), 122–132 (2019)
Effect of Non-conventional Seating Position on Driver Injuries in the Case of a Self-driving Car Laszlo Porkolab(B) and Istvan Lakatos Department of Road and Rail Vehicles, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Szechenyi Istvan University, Egyetem ter 1, Gy˝or 9026, Hungary {porkolab.laszlo,lakatos}@sze.hu
Abstract. Today the automotive industry is making great strides toward driverless driving. Vehicle manufacturers, as well as well-known development service providers, have already developed a large number of concepts and design studies for fully automatic vehicles. In the future, there will be no more limits to driverless driving. However, a fully autonomous vehicle must also protect its occupants in the event of an accident. The driver becomes a passenger, no longer having to take part in driving the vehicle. The big challenge for the future is how the seat that can be rotated in the front row of a car will have an impact on the future passenger safety system. The aim of the research is to build a simulation model that will allow us to study the area. It is absolutely necessary to use a real test to validate the simulations. To investigate the influence of swivel seats on the restraint systems, the seat positions to be analyzed must first be narrowed down. The area examined extends from 0˚, which corresponds to the current driver’s seat position, to the 180˚ position, in which the driver’s view is directed towards the rear. In order to limit the number of variants that are examined, the angle steps are limited. As part of this research, the 0˚, 30˚, 60˚, 90˚, 135˚, 180˚ positions of the seat are examined in more detail and examinations are carried out on the basis of these variants. Keywords: Crash test · Occupant safety · Finite element method · Autonomous driving · Electric vehicles
List of All Abbreviations HIC BrlC a3ms Nij THOR DAB
Head Injury Criterion Brain Injury Citerion Maximum head acceleration within 3ms Neck Injury Criterion Test Device for Human Occupant Restraint Driver Airbag
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 35–49, 2023. https://doi.org/10.1007/978-3-031-15211-5_4
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1 Introduction According to a WHO (World Health Organization) study published in 2020, more than 1.2 million people worldwide die in road accidents each year and another 20 to 50 million suffer non-fatal road injuries. We would think that an average of at least 3,200 deaths a day would already be a sufficient reason to make every effort to support safer technologies and completely remove the human factor capable of fatal errors from control [1]. Because the huge advantage of self-driving software and hardware combinations over flesh-and-blood people is that they don’t drink, don’t get tired, aren’t nervous, don’t rush, don’t reach out, and we could go on for a long time. If we narrow the circle to Europe, we can see that in 2020, an estimated 22,800 fatal road accidents were registered in the EU. This means that almost 7,000 fewer people lost their lives on the roads than in 2010, a decrease of 23%. Compared to 2018, the number of fatal accidents fell by 2%. Although statistics continue to show a declining trend, the pace of improvement has slowed in most countries since 2013 and we will not be able to meet the EU’s target of halving the number of road deaths by 2020 (compared to the 2010 baseline). The 51 fatal road accidents per million inhabitants, which is the EU average, do not show any significant differences between the Member States. Although the gap between the Member States has narrowed significantly since 2000, the worstperforming country still has four times as many road deaths as the best-performing one. According to preliminary data for 2020, the best performing countries were Sweden (22 deaths/million inhabitants) and Ireland (29 deaths/million inhabitants), while the highest death rates in 2020 were Romania (96 deaths/million inhabitants), Bulgaria (89 deaths/million inhabitants) and Poland (77 deaths/million inhabitants) [1]. In 2020, there were 62 fatal road accidents per million inhabitants in Hungary, which is above the EU average. Due to a 5% decrease compared to 2018, Hungary had the lowest number of fatal road accidents this year since 2013. The number of fatalities has fallen by 19% in the last decade. In Hungary, the most common causes of accidents in order are speeding, non-granting of priority, incorrect choice of cornering speed, non-compliance with tracking distance, pedestrian fault, improper overtaking, inattention and only less than 10% are the technical errors [1]. It is clear, then, that the human factor is largely responsible for accidents. The possibility of autonomous driving is increasingly coming to the fore and crystallizing as a wish for the future. Nowadays, technology may be able to move a vehicle completely on its own, but with one hundred percent safety, it is still far from being able to be applied in real traffic. For the time being, artificial intelligence is not suitable for being able to do the same in more complex situations and to make the right decision, to react appropriately to human factors, which they want to exclude from driving the car. At the same time, it is certain that the decade ahead may bring the time for self-driving cars, which could change mobility like nothing else before. Preparing for autonomous vehicles can best be done by physically transforming and digitally covering the urban road network. The most effective way to do this is to create dedicated lanes for them. Cities also need to develop their own data analysis capacities in order to continuously improve the movement and traffic of cars without a driver. The high automation of vehicles will be able to provide greater safety for passengers. Higher-level automated driving systems, available in the near future, will remove the human driver from the chain of events that could lead to an
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37
accident. The development is continuous, and from time to time, the level of automation is higher, serious achievements in terms of occupant safety are expected. The passive safety of the vehicle must be able to protect passengers in all situations and in all seating positions in the future [2].
2 Seating Position Scenarios The idea of driverless driving turns the driver into a normal occupant; the driver becomes a passenger. The responsibility of the vehicle no longer lies with the driver but with the system. This offers the occupants new freedoms and opportunities. In addition, an investigation of new and future measures intended to reduce the consequences of accidents is unavoidable. An approximation of the seating position to the almost endless possibilities with which the occupant can occupy himself during automated driving is necessary. The possibilities for the driver to deal with during driverless driving are extensive, and conceivable seating positions have to be approximated. In stage 3 of autonomous driving, the driver is still involved in what is happening, so the current seating position can be found without any rotation and with an upright seat backrest. Automated or autonomous driving is divided into five levels according to the SAE J3016 standard. This structuring has established itself worldwide in the automotive industry. The stages of autonomous driving are included in a development process and can also be viewed as a timeline. There is no automation at level 0. The occupant is the driver of the vehicle and is independently responsible for steering, accelerating and braking. Except for warning systems, no vehicle systems actively intervene in the control. In the next stage, systems are already in place that controls either the longitudinal or lateral guidance of the vehicle. Once the driver has selected a particular guide, the system is responsible for the other function. Level 2 is partial automation. The driver is able to completely hand over the longitudinal and lateral guidance to the system. The driver is still responsible for monitoring the vehicle and the traffic, because the occupant must be able to regain control of the vehicle at any time. With level 3, semi-automated driving, it is not necessary for the driver to have to constantly monitor the system. Only in the event of a borderline case does it have to be guaranteed that the vehicle will be taken over after a reasonable handover time. Level 3 is also the current series status of the latest vehicles. At the moment, the development is on the threshold of highly automated driving, level 4. The driver is able to completely transfer a specific driving task to the system. These are specific tasks for which the parameters can be narrowed down, which is the main difference from the last level 5, autonomous driving. In the future, there will be no more limits to driverless driving [3]. The seating position of today’s vehicles has already been sufficiently studied. As the driver advances into fully automatic handover of vehicle control to a system, the driver can focus on other activities. Tasks from private life, problems from professional life or switching off and enjoying your free time are conceivable. Conversations with constant eye contact in the vehicle are also conceivable. All you need to do is turn the seat and turn it towards the person you are talking to. The interval of the rotation axis of the swivel seat extends from the normal driving position to the mirrored seating position opposite to the direction of travel.
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Changing the seat rotation angle has a significant impact on occupant protection and the functionality of the restraint systems. The rearward movement of the seat, with the occupant remaining in an upright seated position in the direction of travel, means that current airbag systems have to accept a reduction in their functionality. Since 1998, airbags to protect occupants have been a mandatory accessory for all cars. The driver airbag which is usually between 45 and 60 L, the passenger airbag which is usually between 80 and 130 L so is it much bigger than a driver’s airbag. Nowadays, the weight sensor built into the seats is a requirement in most new cars, it is able to deactivate the airbag by not opening it when a child is sitting there. The airbag can cause injuries to children in all older models that do not have a weight sensor. Even with such minor adjustments, restraint systems lose some of their effectiveness because they are often only designed for standardized borderline cases in order to successfully comply with legal and consumer protection regulations [4]. If the seat is rotated and a frontal crash is still assumed, the occupant’s body will still try to move further in the direction of travel. Accordingly, depending on the angular position of the seat, the occupant hits the airbag or possibly vehicle interior structures. In addition, depending on the position of the seat and the belt system, increased body rotation is to be expected. From a certain angle, the driver’s airbag is no longer able to fully unfold because the backrest is in the unfolding area. The three-point belt also loses part of its restraining effect. If the seat is turned 180z or nearly as much, the front crash develops into a rear crash for the occupant due to the change in the load. Due to the restraining effect of the seat, a lower risk of injury is to be considered. Depending on the angle of rotation, the first contact partner of the occupant with the vehicle changes. At an angle of 180z, the impact on the second row of seats is excluded by the restraining effect of the belt [5].
3 Honda Accord 2008 EUNCAP Crash Test It is absolutely necessary to use a real test to validate the simulations. Validation is important for building accurate simulation models. Computer simulations are suitable for plotting and comparing theoretical scenarios, but they must match the actual measurement results in order to accurately model the actual case studies. Simulation models are essential in engineering development and must be validated in all cases, they can then be used. The simulations were based on the 2008 model of Honda Accord; therefore requisite to use the real test of this model as a basis for validation. This is a free full vehicle model that includes all components. However, since this is a conventional vehicle model, it is necessary to make any modifications that allow the modelling of a fully self-driving car. The main changes are justified in order to create the space needed to rotate the seats. In addition, the underbody of the vehicle model must be adapted to the boundary conditions so that it is possible to turn the driver’s seat into the respective rotational positions. In order to gain space-related advantages, the tunnel to which the center console is attached is flattened. Electric vehicles are often associated with driverless driving. In the case of electrically operated vehicles, the exhaust gas system is omitted due to the lack of exhaust gases, as is the tunnel designed for this purpose, if this is not used for cooling. In addition, in current vehicle models, the battery, and
Effect of Non-conventional Seating Position on Driver Injuries
39
energy storage, is attached below the underbody. An essential change to the base model is made in the attachment of the belt. The seat belt is usually anchored in the B-pillar due to a large amount of force exerted, but the driver’s seat cannot be rotated with the belt attachment as the belt would tighten the occupant’s neck. For this reason, the seat belt holder is integrated within the seat. For this front-impact test, a rigid wall is placed in front of the vehicle model, and the car is hit at a speed of 50 km/h with 100% overlap (Fig. 1).
Fig. 1. Simulation model
The tested Honda Accord Euro model was introduced in 2008 in Australia. The model included two front airbags, a side airbag and a head airbag as standard. ABS (Antilock brakes), EBD (electronic brake distribution) and ESC (electronic stability control) are also standard. Each seat includes an intelligent seat belt reminder system. The belt buckles are attached to the seats, each belt has a height adjustment option. Height adjustment greatly increases the efficiency of the seat belt. Another extremely important element is the seat belt pretensioner, which is designed to reduce belt slack. In the second seat row, the center seat is also equipped with a three-point seat belt that provides much greater protection than the two-point version commonly used here. The Accord Euro scored 14.47 out of 16 in the frontal crash test (5 Stars). The carbody showed great rigidity. With regard to passenger injuries, there is only a small risk of chest injuries to the passenger and minor lower leg injuries to the driver and passenger. Body region scores out of 4 points each: Head/neck 4 pts, chest 3.45 pts, upper legs 4 pts, lower legs 3.02 pts. The accelerator pedal moved rearwards by 41 mm. Approximately 20 mm in x-direction, 7 mm in y-direction and 13 mm in z-direction movement has the steering wheel after the crash. All doors remained closed during the crash. All doors could be opened after the crash. The driver airbag has greatly reduced the driver’s head injuries. There was no chance of a knee injury. In the case of the passenger, it can also be said that the airbag worked properly in all respects [6]. The protection of a 3-year-old dummy deserved maximum points, the dummy was not injured during the crash. The model also features a switchable passenger airbag feature that contributes to the safe transport of a child seat. However, a shortcoming is that the condition of the passenger airbag is not clearly indicated to the driver. Unfortunately, not all European languages have a warning that a rear-facing child seat can be used in the passenger seat without first deactivating the airbag. The ISOFIX anchorages notation are incomplete in the second seat row.
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In terms of pedestrian protection, the model scored maximum in the Euro NCAP crash test. However, further tests have revealed shortcomings with regard to pedestrian protection. The assessment of the hood is also twofold, the front part seems too stiff, but the rear parts where the head of the pedestrian or cyclist can hit have been properly rated (Fig. 2).
Fig. 2. Honda accord 2008 EUNCAP crash test [7]
4 Results of the Simulations Table 2 shows the results of the base model and the modified model in comparison with the crash test. The base model is validated; it can be seen that all differences in the simulation results remain within the 10% limit. The situation is different for the fitted model due to the attachment of the belt in the seat and the associated reduced restraint effect. This results in a significantly changed kinematics of the seat and the dummy and the results also show a partially larger difference compared to the crash test. There is a bigger difference in the values of the neck and chest; the main reason for this is the change of the seat belt. The fitted model should be used for comparison with rotated seat variants; all modifications are summarized in Chapter 3 [8, 9] (Table 1).
Effect of Non-conventional Seating Position on Driver Injuries
41
Table 1. Injury measurements [7] Criteria
Unit
Limit value
Driver
Passenger
Head (HIC15)
[–]
700
416
366
Head (a3ms)
[g]
80
47,4
49,1
Neck (BrIC)
[–]
1,05
0,39
0,72
Neck (Nij)
[–]
0,85
0,64
0,58
Chest (Compression)
[mm]
60
32,8
37,9
Abdomen (Compression)
[mm]
88
72,9
74,2
Femur (Force)
[kN]
7,56
6,03
5,37
Table 2. Results of the base and fitted simulations Criteria
Unit
Limit value
Crash test
Simulation 0° Base model without any modifications
Simulation 0° Fitted model with all modifications
Head (HIC15)
[–]
700
416
437 (+5%)
487 (+17%)
Head (a3ms)
[g]
80
47,4
49,4 (+4%)
55,1 (+16%)
Neck (BrIC)
[–]
1,05
0,39
0,42 (+8%)
0,57 (+46%)
Neck (Nij)
[–]
0,85
0,64
0,63 (−2%)
0,43 (−32%)
Chest (Compression)
[mm]
60
32,8
34,3 (+5%)
41,4 (+26%)
Abdomen (Compression)
[mm]
88
72,9
70,7 (−3%)
68,1 (−7%)
Femur (Force)
[kN]
7,56
6,03
6,25 (+4%)
6,88 (+14%)
The results of the sitting positions for the angles of rotation from 0° to 180° are shown in Table 3, Table 4. With the rotation and translation of the seat into the selected 30° seat position, the highest values for the HIC15 are achieved by a clear margin. With the increase in the angle of rotation, a continuously decreasing trend in the HIC values and the critical head acceleration a3ms can be observed. These determined values for the head area are higher compared to the straight-ahead sitting position. Consequently, the restraint systems for the changed kinematics of the seat in combination with the changed seat positions, have a reduced protective effect on the occupant. There is a risk of irreversible to fatal head injuries. With the increase in the angle of rotation to 90° and 135°, the direction of loading on the dummy changes. The BrIC criterion shows this progression and has an increasing trend for increasing angle numbers, but an abrupt increase in this value can be seen for the angle numbers of 90° and 135°.
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In addition to the head values, the effects of the adjusted seating position can also be felt in the neck area. Initially, a positive trend is to be expected, as the specified values remain below the limit up to an angle of rotation of 60°, but rise sharply again with an angle of rotation of 90°. Removing the head airbag reduces the restraint systems and this leads to a decrease in protection. As the angle of rotation increases, the percentage of hack-crash increases, so stretching the neck is given a separate role.In addition to the head values, the effects of the adjusted seating position can also be felt in the neck area. Initially, a positive trend is to be expected, as the specified values remain below the limit up to an angle of rotation of 60°, but rise sharply again with an angle of rotation of 90°. Removing the head airbag reduces the restraint systems and this leads to a decrease in protection. As the angle of rotation increases, the percentage of hack-crash increases, so stretching the neck is given a separate role. Table 3. Results overview (yellow = maximum value)
Criteria
Unit
Limit value
Crash test
Simulation 0°
Simulation 30°
Simulation 60°
Head (HIC15)
[-]
700
416
487
1159
964
Head (a3ms)
[g]
80
47,4
55,1
91,3
93,1
Neck (BrIC)
[-]
1,05
0,39
0,57
0,93
0,95
Neck (Nij)
[-]
0,85
0,64
0,43
0,59
0,48
[mm]
60
32,8
41,4
50,6
54,9
[mm]
88
72,9
68,1
113,4
103,5
[kN]
7,56
6,03
6,88
4,39
6,11
Chest (Compression) Abdomen (Compression) Femur (Force)
With regard to the torso, the chest and abdominal deflection values are available for assessment. The decisive factor for chest deformation is usually the tensile force of the shoulder strap. By changing the flow of force from the seat belt to the backrest, the backrest moves more in the direction of the impact. The integrated belt system follows this movement and is not able to absorb the chest area sufficiently. According to this, only minor changes in the chest values can be observed; only the sitting position with a rotation angle of 180° shows an increase. The abdominal deformity is subject to the same problem with the seat belt. However, there is an increase up to the angle of rotation of 60°. From the 90° sitting position and the omission of the actual front crash impulse on the measuring dummy, a continuous strong decrease in the determined deformations can be noted since the dummy kinematics changes significantly.
Effect of Non-conventional Seating Position on Driver Injuries
43
Table 4. Results overview (yellow = maximum value)
Criteria
Unit
Limit value
Head (HIC15)
[-]
700
416
833
803
731
Head (a3ms)
[g]
80
47,4
83,3
75,3
72,9
Neck (BrIC)
[-]
1,05
0,39
1,74
1,43
1,18
Neck (Nij)
[-]
0,85
0,64
0,57
0,63
0,61
[mm]
60
32,8
48,3
46,1
56,7
[mm]
88
72,9
91,4
78,6
71,4
[kN]
7,56
6,03
6,32
8,17
7,26
Chest (Compression) Abdomen (Compression) Femur (Force)
Crash Simulation Simulation Simulation test 90° 135° 180°
The femoral forces are already at their maximum in the upright sitting position. It is noticeable that the forces experience a strong decrease to the minimum at the 30° angle and the subsequent angles of rotation produce an increase in femoral forces again. This course can be explained by the change in the seating position and the respective load case proportion (Fig. 3).
Fig. 3. Dummy-belt-seat in different positions
4.1 The Seat Rotated 30°/60°/90° In the following subchapters, a detailed analysis of the causes of the identified trends is presented. Several factors are responsible for the increase in critical head acceleration and HIC values. One of the factors is the sitting position of the dummy, caused by the translations and rotations of the seat. This leads to different impact scenarios of the dummy head. The maximum value of the HIC15 is already reached from a rotation angle of 30° in this simulation model. There is a risk of serious head injuries. Due to the changed impact point of the head, which is no longer in the middle of the air cushion,
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the protective effect of the airbag is seriously reduced. The dummy head does not hit the center of the airbag optimally for all angle variants. In the variants 30° and 60°, direct impacts on the steering wheel occur. The seat with the 30° angle is the worst for the airbag (Fig. 4).
Fig. 4. Representation of the examined angles of rotation (30°/60°/90°)
As the angle of rotation increases, the HIC values decrease, but these values do not reach the level that is achieved in the upright and straight-ahead sitting position. All variants exceed the Euro NCAP HIC15 value limit of 700. The falling HIC values in the range from 30° to 90° are also due to the increasing proportion of energy absorption in the arms and shoulders. Furthermore, with an increasing angle of rotation of the seat, the dummy is more likely to slip sideways out of the driver’s seat. The closer the number of angles approaches 90°, the greater the proportion of a side crash in which the protective effect of the seat belt decreases significantly [10].
Fig. 5. Slipping of the dummy 0°/30°/60°/90°
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The seat ramp, which is usually intended to avoid or reduce a submarining effect, has no protective effect whatsoever for the controlled rotation angle. The seat ramp is designed exclusively for the conventional sitting position, but this ramp is also rotated with the selected angle of rotation, with the front crash impulse or the direction of the load remaining the same. A tilting movement of the seat can also be seen in Fig. 5. This favours the movement of the dummy out of the seat. The increase in head acceleration can be seen in Fig. 6 and supports the statements made. In addition, due to the translations, which are adapted to each seating position for reasons of space, there is a delay in the head impact.
Fig. 6. Comparison of the head accelerations of the rotating sitting positions 0° to 90°
It can be seen that the head acceleration of the conventional sitting position occurs almost simultaneously with the 90° rotation angle; this is due to the reduced distance of the dummy due to the sideways position to the dashboard. In contrast, the 30° and 60° sitting positions show a time delay of up to 10ms. The values of the head accelerations of the swivel seat variants clearly exceed the critical and life-threatening limit value of 800 m/s2 [11]. As can be seen in Fig. 7, the belt forces increase as a function of the angle of rotation. Accordingly, the chest impressions should show an increase. However, this is not the case, as the THOR dummy is not designed for side crashes, and the necessary measuring sensors are missing. Accordingly, the THOR dummy is not able to accurately measure the deformations that occur.
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Fig. 7. Belt forces of the shoulder strap of the rotating seat positions 0° to 90°
While a reduction in the BrIC value to 0.8 can be seen in the 60° seated position compared to the 30° seated position, the dummy experiences almost a doubling of the value to 1.7 in the 90° angular position and thus exceeds the Euro NCAP limit clear. This serious increase can be explained by the side impact of the head of the 90° variant. While the dummy dives into the airbag in the event of an impact with a 60° rotation angle and falls back slightly, the THOR dummy in the present case, which reflects a side crash for the dummy, receives a stronger impulse at the beginning of the impact on the airbag the relocation. Accordingly, the head rotates during the rebound. 4.2 Seat Rotated 135°/180° The increased BrIC value for the seating position with the rotation angle of 135° is related to the removal of the DAB and the lack of restraint of the headrest. Because the head slips sideways past the headrest. The result is an almost unchecked impact of the dummy’s neck on the steering wheel, which leads to hyperextension of the cervical spine. Figure 9 shows the spatial angular velocities of the dummy in the range from 60° to 135°. There is a clear increase in the z-angular velocity for the 90° variant, whereas the proportions of the x and y angular velocities are decisive for the increase in the 135° variant (Fig. 8).
Fig. 8. Representation of the examined angles of rotation (135°/180°)
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Fig. 9. Angular velocities of the rotating sitting positions 60°, 90° and 135°
In addition to the increased BrIC value at a seat rotation angle of 135°, the increase in the Nij value is due to the impact of the neck on the steering wheel. It’s the same behaviour to look at. With the impact of the backrest of the seat on the steering wheel, the steering wheel bends and thus moves the backrest down. This promotes hyperextension of the neck on the steering wheel (Fig. 10). The protective effect of the headrest is only slightly available for this angle of rotation.
Fig. 10. Impact of the neck on the steering wheel at the rotating angle of 135°
The reduction in thigh forces up to the 60° rotation angle and the subsequent increase again for the angles 90° to 180° has to be explained in close connection with the dummy movement during the impact. The upright and straight-ahead seating position has the smallest distance to the respective component internal structures, so this is where the hardest impact occurs and exceeds the limit of the Euro NCAPs. The translation and
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rotation of the seat increase the distance between the legs and the dashboard. The rebound in femoral forces at the 90° rotation angle is due to the dummy slipping out of the seat. In this case, the impact between the dashboard and the legs is worsened. By adjusting the dummy extremities for angles greater than 90°, the dummy’s legs are almost fully extended [12]. In the case of a frontal crash, the upper body is braked earlier in this position than the legs because the seat is in contact with the steering wheel early on, and the safety belt keeps the dummy’s upper body in the seat. The legs can move freely in the air and have no contact, so there is no separate braking of the legs and it occurs when the legs hit the seat console, which results in a renewed increase in femoral forces. Figure 11 shows the right and left femoral forces over time. The time delay of the maxima for the angles of rotation between 0° and 90° is striking since the left leg hits the ground first and then the right leg [13].
Fig. 11. Femoral forces depending on the angle of rotation 0°, 90° and 135°
5 Conclusion I examined the extent to which the seating position of passengers affects their injuries in the event of an accident. In the course of my research, I compared the results of the injuries in the seating positions rotated by 30°/60°/90°/135°/180° with the driver’s seat in the normal basic position. I have pointed out that the kinematics of the movement of the driver in the event of an accident change radically as a result of the modified seating positions. I have demonstrated a large reduction in the effectiveness of passive protection systems, with a high increase in the risk of fatal injuries in the case of rotated seating positions. This is especially true for head and neck injuries, but the value of chest indentation and the extent of leg injuries also become more severe. It is, therefore, necessary to extend the passive protection systems known today. I will look at the possibilities for further development of the driver’s seat in terms of passive vehicle safety, and what protection it can provide for the fully self-driving car of the future. The seat must be able to coordinate the movements of the occupants in the event of an accident, even in rotated
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seating positions, and be involved in the prevention of serious injury. I am building a model to test and verify this.
References 1. European Commission - Questions and Answers: Road safety statistics 2020: what do they mean a numbers? (2020) 2. Wu, H., Hou, H., Shen, M.: Occupant kinematics and biomechanics during frontal collision in autonomous vehicles—can rotatable seat provides additional protection? Comput. Methods Biomech. Biomed. Eng. 23, 191–200 (2020) 3. Novakazi, F., Johansson, M., Strömberg, H.: Levels of what? Investigating drivers’ understanding of different levels of automation in vehicles. J. Cognit. Eng. Decis. Mak. 15, 116–132 (2021) 4. Nie, B., Gan, S., Chen, W., Zhou, Q.: Seating preferences in highly automated vehicles and occupant safety awareness: a national survey of Chinese perceptions. Traffic Inj. Prev. 21, 247–253 (2020) 5. Jin, X., Hou, H., Shen, M.: Occupant kinematics and biomechanics with rotatable seat in autonomous vehicle collision: a preliminary concept and strategy. In: Conference Proceedings International Research Council on the Biomechanics of Injury, pp. 106–113 (2018) 6. Kullgren, A., Lie, A., Tingvall, C.: Comparison between Euro ncap test results and real-world crash data. Traffic Inj. Prev. 11, 587–593 (2010) 7. EUNCAP Crash Tests New Car Safety Honda Accord 2008 8. Petrucelli, E., States, J.D., Hames, L.N.: The abbreviated injury scale: evolution, usage and future adaptability. Accid. Anal. Prev. 13, 29–35 (1981) 9. Nolan, J.M., Lund, A.K.: Frontal offset deformable barrier crash testing and its effect on vehicle stiffness. Insurance Institute for Highway Safety (2001) 10. Bass, C.R., Crandall, J.R., Bolton, J.R., Pilkey, W.D., Khaewpong, N., Sun, E.: Deployment of air bags into the thorax of an out-of-position dummy. SAE Technical Paper, paper no. 1999-01-0764, pp. 1–17 (1999) 11. Golowko, K., Zimmermann, V., Zimmer, D.: Automated driving influences on the restraint system. ATZ-Automobiltechnische Zeitschrift 119, 26–33 (2017). (in German) 12. Tang, L., Zheng, J., Hu, J.: A numerical investigation of factors affecting lumbar spine injuries in frontal crashes. Accid. Anal. Prev. (2020) 13. Choi, W.M., Jeong, H.Y.: Design methodology to reduce the chest deection in US NCAP and EURO NCAP tests. Int. J. Automot. Technol. 13, 765–773 (2012)
Implementation of a System for Signaling the Approach of Emergency Vehicles Within Other Vehicles Dávid Makó1 and Ákos Cservenák2(B) 1 University of Miskolc, Miskolc 3515, Hungary
[email protected]
2 Institute of Logistics, University of Miskolc, Miskolc 3515, Hungary
[email protected]
Abstract. This paper deals with the implementation of a system for signaling the approach of vehicles using a distinguished signal within other vehicles. According to the literature processing, the two main warning systems of emergency vehicles, i.e., the light and sound signal, may not be sufficient. Literature processing also covers vehicle communication technologies and important relationships with DMRS technology. The paper also contains an example of an application described for LTE-V technology and details existing transceiver technologies, such as transmission at conventional car radio frequencies. Taking into account cost-effectiveness, the system used in the paper consists of a radio transceiver using a frequency of 433 MHz, Arduino development platforms, and input and output devices. A joystick was installed on the transmitter side to test the operation of the system, while four LEDs were installed on the receiving side to simulate directions. Electronic circuits containing the Arduino platform have been designed and constructed in order to perform the RF communication among the receiver and transmitter. Keywords: Emergency vehicle · V2X · DMRS · IoT · 433 MHz RF transmitter and receiver module
1 Introduction Nowadays, the automotive industry is undergoing a major transformation. It is true not only for manufacturing systems [1,–3], but also in vehicles themselves. There are fundamental debates about what type of powertrain would be ideal for vehicles from an environmental point of view, as environmental protection has become a major trend in vehicle production. The other equally important issue is safety. Almost everyone agrees that the road to safety transport leads in the development of vehicle communication. This is a direction that should be reflected in as many vehicles as possible, regardless of their type or construction. There are several types of vehicle communication systems. It is not easy to categorise the different systems, as it is difficult to distinguish clearly © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 50–62, 2023. https://doi.org/10.1007/978-3-031-15211-5_5
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between them, as there is not necessarily a fundamental difference between them. The basic idea came from an own unpleasant experience, which almost ended in an accident. According to a video published by the official news provider [4], 70 ambulance accidents occur every year in Hungary, not including accidents involving vehicles using other emergency signs, but roughly 150 accidents involving vehicles using lights and sound signals, for example, because of loud music. In the majority of cases, it is the drivers of civilian vehicles who are at fault fail to see a vehicle with sirens at an intersection, etc. Some statements have been compared using data from the KSH report [5] to show the significance of the above numbers. Referring the data in 2020, the number of road accidents in Hungary in the previous year (13778) has been compared with the number of accidents involving differentiated vehicles (150). The result is 1%. This result is not yet fully realistic, as the next comparison shows the number of accidents resulting from collisions between “passing vehicles” (7367). Here the proportion has already risen to 2%. The next comparison is the number of accidents due to right of way (2660). Still 150 has been taken into account since accidents involving emergency vehicles are always not getting right of way. The result here is already 5%. Lastly, the number of ambulances in use in Hungary (910) was compared with the number of ambulance accidents (70). Here the most striking result is 7%. This information is in themselves revealing, but they are only the really occurred accidents and do not include the number of vehicle obstructions, which are almost impossible to determine but can also cause significant problems. It should also be mentioned that it is not only those directly involved in the accident that needs to be taken into account but also the lives and health of many people who may be affected indirectly by such accidents/obstructions. This makes these situations even more significant. The aim is to implement such a system in order to address the problems mentioned above in order to help everyday drivers to detect vehicles using emergency signals. When approaching such a vehicle, the drivers can often find themselves in unpleasant situations, often failing to pay attention to the signals. The first aim is to develop a system to help in cases where the approach of an emergency vehicle is not detected. This can lead to very dangerous traffic situations, but even if it does not result in an accident, the vehicle being distinguished loses valuable seconds due to the inattention of road users. Section 2 of this article presents vehicle communication technologies since the system under development also implements vehicle communication. Their operation may help to implement the device presented in this article. Section 3 highlights the practical implementation, which is developed through three milestones. Section 4 of the article covers the possibilities for further development. Finally, Sect. 5 summarises the gained results.
2 Vehicle Communication Technologies In the following few subsections, different vehicle communication technologies are presented, which have been selected on the basis of the document [6] and serve as a background for the basic idea of the thesis, as the system to be implemented also allows a certain level of vehicle-to-vehicle communication.
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2.1 D2D (Device to Device) According to the [7] the challenge with 802.11p is that it will only communicate with cars that can receive its signal and will not work with cars that do not carry WLANp. The 802.11p Wi-Fi standard that the IEEE has standardised for wireless access in the automotive environment, as communication over cellular networks is essential to support fully autonomous driving. This new field is often referred to as C-V2X, where “X” stands for “everything”, such as another car (V2V), pedestrians (V2P), networks (V2N), etc. The main advantage of C-V2X is that it uses the existing LTE network infrastructure, facilitating high data rates, high coverage and lower latency with 5G. 2.2 IoT (Internet of Things) The IoT devices range from everyday household objects to sophisticated industrial equipment [8] and also in education and research [22–24]. Today there are more than 7 billion connected IoT devices available and experts expect this number to grow to 22 billion by 2025. According to [9], an IoT device could be, above the household and health purpose devices, a warning system built inside the vehicle using built-in sensors, for example, signing the low tyre pressure. Other objects can be regarded as IoT, which is assigned an Internet Protocol (IP) address and can collect and transmit data over the network. The IoT system consists of web-based smart devices that use embedded system kernels such as processors, sensors and communication hardware to data from their environment and use it to collect, send and act on them. IoT devices share the collected sensor data by connecting to an IoT node or other edge device, where the data is either sent to the cloud for analysis or analysed locally. Sometimes these devices communicate with other connected devices and act on the information they receive from each other. The devices do most of their work without human involvement, although humans can interact with the devices; for instance, they can set them up, give them instructions or access data. The connectivity, networking and communication protocols used with these web-based devices depend heavily on the specific IoT applications installed. IoT can also use artificial intelligence (AI) and machine learning to make data collection processes simpler and more dynamic. RFID technology can be used to control traffic signals by measuring vehicle density [10]. They propose RFID-based technology that eliminates congestion during busy hours and provides protected lanes for emergency vehicles. The main purpose of this system is to manage traffic efficiently and help emergency vehicles to move properly. Emergency vehicles will emit a signal when they detect an RFID signal within a certain distance. When approaching the emergency vehicle, the traffic server notifies the Arduino in the node and then this signal acquired by the Arduino is matched by the RFID signal received from the RFID reader. This confirms whether the vehicle is in an emergency or not. Until the green light is confirmed, the vehicle with the differentiated signal is given a clear path. If the emergency vehicle receives the RFID signal from the Arduino, it will receive a green signal at the traffic lights along its expected route. The traffic information will be sent to the server so that users can determine traffic conditions using web pages.
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2.3 VND (Vehicular Neighbour Discovery) The Neighbour Discovery is a protocol that allows different nodes on the same link, to advertise their presence and collect data about their neighbours [11].
Fig. 1. Internet work between two vehicle internet networks [12]
Figure 1 shows the relations between the mobile internet networks of two adjacent vehicles [12]. There is an internal network for Vehicle1 (Moving network1). Vehicle1 also has a DNS server (RDNSS1), two hosts (Host1, Host2) and two routers (Router1, Router2). Vehicle2 also has another internal network (Moving network2). and similar structure as Vehicle1. The Vehicle1 Router1 (mobile router) and the Vehicle2 Router3 (mobile router) use the 2001:DB8:1:1::/64 external link to V2V networks. 2.4 VANET As described in [13] and [14], in VANETs the vehicles are so-called mobile nodes which are able to communicate with roadside units (RSUs) and each other. This system is illustrated in Fig. 2 illustrated in [15]. It provides many useful applications such as traffic optimisation, payment services, location-based services, security systems, and entertainment. VANET provides V2v or V2i communication. Equipped vehicles used as network nodes can move between each other at will within the constraints of the road infrastructure.
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Fig. 2. Functional flowchart of VANET [15]
Some studies have already explored the possibilities of implementing EVA on a VANET basis [16, 17]. These studies are all based on the fact that the discriminated vehicle sends its expected route to all vehicles and, taking into account the pre-planned route, the receiving vehicle determines whether the message is relevant or not. The feasibility of emergency vehicle approach notification is described in [6]. It is intended to be established by vehicle-to-vehicle communication in the VANET system, using the following equation: ti ∼ = δei + δdi + δpi + δti + δMAC i + (i − 1) · t + tstart
(1)
where ti when the civilian vehicle receives the i-th message from the emergency vehicle, δei and δdi is the embedding and decapsulation delay of an i-th message, δpi is a propagation delay, δti is the message transmission duration, δMAC i is a medium access delay, t is the message generation interval and tstart is the start time of the message forwarding. 2.5 Connected Vehicles-V2X Communication V2X is a collective name, within different types can be distinguished according to [18] document: a. b. c. d.
V2N: Vehicle-To-Network V2V: Vehicle-To-Vehicle V2P: Vehicle-To-Pedestrian V2I: Vehicle-To-Infrastucture
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The different types can be observed in Fig. 3 illustrated in [19]. The article in [11] mentions an application within the V2V network called the Environment Dependent Safe Driving Navigator (CASD). It can provide drivers with a safer driving experience by helping them to detect dangerous situations and obstacles. Also, document [11] mentions use cases for V2I networks. It mentions a navigation service called Self Adaptive Interactive Navigation Tool (SAINT) that uses V2I networks to optimise road traffic globally. The enhanced version (SAINT+) is able to provide the fastest of different routes to the accident scene for differentiated vehicles while also providing civilian vehicles with efficient detours.
Fig. 3. Illustration of V2X communication [16]
3 Practical Implementation This section consists of the practical implementation to perform the vehicle communication among two Arduino devices using different electronic elements. 3.1 First Milestone As a first step, an Arduino development platform and a double-axis analogue joystick were connected, as can be seen in Fig. 4. The joystick has five pins. Pin GND was connected to the ground of the platform and pin + 5 V to the power supply. The joystick has x direction (pin VRx) and y direction (pin VRy). Pin VRx is connected to A0, while pin VRy is connected to the analogue input A1. Then, the values were written out on a serial monitor in the written program, which changed as the joystick was moved. Four different pairs of values were assigned to the four different directions. The values of
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each analog channel varied from 0 to 1023, since the Arduino Nano development board form used has a 10-bit ADC (Analog-Digital Conversion) resolution.
Fig. 4. Arduino with RF radio transmitter and joystick
3.2 Second Milestone The next milestone required two Arduino Nano development platforms, an RF radio signal transmitter and an RF radio signal receiver, as illustrated in Fig. 5. The RF radio signal transmitter comes with three pins. Here again, pin GND is connected to the ground pin VCC is connected to pin 5 V, and pin DATA is connected to pin D12. The RF radio signal receiver has four pins, two of which are connected together. The pin GND is also for grounding, pin VCC for pin 5 V, and only one of the two data pins were connected to pin D11.
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Fig. 5. Arduino with RF radio signal receiver and LED outputs
3.3 Third Milestone/Complete System This is the final milestone in the research presented in this article. The complete system consists of two parts, a transmitter and a receiver side, as can be seen in Fig. 6. The circuit for the system required two Arduino Nano development platforms, a double-axis analogue joystick, a 433 MHz RF radio signal transmitter and a 433 MHz RF radio signal receiver, four LEDs of different colours and four 220 resistors. The analogue joystick with five pins was wired to one of the Arduino platforms, and like the first milestone, the pin GND was connected for grounding, the pin 5 V for supply voltage, pin VRx was connected to analogue input A1, while pin VRy was connected to A0 input and the fifth output (pin SW ) was not relevant in this circuit. Also wired to this same platform was an RF radio signal transmitter with 3 pins and like the second milestone, pin GND was wired to the ground, pin VCC to supply voltage and pin DATA to pin 12. This circuit functions as a signal emitter, this is for use in emergency vehicles. The program for this development platform is written as follows: • Part 1: Inviting libraries: RH_ASK and SPI libraries. The SPI library is not actually used, but is necessary for compilation. • Part 2: Declaration of variables (horizontal, vertical initial positions, auxiliary variables, delay time): horizontal initial position value from readout: 497, vertical initial position value from readout: 493, auxiliary variables declare old position value (100) and new position value (0), delay time 0.1 s value declaration.
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• Part 3: Serial port Setting function: the serial port should communicate at 9600 bitrates. Only needed for debugging, using the “IF” function to monitor the driver’s evaluation, if an error occurs, to print a new line with the “evaluation failed” text. • Part 4: Main function “void loop()”, contains different messages. • Part 5: Different messages: messages are defined as characters with different values. • Part 6: Write out: write out the values “val1” or “val2” in a new line on a serial monitor. • Part 7: Joystick position condition check and assignment: check and assign different positions.
Fig. 6. Implementation of the full system
For the receiver Arduino Nano development platform, an RF radio signal receiver with 4 outputs was connected, and also, like the second milestone, pin GND for ground, pin VCC for power, and only one of the two data pins was connected to the pin D11. The four different coloured LED output light sources were wired to pins 4, 6, 8 and 10 and connected in series with a 220 resistor each, and also wired to the Arduino’s over-ground. This circuit acts as a receiver and is used in civilian vehicles. The program for this development platform is written as follows: • Part 1: Inviting libraries: RH_ASK and SPI libraries. The SPI library is not actually used, but is necessary for compilation. • Part 2: Declaring variables: declaring the position of LEDs by pin, different. Assigning different LEDs to different numbers of digital pins on the Arduino.
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• Part 3: Serial port Setting function: the serial port should communicate at 9600 bitrates. Only needed for debugging, using the “HA” function to monitor the drive’s evaluation, if an error occurs, to print a new line with the “evaluation failed” text. • Part 4: LEDs are interpreted as outputs: each LED is in output Pin mode. • Part 5: The main function: if a message with a good checksum is received, the message will be written out. • Part 6: Write out a message as a string: translate the incoming message and translate and write out character by character on a serial monitor. • Part 7: Operation of each LED as a function of the number in the incoming message: Depending on the message, different numbers of LEDs on the pin will flash for different values, i.e. different coloured LEDs will flash depending on the message in the signal. The circuit worked successfully, the transmitter emitted a signal based on the written program and moving the double-axis joystick to different positions changed the message. This change was also visible on the Arduino’s serial monitor as it was written out for easier data analysis. On the receiver side, the Rf radio signal receiver detected the signal, the message was successfully read and translated and the LED outputs were operated based on the written program. Four LEDs of different colours were wired as outputs, these imitate the warning signals displayed inside the civilian vehicle when approaching an emergency vehicle, the different colours have different meanings; they indicate directions. By moving the joystick to different positions, we get the four different directions and the four different coloured LED flashes. The successful implementation is shown in Fig. 7, which is a flowchart of the flashes of the different coloured LED outputs of the completed warning signal simulation system.
Fig. 7. Flow chart of the implemented system in operation
4 Further Development Opportunities The system described in this article has worked in practice, but no measurements have been made to determine the range of the transmitted signal. As a next step, the necessary device parameters should be determined using equations before the actual measurements. The Friis propagation model will help in this. After performing some basic calculations, it was found that power amplified RF radio antennas are needed to achieve a longer range.
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The Friis propagation model from the [17] article: Pr =
PT · GT · GR · λ2
(2)
(4 · π · R)2
The extended equation with Path-loss (LP ): PT · GT · GR PR = · LP
λ 4·π ·R
2 (3)
where PR the sensitivity of the receiver, PT the effective radiated power of GT the transmitter, the strengthening of the transmitter antenna, GR the strengthening of the receiver antenna, LP the path loss, λ the wavelength, R the distance between the transmitter and the receiver. Where free space path loss can be calculated using the following equation from the [18] article: π Lp ∼ = 20 · lg(d ) + 20 · lg(f ) + 20 · lg(4 · ) − GT − GR c
(4)
Logarithmisation is necessary because the decibel is not a traditional dimensional unit, but a quotient of two values on a logarithmic scale. The calculation of the wavelength can be done with the following formula if the frequency range is known: λ∼ =
c f
(5)
where c is the speed of light, c = 299792458 m/s, f is given in Hz. Often the radiated power of an antenna is given in W , but the above equations require dBm, so they need to be converted and the following formula can be used: P[dBm] = 10 · lg(1000 · P[W ])
(6)
The following relationship can be used to determine the instantaneous power: P[W ] =
U2 R
(7)
where U is the effective voltage, R is the resistance. From the equation it is clear that the voltage has a large influence on the power output, so the choice of supply voltage must be considered to achieve the desired range. Using these equations, it is possible to determine the antenna with the required para-meters to achieve the required range. Since the 2.4 GHz frequency range has much lower dispersion, it is easier to control than the 433 MHz frequency range. The frequency bands are allocated by the National Media and Communications Authority.
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5 Summary In this paper, the development of a device for civilian in-vehicle detection of the approach of emergency vehicles was presented. The topic can be classified as vehicle communication, so different vehicle communication technologies were presented at the beginning of the article. This was followed by the practical implementation, showing the cost-effective device development through different milestones. Finally, further development options were presented, where the efficiency of the tools needed for the system implemented so far was discussed, as well as how to determine the tools needed for further development using different calculations. The further plans include also taking into account cybersecurity with the encrypted channel. Acknowledgement. The described article was carried out as part of the NTP-SZKOLL-21-0026 National Talent Program of the Ministry of Human Capacities.
References 1. Kostal, P., Velisek, K.: Flexible manufacturing system. Eng. Technol. 53, 825–829 (2010). World Academy of Science 2. Holubek, R., Kostal, P.: The intelligent manufacturing systems. Adv. Sci. Lett. 19(3), 972–975 (2013). https://doi.org/10.1166/asl.2013.4816 3. Kerak, P., Holubek, R., Kostal, P.: Novel trends in the intelligent manufacturing systems. In: Proceedings of 8th International Baltic Conference on Industrial Engineering, pp. 19–21 (2012) 4. Gy˝orfi, P.: Évente átlagosan 70 ment˝os baleset történik. ATV Hungary (2019). (in Hungarian). https://www.youtube.com/watch?v=mJGJfV2O844 5. A közúti közlekedési balesetek száma. KSH (2020) 6. Petrov, T., Pocta, P., Roman, J., Buzna, L., Dado, M.: A feasibility study of privacy ensuring emergency vehicle approaching warning system. MDPI (2019). https://doi.org/10.3390/app 10010298 7. IEEE Spectrum: Applications of Device-to-Device Communication in 5G Networks, National Instruments (2018) 8. Oracle: What is IoT (2021) 9. Gillis, A.S.: What is internet of things (IoT)? TechTarget (2021) 10. Naik, T., Roopalakshmi, R., Jain, P., Ravi, N.D., Sowmya, B.H.: RFID-based smart traffic control framework for emergency vehicles. In: 2018 Second International Conference on Inventive Communication and Computational Technologies , pp. 398–401 (2018). https:// doi.org/10.1109/ICICCT.2018.8473001 11. Jeong, J., Shen, Y., Xiang, Z.: Vehicular Neighbor Discovery for IP-Based Vehicular Networks. Sungkyunkwan University (2019) 12. Benamar, N.: IP-based Vehicular Networking: Use Cases, Survey and Problem Statement. Sungkyunkwan University (2018) 13. Badis, H., Rachedi, A.: Vehicular Ad Hoc network (2015) 14. Kovács, S.z.: Vehicular Ad-Hoc Networks. University of Miskolc (2020) 15. Singh, G.: Video streaming communication over VANET. In: Kumar, R., Wiil, U. (eds.) Recent Advances in Computational Intelligence. SCI, vol. 823, pp. 189–197. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12500-4_12
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16. Buchenscheit, A., Schaub, F., Kargl, F., Weber, M.: A VANET-based emergency vehicle warning system. In: IEEE Vehicular Networking Conference (VNC), Tokyo, Japan (2009). https://doi.org/10.1109/VNC.2009.5416384 17. Bhosale, S., Dhawas, D.A., Burkul, A.: A VANET-based communication for emergency vehicles. Int. J. Adv. Res. Comput. Sci. Electron. England 2, 5 (2013) 18. Kovács, S.z.: Járm˝uinformatika Bevezetés. University of Miskolc (2020). (in Hungarian) 19. V2X: What is Vehicle to Everything? Thales (2021) 20. Scahub, F.: A VANET-based emergency vehicle warning system. IEEE Xplore, Tokyo (2009) 21. Free Space Path Loss Calculator. PE Pasternack, U.S. (1972) 22. Matyi, H., Veres, P., Banyai, T., Demin, V., Tamas, P.: Digitalization in Industry 4.0: the role of mobile devices. J. Prod. Eng. 23(1), 75–78 (2020). https://doi.org/10.24867/JPE-202001-075 23. Hardai, I., Illés, B., Bányai, Á.: View of the opportunities of Industry 4.0. Adv. Logist. Syst. Theory Pract. 14(2), 5–14 (2021). https://doi.org/10.32971/als.2020.009 24. Akkad, M.Z., Bányai, T.: Applying sustainable logistics in industry 4.0 Era. In: Jármai, K., Voith, K. (eds.) VAE 2020. LNME, vol. 22, pp. 222–234. Springer, Singapore (2021). https:// doi.org/10.1007/978-981-15-9529-5_19
Safe In and Out of the Car Agnes Takacs(B) University of Miskolc, Egyetemváros, Miskolc 3515, Hungary [email protected]
Abstract. Our cars make our everyday life much easier. With their help, in a day full of programs, we can get anywhere on time without carrying our packages on our own. In our rushing world, there is the probability that even if we have enough time to get there somewhere, we will hurry, which leads to inattention or worse case an accident. Car manufacturers have a huge amount of research projects to install driver-assistance safety electronics into our cars that, even if they are not driving, instead of the driver, alert to an accident and help to avoid a stutter or a life-threatening accident. According to the Hungarian Central Statistical Office (KSH), research on car manufacturers has brought significant results, as while in 1990 there were nearly 37,000 accidents involving personal injuries on the roads, in 2020 there were only 18,000 such accidents. However, in addition to these active and passive safety features, we need to be aware of and follow the written and unwritten rules of road safety. However, the number of passenger cars has increased so significantly in the last 20–25 years—but especially in the last two years due to the coronavirus epidemic—as roads are characterized by such a high degree of congestion that pedestrians and cyclists require much more attention from participants. This way it is important children receive proper education on the rules of walking or cycling at nursery school and preschool age. The aim of the study is to enumerate and present the platforms that help children to learn, know and deepen the rules of safe traffic. Keywords: Traffic-safety · 3E · Child educating programs
1 Elements of Traffic To introduce the topic, it is necessary to examine who has effects on the safe traffic participants of it. In any case, we must realize that, in the current state of science, people are solely responsible for safe traffic. We are the participants in the traffic as: – – – – – –
child, adult, elderly, healthy, sick, passenger, driver, pedestrian, cyclist, car driver, as a supplier, with an agricultural vehicle or an animal-drawn vehicle, during peak hours or just outside these intervals.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 63–70, 2023. https://doi.org/10.1007/978-3-031-15211-5_6
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Accordingly, we need to be aware of our own limits and act responsibly in the face of these limitations. As children, while adults are protecting us, we must learn the rules, and as adults, and possibly as drivers, we must carefully follow the instructions on the signs, lights, and regulations [1]. 1.1 Is the Individual the Only One Responsible for Safe Traffic? From the above, it is clear that the man is the only one responsible for safety on the roads. However, it is not the individual who can be held responsible in any case for a traffic accident, even if he or she was at fault. The safety of children is the responsibility of adults. A child, even if he or she knows what is right, often does different things. The rule is perfectly revoked by the pre-schooler in the vast majority of cases. But his or her developing nervous system is not always able (tired, excited, frustrated, etc.) to follow the rule. That is why they need to have care. An adult is less likely to step off the sidewalk in front of a moving vehicle. However, there are adults who suffer from some form of mental illness (addictions, Down syndrome, autism, dementia, etc.). From a moving car, the mental state of a pedestrian on the sidewalk may not be noticeable to the driver in time. Therefore, adult pedestrians should be paid attention to. The adult can take part in the traffic not only on foot but also as a driver. In many cases, this is also the job of the adult. Our attention is not only influenced by mental illness or our endurance. It is often happening that we need to take medication for a disease that affects our ability to concentrate. Thus, it can be concluded that the doctor, the psychologist, the counsellor, and the family also have an important role in traffic safety. It is also possible that insomnia is the cause of inattentive, impatient driving or even that the driver falls asleep [2]. Occasionally, bypassing the potholes and pits of a road section lined with road defects can be fatally distracting for the driver. 1.2 Vehicles as the Tools of Traffic Vehicles moving on the roads have a major impact on road users. Figure 1 summarizes the main effects of the vehicle on other traffic actors. It is clear that the person who appears here as the owner/operator of the vehicle also plays an important role in the condition of the vehicle. It is his or her responsibility to ensure that the vehicle passes the mandatory tests and that the necessary parts are replaced by a service technician. Thanks to the service, the car can stop exactly where the driver wants it (braking system) and it is clearly visible (lights). Thanks to its clean windscreens, the driver’s view is not disturbed by anything. A well-maintained vehicle poses minimal risk to the environment: emissions do not cause more damage to wildlife than it is allowed [5, 6], parts do not fly off (decorative disc, license plate, etc.), and broken parts do not fly out (e.g. suspension springs). The left part of Fig. 2 shows a broken decorative disc left next to a pedestrian crossing; the right part shows a broken suspension spring. In the case of the spring, the repair was still in time, and the broken piece had not fallen out of the vehicle.
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Fig. 1. The most important effects of a vehicle to the members of the traffic
Fig. 2. Easy-to-leave vehicle parts (Photos: personal)
A flying part can cause serious injury, as it did at the Hungaroring at the Formula 1 Hungarian Grand Prix in 2009: Felipe Massa was hit by a nearly 1 kg damper arrangement spring that ruptured from Rubens Barichello’s car in front of him, causing a lifethreatening injury to the pilot who was, of course, driving in a state-of-the-art safety
Fig. 3. The helmet of Formula 1 pilot Felipe Massa after the accident he suffered at the Hungarian Grand Prix in 2009 (Photo: internet)
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helmet (Fig. 3) [7]. In addition to the above, the design of the vehicle is also an important element (collision - crease zone, wiper blade - running pedestrian, etc.) 1.3 Weather Conditions The weather can not only rewrite a pleasant family program, but as the memories of our history show, it can lead to disaster (the tragic accident of the Challenger space shuttle on January 28, 1986, was caused by a rubber sealing ring that did not provide adequate flexibility at sub-freezing temperatures). There are a lot of accidents on the roads due to limited visibility because of the rain, slippery roads, or just ice on the asphalt after the winter precipitation. Therefore pedestrians should be aware that it is advisable to leave home in more colourful, noticeable clothing when it is rainy. And the driver should expect to detect pedestrians in dark clothing in grey, rainy weather on time. We also encounter many times the problem that after dark, the public lighting is incomplete on a given section of road. This can be a big problem, especially at pedestrian crossings. 1.4 Quality of Roads and Infrastructure The condition of the roads also plays an important role in traffic safety. By avoiding pits and potholes, the driver can scare those coming from the front as well as those walking on the sidewalk. However, if the wheel of the vehicle finds all the faults on the road, it will result in a quick breakdown of the suspension. A deeper road defect can also cause an immediate breakdown, not only causing a vehicle to crash, but if those behinds are slow to respond, a mass crash can also occur. In the case of a faulty road section, it is also very common for the vehicle’s wheels to bounce on the road, so that the connection between the tire and the asphalt becomes intermittent, so in the case of a braking event, the braking distance will be much longer than in the case of a faultless road section and a permanent road-tire connection. It can also cause an accident if the traffic lights are not working properly, or not working at all. This could be due to windy, rainy weather or an electrical failure. Great care must be taken to maintain the system. If the driver is late to notice that not only he or she has got the green signal, but the traffic light is also green for the pedestrians crossing the road in front of him or her, tragedy may occur.
2 3E System The literature [3, 4] also describes another approach to improving traffic safety. This system was named 3E after the three components: education, enforcement, and engineering. Education is a multi-stage process because as a child develops over time, the acquisition of knowledge can take place at an ever-increasing level. Thus, education takes place all the way from preschool and then school education in early childhood through driver training and the education of professional drivers. Mass communication PR activity is also a device for educating since with the help of posters and advertising films,
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in addition to the deepening of the rules, it is also possible to draw the attention of the masses to these changes through these channels. Enforcement means compliance with the rules. The presence and action of the police to ensure that the participants of the traffic pay more attention to the rules, their possible changes and the changes in the traffic order. Many times, it is enough to follow the rules when the police are present. Engineering includes tasks related to infrastructure and vehicle development, traffic engineering and organization.
3 Education for Safe Traffic The education of pre-schoolers and schoolchildren for safe traffic can take place at 5 different levels: – – – – –
tales, games, smart devices, traffic playgrounds, through educational programs (Fig. 4).
Fig. 4. STOP! Közlekedj okosan! – STOP! Walk smart! (Photos: internet)
The beautifully crafted characters of a fairy tale capture the children’s attention to such an extent that they can understand and retell the words of the characters even after the first look at the story. Perhaps one of the best examples of this is the paper-cut animations called STOP! Közlekedj okosan! which debuted in Hungary in the 1970s and has been extremely popular ever since! The dog in the main role explained all the important rules of the traffic precisely through his helpless friend, the cat to generations of young children. A book and a slide film were also made from the story.
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Fig. 5. Interactive performance by Diridongó at the Édenkert Református Óvoda (Garden of Eden Reformed Nursery School, Miskolc, Görgey street) (Photo: Eszter Gömze, nursery school teacher)
Tales can’t just be captivating for children in a movie adaptation. Several puppet performances (Fig. 5) adapt the rules of traffic, through which the children can learn the most important knowledge in an interactive way, together with the characters of the fairy tale. There are so many different products available on the toy market nowadays that it is a great challenge for parents and nursery schools to choose the proper one. Board games are also made on countless topics, so we can also find board games related to safe traffic. But there are also interactive games about road safety (Fig. 6).
Fig. 6. Toys on traffic safety (Photos: internet)
There are also bicycle traffic playgrounds for children. In Hungary, such parks are open in several large cities (Budapest, Esztergom, Gy˝or, Sopron, Tatabánya, Pécs, Komló, Békéscsaba, Paks, Hódmez˝ovásárhely, Nyírbátor, Nyíregyháza, Cegléd, Kiskunfélegyháza). In these parks cross signs, traffic lights and road signs help to learn the rules. Bike-education offers game elements similar to the traffic playgrounds, including game elements that can be used in a group room in a nursery school [8]. As smart devices play an increasingly important role in all areas of life today, app developers have found their way to educating children. There are stand-alone applications for road safety, but there are also some games embedded in complex games. The development of various skills for young children has been the goal of several private educational centres. Development sessions are available on a variety of topics,
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and there is a training centre that also runs a course on education for safe traffic. (https:// alfatanuloklub.hu/kozlekedj-okosan/).
4 Campaign-Like Educational Programs The importance of road safety is undeniable. That is why it is advisable to start educating children about safe traffic as soon as possible. With the right programs, children can easily become receptive to the topic, as what they are involved in will always captivate them. Compared to the options mentioned above, campaign-like education programs offer more intensive programs. Hereinafter, some options that are currently available will be described. 4.1 Safe Nursery School Program (Biztonságos Óvoda Program) The National Accident Prevention Committee of the National Police Headquarters announced the Safe Nursery School program for the first time in 2016 on the recommendation of the Institute for Transport Sciences Non-profit Ltd [9]. The program introduces preschool children to rules and signs that help ensure safe traffic, all with the help of playful educational material (gamification). For pre-schoolers, the directions (left and right) are sometimes not clear, but the program also places great emphasis on spatial orientation. The program has taken place every time in the last 6 years with the participation of several nursery schools (Fig. 7).
Fig. 7. Distance estimation, colouring (sidewalk, curb, road), memory game and aggregation according to colours and shapes in the Safe Nursery School Program at the Édenkert Református Óvoda (Garden of Eden Reformed Nursery School, Miskolc, Görgey street) (Photos: Eszter Gömze, nursery school teacher)
4.2 Mobile-Kids a Mercedes-Benz Initiative The Mercedes-Benz Mobile-Kids initiative is a worldwide global program that allows children ages 6–10 to learn safe street biking and rollerblading in addition to many very important topics and courses through a variety of thematically structured educational materials and courses. The program was launched in Hungary in 2012, the Hungarian founder of which is Mercedes-Benz Hungary Kft, and professionally supported by the National Accident Prevention Committee of the National Police Headquarters. The program operates in Bács-Kiskun county [10, 11].
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4.3 Bicaj-Ricsaj Cycling Traffic Team Competition The 3-round national competition will be announced for the 9th time in 2022 by the Szabolcs-Szatmár-Bereg County Accident Prevention Committee and the Míg Megnövök Kiemelked˝oen Közhasznú Alapítvány (While I Grow Outstanding Public Benefit Foundation) (Accredited Excellent Talent Point) for 7th and 8th grade students. The aim of the competition is to improve the traffic safety of children aged 13–15 with a method, content and form appropriate to their age. The main patron of the competition is dr. Police Colonel Zsolt Hudák; Deputy Chief of Police of Szabolcs-Szatmár-Bereg County, Chairman of the County Accident Prevention Committee. Professional leader Tihamér Szikszai titular Police Lieutenant Colonel, Secretary of the Szabolcs-Szatmár-Bereg County Accident Prevention Committee [12].
5 Conclusion The article provides an overview of the participants in the traffic and the responsibilities of the participants. It describes the various educational tools that can be used successfully in early childhood and gives a brief overview of the traffic safety education programs currently operating in Hungary. Nevertheless, the pattern that the adult, the parent, shows to the child is so important for the child that he or she will copy. This is why the responsibility of adults, and parents in particular, for the traffic model is very important.
References 1. Lévai, Zs.: Közlekedésbiztonság. Dialóg Campus Kiadó, Budapest (2019) 2. Ficzere, P., Horváth, Á.M., Sipos, T.: Elalvásos balesetek csökkentési lehet˝osége additív gyártási eljárással fejlesztett kapszulák segítségével. Közlekedéstudományi Szemle 70(1), 77–85 (2020). https://doi.org/10.24228/KTSZ.2020.1.3 3. Morimoto, A., Wang, A., Kitano, N.: A conceptual framework for road traffic safety considering differences in traffic culture through international comparison. IATSS Res. (2021). https://doi.org/10.1016/j.iatssr.2021.11.012 4. Gogola, M., Ondrus, J.: Road safety perspective of small children. In: 2020 XII International Science-Technical Conference Automotive Safety, Poland. IEEE (2020). https://doi.org/10. 1109/AUTOMOTIVESAFETY47494.2020.9293525 5. Majerova, J.: Cognitive rationality and sustainable decision based on Maslow’s theorem: a case study in Slovakia. Cogn. Sustain. 1(1) (2022). https://doi.org/10.55343/cogsust.8 6. Zoldy, M., Szalmane Csete, M., Kolozsi, P.P., Bordas, P., Torok, A.: Cognitive sustainability. Cogn. Sustain. 1(1) (2022). https://doi.org/10.55343/cogsust.7 7. https://www.motorsport.com/f1/news/felipe-massa-almost-died-hungary-ferrari/4793813/. Accessed 24 Mar 2022 8. https://bringa-city.hu/b-for-kindergartens-hu/. Accessed 24 Mar 2022 9. https://www.police.hu/hu/hirek-es-informaciok/baleset-megelozes/aktualis/palyazati-fel hivas-biztonsagos-ovoda-program-2020. Accessed 24 Mar 2022 10. https://www.mobilekids.net/en. Accessed 24 Mar 2022 11. http://www.eletuton.hu. Accessed 24 Mar 2022 12. https://www.megnovok.hu/hirek/ix-bicaj-ricsaj-kozlekedesi-csapatverseny-2022. Accessed 24 Mar 2022
Possibilities of Using of Online Vehicle Diagnostics in the Future Jozsef Nagy1(B) and Istvan Lakatos2 1 AUDI HUNGARIA Zrt., Audi Hungaria ut 1., Gy˝or 9027, Hungary
[email protected]
2 Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Szechenyi Istvan
University, Egyetem ter 1, Gy˝or 9026, Hungary [email protected]
Abstract. In the premium vehicle category, real-time online internet connection has become a standard in recent years. This trend is likely to spread completely in the automotive industry in the coming years. This fact offers a lot of new options in the field of vehicle maintenance (and predictive maintenance). Another possible use case may be remote diagnostics of in-use vehicles on the market, analysis of their online data and thereby an extension of the product development process after SOP. An additional new option may be to automatically collect, evaluate and generate of onboard diagnostics data to report to different authorities. E.g. OBFCM (onboard fuel consumption) or IUMPR j3 (in use monitoring performance ratio) field reports. In vehicle production, during the test drive, it could be possible to read and log of measurement data of the finished vehicle’s control units online Another application may be to test vehicles online during the production process e.g. to read of DTC’s (diagnostics trouble codes) during technical tests or to monitor of SoC (state of charge) of battery online while moving vehicles within the factory. Keywords: Online OBD · Online vehicle analysis · Online data logging on customer’s vehicles · Predictive maintenance · Online transferred OBFCM/IUMPR reports · Online in-production vehicle analysis
1 History of Analysis and Predictive/Preventive Maintenance Based on Online Data Collection (Flight Applications) Predictive and preventive maintenance, based on online data, first appeared in the aerospace industry. Maintenance costs are very high here, so preventing potential failures J. Nagy—Head of Corporate Quality Vehicle Technology Functions/Geometry. I. Lakatos—Head of Department of Road and Rail Vehicles. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 71–83, 2023. https://doi.org/10.1007/978-3-031-15211-5_7
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also results in high-cost savings and significantly reduces aircraft downtime, resulting in additional cost savings. AIRBUS ACARS (aircraft communication addressing and reporting system) have existed in the aerospace industry since 1978 [1].
Fig. 1. Principle of airbus ACARS system [2, 3]
Fig. 2. Airbus ACARS system pilot interface [2, 3]
Principle of operation of the system: the pilot can communicate with the airline’s headquarters via the interface (see Figs. 1 and 2) placed in the cockpit. The system automatically sends relevant data about the operating parameters of the machine e.g. the engines to the background database via radio and later satellite connection. Thus, in case of a technical failure, the ground maintenance personnel can prepare for possible repairs after landing a few hours later. In the same way, it is possible to correct this technical problem by localizing starting malfunctions before a relevant serious failure has occurred (preventive or predictive maintenance). Boeing OMF operates on a similar principle (Fig. 3):
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Fig. 3. Boeing onboard maintenance function [4])
2 Real-Time Monitoring in China (Beijing, Shanghai) Road Traffic Application According to GB/T 32960-2016 in China, all “new energy vehicles” (PHEV/BEV/FCEV) need to be equipped with RTM system (Real-Time Monitor) to collect data of the vehicle and each component and send the data to the enterprise platform and finally to the public one also for the authority (see Fig. 4).
Fig. 4. Overview of RTM system [5]
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The vehicles send relevant data in appr. every 30 s to the back end (see Fig. 5).
Fig. 5. Reported data through RTM [5]
There are predefined alarm levels 1, 2, 3 (see Fig. 6). This function is also used for emergency calls e.g. in case of accidents (similar to e-call function in EU).
Fig. 6. RTM alarm handling – the 3 levels [5]
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3 Audi’s Pilot Project for Online Data Collecting from Vehicles The automotive industry is adopting the technology from the aerospace industry now for analysis purposes; Audi AG’s first such project was the brand’s first electric (BEV) model, the Audi e-tron.
Fig. 7. Audi’s data collecting model (Audi e-tron) [6]
Way of communication: the vehicle’s gateway unit communicates with Audi’s back end server (AAP: Audi analytics platform) via a software module (fDC: flexible data collector) and the OCU (online connectivity unit) supported by its built-in “e-SIM” function (see Fig. 7). In one direction, “test campaigns” can be configured and sent to the selected VIN vehicle(s). In the other direction, the car sends the requested data e.g. measured values to the back end (see Fig. 8). In the meantime, care must be taken to comply with data protection legislation (DSGVO = GDPR) [8].
Fig. 8. [6] communication schema vehicles gateway (fDC) through network with a backend
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The system is suitable, e.g. to detect critical potential defects with high repair costs based on the experience measured values in a timely manner and to prevent them by sending of affected vehicles ASAP to service (predictive maintenance). These may include electrical powertrain failures such as e-motor electrical short circuits or lithium-ion HV battery cell failures (e.g. short circuits) and then prevention of a potential thermal runaway (see Fig. 9). Data can be used by R&D for design optimization or by after-sales for repair and maintenance.
Fig. 9. [6] example: stored and transmitted high voltage battery data from vehicle to a backend
4 The Possibility of Online Analysis in Vehicle Manufacturing The spreading of 5G data transmission technology offers new possibilities for vehicle analysis and testing during and after production. The following example shows a local 5G network (Audi Hungaria – Szechenyi University Gy˝or joint project). During the test drive, without connecting the scan tool, e.g. DTC entries or measured values of control units can be read out: A 5G interface contacted to the standard OBD socket of vehicles helps, for example, to communicate with an office computer. The same method can be used to reach cars in the production hall and in the parking lots around it, on which the 5G interface OBD device is connected. The data can be analyzed on PC through e.g. “CANoe” software from Vector Informatic LLC. The test drive area is now equipped with a local 5G network, which ensures communication between of vehicle’s diagnosis CAN data and the user’s PC via a 5G interface connected to the vehicle (see Fig. 11). The by Audi employee prepared device communicates as an interface on the diagnosis connector (see Fig. 10) with gateway unit and the vehicle’s diagnosis CAN network and also with the user’s PC using a 5G interface built into the device, where analysis evaluations can be carried out.
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Fig. 10. OBD plug pinout
Fig. 11. How the vehicle communicates with the infrastructure
5 Development Opportunities for Online Diagnostics in the Future 5.1 On-Site but yet not Online Vehicle Diagnostic with a Scan Tool (in Use Already) Example: USA OBD PVE j3 monitoring. One employee of an OEM reads out IUMPR (in use monitoring performance ratio) data via OBD connector. The data of many vehicles are saved in an excel file. This is the basis of the report for CARB/EPA authorities which shows the average IUMPR values of each monitored subsystem of engine management of evaluated cars (see Fig. 12 and a report example in Fig. 13).
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Fig. 12. OBD IUMPR (USA) data collecting from vehicle with using of diagnostic scan tool
5.2 Partially Online but not Real-Time Vehicle Diagnostic (in Use Already) Example: EOBD (EU) field monitoring. The car is connected online to the back end of OEM dealer via scan tool during service or maintenance.
Fig. 13. Example EOBD IUMPR data matrix [7]
Each car’s IUMPR data is automatically transferred to the OEM back end server. From there, they can be imported into Excel for evaluations and reports (see Fig. 14).
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Fig. 14. Creating of IUMPR EOBD reports from data transmitted through diagnostics scan tool
5.3 Online but no Real-Time Data Collection (Already in Use): Statistical Function in the Vehicle MMI (Men Machine Interface) The car’s MMI’s central computer unit (CCU) is linked with the OCU (online connectivity unit) and automatically sends data to the OEM back end server via built-in eSIM. All functions displayed on the central display, e.g. seat ventilation, climate settings, etc., are sub-stored or transferred to the back end. Here you can carry out various evaluations, which will give you the opportunity to optimize product development. Of course, the data is processed here also in accordance with the statutory regulations (GDPR) [8]. This can be done purposefully – to further develop specific functions or to map previously unknown usage characteristics and relationships with the help of big data evaluations (See Fig. 15).
Fig. 15. Transmitting of MMI user’s habits data to backend
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5.4 Real-Time Online Vehicle Diagnostics (Partially Used in the Meantime) There is a data collector function of gateway unit of the vehicle. The car sends data through OCU e-SIM communication to the back end of OEM (LTE, 4G…) => basis for reports for authorities (see also RTM on pages 3 and 4).
Fig. 16. Transfer of OBD IUMPR or OBFCM data from the vehicle to a backend
In the future, for example, it may be possible to read OBFCM (onboard fuel consumption monitoring) field monitoring data, as soon as all cars have an online data collector function. The currently planned EU7 emission standard (planned from 2025) is likely to make it a legal obligation to have the OBFCM statistical function in road vehicles in EU27 Member States (It’s already required for CoP (conformity of production) emission tests in the car plants). For example, the way data are transmitted from the vehicle in this way is already in series in Audi e-tron vehicles. This method with the predictive/preventive maintenance function or other data collection is already used (see page 5). However, this use case doesn’t cover OBD IUMPR or OBFCM functions yet, but the system can theoretically be further developed for these evaluations (see Fig. 16). 5.5 Fully Real-Time Online Vehicle Diagnostics (Possible Future): After the full roll-out of 5G networks, it is likely that all cars will have a data logger function in gateway unit plus a permanent online connection via OCU (online connectivity unit) with back ends of OEM’s. Benefits of 5G data transfer see Fig. 17 [9]: – zero handover time – much higher data transfer speed – better system stability
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Fig. 17. Comparison of several networks/data transfer methods
– up to 10x higher energy efficiency (energy consumption => CO2) The benefit is here, after the warranty period, if there is no service visit longer, the possibility of data collection and remote diagnostics will remain as long the OEM and/or the customer overtake the cost of data packages in order to transfer information from vehicle to the manufacturer’s back end (see Fig. 18).
Fig. 18. Diagnosis data transfer from car to a back end
Further Option: The vehicles could even communicate automatically with the authority’s back end servers and could send automatic reports too. It would be a possible extension of Chinese RTM (real-time monitoring) regulation for the entire market or for other countries like USA or EU too. “Real-time consumption = CO2 monitoring by authorities”.
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Fig. 19. Assumed future: vehicle diagnosis without a general scan tool
6 Possible Future: Vehicle Repair and Diagnostic Without a Generic Scan Tool Hypothesis: all vehicles will have an online diagnostics interface and an online connectivity unit as well and there will be 5G networks everywhere in the future. Thus, a diagnostic scan tool is theoretically no longer required: vehicles could send data directly or via the OEM back end to a cloud according to a standardized protocol. Any subscriber (private persons, car services or authorities) can retrieve certain stored or even real-time data about the vehicles based on their VIN (vehicle identification number) through a subscription model (see Fig. 19).
7 Summary In the future, I would like to dive deep into the online vehicle diagnosis area. Within this, the use of remote, online and real-time diagnostics in vehicle manufacturing may be the focus: • how to reduce vehicle processing time in production (“Harbour time”: EHPVengineered hours per vehicle) • how to reduce or partially eliminate function tests in assembly line using this method or how to perform functional tests on vehicles instead of as an EOL (end of line) test bur during car assembly • how to make vehicle production more efficient overall; thereby reducing production costs and further improving delivery quality.
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References 1. ACARS in Wikipedia. https://en.wikipedia.org/wiki/ACARS#History_of_ACARS 2. Figure ACARS. https://image3.slideserve.com/6147113/aircraft-communications-addressingand-reporting-system-acars-l.jpg 3. ACARS flight deck. https://www.airsatone.com/img/site/iridium/acars-flightdeck.jpg 4. http://www.b737.org.uk/flightinstsmax-maint.htm#overview 5. RTM, Source: Audi Sales Division China 6. fDC, source: Audi AG 7. EOBD IUMPR report extract, source: Audi AG based on regulations of ECE-R83, VERORDNUNG (EU) Nr. 566/2011 DER KOMMISSION, EG (Vo)-692-2008 (Euro5/Euro 6) 8. General Data Protection Regulation GDPR; Regulation (EU) 2016/679. https://gdpr-info.eu/ 9. Benefits of 5G data transfer: figure from Audi AG
Security and Safety Systems on Modern Vehicles József Répás1(B) and Lajos Berek2 1 University of Public Service, Budapest, Hungary
[email protected] 2 Óbuda University, Budapest, Hungary
Abstract. In collaboration with safety, security, infocommunication and transport science, self-driving vehicles are going to transform the future of the transport technology. Due to technological innovations and specificities, new risks and vulnerabilities are emerging in terms of modern road vehicles and more increasingly in terms of autonomous vehicles. These vulnerabilities should be addressed at the design stage already. Autonomous vehicles just as manufacturers of intelligent and networked cyber-physical systems, are supposed to meet the interoperability requirements and consider security threats and safety hazards at the same time. The autonomy level of the vehicles is increasing therefore, they are becoming more and more similar to a rolling computer, evaluating and using more and more information, retrieving information from their environment with their sensors and from each other or gathering it from track elements. Based on this incoming data intelligent vehicle systems are able to identify potential hazards. Irrespective of the communication channel, protocol or information gathering solution, the vehicles must be protected from external threats also by allowing access only to authorized users, ensuring the possibility that the traffic information can be processed, retrieved, and examined retroactively in addition to the security risks of information, communication. The research in this study summarizes the safety solutions from the past and present and the solutions expected in the short and long term. Keywords: C-ITS · Autonomous vehicles · Safety
1 Introduction With the increase in personal mobility, new economic, social, and recreational opportunities have emerged. Due to the continuous development of motorization, mass production, and the automotive industry, transportation vehicles have become more widespread and faster, on the one hand, creating new potential risks on the other [1]. In order to reduce the risks, the development of safety devices has also started, as some developments may not only be exciting but dangerous as well. As more cars appeared on the roads, more and more collisions emerged, affecting cars and even pedestrians, resulting in a fatal accidents. The causes of the accidents were not always clear, “driver behaviour, automobile design, highway engineering, and traffic hazards all were blamed” [1]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 84–100, 2023. https://doi.org/10.1007/978-3-031-15211-5_8
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To preserve the benefit of the transport the focus had to be shifted on the specific problems: from the controlling driver behaviour to redesigning automobiles and improving the driving environment [1]. Manufacturers have been paying increasing attention to reducing the impact of accidents by developing vehicle safety and solutions. “Auto manufacturers have come a long way over the history of auto safety, paving the way for improved global safety standards” [2]. As a result of customers’ needs and the increasingly strict regulations, the car safety has been improved year by year, the number of efficient and patented safety systems has been increased [3]. Some early developments (improved solutions, but with no change in conception) can also be found in today’s modern vehicles and are going to play an important role in the future improvement of transport safety. Starting with the early 1900s where effective solutions had to be found for speeding, accidents, collisions, and irresponsible driving. The primary solution was to regulate and control managerial behaviour, e.g.: “laws, fines, signals, and drunk driving arrests”, trying to put an end to the rising number of mortal accidents. Initially, vehicles were devices working according to the driver’s instructions, performing them without causing any accidents on their own. However, by the late 1920s, manufacturers had to admit that design flaws were compromising safety. Although manufacturers had begun to support new security developments in the 1930s, they did not use the security tools already available such as seat belts, energyabsorbing steering columns, etc. At the same time, it was empathized that the cars were completely safe, and the development of roads, traffic regulation and driving licenses are the necessary factors to prevent accidents and improve road safety.
2 Automotive Safety Automotive safety has been developed for long decades, from installing the first components to the computer-controlled automatic systems. The aim of the improvements was to protect drivers, passengers, pedestrians, the environment, and the vehicle itself. Rapid progress has been made in terms of achieving the goals in two areas: active and passive safety. To passive safety includes the protection of passengers as well as the reduction of damages, thus the reactive safety/protection measurements. Active safety can be interpreted as a technology for preventive and proactive safety solutions aiming to prevent accidents. The individual systems, their purpose and timing, and a vision for the systems of future vehicles will be presented below. 2.1 Passive Safety Solutions The development of individual passive protection systems can be traced back to the 1880s, which led to today’s modern safety systems. The first seat belt patent, developed in 1885 by Edward J. Claghorn can be considered the first passive safety solution such as using kerosene and acetylene operated front lights. Although the first electric headlight by the Electric Vehicle Company appeared in 1889 it was not widespread because of the fragile filament they used. From 1895 began
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the usage of Pneumatic tires developed by André Michelin. This development is still one of the basic elements of today’s traffic safety. In order to meet the increasingly widespread traffic rules, the first mechanical speedometer equipped vehicle (by Oldsmobile) was built that allowed drivers to monitor their speed. The windshields of the vehicle in the early 1900s were still simple panes of glass. To keep them “clean,” Mary Anderson made and patented the Wiper blades (1903), “a hand-operated lever inside the car connected to a rubber blade on the outside”. It was supposed to help removing snow, ice or sleet from the window [4]. The first brake lamps were developed in 1905 and in 1910, it became mandatory for motorists to use signals [5, 6]. In 1911 Ray Harroun started using Rear view mirrors, and Cadillac provided a solution for the cumbersome start-up of vehicles with his first massproduced electric-start cars. As traffic density increased, it became more important to indicate the planned direction of travel, therefore in 1914 the earliest turning indicators called ‘auto-signalling arm’ were invented by Florence Lawrence. This signaling arm was on the back of the fender and could be moved with the electric pushbuttons [7]. In 1914 a patent for an “electric signalling pipe got registered by Bosch” [7]. However, the outbreak of the first world war made it impossible to proceed with any further development [6]. In 1919 inventor William M. Folberth patented the first automatic, non-hand-driven windshield wipers [8]. In 1921 the first Bosh-made horns were applied on cars, and Benjamin Katz invented the headrest [6]. One year later, Frederick Duesenburg Hydraulic applied brakes on all four wheels. Due to an accident pieces of glass windshields caused injuries, therefore, to minimize shattering, Stutz placed horizontal wires in its windshields in 1926. In the same year another safety feature emerged: Stutz’s lowered the center of gravity to reduce sway and roll-over. He also designed heavy steel running boards for side-impact protection [1]. To avoid injuries due to glass breakage in case of a collision, one year later Henry Ford introduced laminated windshields (Triplex glass). In 1930 General Motors installed shatterproof Deplete windshield glass on Cadillac’s [1, 9]. Acknowledging that in the event of an accident, both the driver and passenger usually hit the handlebars, dashboard, windshield, or doors, these surfaces became simplified, clearer, the sharp parts, protrusions, knobs, and handles were replaced with safer shapes. The use of chromium, known as harmful to health, has been phased out [7]. Dr. Claire L. Straith was the first installing lap belts, designing, and patenting a dashboard crash pad in his own car [1]. In 1937, several auto manufacturers decided to improve their cars applying flat, smooth, rounded door handles, windshield wipers and additional cushioning on the back of front seats in order to assure higher safety for backseat passengers [7]. However, for Chrysler, Plymouth, Dodge, DeSoto, and Imperial cars no seat belts and padded dashboards were applied. They had “recessed knobs, rubber buttons, curving door handles that could not snag motorists, and padded seat tops” [1]. In 1939 the electric turn signals were introduced on the Buick [10]. For the protection of both the driver and the passenger in 1947, the first padded dashboard was applied. The next year, additional safety features were introduced by Preston Tucker. He introduced the shatterproof rear-view mirror, the pop-out windshield, as well as the center headlight which turns along with
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the steering wheel [1]. In the year of 1949, the Chrysler Crown Imperial was produced with disk brakes, and the Saab introduced cars with safety cage (see Fig. 1).
Fig. 1. The safety cage [11]
In the 1950s almost every year, a new safety development emerged. In 1950 Walter Linderer created the airbag, in 1952 Bela Barényi, engineer of the Mercedes Benz “presented the crumple zone concept, aiming to absorb the force of impact in the event of a crash”. One year later Jaguar and Dunlop produced even more effective disc brakes the so called Caliper-Type, and John W. Hetrick invented the compressed air airbag [1]. In 1954 the Citroën applied a self-levelling suspension on the Traction Avant truck in order to improve its road-holding and preventing the headlights disturbing the oncoming traffic [13]. “Invented in 1954, breathalyzers help to monitor one of the biggest causes of road accidents: drink driving” [12]. In 1956 Volvo introduced the Safety steering column, which “broke away from the steering system in a major impact” [13], and in 1959, General Motors started working on the Invertube, a steering column that turned inside out in case of force [1]. Later this year, optional supplementary items, the headrests, were introduced for the front seats and Nils Bohlin engineer of the Volvo invented three-point seat belts introducing and making them mandatory for all Volvo cars [14]. In the 1960s fast gas-fired airbags were developed by Ford, General Motors, and Eaton, Yale and Towne [1]. In 1963, Excelsior Motor Company introduced the inertiareel seatbelt adjustable according to passenger’s preference [15]. More than 60 years after its invention, in 1964 the Intermittent wipers and the Volvo-made first rear-facing child seat were introduced. In 1967, General Motors, Chrysler installed the “steering columns with mesh that compacted under pressure”, in the next year, Ford created his own, also aiming to reduce fuel leak-danger and consequent fire by introducing inertia switches to cut off power to the fuel pump in case of an impact [13]. From 1969, Volvo cars were produced with front-seat head restraints.
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The 1970s were productive mostly in terms of active safety. In 1978 Volvo introduced the first belt-positioning booster. It allowed children “from 4 years of age to travel facing forward, with increased protection and comfort” [14]. In the 1980s the fuel tank was replaced so that it became safer in front of the rear axle. In 1981, Mercedes-Benz started to apply supplemental restraint system (SRS) airbags and seatbelt pre-tensioner in Mercedes cars [1, 16]. From 1986, the “third brake lights became standard”, the tire pressure monitoring system (TPMS) was introduced by Porsche and airbags as an optional supplemental item by Ford. From 1988 the Chrysler made driver-side airbags a standard safety item in its cars [1]. The handling of cars mostly depends on the suspension, the most important is, that all tires have contact with the ground [17]. In 1990, Volvo made a further step toward children safety: the first built-in booster cushion was introduced [14]. In 1991 the Japanese Toyota provided the Soarer Limited with rear-view cameras, and the Volvo introduced its side impact protection system (SIPS) [18], which in case of an accident, dissipated most of the impact force distributing it towards the supports, columns, floor, roof and other structural elements of the body. From 1992, Chrysler provided its minivans with integrated child safety seats. From 1994 the Volvo SIPS were completed with side-impact airbags for protecting the chest and hip and other body parts [19]. In 1995 more manufacturers introduced the daytime running lights to create better visibility in daylight. In 1996 the first-ever knee airbag was installed in Kia Sportage and to prevent whiplash injuries the Volvo introduced its Whiplash Protection System (WHIPS) [14]. In 2000 the Cadillac Deville was launched with a night vision interface, displaying an infrared image on the windshield [16]. In the next year by the Nissan the backup camera became an optional item for better visibility [16]. The next step by Volvo was in 2002 in the form of a roll-over protection system (ROPS) [14]. In the year of 2005, the Citroen C6 and Jaguar XK came up with pop-up bonnets: “if the car hits a pedestrian, the bonnet pops up to help absorb the impact” [18]. In 2012, Volvo’s - Pedestrian Airbag were introduced in order to protect pedestrians in accidents, the Run-off road protection keeps the occupants firmly in position in the seats [13, 14]. In 2017, Toyota started working on the Patents Cloaking Device. It is supposed to improve visibility by helping to see “through Car pillars” [20]. In 2019 the world’s first Multi-collision Airbag System was created by Hyundai Motor Group, Mercedes showcased a retractable steering wheel and pedal line in its self-driving car (“the steering wheel and pedal cluster are retracted to reduce the risk of injury during a crash”) [21–24]. In 2020, the Honda applied a new front center airbag placed on the back of the driver’s seat and unfolds between the driver and the passenger [25]. Nowadays, apart from manufacturer-specific products, passive safety solutions help drivers, such as daytime running lights for better visibility, the Adaptive Front Lighting System, for the better visibility at nighttime, Intelligent Auto Headlights, The Headlights turn on automatically during rain or twilight, helping to improve the visibility of your vehicle [26, 27]. With the 360° camera system, the driver can see the obstacles surrounding the car, and it also helps with parking [28]. “The technology is intended to make exiting an automobile safer for everyone in the area, not just people departing the vehicle” [29].
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2.2 Active Safety Solutions From the 1970–80s, electronics have become a key role in vehicle safety technology, starting to introduce the improvement of safety items. In 1971 the Chrysler Imperial has the first mass-produced car with anti-lock braking system (ABS) as standard equipment [16]. In 1974 the General Motors introduced airbags as optional supplement both on the driver and passenger side. In 1976 Aston Martin presented the very first electronic dashboard and digital speedometer [30]. From 1978 the Mercedes-Benz S-Class contains electronic anti-lock braking (ABS) system. Allwheel-drive (AWD) aim for better road holding and efficient traction transmission [17]. From 1987, Mercedes-Benz, Toyota, and BMW vehicles are provided with traction control, which helps to keep traction while accelerating. 10 years later, in 1995, Mercedes-Benz and Bosch introduced electronic stability control (ESC), to improve the stability of cars. In the next year the Brake Assist System (BAS) was launched by Mercedes-Benz to ensure a more efficient emergency braking [13]. In 2000s autonomous safety functions and further solutions aiming pedestrians’ safety have emerged. The Lane Departure Warning System for trucks were also introduced by Iteris [15]. From 2003, Volvo has a new visible alert system (BLIS - Blind Spot Information System) targeting the problem of obstacles in blind spot [31]. Two years later with AEB (autonomous emergency braking) the Volvo cars became capable of breaking before the collision. In 2009 Citroen developed the Intelligent Anti-Skid system Snowmotion [2]. “In 2010, the pedestrian detection system was also introduced by Volvo.” It detects pedestrian and makes vehicles break automatically [3]. In 2013 the Pre-Collision System was implemented by Toyota to identify potential dangers on the road and prevent potential collisions by breaking or bypassing [16]. Transparent Hood – The transparent hood system ensures the driver good visibility regarding the under the hood area [32] (see Fig. 2).
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Fig. 2. The under the hood area is visible to the driver enabling him to have accurate information on the obstacles and the terrain’s type not visible while driving [32, 80].
Volvo’s road safety system of 2016 was the Connected safety. The solution connects the cars via cloud connections, to share important data about the road or the cars in the near environment e.g.: about slippery road sections or vehicles with hazard lights, helping the driver to find enough time to slow down [14]. From 2018, with the Oncoming mitigation by braking, the system is able to manage to reduce the vehicle’s speed and mitigate the force of the collision in case of an unavoidable collision (e.g.: an oncoming vehicle veers into the other lane) [14]. In the same year Car2Car and Car2Infrastructure technology emerged, whereby cars communicate with each other in order to warn of slippery roads or break-downs on the way. It is also possible to inform other cars how long it takes traffic lights to turn green [33]. Nowadays, the following active safety solutions help drivers: • “Speed-dependent power steering increases the steering wheel resistance in pace with the vehicle’s speed” [34]. • “Intelligent Cruise Control (ICC) - measures the distance from the vehicle ahead and controls acceleration and deceleration so that it automatically maintains a suitable following distance” [35]. • Similarly, the Intelligent Distance Control provides the drivers’ distance control between themselves and the vehicle in front [36]. • The Volvo Pilot Assist helps drivers to drive smoothly and safely from a standstill to the speed allowed on the motorway, and the steering wheel automatically keeps the vehicle in the middle of the lane. • Intelligent Park Assist Automatic steering for helping drivers to park.
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• “Intelligent Engine Brake – reduces the frequency and effort foreseen for braking the car enabling a more accessible and more comfortable driving [37]. • Intelligent Ride Control helps to moderate the feeling of fore-aft pitching motion while passing over certain bumps by controlling the engine and brakes, delivering a smoother driving experience [38]. • Intelligent Trace Control assists drivers by applying automatic braking to each wheel, keeping the vehicle on the line as steered [39]. • Emergency Assist for Pedal Misapplication helps to avoid accidents caused by using the wrong pedal [40]. • Rear Cross Traffic Alert assists the driver in avoiding collisions with oncoming vehicles when reversing [41]. • “Rear Automatic Breaking helps to avoid collisions with objects behind the vehicle” [42]. • Intelligent Driver Alertness - Helps detect driver fatigue and alerts the driver when erratic driving occurs, and the Facial Recognition software helps to monitor the driver in a more effective way [32, 43]. • Forward Collision Warning - reduces the danger of distracted driving by using cameras, radar and even lasers to detect objects in front of the vehicle [43]. • Hands Free/Bluetooth for the safe usage of cell phones [44]. • Voice activation –“Talk to the car! Vehicle voice control helps drivers to concentrate on driving without distracting the driver [44]. • “Speed Assistance Monitoring - Speed Assistance monitoring helps drives to comply with speed limits [32]. • The Occupant Safety Monitoring feature permanently monitors the positions of passengers to deploy airbags in the event of a crash [32]. • Proximity sensors and Contact Sensor System (CoSSy) – “This feature helps to detect the objects and people getting too close to the vehicle [32, 45]. Intelligent Intersection has been created to prevent accidents at intersections by providing in-formations to drivers and others on the road [32]. • Teen Driver Technology –Today General Motors is market-leader in terms of teen driver technology offering features like notifications for higher-speed driving, volume limits on sound systems, and safe driving reports on ABS triggering while driving [46]. • Interior Motion Detection prevents fatal accidents potentially caused by leaving a child or a pet in a hot car [47]. • Nissan has developed its Brain-to-Vehicle technology, the world’s first system for detecting and analyzing the driving-related brain activity in real time. It includes intentional movements as steering and motion-related cortical potential (MRCP) as well as activities revealing the differences between driver expectations and experiences, the error-related potentials (ErrP) [48]. • Traffic Sign Recognition keeps the drivers informed on road signs by notifying them on the speed limit and other traffic signs, including speed limit changes and other relevant road and traffic-related information [49]. • To help to address intoxication and distraction in traffic, the driver monitoring cameras and sensors make it possible to detect drunk driving or driving under the influence of drugs or other disturb, and to act and intervene accordingly [50].
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The more vehicle production is intertwined with electronics, informatics, communication, and artificial intelligence, the more are we getting closer to truly self-driving vehicles, which, in addition to perceiving each other and the environment, can communicate with each other, the environment and the track ensuring accident prevention and pedestrian protection. 2.3 Safety Systems of the Future Vehicle safety solutions are increasingly helping the driver to drive safely, becoming more precise, and in some cases providing solutions that go beyond the driver’s abilities. All these changes had to be pursued by the regulatory background as well, thus the traffic regulatory environment has constantly been evolving too. After regulations on the construction of vehicles were announced, the traffic rules were also developed, and the mandatory safety systems were also defined to be applied in the vehicles. In order to ensure a high level of safety and environmental protection “Regulation (EU) 2019/2144 of the European Parliament and of the Council lays down administrative provisions and technical requirements for the type-approval of all new vehicles, systems, components and separate technical units with a view to” [51, 52]. The regulation stipulates the mandatory use of ADAS (Advanced Driver-Assistance Systems) in new vehicles to further improve road safety, facilitate the driving task and reduce the number of fatal road accidents [3, 53]. The first Advanced Driver-Assistance Systems was the anti-lock braking system (ABS), and nowadays also Braking Assist, Lane Keeping Assist, Adaptive Cruise Control, Blind Spot Indication and Parking Assist can be considered here as well [54]. These Comprehensive Crash-Avoidance Technology items could become the standard safety system of future vehicles. Hereby the first step is the Regulation (EU) 2019/2144. According to the Article 6. (1) “motor vehicles shall be equipped with the following advanced vehicle systems: • • • • • • •
intelligent speed assistance, alcohol interlock installation facilitation, driver drowsiness and attention warning, advanced driver distraction warning, emergency stop signal, reversing detection, event data recorder” [51, 55, 56].
According to Article 6. (4) the event data recorders shall contain “the data that they are capable of recording and storing with respect of the period shortly before, during and immediately after a collision shall include the vehicle’s speed, braking, position and tilt of the vehicle on the road, the state and rate of activation of all its safety systems, 112-based eCall in-vehicle system, brake activation and relevant input parameters of the on-board active safety and accident avoidance systems, with high level of accuracy and ensured survivability of data” and that “they cannot be deactivated”. Furthermore, the data shell be recorded and stored as a closed-loop system, and should be anonymized
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and protected from manipulation and misuse. The data also shell include the type of the vehicle, variant, and version. First, the active safety and accident-avoidance systems in the vehicle should be identified. Based on this information, accident-related vehicle data may become available in a unified, standardized format in the future [51]. This list could be upgraded in the future, and it also will, since manufacturers are continuously developing newer versions of driver assistants and safety systems, e.g.: airbags activating on the exterior part of the car, reducing the negative effects and force on the passengers and the vehicles. “These extra crumple zone exterior airbags provide helps reduce passenger injury severity by up to 40%” [57]. There are also airbags able to prevent crashes, thus if a collision is unavoidable, according to the sensor information, it stops the vehicle almost immediately by means of an airbag that opens under the vehicle and a friction coating on it stops the vehicle (see Fig. 3).
Fig. 3. The new friction airbag system enables cars to have double-stopping power [58–61].
Intelligent, Adaptive headlights are designed to improve the safety at night. The headlight system moves the headlight beam with the steering wheel improving the visibility while cornering. Depending on surrounding light conditions and other vehicles on the road, these can also dim and brighten in an automatic way [57]. We can count on the Virtual Door Mirrors and digital rear-view mirror being introduced as a standard soon. For ensuring pedestrians’ safety, the turning assistance solution will be introduced by more and more manufacturers. Also, in case of the most significant elements of the road holding: wheels and tires, intelligent and adaptive new solutions will be introduced in the future. One station on the way to self-driving may be the driver override system, which allows “the car to be able to apply the breaks even in some cases when the driver is accelerating” [62], it can also be an effective tool for forcing speed limit compliance. Due to networking vehicles and providing access to environmental, traffic and track element information, a real-time database is provided for vehicles so that drivers will be enabled to avoid road construction, closures, obstacles, accidents, or road errors or to adjust the driving parameters according to this information [62] (see Fig. 4).
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Fig. 4. Networked vehicles [63]
A monitoring of the driver’s health state will be also possible, high blood pressure or electrolyte imbalances, drowsiness, distraction, fatigue, tiredness will be detected. An active health monitoring technology might help to avoid potential, health related accidents [62, 64].
Fig. 5. Intelligent display [63, 65]
The purpose of the 3D displays, and Google Glass-similar solutions is to display and highlight important information to the driver, to mark obstacles and pedestrians, and to draw attention to safer traffic [56] (see Fig. 5).
3 Automotive Safety IT and electronics have increasingly intertwined with traffic security developments, equipment solutions, vehicles, therefore new security risks have emerged. Initially, there was no need for vehicle protection from theft and burglary, then in connection with the
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wave of auto thefts of the 1970s, vehicle protection moved more towards security with the aim of protecting the vehicle from unauthorized start-up and theft. On the other hand, unauthorized wired or wireless access, cyberattacks have made it necessary to handle the security of software and related hardware as high priority [66–68]. “Around 1900, starting a car was a rather complex undertaking involving some ten steps that only properly trained drivers could master” [69]. The next step was the removable steering wheel, and the first theft deterrence systems was provided by Bosch in 1911, which was the key in the ignition switch: it should be turned on position [69, 70]. Then more and more sophisticated locks were made, and power locks were used from 1914, the keyless entry was introduced by Ford in 1980, and transponder keys were used first in 1990’s. It had a chip in the key and worked with radio signal [70, 71]. In 2004, General Motors provided a factory solution for remote vehicle start-up, with pre-start run diagnostics such as engine oil pressure, engine temperature, and more [74]. Since 2009, vehicle control through cell phone is possible due to the mobile remote access introduced by Mercedes. Nowadays keyless entry is becoming more widespread, the first application was presented in 2015, in addition digital keys or fingerprint-based biometric vehicle access can be used to open or start our cars. Furthermore, comprehensive vehicle control also provides remote monitoring [62, 72–74]. Thanks to the remote control, it will be possible to deactivate the vehicles remotely in case they are stolen. According to the Popular Mechanics Magazine, the first car alarm was invented by an unnamed prisoner from Denver in 1913. It was manually armed and emitted siren-like calls for helps in case someone would try to crank the engine. Based on this idea, the first remote starter was created in 1916 [75]. and first immobilizer was invented in 1918 by St. George Evans and Edward N. Birkenbeuel [70]. To ensure that only the owner or authorized persons drive the vehicles, physical protection measurements may be introduced for the electromechanical protection of the engine compartment and for the installation of steering, shift lock and immobilizer safety switches [76]. Increasingly effective protection needs to be evolved in the future to make vehicle security system ensure integrity and availability of modern vehicles, as well as confidentiality, integrity and availability of in-vehicle user, operational and environmental data (including track information) to make increasingly autonomous vehicles become a safely integrated elements of the future transport. Protecting active and passive safety devices and vehicles is a complex task, Tokody et al., In their study Automotive Cybersecurity, identified the following for protection criteria: • • • • • • •
Communication between in-vehicle systems, communication between vehicles, protection against interference and unauthorized modification, protection against physical access, protection of data collected, stored and transmitted, physical protection of automotive and transport IT systems, protection of the transport system as critical infrastructure [77].
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Accordingly, in addition to all active and passive safety systems in modern vehicles to be installed in the future go hand in hand with their security aspects: the systems need to be protected from forged input information, access to system components, external and internal connections, its unauthorized modification, influencing processing, incorrect operation of intervention systems. The integrity and authenticity of the data generated in the systems, stored and transmitted, is a primary issue, regardless of the communication channel, since a malfunctioning active or passive safety system or a maliciously controlled self-driving vehicle generates a high safety risk. This is borne out by Regulation (EU) 2019/2144, according to which Article 4 (5) requires manufacturers to ensure that vehicles, systems, components, and separate technical units are protected against unauthorized use and cyber-attacks, in accordance with NHTSA (National Highway According to the Traffic Safety Administration), protection should be provided for both wired and wireless access points based on a multi-level security approach [78, 79]. 3.1 Accident-Free Future Safety has always been a priority in terms of vehicle development: while in the beginning passive safety was emphasized, nowadays active safety solutions are given priority. Active and passive protection solutions designed to increase the safety of vehicles and their drivers and to reduce the extent of injuries have now reached the limit concerning their possibilities. As a next step, the integrated safety approach can bring traffic safety to a new level and reduce the number of accidents by combining active and passive safety solutions with the integration of different safety systems, modular situation-decision tools and different escalation levels. The drivers will still be warned by optical, haptic and/or audible warning signals in dangerous situations. However, in case of late or missing intervention of the driver, cars will be able to intervene to mitigate the effects of an accident (breaking and steering). “In the future a holistic approach to safety functions during an accident and the use of a superimposed decision tool will make it possible for the various individual functions to play their part in the total accident response function” [81]. The goal is accident-free driving. With the ever-increasing level of self-driving skills and the implementation of continuous communication between vehicles, vehicle safety will be able to reach a level where even some passive safety solutions might be removed. Since vehicles will have real time information about their environment and other vehicles in their environment, they will also be able to prevent and avoid accidents. In case self-decision for cars become realized and vehicles also have information on the predicted movement of other cars in their environment -for which the necessary computing capacity is provided-, some passive safety solutions (e.g.: airbag, crease zone) could be removed, accident-free driving could be achieved [81].
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4 Summary Our study presents the history of the development of vehicle safety and security systems from its beginnings to the present and provides a review of the technologies under development or used in self-driving vehicles. It has been shown that, in addition to the use of safety systems, the security aspects of these systems should continue to play a key role in the future, considering the risks of malicious use of vehicles. The standards and systems required to protect the IT and communication systems of modern vehicles place a heavy burden on manufacturers and developers building applications for these systems. “Prepared with the professional support of the Doctoral Student Scholarship Program of the Co-operative Doctoral Program of the Ministry of Innovation and Technology financed from the National Research, Development and Innovation Fund.”
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70. Source. https://automobileandamericanlife.blogspot.com/2015/02/auto-theft-alarm-systemsbrief-history.html. Accessed 13 Feb 2022 71. Source. https://galmierlocksmiths.com.au/blog/melbourne-auto-locksmith-explains-evolut ion-of-car-locks-and-security. Accessed 22 Feb 2022 72. Source. https://www.fixautousa.com/blog/the-future-of-car-technology/. Accessed 21 Feb 2022 73. Source. https://gomechanic.in/blog/features-in-2022-cars-in-india/. Accessed 23 Feb 2022 74. Source. https://carbuzz.com/features/evolution-of-car-technology-and-features. Accessed 16 Feb 2022 75. Source. https://www.eagleridgegm.com/evolution-car-security/. Accessed 19 Feb 2022 76. Source. https://www.pajzs.hu/pajzs-biztonsagtechnika. Accessed 14 Mar 2022 77. Dániel, T., Attila, A., László, A., Marcell, T.Z., Zoltán, R.: Automotive cybersecurity. BÁNKI KÖZLEMÉNYEK, 1. ÉVFOLYAM 3. SZÁM (2018) 78. NHTSA: Automated Vehicles for Safety (2018). https://www.nhtsa.gov/technology-innova tion/automated-vehicles-safety. Accessed 04 Apr 2018 79. Source. https://eur-lex.europa.eu/legal-content/HU/TXT/PDF/?uri=CELEX:32019R2144& from=EN. Accessed 20 Feb 2022 80. Source. https://images.hgmsites.net/hug/land-rover-discovery-vision-concepts-transparenthood-technology_100463101_h.jpg. Accessed 14 Mar 2022 81. Gonter, M., Seiffert, U.: Integrated Automotive Safety Handbook. SAE International, Warrendale (2013)
Design and Powertrains
Contact Ratio of Spiral Bevel Gears Miklós Gábor Várkuli(B) , Gabriella Vadászné Bognár, and József Szente University of Miskolc, Miskolc 3515, Hungary {machvmg,v.bognar.gabriella,machszj}@uni-miskolc.hu
Abstract. The relationships used to calculate the contact ratio of Gleason spiral bevel gears are not published or the formulas found are of an empirical nature, and the theoretical background of which is unknown. Since the spiral bevel gears have a localized contact pattern, the actual contact ratio depends on the shape and size of the contact pattern. In the known formulae, no reference was made to this consideration. To solve the uncertainty, we have developed a method that gives a general interpretation of the contact ratio. It is based on the operation of the gear pair and is suitable for taking into account the localized contact pattern. Keywords: Transverse contact ratio · Profile contact ratio · Face contact ratio · Overlap · Total contact ratio · Modified contact ratio · Gleason type spiral bevel gear pair
1 Introduction The contact ratio is a unitless characteristic of the operation of the gears that provides information on how many teeth will carry the load, and whether continuous meshing and smooth motion transmission is ensured. The contact ratio expresses the average number of teeth which are simultaneously engaged. It is very simple to define spur gears, as the task can be solved as a planar problem. For helical gears, the calculation of the profile contact ratio and overlap ratio can still be discussed with relatively simple geometric models. The situation is different with spatially connected gears, which include spiral bevel gears. This is especially true for spiral bevel gear pairs having localized contact patterns, i.e. limited tooth contact, where traditional methods give inaccurate results when calculating the contact ratio.
2 Calculation of Profile Contact Ratio The profile contact ratio (or transverse contact ratio) of spiral bevel gears is determined based on the virtual cylindrical gear shown in Fig. 1. According to the literature [1–3] the profile contact ratio is 2 − r 2 + r 2 − r 2 − a sin α rav1 v vt av2 bv1 bv2 , (1) εα = pmt cos αvt © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 103–110, 2023. https://doi.org/10.1007/978-3-031-15211-5_9
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Fig. 1. The virtual cylindrical gears
where r av1 and r av2 are the tip radii, r bv1 and r bv2 are the base radii, av is the centre distance, αvt is the transverse pressure angle of the virtual cylindrical gears, and pmt is the mean transverse pitch of the bevel gears. The above listed geometric characteristics are calculated in the following formulas: rv1 = Rm sin δ1 , rv2 = Rm sin δ2 , tan αn av = rv1 + rv2 , αvt = arctan , cos βm rav1 = rv1 + ham1 , rav2 = rv2 + ham2 , rbv1 = rv1 cos αvt , rbv2 = rv2 cos αvt .
(2)
In formulas (2) r v1 és r v2 are the pitch radii of the virtual cylindrical gears. Rm is the mean cone distance, δ1 and δ2 are the pitch angles, αn is the normal pressure angle, βm is the mean helix angle, ham1 and ham2 are the mean addendums of bevel gears.
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3 Calculation of the Overlap Ratio on Pitch Plane of Imaginary Crown Gear The overlap ratio (or face contact ratio) is interpreted on the pitch plane of the imaginary crown gear as the quotient of the overlap angle and the angular pitch of the crown gear [1]: εβ =
σ . τ
(3)
Based on Fig. 2 the overlap angle is rc cos βe rc cos βi − arcsin , σ = arcsin Sr Sr
(4)
and the angular pitch of the crown gear is τ=
2π . zc
(5)
In formulas (4) and (5) r c is the nominal cutter radius, β i is the inner helix angle, β e is the outer helix angle, S r is the radial distance of cutter for machine settings, zc is the number of teeth of the imaginary crown gear.
Oc rc βe
Sr Re
rc σ Ri
βi Fig. 2. Overlap angle of a crown gear having circular arc tooth trace
O
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4 Calculation of the Overlap Ratio Based on the Virtual Cylindrical Gear The overlap ratio is calculated with the data of the virtual cylindrical gear in sources [2] and [3]: εβ =
b sin βm , mmn π
(6)
where b is the face width, βm is the mean helix angle, mmn is the mean normal module.
5 Calculation of the Overlap Ratio According to AGMA Standard The overlap ratio is calculated by the following empirical formula in the US standard for bevel gear design [4]: (Kz tan βm )3 Re , (7) εβ = Kz tan βm − 3 π mte where Re is the outer cone distance, mte is the outer transverse module, and according to [5] Kz =
b 2Re − b . 2Re Re − b
(8)
6 A General Interpretation of the Contact Ratio The gear literature calculates the contact ratio from two components, the profile contact ratio and the overlap ratio. The latter characterizes the degree of axial meshing. The total number of contact ratio is determined by some summation from the two components. This can be done by simple addition or by subtracting the square root of the square sums: εγ = εα + εβ , or εγ =
εα2 + εβ2 .
(9)
(10)
(9) is a known formula for helical gears, but it is also used for bevel gears in [3]. (10) has been introduced by Gleason firm. In this paper, we interpret the contact ratio differently from the usual one, and we present a new method, which can be considered general due to the approach, which is suitable for determining the contact ratio even in the case of localized tooth contact. Select a tooth on one of the members of the driving pair and follow up its position in the connection. Denote by ϕ min the angle of rotation of the gear at which the tooth engages and ϕ max by the angle at which the same tooth disengages. The examined tooth
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has contact with a tooth of the mating gear until the gear rotates Δϕ = ϕ max − ϕ min . However, after an angular rotation of the transverse pitch (2π /z), the adjacent tooth is already in the starting position, so the contact ratio becomes εγ =
ϕ z, 2π
(11)
where z is the number of teeth of the tested gear. (11) This is general, so it is applicable to all gear types.
7 The Path of Contact and Contact Ratio A computer program analyzing the tooth contact was developed based on mathematical software to examine the spiral bevel gears, the purpose of which is to determine the path of contact on the tooth surface. The path of contact is the set of contact points formed during operation. The developed program also allows you to specify the contact ratio. The structure of the program is as follows. • The following tool and machine setting data for both gears are known: cutter radius, cutter point width, radial distance, work offset, machine root angle, machine center to cross point, sliding base, and ratio of the roll. • Mathematical models of tooth surfaces are determined using the meshing theory of gears and tool and machine setting data. These can be characterized by position vectors and normal vectors in their own coordinate system: r1 = r1 sp , θp , n1 = n1 θp , (12) r2 = r2 sg , θg , n2 = n2 θg . • Tooth surfaces are placed in an operational position in a common coordinate system, and their common points are determined. At the points of contact, the two position vectors and the two normal vectors coincide; that is (2) (13) r(1) h sp , θp , φ1 = rh sg , θg , φ2 (1)
nh
(2) θp , φ1 = nh θg , φ2 .
(14)
• Vector Eq. (13) is equivalent to three scalar equations, but instead of Eq. (14), only two scalar equations can be considered since normals are unit vectors. It follows that we have five independent equations to determine the six variables in the system of equations. • To determine the points of contact, the angle of rotation of the bevel gear φ 1 is treated as an input parameter and changed in the connection range of a pair of teeth. The system of nonlinear equations of five equations can be solved using the numerical method for the five variables with different values of φ 1 . As a result, the path of contact on the tooth surfaces is given by the following functions: (15) r1 (φ1 ) = r1 sp (φ1 ), θp (φ1 ) ,
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and r2 (φ1 ) = r2 sg (φ1 ), θg (φ1 ), φ2 (φ1 ) .
(16)
To represent the path of contact, it is advisable to switch to cylindrical coordinates because we get a clearer solution in the plane using the radial R and axial L coordinates than with the use of rectangular coordinates. Ri (ϕ) = xi2 (ϕ) + yi2 (ϕ), Li (ϕ) = zi (ϕ),
(17)
i = 1, 2 and ϕ = φ s . The contact ratio is determined as described in Sect. 6. To do this, the values of ϕ min and ϕ max must be generated, which specify the points where the path of contact enters and exits the useful tooth surface. The useful tooth surface is bounded by the face cones of the two gears and the side (front and back) cone surfaces.
8 Examples The applicability of the presented method in practice was verified by numerical examples. The data in Table 1 were taken from a dimension sheet made with Gleason software. Table 1. Dimensions of bevel gear pair Bevel gear 1
Bevel gear 2
Number of teeth
29
30
Pitch angle, °
17.2
17.8
Addendum, mm
3.877
3.715
Outer transverse module, mm
4.791
Outer cone distance, mm
235
Mean cone distance, mm
215
Face width, mm
40
Pressure angle, °
20
Mean spiral angle, °
30
Using the data in Table 1, we performed the calculations described in Sects. 2, 3, 4, and 5. The following results were obtained: • profile contact ratio according to (1) εα = 1.362, • overlap ratio according to (3) εβ = 1.686, • overlap ratio according to (6) εβ = 1.677,
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• overlap ratio according to (7) εβ = 1.685. The contact ratio values on the Gleason dimension sheet are: εα = 1.36 and εβ = 1.685. From all this, it can be concluded that Gleason calculates the contact ratio based on Eqs. (1) and (7). The latter empirical formula provides a theoretical basis for (3), but (6) is also a good approximation. Based on the total contact ratio (10), εγ = 2.167, identical to the value on the Gleason dimension sheet. Hereinafter, two types of localization for contact patterns were applied to the exemplary gear pair. In the first case, a path of contact along the profile was reached; in the second case, a path of contact along the tooth length. Using the program described in Sect. 7, the path of contacts is shown in Figs. 3 and 4. All units are in mm in the figures. 80 R2 ( φ )
75
R A2 R B2 R C2
70
65
R D2 60
55 180
190
200
210
220
230
L2 ( φ ) , LA2 , LB2 , LC2 , LD2
Fig. 3. Localized contact: path of contact along the tooth profile 80 R2 ( φ )
75
R A2 R B2 R C2
70
65
R D2 60
55 180
190
200
210
220
230
L2 ( φ ) , LA2 , LB2 , LC2 , LD2
Fig. 4. Localized contact: the path of contact along the face width
In both figures, R2 (ϕ) and L 2 (ϕ) are the radial and axial coordinates of the contact path, respectively. The coloured dots indicate the boundary of the useful tooth surface.
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The contact ratio corresponding to Eq. (11) of the two solutions was extracted from the program. For connection along with the profile εγ = 1.5, for tooth length connection εγ = 1.69 was obtained. We found that the calculations existing in the literature do not take into account the localization of the contact pattern. This is also true for the calculation used by Gleason, which gives the same result for both models, that is, it applies to a full contact pattern. Own calculations are valid for the real but unloaded state. Under load, the contact pattern “expands”, so the contact ratio under load is larger than we calculate it. The overlap ratio calculated by Gleason is virtually the same as the contact ratio in our second model.
9 Summary We examined the formulas found in the literature for the calculation of the contact ratio of spiral bevel gears. We found that these are based on the use of virtual cylindrical gears. Essentially, the profile contact ratio and overlap ratio of a conjugated action helical gear is obtained. They do not take into account the characteristics of the spiral bevel gears, which are the theoretically point contact and the localized contact pattern. These formulas have been incorporated into the standards, and as a numerical example confirms this, the Gleason software also calculates according to them. Because the currently used formulas are approximate, we found it necessary to determine how much they differ from the actual contact ratio. Therefore, we have developed a calculation method that is suitable for the analysis of tooth meshing, the determination of the path of contact on the tooth surface, and also allows the determination of the contact ratio as a by-product. Through two sample examples, we demonstrated that the actual contact ratio depends on the shape of the path of contact and gives a significantly smaller numerical value than the total contact ratio provided by the currently used models.
References 1. György, E.: Fogaskerekek. Gears, M˝uszaki Könyvkiadó, Budapest (1983). ISBN 963 10 5089 0. (in Hungarian) 2. ISO 10300-1: Calculation of load capacity of bevel gears. Part 1: Introduction and general influence factors. International Standard (2001) 3. DIN 3991: Tragfähigkeitsberechnung von Kegelrädern ohne Achsversetzung (1988) 4. ANSI/AGMA 2005-D03: Design manual for bevel gears (2003) 5. ISO 23509: Bevel and hypoid gear geometry (2006)
Stability Analysis and Optimization of Vehicle Active Motion Control System with Feedback Time Delay Hangyu Lu1,2(B)
, Jianwei Lu1 , Gabor Stepan2
, and Takacs Denes2,3
1 Hefei University of Technology, Hefei 230000, China
[email protected]
2 Budapest University of Technology and Economics, Budapest 1111, Hungary 3 MTA-BME Research Group on Dynamics of Machines and Vehicles, Budapest 1111, Hungary
Abstract. A delayed dynamic model of vehicle lateral motion system is established to investigate the vehicle dynamics under time delay and to optimize the effect of time delay. The dynamic model is based on the non-linear 2-dimensional vehicle equation of motions and includes an active PD torque controller with feedback time delay. The wheel rotational dynamics and the longitudinal-lateral tyre force coupling are also modelled regarding the deformation delay induced by tyre elasticity. Phase plane analysis is carried out to acquire the handling and stability properties of the uncontrolled vehicle, and on this basis, the stability analysis of the delayed system is conducted and summarized in the stability chart of two control parameters, which shows the effects of time delay on vehicle system in various driving conditions and handling characteristics. Finally, simulations validate that the stability analysis for the controlled system dynamics and the eigenvalue-based optimization method performs effectively, which can be used for many considerations in vehicle delayed systems both from theoretical and practical viewpoints. Keywords: Vehicle motion control · Handling characteristics · Time delay · Delayed system · Stability analysis · Optimization
1 Introduction Active stability control of the modern vehicle can significantly decrease potential accidents and increase driving safety in critical manoeuvres [1], and its activation relies on the real-time feedback of vehicle motion status, where during the process, it is unavoidable to generate multiple time delays [2] such as signal sampling, data computing, and physical actuating. When time delay occurs, the update of vehicle status will depend not just on its current state, but also on the past history state of motions [3]. Such time delay is a key component that would deteriorate the vehicle dynamics or even destabilize the whole system, and consequently, it is from many perspectives meaningful to analyze and quantify the effects of existed time delay on the dynamic stability and control performance of the vehicle. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 111–125, 2023. https://doi.org/10.1007/978-3-031-15211-5_10
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Previous research on the time delay problem of vehicle system has also covered many aspects. Longitudinally, [4] investigated the tyre slipping control with the feedback delay of the rotational information and compared the delayed controller in the proportional, derivative, and integral type respectively; [5] explored the mechanism of wheel shimmy phenomenon by introducing the travelling delay of the tyre deformations. In the vertical dimension, [6] studied the active vehicle anti-rollover control with time delay, and determined the closed-loop stability of the controller by Lyapunov function; [7] investigated the active and semi-active suspension with the response and actuation delay. In lateral motions, [8] analyzed the effect of the four-wheel steering system delay on the lateral stability of the vehicle; [9] discussed the vehicle driving stability under the lane-keeping control with the preview delay of road information and the feedback delay of motion status; [10] introduced an integrated control structure for better-automated driving inclusive of the preview and actuation delay. However, rare attention is paid to the time delay effect in the lateral active motion control system of the vehicle, especially for the complex vehicle dynamics under the delayed controller and the optimization for the control parameter. Such systems, like the traditional brake-based ESC (Electric Stability Control) and independent wheel motorbased DYC(Direct-Yawing-Control) [11, 12], contribute a lot to the stability and safety in critical conditions. Here, to investigate and optimize the effect of feedback delay on the vehicle dynamics, this paper constructed a typical vehicle lateral motion system inclusive of the delayed active torque control, to derive the critical boundary and optimization criterion of the vehicle dynamic stability in the presence of various time delays. The research content is organized as follows. In the first part, a 2-dimensional vehicle model containing non-linear characteristics of tire lateral forces is established, and the longitudinal rotational dynamics of the controlled wheel is also modelled with the consideration of the coupling effect between longitudinal and lateral tyre. The dynamics of the uncontrolled vehicle are investigated through phase plane analysis, where two types of steering characteristics are constructed and distinguished. Then, the active motion controller is designed based on status feedback of yaw and sideslip angle, where a constant time delay exists within the process. In the third section, stability analysis is adopted to characterize the vehicle dynamics under several conditions that include various time delays and cornering manoeuvres, and the result is presented in the stability charts of control gains. Finally, simulation is carried out to explore the vehicle dynamics under delayed control, and an eigenvalue-based optimization method is introduced to achieve the optimal control.
2 Modelling of Vehicle Lateral System To describe the whole loop of vehicle lateral stability control, representative modelling is required. Two dominant lateral motions: yawing and side-slip of the vehicle, are considered by constructing a two-dimensional vehicle equation of motions. The non-linear tyre characteristic is described by the updated Magic formula [13], where the parameters are identified through experimental tests, and also, the mutual coupling mechanism between longitudinal and lateral tyre force is derived. Further, the subsystem of the stability controller, the longitudinal wheel rotation with tyre slipping is also modeled, and the form
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of the active torque control is derived based on the driving scenarios when the vehicle encounter understeer or oversteer. Finally, the global diagram for the lateral control loop is listed.
Fig. 1. Mechanical model of the vehicle motion systems
2.1 Equation of Handling Motions The yaw rate r and body side-slip β of the vehicle are introduced to describe the lateral motions. The longitudinal speed u and steering angle δ are considered to be the driver’s inputs that are assumed to be constant for simplifying the steering and longitudinal motion dynamics. The corresponded mechanical model is given in Fig. 1, and two coordinate systems as shown are used for the modelling. By using the Lagrange method, the equations of vehicle handling motion [12] can be established as mu(β˙ + r) = Fyf (αf ) cos δ + Fyr (αr ), Iz r˙ = Fyf (αf )lf cos δ − Fyr (αr )lr ,
(1)
where αf and αr are the front and rear tyre lateral side-slip angle; Fyf and Fyr are the tyre lateral force of front and rear axle, respectively. The parameters of the vehicle are listed in Table 1, that is experimentally acquired from a practical urban SUV vehicle, making it representative for the type of vehicle equipped with active control. Due to the assumption that the steering angle is fixed, the transient properties of the tyre status can be simplified by the steady-state form into r r (2) αf = δ − β − lf , αr = −β + lr . u u
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Parameter
Definition
Value
Unit
m
Total vehicle mass
1344
[kg]
Iz
Vehicle inertia to z-axis
2340
[kg m2 ]
Iw
Wheel inertia of rotations to y-axis
5
[kg m2 ]
lf
Distance of front axle to COG
1.2
[m]
lr
Distance of rear axle to COG
1.3
[m]
R
Wheel radius
0.4
[m]
tt
Wheel base
0.96
[m]
2.2 Rotational Wheel Dynamics When the control torque is applied, the torque acts as the braking/accelerating torque for one designated wheel, and after that, the tyre starts to deform longitudinally due to this input torque, which finally buildup the corresponded tyre force. Thus, the whole process is modeled by using a one-dimensional rotational equation as ˙ = Fxi ω(t), Fy (t) R − Tb (t), (3) Iw ω(t) where Fxi is front or rear longitudinal tyre force, and ω is the angular speed of wheel rotations, R is the wheel radius given in Table 1, and Tb (t) is the torque input generated by the lateral controller through Tb (t) = Mc (t)R/tt, and the longitudinal deformation of the tyre is described by its slip ratio κ as κ = (u − ωR)/u. 2.3 Modelling of the Tyre Characteristics Tyre Model. The strong non-linear characteristics of tyre forces are the dominant factors affecting the dynamics of the vehicle motions, and small changes in the tyre parameters could lead to completely different behaviour even under the same handling inputs. The updated Magic tyre formula [13] is applied to calculate both the longitudinal and the lateral tyre force, and due to its simplicity and accuracy, it has the advantage in the analysis and simulation of the vehicle. The model of the lateral tyre force in the front and rear axle is assumed as , (i = f , r) (4) Fyi (αi ) = Dyi sin Cy arctan By αi − Ey By αi − arctan By αi where Dyi = μyi Fzi λDyi , Fzi are the vertical tyre force that follows the relation: Fzf = mg
lf lr , Fzr = mg lf + lr lf + lr
(5)
and μyi is the maximum contact frictional coefficient that can be manipulated differently in the front and rear tyres. The longitudinal tyre force model assumes a similar form as Fxi (κi ) = Dx sin(Cx arctan(Bx κi − Ex (Bx κi − arctan(Bx κi )))),
(i = f , r)
(6)
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where κ is the longitudinal tyre slip ratio that is calculated from (3), Dxi = μxi Fzi λDxi , and μxi is the available longitudinal contact frictional coefficient. All the parameters of the above tyre models are identified by using the test data of a Giti brand tyre, and the value is listed in Table 2. Table 2. Identified tyre parameters Tyre parameters
Lateral front (yf)
Lateral rear (yr)
Longitudinal (x)
λD
1
1
1.15
C
1.6
1.6
1.4
B
8.5
8.5
8
E
−1.3
−1.5
−2
Coupling Mechanism. For the specific tyre to be controlled longitudinally, the tyre forces in the two dimensions are mutually affecting each other. The frictional circle gives a good approximation for the coupled relations between the longitudinal and lateral force capability, as shown in panel (a) of Fig. 2. Here, under the increase of the lateral force input, the maximum available tyre longitudinal force is decreased correspondingly, and regarding this correlation, the longitudinal contact coefficient μxi in (6) should be revised as 2 2 (7) μxf,r = 1 − Fyf,r /Fmax = 1 − Fyf,r (αi )/ μy Dy . Accordingly, the Magic formula (6) for the longitudinal tyre force is also revised as shown in panel (b) of Fig. 2.
Fig. 2. Tyre force coupling relations. (a) Frictional circle of the contact, (b) Revised longitudinal tyre force characteristics curve.
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3 Dynamic Properties of Vehicle Lateral Motion System A comprehensive understanding of the original uncontrolled vehicle dynamics subjected to various driver inputs is the essential basis for the design of the controller and control loop. With considerations of the non-linear tyre force and the effect of multiple steering angles, the vehicle dynamic model is based on (1–2) where the non-linear tyre force characteristics (4) are considered. The longitudinal effect is not considered here for there is no active control. 3.1 Vehicle Handling Analysis Both analytical and numerical methods can be used to investigate the dynamical behaviour and its dependence on the input parameters. The so-called handling curves give insight directly to the vehicle steering characteristics whether understeer or oversteer occurs under certain driver inputs u and δ, and to find and locate the stable steady-state motions represented by the fixed points β0 and r0 . Here, the exact process of judging different steering properties is not listed for conciseness and can be referenced by [13]. Phase plane analysis, a graphical method for observing global dynamics, numerically provides the illustration of vehicle handling characteristics [14]. The location and the type of the equilibrium points can be obtained accurately in the phase portraits. In the literature, different sets of state variables have been implemented in phase planes, which ˙ front and rear tyre slip angles mainly include side-slip angle and side-slip rate (β − β), (αf − αr ), or side-slip angle and yaw rate (β − r).In this case, a desired stable state of vehicle dynamical responses for the driver’s inputs, and the trajectories of the state variables are presented in the phase portrait of (β − r). The desired vehicle state (βd , rd ) for the controller should be selected from a stable equilibrium, that is, a trivial solution of (1), which should be stable originally. The equilibrium points (β0 , r0 ) are calculated numerically, and they are presented in Figs. 3 and 4 by green dots when they are stable, and by red dots, if they become unstable. 3.2 Phase Plane Analysis for Steering Characteristics The phase planes are presented for two steering characteristics: the understeer and the oversteer, which are presented in Figs. 3 and 4, respectively. For the vehicle handling parameters, that is, for the steering angle δ and for the longitudinal speed u, the kinematic relation δ + (αr − αf ) =
l , R
(8)
can be obtained (see Fig. 1) for steady-state steering. Then, the understeer or oversteer characteristics can be defined by the inequalities αr − αf > 0 ⇒ oversteer,
αr − αf < 0 ⇒ understeer.
(9)
Two types of steering characteristics are modelled by considering a 10% loss of tyreroad contact coefficient μyi either at the front or at the rear axle relative to the maximum
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Fig. 3. Phase portrait of understeered vehicle, with increased steering angle δ input. Green dot means stable equilibrium (node), the trajectories appear as blue lines.
Fig. 4. Phase portrait of oversteered vehicle, with increased steering angle δ input. Green dot means stable equilibrium (node), red dots refer to unstable equilibria (saddles).
reference value of 1: for understeer case, μyf = 0.9 and μyr = 1, while for oversteer case, μyf = 1 and μyr = 0.9. Accordingly, the loss of force capability at the front axle will lead to understeer as the front tyre force saturates before the rear one, while the opposite case will lead to oversteer. Figures 3 and 4 show the phase portraits of both understeer and oversteer cases for different input steering angles δ at a fixed input longitudinal speed u = 15 m/s. The minimum and maximum values of the steady-state yaw rates are plotted with dotted
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horizontal lines according to the formulae: μyf g μyr g |r|max,us = , |r|max,os = , (10) u u which are derived by Carrie [14]. In the understeer case shown in Fig. 3, the only existing equilibrium is always exponentially stable. It is a stable node for steering angles δ < 0.15 rad, while it becomes a stable focus (or spiral) for larger steering angles, as shown by panels (d), (e), and (f) in Fig. 3. The steering angles δ ≥ 0.15 rad could practically be undesired since the lanekeeping manoeuvres may require several oscillations with a long settling time before reaching the stable cornering. The oversteer cases presented in Fig. 4 are more intricate. For steering angle smaller than 0.2 [rad] (see panels (a)–(d) in Fig. 4), there are two unstable saddles located at the yaw rate boundaries given in (10), and a stable node in between. This means that there is a finite domain of attraction of the stable fixed point, that is, large perturbations may result in unsuccessful cornering manoeuvres. Moreover, if the steering angle is large, that is, δ ≥ 0.2rad, the stable node will hit the unstable saddle at a saddle-node bifurcation, and the system becomes globally unstable with a single saddle point only. 3.3 Active Stability Controller The purpose of the active controller is to achieve the driver’s desired path and stabilize the motion even in extreme conditions. By applying the torque on one or several specific wheel, an additional yawing moment can be generated to help the car cornering. The detailed logic for the activation of such types of lateral control systems, such as ESC and DYC, can be found in [12] and is generally explained in Fig. 1. The global control loop is presented in Fig. 5, and the active controller with the feedback time delay τ assumes the form: Mc (t) = −Kr (r(t − τ ) − r0 ) + Kβ (β(t − τ ) − β0 ) = Tb (t)dT /R where Kβ = Iz kβ and Kr = Iz kr are the control gains for β and r, respectively.
Fig. 5. Diagram of the vehicle lateral stability control loop with feedback delay.
(11)
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4 Stability Analysis of the Delayed System With the application of the control torque, the delayed system can be constructed by substituting (11) into (3), and by incorporating the resultant longitudinal tyre force into equations of motion (1), the final form is summarized as ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ β˙ f (β, r; u, δ) 0 ⎣ r˙ ⎦ = ⎣ g(β, r, ω; u, δ) ⎦ + ⎣ Fxi dT /Iz ⎦, (12) Tb /Iw ω˙ h(β, r, ω; u, δ) where f , g, h are the corresponded functional expression after derivation. To analyze the system stability, linearization is conducted around the equilibrium point (β0 , r0 , ω0 ), and ω0 = u/R corresponds to κ = 0 meaning that no longitudinal tyre force exists in steady-state. Consequently, tyre forces (4, 6) are linearized at their steady-state side slip angle (αf0 , αr0 ) in the form of Fyf (αf ) = Fyf (αf0 ) + C˜ f (αf − αf0 ), Fyr (αr ) = Fyr (αr0 ) + C˜ r (αr − αr0 ).
(13)
where C˜ f and C˜ r are the linearized cornering stiffness around the equilibrium point. By substituting (13) into (12), the whole linearized system assumes the form: x˙˜ (t) = A˜x(t) + B˜x(t − τ ), (˜x(t) = x(t) − x0 )
(14)
where x(t) = [β(t), r(t), ω(t)]T and x0 = [β0 , r0 , ω0 ]T , and the coefficient matrix are ⎡
⎤ 0 − C˜ f + C˜ r /mu −1 − Cs/mu2 ⎢ ⎥ A=⎣ (15) −Cs/I −Cq2 /uI C˜ x dT /R ⎦, ˜ 0 0 Cx /Iw ⎡ ⎤ 0 0 0 B=⎣ (16) 0 0 0 ⎦, −Kr R/(Iw dT ) Kβ R/(Iw dT ) 0 where Cs = C˜ f lf − C˜ r lr , Cq2 = C˜ f lf2 + C˜ r lr2 , and C˜ x is the linearized stiffness of longitudinal tyre force that is coupled with lateral side slip angle (αf0 , αr0 ) in (4). 4.1 Stability Charts in the Presence of Time Delay For the delayed system (14), linear stability analysis can be performed by analytically deriving its characteristic function and finding the parametric instability boundary when the system eigenvalues cross the imaginary axis. The system is guaranteed to be stable if and only if all of its characteristic exponents stay negative, and otherwise, it would be unstable or oscillating unstable with nonlinearities. Here, the so-called D-curves [3], which gives the static and dynamic instability boundary of the system (14), are derived in Fig. 6, and two types of steering characteristics under time delays are distinguished. The red straight line in Fig. 6 is the static instability boundary of the system where the vibration frequency is f = 0 [Hz], and one real eigenvalue will become positive if it
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Fig. 6. Stability chart of the delayed vehicle system at τ = 0.2 s, u = 15 m/s, δ = 0.05 rad with (a) understeer at μyf = 0.8, μyr = 1; (b) oversteer at μyf = 1, μyr = 0.8.
is crossed from the stable domain, meaning the occurrence of the divergent instability. The black curves denote the dynamic instability boundary with the frequency increased alongside with circling (see Fig. 6), and crossing it means that two complex conjugate eigenvalues will go through the imaginary axis simultaneously, where the Hopf bifurcation occurs within the system. These two boundaries split the parametric plane consisted of (kβ , kr ) into infinite domains, and within each closed domain, the stability property remains the same. To identify the stable domain from the stability chart, one has to calculate the number of positive system eigenvalues (unstable exponent) out of infinite eigenvalues of the delayed system. Based on the judgment formula in [4], the number of unstable exponents in each parametric domain is indicated in Fig. 6, where the shadowed area is the only stable domain with zero count. Comparing the cases of understeer and oversteer in Fig. 6, the main difference is found that the size of the stable domain of understeer is relatively much larger than oversteer case. Also, it is observed that a double-Hopf bifurcation point appeared in understeer case, where two periodical oscillations with different vibration frequencies will be interacting each other. Generally, the D-curves are similar in structure and the size of the stable domain also follows the conclusion in Figs. 3 and 4 where understeer vehicle has better stability against lateral perturbations. 4.2 Variation of the Stable Domains in Changing Conditions To further explore the effect of increased time delay on system stability, the stability chart of the oversteered vehicle in various time delays is plotted in panel (a) of Fig. 7, where every deeper grayscale area means the increment of time delay at every 0.05s. The shrinking of the stable parametric domain under the increased time delay can be easily observed either from panel (a) or panel (b) of Fig. 7. The size of the stable domain decreases quickly under the small delay range (τ < 0.3 s), which gives that ensuring a smaller feedback delay is still the basis for improving controller performance. For the condition of τ ≥ 0.3 s, the rate of area shrinkage is gradually converging to zeros with the increased time delay, meaning that the delay effect is no longer poses a
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Fig. 7. Stability properties of oversteered vehicle with changing time delay. (a) Variation of the stability chart, (b) Variation of the size of stable domain.
Fig. 8. Stability properties in various conditions with different lateral acceleration ratio. (a) Variation of the stability with the increased ay /g, (b) size of the corresponded stable domain.
major stability problem for the parametric domain. Finally, as shown both in the darkest region in panel (a) and its size in panel (b) of Fig. 7, the inner stale domain will remain its size and area even with infinite large time delays. This is the non-delayed domain with its stability determined by original uncontrolled system dynamics, which means the active control system can choose not to interfere with the vehicle state if a large delay occurs within itself. In Fig. 8, the stability properties of the delayed vehicle system are further discussed in various driving conditions where different levels of lateral accelerations are applied. Here, instead of using steering angle or speed as the conditional change, the concept of steady-state lateral acceleration ratio ay /g (ay = ur0 ) is introduced due to its representative capability for all types of cornering scenarios, and it is also easier to identify the critical non-linear boundaries. The general tendency of area variation Fig. 8 can be summarized that under the increased lateral accelerations, the size of the stable area is also reduced in an approximate
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linear relation. When ay /g reaches its maximum of 0.9, the size of the stable domain is only one-tenth of the straight-line condition ay /g = 0. This indicates that in these critical conditions where the nonlinearity of the tyre is the dominant factor, the requirement for a successful active control application to stabilize the vehicle is much higher, especially in the case of selecting appropriate control parameters. Split the condition in panel (b) of Fig. 8 into linear and non-linear by ay /g = 0.3, it can be found that in the linear range the change of stable domain is rather small and consistent instead of rapid reduction in the non-linear region, thus, it is still important for the vehicle handling to avoid those critical conditions which are a fundamental guarantee for safety.
5 Dynamics of the Delayed System in Time Domain
Fig. 9. Characteristics of vehicle dynamic responses under different control gains of 4 parametric domains in the stability chart.
5.1 Time Histories of the Delayed System Due to the strong nonlinearity and the extra third dimension of wheel rotations, the dynamic response of the delayed system may partly deviate from the analysis, and thus it is worth studying. By using Matlab DDE23 function, the simulation with multiple control gain sets is carried out, and the results are plotted in Fig. 9 where its subpanels correspond to each domain indicated in Fig. 9. The angular speed of wheel rotation is chosen as the plotted variable because it has the advantage to check the intervention of the control system and also reflect the yawing behaviour. Such as in area II and III of Fig. 9, it is observed that the controller is periodically activated to stabilize the vehicle, which will also lead to the yawing oscillation of the vehicle itself. In the stable area and area I, the dynamic response come as expected, where the gains in the stable domain offer stability after the settling period, and the other set damages the stability by exponentially increasing the oscillation amplitude.
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By comparing each panel in Fig. 9, it is can be concluded that the dynamics in the stable domain fully follows the analysis, but the responses in the unstable domain are hard to summarize besides periodical oscillations; due to that the linear stability analysis is incapable of incorporating multiple nonlinearities and investigating global dynamics outside the linearized region. 5.2 Gain Optimization for the Fastest Settling The stability chart of the delayed system in Figs. 6, 7 and 8 helps to locate the stable parameter domain for the control gains, but still, it is unable to select the best gain set out of the stable domain that leads to the fastest decay and settling rather than mere stability.
Fig. 10. Dynamic responses of oversteered vehicle under different levels of parameter optimization. (a) Optimized stability chart, (b) Corresponded simulation responses.
In this case, an optimization method is introduced by calculating the system eigenvalue with the largest real part:
max (Reλi ) = ζ, (Reλi < 0) (17)
i=1,2,...
which corresponds to that the state variables will decay to the equilibrium in the path of e−ζ t . Such optimization can be conducted either by numerically calculating the system eigenvalues as shown in [15] or analytically deriving the ζ as given in [16]. Here, the result of the optimization of the stable domain in Fig. 6(b) is given in Fig. 10, where the ζ is leveled from zero to its maximum in gray-scale. Four different points of gains are selected and applied to the delayed system with their dynamic responses plotted in panel (b) of Fig. 10. It can be observed from the yawing histories that systems with gains A1 and A2 are the ones with better and faster settling dynamics. While in the case of uncontrolled and A3, the decays of amplitude are similar, but case A3 leads to a serious of fluctuations of the system behavior before settling to equilibrium. This is due to the high vibrational frequency of the system at this point, which is a potential risk. Another thing to be noticed is that such optimization is only valid for the linear and
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linearized system, which means it is only functional for a certain range of perturbation of this non-linear system.
6 Conclusion The main conclusion is summarized as follows. 1. The modelling of the delayed vehicle lateral system, which includes the handling motions, the tyre slipping, force coupling, and the active controller, offered a preview about the incorporation of feedback time delay in similar types of the stability control system such as DYC and ESC. Also, the phase plane analysis gives the vehicle handling properties and motion boundaries, which helps to design and validate the desired target of the lateral controller. 2. The stability analysis of the delayed system shows that the understeered vehicle is always easier to stabilize in slow cornering conditions than the oversteered one due to its intrinsic global stability, and the time delay effect is also not principal here. However, when the vehicle is encountering large lateral accelerations or critical cornering, the existence of time delay will quickly shrink the stable parametric domain and leads to the extremely limited selection for the controller, which could potentially convert into motion destabilization. 3. Simulation of the delayed system has validated the analysis through stability charts, but also indicates that the complex dynamic behaviour has appeared in the unstable parametric domain, where the active controller is periodically intervening in the yaw motion and could lead to a limit cycle or extra oscillation around a new equilibrium point. Finally, the method of optimization is introduced and validated through simulation of small initial perturbation conditions, which is very useful to get the optimal gains set of the controller.
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Cogging Torque Analysis of Toyota Prius 2004 IPMSM Motor with the Digital-Twin-Distiller Mihály Katona1,2(B)
, Péter Kiss1
, Krisztián Gadó3
, and Tamás Orosz4
1 Department of Electric Power Engineering, Faculty of Electrical Engineering and Informatics,
Budapest University of Technology and Economics, M˝uegyetem rkp. 3., Budapest 1111, Hungary [email protected] 2 Department of Vehicle Electrification, Robert Bosch Ltd., Gyömr˝oi út 104., Budapest 1103, Hungary 3 MONTANA Knowledge Management Ltd., Lágymányosi u. 11., Budapest 1111, Hungary 4 Department of Automation, Faculty of Engineering Sciences, Széchenyi István University, Egytem tér 1., Gy˝or H-9026, Hungary
Abstract. With the recent advancements of the fourth industrial revolution, many sensors are implemented into the products that collect the data and monitor their performance. The measurement data can be stored with the numerical models as a digital twin, and these can serve as a virtual replica of a physical object or process. These digital twins can improve the design and optimization process. The paper uses an open-source tool, the Digital-Twin-Distiller, which can encapsulate the realized finite element analysis of a model into a web application, which can be integrated easily into an optimization chain or any other business process. The paper proposes a numerical model and cogging torque analysis of an interior permanent magnet synchronous machine. The paper examines the effect of two parameters of the magnet groove shape on the cogging torque of the Prius 2004 motor model. It was found that the cogging torque of the motor is highly dependent on the shape of the flux barrier, even with minor changes. On the other hand, the wedge between the two magnets does not affect cogging torque much. Keywords: FEM · Electrical machines · Digital twin
1 Introduction The optimization of electric machines is a crucial part of the industry. The goal is to meet the customer’s unique expectations or find the most cost-efficient solution. A multiobjective optimization problem needs to be resolved with an ample parameter space [1]. The optimization process results in a data set that assigns an exact value to every investigated parameter. Thus, it assumes that the manufacturing process is precise without tolerances; it neglects the effect of those. Different papers propose that the optimized design is highly affected by manufacturing tolerances, environmental conditions, or material properties [1, 2]. Furthermore, “the cogging torque… can be highly affected by tolerances” [3]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 126–138, 2023. https://doi.org/10.1007/978-3-031-15211-5_11
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The most challenging part of the proposed methods and results is to validate those. The main reason is that there are not many precisely defined motor topologies published with measurement data. One exception is the Toyota Prius 2004 Interior Permanent Magnet Synchronous Motor (IPMSM) measured, disassembled, and published [4–6]. Many papers and investigations have been published that consider the Toyota Prius 2004 electric motor topology analysis. However, these models have minor differences, mainly in rotor topology. There are models published where the flux barrier - which is defined in Sect. 2.3 - is approximated by a triangle [7–9], one that uses a simple circle arc [10], and one that is most similar [11] to the published topology of the benchmark [6]. To reach the desired performance of an interior permanent magnet synchronous motor (IPMSM), the air-gap flux density should be increased. The leakage flux is the short-circuited magnetic flux in the rotor between two neighbouring permanent magnets or the poles of the same magnet. The leakage flux does not contribute to the torque production because it does not cross the air gap of the electric motor. As the magnetic bridge between the flux barrier and the air gap is saturated, it acts like an air gap [12]. The flux density of the magnetic bridge depends on the flux barrier size and shape. If the flux density is high, it results in low permeability and high reluctance. This reduces the magnetic flux leakage. A stronger magnetic isolation effect means a smaller magnetic bridge with lower mechanical strength [13]. Demagnetization of the permanent magnet is related to the magnet’s temperature and the strength of armature reaction from stator windings, and the load angle [14]. Partial demagnetization deteriorates motor characteristics severely [15]. The shape of the flux barrier highly affects the demagnetization of the edge of the permanent magnets [16]. This paper does not address the effects of demagnetization. The published Toyota Prius 2004 electric motors rotor topology [4, 6] is not eligible to fully recreate the model because of a conflict of constraints. This paper presents a possible solution to this problem. A cogging torque analysis using a topology where different variable parameters complement the existing constraints [6] that define the flux barrier shape is carried out. It is investigated how the motor’s cogging torque reacts to the change of the flux barrier shape.
2 Materials and Methods 2.1 Modelling with Digital-Twin-Distiller The simulated geometry used in this paper for locked-rotor test validation purposes is downloaded from the Pyleecan project [11]. The reference calculations and model for cogging torque validation are downloaded from [10], where the author used a more simplistic model during the analyses. In these calculations, the shape of the flux barrier defined in Sect. 2.3 is approximated by a simple curve. The FEM solver used is FEMM [17]. In our research, the FEM simulations were also solved by FEMM, but via the Digital-Twin-Distiller (DTD), which adds many features, that can be used for the analysis. The geometry can be imported from various cad formats or a simple.svg file, and also, Bezier curves can define the shapes. These Bezier curves are replaced by lines with the given precision and exported into a simplified form, which FEMM can
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solve, or other open-source solvers which do not support NURBS-based geometries. Moreover, the realized parametric geometry can be automatically encapsulated into a ¯ server application, which can be invoked by a custom optimization tool, like Artap [18], jMetalPy [19], or Platypus [20]. The DTD is MIT licensed tool that was published [21]. The capabilities and working conditions of the DTD are summarized in the paper [22]. The DTD may extend the capabilities of other open-source electrical machine design tools [23, 24]. This paper presents two models for validation. Figure 1 is used for cogging torque simulation because the associated code is given and written in a different software than DTD, namely Matlab. Furthermore, a full-scale analysis is published [10]. Figure 3 is utilized for locked-rotor simulation as it is a test topology for the Pyleecan project [11, 25]. The proposed methodologies modelled with the DTD were saved as a digital twin, and not only the source code of the model but its encapsulated version can be accessed from [26]. This containerized version of the code contains the model and the simulation together as a digital twin (DT) [27, 28]. It can work via an online application interface without any installation. The proposed solution supports long-term usability and reproducibility for the proposed model. 2.2 Validation The basis of the validation is a study published on the Ph.D. Engineering Electromagnetics [10]. The same model created with DTD (case O(riginal)) gave back the results almost double the one that was published [10]. To solve this contradiction, the code from the website was downloaded. With that, a full model of the motor (case F(ull)) was created with Matlab and one that only represents one pole from it (case P(ole)) with the antiperiodic capability of FEMM [29]. These simulations used the same motor topology (Fig. 1). The results show (Fig. 2) that the cogging torque is almost identical in the three examined cases. However, the published results differ from the analyzed code, as the former is almost double the latter. This difference is not relevant from the comparison point of view. Therefore, for further research, the results of the downloaded code were accepted as a basis for comparison.
Fig. 1. The motor topology used for cogging torque validation [10]
The comparison between the three cases is represented in Fig. 2. The electrical angle is four times the mechanical angle of the rotor, as the motor has eight poles. The electrical
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angle refers to the position of the rotor to the zero angle where the torque is zero. During the simulations, the magnet’s coercive magnetic field (Hc ) was set to 920 kA/m. There is no significant difference in the simulated results. The maximal absolute difference is summarized in Table 1.
Fig. 2. Comparison of cogging torque on Hc = 920 kA/m in three different cases where case O: original model with DTD, case F: full model from [10], case P: pole model from [10]
Table 1. Maximal absolute difference of cogging torque on HC = 920 kA/m Case
F to P
F to O
P to O
Maximal absolute difference
0.161 Nm
0.180 Nm
0.089 Nm
Then the coercive magnetic field was corrected to the temperature of 50 °C (Hc = 782 kA/m) [10] to produce a similar locked-rotor simulation result to the Report on Toyota Prius Motor measurements [5]. The results for locked-rotor simulation created with DTD now use a different topology downloaded from the Pyleecan project [11] shown in Fig. 3 which is the closest to the geometry reported [6]. The results of the written code (case O) - which now uses the topology shown in Fig. 3 - are compared to the results published in [5] and are the basis for validating the locked-rotor test.
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Fig. 3. The motor topology used for locked-rotor validation [11]
The comparison of the locked-rotor simulation results is shown in Fig. 4. Different stator current values were 150 A, 200 A, and 250 A.
Fig. 4. Comparison of the simulated and measurement data for the locked-rotor test
The measurement is published via a figure [5], so the WebPlotDigitizer was used for scalping the data [30]. The measurement carries an error of unknown magnitude, and the scalping too. The measurement contains only 23 measuring points, so a polynomial curve of a quarter degree was calculated to approximate the torque curve constrained by those points. The difference in the motor topology shown in Fig. 3 causes a reduction in the maximal torque compared to the first topology shown in Fig. 1. The approximation of simulated results to the measured data is acceptable. Therefore, the code is eligible for further research. There is a significant difference between the electrical angles of 0°–50°. The cause of this phenomenon is not part of this research. 2.3 Resolving the Conflicting Constraints The geometry dimensions were determined for further analysis as in [6]. The published parameters are not eligible to fully recreate the model because of a conflict of constraints. There are different ways to resolve this conflict. The following parameterization was used for this research, represented in Fig. 5.
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Fig. 5. Parameterization of rotor geometry. The measures are given in millimeters.
There are three different unknown measures marked by A, B, and C in Fig. 5. Parameter C is an independent parameter that controls the height of the wedge between the two magnets. This part of the geometry is meant to hold the magnets in place. In Fig. 5 the graphically non-filled geometrical areas are filled by air in the simulation. In real life, it contains glue to fix the magnets. The approximation that it has air-like properties is acceptable. On the other hand, A and B are linked parameters with the following premises listed below and represented in Fig. 6: • • • •
The point p1 varies by parameter A and moves by the red arrows. The object determined by p1-p2-p3-p4 is fixed and anchored to p1. The point p5 moves by the green arrows as the length of the line p4-p5 is fixed. The point p6 is fixed.
Fig. 6. Flux barrier shape as A (left) < A (right)
As parameter A varies in a given range, parameter B and the angle between line p4-p5 and line p5-p6 change by the given constraints. For further reference, the object determined by points p1 to p6 is called a flux barrier. The material that fills the flux
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barrier is simulated as air as well. As parameter, A increases and the point p4 moves closer to the air gap, significant properties of the magnetic field change. The area of the conductive material - magnetic bridge - between the air gap and the flux barrier reduces. The theoretical background of the effect of the magnetic bridge on motor parameters is summarized in [12, 13] and the demagnetization in [14–16].
3 Torque Analysis 3.1 Range of Parameters and Model Creation For the first iteration, the range of parameter A was set from 0.5 mm to 3.5 mm. The lower boundary is defined by the constraints listed beforehand. The fact that point p4 reaches the air gap creates a topology that cannot be manufactured and simulated correctly defines the upper boundary at 3.5 mm. Parameter C is in the range of 0.0 to 3.0 mm. From a mechanical point of view, too low wedge height cannot perform its functions, but it is neglected to show a tendency presented later for the torque analysis. The value of the steps is 0.1 mm. Every model created follows a nested loop where the parameter of the outer loop is A and the inner loop is C. This loop creates 31 * 31 = 961 cases. The cogging torque was calculated in an electrical angle range of 0.0°–30° in 16 steps. In Fig. 7, a few cases are represented by the simulation results for cogging torque.
Fig. 7. Simulation results for cogging torque in 16 steps, C = 0
It can be seen in Fig. 7 that the cogging torque significantly deviates; moreover, the position of the maximum torque shifts by the change of parameter A. The curves are antisymmetrical to the x = 15° axis. Figure 8 shows that the cogging torque amplitude is not dependent on parameter C as much as on parameter A.
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Fig. 8. Simulation results for cogging torque in 16 steps, A = 0.5
A higher resolution is needed to draw more accurate conclusions given those mentioned above. From here on out the following ranges were used, which creates 806 models and 61256 points of data to calculate the cogging torque using the following parameters: parameter A = [0.5: 0.1: 3.0], parameter C = [0.0: 0.1: 3.0], electrical angle = [15.0: 0.2: 30.0]. 3.2 Cogging Torque Analysis Figure 9 represents the cogging torque maximal values for every model calculated. The calculations with higher resolution support that parameter A has more effect on the torque than parameter C. First, the impact of parameter C on cogging torque is neglected. It will be examined and presented later on.
Fig. 9. Simulation results for cogging torque maximum values
Figure 10 show the maximum (red) and minimum (blue) cogging torque values for every model defined by parameter A. To clarify, Fig. 10 is the front view of Fig. 9. As stated beforehand, because the cogging torque is antisymmetric, the simulations cover only one-half of the whole wave. Because of that, the presented values in Fig. 10
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are symmetrical to the y = 0 axis. The cogging torque is such that the minimum and maximum values define the peaks either. In the case of parameter A from 0.6 mm to 2.1 mm, the cogging torque has only one peak. If the results are broadened to the range from 0° to 30° in electrical angles, which is defined as one period (T), then Fig. 10 shows half a period. In the case of parameter A from 2.2 mm to 3.0 mm and 0.6 mm, the cogging torque reaches its peak values four times, which means a pulsation of double frequency.
Fig. 10. Maximum and minimum of the cogging torque in different models
Figure 11 shows the electrical angle position of the maximum (red) and minimum (blue) of the cogging torque for every model defined by parameter A. In the case of parameter A from 0.6 mm to 2.1 mm, the peak values of one period are getting farther apart. The scattering at a fixed value of parameter A is the effect of parameter C. A second peak is formed from 2.2 mm to 3.0 mm and 0.6 mm, and its position is fixed at around 17°.
Fig. 11. Position of maximum and minimum of the cogging torque in different models
In Fig. 12 the difference between the torque peak values can be seen for every model. t1 = |tmax - tmin | in half a period (blue). If tmin has no value in the case of parameter
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A from 0.6 mm to 2.1 mm, then tmin is considered zero. t2 is the mean value of the cogging torque in half a period (red).
Fig. 12. The difference in torque peak values and mean value of cogging torque
Up to this point, the effect of parameter C was neglected. To complete the results Fig. 13 shows how parameter C affects the maximum torque value and the mean value for half a period. This data shows the simulation results at parameter A = 2.1 mm.
Fig. 13. Effect of parameter C on torque peak values and mean value, A = 2.1 mm
Figure 14 (left) shows the cogging torque at 2.5 mm of parameter A for two periods. This model has the least mean torque value but has a pulsation of double frequency in a period Fig. 14 (right). Furthermore, the peak-to-peak value changes only in one period there. Figure 14 (right) shows the cogging torque at 2.1 mm of parameter A for two periods. In this case, the peak-to-peak value remains constant, the position is almost evenly distributed, and the amplitude is minimal 0.495 Nm. For a more complex analysis with a non-uniform base spline shape of the magnetic parts in the future, the following topology will be used based on this paper, where parameter A is 2.1 mm, and parameter C is 3.0 mm. The topology is shown in Fig. 15. The stator remained the same as in Fig. 3.
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Fig. 14. Cogging torque at 2.5 mm of parameter A (left) and cogging torque at 2.5 mm of parameter A (right)
Fig. 15. Chosen rotor topology
4 Conclusion In the case of IPMSM machines, the shape of the flux barrier has a significant effect on the cogging torque. The paper presented a sensitivity analysis, which considered the impact of the shape of the flux barrier. As parameter A increases, the maximum torque rises first and then decreases. In this process, a negative torque develops in half a period from 2.2 mm. That means a pulsation of double frequency on a period. The position of the peak values in the electrical angle is also increased, which means the distance between two peak values increases in one period. Where the negative torque appears, its position is fixed at around 17°. In the same process, the peak-to-peak value of the torque decreases steadily. The peak value and the average torque on half a period decrease as parameter C increases. The findings of this study have several important implications for future practice; a more complex analysis, which can handle the non-uniform base spline shape of the magnetic parts, should be and will be carried out.
5 Data Availability Statement All the data and the code created for this paper are available [26].
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Acknowledgement. The authors would like to thank the help of Vilmos Paiss for sharing his knowledge in the field of this research; furthermore, Attila Geleta and Csongor Horváth for the help in the administration of the funding of this project. This research was funded by the project Establishment of Electromobility Development Center at Robert Bosch Kft. Phase 1.
Author Contributions. Conceptualization, M.K., T.O.; methodology, M.K., and T.O.; software, T.O., and K.G; validation, M.K.; initial draft preparation, M.K., and T.O.; writing, review and editing, M.K., T.O.; visualization, M.K.; supervision, P.K., and T.O.; All authors have read and agreed to the published version of the manuscript.
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Effect of the Radial Constrain for the Impact Energy-Absorbing Behaviour of the Closed-Cell Metal Foam József Kertész1,2(B)
and Tünde Anna Kovács3
1 School on Safety and Security Sciences, Óbuda University, Budapest 1081, Hungary
[email protected]
2 Air- and Road Vehicle Department, University of Debrecen, Debrecen, Hungary 3 Donát Bánki Faculty of Mechanical and Safety Engineering, Óbuda University,
Budapest 1081, Hungary
Abstract. The application of metal foam in the vehicle industry is not a novelty. Owing to the strict emission and consumption requirements, the use of composites and metal foams can be useful for reducing the mass for car manufacturers. Along with sustainability, higher safety features are also expected by the users in the case of new vehicles. This requirement also confirms the necessity of the metal foam application in the vehicle structure since the porous feature results in high absorbed energy by the low mass. The present study is a prework of subsequent development of a special crumple Crushbox part of the vehicle front, which is working on the principle of radial constrained foam compression. In this study discussed compression experiments confirm the fact, that the energy absorbed capacity of a metal foam can be improved in different radial constrained applications. The significant absorbed energy difference can be expected due to the increase in the densification region. Keywords: Constrain · Aluminium-foam · Impact · Energy-absorption
1 Introduction Many recent investigations are about the metal foam application in vehicle body parts. All of them report the positive effect of the metal foam application in the case of energyabsorbing capacity. [1–5] The typical investigation of the metal foams to reviled the physical properties, the compression test. In the practice, we have many options for manufacturing metal foam, furthermore, the different manufacturing process results in different material properties in the end-product. [6–8] However to get real appreciable and comparable results with the compression test, it is mandatory to provide the conditions and the requirements the standard expects. In the case of the applied compression test to the reveal of the mechanical properties of a metal foam the ISO13314:2011: Compression test for porous and cellular metals, and the ISO17340:2020: High speed compression test for porous and cellular metals must be taken into consideration. [9, 10] The experiments about the mechanical properties of the metal foam can be dived © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 139–150, 2023. https://doi.org/10.1007/978-3-031-15211-5_12
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into two groups. According to this, we can distinguish quasi-static and dynamic compression tests. In the case of the first, the main aim of it is to analyse the full length of the stress-and strain diagram of the compression test and concludes the possibilities of the application and its later development of it. [11–13] However the dynamic test is focusing on the energy absorbed capacity most of all. Both compression experiment results in special stress-and strain character, which has three main typical parts. Helping with these parts the foam’s features can be compared purely. All of the type of foam (opened-cell, closed-cell, syntactic-foam) has the main features under compression test. Our investigation focuses on closed-cell aluminium foam. Lightweight aluminium alloy closed-cell foams are one of the most important classes of cellular materials for structural applications. They have frequently been applied as a core of sandwich panels [14] or a filler material [15] of hollow structures (Crushbox) for several multi-functional construction elements in vehicles as crash energy, sound and vibration absorption. When an object is moving it has kinetic energy, and the amount of it is influenced by the amount of its mass and the velocity. At the moment of the impact, the previous mentioned kinetic energy is converted into impact energy. The vehicle safety crumple zone aims to absorb the most impact energy to reduce the effect of the impact force that would load the passengers. The impact absorbing capacity of the vehicle can be improved from the side of shape or applied-material optimisation. Our investigation is about the absorbing improvement with the application of aluminium foam. A compression load of the foams results in three segmented stress-strain diagrams. Our aim is the maximum utilisation of the plateau region and the incipient densification state for energy absorption. At the beginning of the research, we started with the hypothesis that if we can influence the steepness and shape of the stress-strain diagram of given metal foam with a design solution, we can influence the amount of energy it can absorb. Owing to this assumption, we apply a tube around the foam specimen to hinder its radial displacement of it. The literature describes this construction with the “radial constrained compression” expression. When the constraining tube inner diameter is equal to the initial outer diameter of the foam specimen it is the radial constrained structure. Tons of investigation is about the free compression test of aluminium foams with quasi-static load, but the field of the constrained examines with dynamics load are less. However, these reports have good results in the case of absorbing the energy effect. Li et al. share their study that the performance of the foam absorbing energy per unit volume can be greatly improved by the radial constraints. [16] In the present study, we would like to reveal and prove the impact energy absorbing improvement effect of the applied constraining tubes. Therefore, we have taken a comparison examination, where the same aluminium foam was compressed in three different ways. The first case was about free compression, the second one was about radial constrained compression. Figure 1 represents the set of the different specimen construction.
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Fig. 1. The set of the specimens a. Free compression b. Radial constrained compression [author]
2 Material and Experimental Methods For the comparison test, we applied cylindrical specimens taking into consideration the requirement of the ISO13314 and ISO17340 standards. The diameter and the length of the foam were chosen for 30 mm. For the radial constrained compressing we used a 30 mm inner diameter and 50 mm length cylindrical ended-tube with a 2 mm wall made from AlMgSi1. The foam is a closed-cell aluminium foam with an average 0,3 g/cm3 density. The exact material ingredients of the foam are not detailed in this study, since the aim is to focus on the structure effect and not the material examination. From this point of view, the applied foam type is independent of the comparison test. For the constrained test we applied a rigid cylindrical body made from steel with 30 mm diameter and 50 mm length. 2.1 Experimental Methods and Technology The tests were performed on an Instron 8801 servo-hydraulic material testing machine. This system is suitable for static and dynamic material testing due to its rigid frame design. The research aims to develop a high-efficiency impact energy-absorbing body element, the effectiveness of which I base on the radially inhibited compaction of metal foams. In an accident, the structure is subjected to a dynamic load due to a collision. Therefore, we perform the tests according to the guidelines of the ISO17340: 2020 standard. This standard specifies boundary conditions for high-speed compaction testing of porous materials. A dynamic test is when the compaction speed is at least 0.1 m/s. This standard sets the upper-speed limit at 100 m/s. Accordingly, the test was performed at a
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compaction speed of 100 mm/s. An Instron 8800MiniTower control unit is used to control the Istron 8801 material testing system. To record the measurement boundary conditions and data, the computer is connected to the control unit via an interface cable. The software required to use the system is the WaveMatrix software package. The purpose of the measurement is to record the response properties of the metal foam to its compaction, i.e. to record the engineering stress-strain diagram and to evaluate them. Instron® it is the only manufacturer that develops and manufactures the transmitters and sensors required for the measurements itself, so we use an Instron 149464 100 kN force-measuring cell for the measurements so that we can record the most authentic data. The safety and calibration requirements specified by the manufacturer have also been taken into account in the design and performance of the measurement, as current standards recommend that the interval between calibrations should not exceed 18 months. The measuring system met this requirement. After many empty test-run, we found that the correct data recording must be used a minimum of 125 mm displacement for the test. In which the first 100 mm is for the acceleration of the crosshead of the machine to reach the 0,1 m/s speed, and the rest 25 mm is the compression of the foam. For this displacement, we can reach the 80% strain to get an evaluable result. To get representative results, we repeat every compression test three times. Figure 2 shows the set of the measuring device.
Fig. 2. Instron 8801 prepared for the compression test [author]
3 Interpretation of the Stress-Strain Diagram To compare the effect of the different construction filled with aluminium foam, a standardised interpretation is necessary. In the next, the definitions are defined according to
Effect of the Radial Constrain for the Impact Energy-Absorbing
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the above mentioned ISO standards. In the case of porous and cellular material compression, the compressive stress can be determined with the compressive force divided by the initial cross-section. The horizontal (y-axis) of the diagram indicates the strain, which can be calculated by the proportion of the actual displacement and the initial length of the specimen. We compared the first maximum compressive stress values, so the definition of it is important. In this case, the first local maximum can correspond with the maximum compressive stress. The study focuses on the amount of absorbed energy, so the interpretation of the plateau stress cannot be neglected. The plateau is the arithmetical mean of the stresses between 20% and 40% compressive strain. The results of this region are the base of the calculation of the densification strain point. The end of the plateau and the start of the densification region can correspond with the 1,3 times the plateau stress. Finally, we calculate the absorbed energy in two different ways. First – as the standard also suggests – the energy is the area under the stress-strain curve up to 50% strain or up to the plateau end strain. After it, we calculate the area under the curve up to 80% strain, a significant difference is expected in the densification region due to radial constrain. Figure 3 Represents the typical stress-strain diagram of foam compression. For the area calculation under the curve, we use the integration with the trapezoidal rule, according to the next formula: 0.5−0.8 f (x)dx ≈ (1) 1 N (f + f (xi+1 ))(xi+1 − xi ) (x ) i 0 i=1 2
Fig. 3. Typical stress-strain diagram of the aluminium foam compression [author]
4 Specimen Preparation To avoid the influence of the inner material structural deviation, before the compression we recorded the exact geometry parameters and weight of the specimens. For all tests we use the same nominal size specimens, however, owing to the uneven cellular distribution the density definition is important. We performed three free-compression, three radial constrained compression tests, and all of them are repeated by three times, resulting in six pieces’ specimens. The parameters of the specimens are collected in Table 1.
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D [mm]
L [mm]
m [g]
ρ [g/cm3]
SP1
29.77
29.61
6.98
0.30
SP2
29.88
29.35
6.94
0.29
SP3
29.91
30.17
6.85
0.29
SP4
29.89
30.13
7.51
0.32
SP5
29.84
30.12
6.96
0.30
SP6
29.82
30.02
7.97
0.34
Avge
29.85
29.89
7.19
0.30
Table 1 confirms the specimen’s geometry so the density tolerances are under 1%. The specimens from SP1-SP3 are examined with free-, the SP4-SP6 radial constrained.
5 Results of the Tests 5.1 Compression Test Without Constrain In the first, we analysed the SP1–2-3 specimens, without any radial constraining. The results are reported in Table 2. All of the specimens resulted in the typical stress-strain diagram. The average of the plateau stress is 2.81 MPa was calculated in 20–40% strain range with 3.66 MPa plateau-end stress. The plateau reaches its end at the 50% strain. To analyse the effect of the constrain - in the next - for the plateau region we calculated the absorbed energy up to the beginning of the densification range. Considering the absorbed energy under compression, the significant (more than 80%) part of the whole was performed after the end of the plateau, in the densification range. Numerically up to 60% strain the foam absorbed 1.89 J, however up to 80% the energy was 11.17 J. The absorbed energy density has also an important function in the case of analysing the foam behaviour, therefore we have to define the amount of energy related to the volume. According to this, it has 2104.7 kJ/m3. The manufacturers frequently supply production descriptions; which manual contains normative information about the absorbed energy density related to each type of product [17]. Our specimens have an average 0.3 g/cm3 density means 2000 kJ/m3 energy density according to the product guide. It results in less than 10% difference between the real test and the literature data. The first maximum compression test was an average of 3.66 MPa. After the compression, we measured the diameter of the broken specimens, which was an average of 34.33 mm. Figure 4 shows the specimen before and after the impact load.
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Fig. 4. Specimen before and after the free compression [author]
Figure 5–6 show the occurred stress-strain diagram about the free compression. Figure 5 indicates the compression until 60% strain, and then the full test is shown (until 80% strain). There are many recent studies in which the foam density and the compression behaviour were observed [18, 19]. These studies confirm the correctness of the earned results since they report the same values and results with 0.3 g/cm3 foam density. In Fig. 7 the steps of the compression can be seen at 0–30-50–80% strain. Table 2. Results of the compression of the SP1–2-3 specimen without constrain [author] SP1 σpl
2.95
2.89
Avge
2.62
2.82
3.84
3.75
3.41
3.66
εpl end
51.76
50.76
47.51
50.01
3.03
2.81
2.60
2.81
Eabs.60
1.89
1.14
Eabs.80
11.18
11.42
1.278 11.39
MPa MPa % MPa
1.16
J
11.39
J
Eabs. up to dens
1.45
0.66
0.50
0.59
J
Eabs. in the dens
10.72
10.77
10.90
10.80
J
SP1
SP2
SP3
4 3 2 1
STRAIN Fig. 5. Compression without constrain (up to 60% strain) [author]
60%
50%
40%
30%
20%
0
10%
STRESS [MPA]
5
SP3
σpl end σfirst max
6
SP2
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20 STRESS [MPA]
SP1
SP2
SP3
15 10 5
80%
70%
60%
50%
40%
30%
20%
10%
0
Fig. 6. Compression without constrain (up to 80% strain) [author]
Fig. 7. The steps of the free compression of the foam (0–30-50–80% strain) [author]
5.2 Compression Test with Radial-Constrain In this section, the results of the radial constrained compression tests will be reported. As in the previous two test, the foam has the same mechanical behaviour in the linear-elastic range. The maximum compression strength was 5.434 MPa. The indicated plateau stress reaches 4.183 MPa. The significant difference began to appear in the second stage of the plateau range and the densification period. The stress-strain character was even steeper, and there is a remarkable increase in the second plateau region resulting in a higher amount of absorbed energy. The effect of the tube constrain was felt, since the radial displacement of the cells was constrained, and the hardening effect appeared already at the 44% strain. Figure 8 shows the state of the specimen before and after the impact load. As a result of constraining the absorbed energy was 24.53 J. It means that the impact energy capacity was doubled only because of the application of the radial constrain related to the free compression. A significant part of this energy was absorbed naturally in the second half of the compression because of the hardening effect. Table 3 sums up the indicated results of the constrained test. As can be seen in Fig. 9–10. From the second half of the plateau region, the curve has notable steeper characteristics owing to the hardening effect.
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Fig. 8. Specimen before and after the collision load in half section by 2D/photo view [author]
Table 3. Results of the compression of the SP4–5-6 specimen with radial-constrain [author] SP4 σpl
3.96
SP5
SP6
4.48
4.11
Avge 4.18
σpl end
5.16
5.82
5.34
5.44
εpl end
44.83
44.07
43.21
44.03
3.29
3.52
3.51
3.43
σfirst max
MPa MPa % MPa
Eabs.60
3.78
3.86
3.68
3.77
J
Eabs.80
23.17
28.11
22.34
24.53
J
Eabs. up to dens
1.62
1.37
1.43
1.47
J
Eabs. in the dens
21.55
26.74
20.91
23.06
J
12 SP7
SP8
SP9
8 6 4 2 50%
40%
30%
20%
0 10%
STRESS
10
STRAIN Fig. 9. Compression with radial constrain (up to 60% strain) [author]
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70 60 SP7
SP8
SP9
STRESS [MPA]
50 40 30 20 10
80%
70%
60%
50%
40%
30%
20%
10%
0
Fig. 10. Compression with radial constrain (up to 80% strain) [author]
6 Comparison of the Results
12 10 8 6 4 2 0
With constrain
50%
40%
30%
20%
Without constrain
10%
STRESS
Figure 11 and Fig. 12 contain the two types of compression test in one diagram. In this diagram, the effect of the constrain is very spectacular. In the first elastic region, there is no significant difference, the curve has a similar steep shape, and this similarity stays till the half of the plateau region. However, after half of the plateau, the curves begin to separate. The hatched area indicates the absorbed energy difference. In the case of free compression, the foam was able to absorb an average of 11.391 J. However this value achieved the 22.342 J. It confirms that it can be achieved more effective energy absorbing with the application of a constrain with the same foam.
STRAIN Fig. 11. Absorbed energy difference between the free and constrained test (up to 60% strain) [author]
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STRESS [MPA]
50 40
Without constrain
With constrain
30 20 10 70%
60%
50%
40%
30%
20%
10%
0
Fig. 12. Absorbed energy difference between the free and constrained test (up to 80% strain) [author]
7 Conclusion In this study, the effect of the radial constrain was observed in the case of closed-cell aluminium foam compression. The test was a prework of a latter crushbox optimisation, owing to this the investigation was focused on the absorbed energy performance. According to our prediction, the radial constrain can improve the energy absorbing capacity of the same foam. There was little difference in the linear elastic region; in all set up the specimens resulted in same behaviour. The constrain began to take effect in the second half of the plateau region significantly. The hardening effect was existed earlier with radial constrain, and the plateau line had remarkable steep. Due to the steeper characteristic, the absorbing energy in the plateau stage was higher than without constrain. The improved stress-strain character involved a higher amount of absorbed energy. The significant difference is recorded in the densification region. With radial constrain the character can be lifted remarkably. Considering the absorbed energy, it could be double with the constrain. Furthermore, it can be clearly stated from the measurement that the hardening behaviour (involving the energy absorbing) of metal foams can be influenced by the extent of radial constrain. The aim of the long-term investigation is to develop a vehicle crumple zone crushbox that is suited for more impact energy. We would like to divide the crushbox into more stage and fill it up with different mechanical featured foams and it will work on the principle of the radial constrained foam compression. This study confirms the fact, that with an optimised crumple structure, the impact energy absorbing performance can be improved in the case of the same featured foams. In the shadow of these results, we can start the structure developing and designing. Furthermore, since the constrain is not directly compressed in the impact, the material of it can be replaced by a lighter one which can also contribute to the mass optimisation of vehicles. Acknowledgement. The authors would like to thank the Hungarian State, the National Research, Development and Innovation Office and the European Union for their support in project No. 2020–1.1.2-PIACI-KFI-2020–00081.
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References 1. Joshi, S.Y., Kolhe, V.A.: Investigation on energy absorption properties of Al-foam, foam filled and empty MS tube under 3-point loading condition at room temperature. IOP Conf. Ser.: Mater. Sci. Eng. 409(1), 012033, (2018) 2. Kolhe, V.A., Baviskar, P.R., Patil, M.M.: Bending characteristics of foam filled mild steel bumper beam under gradual loading condition. Int. J. Applied Eng. Res. ISSN 0973–4562 13(5), 64–68 (2018) 3. Yousefi, M.K., Kianirad, A., Vaseghi, M.: Simulation and investigation to the behavior of metallic foam as a bumper in automobile under impact loadings. The First International Conference on Mechanics of Advanced Materials and Equipment (2018) 4. Pandey, R., Singh, P., Khanna, M., Murtaza, Q.: Metal foam manufacturing, mechanical properties and its designing aspects—a review. Advances in Manufacturing and Industrial Engineering, pp. 761–770 (2021) 5. Marx, J., Rabiei, A.: Tensile properties of composite metal foam and composite metal foam core sandwich panels. J. Sandwich Structures and Materials 23(8) 3773–3793 (2021). https:// doi.org/10.1177/1099636220942880 6. Gyöngyösi, S., Gábora, A., Balogh, G., Kalácska, G., Bubonyi, T., Mankovits, T.: Effects of additives on the mechanical properties of aluminum foams. Mechanisms Machine Sci. 109, 307–313. Paper: Chapter 28 , 7 p. (2022) 7. Kim, S., Lee, C.-W.: A review on manufacturing and application of open-cell metal foam. 8th International Conference on Porous Metals and Metallic Foams, Metfoam 2013, Procedia Materials Science 4, 305–309 (2014) 8. Kulshreshtha, A., Dhakad, S.K.: Preparation of metal foam by different methods: a review. Materials Today: Proceedings 26 1784–1790 (2020) 9. ISO 13314:2011 Mechanical testing of metals — Ductility testing — Compression test for porous and cellular metals 10. ISO 17340:2020 Metallic materials — Ductility testing — High speed compression test for porous and cellular metals 11. Kader, M.A., et al.: Macro and micro collapse mechanisms of closed-cell aluminium foams during quasi-static compression. Materials and Design 118, 11–21 (2017) 12. Seitzberger, M., Rammerstorfer, F.G., Gradinger, R., Degischer, H.P., Blaimscheind, M., Walchd, C.: Experimental studies on the quasi-static axial crushing of steel columns filled with aluminium foam. Int. J. Solids Structures 37, 4125–4147 (2000) 13. Mankovits, T., et al.: Structural analysis and its statistical evaluation of a closed-cell metal foam. Int. Review of Applied Sciences Eng. 5(2), 135-143 (2014) 14. Radford, D.D., Deshpande, V.S., Fleck, N.A.: The use of metal foam projectiles to simulate shock loading on a structure. Int. J. Impact Eng. 31(9), 1152–1171 (2005) 15. Duarte, I., Krstulovic-Opara, L., Vesenjak, M.: Characterisation of aluminium alloy tubes filled with aluminium alloy integral-skin foam under axial compressive loads. Composite Structures 121, 154–162 (2015) 16. Li, C., Li, C., Wang, Y.: Compressive behavior and energy absorption capacity of unconstrained and constrained open-cell aluminum foams. Advanced Composites Letters 29, 1–4 (2020). https://doi.org/10.1177/2633366X20923671 17. Metal foam web - https://www.metalfoamweb.com/aluminum-foam/closed-cell-aluminumfoam/. Accessed 11 Mar 2022 18. Lopatnikova, S.L., Gamaa, B.A., Haquea, M.J., Krauthausera, C., Gillespie, J.W.: Highvelocity plate impact of metal foams. Int. J. Impact Eng. 30(4), 421–445 (2004) 19. Beals, J.T., Thompson, M.S.: Density gradient effects on aluminium foam compression behavior. J. Materials Sci. 3(2), 3595–3600 (1997)
Investigation of Tilting Table with Parallel Kinematic István Tüske(B)
and György Heged˝us
University of Miskolc, Miskolc-Egyetemváros, Miskolc 3515, Hungary [email protected]
Abstract. The focus of this paper is on the analysis of the degree of freedom and mobility of a tilting table with parallel kinematics using the mechanism module of parametric software. Parallel kinematics is also used in many places in the automotive industry, for certain workpieces where the orientation movements change only in a narrow range (e.g., draft angles of casting tools), then the workspace constraints of the unit with a parallel kinematic chain can be disregarded, and placement of body parts in the automotive industry. The paper presents an analytical calculation of the degree of freedom and mobility of a tilting table with parallel kinematics using the modified Chebyshev – Grübler – Kutzbach formula. The result of the analytical calculation will be verified with parametric software. This is significant because the Chebyshev – Grübler – Kutzbach formula ignores the geometrical characteristics of the structure during analysis. The model of the investigated structure consists of solid parts created with parametric software. The mechanism modul of the parametric software provides an opportunity to analyse the kinematic properties of the investigated structure. The purpose of the studies on the kinematics of the mechanism is to verify the validity of the analytical calculations. Keywords: Constraints · Degree of freedom · Machine tool · Parallel kinematics
1 Introduction Nowadays, the use of computer tools and programs is essential in engineering practice, automotive design processes and in solving the tasks of research projects. It is extremely important to prove the functionality of the structure without manufacturing any parts of the structure. There are computer programs that can be used to create geometrically realistic models [1, 2], and real material properties can be assigned to a given part of the virtually created structure, for the connected parts can be specified the connection types and kinematic constraints, and the boundary conditions, the forces and torques acting on the components can be defined [3]. All this serves that purpose so that the properties of the given structure can be examined before it is manufactured. Using parametric software, it will be verified that the results calculated by the Chebishev – Grübler – Kutzbach formula – in terms of the degree of freedom of the structure – meet the requirements for the examined structure. The Chebyshev – Grübler – Kutzbach formula is briefly described [4, 5]. This paper provides further insight into kinematic modeling issues. PTC Creo (parametric software) is used for the investigation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 151–156, 2023. https://doi.org/10.1007/978-3-031-15211-5_13
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2 Presentation of the Structure The tilting table with parallel kinematics consists of a platform, three actuators and a central universal joint. The platform is connected to the base via actuators and a central universal joint. All three actuators have the same structural design with two universal joints and a prismatic connection (Table 1). Table 1. Structural design of the tilting table. Marking System
1. actuator 2. actuator 3. actuator Central connection Platform
Platform
-
Actuators
A1
-
-
-
P
A2
A3
-
-
Central universal joint -
-
-
C
-
Upper universal joint
A1UU
A2UU
A3UU
-
-
Prismatic connection
A1P
A2P
A3P
-
-
Lower universal joint
A1UL
A2UL
A3UL
-
-
Fig. 1. Structure of the workpiece moving table.
Fig. 2. Schematic view of the model.
The universal joint has two degrees of freedom and can turn around two axes. The prismatic connection has a degree of freedom and is able to move in a straight line along its axis. The system of the two elements determines the degree of freedom of the structure (Fig. 1, 2).
3 Determination of the Degree of Freedom of the Structure by Analytical Calculation One of the essential tasks in investigating the movement of the mechanism is to determine the degree of freedom. The degree of freedom of a mechanical system is the number
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of independent parameters which determine the configuration of the analysed structure. There are several methods for determining the degree of freedom of a structure; in this case, the formula Chebyshev - Grübler - Kutzbach will be used [6–8]. 3.1 Chebyshev – Grübler – Kutzbach Formula The Chebyshev – Grübler – Kutzbach equation gives the degree of freedom and mobility of the mechanism: g fi . (1) M = d (n − g − 1) + i=1
where: M: degree of freedom, d: d = 6 in case of a spatial mechanism, d = 3 in case of a planar mechanism, n: number of links, including the fixed base of the mechanism, g: the number of joints of the mechanism, fi: the number of degrees of freedom of the i-th joint. The modified Chebyshev – Grübler – Kutzbach equation: g fi − v − ζ. (2) M = d (n − g − 1) + i=1
where: M: degree of freedom, d(=6): d = 6 in case of a spatial mechanism, d = 3 in case of a planar mechanism, n(=8): number of links, including the fixed base of the mechanism, g(=10): g the number of joints of the mechanism, fi ( i=1 fi = 17): the number of degrees of freedom of the i-th joint. ν(= 3): the number of parallel redundant constraints, ζ (= 0): degrees of freedom of passive constraints. Solving the above (2) equation, the degree of freedom (M) of the structure is two.
4 Verification of the Degree of Freedom of the Structure with Parametric Software The parametric software is a suitable tool for kinematic investigations of the structure using the mechanism module. Kinematics is a branch of dynamics that deals with aspects of motion. The kinematic analysis simulates the movement of the mechanism, meeting the requirements of the servomotor profiles and any joint [9] (Fig. 3, 4 and Table 2).
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Fig. 3. Kinematic constraints of the tilting table.
Fig. 4. The tilting table model.
Table 2. Kinematic constraints of the tilting table. Elements/Elements
Basis
Lower cross
Actuator lower fork
Actuator upper fork
Upper cross
Lower fork
F
Lia
Lower cross
-
-
Actuator lower fork
-
-
Actuator upper fork
-
-
Upper cross
-
Platform Cradle
Central cross
Upper fork
-
-
Lib
-
-
-
-
-
-
-
-
Si
-
-
-
-
-
Uia
-
-
-
-
-
-
-
Uib
-
-
-
-
-
Ca
F
F
-
-
-
-
Cb
-
The kinematic constraints listed above form a closed kinematic chain that clearly defines the degree of freedom of the structure. Markings: F – FIX, S – SLIDER, L – LOWER, U – UPPER, i – actuator numbering: i = 1..3, a, b – rotation axis [10, 11]. The platform of the structure via the central universal joint – which is embedded in the cradle – can rotate about two axes, a and b, the rotations being generated by servomotors in the mechanism model according to predefined functions. The angular rotations about the a and b axes are illustrated in Fig. 5.
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Fig. 5. Angular rotation of the axles of the central universal joint as a function of time.
The actuators are fixed to the platform via the upper universal joint. The actuators follow the movement of the platform created by the servomotors to meet kinematic constraints. The resulting displacements on the actuators are shown in Fig. 6.
Fig. 6. Displacement of actuators as a function of time.
The result of parametric software shows the degree of freedom of structure is two.
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5 Summary The paper presents an examination of a closed-loop parallel kinematics tilting table. The analysis was developed in three parts. Investigation of the mobility of the mechanism using the modified Chebyshev – Grübler – Kutzbach formula, which shows that the analysed structure has two degrees of freedom. Examination of the degree of freedom of the tilting table using the mechanism module of parametric software, describing the kinematic constraints. The results generated by the parametric software confirm the results of the analytical calculations.
References 1. Kanife, P.O.: Introduction. In: Computer Aided Virtual Manufacturing Using Creo Parametric, pp. 1–11. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-23359-8_1 2. Kanife, P.O.: Engraving tutorial. In: Computer Aided Virtual Manufacturing Using Creo Parametric, pp. 13–68. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-23359-8_2 3. P. Köhler (Hrsg.), CAD-Praktikum für den Maschinen- und Anlagenbau mit PTC Creo (2016). https://doi.org/10.1007/978-3-658-15389-2_5 4. Staicu, S.: Matrix kinematics of composed motion. In: Dynamics of Parallel Robots. PRTA, pp. 33–55. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-99522-9_3 5. Liu, X.-J., Wang, J.: Parallel kinematics: type, kinematics, and optimal design. Springer Tracts Mechanical Eng. (2014). https://doi.org/10.1007/978-3-642-36929-22 6. Han, M., Yang, D., Shi, B., Li, T., Feng, J.: Mobility analysis of a typical multi-loop coupled mechanism based on screw theory and its drive layout optimization. Advances in Mechanical Eng. 12(12), 1–9 (2020). https://doi.org/10.1177/1687814020976216 7. Huang, Z., Li, Q., Ding, H.: Mobility analysis part-1. Theory of Parallel Mechanisms, Mechanisms and Machine Science, vol 3, (2013). Springer. https://doi.org/10.1007/978-94-0074201-7_3 8. György, H.: Párhuzamos kinematikájú billen˝oasztal elemzése, Multidiszciplináris Tudományok: A Miskolci Egyetem Közleménye 10(3), 102–108 (2020) https://doi.org/10. 35925/j.multi.2020.3.12 9. http://support.ptc.com/help/creo/creo_pma/usascii/index.html#page/simulate/mech_des/ana lysis/AboutKinematicAnalysis.html 10. Hiller, M.: Multiloop kinematic chains. In: Angeles, J., Kecskeméthy, A. (eds.) Kinematics and Dynamics of Multi-Body Systems. CICMS, vol. 360, pp. 75–165. Springer, Vienna (1995). https://doi.org/10.1007/978-3-7091-4362-9_4 11. Flores, P.: Kinematic constraint equations. In: Concepts and Formulations for Spatial Multibody Dynamics. SAST, pp. 31–35. Springer, Cham (2015). https://doi.org/10.1007/978-3319-16190-7_7
Influence of Speed to Rolling Resistance Factor in Case of Autobus Sándor Pálinkás(B) Faculty of Engineering, University of Debrecen, Debrecen 4028, Hungary [email protected]
Abstract. The fuel consumption of autobuses is significantly influenced by the different losses, such as air resistance, tyre rolling resistance of tire and other losses (bearing losses, losses in the drive, gearbox, etc.). The main goal of my current research is to work out indirect methods of measurement via experiments carried out with Mercedes-Benz REFORM 501 autobus to define rolling resistance, one of the most decisive losses. During the evaluating of measurements, I used a special method because the power values were determined based on the power characteristic curve of OM 936/260 kW engine of the bus. During my calculations, I considered the losses due to rolling resistance and air resistance and other losses due to the use of additional auxiliaries furthermore, the losses in the drive elements of the vehicle (3% of the used engine power) were considered as constant. Later on, the defined values will serve as input data for modelling the vehicle energetics calculations. Keywords: Rolling resistance · Air resistance · Fuel consumption
1 Introduction Rolling resistance is basically caused by the motion of tyres. The running surface is deformed and distorted with every rotation of the tyre. Due to being in contact with the road surface during a change in shape, the tyres warm-up and the mechanical energy turns into heat. Most of the rolling resistance of car tyres emerge in this process. Part of the energy passed on to the wheels is consumed by the tyres in the process, which leaves less energy for advancing the vehicle. Thereby the bigger the rolling resistance of the tyres is the more it consumes the mechanical energy provided by the engine. The rolling resistance cannot be eliminated completely but decreasing it as much as possible means a lot lower fuel consumption of the vehicle. In other words, reducing rolling resistance plays an important role in reducing fuel consumption. It is important to note that fuel can only be saved by reducing the losses during operation. These losses are as follows [1]: • rolling resistance, • air resistance, • inertial resistance, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 157–164, 2023. https://doi.org/10.1007/978-3-031-15211-5_14
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gradient resistance, side force resistance, transmission loss, losses from the use of auxiliary equipment, engine friction.
Factors influencing rolling resistance are the following: structure of tyres, sizes and temperature of tyres, air pressure in tyres, speed and mass of vehicle, material and pattern of tyres and sliding of tyres. In recent years, several researches [2–4] are focused on determining the above factors and more equations have been defined to calculate rolling resistance. Studies focusing on characteristics of rolling losses of car tyres have defined the following equation [5]: W ht FG = C· · (1) fr = W D w where: FG = rolling resistance force [N], W = weight on the wheel [N], C = constant reflecting loss and elastic characteristics of the tyre material [-], D = outside diameter of tyre [m], ht = tyre section height [m], w = tyre section width [m].
2 Elaborating an Experimental Plan to Determine the Rolling Resistance 2.1 Choosing the Right Stretch of Road for Measurements In order to investigate the rolling resistance, it is very important to choose a straight road which is long enough for accelerating the autobus to a speed of 90 km/h. The autobus used during the experiments shuttles as scheduled between Debrecen and Haláp so it seemed to be evident for me to choose this nearly 8 km long road (only in one direction from Debrecen to Haláp) section for performing the experiments (Fig. 1).
Fig. 1. Road section chosen for performing the experiment on the Google map.
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2.2 Elaborating the Aspects of Measurement In the course of the experiments, it was of an extraordinary importance that the conditions of measurements were suitable therefore the following aspects were elaborated: • • • • •
It was necessary to choose the suitable weather (calm, dry weather). Suitable temperature (20 °C). Identical tyre sizes with suitable air pressure, The windows of autobus shall be closed. The fuel tanks shall be full of fuel.
2.3 Process of Experiment The main purpose of my present research work is to develop an indirect measurement method for determining the rolling resistance. In order to realize this task, the input parameters necessary for calculating the rolling resistance were determined by measurement methods during the experiment. In case of the Mercedes-Benz REFORM 501 type autobus, the data coming from the engine cannot be obtained so the analogue revolution counter mounted in the instrument panel is used in the course of experiments. So the power values used by the engine can be determined on the basis of the obtained revolution numbers and the factory power curve of autobus in case of the different speeds applied during the experiment. During the evaluation of measurement, the losses due to the rolling resistance and air resistance are taken into consideration and the losses due to the use of the auxiliary units are also considered moreover the losses developing in the drive elements of the vehicle are defined as constant (3% of the used engine power). 2.4 Parameters of Autobus Used for the Experiments A Mercedes-Benz REFORM 501 type autobus was used for performing the experiments, the parameters of which are summarized in Table 1. Table 1. The details of experimental vehicle Parameters (Type of vehicle: Mercedes-Benz Reform 501) Air-resistance coefficient
0.85
Surface perpendicular to the flow
7.829 m2
Mass (own mass + 30 person with packets and the driver)
12650 + 2400 + 70 kg
Targeted speeds
50, 60, 70, 80, 90 km/h
Size of tyre
295/80 R22
Type of tyre
urban
The owner of the autobus used during the experiment (see Fig. 2) is DKV (Debrecen Transport Company); the number of seats is 40 and the number of standing places is 45 in the autobus. The autobus is equipped with the following type of engine: Mercedes-Benz OM 936; 260 kW/1400 Nm/Euro 6.
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Fig. 2. Autobus used for the experimental measurement [source: www.dkv.hu]
3 Experiment 3.1 Environmental Conditions of the Measurement In the course of performing the experiment, I had to take into consideration the suitable weather conditions therefore, the measurements were performed in a calm, dry weather. During the measurement procedure, the outside temperature was stated by using the built-in temperature measurement unit of autobus. The temperature was 19 °C during the measurement procedure. 3.2 Evaluation Process of Measurement On the basis of the revolution numbers measured during the experiment and the factory power-characteristic-curve of the engine of autobus, the power values used by the engine were determined in case of the different speeds applied during the experiment. In the course of evaluating the measurement, the losses due to the rolling resistance and air resistance were taken into consideration and the losses due to the use of the auxiliary and supplemental units were taken into consideration as “other” losses moreover, the losses developing in the driving elements of vehicle were interpreted as constant (3% of the used engine power). So - after determining the loss-factors - the rolling resistance factors were calculated in case of the different speeds applied during the experimental procedure by means of a counter panel developed in the Microsoft Excel. A special method was used during the experiments as the power values were determined on the basis of the power characteristic curve of OM 936/260 kW type engine of autobus. However, the applied characteristic curve does not contain values below 1000 l/min therefore, the missing values were determined by means of a linear interpolation method.
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3.3 Measurement Results In the course of the experimental measurements, my aim was to determine the revolution number of the engine at pre-determined speeds. The revolution-number measuring unit mounted on the instrument panel of the autobus was used for determining the revolution number. The results of the measurements are summarized in Table 2. Table 2. Summary of the measured values Revolution number [1/min]
Speed [km/h]
Method of determination
600
Power [kW] 50
0
linear interpolation
800
90
30
linear interpolation
930
115
50
linear interpolation
1000
130
60
measurement
1180
170
70
measurement
1310
195
80
measurement
1500
220
90
measurement
The power values were determined on the basis of the power characteristic curve of the OM 936/260 kW type engine of autobus as demonstrated in Fig. 3. It can be seen in Fig. 3 that the characteristic curve does not contain values below 1000 l/min therefore the
Fig. 3. Characteristic curve of power of OM936/260 kW type engine.
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missing values were calculated by means of the linear interpolation method performed on the basis of Fig. 4. 250
200
150
100
50
0 0
200
400
600
800
1000
1200
1400
1600
Number of revoluƟon, [1/min] Power, [kW]
Speed, [km/h]
Fig. 4. Determination of the missing values by means of linear interpolation
The air resistance as well as the rolling resistance were determined in the course of the calculations. The data demonstrated in Table 1 were taken into consideration during the calculation of the air resistance. The air resistance (FL ) and the power necessary for coping with the air resistance (PL ) were calculated as follows: FL =
ρ ·A·cw ·v2 2
PL = FL ·v
(2) (3)
where: FL = air resistance [N], PL = the power to cope with airresistance [W], ρ = density of air at 15 °C mkg3 , A = surface perpendicular to the flow [m3 ], [-], cw = air resistancecoefficient v = vehicle speed ms . By means of the afore-mentioned calculation, the air resistance was determined in case of different speed-values. The obtained results are summarized in the diagram demonstrated in Fig. 5. During the free rolling of the vehicle, the engine power was constant in each case; it was interpreted as “other” loss namely a loss due to the use of auxiliary and supplemental
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3000
Air resistance, [N]
2500 2000 1500 1000 500 0 0
20
40
60
80
100
Speed, [km/h] Fig. 5. Air resistance force of autobus as a function of speed
units. It means that the power of the engine was determined from the factory power characteristic curve on the basis of the measured values of revolution number and the air resistance was calculated in accordance with Eq. (1). In the course of the further calculations, the losses due to the use of auxiliary- and supplemental units were taken into consideration as “other” losses moreover, the losses developing in the driving elements of the vehicle were interpreted as constant (3% of the used engine power) so the power necessary for coping with the rolling resistance (PG , [W]) could be determined at a given speed and the force required by the rolling resistance (FG , [N]) could be calculated from it as follows: FG =
PG v
(4)
By knowing the value of force required by the rolling resistance, the rolling resistance factor (f) was determined according to the following equation: f =
FG m·g
(5)
where: m = mass of vehicle together with the staff performing the measurement [kg], g = gravitational constant sm2 . As a result of the calculation described above, the rolling resistance factors were determined in case of different speeds. The results of the calculations are summarized in Table 3.
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Speed, [km/h]
Air resistance, [N]
Power necessary for coping with the air resistance [kW]
Power Rolling necessary for resistance, coping with the [N] rolling resistance [kW]
Rolling resistance factor, f
50
786.531
10.924
50.626
3645.069
0.0245
60
1132.605
18.877
57.223
3433.395
0.0231
70
1541.601
29.975
84.924
4367.542
0.0294
80
2013.519
44.745
94.405
4248.231
0.0286
90
2548.360
63.709
99.690
3987.641
0.0269
4 Conclusion My main goal defined at the beginning of my research to provide a measurement method to define rolling resistance, has been achieved. In the course of my research work. I could develop an indirect measurement method for determining the rolling resistance as one of the most significant loss-factor. It can unanimously be stated from the obtained results that the value of rolling resistance factor is around 0.02 at each speed value; according to the References [6]. it is in accordance with the rolling resistance factor between the tyre and asphalt in case of an autobus. The incidental differences are caused by the fact that the quality of the road was not entirely identical on the investigated road section. Acknowledgement. Project no. TKP2020-NKA-04 has been implemented with the support provided from the National Research. Development and Innovation Fund of Hungary. financed under the 2020–4.1.1-TKP2020 funding scheme.
References 1. Ulf, H., et al.: Road surface effects on rolling resistance – coastdown measurements with uncertainty analysis in focus (ECRPD Deliverable D5a), p. 2 (2009) ˙ Piotr, J., Jerzy, A., Agnieszka, K., Paweł, C.: Rolling Resistance And Tire/Road Noise 2. Beata, Z., On Rubberized Asphalt Pavement In Poland (Proceedings of the Rubberized Asphalt-Asphalt Rubber Las Vegas. USA) (2015) 3. Wiegand, B.: Estimation of the Rolling Resistance of Tires (SAE Technical Paper) (2016) 4. Yudhidya, W.: Determining Rolling Resistance Coefficient on Hauling Road Using Dumptruck in Open Pit Coal Mine. International Symposium on Earth Science and Technology (2011) 5. Gillespie, T.D.: Fundamentals of Vehicle Dynamics (Society of Automotive Engineers), pp. 116–117 (1992) 6. Łukasz, W., Bartosz, W., Mateusz, K.: The determination of the rolling resistance coefficient of objects equipped with the wheels and suspension system – results of preliminary tests. MATEC Web of Conferences 254. 01005, p. 2 (2019)
New Adder and Distribution Gearbox Used in the Transmission of the Heavy Truck Száva Ioan(B) , Vlase Sorin, Gheorghe Vasile, Száva Renata Ildiko, Gálfi Botond Pál, and Popa Gabriel Transilvania University of Brasov B-Dul Eroilor, 29, 500036 Brasov, Romania [email protected], [email protected]
Abstract. The paper aims to study the constructive solutions for an adder and distribution box used in the transmission of a heavy truck used in oil drilling. The main problem that needs to be studied is to reduce the vibrations of this structure and the study of an optimal solution, from the point of view of operation but also of the production price. The adder and distribution box is a complex part of the trucks and is very noisy. Experimental tests come to validate the model used for the mechanical study of the structure. The symmetry of the structure also allows the simplification of the model used. The design, taking into account the concrete conditions of the rocks (accidentally and randomly appeared at the level of technological equipment) in which it is drilled, would lead to technical solutions - obviously unsatisfactory - for the production of sum box constructions adapted to various local conditions drilling, eliminating or greatly reducing the particularly important economic aspects of engineering construction. The new solution found and proposed to the designers is presented in the paper. Keywords: Adder box · Vibration · Heavy truck · Video image correlation
1 Introduction The analysis of the literature in the field in recent years highlights the generalization of researchers’ concerns for modelling the operating regimes of technological equipment and experimental attestation of the results obtained theoretically. This approach is useful both for the manufacturers of technological equipment for drilling and for the proper adoption of the power-train. It is obvious that for a modelling as close as possible to the reality of the engine and transmission, it is necessary to know the operating requirements, necessary to be taken over by the components of the power-train, so the informational feedback for static and dynamic requirements of the technological equipment. As a result of the research carried out - mostly - at the level of technological equipment (characteristic of the current stage of the field), it was concluded that the dynamic stresses subsequent to torsional vibrations are the main demands to be taken by these equipments. At the drilling rig, during operation, the main stress is produced by the torsional vibrations of the system. Vibrations are generally damped or have a low damping value, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 165–178, 2023. https://doi.org/10.1007/978-3-031-15211-5_15
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and the vibration regime is nonlinear. Due to the non-uniformity of the drilling speed, due to the non-uniformity of the resistance of the drilled soil, the friction is high at low drilling speeds and has low value as the speed of the screed increases due to the low resistance of some of the drilled layers [1–4]. The special complexity of the equipment requires the introduction of damping and torsional vibration control systems, especially during operation. The effect of the control systems is manifested both at the level of the active technological equipment and the drilling pipes, as well as of the power-train, which ensures the energy necessary for the drilling. But the research at the moment is multiple approached at the level of technological equipment and only sporadically in the field of power-trains. Even if from the point of view of modelling the power-train apparently belongs to other fields (transport vehicles, installations with stationary engines, etc.), the modelling at the level of accuracy imposed in this dynamic field requires the adoption of feedback data from the good operation and not from the installation which could be considered, more or less justified, similar (Fig. 1).
Fig. 1. Mobile oil drilling rig type TW125 CAA6 mounted on the chassis ROMAN 75.540 MFEG (12 x 8) [10]
Torsional vibration dampers RTS (STRS - Soft Torque Rotary System), H∞, etc. corrects and modifies torsional vibrations induced by operation so that there are always
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complex problems that must be solved not only by the designer of the technological drilling equipment but also by the designer of the power-train of the drilling rigs. The design of the kinematic scheme, the dimensioning and the verification of the assemblies of the summing boxes represents the way of correlating the design theme with the product, the final result reflecting the adopted conception and technology. The current technical design is the result of classic modelling, both in terms of the kinematics of the main motions and controls and the strength. The adder and distribution box sums up the torque (transmitted power) of two high-power diesel engines (> 500 hp), equipped with ALLISON automatic transmissions. Power flows are taken from the transmissions distributed through the adder boxes to the power take-off that drives the technological equipment of the drilling rig and, when the vehicle is in running position, the power flow is directed to the front and rear axles of the self-propelled vehicle for drilling. The adder box also has the role of driving secondary consumers (compressors, hydraulic pumps for coupling systems - decoupling and steering) (Fig. 2).
Fig. 2. Self-propelled chassis equipped with two diesel engines and adder box [10]
2 Critical Analysis of the Adder Box The transmission of power flows to the front, and rear drive axles is carried out by a conical (symmetrical) differential mechanism lockable with electro-pneumatic control and the signalling of the lock with a mechanical micro-switch. In Fig. 3 is presented the design of the drilling vehicle equipped with the CSD 4000 adder and distribution box, intended to transmit the total power of 1010 hp [10] (Fig. 4).
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Fig. 3. Section through the adder and distribution gearbox CSD 4000 [10]
Fig. 4. General view of the adder and distribution gearbox CSD 4000 [10]
The adder box is a complex mechanical assembly that must operate accurately and safely in a high-yield, low-cost manner. Consequently, a modelling is needed that leads to a levelling of operation practically identical to the real phenomena. At the same time, the sizing of all components must be ensured to the degree of safety and economy required by the profile market [6–8].
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The current construction corresponds in terms of kinematics, dynamics and design, to the current level of these assemblies. The problems solved - following the current calculation methods used - are a valuable guide in solving important practical issues, leading to the realization of a product that is appreciated by manufacturers of selfpropelled drilling equipment. The unsolved issues considerably widen the scope of some cases in which technological equipment is additionally randomly requested and consequently lead to new needs for dynamic modelling with a higher quality finality compared to the current way of solving problems. The issue in the field of research-development of adder boxes, contains the following stages, which are practically unanimously accepted. • Establishing the total transmission ratio, distributing the transmission ratio on the gear stages in the gearbox, establishing the gear geometry, the distances between the axles, the predimensioning of the gears, the profile deviations, roughness, recommended technology; • The fundamental relations for the dimensioning of the gears through which the characteristics of shape, dimensions, treatments, roughnesses are elaborated. Calculation methods are highly standardized and widely applied in all industrialized countries. In relation to the measurements on stand and in operation, values of noise and vibration in gears are also obtained, which cannot be determined theoretically, by using the above-mentioned methodologies; • Design and dimensioning of shafts intended for gear assembly, processing of motor torque/moments and their transmission to the power take-off and differential. The current calculation methods are of deterministic type, with the application of the classical relations in the stages of predimensioning and verification to fatigue; • Design of bearings; • Design of the lubrication system of the adder box; • Fatigue calculation methods in the case of infinite durability. • Establishing the parameters of durability/reliability; • Characteristics of the stand test regime of the gearboxes of the motor vehicles. From the enumeration of these fundamental and particular problems, it can be concluded that there is an extended basis for the design and operation of summation boxes, allowing a systematic application of deterministic relations but also of a probabilistic approach. However, this approach requires dynamic modelling of the dynamic regimes, the integrated approach in the design calculations of the transient operation stages. Thus, it can be appreciated that there are defined development directions of the field of summing box modelling, allowing designers to obtain quasi-values and also the possibility to bring the design to a near final stage by studying and simulating behaviour in a virtual environment. In particular, modelling is also possible due to the accumulation of data and results in the field of road vehicles and the determinations made when researching the demands of technological drilling equipment. The continuous refinement of the models will lead to a leap in the quality of the methods applied for the optimization of the mobile oil drilling installations [9–11].
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Following the study of the operating behaviour of the summing box, it was concluded that it is necessary to strengthen and stiffen the box housing to cope with the stresses caused by vibrations in operation. As a result, a welded solution of the summing box was proposed where certain elements are reinforced with ribs (Figs.5 and 6).
Fig. 5. New adder box with reinforcements [10]
Fig. 6. New welded version of the adder and distribution gearbox CSD 4000 [10, 11]
3 Experimental Setup For the performance of high-precision experimental investigations (of the order of microns μm), with magnitudes ranging from a few microns to a few cm and also the simultaneous monitoring of relatively large areas of the structures under analysis, the method of Digital Image Correlation (DIC) was used (named too Video Image Correlation (VIC)). The system produced by ISI-Sys GmbH, Kassel, Germany, with software from Correlated Solutions, USA, was used to perform the experiments. In principle, the
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method is based on the use of images recorded simultaneously by two video cameras, which are similar to the human eye which provide a spatial image of the analyzed object (Fig. 7 and 8).
Fig. 7. Basic elements of the measurement system VIC-3D [34, 35].
Fig. 8. Typical image on the monitor
The position of any point of the deformed object can be identified with an accuracy of approx. one micron. After calibrating the two cameras at the level of the plane in which the actual investigation of the body is to be performed, the actual object is placed, and each camera will capture an image, obviously from a different angle of the initial, unsolicited, state of the body. Based on this information obtained by the two cameras, the spatial displacements (so along the three coordinate axes) of all points can be obtained (obviously, with the accuracy of that number of pixels, which predetermined both the size of the base/reference cell, as well as the step of sweeping horizontally and vertically, respectively).
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Thus, after the analysis of the images, the software will offer the possibility to determine the deformations on the three directions, of the strains, all available, either in the form of maps, or having colour code (similar to the images from the numerical analysis by the Finite Element Method). This optical method of investigation is without direct contact with the surface of the analyzed part, i.e. it is not dependent on its material either. Consequently, it does not intervene in the intimate process of changing the field of displacement and deformation of the structure under the action of external influence factors, to which the part would be subjected (for example: the action of mechanical or thermal loads) or internal (for example: changes of crystalline structure etc.). In view of this, it can be noted that the method can be applied with equal ease to both homogeneous and isotropic materials and non-homogeneous, orthotropic or even anisotropic materials. For example, in the case of the investigation of bones (fresh or prepared/preserved), respectively of soft tissues (skin, tendons, etc.) these advantages of the method make it one of great applicability and future. The accuracy of these displacements, as mentioned, is of the order of 1 micron, and the range of displacements is from microns to a few cm. Saving (extracting data in Excel tables) is another feature of the software, which allows for further detailed analysis of the phenomena.
Fig. 9. Old and new adder and distribution box mounted on the stand
Fig. 10. The heavy truck used for the experimental tests
In Figs. 9, 10 are presented the experimental setup and the heavy truck used for tests.
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4 Results Based on the VIC system application methodology, the authors prepared a significant area of interest in the disk (of the connection flange between the loader and the drilling rig). As mentioned above (in the description of the VIC method), this preparation of the area of interest consists of the application of spots of random shape, size and distribution, with the help of water-soluble paint.
Fig. 11. The VIC system
Measurement methodology supposes (Fig. 11): • Before starting the engines (so the drive of the output flange of the adder box) the system is calibrated with the help of a calibration plate appropriate to the observation distance, the size of the spots applied, respectively the optical system used; • A set of images of the output flange in the rest position is also purchased; • Start the engine on the left and purchase a set of images with the help of the two CCD cameras; the acquisition takes place in stroboscopic illumination mode, corresponding to the nominal rotation frequency of the output flange; the acquisition will take place over a sufficiently long period of time, in order to ensure the highlighting of the probable cycles of torsional oscillations of the output shaft, to which the investigated flange is fixed; • Repeat this set of measurements for both the right-hand motor (single-acting) and the two engines running together. • The analysis of the sets of images captured with the help of VIC-3D software is started. In this regard, the following steps will be performed: • The area of interest is fixed/marked, on which the software can perform the analysis of the displacements fields; • A point is identified (previously marked on the analyzed disk) on all left-right image pairs); • A reference point is chosen, from which the software is to perform the analysis; • Choose a SubSet (a Base Cell), respectively a step of scanning the images;
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• The program is run, finally obtaining the displacement vectors of all the midpoints related to the base cells (SubSet); • Select (in this case) a single point P1 on the surface of the area of interest at which it is desired to follow the angular oscillations with respect to its nominal equilibrium position. This implies that, following the stroboscopic monitoring of the image, ie by fixing it in a virtual-static position, the surface points would have static equilibrium positions (so they would not move), if the rotation of the flange (fixed on the output shaft of the adder box) would be uniform. Given that the flange drive system has finite stiffness (think only of the teeth of the sprocket teeth), angular oscillations will be generated at the output shaft, and consequently, was amplified at a point P1 of flange, arranged at a sufficiently large radius R1 of the axis of rotation. From the constructive solution of the summing box the dimensions are known: R = 280/ = 140 mm; R1 = R − (10 + 20/2) = 120 mm Based on the data horizontal displacements, the value u1 was extracted at this point P1 .
Taking into account the values u1 (t), ie the probable law of variation of the displacement u1 as a function of time t, the law according to which the angular oscillations take place is easily determined: u1 (t)[mm] φ(t) [rad ] = arctan R1 [mm] Figures 12, 13, 14 show these probable cycles in the case of single engine starting from the left, right or both engines running simultaneously.
Fig.12. Horizontal displacements u1 (t) of the point P1 in case of single engine operation on the left
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Fig.13. Horizontal displacements u1 (t) of the point P1 in case of single engine operation on the right
Fig.14. Horizontal displacements u1 (t) of the point P1 in the case of the combined operation of the two motors
Careful analysis of these graphs shows that these probable cycles are not uniformly defined. For example, in the case of single-engine operation on the right, the oscillations are closer to small shocks, while in the other two cases, much more periodic effects can be seen. These can be partly explained by the fact that, during operation, the uniformity of the speeds was not perfectly similar in the three cases, there were small deviations in this respect, even if an identical operating regime was imposed (hence a uniform speed). The liaison between the angle deformation φ, the length of the shaft , the torsional stiffness modulus G · It and the corresponding torque Mt are known from the Strength of the Materials: ϕ=
Mt · . G · It
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The stress is: τmax =
Mt , Wt
where, for the shaft of solid circular section we have. Wt = Wp = Ip /R. Obviously, these relationships can be extended to the analysis of phenomena with angular variations φ, respectively an elastic drive system (which exists before the flange is analyzed). Conclusions can be drawn for the more or less uniform operation of the above-mentioned system, depending on the coupling of one or more of the drive motors.
5 Conclusions and Discussions The technical solutions to be developed for the continuous improvement of the product are based on the quality of the perfected mathematical models, based on the following categories of information and methods of information processing: • the results provided by the test on specialized test stands supplemented by the results provided by the operation; • application of new and modern software, allowing the determination in real time of the characteristic parameters of the operation, by taking into account some dimensional characteristics of material and of operation demands. It should be noted that the factor that requires mathematical modelling is the need for the system to be stable when passing through areas where dangerous vibrations occur. Even in cases where the initial design demonstrate that the limit stresses and limit deformations are well supported by the construction, the entry of the adder boxes in an unstable operating area leads to the exit - for a short time - from the hypothesis category. This has been demonstrated during the 10–12 years of manufacturing adder boxes by the appearance of defects that cannot be properly explained, if only the stationary operating mode is considered. The multiple operating conditions that cannot be fully taken into account when applying classical calculus methods, operating conditions particularly characteristic of drilling regimes, demonstrate the need to address, in a complementary way, vibration modelling for transmissions equipped with adder boxes. The special vehicles currently used, starting with forestry vehicles, vehicles intended for the most varied uses in agricultural exploitation as well as for road construction and urban cleaning, are equipped with transmissions designed both to drive power outlets for technological processes and travel to various work points or other displacements also imposed by extensive technological processes in an area of exploitation. However, the highest levels of demand are from oil exploration. Because of this, it can be said that improving the transmission modelling of drilling vehicles - both in terms of sizing at strength and stability of operation during the period of potentially dangerous vibrations
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- is able to provide high-level technical solutions for all other related areas in which dual-role vehicles are used. As a result of the research of the vibration regimes in the transmissions of the drilling vehicles, with punctual application in the field of adder boxes, the following conclusions can be formulated: • The summing boxes are well-individualized assemblies, with a double role, leading to mechanical stresses near the limit states, requiring the elaboration and correlation of mathematical models; • The major changes are obtained by applying the modelling based on the modal analysis. The decision to introduce constructive changes must be taken only after obtaining the results of the modelling and the experimental researches; • The research shows the need to use new experimental methods based on Digital Image Correlation (DIC) also called Video Image Correlation (VIC), a special experimental method efficient and promising. In the complex process of research-development of adder boxes, other important conclusions were obtained, among which we mention: • obtaining the results characteristic of the set of dynamic loads in the periods of operation (in stabilized and resonant regimes) requires the completion of classical strength calculations, usually applied as a single calculation step, and modelling the dynamic vibration regime by the modal method; • the current approach in design departments, in which the designer generates 3D models used to verify the structure of strength by classical deterministic methods, is outdated in the current conditions that require modelling to obtain results close or virtually identical to those in operation; • admitting the particular importance of the intuition that designers usually have, the current economic requirements, subsequent to the fierce competition on the market of all categories of vehicles, require that the launch of functional models be preceded by probabilistic modelling for calculations of strength (having the role of predimensioning) and validated by the dynamic model to support high loads of the adder boxes when the drilling conditions change suddenly; • the use of the modal analysis method must be continuously accompanied by the design solutions that include all the conditions imposed by the functional requirements of the beneficiary.
References 1. Soami, P.: Modeling vibration and noise in a gearbox. Mech. Eng. 140, 22–24 (2018) 2. Vlase, S., Marin, M., Scutaru, M.L., Munteanu, R.: Coupled transverse and torsional vibrations in a mechanical system with two identical beams. AIP Adv. 7, 065301 (2017) 3. Elisabeth, K., John, L., Lars, H., Magnus, K., Jing, L.: Vibration-based condition monitoring of heavy duty machine driveline parts: torque converter, gearbox, axles and bearings. Int. J. Progn. Health Manag. 10, 14 (2019)
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4. Wu, H., Wu, P.B., Xu, K., Li, J.C., Li, F.S.: Research on vibration characteristics and stress analysis of gearbox housing in high-speed trains. IEEE Access 7, 102508–102518 (2019) 5. Batizi, V., Likhachev, D.: Mass-geometric parameters improvement of gearbox by using vibration analysis. In: Proceedings of the 14th International Conference on Vibration Engineering and Technology of Machinery (VETOMAC XIV), Lisbon, Portugal, pp. 10–13; Volume 211, p. AR 06002 (2018) 6. Guo, W., Chen, C., Xiao, N.C.: Dynamic vibration feature analyses for a two-stage planetary gearbox with a varying crack using a rigid-flexible coupled model. J. Intell. Fuzzy Syst. 34, 3869–3880 (2018) 7. Furch, J., Glos, J., Nguyen, T.T.: Modeling and simulation of mechanical gearbox vibrations. In: Proceedings of the International Conference 20th International Scientific Conference on Transport Means, Juodkrante, Lithuania, pp. 133–139 (2016) 8. Száva, I., Botond Pál-Gálfi, B., Ambrus, C., Vlase, S.: Video image correlation-based method used for the study of the torsional vibrations of an adder gearbox. Energies 14(19), 6129 (2021). https://doi.org/10.3390/en14196129 9. Gillich, N., Sirbu, N., Vlase, S., Marin, M.: Study of metallic housing of the adder gearbox to reduce the noise and to improve the design solution. Metals 11(6), 912 (2021). https://doi. org/10.3390/met11060912 10. Ambrus, C.: Ph.D. Thesis, Transilvania University of Brasov (2014) 11. Bencze, A., Scutaru, M.L., Marin, M., Vlase, S., Toderita, A.: Adder box used in the heavy trucks transmission noise reduction. Symmetry 13(11), 2165 (2021). https://doi.org/10.3390/ sym13112165
Length of Contacting Generating Lines Interpreted in the Regular Rectangular Contact Zone of Helical Gears and Zone Dependence of Their Change Zsuzsa Drágár(B)
and László Kamondi
University of Miskolc, 3515 Miskolc-Egyetemváros, Hungary {machdzs,machkl}@uni-miskolc.hu
Abstract. External helical gears play an important role in the development of drive chains. Powertrain gears are expected to show a minimum level of vibration and noise level. The most significant source of vibration excitation and noise is the contact zone. Effects of errors caused by the manufacture, assembly and load are also displayed in the zone of contact. In this article, we will focus our investigation only on error-free gears (free from manufacturing, assembly and deformation errors). The continuous change in the contact zone is the change in the length of the contacting generating lines of the meshing. The contact zone can change its shape from a regular rectangle to a completely general one as a result of modifying the top land surface. We will examine the effect of the parameters and the shape of the contact zone on the total length of the contacting generating lines. In this article, the first step is to study the regular rectangular contact zone of external-external gear pairs. Keywords: Helical gear · Contact zone · Contacting generating line · Contact ratio
1 Characteristics of the Regular Rectangular Contact Zone by Cylindrical Helical Gears Cylindrical helical gears are important components of drive chains, especially vehicle drive chains. An inclined tooth has a number of advantages by making the transmission of rotation more continuous, the load capacity larger, and thus, the teeth becoming stiffer. However, its disadvantage consists in the arising of an axial load as an effect of the power transmission, which requires more care when selecting the adequate bearing solution. The behaviour of the meshing is determined by three very important areas [1, 4, 10]: – accuracy of manufacture and assembly, – deformations caused by the load, – change of geometrical parameters in the contact zone. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 179–189, 2023. https://doi.org/10.1007/978-3-031-15211-5_16
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When examining the effect of the contact zone, it is assumed that the effect of manufacturing and assembly accuracy, as well as deformation under load, is negligible. In reality, however, this is difficult to accept because they are present. At the same time, the effects of defects and disturbances caused by manufacturing, assembly and loading can already be reduced by introducing corrections in machine tools [3]. In this case, the effects mentioned before can be ignored. The meshing, on the other hand, is due to the geometry of the teeth and can only be changed by modifying the geometry. One of the basic features of the meshing is the summed contact ratio (εγ ), which is determined by two components for oblique teeth [1, 2]: – transverse contact ratio (εα ), – axial contact ratio (εβ ). The sum of the two determines the value of the summed contact ratio (εγ = εα + εβ ). The summed contact ratio and its components are characterized by the fact that they contain an integer part and a fractional part. This can be formally specified as follows: – the integer part E(εγ ) and the fractional part D(εγ ) of the summed contact ratio, – the integer part E(εα ) and the fractional part D(εα ) of the transverse contact ratio, – the integer part E(εβ ) and the fractional part D(εβ ) of the axial contact ratio.
Contacting generating line A p bt
O1 r b1 y N1
z
α
px
A’
E’
pbn
A C
1 E’ E
N2
3
Line of action
α
r b2
A’
2 4
O2
E
a.
Contact zone
b.
Fig. 1. Elements of a regular rectangular contact zone for external-external meshing.
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Integer and fraction parts enumerated before will play an important role in the study of the change in length of contacting generating lines. The regular rectangular contact zone can be divided into 4 areas, which is also illustrated in Fig. 1. Areas are defined by integers and fractions. Figure 1.a. refers to the basic geometry of the gear, in Fig. 1.b. shows the division of the contact zone. Axial base pitch (px ) and base pitch in the transverse section (pbt ) play an important role in determining zone parameters. Zone 1 is defined by the integer part of the contact ratios, zone 2 is defined by the integer part of the transverse contact ratio, and the fractional part of the axial contact ratio, and zone 3 is defined by the integer part of the axial contact ratio and the fraction part of the transverse contact ratio, and zone 4 is defined by the fractional part of both contact ratios.
2 The Original Interpretation of the Contact Ratio The contact ratio is an important parameter for the correct operation of the teeth [1, 2]. It is accepted that the contact ratio in a spur gear tooth action should be greater than 1,15. With a helical gear tooth action, this is easy to ensure, but what is meant by this. It can be seen in Fig. 1. that the tooth remains in the contact zone due to its skew so that the summed contact ratio will be larger than that of the straight tooth. Each contact ratio can be determined using known relationships [1], thus. – the transverse contact ratio with the relation εα = AE /pbt , – the axial contact ratio with the relation εβ = b/px .
3 Determining the Length of Contacting Generating Lines in Regular Rectangular Zone Components and the Complete Contact Zone The entire contact zone, as illustrated in Fig. 1, is defined by 4 components. It is characteristic of the meshing that the teeth enter the meshing at point A and leave at point E. A period in the meshing is the base pitch in the normal section (pbn ) path of the contacting generating lines, which is the base pitch in the transverse section (pbt ) distance. It is necessary to determine the length and total length of the contacting generating lines. – fixing the phase boundaries of the meshing, – calculation of the constant part of the total component length – calculation of the variable part of the total component length. 3.1 Determining the Phase Boundaries of Meshing Examining the complete regular rectangular contact zone (AE’EA’) (Fig. 2.a.), it can be noticed that the partial lengths of the contacting generating lines in zones 1–3 can be easily determined without considering the phase boundaries. In the case of zone 4, the length of the contacting generating lines can only be determined on the basis of the
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phase boundaries of the zone traversal. The field detail (E2 EGH) is shown in Fig. 2.b. illustration. In the figure, the continuous red line shows the initial state of the meshing, and the dashed blue line shows the state after a displacement of length x of these, which corresponds to a rotation of angle ϕ1 = x/r1 of the driving part. Examining the zone, it is difficult to determine the effect of the fractional parts of the contact ratios on the phase boundaries of the meshing and the change in the length of the contacting generating lines. The width (b) of the contact zone can be determined by the axial base pitch (px ), as illustrated in Fig. 3. Examining the possible width ranges of the contact zone, the dimensions can be b, b1 , b2 . The width b contains the increment b0 , the width b1 the increment db1 and the width b2 the increment db2 . The width increment b0 is the dividing line. In the case where the width increase is db1 , then subzone 4 does not contain a contacting generating line at the beginning of the meshing, and db2 contains a contacting generating line. The boundary case is determined by the value of b0 .
Fig. 2. Subzones of contact zone and movement of contacting generating lines.
We can write that b0 = (pbt − D(εα ) · pbt )/tgβb
(1)
px = pbt /tgβb
(2)
substituting
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in (1), we get the limit of the increase in width, b0 = (1 − D(εα )) · px
(3)
Fig. 3. Increase in the width of the contact zone, meshing phase boundary.
By tracing it all back to the fractional parts of the contact ratio elements, two conditions can be written. At the beginning of the meshing (point A), there is no contacting generating line in the sub contact zone if (4) D(εα ) + D εβ ≤ 1. There will appear contacting generating line if D(εα ) + D εβ > 1.
(5)
Within these two basic cases, the meshing process has phase boundaries. The phase boundaries are defined by the fractional parts of the sub contact ratios for two basic cases: D(εα ) ≤ D εβ , D(εα ) > D εβ .
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Fig. 4. Meshing phase boundaries (Phase I. and II.)
The examination of the phase boundaries of the meshing should be based on the following: 1. D (εα ) + D εβ ≤ 1, including a. D(εα ) ≤ D εβ , b. D(εα ) > D εβ .
(6)
a. D(εα ) ≤ D εβ , b. D(εα ) > D εβ .
(7)
2. D (εα ) + D εβ > 1, including
The whole subzone test would require quite a lot of space, so the phase boundary test according to 1.a) case is presented in Figs. 4 and 5. For the other cases, the results are shown in determining the variable part of the summed contacting generating lines: relations (14–29). The phase boundaries can be determined by increasing the parameter x. In phase I., x shows increasing contacting generating line (component) length; in phase II., the component length remains constant; in phase III., the component length decreases, and in phase IV., the component leaves meshing. This change will also be seen in the section on calculating the length of the contacting generating line using Figs. 6 and 7.
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Fig. 5. Meshing phase boundaries (Phase III. and IV.)
3.2 The Total Length of the Contacting Generating Lines Interpreted in the Subzones Examining the zone 1 among the subzones, it can be observed from Fig. 2. that it is composed of elementary zones, the size of which is px · pbt . In this elementary zone, the contacting generating line is alone until the meshing starts at point A. After that, a new contacting generating line enters, the length of the original decreases by this amount, so during a period of meshing, the length does not change; it is only divided into two parts. Zone 1 has an elementary zone E(εα )·E εβ , which is derived from the integer values of the contact ratio elements, meaning the repetition of the base pitch in transverse section and axial base pitch, so the total length of the contacting generating lines in the zone is (8) L1 = E(εα ) · E εβ · pbt / sin βb . In zone 2 (Fig. 2), the individual lengths of the contacting generating lines will change, but the summed length will remain unchanged, which can be determined by the following relation (9) L2 = E(εα ) · D εβ · pbt / sin βb . Zone 3 behaves similarly to zone 2, only the zone sizes change, so the total length of the contacting generating line in this zone is (10) L3 = D(εα ) · E εβ · pbt / sin βb . For zone 4, we distinguish two cases depending on whether there is a contacting generating line in the zone at the beginning of the meshing or not: a. D(εα ) + D εβ ≤ 1, then, L4,0 = 0, (11) b. D(εα ) + D εβ > 1, then, L4,0 = D(εα ) + D εβ − 1 · pbt / sin βb .
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3.3 The Constant Part of the Total Length of the Contacting Generating Lines In zones 1, 2, 3 and 4, the total length of the contacting generating lines interpreted together can be written from the relations (8, 9, 10, 11), ie a. in the case of D(εα ) + D εβ ≤ 1 Lmin =
3 i=1
Li = E(ε∝ ) · εβ + D(ε∝ ) · E εβ · pbt / sin βb
(12)
b. in the case of D(εα ) + D εβ > 1 Lmin =
3 i=1
Li
Lmin = E(ε∝ ) · εβ + +D(ε∝ ) · E εβ + D(ε∝ ) + D εβ − 1 · pbt / sin βb (13) which is constant for the whole phase of the meshing and is a minimum value. 3.4 The Variable Part of the Total Length of the Contacting Generating Lines In subzone 4, the length of the contacting generating lines shall be determined for the cases defined relations (6) and (7). Let the subject of our analysis be the relation by D(εα ) + D εβ ≤ 1 and for this, we change the relative size of the fractional parts. Taking into account the phase boundaries of Figs. 4. and 5., the change in the length of the contacting generating lines within the phase boundaries can be determined: a. in case of D(εα ) ≤ D εβ results four subcases, which are follows: a1. if 0 ≤ x < D(εα ) · pbt ⇒ L = x/ sin βb , a2. if D(εα ) · pbt ≤ x < D εβ · pbt then L = D(εα ) · pbt / sin βb ,
(14) (15)
a3. if D εβ · pbt ≤ x < D εβ + D(εα ) · pbt leads to L =
D(εα ) + D(εβ ) · pbt − x / sin βb ,
a4. if D εβ + D(εα ) · pbt ≤ x < pbt then L = 0 b. in case of D(εα ) > D εβ results four subcases as follows: b1. if 0 ≤ x < D εβ · pbt ⇒ L = x/ sin βb , b2. if D εβ · pbt ≤ x < D(εα ) · pbt then L = D εβ · pbt / sin βb ,
(16) (17)
(18) (19)
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b3. ifD(εα ) · pbt ≤ x < D εβ + D(εα ) · pbt leads to L =
D(εα ) + D εβ · pbt − x / sin βb ,
b4. if D εβ + D(εα ) · pbt ≤ x < pbt then L = 0.
(20) (21)
Consider, under the above conditions, the nature of the change in contacting generating lines in a period of meshing for general cases (Fig. 6) and special cases (Fig. 7). a. in case of D(εα ) ≤ D εβ results four subcases, which are as follows: a1. if 0 ≤ x < D(εα ) + D εβ − 1 · pbt ⇒ L = 0,
(22)
a2. if D(εα ) + D εβ − 1 · pbt ≤ x < D(εα ) · pbt then L =
x − D(εα ) + D εβ − 1 · pbt / sin βb ,
a3. if D(εα ) · pbt ≤ x < D εβ · pbt then L = 1 − D εβ · pbt / sin βb , a4. ifD εβ · pbt ≤ x < pbt leads to L = (pbt − x)/ sin βb . b. in the case of D(εα ) > D εβ will result in four subcases as follows: b1. if 0 ≤ x < D(εα ) + D εβ − 1 · pbt ⇒ L = 0, b2. if D(εα ) + D εβ − 1 · pbt ≤ x < D εβ · pbt leads to L = x − D(εα ) + D εβ − 1 · pbt / sin βb , b3. if D εβ · pbt ≤ x < D(εα ) · pbt then L = [1 − D(εα )] · pbt / sin βb , b4. ifD(εα ) · pbt ≤ x < pbt leads to L = (pbt − x)/ sin βb .
(23) (24) (25)
(26)
(27) (28) (29)
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Fig. 6. General cases of the variable part of the summed length of the contacting generating lines.
It can be observed that the choice of the tooth width, which also influences the value of the fractional part of the axial pitch, has an effect on the fluctuation and the nature of the change in the summed length of contacting generating lines. Figure 7.c. shows a special case where the whole process of meshing is free from fluctuations. Recognizing this fact, the basic literature has suggested that the tooth width should be defined as an integer multiple of the axial base pitch [1, 2], thus eliminating torsional excitation in the drive chain. However, it could also be given a solution by keeping the transverse contact ratio always an integer value. It can be seen that fixing the parameters of the basic profile (module, base profile angle, profile displacement), does not, or only in a very bad direction, affect the parameters of the contact zone. Subsequent research [4, 5] has pointed out that there are other sources of excitation from changes in the length of the contacting generating lines, but further analyses of the contact zone may provide an answer.
Fig. 7. Special cases of the variable part of the summed length of the contacting generating lines.
3.5 The Total Length of the Contacting Generating Lines for the Entire Contact Zone As can be seen from the previous analyses, there are certain zone elements in the regular rectangular contact zone that result in constancy, others in continuous but periodic change. The contact zone is computed by the summation of the constant and variable parts in the meshing process.
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4 Conclusions, Results The article pointed out that the total length of the contacting generating lines in the contact zone varies and produces disturbances affecting the correct meshing. Design recommendations based on the literature [1, 2] have shown that torsional excitation can be reduced if the axial contact ratio is an integer value. Our studies showed that the summed length of contacting generating lines becomes subject to analysis and thus, the further recommendations about the constructive parameters of the gear drive have to move in this direction. Figure 4 also confirms our assumption that efforts should be made to select such parameters, which try to set the summed contact ratio to an integer value. The application of the modified contact zone, which can be achieved by modifying the top land surface, as well as the introduction of asymmetrical tooth profile, which is of great importance in the braking drive (retraction), constitute the subject of further research.
References 1. Erney, Gy.: Fogaskerekek. M˝uszaki Könyvkiadó, Budapest (1983) 2. Niemann, G., Winter, H.: Maschinenelemente. Band II. Zweite Auflage. Springer-Verlag, Berlin (1983) 3. Debreczeni, D.: Evolvens, küls˝ofogazatú, hengeres fogaskerékpárok fogt˝o teherbírásának és egyfogpár merevségének geometriai függ˝osége. PhD értekezés, Miskolc (2021) 4. Drágár, Zs., Kamondi, L.: Exciter effects in cylindric helical gear meshing. In: 4th Agria Conference on Innovative Pneumatic Vehicles, ACIPV 2020, GÉP, LXXI. 2020/3–4, pp. 22– 25. GTE, Miskolc (2020) 5. Linke, H., Senf, M.: Breitenlastverteilung bei Verzahnungen-Berechnung und Diskussion von Einflüssen. Maschinenbautechnik Berlin 32(10), 437–444 (1983) 6. Graf, H.-CHR.: Die Entwicklung der Zahnrad-Technik. Springer-Verlag, Berlin (1965) 7. Litvin, F.L.: Gear Geometry and Applied Theory. Prentice Hall, Upper Saddle River, NJ (1994) 8. Drágár, Zs., Kamondi, L.: A fogalak szerepe a fogazott elempárokat tartalmazó hajtásláncokban. In: 26th International Conference on Mechanical Engineering, OGÉT 2018, pp. 232–235. EMT, Kolozsvár (2018) 9. Drágár, Zs., Kamondi, L.: Tooth root stress calculation for non-symmetric tooth shape. In: Géptervez˝ok és Termékfejleszt˝ok XXIX. Szemináriuma, GÉP, LXIV. 2013/6, pp. 25–28. GTE, Miskolc (2013) 10. Kapelevich, A.L.: Direct Gear Design. CRC Press, Boca Raton, FL (2013)
Diagnostic and Prognostic Strategies for Monitoring of Diesel Engines’ Technical Conditions Hla Gharib(B)
and György Kovács
Faculty of Mechanical Engineering and Informatics,Institute of Manufacturing Science, University of Miskolc, 3515 Miskolc, Egyetemváros, Hungary [email protected], [email protected]
Abstract. Diagnostic strategies play a vital role in achieving high competitiveness, better productivity and improving workplace safety because the technical diagnoses provide solutions for all issues related to the following tasks: determination of the actual technical conditions; detection of the defects; isolation and identification of the component that failed; make the appropriate decision in order to maintain high reliability of machines without having to disassemble any of its parts and explain the malfunction. Furthermore, the prognostic aspect is a very challenging goal in machinery to improve the performance, fixing the error before the malfunction occurs, assessing the condition of the equipment, and whether maintenance is necessary, which will give more specialised recommendations for the operators. The article’s objective is to elaborate the conception of a diagnostic system for marine diesel engines in real-time conditions and find the most proper strategy to develop a diagnostic system that will increase the reliability of machines and reach maximum efficiency. In the first part of the study, the relevant literature is introduced to diagnose machine malfunctions which is vast and varied due to the diversity of systems and components. In the next part of the article, the possibilities of using Artificial Intelligence techniques are described to develop a significantly improved diagnostic system convenient for ship’s operating systems. After that, the determination of diagnostic parameters in diesel engines is described to simplify the complexity and instability of the whole system. In addition, the causes that lead to damages and failures in main engines are introduced. Finally, some perspectives on important challenges and future directions are discussed and highlighted for future research. The main added value of the study is that the conception of the most proper diagnostic and prognostic strategy for the diagnostic system was elaborated, especially for the marine diesel engines, to increase the reliability and efficiency of the engines. Keywords: Diagnostic and Prognostic strategies · Expert systems · Marine diesel engines
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 190–199, 2023. https://doi.org/10.1007/978-3-031-15211-5_17
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1 Introduction Condition diagnostic and prognostic strategies have played a vital role in developing maintenance systems for several machines because of their impact on downtime, maintenance cost and machine availability and reliability levels. Among different process monitoring and supervision techniques, fault diagnosis and prognosis are significantly critical methods for accomplishing this task since most industries work on improving their process performance by a higher level of fault diagnosis and prognosis capability [1]. Park et al. divided machine diagnosis systems into three approaches: (a) ModelBased Approaches, (b) Knowledge-Based Approaches, (c) Data-Driven Approaches [1]. Many relevant papers in the literature were reviewed in this research field. ModelBased Approaches use physical and mathematical knowledge of the system, and modelled conditions, that compute residuals and then use those residuals to detect if a fault has occurred in the system (see Fig. 1).
Fig. 1. General flowchart of model-based approach [2].
Finding the most accurate model is the critical task in Model-Based Approaches. The classification of the Model-Based Approaches can be seen in Table 1. Table 1. Classification of model-based approaches for fault diagnosis and detect (FDD) methods [1]. Quantitative model-based method
Qualitative model-based method
Process history-based method*
Observer-based
Causal model
Expert systems
Parity equation-based
Hierarchical model
Trend analysis
Kalman filter-based
NN, PCA, PLS
*Process History-Based Method also can be divided into quantitative model-based and qualitative model-based methods depending on the data type
Tung et al. accomplish high efficiency by focusing on the model’s accuracy and correctness. However, due to the uncertainties and complexities of modern industrial systems, it is very difficult or impossible to build because of the difficulty of dealing with them or even building an appropriate model. These non-linear mechanisms are difficult to implement using models and are subject to many linear approximations and corrections, resulting in significant errors [2]. On the other hand, others study Data-Driven Approaches necessities to process information that can be extracted directly from huge amounts of recorded data, and they have low design efforts compared to Model-Based
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Approaches. The most known multivariate statistical approaches for process monitoring and fault detection are Principal Component Analysis (PCA), Partial Least Squares (PLS) and Fisher Discriminant Analysis (FDA) [3–5]. Although it does not require the assumption of empirical estimation of physics parameters. It can contain data overload, low-quality data, data misinterpretation [2]. Others build hybrid systems by combining Model-Based and Data-Driven Approaches to diagnose the airflow in an internal combustion engine that accomplishes better identifying faults [6]. Finally, number of papers focus on Knowledge-Based Systems. It is often difficult to efficiently implement fault diagnosis procedures without knowledge and fully understand the machine operation process. So, it would be necessary to consider the knowledge or rules and the process data when we develop or apply FDD methods to various industrial processes [1]. The cause-effect analysis approach is based on the fault model, the Expert Systems based on human reasoning, Neural Network (NN) approaches based on the relationship between the faults and process variables, and a combination of NN and Fuzzy Logic are known as the representative Knowledge-Based FDD methods [1, 7]. Moreover, to make the best benefit of the acquired knowledge, a combination of different techniques can be mixed, such as Fuzzy Logic and Neural Networks, to build a diagnostic system and the critical feature of these systems is that they use input-output patterns to adjust the fuzzy rules sets inside the model [8]. Others used Fuzzy Expert Systems for condition monitoring of power transformers to improve an electrical power system [9]. This system provides thorough information and consultation in an interactive and user-friendly approach. Moreover, it is open to use by all (unless there is authorisation), even by geographically separated users [9, 10]. The goal of the article is to elaborate the conception of a diagnostic system for marine diesel engines in real-time conditions and find the most proper strategy to develop a diagnostic system that will increase the reliability of machines and reach maximum efficiency. The study aims to review the techniques of using the Knowledge-Based Approaches and selecting diagnostic parameters in main marine engines that can be monitored to prevent damages and failure and improve performance. At first the article describes a review of Artificial Intelligence techniques for using Expert Systems and Fuzzy Expert Systems in diagnosing systems in Sect. 2. After it a general explanation about fault prognosis directions is introduced in Sect. 3. Then a brief description of marine diesel engines’ different conditions is discussed in Sect. 4. Finally, a conception of the diagnosis system for marine engines is introduced in Sects. 5–6. The significance of the article is that the conception of the adequate diagnostic and prognostic strategy for the diagnostic system was elaborated, especially for the marine diesel engines in order to increase the reliability and efficiency of the engines.
2 Fuzzy Expert Systems Fuzzy Systems and Expert Systems are two essential Artificial Intelligence (AI) techniques, and they have many applications in various fields. An expert is a person with a high level of knowledge or skill relating to a particular subject or activity [Cambridge English Dictionary]. An Expert System is an interactive system that simulates the behavior and ability of one or more experts within a specific field of knowledge [2]. Moreover,
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Fuzzy Logic is intended to model logical reasoning with vague and imprecise statements [Stanford Encyclopedia of Philosophy], and Fuzzy Expert Systems are oriented towards handling uncertainties generated by incomplete or/and imprecise information and use it in the domains where the input variables do not have fixed values [11]. Using human knowledge in a specific field is usually inaccurate and unorganised, and therefore the process of reasoning this information is often inaccurate. Thus, logical processing of knowledge requires a solution to this problem to analyse and transfer this type of knowledge into certain conclusions and advice. Building a Fuzzy Expert System to diagnose the technical condition in ships’ main engine has many obstacles related to the difficulty of maintaining a stable work system, and the effects of diagnostic parameters by external factors and operational parameters more than structural parameters, in addition to the availability and cooperation of experts. Although the studies related to the development and use of Expert Systems and Fuzzy Systems are many and varied, most of these studies focused on their use in the medical field (21%). At the same time, the remaining application domains had less luck, as (7%) of the developed Fuzzy Systems were in fault diagnosis and (4%) on the domain of performance evaluation [12]. For this reason, many tools may need some modification to achieve more efficiency and convenience for machines’ technical diagnosis. • The main stages of a Fuzzy Expert System are the following: Fuzzification; Inference Engine; Fuzzy Knowledge Base; Defuzzification; Output [11]. 1. Fuzzification: In this part, the input variables were compared to obtain values for each linguistic label. 2. Inference Engine: Combine the values using multiplications or min. Function, to get the degree of belief in each rule, then generate the result for each rule depending on the belief degree. 3. Fuzzy Knowledge Base: Contains all the fuzzy set rules. 4. Defuzzification: In this part, the results are aggregated to produce output. 5. Output: Final values that the system gives as a decision. Figure 2 shows those components of a Fuzzy Expert System.
3 Machines’ Faults Prognosis Fault prognosis in the literature is much like those for fault diagnosis, and the most obvious and typically used prognosis is to use the given current and past machine conditions to predict the lifespan of components before a failure occurs [2]. Machines components’ lifetime is usually called Remaining Useful Life (RUL). Xiongzi et al. defined RUL as the period from the current time to end of useful life for a component [13]. Figure 3 shows the variation in Component Health Index (CHI) by time, this index represents the overall condition of the component, usually normalised between 0 and 1. Over time, it decreases according to many factors such as loading, operating condition, maintenance, etc. (See Fig. 3). An accurate prediction for RUL for manufactured systems is a vital factor to achieve: Sustainable work system; Improve decision making; Enhance reliability and availability
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Fig. 2. Components of a fuzzy expert system [11].
Fig. 3. Component health index against time [13].
by decreasing maintenance cost and time. The methodologies for RUL prediction fall into four main categories: Analytical-Based, Model-Based, Knowledge-Based and HybridBased (See Fig. 4).
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Fig. 4. Classification of RUL predictions methodologies [14].
Moreover, the Multi-Steps ahead prediction techniques to a sequence of values in a time series considered as a challenging task because of the growing uncertainties from many sources [2]. Liu et al. used a Neuro-Fuzzy System for the Multi-Steps model to predict dynamic system behaviour [15]. Furthermore, prognostic systems must consider different variables, including external unrelated parameter effects, which can reduce the accuracy of the prediction output in real-time operation [2].
4 Brief Description of Marine Diesel Engines’ Different Malfunctions Fault and performance monitoring systems have a complexity problem in the model, which require dividing the engine system into multiple subsystems to improve the efficiency of the monitoring process. Although it is better to have one model to describe and control the engine, which considers the overlap between different subsystems’ variables, it is more convenient to study it separately, and then study the effects between the different systems during the operation process. Regarding ships’ engines, it is often divided into five subsystems (Fuel, Cooling, Lubrication, Exhaust, Air). Some of those subsystems most common operation malfunction and their possible causes are listed below to illustrate the local and global variables effects. 4.1 Some Operating Malfunctions and their Possible Causes 1. High-pressure pump fails to reach the required fuels’ pressure: The possible causes are: • Incorrect calibration. • Unsealed in the pump tubes (may lead to fuel leakage and contamination in the camshaft oil). • Corrosion.
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2. Exhaust valve breakdown: The possible causes are: • Increase in gas temperature (due to the difference in injection timing; poor cooling; poor lubrication). • Incomplete combustion: This led to solid particles that settled between the valve stem and the seat. By repeatedly knocking the valve on the seat and the flow of these atoms, surface cracks were formed that increased with time until damage within a short period. • Design reasons (incorrect clearances) lead to high gas temperature. • Weak cooling may lead to high temperatures, which leads to a chemical reaction between the steel alloy and the metal compound that it contains. Vanadium pentoxide and sodium chloride formed due to heavy fuel combustion, causing the valve and base surfaces to corrode. • Long-term operation at low loads exposes the valve stem and bushing to corrosion due to Sulphur oxides that interact with the valve parts. 3. Exhaust gases high temperature: The possible causes are: • If sparks from the funnel follow the increase, it is caused by an unsealed valve or unburned carbon. • In the absence of sparks: the increase is caused by (high pressure of the gases leaving the cylinder; poor cooling; overloading; unsealed valve; lack of air; injector defect problem with the air cooler). 4. Insufficient cooling leading to overheating: The possible causes are: • • • • • • •
Low cooling liquid level (internal and external leaks). Blockage in the temperature regulator (low heat exchange). Residual in the cylinder shirt (low heat exchange). Cooling pump failure (insufficient fluid flow). Relief valves failed. Exhaust gases/air entering the cooling circuit (fluid overflow). Unclean filters (coolant temperature too high). Note: Low coolant level may be due to (leak) or as a result (vaporisation) of high coolant temperature.
5. Increase in the consumption of lubricating oil: The possible causes are:
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Oil entering the combustion room. Change to mist or steam and exit from the crankcase. Leaks from joints or cracks. Decomposition or oxidation. Oil decomposition at high temperatures is responsible for some consumption but in a small proportion.
5 Diagnosing Parameters Selection The stronger the diagnostic parameter responds to a change in the structural parameter, the greater its diagnostic sensitivity is. Thus, the early development of a given fault can be detected based on deviation analysis of this parameter from its reference value [16]. However, building a relatively simple model is also essential; thus, diagnosing parameters should be minimised as possible with correct faults recognition [16, 17]. Several points should be taken into consideration when searching for the most sensitive diagnosing parameters: Provide as much diagnostic information as possible; Practical and on-board convenient that does not require any further analysis; Does not require to add any new sensors or measuring devices; In addition to change sensitivity.
6 The Conception of a Newly Elaborated Diagnosis System for Marine Engines Nowadays, the overall direction of applying diagnostic methods emphasises working with well-studied plans for technical operating and maintenance. Those methods aim to reach ideal periodic times for technical service and to increase the quality of their application, which will positively affect the technical service. Technical diagnosis solves the issue of monitoring the current technical condition, allowing detection and monitoring of deteriorated malfunctions, and explaining the causes of the emergence of a particular fault. In this way, it is possible to predict and prevent its appearance. Depending on Knowledge-Based System developed after discussing the number of collaborated fields’ experts, who had been working on ships for at least five years as a chief engineer, an Expert System had been created by the following steps: 1. After discussing with the fields’ experts about the most sensitive diagnosing parameters for each engine subsystem, we reached the following results: • Fuel system: From a performance monitoring point of view, it is essential to know the technical condition of the fuel system because reduced engine efficiency due to a fault in the combustion process will result in increased fuel consumption at a constant load. (The suggested diagnosing parameters are: Fuel pressure after feeding pump; Fuel combustion pressure; Injection pressure). • Air charging system: According to the experts, most system failures are caused by contamination of its parts, so the air pressure after the turbocharger and air temperature aftercooler are the appropriate parameters to give as much diagnostic information as possible.
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• Exhaust system: Practically, physical analysis is more convenient and easier to process for an on-board diagnostic system. (The suggested diagnosing parameters are: Gases temperature and color). • Lubrication system: several parameters must be checked: Oil pump pressure, oil temperature, oil level) in addition to considering separated lubrication systems. Cooling system: The cooling system’s cooling liquid pressure linked with its temperature gives the necessary diagnosing information. For each subsystem, the expert determines a set of rules related to the changes in those main parameters, each one of those rules is linked to another sub parameter system changes to identify the condition with a more precise output. 2. After the Expert System is created, the experts provide general rules and principles to deal with the different conditions. 3. When it is linked to the ship operation system, the user (engineer or technician) should adjust the diagnosing parameters’ normal operating ranges to accommodate the engine, increasing the initial system efficiency. 4. After running the Expert System, the rules can be adjusted, and the user can add new rules according to operation readings. This technique will improve the system reliability and efficiency and make it the engine specific diagnosing system. The experts’ knowledge covers all the conditions in fuzzy modifiable ranges of the diagnosing parameters, which occur between the normal and failures operation conditions. Then organised into sets on rules and facts. Moreover, the final user being able to change this ranges, increase the Expert System reliability and flexibility. The developed new Expert System works as an expert consulter to the final user by explaining the different changes in parameters and its effect on the engine’s performance. The application of the Expert System can reduce the maintenance and repair time during the operation of the engine; therefore, engines’ efficiency and reliability can be improved.
7 Conclusions Using the appropriate diagnosing strategies and Knowledge-Based Methods to build a diagnostic Expert System for the ships’ main engines requires three essential factors. Firstly, selecting the diagnosing parameters (the most sensitive), which detect early deterioration of malfunctions. Secondly, expert fields can link the changes in the main parameters and the sub ones. Thirdly, qualified user on-board with prior knowledge on adding and adjusting to the end system. The main added value of the article is that the conception of the most adequate diagnostic and prognostic strategy for the diagnostic system was elaborated, especially for the marine diesel engines in order to increase the reliability and efficiency of the engines. Building an Expert system by depending on the knowledge extracted from the fields’ experts and organised in sets of rules (promises and results). This system works as a professional consulter to operators, saving all the possible conditions and the current ones, being able to add and adjust to the knowledge base to specifying this resulting expert system, all those factors improve the operation process and save important service time.
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In the future research the Artificial Intelligence techniques can be used to establish more reliable and accurate diagnosing systems in the marine operation systems with a more significant knowledge base and optimise the performance of the initial system. Acknowledgements. The research was supported by the Hungarian National Research, Development, and Innovation Office - NKFIH under the project number K 134358.
References 1. Park, Y., Fan, S., Hsu, C.: A review on fault detection and process diagnostics. Ind. Process. Process. 8(1123), 1–26 (2020) 2. Tung, T., Yang, B.: Machine fault diagnosis and prognosis: the state of the art. Int. J. Fluid Mach. Syst. 2(1), 61–71 (2009) 3. Yin, S., Member, Ding, S., Xie, X., Luo, H.: A review on basic data-driven approaches for industrial process monitoring. IEEE Trans. Ind. Electr. 61(11), 6418–6427 (2014) 4. Babic, M., et al.: A novel approach for pattern recognition by using graph theory and its application in mechanical engineering. Acad. J. Manufact. Eng. 19(3), 5–10 (2021) 5. Kun-Bodnár, K., Kundrák, J., Maros, Z.: Machining of rotationally symmetric parts with abrasive waterjet. In: IOP Conference Series: Materials Science and Engineering, vol. 448, p. 012053 (2018) 6. Jung, D., Yew Ng, K., Frisk, E., Krysander, M.: A combined diagnosis system design using model-based and data-driven methods. In: 3rd Conference on Control and Fault-Tolerant Systems, pp. 1–6 (2016) 7. Wang, H., Chai, T., Ding, J., Brown, M.: Data-driven fault diagnosis and fault-tolerant control some advances and possible new directions. Acta Autom. Sin. 35(6), 739–747 (2009) 8. Viharos, Z.J., Kis K.B.: Survey on neuro-fuzzy systems and their applications in technical diagnostics and measurement, In: 13th IMEKO TC10 Workshop on Technical Diagnostics, pp. 126–136 (2015) 9. Husain, Z.: Fuzzy logic expert system for incipient fault diagnosis of power transformers. Int. J. Electr. Eng. Inform. 10(2), 300–317 (2018) 10. Patel, M., Virparia, P., Patel, D.: Web-based fuzzy expert system and its applications – a survey. Int. J. Appl. Inf. Syst. 1(7), 11–15 (2012) 11. Thakera, S., Nagorib, V.: Analysis of fuzzification process in fuzzy expert system. Procedia Comput. Sci. 132, 1308–1316 (2018) 12. Tavana, M., Hajipour, V.: A practical review and taxonomy of fuzzy expert systems methods and applications. Benchmark. Int. J. 27(1), 1–7 (2019) 13. Xiongzi, C., Jinsong, Y., Diyin, T., Yingxun, W.: Remaining useful life prognostic estimation for aircraft subsystems or components: review. In: 10th IEEE International Conference on Electronic Measurement & Instruments, pp. 94–98 (2011) 14. Okoh, C., Roy, R., Mehnen, J., Redding, L.: Overview of remaining useful life prediction techniques in through-life engineering services. In: 6th CIRP Conference on Industrial Product-Service Systems, pp. 158–163 (2014) 15. Liu, J., Wang, W., Golnaraghi, F.: A multi-step predictor with a variable input pattern for system state forecasting. Mech. Syst. Signal Process. 23(5), 1586–1599 (2009) 16. Witkowski, K.: The correct selection of diagnostic parameters of marine diesel engine and their minimisation of as a necessary action in the formation of diagnostic algorithm. J. KONES Powertrain Transp. 24(2), 287–292 (2017) 17. Zadr˛ag, R., Bogdanowicz, A.: Identification of indicators sensitivity of emissions as a diagnostic parameter during the dynamic process of marine diesel engine. Diagnostyka 20(3), 79–86 (2019)
Development of a Knowledge-Based System for Diagnosing of Diesel Engines Hla Gharib(B)
and György Kovács
Institute of Manufacturing Science, University of Miskolc, Faculty of Mechanical Engineering and Informatics, H-3515, Miskolc, Egyetemváros, Hungary [email protected], [email protected]
Abstract. Knowledge-Based Systems apply Artificial Intelligence techniques to solve difficult problems in complex systems that well-trained experts can only manage. These systems can support the decision-making of inexperienced people with the necessary tools to do work that requires high expertise. These systems depend on three main resources: human expertise, experiments, and previous observations. Furthermore, Knowledge-Based Systems reduce the complexity of operation and implementation, making them flexible and easy to understand. The combination of knowledge-based diagnostic methods with recording and monitoring of operating variables; furthermore, adding them to the knowledge base improves the efficiency and reliability of detecting the machine’s behaviour and the effectiveness of the whole system. The aim of the study is to develop a Knowledge-Based System including five stages that could be improved separately to optimize the operation of the machines. In addition, this system allows the evaluation, updating, modification, and integration of the rules in the knowledge base, which results in efficiency improvement of the machines’ operation. Firstly, this paper briefly introduces the different methods to analyze knowledge obtained from human experts in the most effective way. We aimed to maintain the quality of the information and define the effect that experts and the types of machines being understudied on selecting the most suitable method to deal with this information to form the final knowledge base. After it, we reviewed the theories that deal with uncertain and qualitative information and the most appropriate theory for the Knowledge-Based System. Finally, different directions in software tools for Expert Systems development were reviewed. The main added value of the study is the development of the new Knowledge-Based System, which can be handled more flexibly by inexperienced users and increase the reliability and efficiency of the marine diesel engines. Keywords: Knowledge-based expert systems · Diagnosis system · Marine diesel engines
1 Introduction Knowledge-Based Expert Systems (KBES) or Expert Systems (ES) are one of the most important applications of Artificial Intelligence (AI) in problem-solving and decision © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 200–211, 2023. https://doi.org/10.1007/978-3-031-15211-5_18
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making [1, 2]. It is an interactive system that simulates the behaviour and ability of one or more experts within a particular field of knowledge. Knowledge-Based Expert Systems help the computer to aid in non-deterministic and mismanaged problem-solving activities. Those systems contain rules, facts, and guidelines for applying them. An efficient shell (developed software used to generate KBES) is a highly recommended in developing the KBES. It will have facilities for information implementation and interaction between the user and the network of experts. The base units of shells contain a model for information acquisition, inference mechanism, and user interface [1–3]. There are several ways of representing Knowledge: Semantic Networks, ObjectAttribute-Value triplets, Rules, Frames, Logic Representation, Blackboard Architecture. The most common form of representing knowledge currently used is a Rule-Based System [1, 4]. In a Rule-Based System the Knowledge is given in “if-then” rules. Generally, it contains two parts: the condition (premise) and the conclusion. The key reasons for writing Rule-Based Expert Systems are that it is intuitive for human expert to communicate their field experience. Rules are a standardized way of expressing information without complex constructs in programming [1]. Therefore, the knowledge will be coded in a simple, extensible, and modifiable form. Many papers were reviewed in the literature on this topic. Several authors discuss Expert Systems designing architecture by explaining the role of the Backward Chaining method, Forward Chaining method, Rule-Value method as three methods involved in solving these problems to differentiate the Knowledge-Based Expert System programming from normal programming [1, 5]. Tavana et al. reviewed the methods and applications in Fuzzy Expert Systems, which could be useful for practising managers developing Expert Systems under uncertainty [2]. Xu found that knowledge of fault diagnosis includes structural knowledge, manufacturing Knowledge, operational Knowledge, etc., and there are differences in the characteristics and expressions of each knowledge [4]. Therefore, constructing a knowledge representation method compatible with basic knowledge, supporting knowledge, supporting algorithm knowledge, and process knowledge is an important issue to consider [4]. Furthermore, in Expert Systems the unavoidable aspect in dealing with human knowledge is the uncertainty of those types of information. Several approaches were developed to deal with it, such as Classical Probability Theory and Conditional Probabilities, Certainty Factors, Confidence Levels, Fuzzy Set Theory, and many more approaches that consider all relevant factors to provide the best possible suggestion rather than exact solution [5–7]. All the approaches have their strength and weakness. There is not an ideal approach for all expert systems. It depends on the understudied case and the uncertainty type [8–11]. After building the knowledge base, the selection of the software tool process began. Many tools and comparative studies were presented to assist and simplify the process of programming a good, readable, and does well what is intended to do to create the required Expert System [12–14]. Sajja et al. presented a review on the two methods on Expert Systems programming: to code it as a normal computer program for each domain using programming languages or to use expert system shells and the key features in selecting the suitable method to be considered, besides support knowledge acquisition
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approach and representational features. There are other features such as price, flexibility, ease of use, and availability which need to be considered before the final selection [3]. The main aim of the study is to explore each developing phase separately and improve it to produce and optimize the operation of the engines. In addition, to provide the feature of evaluation, updating, modification, and integration of the rules in the knowledge base during the operation, which results in constant increasing in the machines’ efficiency. This article contains an overview of the Expert System structure, specifying its development stages in Sect. 2, uncertain information analyzing methods for the knowledge provided by the fields’ experts in Sect. 3, and the software tools used for Expert Systems in Sect. 4. Further, it describes an Expert System’s case study for marine diesel engines’ technical diagnosis in Sect. 5. The primary significance of the article is the introduction of a newly developed Knowledge-Based System. This new Diagnosing System can be provided for the inexperienced users on ships which could replace the necessary physical existence of the fields’ expert in workplace. This tool can improve the flexibility and efficiency of marine diesel engine’s operation and maintenance.
2 Expert SYSTem’s Structure and Development Stages The main components of a Knowledge-Based Expert System are the following: Knowledge base, which includes the sets of rules and facts; Inference engine (software program); Explanation (output) of the suggested decision; User Interface; and the unique feature of Knowledge-Based Expert System represented by the ability to modify and add new rules and facts to the knowledge base which may be either manually or self-updated (Machine Learning) [15, 16]. These components are shown in Fig. 1.
Fig. 1. The architecture of a knowledge-based system [3].
Many stages must be fulfilled to develop an Expert System that monitors machine performances. After identifying the normal and abnormal conditions, the knowledge acquisition stage begins by identifying the fundamental concepts from the experts who describe the machine condition and provide experience advice with as much explanation from the expert as possible [17–20]. Then the second stage is about creating the concepts by the knowledge engineer of the conditions to describe the relationships between the objects and processes in the machine. After that, in a rule-based system, rules will be designed to represent the knowledge in the domain fields. This is a critical part of the development process because many experts can explain what they do but not why; therefore, one of the primary responsibilities is to analyze example situations and filter in from those examples a set of rules which describe expert’s knowledge.
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The next stage is a formalization of the concepts into the software, selected for system development to implement a ‘first-pass’ (prototype) of the Expert System. Finally, the validation and verification stage to test the Expert System quality. It could be applied in two aspects the programing process completeness (verification) and the systems’ output in comparison to normal results (validation). The main purpose is to identify the weaknesses in the structure and implementation of the system and to make the appropriate corrections. This process is not completed until it indicates that the solutions suggested by the Expert System are consistent as valid as those provided by a human domain expert.
3 Uncertain Information Analysis Analyzing information, linking experts’ opinions to common rules, and weighting them with relevant factors are critical in building Expert Systems. Many researchers have developed several theories on how to transform this knowledge held by experts into a knowledge base that can be processed statistically and numerically and build results and recommendations on them [17]. There are several techniques to process uncertainty in Expert Systems, aiming to help the decision making of the end-user. First, a brief explanation of the concept of (Uncertainty) and its characteristics will be presented. In general, any information considers uncertain when it has one or more of the following characteristics [6]: • • • • •
Ambiguity or imprecise language. Incomplete information. Measurement errors (fatal errors - regular errors - random errors). Errors in the inference mechanism. Experts’ disagreement.
3.1 Uncertainty Management Approaches Some of the approaches to deal with uncertainty are the following: 1. Probability Theory and Bayesian Approach: Although Probability Theory is the oldest and best way to deal with inaccurate information and random data, statistical information is not always available in Expert Systems. In addition, conditional independence of evidence cannot be assumed [8, 19]. Bayesians’ Approach is more appropriate if the data is more reliable, and the knowledge engineer can have serious analytical discussions of decisions with experts. With large knowledge bases, Bayesians’ Approach becomes very complex [7]. 2. Certainty Factors Theory: Certainty Theory is a framework used to represent and work with degrees of belief in Expert Systems (known as an alternative to the Bayesians’ Approach). Certainty Factor is a number that measures the degree of confidence of the expert. For example, it can take values between + 1 (Definitely true) and the value -1 (Definitely false). This theory deals with the evidence acquired gradually (it works with partial input also) and hypotheses and evidence with different
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degrees of Certainty Factors. It is used in cases where the probabilities are unknown, difficult to obtain, or expensive to access [6]. 3. Fuzzy Set Logic/Theory: Fuzzy Logic is a logic to describe fuzziness; it is based on the idea that all statements have a degree of validation or not. When dealing with Expert Systems, two types of the fuzzy set can be recognized. Type 1: the expert should determine the degree of belief in the rule. Type 2: the expert determines a range for his rules’ belief degree. Using Fuzzy Logic in the Expert System depends on the nature of the problem and experts’ concepts in representing their knowledge. Selecting an approach to deal with uncertainty in the Expert System depends on many crucial factors related to the purpose of the Expert System. However, many uncertainty approaches are unused in this application. Classical Probability, Bayesians’ Theory, Dempster–Shafer Theory, and fuzzy set are presented for handling uncertainty, but these models cannot express different uncertainties. Bayesian works well where accurate statistical data are obtainable, which may not be available. Dempster–Shafer presents belief functions that permit the experts to use their knowledge to bind the sets of rules when boundaries are not available, but it does not give direction on how to obtain these rules. Therefore, there are not any Expert Systems built using this theory. Certainty Factor is as type 1 Fuzzy set does not express the degree of non-membership and it has not a solution when experts have a hesitancy. This theory describes vagueness but not imprecision, ambiguity, and inconsistency [8]. It has been proven that hybrid methods have more potential to solve different weaknesses and make the most benefits of them [7].
4 Software Tools Used for Expert Systems Expert System tool is a set of software instructions and utilities taken to be a software package constructed to assist the development of Knowledge-Based Systems and simplify the job [3]. Those tools minimize the risks of mistakes made by the knowledge engineer and final user and save time and effort. Many factors affect the selection process of a proper method for developing an Expert System, like the knowledge characteristics, inference mechanism, the time and cost of the project, availability, and required performance. General programing languages can be used to develop Expert Systems using both Front-End Software (CLIPS, PROLOG, VB.NE, etc.) and Back-End Software (MySQL, Ms-Access, Oracle, etc.) [13]. In addition to Experts’ systems shells like Expert System Shell for text Animation (ESTA), LISP. There are approximately 200 Knowledge-Based System tools [3]. For this study, a further explanation of CLIPS will be introduced. 4.1 C Language Integrated Production Systems (CLIPS) CLIPS is a public domain software tool for implementing Expert Systems. It was developed at NASA. It is a rule-based programming language used for creating Expert Systems. CLIPS was written in C language; furthermore, can be installed and used on various platforms [13]. It is an easy language to understand with basic principles capable of creating complex systems.
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CLIPS has undergone continuous development and improvement since its initial release. For a variety of reasons, this software tool was considered for the development of the new Knowledge-Based System. To begin, CLIPS’ Data-Driven nature is an efficient tool for the case study, as the experts in the field depend on the Forward Chaining approach. Second, CLIPS offers high-probability, low-cost (open source licensed) and easy access to most platforms. Third, Clips features an easy code language that makes developing a Knowledge-Based System easier and faster. Fourth, CLIPS offers an effective rule syntax and an interactive environment.
5 Development of a Knowledge-Based System for Diagnosing of Marine Diesel Engines – Theoretical Background A new Knowledge-Based System was developed for diagnosing and identifying the technical condition of the main engine in ships, which would provide the operator with the essential knowledge to make ideal maintenance and repair time decisions. These systems enable the user to identify the defect components and make the necessary changes before the failure occurs [14]. CLIPS was used to assist in developing the Knowledge-Based System by providing a complete structure for the human-made rules. In this software, it can be described in detail or give sets of rules, on how to behave according to set of facts. These rules are referred to as ‘If statements’ as they tend to follow the line of ‘IF X happens THEN do Y’. The ability to link each fact or set of facts to different scenarios provides a more accurate and effective system. For diesel engines, it was easier to divide the engine operation system into multiple subsystems. Several fluids are moving into the engine and their characteristics can be used to define the structural condition of the engine and evaluate its performance. Usually, a diesel engine is divided into five subsystems (Fuel, Cooling, Lubrication, Exhausting, Air). Each subsystem has a set of rules that describe the possible scenarios for each abnormal change in specific parameters. After collecting the information from the marine expert about the different technical conditions of the engine. The fields’ expert explains his knowledge in this field and the methods he uses in monitoring the condition and solving various issues. The information analyzing process began. Expert opinions are considered uncertain because they were obtained from a human being subject to expected influences on his thinking methodology. Large Expert Systems usually involve the knowledge of many experts, and unfortunately, experts may have conflicting opinions, producing contradictory rules. In this study, Certainty Factors Theory was more convenient to apply to solve this inconsistency. Certainty Factors Theory is an alternative approach to Bayesian reasoning where solid statistical data is unavailable, or evidence independence cannot be assumed, and its calculus is based on human expert heuristics [11]. Those Certainty Factors show the measure of certainty of a fact or rule [21]. Because of its simplicity and lack of assumptions, the Certainty Factor technique has been frequently used in uncertain rule-based inference [22]. The knowledge engineer weighted each expert’s opinion individually to obtain composite results.
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• Certainty Factors can be calculated using the following equations [11, 21]: ⎧ Cf 1 + Cf 2 · 1 − Cf 1 ifCf 1 > 0 and Cf 2 > 0 ⎪ ⎨ Cf 1 +Cf 2 if Cf 1 < 0 or Cf 2 < 0 Cf Cf 1 , Cf 2 = 1−min[|Cf 1 |,|Cf 2 |] ⎪ ⎩ Cf 1 + Cf 2 · 1 + Cf 1 if Cf 1 < 0 and Cf 2 < 0
(1)
where: Cf is the rules’ Certainty Factor; Cf 1 is the rules’ Certainty Factor according to the first expert; Cf 2 is the rules’ Certainty Factor according to the second expert. The above formula relates to an operation (with real values in the range [–1, 1]) [22]. In general, we use this function to combine two rules that share the same hypothesis. The Certainty Factor Cf 1 , Cf 2 , determined for each rule by the field’s experts [23]. After calculating different rules Certainty Factors, the knowledgebase will be available to start programming. Working with CLIPS provided complete, free, easy to understand software, which will reduce the time consuming to construct machines’ diagnosis and monitoring rules.
6 Introduction of the Newly Developed Diagnosis System for Marine Diesel Engines Marine engines have many parameters that have their own measuring devices connected to the ship’s operating system, so the parameters that provide as much diagnostic information as possible have been selected as diagnostic parameters, i.e., no need to add new sensors to the engine. After organizing the knowledgebase for technical conditions of each engine subsystem separately, the experts’ opinions who had been worked with (six marine experts with the rank of a chief engineer) were more than 90% consistent, and this means that in programming, the knowledgebase was almost fixed. The difference was related to the Certainty Factor of each rule. As for adding new rules, it was decreasing with each new expert. 6.1 Structure of the new Diagnosis System The Diagnosis System could be set in two modes (manual and automatic). However, in both modes, the main (first) interface had to be filled manually by the user. This interface determines the operation diagnosing parameter accepted ranges for the user. The ability to modify those values to maintain the system effectiveness during the operation is case of the ideal ranges for operation gets changed. Figure 2 shows the main screen for the Diagnosis System. This added feature covers the gap in those kinds of systems which its effectiveness decreases as we deviate from the nominal values. The key difference between the two operation modes is that the manual mode gives the opportunity to test the resulted system. Furthermore, on the next screen in the manual mode, the user can select the subsystem subject to the diagnosing process. Figure 3 shows the five subsystems understudy for the marine diesel engine.
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Fig. 2. The main screen of the diagnosis system.
Fig. 3. Subsystem selection screen.
6.2 Case Study for the Operation of the New DIAGNOSIS System – Diagnosis of the Marine Engines’ Air System Good air charging increases the engine’s power by increasing the air charge (amount) in the cylinder during the absorption process, thus providing an opportunity to increase the amount of fuel injected into the cylinder and as the amount of fuel injected increases, so does the amount of heat generated by combustion, which increases the work that these gases produce on the piston. If the user defines the ideal range for air pressure after the turbocharger between 1 and 1.5 bar, then during the operation, the air pressure value
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decreases below 1 bar. The rule that will be applied requires more facts from the user (whether the values’ change accompanied with surging or not?). In the case of surging happening, the diagnosing procedure will be the following: • Start the diagnosis as follows: 1. Air filter plugged (remove and clean). 2. Polluted turbo (check if the compressor and the turbine had been cleaned regularly). 3. Unsteady shaft (wear damage). 4. Air cooler fouling (accompanied with high temperature). • Immediate action: Reduce speed to the load at which the surging stop, if necessary open the air cooler inspection hole to stop surging. • The Certainty Factor is: CF = 0.9978. Figure 4 shows the screen of the before mentioned case study.
Fig. 4. Engines’ air system screen.
The Certainty Factor shown in the end of each case is the combined factor for the experts’ certainty in the result, calculated by Eq. 1. In addition to the possibility to open CLIPS and modify the results according to the engine’s real condition. MATLAB GUI is used to present it by linking CLIPS to MATLAB in order to visualize the data and results easier and better for the final user. Operations and calculations will be done in the background by CLIPS, and MATLAB GUI will present the result. Installing Knowledge-Based Diagnosis System on ships provides valuable advice to
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operators. This advice helps the operators make timely decisions, increasing the system efficiency and reliability. The key feature of the new diagnosis system for marine diesel engines is represented in two aspects: First, the ability to select and modify diagnostic parameter values. These modifications can be made before and during operation. This feature makes the system applicable to any diesel engine, as the base diagnosis system will be developed to be specific to the engine during use. The second aspect is using Certainty Factor Theory to calculate and combine the expert opinions’ depending on the amount of expertise by years of experience for each expert. Usually, experts provide Certainty Factors for each rule separately, but in this study, after working with the field experts, it was easier for both the experts and the knowledge engineer to work with one Certainty Factor for all the expert opinions.
7 Conclusions The development process of the Knowledge-Based System includes different steps and decisions to obtain an effective system, that improves, and eases the user tasks. At first the appropriate approach has to be selected to analyze the experts’ knowledge, knowledge type, and availability affect the selection decision. Then the programming tool has to be selected, which facilitates the programmer’s mission; the software capability to create complex systems contains the required relations and rules. In this study, the new Knowledge-Based System depends on Certainty Factors Theory to analyze the experts’ knowledge. This theory applies in cases where the possibilities are unknown, difficult to obtain, or expensive to access in the case of marine engines, the experts’ knowledge is not enough to create the knowledge base. Furthermore, the previous faults and conditions of a similar engine may be hard to obtain and access. In contrast, after creating the knowledgebase, the selected software tool used CLIPS to build the system and MATLAB to build the interfaces. This software is available, effective, can be applied on several platforms. These decisions produce a Knowledge-Based System that provides the user with crucial advice to improve the operating process and the engine’s reliability and effectiveness. The main added value of the study is that a new Knowledge-Based Diagnosis System was developed, which can be handled more flexibly by inexperienced users and increase the efficiency and reliability of the marine diesel engines. The future research in Knowledge-Based Systems for diagnosing and monitoring suggested is to enrich the knowledgebase with new experts’ opinions and comparing the effect of the experts’ numbers on the Certainty Factors changes. In addition, it is preferred to use programming languages that support graphical user interfaces like PYTHON and its newly developed library (EXPERTA) strongly inspired by CLIPS, taking the leverage of eliminating the need for the data exchange between MATLAB and CLIPS. Acknowledgements. The research was supported by the Hungarian National Research, Development, and Innovation Office - NKFIH under the project number K 134358.
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References 1. Janjanam, D., Ganesh, B., Manjunatha, L.: Design of an expert system architecture: an overview. J. Phys. Conf. Ser. 1767(1), 1–7 (2021) 2. Tavanaa, M., Hajipourc, V.: A practical review and taxonomy of fuzzy expert systems methods and applications. Benchmark. Int. J. 27(1), 81–136 (2019) 3. Akerkar, R.A., Sajja, P.S.: Knowledge-based Systems: Model, Applications & Research. 1st edn. Jones & Bartlett Learning, United States (2010) 4. Xu, S.: A survey of knowledge-based intelligent fault diagnosis techniques. J. Phys: Conf. Ser. 1187(3), 1–6 (2019) 5. Ali, R., Hacimahmud, A.: Methodology of expert system building. . Technium 2(3), 140–146 (2020) 6. Dubey, S., Pandey, R.K., Gautam, S.S.: Dealing with uncertainty in expert systems. Int. J. Soft Comput. Eng. 4(3), 105–111 (2014) 7. Suresh, G.V., Reddy, E.: Knowledge extraction from uncertain data: a survey. Adalya J. 8(1), 33–50 (2019) 8. Radwan, N.M., Senousy, M.B., Riad, A.E.D.M.: Approaches for managing uncertainty in learning management systems. Egypt. Comput. Sci. J. 40(2), 1–10 (2016) 9. Kun-Bodnár, K., Maros, Z.: Some characteristics of surfaces machined with abrasive waterjet turning. Pollack Period. Int. J. Eng. Inf. Sci. 17, 1–15 (2022) 10. Aggarwal, C.C., Yu, P.S.: A survey of uncertain data algorithms and applications. IEEE Trans. Knowl. Data Eng. 21(5), 609–623 (2019) 11. Roventa, E., Spircu, T.: Management of knowledge imperfection in building intelligent systems. Stud. Fuzziness Soft Comput. 227, 153–160 (2009) 12. Jabbar, H.K., Khan, R.Z.: Tools of development of expert systems: A comparative study. In: 3rd International Conference on Computing for Sustainable Global Development, pp. 3947– 3952. New Delhi (2016) 13. Rani, M.N., Rajesh, T.: Comparative analysis on software’s used in expert system with special reference to agriculture. Int. J. Recent Technol. Eng. 2(2), 85–89 (2013) 14. Krakowski, R.: Diagnosis modern systems of marine diesel engine. J. Kones Powertrain Transp. 21(3), 1–12 (2014) 15. Tripathi, K.P.: A review on knowledge-based expert system: concept and architecture. Int. J. Comput. Appl. 4, 19–23 (2011) 16. Babiˇc, M., Karabegovi´c, I., Martinˇciˇc, S.I., Varga, G.: New method of sequences spiral hybrid using machine learning systems and its application to engineering. Lect. Notes Netw. Syst. 42, 227–237 (2019) 17. Yazdi, M., Hafezi, P., Abbassi, R.: A methodology for enhancing the reliability of expert system applications in probabilistic risk assessment. J. Loss Prev. Process Ind. 58(1), 51–59 (2019) 18. Wagner, W.P.: Trends in expert system development. Expert Syst. Appl. Int. J. 76(3), 85–96 (2017) 19. Hariri, R.H., Fredericks, E.M., Bowers, K.M.: Uncertainty in big data analytics: survey, opportunities, and challenges. J. Big Data 6(1), 1–16 (2019). https://doi.org/10.1186/s40537019-0206-3 20. Benotsmane, R., Dudás, L.: Robotic production oriented engine design and manufacturing. Lect. Notes Mech. Eng. 22, 390–400 (2021) 21. Efendi, R., Jambak, M.M., Marlina, L.: Implementation of fuzzy logic in determining the value of uncertainty factors on expert system. In: Sriwijaya International Conference on Information Technology and its Applications, Atlantis Press, pp. 172, 448–453 (2020)
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22. Yuan, J., Zhang, S., Wang, S., Wang, F., Zhao, L.: Process abnormity identification by fuzzy logic rules and expert estimated thresholds derived certainty factor. Chemom. Intell. Lab. Syst. 209, 1–13 (2021) 23. Xie, N., Han, Y., Li, Z.: A novel approach to fuzzy soft sets in decision making based on grey relational analysis and MYCIN certainty factor. Int. J. Comput. Intell. Syst. 8(5), 959–976 (2015)
Examination of Bolt Connection with Finite Element Method Ferenc Sarka(B) University of Miskolc, Miskolc 3515, Hungary [email protected]
Abstract. Screw threads and bolt connections have appeared already in ancient times in technical practice. Archimedes was an ancient Greek scientist in the IIIrd century AD. He created the mathematical description of the helix, that is the base of several inventions (e.g., a water-lifting screw). Nowadays, screw connections have become the most commonly used fasteners and become the most common machine element. Their simplicity and the possibility of repeating the assembly almost any number of times led to their widespread use in the mechanical engineering industry. It is not different in vehicle manufacturing either. There are many places in vehicles where the precise adjustment of the clamping force created by the joint is an important parameter, like the bolts that secure the wheels or the various elements of the drive units. It is important to create clamping force, but not cause excessive deformation that would adversely affect either the contact surfaces or the gaskets. The aim of the research presented in this publication is to examine how to construct CAD models and FEM models of a bolted joint in order to obtain relevant information for a bolted joint installed in a given location. The publication describes why it is recommended to pay attention to creating CAD geometry so as to the model can be processed by the FEM software. The research examines the displacements caused by the tightening torque. The two presented simulations can be the basis of the tightening sequence of the bolts in the case of a machine element with many bolt connections. Keywords: Finite element method · Screw connection · Bolt connection · Fastening torque · Clamping force
1 Introduction Bolt connections appear in almost every field of technical and scientific practice. Think of almost any device around us, even the chair, the reader is sitting in, we will most likely find a bolt connection in it. From this we can also see that bolt connections play a very important role in technical practice. Looking at it in a slightly different direction in the scientific world, we also encounter it in medical/surgical work, as plates combined with bolts are also used to fix a severely broken bone [1]. In the automotive industry too, we find bolted joints in the fastening of many vehicle components. The sizing of a staticstressed bolt connection can be performed with experimentally validated relationships detailed in the literature. Even with a time-varying load, acting parallel to the bolt axis, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 212–222, 2023. https://doi.org/10.1007/978-3-031-15211-5_19
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the characteristics of the connection can be easily calculated [1]. However, there are cases that can be no longer handled by simple calculations. Several standard test methods have been developed for testing bolted joints, which provide good comparability between products from different manufacturers.
2 Calculation of Tightening Torque, the Problem of Tightening Sequence The calculation of the tightening torque of bolted connection can be found in almost every book dealing with machine elements, where the model of the connection is a body pushed up on a slope [2]. In the case of a single connection, we can calculate from Eq. (1). d2 · tan α + ρ + rw · μw (1) T = Fe · 2 where: • T: tightening torque, • Fe : the tensile force in the bolt shank equal to the compressive force generated by the joint, • d2 : the mean diameter of the thread, • α: the angle of the slope in the thread, • ρ’: angle of the friction cone, • rw : the characteristic diameter of the washer, • μw : the friction coefficient at the washer. The correlation is appropriate for most bolt connection design tasks. There are cases when it is not enough to know what tensile force is generated in the bolt shank due to the applied torque, but we also need to know what happens to the compressed elements and their surroundings. We need to know the extent and direction of the deformation of the compressed elements. This can happen when the parts are fastened together with several bolt connections. In these cases, the relative position of the two components is/may be affected by the tightening order of the bolt connections and the reaching process of the required tightening torque. Not only assembly but also disassembly can be a problem due to bolt loosening in the wrong order, which results in unwanted deformations. There are several examples in the literature that give the tightening order of the bolt for a very special case. One such case is the assembly of the cylinder head of internal combustion engines, where the tightening order of the bolts and the process of constructing the applied tightening torques are very important (how many and what size of steps the required torque is achieved with). Unfortunately, the relationship between the tightening torque and the force generated in the bolt shank is only theoretically as simple as Eq. (1) shows. Due to the change and uncertainty of the friction conditions, there is a large variance in the value of the force in the bolt shank (up to 30%), even at the same tightening torque. Therefore, car manufacturers, together with the manufacturers of gaskets, have
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developed a method that more or less eliminates this problem. The bolts are tightened to such an extent that the shank is subjected to a plastic deformation, i.e. the bolt is set to the same condition as when the tensile test is performed, but it stops before the contraction phase. Under such a load, the shank of the bolt is stretching, but the force required to elongate changes only slightly. And this condition results in a much more accurate compressive force. Furthermore, the bolts are maintenance-free; there is no need to check or retighten the bolt after a certain kilometer, they retain the required clamping force. The bolt tightening sequence for internal combustion engines is based on many years of experience and experimentation. The shape of the shank on the bolts was developed with the above-mentioned requirements. Some examples can be seen in the following figure (Fig. 1) [14].
Fig. 1. Examples of bolts that is capable of elongation [14]
In technical practice, we encounter a case elsewhere where the tightening order of bolts can be an important factor. Such area is the assembly of slot-die heads used in slot-die coating technology. In the case of slot-die technology, the material that is used to make the coating is delivered to the receiving surface through a very narrow gap. The following figure (Fig. 2) shows the working principle of the technology and the names of the elements around the slot-die head.
Upstream lip
Q
Upstream die
Upstream coating gap
w
Upstream meniscus substrate
Downstream Downstream die lip Downstream coating gap Downstream Wet thickness meniscus Coating bead
U
Fig. 2. The overall design of a slot-die head
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Substrate: carrier/receiver surface, for which the coating is applied, it can be rigid or flexible. • • • • • • • •
U: the velocity of the substrate. Upstream die: the upstream part of the slot-die head. Downstream die: the downstream part of the slot-die head. Upstream meniscus: the fluid profile of the upstream side. Downstream meniscus: the fluid profile of the downstream side. Coating bead: a layer of fluid applied from the slot-die head. Wet thickness: the thickness of the applied material when wet. w: the width of the gap.
The slot-die head is basically made up of three main elements (Fig. 3), a spacer element (shim) will be installed between the two halves of the head, which will determine the gap size of the slot-die head.
Fig. 3. Names and design of the interior of the slot-die head [4]
Bolt connections are important when connecting the two outer elements of the slotdie head. Between the two outer elements, there is a very thin spacer, often a few 10 μm thick. Due to its size, it is a very sensitive part. When assembling the head, great care must be taken to ensure that the spacer is not damaged by tightening the bolts. It is of increased importance to tighten the bolts on heads with a large width (up to 2 m). Such large width heads are assembled with many bolt connections (Fig. 4). The spacer between the two halves of the slot-die head plays essentially the same function as the gasket of the cylinder head of the internal combustion engines, as was mentioned previously. Slot-die coating technology is an increasingly used technology in battery manufacturing. And with the proliferation of electric cars, batteries are playing an increasingly important role [4–9]. Due to the compressive force of the bolts, minor or major deformations occur on the components. In order to optimize the shape of the parts to minimal deformation or minimal weight (or something else), we can use several design methods (such as Generative design or Fuzzy logic) that are shown in the literature [15]. From the lines described above, it can be seen that the tightening order of the screws is very important
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Fig. 4. A width slot-die head from Yasuiseiki [10]
in some cases. Therefore, such method would be useful as we can make propose to the tightening sequence at the design stage. The study presented in the following chapters is the initial step in developing such a method.
3 Create the CAD Model of the Screw Connection For finite element simulation, the R19 version of Ansys was used. In the software, it is possible to design bolt connections and test their pretension. We can examine the pretensioned bolts using the “bolt-tool”, “bolt pretension”, or “beam” commands. The elements of the screw connection (nut, bolt shank, bolt head) are approached with simple cylinders [13]. The use of these commands allows a relatively fast calculation but does not take into account the phenomena during tightening and the effect of torque. For this reason, a different path was chosen for FEM studies. In the CAD models, the real threads have been designed. The CAD model of the bolt connection was created in the 2020 version of the Solid Edge design system. Each screw connection is made up of two main elements, the nut and the screw. Because it is necessary to examine the tightening conditions in the FEM software, the screw surfaces of the bolt and the nut (threads) must be fully modelled. The following figure (Fig. 5) shows the bolt used in the simulation. Its dimensions have been chosen based on the literature [11]. The bolt is an M10 inner hexagon head bolt with a shank length of 22 mm.
Fig. 5. CAD model of the bolt used in the FEM simulation
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The design of the nut thread must match the thread of the bolt already made. To make sure that the nut thread fits exactly, the nut thread is created using a Boolean-operation. Place the threadless nut on the shank of the bolt with a cylindrical fit, and then remove the bolt from the nut part to obtain the exact fit nut (Fig. 6).
Fig. 6. The bolt assembled with the nut after the Boolean-operation
If several bolt connections are placed in one assembly (which is quite common in the applications mentioned above), the bolt and the nut created by the Boolean-operation could also be a subassembly, so that, time and energy can be saved by inserting the subassemblies several times. Finite element simulations have shown that if the bolt-nut double is inserted into the assembly as a subassembly, no usable result is obtained. The reason for this has not been determined so far. On the other hand, it has been found that a good result is obtained if the Boolean-operation is carried out one by one in the assembly to be tested for FEM for each bolt connection.
4 Development of a FEM Model for Bolt Connection, Simulations The FEM test is based on a very simple model that consists of two plates and two bolts. The used bolts are the bolts which were described in the previous section. 11 mm diameter holes were made in the plates, 80 mm apart of each of them. The thickness of the plates is 5 mm. The assembled model is shown in the following figure (Fig. 7). To simplify the simulation, the washer will not be installed. Although the washer may have a beneficial effect on the displacement of the plates due to the predictable high computational requirements, they are not included in this study.
Fig. 7. Assembled plates and bolt connections in CAD system.
The model was transferred from the CAD system to the FEM program in a.step intermediate format. The static structural module of the FEM program was used for the
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simulation because the target was that to investigate the situation when the elements of the screw connection (bolt and nut) have already reached the plates and they are tightened. The following figure shows the model and its finite element mesh entered into the FEM program (Fig. 8).
Fig. 8. The mesh of the model
The supports and loads used in the model are shown in the following figure (Fig. 9). The tightening torque is applied through the head of the bolts, which is 100 Nm. The supports are on the outer surface of the nuts, simulating a wrench. The “fixed support” type support was used on the nut. This kind of support does not allow any displacement and can transmit forces and moments.
Fig. 9. The used boundary conditions of the FEM model
In the first simulation, the build-up of torque on the two bolts followed one after the other. When 100 Nm is reached on one of the bolts, the second bolt is then tightened (Fig. 10). The red curve shows the tightening torque of the first tightened bolt, the green shows the second bolt.
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Fig. 10. Development of tightening torques.
For each contact surface of the model, the “bonded” connection offered by the program must be changed to a frictional connection. The connection property must also be changed between the bolt and the nut, between the plates and among the plates and bolt and nut. The coefficient of friction was chosen to μ = 0.1 based on the literature [1]. Setting up the contacts predicts that we may experience increased computation time, but with a computer with the appropriate parameters, it does not have a relevant effect on the computation time. In the case of contacts, make sure that there are no contact surfaces in each contact that appear in more than one contact. If we find one of these, we need to turn it off. Logically, from that contact where it is not relevant. After the settings, the simulations will show the conditions between the contact surfaces. The quantities that are to be solved are the stress distribution and the total deformation (displacement). Once the solution is complete, the results are visible. It took 49 min to solve (machine parameters: Core i5 5th gen., 16 GB RAM). The following two figures show the values of the total deformation. The a) part of Fig. 11 shows the condition after tightening the first bolt, and part b) shows after tightening the second bolt also. For us, the displacement that appears at the plates is important because this is the situation we would like to avoid in a similar situation that was mentioned in Sect. 2. The value of the displacement visible at the plates is between 0.7 and 1.5 mm. Larger displacement values appear because of bolt head rotation, this is not relevant for the test. Our goal is to be able to suggest a tightening sequence for the bolts and suggest a pattern for building up the tightening torque. So that we can keep the displacement of the plates as small as possible.
a)
b)
Fig. 11. The tightening of the bolt one after another
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In order to approach our goal, another simulation was also performed. In which simulation, the tightening torque was divided into two parts. In this case, firstly only 60% of the applicable torque was applied to the first bolt and then also 60% was applied to the second bolt. The remaining 40% tightening torque is then applied separately to the two bolts. The following figure (Fig. 12) shows the change in the value of the tightening torque as a function of the time steps when setting up the simulation.
Fig. 12. Variation of tightening torque as a function of time
All other parameters of the simulation remained unchanged. The evolution of the displacements is shown in the following figure (Fig. 13). It is clear that, the entire plates are in the smallest displacement range. The amount of displacement is between 0 and 0.7 mm. It is significantly smaller than it was in the previous case (Fig. 11). So if we tighten the bolts in several steps, we get less displacement for the parts to be compressed, which is of course, known from experience. Based on the simulations, we can say that based on the presented method, in the case of several bolt connections, we can propose the tightening order of the bolts and the construction of the tightening torque steps.
Fig. 13. The displacement result of the tightening in two steps.
There was another usable result of the simulations. This comes from the examination of the contact zones. Examining the zone between the two plates (Fig. 14), the affecting area of the bolt connections can be seen. Zones marked in orange are the surfaces that have stuck together or slipped over each other. It can be clearly seen that there is a wide (long) zone between the two bolt connections with less compression between the plates
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than around the joints. By using washers, the orange zone in Fig. 14. can be increased at the expense of the yellow zone, which has a beneficial effect on the two plates. The area subject to greater total compression can be increased while the resulting pressure decreases.
Fig. 14. Contact zone between plates
From this observation of the test, we may be able to specify the recommended bolt spacing, or if necessary to change the combined diameter and number of bolts. In the case of finite element analysis, the image of the stress distribution in the structure is often reported. You can see this in the next figure (Fig. 15), although the main purpose of the test is not to find the stresses, but to simulate the displacements in the plates due to the tightening torque with this finite element simulation. We see very high stress values on the stress scale, but they all affect only a very small area (it can’t be seen in the picture). It is clear from the cross-section of the screw shank that only minor stresses appear there, almost the entire part is in the blue and light blue field. The stresses are very similar to each other in the two different cases, because the tightening torque was the same.
Fig. 15. The stress distribution of the bolt shank in the first and second simulation
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The high-stress values are at the tip of the thread because the thread clearance was not developed in the CAD models. In bolted joints operating under real conditions, there is no material at the high-stress locations due to the thread clearance present.
5 Evaluation of the Simulation Results, Possibilities of Use Based on the two cases presented above, we can conclude that in the case of properly created CAD models and a well-adjusted FEM model, we have the opportunity to propose the tightening sequence of the bolt connections and the steps of the tightening torque during the design phase. This makes it possible to replace some of the expensive and timeconsuming real tests. Of course, it is not recommended to omit the real tests altogether, as the simulations presented are more or less good, but they only approximate reality.
References 1. Lovas, L.: Csavarkötés modellezésének kérdései, Gép, LXIX, 2018/4, pp59–62. ISSN 0016– 8572 2. Lovas, L.: Haszonjárm˝u kerékcsavar lazulásának kérdései, Gép, LXXI, 2020/7–8, pp. 43–46. ISSN0016–8572 3. Zsáry, Á., Gépelemek, I.: kötet, Nemzeti Tankönyvkiadó, Budapest, ISBN963 19 4585 5 4. Beguin, A.E.: Inventor. Method of coating strip material. US patent No. 2,681,6941954 5. The homepage of Nordson: https://www.nordson.com/en/divisions/polymer-processingsystems/products/fluid-coating-systems/ultracoat-adjustable-lip-slot-dies. Accessed 31 Dec 2021 6. Chang, H.M., Chang, Y.R., Lin, C.F., Liu, T.J.: Comparison of vertical and horizontal slot die coatings. Polym. Eng. Sci. 47, 11, 1927-1936. ProQuest Central (2007) 7. Ding, X., Liu, J., Harris, T.A.L.: A review of the operating limits in slot die coating process. Transp. Phenomena Fluid Mech. 62(7), 2508-2524 (2016). https://doi.org/10.1002/aic.15268 8. Tanwar, J., Vinjamur, M., Scriven, L.E.: Design principles of integrated vacuum slot arrangement. AIChE J. 53(3), 572–578 (2007). https://doi.org/10.1002/aic.11112 9. Ruschak, K.J.: Limiting flow in pre-metered coating device. Chem. Eng. Sci. 31, 1057–1060 (1976) 10. Homepage of Yasuiseiki. https://www.yasuiseiki.com/slot-die.php 11. ISO 724–1993: ISO general-porpuse metric screw threads – Basic dimensions 12. Horváth Tibor – Márkus Norbert – Torma László: Met Tech csavarozástechnika, Metrikont Kft. (2009). ISBN 978 963 06 7099 9 13. https://www.finiteelementanalysis.com.au/featured/an-overview-of-methods-for-modellingbolts-in-ansys/ 14. Nagyszokolyai, I.: Húzd, ki tudja meddig húzhatod…, Autótechnika 2003/6 15. Piros, A., Farkas, Z.: Fuzzy-based evaluation of a specific drive train. Adv. Mech. Eng. 4(7), 763171 (2012). ISSN 16878132, 16878140
Calculation Methods and Measurement of the Heating of Small Plastic Gears Imre Marada(B) and János Bihari Institute of Machine and Product Design, University of Miskolc, 3515 Miskolc, Miskolc-Egyetemváros, Hungary [email protected], [email protected]
Abstract. The heating is always a more important problem for drive units with plastic gears, even in simpler applications than with steel gears. The strength of plastics can change dramatically even at a temperature that occurs in the everyday environment, such as in places exposed to direct sunlight. In gearboxes, this can have a significant effect on the behaviour without rapid failure of the components, while higher temperatures lead to greater deformation of the tooth surface and greater wear and thus, it can reduce the contact ratio. At the same time, plastic gears are often used without lubrication, and in such circumstances, temperature also has a significant effect on the tribological properties of plastics. There are several methods for calculating the heating of plastic gears, which can give conflicting results. Some of these have become obsolete over the years but are still used today, for example, the VDI 2545 or the modified VDI 2545 calculation method. In the past, we have been able to demonstrate that reactive loads, especially in the drive units with small plastic gears performing a supporting function can lead to significant local temperature rises in the contacting teeth. Pulsating or continuous but variable reactive loads can cause heating similar to or greater than in the case of the normal operation. This heating can significantly affect the operating clearance and the strength characteristics of the gears. The temperature rising effects of reactive loads are measured with the help of special drive units, which proved to be a very useful method. In this article, we present some of the most commonly applied computing methods of heating. This is followed by a description of the specific drive units that we use to investigate the heating induced by the reactive loads and the principles of the measurement that can be performed with them. Keywords: Small plastic gears · Calculation methods · Heating · Measurement · Drive unit
1 Introduction No detailed standards or guidelines exist for the design of small plastic gears, such as DIN 3990. A common method here is the design of plastic gears according to standards for metal gears and substituting the strength properties of plastics into the calculations. However, this approach does not address the issue that the strength of polymers can fluctuate significantly with operating ambient temperature. This issue is addressed by © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 223–236, 2023. https://doi.org/10.1007/978-3-031-15211-5_20
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current standards and guidelines for the design of small plastic gears, either by a calculational method or by a combination of tests and calculations. The VDI 2545 guideline has been for long the fundamental approach in Europe for the design of small plastic gears. It was published in 1981 and withdrawn in 1996. Since no other similar guideline was accessible after the withdrawal of this guideline, it was commonly used in the industry until the publication of the VDI 2736 guideline in 2016, and it is still significant nowadays. Thus, the calculation method is demonstrated in this article to calculate the heating of plastic gears according to VDI 2545. Additionally, the computation method of VDI 2736 guideline is also presented here. AGMA and JIS standards for gears suggest experimental test methods for the determination of the heating, of which we present here the test method of the JIS B 1759 standard. Since there was no commonly used method for about 10 years, several manufacturers have developed catalogues that are well suited to use within given limits. In this article, we show the method of Licharz, which is a modified version of the VDI 2545 method, and it bases on experimental data.
2 The Calculation Methods of the VDI Guidelines 2.1 The VDI 2545 Guideline The VDI 2545 guideline uses the following formula [1]: 17100 1+u k2 k3 · · + 6, 3 · ϑ1,2 = ϑk + 136 · P · μ · z2 + 5 b · z1,2 (v · m)κ A
(1)
where [1]: • • • • • • • • • • • • •
ϑ 1,2 is the temperature of the gear in °C ϑ K is the ambient temperature in °C P is the power in kW b is the width of the tooth face in mm μ is the coefficient of friction z is the number of teeth m is the module in mm v is the tangential velocity in m/s A is the surface of the gear casing in m2 u is the gear ratio k 2 is the material-related factor k 3 is the gear-related factor in m2 K/W κ is a factor related to the gear material
2.2 The Modified VDI 2545 Method by Licharz The solution developed by Licharz is shown in Eq. (2) [2]: 17100 1+i k2 k3 ϑ1,2 = ϑk + 136 · P · μ · · + 7, 33 · · z2 + 5 · i b · z1,2 (v · m) 43 A where [2]:
(2)
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• • • • • • • • • • • •
225
ϑ 1,2 is the temperature of the gear in °C ϑ K is the ambient temperature in °C P is the power in kW b is the width of the tooth face in mm μ is the coefficient of friction z is the number of teeth m is the module in mm v is the tangential velocity in m/s A is the surface of the gear casing in m2 i is the transmission ratio k 2 is the material-related factor k 3 is the gear-related factor, m2 K/W
In the case of infrequent operation, the relative duty cycle ED can be calculated as the percentage of the ratio between the load duration t and the overall cycle time T of one contacting tooth [2]. ED =
t · 100 T
(3)
The correction factor f as a function of ED can be decided from a diagram. Equation (4) shows the modified formula by the f factor for infrequent operation [2]. 17100 1+i k2 k3 ϑ1,2 = ϑk + 136 · P · μ · f · · + 7, 33 · · (4) z2 + 5 · i b · z1,2 (v · m) 43 A
2.3 The Calculation Method of the VDI 2736 Guideline The tooth root temperature can be calculated by Eq. (5) [3]: kϑ,Fuβ Rλ,G ϑft = ϑk + Hv · P · μ · + · ED0,64 3 AG b · z · (v · mn ) 4 The flank temperature can be determined by Eq. (6) [3]: kϑ,Fla Rλ,G ϑFla = ϑk + Hv · P · μ · + · ED0,64 3 AG b · z · (v · mn ) 4
(5)
(6)
where [3]: • • • • •
ϑ K is the ambient temperature in °C P is the nominal output in W μ is the coefficient of friction H v is the degree of tooth loss k ϑ,Fuß is the heat transfer coefficient of the plastic gear for the calculation of root temperature
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• k ϑ,Fla is the heat transfer coefficient of the plastic gear for the calculation of flank temperature • b is the face width in mm • z is the number of teeth • v is the tangential velocity in m/s • mn is the normal module in mm • Rλ,G is the heat transfer resistance of the mechanism housing • AG is the heat-dissipating surface of the mechanism housing in m2 • ED is the relative tooth-engagement time
3 Correlation Between the Methods in the VDI Guidelines All methods present ambient temperature to the equations by an addition. This means that none of the approaches considers that not only the strength but also other properties can vary with temperature in plastics. Resemblances in the approaches are that module m, power P, width b, number of teeth z, tangential velocity v and coefficient of friction μ are taken into account in a similar way. Moreover, all three VDI approaches consider the size of the surface. The method of the VDI 2545 guideline can only be applied for continually operating units. The modified VDI 2545 by Licharz and VDI 2736 methods are not only usable for continuous running but also for infrequently running drive units as well. The first can be modified by the correction factor, which is a function of ED, the VDI 2736 guideline puts this ED value directly into the equation. It is beneficial because plastic drives often perform only setting tasks, i.e., they work infrequently. The VDI 2545 and the modified VDI 2545 approaches show similarities in their structure and the parameters they use. The differences appear in the experimental values of 6,3 in the former equation and 7,33 in the latter, as well as in the possible values of the k2 and k3 factors. The VDI 2736 method alters from the other two approaches in several respects, both in the structure and parameters. The material properties are taken into consideration by the VDI 2545 and the modified VDI 2545 methods with the k2 factor, which can take a different value when determining the temperature of the tooth flank or the tooth root. For the VDI 2736 method, there are two different factors for determining the temperature of the tooth root or the tooth flank. The kϑFuß factor is applied for the root and the factor kϑFla is used for the surface temperature. These factors depend on the material of the gear. The VDI 2545 equation includes a factor that the other two approaches do not. This is the factor κ, which depends on the material pairing. This factor is one of the reasons why the VDI 2545 guideline is still regularly used nowadays, because plastic gears are often paired with steel gears or plastic gears with different properties, for example, to reduce the noise or the operating clearance. The modified VDI 2545 and the VDI 2736 approaches take this κ factor as 0,75, although according to Miklós Antal [1] κ is not 0,75 for all plastics. Different factors are also considered when deciding on the closure of the drive unit. The VDI 2545 and the modified VDI 2545 methods use k3 for this purpose, while the VDI 2736 method uses Rλ,G . They have in common that three different values can be taken depending on whether the drive unit is open, partially closed or closed.
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4 The Method of the JIS B 1759 Standard The JIS B 1759 Japanese standard introduces a possible method for the definition of the strength of plastic gears. For this, the standard introduces a test bench, which is used by making analyses with the standard test parameters in the JIS B 1759 standard, as well as with different parameters, and then comparing these values. The JIS B 1759 standard takes into account the effect of temperature on the allowable dedendum bending stress of the gear material [4]. σFP = σFlim · YNT · Yθ · Yθ · Yl · YM
(7)
where [4]: • • • • • •
σ Flim is the allowable bending stress of gear material Y NT is the life coefficient Y M is the mating gear coefficient Y l is the lubrication coefficient Y θ is the ambient temperature coefficient Y Δθ is the temperature increase coefficient
The ambient temperature coefficient is Yθ = 1 for standard parameters, namely for the ambient temperature of 23 °C. The value of the coefficient for a given temperature other than the standard conditions is calculated by Eq. (8) [4]: Yθ =
σFlim(atθ=θ ∗) σFlim
(8)
In Eq. (8) σFlim is the allowable bending stress at the standard test parameters, σFlim(at θ=θ*) is the allowable bending stress at an ambient temperature different from the standard parameters, which can be determined by test bench measurements [4]. The temperature increase coefficient considers the effect of the temperature increase due to the friction and the hysteresis on the allowable bending stress of the gear material. The temperature growth factor can be greater or smaller than 1, depending on the gear module, tooth width and rotation rate [5]. The approach introduced in the JIS B 1759 standard is very different from the ones used in the VDI methods. Moreover, the JIS B 1759 standard does not acquire the factors from tables or diagrams but introduces the method to make tests to obtain factors that influence the strength. Furthermore, the JIS B 1759 standard does not address the determination of gear temperature. As opposed to the VDI methods, it incorporates the effect of temperature in the form of several factors into the strength computational method. Another difference is that the JIS B 1759 standard does not ignore the effect of the ambient temperature on the material but shows a possibility to measure the effect of ambient temperature as a factor. However, it does not use a specific correlation to determine the additional temperature increase but introduces a benchmark that can be acquired by comparing the parameters of the gearbox we use with standard parameters. This approach has some uncertainty, as
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JIS B 1759 does not determine the scale at which a change in the parameters will change the factor, only whether it will be greater or lesser than one. Finally, the temperature increase coefficient does not consider the friction, nor the tribological properties, nor the fact that the gear is closed or open. It only considers the variety of certain geometrical values of the gear and the speed.
5 Other Calculation Methods The temperature formula in VDI 2736 was created based on the work of Hachmann and Strickle. The equation built-up by the authors is as follows (9) [5]. k2 · a0,75 i+1 k3 · ϑi = ϑk + P · μ · + (9) z+5·i A b · z · λ · (v · mn )0,75 where [5]: • • • • • • • • • • • •
ϑ K is the ambient temperature in °C P is the nominal output in W μ is the coefficient of friction i is the transmission ratio k 2 , k 3 are the factors that take material pairing and lubrication into account b is the face width in mm z is the number of teeth λ is the thermal conductivity a is the thermal conductivity of air v is the tangential velocity, m/s mn is the normal module in mm A is the heat-dissipating surface of the housing in m2
As mentioned before, the temperature Eq. (9) was the basis for the formula in the VDI 2736 guideline, so it bears many similarities to it. However, due to its structure and the factors used in it (k2 , k3 ), it shows more similarities with the VDI 2545 and modified VDI 2545 methods. Equation (10) shows the gear temperature according to [6]: ϑ = ϑK + ϑb + ϑf
(10)
where ϑK is the value of the ambient temperature. The increase in gear body temperature is [6]: ϑb = where [6]: • C p is the heat capacity of air
0, 625 · μ · T Cp · ρ · z · L · r02 − rp2
(11)
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• • • • •
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ρ is the air density T is the transmitted torque L is the gear face width r 0 is the outer radius of the gears r p is the pitch radius The flash temperature [6]: ϑf =
0, 555 · δH L · (k · ρ · c · w · vs )−1/2
(12)
where [6]: • the instantaneous energy loss due to friction: δH = • • • • • • •
π ·P·μ z
(13)
k is the thermal conductivity ρ is the density c is the specific heat of the surfaces vs is sliding velocity w is the Hertzian contact width P is the transmitted power z is the number of teeth
The formula, like the VDI equations, does not consider the effect of ambient temperature on the material. The difference is that this approach takes different parameters into account to determine the temperature increase. Compared to the other equations, it breaks down the temperature change into two terms: one term for the temperature inside the gear body and another for the temperature on the surface. The first depends on the gear dimensions, torque and on the air, while the other depends on the gear material properties, surface friction and the value of Hertzian contact.
6 Calculations Based on the VDI Methods Using the presented VDI methods, calculations have been made for different modules, powers, and ratios for several different materials. We wanted to see if there were any significant differences between the results and, if so, to what extent the size of the gears influenced these results. For the VDI methods, the only significant differences in the module range above 0,5 mm are in such extreme cases where the usability of a size range or a material grade is arguable. However, for small plastic gears, significant differences can be noticed, especially for a module of 0,3 mm and below. Table 1 Table 2 and Table 3 show these calculations. The calculations were made for a gear pair of POM (Polyoxymethylene) material grade with 10/50 gear ratio and 0,3 mm module.
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For this gear pair, the surface load capacity at the critical starting temperature of 60 °C, which is not an extraordinary value for example, in a summer day in a car dashboard, at 10,000 rpm and 10 watts of power, the tooth surface safety factor is only around 1,8, which is not practical for continuous running, but it can be adequate for infrequent operation. Table 1. The resulting temperatures in °C computed with VDI 2545 method. VDI 2545 Assembly lubrication
Dry
Power
Flank
Root
Flank
Root
10 W
62,247
61,18
66,99
63,66
5W
61,123
60,59
63,49
61,83
Table 2. The resulting temperatures in °C computed with the modified VDI 2545 method. Modified VDI 2545 Assembly lubrication
Dry
Power
Flank
Root
Flank
Root
10 W
63,06
61,255
66,81
62,79
5W
61,53
60,628
63,40
61,39
Table 3. The resulting temperatures in °C computed with the VDI 2736 method. VDI 2736 Assembly lubrication
Dry
Power
Flank
Root
Flank
Root
10 W
60,041
60,016
60,041
60,016
5W
60,020
60,008
60,020
60,008
The values in the tables show that with VDI 2545 and VDI 2736 methods, we can get significantly different values. The calculations show that as the module was reduced, the different approaches gave increasingly different results. This is not the case in the range above a module of 1 mm. However, it is impossible to decide by only calculations which method gives the best result; tests are needed. Therefore, the next step of our research on the topic of heating is to carry out a series of tests to determine which calculation method is the most applicable for small plastic gears and whether the use of known methods is even practical.
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An even more interesting question, however, is which method is more suitable for calculating the heating caused by the reactive loads. Reactive loads can be particularly dangerous for drive units that also have a supporting function. These are loads that are applied repeatedly and systematically from the held element back to the gears of the gearbox, e.g., in cases where the holding element is being displaced by pulsating air in an air duct. The resulting heating can be approximated by replacing the frequency of load build-up and decay with the rotational speed. When the gears rotate, a given tooth engages and disengages at given intervals according to the speed of rotation. For a steadily pulsating load, this corresponds to the frequency at which the load is cycled off and on. The effect of reactive loads on heating has been successfully demonstrated experimentally before, but the previous method was only capable of proving the existence of the problem, not of providing guidance on the exact amount of heating. However, more accurate methods of measuring the effect of reactive loads make it easier to check which calculation method is applicable, as the gears are stationary in these measurements, so the temperature of the teeth in contact can be measured more accurately at a given point or range.
7 The Former Equipment for Measuring Heating Caused by Reactive Loads The testing of the gears in service needs equipment that can fix them in the right positions and also makes it possible to drive and load them, thus allowing the analysis of the whole system. They are usually different test benches. Creating universal test benches for small plastic gears is usually an expensive method. Thus, for the tests of the reactive loads and the recirculation torque, a special type of drive unit has been developed which, when we installed it in simple devices, was able to make measurements at specific settings. With this drive unit, we were able to eliminate the complicated adjustment probabilities of the expensive test benches. During the design process of the drive units, we considered that in many analyses, it could be desirable to rotate the entire drive unit, not just the shafts of the gears, because it makes the use of static torque measuring devices possible [11]. Mainly, two types of these special drive units were designed and constructed in different versions, one for plain bearings and another for roller bearings. The tests at the time required four wheelbases different from each other, as well as a fifth, simulating housing distortions. The simulated error in our design was the skew that was formed when the housing of the drive unit was distorted. To simulate this, one of the shafts was modified by 3° [7, 11] (Fig.1 ).
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Fig. 1. Design of the housing for roller bearing [11].
8 Applications of the Previously Used Drive Units and the Results Achieved Reactive loads can create problems that are difficult to diagnose in drive units with small plastic gears if they also perform a support function [10]. As supporting function is a typical additional application in the case of small plastic gears, especially for multi-stage drives, it is worth measuring the effects of these loads in details. To measure the effects of these loads, one of the gears was stopped, and we applied repetitive load to the other shaft. In former drive units, this was done with the help of a screw through a hole created in the fixed gear, and the load was made by weight. A lever (3) was attached to the output shaft. The drive unit (1) and a drive motor (5) were installed on the base (2). An eccentric disc (4) was fixed on the shaft of the motor, we placed the lever above it. When the motor was working, the lever was lifted. The lever loaded the gears when the eccentric disc was rotated by the motor. The load was provided by weight (6), attached at the lever through a joint (7). By adjusting the position of the motor, we could adjust the angular rotation of the lever. The size of the load could be changed by the size of the weight [11] (Fig. 2).
Fig. 2. Figure of the test [11].
Using these drive units, we have been able to make several measurements and show that the reactive loads can make relevant local temperature increases in connected teeth [11].
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9 Problems with the Previously Used Drive Units and Expectations for the New Equipment With the help of the previously used drive units, the effects of the reactive loads could be shown, but they are not suitable for telling the exact values of these effects. A greater problem is that tests can only be made on one stage, which is unfavourable. In drive units which also have a supporting function, the drive motor’s normally limited moment of inertia is tremendous and effects the supported elements through a ratio of between 1:400 and 1:1600. This means many connected components, as well as a backlash at the connections. Thus, analysing with a single stage and with one fastened gear is inaccurate because only the connected number of teeth are the ones corresponding to the contact ratio, while in real-life scenarios, due to backlash in the system, there is always a displacement. This displacement can be favourable for the heating, specifically if the held element flexibly goes back to the position it was before because less teeth are engaged in the fastened case. However, in the fastened case, there is commonly no wear, which is also not happening here, because several of the gearboxes have been shown wear on the tooth surfaces because of small value but high repetition rate displacements. Another property of these small plastic drive units is that, because of the big number of stages, the torque and speed differences between the input and output of the drive unit are also tremendous. To reduce the volume and minimise it, it is common to alter the tooth widths, a number of teeth and modules for the same gear ratio, even per stage. Because of this reason, the effect of the reactive loads does not only appear on the last stage. Additionally, the solution of the fastened gear does not let the positions to be varied during the analysis. The damage from the repetitive reaction loads appears when the gearbox starts to run after a long period of inactivity. The tests so far have been executed at an ambient temperature between 20 and 25 °C. However, the effects of these reactive loads are regularly more dangerous at higher temperatures, so it is necessary to make measurements at ambient temperatures of at least 60 °C. A further disadvantage of the drive units used before is that their housing is made of plastic material. In real applications, this is a popular solution, but because of the thermal expansion of the housings, the wheelbases and with them the operating clearances change more with the increase of ambient temperature than in the case of steel housings. Therefore, the measurements have to deal with both the change in the gears’ own dimensions and the change of the wheelbases. As we are presently primarily interested in the behaviour of the small plastic gears, this makes it harder to evaluate the results. Commonly, gearboxes with a supporting function must be created so that if the reactive load becomes larger than the designed supporting force, the gearbox can rotate backwards without any possible damage. In cases like that, the gearbox may be subjected to a far larger load than the designed working load. This type of test cannot be carried out with fastened gears. The drive units we used for the previous tests could only accommodate gears with the same number of teeth. For the newly designed ones, different gear ratios have to be achievable. This will be solved with interchangeable covers [11].
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The previously used drive units could not be returned to the clamp in the same way because of the clamping surfaces, which always meant different impacts during the rotation. We must design the new drive units in such a way that they can be always installed within the required accuracy [11]. However, while the drive units used before were needed to be lightweight so that the mass did not make any effect on the measurement of the recirculation torque, this is no longer important for the new drive units. If they fit exactly into the device, the effect of the increased mass can be calibrated. This allows the housings to be manufactured of steel. The drive units we used previously could not be combined. This would not have been important, as their gear ratio in every case was 1:1. However to make analyses of the interaction of the faults, it is important to be able to make multi-stage drive units [11].
10 The New Drive Units Made for the New Tests The new drive units are designed to have housing which consists of three parts. The main part is a central housing part, and there are also two covers. The accurate positioning of the covers is guaranteed by the help of positioning pins. The covers are fixed to the central part with through bolts (Fig. 3).
Fig. 3. The housing of the newly designed drive unit
The central housing part is a constant component of the drive unit; it is the same for every ratio and wheelbase. The covers are replaceable and can be manufactured in many variations, allowing us to design drive units for different wheelbases and ratios. These different wheelbases are related to different cover pairs, of which the appropriate one must be installed. Therefore, these newly designed drive units can accommodate not only gear with an identical number of teeth but also gears with different ones. Temporarily these covers will be manufactured for only plain and roller bearings, but other designs will also be possible (Fig. 4). The accurate positioning of the new type of drive unit into the clamp is made possible with the help of the positioning holes on the central housing part. Thus, they can be always positioned the same way within the required accuracy in the clamp, allowing exact repeatability. Additionally, the mounting with a pin makes it possible to calibrate accurately, so the inertia due to the increased mass of the newly designed drive units will be not a problem for the other tests.
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Fig. 4. Design of the cover for roller bearings in the case of new drive units
We can also combine these new types of drive units, therefore multi-stage drives can be created. This is an important because the gear ratio is no longer 1:1, but can be any favoured ratio. With the combined, multi-stage drive units, we can investigate the reactive loads and the interaction of faults (Fig. 5).
Fig. 5. The 3D model of a three-stage combined drive unit with plain bearings
With the new type of drive units, the measurement of the effects of the reactive loads will be changed. As we mentioned before, the drive unit used for the measurements will not only be single-stage but combined, multi-stage. The gear ratio will not be 1:1 but will be similar to the actual application. In the new measurements, none of the gears will be fixed with screw or otherwise. Instead, the shaft at one end of the gearbox will be connected to a stationary electric motor, and the other end will be fitted with the lever solution described in the previous measurements. This should produce more realistic results because the system can move if the torque exerted by the lever is high enough to overcome the moment of inertia of the electric motor. This new type of drive unit also makes it possible for the tests to be executed at higher ambient temperatures. The housings made of steel simplify the installation of sensors, as the covers or the main housing part can be manufactured with holes, so the measurement of heating can be made independently for each step, speeding up the data collection.
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11 Summary The first part of the article presents several methods that can be used to calculate the in-service heating of small plastic gears. We used three of these approaches for comparative calculations to determine the extent to which the results acquired by the different approaches differ from each other. The second part describes the method of measuring the heating caused by the reactive loads, the equipment used previously for this purpose, its further development and the explanation and requirements of the need for further development. It also gives a brief insight into how the tests that can be carried out with the new type of drive units may be different from the old method.
References 1. Antal, M.: M˝uanyag fogaskerekek, M˝uanyagok gépészeti alkalmazása II. kötet, Plastic gears, Mechanical application of plastics II. volume, p: 482 – 584. Gépipari Tudományos Egyesület Kiadványszerkesztési és M˝uszaki Iroda, Budapest, 1986. (in Hungarian) 2. LICHARZ GMBH: Designing with engineering plastics. Licharz GmbH, Buchholz, Germany (2016). https://www.licharz.de/downloads/en/04_Design%20Guildelines/01_Design ing_with_engineering_Plastics.pdf. March 2021 3. VDI 2736 Thermoplastische Zahnräder, Blatt 2, BeuthVerlag, Berlin (2014) 4. JIS B 1759:2019 (JGMA/JSA) Estimation of tooth bending strength of cylindrical plastic gears, Japanese Standards Association, Tokyo (2020) 5. Hachmann, H., Strickle, E.: Polyamide als Zahnradwerkstoffe. Konstruktion 18(3), 81–94 (1966) 6. Hooke C. J., Mao K., Walton D., Breeds A. R., Kukureka S. N.: Measurement and prediction of the surface temperature in polymer gears and its relationship to gear wear. J. Tribol. 115(1), 119–124 (6 pages) (1993) 7. VDI 2731 Mikrogetriebe, Grundlagen. BeuthVerlag, Berlin (2009) 8. Bihari, J.: Kisméret˝u m˝uanyag fogaskerekes hajtások hibái defects small plastic gear drives. GÉP 65(2), 19–22 (2014). (in Hungarian) 9. Bihari, J., Kamondi, L.: Kis méret˝u m˝uanyag fogaskerekek vizsgálata. GÉP 62, (7–8), 21–24 (2011) 10. Sarka, F., Bihari, J., Takács, Á., Tóbis, Z.: Test method for investigation of reactive loads on gear drives with supporting function. In: Jármai, K., Voith, K. (eds.) VAE 2020. LNME, pp. 265–272. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9529-5_23 11. Marada, I., Bihari, J.: Designing a new type of drive unit for the analysis of small plastic gears. Multidiszciplináris Tudományok: a Miskolci Egyetem Közleménye 11, 5, 245–250, 6 p. (2021)
External Tyre Loading Predictions from Inner Tyre Deformation Measurements R. Gast1
, P. S. Els1
, D. N. Wilke2(B)
, S. Kok2
, and T. R. Botha1
1 Vehicle Dynamics Group, University of Pretoria, Pretoria 0086, Gauteng, South Africa 2 Center for Asset Integrity Management, University of Pretoria, Pretoria 0086,
Gauteng, South Africa [email protected]
Abstract. Tyre forces are the primary external forces applied to a vehicle, especially at lower speeds, but are notoriously difficult to measure or predict. An intelligent tyre that allows the external forces to be determined from simple measurements can be used to improve vehicle safety systems like ABS and stability control and mitigate collisions. An intelligent tyre has been developed at the University of Pretoria using stereovision cameras to measure strain and displacement on the inner surface of a tyre, but a model to predict force from these measurements has not been developed yet. This initial study investigates the viability of predicting external tyre loadings from inner tyre deformations. Keywords: Intelligent tyre · External load · Inner tyre · Deformation · Prediction
1 Introduction The pneumatic tyre has been the subject of many studies since its invention, which can be attributed to the fact that it is the primary mechanism by which external forces act on a vehicle, especially at lower speeds where aerodynamic effects are not significant. The complex construction of the modern tyre and its frictional interaction with the road surface contribute to the difficulty in modelling and predicting the force generation of the tyre. For example, optimal braking is often modelled and controlled based on the longitudinal slip of the tyre since this quantity can be estimated readily [2]. Road conditions are generally unpredictable and have a significant influence on the behaviour of tyres, further complicating the development of Advanced Driver Assistance Systems (ADAS). For example, optimal braking is often modelled and controlled based on the longitudinal slip of the tyre since this quantity can be estimated readily [17]. Still, it is clear that if one can instead accurately estimate or measure the tyre force directly, then ADAS will perform significantly better. Additionally, the capability to accurately predict the tyre-terrain pressure distribution is invaluable in terramechanics studies. Therefore, we seek a system or strategy that can estimate or measure tyre force accurately and over a wide range of driving conditions to further the development of ADAS and ultimately save lives. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 237–247, 2023. https://doi.org/10.1007/978-3-031-15211-5_21
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An intelligent tyre that allows the external forces to be determined from accessible measurements can be used to improve vehicle safety systems like ABS and stability control and mitigate collisions. An intelligent tyre has been developed at the University of Pretoria using stereovision cameras to measure strain and displacement on the inner surface of a tyre, but a model to predict force from these measurements has not been developed. Previous research efforts at the University of Pretoria have led to the development of the Tyre-Terrain Camera System (T2Cam) [2, 3, 18], which can measure full-field deformation and strain of the inner surface of a tyre in the region of the contact patch using stereovision. T2Cam has the potential to be used as an intelligent tyre, but the appropriate modelling strategy to accomplish tyre force prediction has yet to be determined. Much of the existing research in modelling is empirical, but ideally, a physics-based model should be used for research and development. Moreover, it remains unknown whether or not there exists a unique solution to such a problem in general. Saint Venant’s principle dictates that the effects of statically equivalent loads become similar at sufficiently large distances from the load application. It is expected that it may be challenging to identify the distribution of external tyre forces by measuring the deformations and strains at different locations. This study first summarises tyre road interaction studies conducted over the last decade in Sect. 2. The finite element model used in this study is outlined in Sect. 3. Results for a data-driven approach to mapping inner tire displacements to applied loads are offered in Sect. 4. Conclusions and future work are offered in Sect. 5.
2 Tyre-road Interaction The tyre-road interaction is highly non-linear due to complex geometry, composite materials, and the frictional interaction between the tyre and the road. Tyre modelling is a mature field, but models that apply to intelligent tyres are in their infancy. Table 1 shows a summary of journal papers, conference proceedings, and projects in which tyre force is estimated, the type of sensor used, the tyre parameters estimated, the estimation method, and whether or not validation was performed. While is it clear that strain gauges are the preferred sensor, there does not appear to be much agreement between researchers as to the best method to use this data. Earlier work mainly relied on more traditional semi-empirical or physical methods [4, 6, 7, 21] (The European Commission, 2008; [4, 6, 7], whilst the most recent work has shifted to Neural networks and Fuzzy logic approaches [14–16]. The lack of consensus among researchers on the modelling approach points to a lack of understanding of the relationship between the tyre-road interaction and the resulting behaviour of the tyre. Regardless of the sensor type used by an intelligent tyre, the input-output relationship is unknown and highdimensional. This makes it challenging to propose models to recover the distribution of forces. Uncertainties in the relationship between the measurements that can be taken of the tyre and the tyre-road interaction make it clear that physical models have the highest likelihood of successfully investigating the identifiability of these forces. The relationship between strain and displacement measurements on the inner surface of the tyre and the external loads that caused them is not well understood. This study explores the relationship between these quantities through simulation.
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Table 1. Summary of tyre force-predicting intelligent tyre concepts Year
Author(s)/Project Sensor
Tyre Parameter(s) Method
Validated?
2009 [4]
Optical
Fy Fx Fz
Empirical
Yes Yes Yes
2010 [5]
Strain Gauge
Fz Fx
Physical
Yes (FEM) Yes (FEM)
2014 [6]
Strain Gauge
Fx Fy Fz
Physical
No No No
2015 [7]
Strain Gauge
Fx Fy Fz
Physical
Yes Yes Yes
2016 [8]
Accelerometer Contact Length Fz
Neural Network Yes Yes
2017 [9]
Strain Gauge
Fz Angular Velocity
Fuzzy Logic
Yes Yes
2018 [10]
Strain Gauge
Fy Fz α
Empirical Fuzzy Logic
Yes Yes Yes
2018 [3]
Optical
Fy Fz
Empirical
Yes Yes
2019 [11]
Laser
Strain Profile
Semi-Empirical
Yes
2019 [12]
Strain Gauge
Fz Contact Length
Semi-Empirical
Yes Yes
2019 [13]
Strain Gauge
Fz
Semi-Empirical
Yes
2019 [14]
Accelerometer F y Fx Fz
Neural Network Yes Yes Yes
2020 [15]
Strain Gauge
Fuzzy Logic
2020 [16]
Accelerometer F y Fx Fz
Rolling Radius Contact Length
No No
Neural Network Yes Yes Yes
3 Finite Element Tyre Models The finite element model (FEM) used in this study has been validated, and the vertical stiffnesses were shown to correlate well [22]. The identifiability of the tyre loading from the strain measurements was investigated using virtual experiments [18]. In a virtual experiment, the experiment under investigation is only simulated. The benefits are that the conditions, epistemic and aleatoric uncertainties can be explicitly controlled and that
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both the pressure and strain fields are known. This allows the identifiability of the tyre loading from the strain fields to be investigated. Gast et al. [19] showed that identifying the tyre loading in the measured high-dimensional data domain is fundamentally ill-posed, mainly because the loading direction is transverse to the measured strain components. As expected from St. Venant’s principle, only the resulting spatially integrated macroscopic loads and moments from the tyre loading can be estimated. However, the reduced dimensional deformation and strain domains are spatially informative over the tyre. This enables their utilisation as a potential regularisation strategy enabled by a physics-based simulation. In this study, the same FEM is used, and the ability of principal component regression as a regulariser to predict the tyre forces from the measured inner displacements is investigated. The problem is again conducted using virtual calibration [20]. Figure 1 depicts the deformed finite element model and the resulting contact forces.
Fig. 1. Finite element simulation of a tyre loaded with a flat plate and the (a) deformed geometry (b) vertical reaction forces, bottom view
4 Load Predictions Gast et al. [19] initially used an optimisation algorithm to find the contact force at each contact node in order to minimise the difference between the simulated strains and (virtual) experimental strains on the inside of the tyre. One challenge the optimisation algorithm faces is that the input space is large, and many terms have little influence over the objective function. Similarly, the virtually measured deformations/strains used to penalise deviations from the true answer are also high-dimensional. Few of these strains are theorised to have a strong relationship with the external force. Suppose one generates a virtual experiment dataset of many possible tyre loading conditions. In that case, one can leverage Principal Component Analysis (PCA) to reveal, for the given
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dataset, where the strongest correlation between the input and output variables lies. This allows one to find which specific regions of the inside of the tyre explain most of the variance in the external force and how much of the variance those regions explain. This can be done by analysing the Principal components of the cross-correlation between the nodal deformation/strain and external forces for the given dataset. K virtual experiments are considered, with N nodes on the inside of the tyre and M nodes on the outside of the tyre. The input matrices X and X U contain all the N nodal strains and displacements (for a particular direction) in overall K virtual experiments. In contrast, the output matrix Y contains all the M nodal reaction forces over the same K virtual experiments. Therefore, there are six input matrices (one for each direction of strain and deformation), and one output matrix. The strain referred to here simply as is the strain extrapolated from the integration points to nodal locations. One can calculate the cross-covariance T between any mean-centred input matrix (X , X U ) and the mean-centred output matrix (Y) as T
T=X Y
(5.1)
where X is any X or X U for a particular direction. The cross-covariance matrix T is generally non-square and therefore one cannot extract the principal components using a normal eigenvalue decomposition. Instead one can use the Singular Value Decomposition (SVD) of the cross-covariance to extract the same information, which is defined as T = W Σ VT
(5.2)
where W, V are real, orthogonal matrices and Σ is diagonal. The product WV T reconstructs the original cross-covariance matrix T. In this case, the W matrix can be viewed as the “eigen-displacement” or “eigen-strain” matrix, and similarly, the V matrix can be interpreted as the “eigen-forces” matrix. The Σ matrix is analogous to the eigenvalues in the eigenvalue decomposition of a square matrix, but it has as many diagonal entries as the smallest dimension of the problem. The Σ matrix contains the singular values/principal components of T. One can reconstruct the original matrix T with one or more of the principal components by selecting those components in Σ which are desired, setting the rest to zero, and performing the product WV T . The entries in Σ are arranged in descending order of magnitude, and the more significant the magnitude of an entry in Σ the higher the influence that particular combination of displacements and strains has on the forces. Therefore, plots of just the principal components of the cross-correlation indicate the dominance of each “mode” of T. Based on the principal components, one can clearly understand which regions of the tyre explain the most variance of the external forces. The columns and rows of W, V T are hierarchically ordered such that the columns of W and the rows of V T each explains the variance of T from highest to lowest. The entries in the first column and row of W and V T explain the most variance. Figure 2 shows the principal components of the cross-covariance between the dataset’s interior deformation measurements and exterior forces. The top plot shows all principal components on a logarithmic scale, while the bottom plot shows the first three principal components on a linear scale. One would expect that U z measurements
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Fig. 2. Principal Components of the cross-covariance between internal displacements and external forces
explain most of the variance of the external force, which is confirmed by Fig. 2. It is also clear that the vast majority of the variance in the external forces can be attributed to the first principal component of the measurements, which matches the hypothesis that the problem has a single degree of freedom essentially since it involves only vertical displacements on a frictionless plate. The principal components tend to have similar values for many nodes, which are explained by the fact that adjacent nodes are likely to have similar displacements and, therefore, a similar principal component. Suppose one isolates the first column of the W matrix (the “eigen-deformation” matrix) from the SVD. In that case, a heat map can be generated, which can be used to show to what degree each measurement on the inside of the tyre contributes to explaining the variance in the external force, which is pictured in Fig. 3 for each direction. In the top three plots of Fig. 3, the contributions are normalised according to their highest principal component compared to the highest principal component for all directions (U z in this case), making it clear which directions are more important than others. In the
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Fig. 3. Normalized and un-normalised plots of the correlation between internal deformations and external forces
left three plots, the figures are not normalised. One can see that measurements on the inside of the tyre in the region of the contact patch explain most of the variance, less is presented by the sidewall adjacent to the contact patch, and the rest of the measurements explains almost none. Suppose one has all three components of deformation available. It is sensible to only measure U z near the contact patch, but it would also be feasible to measure U y at the sidewall near the contact patch. The centre-left plot shows that there is still a relationship between the U y deformations and the external force, even though these measurements explain very little of the variance in the external forces.
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PCR models the response of a variable by linearly mapping the directions of maximum variance in the input and output space. It should be noted that PCR will be successful if one intends to map the directions of maximum variance of the input space to the output space, which may not represent the actual relationship between these quantities depending on how the data was generated. One can approximate a non-linear relationship
Fig. 4. Tread forces predicted from displacements with a PCR model for a varying number of modes
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between the input and output space by modelling it with as many modes as required to achieve the desired accuracy, even though the model itself is still linear in the principal component space. Figure 4 shows the tread forces predicted by the PCR model from displacement and strain, respectively for varying numbers of modes for a total displacement of 30mm at 200kPa inflation pressure. The predictions using 3 or more principal components correlate with the reference virtual experiment results. The eigenvalues of the output covariance matrix can be used to plot the percent of variance explained in the output by each mode, which is shown in Fig. 5. The first and second modes only explain 64% and 78% of the variance in the output, respectively, while the third mode increases to 91%. The dataset comprises virtual experiments wherein there is a single known latent variable, the total displacement of the tyre. The non-linearity of the tyre means that it is not accurate to model this relationship using a linear mapping with a single variable, and as such a PCR model with one mode is not sufficient to represent the relationship between displacements or strains on the inner surface of the tyre and the distribution of forces on the outside. This shows that although it is relatively easy to identify the latent variable, modelling it in a low dimensional space may be difficult. However, using three or more modes is sufficient to model the non-linear behaviour of the tyre with linear variables.
Fig. 5. Percent of variance in the output explained by each mode of the PCR
5 Conclusions This study investigates a data-driven approach to analysing the relationship between external forces and internal strains and deformations. A small dataset is generated to find the principal components of the cross-covariance between the external forces and
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internal strains and deformations. This analysis shows that the first principal component dominates the problem and could be used to reduce the dimensionality. Certain measurement directions are more informative than others, and it is found that vertical deformations and longitudinal strains are the most informative deformation and strain measurements, respectively. Certain regions of the tyre are informative about the vertical force in different directions. The displacement measurements have very clear regions where each measurement is informative, and these regions have little to no overlap. The strain measurements are weakly informative in a similar region to one another, and this region is similar to where high strains are observed in laboratory experiments. This analysis shows a clear hierarchy of which measurements are important from an experimental perspective and regularisation. Many regions can be ignored to ease the identifiability of external forces. Moreover, deformation is the preferred measurement as it is the most informative of the external forces. Principal components regression is considered to model between the strains or deformations on the inner surface of the tyre and the external forces. Principal component regression is a linear model of the principal components of the input and output. While these data-driven methods presented are not suitable for predicting information outside of their training dataset, they are beneficial for studying the identifiability of parameters in an inverse problem. While it is clear that PCR can construct maps between the internal deformations/strains and the external forces, there is no guarantee that the direction of maximum variance in the output corresponds to that of the output. Future work will consider a more robust approach, such as partial least squares regression, whereby the input direction is varied to maximise the variance in the output explained by the input.
References 1. Brodsky, H., Hakkert, A.S.: Risk of a road accident in rainy weather. Accid. Anal. Prev. 20(3), 161–176 (1988) 2. Guthrie, A.G., Botha, T.R., Els, P.S.: 3d contact patch measurement inside rolling tyres. J. Terrramech. 69, 13–21 (2017) 3. Feldesi, F., Botha, T.R., Els, P.S.: Full-field strain measurements of the inside of an agricultural tyre using digital image correlation. J. Terrramech. 91, 309–318 (2020) 4. Tuononen, A.: On-board estimation of dynamic tyre forces from optically measured tyre carcass deflections. Int. J. Heavy Veh. Syst. 16(3), 362–378 (2009) 5. Matsuzaki, R., Hiraoka, N., Todoroki, A., Mizutani, Y.: Analysis of applied load estimation using strain for intelligent tires. Journal of Solid Mechanics and Materials Engineering 4(10), 1496–1510 (2010) 6. Oertel, C., Hempel, J.: Smart tire: a pattern based approach using fem. In: 10th Annual Conference Intelligent Tire Technology (2014) 7. Yang, X., Olatunbosun, O., Garcia-Pozuelo, D., Bolarinwa, E.: Fe-based tire loading estimation for developing strain-based intelligent tire system. Technical Report, SAE Technical Paper (2015) 8. Khaleghian, S., Ghasemalizadeh, O., Taheri, S.: Estimation of the tire contact patch length and normal load using intelligent tires and its application in small ground robot to estimate the tire-road friction. Tire Sci. Technol. 44(4), 248–261 (2016)
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9. Garcia-Pozuelo, D., Olatunbosun, O., Yunta, J., Yang, X., Diaz, V.: A novel strain-based method to estimate tire conditions using fuzzy logic for intelligent tires. Sensors 17(2), 350 (2017) 10. Yunta, J., Garcia-Pozuelo, D., Diaz, V., Olatunbosun, O.: A strain-based method to detect tires’ loss of grip and estimate lateral friction coefficient from experimental data by fuzzy logic for intelligent tire development. Sensors 18(2), 490 (2018) 11. Garcia-Pozuelo, D., Olatunbosun, O., Strano, S., Terzo, M.: A real-time physical model for strain-based intelligent tires. Sens. Actuators A 288, 1–9 (2019) 12. Lee, H., Taheri, S.: A novel approach to tire parameter identification. Proc. Inst. Mech. Eng. Pt. D J. Automobile Eng. 233(1), 55–72 (2019) 13. Garcia-Pozuelo, D., et al.: Development and experimental validation of a real-time analytical model for different intelligent tyre concepts. Veh. Syst. Dyn. 57(12), 1970–1988 (2019) 14. Khaleghian, S., Ghasemalizadeh, O., Taheri, S., Flintsch, G.: A combination of intelligent tire and vehicle dynamic based algorithm to estimate the tire-road friction. SAE Int. J. Passeng. Cars-Mech. Syst. 12(2), 81–98 (2019) 15. Mendoza-Petit, M., García-Pozuelo, D., Díaz, V., Olatunbosun, O.: A strain-based intelligent tire to detect contact patch features for complex maneuvers. Sensors 20(6), 1750 (2020). https://doi.org/10.3390/s20061750 16. Xu, N., Askari, H., Huang, Y., Zhou, J., Khajepour, A.: Tire force estimation in intelligent tires using machine learning. arXiv preprint arXiv:2010.06299 (2020) 17. Tsiotras, P., De Wit, C.C.: On the optimal braking of wheeled vehicles. In: Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No. 00CH36334), vol. 1, pp. 569–573. IEEE (2000) 18. Pegram, M.S., Botha, T.R., Els, P.S.: Full-field and point strain measurement via the inner surface of a rolling large lug tyre. J. Terrramech. 96, 11–22 (2021) 19. Gast, R.G., Els, P.S., Kok, S., Wilke, D.N., Botha, T.R.: Identifiability of tyre force contact prediction from deformation measurements. MATEC Web Conf. 347, 00027 (2021) 20. Ben Turkia, S., Wilke, D.N., Pizette, P., Govender, N., Abriak, N.-E.: Benefits of virtual calibration for discrete element parameter estimation from bulk experiments. Granul. Matter 21(4), 1–16 (2019). https://doi.org/10.1007/s10035-019-0962-y 21. The European Commission: On-board Measurement of Friction and Road Slipperiness to Enhance the Performance of Integrated and Cooperative Safety Systems. Finland (2008). https://cordis.europa.eu/project/id/027006 22. Gast, R.G.: Tyre Force Prediction from Deformation Measurements, Master’s Thesis, University of Pretoria (2022)
Design Study of the Low-Cost Advance Rider Assistance System Václav Mašek(B)
ˇ and Roman Cermák
University of West Bohemia, Univerzitní 2732/8, Pilsen 301 00, Czech Republic [email protected]
Abstract. This work describes the design and implementation of a low-cost Advance Rider Assistance System (ARAS). Motorcycle riders are more prone to the injury during an accident than passengers of the car. For riders those accidents often end up tragically and additionally, there is a higher chance that rider will be involved in the accident than the passenger of a car. Therefore, there is a need for devices that can increase the passive and active safety of bikers. The work describes the design verification and implementation of a simple and affordable assistance system with traffic sign recognition, pedestrian recognition and proximity alert function. Device contains sensory unit equipped with a camera for pedestrian and traffic sign recognition, infrared (IR) rangefinder for proximity measurement and a combination of Global Position System (GPS) sensor and Inertial Measurement Unit (IMU) for the independent speed measurement of the motorcycle. Displaying unit contains Head-Up Display (HUD) and is placed on the helmet. Methodology part describes considered scenarios which could be prevented and possible solutions. In addition to the mentioned functions, the possibility of future extension with smart infrastructure communication functions like Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) is taken into account. Based on these considerations, a suitable mechanical solution and used hardware was selected. Design study describes mechanical and mechatronic design and is supplemented by analyses. Implementation part describes software solution (both of sensory and displaying unit) and prototype manufacturing using 3D printing. Test part describes conducted tests and their results, with special emphasis on proximity alert response rate and capability of vision system using cascade classifier. Several further improvements (features which are currently under development, such as night vision, improved scene recognition, people on wheelchairs recognition, etc.) are described at the end of the article. Conclusion involves further work and new ideas that came up during the process. Keywords: Advanced rider assistance system · Head-up display · Prevention · Sensor fusion · Design · Simulation · Testing · Traffic sign recognition · Pedestrian detection · Proximity warning system
1 Introduction In the Czech Republic, there was an average annually over 90 000 accidents between 2010 to 2020, with over 600 fatalities annually [1]. One of the most common causes of the traffic accident was the driver’s mistake. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 248–260, 2023. https://doi.org/10.1007/978-3-031-15211-5_22
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According to the study [2], up to 9% of fatal traffic accidents are caused by overlooking of the important fact when driving. According to the study [3], the most important contributing factor of mortal accidents of bikers is not seeing another road user. One of the most threatened groups of road users are motorcycle drivers. Per 100,000 motorcycles, there is, on average six times more fatal accidents than in the same number of cars [3, 4]. In addition, a motorcyclist has almost 30 times less chance of survival than a passenger in a car during a collision [5]. The subject of this work is a proposal for a simple assist system that could reduce the risk of accident and prevent basic traffic accident scenarios. It is based on the original study (which is already accepted for DSM:IE 2022, but not published yet), which had to map usable hardware, and demonstrate the mechanical and software design. This work focuses on verifying the projected functionalities and suggesting new features and improvements.
2 Literature Review Significant part of the literature review was presented in the original study and presents primarily available solutions. It shows that in 2021 functional ARAS systems were under development with no commercial application. All available solutions just showed additional information to the rider such as navigation data. However, none of them did not perform the function of the ARAS system, which is the intention of this work: a low-cost universal assistance system. In the area of assistance systems was promising the concept presented by company Bosch, which is ARAS system containing adaptive cruise control, forward collision warning and blind-spot detection. In 2022 Kawasaki started to sell the first model equipped with this system – Ninja H2 SX SE [6–8] (Fig. 1).
Fig. 1. Bosh advanced rider assistance system [8]
In the area of imaging devices is an interesting and already commercially available NUVIZ HUD set. This unit, which is connected to the helmet and controlled by a special
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controller attached to the handlebars, allows displaying e.g., satnav data. System is also equipped by a camera which allows recording in a dashcam mode [9] (Fig. 2).
Fig. 2. NUVIZ HUD set [9]
Furthermore, a research analysis of possible traffic accidents was conducted. According to the study [10] that is focused on the issue of motorcycle accidents in metropolitan areas (presented system is designed for use in urban areas where the speeds are relatively low), are the most common types of accidents: • Collision with a front vehicle • Collision with a pedestrian • Frontal collision with another vehicle These types of accidents also most often involve fatal injuries. For the prevention of those types of accidents is suitable an assistance system with sensors covering only limited field of view (in front of the motorcycle). This type of assistance system can be implemented by a sensory unit, placed in the front part of the motorcycle. For the prevention of other types of motorbike accidents (side crash, collision with a vehicle which is in a blind spot during overtaking etc.) will be necessary to install additional sensors in other parts of the bike.
3 Research Methodology 3.1 Original Study This paper is based on the original design study of the assistance system, which included an electrical and mechanical design draft and supposed software design. Presented system is independent on the type of used motorbike. System has its own power supply and sensor kit, allowing independent speed measuring.
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The aim of this work is to verify the design of the original study of the ARAS system. First part of the work is verification of the original study, and the second part is optimization of the system to enhance the functionality. The whole set contains two parts – sensory unit, placed on the front fork of the motorcycle, and display unit, placed on the rider’s helmet. Both communicate using the Bluetooth interface (Fig. 3).
Fig. 3. Original design study of the ARAS system
Sensory unit contains Raspberry Pi 3b+ as a microcontroller. This board communicate with sensors during the General-Purpose Input/Output (GPIO) pins, loads camera video stream using Camera Serial Interface (CSI) connector and IR sensor data using Universal Serial Bus (USB). Communication with a smart infrastructure can be carried out using Wi-Fi. IMU and GPS modules are used for speed measurement. Camera is used for recognition of the pedestrians, traffic signs and other important objects on the way. IR sensor measures the distance from the front vehicle. Display unit uses ESP32 microcontroller and two displays for information and alert displaying. Both units are powered by power banks in the original study. 3.2 Component Testing For the attachment of all tested sensors was designed a platform. Camera function is tested separately. It allows easy access to all electronic parts during the testing and it is a sufficient replacement for the sensory unit in the way of the manipulation and attachment. Primary purpose of this platform is proximity alert function testing (Fig. 4). Image processing test was performed with a cascade classifier using Haar-like features. For traffic sign recognition was used dataset [11]. For the pedestrian detection and car detection was used classifier [12]. Testing was performed on the video captured during car drive using Raspberry Pi camera module due to safety reasons in the first phase of testing.
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Fig. 4. Test platform design (on the left) and the physical realization
3.3 Improving of the Mechanical Design of the Sensory and the Display Unit Previous solution, which was presented in the original is satisfactory in terms of strength. The biggest problem was relatively high mass and dimensions, mostly because of the relatively big power source (power bank). In case of the displaying unit, there can be an ergonomic issue related to using a heavy power source. On the other hand, the pros of using the power bank are stable voltage and relatively high provided current together with appropriate capacity. The task was to find an alternative power source, capable of powering all components and providing better energy density. After that, the design of both units was modified for the new power source.
4 Results 4.1 Image Processing After testing the recorded data, the classifiers appear as inconvenient for use in the ARAS system. Used datasets have strong tendency for errors in detections. Datasets for traffic sign detection show strongly false-negative behaviour. Traffic sign detection is not reliable. On the bottom part of Fig. 5 is visible that despite relatively good light conditions, the classifier wasn’t able to detect the traffic sign. On the top part of Fig. 5 is shown correct traffic sign detection.
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Fig. 5. Correct (top) and false negative detection of traffic signs
On Fig. 6 is shown the moment when the classifier starts to stable detect the red traffic light. This classifier also has several false-positive detections but only for a very limited amount of time.
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Fig. 6. Detection of the red light
Pedestrian detection dataset has worked well if there was a sufficient contrast with a background. Unlike previous datasets this one has a tendency to false-positive detection, as can be seen on Fig. 7, where the handle placed on the van was continuously detected as a pedestrian.
Fig. 7. False positive pedestrian detection (part of the van) and correctly detected pedestrian on the left
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Dataset for the car detection performed the best results, on the other hand has the highest number of false-positive detections. Often happened that shadow or road cracking was detected as a car. This can be seen on Fig. 8.
Fig. 8. False-positive detections of the car
It’s likely that the incorrect detection is caused by the fact that datasets were made in different conditions that are common in the Czech Republic. Because of this fact, the further research will be performed using classifiers made by our team. 4.2 Speed Measurement and the Proximity Warning All sensors used for proximity warning function (GPS, IMU and IR sensor) were tested separately. Inertial sensor MPU6050 shows a relatively high noise level during the measurement. On Fig. 9 is shown the output of the sensor when the sensor is lying still on the table.
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Fig. 9. Accelerometer data; sensor is still
It will be necessary to develop a sufficient filter to reduce IMU drift. Simple moving average filter, which was used for GPS data smoothing, is not suitable for this. On the Fig. 10 is shown the output of the GPS measurement. Because in the GPS data the noise is not propagated on the next measurement and the error is not accumulated, moving average filter with factor 3 provides a sufficient level of noise filtration.
Fig. 10. GPS speed measurement
On Fig. 11 is shown the measurement of the distance during the approach to the wall. First second of the measurement was sensor still and then started to move against the obstacle.
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Fig. 11. Distance measurement
Even though the measuring of the data is not a problem, it turned out, that the problematic was simultaneous data acquisition from all sensors, connected using different buses. Partial solution was using multithreading, using the Threading library in Python. However, for further research will be necessary to use low-level programming language for simultaneous data and image processing. In that case is assumed that even the Raspberry Pi Zero 2W board would be used. This single-board computer provides more powerful processor but it has less RAM. Due to his compact dimensions is assumed, that this board (or its modernized version) will be used in future concepts of the system. 4.3 Improved Design of the Sensory Unit In the original mechanical design, each component was positioned independently. The improved version uses printed circuit board (PCB) for the attachment of all components. Final component placement is shown on Fig. 12.
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Fig. 12. PCB design (A), implementation of the PCB (B) a component placement (C)
Furthermore, a better power source was found. As a good source of power, considering maximal provided current and capacity, is an accumulator type 18650, used in a serial-parallel configuration. This type of wiring provides an output voltage 6,4 V, therefore the step-down converter (like LM2596) is necessary.
Fig. 13. Original design of the sensory unit (left) and improved design
Reducing the multiple parts, the new design of the sensory unit is significantly smaller than the original one. The new solution is more practical, lighter and less material is needed. Furthermore, because the sensory unit is placed on the spot where it’s exposed
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to mechanical vibrations, the new solution would be less prone to mechanical load (Fig. 13). 4.4 Improved Design of the Display Unit
Fig. 14. Original design of the display unit (left) and the improved design
The same source of power was used in the display unit. Whereas original idea seems to be sufficient so far, the only modification of the unit was a reduction of the power pack. In addition, this solution is lighter, so from an ergonomic point of view, it would be better for the user (Fig. 14).
5 Conclusion This work is focused on testing the design study of the universal low-cost ARAS system. During the work, the testing of data acquisition was performed. In the second part of the work is suggested the optimized mechanical design. During the testing of selected image classifiers it shown up that their functionality is very limited. Based on this finding was decided that its necessary to make our own datasets. Beyond the used types of datasets, a classifier for detection of the impaired persons and persons on the wheelchair will be made because classical pedestrian classifier might not be successful in this task. For the speed measuring in areas where the GPS signal is not available, is necessary to design a robust solution for reducing accelerometer drift. Possible solutions are filter design or IMU units’ redundancy. Software written in python work only with restrictions in the real scenario. For further development will be necessary to prepare more optimized software. For the optimized design of sensory and display unit was chosen a new source of energy. Because of the higher energy density, both space and the weight of the unit were reduced. New design suggests using the Raspberry Pi Zero 2W instead of the Raspberry Pi 3B+ as a sensory unit computer. For further research is assumed that the functional prototype of sensory unit will be made and tested on the motorbike riding on the closed track. Acknowledgement. This research is partly supported by project SGS-2022–009.
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References 1. CZSO: Transport accidents - time series. [Online]. Available at: CZSO. https://www.czso.cz/ csu/czso/transport_accidents_time_series (2022). Accessed 1 Mar 2022 2. Insurance Information Institute: Facts + statistics: Distracted driving. [Online]. Available at: https://www.iii.org/fact-statistic/facts-statistics-distracted-driving (2022). Accessed 1 Mar 2022 3. Milling, D., Affum, J., Chong, L., Taylor, S., Eveleigh, M.: Infrastructure Improvements to Reduce Motorcycle Casualties. Austroroads. [online]. Austroads Ltd, Sydney. Available at: https://www.amda.org.au (2016). Accessed 5 Mar 2022 4. Insurance Information Institute: Facts + statistics: Motorcycle crashes. [Online]. Available at: https://www.iii.org/fact-statistic/facts-statistics-motorcycle-crashes (2019). Accessed 14 Oct 2021 5. Dimovski A. (2021) Motorcycle accidents - statistics, facts, and trends in 2021. [Online]. Available at: carsurance. https://carsurance.net/insights/motorcycle-accidents/. Accessed 14 Oct 2021 6. Robert Bosch GmbH: Advanced rider assistance systems. [Online]. Available at: https:// www.bosch-mobility-solutions.com/en/solutions/assistance-systems/advanced-rider-assist ance-systems-2w/. Accessed 13 Mar 2022 (2022) 7. Vassallo, J.: Kawasaki Ninja H2 SX SE debuts with Bosch ARAS technology. [online] World of Technology, Video Games and Digital Entertainment. Available at: https://techgameworld. com/kawasaki-ninja-h2-sx-se-debuts-with-bosch-aras-technology/ (2021). Accessed 22 Mar 2022 8. Lookcharms: Kawasaki is developing a camera system for the upcoming Ninja H2 SX. [online] US Sports. Available at: https://lookcharms.com/street-kawasaki-is-developing-a-camera-sys tem-for-the-upcoming-ninja-h2-sx/ (2022). Accessed 1 Apr 2022 9. McMurphy, R.: Nuviz all-in-one heads-up display – gear review. [Online] Available at: https:// www.rideapart.com/reviews/253531/nuviz-all-in-one-heads-up-display-gear-review/ (2017). Accessed 18 Mar 2022 10. Akter, T., Pervaz, S.: 1. Assessing motorcycle accident and injury characteristics in Dhaka metropolitan city. International Conference on Urban and Regional Planning, 5–6 Oct 2019 (2019) 11. Fizzete C.: Road sign cascades. Source code. GitHub repository. Available at: https://github. com/cfizette/road-sign-cascades (2017). Accessed 12 Feb 2022 12. Sathwick P.: Real time detection and classification of vehicles and pedestrians using haar cascade classifier. Source code. GitHub repository. Available at: https://github.com/sathwi ck9/opencv (2020). Accessed 14 Feb 2022 13. Mašek, V., Cermák, R.: Motorcycle rider assistance system for obstacle detection with visualization in the rider’s visual area. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Rauch, E., Perakovi´c, D. (eds.) DSMIE 2022. LNME, pp. 41–50 (2022). https://doi.org/10.1007/978-3031-06025-0_5. ISBN 978-3-031-06025-0. ISSN 2195-4364
Load Testing of Alternating Current Hydraulic Drive Tamás Fekete(B) University of Miskolc, Miskolc, Hungary [email protected]
Abstract. Several types of research related to hydraulics are currently underway at our institute [6–9], but this paper aims to investigate the hydraulic drive. The energy transfer at the hydraulic drives can be solved by electrical analogy with direct current hydraulic drives and alternating current hydraulic drives [2–4]. At the direct current hydraulic drives, the operating fluid flow in one way besides the alternating current hydraulic drives, where it alternates periodically between the hydrogenerator and the hydromotor. The alternating current hydraulic drives have two types. The alternating current synchronous drive and the alternating current asynchronous drive. The alternating current hydraulic drives have two main units: the alternating current hydrogenerator and the alternating current hydromotor. The angular position of the hydrogenerator and the hydromotor is changed by the loads that occur during operation. The aim of the research was to measure this angular deviation in the case of the implemented synchronous drive. With the help of this, it is possible to determine how much load the system can withstand under certain parameters. Keywords: Alternating current hydraulic drive · Load angle · Synchronous drive
1 Introduction One possible grouping of hydraulic drives is shown in Fig. 1.
Hydraulic drives
Direct current hydraulic drives
Alternating current hydraulic drives
Alternating current synchronous hydraulic drives
Alternating current asynchronous hydraulic drives
Fig. 1. A possible grouping of hydraulic drives according to the electrical analogy © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 261–267, 2023. https://doi.org/10.1007/978-3-031-15211-5_23
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Hydraulic drives can be divided into two large groups based on electrical analogy; for direct current (DC) and alternating current (AC) hydraulic drives. AC hydraulic technology belongs to the group of hydrostatic drives. In the case of direct current hydraulic drives, the fluid flows in one direction between the pump and the working elements (e.g. hydromotor), even in the case of alternating current hydraulic drives it pulsates between the hydrogenerator and the hydromotor. The two main building blocks of AC hydraulic drives are the hydrogenerator (AHG) and the hydromotor (AHM). The hydraulic motor converts the hydraulic energy provided by the hydrogen generator into mechanical energy.
2 Load Angle of Alternating Current Hydraulic Drives The forces acting on the phase pistons of the hydraulic motor and the resulting torque must be examined under the action of the load module attached to the output shaft of the hydraulic motor. The shaft of the hydraulic motor is loaded with torque. The torque exerted on the hydraulic motor phase pistons must be examined. This torque is in balance with the load torque. I performed the calculation based on Fig. 2.
Fig. 2. The axle load force system of the motor unit in the case of a phase piston.
Based on Fig. 2, the drive torque can be written: Mh = FN kN − FS kS .
(1)
During operation (but not under constant load) it can be stated that FN >> FS
(2)
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therefore FN ≈ F
(3)
kN = −e sin ϕm .
(4)
and
With the approximations established above, the phase pistons in direct contact with the eccentric of the hydraulic motor produce the driving torque on the shaft of the motor unit. The kinematic model is shown in Fig. 3.
Fig. 3. The generation of Mh is the resulting driving torque.
Figure 3 shows the time of the starting phase when the driving torque generated by the rotation of the eccentric disk ϕg of the generator unit as a result of the phase pressures acting on the phase pistons of the motor unit, balanced with the load torque (Mh = Mt ), moves the shaft of the motor unit. The resulting driving torque Mh can thus be determined by the following relation: Mh =
3
FNi kNi
(5)
1
The angle ϕg required to start the hydraulic motor unit is called the load angle ϕt for the load torque. This phenomenon occurs because the synchronous position of the hydrogen generator and the hydraulic motor is lost due to the load. The eccentric disc of the motor unit lags behind the eccentric disc of the generator unit by a load angle value of ϕt . Based on Fig. 3, on the motor unit shaft: Mh = F1 k1 + F2 k2 + F3 k3
(6)
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driving torque occurs. The sources of the forces F1 , F2 and F3 are the phase pressures occurring in the phase space. Phase pressures can be determined from capacitive fluid flows in the phase spaces. The resulting driving torque Mh results in a pressure increase in the phase lines (one or two). The rate of increase in pressure depends on the magnitude of the load, since the higher the load, the greater the driving torque the phase pistons must exert on the shaft of the hydromotor.
3 Measurement of the Load Angle In the case of AC hydraulic drives, when the hydrogenerator is driven, the hydromotor starts even with a slight delay. This phenomenon is called the load angle. This is because loads also occur when idling, for which the right amount of starting torque must be built up, which can develop as the pressure increases. This delay can be examined by measuring the relative angular positions of the hydrogenerator and the hydromotor. The higher the load, the greater the angular misalignment between the hydrogen generator and hydraulic motor shafts. I was able to determine the extent of this deviation by placing a disc with an inductive encoder on the shafts of the hydrogenerator and the hydromotor (Fig. 4). I was able to determine the load angle from the time difference between the two signals.
Inductive transmitters
Discs
Fig. 4. Placement of inductive transducers on the experimental equipment.
The inductive encoder travels from point P along the arc to point P’during time t (Fig. 5). The s - the master taken (circular arc), the r - the displacement vector (string).
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Fig. 5. Displacement of the inductive encoder.
In the case of a smooth circular motion, the angular rotation changes evenly with time (periodic motion). Angular rotation for time t: ϕ (Fig. 6).
Fig. 6. Uniform circular motion of inductive encoder.
The corresponding arc (path): s = Rϕ. Due to the uniform circular motion: ϕ ∼ t. Thus the angular rotation during time t is: ϕ = ϕ0 + ωt
(7)
The time required for a complete revolution is ϕ = 2π . So it turns out that T=
2π . ω
(8)
During the experiment, I was able to measure three parameters; one is the time difference between the speed (frequency) of the shaft of the hydrogen generator and the hydromotor, the distance (radius) between the inductive encoder and the center line of the shaft, and the signal emitted by the two inductive encoders. The measurement results are shown in Table 1.
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Table 1. For preset eccentricity values (a, e = 6 mm; b, e = 10 mm; c, e = 14mm) and variable load and speed, the average value of the load angle for rigid and flexible phase conductors.
a,
Rev [1/min]
60
120
240
Load [Nm]
30
40
50
Load angle [0] rigid phase space
19
26
36
Load angle [0] flexible phase space Dropout from the synchronous position [°] for rigid phase space Dropout from the synchronous position [°] for flexible phase space
17
26
34
27
38
54
24
38
51
6
6
6
60
120
240
Eccentricity [mm] b, Rev [1/min] Load [Nm]
30
40
50
Load angle [0] rigid phase space
14
22
30
Load angle [0] flexible phase space Dropout from the synchronous position [°] for rigid phase space Dropout from the synchronous position [°] for flexible phase space
13
20
29
20
32
45
18
29
44
Eccentricity [mm]
10
10
10
Rev [1/min]
60
120
240
Load [Nm]
30
40
50
Load angle [0] rigid phase space
10
17
26
c,
Load angle [0] flexible phase space Dropout from synchronous position [°] for rigid phase space Dropout from synchronous position [°] for flexible phase space
8
15
25
14
25
39
11
22
38
Eccentricity [mm]
14
14
14
Based on the measurements, I found that the load angles also depend on the different eccentricity values, more precisely on the Re ratio. It can be observed that due to the capacitive resistance introduced into the system by the flexible phase wires, the magnitude of the load angle decreased despite the same parameters.
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4 Summary Measurements performed on an experimental device have shown that, in contrast to the alternating current hydraulic drive with a rigid phase space, the alternating current hydraulic drive with a flexible phase space drops out of sync at the same settings, that is the load angle is smaller.
References 1. Ponomarjov Sz, D.: Szilárdsági számítások a gépészetben c. kötet (Strength calculations in mechanical engineering). Rezgések Ütések. M˝uszaki Könyvkiadó, Budapest ( 1966) 2. Erdélyi, J., Fekete, T., Lukács, J.: A kontrakciós henger konstrukciós és m˝uködési tulajdonságai.(Construction and operating properties of the contraction cylinder) Pneumatika, hidraulika, hajtástechnika, automatizálás XII. Évf, pp. 3–5 (2008) 3. Fekete, T.: The alternating current synchronous hydraulic drive. Annals of Faculty of Engineering International Journal of Engineering 12, 235–238 (2014) 4. Fekete T.: alternating current hydraulic drive the possibility of applying in the automotive industry. Vehicle and Automotive Engineering, 49–57 (2017). ISBN978-3-319-51188-7 5. Fekete T.: Analyzing the temperature of the alternating current hydraulic drive. Eng. Appl. Sci. (2020) 6. Tóth, S.G., Takács, G.: Examination of machine tool slideway combined with pressure chambers. Des. Mach. Struct. 10(2), 160–164, 5 (2020) 7. Tóth, S.G., Takács, G., Szilágyi, A.: Hidrosztatikus hordozó csapágyakra jellemz˝o határfordulatszám fizikai hátterének vizsgálata, (Investigation of the physical background of the limit speed characteristic of hydrostatic carrier bearings) In: Barabás, István (szerk.) XXVII. Nemzetközi Gépészeti Konferencia OGÉT (2019) 8. Tóth, S.G., Takács, G.:, Hidrosztatikus f˝oorsó csapágyazásának vizsgálatára alkalmas mér˝oberendezés tervezése (Design of a measuring device suitable for testing the bearing of a hydrostatic spindle) Multidiszciplináris Tudományok: A Miskolci Egyetem Közleménye, vol. 10, 3, pp. 413–420, 8 (2020) 9. Rónai L.: Development of a low-cost pressure sensor. International Journal Of Engineering And Management Sciences / M˝uszaki És Menedzsment Tudományi Közlemények 5(4), 33–38, 6 (2020)
A Review on HCNG/Diesel Tri Fuel Engine Performance Hassan Sadah Muhssen1,2(B) , Ákos Bereczky1 , and Máté Zöldy3 1 Department of Energy Engineering, Budapest University of Technology and Economics,
Müegyetem rkp. 3-9, Budapest 1111, Hungary [email protected] 2 Department of Mechanical Engineering, Wasit University, Wasit, Iraq 3 Department of Automotive Technologies, Budapest University of Technology and Economics, 6 Stoczek St, building J, Budapest 1111, Hungary
Abstract. High torque and thermal efficiency, durability, reliability, fuel economy, and low emission of carbon dioxide are the factors that made of diesel engine as a preferable engine in passenger transport, trucks and power generation. In contrast, high localized temperatures along with heterogeneous combustion of the fuel-air mixture have made the diesel engine yields a high level of particulate matter and nitrogen oxide emissions. In addition to the problems of diesel engines, the fear of depleting energy sources along with environmental pollution and global warming as a result of using fossil fuels prompted researchers to find many types of alternative fuels. HCNG is a mixture of hydrogen (H2 ) and compressed natural gas (CNG) that is used as an alternative fuel for diesel engines at varying substitution ratios. The objectives of using HCNG are to maintain or improve engine efficiency and reduce exhaust emissions in addition to the cheapness of this fuel compared to diesel. Many researchers have conducted theoretical and experimental research on the use of HCNG fuel with diesel engines, but there are few literature review studies in this area. Therefore, in this research, the results of a number of pre-prepared studies regarding the performance and exhaust emissions of a HCNG tri-fuel diesel engine were reviewed and summarized. Engine performance is represented by in-cylinder pressure, heat release, brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), brake power (BP), and brake torque (BT). Keywords: Hydrogen enrichment · Natural gas · HCNG · Diesel engine · Performance
Abbreviations AFR ATDC BP BSFC BT BTDC
Air-fuel ratio After the top dead center Brake power Brake specific fuel consumption Brake torque Before the top dead center
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 268–288, 2023. https://doi.org/10.1007/978-3-031-15211-5_24
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BTE CA CH4 CI CNG CO CO2 CR DF FC DFS PDF H2 HC HCNG HNG HRR IMEP IP Mix NG NOX PM TDC λ
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Brake thermal efficiency Crank angle Methane Compression ignition Compressed natural gas Carbon monoxide Carbon dioxide Compression ratio Dual fuel Fuel consumption Diesel fuel system Pilot diesel fuel Hydrogen Hydrocarbons Mixture of hydrogen and compressed natural gas Mixture of hydrogen and natural gas Heat release rate Indicated mean effective pressure Injection pressure Mixture Natural gas Nitrogen oxides Particulate matter Top dead center Excess air factor
1 Introduction Diesel engines are extensively used in the world due to their cost-effectiveness, high combustion efficiency, adaptability and reliability [1–3]. In spite of this, diesel engines are one of the major environmental pollution resources [4, 5]. NOX and PM are the main harmful pollutants from diesel engines. NOX emission is one of the main causes of acid rain and photochemical smog. PM from diesel engines consists of many types of chemical components like elemental carbon, inorganic ions, organic carbon, trace elements etc. [6, 7]. These particles are extremely harmful to human health and the environment. In order to reduce the contradiction between the decreasing oil resources and the need for increased energy simultaneously with the reduction of pollutant emissions, alternative fuel utilization has been established to be an attractive solution [8]. Among many alternative fuels, natural gas (NG) is very promising and attractive in the transportation sector. Firstly, natural gas is available in several areas worldwide with attractive prices [9–12]. In addition to oil fields and natural gas fields, the NG industry is producing gas from increasingly more challenging resource types such as sour gas, tight gas, coal-bed methane, shale gas, and methane gas hydrate [9]. Secondly, although the main component of NG, namely methane, is a greenhouse gas, NG still is an eco-friendly
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fuel; it contributes to reducing CO2 emission due to its lowest carbon-to-hydrogen ratio of wholly fossil fuels. Natural gas can also significantly reduce the NOX emission and, at the same time, produce nearly zero PM and smoke [13, 14]; which is extremely difficult to achieve in conventional diesel engines. Thirdly, NG is not prone to knock because of its high methane number under normal circumstances. Therefore, it could be used in engines with a reasonably high compression ratio and obtain a higher thermal efficiency compared with that of a standard fuel engine [9, 10]. There are two solutions for the use of gaseous fuels in diesel engines (CI engines). One solution is to convert the engine to a gas engine, which is not addressed in this paper, and the other is the so-called dual-fuel (DFS) system. In this solution, either an air mixture of the gas fuel is created in the intake manifold, or it is injected directly into the combustion chamber and ignited by a dose of diesel. In spite of the advantages of NG, it has some disadvantages, such as poor lean-burn capability and low burning velocity. These troubles lead to engines having longer combustion duration, high cyclic variation, lower power output, decreasing in heat release and in-cylinder pressure [9, 10]. Moreover, the CO and HC emissions may increase several times or even more than 100 times, in addition to suffering from low BTE at low and intermediate loads, while under high engine load the BTE is a little higher or similar as compared to normal diesel mode [9]. According to the disadvantages of using natural gas with diesel engines, many researchers sought to find alternative gaseous fuels like hydrogen (H2 ) and others. H2 supply in compression ignition (CI) engines significantly reduces smoke, HC, CO, and CO2 levels that can reach as much as over 50% under optimum conditions. High H2 rates tend to have an apparent effect on the combustion, which is depicted as a sharp increase in brake thermal efficiency and heat release rate. However, the combustion of the higher energy content of fuel causes the increased in-cylinder temperatures that result in significant growth of NOx, particularly at high load conditions as well as high in-cylinder pressure [15]. Hydrogen-enriched natural gas (HCNG) is a new engine fuel that not only owns the advantages of CNG and hydrogen but also overcomes their disadvantages. H2 is the best additive candidate to NG due to its unique characteristics. Moreover, hydrogen has higher flame stability, and low ignition delay, in addition to having a wide flammability range which allows higher efficiency with a leaner operation for reduced toxic emissions. The effect of adding H2 to NG can lead to extended flammability, shorter burning time and leaner limits of the mixture. This, in turn, leads to improve combustion in addition to increasing heat release and pressure inside the combustion chamber [16–19]. Moreover, HCNG used with diesel engines results in a reduction in fuel consumption and improvement of BTE [18, 20–22]. Likewise, the BP and BT improved by using HCNG with the diesel engine. Besides that, H2 addition can broaden the range of EGR while maintaining low cyclic variations and low levels of NOX emissions [23]. The main purpose of this study is to provide a comprehensive review of the works of literature related to the potential use of HCNG in a diesel engine. This literature review focused on the effects of engine size, type, and operation parameters in addition to fuel blends on the combustion and performance characteristics.
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2 Engine Combustion Performance Characteristics 2.1 In-Cylinder Pressure Arslan & Kahraman [20] conducted experiments work at two engine speeds of 1500 and 1750 rpm, no-load and four different engine loads. They found an engine cylinder pressure of 59.16 bar that is closed to the 59.39 bar obtained with using of diesel fuel, was obtained at 1500 rpm for “Diesel NG(500 g/h)” and 59.9 bar (highest values) was obtained for “Diesel (500 g/h) [80%NG + 20%H2 ]” at 1750 rpm. For “Diesel-NG (250 g/h)” and “Diesel-NG (500 g/h)”, as the engine speed increases, at the point where the maximum in-cylinder pressure is obtained further to the after top dead center (TDC). With adding of 500 g/h NG, an increase of 4.5% was obtained in the cylinder pressure at full load, while an increase of 6.5% was reached in the case of using “Diesel (500 g/h) [80%NG + 20%H2 ]”. In brief, with the diesel engine, the addition of NG and H2 /NG mixtures to the combustion air, the in-cylinder pressure increased. Zhou et al. [16] found that the in-cylinder pressure slightly changed at low to medium loads but increased at high loads. At low to medium loads (10, 30 and 50% loads), it is observed that the in-cylinder pressure decreases with adding of each gaseous fuel when the reduction is within 2%. At 50% load, under diesel-HCNG operation, the in-cylinder pressure increases with an increase of H2 fraction in NG. For diesel-(70%NG + 30%H2 ) operation, the in-cylinder pressure begins to exceed that of diesel fuel operation. At 70 and 90% loads, as reflected by the substantial increase in peak in-cylinder pressure. In compared with diesel fuel operation, at 90% load, the peak in-cylinder pressure increases by 6.0, 9.5, 14.3, 16.8 and 27.1% for adding 100%NG, 70%NG + 30%H2 , 50%NG + 50%H2 , 30%NG + 70%H2 and 100%H2 , respectively. In addition, the corresponding increases of in-cylinder pressure are −21.5, −15.6, −13.1, −9.7 and −19.5% at 10% load, and 82.3, 81.9, 111.8, 141.8 and 78.2% at 90% load. At 90% load, there is a significant influence of the gaseous fuel on the engine combustion process. Compared with diesel fuel operation, diesel-H2 combustion becomes uncontrolled and unstable with a remarkable increase of peak in-cylinder pressure of 25.3%. The in-cylinder pressure at 90% load almost increases linearly with the increase of H2 fraction in methane. For diesel-(70%H2 + 30%NG), compared with diesel fuel operation, the increase in peak incylinder pressure is 18.1%, and compared with diesel-NG operation, the corresponding increase is 5.8%. Moreover, at 90% load, compared with diese-H2 operation, NG can improve the durability and safety of H2 combustion in a diesel engine by avoiding uncontrolled combustion, such as the pointed increase of peak in-cylinder pressure. Alrazen et al. [24] reported that the peak in-cylinder pressure increases with the addition of gaseous fuels at low and medium values of the excess air. Compared with DieselCNG operation at 2.4 excess air, the peak pressure increases by 28.57% and 33.414% by way of adding the limit value of hydrogen to CNG, such as (H30%H2 + 70%CNG) and (50%H2 + 50%CNG), respectively. Compared with (50%H2 + 50%CNG), it to decreases by 0.726% and 3.81% with (70%H2 + 30%CNG) and Diesel-H2 operations, respectively due to low value of fuels in air compared with other cases. The addition of CNG in H2 produces smoother combustion of H2 and ascertains that the engine is safe with mechanical durability.
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Zareei et al. [21] investigated the cylinder pressure at engine full load conditions. Their results showed that the optimal fuel was the H2 -CNG blend with 30% of hydrogen (30%H2 + 70%CNG) content. Also, this ratio of H2 -CNG can improve the cylinder pressure up to 10% compared to pure diesel operation. Comparing maximum cylinder pressure between different fuels and various injection pressures with the AFR of 40:1 at full load engine proves that the highest value for maximum cylinder pressure belongs to (30%H2 + 70%CNG) and injection pressure of 800 bar. Besides that, it would be increased up to 7.46% and 11.2% at 1200 and 2400 rpm, respectively. Tangöz et al. [25] reported that the maximum pressure values of 57, 57.4, 57.6 and 56.6 bar are created at 24.2, 22.4, 20.6 and 18.8 crank angle (CA) ATDC for λ 1.01 using HCNG, (5%H2 + 95%CNG), (10%H2 + 90%CNG) and (20%H2 + 80%CNG) for CR of 9.6, respectively. It could be said that maximum pressure values are generally obtained at lower CA by the addition of H2 to CNG since the adding of H2 causes an increase in the flame speed of blends [26, 27]. The maximum pressure values of 57.4, 79.1 and 82.1 bar are achieved at 22.4, 20.6 and 19.1 CA ATDC around λ 1.0 using (5%H2 + 95%CNG) for 9.6, 12.5 and 15 CR, respectively. The maximum pressure values are closed to TDC with increasing CR. CR increases the flame speed of blends. The maximum pressure values are increased by CR, as in some studies [24–26]. While, IMEP values of 7.4, 8.68 and 8.12 bar are found using CNG for 9.6, 12.5 and 15 CR, respectively. The same results are obtained using the other fuels. However, it can wait that IMEP values increase with CR. IMEP values decreased for CR of 15 due to increasing of BSFC and decreasing of cylinder air. Benbellil et al. [17] show that for all examined test blends, the in-cylinder pressure peak rises with increasing load. At low loads (20% and 40% of full engine load), the lower in-cylinder pressure is observed for DF mode relative to conventional diesel, either for pure NG or HNG mixtures. The corresponding peak pressure values are 56.8 bars for diesel mode, 51.7 bars for pure NG case and 49.9 bars for the blend composed of 50% H2 at 20% engine load. However, the DF operating mode shows higher pressure peaks than the diesel model at high loads. Selim [28] also confirms that the peak pressure is higher for dual fuel mode than diesel operation at high loads. The highest pressure peak value corresponding to the blend composed of 50% H2 is 8.8 higher than pure NG and 10.8 bars higher than that of diesel mode at 80% engine load. Ouchikh et al. [18] presented in-cylinder pressure curves for different engine loads of (30, 40, 50 and 70%) that are representing of low, medium and high engine loads respectively. They told that obviously, a larger amount of fuel is inducted at high engine loads resulting in higher peak pressure for all examined test blends. Under dual fuel mode, a slightly lower in-cylinder pressure is obtained during the compression stroke due to the higher specific heat capacity of the air-NG mixture and, consequently a lower in-cylinder temperature [9]. Adding of H2 to NG resulted in general higher incylinder pressure compared to pure NG-DF mode as a result of enhanced heat release rate. However, at low to moderate engine load with consideration of in-cylinder pressure sensor accuracy, the change in the peak pressure is insignificant. At 70% engine load, a distinct increase (4.1 bar) is observed as a consequence of the improvement of the gaseous fuel combustion.
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Tutak et al. [29] reported that for the engine fueled with 90% CNG, peak pressure was lower by 0.3 MPa compared to diesel fuel mode, while at 21% H2 content, peak in-cylinder pressure increased by 1 MPa. The maximum H2 content caused a large dispersion of combustion pressure in consequent engine cycles with a clearly audible knocking effect for (21%H2 + 69%CNG). It could be stated that the acceptable H2 content in the CNG mixture is 19% of the energy fraction. With 19% of H2 energy share, the effects of knocking were already noticeable. For 19% of H2 , the distribution of combustion pressure for the engine is still acceptable. For a 21% share of H2 , a significant of pressure waveforms are visible spreading. Lee et al. [19] observed that under 13°CA BTDC and 10°CA BTDC diesel fuelinjection angle, the pressure peaks are astonishingly close. Compared to the 10°CA BTDC main injection condition, the mixture appears to be over-mixed, resulting in a shorter premixed peak on the in-cylinder pressure curve. The pressure peak at the main injection timing of 5°CA BTDC is much lower than in the other two examples, regardless of whether hydrogen is added or not. The pressure curve’s variation trend is comparable for varied hydrogen volume ratios. The pressure peak and phase of distinct pressure curves alter dramatically after 30%hydrogen (30%H2 + 70%CNG) is added. The pressure curve is sensitive to changes in main injection timing, with the pressure peak falling by about 4.3%. With hydrogen blending, as the main injection timing is advanced, the ignition delay period lengthens, resulting in a faster combustion rate and higher maximum in-cylinder pressure and temperature. The pressure peak increased from 4.6 MPa to 5 MPa when the main injection timing was advanced from 5°CA BTDC to 13°CA BTDC, an increase of roughly 8.7%. The pressure increase rate curves’ tendency corresponds to the pressure curves in each scenario. Low hydrogen ignition energy and high heat of combustion result in increased pressure, pressure rise rate, and heat release rate peaks as compared to the scenario without hydrogen. With the addition of H2 , the phase interval of peak pressure rise rate increased at the main injection conditions. Lounici et al. [30] investigation results show that the peak in-cylinder pressure peak becomes higher for HCNG-dual fuel mode as a consequence of gaseous fuel combustion improvement. 2.2 Heat Release Rate Zhou et al. [16] show that at 10% load, in general, the two gaseous fuels (H2 and methane(CH4 )) have a similar effect on diesel fuel combustion as both of them reduce the peak heat release rate. At 90% load, diesel-H2 combustion becomes uncontrolled and unstable with a noticeable increase of peak heat release of 79.2%, compared with diesel fuel mode. On the other hand, for diesel-CH4 , the increase of peak heat release rate is 28.7%. The combustion of diesel-H2 -CH4 is observed to be similar to that of dieselCH4 , which is possibly due to the relatively small energy share of hydrogen (less than 50%). The heat release rate at 90% load increase almost linearly with the increase of H2 fraction in CH4 . For diesel-(70%H2 + 30%CH4 ), compared with diesel fuel operation, the increase in peak heat release rate is 42.8% and compared with diesel-CH4 operation, the corresponding increase is 10.9%. In addition, at 90% load, compared with dieselH2 operation, CH4 can improve the safety and durability of H2 combustion in a diesel
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engine by avoiding uncontrolled combustion, like the sharp increase of peak in-cylinder pressure. Alrazen et al. [24] found that with Diesel-H2 operation at excess air 1.2 to 2.4 the in-cylinder temperature decreases exponentially from 2484.89 to 1584.44, respectively. Although the ultra-value of excess air was taken with hydrogen, the in-cylinder temperature was still high. On the contrary, for the Diesel-CNG operation, the in-cylinder temperature decreased monotonously from 1481.41 to 1169.51 K at excess air 1.2, to 2.4, respectively. At a lower excess air ratio, the in-cylinder temperature increased observably during the process of combustion and power strokes. For the Diesel-(30%H2 + 70%CNG) operation, a temperature was lower than Diesel-(50%H2 + 50%CNG), Diesel-(70%H2 + 30%CNG), and Diesel-H2 operations at all excess air. At 1.2 excess air, when adding hydrogen fraction to CNG the peak temperature was increased from 1481.41 to 2484.89 respectively, for mentioned fuel blends. Meanwhile, at 2.2 and 2.4 excess air, no effects on the peak temperature were noted between Diesel-(70%H2 + 30%CNG) and Diesel-H2 due to the low value of fuels. However, with Diesel-(70%H2 + 30%CNG), the temperature was early raised due to the early combustion. Tangöz et al. [25] reported that the curves of rate of heat release are generally advanced with the increase of CR and H2 fraction in the blends. Namely, the maximum rate of heat release rate of 84, 88.19, 85.18 and 78.06 J/°CA are gotten at 17.0, 15.3, 13.4 and 11.6 CA ATDC at λ = 1.01 for 9.6 CR and the values of 89.56, 102.06, 90.69 and 80.36 J/°CA are obtained at 13.4, 13.4, 11.3 and 9.8 CA ATDC at λ = 1.0 for 12.5 CR for CNG, (5%H2 + 95%CNG), (10%H2 + 90%CNG) and (20%H2 + 80%CNG), respectively. This is caused by the increasing of CR and addition of hydrogen that increased burning velocity of blends, bringing the maximum heat release phase closer to the top dead center. [31–33]. Benbellil et al. [17], investigating results show that at low engine load, the peak HRR for DF mode (20% and 40% of engine load) is lower than for diesel mode. The maximum HRR is 21.8 J/°CA for diesel compared to 20.1 J/°CA for pure NG and 17.6 J/°CA for the blend that is composed of 50% H2 at 40% engine load. In terms of the influence of adding H2 to NG, the peak HRR at the premixed combustion process reduces at low loads when compared to baseline NG (20% and 40%). Because of the modest volumes of H2 used with NG, relatively lean mixes result, in reducing combustion efficiency. Nonetheless, from medium to high engine loads, the peak HRR increases for HNG-DF mode in comparison with baseline NG mode. The highest HRR for a 50% H2 blend at 80% engine load is 53.6 J/°CA, compared to 41.7 J/°CA for pure NG and 22.6 J/°CA for diesel mode. Furthermore, when employing HNG dual fuel mode at high engine load, the heat released through the late combustion phase is lower than of using pure NG mode. Ouchikh et al. [18] reported that the HRR is mostly has same trend for both conventional and DF operation at low load. Regarding the effect of H2 addition to NG, it could be noticed that, at the medium to high loads, the maximum HRR increased. The maximum HRR was achieved with 10% of H2 addition. Tutak et al. [29] found that a multi-fuel engine’s maximum HRR values were attained over a dozen CA degrees later than a normal diesel engine, and that increasing hydrogen
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addition clearly increased peak HRR and moved it closer to TDC. The addition of 21% H2 caused an increase in heat release of about 15 J/deg. 2.3 Brake Thermal Efficiency (BTE) Arslan & Kahraman [20] results show that at low load and 1500 rpm, the best BTE for Mix5 (diesel-(250 g/h) [80%NG + 20%H2 ]) was about 19%, while at the maximum load it was about 36% for Mix2 (diesel-500 g/h NG). At a high load, the combustion became more efficient with the addition of 500 g/h NG (Mix2 ) to the combustion air. While the optimum efficiency in diesel fuel was roughly 21% at 1750 rpm and low load, the load was steadily increased, culminating in approximately 35% efficiency for Mix2 . In addition, Mix3 (diesel-(250 g/h) [90%NG + 100%H2 ]) also performed well at maximum load. Zhou et al. [16] found that for diesel-CH4 operation, generally at all loads, the BTE is lower than those of diesel fuel operation. Especially, at 10 and 30% loads, unacceptable performance is observed where the BTE decreases by 27.9 and 16.7%. At 50, 70 and 90% loads. The engine performance under diesel-CH4 mode improves gradually with increasing of engine load when the average reduction of BTE is 6.1% compared with diesel fuel operation. At low loads, the engine performance is generally similar for dieselH2 and diesel-CH4 operations. Compared with diesel fuel mode, the BTE decreases by 27.1 and 12.6% at 10 and 30% loads, respectively. The engine performance under dieselH2 operation is better than that under diesel-CH4 operating at medium to high loads, and the average decrease in BTE on 50, 70, and 90% loads is 2.0, respectively, as compared to diesel fuel operation at 90% load, diesel-H2 operation. In terms of tri-fuel operation, there is a potential for improved engine performance at lower loads. Compared with diesel fuel operation, the BTE still decreases by 16.6% for diesel-(70%H2 + 30%CH4 ) operation at 10% load. However, it is noticed that the increase of H2 fraction in CH4 can gradually increase the engine performance at 30% load, where the BTE becomes close to its value under diesel fuel mode. The effect of H2 addition on CH4 combustion at medium-to-high loads is also positive, but the best combination of methane-hydrogen is (30%H2 + 70%CH4 ). Compared with (30%H2 + 70%CH4 ), (70%H2 + 30%CH4 ) contains higher fraction of hydrogen which increases the hydrodynamic instability and thermal diffusion of the flame [35] causes higher energy loss and lower efficiency. Arat et al. [34] in their results reached to fact that the average of BTE reductions are 3% and 3.6%, respectively for ((50% diesel) + (15% H2 + 35% CNG)) and ((75% diesel)-(7.5% H2 + 17.5% CNG)) when compared with diesel operation. Zareei et al. [21] compared the amount of BTE for HCNG with different contents of H2 and also for pure diesel to each other in different AFRs and injection pressures with (AFR of 40:1) at two constant engine speeds (1200 and 2400 rpm). The results show that in comparison to pure diesel and CNG, brake thermal efficiency improved by increasing hydrogen fraction in the HCNG blend due to a better homogeneity of H2 -air mixture that related to higher diffusivity of hydrogen. Based on results, by using HCNG blend with 40% H2 compared with pure diesel, BTE would increase up to 14.85% and 8.44% at 1200 and 2400 rpm, respectively. Also, a higher CR using H2 blended fuel is another reason for higher thermal efficiency compared with fossil fuels.
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Benbellil et al. [17] found that the BTE is lower for DF mode either with pure NG or HNG blended than pure diesel at all loads. The findings also show that for low engine loads, the thermal efficiency of the NG dual fuel mode is significantly lower than that of diesel operation. Adding H2 to NG, on the other hand, diminishes this impact. All H2 blends show a significant gain in thermal efficiency; this increases with increasing enrichment percentage and reaches a 16% increase in BTE for the 50% H2 mixture compared to the pure NG instance at 80% load. Ouchikh et al. [18] explain that for instance, at 30% engine load, the BTE decreased from 25.5% with diesel operation to 20.8% with NG-DF mode. Regarding hydrogen addition, a significant improvement was gained with almost H2 blends as a result of gaseous fuel combustion improvement. With 10% hydrogen enrichment at 70% engine load, the maximum BTE of 32.5% was mostly similar to the one under diesel operation. Quadri et al. [22] observed that the pure diesel has shown 30.61% BTE at 220 bar that is 1.2% more than that of at 200 bar, and decreased by 0.96% at 240 bar. When compared to pure diesel mode, CNG has a higher BTE due to its stronger knock resistance at lean mixes and the extreme flame propagation rate of H2 . The BTEs were 1.5 and 1.46% higher at 10% and 20% HCNG replacements, respectively, than at 200 bar, and thereafter decreased by 0.95 and 1.1% at 240 bar. With 10% and 20% HCNG substitutions, the BTEs were higher at 220 bar than at 200 bar. A higher IOP, such as 240 bar, reduces the ignition delay period, which reduces the potential of homogenous mixing, resulting in incomplete combustion and lower BTE. 2.4 Brake Specific Fuel Consumption (BSFC) Arslan & Kahraman [20] reached to the fact that the highest fuel consumption (FC) was obtained in Mix6 (diesel-(500 g/h) [80%NG + 20%H2 ]) as 556 g/kWh and the lowest FC was obtained in Mix5 (diesel-(250 g/h) [80%NG + 20%H2 ]) around 403 g/kWh at 1500 rpm and 10.2 Nm (low load). As the engine load increased from 10.2 Nm to 40.8 Nm, the FC inclined to decrease in general, and the lowest FC was around 240 g/kWh in Mix6 . The highest FC was obtained in Mix3 (diesel-(250 g/h) [90%NG + 10%H2 ]) as approximately 282 g/kWh at the maximum load. Compared to low load at both speeds, the most significant decrease in BSFC at the maximum load and 1500 rpm was obtained for Mix2 (diesel-NG (500 g/h)) and Mix6 with 54% and 57%, respectively. At the maximum load and 1750 rpm, a decrease of 53% and 50% in BSFC was obtained for Mix4 (diesel-(500 g/h) [90%NG + 10%H2 ]) and Mix6 , respectively. Maximum FC of approximately 574 g/kWh was observed for Mix4 at low load and at 1750 rpm, the minimum BSFC was achieved as 408 g/kWh at the same conditions when diesel fuel was used. As the engine load increased, an overall decrease in FC was observed for all test cases, while the least FC at maximum load was 240 g/kWh for Mix6 at 1500 rpm and 243 g/kWh for Mix3 (diesel-(250 g/h) [90%NG + 10%H2 ]) at 1750 rpm. Therefore, with the addition of HNG mixture, better combustion was achieved at a high load. Zhou et al. [16] conducted that in comparison with diesel fuel mode, the BSFC still decreased and increased by 15.0% for (diesel-(70%H2 + 30%CH4 )) operation at 10% load. However, at 30% engine load, the increase of H2 fraction in CH4 can gradually increase the engine performance where the BSFC become close to those under diesel fuel operation.
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According to Arat et al. [34], 30HCNG fuel variations with diesel fuel substitution, such as pilot diesel injection, reduced the BSFC and resulted in more profitable fuel usage. The ((75% diesel)-(7.5% H2 + 17.5% CNG)) and ((50% diesel)-(15% H2 + 35% CNG)) fuel mixtures improved fuel economy by 18% and 26.8%, respectively. Zareei et al. [21] results showed that using HCNG with 30% of H2 instead of pure diesel would reduce the BSFC by 38 g/kWh and 10 g/kWh at 1200 and 2400 rpm, respectively. With AFR of 40:1 and higher IP, the amount of BSFC becomes less. Tangöz et al. [25] found that the lowest BSFC values were obtained at CR of 12.5 for all blends. For a given excess air ratio, the BSFC values at 12.5 CR were about 23–30%, 19–27%, 20% and 23–32% lower than with 9.6 CR fueled by CNG, Mix1 (5%H2 + 95%CNG), Mix2 (10%H2 + 90%CNG) and Mix3 (20%H2 + 80%CNG), respectively. The BSFC values were about 0.5–8%, 0.5–6%, 0–4% and 5–11% lower than with CR = 15 fueled by CNG, Mix1 , Mix2 and Mix3 , respectively. According to Benbellil et al. [17], the current findings corroborate the notion that the BSFC for low engine loads is much higher in the DF mode than in the typical diesel scenario. At low to medium loads, BSFC for NG dual fuel operation is much greater than for diesel fuel operation. NG dual fuel mode results in a small drop in BSFC for high loads when compared to diesel operation. For moderate to high loads, the overall BSFC is significantly lowered when H2 is added to NG. It’s a lot less than diesel mode. It achieves a 20% reduction with a 50% H2 mixture compared to pure NG and a 29% reduction compared to normal diesel at 80% engine load. Ouchikh et al. [18] reported that for NG-DF operation, at low to moderate loads, BSFC is higher in comparison with diesel fuel operation. The BSFC increases by 17% and 6.2% at 30 and 40% engine loads, respectively. This difference reduces with the increase of engine load. That confirms the fact that the BSFC is significantly higher for NG-DF mode than diesel operation at these conditions BSFC decreased from 388.5 g/kWh under pure NG to 343.8 g/kWh with (10%H2 + 90%NG) at 30% engine load, namely 11.5% reduction. This reduction is noticed almost for all H2 blends, especially for low to moderate engine loads. When compared to diesel operation, NG dual fuel mode results in a modest reduction in BSFC for high loads. Furthermore, the lowest BSFC is observed with a hydrogen concentration of 10%. When compared to pure diesel operation, Quadri et al. [22] found that as the percentage of HCNG grew, BSFC declined. When compared to 0.28 kg/kWh for pure diesel, the BSFC for rated injection opening pressure of 200 bar is 0.2589 kg/kWh, 0.2356 kg/kWh for Mix1 (10%H2 + 90%CNG), and 0.2356 kg/kWh for Mix2 (20%H2 + 80%CNG). From 200 to 220 bar injection opening pressure, the BSFC trend was seen to be decreasing and then increasing at 240 bar. When compared to 0.25 kg/kWh for pure diesel at 220 bar, the minimum BSFC is 0.2302 kg/kWh for Mix1 and 0.2114 kg/kWh for Mix2 . 2.5 Brake Power (BP) Arat et al. [34] reported that similarly engine torque values, pilot diesel fuel injection with gas fuel mixtures decreased the BP of engine using Mix1 ((75% diesel)-(7.5% H2 + 17.5% CNG)) and Mix2 ((50% diesel)-(15% H2 + 35% CNG)) with the ratio of 9% and 8%. Additionally, in lean burning, Mix2 power values were better than Mix1 values caused of more H2 concentration was inducted.
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Zareei et al. [21] investigated engine BP with pure diesel, and different HCNG blends. Two constant engine speeds (1200 and 2400 rpm), once with various AFRs and once with various IP (with AFR of 40:1) were employed. In comparison to pure diesel and CNG, BP can be enhanced by increasing hydrogen fraction in the HCNG blend, along with higher IP. Result data show that in comparison to diesel fuel mode, by blending 40% H2 with CNG, the BP would increase up to 8.3 kW and 10.01 kW at 1200 and 2400 rpm, respectively. 2.6 Brake Torque (BT) Arat et al. [34] show that the mixtures of gaseous fuel are relatively played a negative role and reduced the engine torque. When BT values of fuels were compared, Mix1 ((75% diesel)-(7.5% H2 + 17.5% CNG)) and Mix2 ((50% diesel)-(15% H2 + 35% CNG)) fuels reduced the torque by 6.2% and 4.3% respectively. Although replacement rate of diesel fuel for Mix1 is higher than Mix2 , the hydrogen quantity of Mix2 occurs in more powerful combustion. Tangöz et al. [25] found that the maximum BT values are found at stoichiometric and 12.5 CR conditions. When λ is around 1.0, the BT values of 228, 229, 228 and 217 Nm; 232, 235, 233 and 226 Nm; 231, 231, 228 and 221 Nm are obtained with fueled by CNG, Mix1 (5%H2 + 95%CNG), Mix2 (10%H2 + 90%CNG) and Mix3 (20%H2 + 80%CNG) for 9.6, 12.5 and 15 CR, respectively. When CR is increased from 9.6 to 12.5 torque values increase, but, it decreases at 15 of CR. By increasing the CR from 12.5 to 15, total volume of the cylinder is decreased, so the amount of fuel supplied to the cylinder and torque values also decreases. Moreover, the torque values gradually increase and then decline with increasing of λ. The maximum torque values of 221, 227 and 222 Nm; 229, 235 and 231 Nm; 215, 225 and 221 Nm are found at λ = 0.95, λ = 1.0 and λ = 1.15 for 9.6, 12.5 and 15 CR fueled by Mix1 , respectively. The higher torque values are achieved by using pure CNG under a rich mixture (λ = 0.9–0.95). At stoichiometric conditions, the best torque values are observed using Mix1 for CR of 9.6 and 12.5 and are found at 15 CR using CNG and Mix1 . When mixture conditions are lean, the best values are obtained Mix2 , Mix1 and Mix1 at 9.6 CR, 12.5 and 15 CR, respectively. So, it could be said that the CNG losses the advantages at high excess air ratios. This is caused by the low flame speed of CNG [23, 26]. Additionally, when the CR values are 9.6 and 12.5, torque value increases with the addition of H2 (about 5–10%) to CNG. 2.7 Summary of Engine Performance Table 1 summarizes the recent studies on the effect of using HCNG gaseous fuel with diesel engines on the engine performance. This table mentioned the engine details, testing conditions, fuel blends, and performance findings.
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Table 1. Summary of recent studies that used HCNG as a blend with diesel in various diesel engines Ref
Engine
Testing conditions
Fuel
[20] DF, 4S, 3C, WC, CR22.8:1, DS1028 m3
1. Loads: 0.0, 10.2, 20.41, 30.61, and 40.81 Nm 2. Speed: 1500, 1750 rpm
[16] DF, 4S, 4C, CR19:1, DS4334 cm3
1. Load: 10, 30, 50, 70, and 90% 2. Speed: 1800 rpm
Engine performance results In-cylinder Heat pressure release
BTE
BSFC
BP
BT
1.DF 2. PDF + N (250 g/h), 3. PDF + N (500 g/h), 4. PDF + (250 g/h) [90%N10%H], 5. PDF + (500 g/h) [90%N10%H], 6. PDF + (250 g/h) [80%N20%H], 7. DF + (500 g/h) [80%N20%H]
∧ for DF at low load for all rpms and ↑ with ↑ engine load and H2 add. ∧ for fuel 3 at 1500 rpm and F7 at 1750 rpm
∧ for F6 at 1500 rpm and for DF at 1750 rpm for highest load. ∨ for DF at both rpms and no load
∧ for F6 at 1500 rpm and for DF at 1750 rpm for lowest load. ∧ for F3 at both rpms and highest load. ∨ for F7 at both rpms and all loads, except highest load
∧ for F7 at 1500 rpm and for F5 at 1750 rpm for ∨ load, while ∨ for F6 at 1500 rpm and F2 at 1750 for ∨ load. ∧ for F4 at 1500 rpm and for F6 at 1750 rpm for ∧ load, while ∨ for F3 at 1500 rpm and F2 at 1750 for ∧ load
N-M
N-M
1. DF 2. PDF + N 3. PDF + (H30-N70) 4. PDF + (H50-N50) 5. PDF + (H70-N30) 6. PDF + H
∧ for DF at 10% load and ↑ with ↑ engine load and H2 add. ∧ for fuel 6 at 90% load. ∨ for F4 at 10% load and DF at 90% load
∧ for DF at 10% load and ↑ with ↑ engine load and H2 add. ∧ for fuel 6 at 90% load. ∨ for F3 at 10% load and for DF at 90% load
∨ for F2 at all loads. ↑ with ↑ load for all fuels, except fuels 1 and 6 slightly ↓ at highest load compare with previous value. ∧ for fuels 1 and 6 at load of 70%
∨ for DF N-M at 70% load. ↓ with ↑ load for all fuels, except fuels 1 and 6 slightly ↑ at highest load compare with previous value. ∧ for fuels 2 and 6 at load of 10%
N-M
(continued)
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H. S. Muhssen et al. Table 1. (continued)
Ref
Engine
Testing conditions
Fuel
Engine performance results
[24] DI DF, 4S, 1C, CR20.36:1, SV0.45L
1. Load: 20.18 Nm 2. Speed: 2000 rpm 3. λ: (1.2), (1.4), (1.8), (2), (2.2), and (2.4)
1.PDF + N 2. PDF + (H30-N70) 3. PDF + (H50-N50) 4. PDF + (H70-N30) 5.PDF + H
∨ for DF and ∧ for F5 at all λ. ↓ with ↑ of λ for all Fs but still ∧ gaseous Fs than DF. ↑ with H2 add. ∧ for F5 at λ = 1.2, and ∨ for F1 at λ = 2.4
∨ for DF N-M at all λ and ↓ with ↑ of λ but still ∧ for gaseous fuels than DF. ↑ with H2 add. ∧ for F5 at λ = 1.2, and ∨ for F1 at λ = 2.4
[34] DI Diesel, 4S, 4C, WC, CR17.5:1, DS3567 cm3
1. Load: 100% 2. Speed: 1200–2600 rpm with an interval of 100 rpm at WOT 3. λ = 1.35, ϕ = 0.74, AFR = 21.04 AFR for 30HCNG = 16.4
1. DF 2. (75% PDF) + (7.5% H + 17.5% N) 2. (50% PDF) + (15% H + 35% N)
N-M
N-M
In-cylinder Heat pressure release
BTE
∧ for F1 at all rpms. ∨ for F2 at all rpms
BSFC
BP
BT
N-M
N-M
N-M
∧ for F1 at all rpms. ∨ for F3 at all rpms
almost ↑ with ↑ of rpm for all fuels and full load. ∧ for F1 and ∨ for F3 with all rpms at full load
almost ↓ with ↑ of rpm for all fuels and full load. ∧ for F1 and ∨ for F2 with all rpms at full load
(continued)
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Table 1. (continued) Ref
Engine
Testing conditions
Fuel
[21] Diesel, 4S, 3C, CR17:1, DS3.2L,
1. load: 100% 2- speed: 1200 and 2400 rpm 3. AFR: 40, 45, 50, 55, 60, and 65 4.Injection pressure between 300 and 800 bar
[25] Modified Diesel, 4S, 4C, CR17.5:1, DS3.9L
1. load: 100% 2- speed: 1500 rpm 3. λ: 0.9, 1.1, 1.15, 1.2, 1.25, and 1.3 4. CR: 9.6, 12.5, and 15
Engine performance results In-cylinder Heat pressure release
BTE
BSFC
BP
BT
1.DF 2. PDF + N 3. PDF + (H10-N90) 4. PDF + (H20-N80) 5. PDF + (H30-N70) 6. PDF + (H40-N60)
∨ for F2 at N-M all rpms and IP and ↑ with H2 add. ∧ for fuel 5 at all rpms and IP. All results with AFR of 40:1
∧ for F5 and F6 with all rpms, AFRs, and IPs. ∨ for F2 with all rpms, AFRs, and IPs. ↓ with ↑ of AFRs, and ↑ with ↑ of rpms and IP for all fuels
∨ for F5 and F6 with all rpms and AFR of 40:1 and all IP. ∧ for F2 with all rpms and AFR of 40:1 and all IP. ↓ with ↑ of rpms and IP for all fuels
N-M
N-M
1.PDF + N 2. PDF + (H5-N95) 3. PDF + (H10-N90) 4. PDF + (H20-N80)
↑ with ↑ of H2 for all CRs. ∧ at fuel 1, λ = 1.025 and CR 15. While ↓ with ↓ of CR although ↑ of H2 and λ
N-M
∧ for F4 with all λs, and CRs. ↓ with ↑ of λ until λ:1.2 ∨ for F1 with all λs, and CRs of 12.5 and 15. ∨ at CR of 12.5 for all fuels
N-M
N-M
∧ at fuel 2, λ1 and CR 12.5
(continued)
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H. S. Muhssen et al. Table 1. (continued)
Ref
Engine
Testing conditions
Fuel
[17] DI Diesel, 4S, 1C, AC, CR18:1, DS630 cm3
1. Load: 20, 40, 60, and 80% 2. Speed: 1500 rpm
[22] DI Diesel, 4S, 1C, WC, CR17.5:1, DS661 cm3
1. Load: 25, 50, 75, and 100% 2. Speed: 1500 rpm 3 4. injection pressure: 200, 220, and 240 bar
Engine performance results In-cylinder Heat pressure release
BTE
BSFC
BP
BT
1. DF 2.PDF + N 3.PDF + (H20-N80) 4. PDF + (H30-N70) 5.PDF + (H40-N60) 6. PDF + (H50-N50)
∧ for DF at 20% load and ↑ with ↑ engine load and H2 add. ∧ for fuel 1 at 20% load and F6 at 80% load. ∨ for F5 at 20% load and fuel 1 at 80% load
∧ for DF at 20% load and ↑ with ↑ engine load and H2 add. ∧ for F1 at 20% load and F6 at 80% load. ∨ for F5 at 20% load and fuel 1 at 80% load
↑ with ↑ engine load for all fuels. ∧ for F1 with all loads. ↑ with ↑ of H2 for gaseous fuels especially from medium to high loads, but still ∨ than DF
↓ with ↑ N-M engine load for all fuels. For F1, ∨ with loads of 20 to 50% and ∧ from loads 50 to 80%. ↓ with ↑ of H2 for gaseous fuels especially from loads 50 to 80% and still ∨ than DF
N-M
1. DF 2. DF + (H10-N90) 3.DF + (H20-N80)
∨ for DF at 100% load and ↑ with H2 add. ∧ for fuel 3 at 100% load
∨ for DF at 100% load and ↑ with H2 add. ∧ for fuel 3 at 100% load
∧ for F3 Than other fuels with all IP. ∧ at IP of 220 for all fuels. ∨ for F1 than other fuels with all IP. These results at 100% engine load
∨ for F3 N-M Than other fuels with all IP. ∨ at IP of 220 for all fuels. ∧ for F1 than other fuels with all IP. These results at 100% engine load
N-M
(continued)
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Table 1. (continued) Ref
Engine
Testing conditions
Fuel
[18] DI Diesel, 4S, 1C, AC, CR18:1, DS630 cm3
1. Load: 30, 40, 50, and 70% 2. Speed: 1500 rpm
[29] DI Diesel, 4S, 1C, AC, CR17:1, DS573 cm3
1. Load: IMEP = 0.70 Mpa 2. Speed: 1500 rpm
Engine performance results In-cylinder Heat pressure release
BTE
BSFC
BP
BT
1.DF 2. DF + N 3. DF + (H10-N90) 4.DF + (H20-N80) 5.DF + (H30-N70)
∧ for DF at 30% load but ↓ for DF and ↑ for gaseous Fs with ↑ engine load. ∧ for fuel 1 and ∨ for F5 at 30% load. ∧ for fuels 3 and 4 and ∨ for F1 at 70% load
∧ for DF at low load and ↑ with ↑ engine load and H2 add. ∧ for fuel 1 at 30% load and fuels 3 and 4 at 70% load. ∨ for fuel 5 at 30% load and fuel 1 at 70% load
∧ for F1 with all loads. ↑ with ↑ load for all fuels. ∧ for F1 and ∨ for F2 at 30% load. ∧ for F1 and ∨ for F5 at 40% load. ∧ for F1 and ∨ for F5 at 50% load. ∧ for F1 and 3, and ∨ for F4 at 70% load
∨ for F1 with all loads. ↓ with ↑ load for all fuels. ∧ for F2 and ∨ for F1 at 30% load. ∧ for F5 and ∨ for F1 at 40% load. ∧ for F5 and ∨ for F1 at 50% load. ∧ for F4 and ∨ for F1 at 70% load
N-M
N-M
1. DF 2. 10PDF−90N 3.10 PDF + (2H-88N) 4.10 PDF + (8H-82N) 5.10 PDF + (12H-78N) 6.10 PDF + (19H-71N) 7.10 PDF + (21H-69N)
∨ for fuel 2 and ↑ with H2 add. ∧ for fuel 6
∨ for fuel N-M 1 and ↑ with H2 add. ∧ for fuel 6 and 7
N-M
N-M
N-M
(continued)
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H. S. Muhssen et al. Table 1. (continued)
Ref
Engine
Testing conditions
Fuel
Engine performance results
[19] DI Diesel, 4S, 1C, CR17:1, DS0.664 L
1. Load: Equivalent Diesel Load = 25(mg/cycle) 2. Speed: 1200 rpm
1. 30PDF + 70N 2. 30PDF + (H30-N70)
↑ with advancing injection timing for all fuels
[30] DI Diesel, 4S, 1C, AC, CR18:1, DS630 cm3
1. Load: 30, 40, 50, and 70% 2. Speed: 1500 rpm
At 30% load: 1. DF 2. PDF + N 3. PDF + (H16-N84) 4.PDF + (H20-N80) 5.PDF + (H30-N70) At 40% load: 1. DF 2. PDF + N 3. PDF + (H11.4-N88.6) 4. PDF + (H20-N80) 5. PDF + (H30-N70) At 50 and 70% load: 1. DF 2. PDF + N 3. PDF + (H10-N90) 4. PDF + (H20-N80) 5. PDF + (H30–70)
∨ for DF and fuel 2, while it ∧ with fuel 3 at 70% load. ↓ with ↑ of H2 in fuels 4 and 5 at 70% load
In-cylinder Heat pressure release
BTE
BSFC
BP
BT
↑ with N-M advancing injection timing for all fuels
N-M
N-M
N-M
∨ for DF, while it ∧ with fuel 3 at 70% load. ↓ with ↑ of H2 in fuels 4 and 5 at 70% load
↓ with ↑ load for all fuels. ∧ for F2 and ∨ for F4 at 30% load. ∧ for F2 and ∨ for F3 at 40% load. ∧ for F2 and ∨ for F3 at 50% load. ∧ for F1 and ∨ for F3 at 70% load
N-M
N-M
↑ with ↑ load for all fuels. ∧ for F1 and ∨ for F2 at 30% load. ∧ for F1 and ∨ for F5 at 40% load. ∧ for F1 and ∨ for F5 at 50% load. ∧ for F3 and ∨ for F4 at 70% load
∧: highest, ∨: lowest, ↑: increase, ↔: intermediate, ↓: decrease, DF: diesel fuel, PDF: pilot diesel fuel, Fs: fuels, F: fuel, rpm: revolution per minute, IP: injection pressure, H and H2 : hydrogen, N: natural gas or compressed natural gas, λ: excess air, S: stroke, C: cylinder, CR: compression ratio, DS: displacement, WC: water-cooled, N-M: Not mentioned.
3 Conclusions This review article focused on the effects of hydrogen enrichment of natural gas on the combustion and performance characteristics of Tri-fuel diesel engines. According to previous studies, adding hydrogen to natural gas resulted in addressing of the disadvantages of using each gas separately with diesel especially from medium to high engine loads. For example, the high flame speed of hydrogen resulted in a reduction of ignition delay caused by suppressing the auto-ignition of diesel when enriched by natural gas. Moreover, hydrogen addition results in improving of in-cylinder pressure and heat release
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that drop down in using of NG-diesel dual fuel mode. Furthermore, using hydrogen with natural gas resulted in combustion improvement of gaseous fuel, more safety, durability, and less mechanical problem of the engine. In addition, there are many parameters that have a direct effect on the engine combustion and performance characteristics. These parameters could be summarized as engine load and speed, fuel injection pressure and timing, excess air, air-fuel ratio, fuel blends, and compression ratio. The outputs of this research could be summarized as below: • The parameters of in-cylinder pressure and heat release are higher for the lowest hydrogen content in fuel like (diesel and diesel + natural gas) fuel operation at low engine load and higher with higher hydrogen content at full load. That indicates a positive relationship between the above-mentioned parameters and hydrogen content at high load and an inverse relation at low engine load. While at the medium load, the values of pressure and heat release are mostly same for all fuels. In terms of fuel injection pressure at full engine load, the pressure and heat release are increasing with increasing of fuel injection pressure for all fuels and engine speeds. Moreover, these two parameters are increasing with increase of engine speed at full load for all fuels and injection pressure. In addition, at full load, the lowest values of these two parameters are for (diesel and diesel + natural gas) and increase with increasing hydrogen with all engine speeds and fuel injection pressures. In terms of excess air impacts with full load and fixed engine speed, the pressure and heat release are decreasing with increasing of excess air for all fuels. The lowest values for diesel that dramatically increase with hydrogen fraction increasing for all excess air. Also the pressure and heat release are increased with increasing of compression ratio and pre-injection angle but not always. • Brake thermal efficiency is higher for diesel fuel mode at low engine load for all engine speeds and increases with increases of engine load for gaseous fuel. Also the BTE parameter decreases with increasing of air-fuel ratio, increasing with increasing of engine speed. Moreover, BTE increases with increasing hydrogen fraction in gaseous fuel with all AFRs, engine speeds and injection pressures. Adding hydrogen results in better homogeny of the gaseous fuel-air mixture due to the high diffusivity of hydrogen, and this in turn, enhances the combustion of air-fuel mixture. Moreover, high flame speed of hydrogen resulted in a reduction of ignition delay caused by natural gas, and that resulted in avoiding of reduction of thermal efficiency. • Brake specific fuel consumption is lower for diesel fuel mode at low engine load for all engine speeds and decreases with increases of engine load for gaseous fuel. Also the BSFC parameter increases with increasing of air-fuel ratio, decreasing with increasing of engine speed. Moreover, BSFC decreases with increasing hydrogen fraction in gaseous fuel with all AFRs, engine speeds and injection pressures. This is attributed to the higher calorific value of hydrogen, so, the more hydrogen concentration increases lead to less fuel consumption occurs. Moreover, the enrichment of natural gas by hydrogen results in wider lean burning that enhances better mixing of air-gaseous fuel mixture and better combustion and results in lower BSFC. Additionally, BSFC has an inverse relation with BTE, and hence, with the lower BTE, higher BSFC is observed and vice versa.
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• Brake power is almost increased with increasing engine speed for all fuels at full load. Highest for diesel fuel and increases for gaseous fuel with increasing of hydrogen fraction in gaseous fuel at all rpms and full load of engine. • Brake torque almost decreases with increasing engine speed for all fuels and engine full load. Highest for diesel fuel and increases for gaseous fuel with increasing of hydrogen fraction in gaseous fuel at all rpms and full load of engine.
Acknowledgements. The authors would like to express thanks to Stipendium Hungaricum and The Ministry of Higher Education, University of Wasit-Iraq for the financial support of the first author. Also, the authors would like to extend their thanks to Budapest University of Technology and Economics for scientific supporting at all fields and levels. The research was supported by OTKA - K21 - 138053 - Life Cycle Sustainability Assessment of road transport technologies and interventions by Mária Szalmáné Dr. Csete.
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Electric and Thermal
Investigations on the Effects of Capacitive Couplings in an Automotive Phase-Shifted Full-Bridge Power Supply Used in Electric Vehicles Róbert Orvai1(B) and Márk Csörnyei2 1 Óbuda University, Budapest, Hungary
[email protected] 2 Robert Bosch Kft., Budapest, Hungary
Abstract. DC/DC converters are essential parts of an electric car. Converting high voltage to 12 V is the task of the main DC/DC converter. The phase-shifted full-bridge (PSFB) is a popular circuit topology for this role due to its high efficiency. However, designing the PSFB for electromagnetic compatibility (EMC) is a significant challenge during the development phase. Therefore, we conducted simulation-based experiments on the connection between the unintentional parasitic capacitances and the electromagnetic emissions to support the EMC design of automotive DC/DC converters. Keywords: Electromobility · Power electronics · DC/DC converters · Electromagnetic compatibility
1 Introduction The high-voltage (HV) energy transmission system of an electric car usually consists of five main parts (in the automotive industry, HV refers to voltages above 60 V DC or 30 V AC). Firstly, the traction battery or some HV DC source supplies the necessary electrical energy to power and drive the vehicle. The next component is the inverter circuit which provides alternating current for the electric motor (or motors). The fourth element is the onboard charger which enables charging the traction battery from the AC grid. Besides the HV network, the 12 V system is also used in electric cars to power LV on-board control units, headlights, etc. Therefore, an isolation barrier is required to galvanically separate the two networks for safety reasons. Last but not least, the device which implements the HV-LV power conversion while providing isolation is the main DC/DC converter. This component is the subject of this discussion (Fig. 1). The task of the DC/DC converter circuit in the HV energy transmission system is charging the 12 V battery and supplying power to the LV loads. This is achieved by stepping down the high voltage of the traction net. It can be viewed as the equivalent of a common alternator in terms of its general function. Compact size and high efficiency are among the main requirements of this switched-mode power supply [1]. The term © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 291–301, 2023. https://doi.org/10.1007/978-3-031-15211-5_25
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‘switched-mode’ refers to the way of operation during which transistors are turned on and off to convert the input voltage. One of the most desirable features of these circuits is soft switching which can only be achieved by certain topologies. In the course of soft switching, the losses occurring when turning a transistor on or off is greatly reduced compared to hard switching [2]. Another important property is the switching frequency which is in the range of several hundred kHz. The higher the switching frequency, the more efficiently the converter operates. Of course, this has limits because there is a point beyond which choosing faster switching frequencies would only increase the dynamic switching losses. Faster switching also results in increased electromagnetic interference (EMI). Thus, the growing demands in terms of efficiency and power density make EMI reduction more and more challenging.
Fig. 1. Parts of the high-voltage energy transmission system in electric cars (battery-powered).
The structure of this paper is as follows. In Sect. 2, the PSFB is presented with the basic explanation of its operation. Section 3 provides an insight into the vast topic of electromagnetic compatibility and describes the modelled test setup. In Sect. 4, PSFB and EMC are connected by presenting the parasitic capacitances of the converter. Results of the simulations are presented in Sect. 5. Lastly, Sect. 6 terminates the paper with the main conclusions.
2 The Phase-Shifted Full-Bridge The PSFB is a popular choice for mid to high power (several kW) DC/DC conversion. The main reason is that the topology achieves soft-switching relatively easily by taking advantage of the parasitic properties of certain circuit components. The schematic diagram of the converter is illustrated in Fig. 2. On the left is a DC voltage source which symbolizes the 400 V HV battery. The input capacitor supplies the high-frequency components occurring at the switching of the transistors. The four N-channel power metal-oxide-semiconductor field-effect transistors (MOSFET) form the phase-shifted full-bridge on the HV side of the transformer. The drain-source parasitic capacitances of these devices are also drawn due to their role in achieving soft switching. Between the two full-bridges is the transformer with its leakage inductance indicated. The leakage inductance originates from the imperfect magnetic coupling between the primary and
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secondary windings. It is a non-ideal parasitic component, but without it, soft switching would not be possible at all. The transformer provides the necessary galvanic isolation between the HV-LV sides. The rectifier circuit on the secondary side is one of the most complex available for synchronous operation. Rectification can also be achieved with simpler asynchronous diode circuits at the expense of efficiency. The output coil and capacitor provide filtering, and the resistor represents the 12 V battery as the load. In the investigated 1.5 kW application, the input current is in the range of a few amperes and the output current can be more than 100 A at full load.
Vin: input voltage source (HV traction battery) Cin: input capacitor S1...S4: high-voltage primary MOSFETs C1...C4: parasitic capacitances of primary MOSFETs Llkg: leakage inductance of the transformer Lt: planar transformer S5...S6: low-voltage secondary MOSFETs C5...C8: parasitic capacitances of secondary MOSFETs Lout: output inductor Cout: output capacitor Rl: load resistor (12V battery)
Fig. 2. The phase-shifted full-bridge with the necessary parasitic components for soft switching.
The essence of the converter’s operation is applying a phase shift between the switching of the primary half-bridges (S1 –S2 and S3 –S4 ). By doing so, these become resonant and non-resonant branches as depicted in Fig. 2. Resonant means that this half-bridge is phase-shifted compared to the non-resonant branch. Soft switching DC/DC converters are also called resonant converters [2]. They use a resonant tank to decrease the voltage (or current) of the transistor before switching by discharging its drain-source parasitic capacitance. This is called zero-voltage switching, which feature makes the PSFB a popular choice. The resonant tank in this case consists of the leakage inductance of the transformer and the parasitic capacitances of the primary transistors. The transformer needs an alternating current to induce voltage in the secondary winding. Therefore, the PSFB itself functions as an inverter on the primary side. It creates a stepped square wave by switching the input voltage to the primary winding in opposite directions. This is done by controlling the diagonal transistors (S1 –S4 and S2 –S3 ) to be turned on at the same time, as depicted in Fig. 3. The high states of the four gate-source voltages show when the respective transistor is in conductive state. By
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increasing the phase shift of S1 and S2 MOSFETs (the resonant branch) to 45° from 90°, the duty cycle of the transformer primary voltage also decreases to 25% from 50% as depicted on the right in Fig. 3. The switching of the non-resonant half-bridge (S3 – S4 ) remains unchanged. When the converter is in its freewheeling period (0 V primary voltage), only the high-side or low-side transistors are turned on. By modifying the phase shift and thus the duty cycle, and the energy transfer can be controlled very precisely. The timing of the secondary side transistors is adjusted according to the switching of the primary ones to rectify the voltage stepped down by the transformer.
Fig. 3. Operating periods at 50% (left) and 25% (right) duty cycle. Vgs1 …Vgs4 : HV MOSFET gate-source voltages, Vp : transformer primary voltage, Vs : transformer secondary voltage. Switching frequency is 100 kHz.
3 Electromagnetic Compatibility EMC is the field of electrical engineering which deals with electromagnetic disturbances and their suppression. An electric system is electromagnetically compatible if it does not cause EMI in other systems, is not sensitive to EMI from other devices and does not interfere with itself. The first case is called electromagnetic emissions, which can be either conducted or radiated. Conducted emissions propagate through any electrical conductor in the system whereas radiated interference travels through the air in the form of electromagnetic waves. For the measurement of robustness against EMI, there are also conducted and radiated immunity tests. The cause of EMI is different noise sources in the circuit. In switched-mode power supplies, this is typically the switching of the input voltage on and off for power conversion as shown above. By fast switching, high-frequency signal components are generated whose propagation paths are difficult to identify and, because of this, their filtering is also troublesome. The higher the voltage which is being switched, the higher the generated EMI. The disturbance propagates through the parasitic components of the circuit to interference-sensitive devices. This can cause malfunctions and damage in the system. However, these effects cannot be eliminated entirely, so certain limits are defined for
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conducted and radiated EMI, which are considered low enough for a reliable operation. These limits are determined by various regional and international standards. For the automotive industry, the CISPR 25 and CISPR 12 regulations are used most often. In Fig. 4, the CISPR 25 limits for conducted emissions are illustrated [3]. Frequency is on a logarithmic scale from 100 kHz to 108 MHz. 108 MHz is the upper limit of the FM radio band. The unit of the vertical axis is dBµV. The standards also define the measurement procedures to investigate a certain product for EMC. The passing of these tests is critical since the product cannot be placed on the market while it violates the homologation limits.
Fig. 4. CISPR 25 Class 5 peak limits for conducted emissions.
3.1 Conducted Emissions Measurement In the following, we focus on the measurement of conducted emissions according to the CISPR 25 standard. The test is done by using line impedance stabilization networks (LISN). The LISN provides a constant impedance over the frequency range under investigation so the measurement can be repeated. The test setup is shown in Fig. 5. The metal test table serves as an electrical reference representing the vehicle’s chassis. The equipment under test (EUT) is connected to the HV DC source through a HV LISN on each line. Under the EUT is an isolating foam with a thickness of 5 cm represented by the blue rectangle. An asymmetric setup can be seen on the low-voltage side because the output of the DC/DC converter and the 12 V system are both referenced to the chassis. Every LISN is connected to the ground plane and also routed to the test receiver – which is not indicated in the figure – to measure the conducted interference voltage as a function of frequency. The test takes place in an electromagnetically isolated room or test chamber. When the EUT reaches the operating point at which the emissions tests need to be carried out, the measurement can begin. These operating points usually represent common use cases and worst-case scenarios regarding the generated EMI.
4 EMC Issues of the PSFB As mentioned earlier, the PSFB we are investigating is a high-voltage switched-mode power supply and, because of the switched-mode operation, generates a significant
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amount of noise. For this noise to appear in the EMC measurements, it needs to find propagation paths to the LISNs and hence to the test receiver. The most significant of these paths is capacitive coupling [4]. Several parasitic capacitances can be found within the PSFB, which serve as a propagation path for EMI. One can think about these as unintentional capacitors which cannot be eliminated entirely, only reduced to a certain degree by various techniques [4–7]. The source of capacitive couplings is the high dv/dt points in the circuit, so the nodes whose voltage changes rapidly and within a wide range. Typically, these are the high-voltage switch-nodes on the primary side and the primary winding itself of the transformer in PSFB converters. In the following, we present the two main capacitive propagation paths from these high dv/dt circuit parts.
Fig. 5. Conducted emissions test setup.
The most significant parasitic capacitance of the PSFB is the interwinding capacitance of the transformer. It represents the capacitive coupling between the primarysecondary windings. Ideally, the transformer only transfers energy in the form of a magnetic field. In reality, this is not the case because the interwinding capacitance is always present, especially in transformers with a planar structure. Planar transformers use the multilayer design of printed circuit boards (PCB) for implementing the winding structure and hence the required turns ratio. It is a preferable design because of manufacturability and thermal properties compared to conventional structures (Fig. 6). However, it has considerable disadvantages regarding EMI. Since the distance between the primary-secondary windings is reduced to the thickness of the isolating material between the PCB layers, the interwinding capacitance is increased significantly which deteriorates the EMC performance. This is easy to understand by looking at the equation of the parallel-plate capacitor: C=ε
A d
(1)
where C is the capacitance, ε is the electric permittivity which is the product of ε0 and εr (specific to the isolating material), A is the area of the plates and d is the separation distance between them. This is a simplification of the distributed interwinding capacitance, but it is suitable for the purpose of this study. The other capacitive coupling path is well-known from the design of traditional buck or boost DC/DC converters. The parasitic capacitance of the switch-nodes is the second considerable propagation path from the high dv/dt points (Fig. 7). The two main factors
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determining the effect of this coupling path are the surface area of the switch-nodes on the PCB and the distance between the circuit and the ground plane or a metal housing. Since the first one is not modified easily, the second factor can be a variable to understand the effect of this parasitic capacitance more thoroughly. If the circuit, and therefore the switch-nodes are close to a metal surface, the capacitive coupling increases. This will be examined in the simulations.
Fig. 6. Interwinding capacitance of the conventional (left) and planar (right) transformer in crosssectional view. The figure on the right represents a common four-layer PCB structure as an example. The Ci interwinding capacitance is between the bottom two (primary-secondary) layers.
Fig. 7. Capacitive coupling paths of the phase-shifted full-bridge. Ci : interwinding capacitance of the transformer; Cs1 , Cs2 : switch-node parasitic capacitances. The HV switch-nodes are indicated by A and B.
5 Simulation Results To inspect the effects of the aforementioned capacitive coupling paths on the PSFB’s conducted emissions, a model was created in MATLAB Simulink. The basis of the simulation was the conducted emissions test setup depicted in Fig. 5. In every aspect of the model, ideal circuit components were used except for the PSFB as the equipment under test. In addition to the circuit in Fig. 2, unoptimized input and output filters were attached to the converter. Fast Fourier Transform was used to calculate EMI in the frequency domain from the time domain results. It is important to note, that this does not represent the measurement procedure of the test receiver which can also affect the results. The simulations are divided into two categories: the effect of the interwinding capacitance and the switch-node capacitances.
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5.1 Interwinding Capacitance Table 1 contains different values of the variables in (1) which depend on the parameters F of the investigated planar transformer. ε0 is 8.86 · 10−12 m and εr is taken as the typical value of 4.7. The fourth row shows how the capacitance would change if εr were reduced to 4 with a different isolating material. The base value of the interwinding capacitance was assumed to be 100 pF with an area of 10 cm2 and 400 µm separation distance, as shown in the second row. This corresponds to that of a well-designed symmetric planar transformer [7]. Symmetry means that the parasitic capacitance is evenly distributed across the primary-secondary sides. This case is represented by the green curve in Fig. 8 which contains the simulated conducted emissions on the HV side, measured at T+ (Fig. 5). We carried out simulations in which the value of Ci was increased and decreased by one order of magnitude. By increasing its value to 1 nF, conducted emissions became higher below 6 MHz (purple graph). This represents a poorly designed transformer with a large interwinding capacitance and therefore a PSFB with an increased amount of EMI in the lower frequency range. On the other hand, if Ci were reduced to 10 pF from 100 pF, emissions would be significantly lower below 20 MHz (red curve). We examined the impact of the same modifications on the LV side which can be observed in Fig. 9. The differences occur at the same frequencies by changing the value of Ci . Note that emissions on the LV side are generally lower because the main noise source is on the primary side (switching of the input voltage). The interwinding capacitance provides a path through the galvanically isolated transformer. By increasing its value, lower frequency noise components propagate to the secondary side. Because of the 50% duty cycle, the odd harmonics of the 100 kHz switching frequency are dominant (300 kHz, 500 kHz, etc.). Table 1. Typical ε, A and d values and the resulting capacitances regarding the investigated planar transformer F ε m
A [cm2 ]
d [µm]
C [pF]
4 · 10−1
10
400
100
4 · 10−11
10
300
133
3.5 · 10−11
10
400
87.5
4 · 10−11
20
400
200
5.2 Switch-Node Capacitances As mentioned in Sect. 4, the value of the switch-node capacitances mainly depends on the area of the switch-nodes and the distance between the PCB and a metallic reference. Since the surface area was taken as a constant value of 10 cm2 , the distance was considered a variable factor. Basically, the thickness of the foam under the EUT determines the distance between the PCB and the grounded metal table (Fig. 5). Nevertheless, this 5 cm
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value is not always true for the distance between the circuit and a metal surface in the car. This in turn, can affect the conducted emissions. Figure 10 shows three capacitance values which represent a certain distance according to (1). In addition to the surface area, the permittivity was also taken constant, assuming air as the dielectric between the “plates” of the stray capacitances. By decreasing the distance, the parasitic capacitances of the switch-nodes increase as well as the conducted emissions as shown in Fig. 10 by the LV side (B+) emissions. Note that in this case there was a smaller change in the level of EMI than with the modification of Ci by the same extent. This indicates the fact that the effect of the switch-node capacitances is less significant compared to the interwinding capacitance. A good transformer is therefore critical and is one of the most important factors when it comes to the EMC of PSFB converters.
Fig. 8. Effect of Ci interwinding capacitance on the conducted emissions captured at the positive HV termination (T+) of the circuit (50% duty cycle).
Fig. 9. Effect of Ci interwinding capacitance on the conducted emissions captured at the positive LV termination (B+) of the circuit (50% duty cycle).
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Fig. 10. Effect of Cs1 and Cs2 switch-node capacitances on the conducted emissions captured at the positive LV termination (B+) of the circuit (50% duty cycle).
6 Conclusions This paper presented the simulation results of an automotive PSFB power supply regarding its conducted emissions. The main parasitic capacitances were identified and their effect was represented by a model. Emission results show that out of the two capacitive couplings, the interwinding capacitance is the most significant. Therefore, when choosing or designing a planar transformer for automotive DC/DC converters, great consideration should be taken when it comes to its parasitic capacitances. Also, investigations on the switch-node capacitances showed that their effect is only noteworthy at very small distances between the circuit and a metal reference plane. This confirms the disadvantages that result from the planar structure of the transformer and allows for a more distinct separation of the DC/DC converter from other parts of the HV energy transmission system from an EMC point of view. Acknowledgements. The authors would like to express their gratitude to Robert Bosch Kft. Furthermore, they are also thankful for the support under project “Establishment of Electromobility Development Center at Robert Bosch Kft. – 1. phase” (2020-2.1.1-ED-2020-00062) by the National Research, Development and Innovation Fund.
References 1. Sakka, M.A., Mierlo, J.V., Gualous, H.: DC/DC converters for electric vehicles. In: Soylu, S. (ed.) Electric Vehicles – Modelling and Simulations. IntechOpen, London (2011) 2. Hui, S.Y. (Ron), Chung, H.S.H.: 15 – Resonant and soft-switching converters. In: Rashid, M.H. (ed.) Power Electronics Handbook. 1st edn. Academic Press (2001) 3. CISPR 25 – Vehicles, boats and internal combustion engines – Radio disturbance characteristics – Limits and methods of measurement for the protection of on-board receivers (2016) 4. Xie, L., Ruan, X., Ye, Z.: Reducing common mode noise in phase-shifted full-bridge converter. IEEE Trans. Ind. Electron. 65(10), 7866–7877 (2018)
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5. Lu, J., Dawson, F.: Characterizations of high frequency planar transformer with a novel combshaped shield. IEEE Trans. Magn. 47(10), 4493–4496 (2011) 6. Saket, M.A., Ordonez, M., Craciun, M., Botting, C.: Improving planar transformers for LLC resonant converters: paired layers interleaving. IEEE Trans. Power Electron. 34(12), 11813– 11832 (2019) 7. Vedde, A., Neuburger, M., Spanos, K., Reuss H.-C.: Reduction of common mode EMI emissions by optimizing the planar transformer of phase-shifted full-bridge DC/DC converters. In: 2021 23rd European Conference on Power Electronics and Applications (EPE’21 ECCE Europe), pp. 1–9 (2021)
A Literature Review of a Dual-Purpose Solar Collector Mustafa M. Hasan1,3(B) and Krisztián Hriczó2 1 University of Miskolc, Miskolc 3515, Hungary
[email protected]
2 Institute of Mathematics, University of Miskolc, Miskolc 3515, Hungary
[email protected]
3 Power Mechanics Techniques, Basra Technical Institute, Southern Technical University,
Basra, Iraq [email protected]
Abstract. Solar energy is an abundance, inexpensive and clean source of energy. Using this energy source can be widely spread as the efficiency of solar systems improves. The main component of any solar thermal system is the solar collector which absorbs the incident solar radiation and converts it into heat. Solar thermal systems are categorised into two major types as air and liquid heaters. A solar air heater (SAH) is used for space heating and crop drying. A solar water heater (SWH) is used to supply hot water for domestic and industrial applications. However, air and liquid collectors are considered as a single purpose collector since the working fluid is air or liquid only. One way to enhance the performance of solar thermal systems is by combining both air and liquid heaters in one facility called dual-purpose solar collector (DPSC). This collector is basically a flat plate solar collector (FPSC) with two sections, one for air heating and the other for water heating. Therefore, it can produce hot air and hot water simultaneously. Using DPSC can attain high temperature, and high thermal performance with a reduction in cost and space. Despite many significant investigations on DPSC, no review paper has been seen. This article presents the different designs and applications of DPSC and the parameters affecting its performance. A comparison between single and dual-purpose solar collectors is also discussed. Moreover, the possibility of integrating DPSC in some automobile manufacturing processes is suggested in this article as well. Keywords: Solar energy · Solar thermal systems · Solar heaters · Dual- purpose solar collector (DPSC) · Automobile industry
1 Introduction Energy is getting into all sectors that deal with our daily life. It comes either from conventional sources like coal, oil and natural gas, which known as fossil fuels or from alternative sources like solar, wind, hydro, nuclear, and geothermal which known as renewable energy. Among the renewable energy sources, solar energy is an environmental- friendly, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 302–321, 2023. https://doi.org/10.1007/978-3-031-15211-5_26
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inexpensive, clean, and carbon- free source of energy [1]. Both light and heat are emitted from the sun and can be invested by producing electrical energy from the first and thermal energy from the latter. A photovoltaic panel (PV) is the well- known device that directly converts the emitted light from the sun into electrical energy. While a solar collector is a device that converts the radiant heat from the sun to thermal energy and transfers it to the heat transfer fluid (HTF) [2]. Solar collectors can be classified based on the required temperature of the working fluid into a concentrating and non-concentrating collector [3]. Among the non- concentrating collectors, the low- temperature flat plate solar collector (FPSC) is a widely spread collector and is commonly designed to perform as a single- purpose collector. FPSCs have been used to heat air and water individually through an absorber plate. Therefore, FPSCs can be subdivided according to the type of flowing fluid into solar air heaters and liquid heaters [4]. Solar air heater (SAH) is characterised by its very low manufacturing, maintenance, and operational costs. It has a vital role in space heating [5–7] and crop drying [8, 9]. The main drawbacks of SAH are the low thermal conductivity of air and high heat loss to the ambient [10]. Whereas solar water heater (SWH) has a distinctive importance in producing hot water for domestic, residential, and industrial applications due to its effective operation, simple design, and low maintenance cost [11]. In general, the main merits associated with FPSCs are relatively low manufacturing cost, the ability to collect incident solar radiation, and needless for sun’s tracking system [12]. Whilst the major demerit is the low thermal efficiency due to the low heat delivered from the absorber plate to the circulating fluid [13]. Thus, a numerous studies and modifications [3, 14–16] have been conducted to enhance the rate of heat transfer between the absorber plate and the circulating fluid and thus increasing the thermal performance of FPSCs. The combination of two solar thermal technologies (i.e., air and liquid heaters) in one facility called dual-purpose solar collector (DPSC) is one of the most feasible solutions to overcome the drawbacks of FPSCs. It is one of a novel avenues by which the following benefits can be obtained: • increase the thermal performance and annual application of solar energy. • reduce the required install area and cost. • achieve high temperature with high heat delivery of solar thermal systems. This hybrid collector is mainly a FPSC with two sections, one for air heating and the other for liquid heating, therefore it generates hot air and hot liquid simultaneously. Moreover, DPSC can also perform as a single- purpose collector according to the requirements. The concept of dual- purpose collector is not limited on combining two thermal technologies in one device, it can also be obtained by combining another two solar technologies (i.e., solar thermal and PV technologies) in one device called photovoltaic/ thermal (PV/T) collector. This dual function collector recovers the accumulated heat in PV module to produce thermal energy for low and medium temperature applications and ensure high electrical efficiency of PV module as well [17]. Thus, PV/T collector has the advantage of generating both thermal and electrical energy simultaneously with a
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high overall efficiency [18]. Depending on the coolant used in PV/T collector, two main categories such as PV/T air and PV/T water can be noticed with a brief description [19]. In spite of many types of research, no review study on DPSC is seen. The objective of this article is to present the various works that have been carried out for DPSC concerning different designs and operational parameters. This paper hopefully assists researchers in having state of the art review of recent works in this field. Besides, it presents a new trend for incorporating DPSC with the different automobile industrial processes such as parts washing, paint drying, and corrosion protection.
2 Overview of Different Design Approaches Based on the previously published literature, a researcher can observe that the dual function concept in solar energy systems includes a combination of any two different solar technologies in one device. Figure 1 shows the classification of dual- function collectors. The studies on DPSC are sought in the following subsections.
Fig. 1. The classification of dual- function collectors.
2.1 A Standalone DPSC In this section, we referred to DPSC, which is installed and operated independently as a standalone. The first design and study of such a collector was carried out by Assari et al. [20]. The collector consists of water pipes in the top section which used for heating water and a V- shaped air channels in the bottom which used for air heating (see Fig. 2). The DPSC was investigated theoretically and experimentally as a single (i.e., air and water) and combined collector. Tests were carried out according to the American Association of Thermal Engineering (ASHRAE93–77) standards [21]. Good agreement between the calculated and experimental results was obtained. They observed an increase in the outlet air temperature by 20% after 4 p.m., which means that DPSC can be used after sunset. Also, they deduced that this collector could attain high temperature and high heat delivery with a 50% reduction in space and cost. The same DPSC designed by Assari et al. [20] was further studied by Assari et al. [22]. They examined three types of air channel (i.e., straight fin, triangular fin, and without fin). They developed a mathematical model based on effectiveness- NTU method to calculate the outlet air and water temperatures, heat delivery, and heat exchange effectiveness. Parameters (solar radiation, inlet water temperature, airflow rate, and geometry of air
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Fig. 2. The design details of DPSC: (a) the cross- sectional view (b) the schematic layout [20].
channel) were adopted to analyse the performance of DPSC. Increasing the inlet water temperature caused a decrease in both heat delivery and thermal efficiency in water section, while it caused an increase in both heat delivery and thermal efficiency in air section. Further, increasing airflow rate caused an increase in heat delivery and decrease in heat exchange effectiveness for various air channel geometry. In a related context, the energy and exergy analysis of the same DPSC designed by Assari et al. [22] was further studied by Assari et al. [23]. In the water section, increasing the inlet water temperature increased up to 60 °C led to an increase in the exergy efficiency and a decrease in the energy efficiency, whereas both of these efficiencies are decreased as the inlet water temperature increased above 60 °C. In air section, increasing airflow rate and inlet water temperature led to an increase in both energy and exergy efficiencies for all types of air passages. Energy and exergy analysis of DPSC with triangle air channel geometry was further studied by Jafari et al. [24]. Effectiveness- NTU method was used for analysis under a variety of inlet water temperature and airflow rate. They gained the same results presented in Ref. [23] with a conclusion that DPSC with triangle air passage has better energy and exergy efficiency than single purpose collector. Ma et al. [25] modified a conventional solar water heater by adjusting the interior air gap of the collector to construct DPSC. They also bended the absorber fins as L-shape to increase the heat transfer in air section (see Fig. 3). Experiments have been conducted to investigate the thermal performance of the collector in both air and water heating modes. The experimental results showed that the average water heating efficiency was 50% and the daily mean and instantaneous efficiencies in air heating mode reached 52% and 55%, respectively. Theoretical results revealed an increase in the efficiency of the L-shape air channel accompanied with a decrease in outlet air temperature when the airflow rate increased. Another different design of DPSC was fabricated by Venkatesh and Christraj [26]. The novelty in their design is by replacing the storage tank of water heater with a riser tubes and header which are fitted in the bottom of air heater (see Fig. 4). The experimental tests have been carried out for load and no-load conditions for different air and water flow rates. Two scenarios were adopted in experiments: (i) both the water and air heaters are combined together to act as a multipurpose solar air heater (MPSAH), and (ii) both the water and air heaters are combined together to act as a multipurpose solar water
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Fig. 3. Schematic of the dual function solar collector [25].
heater (MPSWH). The results of MPSAH for no load condition showed a maximum temperature of the stagnant air of 88 °C when the ambient temperature was 37 °C. For load condition, the maximum efficiency and maximum temperature difference between outlet and inlet air temperature were 85% and 39.6 °C, respectively. While the results of MPSWH for load condition, showed a maximum efficiency and maximum outlet water temperature of 67.69% and 79 °C, respectively. The novelty made by the authors increased the performance of the system as compared with a conventional collector. Incorporating a porous medium with DPSC was a new trend adopted by Arun Venu et al. [27]. They added a matrix of the porous medium below the absorber plate (see Fig. 5). The modified DPSC was analysed numerically using ANSYS 13 software. The simulation results show that for a solar irradiance of 1000 W/m2 , the temperature difference between outlet and inlet air reached 68 °C and 24.7 °C in the lower and upper channels, respectively. Also, an increase of 11.1 °C was obtained for the water temperature. Water heat gain decreased as the inlet water temperature increased. Whereas air heat delivery increased as air flow rate increased. The thermal efficiency of the collector was enhanced due to the presence of porous matrix which enhanced the heat delivery. One of the valuable usage of DPSC is by coupling it with a drying system to preserve the agricultural products. Solar dryer characterised by several benefits and received a lot of investigations and developments [28, 29]. The technology of solar drying is simple and can be easily adopted to domestic sector [30]. In this context, Mohajer et al. [31] presented a new hybrid system which combines the same DPSC designed by Assari et al. [22] with a domestic scale solar dryer. In their study, the outlet hot air from DPSC was applied to an indirect forced convection solar dryer. Furthermore, the outlet hot water from DPSC can be: (i) supplied for domestic usages, (ii) used as phase change material (PCM) to continue drying process during night or off- sunshine period. The results revealed the ability of the system to be used as a solar dryer and provide domestic consumptive hot water as well. Nematollahi et al. [32] used a vertical water storage tank instead of a horizontal one with the same DPSC designed by Assari et al. [22]. The height difference between
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Fig. 4. A (a) Schematic layout of typical solar water heater cum air heater with storage tank. (b) Pictorial representation of multipurpose solar heating system [26].
Fig. 5. DPSC with porous matrix (a) Schematic diagram. (b) Cross sectional view [27].
the inlet and outlet ports of the vertical storage tank can ensure that the temperature of the water entering collector does not alter greatly. Hence, the heat losses from collector is decreased and the thermal efficiency is increased. The average results revealed that the dual-purpose collector has a significant higher efficiency than for single purpose collector. However, the average temperature of water inside the tank was equal to 65.2 °C which can be used at night or off- sunshine hours. The effect of inner parameters (insulation thickness, upper and lower air channels height, and diameter and number of copper tubes) on the performance of DPSC was studied by Ma et al. [33]. They developed a dynamic model which is based on a finite
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difference method to optimise the structure parameters. Experiments were conducted for water and air heating modes under a different air mass flow rate, different inlet water temperature, and environment conditions. However, the efficiency in air heating mode was increased as the insulation thickness increased whereas it was slightly affected as the inner diameter and number of copper tubes increased. In water heating mode, the efficiency has a maximum value of 62.5% when the inner diameter is fixed at 0.008m and for this diameter the efficiency increased remarkably as the number of tubes increased until 8. Velmurugan et al. [34] introduced another design configuration of DPSC. They fabricated a dual function solar heating system (DFSHS) by connecting SWH, SAH, and heat exchanger in series (see Fig. 6). The system can operate in water heating (WH) mode or air heating (AH) mode, depending on the requirements in different seasons. Experiments were carried out for the two modes under different water flow rate. The maximum outlet temperature and efficiency for WH mode were 74 °C and 73.68%, respectively. Whereas, in AH mode, the maximum recorded efficiency was 69.18%.
Fig. 6. Photograph of DFSHS experimental apparatus [34].
Zhang et al. [35] modified DPSC from a conventional liquid collector. The collector consists of 8 copper water tubes and two (top and bottom) diagonal arranged air channels. Experiments were carried out for three different operating modes: (A) air heating, (B) water heating and (C) air- water compound heating. In mode A, the average efficiency was 50% at a constant air flow rate of 0.024 kg/s. In mode B, the average thermal efficiency was 51.4% at a constant water flow rate of 0.13 kg/s. In mode C, experiments were conducted at constant air flow rate of 0.024 kg/s and for a variety of water flow rate. An average efficiency of 73.4% was obtained which is higher than that of mode A and B. On the contrary, the maximum temperature rise and the temperature difference of mode C are lower than that of mode A and B. Regarding the developments in desalination technologies, researchers observed that integrating different solar collectors with humidification dehumidification desalination (HDH) system can enhance the freshwater production [36]. In this context, the effect of
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integrating DPSC with HDH system was experimentally studied by Rajaseenivasan and Srithar [36]. The system consists of three major parts: (i) DPSC, (ii) humidifier and (iii) dehumidifier (see Fig. 7). The DPSC supplies hot water and hot air to the humidification chamber in which the two fluids were mixed in a direct contact counter flow pattern by means of a packing material. Then, the humid air is directed to the dehumidification chamber for condensation in which, both humid air and cooling water are mixed in an indirect contact counterflow pattern. It was found that freshwater productivity can be increased by increasing the air, water, and cooling water flow rates. Also, it can be increased by increasing the outlet air temperature, which was occurred by using a convex and concave semi-circular turbulators in the air section of DPSC. The overall efficiency of the system was 68% for the absorber with a concave turbulator.
Fig. 7. Schematic diagram of HDH system with dual-purpose collector [36].
DPSC with rectangular air channels was designed and investigated by Kavoosi and Saidi [37]. The experimental results showed that the thermal efficiency of air heater, water heater and DPSC were 45%, 20% and 60%, respectively. In addition, an increment of 11% in the air heater efficiency with rectangular passages was observed as it compared to a single V-corrugated and flat plate air heaters. Another refined design of DPSC was adopted by More and Pote [38]. It consists of horizontal copper tubes instead of parallel ones mounted on the top of an absorber plate and a triangle air channel connected to the bottom of the absorber. The maximum experimental and theoretical efficiencies were 72.4% and 68.81%, respectively and the maximum temperature in the horizontal storage tank was 50 °C at noon. Besides, the heat losses in DPSC were 20%, while for a conventional flat plate collector are about 33–50% [39]. Furthermore, the results revealed that the efficiency of DPSC always higher than 50% during the whole day. A detailed mathematical model for DPSC was developed by Shemelin and Matsuka [40] to simulate two different designs of a dual air/water solar collector (DAWC) (see Fig. 8). The model was simulated in TRNSYS 17 software and it was experimentally validated. Good agreement between the simulated and experimental results were obtained. Therefore, the model was further used to analyse the performance of three DAWC for three houses from three different locations with different energy performance level for each. The results revealed that DAWC is more efficient for buildings with high heat energy consumption and performs better in cold and moderate climates than in warm climates.
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Fig. 8. The DAWC designs considered [40].
A simulation analysis based on computational fluid dynamics (CFD) was studied by Shandal et al. [41]. COMSOL Multiphysics 5.4 software was used to model and simulate the DPSC by considering it consists of four domains: water tubes (A), absorber plate (B), triangular fins (C), and air channel (D) (see Fig. 9). The model was validated against the experimental results and good convergence was observed. Thus, it was further used to study the thermal behaviour of water and air inside the DPSC during Winter, Spring and Summer for different fluid flow rates and water inlet temperatures. As an expected, the outlet air temperature decreased as the air velocity increased. The efficiency increased as the water flow rate increased, whereas it decreased as inlet water temperature increased.
Fig. 9. The domains of the simulated DPSC [41].
Somwanshi and Sarkar [42] developed a new collector known as dual-purpose cumstorage air-water heater (DCS- AWH). It consists of an upper air heating section and lower water heating section which are separated by an absorber plate (see Fig. 10). Both the outlet water and air temperatures were computed theoretically based on a simple mathematical model and compared with an experimental values. The model was further utilised to analyse the effect of covering the collector during the night and the effect of air flow rate on water and air temperatures. It was found that about 19.9% of the heat delivered to the air and water was conserved by using the cover. Also, the collector performed well at a low airflow rate rather than at a heigh one. The maximum water and air temperatures in winter and summer were 56 °C, 50 °C and 89.6 °C, 81.1 °C, respectively. Finally, the average thermal efficiency of the system was 5.5% and 24.6% higher than that found in Ref. [32] and [38], respectively.
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Fig. 10. Cross-section view of DCS- AWH [42].
Recently, Kumar et al. [43] experimentally investigate a novel hybrid DFSC. They used a pressurised shot- blasting technique for roughening the inner surface of air channel and the absorber plate of water heater to enhance thermal performance. Moreover, solar glycol (SG) with multi-walled carbon nanotube (MWCNT)-based nanofluids with two-volume percentages (0.1 vol% and 0.2 vol%) were tested to further improve the convective heat transfer coefficient. The water and air heaters were tested separately under a variety of air and water flow rates. In the air heater test, the maximum difference between the outlet and inlet air temperatures was 25 °C, the heat transfer rate recorded 452W, and thermal efficiency attained 33.2%. In the water heater test, the maximum difference between the outlet and inlet water temperatures was 18.32 °C, the heat transfer rate recorded 680W, and thermal efficiency attained 51.03%. for the 0.2 vol% SG/MWCNTbased nanofluid. Harvesting solar energy and producing both hot water and hot air are not the unique function of DPSC. It can serve as heat collection during daytime and cold collection (rejecting heat) during night as was indicated by Miao et al. [44]. They innovated a new design, in which the single-pane glass cover of a conventional FPSC was replaced by a double-pane polycarbonate cover (see Fig. 11).
Fig. 11. Design of DPSC for heat and cold collection [44].
In the heat collection mode, water or other liquid gets heated as it flows in the copper tubes embedded in the absorber plate, while in the cold collection mode, water or other liquid rejects heat to the outside by radiation and/or convection as it flows in the channels between the two panes. The experimental results indicated that this collector has higher cooling and heating capacity than uncovered collectors, which in turn lowers the energy consumption of Heating, Ventilation, and Air Conditioning (HVAC) systems.
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2.2 Building- Integrated DPSC Utilisation of DPSC is not limited to a standalone collector only, but also can be used as building- integrated collector. The reason behind using such a collector is summer overheating which is a common problem in temperate climates that appears with passive space heating designs such as trombe-wall, composite Trombe- Michel wall and PVTrombe wall [45]. Building- integrated collector is widely used in building sector since it reduces building energy consumption and provides space heating in winter, water heating and lowers the cooling load in summer as well. Ji et al. [46, 47] proposed building- integrated dual function solar collector which operated with natural circulation of water (see Fig. 12). The newly designed collector was examined experimentally and numerically under water heating mode. The results showed that the daily cooling load of a test room with collector is 2% lower than that without collector on a typical summer day. On winter, the mean indoor air temperature was up to 24.7 °C while the mean ambient temperature was 4.8 °C. Moreover, the numerical model can give an accurate prediction of system performance.
Fig. 12. Schematic diagram of the building- integrated dual function solar collector: (a) water heating circuit, (b) section view of passive space heating [46].
The effect of DFSC on the cooling load of a building in summer was investigated by Jie et al. [48]. The collector mounted on the south façade of the building and operated in water heating mode with natural circulation. The results obtained from the developed numerical model was much more agreeable with the experimental results. Therefore, the model was used to predict the cooling load of a room with and without the collector. The simulation results showed that the cooling load of a room with the collector is 2.05% lower than that without the collector. Moreover, the DFSC can enhance the thermal environment of the building and provide domestic hot water in summer without overheating problems caused by other conventional passive solar heating systems. A hybrid solar system composed of two DPSCs was presented by Zhi et al. [49] to heat a solar demonstration building. One of the DFSCs performed as a Trombe wall and provided passive space heating. Whereas the other performed as a solar air heater and provided active space heating. The results revealed that the strategy of passive solar heating for southern rooms and active solar heating for northern rooms could maintain
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the average indoor temperature at 17 °C. TRNSYS 17 simulation software was used to predict the solar fraction. It was observed that solar fraction of the hybrid system is low for low irradiance areas and vice versa. The mismatch between the conventional solar collector and the tile roof of a traditional building in appearance motivated some researchers to propose a novel DPSC which has a good match with the local special feature building culture. Luo et al. [50] proposed a novel tile- shaped DPSC in which the glass cover was further covered by Polymeric Methyl Methacrylate (PMMA) covers to enhance the aesthetical view and minimise the heat loss (see Fig. 13). The collector was experimentally tested and compared with DPSC without PMMA covers and with DPSC has a semicircle covers. The daily thermal efficiency of the tile- shaped collector when it was operated in water heating mode varied from 54% to 61.8%. While it varied from 44.7% to 59.2% and from 35.5% to 67.4% for the DPSC with semicircle covers and DPSC without PMMA covers, respectively.
Fig. 13. Schematic of the tile-shaped dual-function solar collector [51].
He et al. [51] further studied a tile- shaped DPSC in water heating mode by using CFD. The numerical results obtained were compared with an experimental ones and reveled a good agreement. The influence of inlet water temperature, water mass flow rate, solar radiation, and ambient air temperature on thermal efficiency were also studied. The results showed that lower inlet water temperature, higher water flow rate, higher ambient air temperature and lower solar radiation enhanced the thermal efficiency of the module. Moreover, a comparative study revealed that the collector with tile-shaped
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cover can achieve higher efficiency than a flat plate collector does at higher temperature operation. In the same context, Hu et al. [52] designed a novel roof type dual function solar collector named (Type 1) by using a wavelike PMMA top cover instead of the glass cover (see Fig. 14). An absorber plate divided the gap between the cover and the bottom of the collector into up and down channels. A dynamic model was developed using MATLAB software and validated experimentally. The model was adopted to predict the thermal efficiency and outlet water temperature of this collector under the same operating conditions used in Ref. [51]. The simulated results indicated that higher thermal efficiency as well as lower heat loss coefficient can be achieved with Type 1 collector. Further, it was found that the wavelike collector provided better consistency for traditional Chinese buildings than other roof-integrated solar collectors.
Fig. 14. Cross-sectional view of wavelike roof solar collector [52].
Ma et al. [53] tested the thermal behaviour in passive heating mode for a room integrated with DPSC (test room) and a room without DPSC (reference room). Both of the test and reference rooms were examined under a controlled and non- controlled conditions. For the non-controlled condition, the average room temperature for both rooms were 8.24 °C and 4.81 °C, respectively. For a controlled condition, the room temperature was set to 18 °C, the power consumption for both rooms were 4.322 kWh and 7.796 kWh, respectively. Moreover, the influence of the copper water tubes in DFSC and the depth of air channel on thermal efficiency were studied. An enhancement in efficiency was observed as compared with traditional passive solar air heater without water tubes. 2.3 New Trend Apart from that, the automobile industry depends on the non- renewable energy sources (i.e., fossil fuels) and electricity for manufacturing processes of vehicles. The maximum temperature range required in different process in an automobile industry is 120 °C [16]. Among these processes, parts washing, paint drying, and corrosion protection can be accomplished by integrating solar systems to save energy [54]. In parts washing, hot water is used before the assembly process to remove debris from the parts, such as dirt, grime, and metal chips. The temperature of the washing water is between 70 °C- 90 °C and it is usually heated by electric heaters [55]. Hot air is required in paint shops to dry the painted parts. The air temperature needed for this purpose is in the range of 80 °C to 150 °C and it is usually gained by fossil fuels [55]. Phosphate coating is a pretreatment process applied to automobile parts to protect them against corrosion. In this process, the parts are immersed in a solution path of phosphate of around 90 °C.
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It is worthy of being highlighted on the idea of integrating DPSC, storage tank, and an auxiliary heater in the automobile industry to reduce energy consumption and make this industry more sustainable.
3 Mathematical Model of DPSC The useful energy QU and thermal efficiency ηc of DPSC are given by the following relationships [20]: QU = (QU )L + (QU )a ηc =
QU AIT
(1) (2)
where; A is the area of the collector, IT is the solar radiation, and the indexes a, L are for air and liquid part, respectively. The useful heat gain to the fluid is calculated based on the energy balance as: (3) QU = m ˙ f Cf Tfo− Tfi or QU = AP S − QL
(4)
where AP is absorber plate area, S is absorbed incident solar flux by the absorber, and QL is the heat loss through top, bottom and side: QL = UL AP Tpm− Tamb (5) where Tpm , Tamb are the absorber and ambient temperatures, UL is overall loss coefficient and calculated as: UL = Ut + Ub + Ue
(6)
Ut , Ub , Ue are top, back and side loss coefficient respectively. A detailed calculations for these coefficients can be found in Ref. [56]. Thermal efficiency of DPSC is expressed as [56]: ηc = FR (τ α) − FR UL
(Ti− Ta ) IT
(7)
Here, τ α is transmittance-absorptance product of cover, FR is the heat removal factor which is defined as the ratio of actual heat transfer to the maximum possible heat transfer and can be calculated as [56]: ⎡ ⎤ − AUL F mC mC ˙ P⎣ ˙ P ⎦ 1−e (8) FR = AUL
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Here, m ˙ is the mass flow rate, CP is the specific heat of fluid and F is the collector efficiency factor. Assari et al. [22] developed a mathematical model based on effectiveness- NTU method. The effectiveness is defined as heat delivery to maximum heat delivery that can transfer to fluids: m ˙ f CP,f Tf 2− Tf 1 Tf 2− Tf 1 = (9) εf = Tpm− Tf 1 m ˙ f CP,f Tpm− Tf 1 or h A − m˙ fC f
εf =
f P,f (Tpm− Tf 1 ) − exp (Tpm− Tf 1 ) hf Af = 1 − exp[− ] (Tpm− Tf 1 ) m ˙ f CP,f
(10)
where Tf 2 , Tf 1 are the fluid temperatures at outlet and inlet, hf is the convection heat coefficient, NTU is defined as: NTU =
hf Af m ˙ f CP,f
Therefore, the useful energy of collector is given as: ˙ f CP,f ˙ f CP,f εf m εf m Qu = AP S − UL Ap (Tfi− Tamb ) UL Ap + εf m ˙ f CP,f UL Ap + εf m ˙ f CP,f And the efficiency as: (Tfi− Tamb ) εf m εf m ˙ f CP,f ˙ f CP,f η= UL (τ α)av − UL Ap + εf m ˙ f CP,f UL Ap + εf m ˙ f CP,f IT
(11)
(12)
(13)
Here, equations are for fluids (water and air) and the subscript f means fluids. The exergy analysis of DPSC was studied by Assari et al. [23]. The exergy balance can be written as follows: ˙ work + Ex ˙ u = Ex ˙ dest ˙ heat − Ex Ex
(14)
˙u ˙ heat = 1 − To Q Ex Ts
(15)
where;
˙ u , the total rate of the energy, is received by the ˙ heat is exergy due to heat, Q where Ex collector absorber area from the solar radiation, and Ts is the black body temperature (6000 K). The exergy destroyed is the total entropy generated S˙ gen times the ambient temperature To as: ˙ dest = To S˙ gen = I˙ Ex
(16)
A Literature Review of a Dual-Purpose Solar Collector
Thus, the exergy balance for collector become: Tfo To ˙ Qu = m 1− ˙ f Cf Tfo− Tfi − To ln Ts Tfi
317
(17)
The exergy efficiency is the ratio of useful exergy to the exergy of the solar radiation: ηex = 1 −
I˙ ˙ heat Ex
(18)
4 Conclusion and Future Work Several studies have been conducted to increase the annual application of solar energy and maximise the heat delivery of solar thermal collectors. Combining both air and liquid heaters in so-called DPSC is a distinctive innovation in this field. Based on the literatures presented in this overview, the following can be deduced: • The overall performance of DPSC is directly affected by: – Design aspects such as the roughness elements on the absorber plate, air channel geometry, inserting a porous medium, using PMMA covers, insulation thickness, and water tubes. – Operational parameters such as solar irradiance, inlet fluid temperature, fluid flow rate, and the geometry of air channel. • When the inlet water temperature is increased, both the heat delivery and thermal efficiency in the water section of DPSC are decreased whilst in the air section of DPSC they are increased. Thus, it is preferred to utilise a vertical water storage tank rather than a horizontal one since it offers a temperature stratification between the inlet and outlet openings and keeps the temperature of water entering the collector at the initial one. • When the airflow rate is increased, both the heat delivery, energy, and exergy efficiencies in the air section of DPSC are increased for all air passages with best results for triangle one. • Integrating a porous matrix in the air channel of DPSC has a significant advantage to enhance thermal performance as it increases the heat transfer area. • Using the horizontal water tubes in DPSC instead of the vertical ones has an intangible effect on the efficiency enhancement. • Using the tile- shaped PMMA covers with DPSC has two different advantages. First, enhances the aesthetical view of the Chinese traditional buildings. Second, minimises the heat loss of DPSC and thus maximises its efficiency. • The hot air produced in DPSC can be used to dry agricultural products in drying system, while the hot water can be used as a PCM to continue drying process through the night or off- sunshine periods.
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• Integrating DPSC with a distillation system can improve the distillation productivity and reduce the desalination cost to the lowest value of 0.0257 $/kg. • DPSC can reduce the greenhouse emission by lower the energy consumption of HVAC (Heating ventilation and air conditioning) systems like cooling towers and chillers when it used as a heat and/ or cold collection. • Integrating DPSC with buildings has several advantages: – In winter, it can perform as space heating and maintain the indoor air temperature higher than the ambient by 20 °C under specific conditions. – Reduce the cooling load of a building in summer by approximately 2%. – Hot water can be supplied normally for domestic use in both seasons. – Save about 3.5 kW/h of the daily power consumption when the indoor temperature is controlled to be at 18 °C. • In the automobile industry, there is a reasonable possibility to use DPSC in some manufacturing processes and reduce the dependency on conventional sources of energy. As a summary, DPSC is not only used to supply hot air and liquid simultaneously or separately, but it also performs better than the single purpose air and water collectors with high energy savings and approximately 50% reduction in space and cost. Besides, it is an efficient device for any region, any application (i.e., domestic, agricultural, and industrial), and under any meteorological conditions.
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9. Sanghi, A., Ambrose, R.P.K., Maier, D.: CFD simulation of corn drying in a natural convection solar dryer. Dry Technol 36(7), 859–870 (2018) 10. Varun, S.A., El-Sebaii, AA.: A thermodynamic review of solar air heaters. Renew Sustain Energy Rev 43, 863–90 (2015) 11. Khan, M.M.A., Ibrahim, N.I., Mahbubul, I.M., Ali, H.M., Saidur, R., Al-Sulaiman, F.A.: Evaluation of solar collector designs with integrated latent heat thermal energy storage: A review. Sol. Energy 166, 334–350 (2018) 12. Garcia, R.P., Oliveira, S. del R., Scalon, V.L.: Thermal efficiency experimental evaluation of solar flat plate collectors when introducing convective barriers. Sol. Energy. 182, 278–285 (2019) 13. Zayed, M.E., Zhao, J., Du, Y., Kabeel, A.E., Shalaby, S.M.: Factors affecting the thermal performance of the flat plate solar collector using nanofluids: a review. Sol. Energy. 182, 382–396 (2019) 14. Mund, C.: Sushil Kumar Rathore, Ranjit Kumar Sahoo: A review of solar air collectors about various modifications for performance enhancement. Sol. Energy. 228, 140–167 (2021) 15. Gorjian, S., Ebadi, H., Calise, F., Shukla, A., Ingrao, C.: A review on recent advancements in performance enhancement techniques for low-temperature solar collectors. Energy Convers. Manage. 222, 113246 (2020) 16. Vengadesan, E., Senthil, R.: A review on recent development of thermal performance enhancement methods of flat plate solar water heater. Sol. Energy 206, 935–961 (2020) 17. Zondag, H.A., de Vries, D.W., van Helden, W.G.J., van Zolingen, R.J.C., van Steenhoven, A.A.: The yield of different combined PV-thermal collector designs. Sol Energy 74, 253–269 (2003) 18. Chow, T.T.: A review on photovoltaic/thermal hybrid solar technology. Appl Energy 87, 365–379 (2010) 19. Diwania, S., Agrawal, S., Siddiqui, Anwar S., Singh, S.: Photovoltaic–thermal (PV/T) technology: a comprehensive review on applications and its advancement. International Journal of Energy and Environmental Engineering (2019) 20. Assari, M.R., Basirat, T.H., Kavoosi, H., Moravej, M.: Design and performance of dualpurpose solar collector. In: 3rd International Energy, Exergy and Environment Symposium (IEEES-3). University of Évora, Portugal (2006) 21. ASHRAE: Methods of testing to determine the thermal performance of solar collectors, American Society of Heating. Refrigeration and Air Conditioning Engineers, New York (USA) (1977) 22. Assari, M.R., Basirat, T.H., Jafari, I.: Experimental and theoretical investigation of dual purpose solar collector. Sol Energy 85, 601–608 (2011) 23. Assari, M.R., Basirat, T.H., Jafari, I., Najafpour, E.: An energy and exergy analysis of water and air with different passage in a solar collector. Energy Sources Part A Recovery Utilization and Environmental Effects 36, 747–754 (2014) 24. Jafari, I., Ershadi, A., Najafpour, E., Hedayat, N.: Energy and exergy analysis of dual purpose solar collector. Acad Sci Technol 81, 259–261 (2011) 25. Ma, J., Sun, W., Ji, J., Zhang, Y., Zhang, A., Fan, W.: Experimental and theoretical study of the efficiency of a dual-function solar collector. Appl. Therm. Eng. 31, 1751–1756 (2011) 26. Venkatesh, R., Christraj, W.: Experimental investigation of multipurpose solar heating system. Journal of Energy Engineering 141(04014009), 2013 (2013) 27. Arun, A.K., Venu, Arun, P.: Simulation studies on porous medium integrated dual purpose solar collector. International Journal of Renewable Energy Research (IJRER) 3(1), 114–120 (2013) 28. Çakmak, G., Yıldız, C.: Design of a new solar dryer system with swirling flow for drying seeded grape. Int Commun Heat Mass Transfer 36, 984–990 (2009)
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29. Fadhel, M.I., Sopian, K., Daud, W.R.W., Alghoul, M.A.: Review on advanced of solar assisted chemical heat pump dryer for agriculture produce. Renew Sust Energy Rev 15, 1152–1168 (2011) 30. Maiti, S., Patel, P., Vyas, K., Eswaran, K., Ghosh, P.K.: Performance evaluation of a small scale indirect solar dryer with static reflectors during non-summer months in the Saurashtra region of western India. Sol. Energy 85, 2686–2696 (2011) 31. Mohajer, A., Nematollahi, O., Joybari, MM., Hashemi, SA., Assari M.R.: Experimental investigation of a Hybrid Solar Drier and Water Heater System. Energy Conversion and Management 76, 935–944 (2013) 32. Nematollahi, O., Alamdari, P., Assari, M.R.: Experimental investigation of a dual purpose solar heating system. Energy Convers. Manage. 78, 359–366 (2014) 33. Ma, J., Wang, H., Wang, Y., Sun, W., Ji, J.: Performance investigation and structure optimisation of a flat dual-function solar collector. Int. J. Photoenergy 2015, 1–11 (2015) 34. Velmurugan, K., Christraj, W., Kulasekharan, N., Elango, T.: Performance study of a dualfunction thermosyphon solar heating system. Arab. J. Sci. Eng. 41(5), 1835–1846 (2015). https://doi.org/10.1007/s13369-015-1994-1 35. Zhang, D., Li, J., Gao, Z., Wang, L., Nan, J.: Thermal performance investigation of modified flat plate solar collector with dual function. Appl. Therm. Eng. 108, 1126–1135 (2016) 36. Rajaseenivasan, T., Srithar, K.: Potential of a dual purpose solar collector on humidification dehumidification desalination system. Desalination 404, 35–40 (2017) 37. Kavoosi, H., Saidi, M.H.: Experimental investigation of dual-purpose solar collector using with rectangular channels. Journal of Thermal Engineering 3, 1052–1059 (2017) 38. More, N.G., Pote, R.S.: Numerical and experimental investigation of dual purpose solar collector. International Journal of Engineering Research & Technology (IJERT) 7, 81–88 (2018) 39. Tiwari, G.N., Suneja, S.: Solar Thermal Engineering System. Narosa publication house, New Delhi (1997) 40. Shemelin, V., Matuska, T.: Performance Modelling of Dual Air/Water Collector in Solar Water and Space Heating Application. Int. J. Photoenergy 2019, 1–10 (2019) 41. Shandal, J., Abed, Q.A., Al-Shamkhee, D.M.: Simulation analysis of thermal performance of the solar air/water collector by using computational fluid dynamics. E3S Web of Conferences 180, 02015 (2020) 42. Somwanshi, A., Sarkar, N.: Thermal performance of a dual-purpose collector-cum-storage type air- water heater. Appl. Therm. Eng. 171, 115094 (2020) 43. Ganesh Kumar, P., Balaji, K., Sakthivadivel, D., Vigneswaran, V.S., Velraj, R., Kim, S.G.: Enhancement of heat transfer in a combined solar air heating and water heater system. Energy 221, 119805 (2021) 44. Miao, R., Hu, H., Yu, Y., Zhang, Y., Wood, M., Olson, G.: Experimental study of a newly developed dual-purpose solar thermal collector for heat and cold collection. Energy & Buildings 252, 111370 (2021) 45. Gan, G.: A parametric study of Trombe walls for passive cooling of buildings. Energy build 27, 37–43 (1998) 46. Ji, J., Luo, Ch., Chow, T.T., Sun, W., He, W.: Thermal characteristics of a building-integrated dual-function solar collector in water heating mode with natural circulation. Energy 36, 566e574 (2011) 47. Ji, J., Luo, C., Sun, W., He, W., Pei, P., Han, C.W.: A numerical and experimental study of a dual-function solar collector integrated with building in passive space heating mode. Chin. Sci. Bull. 55, 1568–1573 (2010) 48. Jie, J., Luo, C.L., Sun, W., He, W., Jiang, Q.Y.: Effect of a dual-function solar collector integrated with building on the cooling load of building in summer. Chinese Science Bulletin 55, 1568–1573 (2010)
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Overview of the Market of Electric Cars by Multilogistic Curves Ferenc János Szabó(B) Institute of Machine- and Product Design, University of Miskolc, Miskol, Egyetemváros 3515, Hungary [email protected]
Abstract. Sigmoid functions (growth function, logistic function, evolution function, etc.) are used to describe, study and forecast several phenomena of the life. In some cases (for example, in case of the COVID-19 disease), the phenomenon has several waves, which needs to apply multilogistic (multiwave logistic) curves in order to perform realistic investigation. In product design, the logistic curve can describe the lifecycle of a product. A product lifecycle can be finished by the significant decrease of the market, but in some cases, several new developments and innovations can regenerate the increase of the market by starting a new boom. This renewing process can invoke several waves of the phenomenon, which will make necessary the application of multilogistic curves for the correct study. This multiwave behaviour of the product lifecycle makes this phenomenon very similar to the time history of the COVID-19 disease which also has several waves, because of the newer and newer virus variants. Analysis and comparison of several phenomena described by logistic curves, or bi- logistic, tri- logistic or multilogistic curves can be made easier by the application of the EBSYQ (Evolutionary Based SYstem of Qualification and comparison of group achievements) comparison and qualification system. The similarity between the multiwave characteristics of the product lifecycle and coronavirus time history makes possible to apply several results, skills and methods of comparison and investigation, which were developed and used previously during the analysis of several waves of the disease also for the case of product lifecycle analysis. Keywords: Logistic curves · Multilogistic curves · Electric cars · Market of electric cars
1 Introduction Sigmoid curves describing growth or saturation phenomena are used in many fields of life for description, study and forecast of these kind of situations. These curves are highly multidisciplinary curves because one can find many different applications of these curves in a large variety of problems (biology – population dynamics, economy – lifecycle curve of products, medicine – growth of tumors or time history of pandemic disease just like COVID 19, environmental protection – plastic waste in oceans, agriculture – growth of fishes and forests, optimisation – iteration history curve of optimisation algorithms). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 322–329, 2023. https://doi.org/10.1007/978-3-031-15211-5_27
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Discovery and investigation of the sigmoid curves started in the years of 1700. Malthus (1798) [1], who proved that the increase in the number of members of a species is dependent from the actual value of this number. This is the basis of the Moore (1965) [2] law for computer capacity increase. Verhulst (1847) [3] derived the sigmoid curve describing the case of saturation, introducing the denomination of this type of curve “logistic curve” or logistic function. Pearl and Reed (1920) [4] applied the logistic curve for the study of the population growth of USA. The S –like the shape of the curve made possible to use the attribute “sigmoid” for these kind of curves. Fisher and Pry (1971) [5] developed a transformation of the curves from S- shape into linear function, which makes it easier to calculate the regression coefficient in case of approximation of the curves. Bertalanffy (1938) [6] used sigmoid curves for the description of the growth of the length of sharks. These results are useful also for the study of several fish species and in forestry too. Kozuko and al. (2003) [7] applied this growth function for the study of the growth of tumors in medicine. The growth function modified by Richards (1959) [8] is applicable for the studies of the growth of several plants, too. Mansfield (1961) [9] and Rogers (1962) [10] described the product’s lifecycle as sigmoid curve. Jang Show- Ling and al. (2005) [11] shown that the spread of the mobile phones in 29 OECD countries and Thaiwan can be described also by sigmoid curves. Investigating some pulsating or multiwave phenomena by Meyer (1994) [12] shown the possibility of the application of bi- logistic, tri- logistic or multi- logistic curves, which were used by Silverberg and Lehnert (2003) [13] for the investigation of the evolutionary models of economic growth. Nikosz (2009) [14] gave interesting examples of the application of sigmoid curves for social phenomena. Szabó (2011- 2021), [15–24] investigated several phenomena by sigmoid curves: one hundred years history of sports world records (2011) [15], proposed a comparison and qualification system using sigmoid curves (2017) [16] having the name EBSYQ (Evolutionary Based System for Qualification and comparison of group achievements), investigated the iteration history curves of optimisation algorithms (2018) [17], studied the possible future of the plastic waste in oceans of the Earth (2019) [18], investigated wear curves of tools [20] (2020), shown that product lifecycle can also be described by sigmoid curves [21] (2021), investigated the time curve of COVID 19 disease in Hungary [22] (2020). For all these investigations, Szabó applied the approximation procedure based on the Nelder- Mead (1965) [19] optimisation algorithm, defined the approximation process as an optimisation procedure searching for the minimum of the square differences. Rézsó F.-né [23] (2020) shows an example of the application of the EBSYQ system for the comparison of several student groups writing the same exam test.
2 Approximation of Product Lifecycle Curves as Sigmoid Curves Investigation of the product lifecycle curves is a more and more important part of the interdisciplinary and holistic approach of the entire product lifecycle, as it is shown by Vajna, (2020) [24]. The lifecycle curve of a product has three important phases (see Fig. 1): first is the increasing phase, which contains the introduction of the product and the growth of the market; the second phase is the saturation, where the growth of the
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market is very slow; the final phase is the decreasing of the market, the end of this phase could be even the rollout of the product.
Fig. 1. Typical lifecycle curve of a product.
The first three-phase of the lifecycle curve (introduction, growth and saturation) shows a typical sigmod (S- shape) curve, the logistic curve, the beginning of it (introduction phase) is an exponential growth curve. Because of some developments and innovations, the decreasing phase can be stopped and a new increasing part can be started (Fig. 2). This repeating increase will change the shape of the lifecycle curve into a multiwave logistic curve.
Fig. 2. Lifecycle curve showing repeating increase (multiwave logistic curve)
The time history curve of the COVID 19 pandemia, because of newer and newer mutations and virus variants shows also multiwave shape and it can be described very accurately by multiwave logistic curves by Szabó (2022) [25], as shown in Fig. 3. This similarity between the characteristic curves of the pandemic and of the product lifecycle curve makes possible to describe both of them by the multiwave logistic curve. This description could give the possibility of a better understanding of the behaviour of
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these phenomena and by discovering more and more details of the multiwave logistic curves, it could be possible to create several forecasts concerning the future of the market in case of a given product. This could give useful new points of view for product developers and engineers to keep the market at a permanent high level.
Fig. 3. Multiwave logistic curve of the COVID 19 pandemy in Hungary
3 The Market of Electric Cars Figure 4 shows the curve of the market of electric cars in several regions of the world.
Fig. 4. Market of electric cars in the new cars selling in percents, regarding USA, China, Europe (2022) [26].
It can be seen in Fig. 4 that the market of electric cars has several decreasing and increasing periods, so this is a phenomenon having multiwave behaviour. According to Fig. 4, in case of Europe and USA this curve has two waves, in case of China 3 waves. In Hungary one can detect the second wave (Fig. 5). Regarding the 2014- 2020 part of the Hungary curve, it is a typical logistic curve. The 2020- 2022 part of the curve is the
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beginning of the second wave, exponential curve. 2022 point of the curve is the inflection point, the end of the exponential increase and starting of the saturation part. Bihari and Sarka (2018) [28] also shown that the market of the electric and hybrid vehicles is increasing in our days in Hungary. Figure 6 shows the approximation of these curves, the values of the parameters in the equation of the curves can be found in Table 1.
Fig. 5. The number of electric cars in Hungary in the last decade (2022) [27]
Fig. 6. Approximation of the exponential growth and saturation parts of the curves of Fig. 5.
Table 1. Parameters of the approximated curves Parameter Exponential Logistic
First wave
Second wave
c
300.00
0.336834
r
2.368483
4.659909
K
21286.89128
177919.5
r
1.220149316
0.347306
c
236.99
254.5286
Equations of the curves: y = cxr , y =
K 1 + ce−rx
(1)
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The derivative of the equation of the logistic function: Kcre−rx dy(x) = 2 dx 1 + ce−rx
(2)
The parameters in Eq. (2) are the same as in Table 1.
4 Conclusions Sigmoid curves can describe many phenomena of the life. Discovering important charasteristics of these curves and understanding the effects of the parameters in the equations of the curves could give the possibility of deeper understanding the investigated phenomena, comparing several exemplars of a process or comparing the several waves of one process. The lifecycle curve of a product is sigmoid curve, especially the Pearl- Reed logistic curve, having two important parts: first part is an exponential growth, the second part is the saturation period. In some cases, the lifecycle curve could show a multiwave behaviour because of newer and newer developments and new variants of the product. The multiwave behaviour of the product lifecycle curves results in that these curves are very similar to the time history curve of the COVID-19 disease, which shows also multiwave shape. This gives the possibility to use and apply the comparing and analysis techniques and methodology developed for comparison and evaluation of several waves of the COVID-19 disease (2021) [25]. The comparison and evaluation process of sigmoid curves and several waves of a phenomenon starts with the approximation of the curves and by the evaluation of the equations of the curves. The next step is the understanding the effects of the parameters in the equations and comparison of the shapes of the curves. As a next step it is possible to write the derivatives of the curves, which is in connection with the “agressivity” of the phenomenon, or with the velocity of the growth process. The ending point of the exponential growth is the starting point of the saturation part (point of inflection of the logistic curve). Comparing the place of the inflection point is also interesting, because if this point will appear later, it means that longer period will be with exponential growth, therefore the first part of the curve will give higher function value, which means higher market of a product. Using the equation of the curves it is possible to give some forecast to the future possibilities of the market. This could be the most important and most interesting result of this investigation. In this paper the multiwave behaviour of the market of electric cars is shown in USA, in Europe and in China. The Hungarian curve of this market is approximated and the parameters of the equations are determined. For the approximation the Nelder- Mead optimisation algorithm is used, solving the minimisation problem of the least squares approximation. The regression coefficient is calculated by the application of the Fisher- Pry transformation. Continuing this study, as further investigation the detailed comparison of the waves of the curves will be made and it will be necessary to transform the curves into a common origin point in order to make better comparison of their shapes. In that phase of the investigation, several forecasts can be derived regarding the possible future of electric cars in Hungary.
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References 1. Malthus, T.: An Essay on the Principle of Population. Printed for J. Johnson in St Paul’s Church- Yard, Londodn (1798) 2. Moore, E.G.: Cramming more components onto integrated circuits. Electronic Magazine 38(8), 114–117 (1965) 3. Verhulst, P.-F.: Deuxieme mémoire sur la loi d’accroissement de la population. Mémoires de l’Académie Royale des Sciences, des Lettres et des Beaux- Arts de Belgique 20, 1-32 (1847). Retrieved 18 February 2013 4. Pearl, R., Reed, L.J.: On the Rate of Growth of the Population of the United States since 1790 and its Mathematical Representation. In: Proc. of the National Academy of Sciences 6(6), pp. 275–288 (1920) 5. Fisher, J.C., Pry, R.H.: A simple substitution model of technological change. Technol. Forecast. Soc. Chang. 3, 75–88 (1971) 6. Bertalanffy, L.: Principles of theory of growth. In: Fundamental Aspects of Normal and Malignaent Growth. Amsterdam, pp. 137–259 (1960) 7. Kozuko, F., Bajzer, Z.: Combining gompertzian growth and cell population dynamics. Math. Biosci. 185, 153–167 (2003) 8. Richards, F.J.: A flexible growth function for empirical use. J. Exp. Bot. 10, 290–300 (1959). https://doi.org/10.1093/jxb/10.2.290 9. Mansfield, E.: Technical change and the rate of imitation. Econometrica 29(4), pp. 741-766 (October 1961) 10. Rogers, M.E.: Diffusion of Innovations. (Third edition), p. 236. The Free Press, Macmillan Publishing Co. Inc., New York, USA (1962) 11. Jang, S.L., Dai, S.C., Sung, S.: The pattern and externality effect of diffusion of mobile telecommunications: the case of OECD and Taiwan. Inf. Econ. Policy 17, 133–148 (2005) 12. Meyer, W.B., Turner, B.L. (eds.) Changes in land use and land cover: a global perspective. Cambridge University Press, p. 537 + Xi (1994). ISBN 0 521 47085 4 13. Silverberg, G., Lehnert, D.: Evolúciós káosz: növekedés és fluktuációk az “alkotó rombolás” Schumpeter- féle modelljében. In: Fokasz Nikosz (szerk.) Káosz és nemlineáris dinamika a társadalomtudományokban. Typotex kiadó (2003) 14. Nikosz, F.: Növekedési görbék, társadalmi diffúzió, társadalmi változás. http://www.social network.hu/cikkek/FokaszDiffuzio.pdf Last visit (nov. 22 2019) 15. Szabó, F.J.: Analógia a sport- világcsúcsok története és az evolúciós optimáló algoritmusok iteráció- története között. In: GÉP, LXII; 9- 10, pp. 28–31 and 4 (2011). (Analogy between history of sports world records and iteration history of optimization algorithms, in Hungarian) 16. Szabó, F.J.: Evolutionary based system for qualification and evaluation of group achievements (EBSYQ). International Journal of Current Research 9(08), pp. 55507–55516, (August 2017). ISSN: 0975–833X, www.journalcra.com/sites/default/files/21246.pdf 17. Szabó, F.J.: Optimumkeres˝o algoritmusok iterációtörténetének vizsgálata. GÉP 69(4), pp. 8285 (2018). (ISSN 0016- 8572) (Investigation of iteration history curves of optimisation algorithms. In Hungarian) 18. Szabó, F.J.: Application of sigmoid curves in environmental protection. In: Szita Tóthné, Klára, Jármai Károly, Voith Katalin (szerk.) Solutions for Sustainable Development: Proceedings of the 1st International Conference on Engineering Solutions for Sustainable Development, (ICESSD 2019), pp. 1-7. CRC Press, London, Egyesült Királyság / Anglia (2019) 19. Nelder, J.A., Mead, R.: A simple method for function minimisation. Computer Journal 7, pp. 308-313 (1965). https://doi.org/10.1093/comjnl/7.4.308
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20. Szabó, F.J.: Analysis of wear curves as sigmoid functions. Lecture Notes in Mechanical Engineering 22, pp. 273–281 and 14 (2021). https://doi.org/10.1007/178-981-15-9529-5_24 21. Szabó, F.J.: A szigmoid görbék multidiszciplinaritása. GÉP, LXXII 3(4), pp. 61–64 and 4 (2021). ISSN 0016–8572 (Multidisciplinarity of sigmoid curves, in Hungarian) 22. Szabó, F.J.: A COVID-19 járvány id˝obeli alakulásának vizsgálata szigmoid görbék-kel. Multidiszciplináris Tudományok 10(3), pp. 294–306 and 13p (2020). (Investigation of time history curve of COVID-19 by sigmoid curves, in Hungarian). https://doi.org/10.35925/j.multi.2020. 3.35 23. Rézsó, F.: Szigmoid görbék alkalmazása tanulói csoportok eredményeinek vizsgálatához. (Application of sigmoid curves for study of results of student groups, in Hungarian). Multidiszciplináris tudományok, (Multidisciplinary Sciences) 10(3), pp. 195–211 (2020). https:// doi.org/10.35925/j.multi.2020.3.25 24. Vajna, S. (ed.): Integrated Design Engineering . Interdisciplinary and holistic product development, Ist edition. Springer Nature, Switzerland (2020) 25. Szabó, F.J.: Investigation of the time history curve of the COVID-19 disease II. – Comparison of several waves. (In Hungarian). Multidiszciplináris Tudományok (Multidisciplinary Sciences), publication is in process 26. Market of electric cars in USA: China and Europe: https://theicct.org/ last reading: the 16 February 2022 27. Market of electric cars in Hungary: www.pwc.com/hu last visit: the 16 February 2022 28. Bihari, J., Sarka, F.: Human- electric hybrid drives in medium- sized cities by daily traffic. In: Károly Jármai, Betti Bolló, (eds.) Vehicle and Automotive Engineering 2. Proceedings of the VAE 2018 Conference. Springer, Hungary. https://doi.org/10.1007/978-3-319-75677-6_5
Electromobility: The Spreading of Electric Cars Versus Internal Combustion Engine Vehicles Dénes Kocsis(B)
, Judit T. Kiss , Gábor Bellér , and István Árpád
University of Debrecen, 1 Egyetem sqr, 4028 Debrecen, Hungary [email protected]
Abstract. In terms of road transportation, the ratio of newly purchased electric passenger cars is increasing to the detriment of internal combustion engine vehicles. According to current assumptions, this process will continue, but the speed and extent of the change are still open questions. The entire road transport sector is just before a very significant transformation and forecasting this conversion is a burning issue. The changes will arise many challenges, among which growing electricity demand is a great concern. It simultaneously appears to shift towards renewable energy sources in electric power generation. In this work, the growth of newly purchased and already used electric light vehicles is investigated based on the example of Hungary. Taking into consideration the latest available data, the spreading of electromobility is forecasted using an exponential model. It is assumed that the annual new electric car’s number shows exponential growth until the year when all newly purchased light vehicles will be fully electric. Three different scenarios are investigated in the manuscript: one assuming a slow increase, one calculating with a rapidly spreading, and one between these two other options representing the most likely case based on the authors’ expectations. Calculating the expected numbers of electric vehicles and their ratio among all light vehicles makes it possible to forecast the additional electricity demand. Based on our opinion, it is a very important and urgent issue, taking into consideration the very long investment and realization time of projects aiming at generating surplus electrical power. Keywords: Electromobility · Modelling · Electric cars
1 Introduction The fight against climate change has accelerated the use of new technological developments, which affect different aspects of our daily life. Recently, transportation became the highest greenhouse gas emitter sector in the European Union, taking first place from the energy supply sector. Thus, more focus turns to transportation, with special emphasis on road transport, responsible for most CO2 emissions. Nowadays, a great transformation of the transportation sector is ongoing. Alongside the previously and currently dominating internal combustion engine vehicles, electric cars are on the rise. Their spreading is simultaneously supported by governments, companies, and researchers [1]. From the sustainability perspective, the greatest strength of © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 330–338, 2023. https://doi.org/10.1007/978-3-031-15211-5_28
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electric vehicles is the possibility to achieve more favorable positions in the sustainability matrix [2]. For this, the electricity mix is essential, as it strongly influences the energy-specific CO2 emissions of electric vehicles. Based on the International Energy Agency’s (IEA) “Global EV Outlook 2021” [3] report, the total number of electric cars will have reached 10 million by the end of 2020. Despite the pandemic situation, which resulted in a drop in car sales, the electric car registrations increased by 41% in 2020 [3], and the European e-car market became the biggest one, overtaking the former leader Chinese market. According to the “Vehicle in use, Europe 2022” [4] report published by the European Automobile Manufacturers’ Association (ACEA) the ratio of electric cars in the EU reached 0.5%. Forecasting the spreading of electromobility becomes an urgent issue, as many future consequences will follow. In some forecasts, linear models for the spreading can be found, as in the Hungarian National Electromobility Strategy (named as Ányos Jedlik Plan, the current version is 2.0, published in 2019) [5]. We believe that an exponential model will be much closer to reality, thus such a model is applied in this manuscript with the latest available data to prognose the spreading of electromobility in Hungary.
2 Materials and Methods 2.1 Background Data for the Modelling In the forthcoming, the calculation method will be introduced, which forecasts the spreading of electric cars among all light vehicles (LV). This work focuses only on the purely electric car category (battery electric vehicle (BEV)), hybrid electric vehicles (HEV) are not taken into consideration. The actual number of BEV is an important baseline data for us, with the number of newly registered e-cars in the last year (2021). In Hungary, the Ministry of Interior coordinates and shares the information regarding the “green” vehicle registration plates, which are available for purely electric cars and internal combustion engine vehicles capable of travelling at least 25 km distance in electric mode. Table 1. summarizes the actual status of the Hungarian light electric vehicles (LEV). Table 1. The number of green vehicle registration plates in Hungary [6] Type
Dec 2020
Dec 2021
Febr 2022
Battery electric
12,566
21,544
23,274
Extended range externally charged HEV
8,701
13,085
13,961
Plug-in hybrid
5,856
8,002
8,299
Other zero emission cars
0
2
2
Total
27,123
42,633
45,536
Last full year’s BEV data will be used for the calculation, thus the annual growth in 2021 was 8,978 vehicles.
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2.2 Exponential Model to Forecast the Spreading of Electric Cars To evaluate the composition of the electric car stock, the annual input numbers (newly registered BEV) and the output (obsoleted vehicles removed from the system) are taken into consideration. Therefore, the process can be described as a balance equation. Let us assume that the overall number of vehicles is unchanged for the investigation period, which simplifies the calculation. Another assumption is required for the average stay-in time for electric cars, as there is no practical experience in this field. We assume that BEV will be in the system for 15 years, which equals the average age of passenger cars in 2021, according to the Hungarian Central Statistical Office [7]. That means all electric cars will leave the system 15 years later. For simplification, all BEVs currently in the system (21,544 as of December 2021, see in Table 1) will leave it in 2036. The total number of good light vehicles is fixed at 4,020,159, which is the total size of the Hungarian light good vehicle stock in December 2021 [8]. If we assume that the total fleet will be replaced in 15 years, then the annual newly registered car’s number is 268,011. An exponential model is used for the calculation, which describes spreading more adequately than a linear model. Similar modelling is applied as in Árpád et al. [9]. If we assume that all new cars will be electric after 15 years, the increase until then can be described by the following equation: i = e(a+i·b)t , i = 0...15 Ne,in
(1)
i Ne,in = exi ·t , i = 0...15, xi = a + i · b
(2)
where Δt is the period (1 year), a and b parameters are constants, and i is the number of years. From this, the x0 and x15 can be calculated, as the starting year’s (2021) new electric car number is 8,978 (according to the data in Table 1) and the fifteenth year’s (2036) is 268,011: 0 = e(a+0)1 = 8, 978 → x0 = a = ln8, 978 = 9.10 Ne,in
(3)
15 Ne,in = ex15 ·1 = 268, 011 → x15 = ln268, 011 = 12.50
(4)
Thus, the b parameter from the previous results: b=
x15 − x0 ln 268, 011 − ln 8, 978 = = 0.23 15 15
(5)
Then Eq. (1) with the calculated parameters: i Ne,in = e(9.10+i·0.23)t , i = 0...15
(6)
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2.3 Different Scenarios to Forecast the Spreading Based on the author’s expectations, the previously mentioned 15 years is the most likely scenario (named as realistic) to reach the point when all new passenger cars will be electric. Two other scenarios are also identified as a rapid and a slow increase. In the case of the rapid one, we assume that after 10 years all newly purchased cars will be electric, while in the slow scenario the same thing is assumed only after 20 years. A similar calculation introduced in the previous subsection is done for all scenarios. The obtained a and b values for the different cases are summarized in Table 2. Table 2. Calculated parameters for the different scenarios (see the parameters in Eq. (1)) Realistic
Rapid
a
9.10 for all scenarios
b
0.23
0.34
Slow 0.17
3 Results 3.1 Realistic Spreading Scenario By the method, introduced in the previous section, the input and output number of e-cars can be calculated for all years. In the case of the realistic spreading, the results can be seen in Fig. 1.
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Input new e-cars/year
4.0
Output leaving e-cars/year
Number of cars [million]
3.5
Total number e-cars
3.0 2.5 2.0 1.5 1.0 0.5
2045
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2022
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0.0
Fig. 1. The realistic spreading scenario’s results
In the case of the realistic scenario, all light vehicles will be electric by 2050. 3.2 Rapid Spreading Scenario Based on the last few years’ data, it is adequate to examine a faster spreading, which is represented here by the rapid scenario. Here, it is assumed that after 10 years all new light vehicles will be electric. The results can be seen in Fig. 2.
Electromobility 4.5
Input new e-cars/year
4.0
Output leaving e-cars/year
3.5
Number of cars [million]
335
Total number e-cars
3.0 2.5 2.0 1.5 1.0 0.5
2050
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2032
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2023
2022
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0.0
Fig. 2. The rapid spreading scenario’s results
In this case, the first year to reach the 100% ratio of electric cars among all light vehicles is 2045. 3.3 Slow Spreading Scenario The slow scenario represents the case, where all new passenger cars will be electric after 20 years. The result of this calculation is summarized in Fig. 3.
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Input new e-cars/year
3.5
Output leaving e-cars/year
Number of cars [million]
3.0
Total number e-cars
2.5 2.0 1.5 1.0 0.5
2050
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0.0
Fig. 3. The slow spreading scenario’s results
As it can be seen in Fig. 3, the 100% ratio will not be reached by 2050, but already the majority (87.28%) of light vehicles will be electric by then. 3.4 Comparison of the Scenarios In Fig. 4. the changes in the percentage of electric vehicles are illustrated for all scenarios. The exponential characteristics can be seen until the point when all newly purchased will be electric (10, 15 and 20 years from now in the different cases). After that, a closely linear growth appears, which is altered a little by the increasing number of electric cars leaving the system (reaching the age of 15). Altogether, after a couple of years, the increase will be accelerated due to the exponential character. By 2040, the ratio of e-cars will already be between 33.20% (slow scenario) and 80.13% (rapid scenario). Based on our model, most light vehicles will be electric by 2045, having at least 62.76% in the case of slow-spreading.
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90
Slow
80
Realisc
Percentage of e-cars [%]
70
Rapid
60 50 40 30 20 10
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0
Fig. 4. Changes in the percentage of electric cars among all light vehicles according to the three different scenarios
4 Conclusion The spreading of electric cars among all light vehicles is modelled in this article. Three scenarios are presented: slow, realistic, and rapid spreading, in which cases the first year, when all new passenger cars will be electric is 10, 15 and 20 years from now, orderly. Until that point, an exponential model is applied to calculate the e-car numbers and their ratio to all light vehicles. The latest national data is used to forecast the spreading in the case of Hungary. Simplifications are applied in the calculations, like fixing the vehicle fleet size on the current level, but the authors believe the approach is adequate to see the magnitude of the future changes. Many related issues are appearing due to this transformation of the transportation sector. It influences numerous parts of our economy, society, and environment thus, describing the changes and preparing for the consequences is essential. As in many fields, the reaction and implementation times are long, like in the field of electricity, where very significant surplus demand will appear due to electric cars. Therefore, preparations to meet future demands should start now.
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Acknowledgment. The research was supported by the Thematic Excellence Programme (TKP2020-NKA-04) of the Ministry for Innovation and Technology in Hungary.
References 1. Waclaw, A., Betz, J., Lienkamp, M.: Techno-economical implementation of holistic electromobility solutions in commercial companies. In: 2019 Fourteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), pp. 1–11. IEEE (2019). https://doi.org/ 10.1109/EVER.2019.8813533 2. Árpád, I., Kiss, J.T., Bellér, G., Kocsis, D.: Sustainability investigation of vehicles’ CO2 emission in Hungary. Sustainability 13(15), 8237 (2021). https://doi.org/10.3390/su13158237 3. IEA: Global EV Outlook 2021 (2021) 4. ACEA: Vehicles in use, Europe 2022 (2022) 5. Hungarian Ministry for Innovation and Technology: National Electromobility Strategy (Ányos Jedlik Plan 2.0) (2019) 6. Ministry of Interior Deputy State Secretariat for Data Registers: (2022). https://nyilvantarto. hu/hu/statisztikak?stat=monitoring. Accessed 15 Mar 2022 7. Hungarian Central Statistical Office: (2022). https://www.ksh.hu/stadat_files/sza/hu/sza0026. html. Accessed 15 Mar 2022 8. Hungarian Central Statistical Office: (2022). https://www.ksh.hu/stadat_files/sza/hu/sza0040. html. Accessed 15 Mar 2022 9. Árpád, I., T. Kiss, J., Bellér, G., Kocsis, D.: Investigation of the impact of EU and governmental measures on the spread and the energy supply of electromobility in Hungary. Env. Prog. Sustain. Energy 40, 1–11 (2020). https://doi.org/10.1002/ep.13575
Investigation of the Effect of a Coolant Inlet Duct on the Thermal Performance of Car Radiators Máté Petrik(B)
and Gábor L. Szepesi
University of Miskolc, Miskolc, Hungary [email protected]
Abstract. Electric and conventional combustion engine cars require a device, the radiator or air cooler, to ensure operation. Although, it is used to cool a completely different part of the vehicle, an indirect heat exchange takes place by flowing coolant through this radiator. Heat transfer conditions are a function of flow, geometry, and temperature characteristics. A common feature of the finned tube heat exchangers is that the flow cross-sections are relatively small and are constructed parallel to each other. This implies that the geometry is the same in these radiators regardless of which part of the device is considered. Therefore, the local in-homogeneities are dependent on the other two factors. The coolant flows in these parallel tubes, transferring the heat from the equipment to be cooled to the ambient air temperature. The present paper deals with the effect of the inlet duct on the flow conditions and indirectly investigates the heat transfer conditions which are investigated by this numerical simulation. Keywords: Automotive radiator · Flow distribution · Heat transfer · CFD analysis
1 Foreword Burning fuel in an internal combustion engine generates a high amount of heat. The combustion gas temperature can reach 2200 °C during this combustion process of mixing air and fuel in the cylinder. This generated heat is converted into kinetic energy by the pistons and other mechanical components. In the case of an ideal thermodynamic system, the total generated heat can be converted into other forms of energy, which is a useful mechanical power for vehicles. Unfortunately, a real thermodynamic system has a lot of losses, which can reach two-third of the total produced energy. Half of the losses, one-third of the total energy leave the system with exhaust gases. Meanwhile, the other third increases the energy of the engine, which results in an increase in temperature. The task of the cooling system is to dissipate this energy into the ambient air, which can be achieved by the usage of a heat exchanger, the air radiator [1]. However, the cooling process is further complicated by the fact that internal combustion engines work best at higher temperatures. The different mechanical parts wear out easily if the engine is cold, which results in emitting more pollution and the engine’s efficiency lowering in value. According to these facts, the control system has a dual © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 339–345, 2023. https://doi.org/10.1007/978-3-031-15211-5_29
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role: on the one hand, to ensure the fastest possible warm-up during start-up or in cold weather, and on the other hand, to always maintain this operating temperature. Failure of any of these components in the cooling system could cause the engine to overheat, resulting in short-term inoperability [2, 3]. Even though the engine of electric vehicles works in a very different way, in this case, a cooling system must be installed. However, this is needed to cool the battery packs, not the engine. The key issue is still the cooling solution since advances have been made in these batteries that allow more power and less charging process. During power discharge, the temperature of these battery cells increases, even though due to the control electronics, power is drawn from different cells at another time. This is because discharging the battery produces heat, and the amount of heat is higher value in a faster discharge process. Batteries work on the principle of the voltage difference, and at high temperatures, the electrons inside the batteries are excited, which reduces the voltage difference between the two sides of the battery. Since these batteries are manufactured to operate in a certain temperature range, in case of inadequate cooling, the performance will decrease [4].
2 The Cooling System The schematic assembly of a cooling system is drawn in Fig. 1.
water pump
termostat heater fan
upper radiator hose
radiator
air flow
coolant expansion tank
lower radiator hose
Fig. 1. Cooling system of a vehicle with a combustion engine [5]
The main part of the system is the device in which the heat transfer phenomena take place, the automotive radiator. This radiator is a classic extended surfaced heat exchanger. As with other surface heat exchangers, the heat transfer process between two flowing fluids can be achieved without mixing the two fluids. The coolant circulates on
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the inner side (it can also be called the tube side). This coolant has a higher enthalpy since it also circulates in the channels of the engine. As it can be seen in Fig. 1, it is a closed system, which is slightly pressurised to allow the water pump to operate. The other medium is the ambient air, which flows across the device. This is an open system, where the flow is induced by the motion of the vehicle. The fan will also operate if higher performance is needed. To increase the performance of the radiator, it is not necessary to increase only the value of the heat transfer coefficient itself (which can be performed by using the fan to increase the velocity). According to the poor material properties related to the heat transfer process, the heat performance can be increased in order to use a higher heat transfer area. Even though in this type of heat transfer process, the thermal conductivity has more importance, and the weight reduction also has, and as a result, aluminium will be the optimal material. The water pump is vital to the operation of a car engine, ensuring that the coolant flows continuously through the engine block, cylinder head, hoses, and radiator, moreover, maintains an optimum operating temperature. It is usually driven by a belt from the pulley or sprocket of the crankshaft. The water pump is the other essential device in the cooling system. It uses impeller blades and centrifugal force to move the coolant through the various channels and hoses that make up the cooling system. Once the coolant has circulated around the engine, hoses guide it to the radiator, usually located at the front of the car, where the hot coolant is cooled by the movement of air over the radiator fins. It then exits the radiator and flows back into the water pump, where the process starts all over again. As it is seen in Fig. 1, there is another radiator in the system, the heater, which heats the passenger compartment. When the thermostat is switched to heating mode, part of the coolant flows through this heater, so the dissipated heat is shared between the two radiators. It should be noted that this radiator has a lower heat performance since a lower performance fan is used to circulate a higher temperature air, therefore the driving force of the heat transfer process will be lower [6, 7].
3 The Investigated Radiator It has been shown in the previous section that the safe functioning of the radiator is vital for the operation of the engine. The goal of the present study was to numerically investigate the flow velocities in the small diameter channels. The authors have several papers in which they have investigated and measured the air-side heat transfer coefficient. Compared to other heat transfer possibilities, the calculation of the empirical Nusselt number is also a challenge due to the extended surface. In the previous calculations, it was assumed that the coolant flows at the same velocity through the channels. However, analysis of thermal imaging taken during the measurement showed that this assumption is not met, as the geometry of the inlet divider results in different velocities in the channels at different elevation positions. Figure 2 shows the geometric dimensions of the investigated automotive radiator. The channels are characterised by an internal cross-section of 11 × 1 mm and rounded corners.
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0,9 mm
inlet coolant
0,1 mm
415 mm
outlet coolant 35 mm 650 mm Fig. 2. The dimensions of the investigated radiator
It is seen from Fig. 2., that the numerical analysis (computational fluid dynamics, CFD) of the complete geometry is challenging but not necessary. As the aim of the investigation was to determine the speed profile, it was sufficient to analyse the upper left quarter of the figure (marked as red area). This simplification was inevitable as a mesh refinement at the surface of the channels was required to analyse the flow condition at the appropriate level. This results in relatively high cell numbers due to the small flow channels. Seeing these two considerations, it can be stated that the results obtained from the modelling will be consistent with reality. Generally, the automobile water pumps maximum volumetric flow rate is between 25–150 l/min. This flow rate depends on the engine speed, in the case of higher engine speed, the flow rate will increase due to the escalated amount of heat. In this paper 3–12 l/min was investigated, which belongs to a normal (low power) urban speed (~50 km/h).
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Inlet Outlet (Oi_j)
Fig. 3. Geometry for CFD simulation
Fig. 4. Details of the used mesh
In CFD calculation 1 face is use as inlet and 32 as an outlet. Figure 3 shows that the outlet faces were marked as oi_j, where “i” belongs to the row number and “j” is the column index. The mass flow was calculated at all outlet faces. Tetrahedron and hexagonal elements were used to prepare the mesh, where a sweeping technique was also applied to the small chambers to avoid a huge number of elements. A mesh independence test was also carried out, in which the results show that the 1.2 million cells are enough to get a numerically stable result. The skewness value for this mesh was below 0.8, which means that the mesh quality is acceptable. The mesh details are presented in Fig. 4. In the simulation, the laminar model was used for modelling the flow due to the Reynolds number, which is below 2300 in the channels in all cases. The channel hydraulic equivalent diameter was 1.818 mm.
4 Results and Discussion The CFD calculation was carried out at 0.3, 0.6, 0.9 and 1.2 m/s inlet velocities, which means 3.12, 6.24, 9.37 and 12.49 l/min volumetric flow respectively. As mentioned previously, these values belong to a normal engine load. In an ideal case, all channels
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have the same mass flow, which causes uniform temperature distribution in each channel. The previous studies point out that real cases can be distinct from ideal cases and cause heat performance decrease. Table 1 demonstrates the maximum deviation of the mass flow within the channels. Table 1. Differences on the mass flows Inlet velocity, m/s
0.3
0.6
0.9
1.2
Difference, %
7
14
15
17
With the help of CFD simulation, the mass flow can be calculated in each chamber with different inlet velocities. According to Fig. 5, the mass flow in the channels is increasing away from the point of the inlet at 0.6 m/s inlet velocity and above. At 0.3 m/s inlet velocity, there is an “ideal” case where in all channels have the same mass flow. The largest deviation is observed at a speed of 1.2 m/s near the inlet sections.
Volumetric Flow, l/min
0.45
0,3
0,6
0,9
1,2
0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0
Sections Fig. 5. Results of the analysis
This deviation will cause heat performance loss in the heat exchanger, which can influence the engine efficiency. The generally used inlet duct (like this model) creates a flow pattern which has the previously presented effect on the channel’s mass flow. The main goal of our further investigation will be to develop a new inlet duct and flow distributor design where fluctuation in the velocity field and temperature field would be avoided. To achieve this, we want to use different inlet cross-sections and deflectors for future work.
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References 1. Tasuni, M.L.M., Latiff, Z.A., Nasution, H., Perang, M.R.M., Jamil, H.M., Misseri, M.N.: Performance of a water pump in an automotive engine cooling system. Jurnal Teknologi 78(10– 2), 47–53 (2016). https://doi.org/10.11113/jt.v78.9667 2. Lyu, Y., Siddique, A.R.M., Majid, S.H., Biglarbegian, M., Gadsden, S.A., Mahmud, S.: Electric vehicle battery thermal management system with thermoelectric cooling. Energy Rep. 5, 822– 827 (2019). https://doi.org/10.1016/j.egyr.2019.06.016 3. Bencs, P., Alktranee, M.: The potential of vehicle cooling systems. J. Phys.: Conf. Ser. 1935(1), 012012 (2021). https://doi.org/10.1088/1742-6596/1935/1/012012 4. Rawashdeh, M.O.: Development of the cooling system in vehicle engine. Mater. Today: Proc. (2021) https://doi.org/10.1016/j.matpr.2021.05.301 5. https://www.cars.com/auto-repair/glossary/engine-cooling-system/. Accessed 30 Mar 2022 6. Wang, T.: Investigation of advanced engine cooling systems - optimisation and nonlinear control. https://tigerprints.clemson.edu/all_dissertations (2016) 7. Zhang, C., Uddin, M., Robinson, A.C., Foster, L.: Full vehicle CFD investigations on the influence of front-end configuration on radiator performance and cooling drag. Appl. Therm. Eng. 130, 1328–1340 (2018). https://doi.org/10.1016/j.applthermaleng.2017.11.086
Comparison of Thermal Insulation Performance of Different Materials Used for Aircrafts Ákos Lakatos1(B)
and Alagba Henry Eze2
1 Faculty of Engineering, Department of Building Services and Building Engineering,
University of Debrecen, Ótemet˝o Str 2-4, Debrecen 4028, Hungary [email protected] 2 Faculty of Engineering, University of Debrecen, Debrecen, Hungary
Abstract. For thermal insulating vehicles usually, lightweight materials are used, such as polymeric foams or microfiber insulations. But, the use of novel so-called super insulation materials can also be a good solution for this. Vacuum insulation panels can be reliable insulators for electric vehicles, too. In the paper, we will give a comprehensive review of possible applications of aerogels, polymeric foams, and microfiber insulations. Moreover, a brief introduction will be given about their thermal properties, especially focused on thermal conductivity and compressibility. Finding appropriate solutions for the aircraft industry is very important. There are several requirements for materials used by aircraft to fulfil the tightening demands, such as low weight, good noise and thermal insulation. Keywords: Thermal insulation · Materials · Thermal conductivity
1 Introduction In the European Union, almost 40% of all energy consumption comes from buildings, while another 20–40% comes from vehicles [1, 2]. In these cases, energy losses in mentioned sectors must be reduced. Thermal insulation of vehicles is a key point from thermal comfort, noise reduction and energetic perspective points of view. Aircraft must fly at too high an altitude to furnish, in terms of fuel economy, the best performances. Aircraft are flying at high altitudes, which can be favourable from a fuel consumption point of view but parallel to it the extreme air thermal properties should be isolated from the passengers. The air handling system is an important and essential part of aircraft because it is responsible for comfortable air distribution. The good operation of the air distributor unit of aircraft is an important task because the occupant density (passenger/m2 ) is much greater in an aircraft than in a building. Moreover, the layout and the geometry are also much different [3]. It is very important for passengers to be satisfied with the noises. Noise generated by the flight and engines should be reduced as much as possible. Insulation materials are also commonly used in aircraft avionics and electrical compartment bay to protect avionics and electrical systems, which generate a lot of heat during operation. It is also used in electromagnetic interference reduction, suppression © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 346–356, 2023. https://doi.org/10.1007/978-3-031-15211-5_30
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of surface waves and absorption of microwaves, and temperature up to 600 °C of the aircraft radar system. As a result of technological developments, the manufacturing technique of the insulation slabs is also progressed. In the last two decades, the most used thermal insulation was the plastic foams and micro/nano fiberglass wool insulations due to their low cost and effective thermal insulation capability. But in the preview 10 years, new materials have been put on the market, such the silica aerogel or vacuum insulation panels or thermal insulation paints. These materials are usually called to nano-technological or advanced insulation materials. The goal is to achieve the required thermal level with the thinnest layer thickness as much as it is possible. Today, the term “super insulation material” is often used for these materials. These products have much better thermal insulation properties and thus can be used in much smaller volumes and by vehicles, too. Current energy conservation standards often require space-saving insulation techniques, especially for aircraft. As the definition, therefore, new super insulation materials (SIM), such as vacuum insulation board (VIP) types, gas-filled panels (GFP) and aerogel based (ABP) products, are rapidly spreading in the insulation market. They have become an attractive alternative that reduces insulation thickness by five times [4–10]. These materials should be used between − 50 °C and +70 °C, since they must stand the rapid change of temperature and the strong freeze-thaw cycles, too. For aircraft, another most important requirement to be kept is the weight. The used materials should be lightweight, but this also implies that they are compressible, and with this, it loses their thermal resistivity. Moreover, these materials should be fire-resistant, too [11, 12]. It is reported by Zhanga et al. that materials should not absorb moisture from the environment because the humidity in solid form can cause undesirable changes in the structure [13]. As it is well known, these materials should stand in extreme circumstances at an altitude of 11,000, where the pressure is about one-fifth of that of the sea level and the dampness is around zero [14, 15]. Figure 1 presents the cross-section of the cabin of an aircraft, where the insulation is highlighted by pink colour. While Fig. 2 represents an own photo of the cabin without linings and inner shell. In both figures, the thermal insulation of the cabin is shown.
Fig. 1. Sketch of an aircraft cross-section
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Fig. 2. The fixing of the insulation
The main purpose of the paper is to give a comparison of the thermal properties of insulation materials used for aircraft. Thermal tests executed on three different types of insulations are presented in the paper.
2 Materials and Methods Thermal insulation materials are responsible for separating spaces having different temperatures. Further tasks and requirements for them, besides the thermal insulation and the reduction of energy use, is the fire prevention, good mechanical load-bearing capacity, and protection of materials and equipment nearby against chemical and mechanical damage, too. 2.1 The Tested Materials Microfiber Insulations. Microfiber blankets are weightless, bouncy, thermal and audial insulating materials designed as insulting air spaces to prevent body heat dissipation. These are equipped with a water repellent thermoplastic phenoplast coating which is flame-retardant and provides outstanding planar balance. An additive is used to supply water repellency to the healed blanket for repair in areas where towering altitude moisture condensation may occur. In a situation where moisture is not a concern, the clear phenoplast layer can be characterized. These blankets tender excellent audial and thermal pursuance per unit weight of insulation used. Blankets are phenoplast shackled, fireproof, and easily meet the overall heat release standards. Since the blankets are sub-cellular and moisture-resistant, they
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will not assist life growth. They also provide excellent balance with age. The extraordinary flexibility of the optical fiber prevents single-particle arbitrating and holds their outstanding sound debilitation and thermal effects [10]. Two types of insulations have been tested, an orange and a pink one (see Fig. 3. and Fig. 4.).
Fig. 3. The tested microfiber insulate #1
Fig. 4. The tested microfiber insulate #2
Polymeric Foam. Polymeric aircraft foam products can be good insulation material to meet the strict requirements of the aerospace and airplane industries. They provide outstanding audial and thermal insulation at a greatly low weight and keep planar stability and alterability over a full temperature range. With an evidenced in-service long-lasting record in humid, hot surroundings, they outmatch conventional fiberglass insulation by holding insulating features far longer. Polymeric foams are independent and non-stringy making them simple to handle, fast to install and requiring fewer fasteners. They can be found in falcon, Airbus, Boeing, hawker, Embraer, business jets, military aircraft and helicopters, equally in particular aerospace applications, along with the worldwide Space Station, Mars Rover, solar-energy shields and cryostat fuel tanks. The tested material has a density of about 15 kg/m3 , see Fig. 5 [16].
Fig. 5. The polymeric foam
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Slentex Aerogel. Sensex is a blanket based on silica aerogel that is non-combustible, thus meeting the increased fire protection requirements and can be directly moulded, i.e. easily fitted to surfaces that we would not normally be able to reach. Due to this, it is also suitable for hard-to-reach surfaces where an elevated level of fire is mandatory. It is often used to insulate ventilated facades as it is a thin quilt so we can save valuable space for the benefit of the ventilation system. It can also be used as insulation for vehicles. Slentex thermal insulation is also known as Spaceloft A2 (see Fig. 6.).
Fig. 6. The tested slentex insulation
2.2 Thermal Conduction Thermal conduction is a form of heat dissipation in which particles of matter do not move out of their macroscopic equilibrium. Heat is propagated from molecule to molecule by the collision of different medium velocity molecules and by the diffusion of free electrons in metals, as a result of the thermal energy, the vibration energy of the particles increases. Materials conduct heat to varying degrees. To characterize the degree of conductivity, we use the thermal conductivity (λ in W/mK), which is a material constant. One of the most important thermophysical properties of thermal insulation materials is thermal conductivity. The thermal conductivity of homogeneous solids can be easily determined by measuring the equilibrium heat flux (jq in W/m2 ) that flows in the sample under the influence of a temperature gradient (grad T ); this is written by Eq. (1) and also presented in the latest paper [10]: jq = −λ × (grad (T ))
(1)
2.3 Test Procedure In our laboratory, the thermal conductivity of the materials is measured with a Netzsch HFM 446, presented in Fig. 7; with this apparatus, less than 3% accuracy can be reached. This machine can be used to test thermal insulation materials. The device operates as
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described in ISO 8301 [17]. Materials with geometries 20 × 20 × 1–5 cm thick can be tested in the measuring chamber. The measurements were performed in each case after drying them to constant weight in a Venticell (111) desiccator. Drying of the tested samples did not yield measurable moisture content due to the 24 h drying.
Fig. 7. The Netzsch 446 HFM
The device is also suitable for measuring the specific heat capacity (cp , J/kgK) of materials, and its thermal conductivity can be examined under different loads and compressive forces. Moreover, the equipment can measure the change in the thickness of the samples within the calliper after applying different compressing loads from 0,5 to 15 kPa (Fig. 8).
Fig. 8. The test section of the equipment
In this heat flow meter, the 20 cm × 20 cm sample is placed between two heated plates, as in the case of the equipment mentioned above. The heat flow (J q in W ) through the sample is generated by a temperature difference ΔT (in °C or °K). Moreover, the
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heat flow further depends on both the thermal conductivity and the thickness of the material (d, cm), see Eqs. (1) and (2), where A (in m2 ) is the surface area of the sample. The measurement details are written in the latest paper by the Authors [10]. Jq = λ × A × T /d
(2)
3 Results Thermal conductivities of four insulation materials, polymeric foam, two microfiber insulation and an aerogel, were measured with Netzsch 446 HFM equipment. The mean temperatures during the measurement were fixed to 0, 10, 20 and 30 °C, and the temperature difference between the plates was fixed to 20 °C in each case. In Table 1, we have represented the initial data of the samples, such as the density and thickness. Table 1. The properties of the tested materials Tested materials
Density [kg/m3 ]
Thickness [cm]
Slentex - aerogel
191.1
1.05
Pink - microfiber
13.64
3.02
Orange - microfiber
29.58
4.02
Plastic foam
17.6
1.2
Figure 9 presents the measured thermal conductivities of the samples. It is noticeable that the thermal conductivities are increasing with the increasing mean temperatures. The values of the polymeric foam and the pink microfiber increase by about 16% if the temperature difference rises from 0 to 30 °C. The values of the orange microfiber raise under the same temperature change by about 14%, however, the values of the slentex aerogel rise only by 5%, and it also has the smallest measured thermal conductivity. It is further noticeable that the density of the slentex aerogel is much greater than the others,
Fig. 9. Comparison of the measured thermal conductivities
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which gives an applicability limit to this material. To the weight problems of aircraft, this material cannot be used in all places of the aircraft. To better understand the thermal conductivity results and to suggest a possible application, we have calculated the thermal resistances (R, m2 K/W ) of the samples at different temperatures from the tested materials by following Eq. (3): R = d /λ
(3)
Fig. 10. The calculated thermal resistances
The calculated thermal resistances in the function of the temperature are shown in Fig. 10. One can see the highest thermal resistance belonging to the orange and the pink, while the polymeric foam and the slentex have less. It should also be mentioned that the orange and pink microfibers are very compressible (lightweight), so they cannot be used where load-bearing insulations are needed; we suggest their use by the deck, while in those cases where the materials should stand the weight, slentex with its increased density and low thermal conductivity is suggested to use. Compressibility Experiments With the Netzsch HFM, we have tested the compressibility of the samples through the registration of the thicknesses after applying different loads from 1 to 15 kPa. The results are presented in Fig. 11. From this figure, one can see that the relative change in the thickness of the samples increases with the increasing loads, but the changes are the greatest for the two micro-fiber insulations (pink and orange) followed by the plastic foam, and the change is less for the slentex aerogel. Mass to Area Ratio To see the effect of the density of the samples, we have calculated the specific mass of the materials in kg/m2 units. If one multiplies the density with the thickness of the materials, one can reach a specific mass value. From this by using the data in Table 1, we have reached the results presented in Table 2. However, we have mentioned above that the lowest thermal conductivity belongs to the aerogel as well as it also takes the mass to area ratio of 2 kg/m2 .
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-0.8
dl/lo
-0.6
slentex pink - microfiber orange - microfiber plastic foam
-0.4
-0.2
0.0 0
5
10
15
Load [kPa]
Fig. 11. The compressibility test results
Table 2. The mass/area ratio Tested materials
Mass/area [kg/m2 ]
Slentex - aerogel
2
Pink - microfiber
0.4
Orange - microfiber
1.2
Plastic foam
0.21
Evaluation of the Results To compare the most important properties of the thermal insulation used for aircraft we have collected the resistances, compressibility and the mass to area ratios in a table (see Table 3.) We have evaluated the samples from 1 to 4 where 1 means the best, and 4 means the worst value from a usability point of view. Table 3. Evaluation of the samples The property
Slentex
Pink microfiber
Orange microfiber
Plastic foam
Resistance 0 °C
3
2
1
4
Resistance 10 °C
3
2
1
4
Resistance 20 °C
3
2
1
4
Resistance 30 °C
3
2
1
4
Compressibility
1
4
3
2
m/A ratio
4
2
3
4
Sum value
17
14
10
22
Final result
III
II
I
IV
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As is visible from Table 3, the overall rate for the samples resulted in the benefit of orange microfiber insulation, followed by the pink microfiber, the aerogel and the plastic foam, respectively. We consider the insulation most appropriate if it has a less sum value.
4 Conclusions Aircrafts must fly at too high an altitude to furnish, in terms of fuel economy, the best performances. Thermal insulation materials used for aircraft must fulfil several requirements such as a) reducing heat loss, b) should be non-flammable, should reduce noise and must be lightweight. Advances in airplane thermal insulation supply better passenger safety, less fuel use, greater plane availability, better noise reduction as well as faster and easier operation, maintenance, and installation. In this paper, we have presented results of thermal conductivity and measurements executed on the most important insulations used by aircraft. We have compared the thermal conductivities and the thermal resistances of the samples. Firstly, we have revealed that the thermal conductivity change of the samples by the temperature is the least for the slentex aerogel, having the smallest thermal conductivity. After calculating the thermal resistance of the samples, we have reached that the orange and pink microfibers have the highest resistance value. Secondly, we have shown that the greatest change in the thickness of the samples by compression loads belongs to the pink and orange microfibers, followed by the plastic foam and the slentex aerogel. We have stated that in places where the load-bearing is not necessary, the conventional colourized microfibers can be used, taking into account the sensitivity of the thermal conductivities from temperature, while in places where the weight should be considered, such as luggage compartments, the use of slentex insulation, is recommended. It was started with compressibility measurements, too. Moreover, we have executed an overall rating procedure where we have taken into account the resistances at different temperatures and the mass to area ratio. Acknowledgements. This paper was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences, grant number: Ákos Lakatos/BO/269/20.
References 1. Berardi, U.: A cross-country comparison of the building Energy consumptions and their trends. Resour. Conserv. Recycl. 123, 230–241 (2017). https://doi.org/10.1016/j.resconrec. 2016.03.014 2. Directorate-General for Mobility and Transport, New transport proposals target greater efficiency and more sustainable travel, European Commission Mobility and Transport, NEWS ARTICLE (14 Dec 2021) 3. Viscardi, M., Arena, M., Porpora, V., Di Paola, G., Aubry, E.: Feasibility investigation of a smart thermoacoustic configuration for general aviation aircrafts. MATEC Web Conf. 233, 00012 (2018). https://doi.org/10.1051/matecconf/201823300012 4. Baek, S.W., Lee, S.W., Kim, C.S.: Experimental verification of use of vacuum insulating material in electric vehicle headliner to reduce thermal load. Appl. Sci. 9(20), 4207 (2019). https://doi.org/10.3390/app9204207
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5. Capozzoli, A., Fantucci, S., Favoino, F., Perino, M.: Vacuum insulation panels: analysis of the thermal performance of both single panel and multilayer boards. Energies 8, 2528–2547 (2017). https://doi.org/10.3390/en8042528 6. Berardi, U., Nosrati, R.H.: Long-term thermal conductivity of aerogel-enhanced insulating materials under different laboratory aging conditions. Energy 147, 1188–1202 (2018). https:// doi.org/10.1016/j.energy.2018.01.053 7. Semeniuk, B.P., Göransson, P., Dazel, O.: Dynamic equations of a transversely isotropic, highly porous, fibrous material including oscillatory heat transfer effects. The J. Acoustical Soc. Am. 146(4), 2540–2551 (2019). https://doi.org/10.1121/1.5129368 8. Semeniuk, B.P., Göransson, P.: Microstructure based estimation of the dynamic drag impedance of lightweight fibrous materials. The J. Acoust. Soc. Am. 141(3), 1360–1370 (2017). https://doi.org/10.1121/1.4976814 9. Lakatos, Á.: Stability investigations of the thermal insulating performance of aerogel blanket. Energy and Buildings 139, 506–516 (2019). https://doi.org/10.1016/j.enbuild.2017.01.054 10. Lakatos, Á., Csarnovics, I., Csík, A.: Systematic analysis of micro-fiber thermal insulations from a thermal properties point of view. Appl. Sci. 11, 4943 (2021). https://doi.org/10.3390/ app11114943 11. Johnston, A., Cole, R., Jodoin, A., MacLaurin, J., Hadjisophocleous G.: Evaluation of fire performance of composite materials for aircraft structural applications. In: ICCM-12. July 609, pp 1–10 (1999) 12. Aircraft Thermal/Acoustic Insulation Materials Functions and Requirements. https://www. fire.tc.faa.gov/pdf/insulate.pdf. downloaded, 16 Mar 2022 13. Zhanga, T.T., Lia, G., Linc, C.H., Weid, Z., Wang, S.: Experimental identification of key parameters contributing to moisture accumulation in an aircraft section. Build. Environ. 126, 339–347 (2017). https://doi.org/10.1016/j.buildenv.2017.10.012 14. Bianco, V., Manca, O., Nardini, S., Roma, M.: Numerical investigation of transient thermal and fluidynamic fields in an executive aircraft cabin. Appl. Therm. Eng. 29, 3418–3425 (2009). https://doi.org/10.1016/j.applthermaleng.2009.05.020 15. Blosser, M.L.: Mass efficiency considerations for thermally insulated structural skin of an aerospace vehicle. In: 43rd AIAA Thermophysics conference 25–28 June 2012, New Orleans, Louisiana, AIAA2012–3006 (2012). https://doi.org/10.2514/1.T4008 16. https://www.alltherm.ca/_uploads/_tech_pdf/tech_pdf_id134.pdf. Downloaded: 16 Mar 2022 17. EN ISO 8301:1991: Thermal Insulation—Determination of Steady-State Thermal Resistance and Related Properties—Heat Flow Meter Apparatus. ISO, Geneva, Switzerland (1991)
ANN Modeling for Thermal Load Estimation in a Cabin Vehicle Ali Habeeb Askar1,2(B)
, Endre Kovács1
, and Betti Bolló1
1 University of Miskolc, Miskolc, Egyetemváros 3515, Hungary
[email protected] 2 University of Technology – Iraq, Baghdad 10066, Iraq
Abstract. People’s need for comfort has accelerated the development of air conditioning systems. The cooling system is an essential system to analyze since it affects passenger comfort and the vehicle’s transient responsiveness. In this paper, a thermal load estimation has been developed for the Arteon vehicle cabin travelling in Miskolc. The outside conditions include a minimum and maximum temperature of −3 and 35 °C, respectively, while the air velocity is between 0.83 and 7.5 m/s. The Arteon’s vehicle air-conditioning system plays a crucial part in providing a comfortable environment. The heat generated in the cabin is a combination of passengers, solar radiation, ambient, exhaust, engine, and ventilation. In this investigation, a negative load was applied to the cabin in order to cool or heat it. The air conditioner (AC) loads have been computed for three distinct time periods, namely three days in each summer and winter month. Based on these data, an Artificial Neural Network (ANN) was developed to predict the heat load in the car during the year. The agreement with the theoretical calculations was good even if we used a seventh month, namely September, for verification. The maximum error between the thermal load and ANN was 3%, 5%, and 11% for August, December and September, respectively. Keywords: Cabin load · Thermal load · Heat load prediction · Heat transfer · ANN
1 Introduction The first step in correctly sizing a vehicle’s AC system is determining the expected heat load. A model to precisely define a car’s heat load is still not known. Zheng et al. [1] proposed the cooling load temperature difference (CLTD) method to calculate a vehicle’s heat gain. Their computations considered the geometrical vehicle compartment design with glazing, the angle of the windscreen and roof, as well as the vehicle, the computations of direct and diffuse solar radiation, the skylight and windshield heat from the vertical glass. Their results were validated with wind tunnel tests. It was found that the difference between the calculated and tested heat loads was smaller in the fresh air mode than in the recirculation mode. Ye [2] used Design for Six Sigma statistics to link a CFD cabin model for the volume-averaged temperature of the hot-soak terminal with test findings. Hot-soak terminal temperature occurs in the cabin of a parked car when total heat transmission © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 357–373, 2023. https://doi.org/10.1007/978-3-031-15211-5_31
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reaches equilibrium. For regular design analysis, Ye employed a simpler 3D CFD cabin model for developing automotive Heating-Ventilation-Air-Conditioning (HVAC) systems. That research offered a DFSS statistical technique to construct an approximation (meta- model) of the costly computer simulation of the hot-soak process. For this purpose, a small number of CFD simulations were used, which took very little computing power. His method can be used with different modelling outputs and CFD models other than the cabin model. He calculated cooling capacity from the load estimate to determine the necessary refrigeration tonnage. The Heat Balance Method (HBM) was applied to estimate car cabin heating and cooling demands. The various load categories were calculated using mathematical heat transfer models. Load estimation uses mathematical load calculation models from diverse sources. Vaghela et al. [3] presented a Wagon R case study under random driving situations. Their study used the simplified geometry and material qualities of a Wagon R automobile as input parameters. The engine, exhaust, and reflected radiation loads were ignored in the Wagon R Car case study. However, they considered direct and diffuse radiation loads as important AC loads that influence cabin temperature. The cabin temperature dropped from 80 °C to a comfortable temperature in roughly ten minutes. After the pull-down time, the weights were balanced, and the cabin had a zero net load for the remainder of the duration. Jha et al. [4] at Mahindra & Mahindra company created a computer model to take into account solar radiation, air leakage, electrical fittings, and inhabitants. They drew up estimations for the thermal loads in the interior of cars to determine the necessary cooling capacity. In the case of the solar load, the location and time-specific sun irradiance (direct and diffuse) are considered. They calculated heat transfer coefficients for all panels using boundary layer conditions. The correctness of their model was confirmed through wind tunnel experiments. Conceição et al. [5] employed a computer model for predicting the thermal behaviour of car passenger compartments. Their model used space-integral energy balance equations for the compartment’s air and the vehicle’s primary bodies and surfaces. Their model was validated using an immobilized and operating railway vehicle in the summer. The model effectively recreated experimental temperature and heat flow evolutions. Using the convective heat transfer coefficients that were found in their study, the numerical simulation matched the data very well. Abdulsalam [6] created a cabin model and solved the mathematical equations by MATLAB. The heat generated in the cabin was estimated for three distinct time periods using MATLAB/GUI’s cooling load simulation tool. A negative cabin load lowers the cabin’s temperature to a comfortable level. Solmaz et al. [7] implemented an ANN method for the prediction of the hourly cooling load of a vehicle cabin in different cities of Turkey in the hottest five months. In their ANN model, seven neurons were used for the input data and one neuron to produce the output, which was the cooling load. They showed that the cooling load of a vehicle could be predicted by the ANN. The aim of our study is to calculate the thermal load for a cabin of a vehicle. We use MATLAB and Excel to calculate all the components of the cooling load and then build an ANN to predict the thermal load for the cabin and compare it with the calculation of
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the thermal load. Finally, we will verify the ANN by entering data that was not used in building the neural network and comparing the results.
2 Methodology 2.1 The Model Selected There are a lot of Arteon vehicles in Hungary, so this type was chosen for calculations. In order to calculate the cooling load, the front side of the vehicle was supposed to be facing to the south while travelling, the engine revolution per minute was 3000 rpm. Figure 1 depicts all the necessary dimensions to calculate surfaces related to the cooling load components.
a. Dimensions front and back of the vehicle [8].
b. Dimensions and areas of the vehicle [8].
Fig. 1. a. Dimensions front and back of the vehicle [8]. b. Dimensions and areas of the vehicle [8].
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2.2 The Cooling Load Calculation For thermal load computation, a lumped model of the interior of a vehicle was used. The vehicle cabin is subjected to a variety of loads, which are categorized into seven categories: passengers, solar, ambient, exhaust, engine, ventilation, and air-conditioning loads. The numerous thermal load categories are seen in a typical car interior are shown graphically in Fig. 2. Some of the aforementioned loads flow through the vehicle’s body plates and parts, while others are surface independent.
Fig. 2. Different thermal loads.
The solar load effect on the vehicle has three components: beam, diffuse, and reflected radiation. All these components are absorbed or transmitted to the cabin of the vehicle, in addition to the heat gain from the engine, exhaust, ambient, passengers, and ventilation, see Fig. 3. The immediate cabin total heat load growth will be the sum of all load
Thermal load calculation Passenger Load
Solar Radiation Load
Ambient Load
Exhaust Load
Engine Load
Ventilation Load
Beam radiation Diffuse radiation Reflect radiation Fig. 3. Flow chart of thermal load categories
AC Load
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categories. Thus, mathematically the model can be formulated as follows: QTotal = QPass + QSolar + QAmb + QExh + QEng + QVent + QAC [W].
(1)
2.2.1 Passenger Load The passenger in the vehicle is constantly generating heat that is released into the cabin air. This is known as the passenger load, and it is regarded as a heat gain by the cabin air. The passenger load can be calculated using the formula PADU , (2) QPass = Pass
ADU = 0.202W 0.425 H 0.725 ,
(3)
where P is the passenger metabolic rate (W/m2 ), ADU is du Bois body surface area (m2 ), which is calculated as a function of weight and height; W: Human body weight (kg), H: Human body height (m) [9]. 2.2.2 Solar Radiation Load Solar radiation is the amount of solar energy that falls on the earth’s surface is referred to as solar radiation. Because solar energy is scattered and absorbed by particles in the atmosphere, the quantity of solar radiation that reaches the earth’s surface is less than that which is outside the atmosphere. The solar constant is the amount of solar energy emitted by the sun and received by the perpendicular surface along the solar radiation path outside the atmosphere (Gsc ). With a 1.6% error, the solar constant is roughly 1367 W/m2 [10]. After knowing the time and location, solar radiation (GT ) calculation include computing the beam (Gb ), diffuse (Gd ), and ground-reflected (Gr ) solar radiation [10]. GT = Gb + Gd + Gr [W/m2 ].
(4)
Beam Radiation Calculation To calculate the quantity of the beam radiation (Gb ) that impacts a vehicle’s surfaces at a given angle, we apply the following equation: Gb = GDN cos θ,
(5)
where GDN is the direct normal solar radiation (W/m2 ), θ is the angle of incidence of beam radiation on the surface. The value of (GDN ) is given by the following equation:
GDN = Ae
−B sin αs
,
(6)
where A is the apparent solar radiation (W/m2 ), at air mass = 0, B is the atmospheric extinction coefficient. The value of A and B are given by [10]: 360 n , (7) A = Gsc 1 + 0.033 cos 365
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B = 0.1745 − 0.0325 cos
360(n − 21) . 365
(8)
Equation (6) yields values for cloudless days, while values in exceptionally clear atmospheres are 15% greater. This equation yields greater numbers than the actual genuine values recorded in practice. The correction factor (K o ) for the practical values of Miskolc city is determined by Ko = 1 + Mo sin αs ,
(9)
where M o is a numerical variable given for each month, as it can be seen in Table 1. Table 1. Values of the variable (M o ). [8]. Month Jan. Mo
Apr. May Jun.
2.15 2.05 1.925 1.75 1.6
Month Jul. Mo
Feb. Mar.
Aug. Sep.
1.512
Oct. Nov. Dec.
1.462 1.487 1.58
1.736 1.975 2.050
The correction factor K o is inserted into Eq. (6):
GDN = Ae
−Ko B sin αs
.
(10)
Diffuse Radiation Calculation This component of solar radiation can be calculated for vertical or tilted surface as follows: Gdv = YCGDN ,
(11)
Gdh = CGDN ,
(12)
and for horizontal surface:
where Y is the ratio of sky diffuses radiation on vertical surface to that on a horizontal surface, C is the dimensionless value that represents the average ratio of diffuse to normal beam radiation. The values of Y and C can be calculated by [10] Y = 0.55 + 0.437 cos θ + 0.313 cos(2θ ), C = 0.0965[1 − 0.42 cos
360 n 370
(13)
− 0.0075[1 − cos(1.95 n)].
(14)
Ground Reflected Radiation Calculation Ground reflected radiation can be found in the expression below: Gr = 0.5ρg GDN (C + sin αs )(1 − cos β),
(15)
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where ρ g the ground reflectivity depends on the quality of the ground [11]. The ground near the floor of a vehicle is concrete and so a ground reflectivity (0.23) is used in this research. So the total heat gain from the beam radiation part is: Qb = Sτ Gb , (16) surfaces
and for diffuse radiation, it is
Qd =
Sτ Gd ,
(17)
Sτ Gr .
(18)
surfaces
and for reflected radiation, it is
Qr =
surfaces
where S is the cabin surface element area (m2 ), the area of the body, glass, exhaust and engine is (5.5, 3.7, 0.42 and 1.125 m2 ), respectively and because the body is not uniform, we approximate some value. τ is the transmissivity, where surface element transmissivity is 0.5. 2.2.3 Ambient Load For ambient load, the weather data effect is especially important since the change in ambient temperature influences the calculation of the external and interior thermal loads QAmb = SU (Ts − Ti ), (19) surfaces
where U is the surface overall heat transfer coefficient (W/(m2 K)), subscript i means internal. 2.2.4 Exhaust Load Internal combustion engines in vehicles generate exhaust gases. The high temperature of the exhaust gas can contribute to the thermal gain of the cabin via the floor, which can be estimated by the following formula [12] QExh = SExh U (TExh − Ti ), (20) surfaces
TExh = 0.138RPM − 17,
(21)
where RPM is the engine revolutions per minute (1/min), subscript Exh means exhaust gases [13].
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2.2.5 Engine Load Engine load calculation should take into account the engine installation’s harsh working circumstances, such as high engine power, low vehicle speed, and/or hot ambient temperatures [13] as follows (22) SEng U TEng − Ti , QEng = surfaces
TEng = −2 · 10−6 RPM2 + 0.0355RPM + 77.5.
(23)
2.2.6 Ventilation Load Passengers exhale CO2 , so air quality in the passenger compartment is gradually deteriorating. Fresh air must be introduced into the cabin to maintain passenger comfort. The heat input to ventilation is divided into sensible and latent loads in psychometric calculations. The flow rate of fresh air entering the cabin is known as what can be used to calculate the thermal heat gain as shown below: ˙ vent (ho − hi ), Qvent = m
(24)
where m ˙ vent is the ventilation mass flow rate, h is the enthalpy of air (J/kg), and subscript o and i mean outside and inside [14]. 2.2.7 AC Load The air conditioning load is used to keep the passengers comfortable while riding. Estimates of how much heat is in a vehicle cabin may be used to calculate how much AC power is consumed when it becomes very hot or very cold.
QAC = − QPass. + QSolar + QAmb. + QExh. + QEng. + QVent. Ti − Tcomf , (25) − (ma ca + DTM ) tc tc =
tp , ln Tin − Tcomf
(26)
where t c is a Pull-Down Constant (s/K), t p is a Pull-Down Time (s), T in is the Initial Cabin Temperature (°C), ca is the specific heat of air (J/kg K), DTM is the Deep Thermal Mass (J/K) and subscript comf is Comfort Condition, a is air [15].
3 Artificial Neural Networks 3.1 Structure of ANNs Artificial neural networks (ANN) are a prominent field of artificial intelligence, which is described as a computer’s capacity to learn, comprehend and think. However, the major
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Fig. 4. The architecture of the ANN model for the thermal load prediction.
thing computers can accomplish nowadays is to follow the stated algorithms. So the ANN structure is the neuron, composed of an adder and an activation function, as shown in Fig. 4. On the other hand, the inputs to the neuron are Q1 , Q2 , Q3 … Qn, and the weights of the inputs are w1 , w2 , w3 , etc. The weights replicate the nervous biological system’s linkages and react by decreasing or increasing input signals to the neurons. The inputs may have a ‘b’ threshold value. Neuronal inputs, weights, thresholds, and outputs are usually real, binary, or bipolar. The net input to the ANN model is created by multiplying all inputs by their weights and summing them together. As indicated in Fig. 4, the input parameters for the different neurons are the heat gain, the outside temperature, the enthalpy of the air, the velocity of the air, and the solar radiation components, whereas the output parameter is the thermal load. To avoid a certain element dominating the learning, the input and output data must be normalized for ANN learning. Normalization, in general, yields a number between 0 and 1. In this work, however, Eq. (27) is used to normalize values between 0.1 and 0.8 in order to avoid the creation of 0 and 1 by [16] ‘
X= a +
(XR − Xmin )(b − a) , Xmax − Xmin
(27)
where X , X R , X min , and X max are the normalized, real, minimum, and maximum values, respectively. We take into consideration two passengers. The temperature inside is prescribed to T comf = 23 °C. The weather variables are listed in Table 2 [17]. Based on three days in each month (1, 11, and 21), the various forecasting model architectures are built to find the best match between model and architecture.
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City: Miskolc, Hungary, latitude: 48.1, longitude: 20 Month
Temperature (o C)
Radiation GT (W/m2 )
Velocity of air out (m/s)
Min.
Min.
Max.
Min.
Max.
699.7516
0.83
5.8
Max.
December
−1
5
January
−3
10
50.1525
764.1715
2.7
7.5
1
11
269.8381
860.3716
0.83
7.2
14
29
364.9429
812.2441
1.1
3.6
February June
0.00032
July
21
29
369.1247
829.2584
1.9
5.3
August
24
35
370.0078
860.6864
1.6
4.7
Artificial neural networks can be used to accurately map the relationship between input and output vectors of n-dimensions. In the ANN design, there are three layers: an input layer that represents input variables, an output layer that represents output variables, and the layers that are between the input and output layers. These are called “hidden layers.” As seen in Fig. 3, each of these three levels is connected to the others. In this article, a feed-forward neural network is used to anticipate the homogenized features. The network is made up of eight neurons in the input layer, three in the hidden layer, and one in the output layer, based on the factors that affect the central nervous system’s heating and cooling load. For the output layers, the back-propagation Levenberg-Marquardt technique is utilized [18]. 3.2 Neural Network Performance The data is divided into three subsets throughout the training process: the training set, the validation set, and the test set. The training (114 samples) set is used to compute and update the network’s weights and biases. The validation (24 samples) set is used to track errors throughout the training phase. Meanwhile, weights and biases are chosen to be as close to the validation set error as possible. During the training phase, the test (24 samples) set is not utilized. It is, nevertheless, utilized to test the generality of various models. The mean square error (MSE) in Eq. (28) and linear regression results are utilized to assess ANN’s prediction ability (R) in Eq. (29). When the MSE value decreases, it indicates that the network has high predictive performance and is close to the analytical model findings. However, when R approaches one, there is a high connection between the analytical model findings and the ANN forecast (outputs) [7, 19]. n (aN ,i − pN ,i )2 , (28) MSE = i=1 n n 2 i=1 (ai − pi ) 2 R =1− , (29) n 2 i=1 (pi ) where ai is the actual value and pi is the predicted value.
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4 Results and Discussion The results have two aspects. The heat load of a car travelling in the city of Miskolc was calculated. Different times were chosen for the whole year. In the summer, three months were picked (June, July, and August), and the same in the winter (December, January, and February). Three days were selected for each month, namely 1, 11, and 21. For the Arteon vehicle, potential heat loads have been estimated. Figure 5 shows the thermal load distribution with the time selected from 8 am to 16 pm. These are the results for the first of August, with all of the thermal load components. As you can see in the figure, the thermal loads begin to increase from morning to noon, and then the amount of solar radiation changes, as well as weather conditions when approaching the evening, and the heat load decreases, meaning that the greatest load is at midday. Figure 6 shows the thermal load on the 21 of December, the primary source of heat is the sun and the absorption of heat by the structure of the vehicle. In the winter, thermal load components depend on weather conditions and are less than in the summer because other load vehicles are gained.
Fig. 5. Thermal load with time, on August 1.
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Fig. 6. Thermal load with time, 21 of December.
The location is different, the type of car is not the same, and another important reason is the solar radiation load. They take a beam load of only 1000 W and we take it changed depending on the time of the day and the range of it (3763–1606) W. It is larger. The mean square error (MSE) and linear regression (R) for training, validation, and testing have been investigated to verify the constructed neural network in homogenized attributes prediction. Figure 7 depicts the obtained MSE in terms of training sessions (epochs). The value of MSE is quite minimal; the less the MSE, the greater the prediction accuracy. The regression coefficients (R) are close to one, indicating a significant connection between predicted and target values. According to these findings, the neural network accurately represents the homogenized features.
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Fig. 7. The MSE for a neural network.
Figure 8 shows the objective and output values of the training and testing data for the three neuron model are compared. The distances of points to a solid line with a 45° angle show the degree of agreement between the objective and output values. This means that a thermal load forecast for every day of the year can be used for this model. Figure 9 and 10 display a comparison between neural network prediction and vehicle thermal load. For clarity, only the outputs of two data sets from August 1 and December 21 are shown. There are some good results from the neural network-based model. It can predict the homogenized thermal load based on changes in the parameters of the components with good accuracy.
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Fig. 8. A linear regression for a neural network.
Verification The constructed neural network model is verified by choosing a month which is not present in the input data, namely September. During this month, we evaluate the neural network using data that have never been an input before, and the findings are favorable for the neural network as can be seen in Fig. 11.
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Fig. 9. Thermal load and ANN model on August 1.
Fig. 10. Thermal load and ANN model on December 21.
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Fig. 11. Thermal load and ANN model for September 21.
5 Conclusions An ANN model has been constructed in this work to forecast the thermal load of a car in Miskolc, Hungary, utilizing several environmental data points such as latitude, longitude, temperature, air velocity, enthalpy of air, components of solar radiation, day of the year, and hour of the day. First, energy balance equations have been used to compute the thermal loads of the vehicle throughout the summer and winter seasons. To find the optimal structure of the ANN model, MATLAB software has been used to construct and test neurons ranging from 1 to 10. The model with three neurons produced the most accurate results, with an MSE of 6.3 x 10–6 and an R2 (Training, Testing, and Validation) of 0.99. Based on these findings, our model can be applied to forecast the thermal load of the vehicle on any given day of the year. We believe that the agreement of the thermal load values between measurements and predictions is quite promising for the model’s training and testing data. Similarly, the model can be used anywhere in Hungary or the globe to build a car’s air conditioning system. So the maximum error between the thermal load and ANN was 3%, 5%, and 11% for August, December, and September, respectively.
References 1. Zheng, Y., Mark, B., Youmans, H.: A simple method to calculate vehicle heat load. In: SAE 2011 World Congress and Exhibition (2011). https://doi.org/10.4271/2011-01-0127 2. Ye, T.: A statistical approach for correlation/validation of hot-soak terminal temperature of a vehicle cabin CFD model. SAE Technical Papers, vol. 2 (2013). https://doi.org/10.4271/ 2013-01-0854
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3. Vaghela, J.K., Kapadia, R.G.: The load calculation of automobile air conditioning system. Int. J. Eng. Dev. Res. 2(1), 97–109 (2014) 4. Jha, K.K., Bhanot, V., Ryali, V.: A simple model for calculating vehicle thermal loads. SAE Technical Papers, vol. 2 (2013). https://doi.org/10.4271/2013-01-0855 5. SAE Technical: A computational model to simulate the thermal behaviour of the passengers compartment of vehicles, no. 724 (2018) 6. Abdulsalam, O., Santoso, B., Aries, D.: Cooling load calculation and thermal modeling for vehicle by MATLAB. Int. J. Innov. Res. Sci. Eng. Technol. 3297(5), 3052–3060 (2007). https://doi.org/10.15680/IJIRSET.2015.0405076 7. Solmaz, O., Ozgoren, M., Aksoy, M.H.: Hourly cooling load prediction of a vehicle in the southern region of Turkey by Artificial Neural Network. Energy Convers. Manag. 82, 177–187 (2014). https://doi.org/10.1016/j.enconman.2014.03.017 8. Arteon: Vehicle. https://www.scribd.com/document/415574771/arteon-dimensions-pdf 9. ISO Standard: Ergonomics of the Thermal Environment-Determination of Metabolic Heat Production. ISO Standard (2004) 10. Deceased, J.A.D., Beckman, W.A.: Solar engineering of thermal processes, vol. 3, no. 3 (1982) 11. Thevenard, D., Haddad, K.: Ground reflectivity in the context of building energy simulation. Energy Build. 38(8), 972–980 (2006). https://doi.org/10.1016/j.enbuild.2005.11.007 12. Talbi, M., Agnew, B.: Energy recovery from diesel engine exhaust gases for performance enhancement and air conditioning. Appl. Therm. Eng. 22(6), 693–702 (2002) 13. Khayyam, H., Kouzani, A.Z., Hu, E.J.: Reducing energy consumption of vehicle air conditioning system by an energy management system. In: 2009 IEEE intelligent Vehicles Symposium 2009, 752–757 (2009) 14. Singh, A.K., Singh, H., Singh, S.P., Sawhney, R.L.: Numerical calculation of psychrometric properties on a calculator. Build. Environ. 37(4), 415–419 (2002) 15. Fayazbakhsh, M.A., Bahrami, M.: Comprehensive modeling of vehicle air conditioning loads using heat balance method. SAE Technical Papers 2013, 1507 (2013) 16. Solmaz, O., Ozgoren, M.: Prediction of hourly solar radiation in six provinces in Turkey by artificial neural networks. J. Energy Eng. 138(4), 194–204 (2012). https://doi.org/10.1061/ (asce)ey.1943-7897.0000080 17. Weatheronline. https://www.worldweatheronline.com/miskolc-weather-history/miskolc/hu. aspx 18. Moradzadeh, A., Mansour-Saatloo, A., Mohammadi-Ivatloo, B., Anvari-Moghaddam, A.: Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings. Appl. Sci. 10(11) (2020). https://doi.org/10.3390/app101 13829 19. Xue, G., Pan, Y., Lin, T., Song, J., Qi, C., Wang, Z.: District heating load prediction algorithm based on feature fusion LSTM model. Energies 12(11) (2019). https://doi.org/10.3390/en1 2112122
A Critical Review of Multiple Impingement Jet Mechanisms for Flow Characteristics and Heat Transfer Augmentation Mahir Faris Abdullah1,2(B) , Humam Kareem Jalghaf3,4 , and Rozli Zulkifli1,3,4(B) 1 AL-Rafidian University College, Baghdad, Iraq
[email protected]
2 Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, National
University of Malaysia (UKM), Bangi, Malaysia 3 University of Miskolc, Egyetemváros, Miskolc 3515, Hungary
[email protected] 4 University of Technology – Iraq, 10066 Baghdad, Iraq
Abstract. Impingement jet is one of the most important ways to enhance the heat transfer and fluid flow characteristics. The Heat transfer augmentation research has been carried out over the last several decades to enhance the heat transfer augmentation used currently. Although, data is still limited regarding twin jet impingement and heat transfer augmentation using twin jets. This critical review illustrates a very comprehensive analysis of both experimental and numerical. This article aims to provide a detailed review of the twin impingement jets mechanism and the design’s physical knowledge. The article also intends to give a detailed review on using twin impingement applications, where the factors that influence heat transfer are categorized. The impingement heat transfer and characteristics of twin impingement jets are mainly focused on. This critical review aims to confirm the deficiency of knowledge on the impingement of twin jets on flow and heat transfer enhancement and to identify the crucial parameters regarding this issue. This critical review aims to epitomize current advancements in research on the characteristics of heat transfer of twin impingement jets to come up with several possible reasons why the change of parameters and applications of jets can augment the transfer of heat of traditional fluids and come up with a guideline for research in the future. The influence of pulsation frequency on the augmentation of heat transfer was discussed, and it can offer a view regarding the behaviour of heat transfer development and jet flow. Furthermore, the future directions of Nanocoating in the impingement jet techniques need more investigation regarding the deficiency in the literature. Keywords: Nano coating · Twin impingement jet · Heat transfer enhancement · Steady jet · Pulsating jet
1 Introduction In engineering applications, there is a significant need to enhance heat transfer efficiency [1], the jet impingement heat transfer mechanism brought the interest of numerous © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 374–393, 2023. https://doi.org/10.1007/978-3-031-15211-5_32
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researchers due to the high heat transfer coefficients from the forced convection action. Many engineering and industrial applications, especially those involved in electronic chip cooling, the food industry, annealing of metals, drying of textiles and tempering of glass, and cooling of turbine blades, can be advanced by utilizing jet impingement methods. Comprehensive research was done to investigate the impacts of using twin impinging jets on heat transfer characteristics as well as fluid flow. The impact of altering the Reynolds number has been actively studied. The impacts of different velocities, nozzle-plate distances, the spacing between nozzles, the convection and conduction processes on the impingement heat transfer rates and jet flow structure have been studied in many articles [2–11]. The effect of both twin impingement jets on the fluid flow and the behaviour of heat transfer is currently being investigated. Previous literature mentioned the insufficiency of information on this matter. The objective of this critical review is to confirm the lack of knowledge regarding the use of twin jets for fluid flow and augmentation of heat transfer and to identify the important variables relating to this matter. Subsequently, the article demonstrates the impingement heat transfer as well as the twin jets’ flow characteristics that have been discussed and investigated. The article demonstrates the impingement heat transfer as well as the twin jets’ flow characteristics that have been discussed and investigated in a steady and pulsating twin jets impingement state. The taxonomy of heat transfer enhancement mechanisms can classify heat transfer augmentation techniques into passive and active techniques. A passive technique does not supply external energy, the passive manner uses a modified plate or the surface of the heated flat plate, or the vortexes cover the flow
Fig. 1. Classification of enhancement heat transfer techniques
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field. Passive techniques are valuable compared to active techniques. Active techniques are complicated due to the external power supply, even though this style has a remarkable force and can control the heat. See Fig. 1. Heat transfer coefficient and ways to develop it is an important factor that leads to the many usages of impingement jets. In this research, the challenges are: 1- What’s the impact of using the nanocoating technique on the surfaces in the impingement jet? 2- What is the impact of the cross-flow region between the twin nozzles and heat flux? 3- How to enhance the twin impingement jets mechanism using the experimental and numeric methods? 4- There is a clear gap in the impact of turbulence intensity measurement, pulse amplitude, and nozzle configuration. 5- The impact of nozzle geometry on heat transfer and fluid flow is worth studying because of the limited information on this matter. 6- What is the real behaviour of the heat transfer distribution and flow characteristics that are captured by using particle image velocimetry (PIV) and a high-speed camera? There are two types of jet impingement flows that we can classify as follows: 1. Steady twin impingement jets. 2. Pulsating twin impingement jets. 1.1 Steady Twin Impingement Jets Steady twin impingement jets is the enhancement of heat transfer by steady flow impingement jet, for that, in 2001, Makabe [12] investigated the twin jets impingement mechanism and discussed the heat transfer enhancement and fluid flow characteristic. The intervention between the two staggered arrangements of inclined inline with the crossflow, explains the test section of a rectangular duct with two inclined impinging jets issuing into a fully developed turbulent cross-flow. The duct’s height was 21 and the duct’s width was 432 mm, as shown in Fig. 2 and they were studied experimentally by [13]. Flow visualization was studied using PIV techniques and fluorescent dyes while heat transfer measurements were conducted using a neural network algorithm and thermochromic liquid crystal method. The outcomes clarified the intervention of the jets was impacted by the geometrical arrangements of the jets, and the enhanced regions of heat transfer were affected as well. For the case of the staggered jets, there were four longitudinal vortices made, and three vortices were produced in the inline arrangement case only. Likewise, the Nusselt number has a higher upstream peak than that downstream in the inline case. By contrast, staggered jets presented a behaviour contrary to that of the inline arrangement.
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Fig. 2. Twin jet nozzle (A) in line arrangement (B) in a staggered arrangement
Abdel-Fattah [13] investigated experimentally and numerically the impact of the nozzle–nozzle centerline spacing, jet angles, Reynolds number, and nozzle–plate spacing for 2D impinging circular twin jets. This article employed the finite volume method to resolve the turbulent kinetic energy dissipation rate, governing mass, turbulent kinetic energy, and momentum. The result revealed a decrease in the Reynolds number impact on the pressure value of the secondary stagnation point by decreasing this value and/or rising the angle of the jet. The area of the re-circulation’s intensity between the two jets went down by raising the nozzle-plate spacing and jet angle, while within each vortex region, the turbulent kinetic energy increased. The conclusion of this article is as follows, a decreased nozzle plate spacing and jets with a higher Reynolds number results in a sub-atmospheric zone. The pressure in the atmospheric zone increases when the Jet Reynolds number increases as well as making the sub-atmospheric zone stronger when the jet angle increases the impact of the pressure by decreasing its maximum value. The dissimilarity of the pressure at the primary stagnation points and secondary stagnation point goes down by reducing the Reynolds number and expanding the nozzle to plate spacing. Turbulent kinetic energy grew in each zone of the vortex, this enhancement was reduced by expanding the spacing of the nozzle to plate and/or jet angle. Ozmen [14] studied experimentally the twin jets mechanism and the characteristics of the flow structure, while turbulent intensity was measured at instantaneous velocity. The measurements at the axial jet centerline were executed by employing hot-wire anemometry. Measurement of the distributions of pressure on the impingement flat surface and confinement surface as seen in Fig. 3. High values of Reynolds number at (30000 to 50000) were used along with nozzle–nozzle distances as well as different nozzle–plates. Outcomes presented the centerline turbulence level was raised during the development. The distributions of pressure on impingement jet flat plate and confinement surfaces were not dependent on the Reynolds number and were based on the
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jet–jet spacing and nozzle–plate spacing, because of the impact of the twin jets near the impingement flat plate, two counter-rotating circular vortices, and downwash fountain flow were developed and occurred between the jets, existence the impinging flat plate grant rise to the deflection and deceleration of the flow, the decreasing of interaction has been done with the twin jets by raising H/D and L/D. Difference values of primary and secondary stagnation points decreased when increasing the spacing of nozzle-to-plate. The primary and secondary sub-atmospheric zones happen on impingement and confinement flat surfaces, and they lie up nearly to the same location on both services for H/D < 1. The sub-atmospheric zones are stronger when the spacing of the nozzle-to-plate is reduced.
Fig. 3. Schematic of confined twin impinging jet
When raising the spacing of the nozzle-to-plate, the secondary sub-atmospheric zones are stronger than the primary sub-atmospheric zone when the jet-to-jet spacing is increased. The interaction of the twin air jets influences heavily the distributions of surface pressure on both impingement and confinement flat plates. In comparison to the results of the heat transfer in the literature articles, it is evident of a correlation between sub atmospheric zones and the secondary peaks in the value of Nusselt numbers. Javad et al. [15] investigated the twin impingement jets technique numerically to discuss heat transfer characteristics and the flow of fluid linked with hybrid-type turbulence modelling based on large-eddy simulation (LES). The shear stress transport (SST)–scale-adaptive simulation (SAS) hybrid model was carried out for the first time. The view behind the SST–SAS, k − ω model was to give an extra production expression, which was sensitive to determine fluctuations and unsteadiness. A numerical investigation has been executed using differing Reynolds numbers as well as spacing. A domain of values as seen in Fig. 4 was considered for different nozzle-plate spacing, nozzle-nozzle spacing, and Reynolds number, which was installed so that two stagnation regions comprised the re-circulation framework. The first and second zone at the impinging zone
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happens in the zones between two impinging jets with an upwash fountain-like structure. The initial one is in the impinging zone, and the following one occurred in the area in the middle of two impinging jets that has a structure similar to an upwash fountain. The final one brings about by the impact of ambient air on the mainstream jet; the pressure distributions are also greatly affected by the spacing of nozzle-to-plate and nozzle-tonozzle spacing. Moreover, the nozzle-nozzle spacing ratio is responsible for the creation of a sub-atmospheric zone. Likewise, the Cp distribution value is independent of the Reynolds number values ranging between 3 * 10ˆ4–5 * and 10ˆ4.
Fig. 4. Computational domain
Singh et al. [16] investigated the twin impingement jet with and without Nanofluid to augment cooling in central processing numerically by using the ANSYS FLUENT software. At first, the thermal fields and flow for a normal contained slot-jet impingement were studied based on the SST k- ω model and the RNG k-ε model. The Nusselt number increased as the heat transfer coefficient (h) increased. However, the Nusselt number continued constantly at 831 at Reynolds number values 23000 to 50000, and the angles of the impingement jet were 30 and 75, which is in a turbulent zone. There are no noticeable main changes in the jet to diameter ratio as it is optimized by the previous researchers in their articles. Increasing jet impingement angle arranging from 30 to 75 then we spot that the Nusselt number remains constant at 831 which lies in the turbulent zone. The preferable optimum rate of his three as at this rate neither will there be greater geometry arranging, the authors seek to make Nusselt number in the assortment to Reynolds number as this increases heat transfer coefficient. Heat transfer characteristics and fluid flow of twin gas turbulent slot impinging jets on rough and smooth target surfaces were studied based on a computational fluid dynamics model (CFD). [7] has compared using a smooth surface and a single impinging jet. The performance of the twin jets impinging on heat transfer on a rough surface differs significantly from smooth surfaces as shown in the outcome. The heat transfer rate of the surface went down in the twin impinging jets by the fountain-like flow structure in between the twin jets impinging flows. The reaction between the twin jets decreased the
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effectiveness of heat transfer of the jets in the area. A single jet performed better than twin jets. It is noteworthy that roughness can reduce the performance of transfer of heat in the impingement region, a rough sea surface develops the stagnation impact of the twin jets impinging, this may be enhanced by improving the roughness of the surface and jet flow Reynolds number. The Nusselt number around the stagnation points can be decreased as the amplitude of roughness and frequency increases, and the Nusselt number in the area of the wall jet can be improved by the increase of the roughness of the surface. It is evident changing the twin jets configuration could clearly enhance the performance transfer; this then could have great potential in the drying of sheet products and cooling of electronic components, etc. See Fig. 5.
Fig. 5. Schematic of the 2-D twin-jet of slot nozzles onto a sinusoidal wave surface.
Mahir et al. [18] extensively investigated the comparison of local Nusselt numbers based on the Twin jet impingement technique at different parameters experimentally, such as the spacing between nozzles and the nozzle-plate distance to cover all the surfaces measured from the stagnation point t to the end of the aluminium plate and to determine the impact of various Reynolds (Re) numbers on the local heat transfer of an impinged flat surface. The present article used thermal data that was obtained by Graphtec GL820 multichannel data logger and Fluke Ti25 to capture the distribution of temperature in front of the surface plate. It included IR thermal imaging and heat flux–microsensor, the thermography capturing process was carried out on the surface of the aluminium plate target, while the heat flux–temperature data was collected for the nine models with nozzle-nozzle space, ng (1, 2 and 3 cm) and nozzle-plate distance (1, 6 and 11) at different Reynolds number that was 17,000 and 13,000. The setup was used to measure the heated plate’s heat flux, which was recorded using a micro-sensor of heat flux at radial positions 1–15 cm away from the point of stagnation to the end of the plate zone. This study calculated the local Nusselt (Nu) number for stable impingement twin air jet. The results showed that the local Nusselt number was accounted for all points of measurement on the aluminium plate surface. Added to that, the Nusselt number increased with the increase of value in the Reynolds number. The connection between the findings presents that a higher flow velocity results in a higher localized heat flux of the steadily heated air jet impinging on the aluminium plate. Furthermore, the results showed significant improvement in the
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localized Nusselt number and heat transfer coefficient at radial distance positions of 1–5 cm on the surface and gradually decreased whenever the twin air jet action moves away from the center of the interference region. In addendum, the best heat-transfer coefficient in the area near the nozzles and aluminium plate and the nearest distance between the nozzles, especially in the initial points at the aluminium plate surface after that decrease away from the center of the aluminium plate for all Re numbers used. The conclusion of this paper shows that the results describe the impact of the different nine models on heat-transfer characteristics of this technique, which might also impact and improve the efficiency and efficiency improvement of different industrial and engineering applications. And also, Abdullah et al.[18] in another paper, he is studying of convective heat transfer over a flat aluminium plate based on twin jets impingement Mechanism in different Reynolds numbers (17000, 13000, and 10000) to investigate the impact of changing e Reynolds number, Spacing between Nozzle, ss, and distance between, nozzles and plate surface which decreases gradually these the datacenter of the surface plate is increased for all Reynolds numbers measured. This studying is based on thermal imaging (IR) and measurements of heat flux–temperature micro foil sensor. The findings illustrate significant enhancement of localized heat transfer coefficient (h) and Nusselt number of the steady flow at a radial distance on the aluminium with a measured flat plate surface of 0–4 cm. Which could contribute to the o development of the performance for several industrial and engineering applications, as shown in Fig. 6.
Fig. 6. Schematic of twin impingement jets tests setup
Attalla [19] investigated the impact of the inclination angle of twin air jets on the enhancement of heat transfer on an aluminium plate surface. A flat surface is cooled by twin oblique jets. The inclination value of the angles are 0°, 10°, 20°, 30° 45°, and 60°, while the Reynolds number ranges between 10,000 to 40,000, and the spacing distance, as well as the separation distance, is at (2, 4, 6 and 8). The results show that the maximum magnitude of the average heat transfer coefficient is examined in the range of inclination angle from 10° to 20°. In the case of oblique jets, the profiles of the local
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Nusselt number are asymmetric, the maximum value of the average Nusselt number is gained at the inclination angle ten ≤ θ ≤ 20 for spacing distances of 2 ≤ S/D ≤ 4. Finally, three correlations of the average Nusselt number are obtained as a function of the Prandtl number, Reynolds number, and inclination angle for twin impinging inclined jets. Table 1. Twin steady impingement jets No
Author
Type of study
Year
Factors
Problem statement-“Methods-Results”
1
Kazuyosh, et al.
Experiment
2001
1. Jet arrangement 2. Using thermochromic liquid crystal, Particle Image Velocimetry (PIV)
1. Examining the interaction between two inclined impinging jets in staggered and line arrangements with cross-flow 2. The geometrical arrangement of inclined jets is affecting heat transfer for impingement jet jets. It was spotted that the geometrical arrangement influences of the inclined jets had an on the Interaction between the twin jet flows
2
Abdel-Fattah, A.
Experiment + Numerical
2007
1. (9.5 × 104 Reynolds number 22.4 × 104), 2. Three Nozzle-plate spacing 12, 3. Nozzle-nozzle centreline spacing = 3, 5 and 8 4. (0° jet angle 20°)
1. Study of the two-dimensional twin impinging Circular with no cross-flow 2. The primary stagnation point moves away in the radial main flow direction by increasing the jet angle 3. It is strongly increased by the Reynolds number increases and decreasing the jet angle and/or increases nozzle-plate spacing
3
Ozmen, Y.
Experimental
2011
1. Reynolds number is arranging from 30,000 to 50,000 2. Nozzle-to-plate spacing in the range of 0.5–4 3. Jet-to-jet spacing in the Range of 0.5–2
1. Confined impinging twin air jets at high Reynolds numbers 2. The smoke-wire mechanism has been used to visualize the flow behaviorism between the sub-atmospheric ones and peaks in heat transfer coefficients for low spacing on the impinging jets
(continued)
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Table 1. (continued) No
Author
Type of study
Year
Factors
Problem statement-“Methods-Results”
4
Taghinia et al.
Numerical
2014
1. LES & SST–SAS hybrid model was Applied for the first time for impinging twin-jets 2. 3 × 104 < Reynolds Number < 5 × 104
1. SST–SAS generated good results regarding velocity and pressure distribution 2. Both approaches were not qualified for producing accurate results for heat transfer 3. Results illustrate that the SST–SAS model can output fairly accurate results 4. The SST–SAS model is qualified of predicting the values of the local Nusselt number at proper locations 5. The maximum Nusselt number is around 45
5
Singh et al.
Numerical
2016
1. Using Ansys Fluent program 2. RNG k-ε model 3. SST k- ω mode
1. Using jet impingement technique with and without Nanofluid 2. The peak Nusselt number at the flat impingement plate is gradually reduced 3. Results show that impingement jet angle is reduced from 900 4. The peak Nusselt number at the flat impingement surface is gradually reduced with the slight decrease in the average Nusselt number for any combination of Re
6
Peng et al.
Numerical
2016
1. Using computational fluid dynamics model (CFD)
1. A Computational investigation of Heat Transfer under Twin Turbulent Slot Impinging jets on Rough Surfaces and Planar Smooth 2. The maximum Nusselt number is around 50 3. Roughness can lower the heat transfer effectiveness in the impingement region as the fluid can get trapped in the valleys on the rough surface 4. The interaction between jets lowers the heat transfer effectiveness of each jet in the regions where the wall jets collide
(continued)
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M. F. Abdullah et al. Table 1. (continued)
No
Author
Type of study
Year
Factors
Problem statement-“Methods-Results”
7
Abdullah et al.
Experimental
2017
1. Re numbers of 17,000 and 13,000 2. Using Heat flux–temperature micro foil sensor and IR thermal imaging
1. The study discussed the effect of the twin jet impingement transfers as a technique for enhancing heat transfer at different parameters 2. The finding shows a considerable enhancement in the localized Nu number at positions of radial distance on the flat plate from 1–5 cm 3. The maximum Nusselt number is around 160
8
Attalla et al.
Experimental
2017
1. The inclination angles of 0°, 10°, 20°, 30° 45°, and 60°. 2. Reynolds number values from 10,000 to 40,000. 3. Jets to impingement plate distance (L/D) as well as the spacing distance (S/D) are changed from 2 to 8
1. Impact of the inclination angle of a pair of air jets on augmentation of heat transfer into the plate measured 2. A flat surface cooled by a pair of oblique jets 3. The maximum magnitude of the average heat transfer coefficient is achieved in the range of inclination angle from 10° to 20°
9
Abdullah et al.
Experimental
2017
1. Values of Reynolds numbers from 10000 to 17000. 2. Nozzle-to-plate spacing from 1 to 11 cm 3. Jet-to-jet spacing from 1 to 3 cm
1. Studying convective heat transfer over a flat aluminium plate based on twin jets impingement Mechanism 2. The nearest spacing between nozzles and plate is the best heat transfer coefficient and Nuselt number
10
Abdullah et al.
Exp and Num
2021
1. Different Re 2. TiO2 nano-coating 3. VariableNozzlee-nozzle spacing 4. Different Nozzle-plate distance
1. Enhance the heat transfer coefficient and fluid flow characteristic 2. Fabricate the TiO2 nanoparticle 3. Capture the thermal image using a thermal imager device
1.2 Pulsating Twin Jets Impingement The development of the pulsating twin jet mechanism (TMJ) in the literature review was to investigate the impact of the pulsating twin jet mixing zone on heat transfer augmentation. A notable improvement in heat transfer was achieved from the previous studies with the increase in pulsation frequency. With that, we are going to discuss this matter. The first group of researchers who addressed the pulsating twin jet mechanism were Gitan et al. 2014 [20], in the article, the development of the pulsed jet mechanism was achieved using the research objectives and factors that were considered. Authors have reviewed many pulsed jets producing techniques to design a methodology of the pulsed
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jet system based on logical design proceedings. Furthermore, the developed pulsed jet technique has been described in detail. Moreover, the operation of the pulsed jet system was given to present the novel design idea of the twin pulse jets mechanism has examined in different ways. 1. Conduct the flow field tests inclusive of pulse velocity profile during a velocity profile and a specific time for twin jets impinging through their cross-sections. The maximum velocity is around 64 m/s for both jets gained by measuring velocity with time under pulsation impact and about 20 m/s by experimenting with velocity across the diameters of steady twin jets. 2. The mechanism of the pulsating jet has been examined from the point of heat transfer by measuring the heat transfer on the hot flat surface impinged by twin jets at the middle point between the jets with different factors. Noticeable improvement of heat transfer was accomplished with pulsating twin jets techniques. In conclusion, a more competent and verified pulse twin jets impingement mechanism has been done to achieve the requirement of experimental tests by considering different important factors and thus presenting a clear characterization of twin pulsed jet impingement for heat transfer matters. As shown in Fig. 7.
Fig. 7. Graphical representation of the experimental setup
Rozli et al. [21] investigated experimentally the augmentation of heat transfer based on twin pulsating impingement jets to improve the design and study the impact of pulsating twin jets impingement on the augmentation of heat transfer. Stable characteristics of twin pulsed air jets have been the main target of the design to investigate the impact of twin jets’ interaction at the mixing zone. Noticeable improvement in heat transfer has
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been achieved with the increase of pulsation frequency at values of Reynolds number at 5000, 7000, and 9000, various nozzle-plate spacing, variable nozzle-nozzle distance, and pulse profile at a frequency of 20 Hz equal velocity peak of around 64 m/s for both jets was obtained. As seen in Figs. 8, 9 and 10.
Fig. 8. Drawing on PTJM mechanism
Gitan et al. [22] Also studied the pulse twin jet mechanism with different applications and parameters for reheating automotive fuel, a fuel tank was used with exhaust impingement on it. The present article investigated heat flux microsensor measurements as well as IR thermal imaging. This paper discussed the environmental matters such as lessening pollution and depletion of fossil fuels to be used by vehicles with the use of alternative bio-fuel and enhancing the ignition process in an engine. Preheating fuel is a method that can be used. The augmentation of heat transfer between twin pulsating impingement hot air jets and the plate surface copper target was examined as an implementation for the preheating of automotive fuel to enhance the engine ignition method. The logical behaviour can be noticed from the results of all parameters under observation. The fluid flow rate difference between steady and pulsating jets significantly alters the distribution of temperature. The identical thermal impact of twin jets impingement also verifies the efficiency of the twin jets mechanism, which was generated into two identical jets by designing the system. Furthermore, a 20-mm nozzle was utilized to output an air jet with Reynolds number re 5500 and a temperature of 54 °C. The impinged target was imposed on still surrounding air at a temperature of 24 °C and a pulsation frequency of 10 Hz to 50 Hz, which can be applied to twin air jets to obtain higher heat transfer rates for the present matter. The results obtained show the effect of pulsation frequency on heat transfer characteristics of the jet impingement fuel reheating system that can assist in enhancing the efficiency of internal combustion engines. The highest Nusselt number, nu 7.2, was obtained at a pulsation frequency of 20 Hz, as shown in Fig. 11.
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Fig. 9. Pulsating twin jet mechanism
Fig. 10. Graphical representation of the experimental setup
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Fig. 11. Twin impingement Jets Effect
2 Discussion This review investigated the steady and pulsating flow as follows: Some of the articles are a mix of more than one kind of analysis. This critical review displays an in-depth review of experimental and numerical research that brings forth the influential factors in twin jets impinging and associated flow and efficiency of heat transfer that we have addressed. The research is shown in the last layer in the taxonomy of heat transfer enhancement in Fig. 1, which is a very detailed review covering all studies on the twin impingement jets mechanism. A small number of studies focused on the twin impinging jet technique and literature remains limited as shown in the literature review. To the best knowledge of the author, some articles conducted experimental and numerical analyses on the twin impingement jets mechanism, but it is still limited. Tables 1 and 2 displayed the studies that are the most relevant to steady and pulsating impinging jets. Despite a large number of articles published, information on augmentation of heat transfer by the approach of twin jets impingement is insufficient. Twin impingement jets have been done as shown in Fig. 8 by controlling all the significant parameters mentioned above, a special mechanism designed to test and enhance thermal efficiency. Likewise, the interference region between two neighbouring jets and the impact of twin impingement jets in this zone on impingement heat transfer and flow structure was not researched enough. No correlation related to the Nusselt number with significant parameters was found in previous literature. The real behaviours of the fluid flow, heat transfer distributions, turbulence intensity measurements, pulse amplitude impact, and impact of nozzle configuration were not researched enough. Moreover, there is a clear gap in the nozzle geometry affecting the heat transfer coefficient, and flow characteristics and the interactions between the correlated factors were not discussed in detail. For all the shortcomings mentioned above, there is a need to address them to enhance the knowledge of characteristics of heat transfer in many engineering applications. We reviewed the articles on the impact of twin jets’ impingement regarding augmentation of heat transfer and flow behavior characteristics. An extensive review of
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Table 2. Twin pulsating impingement jets No Authors
Type of study Years Factors
Problem statement and results
1
Zulkifli et al. Experimental 2012
1. Reynolds number values (5000, 7000, and 9000) 2. Different Nozzle-Nozzle spacing 3. Nozzle-plate distance 4. Pulse frequency = 20 Hz 5. Velocity peak is around 64 m/s
1. The main objective of the pulsating twin jet mechanism design is to develop the design and investigate the impact of (PTJM) on the augmentation of heat transfer Controllable characteristics of twin pulsed jets 2. Investigate the impact of twin jets’ interaction at the mixing zone 3. Increasing pulsation frequency leads to Noticeable augmentation in heat transfer
2
Gitan et al.
1. The diameter nozzle of 20 mm 2. Reynolds number value 5500 3. Temperature = 54 C 4. Pulsating frequencies arranged for 10–50 Hz 5. Heat flux-temperature micro foil sensor and IR camera were used in the study
1. Investigated the Fuels preheating based on exhaust impingement on the tank of fuel 2. Environmental pollution and depletion of fossil fuels 3. Enhance the ignition process in the engine 4. The results showed by both of these manners presented good agreement 5. There is a significant influence on the flow rate difference between pulsating and steady jets 6. The highest Nusselt number 7.2, was acquired at a pulsation frequency of 20 Hz
Experimental 2014
(continued)
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M. F. Abdullah et al. Table 2. (continued)
No Authors
Type of study Years Factors
Problem statement and results
3
Experimental 2014
1. Investigate the impact of pulsating twin jets mixing zone on the augmentation of heat transfer 2. The main objective of this design is to Controllable the characteristics of twin pulsed jets 3. The profile of jet velocity at different pulsation frequencies was examined to verify system efficiency 4. The impact of pulsation frequency on the surface temperature of the hot flat surface was investigated experimentally at the midpoint between twin jets 5. There is a Noticeable augmentation in heat transfer with the rise of pulsation frequency
Gitan et al.
1. Pulse frequency of 20 Hz 2. The variable nozzle-nozzle distance 3. Velocity peak of around 64 m/s
the twin impingement jets’ impact on twin jets’ flow and heat transfer was conducted to concentrate on an area of research that was not previously covered. The parameters of the nozzle–plate distance, Reynolds number, turbulence intensity, nozzle–nozzle spacing, pulse amplitude, and heat flux can be considered the most significant factors related to impingement heat transfer for twin impingement jets. Likewise, the interference region between two neighbouring jets was not considered adequately, and the impact of impingement at this zone on the impingement heat transfer and flow structure was not investigated sufficiently. In addition, the interactions between the correlated factors were not investigated in detail. An extensive study of twin jets’ impact on heat transfer systems and twin jet flow was chosen to address an area of study that was not covered previously. In general, the literature proposes that the characteristics of heat transfer can be improved when the suitable impingement system and the optimum levels of the influential factors are chosen. Quite a several studies considered pulsating and steady flow
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are impinging jet states, but the literature on twin jets is uncovered. The conclusions of this critical review are based on currently available possibilities and information. See Tables 1 and 2.
3 Conclusion This critical review illustrates a detailed review of experimental and numerical investigations that introduced the influential factors in steady and pulsating impinging jets and associated flow and efficiency of heat transfer with and without excitation, the authors reviewed the heat transfer augmentation and flow characteristics based on twin impingement jets. Research on the pulsating and steady twin jets technique is still unreported and limited, heat transfer augmentation using twin jets impingement mechanism jets uncovered yet at a radial distance for stagnation point experimentally and numerically with different and suitable parameters as per authors’ knowledge. The authors highlighted the deficiency of information on the twin jet impingement of heat transfer matter. The important factors of the behaviours of heat transfer and fluid flow characteristics, as well as the potential of increasing these characteristics, were the main matters. Also to study the modifying the metal surface on the heat transfer rate by using any Nano coating. Also, the influence of pulsation frequency on the augmentation of heat transfer was discussed, and it can offer a view regarding the behaviour of heat transfer development and jet flow.
4 Future Recommendations 1. The future directions of Nanocoating in the impingement jet techniques need more investigation regarding the deficiency in the literature. 2. Authors suggested that heat transfer characteristics can be improved by taking into consideration the optimum levels of the influential factors and by choosing a suitable impingement mechanism. 3. Flow characteristics and heat transfer enhancement investigation are important processes to grasp the thermal behaviour of twin impinging jets. 4. We recommend investigating the impact of the use of nanotechnology on the efficiency of heat transfer enhancement. 5. The effect of nozzle geometry on heat transfer and fluid flow is worth studying because of the limited information on this matter. 6. Authors recommend studying particle image velocimetry (PIV) and high-speed cameras to capture the real behaviours of the fluid flow and heat transfer distributions. 7. Authors also recommend investigating the turbulence intensity measurements, pulse amplitude impact, and the impact of nozzle configuration on heat transfer and flow as an important matter due to the significant effect of wall jet characteristics.
Acknowledgments. We would like to thank the support provided.
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References 1. Alam, T., Kim, M.-H.: A comprehensive review on single-phase heat transfer enhancement techniques in heat exchanger applications. Renew. Sustain. Energy Rev. 81(Part 1), 813–839 (2018) 2. Abdullah, M., Zulkifli, F., Harun, R., Abdullah, Z., Ghopa, S.W.A.W.: Experimental and numerical simulation of the heat transfer enhancement on the twin impingement jet mechanism. Energies 11(4), 927 (2018) 3. Abdullah, M., Zulkifli, F., Harun, R., Abdullah, Z., Ghopa, S.W., Aizon, W.: Impact of the TiO2 nano solution concentration on heat transfer enhancement of the twin impingement jet of a heated aluminum plate. Micromach. J. 10(3), 176 (2018) 4. Yu, Y., Zhang, J., Xu, H.: Convective heat transfer by a row of confined air jets from round holes equipped with triangular tabs. Int. J. Heat Mass Transf. 72, 222–233 (2014) 5. Abdullah, M.F., Zulkifli, R., Moria, H., Soheil-Najm, A., Harun, Z., Abdullah, S.: Assessment of TiO2 nanoconcentration and twin impingement jet of heat transfer enhancement - a statistical approach using response surface methodology. Energies 14(3), 595 (2021) 6. Jalghaf, H.K., Askar, A.H., Abdullah, M.F.: Improvement of heat transfer by nanofluid and magnetic field at constant heat flux on tube. Int. J. Mech. Mechatron. Eng. IJMME-IJENS 20(03), 110–120 (2020) 7. Xu, P., Sasmito, A.P., Mujumdar, A.S.: A computational study of heat transfer under twin turbulent slot jets impinging on planar smooth and rough surfaces. Therm. Sci. 20, s47–s57 (2016) 8. Alimohammadi, D., Persoons, S, Murray, T.: A numerical-experimental study of heat transfer enhancement using unconfined steady and pulsating turbulent air jet impingement. In: Proceedings of the 15th International Heat Transfer Conference, IHTC (2014) 9. Abdullah, M.F., Zulkifli, R., Harun, Z., Abdullah, S., Ghopa, W.A.W.: Heat transfer and flow structure of multiple jet impingement mechanisms on a flat plate for turbulent flow. Int. J. Mech. Mechatron. Eng. IJMME-IJENS 19(03), 141–160 (2019) 10. Abdullah, M.F., Zulkifli, R., Harun, Z., Abdullah, S., Ghopa, W.A.W., Abbas, A.A.: Heat transfer augmentation based on twin impingement jet mechanism. Int. J. Eng. Technol. 7(3.17), 209–214 (2018) 11. Abdullah, M.F., Zulkifli, R., Harun, Z., Abdullah, S., Chopra, W.A.W.: Discussion paper: effect of the nano solution concentration on a heated surface of the heat transfer enhancement using twin impingement jet mechanism. In. J. Eng. Technol. 7(4), 6200–6206 (2019) 12. Varun, Garg, M.O., Nautiyal, H., Khurana, S., Shukla, M.K.: Heat transfer augmentation using twisted tape inserts: a review. Renew. Sustain. Energy Rev. 63, 193–225 (2016) 13. Nakabe, K., Fornalik, E., Eschenbacher, J.F., Yamamoto, Y., Ohta, T., Suzuki, K.: Interactions of longitudinal vortices generated by twin inclined jets and enhancement of impingement heat transfer. Int. J. Heat Fluid Flow 22(3), 287–292 (2001) 14. Abdel-Fattah, A.: Numerical and experimental study of turbulent impinging twin-jet flow. Exp. Therm. Fluid Sci. 31(8), 1061–1072 (2007) 15. Ozmen, Y.: Confined impinging twin air jets at high Reynolds numbers. Exp. Therm. Fluid Sci. 35(2), 355–363 (2011) 16. Taghinia, J., Rahman, M.M., Siikonen, T.: Numerical investigation of twin-jet impingement with hybrid-type turbulence modeling. Appl. Therm. Eng. 73(1), 648–657 (2014) 17. Singh, S.: Enhancement of cooling in central processing CPU by using jet impingement with and without nano fluid. J. Innov. Res. Sci. Technol. 2(10), 9–16 (2016) 18. Abdullah, M.F., Zulkifli, R., Harun, Z., Abdullah, S., Ghopa, W.A.W., Abbas, A.A.: Experimental investigation on comparison of local Nusselt number using twin jet impingement mechanism. Int. J. Mech. Mechatron. Eng. 17(4), 60–75 (2017)
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19. Abdullah, M.F., Zulkifli, R., Harun, Z., Abdullah, S., Aizon, W., Ghopa, W.: Studying of convective heat transfer over an aluminum flat plate based on twin jets impingement mechanism for different Reynolds number. Int. J. Mech. Mechatron. Eng. 17(6), 16 (2017) 20. Attalla, M., Maghrabie, H.M., Specht, E.: Effect of the inclination angle of a pair of air jets on heat transfer into the flat surface. Exp. Therm. Fluid Sci. 85, 85–94 (2017) 21. Gitan, A.A., Zulkifli, R., Abdullah, S., Sopian, K.: Development of pulsating twin jets mechanism for mixing flow heat transfer analysis. Sci. World J. 2014, 1–8 (2014) 22. Zulkifli, R., Gitan, A.A., Sopian, K., Abdullah, S.: Enhancement, Multiple pulsating jets mechanism for heat transfer. Int. Rev. Mech. Eng. 6(3), 5 (2012) 23. Gitan, A.A., Zulkifli, R., Sopian, K., Abdullah, S.: Twin pulsating jets impingement heat transfer for fuel preheating in automotives. Appl. Mech. Mater. 663, 322–328 (2014)
Logistics and Sustainability
Process-Based Selection of Handling Equipment in the Automotive Production Péter Telek(B) University of Miskolc, Miskolc, Hungary [email protected]
Abstract. There are many excellent papers published in the international literature which reflect different logistic aspects of automotive production and give numerous useful solutions. Among them, many publications deal with the scheduling and optimization of the logistic activities of the production processes, but most of them ignore the analysis of the effects of the handling machines. This paper gives an overview of the process-based planning concept and its application possibilities in automotive production focusing to equipment selection. This design concept uses an integrated, task-based approach however; it is mainly focusing to the handling processes. By the help of the process-based planning concept the logistic tasks of the automotive production processes can be solved in a more efficient and economical way. Keywords: Material handling · Handling process · Equipment selection
1 Introduction Nowadays, automotive production is one of the most important sectors of the economy, so any task related to it also has large significance. There are many excellent papers published in the international literature which reflect different logistic aspects of automotive production and give numerous useful solutions. Among them, many publications deal with the scheduling and optimization of the logistic activities of the production processes, but most of them ignore the analysis of the effects of the handling machines. Based on the research related to the process-based planning of material handling solutions, published in 2018, the work published in this paper tries to use the elements of process-based planning for the logistic activities of the automotive production processes. This design concept uses an integrated, task-based approach however; it is mainly focusing on the handling processes. The main objective of this concept is not the selection of the optimal solution; it targets building effective, traceable handling processes. This paper gives an overview of the application of the process-based planning concept in automotive production, focusing to equipment selection. The main objective of the research is to find optimal handling devices and solutions for the handling system and to enable the examination of the effects of the handling device parameters to the logistic performance and the efficiency of the production process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 397–411, 2023. https://doi.org/10.1007/978-3-031-15211-5_33
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2 Material Handling Processes Material handling means the simple task of moving units from a source object to a destination point. Naturally, the characterizations of the given task can be very different and the realization process can also be very complicated, but the basic task is very simple (Fig. 1).
O1
O2 T
a) Single handling task O1
O2 T1
O3 T2
O4 T3
b) Linked handling tasks Fig. 1. Explanation of the handling tasks.
Of course, handling tasks in generally occur together with other ones, so for the realization of the tasks, the system concept has to be applied. Material handling system means handling solutions where more than one handling machine or module elements are applied for different handling processes. These systems, in general, integrate the specifications of the individual material handling machines, but in certain cases, new specifications can appear during the integrated operation [1]. 2.1 Role of the Material Handling Processes During the planning and operation of material handling systems usually, several handling processes are applied to realize the handling tasks. The material handling process means a given set of handling tasks which are linked to each other and suited to certain logic [2]. The realization of a task in a given handling process always influences the other tasks. The logic of the allocation can be based on the handling machine (linked tasks are realized by the same device), related groups of the tasks (e.g. manufacturing relations) the or same working area, etc. Usually the material handling system of a manufacturing procedure is built from different linked or independent handling processes. The explanation of the handling tasks, processes, systems and their relations are presented in Fig. 2. In the practice, the handling process means several related handling activities linked by one or more handling machines, where the relationship can be linear or parallel among the process elements. Different handling process variations can be defined depending on the quantity of the tasks and the devices (Fig. 3).
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Process A
Relation 1
Process B
Process C Relation 2
Process C Handling system
Fig. 2. Explanation of the structure of handling systems [2].
L
T
L
1 C
B
A
B E
T
B
B D
E 3
L
T
L
2
L-loading, T-transport, E-empty route, A, B, C, D, E–handling machines, 1, 2, 3–handling tasks
Fig. 3. Example for linear handling process, containing 3 handling tasks, 5 handling devices.
2.2 Handling Processes in the Automotive Industry There are many materials handling processes and equipment used in the automotive industry. Handling solutions fundamentally depend on the production procedures. Segmented production results in modular structure of the car, which divides the production into different phases [3]. Related to the production phases, automotive parts have to be linked to different levels, which can be: final assembly, module production, part production and raw material/element production. Final assembly is the key process of automotive production, where cars are finished [4]. During this procedure, the chassis of the car is moving along a given line and the main modules and parts are built on it (Fig. 4). At the end of the line, finished cars roll down from the transport equipment [3]. There are many different modules and parts built into a car, but most of them are independent from the final assembling process. Depend on the structure, complexity and function of the modules and parts, the production processes can also be very different, however typical production types can be described [5]: one workplace production, assembling cell production, production line, complex production process, etc.
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Fig. 4. General steps and handling solutions for final assembly [3].
In the aspect of raw material/element production there are much more differences among the individual production processes. Applied handling solutions of them cannot have significant effects to the automotive processes. Handling processes applied in the automotive industry can be overviewed by the related production process types (Table 1). Table 1. Typical handling process types for different production types. Production type
Main process
Supply processes
Output process
Final assembling
One linear process
One linear process or more parallel processes
One linear process
Production line
Independent linear processes
Independent linear or parallel processes
Independent parallel processes
Assembling cell
Linear processes
Independent linear or parallel processes
One linear process or more parallel processes
Individual workplace
None
Independent linear or parallel processes
Independent linear or parallel processes
As it can be seen in Table 1, the handling processes of the final assembling and one individual assembling cell are similar and very often used in practice.
3 Planning of Handling Processes 3.1 Planning of Material Handling During the planning of material handling, we are looking for suitable equipment and procedure to satisfy the supplying requirements of production processes. The planning process can be realized by a system-based or task-based approach.
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The system-based approach analyses the whole production and handling system and is based on the system relations [6]. The most important element of the planning process is the comparison of different handling systems in objects, in devices, in handling tasks or in technology processes. The result of the planning is the adaptation of a similar handling system (e.g. similar production firms of multinational companies) [7]. The taskbased approach follows a given or iterative order of different planning subtasks (facility planning, element planning, functional planning, allocation planning, etc.) [8]. Planning process can be different based on the subtasks: single-task-planning, or augmentedplanning, complex-planning (2–3 subtasks) or integrated-planning (all subtasks). There are many solution techniques and methods to solve single planning tasks [9], but their results are limited. During augmented planning the focus is on a single task, but some parameters of other subtasks are also taken into consideration. Complex planning combines 2–3 subtasks, which are linked by a certain aspect (e.g. technology), its complexity depends on the involved subtasks [10]. The integrated planning concept theoretically realizes all planning subtasks, but because of the volume, complexity and iterative manner of the different tasks, in practice, it can be solved only at simple planning cases [11]. According to the increase of computational performance and to the development of optimization methods, the integrated-planning can be applied to more and more complex handling systems, but because of the complexity of the required methods and software applications, users can hardly understand the procedures (black-box effect) so they cannot easily accept its using [12]. To avoid the black-box effect, new research concepts started during the last years, e.g. process-based planning, which does not target to find the global optimum but searches for a suitable and understandable solution using an easier logic [2]. One of the most important principles of process-based planning is that equipment selection is based on the parameters of handling processes. 3.2 Equipment Selection Principles for Handling Tasks There are four different approaches to selecting the best material handling machine for a handling system: 1. 2. 3. 4.
Taking workplace parameters into consideration, Using material handling relations as the basis of the selection, Building processes from the relations and using them for the selection, Looking for solutions for the whole handling system.
If we take workplace parameters (e.g. geometric, manufacturing, handling parameters) into consideration at the planning of material handling, the results will suit only the objects and cannot fit the requirements of the production environment. The use of material handling relations as the basis of the selection gives the possibility to take all environment effects (line parameters, route complexity, etc.) into account, besides the object parameters. One disadvantage of this approach is that the linking of the different handling solutions is limited and not flexible. If we build handling processes from the relations before the equipment selection process, it is possible to differ from the best solution – in
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the aspect of the relations - and to use alternative versions which can be better for the given handling requirements. In many practical cases, experts look for system solutions for handling tasks, which means the using of the same (or near the same) type of machine in every object. It is a cost-effective and simple solution, but usually, it is too far from the optimal handling system. Above mentioned approaches can be used in different cases and result in different solutions, which can be a suitable application for a given system, however only the 3rd version gives a general usable and near-optimal handling version. Because the quantity and complexity of the applied handling processes largely influence the efficiency of the planning procedure, it is worth analyzing the characterization of the occurring handling processes. 3.3 Planning of Handling Processes in the Automotive Industry To determine the practical role of the handling processes in the planning of material handling, the related literature in the Scopus database has been analyzed. In this paper, the general objective was the examination of the applicability of the process-based planning concept in automotive production, so the search was realized only among papers related to material handling and automotive production. During the research, until February 2022, 425 matches were found among research papers in the Scopus database related to the search: TITLE-ABS-KEY (material AND handling AND automotive AND production). After reading the abstracts of the publications, only 114 papers remained that deal with real logistic aspects, the others have only some minor relations to the material handling procedure. Further examination of the papers resulted in 92 publications which have a direct relation to the handling processes, the other 22 involved only other logistic topics (e.g. supplying aspects, lean methods, performance metrics). Detailed analysis of the related papers is presented in Fig. 5.
18
22
Other logistic topics Machine of the handling process Theoretical aspects of the handling process
31 43
Handling process and machines
Fig. 5. Distribution of the topics of related papers.
Papers related to the material handling processes of the automotive industry can be distributed into 3 categories based on the main focus of the papers, which can be only the handling machines, only the theoretical aspects of the handling processes or both the handling processes and the machines.
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Almost half of the related papers (43) deal only with the handling machine, focusing on the structural building, certain elements or other technical aspects. In the aspect of the research, these papers cannot give useful information about the structure, design or operation of the handling processes. One third of the publications (31) examine the handling processes; however they are focusing mainly on the operation (facility planning, route planning, scheduling, etc.). It means that these papers try to find optimal solutions for a given handling process or processes, and they use predefined machines or exact machine parameters. Analysis of real process variations or the effects of the handling machine types are not the scope of these papers. Only the 3rd category can be directly linked to this research work, in which the topic contains information about the handling processes and also the applied handling machines. 8 papers [13–20] deal with alternative technical solutions for a given handling process, other 8 papers [21–28] analyze the handling process parameters at one given handling machine type. Only 2 papers [29, 30] look for the optimal handling device type for the handling processes. In the aspect of the process types, 3 papers [27, 29, 30] look for a solution for the whole handling system, 4 papers [13–15, 17] deal with the main handling process (using conveyors), other papers (11) focus on the supplying processes (using mobile devices or conveyors). Distribution of the applied handling equipment is presented in Fig. 6. 9 8
Related papers
7 6 5 4 3 2 1 0 Mobil device
Conveyor
Robot
Manual
More than one soluon
Fig. 6. Distribution of the handling equipment in the related papers.
Based on the above-mentioned analysis, papers related to the handling processes deal mainly with the supplying process using mobile handling devices and the main handling processes using conveyor handling.
4 Scenario for the Equipment Selection In this chapter, a scenario will be presented to compare the applicability of the equipment selection approaches defined in Sect. 3.2. For the comparison, a simple production
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procedure was used, taking those handling process types into account, which were the results of the previous analysis based on the literature review. Figure 7 presents the structure of the examined production procedure, and Table 2 contains the handling parameters of the objects of the production system.
Input
Input store
Forming
Output
Input
Assembly 1
Output
Input
Input
SP
Pallet
Assembly 2
Pallet SP
SP
SP
SP Output
Input
SP
SP
SP Input
Pallet
Input
Input
Packing
Output
SP
Quality control
Pallet SP
SP Output
Box
SP
Pallet
Output store
Fig. 7. Structure of the production procedure.
The structure on Fig. 7 presents only those production elements, which have effects to the handling process and the units are handled in pallet, boxes or as a single piece (SP). Table 2. Parameters of the objects of the production system. No. Object
Handled unit
Handling characteristics
1
Input store
Pallets
Individual store handling, only pick up and transport to the objects
2
Forming
Single pieces
Manual handling during the production process, single production (continued)
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Table 2. (continued) No. Object
Handled unit
Handling characteristics
3
Assembly 1
Single pieces
Manual handling during the production process, single production
4
Assembly 2
Single pieces
Manual handling during the production process, single production
5
Quality control Single pieces
Manual handling during the production process, single checking
6
Packing
Single pieces, boxes Manual handling during the production process, one piece into one box
7
Output store
Pallets
Individual store handling, only transport for the objects and leave at given point
4.1 Handling Equipment Selection Based on the Workplace Parameters The easiest way to select suitable equipment for handling is the use of only the object parameters. Based on Table 2, the best solutions are the application of forklift for the stores and manual material handling, among the other production objects. 4.2 Handling Equipment Selection Based on the Handling Relations If we want to take other system parameters into consideration during the selection procedure, we have to determine the characterizations of the handling relations. Table 3 presents the handling relations of the system and their most important parameters. Table 3. Parameters of the handling relations. No.
Relation name
Source
Destination
Distances [m]
Quantity [/8 h]
1
I-F
Input store
Forming
11
3 pallets
2
F-A1
Forming
Assembly 1
10
240 pcs
3
I-A1
Input store
Assembly 1
19
1 pallets
4
A1-A2
Assembly 1
Assembly 2
5
240 pcs
5
I-A2
Input store
Assembly 2
24
8 pallets
6
A2-Q
Assembly 2
Quality control
5
240 pcs
7
Q-P
Quality control
Packing
10
240 pcs
8
I-P
Input store
Packing
38
3 pallets
9
P-O
Packing
Output store
5
240 boxes
Based on Table 3, at the relations 1, 3, 5 and 8 where the use of pallets and the high distance determine the application of forklifts. The using of this handling equipment requires only small additional storage place for one pallet at all production objects.
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There is a similar situation for relations 4, 6 and 9 where the low distances and the manual production handling determine manual material handling among the objects. At relation 9 (P-O) the manually transported boxes will be filled into pallet at the input point of the output store. At the relations 2 and 7, the manual material handling is not efficient because of the high transport distances, so we have to use small pallet cars, however it requires additional storage places and unit building at the objects. 4.3 Handling Equipment Selection Based on the Handling Processes For the realization of the handling tasks we have to link and harmonize the operation of different handling machines. In certain cases, during the harmonization capacity problems and material flow disturbances can issue, because of linking problems. Avoiding the negative effects, we can build handling processes linking the individual material handling relations into different chains before the equipment selection procedure. If we realize the equipment selection procedure based on the material handling processes, we can take further system parameters (location limits, crossing roads, etc.) into account. Process building is a principal element of the process-based planning of material handling which is described in [2]. Of course, the process building can be very complex task and in many cases requires hard optimization procedure, but in our simple handling system, we can easily link the relations into 2 different handling processes based on Table 3: • Process 1 links relations 1, 3, 5 and 8 based on the common source and pallet unit. • Process 2 links relations 2, 4, 6, 7 and 9 based on the manual handling and direct relations. If we select material handling devices for the individual handling processes, we can use only uniform machines which can suit for all involved relations. The equipment selection for Process 1 is easy, because the application of forklifts is evident for pallets. In case of Process 2, we have to make a longer selection procedure and we can apply some modifications. The first approach is the application of machines which can be used for the individual relations: • Pallet car suit for relations 2 and 7, and can be used for the other relations, but it requires additional storage places and unit building for all related objects. • Manual material handling suit for relations 4, 6 and 9, and can also be used for the other relations, but because of the high distances it requires many human resources, or fewer persons with more than one transported piece, which also requires additional storage places and unit building at all related objects. The linking of the relations suggests a new solution, which is not the best suit for the individual relations. For longer transportation processes, the using of different conveyors (belt or rollers) can also be efficient. Advantages of the conveyors are the direct linking of the objects, transportation in pieces, no need for storage places and human resources.
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Based on the above-mentioned facts, the best material handling equipment for Process 2 is a roller conveyor system because it is also usable for workplace storing. 4.4 Handling Equipment Selection Based on the Handling System System approach in the equipment selection procedure means the application of one machine type for the whole handling system. In this aspect, the usable machines of the individual relations can be the basic solutions: manual handling, forklifts, pallet cars and conveyors. In our simple case, the overview and evaluation of the possible variations are an easy task. The manual handling for the whole system is not efficient because of the high transport distances among certain objects (near 40 m). On the other hand, the low distances (5 m) avoid the application of forklifts in three relations. Using of small pallet cars can be efficient, but it requires storage places and unit building at all objects, which means additional places and a manual workforce. Application of conveyors is also suitable solution, but it could be very large and complex with additional manual work to dismount the units at the input store. Based on the above-mentioned aspects, we can say that none of them is a real efficient solution for the example system. 4.5 Comparison of the Handling Solutions Summarizing the suitable handling system variations defined in the previous chapters (Table 4), we can determine the most important advantages and disadvantages. Table 4. Handling solutions resulted from the different approaches. No.
Relation name
Handling solution Workplace-based
Relation-based
Process-based
1
I-F
Forklift
Forklift
Forklift
2
F-A1
Manual handling
Small pallet car
Roller conveyor
3
I-A1
Forklift
Forklift
Forklift
4
A1-A2
Manual handling
Manual handling
Roller conveyor
5
I-A2
Forklift
Forklift
Forklift
6
A2-Q
Manual handling
Manual handling
Roller conveyor
7
Q-P
Manual handling
Small pallet car
Roller conveyor
8
I-P
Forklift
Forklift
Forklift
9
P-O
Forklift
Manual handling
Roller conveyor
Evaluate the applicability of the handling variations; the most important handling parameters of the solutions were collected in Table 5.
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Parameters
Handling solution
1. Handling devices
Workplace-based
Relation-based
Process-based
1. Manual handling
1. Manual handling
1. Forklift
2. Forklifts
2. Forklifts
2. Roller conveyor
3. Small pallet cars 2. Additional handling units
–
Small containers
–
3. Puffer needs
4 (F, A1, A2, P)
4 (F, A1, A2, P)
4 (F, A1, A2, P)
+ 4 (F, A1, Q, P) 4. HR needs for handling
3 persons
2 persons
–
5. Unit load building
1 (P)
–
–
6. Unit load dismounting
4 (F, A1, A2, P)
3 (F, A2, P)
4 (F, A1, A2, P)
Checking the operation of the handling variations, a simulation analysis was made using Technomatix Plant Simulation software [31]. Only one exact set of parameters and 2 min production time were used on all manufacturing objects to compare the variations, finding the optimal version was not targeted (Table 6). Table 6. Comparison of the operation of the handling solutions. Parameters
Handling solution Workplace-based
Relation-based
Process-based
1. Manual handling
98 + 86 + 95
57 + 16
–
2. Forklifts (1)
44
19
19
1. Used capacities [%]
3. Small pallet cars (1)
–
84
–
4. Roller conveyor (35 m)
–
–
62
2. Output quantities [pcs/ 8 h]
216
224
227
Based on Table 5 and 6 we can compare the material handling solutions of the different approaches. The workplace-based equipment selection uses only 2 different devices, but requires 3 human workforces and has the lowest production capacity. The relation-based solution has the better production capacity, however it requires 3 device types and 2 workforces for the manual handling procedure. As it can be seen on Table 6, there is theoretically possible to realize the manual handling using one person, but in our example, it is not applicable because of the large distances. The process-based approach resulted in the best production capacity and the simplest structure using only 2 handling
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equipment without human handling. Beside it, the low using ratio of the forklift is also enable the distribution of the transport capacity with other production procedures. Of course, this analysis is not complete because a predefined structure and parameter set was used however, the objective was only the comparison of the equipment selection approaches.
5 Summary Based on the research related to the process-based planning of material handling solutions, published in 2018, this research tried to use the elements of process-based planning for the logistic activities of the automotive production processes. This design concept uses an integrated, task-based approach however; it is mainly focusing to the handling processes. This paper gave an overview of the application of the process-based planning concept in automotive production, focusing to equipment selection. Main objective of the research was to find optimal handling devices and solutions for the handling system and to enable the examination of the effects of the handling device parameters to the logistic performance and efficiency of the production process. With the help of the process-based planning concept, the logistic tasks of the automotive production processes can be solved in a more efficient and economical way. In this paper, the objective was only the comparison of the equipment selection approaches; finding the optimal material handling version of the examined production system was not targeted, and also the financial aspects were not taken into consideration. As a result, we can say that using the process-based approach gives a much more effective and easily traceable handling system. Of course, this work is not complete, it was only the first step of the analysis of the material handling processes, and further research projects will target the full examination of the planning concept and its application possibilities.
References 1. Cselényi, J., Illés, B. (eds.): Logistic Systems I. University Press, Miskolc (2004). (in Hungarian) 2. Telek, P.: Process-based planning of material handling in manufacturing systems. IOP Conf. Ser. Mater. Sci. Eng. 448, 012018 (2018). https://doi.org/10.1088/1757-899X/448/1/012018 3. Telek, P., Bányai, T.: Advanced materials handling processes and devices in the automotive industry. In: Jármai, K., Bolló, B. (eds.) VAE 2018. LNME, pp. 315–328. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75677-6_26 4. Kern, W., Rusitschka, F., Bauernhansl, T.: Planning of workstations in a modular automotive assembly system. Procedia CIRP 57, 327–332 (2016) 5. Brankamp, K.: Production and Assembling Handbook. Technical Press, Budapest (1980). (in Hungarian) 6. Bányai, T.: Integrált anyagáramlási rendszerek strukturált modellezése. GÉP 63(4), 83–86 (2012) 7. Telek, P., Cservenák, Á.: Planning of material handling – literature review. Adv. Logist. Syst. Theory Pract. 13(2), 29–44 (2013)
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8. Cselényi, J., Illés, B. (eds.): Anyagáramlási rendszerek tervezése és irányítása I. Miskolci Egyetemi Kiadó, Miskolc (2006) 9. Jiang, S., Nee, A.Y.C.: A novel facility layout planning and optimization methodology. CIRP Ann. Manuf. Technol. 62, 483–486 (2013). https://doi.org/10.1016/j.cirp.2013.03.133 10. Ortner-Pichler, A., Landschützer, C.: Improving geometry manipulation capabilities of Knowledge-based Engineering applications by the versatile integration of 3D-CAD systems. In: CD Proceedings of MultiScience - XXXI. microCAD International Multidisciplinary Scientific Conference (C1: 3), University of Miskolc (2017). https://doi.org/10.26649/musci.201 7.044 11. Telek, P.: Equipment preselection for integrated design of materials handling systems. Adv. Logist. Syst. Theory Pract. 7(2), 57–66 (2013) 12. Welgama, P.S., Gibson, P.R.: A hybrid knowledge based/optimisation system for automated selection of materials handling system. Comput. Ind. Eng. 28(2), 205–217 (1995). https:// doi.org/10.1016/0360-8352(94)00200-7 13. Anon: Conveyor systems synchronize production. Robot. World 5(8), 30–31 (1987) 14. Anon: Handling overheads. Prod. Eng. Lond. 67(10), 18–19 (1988). https://doi.org/10.1049/ tpe.1988.0238 15. Anon: CI conveyor system forms vital part of Europe’s most advanced car seat manufacturing plant. Finishing 12(9), 21–23 (1988) 16. Kuo, C.-T., Lim, J.-T., Meerkov, S.M.: Improvability analysis of a machining transfer line: an application. In: Proceedings of the IEEE Conference on Decision and Control, vol. 1, pp. 603–604 (1994) 17. Brown, J.: Belts and planetary gearboxes add up to reliable parts-handling. Power Transm. Des. 37(5), 47–49 (1995) 18. Tower picks up the pace. Mod. Mater. Handl. 59(5), 25–28 (2004) 19. Sprovieri, J.: No more forklifts. Assembly 47(2), 60–64 (2004) 20. Du, J.-Y., Apmann, H., Schmider, M.: Time and cost effective handling for building up carbon fiber tailored blanks. In: ECCM 2016 - Proceeding of the 17th European Conference on Composite Materials (2016) 21. Seha, S., Zamberi, J., Fairu, A.J.: Design and simulation of integration system between automated material handling system and manufacturing layout in the automotive assembly line. IOP Conf. Ser. Mater. Sci. Eng. 257(1), Art. No. 012017 (2017). https://doi.org/10.1088/ 1757-899X/257/1/012017 22. Macedo, R., Coelho, F., Relvas, S., Barbosa-Póvoa, A.P.: In-house logistics operations enhancement in the automobile industry using simulation. In: Relvas, S., Almeida, J.P., Oliveira, J.F., Pinto, A.A. (eds.) APDIO 2019. SPMS, vol. 374, pp. 39–51. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85476-8_4 23. Hrušecká, D., Lopes, R.B., Juˇriˇcková, E.: Challenges in the introduction of AGVs in production lines: case studies in the automotive industry. Serb. J. Manag. 14(1), 233–247 (2019). https://doi.org/10.5937/sjm14-18064 24. Subulan, K., Cakmakci, M.: A feasibility study using simulation-based optimization and Taguchi experimental design method for material handling-transfer system in the automobile industry. Int. J. Adv. Manuf. Technol. 59(5–8), 433–443 (2012). https://doi.org/10.1007/s00 170-011-3514-0 25. Kousi, N., Koukas, S., Michalos, G., Makris, S.: Scheduling of smart intra–factory material supply operations using mobile robots. Int. J. Prod. Res. 57(3), 801–814 (2019). https://doi. org/10.1080/00207543.2018.1483587 26. Javed, M.A., Muram, F.U., Punnekkat, S., Hansson, H.: Safe and secure platooning of automated guided vehicles in Industry 4.0. J. Syst. Archit. 121, Art. No. 102309 (2021). https:// doi.org/10.1016/j.sysarc.2021.102309
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27. VanGilder, K.C., Usher, J.S.: Using FactoryFLOW to predict material handling manpower requirements for a future increase in production. In: International Industrial Engineering Conference Proceedings, pp. 121–130 (1996) 28. Bookbinder, J.H., Kirk, M.D.: Lane selection in an AGV-based asynchronous parallel assembly line. Comput. Ind. Eng. 32(4), 927–938 (1997). https://doi.org/10.1016/s0360-835 2(97)00021-1 29. Biles, W.E.: Simulating material handling alternatives for automotive machining processes. SAE Technical Papers (1978). https://doi.org/10.4271/780669 30. Berlec, T., Tansek, B., Kusar, J.: Selection of the most suitable material handling system in production. Int. J. Simul. Model. 20(1), 64–75 (2021). https://doi.org/10.2507/IJSIMM201-542 31. Technomatix Plant Simulation. https://www.plm.automation.siemens.com
Evolution of Startups in Automotive Supply Chain Tamás Bence Venczel(B) , László Berényi, and Krisztián Hriczó University of Miskolc, Miskolc 3515, Hungary [email protected]
Abstract. The automotive industry is highly exposed to the changes in the current economic situation. Product development practices must be evolved to exploit the new possibilities. Startups play an increasingly dominant role in the economy through empowering risk-taking and innovative entrepreneurial behaviour. Collaboration with startups in product development leads to particular management challenges. While the automotive industry is sensitive to quality and requires controlled procedures, startups’ risk-taking behaviour can be hazardous in the entire supply chain. Furthermore, the expectations and opportunities of startups and usual firms are different, resulting in conflicts and risks in the product development process. The study investigates the evolution of the automotive supply chain focusing on enhancing the relevance of startups and highlighting critical factors and risks of the transition. The literature on automotive product development raises the consideration of risk management. The paper applied this knowledge base to startups. The goal is to map the factors, which are the opportunities and risks of the startups in the supply chain considering industry experience. Keywords: Automotive · Project management · Supply chain · Startup · Management · Production
1 Introduction The article aims to study the effect of startups within the automotive supply chain. The first part describes the situation of the industry and startups, considering the different approaches to product development and risk management strategies. The second part differentiates startups in the supply chain as customers and suppliers and analyses the risks and opportunities of their cooperation with traditional, non-startup companies.
2 Industry Overview Historically, the automotive industry has always been a complicated and relatively slowgrowing industry (see Fig. 1). High industry, safety standards, and customer requirements forced car makers to implement processes and methods to reduce the risks of new product launches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 412–420, 2023. https://doi.org/10.1007/978-3-031-15211-5_34
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The automotive market is a highly competitive environment where a constant race exists between the different OEMs and suppliers (60% of a passenger car’s value comes from suppliers) - especially considering the increasing trend of outsourcing since the beginning of the 21st century [1]. 100
million units
80
71
74.9
2010-2019 avg.
2019
63.8
66.7
2020
2021
60 40 20 0 years
Fig. 1. Number of vehicle sales worldwide 2010–2021, based on [2]
A relevant characteristic of a startup is innovative entrepreneurship and risk-taking behaviour. So how can startups evolve in this highly standardized environment and compete with others within an industry which already seems to be complete? Industry trends confirm two major factors on why startups can evolve in the automotive sector, mostly considered passenger cars, but similar factors can be seen in transportation and heavy-duty vehicles. First, the emerging trend of electric vehicles must be highlighted. Market share of electric vehicles grows year by year, but more interestingly, the number of patents related to electric vehicles increased over the last years, while the internal combustion engine patents decreased (see Fig. 2).
market applications
3000 2500 2000 1500 1000 500 0 2006
2009
2012 years
Internal Combustion Engine (ICE)
2015
2018
Electric Motor
Fig. 2. Market share of electric vs. ICE vehicles, based on [3]
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The second trend is the general and overall emerging innovation in the industry. There was a slight drop in the number of patents in 2008, but innovation has continued since then. Three major areas of new patents are Vehicle Controlling Systems, Ancillary Vehicle Systems, and AI-Integrated vehicles (Figs. 3 and 4). As the knowledge about these areas is limited, startups can leverage advantages from the business opportunity to develop products and services.
number of applications
25000 20000 15000 10000 5000 0 2007
2009
2011 2013 years
2015
2018
U.S. applications
Fig. 3. Number of patents in the automotive industry, based on [3]
20000
AI integrated vehicles
15000
Ancillary Vehicle Systems
10000 5000 0 2006
2009
2012 years
2015
2018
Fig. 4. Number of patents yearly and yearly by top categories, based on [3]
The latest forecasts show a decreasing trend in the industry caused by the effects of the COVID-19 pandemic [4]. 2.1 Automotive Product Development and Risk Management Automotive product and risk management for development issues have a broad literature [5–12]. The studies propose risk management in automotive as an overall integrated management approach rather than only a specific area through the product development
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process. Investigations also found differences between the risks depending on where we are in the supply chain (e.g., based on probability and impact analysis, Tier1 and Tier2 suppliers have different risk categories). There are several methods to analyze technical and business risk. It is essential to highlight that most studies concentrate on theoretical questions rather than empirical confirmations. Empirical studies are mainly available about the German automotive industry, including a detailed survey-based analysis among suppliers. According to new product development, the automotive industry requires a significant amount of capital investment without information about the product’s future success, market opportunities, and profitability. The automotive product development standards have a similar approach with some differences over the life cycle. A common model is the third generation stage-gate model (see Fig. 5) [13].
Fig. 5. Third generation stage-gate model [13]
There are different success factors for projects and programs [14]. In the automotive industry, priority success criteria are sales and fulfilment of technical requirements. The risk-taking approach of the industry is very low. Usually, both customers and suppliers tend to mitigate risks, even if it means ignoring possible opportunities. This attitude comes from the fact of high-level requirements and also the financial consequences of a possible risk event. Low-risk acceptance attitude can be seen over the whole supply chain considering customers and suppliers [15, 16]. 2.2 Startup Product Development and Risk Management Startups have basically different product development and risk management practices compared to other companies. Startups usually take high risks and try to enhance opportunities of unknown circumstances. Moreover, most companies do not use structured project and risk management procedures. Even if they use some kind of strategy, the resources allocated for these activities are usually extremely low. Meanwhile, it is not examined yet in detail (no empirical evidence found by the research) if there is a difference between startups as customers or suppliers regarding their product development and risk management practices.
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3 Startups in the Automotive Supply Chain The study considers startups from two aspects: • Startups as OEMs (original equipment manufacturer, sales to end-users) • Startups as Tier-n suppliers (direct (Tier1) or indirect (Tier2) onward) supply of products and services to an organization (Fig. 6)
End User
OEMs
Tier...
Tier-n
Tier1
Tier1
Tier...
Tier...
Tier-n
Tier1
Tier-n
Fig. 6. Visualization of typical automotive supply chain
The number of startups as OEMs has increased worldwide (Table 1) in the previous 20 years. The reason for the higher number is the increase in innovations and new technologies. This is an opportunity for startups and a challenge how to find a business model that can succeed in this competitive environment [17]. Beyond this fact, the increasing trend of the Industry 4.0 approach and Internet Of Things boosts innovation in the industry [18]. Table 1. Top10 automotive startup OEMs (based on public company information) Company
Market cap [$]
Tesla
1.048 trillion
Rivian
101.52 billion
Lucid Motors
71.94 billion
NIO
52.75 billion
XPeng
40.36 billion (continued)
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Table 1. (continued) Company
Market cap [$]
Li Auto
31.92 billion
Fisker
5.4 billion
Arrival
5.06 billion
Nikola
4.21 billion
Canoo
2.37 billion
The reason why startups evolved as suppliers is that they reflect on the trends and demands of OEMs. At the same time, startups as suppliers are “handicapped” as giant Tier-n suppliers rule the market (Table 2), which makes it hard for suppliers to compete. Even if a startup supplier has a creative solution for a specific problem, traditional suppliers can apply the solution with their resources or simply acquire the company (as seen in a couple of years by Bosch, Denso, and ZF). Table 2. Top 5 automotive suppliers (based on public company information) Company
Sales in FY2020 to automotive [$]
Bosch
48.1 million
Denso
44.9 million
ZF
33.9 million
Magna
32.6 million
Aisin
32.2 million
3.1 Risks and Opportunities for Cooperation Risks and opportunities are different between the “types” of connecting partners in the supply chain. There are four possible cases to be considered. The risk is highest in the first category and lowest in the fourth category. The level of risk is higher as we have more partners of startups. Of course, the view could be extended to a whole supply chain with multiple levels of Tier-n partners and OEMs. 1) 2) 3) 4)
Startup customer with startup supplier −→ high risk Startup customer with non-startup supplier −→ moderate risk Non-startup customer with startup supplier −→ moderate risk Non-startup customer with non-startup supplier −→ low risk
Based on industry experience, we investigate level #2 (Startup customer with nonstartup supplier) and level #4 Non-startup customer with non-startup supplier) as we can
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leverage personal industrial experience in this field. As an employee of a non-startup automotive supplier (founded more than 100 years ago, about 10.000 employees, stock on NYSE), we have first-hand experience in cooperation with startup and non-startup OEMs. Key differences are collected in Table 3. The separation considers based on the key factors which can lead to project failure or success. Based on industrial experience, the comparison seems to be independent of the individual actors; it can be considered standard for every startup we worked together, and no significant difference was found between startups. Table 3. Differences of Level #2 and Level #4 cooperation Factor
Level #2
Level #4
Project Management Framework
Not clear, different understanding of parties
Clear, usually same framework (e.g., AIAG, VDA)
Risk Management Practice
Not clear, common risks are usually tracked on the supplier side
Clear, following built-in Project Management Framework
Escalation Process
Regular “over-escalation” from the OEM side towards the supplier. Early involvement of higher management levels
Defined. Problems are usually solved at the project team level; critical items are escalated
Communication
Not defined or defined but not always executed. OEM frequently spreads information to unnecessary functions
Defined by agreed Communication Matrix and usually followed
Financial awareness
Lower, the project budget is usually flexible on the OEM side to reach functional or timing requirements
High awareness on both sides on meeting the initial budget plan. If not, following g the escalation matrix
Product knowledge
Moderate. OEM has less knowledge on supplied parts than Tier-n suppliers. Startup OEM rely on the supplier
High. Usually, both parties understand the product well and its function
Supporting systems maturity
Low. Systems (e.g., PPAP submission site, milestone tracking system) are not existing on the OEM side at the beginning of the project
High. Systems existing and training usually happens at the early stages of the project for both parties
The summary above highlights the most important differences in the type of cooperation. Level #3 cooperation is possibly similar to Level #2 cooperation on the level of two actors but could change depending on the number of OEMs and suppliers in the supply chain. Level #1 results in the highest risks as it manifests the risks of being a startup on any side.
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Cooperation can also result in opportunities when startups are involved in the supply chain (Table 4): Table 4. Opportunities with startups in the supply chain Factor
Explanation
Flexibility
Approvals are easier in startups as the level of bureaucracy is lower
Creative product solutions Parties can acquire knowledge from each other and think outside the box, which drives solutions within their usual framework Market (sales) success
In the beginning, only approximate estimations were available on market acceptance. If the innovation succeeds, sales can be higher than planned
New product ideas
Startups can generate new ideas over the supply chain, including new products or improving current products, especially on the Tier-n level
4 Conclusion Extent literature in the field proves that risk management of the automotive industry is an integral part of current research. However, the number of studies is limited about the role of startups in the supply chain. These companies are the heart of economic growth nowadays and can help the recovery after the COVID-19 pandemic. Startups usually do not have well-established project and risk management practices. Some of them are using traditional or agile practices as business management methods. The failure rate of startups is exceptionally high, regardless of the industry; it seems to be caused by the high amount of risk that startups must ace. The question arises of how automotive startups can succeed in the highly standardized automotive industry. The increasing number of patents forecasts an even more important role for startups in the future. Therefore, the topic is worth further investigation. Industrial experience shows differences between risks and opportunities when startups are in the supply chain or when not. The inappropriate adaptation of project and risk management tools can be considered the main driving force of the changes. It is recommended for the startups in the automotive supply chain to apply an existing project and risk management framework and allocate necessary resources to control the product development process from a project and risk management perspective. It is also recommended to use checklists throughout the project life cycle since these can reduce the effect of the capabilities of individuals in the teams. It is to note that a startup OEM can learn a lot from the experience of a traditional supplier, including administration, documentation and management systems. Acknowledgments. “Supported by the ÚNKP-21-3 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.”
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References 1. MacNeill, S., Chanaron, J.-J.: Trends and drivers of change in the European automotive industry: (I) mapping the current situation. Int. J. Automot. Technol. Manag. 5, 83 (2005). https://doi.org/10.1504/IJATM.2005.006881 2. Global car sales 2010–2021. Statista. https://www.statista.com/statistics/200002/internati onal-car-sales-since-1990/. Accessed 14 Mar 2022 3. Patent Trends Study Part Five: Automotive Industry. IPWatchdogcom Pat. Pat. Law (2019). https://www.ipwatchdog.com/2019/05/07/patent-trends-study-part-five-automotive/ id=108960/. Accessed 5 Mar 2022 4. LMC Automotive: Western European Passenger Car Sales Update, São Paulo, 10 (2022) 5. Yoga Irsyadillah, N., Dadang, S.: A literature review of supply chain risk management in automotive industry. J. Mod. Manuf. Syst. Technol. 4, 12–22 (2020). https://doi.org/10.15282/ jmmst.v4i2.5020 6. Vanalle, R.M., Lucato, W.C., Ganga, G.M.D., Alves Filho, A.G.: Risk management in the automotive supply chain: an exploratory study in Brazil. Int. J. Prod. Res. 58, 783–799 (2020). https://doi.org/10.1080/00207543.2019.1600762 7. Mirboroon, L., Razavi, H.: A case study of risk management of automotive industry projects using RFMEA method. Mapta J. Mech. Ind. Eng. MJMIE 4, 42–50 (2020). https://doi.org/ 10.33544/mjmie.v4i1.132 8. Blos, M.F., Quaddus, M., Wee, H.M., Watanabe, K.: Supply chain risk management (SCRM): a case study on the automotive and electronic industries in Brazil. Supply Chain Manag. Int. J. 14, 247–252 (2009). https://doi.org/10.1108/13598540910970072 9. Dias, G.C., Hernandez, C.T., de Oliveira, U.R.: Supply chain risk management and risk ranking in the automotive industry. Gest Produção 27, e3800 (2020). https://doi.org/10.1590/ 0104-530x3800-20 10. González-Benito, J., Lannelongue, G., Alfaro-Tanco, J.A.: Study of supply-chain management in the automotive industry: a bibliometric analysis. Int. J. Prod. Res. 51, 3849–3863 (2013). https://doi.org/10.1080/00207543.2012.752586 11. Thun, J.-H., Hoenig, D.: An empirical analysis of supply chain risk management in the German automotive industry. Int. J. Prod. Econ. 131, 242–249 (2011). https://doi.org/10.1016/j.ijpe. 2009.10.010 12. Gerhard, D., Brem, A., Voigt, K.I.: Product development in the automotive industry: crucial success drivers for technological innovations. Int. J. Technol. Mark. 3, 203 (2008). https:// doi.org/10.1504/IJTMKT.2008.019922 13. Cooper, R.G.: Overhauling the new product process. Ind. Mark. Manag. 25, 465–482 (1996). https://doi.org/10.1016/S0019-8501(96)00062-4 14. Venczel, T.B., Berényi, L., Hriczó, K.: Project management success factors. J. Phys. Conf. Ser. 1935, 012005 (2021). https://doi.org/10.1088/1742-6596/1935/1/012005 15. Braese, N.: The Dynamics of Supply Chains in the Automotive Industry. Massachusetts Institute of Technology (2005) 16. Nagy, G., Bányainé Tóth, Á., Illés, B., Glistau, E.: Analysis of supply chain efficiency in blending technologies. In: Jármai, K., Bolló, B. (eds.) VAE 2018. LNME, pp. 280–291. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75677-6_23 17. Dovleac, R., Lorincz, A., Ionica, A., Leba, M.: A business and technical approach on startups applied on an automotive system. Int. J. Econ. Manag. Syst. 1, 6 (2016) 18. Halmosi, P.: the interpretation of Industry 4.0 by Hungarian technology-oriented startups. Timisoara J. Econ. Bus. 12, 149–164 (2019). https://doi.org/10.2478/tjeb-2019-0008
Investigation the Effect of the Data Frequency on the Driving Cycle of an Urban Bus Route Attila Vámosi, Dániel Nemes, Levente Czégé(B) , and Imre Kocsis Faculty of Engineering, University of Debrecen, Ótemet˝o u. 2-4, Debrecen 4028, Hungary {vamosi.attila,nemes.daniel,czege.levente,kocsisi}@eng.unideb.hu
Abstract. In this paper, focusing on a designated route of public transport in Debrecen, we examine the possibilities of creating a driving cycle representative for the route. The background of the work is, that in the framework of a larger-scale research project we examine the possibility of how to reduce the bus’s emissions by modifying the drive chain of the current diesel vehicle. A dynamic model suitable for performing the calculations was developed, in which the movement of the vehicle in traffic is described by the driving cycle. The data provided by the data acquisition equipment of the bus on the designated route is available for the creation of the special driving cycle. In this paper, we look for the answer to the question: how the difference in data density affects the driving cycle and how it modifies the parameters describing its representativeness. Keywords: Real traffic data · Micro-trip method · Driving cycle · Urban bus route
1 Introduction 1.1 Background of the Research In this paper, focusing on a designated route of public transport in Debrecen, we examine the possibilities of creating a driving cycle representative for the route. The driving cycle is a route-specific diagram that describes the variation in vehicle speed as a function of time. The driving cycle is typically created using statistical procedures based on real travel data collected over a long period of time. The real load may be represented by different driving cycles, depending on the actual situation that is to be simulated [8]. The sustainability of the public transport is widely investigated in the literature [9]. The background of this work is, that in the framework of a larger-scale research project we examine the possibility of how to reduce the bus’s emissions by modifying the drive chain of the current diesel vehicle [7]. The definition of the drive chain parameters was formulated in the form of an optimization task. For the solution of this optimization task a dynamic model suitable for performing the calculations was developed. In the simulation based on the dynamic model, the movement of the vehicle in traffic, and thus its load, is described by the driving cycle. It has therefore become necessary to create a driving cycle that can serve as an input to run a simulation based on a bus-based vehicle dynamics model. The data provided © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 421–427, 2023. https://doi.org/10.1007/978-3-031-15211-5_35
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by the data acquisition equipment of the bus on the designated route is available for the creation of the special driving cycle. The collected data cover a time interval of several months, which is sufficient to produce a representative driving cycle. However, the density (frequency) of the data is different from what is usual in the literature, thus it is not sufficient. The driving cycles discussed in the literature typically use a data frequency of 1 s, while the data available to us have a variable sampling frequency of 1– 20 s. In order to get an accurate picture of the movement of the vehicle, we supplemented the existing data with our own GPS-based measurements. In this paper, we look for the answer to the question: how the difference in data density affects the driving cycle and how it modifies the parameters describing its representativeness. In the second part of the paper, we discuss a method that is suitable for generating a data set that satisfies the requirements of both the frequency and the amount of data (time interval). To make this data set the long-term but less frequently sampled data set and the short-term but more frequently sampled data set is used. 1.2 Short Introduction of the Dynamic Model For the construction of the dynamic model, the AVL software was used. To avoid later inaccuracies of the simulation originating from not proper values of certain model parameters, we tried to keep the model as simple as possible and took into consideration the elements with the highest effect on the behaviour.
Fig. 1. The dynamic model of the bus in the AVL software.
The bus is a Mercedes-Benz REFORM 501 LE with the diesel internal combustion engine Mercedes-Benz OM 936 LA.6-1 and ZF EcoLife-AP 6+R gearbox. The power of
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the engine is 260 KW, the weight of the vehicle is 18 600 kg. In the simulation only the dynamics in the direction of progress were considered. The model includes the vehicle body, the engine the automatic transmission, and the wheels. By rigidly simulating the suspension, the foot forces are inaccurate, but since the vehicle does not operate near the wheel grip limits, this doesn’t have a significant effect on consumption. The AVL generated dynamic model of the bus is shown in Fig. 1. Detailed information about it is given in [1].
2 Methods of Creating Driving Cycles In the literature, one can find several different methods for constructing the driving cycle. These involve trip segment-based cycle construction, micro-trip cycle construction, modal cycle construction and cycle construction based on pattern classification, [2]. Among them, the Markov-chain approach and the micro-trip based approach are the most common techniques. The micro-trip is a segment between two stops in the vehicle’s movement. Origin of stops may be bus-stops for passengers getting on and off the bus, red traffic light signals, or various other traffic situations. A micro-trip segment of movement includes idling sections, cruise, acceleration and deceleration sections. Some particular examples of a travel section are given in Fig. 2: the first is a short one with one peak point, while the third one is a longer one with relatively high speed. For micro-trip clustering common driving features, such as Average speed, Average acceleration and Idle time may be used. After clustering the proportion of the micro-trips belonging to the clusters in the full dataset must be calculated. Finally, the micro-trips from the determined clusters must be joined together according to the calculated ratios to get the driving cycle. Due to their advantages, the different variations of the micro-trip-based approach are widely applied. In case the goal of the simulation is getting information about the emission, emission sensitive driving characteristics are important in the driving cycle. However, in micro-trip-based cycle constructions, the applied criteria and parameters are not directly related to emissions, as it is discussed in numerous publications, see e.g. [3] or [4].
Fig. 2. Micro-trip samples
The stochastic nature of the driving data may be revealed by the Markov modal events [5]. While the micro-trip approach cannot differentiate significantly between modal
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operating conditions, it is a significant advantage from point of view of the emissions estimation, that the Markov method can replicate modal events and driving variability [5]. With the application of the Markov chains replicating the global driving characteristics as well as preserving small timescale velocity fluctuations may be achieved [6]. In the Markov process theory a transition probability is applied to characterize how the actual driving appears.
3 Creating the Driving Cycles of a Bus Route of Debrecen 3.1 Micro-trip Method If the speed-time data is recorded at less than 1 Hz frequency, the shorter periods of idle sections may disappear and become blurred, making the partitioning of the sample into micro-trips less efficient, and less representative of the real structure of the full dataset.
Fig. 3. Comparison of 1 Hz frequency and lower frequency sampling
In Fig. 3, these differences and problems are clearly visible. The black-coloured dashed line shows sampling at a frequency of 1 Hz and the solid blue line shows sampling at a lower frequency. It follows from the description of the Micro-trip method that sampling with a lower than 1 Hz frequency is not suitable for making a representative driving cycle by the Micro-trip method. 3.2 Markov Chain Method In the case of the Markov-chain method, the speed and the measured or calculated acceleration data are encoded into operational states, which are registered in a state matrix using some bins for speed and acceleration. The probability for moving from state x i to state x i+1 is computed, and recorded in a probability transition matrix. This matrix will be used to select states, thus forming a states vector. Finally, driving cycle is generated from this states vector in terms of speed and time. 3.3 Simulation Results The driving cycle created with the Markov chain method will be used to determine the expected fuel consumption of the vehicle. Since the missing data from the lower
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frequency sample have been replaced by interpolation during the preparation of the driving cycle, the driving cycle does not give a real value for the consumption, because the acceleration-deceleration values that affect the fuel consumption are missing. Comparing the consumption values given by the driving cycle made from a sample taken at a 1 Hz frequency and the driving cycle made from data recorded at a lower frequency with a simulation software made by us [1], the difference is significant. The reason of this difference is the lost data; thus due to the missing accelerating time segments and idle times, the structure of the micro-trips is different.
Fig. 4. Calculated fuel consumptions
In Fig. 4 simulated fuel consumptions are illustrated. Investigating the relationship between time and difference of fuel consumptions, we find a large positive correlation (r = 0.9912), indicating a strong positive linear relationship, see Fig. 5.
Fig. 5. Difference of fuel consumptions
Our aim is to give an estimation for the slope and intercept of the linear regression line. These parameters may be determined by the Least Squares Method. Adding the calculated linear regression function to the consumption values given by the driving cycle made from data recorded at a lower frequency gives a deviation of less than 1% from the actual consumption (Fig. 6).
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Fig. 6. Corrected fuel consumptions
4 Conclusion Data collected at less than 1 Hz frequency cannot be used to generate a driving cycle when using the micro-trip method, but are suitable for a Markov chain solution. The result obtained by Markov chain method must be corrected, the required regression function is determined by the method described in this paper. The regression function is not general; it must be recalculated for specific datasets using the presented method to reach an acceptable level of reliability. The determination of the correction factor creates the opportunity of using a large set of data gathered with lower sampling frequency which is available for the route bus studied with dynamic simulation. Acknowledgement. The research was financed by the Thematic Excellence Programme of the Ministry for Innovation and Technology in Hungary (TKP2020-NKA-04), within the framework of the (Automotive Industry) thematic programme of the University of Debrecen. The authors are thankful to AVL List GmbH for providing licenses to AVL CRUISE under AST-University Partnership Program with University of Debrecen, Debrecen, Hungary.
References 1. Nemes, D., Pálfi, T., Hajdu, S.: Vehicle dynamic simulation possibilities using AVL Cruise M. Int. J. Eng. Manag. Sci. (IJEMS) 5 (2020). https://doi.org/10.21791/IJEMS.2020.2.35 2. Dai, Z., Niemeier, D., Eisinger, D.: Driving cycles: a new cycle-building method that better represents real-world emissions (2008). https://www.academia.edu/26820568 3. Silvas, E.: Integrated optimal design for hybrid electric vehicles. Doctor of Philosophy, Department of Mechanical Engineering, Eindhoven (2015). https://research.tue.nl/files/8809090/201 51130_Silvas.pdf 4. Delgado-Neira, O.F.: Driving cycle properties and their influence on fuel consumption and emissions. Graduate Theses, Dissertations, and Problem Reports, 3568 (2012). https://resear chrepository.wvu.edu/etd/3568
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5. Lin, J., Niemeier, D.: An exploratory analysis comparing a stochastic driving cycle to California’s regulatory cycle. Atmos. Environ. 36, 5759–5770 (2002). https://doi.org/10.1016/S13522310(02)00695-7 6. Li, Y., Peng, J., He, H., Xie, S.: The study on multi-scale prediction of future driving cycle based on Markov chain. Energy Procedia 105, 3219–3224 (2017). https://doi.org/10.1016/j. egypro.2017.03.709 7. Vámosi, A., Czégé, L., Kocsis, I.: Development of bus driving cycle for Debrecen on the basis of real-traffic data. Periodica Polytechnica Transp. Eng. 50(2), 184–190 (2022). https://doi. org/10.3311/PPtr.16109 8. Ficzere, P., Borbás, L.: Járm˝utrajektóriák definiálásához szükséges határértékek meghatározása klasszikus módszerekkel. Acta Periodica (EDUTUS) 23, 37–45 (2021). 9 p. 9. Lakatos, I., Szauter, F., Pup, D.: Alternatív hajtású autóbuszok nagyvárosi közösségi közlekedésben, M˝uszaki Szemle 74/2019 (2019)
Logistics and Mechatronics Related Research in Mobile Robot-Based Material Handling Tamás Bányai(B)
and Ákos Cservenák
Institute of Logistics, University of Miskolc, Miskolc, Hungary {alttamas,cservenak.akos}@uni-miskolc.hu
Abstract. Mobile robots play an important role in the operation of automatised, flexible manufacturing plants because flexible, reliable and cost-efficient material handling solutions can be performed. The fourth industrial revolution makes it possible to integrate new technologies and tools and transform conventional material handling solutions into high-tech, state-of-the-art cyber-physical solutions. Within the frame of this article, the authors are focusing on two important fields of mobile robot-based material handling solutions focusing on both mechatronics and the logistics of mobile robots. After a systematic literature review, the potentials of mechatronics and logistics-related topics are discussed, focusing on trajectory planning, controlling and Petri-net based simulation of mobile robot-based material handling solutions. The discussed results show that both mechatronics and logistics are important for the efficiency improvement of mobile robot-based material handling solutions. Keywords: Mobile robots · Automated guided vehicles · Material handling · Efficiency improvement · Logistics · Trajectory design
1 Introduction Logistics processes play an important role in the life of production and service companies and they influence the efficiency of both in-plant and external processes. The improvement of logistics operations is unavoidable to fulfil customers’ demands. These market-related aspects are especially important in the case of the automotive industry, automotive suppliers and players of the mechatronics assembly industry, where dynamically changing demands must be fulfilled with the efficiency of mass production. The cost-efficient operation of manufacturing systems is based on both technological and logistics subsystems. The logistics operations include material handling, transportation, warehousing, packaging, loading and unloading. These material handling operations can be robotised, but in the case of static robots, the utilisation can be lower, than in the case of mobile robots therefore manufacturing companies are investing more and more money into mobile robot-based material handling technologies to improve the efficiency, reliability and availability [1]. Kuka Robotics created a revolutionary new concept of flexible manufacturing, matrix production, which is based on the potential of mobile robots [2]. Mobility means the integration of robot arms, grippers and mobile platforms (Fig. 1). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 428–443, 2023. https://doi.org/10.1007/978-3-031-15211-5_36
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Fig. 1. Integration of robot arm and mobile platform to increase the availability and utilisation of robotised material handling solutions [3]
However, the development and optimisation of supply chain solutions play an important role in the efficiency improvement of automotive manufacturers and suppliers [4], especially in the case of disrupted supply chains in the pandemic era, but mobile robots are generally used for in-plant solutions to improve the performance of in-plant material handling processes. Based on this fact, the authors focus on the mechatronics and logistics-related aspects of mobile robot-based material handling solutions. The paper is organised as follows. Section 2 presents a systematic literature review, which summarises the research background of in-plant material handling with mobile robots. Section 3 describes the mechatronics related aspect of mobile robots in material handling, while Sect. 4 focuses on logistics aspects from process simulation with Petrinet objects point of view. Conclusions and future research directions are discussed in Sect. 5.
2 Systematic Literature Review Within the frame of this chapter, we would like to summarise the main research results in the field of the mobile robot-supported material handling. The literature review has four main parts. Within the frame of the first part, the methodology of the systematic literature review is summarised. In the second part, the descriptive analysis of the research results is discussed, focusing on the statistical analysis of articles and research results. In the third part, we focus on the content analysis of the related research results. Based on the descriptive and content analysis, we are summarising our conclusions in the fourth part of the literature review. 2.1 Methodology of the Systematic Literature Review The systematic literature review methodology is based on six phases. The first phase is the definition of research questions and finding the most suitable keywords to find related research results in the chosen database. The second phase is the search process.
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The third phase focuses on the performance of exclusions and inclusions. We can define exclusions regarding language, sources or date of publication to exclude old research results or non-English articles. The fourth phase is the descriptive analysis, where we can describe the published articles from the following aspects: • • • •
classification of articles by year of publications, classification of articles considering subject areas, classification of articles considering keywords, classification of articles considering sources (journals).
The fifth phase is the content analysis, where we focus on the scientific motivation, research results, and potential applications. Based on the results of the systematic literature review, the sixth phase describes the main research directions, research gaps and future potentials regarding mobile robot-based material handling. 2.2 Descriptive Analysis We have defined the following keywords to search in the Scopus database: (TITLEABS-KEY (“mobile robot”) AND TITLE-ABS-KEY (“material handling”)). Initially, 393 articles were identified. The robot- and mobile robot-based material handling has been researched in the past 50 years. The first articles in this field were published in 1973, focusing on the application of robots in automatised warehouses [5] and tactile arms for mobile robots [6]. The number of published papers has significantly increased in the last years; it shows the importance of this research field (Fig. 2).
Fig. 2. Classification of articles by year of publication based on search in Scopus
The research results represented by the articles in Scopus can be analysed from a subject area’s point of view. Figure 3 shows the results of the classification of the 393 mobile robot-related articles in the field of material handling design. This classification shows the majority of engineering and computer sciences. The huge number of mathematics and decision sciences related articles validates the importance of the design and
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Fig. 3. Classification of articles considering subject areas based on a search in Scopus database
optimisation of mobile robot applications, while the business, management and social sciences show the multidisciplinary aspect of this research topic. The classification of a research article from Scopus keywords point of view shows the majority of manufacturing applications (Fig. 4), while the control, navigation, collision avoidance with computer vision and other technologies represent important areas. As the keywords show, mobile robot-based material handling is a key to the flexibility of manufacturing systems, especially in the case of flexible manufacturing systems. Mobile robots integrate static robot arms and mobile platforms through a controller, and this integrated solution increase mobility, which requires the optimisation of routing and scheduling.
Fig. 4. Classification of articles considering keywords based on a search in Scopus database
The multidisciplinary characteristics of mobile robot-related research can be demonstrated by the wide range of scientific journals where the research results of this field have been published, including the fields of computer science, production research robotics, automotive industry, material science, manufacturing technologies, optical engineering, artificial intelligence and bioinformatics (Fig. 5).
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Fig. 5. Classification of articles per year by sources based on a search in Scopus database
The above mentioned descriptive analysis was based on the basis of 393 articles, but for the content analysis this huge number of articles must be reduced; therefore the Scopus search was extended using the following keywords and exclusion criteria: (TITLE-ABS-KEY (“mobile robot”) AND TITLE-ABS-KEY (“material handling”)) AND (LIMIT-TO (PUBSTAGE, “final”)) AND ( LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (SRCTYPE, “j”)) AND (LIMIT-TO (OA, “all”)). We have taken only published journal article with open access into consideration. This second extended search lead to 33 scientific publications, which focus on the research field of mobile robot-based material handling. 2.3 Content Analysis Mobile robot systems can be used in integrated technological systems, where humanmachine or machine-machine cooperation or collaboration can be performed. Case studies and applications validate the suitability of mobile robots for integration. As a case study in the field of composite material manufacturing shows, mobile robots, fixedbase manipulators and a machine vision subsystem can improve the material handling efficiency in a state-of-the-art material handling system [7]. The application fields of mobile robot systems are very different, they are used in assembly lines in the automotive industry [8], home robotic systems for rehabilitation [9], autonomous vehicles for construction fields [10], and hospitals [11]. Modularity increases the efficiency, flexibility and availability of mobile robot systems, as approaches show focusing on a modular robotic system with aisle-captive robots for small and medium-sized logistics warehouses [12]. The scheduling problems represent an important problem in the field of mobile robotbased material handling, because not only the technological restrictions and constraints but also some energy-related aspects must be taken into consideration. The transportation requests involve soft or hard time windows, and the mobile robots must perform the
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requested transportation and material handling operations without exceeding the available battery capacity. Different loading strategies influence the used scheduling strategy [13]. The scheduling and the task allocation problems can be optimised, separated or integrated. The multi-robot task allocation problem is discussed from the energy point of view in important research works, where different complexities and sizes of the systems and environments are tested [14]. The scheduling is important in the case of mobile robot-based milk-run systems, were not only time windows but also inventory levels at the manufacturing and assembly lines must be taken into consideration [15]. The scheduling problems of mobile robots can be solved with different methods, including analytical scheduling methods, heuristics, metaheuristics or game theory [16]. The trajectory optimisation plays an important role in the improvement of the performance of mobile robot-based material handling. Digital twin supported real-time simulation has a great impact on the efficiency of trajectory optimisation because the trajectory control of the physical robot can be supported by the performance of the digital robot [17, 18]. Routing and trajectory design represents complex problems of mobile robot and autonomous vehicles. The research works focus on the following important fields of this topic: optimisation of the flexibility of robots passing through narrow corridors focusing on the corners [19], Fuzzy control-based routing and trajectory design and its improvement through genetic algorithm and particle swarm optimisation-based heuristics [20], path following hybrid control for vehicle stability [21], Fuzzy-logic navigation and obstacle avoidance in an unknown dynamic environment [22]. These control problems are especially complex in the case of dynamic, reconfigurable manufacturing systems [23, 24]. Simulation tools are used in many fields of the design and operation of mobile robot systems: movement of mobile robots in production logistics [25], maintenance policy optimisation of the mobile robot and AGV systems [26, 27], dynamic role assignment for cooperative robots [28], alleviating the collision states in material handling systems [29]. The research of mobile robots and systems includes the development of special machine parts, for example, special wheels, grippers, and arms. In the case of special environments, the stare-climbing wheel can significantly increase the efficiency of mobile robots and extend their working space [30]. Recognition is especially important in dynamic manufacturing and service environment, where the recognition of products and components is a core skill of the robot for safe and secure grasping and placing operations [31]. Another field of recognition problems focuses on navigation-related recognition, where vision-based navigation allows mobile robots to navigate in unknown, uncertain dynamic environments [32]. In a complex service environment, the localisation of mobile robots and autonomous guided vehicles plays an important role because a robust localisation solution has a great impact on the robustness, efficiency, flexibility, utilisation and availability of the robots [33]. 2.4 Conclusions of the Systematic Literature Review More than 50% of the articles were published in the last ten years. This result indicates the scientific potential of mobile robot based material handling. The articles that address
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the theoretical and practical aspects of mobile robots and their applications focus on a wide range of scientific topics. Figure 6 shows the research framework of mobile robot-based material handling related to the descriptive and content analysis of articles in Scopus.
Fig. 6. The research framework of mobile robot-based material handling
Based on these researches, the following findings can be made: • Mobile robots are able to integrate technological and logistics subsystems in production and service environments. This integration can improve the efficiency of material handling processes and lead to the more flexible fulfilment of customers’ demands. • The optimisation of mobile robot systems defines new mathematical problems and mathematical models in the field of scheduling, assignment and routing, and the solution methods of these problems are from the field of both analytical and heuristic algorithms. • The trajectory optimisation and path following problems can be described using Fuzzy models because Fuzzy models can describe the uncertain environment. • Simulation tools are used in many fields of the design and operation of mobile ro-bot systems, including movement, maintenance, assignment, scheduling and routing. Within the frame of the next chapters, the authors describe the research results regarding mobile robot-based material handling focusing on both mechatronics and logistics aspects.
3 Mechatronics Related Aspects of Mobile Robot Systems A driverless transport vehicle with a robot is located in the High-Tech Logistics Systems Laboratory of the Institute of Logistics at the University of Miskolc [34]. The carrier vehicle is part of a handling system as can be seen in Fig. 7. This carrier is a custom-made prototype made by Gamma Digital in 2009. This AGV was no longer able to function automatically after the laboratory moved, like motion control [35] was developed in its
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previous location, and the formerly method was no longer suitable for the relocation of the mobile robot. A manually controlled carrier vehicle can move between different stations in a material handling system. A carrier vehicle typically picks up goods at an incoming goods location, takes them, for example, to a station on a roller conveyor system and drops them off. It can also make the same movement in the opposite direction. Usually, there are more than two stations in such a system, so the carrier vehicle can take several different routes. The mobile robot has the following major components, as shown in Fig. 6: • • • • • • •
LIDAR Navigation, 6 degrees of freedom industrial robot, training panel and robot controller, Conveyor belts, front and rear safety sensors, PLC and PC, 30V DC servo motors, 1:25 drive ratio gear driven wheels, retractable wheels.
The industrial robot is a Mitsubishi brand RV-2SDB type robot, which can manipulate piece goods or unit loads using its electric gripper. The conveyor belts can physically interact with the roller conveyor system in the laboratory by transferring and picking up unit loads. Sick brand safety sensors on the front and rear of the mobile robot are designed to detect the environment. The sensors have two zones: the warning zone, where the mobile robot does not stop and this data can be used in programming, and the disabling zone, where the sensor uses a relay to cut the power to the motors, making the AGV to an immediate stop. The PLC handles the simpler output and input data on the mobile robot, while the PC runs the algorithm responsible for the movement and performs the necessary mathematical calculations. The PC is also responsible for controlling the drive motors and conveyor belt motors via a parallel port. The drive motors are DC servo motors, each of which drives the wheels via a gearbox with a drive ratio of 1:25. The drive of the two wheels is independent of each other, so the drive is a differential drive. The other two wheels of the mobile robot are fixed wheels. The individual network devices can communicate with each other via a switch and with the outside world via a Wi-Fi bridge. In general, various technologies are available to determine the position of a mobile robot, such as inductive sensor [36, 37], image processing [38] or LIDAR sensor. Older mobile robots are physical track-attached, following a physical path such as a magnetic track or magnetic points or optical tracking. The position of the vehicle tested in the paper is also detected by the latest method and uses a virtual trajectory, such as the mobile robot developed, but in some cases, LIDAR sensors only detect the environment through a complex transformation algorithm. Since the former control algorithm cannot be performed in the new place, it was necessary to develop a new control algorithm. The goal of this research was the development of the modular system for motion control and simulation capable of pre-planning
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LIDAR sensor Mitsubishi Andon lamp
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and antenna Robot Teaching Panel AGV control panel Battery
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Fig. 7. Driverless transport carrier
the movement of the AGV and then controlling the servo motors to travel the planned trajectory at the desired velocity and angular velocity in order to achieve lower current consumption, in addition, to make a simulation to detect each parameter. This new solution can also be reused after a relocation. For robot control, such as in [39, 40] literature, different methods were used to control the robot’s motors for mobile robots of different designs. In one case, the control is based on less reliable image processing, while in the other case, the control was only discussed theoretically. The following modules are used for the new motion control and simulation between the two points: a. path planning module, b. trajectory planning module, c. speed-voltage conversion module using the velocities obtained from the trajectory planner, d. motion control and simulation of the electrodynamic motor model using voltages from the converter, e. simulation of the motion and f. communication module.
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The first purpose of the research was that the new path planning solution defines track points with two different methods at the same time, taking into account the geometric structure of the AGV. The second purpose was to develop a new trajectory planning solution that calculates the velocities of the vehicle’s driven wheels based on geometric and time data from the track points. In addition, the new path and trajectory planning solution was also designed to provide a more favourable solution for the power consumption of the drive. The determination of the rate of current consumption is based on the current consumption of the propulsion motors, which can be simulated by models and measured by measuring instruments. The third purpose of the research was to develop a new modular system for determining the current consumption of the drive and simulation of the track. The final purpose of the research was to develop a new AGV monitoring measurement system that measures the voltage of the power supply, drive current and receives navigation data.
Initial position and orientation
Track simulation Path and trajectory planning with Hermite-curve
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Velocity-voltage conversion DC motor dynamical model
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Fig. 8. Construction of the design and control system for the AGV
The structure of the control of the AGV implemented during the research and the operation of its main program are set out in Fig. 8. The first step is to receive navigation data, such as position and orientation. This is linked to the pre-entered optional transit and target positions and orientations through which the AGV passes or arrives at the finish line. Based on this data, the velocities of the wheels are produced in the main program using the two types of path and trajectory planning methods. Transmits wheel speed data to the conversion module and the DC motor electrodynamic model module. The latter module transmits angular speed values on one branch and current values to the other branch to determine data transmission and current consumption. By comparing the power consumption, the main program selects the control method and generates the control signal for the motor controllers.
4 Optimisation of Mobile Robot-Based Material Handling The main objective of the optimisation of mobile robot-based material handling systems is to develop design methods which can be used to determine the optimal system parameters for which the system is economically feasible. In order to achieve this goal,
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the first step is to define a general system structure for robotic systems, from which the individual system variants can be derived and described by mathematical models. It is necessary to investigate which variants of possible robotic systems are suitable for modelling the relevant material handling system and from which typical mobile robotbased solutions can be derived, depending on the nature of the preceding and following material handling tasks directly related to the main technological environment. For the derived typical unit-loading systems, the applicability limiting cases of fixed robots and mobile robots can be investigated, and possible service strategies can be defined. Then, for the selected characteristic model variants, heuristic optimisation methods based on evolutionary strategies can be developed to determine the system parameters that ensure minimum cost for the identified possible service strategies, assuming stationary operation for the deterministic parameters of the mobile robot-based material handling system. Simulation model-based problem-oriented Petri net modelling can be developed to investigate the stochasticity of the parameters and the non-stationary processes. The Institute of Logistics at the University of Miskolc has taken part in various research works in this field, and the results of these research works have been presented in PhD theses, article journals and conference talks. The results of this system-oriented research work can be summarised in seven main achievements, as follows: 1. A novel structure for robotic systems (with technological and logistical functions) was defined, from which each robotic system version can be mapped from building blocks. A mathematical model for the exact description of each system variant was developed. 2. A general system structure for mobile robot-based material handling systems was discussed, focusing on loading unit building and packaging systems, from which all characteristic system variables can be mapped, depending on the nature of the preceding and following material handling tasks directly related to unit-loading. 3. The potential service strategies were defined. A special structure was developed, from which the main directions of service strategies can be derivate. The comparative analysis makes it possible to identify possible service strategies, and their advantages and disadvantages. 4. Analysis of applicability of fixed robots and mobile robots. 5. Development of heuristic optimisation algorithm based on evolutionary strategies for determining the optimal system parameters of single-robot-based material handling systems based on cost functions as objective functions. Sensitivity analysis of objective functions. 6. Development of heuristic optimisation algorithms for multi-robot system design, which, in combination with the algorithm for single-robot systems, can be used to determine the optimal number of robots and the optimal system parameters. 7. Development of a pilot model system for simulation tools based on Petri-net modelling to study the effects of stochastic parameters and unsteady phenomena, exploring the advantages offered by the model system. Within the frame of this chapter, we are focusing on the Petri-net modelling-based simulation of mobile robot-based service systems. The main building modules of the
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mobile robot-based material handling system are the followings: input and output of the material flow, conveyor systems, the building of loading units, transportation units and material handling (packaging). The developed modules of the Petri-net objects can be divided into two main parts: a physical subnet and a logical subnet. In the physical subnet, the tokens represent the material flow process, while the logical subnet is responsible for the control and operation of the material flow. As an example, the Petri-net model of a material handling unit and a transportation unit is shown in Fig. 9.
Fig. 9. Petri-net model of a material handling object and a transportation object in a mobile robot-based material handling system.
The description of the physical and logical functions of the Petri net model of a material handling object is demonstrated in Table 1. AF is for states of the material handling system, while EF is for events. Table 1. Description of physical and logical functions of the Petri net model of a material handling object State/Event
Physical and logical function of states and events
AF-1
Product is waiting for palletising
AF-2
The robot arm is in the start position with the product
AF-3
The robot arm is in the end position with the product
AF-4
Product is on the loading unit (continued)
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State/Event
Physical and logical function of states and events
AF-5
There are free positions on the loading unit for the product
AF-6
The empty robot arm is in the end position
AF-7
The empty robot arm is in the base position
AF-8
The empty robot arm is in the start position
AF-9
The robot is at the current workplace
EF-1
Robot gripper is working
EF-2
Robot gripper is moving between start and end position
EF-3
Robot gripper is releasing the product
EF-4
Robot gripper is moving between base and start position
EF-5
Robot gripper is moving between end position and base
AL-1
Logical state
EL-1
Beginning of material handling (palletising)
The application of the above-mentioned modelling method in the field of design and simulation of material flow systems has the following advantages: • The static structure of Petri-nets can be used to model the structure of stationary material handling equipment (conveyors) and manufacturing equipment. Using tokens, it is possible to model the dynamic behaviour of the system (material and information flow, movement of mobile equipment). • The time horizon of Petri-nets allows the modelling and description of not only pure logical but also logical-time processes. This time modelling can be either deterministic or stochastic. In the case of a stochastic time horizon, the duration of actions can be parametrised by means of distribution functions (uniform distribution, normal distribution, exponential distribution, Erlang distribution, etc.). Another possibility is the use of fuzzy Petri-nets [41]. • With the capacity values of the conditions, it is possible to model the capacities of transport, storage and technological objects, while the switching times of the events can define transport, storage, technological and other, even logical, durations. • With coloured Petri-nets it is possible to distinguish between different types of products. It is possible to build a Petri-net simulator without requiring the user to know more than the basics of Petri-net theory [42, 43].
5 Conclusions Within the frame of this article, the authors are focusing on both mechatronics and logistics aspects of mobile robot-based material handling solutions, which are represented in a wide range of intelligent manufacturing plants [44], especially in the field of the automotive industry. As the described research fields show, mechatronics research fields are
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represented by trajectory design and development of new mechatronics parts for mobile robots to improve mobility, flexibility and security. Logistics researches are important from process development and optimisation point of view [45], because the optimal solution to scheduling, assignment and routing problems can significantly increase the performance of material handling processes.
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New Generation Hydrogen – How to Package Pastous Hydrogen for Mobility Applications Julius Brinken(B) , Björn Könecke, Malte Kania, and Tom Assmann Otto-von-Guericke-Universität Magdeburg, Universitätspl. 2, 39106 Magdeburg, Germany [email protected]
Abstract. Future mobility will depend on low-emission technologies to achieve the climate targets sets and to reduce the effects of climate change. Therefore, green hydrogen can be a key component in specific mobile applications in order to provide low-emission mobility. Unlike conventional hydrogen storage, the newly developed Powerpaste does not have high pressure and temperature requirements. The paste-like hydrogen storage system, therefore, enables simplified storage, handling and transport. Likely mobile applications for Powerpaste are car sharing, intralogistics or courier, express and parcel services. These three application scenarios lead to specific requirements for a suitable Powerpaste container. A literature study is conducted to set up criteria which support the selection of a suitable container. The criteria are categorised using expert workshops and evaluated using the methodology of pairwise comparison. The resulting categories for the criteria are application, logistics, recycling and safety. Two exemplary container concepts are compared and evaluated on the basis of the criteria sets: bag-in-box and piston cartridges. The piston cartridge is rugged and suitable for the transport of dangerous goods and enables simple integration into the car. At the same time, the bag-in-box concept is advantageous from a logistical point of view. Keywords: Hydrogen · Packaging · Mobility applications
1 Introduction The transition to environmentally friendly transport is one of the major challenges in the traffic and transport sector. Therefore, new technologies are needed to drive decarbonisation [1, 2]. In addition to synthetic fuels and battery solutions, hydrogen is also being discussed as a key technology. Powering fuel cells with green hydrogen can enable low-emission mobility. Furthermore, the option of store surplus energy in hydrogen seems to be a promising solution to counteract the volatility of renewables like wind or solar energy [3]. The use of hydrogen-powered vehicles requires the construction of cost-intensive infrastructure [4]. High-pressure ratios are required to store compressed gaseous hydrogen (CGH2 ). Commercial vehicles, for example, have a pressure level of 350 bar, while passenger cars have tanks with 700 bar. The required compressed gas storage tanks are heavy and take up a lot of space, which is why these tanks are only suitable for transport to a limited extent. [5] So-called CGH2 tube trailers bundle several © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 444–455, 2023. https://doi.org/10.1007/978-3-031-15211-5_37
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cylindrical pressurised gas cylinders. At particularly high-pressure ratios, CGH2 can be transported in container trailers. [6] Due to its material properties, hydrogen liquefies under normal pressure conditions only at –252.76 °C [7]. Therefore, storage is only possible in cryotanks. These tanks have insulation that prevents heating and the associated evaporation of liquid hydrogen (LH2 ). [8] LH2 -trailers can be used to supply the users. However, the operating range of the liquid trailers is limited to 4000 km, as the cryogenic LH2 heats up during transport. This inevitably leads to an increase in pressure in the tank and a loss of the energy carrier as it diffuses through the walls of the cryotanks. [9] Fig. 1 summarises the transport variants of hydrogen.
Fig. 1. Overview hydrogen transportation based on [9]
Powerpaste is a chemical hydrogen storage system. Magnesium hydride acts as a starting material for the newly developed hydrogen technology. The pasty consistency as well as the increased reaction speed, is achieved by adding esters and additives. In combination with water, Powerpaste reacts in a hydrogen generator to form hydrogen and can then power a connected fuel cell. Unlike conventional forms of hydrogen, the transport and handling of Powerpaste do not require any special pressure or temperature conditions [10]. The non-toxic ingredients and the binding of the hydrogen in a paste also result in increased safety compared to gaseous hydrogen [11]. Because of these reduced pressure and temperature requirements, the pastous hydrogen addresses previously mentioned problems in hydrogen infrastructure and logistics and can be transported in small quantities, providing logistical access to decentralised users. Therefore, Powerpaste can penetrate consumer segments that could not be tapped before due to technical challenges. Accordingly, Powerpaste is particularly suitable for mobile applications such as car sharing, intralogistics or courier, and express and parcel service [12]. However, there is strong competition among energy storage systems in these fast-growing markets. Established technologies represent barriers to market entry, which is why a comparison with competing technologies is essential. Compared to battery electric vehicles, refuelling time can be significantly reduced. At the same time, end-users are not dependent on a charging station. The refuelling of the vehicles can thus take place more flexibly and does not require cables or a sufficiently strong electrical grid. Since only a small battery is used as a buffer for the fuel cell, a large accumulator can be dispensed with, which
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reduces vehicle weight and embedded energy. Disadvantages, however, are the lower total efficiency compared to batteries and the high cost per unit of energy delivered. The packaging of the Powerpaste has to fulfil two different tasks. On the one hand, the container acts as a transport container, bridging the gap between the production site and the place of use. At the same time, it must be able to be inserted into the vehicle so that the packaging acts as a tank. This results in specific requirements for exchangeable and reusable packaging. Accordingly, the problem is to find suitable packaging. Previous publications, which establish criteria for container selection, cover very specific application areas like biobased food packaging [13]. The authors are taking into account environmental, circular, economic and social concerns but fail to address other areas like handling or logistics [13]. The focus on food packaging makes it difficult to transfer specific criteria like moisture content or thermal conductivity to this case [14]. The sustainability of packaging is a topic which is researched and discussed in many other publications [15–17]. Another focus of research is on the material selection for packaging (i.e. in cold chains), but again the results are not directly applicable to this packaging selection problem as the functional requirements differ (i.e. no insulation necessary) [14, 18]. Guidance on container selection was provided by Olsmats and Dominic’s Packaging Scoreboard, which allows the assessment of strengths and weaknesses of packaging concepts from the perspective of suppliers, distributors, retailers and consumers [19]. The very fundamental functions of packaging are containment, protection, convenience, and communication [20]. Those functions are also found in the definition of packaging requirements by the Guideline 4460 “Reusable Transport Packaging and Reusable Systems for Rational Load Transport” issued by the Association of German Engineers. With regard to packaging functions, the guideline formulates six categories [21]. These functional profiles as well as excerpt requirements for reusable transport packaging, are shown in Table 1. Table 1. Extract from VDI guideline 4460 [21] Function profile
Requirements
Protective function
- Protection from climatic influences - Chemically and biologically neutral - Ventilated …
Protection and material flow function
- Volume-preserving - Dimensionally stable - Mechanical resistance - Corrosion resistance - Lockable/sealable … (continued)
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Table 1. (continued) Function profile
Requirements
Material flow function
- High frequency of circulation - Area modularity - Space utilisation - Carrying capacity - Stackability - Handleable - Design suitable for filling and emptying - Volume reducible …
Environmental and utilization function/marketing function
- Standardized - Repairable - Recyclable …
Identification and information function/marketing function
- Informative - Identifiable …
Marketing function
- Economic
Production function
- Positionable - Mechanical removal …
Since Powerpaste is a hazardous material, the protective function of the packaging is essential. The starting material, magnesium hydride, is classified in dangerous goods class 4.3. These are substances which evolve into flammable gas in contact with water [22]. Accordingly, the transport of novel forms of hydrogen is subject to dangerous goods regulations, which are defined in the European Agreement Concerning the International Carriage of Dangerous Goods by Road (ADR) [22]. The ADR also formulates requirements, tests and certificates for packaging that must be complied with in order to guarantee the safe transport of dangerous goods. This paper presents Powerpaste as a new hydrogen storage technology for mobile applications and the supply of low-emission vehicles. The aim of this work is to develop a set of criteria for the selection of a suitable Powerpaste container, which can also be useful to assess other energy or hydrogen storage. The methodology is explained in the second section. In the third section, the criteria are assessed for two competing container concepts in different expert workshops and using pairwise comparison. This is followed by an outlook and a critical appraisal of the procedure.
2 Methodology First, the literature is analysed with the aim of finding a methodology for comparing container concepts. As explained in Sect. 1, many criteria for container selection presented
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in the publications are not suitable for this problem. Therefore, a new methodology is developed in this paper, including criteria from the literature review (Table 1). After collecting the criteria, the second step was to regroup them in an expert workshop. In this workshop, the expertise of logistics engineers was used to define the functional profiles for packaging specified for the Powerpaste application. The workshop, which lasted several hours, was conducted digitally, and the most important statements and findings were recorded in a protocol. The third step is the preselection of suitable container concepts, as a variety of packaging concepts are theoretically available. The preselection took place during another several hour digital expert workshop, with a broader range of participants. This time vehicle development engineers and logistics engineers, as well as filling machine and chemistry experts took part in the workshop. By pooling the various expertise, it was possible to share experiences and preferences, resulting in a constructive consensus. In several iteration rounds, different container types were discussed with regard to their suitability. In order to reflect on the findings, the workshop was minuted so that the results and statements could be processed transparently for all participants afterwards. Following the preselection, two suitable packaging concepts were compared to determine the application-specific packaging for Powerpaste. To quantify the relevance of the requirements, a pairwise comparison was conducted based on a methodology described by [23]. This comparison took place in a third workshop round. The workshop was again conducted with logistics engineers with expertise in packaging and hydrogen transport. If a packaging concept fits a requirement better, it receives two points. In return, the less suitable packaging receives zero points. If both packaging concepts fulfil a requirement equally, both alternatives are awarded one point. The preference matrix used is shown in Table 3. The sum of points resulting from the comparison of the assigned requirements enables a statement about the suitability of the packaging for the formulated categories. Table 2. Preference matrix Assessment
Option 1
Option 2
Option 1 meets requirement better
2
0
Both options meet requirements equivalently
1
1
Option 2 meets requirement better
0
2
In the next Sect. 3 the results of the evaluation are presented for each of the four categories. As some categories (i.e. safety) are more important than others and the number of requirements differs between each category, it is not reasonable to sum up the gained points of both options. Therefore, the results are discussed for each category separately in Sects. 3.1 until 3.4.
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3 Results Based on the literature research, it could be determined that the current state of science on packaging criteria is often related to individual application areas (like food packaging [13] or cold chain packaging [18]). In this respect, no specific elaborations of packaging criteria for hydrogen transport were identified. With regard to the evaluation of packaging, it should be noted that the current scientific work often concentrates on the evaluation of certain aspects like sustainability [16, 18]. While a methodology for the rating of packaging from different supply chain perspectives (i.e. supplier, retailer and consumer) already exists [19], this publication’s methodology can be used to holistically compare two competing packaging solutions from different perspectives. As a comprehensive collection of packaging requirements was found [21] the result of the first workshop was to regroup the different requirements similar to Olsmats and Dominic’s balance scorecard [19]. A differentiation between necessary criteria (i.e. regarding safety legislations) and relative criteria for comparing the suitability for different supply chain sections (i.e. logistics/distribution or application/consumer) was decided. The resulting categories for the requirements are safety, logistics, application and recycling. Due to the requirements of dangerous goods legislation, the safety category is rated superior to the others. The requirements from Guideline 4460 (Table 2) and Olsmats and Dominic [19] have been assigned to the developed categories, while some of them were omitted as they are not applicable for Powerpaste use cases. The relevance of each requirement shown in Table 3 can differ for specific Powerpaste use cases and their design of distribution or recycling processes. For example, a distribution which uses little automatic material flow technologies does not need a positionable container. Another example is that disposable containers have lower requirements for their cleanability than reusable containers. The high number of theoretically available container concepts made a reduction of variants necessary, which was done in the second workshop. A green concept, which for example, allows the reuse of the packaging, was declared as an important criterion. Disposable and material intensive packaging concepts were excluded in this respect. The selected concepts need to be either directly reusable or made from recycled or renewable material. Other aspects which were stressed in the workshop were the necessity to hermetically seal the paste and, therefore to meet the requirements of dangerous goods legislation. Cost intensive packaging concepts were also excluded, as they would further inhibit the paste from competing with other energy storage, as it already is relatively expensive. Some packaging concepts were limited due to the filling quantity of the system, so they were also not considered further. Following the preselection, two types of containers were declared as potential packaging options for Powerpaste: bag-in-box concept and piston cartridges. A visual comparison of the two concepts is provided in Fig. 2. The bag-in-box concept is very affordable and can be used in different shapes and sizes. One advantage is that the carton box serves as an outer packaging, which on the one hand, fulfils dangerous goods requirements and, on the other hand, allows for spaceefficient forming for the transport. Challenging are the discharge possibilities of the paste and a complete residual emptying as possible. Piston cartridges are limited in terms of designs and sizes. From an economic point of view, the higher unit costs represent
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Safety
Logistics
Application
Recycling
Mechanical resistant
Positionable
Easy to open
Reusable
Shockproof
Automation-friendly
Informative
Ecological/Renewable
Pressure resistant
Fillable design
Identifiable
Disposal-friendly
Tear resistant
Stackable
Dimensionally stable
Recyclable
Temperature resistant
Non-slip
Drainable
Cleanable/residual emptying
Leakproof/tight
Standardised
Easy to handle
Reduced volume when empty
Corrosion resistant/moisture resistant
Handleable
Chemically/biologically neutral
Unit forming
Reclosable
Lockable/sealable Flame retardant
a further disadvantage of this container concept. However, this container features a high degree of dimensional stability, which leads to advantages in the discharge of the paste and in-vehicle integration. Due to the round design, lower space utilisation is achieved during transport. Repackaging with an insert to accommodate the round cartridges is necessary and leads to additional process steps and costs. Depending on the container concept, different ADR guidelines for the transport of PowerPaste have to be considered since cartridges as special packaging are subject to separate testing and application requirements. For the bag-in-box concept, the ADR Guideline UN-2813 has to be followed. ADR Guideline UN-3476 must be followed if piston cartridges are used. In the following, the pairwise comparisons of the two competing packaging concepts are considered for each category to show the results of the evaluation method in a transparent and comprehensible way. 3.1 Safety Requirements The metal construction of the piston cartridge is more durable than the mixture out of carton and synthetic material. The higher durability leads to a higher resistance against shocks, pressures or tearing and temperatures for example, during a traffic accident. The piston cartridges are also less flammable in this case. Both container types can be constructed in a way to be tight and sealable. They can be made from not corroding material, which is chemically and biologically neutral. So, these requirements are rated even. Based on the pairwise comparison, it can be seen that the cartridge is at least equally suitable for all requirements and, in most cases, even better than the bag-in-box system.
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Fig. 2. Comparison bag-in-box and cartridge (authors illustration)
This leads to a very clear result in this section, although the legislative regulations can be met with both container concepts. Table 4. Pairwise comparison safety requirements Safety
Bag-in-Box Piston cartridge
Mechanical resistant
0
2
Shockproof
0
2
Pressure resistant
0
2
Tear resistant
0
2
Temperature resistant
0
2
Leakproof/Tight
1
1
Corrosion resistant/moisture resistant 1
1
Chemically/biologically neutral
1
1
Lockable/sealable
1
1
Flame retardant
0
2
Total
4
16
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3.2 Logistics Requirements In contrast to the cartridge, the bag-in-box concept does not require additional outer packaging (legal requirement), which leads to a simplification of logistical processes. Due to the square shape of the box, the bag-in-box system enables better positioning, stacking and unit forming. This leads to better material flow properties. The cylindrical and simple design of the cartridges makes it easier to fill the container with automatic filling machines. The boxes and the cartridges both theoretically allow automation and standardisation. Overall the bag-in-box has the potential to make the logistics process simpler and more efficient. Table 5. Pairwise comparison logistics requirements Logistics
Bag-in-Box
Piston cartridge
Positionable
2
0
Automation-friendly
1
1
Fillable design
0
2
Stackable
2
0
Non-slip
2
0
Standardized
1
1
Handleable
2
0
Unit forming
2
0
Total
12
4
3.3 Application Requirements Due to the built-in piston, the cartridge can be emptied more easily so that, in contrast to the bag-in-box, residual stocks of unused Powerpaste can be avoided. This is important as the users are interested in retrieving the most energy out of every container. Due to the robust design, the cartridge is also more dimensionally stable, this makes the vehicle integration and the paste extraction easier from a technical perspective. The handling, for example, during the refuelling of the vehicle, is easier with a dimensionally stable container. Both packaging concepts are easy to open and can display information (for marketing or for identification). In the application category, the cartridge is in total the better-rated concept.
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Table 6. Pairwise comparison of application requirements Application
Bag-in-Box
Piston cartridge
Easy to open
1
1
Informative
1
1
Identifiable
1
1
Dimensionally stable
0
2
Drainable
0
2
Easy to handle
0
2
Total
3
9
3.4 Recycling Requirements With regard to reusability, it should be noted that the cartridge can be used several times. The cleanability, as well as the possibility of recycling, are further advantages compared to the bag-in-box system. Due to the robust materials, a reduction in volume is not possible. A sustainable consideration of the materials used also showed that the bag-inbox system is made with more sustainable materials. However, due to the reusability and the general possibility of recycling, the cartridge is prioritized. Table 7. Pairwise comparison of recycling requirements Recycling
Bag-in-Box
Pistons cartridge
Reusable
0
2
Ecological/Renewable
2
0
Disposal-friendly
1
1
Recyclable
0
2
Cleanable/residual emptying
0
2
Reduced volume when empty
2
0
Reclosable
1
1
Total
6
8
The results of the evaluation make it clear that the cartridge is the more optimal packaging for Powerpaste, as the cartridge is rated better in three out of four categories. The lower logistic suitability complicates the distribution but still represents a simplification compared to the previous hydrogen distribution.
4 Outlook and Critical Appraisal This paper presents Powerpaste as a new hydrogen storage technology suitable for mobile applications and the supply of low-emission vehicles. A set of criteria for the selection
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of a Powerpaste container is developed, which can also be useful to assess other energy or hydrogen storages (i.e. liquid organic hydrogen carriers, battery-swap systems). The assessment of competing container types is a contribution to the decarbonisation of the transport sector, as specific requirements from a logistical and handling perspective are integrated in the scientific debate about different ways to store or transport the necessary energy. The result of the performed pairwise comparison led to the identification of suitable packaging for Powerpaste. Although the bag-in-box concept has very useful attributes from a logistics perspective, the piston cartridges were evaluated as savers concerning the hazardousness of Powerpaste. The stable packaging enables an easier vehicle integration and refuelling process. Using metal cartridges also allows a high recycling rate of emptied containers. Different vehicle types will require a variable design of the cartridge size or the parallel integration of multiple cartridges to provide an adequate range of the vehicle. Therefore, the characteristics of the application areas must be considered in the future design process. Partly it is a trade-off decision between a lighter cartridge which is easy to handle, and heavier cartridge, which allows for longer distances between refuelling. The aim of the research project is to integrate a complete Powerpaste system on a small vehicle as a demonstrator platform. The piston cartridges will be used in this system with a capacity of 3–4 l aiming to supply around 7 kWh of electric energy for the propulsion of the vehicle. The demonstrator platform is seen as a precondition for a market launch of the technology, which depends on the one hand on successful testing of the technical implementation and on the other hand on the design of a complete technology ecosystem, with distribution channels, vehicles for different use case and collection and recycling of the used cartridges. Once it is ready for the market, it will become clear in which niche market hydrogen technology will be used. As shown in this paper Powerpaste presently is researched for mobile applications. Future investigations might examine the use of Powerpaste systems for stationary applications (e.g. emergency power supply), as well as an energy carrier for transporting green hydrogen. Those use cases will result in different Powerpaste containers. Finally, it can be stated that Powerpaste is a valuable addition to current energy storage for mobile applications. The possibility to safely store and ship small quantities to decentralised consumers might improve the position of hydrogen technologies in the mobility sector, especially concerning the competition with battery storage. Acknowledgement. Financial support by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) under the project POWERPASTE, grant no. 03EI3026C is gratefully acknowledged.
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3. Bullmann, T., Gollnick, C., Schorpp, J.: Wasserstoff - DIHK Faktenpapier. Berlin (2020) 4. Agnolucci, P., Akgul, O., McDowall, W., Papageorgiou, L.G.: The importance of economies of scale, transport costs and demand patterns in optimising hydrogen fuelling infrastructure: An exploration with SHIPMod (Spatial hydrogen infrastructure planning model). Int. J. Hydrogen Energy 38, 11189–11201 (2013). https://doi.org/10.1016/j.ijhydene.2013.06.071 5. Wolf, J.: Die neuen Entwicklungen der Technik: Elemente der Wasserstoff-Infrastruktur von der Herstellung bis zum Tank (2003) 6. Wolter, W.: Wasserstofflogistik auf der Straße. In: Energie Agentur. NRW GmbH (ed.) Wasserstoff-Schlüssel zur Energiewende, p. 36. Düsseldorf (2018) 7. Romm, J.J.: Der Wasserstoff-Boom Wunsch und Wirklichkeit beim Wettlauf um den Klimaschutz. WILEY-VCH, Weinheim (2006) 8. Schadowski, A.: Flüssiggasspeicher. In: EnergieAgentur.NRW GmbH (ed.) WasserstoffSchlüssel zur Energiewende, p. 38. Düsseldorf (2018) 9. Adolf, J., et al.: Shell Wasserstoff-Studie. Energie der Zukunft? Nachhaltige Mobilität durch Brennstoffzelle und H2 (2017) 10. Röntzsch, L., Vogt, M.: PowerPaste for off-grid power supply. Dresden (2019) 11. Röntzsch, L., Tegel, M.: Wasserstoff ohne Tankstellennetze. HZwei Das Magazin für Wasserstoff und Brennstoffzellen 16, 35–37 (2016) 12. Brinken, J., Schulz, T., Assmann, T. (eds.): Hydrogen as a paste - Application scenarios in the transport sector (2021) 13. Arias, A., Feijoo, G., Moreira, M.T.: Establishing the multi-criteria roadmap and metrics for the evaluation of active films for food packaging. Curr. Res. Green Sustain. Chem. 4, 100160 (2021). https://doi.org/10.1016/j.crgsc.2021.100160 14. Ali, H.T., et al.: Multivariable analysis for selection of natural fibers as fillers for a sustainable food packaging industry. Mater. Res. Express 8, 095504 (2021) 15. Cabot, M.I., Luque, A., de Las Heras, A., Aguayo, F.: Aspects of sustainability and design engineering for the production of interconnected smart food packaging. PLoS ONE 14, e0216555 (2019). https://doi.org/10.1371/journal.pone.0216555 16. Ramos, M.H., Bono, J.L.V., Romero, S.A., Gallart, J.J.E.: Methods for evaluating packaging sustainability. Environ. Eng. Manag. J. 8, 1207–1211 (2009). https://doi.org/10.30638/eemj. 2009.176 17. Dang, S., Chu, L.: Evaluation framework and verification for sustainable container management as reusable packaging. J. Bus. Res. 69, 1949–1955 (2016). https://doi.org/10.1016/j.jbu sres.2015.10.086 18. Vamza, I., Valters, K., Dzalbs, A., Kudurs, E., Blumberga, D.: Criteria for choosing thermal packaging for temperature sensitive goods transportation. Environ. Clim. Technol. 25, 382– 391 (2021). https://doi.org/10.2478/rtuect-2021-0028 19. Olsmats, C., Dominic, C.: Packaging scorecard - a packaging performance evaluation method. Packag. Technol. Sci. 16, 9–14 (2003). https://doi.org/10.1002/pts.604 20. Bauer, A.-S., et al.: Cereal and confectionary packaging: Background, application and shelflife extension. Foods (Basel, Switzerland) 11, 697 (2022). https://doi.org/10.3390/foods1105 0697 21. Verein Deutscher Ingenierue e.V. VDI: Mehrwegtransportverpackungen und Mehrwegsysteme zum rationellen Lastentransport. Berlin (2003) 22. United Nations: European Agreement - Concerning the International Carriage of Dangerous Goods by Road. New York (2018) 23. Wartzack, S.: Auswahl- und Bewertungsmethoden. In: Bender, B., Gericke, K. (eds.) Pahl/Beitz Konstruktionslehre, pp. 307–334. Springer, Heidelberg (2021). https://doi.org/ 10.1007/978-3-662-57303-7_11
Transformation of Conventional Manufacturing and Service Systems into a Cyber-Physical Environment: Review of Potential Solutions Tamás Bányai(B) Institute of Logistics, University of Miskolc, Miskolc, Hungary [email protected]
Abstract. The Fourth Industrial Revolution offers new potentials to increase the efficiency, availability, sustainability and transparency of manufacturing systems. The Industry 4.0 technologies are widely used in the field of purchasing, production, distribution and reverse processes to enhance the efficiency from a technological and logistics point of view. The application of these new technologies leads to the transformation of the conventional manufacturing and service systems into cyber-physical systems, where the design and operation of the new, globalised, interconnected and hyper-connected systems and supply chains require new optimisation approaches. Within the frame of this article, the author focuses on the potential of these mentioned Industry 4.0 technologies from a digitalisation point of view. After a systematic literature review, this paper introduces some potential ways to transform conventional manufacturing and related service systems into cyber-physical environment in the field of manufacturing processes in the automotive industry, hyper-connected collection and distribution systems in city logistics and switch pool packaging logistics in Industry 4.0 era, where smart sensors, intelligent tools, gentelligent products, digital twin solutions and edge computing support the transformation. Keywords: Cyber-physical systems · Matrix production · Services · Hyper-connected networks · Digital twin technology · In-plant supply
1 Introduction The new generation of intelligent manufacturing systems in the Industry 4.0 era can be characterised by an in-depth integration of new-generation technologies of Internet of Things solutions and advanced manufacturing technologies [1]. Digital twin solutions play an important role in the development and transformation of conventional systems into cyber-physical systems [2]. This transformation influences not only the operation but also the design of the manufacturing system because this mirroring can bridge the gap between the optimisation aspects of system design and the strategic and tactical aspects of the operation. The transformation of conventional manufacturing systems into a cyberphysical system can be performed in a wide range of manufacturing structures, and this © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 456–471, 2023. https://doi.org/10.1007/978-3-031-15211-5_38
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transformation focuses on the physical layer, virtual layer, related service systems and digital twin data [3]. Today, the challenge for manufacturing and service companies is to meet increasingly dynamic and customised customer needs with mass production efficiency. Meeting these needs in an economical and sustainable way requires the implementation of new production and service paradigms, for which the digitalisation technologies emerging in the context of the fourth industrial revolution provide an excellent technological background. In the era of the fourth industrial revolution, so-called Industry 4.0 technologies are enabling the transformation of traditional production and service systems into cyberphysical systems. This transformation can result highly efficient, flexible, sustainable, cost-effective production and service systems that can exploit the opportunities offered by digitalisation to the mutual benefit of both manufacturers, service providers and customers. During the pandemic, these globalisation efforts have been somewhat overshadowed in the case of supply chains. This is because supply networks have undergone a major transformation, as manufacturing companies have sought to replace suppliers from the Far East with local suppliers to reduce supply risk. Nevertheless, it can be argued that the development of cyber-physical systems offers significant benefits not only for manufacturing but also for service systems. In this article, the main aspects of cyber-physical system design are presented and some Industry 4.0 technologies used are described through some examples. This paper is organised as follows. Section 2 presents a systematic literature review, which summarises the research background of cyber-physical systems. Section 3 describes the model framework of the transformation of conventional manufacturing systems into a cyber-physical system focusing on matrix production. Section 4 presents the potential of a hyper-connected collection and distribution system in city logistics. Section 5 shows a potential solution for switching pool packaging logistics in Industry 4.0 era. Conclusions and future research directions are discussed in Sect. 6.
2 Systematic Literature Review Within the frame of the systematic literature review (SLR), the main research directions and the main research gaps are identified. The systematic literature review includes the description of the methodology of SLR, the descriptive analysis focusing on the statistical and numerical analysis of research results published in articles, the content analysis of published articles focusing on the main research results and typical approaches, and the consequences focusing on main research directions and research gaps. 2.1 Methodology of the Systematic Literature Review Within the frame of this systematic literature review, a 5 phase review process was used (Fig. 1), including the definition of research questions, performing the search in the Scopus database, include and exclude article, which are out of scope, descriptive analysis, content analysis, and conclusions [4].
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Fig. 1. Methodology of the systematic literature review
2.2 Descriptive Analysis Firstly, the relevant search terms were defined, focusing on the research topic. It is a critical phase of the systematic literature review because there is a wide range of sophisticated review articles in the field of application of Industry 4.0 technologies and Internet of Things [5]. The following keywords were used in the Scopus database: (TITLE-ABS-KEY (“cyber-physical”) AND TITLE-ABS-KEY (manufacturing) OR TITLE-ABS-KEY (production) AND TITLE-ABS-KEY (optimisation)). Initially, 519 articles were identified. This list was used for the descriptive analysis of the research field. The search was conducted in March 2022; therefore, new articles may have been published since then. The transformation of conventional manufacturing processes has been researched in the past 10 years. The first articles in this field were published in 2011, focusing on the conceptual framework for dynamic manufacturing resource service composition and optimisation in service-oriented networked manufacturing. The number of published papers has increased in the last years; it shows the importance of this research field (Fig. 2).
Fig. 2. Classification of articles by year of publication based on search in Scopus
The articles can be classified depending on the research area. Figure 3 shows the classification of these articles considering ten subject areas. This classification shows the majority of engineering and computer sciences, while mathematics and decision making
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show that cyber-physical systems offer new optimisation potentials for the design and operation of these complex systems and networks.
Fig. 3. Classification of articles considering subject areas based on a search in Scopus database
2.3 Content Analysis The application of Industry 4.0 technologies is based on the solution of different scientific problems, which can be categorised as follows: (a) intelligent sensing and data acquisition from manufacturing and logistics, (b) collaborative decision making in the global value chain including purchasing, production, distribution and reverse processes, (c) life cycle assessment, security, maintenance and sustainability, (d) cooperative control and optimisation of human-machine interactions [6]. Data acquisition plays an important role in the efficiency of digital twin solutions, especially in the case of distributed production and service processes, therefore it is important to apply multi-modal data acquisition techniques and approaches to support the sophisticated design based on digital twin and discrete event simulation [7, 8]. However, the basic motivation of cyber-physical systems can be found in the expected technological and economic benefits, but social aspects should also be taken into consideration, focusing on the following problems: problem-solving efficiency of human stakeholders [9], efficiency improvement of human-based production tasks through the integration of sensor data from the technological and logistics resources and motion recognition of human operators [10], control of intuitive and efficient human-machine interactions between human operators and cyber-physical machine tools [11]. Cyberphysical systems have more favourable key performance indicators including availability, flexibility, efficiency, sustainability and transparency related parameters. The flexibility of cyber-physical manufacturing systems, including matrix production systems, is based on open-architecture machine tools, smart infrastructures [12] and other manufacturing and assembly resources, which make it possible to perform rapid changes through the application of individualised modules [13]. Other approaches focus on instructiondomain based solutions, where the work process of the cyber-physical system can be established on the basis of real-time status information of resources and processes [14].
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Cyber-physical systems “create” new professions and lead to the birth of new methods and tools, because complex, global, interconnected systems generate new types of problems in the field of product design, process planning, inventory management, scheduling, and distribution [15]. The design and optimisation problems of cyber-physical systems can be solved using a wide range of algorithms and tools, including discrete event simulation [9], decision-making methods [16], special design architectures (i.e., configuration design – motion planning – control development – optimisation decoupling) [17], holistic matching approach [18], neural networks [19], swarming heuristics [20], evolutionary algorithms [21, 22], ontology-based resource reconfiguration [23], multi-layer decision making [24], clustering [25]. The transformation of conventional systems into cyber-physical systems lead to the transformation of objective functions and constraints of the design and operation problems, because previously isolated functions become integrated functions influencing the key performance indicators of the whole value chain [26]. The objective functions of CPSs are focused on the following aspects: costs, logistics performance, energy efficiency, sustainability [27], environmental impact, quality, availability, and efficiency. The efficiency of the transformation of conventional systems into cyber-physical systems is influenced by the architecture used; cloud-based self-organising architectures can support the improvement of key performance indicators through the reconfiguration options of agents in a collaborative way [28]. One of the key factors leading to the revolutionary new concept of cyber-physical supply chain solutions is the integration of digital technologies, blockchain and real-time data analytics. This integration has the potential to achieve a new quality in decisionmaking support by combining discrete event simulation, analytical and heuristic optimisation, and real-time data analytics supported by big data [29] and edge computing solutions [30, 31]. Lean management is also an unavoidable approach of cyber-physical systems because the lean paradigm supports the continuous value stream using a continuous optimisation and standardisation of resources and processes [32]. 2.4 Conclusions of the Systematic Literature Review More than 60% of the articles were published in the last five years. This result indicates the scientific potential of cyber-physical systems. The articles that addressed the transformation of conventional manufacturing systems into cyber-physical environment focus on a wide range of research topics, and based on the results of these researches, the following findings can be made: • The in-depth integration of state-of-the-art technologies of IoT solutions and advanced, configurable, flexible manufacturing technologies is the key factor of cyber-physical systems. • The transformation of conventional manufacturing and service systems influences not only operation but also the design of the manufacturing system through the integration of physical and digital layers of operations. • The optimisation of human-machine cooperation is a key factor in manufacturing and assembly processes, where advanced design and planning architectures represent potential support.
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• A wide range of optimisation methods and tools are used for the design and operation of cyber-physical systems, including simulation, heuristics, ontology and neural networks. Only a few of the analysed articles focuses on the real-time data acquisition and digital twin-based transformation of conventional manufacturing systems into cyberphysical systems; therefore, this research topic still needs more attention and research. According to that, the main focus of this article is the demonstrate the main findings in this field, focusing on manufacturing processes in the automotive industry, collection and distribution systems in city logistics and packaging logistics.
3 Efficiency Improvement Through Matrix Production in Automotive Manufacturing Systems In a conventional manufacturing environment, the static nature of material flow processes makes it difficult to achieve a level of flexibility in the manufacturing process that would allow for the increasingly dynamic and changing individual customer needs to be met with mass production efficiency. The matrix production concept of KUKA Robotics, where technological and logistics processes are separated from each other and autonomous material handling solutions perform in-plant supply between storage and matrix cells, can provide a solution (Fig. 4).
Fig. 4. The matrix production concept of KUKA [33]
Within the frame of this section, the basic concept of the transformation of conventional production systems into cyber-physical systems is described focusing on both the functional model of cyber-physical systems and the potential optimisation problems and solutions [34]. In the case of conventional production systems, the production planning and scheduling are based on ERP (Enterprise Resource Planning) and MES (Manufacturing Execution System) data [35]. However, a wide range of computer-aided technologies are available from the ERP to support the digital simulation-based design and operation, including purchasing, manufacturing, inventory and order management, customer relationship management, human
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resource and workforce management and distribution, but in this case, the design and operation cannot take all real-time information into consideration. The application of Industry 4.0 technologies makes it possible to gather real-time data (failure data, status information, product information) and support the decision making process in the manufacturing system. While transforming conventional manufacturing system into cyberphysical system, smart sensors can be used to gather data from the physical processes (technological and logistics processes) of the manufacturing system. The data analysis is usually based on both edge and cloud of fog computing solutions. Edge computing focuses on the data analysis near the data source in order to increase the speed of communication. The data acquisition is especially efficient from intelligent tools and gentelligent sensors, where in-built smart microsensors are available. The analysed data is sent to digital twin aggregates, digital twin prototypes or digital twin instances, which represent the digital reflection of the physical system. Instances, prototypes and aggregates are integrated into a digital twin environment, where the real-time model of the physical production system exists. This real-time model is transferred to the discrete event simulation tool, where the assignment, routing, scheduling and facility location planning problems can be solved based on the real-time status of the physical system (Fig. 5).
Fig. 5. Transformation of conventional manufacturing system into a cyber-physical system using Industry 4.0 technologies [33]
In a matrix production system, the application of green technological and logistics resources plays an important role, and it is especially important in the case of logistics resources, because the application of autonomous material handling solutions (autonomous guided vehicles -AGVs) can be realised by e-vehicles. In this case, not only the technological and logistics performance can be increased but also the costs and the environmental impact can be decreased. The real-time optimisation model of a cyber-physical manufacturing system can be divided into two main phases. The first phase is the conventional planning, where the
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routing and scheduling of clustered in-plant supply demands are performed. The second phase is the real-time scheduling, where the parameters of the dynamically changing environment of the manufacturing system can be taken into consideration. The optimisation of the cyber-physical system focuses on the minimisation of required time for the performance of in-plant supply processes. The logistics processes include the materials handling operations between raw material and component warehouses, tool storage, AGV pools and matrix grids. As constraints, we can take either time or capacity-based constraints into consideration, but also energy efficiency and emission-related parameters have a great impact on the optimal solution of the in-plant supply problems. In the literature, we can find different heuristic solutions for these kinds of integrated optimisation of logistics problems. One typical solution is based on the sequential metaheuristics integrating black hole heuristics and the discretised flower pollination-based routing, scheduling and real-time optimisation [33, 34].
4 Hyper-Connected Collection and Distribution Systems in City Logistics Conventional city logistics solutions involve direct supply to pick-up and delivery points, which can be households, offices, shops, service providers or supermarkets. In the case of conventional city logistics solutions, the collection and distribution processes are carried out independently by each logistics service provider. This is partly due to the fact that the collection and distribution processes are so different that the logistics services provided by the different service providers cannot be combined due to the different resources and technological conditions required. It is therefore not possible to perform these collection and distribution processes in a cooperative manner. Another important reason for the lack of cooperative implementation is that logistics service providers have separate business management systems and do not use technologies that would allow coordination of previously separate and independent systems. This chapter will show how conventionally operated city logistics processes can be linked using Industry 4.0 technologies to create a hyper-connected service environment in which the service processes of previously independently operating service providers can be coordinated. This coordinated operation can result in a cost-effective operation that is also optimised in terms of environmental impact [35]. There are two main pillars for the design of a cyber-physical collection and distribution system: • Transformation of the distributed, decentralised physical processes of collection and distribution into a centralised, integrated solution: The implementation of conventional collection and distribution processes in an urban environment allows conventional vehicles to travel almost unrestricted in inner-city areas, with significant environmental impacts. This environmental impact can be measured in terms of greenhouse gas emissions or calculated from consumption related to transport processes carried out by independent logistics service providers. The collection and distribution system could be adapted to include intermediate warehouses around the designated downtown zone, which could act as logistics service centers or cross-docking facilities to stop the
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material flow to the downtown area. At these cross-docking facilities, incoming goods can be transshipped from conventional delivery vehicles to vehicles serving locations in the downtown area, which can be predominantly electric vehicles or micro-mobility vehicles. In the case of collection processes, the process is reversed, with electric vehicles and micro-mobility vehicles performing the necessary collection tasks in the downtown area and the collected goods being transported to the cross-docking facility, from where the service providers will then use the conventional resources of transportation to transport the corresponding goods. • Digital mirroring of the collection and distribution process: For conventional collection and distribution processes, integration is often not feasible because there is no solution to coordinate collection and distribution processes and operations performed by independent logistics service providers. The reasons for this are twofold. On the one hand, the service providers concerned may be competitors and therefore, most of the data relating to their processes are confidential. Secondly, they do not use technologies to retrieve the status of the processes of the individual service processes in real-time. Real-time data and the quasi-real-time information derived from them are of great importance since real-time status information of real physical processes and resources can be used to create an intelligent agent for design and optimisation, which can be used to consolidate and optimise in real-time the processes and operations of the individual service providers, which have been operating independently. This realtime optimisation can be achieved by using the digital twin technology, which allows replacing the independent logistic processes of the service providers operating in conventional city logistics systems with an integrated digital twin of the processes of these service providers based on the parameters describing the real-time status of these processes, which can be used to perform the necessary scheduling, assignment, routing, layout planning and controlling tasks in the cyber-physical collection and distribution process (Fig. 6). The most important design parameters of the cyber-physical system are the followings: location of pick-up/delivery points within the urban area, volume, weight and required transportation and loading devices of pick-up/delivery tasks, preliminary or forecasted schedule of time frames available to perform collection and distribution operations. The potential mathematical model of the optimisation problem related to this transformed collection and distribution systems includes the objective function focusing on both costs and environmental impact. The constraints could be the followings: predefined service levels, available capacities of cross-docking facilities or transportation resources, predefined time windows to perform services, and predefined other key performance indicators, such as availability, flexibility or suitability.
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Fig. 6. Transformation of conventional city logistics operations into a cyber-physical collection and distribution environment through integration of independent logistics service providers using Industry 4.0 technologies [35]
5 Switch Pool Packaging Logistics in Industry 4.0 era The industrial packaging market can be described as a dynamic and changing area of manufacturing-related services. The Allied Market Research reported that the size of the packaging market is expected to grow to about 70,000 million USD by the end of 2023. This growth can be characterised by annual growth of about 4%, and this fact validates the importance of continuous improvement of packaging solutions in the field of manufacturing and related services. One typical solution of packaging systems is the switch-pool system, where each actor in the supply chain has its own returnable packaging and is responsible for its cleaning, maintenance [4, 36] and storage. Two typical variants of switch-pool systems are in use. In the first, only the sender and the receiver have their own returnable packaging. The return of the emptied returnable packaging means that transportation process is performed when the carrier delivers the packaged goods to the receiving party (manufacturer), so that the carrier can in this case, perform the following transportation: either transport the packaged goods from the supplier to the receiving party or return the empty returnable packages to the supplier. In the case of this option, there is no guarantee that
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the supplier will receive as many empty returnable packages from the manufacturer as the number of returnable packages sent with the packaged goods. It is of course, possible to ensure, by means of an appropriate contract, that the receiving party (manufacturer) returns to the supplier the same amount of empty packaging as received with the packaged goods, but in this case, this may imply additional material handling operations from the manufacturer, for example by unloading goods to free returnable packages for reverse transportation in manufacturer supplier relation. In another variant of the switch-pool solution, the carrier also has its own packaging pool. When the carrier receives the packaged goods from the supplier, it gives an appropriate amount of empty packaging, thus ensuring that the number of empty packaging that can be used at the supplier is kept at a constant and safe level. In this version of the switch-pool, the receiving party does not carry out any administrative activity in respect of empty means of transportation. The processes of the switch-pool packaging systems can be described as follows: (a) From the supplier, the carrier delivers the consigned packaged goods to the manufacturer. (b) The manufacturer receives the packaged goods from the carrier. (c) From the manufacturer, the carrier receives a quantity of empty returnable packaging corresponding to the quantity of packaged goods delivered. (d) If this quantity of packaging is not available at the supplier’s demands, the carrier added the quantity of packaging from its own pool and delivers the resulting quantity of packaging to the supplier. Based on the above reasoning, three typical processes can be formed within a cycle for a switch-pool. In the first case, the quantity of empty returnable packages to be returned is available at the manufacturer, and exactly the required quantity is shipped from the manufacturer to the supplier. In this case, the freight forwarder can carry out a direct transport between the two sites, as there is no need to unload or load empty packaging at a central depot (Fig. 7).
Fig. 7. One typical cycle of a switch-pool, if the quantity of empty packaging to be returned is available at the manufacturer and the carrier delivers exactly that quantity
In the second case, the quantity available at the manufacturer is greater than the quantity of empty returnable packages, so the carrier will ship the full quantity available. Since the supplier only needs to receive the quantities returned with the goods packed in that cycle, the excess quantity is deposited by the carrier in a central depot (Fig. 8).
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Fig. 8. One typical cycle of Switch-pool, if the quantity available at the manufacturer exceeds the quantity of empty packaging to be returned and the carrier delivers the full quantity
In the third case, the quantity available at the manufacturer is less than the quantity of empty returnable packages, and the carrier shall therefore carry the available quantity. Since the supplier has to receive the exact quantity of the goods packed in the given cycle in return, the missing quantity of empty multi-package is replenished from a central depot (Fig. 9).
Fig. 9. One cycle of a switch-pool, if the quantity available at the manufacturer is less than the quantity of empty packaging to be returned, the carrier will replenish it from its own pool
There are a number of technological and logistical aspects of packaging processes that need to be taken into account in order to design and operate highly efficient, sustainable, flexible and environmentally friendly packaging systems. The characteristics of packaging systems has a great impact on almost all aspects of procurement, production, distribution and recycling. It is therefore important to develop packaging systems using advanced technologies that can transform conventional packaging systems into
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cyber-physical networks. In these cyber-physical networks, we can take advantage of the benefits of Industry 4.0 technologies to increase the availability, flexibility, efficiency, sustainability and transparency of technological and logistical processes. Using Industry 4.0 technologies, including cloud computing, fog computing, edge computing, RFID technologies, simulation, augmented reality, big data analytics, cyber security solutions and smart devices, it is possible to make a transformation, as shown in Fig. 10.
Fig. 10. Packaging logistics in Industry 4.0 era using Internet of Things technologies
The operation of the packaging system can be represented by a wide range of technological and logistics operations depending on the characteristics of the packaging (returnable or non-returnable, primary, secondary or tertiary). Typical packaging systems are working between suppliers of manufacturers and customers. Within the frame of this model, the focus is on the industrial part of the packaging systems, including the supplier-manufacturing relation. The supplier sends packaging from their warehouses to the warehouses of the manufacturer. Agencies take part in the coordination of the supply chain process and integrate the logistics processes of suppliers, manufacturers and carriers from a packaging logistics point of view. Carriers are responsible for the transportation of returnable and non-returnable packaging from the supplier to the manufacturer and return back from the manufacturer to the supplier. This material flow process can be interrupted by cleaning and maintenance operations because, depending on the quality (status) of the packaging, cleaning, maintenance or recycling operations can be added to the standard transportation, warehousing and loading operations. The supply chain of the packaging systems can be created in different ways, depending on the characteristics of the participants and required services.
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6 Conclusions Today, changing markets, increasing customer demands, shorter product lifecycles and the need for continuous availability are major challenges for companies. Companies that recognise and prepare for this process in time will be able to maintain their competitive position. In order to meet the dynamically changing customer needs, developing the Industry 4.0 capabilities of companies engaged in production and service activities is essential to increase their efficiency and expand their capacity. Every company needs flexible and reliable resources and processes to carry out production and service tasks efficiently. Production systems involve a wide range of resources, the effective management of which is essential for cost-efficient, reliable and sustainable operations. The management of resources is becoming increasingly important in a globalised, networked economy, as achieving optimal key performance indicators for cooperative production and logistics systems is a complex problem. Within the frame of this article, some potential ways for the transformation of conventional manufacturing or service systems are described. More generally, this paper focused on the potential of cyber-physical systems and demonstrated how IoT technologies could support this transformation. The main findings of this research work are the followings: • The matrix production concept of KUKA Robotics is a suitable way to improve the flexibility and availability of manufacturing systems, especially in the field of the automotive industry. The separated technological and logistics processes can be controlled by a digital-twin enabled discrete event simulation, which makes it possible to optimise the processes using real-time status information and failure data. • The separated and parallel conventional city logistics solutions can be linked using Industry 4.0 technologies to create a hyper-connected service environment for costefficient and sustainable first mile and last mile operation, where electromobility and micro mobility become more and more important. • Packaging logistics plays an important role in the life of manufacturing companies. There are different types of packaging systems, but cyber-physical solutions can be used for both non-returnable and returnable primary, secondary and tertiary packaging.
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A Compendium Analysis on the Possible Usage of Advanced Biofuels in the Transport Sector from a European Perspective Baibhaw Kumar(B)
, Gábor L. Szepesi , and Zoltán Szamosi
Institute of Energy Engineering and Chemical Machinery, University of Miskolc, Miskolc 3500, Hungary {vegybk,gabor.szepesi,zoltan.szamosi}@uni-miskolc.hu
Abstract. The European transport sector is evolving rapidly, and so do the challenges associated with its fuel needs. The advanced biofuels of second and third generations based on Lignocellulosic (LC) and microalgae biomass have emerged as promising alternative biofuels producers. The paper reviews the renewable energy scenario and its contribution to the transport sector in the European region. A techno-economic analysis is presented for LC, algae-based advanced biofuels. A SWOT analysis is performed to understand the challenges and opportunities associated with 2nd and 3rd generation advanced biofuels for making them market-ready. Keywords: Advanced biofuels · Transport sector · Renewable energy · SWOT analysis
1 Foreword The colossal rise in the consumption of conventional fuel sources in transportation could be observed in the 21st century. The depletion of petroleum resources has furthered the research dimensions in alternative fuel options in the automobile and transportation sector. Several options, such as gaseous fuels, e.g., compressed natural gas (CNG) and liquefied petroleum gas (LPG), are already very much in use. However, biofuels based on (alcohols, vegetable oils, etc.) are being explored extensively by researchers worldwide [1]. For a long time, the transportation sector has been considered a separate entity in research, while electricity and thermal utilities were prime research directions. However, there has been a shift observed in this outlook, and more emphasis could be seen on the integration of transportation among various energy modelling systems of biofuels. The market share of transport biofuels is still competitive as the easy replacement could be electric vehicles or hydrogen-fueled automobiles [2]. Ethanol and biodiesel are two primary examples of the most used biofuels to replace petroleum-based fuels. In addition, the second and third-generation biofuels are not limited because of the production quantity required but the costs associated with their production. Henceforth, new avenues of metabolic pathways are pursued to overcome this challenge and formulate © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 472–482, 2023. https://doi.org/10.1007/978-3-031-15211-5_39
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new advanced biofuels to support the transport ecosystem. Researchers are working with functional genomics and machine learning to curb the difficulties in toxicity and substrates related to the high-scale production of advanced biofuels [3]. Liquid biofuels generation processes such as Lignocellulosic conversion are getting attention. The presence of inhibitors in hydrolysates in Lignocellulosic is a deterrent to microbial performance and degrades the efficiency of this pathway for advanced biofuel’s efficient production [4]. Several countries worldwide can identify the need for conversion from conventional fuels towards alternative renewable energy sources. The burgeoning requirement of this paradigm shift towards renewable energy sources (RES) in the European Union (EU) can also be observed. In 2018, the EU adopted the renewable energy directive (2018/2001), also known as RED II, with a set target of 14% share of RES in the transportation sector by 2030 [10]. This policy shift brings many challenges and opportunities for the researchers and industry sector in the advancement of biofuels[5]. The pathway for this transition yet seems complicated in economic aspects. Paris et al. [6] Investigated a financial analysis on the use of alternative fuels such as advanced biofuels for a timeline until 2050 for Greece as a case study. Results suggested that infusion of bio methane in the natural gas grid makes it cost-competitive with natural gas, tipping from 21 euros/ton in 2020 to 15 euros/ton in 2050. This, however, depends also on the advancements in exact fuel and technology combinations in the future [6]. However, further insights are required on advanced biofuels’ technological and readiness level to achieve the EU targets. Economic assessments and life cycle analysis are inevitable in this regard. This paper presents an overview of the status of renewable energy share in the transport sector of the EU and the potential usage of advanced biofuels as an alternative fuel. Furthermore, we tried to outline the potential opportunities and barriers in the evolution of some advanced biofuels. The biofuels from Lignocellulosic (LC) biomass and micro algal biomass are briefly discussed with their role as an alternative fuel in the transport sector.
2 Advanced Biofuels According to the RED II of the EU, advanced biofuels are such fuels that are produced from the feedstocks listed in part A of Annex IX [7, 10]. Over time, biofuels have evolved into many forms based on their utility. The literature sometimes refers to the evolution of biofuels as “chameleonic evolution” as it changes from legislation to legislation [8]. The most common classification of all is based on the generation of biofuels, i.e. • First-generation: The well-established conventional biofuels produced from food and energy crops. E.g., Palm oil. • Second generation: The biofuels that have evolved technically over the years still face production efficiency and are produced out of non-food and energy crops. E.g., from wastes, straw, etc. • Third generation: Futuristic or advanced biofuels produced from substances such as microbial algae belong to third-generation biofuels or advanced biofuels. These biofuels are considered technologies with low technological readiness levels (TRL).
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Policymakers sporadically mention the use and promotion of these advanced biofuels for future automobiles and the transport sector. First-generation biofuels have already been used in many fields, but it’s time to look beyond the current scenario. The utility of advanced biofuels is as diverse as the engine advancement in the automobile sector. Fuel properties and the related emissions from the automobile also reflect on the performance and utility of the biofuels. Lignocellulosic ethanol, biomethane, hydrotreated vegetable oils (HVO), biomass to liquid (BTL), dimethyl ether, etc., are some common pathways to extract advanced biofuels for automobile fuels. Figure 1 below depicts the primary classification of advanced biofuels and the market perspective for the biofuels.
Fig. 1. Advanced biofuels for transport applications in the European region are reframed from information on European Technology and Innovation Platform Bioenergy [7].
3 Share of RES in the Transport Sector of the EU In the energy-economy modelling scenarios for a long time, the prime focus has been led on stationary energy sectors. With technological advancements in biofuels, the transportation sector has been integrated with such modelling and studies. Few studies suggested that the transport biofuel market share in the medium (15–30 years) and long term (above 30 years) will reflect fruitfully [2]. The EU has emphasized recuperation from fuel import dependency from NON-EU countries. The European Commission (EC) directive 2009/28/EC empirically promotes and sets targets for using renewable energy sources in the European region. For 2020, the target share of energy from RES was set for 10%, which was relatively achieved. Figure 2 depicts the recent trends of the last five years for the European countries (Data source: Eurostat)). Interestingly, some countries performed exceptionally well with a very high percentage share, e.g., Sweden, Iceland,
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Norway, etc. This trend analysis gives insights into the bright future of the advanced biofuels market in the transport sector.
Fig. 2. Recent trends in the share of RES in transport for the European region based on EUROSTAT data accessed on (15th March 2022) [9].
As per RED II of the EC, the overall RES share targets 32% and 14% of energy for the transport sector by 2030. The national energy action plans of the member states will play a crucial role in its development. The RED II also emphasizes sustainability and greenhouse gas (GHG) emissions as severe concerns for the target led. In the subcategories, the targets for the advanced biofuels as also been projected as 0.2% in 2022, 1% in 2025, and 3.5% in 2030. Thus clearly stating the decisive future of advanced biofuels in future transportation of Europe. Figure 3 shows the greenhouse gas saving thresholds for the RED II. The graph shows the rising savings with the percentage share after 2026 [10].
4 Recent Developments in Advanced Biofuels for the Transport Sector 4.1 Bioethanol (C2 H2 OH) from Lignocellulosic Biomass Ethanol is a multifaceted fuel that could potentially be used as a transportation fuel and an additive. Frequently ethanol has been blended with gasoline for improved efficiency as a blended fuel. Ethanol has a better octane number and enhances the octane of the blended fuel, reducing its dependency on toxic additives. Ethanol can also be an excellent fuel to replace gasoline in spark ignition (SI) and internal combustion (IC) engines. The high octane number, specific energy, and heat of vaporization make it a suitable replacement for gasoline. Bioethanol can percolate itself in the blend fuel submarket in the transport fuel market. Replacing gasoline as prime fuel, ethanol has to cover a long path of technological advancements in production and price controls [11]. Due to the carbon-neutral property of bioethanol, it is now used worldwide as an advanced biofuel that is extracted from Lignocellulosic (LC) residues. The feedstock availability
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Percentage
Greenhouse gas savings thresholds in RED II 100% 80% 60% 40% 20% 0%
70% 70%
65% 65% Transport biofuels
70% 80%
Transport renewable Electricity, heaƟng and cooling fuels of non-biological origin
AŌer January 2021
AŌer January 2026
Plant operaƟon start date
Fig. 3. Greenhouse gas savings thresholds as per RED II data source(European commission report, accessed on 15/03/2022) [10].
and a cost-efficient pre-treatment technology for lignin removal are the significant complexities involved in producing LC-based bioethanols. However, genome engineering and cell wall modifications have been identified as possible pathways for the technical boost in production process optimization [12]. Holmatov et al. [13] performed a critical analysis of LC-based bioethanol as a future transport fuel in the context of associated land water and carbon footprints. Currently, the global net production of LC-bioethanol is ranged between 7.1- 34 EJ/annum. Land water and carbon footprints for bioethanol have been estimated as 0.14–0.24 m2 land MJ−1 , 74–120 L water MJ−1 , and 28–44 gCO2 eq. MJ−1 , respectively. Also, the emissions savings range between 4% to 23% of total transport emissions in real versus theoretical potential from bioethanol usage. Henceforth, the LC-based bioethanol could be considered an advanced yet sustainable biofuel. 4.2 Microalgal Biofuel Since 1980, Europe has witnessed a change in attitude toward biofuels production through refineries. There has been a paradigm shift in challenges related to the transition from first-generation biofuels to the recent third-generation biofuels [14]. Many microalgae-based biofuels, such as Pure P. moriformis, are in prime focus as a potential transport fuel. Tsaousis et al. performed experiments with oil extracted from raw biomass for an internal combustion (IC) engine. Croton oil was considered as a base fuel for the relative comparison with the performance. Results revealed the lower engine power output, NOx emissions, and higher break efficiency fuel consumption [15]. Apart from being a neat fuel, sometimes blending microalgal oils with other oils can give better results. Researchers have always seen blending high viscosity oils with low viscosity oil could replace biodiesel in Compression ignition (CI) engines. Chlorella Vulgari [16], a micro algal-based oil, is commonly extracted from degraded soils on seashores. Karthikeyan
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et al. investigated the blending of Chlorella Vulgari with turpentine oil on various proportions (Volume basis) and its performance on CI engines. The diesel engine performed consistently on the ratio of 50% proportions of both the oils. Some researchers have suggested that biodiesel from the microalgal is the only biofuel to replace petroleumbased conventional transport fuels. The humongous transport fuel requirement could be addressed with the technological and economic assessment of the production through photo bioreactors [17]. The growing interest of researchers in microalgal biofuels is also because of their high oil content, which is relatively 15–20 times the other oils to produce biofuels. Interestingly, there are around 50 thousand microalgae species, which are merely thirty thousand, discovered yet. These biofuels also have no requirement of a massive land area for feedstock. Such properties make it a sustainable choice among other biofuels [18]. The three prime impediments identified in the commercialization of microalgal-based biofuels are production cost, high demand for critical resources, and a suitable energy ratio above unity [19].
5 Techno-Economic Comparative Analysis- Identifying Challenges and Opportunities in Biofuel Production The distinct characteristics of the feedstock make the production of biofuels a complex process. The LC biomass consists of lignin, a deterrent to other components and affects the hydrolysis process. More research needs to be carried out to explore costeffective pre-treatment methods with minimum inhibition to subjugate this problem [20]. Similarly, for micro algal-based biofuels, researchers are exploring the hydrothermal liquefaction process. Techno-economic analyses were developed with modelling through Aspen Plus software. Chen et al. [21] found that the baseline conversion cost for microalgal biofuels is 0.45 dollars/liters of gasoline equivalent. Such studies explain the necessity of the techno-economic aspects and the consideration of ecological footprints beyond carbon emissions. Often it is observed there is low accessibility to cellulose and hemicellulose in Lignocellulosic ethanol. It is challenging to ferment the 5-C sugars from hemicelluloses to ethanol due to the insufficient activity of cellulase enzymes. The pre-treatment can assist the conversion process, but the cellulase enzymes play a vital role in successful and efficient hydrolysis, henceforth enabling cost-effective pretreatment processes [22]. For the LC-based biofuels, there are several thermochemical pathways proposed for production by several researchers because of the analytical and processing differences. For techno-economic assessments, researchers suggest two factors to take into consideration primarily. One is the inclusion of biorefinery lifespan in the sensitivity analysis. In addition, the second is to perform an uncertainty analysis of the pathway. This approach helps better picturization of the futuristic sustainable pathways for thermochemical extraction of biofuels from LC [23]. Further improvement in the raw material sources describes Lignocellulosic biomass sources, which can be classified based on their homogeneity. Second-generation non-food Lignocellulosic biomass sources are homogeneous (wood chips), nonhomogeneous (municipal and industrial wastes), and quasi-homogeneous (agricultural residues). Microalgae have emerged as the prime source of third-generation feedstocks [24]. The microalgal bio refinery production has been identified as two streams, namely upstream processing and downstream
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processing. The amalgamation of co-products or bi-products can aid this process to make this process financially viable [25]. Table 1 compiles the technical, commercial, and complications related to the functional prospects of the processing of the biofuels based on Lignocellulosic and micro algal biomass. Table 1. Conspectus analysis of technical, commercial, functional, and feedstock-based assessment for advanced biofuels from Lignocellulosic and micro algal biomass. Type of advanced biofuels
Technical
Commercialization
Functional
Lignocellulosic ethanol
Conversion of 5-C sugars to ethanol
Commercialization of production is constrained with high costs of pre-treatment
Enzymes used in hydrolysis
Micro algal Biomass 2nd Generation
Improvement of the tolerance oil-rich microalgae to high and/or low temperatures
Development of cost-effective microalgae harvesting systems
Selection and development of high-yield, oil-rich microalgae
Application of the [26] bio refinery concept to micro algal biodiesel production system
Micro algal Biomass 2nd Generation
Energy efficient harvesting
Economic nutrient Recycling
Strain choice
Strain evaluation and pond stability
[27]
Thermochemical cellulosic biofuel
Stream factors in sensitivity analyses
Bio refinery lifespan
Costings of feedstock
[23]
Lignocellulose from thermophilic bacteria
Complexity of butanol formation pathway
Solventogenic thermophiles could only produce ethanol
Genetic tools limitation and thermostable key enzymes deficiency
Cellulosic hydrocarbons
Technological readiness
Capital costs
Policy level uncertainty
Lignocellulosic biomass
Complex thermochemical conversion routes
Pre-treatment costs
Micro algal biomass
Catalytic upgrading Integrative of renewable diesel utilization of whole and bio-jet fuel from biomass algal neutral lipids
Cellulosic biofuel
Trade-off mitigations
Diversification of products from lipid-extracted biomass Inadequate stakeholder synergy
Feedstock
Reference [22]
[28]
Feedstock costs/availability
[29]
Dedicated energy crops identification e.g. perennial grasses
[24]
Mass cultivation of lipid-rich algal biomass using CO2-rich flue gas and wastewater
[30]
[31]
(continued)
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Table 1. (continued) Type of advanced biofuels
Technical
Micro algal biofuel Biomass recovery and contamination
Commercialization
Functional
Feedstock
High maintenance and operational costs in closed systems
Constant irradiance Feedstock growth maintenance inhibitions
Reference [25]
6 SWOT Analysis- Biofuels in the Transport Sector SWOT (Strength, weakness, opportunity, and threats) analysis is a frequently used tool to establish a relationship between the status quo and the future perspective of a business or technology. It helps explore and identify the barriers and challenges of a particular technology, taking into account a heuristic viewpoint. This tool is often used in biofuels’ technological and market analysis and their commercialization potential [32]. For a long time, In Europe, first-generation biofuels have dominated the biofuels sector. Previously SWOT analysis has been performed with conventional fuels like gasoline or first-generation biofuels such as biodiesel. It is observed that even previous studies suggested that the advanced biofuels might seem costlier to the traditional fuels as mostly the external costs (adverse environmental hazards) remain unaccounted for [33].
Fig. 4. SWOT analysis for the second and third-generation advanced biofuels in the transport sector.
Figure 4 illustrates the SWOT analysis for second and third-generation advanced biofuels.
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The SWOT analysis has the potential to serve as a documented bridge between policymakers, industrialists, and researchers [34]. Advanced biofuels produced from microalgal sources are available in abundance and could curb the restrictions of biofuel production, which requires a large land area. The new genomic studies of microalgae could be revolutionary in developing biofuels with enhanced fuel properties for automobile engines. The opportunity even opens the door not only for neat individual fuels but the blended fuel options too. The EU directives succour to such advancements on the policy level. However, the pathway to success is not easy because of several weak points. The socio-economic impacts affect the setup of futuristic biorefineries. The market share is not enough to support the creation of indirect jobs in this field [35]. The return on investments for developing novel biofuels with LC and microalgae is not yet very promising. The TRL is yet to mature and be market-ready. Henceforth the current scenario suggests putting more efforts toward making the second and third-generation biofuels as price-competitive as possible for a better economic return to the consumer.
7 Conclusion The advanced bioenergy in the new age is primarily utilized as a heat and electricity utility or as a transportation sector fuel. The EU has clearly stated the advanced biomass in RED II established in 2018. Since the last decade, a surge in the use of RES for the transport sector could be observed in the EU. • Few countries, such as Sweden and Norway, performed exceptionally well in achieving the targets of 2020 and are progressing towards the 2030 goal of a 14% share of RES in the transport sector. This period is the golden time for advanced biofuels to establish their niche in the alternative fuel domain. • The greenhouse gas savings is an undermined parameter in many scopes of future RES sources. The current contribution of RES shows promising trends in GHG savings for the current decade. • The second and third-generation advanced biofuels have emerged as the potential alternative fuels in the transport sector. Blended fuels are being explored extensively with LC-based biomass. • Microalgal advanced biofuels for engine performances are being explored for future utilities in engines. However, the pre-treatment of algal-based biofuels is a complex process. • The fourth-generation biofuels development requires humungous investment for advanced facilities and investments in research. The TRL of the 2nd and 3rd generation needs upliftment to be market-ready and price-friendly for the transport consumer. • Advanced biofuels could promote a circular bio-economy in the future. The socioeconomic impact of the alternatives needs further investigation. Policy-level efforts and consumer behaviour change can achieve the overall success for future transport fuel through awareness.
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The Application of CFD Software for Modelling the Dispersion of Hydrogen Gas at Renewable Energy Fueling Stations Levente Tugyi(B) , Zoltán Siménfalvi, and Gábor L. Szepesi University of Miskolc, Miskolc-Egyetemváros 3515, Hungary {levente.tugyi,zoltan.simenfalvi,gabor.szepesi}@uni-miskolc.hu
Abstract. Hydrogen as a fuel can replace non-renewable fossil fuels, however, its explosive properties make it a more dangerous substance than petrol or diesel. This research aims to develop a Computational Fluid Dynamics (CFD) method to model hydrogen gas release at fueling stations. Ansys Fluent software is being used to investigate the formation, dispersion and distribution of explosive atmospheres and gas concentrations. The extent of hazardous atmospheres depends on the operating pressure, the temperature, the size of the leak, the physical properties of the hydrogen and the characteristics of the wind. The model can be useful for the scientific study of leaks and dispersion at the growing number of hydrogen fueling stations. What is more, it can be studied how it can endanger people in these areas. All this can be achieved without any human exposure to the extremely hazardous environment during the simulation. Keywords: CFD · Dispersion of hydrogen · Fueling station · Explosive atmosphere
1 Introduction and Motivation Renewable energy sources are making huge inroads at the dawn of the 21st century. Fossil fuels are being replaced by solar power, wind power and vehicles are powered by electricity or hydrogen instead of fuel. There are currently 658 hydrogen fueling stations in the world, of which 142 were opened in 2021 [1]. Germany is at the forefront of the industrial hydrogen strategy in the world [2], furthermore, Germany is the leader in Europe with 101 hydrogen fueling stations. Apart from the fact that the Earth’s hydrogen resources are very rich [3], there is also the danger of it being extremely explosive. A hydrogen explosion or bomb [4, 5] can have very serious consequences for life on Earth (human life). There have been several serious accidents at hydrogen fueling stations in recent years, such as in America [6] and Norway [7]. These incidents demonstrate the need to exercise caution around these sites. The purpose of this study is to investigate, through a CFD simulation, potentially explosive areas in the event of a leak from a hydrogen tank to a fueling station. The fueling station shown in Fig. 1 and Fig. 2 demonstrates an approximately reconstructed model, where demonstrating the propagation of hydrogen gas will be shown under different air flows in the simulation model. The exact dimensions of the base geometry have been created with values relative to reality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 483–490, 2023. https://doi.org/10.1007/978-3-031-15211-5_40
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Fig. 1. A typical hydrogen fueling station in Germany [8].
Fig. 2. The structure of a hydrogen filling station model.
2 Previous Studies With the increasingly growing presence of hydrogen fueling stations, much research and studies have been written on them. A 2002 study by Sandra Nilsen et al. [9] provides a detailed analysis of the sources of hazards identified in the on-site production of hydrogen fueling stations. It suggests ways to reduce the potential risks. The study uses CFD-FLACS simulation to demonstrate the possible hydrogen gas cloud formation at the half of lower explosive limit (LEL), which is 2 vol%. Norwegian researchers published a study in 2009 [10], also using CFD-FLACS to investigate the subsonic and sonic propagation of hydrogen. One exception, this same year, these researchers carried out a study using CFD-FLACS to investigate the gas cloud and its combustion at fueling stations [11]. To investigate the propagation and evaporation of hydrogen in the liquid state [12], Norwegian researchers have again used CFD-FLACS simulations to investigate the potential for a gas phase to form a liquid phase. In 2016, some Japanese researchers investigated the fire penetration of hydrogen fueling station tanks in ANSYS
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[13]. The FLACS part of the CFD software has proven to be a suitable simulation program to study the propagations, as it can be used to simulate the formation of an explosive gas cloud for a hydrogen fueling station in 2021 under different operating parameters [14].
3 Hydrogen Fueling Station by CFD Simulation Study Several correlations are available for the mathematical determination of release from liquids or gases. In this study, I determined the value of gas release using Eq. 1, which is applicable according to the standard [15] for the formation of explosive atmospheres. ⎡
⎤ 1 γ −1 γ γ 2 · γ M p a ⎦ · pa ⎣1 − · (1) Wg = Cd · S · p Z ·R·T γ −1 p p where: – – – – – – – –
Cd is the discharge coefficient (1.0),
S is the cross-section of the opening (hole) 50.27 mm2 p is the overpressure inside the equipment (250
000 Pa) kg M is the molar mass of hydrogen 1.01 kmol , Z is the compressibility factor (1.0), T is the temperature of the gas (25 ◦ C) γ is the polytropic index of adiabatic expansion (1.41) pa is the atmospheric pressure (103 125 Pa).
The study assumes a low pressure in the tank, which is always present during continuous feeding. Therefore, the release will be constant. In the simulation, the dispersion with two different airflows was investigated. The following Table 1 summarises the main parameters of the simulation. Table 1. Simulation data. Airflows [m/s]
Pressure [Pa]
Temperature [°C]
Mass flow rate [kg/s]
Simulation time [s]
2
250 000
25
0.0078
4.8
11
250 000
25
0.0078
7.5
The average airflow in Hungary’s territory is 2–8 m/s, but in one-third of the year, airflow speeds of 11–15 m/s can occur [16]. The lower limits of these two ranges were used in the analysis. The gas discharge is to be considered sonic because the internal pressure of the tank is higher than the critical pressure [17]. The simulation was done using a standard CFD module. The general option was set in transient mode with the SST k omega turbulence model, and the gas density was determined using Peng-Robinson
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[18]. User-Defined Function was not justified. The boundary conditions of the airflow are defined as flow from direction -x, from the plane behind the tank and building. All other boundary planes of the space are defined as the outlet. 3.1 Effect of Airflow with 2 m/s Figure 3 shows the airflow distribution in the investigated volume. The low airspeed will practically disappear after the collision with the obstacle; it will remain minimal.
Fig. 3. Airflow with 2 m/s.
In the two simulations, I test models at 20% of LEL of hydrogen (0.8 vol. %) and at a much lower concentration. The 20% of LEL is well known to be the first interlock condition in explosive technology where a gas detector is installed. When this level is reached, light alarms and a visual warning of the potential hazard. The low airflow has no effect on the release of hydrogen, as it will flow out of the container at a much higher velocity (see Fig. 4). By not having enough power to distort the dispersion, it will propagate in the normal direction.
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Fig. 4. 0.8 vol.% of hydrogen spread with 2 m/s airflow.
Figure 5 shows that the direction of the jet does not change at a lower concentration of 0.05 vol%.
Fig. 5. 0.05 vol.% of hydrogen spread with 2 m/s airflow.
3.2 Effect of Airflow with 11 m/s Airflow of more than five times the normal speed creates turbulent conditions around the obstacles in the area. These flow distributions are shown in Fig. 6. In the figure, it can be observed that, in addition to the obstacles, there are residual air currents along the axis of the fuel well and tank.
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Fig. 6. Airflow with 11 m/s.
Under these boundary conditions, it can be seen in the next figure (see Fig. 7) that the 0.8 vol.% radius is already rather tangential to the source of release.
Fig. 7. 0.8 vol.% of hydrogen spread with 11 m/s airflow.
Figure 8, for the lower concentration of H2 , it is already clear that this airflow was sufficient to influence the release.
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Fig. 8. 0.05 vol.% of hydrogen spread with 11 m/s airflow.
4 Conclusions In simulations with two different airflows, significant differences are observed. The low airflow is unlikely to be able to affect the spread of gas from sonic emission in contrast to the much higher airflow. In both cases, the presence of a gaseous atmosphere is already considered to be hazardous in the immediate vicinity of the release. These propagation conditions were determined under normal operating conditions according to literature and standards. There is a high probability that, with proper supervision and regular maintenance, there will be little chance of an explosive zone developing around a hydrogen fueling station when the airflow direction is equal to x- direction. Although the experimental results used for validation are not ideal, further research will be carried out to validate the strengths of the presented modelling could be explored and reasonably evaluated. The goal of this research is to conduct further experiments where we can find the point the explosive atmosphere reaches the well under a combination of longer time periods, higher release and different airflow conditions.
References 1. Staff, E.: Hydrogen refueling station network growing worldwide: 142 new stations opened in 2021 (2022). https://www.sustainable-bus.com/fuel-cell-bus/hydrogen-refueling-stations2021/. Accessed 03 Feb 2022 2. Huber, I.: Germany’s Hydrogen Industrial Strategy. Center for strategic & international studies (2021). https://www.csis.org/analysis/germanys-hydrogen-industrial-strategy. Accessed 28 Oct 2021 3. Zgonnik, V.: The occurrence and geoscience of natural hydrogen: A comprehensive review. Earth Sci. Rev. 203, 103140 (2020). https://doi.org/10.1016/j.earscirev.2020.103140 4. Leipunskii, O.I.: Radioactive hazard resulting from the explosions of a ‘clean’ hydrogen bomb and of a conventional fission bomb. J. Nucl. Energy Part A Reactor Science 9(1–4), 28–40 (1959). https://doi.org/10.1016/0368-3265(59)90135-5
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5. Dorofeev, S.B., Kochurko, A.S., Efimenko, A.A., Chaivanov, B.B.: Evaluation of the hydrogen explosion hazard. Nucl. Eng. Des. 148(2–3), 305–316 (1994). https://doi.org/10.1016/00295493(94)90116-3 6. Sandru, O.: Hydrogen Tank Exploded in Green Fuel Station. Is The Technology Safe Enough for Cars? (2010). https://www.greenoptimistic.com/hydrogen-tank-explosion-mon roe-county-20100828/. Accessed 28 Aug 2010 7. Randall, C.: Norway: Explosion at hydrogen filling station (2019). https://www.electrive. com/2019/06/11/norway-explosion-at-fuel-cell-filling-station/. Accessed 11 Jun 2019 8. Randall, C.: Hydrogen Fuelling station boom in Germany (2018). https://www.electrive.com/ 2018/02/15/hydrogen-fuelling-station-boom-germany/. Accessed 15 Feb 2018 9. Nilsen, S., Andersen, H.S., Haugom, G.P., Rikheim, H.: Risk assessments of hydrogen refuelling station concepts based on onsite production. Eur. Integr. Hydrogen Proj. 2, 15 (2002) 10. Middha, P., Hansen, O.R., Storvik, I.E.: Validation of CFD-model for hydrogen dispersion. J. Loss Prev. Process Ind. 22(6), 1034–1038 (2009). https://doi.org/10.1016/j.jlp.2009.07.020 11. Middha, P., Hansen, O.R.: Using computational fluid dynamics as a tool for hydrogen safety studies. J. Loss Prev. Process Ind. 22(3), 295–302 (2009). https://doi.org/10.1016/j.jlp.2008. 10.006 12. Middha, P., Ichard, M., Arntzen, B.J.: Validation of CFD modelling of LH2 spread and evaporation against large-scale spill experiments. Int. J. Hydrogen Energy 36(3), 2620–2627 (2011). https://doi.org/10.1016/j.ijhydene.2010.03.122 13. Sakamoto, J., Nakayama, J., Nakarai, T., Kasai, N., Shibutani, T., Miyake, A.: Effect of gasoline pool fire on liquid hydrogen storage tank in hybrid hydrogen-gasoline fueling station. Int. J. Hydrogen Energy 41(3), 2096–2104 (2016). https://doi.org/10.1016/j.ijhydene.2015. 11.039 14. Ustolin, F., Åsholt, H.Ø., Zdravistch, F., Niemi, R., Paltrinieri, N.: Computational fluid dynamics modeling of liquid hydrogen release and dispersion in gas refuelling stations. Chem. Eng. Trans. 86, 223–228 (2021). https://doi.org/10.3303/CET2186038 15. IEC 60079-10-1: Explosive atmospheres – Part 10–1: Classification of areas – Explosive gas atmospheres. Switzerland, Geneva (2021) 16. Monthly weather forecast and climate Hungary. https://www.weather-atlas.com/en/hungaryclimate. Accessed 29 Mar 2022 17. Chernyavsky, B., Wu, T.C., Péneau, F., Bénard, P., Oshkai, P., Djilali, N.: Numerical and experimental investigation of buoyant gas release: Application to hydrogen jets. Int. J. Hydrogen Energy 36(3), 2645–2655 (2011). https://doi.org/10.1016/j.ijhydene.2010.04.130 18. Liu, X., Michal, G., Godbole, A., Lu, C.: Decompression modelling of natural gas-hydrogen mixtures using the Peng-Robinson equation of state. Int. J. Hydrogen Energy 46(29), 15793– 15806 (2021). https://doi.org/10.1016/j.ijhydene.2021.02.129
Materials, Technology and Education
The Current Situation of the Rare-Earth Material Usage in the Field of Electromobility Csongor Horváth(B) Department of Vehicle Electrification, Robert Bosch Ltd., Gyömr˝oi út 104., Budapest 1103, Hungary [email protected]
Abstract. Rare-earth materials play an essential role in the field of electromobility. These materials are frequently used in automotive components such as electric machines, battery packages, controller units, automotive actuators, and car multimedia applications. The research aims to examine the ecological effects of the rare-earth material usage and carefully analyse the market needs for rare-earth materials. It is essential to investigate how the other fields of the market (like consumer electronics or telecommunication) can affect the field of electromobility regarding rare-earth content usage. Rare earth magnets are key components for tablets, cell phones and many other electronic devices. It is very unlikely that the major electronic device manufacturers are decreasing their rare-earth material consumption despite the increasing demand for e-mobility. Statistic methodologies are used to conclude the rare-earth material usage. The geopolitical situation and the geographical distribution of the rare-earth production are all essential parts of this article. The recycling rates of the rare-earth materials and the rare-earthmaterial-based magnets are relatively low compared to other base materials in electromobility. The impact of the low recycling rates, together with the increasing demand for rare-earth materials, lead to a business situation which triggers engineering activities focusing on partially or fully rare-earth material free solutions. In this article, the current situation in the rare-earth market is analysed and conclusions are drawn regarding the predicted future situation. The future challenges are also identified, and solutions are proposed as well. Keywords: Rare-earth materials · Rare-earth magnets · Electromobility · Electric vehicles · Automotive
1 Introduction Nowadays, rare earth elements (REEs) focus on different areas of business. These elements can be found in the periodic table between Lanthanum (atomic nr. 57.) and Lutetium (atomic nr. 71.). Besides this series, Sc and Y are also considered rare earth elements. [1] It is expected that the field of electric mobility will be a heavily growing area regarding rare earth element usage, as illustrated in Fig. 1.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 493–504, 2023. https://doi.org/10.1007/978-3-031-15211-5_41
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Fig. 1. Expected NdFeB magnet usage in the different fields of application [26]
If all relevant business fields are taken into account, as shown in Fig. 1, a clear increment of the rare earth element usage can be expected in the upcoming years. The most interesting question for the future is: how the material amount will be distributed between the different industrial fields. There are also ecological effects of rare-earth material usage, which are concerning. Production of the rare-earth magnets is a process with considerable CO2 emission. In case of the consumer electronics, the footstep of the rare-earth magnet production is not heavily in focus. However, the continuously forming eMobility business area pays great attention to the “0 km footprint” emission category. The 0 km footprint of the vehicle is defined as cradle to grave. It is the sum of the emission during the manufacturing of the base parts and the complete supply chain during the production of the vehicle (for instance, the electrical drive-train) [35]. Since a lot of criticism can be experienced by lawmakers in Europe and overseas regarding the freshly formed norms [14], a new definition of vehicle emission calculation will be essential to measure the ecological effect of a vehicle correctly, considering its production processes well. These effects motivate the automotive OEMs and their suppliers to open developments in more environment-conscious directions.
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The geopolitical problems around the rare earth material market are also factors which should be considered carefully. Supply chain issues are getting more and more common nowadays. The Covid-19 pandemic situation and the war between Ukraine and Russia (which started in 2022 February) have a significant effect on the pricing of the different materials. The rare earth material market is not an exception.
2 Situation Analysis of the Rare Earth Elements Usage and Supply Chain 2.1 Ecological Effect of Rare Earth Ores and Magnet Production The ecological effect of rare earth ores mining can be considered significant [3]. The biological, environmental, and human health effects of rare earth mining hit the warning signals in many countries. The cost of sustainable rare earth mining is related to the current monopoly. China dominates the rare earth market but is based on the last decade’s environmental analysis [3, 9]. It can be concluded that the high-volume rare-earth ore mining without proper health and safety protection or environmental consciousness can lead to serious degradation of wildlife and human health in the region. The pollution caused by chemical agents (mainly sulphates, oxalates and extract solvents) is critical if discharged into the environment without any pre-treatment [2]. Another significant environmental side effect of the mining is when the dust is released. Rare earth ores and metal particles can pollute the air, soil and water in the region in the form of particulate matter [3]. The most worrisome ecological effect is when some radioactive elements reach the surface during the mining activities. These elements are mainly thorium and uranium. The lack of reliable environmental regulations, on the one hand, was a decision which supported to reach of the monopole situation in China. On the other hand, serious environmental damage can be detected around the Bayan Obo mine [3], and several rivers were affected by the pollution: Yellow, Jinsha, Huaihe, Yangtze and Pearl River [4]. It is important to emphasise that the environmental impact will persist even after the reclamation of the mine site [5], which should trigger developments to reduce the rare earth element usage and, besides that, to develop rare-earth fewer solutions in many fields of the industry. 2.2 The Geopolitical Situation Regarding Rare Earth Usage From a geopolitical perspective, rare earth elements (REEs) are known as critical materials. As mentioned before, China dominates the market because of two main reasons: they have geological benefits due to their rare earth element rich mining. Areas and besides that, they have cost benefits due to the insufficient handling of the environmentally critical materials. Nowadays, three rare earth elements landed in the focus of the different industrial business fields (see Fig. 2). Neodymium (Nd), praseodymium (Pr) and dysprosium as the main base material of the frequently used permanent magnets [6] are the focus of continuously growing industries like telecommunication, and consumer electronics, manufacturing industry, defence, green energy and electric mobility [1].
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Fig. 2. Industrial applications of REE [2]
China’s dominance of the rare earth minerals market was 97% in 2012. In parallel, it was known that they had owned just about 30% of the known reserves behind the market dominance [7]. However, multiple countries are fighting against China’s dominance in the market, and significant improvements have been made worldwide to decrease the dependency on Asian countries. China produced 63% of the rare earth elements entering the global market in 2019. 37% of the global land-based reserves were located in China [8]. China is strategically focusing on the reduction of rare earth element export prices. That’s how they own the market and makes it difficult for anyone else to compete with them. However, due to the market needs and the political pressure in 2019 November, U.S. and Australia joined their forces to start cooperation on securing rare earth resources. The U.S. started similar cooperation also together with Canada [9]. The global distribution of the rare earth element oxides can be seen in Fig. 3. The status represented the resources discovery situation in 2018.
Fig. 3. Geographical distribution of rare earth reserves [3]
Based on the resource distribution, it can be concluded that China is in the leading position not just from the mining/and production point of view but also from the reserve’s perspective. Brazil, Vietnam, and Russia also have significant reserve rates. On the one hand, this situation seems to be beneficial because it includes the potential that the rare earth element market can be made more diverse in the future. On the other hand, the military conflict between Russia and Ukraine triggered strategic decisions on the E.U., USA and also from China’s side in March of 2022. These strategic decisions
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are intended to eliminate the supply chain dependency on Russia. Embargoes were introduced worldwide against the country [30], and the effects of the conflict can already be seen in the oil prices and the gas market [31]. The military conflict may further affect rare earth element prices or rare-earth magnet prices as its end product. Another important relation can be the question of strategical reserves. The contribution to the rare earth market of the U.S. seems to be quite small, however, they have one of the largest military forces around the globe. The rare earth element usage in military applications is a considerable section of the rare earth market.
Fig. 4. Production distribution of rare earth elements [10]
Since the official production values of the U.S. decreased almost to zero (Fig. 4), the U.S. defence activity can be highly dependent on other exporting countries. A possible scenario can be an unofficial strategical reserve for target productions like the military applications, which results in the rare earth materials produced in the U.S. cannot being detected on the market.
3 Rare Earth Magnet Usage in the Field of Electric Mobility 3.1 Summary of Cause-and-Effect Relations in the Frame of eMobility Needs The rare earth industry has a diverse product portfolio to serve the different fields of the industry (see. Fig. 5).
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Fig. 5. Rare earth industry product portfolio [5]
It can be seen based on Fig. 5 that eMobility affected several different product families such as: Magnets, Glass & Ceramics, Metals & Alloys. Magnets play the most significant role from the rare earth product families and the field of electric mobility uses them in electrical machines and in position sensors for instance. The highest amount of rare earth magnets can be found mainly within the electrical machines, namely: permanent magnet synchronous machines (PMSMs). The PMSM machines have a dominant role in the field of electrical mobility mainly due to their superior characteristics and efficiency [32–34]. Nd, Dy, and Sm materials are mainly used in the permanent magnets. NdFeB- magnets are the most frequently used permanent magnets, but SmCo magnets are also used in some cases. Dy plays an important role as an alloying material to extend a permanent magnet’s temperature limitation. Other industry fields are also using permanent magnets, like the renewable energy industry [11], telecommunications, consumer electronics and military applications. These are the four main fields where the rare earth magnet usage can be considered significant. Magnets are necessary elements of speakers and microphones and also these parts are essential components in telecommunications and consumer electronics. In the case of the renewable energy industry, the highest rare earth magnet material needs can be detected from the wind turbine generator side. From the generator design perspective, a PMSM generator has benefits in terms of generator efficiency.
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The telecommunications industry is developing in a way, that smaller and thinner mobile phones tending to be the target design of manufacturers. If the geometrical constraints are strict, a rare earth magnet-based microphone or speaker seems to be an ideal choice. In case of the consumer electronics, the physical size of a device also plays a key role but beside that, in a case of speakers the reachable volume level and characteristics can also be considered as a significant parameter for the customer. In case of the military applications, rare earth magnets are essential parts in guidanceand control systems and besides that in the targeting- and weapon systems, too [12] (Fig. 6).
Fig. 6. Rare Earth Elements and magnets in guidance- and control systems & Targeting- and weapon systems [12]
As it is illustrated on Fig 1 the field of consumer electronics is growing slightly but steadily. The REE usage in speakers is also showing a continuously increasing tendency together with the increasing need for wind turbines. It is very unlikely that the manufacturers in the fields of telecommunications, consumer electronics, and military applications will switch their technologies and will replace the rare earth magnets, for example, with ferrite magnets, or in the case of electrical motors, with a rare earth less solution. Physical dimensions of these products are essential aspects from the end customer’s point of view, and the mentioned modifications usually cause a physical dimension and/or weight increment if the same technical parameters have to be reached as with rare earth magnet-based technical solutions. However, electric mobility and the renewable energy industry have the chance to catch the opportunity to proceed with their developments by reducing the amount of the rare earth material or even steering the technology towards a magnet-less direction. The electrical machines used in wind turbines and in electric vehicles have higher design freedom compared to telecommunications or consumer electronics or even military applications due to the larger available design space. Permanent magnet free solutions exist, and nowadays are developing further in various directions. Asynchronous machines, reluctance machines and electronically excited synchronous machines are getting more and more popular in the field of electric mobility. There are already existing future scenarios (see Fig. 7 for a low-demand scenario) where based on political ambition, technological advancements and optimisations, the rare earth magnet demand can decrease [13].
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Fig. 7. Future scenarios regarding rare earth element usage (mainly magnet base materials) [13]
3.2 The Importance of the 0 km Footprint Since political ambitions tend to reduce the emission of mobility [14] another important question could be in the future the 0 km footprint of an electric vehicle. Of course, an electric vehicle cannot have local emissions, but the manufacturing emission and the whole-supply chain emission should be reduced for a sustainable approach (see Fig. 8). The production of the rare earth magnets has a considerable CO2 footstep and besides that, environmental effects which cannot be turned back easily.
Fig. 8. Nd producing process steps [15]
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As it can be seen in Fig. 8 there are 16 main steps of Nd refinement, from mining to having the usable refined material available. In each step, a considerable amount of energy should be used for moving, filtering, grinding, and separating the materials. These steps also result in emission values which should be taken into account in the environmental footstep of the produced electrical machine or drive-train and which should be added to the overall 0 km footstep of a freshly produced vehicle. And this is a refinery summary related to just one rare earth magnet base material. When our civilisation arrives to that point, where electrical vehicles (EVs) will be the most common component of the mobility, nowadays approach regarding the emission most probably will change. In the case of EVs in the future, two main options can be seen, one is to consider and regulate the 0 km footstep of a vehicle and the second one is to regulate the indirect emission of the vehicle caused by the energy supply chain footstep. Since the second solution will be a changing value which will be handled independently of the vehicle, the 0 km footstep seems to be the most important value related to our vehicle besides the consumption. 3.3 Tendencies in the Field of Electric Mobility Several automotive manufacturers (OEMs) and Tier 1 suppliers are tending to replace the industry-standard REE based motor technology with alternative solutions. The Japanese manufacturer Yaskawa Electric Corporation started to eliminate REE magnets from their drive-trains and replaced them with ferrite magnets [16]. The BMW and the Renault Group have changed the rotor technology. Instead of implementing the currently widely used permanent magnets for generating the rotor field, a winding is installed on the rotor, which is excited electrically by DC-current. This technology results in an electrically-excited synchronous machine (ESM). This technology uses contact-based (slip-ring) [18] or contactless [17] power transfer towards the rotor. A Tier 1 automotive supplier Mahle is using contactless power transfer in their drive-train solution [19, 27–29]. There are powertrain manufacturers tending to support other REE-free motor technologies. For example, British powertrain manufacturer Advanced Electric Machines are applying switched reluctance motors (SRMs) instead of conventional synchronous motors [20–24]. The manufacturer is planning to install the SRM based drive-train into a premium-category passenger car, its OEM is considered as one of the top companies in the premium segment [25]. This is a breakthrough because this segment usually supports the conventional solutions, like the state-of-the-art PMSM machines or occasionally induction machines.
4 Conclusion Rare earth material usage plays a key role in the industry. Several business sectors are implementing rare earth material-based parts into their products. The ecological effects of the rare earth material usage are alarming. Although the supply chain is transforming into a more diverse one, China is still dominating the market. Tendencies can be identified that several countries are creating a strategical reserve to avoid dependence
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on other countries in case of crucial industry fields like defence. Regarding the industrial consumption of rare earth materials, it can be concluded that parallelly growing business fields (telecommunication, consumer electronics, renewable energy generating devices and electric mobility) will also have a huge demand for the rare earth materials, mainly in the form of rare earth magnets. This situation can cause a significant price increase and can turn the attention of lawmakers to limit the emission of the rare earth magnet processing or even to cease their production for certain applications, e.g. electric vehicles. There are two fields of the industry where alternative solutions can be easily implemented to avoid the above-mentioned risks. These are the wind turbines and the electric mobility industry, where the geometrical design freedom can create an opportunity to implement electric motor solutions which are free of rare earth materials. Several OEMs and Tier1 suppliers are moving towards the reduction of the rare earth content of their electrical drive-trains, and some of them are even using alternative motor technologies, eliminating the need for rare earth magnets. Acknowledgement. The author would like to thank the help and proactive support of Tibor Vajsz PhD for sharing his deep knowledge in the field furthermore, to Péter Gergen, Dániel Potzta and Attila Geleta from the Robert Bosch Kft. management side to make this research activity possible from the financial perspective and finally, Zoltán Donát Varga for the help in the administrative questions. This research was funded by the project Establishment of Electromobility Development Center at Robert Bosch Kft. Phase 1.
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28. Gregor, B., Lurij, P., Ambroz, V.: Electric Power Inverter, U.S. Application No. 16/533,746, US20200052G09 Al (2020) 29. Daniel, D., Jens, G., Achim, G., Alberto, H.P., Simon, S., Hans, C.U.: Electric Machine, U.S. Application No. 17/086,170, US20210135535 A1 (2021) 30. https://www.consilium.europa.eu/en/policies/sanctions/restrictive-measures-against-russiaover-ukraine/. Accessed 08 June 2022 31. https://tradingeconomics.com/commodity/natural-gas. Accessed 08 June 2022 32. Orosz, T., Gadó, K., Katona, M., Rassõlkin, A.: Automatic tolerance analysis of permanent magnet machines with encapsuled FEM models using digital-twin-distiller. Processes 9(11), 2077 (2021). https://doi.org/10.3390/pr9112077 33. Demidova, G.L., et al.: Implementation of Digital twins for electrical energy conversion systems in selected case studies. Proc. Est. Acad. Sci. 70(1), 19–39 (2021). https://doi.org/ 10.3176/proc.2021.1.03 34. Pavel, K., David, P., Tamás, O., Iveta, P., Ivo, D.: FEM based robust design optimization with ¯ Agros and Artap. Comput. Math. Appl. 81, 618–633 (2021). https://doi.org/10.1016/j.camwa. 2020.02.010 35. Wolfram, P., Weber, S., Gillingham, K., et al.: Pricing indirect emissions accelerates low— carbon transition of US light vehicle sector. Nat. Commun. 12, 7121 (2021). https://doi.org/ 10.1038/s41467-021-27247-y
A New Approach to Steel Grade Selection for Automotive Parts Béla Kondás(B)
and Zoltán Péter Kovács
University of Miskolc, Miskolc 3515, Hungary [email protected]
Abstract. Selecting the most suitable steel grade from the available set is a difficult task, therefore it is essential that the selection is carried out in a planned way. Because of its importance, the theme of material selection has been the subject matter of several research. In spite of the fact that the literature is very rich, there are obstacles in the implementation of theoretical processes in the steel industry that prompt us to review the well-established practice. The aim of this paper is to present an optimisation proposal for the current material selection process, with a special focus on rolled steel products, due to the problems encountered in industrial practice. In our work, we thoroughly studied the literature on material selection methods and related processes and the characteristic features of rolled steel products that could distinguish them from other raw materials. We found that due to these differences, the application of the standard material selection method is hazardous and it needs to be optimised. Our proposed model eliminates the current uncomfortable situation where steel producers, part manufacturers and part designers argue about each other’s responsibilities following a less than successful material selection. The model presented in this paper is intended to provide a practical tool for all interested parties in the automotive and mechanical industry in order to avoid design errors and parts deficiency. Our model can be incorporated in the currently valid first-sampling regulations of the automotive industry. Keywords: Part design · Material selection · Automotive industry · Steel Industry · Rolled steel products
1 Introduction These days, the continuous increase in customer demand forces companies to constantly improve their products and services. Moreover, they are forced to do this at an accelerating pace in the race for market share. A product can be successful if it meets customer requirements in a cost-effective way. Raw materials play a major role in this, as they are not only a significant cost factor but also contribute a large extent to the manufacturability of a part. Although the use of steel declines in the automotive industry, it is impossible to replace it completely, as it is still used for the production of many safety-related parts (e.g. brakes, axles, steering gears, chassis, body elements, etc.), even with the progress of electromobility. Its importance is illustrated by a large number of steel grades available on the market. Today, design engineers can choose from more than 3 500 grades. Such © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 505–520, 2023. https://doi.org/10.1007/978-3-031-15211-5_42
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number is constantly increasing due to the ongoing research in the steel industry, e.g. the developing variants of green steel [1]. Selecting the most suitable steel grade from the available set is a difficult task, therefore it is essential that the selection is carried out in a planned way. Because of its importance, the theme of material selection has been the subject matter of several research. Inspite of the fact that the literature is very rich, there are obstacles in the implementation of theoretical processes in the steel industry that prompt us to review the well-established practice. The aim of this paper is to present an optimisation proposal for the current material selection process, with a special focus on rolled steel products, due to the problems encountered in industrial practice.
2 Current Practice in Material Selection Material selection is only one step in the component (or part) design process, but its role is critical. Before analysing the details of material selection, it is useful to define the context of the design work. The introduction of a new product to the market is the result of a series of interdependent processes, which because of their complexity, are covered in many of the requirement specifications used in automotive industrial practice. Two of the most widely used ones are the “APQP” Handbook of the Automotive Industry Action Group (AIAG) and the “RGA” publication of the German Association of the Automotive Industry (VDA). These requirements roughly cover the same sub-processes but with different timing and scope [2, 3]. From the specifications of the two booklets of requirements, a map of the product life cycle can be defined and is shown in Table 1. The whole process is based on innovation and project planning as well as product design and development, these phases largely determine the market success of the future product. According to the generally accepted theory of Pahl and Beitz, the product design process can be further divided into four sub-processes, i.e. planning and task verification • conceptual design • embodiment design • detail design [4] Further analysis is presented here according to these four sub-processes. 2.1 Task Clarification and Activity Planning The product design task may come to the designer from different sources. The assignment is either a design of a completely new product (e.g. based on customer order, market research) or a design of a new variant of an existing product, alternatively to optimise an existing design (e.g. due to quality problems). In this study, we use the process of designing completely new products to formulate our proposal since the design work in the two other cases also relies on the process steps formulated here. It is necessary to have detailed market research from which the most important factors influencing the product design can be derived, such as customer/market/societal needs, available budget and expected number of pieces. Such variables are used to draw up a “list of
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Table 1. Context of product design
requirements” that the final product will have to meet. Its content is maintained and updated throughout the life cycle of the product, thus providing input for a possible future design optimisation. Based on the defined requirements, a product structure is set up, to which a function structure is coupled. Here it is checked whether previous results (models) can be used for the design, thereby the true design goal is clarified [5–7] pp 131, 301, [8] pp 8,32,154. 2.2 Conceptual Design Once the requirements and design tasks are clear, the conceptual design phase can begin. First, the collected requirements are prioritised, and the KO criteria for the concept are defined. Based on the function structures, the functional principles and the components implementing the main functions are selected. The preliminary forms are developed, the enclosing dimensions and layout sketches are defined, and the rough groups of materials (e.g. steel, aluminium, plastic, etc.) and the component or part manufacturing methods (e.g. casting, machining, forging, etc.) are also defined. The material properties are identified, which guarantee the achievement of the main functions. From the selected material groups, those that are not at all suitable to fulfil the predefined requirements are excluded, and the remaining are prioritised. The developed solution outlined earlier is evaluated against the list of requirements and the concept considered to be the best is selected [6, 7] pp 131, [9] pp 45–78 and pp 81–85. 2.3 Embodiment Design In this phase, the technical details of the concept(s) best considered are designed. After a precise selection of the operating principles that will implement the function, the
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engineer designs the individual parts and their interrelationships and then determines the types and availability of parts (standard, catalogue, in-house or purchased parts). Based on the requirements in the “requirements list”, the expected loads of the parts are determined, and quantified material properties are assigned to ensure that they can withstand these loads. A specific material grade is selected from a group of materials previously selected in the conceptual design phase. (e.g. structural steel S235) [9] pp 284–318 see Table 2. Table 2. Determination of material properties based on product functions
The reason why we cannot talk about a final material specification at this stage is that the manufacturability of the part has to be assessed. This involves deciding which processing method can economically achieve the shapes, dimensional accuracy and surface qualities envisioned by the designer. Consideration should also be given to whether the chosen processing method will not cause damage or harmful changes in the material structure that would lead to a degradation of function in the final product. As we can see, the more specific definition of the material grade is of particular importance as it fundamentally determines the dimensions (tolerances) of the product and the choice of the material processing technology but also significantly influences the degree of functional performance. For this reason, material grade selection is an extremely risky step in the design process. Presumably, to reduce the risks, despite a large number of steel grades available, design engineers often prefer to use proven, well-known material grades for which they have a wealth of knowledge and experience [9] pp 402. The material grade selection is based on material data sheets, standards or steel guides. Today’s designers are supported by a wide range of software and electronic databases. Specific software is available to design engineers for steel grade selection, modelling, dimensioning, and simulation of processing technology. Nevertheless, the design of complex parts is still a team effort. Complex design work requires the consideration of many aspects that the design engineer cannot undertake alone, although the final decision is always his responsibility. To facilitate multidisciplinary thinking, a “Simultaneous Engineering Team” is set up, most often with members from sales, purchasing, manufacturing, quality and controlling departments. The design stage results
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in a preliminary technical drawing of the parts [9] pp 132, [10, 11] pp 123–128, [12] pp 218–220. 2.4 Detail Design In the final design stage, the final dimensions, tolerances and surface requirements are precisely defined. The specific types of material grade (e.g. structural steel S235 JR + AR) and the exact material processing technology are defined. When verifying the material grade, all parts are reviewed, processing simulations and functional tests are carried out before the part design is approved (design freeze), and the results of previous experience are used to develop the final design. These may be customer or supplier complaints, troubles in initial part sampling process, deviation approvals from part serial production, risk analyses (DFMEA and PFMEA) of previous similar parts. These will be used as the basis for a final cost calculation. The models are finalised and used to produce technical drawings and documentation to accompany the product and production. The final technical drawing of parts must contain supporting information on the processing technology of the material. On the part drawings should also specify the precise name of steel grade, which will provide help for ordering rolled steel commodities for the buyers of the manufacturer of the parts (steel processors) [9] pp 401–416, [11] pp 512–519. 2.5 Associated Processes to Design Process The design work described above is supported by parallel so-called ‘associated processes’, out of which the most important ones are the cost, quality and change management [8] pp 33. Cost Management The pursuit of cost minimisation in the design process has a decisive influence on the market success of the product, so cost calculations must be made at the start of the new product’s project. Based on the market forecast of the new product, a target selling price per unit is set at the beginning of the project. Taking into account the product’s lifetime and the total number of units to be sold during this period, the cost price per product, as well as the attainable profit are determined. The largest component of the cost price is the manufacturing cost, the two main components of which are the material costs and the production cost, which are closely related. At a cost per unit ratio of 2/3, the material cost is about 1/3 of the selling price and thus becomes a significant cost element in the design [8] pp 66, [13]. In the case of simple parts made of rolled steel, the share of material costs may be higher. Companies producing parts from rolled steel determine the size of the rolled steel commodity required for production on the basis of the drawing dimensions of the part. Alloys and size largely determine the availability of rolled steel commodities on the market and, therefore their price. It is well known that standard, mass-produced rolled steel commodities used in industrial practice are produced in large quantities by steelworks and are, therefore, quicker to obtain and come at a more favourable price than so-called “slow mover” sizes or alloys [4] pp 137–141. In addition to the material cost, the manufacturing cost of the part is the other main component of the cost price. There is a close link between the material grade and the
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processing technology since the material grade chosen by the designer has a decisive influence on the technology by which the part can be produced in the first place. The processing steps and their cycle time depend to a large extent on the shape, size and tolerances of the part to be produced, as well as the surface quality to be achieved. For the selection of the processing technology, the designer uses tables based on empirical values, which include the dimensional accuracies achievable with each processing method and the number of parts that can be produced economically [14] pp 202–267. Surprisingly, some steel processor pays very little attention to cost calculation. These are mostly smaller companies with limited resources and know-how, as opposed to large international industrial corporations [4] pp 125. This may even be critical in this segment, where a large number of competitors are competing with each other in a huge price war. Anyone who simply adjusts his own offer to the selling price set by his competitors without a prior cost calculation must be prepared to take serious risks, the effects of which may even spill over into the entire supply chain [14] pp 202–267. Quality Management Each new product must face a certain degree of risk. The cost of correcting a design failure after market introduction can be horrendous, not to mention the moral hazard that can threaten the market position of the company. Therefore, keeping risks at an acceptable level is a crucial task. In the domain of quality management, there are a number of tools available to assess and analyse risks. The best known and most widely used of these is the “Design Failure Mode and Effect Analysis” (DFMEA) [15]. It is used by a multidisciplinary team to continuously analyse and prioritise potential product/part failures and their effects during the design process. The aim is to increase the reliability of the design. DFMEA provides a continuous input to iterative design work and their content should be maintained throughout the product life cycle in order to allow for timely optimisation of a design when necessary or to build on the experience gained when designing a new variant. The results of the DFMEA can also be shared with partner departments within the organisation. It can provide important information to create additional part or material specifications, to prepare manufacturing Process FMEA, and to prepare product or part control plans [11] pp 291–295, [15, 16]. Change Management After a new product has been put into serial production, the need for changes to the design and its manufacture may arise for a number of reasons. The implementation of changes certainly imposes risk on customer supply and product quality, whose impact needs to be assessed. The most common reasons for change are: • • • • • •
Constant customer complaints New customer needs Potential for optimisation in production Contracting a new supplier (e.g. due to cost reduction!) Relocation of production (e.g. due to cost reduction!) Due to changes in standards or legal regulations [8] pp 79
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2.6 Summary of Current Practices in Material Selection Material selection takes place in several stages during the component and part design. The actual material selection takes place during the finishing phase, which concludes the design. Ashby, Farag and Bernst also agree that the material properties on which material selection is based are determined by the interactions between the function, form and manufacturability of the part to be designed [9] pp 19, [17, 18]. Unfortunately, the result of material selection can never be equally favourable to these three influencing factors, and one of them must always be favoured at the expense of the others, so that the material (grade) chosen is always the result of a compromise [12] pp 49–57. Grosse assessed the priorities of material selection criteria by questionnaire surveys and in deep interviews conducted with designers. The results of his research show that the main considerations are to ensure the component’s function and to minimise costs. This should come as no surprise, given that all the reference literature we have studied emphasises the importance of keeping costs low throughout the design phase. Grosse also found in his research that 66% of the companies surveyed select the material grade regardless of the machinery required for manufacture the part [19]. Before exploring the background to this finding, let us consider the characteristics of rolled steels as the focus product of this paper.
3 Characteristics of Rolled Steel Products The rolled steel products are the raw materials of numerous parts in the automotive industry. Therefore, a lot of part producer processes rolled steels. Rolled steel products can be classified according to several criteria. Regarding the steel grade the EN 10020 standard distinguishes non-alloy-, stainless- and other alloy steels [20] pp 3. According to their conditions, they can be: • • • • •
Hot-rolled (pickled) long products and – sheets/plates; Cold-rolled (coated) sheets and strips; Cold drawn-, peeled-, grinded bars and wires; Welded pipes/tubes and -hollow sections; Seamless pipes/tubes and -hollow sections
Rolled steel products are machined by a wide variety of processing technologies to produce parts which are subsequently used to make assembled components. The most common of these are as follows: • • • • • • • •
Forging Deep drawing Bending Stamping Turning Cutting Welding Surface coating
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• Shot blasting [21]. The extent to which a rolled steel product can be efficiently processed by the technologies above is indicated by the material properties (see Table 3). The most prominent of these are the chemical composition and the mechanical properties, which are closely related since the chemical composition and the metallographic structure are fundamental determinants of the mechanical properties (e.g. tensile strength, yield strength, impact energy) [22] pp 98. Table 3. Material properties of rolled steels
During the design phase, the designer determines the value of the loads (e.g. tension, bending, torsion, compression, etc.) that the part of being produced will have to withstand without any plastic deformation. This value will determine the minimum yield strength of the steel grade to be selected. It is also essential to know the maximum load that the part can withstand before fracture. The designer can obtain information on this from the tensile strength of the material. The impact strength value indicates how ductile or brittle the steel grade is, i.e. how well it can withstand repeated pulsating loads. The designer must also take into account during dimensioning that the mechanical properties of the chosen steel grade may be degraded by surface- or internal discontinuities, which are inevitable in rolled steel products (see below for details) [23]. The specific values of the material properties are determined by the delivery conditions of the rolled steel products, which are most often national or international standards. The authors of these standards determine the delivery conditions for widely used steel
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grades with a view to the economical production of rolled steel products. For this reason, the acceptance limits for material properties form a relatively wide range [24]. According to our research, 622 companies in the EU-27 produced standard rolled steel products in 2019. These companies use different technologies and various levels of technological equipment, so the variation in the properties of their products in comparison with each other varies [25]. Not surprisingly, products of more steady quality are available on the market at higher price levels. When selecting rolled steel materials for critical parts, designers have the option of extending the set of requirements in the international standard for the rolled product or of setting tighter limits on the existing ones (e.g. restricted dimensional tolerance, chemical composition or mechanical properties). It may be critical due to the complexity of the manufacturability (processability) of a part or due to the risk of a functional failure [26]. Where the number of extra specifications exceeds a manageable quantity, designers will create their own booklet of requirements or a company standard. At the time of writing this paper, we have reviewed 64 international (EN) standards and 41 company standards from 12 major European multinational companies using rolled steel products. It was found that companies make deviations from international standards only in truly justified cases where a particular material property has a critical impact on processability or part function. In the case of a major requirement change, it is also the case that a completely new steel grade (e.g. ZF7B) is created as a result of the many restrictions, but this is not the typical case. If the material properties of a rolled steel product, defined by the function of the part, is closely related to its manufacturing technology, it also happens that designers, with the help of metallurgists, specify in their requirements the route and technological equipment of production. In such cases a product or/and production process approval of the rolled steel product is needed by the customer. Because of the enormous amount of know-how and efforts required, this is standard procedure only in large steel using multinational companies that design and produce safety critical parts. However, whether we are talking about international or company standards, material properties can only be kept within quite wide tolerances due to the specialities of steel production and rolling technology, since rolled steel products are the result of complicated physico-chemical reactions that can only be influenced to a limited extent. For this reason, individual pieces of rolled steel products are never completely homogeneous, even those made from the same cast. The chemical composition of the rolled products can vary from point to point within certain limits due to segregations of several chemical elements during crystallisation, and the mechanical properties can vary accordingly. For this reason, it is common for standards to specify only minimum or maximum values of the specific chemical elements and the limit values of the mechanical properties as well. Another important characteristic is that it is not possible to produce rolled steel products that are completely dense because even the most carefully controlled steel making or rolling technology can lead to discontinuities on the surface or in deeper layers due to the physico-chemical axioms of crystallisation [27–30]. The finest surface finishes for rolled steel products are achieved in drawn/peeled and polished bar products. Here the best surface quality class is the “technically crack-free” surface. This means that only a crack deeper than 0.2 mm is only considered as a defect on the surface (!) and 0.2% of the total quantity delivered is acceptable for this type of
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defect. According to the usual calculation of defective parts in the automotive industry, such a product can be delivered with at best only 2000 ppm defects [31] pp 19. This is an order of magnitude higher than the 50 or 100 ppm normally defined for machined parts in the automotive industry. Given the characteristics of rolled steel products described above, it is therefore of paramount importance to accurately define the material properties and their limits when selecting steel grades and ordering rolled steel products. Why are the abovementioned specialities of rolled products so important in terms of part designing? Because failure can also occur in a design with a seemingly sufficiently large safety factor if the dispersion of material properties are unfavourable. After Danzer [32] a doubling of the dispersion of tensile strength results an increase in the probability of disfunction (breakdown) by a factor of 106 . The designer should therefore pay more attention to safety factors instead of just the pure target value of yield strength or tensile strength written in the standards. To calculate this correctly, the designer needs exact information about the distribution of material properties from practice (daily business).
4 Frustrations with the Current Material Selection Method in the Case of Rolled Steel Products As seen above, the method described in the material selection literature does not distinguish between the selection of different types of materials (e.g. rubber, plastic, steel) but describes a uniform methodology. However, we have also seen that rolled steel products have a number of specialities that make the material selection complicated for the designer. The reference literature studied is consistent in identifying the factors that influence material selection: product function (shape design), part manufacturability, raw material price. The choice of material (steel grade) in the design process is always a trade-off, with designers giving priority to guaranteeing the function and keeping costs as low as possible. This raises the following questions: Can the Design Engineer Be Expected to Know All the Steel Grades with Their Characteristics and to Make a Realistic Assessment of the Availability of Each Individual Steel Grade? Although the designer’s work is supported by a wide variety of material selection software (e.g., CES, GRANTA, Total Materia, etc.), but despite all efforts, their database is not always complete. Certain newly emerging international standard products or ones with changed properties (e.g., EN, DIN, ASTM) can be updated quickly by the more reputable software, but even they have difficulty in maintaining the database of company standard products, and in particular of steel manufacturers’ branded products (trade mark products). Even though, the latter are products with optimised material properties for a particular processing method, making them particularly more suitable for laser cutting, turning, forging, etc. By using these trade mark products, production costs can be drastically reduced through shorter processing cycle times and reduced rate of scrap. In addition to knowledge of the existing steel grades, the selection of the proper rolled steel product also requires knowledge of their availability on the market, since it is useless for a designer to specify an alloy that is ideally suited to the function of the part if it is not available locally or only to a limited extent. It is unrealistic to expect designers to know
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the current market availability of more than 3500 steel grades available worldwide. It is no wonder that designers stick to well-known and proven standard steel grades that are readily available but whose applicability for processing a complicated part is often severely limited for the reasons described above. Why is the processability of parts relegated to the background in the ranking of material (steel grade) selection criteria, or should the design engineer be expected to make a realistic judgement of processability? To answer this question, let’s examine the concept of material selection from the perspectives of Functional - Price and Processability - Price: Functionality – Price. The function of parts is ensured by the right design, the right choice of dimensions and tolerances, and the selection of materials (steel grades) that can withstand the loads. It is clear from the literature that the logic of design engineering is that if the risk analysis of the part design does not identify a possible material failure mode that, given the part geometry, could significantly degrade the usability of the product, then no additional material properties beyond the standard ones are required, and the existing ones are not restricted, i.e. the aim is to select standard low-cost, readily available material (steel) grades. Manufacturability (Processability) – Price. The design engineer selects the processing method(s) and if it is necessary defines the advanced processing route on the basis of the geometry of the part, the dimension, the accuracy of tolerances and the number of pieces to be produced. Since parts made from rolled steel products are only exceptionally processed in the same company where the parts are designed, the design engineer may not have an accurate knowledge of the processing equipment. The machinery of companies processing rolled steel products is extremely diverse. The technical level (e.g. the control and the precision) of the machines can be strongly varied. The design engineer knows only in rare cases the machinery of the company that will receive the purchase order for part production. Why is this important? In the literature on metal processing, the manufacturability of the work material is defined as the ease with which it can be processed. It is often pointed out that processability depends on the material properties (e.g. chemical composition, material properties, surface quality, microstructure) of the work material [33–39]. Different types of processing machines are influence to different material properties and to their variation. Material inhomogeneities (e.g. surface or internal discontinuities, segregations of chemical elements, dimensional variations, etc.), which can occur to a greater extent in the standard rolled steel products mentioned earlier, can result for the part manufacturers in increased processing cycle times, faster tool wear, more frequently repeated correction of processing parameters, increased rates of internal scarps. Hi-tech processing machines are able to reduce the down-times with the help of numerous services like continuous measurement of part dimension, automatic parameter correction or calling for another tool in case of any dangerous vibrations and therefore keep stable the non-conformity costs as well. Usually, for new part projects, well-known suppliers are hired at the start of the project. These suppliers use high-tech processing equipment, and employs experts; therefore are able to achieve a good quality performance during the serial production of the part. It
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is natural that as the project progresses, suppliers will be replaced as price reductions lead to cheaper suppliers whose margins will also fall, forcing them to source their raw materials (rolled steel products) from the cheapest source. This obviously entails the sourcing of rolled steel products that meet the specifications only to a minimum level or have a high degree of inhomogeneity and variation in material properties. It could be said that this is the supplier’s own risk, a long-term material selection should not be made more expensive just to favour lower quality firms that will enter the supply chain later. This is a convenient but very dangerous approach, as an increased level of internal defects also means a higher risk of undetected defective products being released, which can lead to customer complaints. Steel grade and rolled steel commodity selection must therefore take into account not only the processing technology but also other processes in the supply chain. However, this can only be expected from the design engineer if he receives continuous feedback on processing/assembly or usage deficiency relevant for steel grade or rolled steel commodity selection after the product has been introduced on the market, which he can be used as a lesson learned in future projects. Summary of Frustrations: The ever-increasing number of steel grades on the world market is both a problem and an opportunity for designers. The local availability of a large number of steel grades in the market cannot be judged by the designer, especially without knowing the current and future production locations of the part. An exact processing technology (processing route) and a proper raw material can only be determined with knowledge of the machinery. As steel processing companies are rarely involved in the design of parts, if so only the “first-round” suppliers, the designer does not have enough information to make a detailed judgement on processability. It is therefore logically explained why processability is relegated to a lower priority in the material (steel grade) selection criteria system and why the majority of designers do not take into account the part manufacturer’s (steel processor’s) machinery when selecting materials. For these reasons, it is essential that part manufacturers are involved in the design process, and more specifically, in the selection of steel grades. But it is equally important that the designer receives feedback on the relevant information regarding the raw material after the part SOP (Start of Production). Unfortunately, the latter is not currently required by any automotive system standard or industry regulation and a need is manifest for a structured process to provide this information.
5 Proposal for a New Approach to the Selection of Materials in the Steel Industry As we have seen, the processability and availability of rolled steel products are two important aspects that have been unreasonably neglected in current material selection practices at least in the steel industry. The risk of these aspects is best assessed by the part manufacturer (steel processor) and it is, therefore, obvious that they should be more deeply involved in the material selection process. As we propose, the one-stage material selection process as described so far (we consider the material selection activity in the
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“conceptual design, embodiment design and detail design” operations as one stage here - Stage I - due to the iterative nature of the design work) is extended by a second stage (Stage II) at the supplier (see Table 4). Table 4. A two-stage (Stage I, Stage II) material selection concept
In this stage, part manufacturers (steel processors) should consider the relevance of all material properties, but especially those not specified by the designer, which should be specifically defined where necessary. If the local specialities of the processing technology make it necessary, the part manufacturer may apply more stringent values to the material properties already specified by the designer (in Stage I), but only with appropriate justification. In future, designers may also draw the attention of part manufacturers to this fact in the technical drawing by means of a note in the text field. The material properties established in the second (Stage II) stage shall be included in the mandatory initial sampling report (PPAP) to be submitted before the start of serial production, on the form proposed in Table 5. The proposal received shall be checked by the design engineer for functionality and, if acceptable, approved. The second (Stage II, part manufacturer 1) is only valid for the specific part manufacturer and will not be included in the documents (e.g. drawing) prepared by the designer, since in case of a possible change of production or supplier, the material (steel grade) specification of the “second-round” (Stage II, manufacturer 2) must be performed again by the new part manufacturer as part of producing the initial sample. This would provide a controlled way to feedback information on the details of the processability-centric material specification to the design engineers. In this way, both the designer and the part manufacturer gain an important opportunity. On the one hand, the designer will have an up-to-date picture of the availability of raw materials (rolled steel products) on local markets and their processability, and on the other hand, the processing industry will be empowered to fine-tune the raw materials in order to further improve the economic efficiency of their production in this respect. This would enhance the focus of the actors in the processing industry on the suitability
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of raw materials, thereby actively contributing to the stability of the quality of the final product and thus directly increasing customer satisfaction. Table 5. Product specification document for the initial sampling report
6 Conclusions The selection of raw materials for automotive parts is an extremely complex task. In applying the method known so far in the literature to the steel industry, we have found that design engineers’ attention is focused on minimising cost while guaranteeing function. In this paper, we have highlighted the importance of additional considerations such as part manufacturability (steel product processability) and availability of rolled steel commodities. Without taking these two aspects into account, an optimal material selection cannot be made, so the planned involvement of part manufacturers in the process is necessary. The proposed model would eliminate the current uncomfortable situation where steel producers, part manufacturers and part designers argue about each other’s responsibilities following a less than successful material selection. The model presented in this paper is intended to provide a practical tool for all interested parties in the automotive and mechanical industry in order to avoid design errors and parts deficiency. This effort is in line with the intention of the International Automotive Task Force (IATF), as reflected in the IATF16949 system standard, which states that companies should not work in isolation from each other, but should work closely together to create the perfect part possible [40]. Our model can be incorporated into the currently valid initial-sampling regulations of the automotive industry. The application of the model presented in our paper has not been examined on other materials (e.g. plastics, rubber, aluminium, copper, etc.). Due to its large scope, this could be the subject of a separate research in the future.
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References 1. https://www.worldsteel.org/about-steel.html Downloaded on: 15 June 2021 2. Verband der Automobilindustrie: Produktentstehung, Reifegradabsicherung für Neuteile, 2. Auflage, Verband der Automobilindustrie, Berlin (2009) 3. Chrysler Corporation: Ford Motor Company and General Motors Corporation: Advanced Product Quality Planning and Control Plan Reference Manual, 2nd edn. AIAG, Michigan (2008) 4. Pahl, G., Beitz, W.: Konstruktionslehre, 8th edn. Springer-Verlag, Berlin (2013) 5. Verein Deutscher Ingenieure: Design of Technical Products and Systems, Model of Product Design, VDI 2221Part 1. Verein der Deutsche Ingenieure e.V., Düsseldorf (2019) 6. Verein Deutscher Ingenieure: Design of Technical Products and Systems, Model of Product Design, VDI 2221Part 1. Verein der Deutsche Ingenieure e.V., Düsseldorf (2019) 7. Pahl, G., Beitz, W., Feldhausen, J., Grote, K.H.: Engineering Design, Third Edition. SpringerVerlag, London Ltd (2007) 8. Naefe, P., Luderich, J.: Konstruktionsmethodik für die Praxis, 2. Auflage, Springer Fachmedien GmbH, Wiesbaden (2020) 9. Ashby, M.F.: Materials Selection in Mechanical Design, Third edition; ButterworthHeinemann Linacre House. Jordan Hill, Oxford (2005) 10. Verein Deutscher Ingenieure: Systematic Embodiment Design of Technical Products, VDI 2223. Verein der Deutsche Ingenieure e.V., Düsseldorf (2004) 11. Dieter, G.E.: ASM Handbook, Material Selection and Design, vol. 20. ASM International Handbook Committee (1997) 12. Reuter, M.: Methodik der Werkstoffauswahl, 2nd edn. Fachbuchverlag, Leipzig (2014) 13. Verein Deutscher Maschinen- und Anlagebau: VDMA Kennzahlenkompass 2014. VDMA Verlag, Frankfurt am Main (2014) 14. Ehrlenspiel, K., Kiewert, A., Lindemann, U., Mörtl, M.: Kostengünstig Entwickeln und Konstruieren, 7. Auflage, Springer-Verlag, Berlin/Heidelberg (2014) 15. Automotive Industry Action Group: Verband der Automobilindustrie: FMEA Hanbook, First Edition; Automotive Industry Action Group, Michigan (2019) 16. Verband der Automobilindustrie: VDA Band 4, Sicherung der Qualität in der Prozesslandschaft, Abschnitt 1: Allgemeines, 3. Auflage, Verband der Automobilindustrie, Berlin (2020) 17. Farag, M.M.: Selection of Materials and Manufacturing Processes for Engineering Design. Prentice Hall, London (1989) 18. Bernst, R.: Werkstoffe im wissenschaftlichen Gerätebau. Akademische Verlaggesellschaft, Leipzig (1975) 19. Grosse, A.: Analyse der Werkstoffauswahl in der industriellen Praxis und Konsequenzen für die rechneruntersctüzte Stahlauswahl, p. 22. TU Clausthal, IMW- Institutsmitteilung Nr. (1997) 20. Deutsches Institut für Normung: DIN EN 10020, Definition and Classification of Grades of Steel. Deutsches Institut für Normung e.V., Berlin (2000) 21. Deutsches Institut für Normung: DIN 8580, Manufacturing processes, Terms and Definitions, Division. Deutsches Institut für Normung e.V., Berlin (2003) 22. Bleck, W., Moeller, E.: Handbuch Stahl. Carl Hanser Verlag, München (2018) 23. Gulyaev, A.P.: Selection of the Type of Steel for Machine Parts (Basic Fundamentals). Met. Sci. Heat Treat. 25, 68–75 (1983) 24. Stahlschlüssel, V.: Key to Steel, 25th edn. Verlag Stahlschlüssel, Marbach (2019) 25. Fastmarkets: Iron & Steel Works of the World, 26th Edition. Fastmarkets, London (2018)
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26. Verband der Automobilindustrie: Prozessbeschreibung, Besondere Merkmale, 2. Auflage, Verband der Automobilindustrie, Berlin (2020) 27. Sakai, T., Nakajuma, M., Tokaji, K., Hasegawa, N.: Statistical distribution patterns in mechanical and fatigue properties of metallic materials. Material Science Research International 3(2), 63–74 (1997) 28. Gündel, M.: Herleitung des Überfestigkeitsbeiwerts auf Basis statistischer Kennwerte europischer Baustähle. Stahlbau 89(H1), 28–37 (2020) 29. Strauss, A., Kala, Z., Bergmeister, K., Hoffmann, S., Novak, D.: Technologische eigenschaften von stählen im europischen vergleich. Stahlbau 75(H1), 55–60 (2006) 30. Deutsches Institut für Normung: DIN EN 10021, General Technical Delivery Conditions for Steel Products. Deutsches Institut für Normung e.V., Berlin (2007) 31. Deutsches Institut für Normung: DIN EN 10277, Bright Steel Products, Delivery Conditions. Deutsches Institut für Normung e.V., Berlin (2018) 32. Danzer, R.: Streuung der Werkstoffestigkeit und die Wahrscheinlichkeit für das Versagen von Probestäben an ausgesuchten Beispielen. Material Science and Engineering Technology 18(1), 19–26 (1987) 33. Mills, B., Redford, A.H.: Machinability of Engineering Materials. Springer, Dordrecht, Essex (1983) 34. Davim, P.: Machinability of Advanced Materials. ISTE Ltd., London (2014) 35. Jalali, S.A., Kolarik, W.J.: Tool life and machinability models for drilling steels. Int. J. Mach. Tools Manuf 31(3), 273–282 (1991) 36. Ebrahimi, A., Moshksar, M.M.: Evaluation of machinability in turning of microalloyed and quenched-tempered steels: Tool wear, statistical analysis, chip morphology. J. Mater. Process. Technol. 209(2), 910–921 (2009) 37. Akasawa, T., et al.: Effects of free-cutting additives on the machinability of austenitic stainless steels. J. Mater. Process. Technol. 143, 66–71 (2003) 38. Capello, E.: Residual stresses in turning: Part II. Influence of the machined material. J. Mater. Process. Technol. 172(3), 319–326 (2006) 39. Lugan, A., Hilton, P.A., Taylor, D.W.: The effects of steel composition on the laser cut edge quality of carbon and C-Mn steels. In: International Congress on Applications of Lasers & Electro-Optics. Vol. 2002. No. 1. Laser Institute of America (2002) 40. International Automotive Task Force: Quality Management System Requirements for Automotive Production and Relevant Service Parts Organisations, 1st edn. IATF (2016)
Iron Oxide and Tungsten Trioxide Nanofluids to Enhance Automotive Cooling Radiators: Experimental Analysis Mohammed Alktranee1,2(B) , Mohammed A. Shehab3 , Zoltán Németh4 , and Péter Bencs1 1 Department of Fluid and Heat Engineering, Faculty of Mechanical Engineering and
Informatics, University of Miskolc, Miskolc HU-3515, Hungary 2 Department of Mechanical Techniques, Technical Institute of Basra, Southern Technical
University , Basrah, Iraq [email protected] 3 Faculty of Materials and Chemical Engineering, University of Miskolc, HU-3515 Miskolc, Hungary 4 Advanced Materials and Intelligent Technologies Higher Education and Industrial Cooperation Centre, University of Miskolc, HU-3515 Miskolc, Hungary
Abstract. The current cooling systems of internal combustion engine vehicles have several functions; in addition to removing excess heat from the engine, it plays an essential role in ensuring that the engine quickly reaches operating temperature and keeping the passenger compartment at the right temperature. Nanofluids possess better heat transfer properties so that current and future cooling systems can work more efficiently. The common coolant used in cooling systems is water or what equivalent substance; these coolants suffer from a decrease in thermal conductivity, which negatively affects internal combustion engine efficiency. This study presents an experimental investigation using iron oxide (Fe2 O3 ) and tungsten trioxide (WO3 ) nanopowder suspension at a specific volume concentration in deionized water as proposed nanofluids. Thermal analytical calculations were conducted of the proposed nanofluids under laboratory conditions with validation of the results and comparison with previous studies. The study indicates an improvement in the heat transfer rate, Nusselt number and heat transfer coefficient, drop in the friction coefficient with an increase in Reynolds number, and convergence of the experimental and simulation results confirms the accuracy of the results. The current developments contribute to increasing the usability of new cooling media by the vehicles and the possibility of achieving greater efficiency. Keywords: Cooling system · Nanofluids · Heat transfer rate · Thermo-physical properties · Heat exchanger
1 Introduction Recently, energy management is an essential issue in reducing the increased energy consumption produced by industrial applications, fluctuations in oil prices, and increased © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 521–537, 2023. https://doi.org/10.1007/978-3-031-15211-5_43
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greenhouse emissions is encouraging many researchers to adopt alternative methods that improve systems efficiency [1]. Increasing thermal efficiency with energy savings and reducing gas emissions by adopting less environmentally aggressive systems is a major challenge in automotive cooling systems [2]. The main requirement of any heat exchanger is its fast effectivity of heat transfer and its heat load, which can be achieved by either increasing the heat transfer coefficient or heat transfer surface area. The heat transfer coefficient of fluids is not changeable, but it can increase the temperature or area of the heat exchanger [3]. Increasing heat transfer area in automotive systems cannot be achieved only by increasing the heat exchangers size, which that causes unwanted increases in weight [4, 5]. In automotive cooling systems, the base fluids (water, glycerol, and ethylene glycol) are the conventional coolant fluids used in an automobile radiator, and these base fluids offer poor thermal conductivity [6]. Thus, that causes equipment limitations and reduced cooling system efficiency [7]. In the automotive industry, applying nanofluid as an alternative to conventional coolant fluids increased heat transfer and the cooling system’s efficiency, thereby helping to use radiators with the smaller radiators size [8]. Dispersion of solid metallic or nonmetallic nanomaterials in base fluids produces a nanofluid with higher thermal properties than base fluid and achieves heat transfer improvements [5]. Recently, nanofluids have taken an advanced place in cooling fluids due to enhanced thermal conductivity and their effectiveness in heat transfer due to the large surface area of nanoparticles compared to their volume ratio. Thereby, scaled-down of heat-transfer devices are possible by applying nanofluids as a cooling fluid which gives better cooling processes [9]. Thermophysical properties of nanofluids such as thermal conductivity, specific heat, density, and viscosity play the main role in improving automotive cooling systems. Thus, some parameters that influence the evaluation of nanofluids performance must be taken into account, such as the variation of volume concentration in the base fluid, size, preparation of nanoparticles, operation temperature, etc., that affect the heat transfer coefficient and losses in pumping power. In the recent decade, nanofluids have been applied as alternative conventional cooling fluids; Fig. 1 shows the number of publications related to nanofluids used in automotive cooling systems. For preparing nanofluids that have enhanced thermal properties higher than base fluid, Zinc and Zinc oxide nanoparticles were dispersed into the water as nanofluids to use in an automobile radiator as a cooling fluid. Different volume concentrations of Zinc, Zinc oxide, 0.15%, 0.25%, and 0.5% were used to evaluate the improvement of the heat transfer coefficient and pressure drop compared with water. Zinc/water nanofluids achieved the best heat transfer performance at 0.5% volume concentration than other considered nanofluids; thus, it can be an energy-efficient cooling fluid compared to water and are suitable for the engine cooling system [10]. Experimentally study using two-volume concentrations of 0.005%–0.05% vol of tungsten oxide nanofluids and deionized water to investigate the heat transfer coefficient behaviour under atmospheric pressure conditions by a typical horizontal heated copper tube. The results show an increment in heat transfer coefficient with an increment in the applied heat flux for both tungsten oxide nanofluids and deionized water. Volume concentrations impact the heat transfer coefficient with better performance nanofluids than deionized water [11].
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An experiment was conducted on the effect of used Al/water nanofluid in the automobile radiator to investigate the effect of heat transfer characteristics on thermal properties of the nanofluid at volume concentrations of nanoparticles in the water by 0.2% and 0.3%. The results showed a gradual increase in Nusselt numbers and friction factor with increased volume concentration at 318.15 K inlet temperature. In contrast, the friction factor decreased at the same temperature when using base fluid [12]. A heat exchanger of the automobile radiator used SiO2 /water nanofluid with 0.04% volume concentration to investigate the improvement of heat transfer characteristics. The study indicated that an increase in volume concentration achieved increased by Nusselt number, which causes an increase in Reynolds number and the inlet temperature of nanofluid. However, at 0.04% volume concentration, the heat transfer characteristics improved by about 3.8% compared with base fluid [13]. In another study, the influence of nanofluids on Nusselt number, heat transfer rate, and heat transfer coefficient is applied with an automobile car radiator with varying flow rates. Different volume concentrations of 0.05% to 0.8% of CuO nanoparticles were mixed with base fluids that consisted of distilled water and ethylene glycol as nanofluids at inlet temperatures from 35 °C, 44 °C, and 54 °C. The study revealed that the Nusselt number increased, the heat transfer coefficient increased to 55% at 0.8% of volume concentration, and an increase in the flow rate of nanofluids compared with base fluids [14]. To evaluate the heat transfer performance by using nanofluids in automotive radiators, Cu/water and Fe2 O3 /water nanofluids have been used at a concentration of 0.65%. An increase in the overall heat transfer coefficient was noticed up to 9% compared to water [15]. On the other hand, an experiment was conducted to enhance the performance and efficiency of the radiator by applying TiO2 /water, Al2 O3 /water nanofluid at a different volume concentration of nanoparticles in a car radiator to evaluate the efficiency factor, pressure drop, and rate of heat dissipation. The result shows that an increase of nanofluid concentration at different values of Reynolds number leads to an increase in the efficiency factor to a larger extent with an increase in the rate of heat dissipation. In contrast, a slight increase occurs with ethylene glycol/water. On the other hand, TiO2 /water nanofluids show high effective heat dissipation than Al2 O3 /water nanofluids [16]. In contrast, a hybrid of nanofluids consisting of silver and graphene nanoparticles mixed with water/ethylene glycol as base fluids is used in automotive radiators to investigate the performance of nanofluids at different volume concentrations and mass flow rates. At inlet temperatures of 55 °C and 85 °C, the nanofluid increased the heat transfer rate to 4.1%; the silver nanofluid achieved an increment of the heat transfer rate of about 4.4%; the graphene samples show low performance compared with the base fluid [2]. This study proposed iron oxide (Fe2 O3 ) and tungsten oxide (WO3 ) nanofluids as cooling fluids for the automotive radiator instead of conventional cooling fluids. Two volume concentrations of nanoparticles into deionized were used to investigate the thermal behaviour of nanofluids by the heat exchanger proposed as a replacement for the automotive radiator. The experiment was conducted with a specific volume concentration of nanoparticles, temperatures, and flow rate to study the thermal characteristics of the nanofluids proposed. MATLAB/Simulink has been used to compare the experimental results to validate the system performance and compare the results with other
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studies, thus determining the fluid that can achieve better performance for an automotive cooling system.
Fig. 1. Statistics of publications on using nanofluid as cooling fluid with vehicles from 2013 to the present (considering the Scopus database, accessed on January 25, 2022)
2 Experimental Methods 2.1 Nanofluid Preparation Nanofluid consists of nanoparticles that suspending by base fluid. suspension of nanoparticles into deionized (DI) water for a long time is essential to obtain a stable nanofluid. In this study, Iron oxide Fe2 O3 and Tungsten trioxide WO3 nanoparticles were used two-step methods to prepare for the material sciences and engineering faculty at the University of Miskolc. The volume concentration of nanoparticles suspended in a base fluid is conducted according to Eq. (1) [17, 18], the volume concentration of Fe2 O3 was 0.015%, and WO3 was 0.01%. The quantity of nanoparticles dispersed into deionized water has been scaled with an electronic scale (type: BOECO BAS 31 Plus with an accuracy of 0.0001g). Then nanoparticles were mixed with a magnetic stirrer (Model: SH-II-4C) for 25 min. Then, an ultra-sonication probe (Type: Q SONICS, Model: Q 125) was used for 40 min. Nanofluid has tested under three values of voltage and frequency of ultra-sonication probe (80V, 13 Hz, 100V, 16 Hz, and 125V, 20 Hz); the value of (80V, 13 Hz) achieved better stability of nanofluid in deionized water compared with other values. According to the volume concentrations of 0.015% Fe2 O3 and 0.01% WO3 of nanoparticles into deionized water by visualization and time sediment method from 3–6 h after the sonication, as shown in Fig. 2. The time of nanofluid’s stability was good
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that directly used in the experiment that started after the sonication step; the volume concentration of nanoparticles was calculated according to Eq. (1) [19]. ⎤ ⎡ m np
ϕ = ⎣ mnp ρnp
ρnp
+
mbf ρbf
⎦ × 100
(1)
where ϕ is the volume concentration in percent, mnp the mass of the nanoparticle mbf the mass of the base fluid ρnp the density of the nanoparticle ρbf is the density of the base fluid. For qualitative characterization, the scanning electron microscope (FEI Technai G2 F20 HRTEM) was used to investigate the morphology of iron oxide. The Fe2 O3 nanoparticle diameter was 8–34 nm, determined by ImageJ software utilizing HRTEM images using the original scale bar. Scanning electronic microscope SEM shows the morphology of WO3 , which appeared as nanoflakes; Fig. 2 shows the morphology of both nanoparticles used. Table 1 shows the characteristics of the nanoparticles that play a vital role in improving nanofluid behaviour; DI water has been used as a base fluid that mixes with nanoparticles. Table 1. Thermophysical properties of the nanoparticles and base fluid. Properties
Deionized Water [20]
Fe2 O3 [21]
WO3 [22, 23]
Density (kg/m3 )
997
5240
7160
Thermal conductivity (W/m·K)
0.613
20
1.63
Heat capacity (J/kg·K)
4179
650
335
2.2 System Description The system consists of an electric water heater with 81 L (Model: GCVS 804420 B11 TSR) and a heat exchanger with a surface area of 0.21 m2 inside the electric water heater. The heat exchanger of the electric heater is connected to a water tank (90 L) which involves a second heat exchanger inside it with a 15 mm diameter connected with an electric water heater by an inlet pipe. The second tank contains a nanofluid with a pump and mass flow rate sensor (Model: YF-S201) connected to an inlet pipe, a nanofluid tank, and an electric water heater. Eight thermocouples (T-type of 0.2 mm ± 1 °C accuracy and ± 0.5 °C limits of error) to measure the temperature of each part of the system, as shown in Fig. 3. Thermocouples were distributed of the inlet, outlet, water tank, nanofluid tank, electric heater wall, water inside of the electric heater, ambient temperature, and temperature between the water tank and nanofluid tank. The measured temperature is recorded by the National instrument model NI 9213 and read by IN SignalExpress 2015 software. The National Instruments Compact DAQ (NIC) system contacts the thermocouples read and recorded by driver software installed by the laptop.
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Fig. 2. Stability of Fe2O3 and WO3 nanofluids with different periods with the morphology of nanoparticles.
Fig. 3. Components of the water electric heater system.
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2.3 Data Reduction Density is an essential property of the nanofluid that significantly affects the thermal system’s sustainability and achieves a suitable extent of nanofluid stability. Equation (2) is used to calculate nanofluids density [24]. ρnf = ϕρnp + (1 − ϕ)ρbf
(2)
ρnf is the density of nanofluid, ϕ, ρnp are the volume concentration and density of nanoparticles and ρbf is the density of the base fluid. In nanofluids, specific heat differs depending on the nanomaterials used and their concentration in the base fluid. Pak and Cho formula was used to calculate the specific heat of nanofluids [25]. Cpnf = Cpnp φ + (1 − φ)Cpbf
(3)
where Cpnf , Cpnp , Cpbf are specific heat of nanofluid, nanoparticles, and base fluid. Thermal conductivity is the important nanofluid property in the heat transfer, which calculate by using the Maxwell formula by Eq. 3 [26]. knp + 2kbf + 2∅ knp − kbf knf = (4) kbf knp + 2kbf − ∅ knp − kbf where knf is the thermal conductivity of nanofluid, kbf thermal conductivity of the base fluid, knp represents the thermal conductivity of nanoparticles. Viscosity represents the essential factor that significantly affects the nanofluids’ behaviour; thus, the ideal nanofluids must have high thermal conductivity with low viscosity, Brinkman formula was used to determine as Eq. (5) [27]. μnf =
μbf (1 − φ)2.5
(5)
˙ where μnf , μbf the viscosity of the nanofluid and base fluid. Heat transfer rate (Q) calculated by the relation presented in Eq. (6) [21]. ˙ = mC Q ˙ p × (Tout − Tin )
(6)
where m ˙ is mass flow rate, Cp is specific heat capacity, TOut and Tin are outlet and inlet temperature, respectively. The heat transfer coefficient of the liquids is estimated by Eq. (7) [21]. h=
mC ˙ p × (Tout − Tin ) A(T − Tw )
(7)
Here, A is a pipe’s peripheral area, which calculates in Eq. (8) [21], while T represents the average inlet and outlet temperature. A = 2 × (W + H )l
(8)
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W, H, and l are the tube’s width, height, and length. Nusselt number relation is given in Eq. (9) [28], h is the convective heat transfer coefficient, k is the thermal conductivity of the liquid, and Dh is the hydraulic diameter. Nu =
mC ˙ p (Toutlet − Tinlet )Dh A(T − Ts )k
(9)
Reynolds number is determined depending on the velocity at the inlet, the working fluid’s density, and the pipe’s diameter as Eq. (10) [29]. Re =
ρ × Dh × u μ
(10)
The friction factor is affected by nanoparticles’ volume concentration and is considered a parameter to measure the pressure drop in the tube [29]. At turbulent flow, it can calculate the friction factor of nanofluids from Eq. (11) suggested by Filonenko [12]. −2 fnf = 0.79lnRenf − 1.69
(11)
3 Validation of Experimental The system has been modelled with MATLAB/Simulink; this model represents a small water electric heater system set to work between the temperature of 60–63 °C and a pressure of 0.6 MPa. Four ports were connected heat exchanger; the first port represents the (inlet cold fluid) connected by a reservoir involving cold water at a specific temperature with a pump and mass flow rate sensor, as shown in Fig. 4. The second port is (hot outlet fluid) connected by a temperature sensor to measure the temperature variation between the inlet and outlet. The other ports are connected to an electric water heater to circulate the hot water and make it homogeneous. The heat exchanger models the heating of fluids through conduction heat from hot water on the outer wall to water circulating inside pipes of the heat exchanger that have a temperature less. The thermophysical properties of the liquid are defined and set on the thermal liquid tab. The simulation system undergoes governing equations into Simulink blocks that depend on variable parameters. The blocks of Simulink have been built according to actual experiments; results were taken for a specific period to validate the effectiveness of the actual system.
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Fig. 4. Simulink model of the heat exchanger.
4 Results and Discussion The present experiment used Fe2 O3 , WO3 nanofluids under 0.015% and 0.01% volume concentration in DI water. The experiment dealt with the effects of type, the volume concentration of nanoparticles in the base fluid, temperature range, and flow rate on thermal properties of working fluids for determining a proposed coolant. The temperature range of the heat exchanger proposed as a replacement for the automotive radiator is between 61–63 °C with a flow rate of 0.28 to 3 L/min for three hours. 4.1 Effect of Temperature with Volume Concentration on Friction Factor and Reynolds Number Adopted Fe2 O3 /DI water and WO3 /DI water as nanofluids, which achieved interesting changes in the heat transfer, thus affecting other parameters by improving the thermal characteristics. Heat transfer of the Fe2 O3 nanofluid used achieved better performance than WO3 nanofluids, reflecting enhanced working fluid characteristics due to increased thermal conductivity and decreased viscosity at the outlet. Figure 5(a) shows the variation of inlet and outlet temperature of the DI water and nanofluids that on the thermal performance of the working fluid. An increase in inlet hot fluids temperature causes decreased friction factor and a slight decrease in viscosity of fluids. In contrast, the increase in Reynolds number leads to a decrease in friction factor. The friction factor is affected by the volume concentration of nanoparticles; at 0.015% and 0.01% nanoparticles volume concentration into base fluid, the viscosity of nanofluids increases compared to DI water, and the friction factor increases. Figure 5(b) shows the decreased friction factor with an increase of the average Reynolds number due to the volume concentration and viscosity of Fe2 O3 nanofluid being the highest compared with WO3 nanofluid and DI water.
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Fig. 5. (a) The temperature profile of working fluid, (b) influence of Reynolds number on friction factor.
Reynolds number is a measure of flow pattern, influenced by fluid inlet temperature and volume concentration of nanoparticles into the base fluid. Figure 6 shows the effect of mass flow rate on increasing Reynolds number at a different volume concentration of nanofluids. It can conclude that the increase of Reynolds number is related to the volume concentration of nanoparticles, flow rate, and inlet hot fluid temperature. Fe2 O3 nanofluid achieved increment by Reynolds number about 2.5% and 3.8% compared with
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WO3 nanofluid and DI water. Thus, increasing the Reynolds number and Nusselt number leads to an increased heat transfer rate to a more significant extent.
Fig. 6. Effect of flow rate and volume concentration on Reynolds number.
4.2 Heat Transfer Coefficient An increment in thermal conductivity of the fluid has a positive effect on the convective heat transfer coefficient, thus enhancing of Nusselt number. Increasing the mass flow rate of the nanofluids achieved an increment in the heat transfer coefficient; thereby, the volume concentration of nanoparticles, inlet temperature, and flow rate contributed by an increment of the heat transfer coefficient of working fluid. Figure 7 shows the heat transfer coefficient enhancement with Fe2 O3 nanofluid at about 9.6%, higher than other studies conducted by [15] that used Fe2 O3 and CuO/water at 0.15%, 0.4%, 0.65 vol.%, and temperatures from 50 to 80 °C. Using WO3 nanofluid at 0.01 vol% achieved an increment of heat transfer coefficient higher than DI water and less than Fe2 O3 nanofluid by 6%. A slight variation was observed by the experimental and simulation results regarding the heat transfer coefficient of about 2% for the fluids used. Therefore, this experimental work adopting a low concentration of nanoparticles into base fluid achieved better heat transfer characteristics. Avoiding agglomeration and sedimentation of nanoparticles into pipes required a specific volume concentration for the nanoparticles that contribute to enhancing of thermal properties of nanofluid. Thus, avoiding increasing the viscosity of nanofluids causes more pump power consumption.
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Fig. 7. Influence of flow rate and inlet temperatures on heat transfer coefficient.
4.3 Heat Transfer Rate Depending on the thermophysical properties of nanofluids and deionized water to determine the heat transfer rate for both fluids, it considered the volume concentration of nanoparticles suspended by water and its effect on the thermal behaviour of the liquid. The experiment was conducted under a temperature range between 61–63 °C with a fluctuation flow rate between 2.8–3 L/min. Fe2 O3 nanofluid was recorded increment by the heat transfer rate higher than WO3 nanofluids and deionized water about 4.1%, 5.8%, respectively, due to the high potential of their thermal properties that positively reflected on the heat transfer performance from the boiler to the storage tank. Figure 8 indicates the difference in heat transfer rate between used nanofluids and deionized water, the increment of heat transfer rate was uniform under the operating temperature range and flow rate at three hours which confirms the stability of nanofluid used during the operation. Comparison to another study conducted by [2] that used silver nanoparticles mixed with water with 0.05 vol.% as a nanofluid, it achieved an increment of the heat transfer rate of about 4.4%. In the present study, 0.015 vol% of Fe2 O3 gives an increment of about 5.8% of heat transfer rate, which indicates that Fe2 O3 nanofluid has the potential to be a cooling fluid more efficient than WO3 nanofluid that shows enhancing better than conventional cooling fluid. A slight variation in the heat transfer rate accentual during simulation compared with experimental results validate the measurement of experimental results, which is acceptable.
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Fig. 8. Heat transfer rate of working fluids at variation temperatures and flow rate.
4.4 Nusselt Number (Nu) Nusselt number is influenced by a volume concentration of nanoparticles and fluids temperature, as shown in Fig. 9(a). Nusselt number significantly increases with an increase in the temperature of the working fluids; it can see the difference in the Nusselt number which used Fe2 O3 nanofluid compared to WO3 nanofluid and DI water. Using Fe2 O3 nanofluid led to an increment of Nusselt number about 6.2%, 8.5% higher than WO3 nanofluids and DI water at a maximum temperature of 63 °C. It is evident in this study that three parameters have a significant effect on the Nusselt number, such as thermal conductivity of the working fluid, Hydraulic diameter, and heat transfer coefficient of the working fluid. Increasing the thermal conductivity and heat transfer coefficient of the working fluid causes enhancement of the Nusselt number, which appeared on the used Fe2 O3 nanofluid. On the other side, the mass flow rate has a vital role in the Nusselt number; Fig. 9(b) show the increase in Nusselt number with an increase in mass flow rate, the range of flow rate help suspend nanoparticles in the base fluid, thereby more stability of nanofluid. Compared with this study, an experiment conducted by [21] had used aluminium nanoparticles mixed with water at 0.2% and 0.3 vol % enhanced the Nusselt number by 3.37% and 5.0877%. In present experiment used Fe2 O3 /DI water at 0.015 vol% enhancement of Nusselt number by 8.5%. Thereby, with lower volume concentration of Fe2 O3 / DI water gave a high Nusselt number of Al/waters, and the variation of simulation and experimental results was convergent.
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Fig. 9. A (a) Influence of inlet temperatures, (b) flow rate on enhancing of Nusselt number.
5 Conclusion In this study, Fe2 O3 , WO3 nanofluid, and deionized water have been used with a heat exchanger as a replacement for the automotive radiator to investigate the performance of the fluids as an alternative to conventional fluids. Heat transfer of the nanofluids used achieved better performance than DI water due to increased thermal conductivity. The volume concentration of nanoparticles, flow rate, and inlet hot fluid temperature influence an increase of Reynolds number. Increasing of volume concentration of nanoparticles
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cause an increased friction factor, while the increase in Reynolds number leads to a decrease in friction factor. Fe2 O3 nanofluid achieved increment by Reynolds number about 2.5% and 3.8% compared with WO3 nanofluid and DI water. Furthermore, volume concentration, inlet temperature, and flow rate contribute to an increment of the heat transfer coefficient of nanofluids by 9.6% and 6%, respectively, compared with DI water. Thereby, suspending 0.015%vol of Fe2 O3 nanoparticles into DI water led to an increment of Nusselt number by 6.2%, 8.5% higher than WO3 nanofluids and DI water at a maximum temperature of 63 °C. Increasing the thermal conductivity and heat transfer coefficient of the Fe2 O3 nanofluid causes enhancement of the Nusselt number better than increases in heat transfer rate to a larger extent. A slight variation observed by the experimental and simulation results of about 2% confirms the accuracy of the results. This study adopts a low volume concentration to avoid the agglomeration and sedimentation of nanoparticles into pipes, thus avoiding increasing the viscosity of nanofluids viscosity, causing more power consumption of the pump. Acknowledgements. The authors would like to thank Mate Zs. Lesko (3DLab) Institute of Mineralogy and Geology, University of Miskolc and Dr. Andrea Simon (C-Therm TCi) Institute of Ceramics and Polymer Engineering, University of Miskolc and Dr. Erzsébet Nagy (XRD) MTA-ME Materials Science Research Group, University of Miskolc, Bozzay Peter Laboratory of Department of Fluid and Heat Engineering, Faculty of Mechanical Engineering and Informatics for support to this research.
References 1. Amiri, A., et al.: Synthesis of ethylene glycol-treated graphene nanoplatelets with one-pot mi-crowave-assisted functionalization for use as a high-performance engine coolant. Energy conversion and management 101, 767–777 (2015) 2. Cardenas Contreras, E.M., Oliveira, G.A., Bandarra, F.E.P.: Experimental analy-sis of the thermohydraulic performance of graphene and silver nanofluids in automo-tive cooling systems. Int J Heat Mass Transf 132, 375–87 (2019) 3. Mahay, N., Yadav, R.K., Sharma, S.: Fabricating Experimental set-up to study the effect of titanium/water nanofluid concentration on heat transfer and fluid flow characteristics in a single pass cross flow compact heat exchanger. Int J Adv Res Sci Eng. 6(4), 93–117 (2017) 4. Esfe, M.H., Behbahani, P.M., Arani, A.A.A., Sarlak, M.R.: Thermal conductivity enhancement of SiO2 –MWCNT (85:15%) E.G. hybrid nanofluids, ANN designing, experimental investigation, cost performance and sensitivity analysis. J Therm Anal Calorim 128(1), 249–58 (2017) 5. Akbari, O.A., Afrouzi, H.H., Marzban, A., Toghraie, D., Malekzade, H., Arabpour, A.: Investigation of volume fraction of nanoparticles effect and aspect ratio of the twisted tape in the tube. J. Therm. Anal. Calorim. 129(3), 1911–1922 (2017). https://doi.org/10.1007/s10973017-6372-7 6. Finn, J., Ewing, D.J., Ma, D.J., Wagner, J.: Nanofluid augmented coolant rail ther-moelectric cooling of electronic systems modeling and analysis. In: American Control Conference on O’Farrell Street, pp. 3077–3083. San Francisco, CA, USA (2011) 7. Eastman, J.A., Choi, S.U.S., Li, S.: Anomalously increased effective thermal conduc-tivities of ethylene glycol-based nanofluids containing copper nanoparticles. Appl Phys Lett 78, 718–720 (2001)
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8. Bencs, P., Alktranee, M.: The potential of vehicle cooling systems. IOP Conf. Physics, Ser 1935, 1–10 (2021). https://doi.org/10.1088/1742-6596/1935/1/012012 9. Karthik, V., Sahoo, S., Pabi, S.K., Ghosh, S.: On the phononic and electronic contri-bution to the enhanced thermal conductivity of water-based silver nanofluids. Int J Therm Sci. 1(12), 168–178 (2012) 10. Sonage, B.K., Mohanan, P.: Miniaturization of automobile radiator by using zinc-water and zinc oxide-water nanofluids. J. Mechani. Sci. Technol. 29(5), 2177–2185 (2015). https://doi. org/10.1007/s12206-015-0438-x 11. Kamel, M.S., Lezsovits, F.: Experimental investigation on pool boiling heat transfer performance using tungsten oxide WO3 nanomaterial-based water nanofluids. MDPI, Materials (13), 2–17 (2020). https://doi.org/10.3390/ma13081922 12. Sharma, S.: Fabricating an experimental setup to investigate the performance of an automobile car radiator by using aluminum/water nanofluid. J. Therm. Anal. Calorim. 133(3), 1387–1406 (2018). https://doi.org/10.1007/s10973-018-7224-9 13. Ebrahimi, M., Farhadi, M., Sedighi, K., Akbarzade, S.: Experimental investigation of force convection heat transfer in a car radiator filled with SiO2–water nanofluid. IJE Trans B Appl 27(2), 33–40 (2014) 14. Heris, S.Z., Shokrgozar, M., Poorpharhang, S., Shanbedi, M., Noie, S.H.: Experi-mental study of heat transfer of a car radiator with CuO/ ethylene glycol-water as a coolant. J Dispers Sci Technol 35(5), 677–684 (2014) 15. Peyghambarzadeh, S.M., Hashemabadi, S.H., Naraki, M., Vermahmoudi, Y.: Experimental study of overall heat transfer coefficient in the application of dilute nanofluids in the car radiator. Appl. Therm. Eng 52, 8–16 (2013) 16. Nieh, H.M., Teng, T.P., Yu, C.C.: Enhanced heat dissipation of a radiator using oxide nanocoolant. Int J Therm Sci 77(2), 52–61 (2014) 17. Bang, I.C., Chang, S.H.: Boiling heat transfer performance and phenomena of Al2 O3 –water nanofluids from a plain surface in a pool. Int. J. Heat Mass Transf 48, 2407–2419 (2005) 18. Harish, G., Emlin, V., Sajith, V.: Effect of surface particle interactions during pool boiling of nanofluids. Int. J. Therm. Sci 50, 2318–2327 (2011) 19. Sajid, M.U., Ali, H.M., Sufyan, A., Rashid, D., Zahid, S.U., Rehman, W.U.: Experimental investigation of TiO2 -water nanofluid flow and heat transfer inside wavy mini-channel heat sinks. J Therm Anal Calorim (2019) 20. Giwa, S.O., Sharifpur, M., Meyer, J.P.: Experimental study of thermo-convection performance of hybrid nanofluids of Al2 O3 - MWCNT/water in a differentially heat-ed square cavity. Int: J. Heat Mass Transf 148, 119072 (2020) 21. Farrukh, A., et al.: Towards convective heat transfer optimization in aluminum tube automotive radiators: Potential assessment of novel Fe2 O3 -TiO2 /water hybrid nanofluid. J. Taiwan Inst. Chem. Eng. 124, 424–436 (2021) 22. Hanumanth Ramji, K.S., Vinoth Kumar, J., Amar Karthik, A.: Experimental investigation of automobile radiator using tungsten trioxide nano-fluid. IOP Conf. Series: Mater. Sci.and Eng 995, 1–8 (2020). https://doi.org/10.1088/1757-899X/995/1/012017 23. Han, B.-Y., et al.: WO3 thermodynamic properties at 80–1256 K revisited. J. Therm. Anal. Calorim. 142(4), 1533–1543 (2020). https://doi.org/10.1007/s10973-020-09345-z 24. Pak, B.C., Cho, Y.I.: Hydrodynamic and heat transfer study of dispersed fluids with submicron metallic oxide particles. Experimental Heat Transfer an International Journal 11, 151–170 (1998) 25. Xuan dan., Y., Roetzel, W.: Conceptions for heat transfer correlation of nanoflu-ids. J. Heat Mass Transf 43(19), 3701–3707 (2000) 26. Yu, W., Choi, S.: The role of interfacial layers in the enhanced thermal con-ductivity of nanofluids: a renovated maxwell model. Journal of Nanoparticle Re-search 5, 167–171 (2003). https://doi.org/10.1023/A:1024438603801
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Investigation of Shape Correctness of Thermally Tested Alternator Stators Viktoria Ferencsik(B) and Gyula Varga University of Miskolc, Miskolc Egyetemváros, Miskolc 3515, Hungary [email protected]
Abstract. The paper analysis the changing of internal roundness of alternator stators which is caused by different (relatively higher and lower) temperature storage tests and calculated with statistic method. The specific power and efficiency of the automotive alternator can be significantly decreased due to this kind of failure as the extending stator points crash to the rotor, which cannot rotates adequate speed. The main purpose of this publication is to investigate the effect of thermal testing parameters (temperature, running time, set or not with damping element) on shape correctness. In this investigation, a specific algorithm and the full factorial experimental design method are used to plan and execute the experiment. The measurement of the roundness of the specimens is done with a circular and position error measuring equipment. Special improvement ratios are determined from the measured data to clarify the suitable range of testing parameters which results in greater deformity. Keywords: Alternator · Roundness · Thermal test · Full factorial experimental design
1 Introduction Nowadays, in passenger cars, several percentages of the total energy are used to power the alternator, which converts mechanical rotational energy into electrical energy due to increased electric loads in the vehicles. These enhanced electrical loads are the consequence of improvements in safety, comfort and electric controls [1]. Alternators are used not only in small cars but also in agricultural and structural engineering and in other pieces of equipment e.g. stationary alternators, etc. They are constructed in a variety of power and voltage levels and normally are always checked out from many aspects, such as effectiveness, dimensions, reliability, weight and costs [2–6]. The noise of claw pole alternators has also been the topic of a substantial investigation plan when the aim is to diagnose and decrease noise radiation mechanisms in claw pole alternators that are used in automotive vehicles. Investigators found that according to different models and experimentations, there is a high correlation between electromagnetic sources, torque ripple, and radiated noise [7], while previous research supposed that in alternators, the noise radiation mechanism is a dynamic response of the stator, and consequently the housing, to rotating electromagnetic forces between the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 538–548, 2023. https://doi.org/10.1007/978-3-031-15211-5_44
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rotor and stator [8], so from this point of view measuring of the shape accuracy of the stator element is necessary to be performed. In close connection with the noise mechanisms in alternators, the induced vibrations are also big problems because of it accelerated vibration tests are common in the automotive industry. With these types of testing methods, for example, with a fatigue-damage model, companies can ensure the durability of components [9]. Of course, other different mechanical behaviours and different damage mechanisms are subjected to investigation, which are mostly identified by SEM (scanning electron microscopy), TEM (transmission electron microscopy) and EBSD (electron back-scattered diffraction) techniques [10, 11]. As the output energy and energy density requirements of automotive alternators are increasing significantly, many papers deal with the investigation of solutions how to achieve an increment in output power [12–14]. These studies were concerned with different model methodologies and the optimization of the alternator design for maximum output power. Previous and further researchers have shown that many factors can lead to a reduction in performance, such as high-amplitude and high-frequency air gap harmonics [15, 16] and they thought the solution to the problem is the modifying the rotor geometry [17, 18], but for example Liu et al. [19] examined the problem from a different perspective: they have investigated the influence of stator deformations of the alternator on its torque and others [20] also found relationships between oval stators and harmonics. Our attention is paid to diagnosing the failure problem of internal roundness of the alternator stator (which was set with the front and the rear bracket) that was tested with high and low-temperature storage tests. The full factorial experimental design method was used in this examination [21–23], which is a ratio between the maximum and minimum values (so-called levels) of the testing parameters. In this inquiry, these parameters were: testing temperature (T), the running time of the test (t), and that the stator was equipped with damping element (DE) or not. The purpose of the latter component is to attenuate noise during operation.
2 Experimental Investigations 2.1 The Applied Experimental Design Method The full factorial experimental design method was chosen, which is an active, effective experimental technique, and the aim of it is to determine the functional relationship between a dependent (roundness) and independent (thermal testing parameters) variables that are called factors, which can take more rates. The matrix of this practice is shown in Table 1, which includes the pre-test parameters for both the transformed and natural dimensions. Related to the damping element in Table 1 “O” means the lack of damping element “withOut” while “W” means the presence of damping element “With”.
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V. Ferencsik and G. Varga Table 1. Applied testing parameters.
No.
Testing parameters
Transformed parameters
T [°C]
t [h]
Damping element
x1
x2
x3
1
−40
150
O
−1
−1
−1
2
130
150
O
+1
−1
−1
3
−40
300
O
−1
+1
−1
4
130
300
O
+1
+1
−1
5
−40
150
W
−1
−1
+1
6
130
150
W
+1
−1
+1
7
−40
300
W
−1
+1
+1
8
130
300
W
+1
+1
+1
To make the changing of roundness error more obvious, dimensionless ratios (Formula 1–2) were generated: ρRON =
RONtested RONbefore
ρRON % = (ρRON − 1) · 100%,
(1) (2)
where: ρRON Improvement ratios of different roundness (RON) parameters, e.g., ρRONp , ρRONv , ρRONt . These are dimensionless ratios, which characterize the changes that occur as a result of testing, RONtested Roundness parameters remain after the temperature test, RONbefore Roundness parameters being before the temperature test, ρ% The percentage value of the changes. The smallest the value of ρRON , the greater the improvement. 2.2 The Testing Processes As automotive alternators are the primary sources of power on board vehicles, different diagnostics of technical conditions about them is an inherent task [25]. The purpose of the test is to assess the product’s suitability for storage under lower (−40 °C) and higher temperatures (130 °C) and put it into operation. Proof of the influence of cold and warm on the function and (irreversible) changes to the material properties (e.g., embrittlement, hardening, etc.). The high and low-temperature storage test was carried out with a floor standing temperature and climatic test chamber (Weiss WK1 180) with a temperature range − 40 °C to 180 °C. The apparatus includes a climate and temperature conditioning system, adjustments standards in the aspect of ease of use, achievement and installation. The rate of changing of temperature during heating and cooling was the same: 4 °C/min.
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Stators assembling with shields were positioned in horizontal in the chamber to prevent deformation because of improper placement. 2.3 Measuring of Roundness The roundness measurement of a circular workpiece is a common problem in quality control and inspection [26]. In this investigation, the process was done with a Talyrond 365 Shape error and Roundness Measurement equipment [27], and an inductive sensor was applied for measuring before and after thermal test in 2 mm distance on 30 mm length. In all 16 roundness indices were analyzed and 3 of them were chosen and examined, which mostly determine operating properties. “Parameter RONt (roundness total) is the most used parameter. It is the maximum difference in- and outside the reference circle and is the amount of RONp (Roundness Peak) and RONv (Roundness Valley), which are companion parameters”. The above-mentioned roundness parameters are included in standard ISO 12180–1 as RONt - Peak-to-valley roundness divergence RONp - Peak-toreference roundness deviation; RONv - Reference-to-valley roundness deviation [27]. As the stators were assembled with the brackets, we had to turn a hole with proper diameter on the front bracket in order to the probe can border on the surface to be measured (Fig. 1).
Fig. 1. Measuring position.
Another problem was that due to the geometry of the assembled piece, its face surface couldn’t be placed horizontally on the rotating desk of the measuring machine, so a fixture was designed as a solution.
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During the measuring process, the maximal permissible eccentricity of the axis of the stator and the measuring table was adjusted to 1 μm by the own software of the equipment. The sensibility and range of motion of the probe were set up as well in order to prevent the circular error caused by the lamellae. 2.4 Analysis of Roundness Parameters In spite of the preliminary adjustments, the analyzation of the results was required, during which we excluded the errors caused by the 1.5 mm protrusion of the lamellas from the measurement results (Fig. 2).
Fig. 2. Correctness of circularity before (a) and after (b) assaying.
As this filtering is just approximately accurate, we filtered out irregularities point by point with MATLAB program, which required, as a first step, the evaluation of the 16 roundness results (RONp, RONv, RONt) per stators before and after the thermal test (Fig. 3/a). In the report of the roundness measurement, individual numerical values have been transformed from cylindrical coordinate system to Descartes coordinate system: the mean value of the given segments on Y-axis was taken as zero, and the X-axis shows the angular rotation in tenths of a degree (Fig. 3/b). For a total of 3600 measurements, the distances from the mean are plotted in blue colour, then deriving the function (orange), we inspected where there is an abrupt alteration in the slope of the function, which refers to the slot in the stator and not the manufacturing feature (where the material is). Selecting the ranges obtained, a more accurate data set was given (purple) at the measured values, as it can be seen in Fig. 3/b.
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Fig. 3. Analyzed circularity in the cylinder (a) and in Descartes (b) coordinate system.
3 Results Table 2–4 summarizes the results of the calculations with the percentile values of the improvement ratios calculated by Formulas (1–2). The individual values were determined from the average of the values per segment (16 segments), ignoring the minimum and maximum values.
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V. Ferencsik and G. Varga Table 2. Measured values of RONv. No.
RONv [μm]
ρRONv [%]
Before
After
1
0.2334
0.1989
−14.41
2
0.2712
0.2422
−10.69
3
0.3406
0.2438
−28.42
4
0.1020
0.1043
2.25
5
0.0771
0.1325
71.85
6
0.0666
0.0399
−40.09
7
0.1671
0.1759
5.27
8
0.0394
0.0330
−16.24
Table 3. Measured values of RONp.
Before
After
ρRONp [%]
1
0.0767
0.0687
−10.17
2
0.0782
0.0615
−21.36
3
0.0679
0.0669
−1.47
4
0.0719
0.0534
−25.73
5
0.0768
0.0782
1.82
6
0.0536
0.0322
−39.93
7
0.0362
0.0385
6.35
8
0.0365
0.0344
−5.75
No.
RONp [μm]
Table 4. Measured values of RONt.
before
After
ρRONt [%]
1
0.3207
0.2658
−17.12
2
0.3524
0.2871
−18.53
3
0.4046
0.3076
−23.97
4
0.1709
0.1895
10.88
5
0.1535
0.2135
39.09
6
0.1209
0.0724
−40.12
7
0.2182
0.2168
−0.64
8
0.0746
0.0693
−7.10
No.
RONt [μm]
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Using Full Factorial Experiment Design method, empirical formulas could be determined by Eqs. (3–5). MathCAD software was used for the calculations and to demonstrate the functions (Figs. 4–6).
Fig. 4. Experiment results from the point of view of valleys.
Fig. 5. Experiment results from the point of view of peaks.
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Fig. 6. Experiment results from the point of view of largest deviations.
ρRONp = −18.969 − 0.204 · T + 0.057 · t − 0.541 · DE + 3.251 · 10−4 · t − 0.216 · T · DE + 0.02 · t · DE + 8.376 · 10−4 · T · t · DE ρRONp = 42.474 − 0.664 · T − 0.177 · t + 48.34 · DE + 2.302· 10−4 · t − 0.577 · T · DE − 0.125 · t · DE + 1.245 · 10−3 · T · t · DE ρRONt = 11.966 − 0.558 · T − 0.07 · t + 31.099 · DE + 2.137· 10−3 · t − 0.366 · T · DE − 0.081 · t · DE + 7.155 · 10−4 · T · t · DE
(3)
(4)
(5)
The manufacturing process of the alternator can cause 2 types of the strain on the stator [7]. Firstly, according to the stator manufacturing: the cold−-rolled non-oriented electrical steel (EN 10106 (2015)) preform as in strip than cut at the appropriate length, plasma jet welded and coiled. Therefore, tensile and residual stresses are induced in the piece, which can relax because of the applied test. Then there is a deformation due to the assembly, as the stator is clamped between two aluminium brackets in the axial direction with four screws.
4 Conclusions This paper deals with the experimental analysis of the alteration of inner roundness in the case of automotive alternator stators, which were tested with so-called high and lowtemperature storage procedure, and according to it the investigated influencing parameters were: temperature (T) and running time (t) of the test, and that the stator was equipped with damping element (DE) or not.
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The full factorial experimental design was used with the calculated dimensionless ratios to determine how testing parameters have an effect on the shape correctness, and it can be stated on the base of this study: • As a lower value of the improvement ratios means a greater refinement, in the vast majority, there is an improvement because of the applied test method, especially at higher temperatures, but the exact explanation of this phenomenon needs further investigation as we suspect that the improving of roundness is a consequence of the changing of stress conditions between the set and tested components and it can be examined with X-ray diffraction measuring method. • It cannot be clearly established that the setting of the damping element has a positive or a negative effect on the roundness. • The most favourable values were found for stator no. 6 by the application of the testing parameters as: T = 130 ◦ C t = 150 h Applied with a damping element • But in stark contrast, the worst results were obtained in the case of stator no. 5 where only the test temperature was different (−40 °C); it also follows that temperature plays a crucial role in changing circularity.
Acknowledgement. “Project no. NKFI-125117 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the K_17 funding scheme.”
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Optical Investigation of the Strain Distribution with Different Orientations on Aerospace Composite Material Máté File1 , Imre Kállai2 , Dávid Huri1,3 , and Tamás Mankovits1(B) 1 Department of Mechanical Engineering, Faculty of Engineering, University of Debrecen,
Ótemet˝o u. 2-4, 4028 Debrecen, Hungary [email protected] 2 Diehl Aviation Hungary Ltd., Vágóhíd u. 2, 4034 Debrecen, Hungary 3 Doctoral School of Informatics, Faculty of Informatics, University of Debrecen, Kassai u. 26, 4028 Debrecen, Hungary
Abstract. The orientation in composite materials determines the mechanical properties of the structure. Different orientations have different usages, and by combining multiple layers with changing orientations, the best mechanical properties can be achieved for the given load case. This work aims to investigate the effects of the different orientations on the mechanical properties of a glass-fibre reinforced composite material used in aerospace components. Multiple specimens were prepared using the vacuum bagging technology with 0°, 45° and 90°orientations based on the warp direction. The laboratory measurements were done with the INSTRON 68TM-10 material testing machine and the GOM Aramis 12M full-field optical measurement system, which works based on the digital image correlation method. The Aramis system enables the real-time tracking and visualisation of the displacement and strain values on the whole surface of the specimen. Under the same conditions, tensile tests were performed on each specimen, and the results were compared. The mechanical material properties of each orientation were determined. Keywords: Digital image correlation · Material testing · Fibre orientation · Pre-preg composite material
1 Introduction Composite materials are used more and more frequently in aerospace engineering because of their lightweight and their excellent mechanical properties. From all the different types of composites, woven composite laminates have the widest usage in the aerospace industry. These materials are generally used in a pre-preg form and then layered in different orientations to create the structure [1]. The reason behind the different orientations is the strong relationship between the mechanical properties and the waft direction. Every orientation has different tensile properties, as the tensile strength comes © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 549–557, 2023. https://doi.org/10.1007/978-3-031-15211-5_45
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from the weave itself, meaning that under more complex load cases, it is better to use a combination of different orientations [2]. By performing tensile tests on the specimens with different orientations under the same conditions, the mechanical properties of the different orientations can be evaluated and compared. Specimens prepared with unidirectional orientations, where the waft or fibre direction is constant between the layers of the specimen, were compared in [3–5]. A multidirectional approach, where the orientations changed between the layers of the specimen, was investigated in [6, 7]. The behaviour of the different multidirectional laminates was measured using a complex tension/compression and shear load in [8]. The measurement can be followed in real-time with optical measurement systems, which work based on the digital image correlation (DIC) method [9]. Such systems are widely used in material testing [10, 11]. DIC measurements are also used in composite testing [12–14]. The behaviour of the different orientations under tensile load was measured using optical full-field measurements in [5]. Strain data was measured on the surface of the specimens with different orientations during the complex tension/compression and shear loading in [8]. The fracture behaviour of different fibre orientations was evaluated using optical full-field measurements in [15]. This paper deals with the optical investigation of the effect of different orientations on the mechanical behaviour in aerospace using satin woven composite material. The goal of the article is to provide information about the possible use cases of the different orientations and to detail their mechanical properties.
2 Materials and Methods 2.1 Specimen Preparation The composite material was procured as a pre-preg, which consists of a glass-fibre satin weave impregnated by a phenolic resin matrix. 300x200 mm sheets were cut from the roll in 0°, 45° and 90°orientations. Three of each orientation were layered on top of each other and onto a metal sheet which was treated by a release detergent to help the removal of the finished product. The laminates were then covered by a peel-ply, which also aids the cleaning of the finished product. After the peel-ply a perforated film and a breather layer were placed onto them, these layers help the flow of the excess resin. Finally, the laminate was covered by a vacuum bag and placed into a curing oven. The curing conditions can be seen in Table 1, and images from the vacuum bagging process can be seen in Fig. 1. Table 1. Curing conditions Curing temperature
120 °C
Warm-up time
45 min
Curing time
120 min
Cool-down time
30 min
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Fig. 1. Images from the vacuum bagging process
After the curing process, 200x25 mm specimens were cut out of the 0.8 mm thick sheets using a waterjet machine. To the ends of the specimens, 60 mm long and 1 mm thick aluminium tabs were glued using LOCTITE HY 4080 adhesive. The finished tensile specimens can be seen in Fig. 2.
Fig. 2. The prepared tensile test specimens with different orientations.
2.2 Tensile Testing The tensile tests were performed using the INSTRON 68TM-10 uniaxial material testing system and the GOM Aramis 12M optical measurement system, which uses the DIC method. The tensile specimens were measured and painted with a stochastic pattern using matte white powder paint for the background and matte black acrylic paint for the points. This pattern provides reference points for the Aramis system to calculate the strain and displacement data during the evaluation. To ensure the proper contrast between the colours of the pattern, the Aramis uses polarised blue light to illuminate it. An image of the painted pattern can be seen in Fig. 3.
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Fig. 3. Software image of a specimen with the painted stochastic pattern
The specimens were gripped using mechanical wedge-action tensile grips. The testing rate was set to 5 mm/min with a data collection of 4 Hz. The failure of the specimens marked the end of the measurement, which was detected if a 40% decrease occurred in the force values. The testing setup with a gripped specimen can be seen in Fig. 4.
Fig. 4. The testing setup with the Instron 68TM-10 and GOM Aramis
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2.3 Data Evaluation Data were evaluated using the GOM Correlate Professional software. First, the Young’s modulus of all the specimens was calculated by fitting a line to the linear section of the curves. Then from each orientation, the specimen representing the median value was selected for further investigation. Images before and after the failure were inspected to find any signs of the failure. Stress-strain curves were established to understand the relation between the three orientations. From these curves, the tensile strength and the maximum strain values were evaluated and compared to each other.
3 Results and Discussion After measuring all 9 specimens the Young’s modulus values were evaluated, the values can be seen in Table 2. Table 2. Young’s modulus values of the 9 specimens [MPa] Specimen Nr.
Orientations 0°
45°
90°
1
16937
7742
15202
2
16539
7693
14924
3
15767
7794
15377
Median
16539
7742
15202
Relative error based on the median
4.67%
0.67%
1.83%
The specimens representing the median values of each orientation were selected for further evaluation. Images were evaluated before the failure of the three orientations. Because of their rigidity, the 0° and 90° specimens showed no signs before the failure, however in the case of the 45° specimen, a stress singularity is visible along the main fibre direction. The images before the failure can be seen in Fig. 5.
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Fig. 5. Strain images of the three orientations before the failure
Images after the failure show that in the case of the 90° orientation, the stochastic pattern completely vanished from the surface of the specimen due to the rapid failure, thus disabling any further data evaluation. In the case of the other two orientations, after the failure, the pattern was unreadable for the software near the break. Further away from the failure, however it remained usable. From these images, it is also visible that all the orientations ruptured along the main fibre direction. The images can be seen in Fig. 6.
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Fig. 6. Images of the three orientations after the failure
Average engineering stress-strain data was also evaluated from the software, Fig. 7 shows the stress-strain relations of the different orientations.
Fig. 7. The stress-strain curves of the three orientations
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Finally, from the curves, the tensile strength and maximum strain values of the specimens were evaluated, which is shown in Table 3. Table 3. Tensile strength and maximum strain values of the three orientations Orientations
Tensile strength [MPa]
Maximum strain [%]
0°
407.3
2.514
45°
87.9
6.166
90°
281.2
2.015
4 Conclusion The results of the measurements clearly show the differences between the orientations. As expected, the 0°orientation proved to be the strongest with the 407 MPa tensile strength, but also the most rigid, as it failed rapidly without any signs. This orientation had the highest Young’s modulus of the three with a value of 16539 MPa. The 90°orientation also showed rigidity, but it was not able to withstand the amount of deformation or load, that the 90° orientation was able to. In the case of the 45° orientation, the specimen showed significantly more strain than in any of the other cases, with a maximum strain value of 6%. It was also the only specimen that showed necking before the failure occurred. Overall, because of its rigidity and rapid failure, the 0°orientation alone may not be suitable for some applications, the same can be stated about the 90° orientation. To utilise the strength of the 0°orientation, it is recommended to layer it with additional 45° layers to decrease the rigidity of the finished structure. The optical full-field measurement system has proven to be appropriate equipment to localise the failure with high accuracy. Acknowledgements. The research was supported by the Thematic Excellence Programme (TKP2020-NKA-04) of the Ministry for Innovation and Technology in Hungary, within the framework of the (Automotive Industry) thematic program of the University of Debrecen. We thank Diehl Aviation Hungary Ltd. For providing the material and supporting the research.
References 1. Rana, S., Fangueiro, R.: Advanced composites in aerospace engineering. In: Advanced Composite Materials for Aerospace Engineering. Elsevier, pp 1–15 (2016) 2. Vasiliev, V V.: Advanced Mechanics of Composite Materials. Elsevier (2013) 3. Cordin, M., Bechtold, T., Pham, T.: Effect of fibre orientation on the mechanical properties of polypropylene–lyocell composites. Cellulose 25(12), 7197–7210 (2018). https://doi.org/ 10.1007/s10570-018-2079-6 4. Patel, H.V., Dave, H.K.: Effect of fiber orientation on tensile strength of thin composites. Mater Today Proc 46, 8634–8638 (2021). https://doi.org/10.1016/j.matpr.2021.03.598
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5. Holmes, J., et al.: Characterisation of off-axis tensile behaviour and mesoscale deformation of woven carbon-fibre/PEEK using digital image correlation and X-ray computed tomography. Compos Part B Eng 229, 109448 (2022). https://doi.org/10.1016/j.compositesb.2021.109448 6. Senthilkumar, K., et al.: Effect of inter-laminar fibre orientation on the tensile properties of sisal fibre reinforced polyester composites. IOP Conf Ser Mater Sci Eng 152 (2016). https:// doi.org/10.1088/1757-899X/152/1/012055 7. Abdallah, M.H., Braimah, A.: Numerical design optimisation of the fiber orientation of glass/phenolic composite tubes based on tensile and radial compression tests. Compos Struct 280, 114898 (2022). https://doi.org/10.1016/j.compstruct.2021.114898 8. Laux, T., Gan, K.W., Dulieu-Barton, J.M., Thomsen, O.T.: Ply thickness and fibre orientation effects in multidirectional composite laminates subjected to combined tension/compression and shear. Compos Part A Appl Sci Manuf 133, 105864 (2020). https://doi.org/10.1016/j. compositesa.2020.105864 9. McCormick, N., Lord, J.: Digital Image Correlation. Mater Today 13, 52–54 (2010). https:// doi.org/10.1016/S1369-7021(10)70235-2 10. Jerabek, M., Major, Z., Lang, R.W.: Strain determination of polymeric materials using digital image correlation. Polym Test 29, 407–416 (2010). https://doi.org/10.1016/j.polymertesting. 2010.01.005 11. Filho, J.C.A.D., Nunes, L.C.S.: Experimental determination of deformation homogeneity and shear states using the digital image correlation method. Polym Test 96, 107114 (2021). https:// doi.org/10.1016/j.polymertesting.2021.107114 12. Lehmann, T., Ihlemann, J.: Strain analysis of cord-rubber composites using DIC. Mater Today Proc 32, 183–186 (2020). https://doi.org/10.1016/j.matpr.2020.04.537 13. Huang, J., et al.: Damage evolution of 3D woven carbon/epoxy composites under the tension– compression fatigue loading based on multi damage information. Int J Fatigue 154, 106566 (2022). https://doi.org/10.1016/j.ijfatigue.2021.106566 14. Spencer, R., et al.: Fiber orientation evaluation in reinforced composites using digital image correlation and thermal excitation. Compos Part B Eng 234, 109713 (2022). https://doi.org/ 10.1016/j.compositesb.2022.109713 15. Koohbor, B., Mallon, S., Kidane, A., Sutton, M.A.: A DIC-based study of in-plane mechanical response and fracture of orthotropic carbon fiber reinforced composite. Compos Part B Eng 66, 388–399 (2014). https://doi.org/10.1016/j.compositesb.2014.05.022
Multilayered Aluminum Clinch Joints: An Experimental and Numerical Investigation of the Manufacturing Process Szabolcs Jónás1(B) and Péter Zoltán Kovács2 1 Budapest University of Technology and Economics, M˝uegyetem rkp. 3, 1111 Budapest,
Hungary [email protected] 2 University of Miskolc, Miskolc, Egyetemváros 3515, Hungary [email protected]
Abstract. The classical clinch joints are two-layered spot joints; however, several different variants have been developed so far. Although, the technology is capable to join more than two sheets. In this study, aluminium spot joints were produced and examined because in the past few decades, the importance of aluminium sheets is increasing in car body manufacturing due to their low weight and relatively high strength. The aim of this study is to investigate the multilayered aluminium clinched connections from the point of view of forming process, and resultant parameters. Experimental and numerical studies were performed to analyze the process itself and analyze the resultant joint geometrical values which are primarily indicators of the joint strength. In this study, the applied material was EN AW-5754 aluminium alloy, and the total connected thickness of sheets was 2 mm. The joints were manufactured by a TOX clinching tool at the University of Miskolc. The most important parameters of the joints from a mechanical strength point of view are the neck thickness and the undercut, besides the only measurable parameter, the residual bottom thickness, which were analyzed. The cross-sections of the clinched joints were analyzed by microscopical investigations. The joints, and basically the joining by forming process were also analyzed numerically by 2D asymmetrical finite element model to virtually analyze the forming load, material flow inside the clinching tool and analyze the stress and strain state of the joints. Keywords: Aluminium clinch joint · Multilayer joining · Joining by forming · Clinching · Process simulation
1 Introduction Lightweight design in the automobile industry is a key target for numerous reasons. High strength steels and aluminium alloy sheets are highly important from the strength, mass and manufacturability points of view. Clinching is a widely used mechanical joining technology, to join materials together without any further materials during local plastic deformations. The joining mechanism © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 558–567, 2023. https://doi.org/10.1007/978-3-031-15211-5_46
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is the following: the punch presses the sheets to the die, and during the process an undercut is formed between the sheets due to the special die geometry [1]. The classical clinch variant is the round-spot clinch joint, but several other variants have been developed during the last few decades such as rectangular clinching [2], flat-clinching [3] and other special variants [4–6]. The clinching is a closely related technology to self-pierce riveting [7] and to spot welding [8]. The importance of the clinching technology comes from its flexibility in dissimilar material joining purposes [9]. Mostly two sheets are clinched together, but there is also a possibility to join more sheets. Multilayer joining by clinching is a challenging process [10]. About the threelayer clinching processes the available literature is very limited, furthermore they deal with different variants [4, 10–14]. In the case of conventional, two-layer joints the most important geometrical parameters are the bottom thickness (tB ), the neck thickness (tN ) and the undercut (C), in the case of three-layer joints, there is a second neck thickness and a second undercut, as well (see Fig. 1). These values affect the strength of the joint, and only the bottom thickness is measurable easily after the process, therefore it is an important quality indicator of the joints. Furthermore, the bottom thickness and the other two parameters, the neck thickness and the undercut, are related.
Fig. 1. Geometrical parameters.
This study is focusing on the joinability of three-layer aluminium joints by a conventional round-spot clinching tool. Experimental and numerical studies were conducted to analyze the main geometrical parameters of the joints and the forming load during the process.
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2 Experimental Investigation The experiments were conducted with EN AW-5754 type of aluminium alloy. The alloy is a non-heat treatable type of aluminium alloy, however, it has good weldability and formability, therefore it is a widely used material in such industries as railway and automotive, and furthermore according to EN 602 it is allowed to contact with food. The applied clinching tool is a conventional TOX produced round-spot clinching tool. The tool was installed into an MTS 250 universal testing machine. The clinching tool is basically developed for the joining of two 1 mm thick sheets, therefore the total clamped thickness is 2 mm. The thickness of the available aluminium sheet was 1 mm, thus t = –.66 mm sheets were manufactured by milling at the laboratory of the University of Miskolc in order to produce the three-layer joints with the available clinching tool. The surfaces after milling were polished to minimize the effects of the surface irregularities and undesirable frictional behaviour between the connected surfaces. The unmachined surfaces of the sheets were in contact with the punch and die of the clinching tool. In between the upper (punch side) and middle sheets two milled surfaces are in connection, therefore the coefficient of friction may differ from the other connected surfaces. The joints for the cross-sectional investigations can be seen in Fig. 2. It is important that although the joints are relatively closely produced to each other, the joints are not affecting the other ones in any sense.
Fig. 2. Clinch joints for cross-sectional investigations.
Three joints were made with different bottom thicknesses for microscopic investigation of the formed geometries. The resultant parameters of joints are in the following table (see Table 1). The values indexed as 1 are associated with the upper (punch side) and middle sheets, and the values indexed as 2 are associated with the middle and lower (die side) sheets, respectively. With the decreasing bottom thickness both undercut values are increasing, which is favourable from unbuttoning point of view, but the tN,2 neck thickness is decreasing. The thinning neck should be avoided, due to the importance of the neck thickness from tensile shear-like loads of view. However, the tN,1 neck thickness shows a nearly constant value (tN,1 = –0.11 mm).
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Table 1. Measurement data. Joint ID
Fmax [kN]
tB [mm]
tN,1 [mm]
tN,2 [mm]
C1 [mm]
C2 [mm]
101
13.4
0.629
0.112
0.339
0.150
0.022
102
15.0
0.498
0.104
0.304
0.196
0.058
103
18.0
0.475
0.116
0.289
0.200
0.64
The measured values are compared and presented as graphs in Fig. 3. The behaviour of the parameters with the same indexes is similar in pairs.
Fig. 3. Neck thickness and undercut values with respect to the bottom thickness.
The cross-sections of the joints can be seen in Fig. 4. The punch side neck (tN,1 ) has suffered excessive thinning, so as the upper and middle sheets in the center of the dome of joints.
Fig. 4. Cross-sections of the joints.
Three specimens were prepared for tensile shear tests with similar residual bottom thickness values. The sheets ‘A’ and ‘C’ were also clamped in the MTS 250 universal testing machine.
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The specimen lay-out can be seen in Fig. 5. The third sheet is perpendicular to the direction of the tensile shear force and is not affected by any direct load. The effect of the bending due to the eccentric clamping is not compensated by added sheets at the clamping zone. During the test, load-displacement curves were registered (see Fig. 6).
Fig. 5. Tensile shear specimens.
The force-displacement curve is unusual compared to conventional two-layer sheet measurements, due to the plateau following the maximal force values. In common the force drops down immediately after the maximal force as a distinct sign of the joint failure, which is most commonly rupture of the neck. All the specimens have the same failure mode, the tN,1 neck is ruptured. When the undercut and the neck thickness is the highest possible at the same time, the joint can consider as optimal. According to Fig. 3 the best joints are the ones with tB = –0.5 mm. Because of the previous optimality criterion, the highest tensile shear load occurred at tB = –0.5 mm, however the difference is not significant.
Fig. 6. Force-displacement curves of the tensile shear specimens with different bottom thicknesses.
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The forming force-displacement curves were registered for all the six prepared joints (three for microscopic investigation and three for tensile shear tests). The measured data show the well-known characteristic nature of the clinching process despite of the third layer. The measured values can be seen in Fig. 7. The bottom thickness values correspond to the maximum forming force. The higher the load, the smaller the bottom thickness.
Fig. 7. Forming force-displacement curves of the joints.
3 Finite Element Analysis of the Joint Forming Process 2D axisymmetric FE model has been developed for the numerical analysis of the joining process. The applied commercial FE code is the MSC.Marc&Mentat 2019. The applied model can be seen in Fig. 8. The thickness of the sheets is 0.66 mm. The applied finite elements are 4-noded linear elements. The contact behaviour is frictional between the parts according to Coulomb friction with the coefficients of friction indicated in the figure. A simplified spring row is modelled as a cylindrical part with the linearelastic properties of E = 25 GPa and ν = 0.3. The holder and the die considered as rigid bodies. The model is displacement driven through a rigid element, named as disp. Remeshing is applied to the sheets during the forming process.
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Fig. 8. Finite element model.
The sheets are modeled as elastic-plastic materials. The elastic properties of the aluminium are the commonly used E = 70 GPa and ν = 0.33. The applied material law which describes the material behaviour of the formed sheets and used for extrapolation to the high strain regime, which can reach 2 ÷ 3 during clinching, is the so-called HockettSherby model. The material model is fitted to tensile test measurement data in order to determine the model parameters. The equation of the model is described as follows: (1) σ = σs − exp −(N ε)p (σs − σi ) Table 2 gives the fitted model parameters of the aluminum alloy. Table 2. Material constants of Hockett-Sherby model. σs [MPa]
σi [MPa]
N [–]
p [–]
464,3
98.5
5.98
0.58
The applied flow curve can be seen in Fig. 9 as fitted to the measured ones. Tensile tests 2 and 3 are smoothed measured data for better visibility, although Tensile test 1 is the raw, measured data.
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Fig. 9. Measured and fitted flow curve of EN AW-5754.
As a result of the finite element simulation, the equivalent plastic strain distribution can be seen in Fig. 10. As it can be seen, the maximal plastic strain exceeds the value of 2 in the neck region of the upper sheet, which is the primary zone of the failure according to the experiments.
Fig. 10. Equivalent plastic strain distribution.
The simulated cross-section is compared to the measured one (see Fig. 11) and shows a good agreement. The resultant force-displacement values from the simulation are also compared to the experiments, and as it can be seen in Fig. 12, also shows a good agreement. The correlation between measured values and the simulated ones are
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RIexp.1 = 0.9932 and RIexp.2 = 0.9974. The correlation was analyzed according to the following equation: 2 m i=1 FExp. − FFEA (2) RI = 1 − 2 m i=1 FExp. − FExp.,avg. The measured and simulated data have no discrete common points, however the data points are close to each other, so the analyzed force value points were calculated by linear interpolation in order to give an estimation of the goodness of the model. The average distance between two raw data points used for the interpolations is 0.02 mm. According to the comparisons, it can be stated that applied FE model is suitable for analyzing the forming process of the three-layer clinched joints.
Fig. 11. Deformed mesh fitted to the measured cross section (101 specimen).
Fig. 12. Comparison of the measurement and simulation.
The comparison of the measured and simulated geometrical parameters is given in Table 3. Despite the cutting process of the joints, the parameters are in good agreement. Table 3. Comparison of the measured and simulated geometrical parameters. tN,1 [mm]
tN,2 [mm]
C1 [mm]
C2 [mm]
101
0.112
0.339
0.150
0.022
FEA
0.120
0.324
0.099
0.016
4 Summary Three-layer aluminium alloy joints were made by a TOX clinching tool. The joining process was analyzed experimentally and numerically as well. According to the results
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the Hockett-Sherby material model is a good choice to describe the material behaviour at high strains in case of EN AW-5754 aluminium alloy. The numerical model shows a very good agreement with the experimental one both from geometrical and reaction force point of views. The tensile shear tests were also performed. The results show that a plateau occurs during the test, which is an uncommon phenomenon. To understand further investigations are needed. Due to the fact that these tests were the first trial experiments of the multilayer clinch joints, several further combinations of materials and examination of further possible loading conditions can be performed in the future for better understanding of the multi-layered clinch joints. An advanced FE model could be built to analyze the tensile shear behaviour of the joints, which would be beneficial for joint optimization.
References 1. Mori, K., Bay, N., Fratini, L., Micari, F., Tekkaya, A.E.: Joining by plastic deformation. CIRP Ann. Manuf. Technol. 62, 673–694 (2013) 2. Ran, X., Chen, C. Zhang, H., Ouyang, Y.: Investigation of the clinching process with rectangular punch. Thin-Walled Struct. 166, 108304 (2021) 3. Gerstmann, T., Awiszus, B.: Recent developments in flat-clinching. Comput. Mater. Sci. 81, 39–44 (2014) 4. Wang, J., Wang, Y., Wang, S., Gouxin, L., Zheng, C., Ji, Z.: Experimental and numerical investigation on incremental laser shock clinching for joining three sheets of copper/aluminum/stainless steel. Optics Laser Technol. 141, 107141 (2021) 5. Bablo, V., Fazli, A., Soltanpour, M.: Electro-hydarulic clinching: a novel high speed joining process. J. Manuf. Process. 35, 559–569 (2018) 6. Vitzthum, S., Hiller, M., Dinh, D.T., Volk, W.: Tool setup to investigate scalability of roller clinching process. Proc. Manufact. 15, 1338–1345 (2018) 7. Grimm, T., Drossel, W.: Process development for self-pierce riveting with solid formable rivet of boron steel in multi-material design. Proc. Manuf. 29, 271–279 (2019) 8. Gáspár, M., Tervo, H., Kaijalainen, A., Dobosy, Á., Török, I.: The effect of solution annealing and ageing during the RSW of 6082 aluminium alloy. In: Jármai, K., Bolló, B. (eds.) VAE 2018. LNME, pp. 694–708. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75677-6_59 9. Lee, C., Lee, J., Ryu, H., Lee, K., Kim, B.: Design of hole-clinching process for joining of dissimilar materials – Al6061-T4 alloy with DP780 steel, hot-pressed 22MnB5 steel, and carbon fiber reinforced plastic. J. Mater. Process. Technol. 214, 2169–2178 (2014) 10. Wiesenmayer, S., Merklein, M.: Potential of shera-clinching technology for joining of three sheets. J. Adv. Join. Process. 3, 100043 (2021) 11. Chen, C., Zhang, H., Xu, Y., Wu, J.: Investigation of the flat-clinching process for joining three-layer sheets on thin-walled structures. Thin Walled Struct. 157, 107034 (2020) 12. Lei, L., He, X., Xing, B., Zhao, D., Gu, F., Ball, A.: Effect of foam copper interlayer on the mechanical properties and fretting wear of sandwich clinched joints. J. Mater. Process. Technol. 274, 116285 (2019) 13. Wang, J., Yu, Y., Fu, C. Xiao, H., Wang, H., Zheng, X.: Experimental investigation of clinching CFRP/aluminum alloy sheet with prepreg sandwich structure. J. Mater. Process. Technol. 277, 116422 (2020) 14. Kaðèák, L., Spiðák, E., Kubík, R., Mucha, J.: Finite element calculation of clinching with rigid die of three steel sheets. Strength Mater. 49(4), 488–499 (2017). https://doi.org/10.1007/ s11223-017-9892-2
Extending an Industrial Robot with Image Processing System József Lénárt(B) University of Miskolc, Miskolc, Egyetemváros, 3515 Miskolc, Hungary [email protected]
Abstract. Today, industrial robots play a significant role in the automotive industry. There are several workflows in the industry that can only be solved efficiently and productively with robots. These robots are equipped with various accessories, such as grippers, sensors and, if necessary, an image processing system. There are tasks where traditional robot programming with fixed coordinates is not enough; visual information about the workspace and workpiece is needed. The problem, especially in smaller companies, is that the existing robot needs to be upgraded, for example, with a visual sensor and an image processing system, but this is usually difficult and costly for older robots and robot controllers. The aim of my research is to create a solution that allows a robot controller to be economically equipped with an image processing system that is not prepared for this at the factory. All that is required for the controller to be able to communicate with the outside world via some communication interface. An external image processing system, which can be a standard PC with the appropriate camera and software, is required. The image processing software running on the PC communicates with the robot controller, and the robot program receives the necessary information from the image processing system via the communication channel. Keywords: Robotics · Machine vision · Image processing
1 Introduction The development of image processing systems for industrial robots goes back much further than we think. The first research began in the 1970s. In 1973, KUKA GmbH patented the first 6-axis industrial robot. Considering that the first IBM personal computer came out in 1981, we can see how much of a task it was to implement such an image processing system at the time. In 1981, Perkins introduced a “Technical vision system” [1] that was able to recognize complex shapes. This system used a powerful computer at the time, an IBM 370 mainframe, but it took 20 s to process an image. In 1983, M. Grishkin introduced an “Industrial robot vision system” [2], which already used a CCD camera with a resolution of 288 * 232 pixels, and ran on an Elektronika-60 computer, a Soviet version of the well-known Digital PDP-11 minicomputer. This system was able to process an image in 0.7 s.
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 568–574, 2023. https://doi.org/10.1007/978-3-031-15211-5_47
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Industrial robot manufacturers usually offer an image processing system for their robots that is either proprietary (Fanuc iRVision, KUKA VisionTech, etc.) or a robotintegrated product from a well-known manufacturer (Yaskawa Motoman – Cognex, ABB – Cognex, etc.). These vision systems provide convenient interaction with the robot controller, making programming easier. Because they are matched to the specific robot controller by the manufacturer, there is usually no need for more complicated settings and adaptations; they can be put to work almost immediately. However, especially smaller companies may have an older robot or purchase a used, refurbished robot that does not have a vision system but would need it for efficient production. There are two typical application areas where efficiency can be significantly increased: • sorting of parts (pick & place, palletizing) • quality control Acquiring a new vision system in such cases can be very costly, even exceeding the current market value of the robot. This is mainly because these are very complex systems, usually with much more functions than required. In such cases, a simple vision system that is suitable for typical tasks and can be used to expand the robot system cost-effectively would be useful. This is relatively easy to solve today. A PC-based image processing system is easy to implement, the only obstacle being the feasibility of communicating with the robot controller. The aim of my work is not to create a competing product for the existing robot vision systems, on the one hand, because I do not want to compete with the development teams of the big manufacturers. On the other hand, overcomplicating the system to be created would increase its cost, so the primary goal, cost efficiency, would be compromised. The designed system is intended to be a simple, lightweight solution with enough capability for the most common tasks. 1.1 Fanuc iRVision A Fanuc LR-Mate 200iC industrial robot with an R-30iA Mate controller and a Fanuc iRVision 3DL image processing system is available in our institute, so I will base my work on its parameters and features. In addition to teaching, we also use it in our institute for research tasks [3–5], so we gained experience with it in several different case studies. Similar products from other manufacturers usually have similar features. The Fanuc iRVision system consists of three main parts [6]: • camera: 640 * 480 resolution, black and white • camera interface unit (vision board, multiplexer) that connects the camera to the controller • software module in the robot controller, which consists of two parts: – the image processing part – the unit providing the interface through which training and supervision can be performed
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The iRVision system interface (Fig. 1.) can be accessed via HTTP protocol from a computer on the same network as the robot controller, which can be done by opening the controller’s IP address in a web browser. The main components of the system are available on this interface: • • • • •
Camera setup tools, Camera calibration tools, Vision process tools, Robot ring, Configuration.
Fig. 1. The Vision setup menu of iRVision
2 Communication Most robot controllers have some form of communication to the outside world through which we can connect an external image processing system. The most common communication interfaces on robot controllers are: • RS-232, simple asynchronous serial communication interface, • Ethernet, which we have several options for communication, their availability usually depends on the software options purchased for the controller; ideally, we have the possibility of TCP/IP socket-based data transmission, • Profibus, simple master/slave connection, • Other: EtherCAT, DeviceNet, CC-Link, etc.
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In our case, I will focus primarily on the two most common communication standards that are easy to implement with simple devices and do not involve licensing costs: RS-232 and TCP/IP socket messaging over Ethernet. If none of these communication interfaces are available on the robot controller, as a last resort, a very simplified communication is available on almost all controllers: digital I/O. This makes data transfer very difficult, but simple binary communication is possible (e.g. a “Processing Start” signal to the vision unit and an “OK/nOK” signal to the robot controller). While this greatly narrows the possibilities, a simple case can provide a sufficient solution, such as: • the workpiece is in the specified position • workpiece size/shape/orientation is correct
3 A Possible Solution During the implementation, I strived for flexible applicability so that the implemented system can be used on different communication channels of different robot controllers. To do this, I designed the modules for two communication interfaces: • RS-232 serial port, and • Ethernet TCP/IP user socket messaging The communication module of the image processing software is designed to provide the same functions regardless of the mode of communication. The communication unit consists of two parts: • the “server” side on the image processing unit, which receives instructions and responds to requests to the robot controller, • the “client” side running on the robot controller, which creates the possibility to communicate with the image processing module from the robot program. The “client” side, i.e. the module running on the robot controller, requires that the robot be able to run a Karel program, as this is the only way to access the communication capabilities of the controller. The robot program (motion program) will be able to access the image processing unit by calling modules written in Karel. 3.1 The Image Processing Unit The image processing software can be run on any PC or even on a SBC (e.g. Raspberry Pi), which can be connected to a camera – which can be even a cheap USB webcam – and has the appropriate communication interface (Ethernet, RS-232). The user interface is provided by a Qt-based program, which plays a primary role in teaching image processing. Its functions are similar to those of the Fanuc iRVision system interface:
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camera management, setup, image display, camera calibration, image processing functions, learning interface.
Another important task of this software unit is to perform real image processing according to the settings specified in the user interface (Fig. 2.). It uses the open-source OpenCV library [7] to perform the processing steps. All the necessary image processing algorithms are implemented in this free library; only the necessary functions need to be called. The most commonly used algorithms are: • colour space conversions, e.g. colour – black & white, RGB-YUV, etc., • edge detection (Sobel, Canny, Laplacian of Gaussian algorithms), search for contours (continuous edges), • measurements [8].
Fig. 2. User interface of the image processing module
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3.2 Client Module on the Robot Controller The motion program running on the robot controller cannot communicate directly with the outside world; this is usually possible by running a Karel program module. The Karel program is called by the motion program with the appropriate parameters, which is transmitted to the image processing system and then the received responses are returned to the motion program. An example of communication: • the robot program runs the selected image processing process: – VISION (“RUN, proc1”); // Call Karel ‘VISION’ program, the image processing system runs a visioning process called ‘proc1’ • the robot program queries the image processing system for the received coordinates – VISION (“GET, proc1, PR [1]); // retrieve the coordinates to the position register It depends on the configuration of the Karel program that performs the communication, which communication interface it uses with the processing system, or even separate communication modules can be created for the different interfaces, and the data exchange can be solved by calling the appropriate module. 3.3 Positioning the Camera An important consideration in robot vision solutions is where to place the camera. There are basically two different options for this (Fig. 3.): • the camera attached to the robot: the camera is attached to an element of the robot arm, usually the last joint, which thus moves with the robot, • the camera fixed in the workspace: the camera is fixed somewhere in the workspace of the robot arm, from where there is a good view of the workpiece, so it does not move with the robot.
Fig. 3. Left: camera mounted on the last joint of the robot. Right: fix mounted camera in the workspace of the robot
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The most important difference between the two placement modes is that in the first solution, the obtained coordinates were relative to the robot, while in the case of the fixed camera, they were interpreted in a fixed coordinate system. Thus, in the case of a camera fixed to a robot, in order to determine the position and dimensions of the workpiece, the processing module must know the current position of the robot – and thus the camera. If we just want to measure the workpiece, we need to know at least the camera-workpiece distance. In this case, the proper flow of information between the two parts of the system must be ensured: the robot program describes the coordinates of the robot with the processing module when requesting the start of processing, the image processing module performs the calculations accordingly and then communicates the results.
4 Summary I have designed the communication options between the Fanuc R-30iA Mate robot controller and an image processing software running on a PC. The image processing is done by using the OpenCV library. The user interface of the vision system is built with open source and platform-independent components. The designed system has fewer features than the industrial solutions available on the market, but covers the most common cases in practice, thus cost-effectively expanding the application possibilities of an existing robot. This work is not related to any industrial task or project, so it progresses slowly as I am only working on it in my spare time. Further development still needs to be done on the client software module running on the robot, which can communicate with the image processing system.
References 1. Perkins, W.A.: A model-based vision system for industrial parts. IEEE Trans. Comput. c-27, 126–143 (1978) 2. Grishkin, V.M., Kulakov, F.M.: Industrial robot vision system. In: IFAC Proceedings Volumes, vol. 16, issue no. 20, pp. 337–341. ScienceDirect, Leningrad (1983) 3. Takács, Gy., Patkó, Gy., Csáki, T., Szilágyi, A., Heged˝us, Gy.: Development of mechatronic systems at the Institute for Mechatronics at the University of Miskolc. In: IEEE International Conference on Mechatronics, pp. 326–331. Piscataway (NJ), USA (2006) 4. Heged˝us, G.: Application of knowledge-based design in computer aided product development. In: Jármai, K., Bolló, B. (eds.) Vehicle and Automotive Engineering. LNME, pp. 109–114. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51189-4_11 5. Rónai, L., Szabó, T.: Modeling and robotic handling of a snap-fitting box buckle. Pollack Periodica 15(2), 94–105 (2020) 6. Fanuc robot series R-30iA/R-30iA Mate controller iRVision Operator’s Manual. Fanuc B82774EN/03, Japan (2009) 7. Bradski, G., Kaehler, A.: Learning OpenCV. O’Reilly Media, Inc. (2008) 8. Shirmohammadi, S., Ferrero, A.: Camera as the instrument: the rising trend of vision based measurement. IEEE Instrum. Meas. Mag. 17(3), 41–47 (2014)
Investigation of Turbocharger Compressor Wheel Damage Due to Collision with Condensed Water Droplets Richárd Takács(B)
, András Lajos Nagy , and Ibolya Zsoldos
Széchenyi István University, Egyetem tér 1, Gy˝or 9026, Hungary [email protected]
Abstract. The compressor wheels used in turbochargers have typically been made of aluminium alloy for decades. The primary reason is to achieve the lowest possible rotor inertia. However, while in the past this component was only encountered with filtered air, nowadays, due to developments in compliance with tightening emission standards, various fluids also collide with the spinning blades, which can cause mechanical damage. One such fluid is condensed water in the low-pressure exhaust gas channel (LP-EGR) formulated at cold starts and low-load conditions. This kind of design has been developed to reduce the nitrogen oxide emission and is used in both Otto and Diesel engines. This paper presents a testing method - implemented on a component testbench - and its results for this phenomenon. First, the effect of the volume flow of the condensed water colliding with the spinning blades was analyzed, and then, in addition to a constant volume flow, the speed dependence of the degree of damage was also determined. Next to the visual inspections, the physical changes on the blades were also detected by vibration diagnostic tools, mainly by analyzing the amplitude of the order correlated to the number of blades, from which we can deduce the changed balance level of the rotor. Keywords: Turbocharger · Compressor wheel · Emission · LP-EGR
1 Introduction Today, the main drivers for the development of internal combustion engines are to comply with the legal maximum levels of exhaust emissions and to reduce consumption and thus CO2 emissions. For diesel engines, NOx reduction is the main challenge [1]. The high peak combustion temperatures present here provide a chemically superior opportunity for the dissociation and subsequent fusion of nitrogen and oxygen molecules from the intake air, and Dhyani et al. [2] have found, through research and measurements, that the amount of NOx produced increases almost exponentially with increasing this factor. By recirculating a certain amount of exhaust gases back into the cylinders, it is possible to reduce the peak temperature while maintaining the desired power level. The underlying phenomenon is that, for a given amount of fresh air and fuel, the heating of the recirculated exhaust mass removes heat from the process [3]. The recirculation can be © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 575–581, 2023. https://doi.org/10.1007/978-3-031-15211-5_48
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achieved by valve timing (Internal EGR) or using an external actuator (External EGR). The former can be applied only at certain engine speeds due to the variation in throttle response, while the latter can be applied over the entire engine’s operating range. In the case of external EGR, two different strategies are commonly used, the so-called LP-EGR and HP-EGR. Lapuerta et al. [4] has investigated the hybrid application of these two systems under engine-cycle conditions, and a series of measurements were carried out to reduce NOx emissions significantly, highlighting the potential of each system. The main difference is that in the former, the exhaust gas is recirculated from the upstream turbine section, whereas in the latter, it comes from the downstream turbine section, flowing directly towards the compressor inlet. Figure 1. Shows a schematic representation of these solutions.
Fig. 1. Schematic comparison of LP-EGR (a) and HP-EGR (b) [5].
The use of LP-EGR has spread mainly to heavy-duty diesel engines but is also becoming more common in passenger cars and even Otto engines, mainly due to the improved efficiency achieved by reducing throttling losses. The main advantage of this system over HP-EGR is that it allows the recirculation of a certain amount of exhaust gas at high boost pressures, and better efficiency can be achieved at the turbocharger turbine since the gas is extracted from the downstream section of this element [6]. However, particle emissions produced by the malfunctioning of the DPF or OPF can cause problems, with some of the particles hitting the vanes as a medium, and in certain quantities even causing complete failure of the vanes in the form of erosion wear. The methodology of erosion wear appears by repeated impacts of solid particles on a surface. Another significant problem - mentioned by Karstadt et al. [7] - is the condensed water in the system, which also hits the spinning compressor blades as liquid or ice particles at cold start-up and low load operating points and causing erosion wear on it. In their studies, they found correlations between several parameters as a function of blade damage.
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This paper presents the correlation of the extent of damage with speed and water flow, considering the mass flow presented in the LP-EGR system. In order to manage a timeand cost-effective way of testing, a turbocharger component testbench was developed. The further aim was to create a measuring system which could detect the damage on the spinning compressor wheel in real-time.
2 Investigations on Turbocharger Component Testbench Component-level testing is used in many areas to achieve time- and cost-efficient testing. For example, Reihani et al. [8] investigated flow conditions on a hot gas component test bench in relation to the mixing of incoming ambient air and LP-EGR. The Department of Internal Combustion Engines and Propulsion Technology at the Széchenyi István University in Gy˝or also operates a similar type of component test bench, which is structured as follows to investigate the erosion of the condensed water on a spinning compressor wheel as mentioned in the previous chapter (Fig. 2).
Fig. 2. Schematic drawing of the test facility
The turbine is driven by compressed air regulated by an electro-pneumatic valve. The desired rotor speed is achieved by the mass flow rate set here. The gases are then discharged from the turbine outlet into a central chimney system. A mass flow meter has been installed on the intake air branch for further testing, and temperature and pressure measurements are possible downstream of the compressor. In order to obtain as accurate as possible a realistic representation of the condensate flow pattern in the exhaust gas recirculated upstream of the compressor, stock elements were used, and the exact mass flow was set using an electro-pneumatic valve, designated LP-EGR inlet in the figure,
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based on measured data from previous engine testbench investigation. In reality, this value varies with the engine and turbocharger speeds, so it is necessary to set the actual value instead of a constant value, which has not been discussed in previous publications. A unique injection system has been designed to inject the condensate. A pressurized water reservoir was connected to an SCR injector, which is also used in vehicles, and this was connected to control electronics. The desired flow rate was thus obtained by adjusting the pressure of the tank and the filling factor of the controller. Based on endoscope images, it can be stated that the tested intake manifold and LP-EGR geometries created a complete wall-flow for the condensate.
3 Results The first series of tests is designed to investigate the relationship between the time profile of blade wear and the sub-applied turbocharger speed. In this case, the following speed levels were investigated with the same water flow rate but varying LP-EGR mass flow rates (Table 1). Table 1. Load cases of the investigation
In this case, the date of wear and damage was determined by the first visible material removal. Accordingly, in addition to continuous testing, visual inspection was carried out from time to time. At 100% rpm, after 63% duration testing, the previously defined visual damage appeared on the outer edge of the blades, as illustrated and compared with 0% of running in Fig. 3. At 88% speed, the same phenomenon occurred after 67% duration, while at 67% speed, it did not occur after 100% duration (Fig. 4). Two different relationships can be inferred from the measurements. On the one hand, increasing the speed decreases the time to damage or causes more damage in the same amount of time, as confirmed by [7]. On the other hand, it is assumed that damage does not occur below a certain speed or only occurs as a result of a drastic increase in time. Further investigation was required to analyze the effect of the flow rate, whereby the previous flow rate of 100% was reduced to 50% at a speed range of 100%. This was achieved by reducing the fill factor of the controller that controls the water injection. The experiment showed that during the predefined 100% duration, the compressor blades do not suffer any visible damage.
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Fig. 3. Blade damage after 63% time interval on 100% rpm compared to the base condition
Fig. 4. Testing time till damage appearance in the function of turbo rpm
In none of the cases did the erosive wear observed cause rotor failure, but the measurements gave an idea of when and where on the blades the first significant damage occurred. In order to be able to characterize and detect damage to the impeller blades not only during a periodic visual inspection but also continuously with a numerical value, a vibration sensor was placed on the turbocharger’s compressor housing in the radial direction. It was placed at this point in order to obtain a vibration image with the highest possible amplitude of the harmonic describing the noise of the blades. The Fast Fourier Spectrum of the vibration image produced by this connected sensor clearly shows that this value is indeed dominant (Fig. 5). The amplitude of the sixth order vibration at the 67% rpm working point was calculated using the Root Mean Square (RMS) method at the given time intervals, and the results were summed over time and polynomially fitted to the results (Fig. 6).
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Fig. 5. Fast Fourier Spectrum of the turbocharger at a given rpm
Fig. 6. RMS analysis of the 67% rpm Load case
It is clear from the data that there is no significant change in the acceleration value up to 90% of the testing time, and no trend of change can be detected. However, after this time, there is a sudden increase of ~50%, and the maximum reached later becomes a constant value. Although there is no visible damage to the impeller blades, the increase in the amplitude of the vibration noise suggests micromaterial removal. This can be confirmed by microscopic analysis.
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4 Conclusions The experiments have reproduced, as many researchers have done, that water condensed in the LP-EGR duct of an internal combustion engine, which collides with the spinning compressor wheel, causes damage and erosion wear under certain conditions. By building a component test bench structure, a time- and cost-efficient test facility was provided, which allows the real mass flow in the LP-EGR duct to be adjusted. The extent of wear and damage to the impeller blades as a function of both speed and flow rate was demonstrated in accordance with the literature cited. A vibration measurement methodology was developed to continuously determine the extent of blade damage, even in real-time, and the relevant order induced by vane pass noise was successfully detected. At a speed value of 67%, a sudden increase of 50% in the noise was detected using the RMS method, presumably caused by microparticle detachment, but this needs further investigation. A possible way of it could be an electron microscope measurement, where the material removal could be detected much more precisely than the basic visual inspection.
References 1. Warey, A., Bika, S.A., Long, D., Balestrino, S., Szymkowicz, P.: Influence of water vapor condensation on exhaust gas recirculation cooler fouling. Int. J. Heat Mass Transf. 65, 807–816 (2013) 2. Dhyani, V., Subramanian, K.A.: Control of backfire and NOx emission reduction in a hydrogen fueled multi-cylinder spark ignition engine using cooled EGR and water injection strategies. Int. J. Hydrogen Energy 44, 6287–6298 (2019) 3. Christensen, M., Johansson, B.: Homogeneous charge compression ignition with water injection. SAE Special Publications, vol. 1999. Society of Automotive Engineers. https://doi.org/ 10.4271/1999-01-0182 4. Lapuerta, M., Ramos, Á., Fernández-Rodriguez, D., González-García, I.: High-pressure versus low-pressure exhaust gas recirculation in a Euro 6 diesel engine with lean-NOx trap: Effectiveness to reduce NOx emissions. International Journal of Engine Research 1–9 (2018). https:// doi.org/10.1177/1468087418817447 5. Wei, H., Zhu, T., Shu, G., Tan, L., Wang, Y.: Gasoline engine exhaust gas recirculation – a review. Appl. Energy 99, 534–544 (2012). https://doi.org/10.1016/j.apenergy.2012.05.011 6. Serrano, J.R., Piqueras, P., Navarro, R., Tarí, D., Meano, C.M.: Development and verification of an in-flow water condensation model for 3D-CFD simulations of humid air streams mixing. Comput. Fluids 167, 158–165 (2018). https://doi.org/10.1016/j.compfluid.2018.02.032 7. Karstadt, S., Werner, J., Münz, S., Aymanns, R.: Effect of water droplets caused by low pressure EGR on spinning compressor wheels. Borg Warner Turbo Systems Engineering GmbH 8. Reihani, A., Hoard, J., Klinkert, S., Kuan, C.K., Styles, D., McConville, G.: Experimental response surface study of the effects of low-pressure exhaust gas recirculation mixing on turbocharger compressor performance. Appl. Energy 261, 114349 (2020)
Investigation of the Applicability of Topological Methods Kristóf Szabó(B) Institute of Machine Tools and Mechatronics, University of Miskolc, Miskolc, Hungary [email protected]
Abstract. In many areas of the industry, continuous and rapid changes can be observed, which are setting a unified direction for product design and creation of the product. Classic examples include the spread of modern production equipment, the consecutive research and development of material technology, computer support and development that allows the extension or supplement of traditional manufacturing technologies. The latter includes additive manufacturing technology, which provides a new opportunity to produce everyday products that have a significant impact on serving market demand. Integrated CAD systems have taken their place in the process of product design and development for decades, partially reforming classical design methods and its steps. The optimization processes have emerged in recent years and are becoming more widespread in integrated CAD systems; these include shape optimization, topology optimization, and the new generative design process, all of which provide an effective solution for design engineers in an increasing number of industrial applications, meaning that these methods can be used in numerous areas of industry. Until now, it was not possible to test the designed products during long-term operation in case of the classic rapid prototyping procedures. However, the appearance of metal powder printing and additive technology already allows the long-term testing of designed prototypes and even the production of final products if the deviation from the required properties of the product is negligible. As a result, using the generative design process, design engineers also have the opportunity to create products that seem to be unfeasible. The following article seeks to prove the facts mentioned above based on a case study. The study describes the product or part that has been traditionally designed and manufactured to replace it with new design methods. And finally details and summarizes the steps required to create a new product or solution. Keywords: Product design methodology · Topology optimization · Generative design
1 Introduction Engineering design is intended to create an adequate solution to a formulated problem both technically and economically. Product design and development require a great deal of experience and a unique vision. A long time ago, it was accepted that this science is an internal ability that cannot be mechanized. It has been recognized that the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 582–591, 2023. https://doi.org/10.1007/978-3-031-15211-5_49
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quality of a product is greatly influenced by the series of decisions that arise during the design process. Accordingly, design tasks need to be transformed into a task that can be completed by many and in which each stage and step can be well followed and achieved [1]. Methodical design is a monotonously advanced process, the first step of which is to define the task precisely, and based on existing technical solutions or intuitions, conceptual and construction plans can be drawn up. The developed solutions are checked on the basis of the formulated requirements. Before manufacturing a physical prototype, the part is pre-inspected in a virtual environment, the aim of which is to reduce time and costs. During the test carried out, each solution is evaluated based on different aspects, such as geometric collision testing, internal stress distribution, degree and nature of deformation, natural-frequency testing, the effect of temperature, and inspection of assembly and manufacturing processes. These steps are called virtual prototyping, which can be related to the parallelization of methodical design [1–4]. Furthermore, additive manufacturing today is an emerging technology that can be considered the next industrial revolution. In fact, the use of this technology is projected to expand steadily [5, 12, 13]. Compared to traditional manufacturing technologies, additive technology offers greater design freedom for both prototype and finished products and also does not require expensive tools and molds. Furthermore, computer-aided design CAD technologies, such as generative design, can further increase the relevance of additive manufacturing. Generative design tools are similarly showing growth and expansion in many areas of the industry. The main CAD system providers have also developed their own generative design systems, adapting to the trends [6, 7, 11]. The interactive design seems to connect with different engineering cultures [8]. Considering the novelty of additive manufacturing and generative design technologies, the methodology is currently thought to be incomplete. This article seeks to answer the following research question (RQ): how effectively can the generative design method be applied to a properly designed part based on the methods explored?
2 The Basis of the Case Study The case study in this article is based on a previous 2019 competition development proposal. The main theme of it is an experiment in the development of chassis components for a racing car participating in student competitions, which is concentrated on the wheel hubs located on the front axle. The main objective of the study is to reduce the weight of the components in the wheel suspension. The automotive and aerospace industry is characterized by the desire to reduce the weight of the vehicle while, of course, maintaining adequate performance and safety. In the case of vehicles designed for racing, this demand is even more emphasized as increasing the acceleration potential of the vehicle can be understood by an increase of the traction force and/or reduction of the mass. Furthermore, the unsprung mass of the wheels is also important for acceleration and braking performance, as, with more weight, more force must be overcome against inertia during acceleration and deceleration. The chosen topic may be appropriate for several reasons to examine the applicability of the given design method, as the role of mass reduction, especially the reduction of unsprung masses, is particularly important
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in the case of vehicles designed for racing. In addition, parts should be sufficiently rigid that the chassis can provide the expected properties. The individual connection points were determined during the entire chassis design, which are located on different planes and axes capable of each other, which can generate complex and spectacular results.
3 Design of the Components of the Landing Gear The wheel hub, also known as the stump stand, is one of the most important and most functional components of a wheel suspension. The main task of this is to connect the wheel and the chassis. Accordingly, it includes bearing points, fixing points of brakes, rocking-arms and connecting rods. It can also include optional features such as mechanical interfaces for temperature and speed sensors. The current design also provides the ability to set basic chassis parameters.
Fig. 1. The traditionally designed model of the wheel hub.
Figure 1 shows a 3D model of the part under consideration, which was designed on the basis of traditional design methods. The raw material of the component is 6061 aluminium with a density of 2.7 [g / cm3 ], a yield point of 275 [MPa] and tensile strength of 310 [MPa]. The overall size of the stump stand is 275x147x55 [mm], and the weight of the current design is 1.05 kg. The indicated hub can be manufactured using conventional cutting operations. The entire component features cuts, breakthroughs and pockets, which are said to be the tools of traditional design and manufacturing methods.
4 The Generative Design Process in iCAD System Generative design is a design process in which an algorithm optimizes the shape of the component for a particular boundary condition. The design of the shape is not a manual design task. The designer determines the functional boundary conditions of the part and
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adds them to the software, which calculates the shape of the optimized part according to the specified criteria during iteration processes [7, 11]. Recently, several articles have been devoted to the description of generative design processes [9, 10], on the basis of which the outlined task is solved (Fig. 2).
Fig. 2. Steps of the generative design process [10].
It is advisable to carry out the design process step by step based on the indicated flowchart because this is the only way to get an effective solution based on preliminary research. The case study in this article is solved in the Generative Design Support module of the Fusion 360 Integrated Design System. Examination of the installation environment of the chassis components is examined in the Design Module, the mass optimization task is performed using the Generative Design Module, and the check of the results is carried out in the Simulation Module. 4.1 Determination of Design Space In accordance with the recommended design process, the installation environment of the part should be examined and then the necessary and limiting volumes using a method should be constructed.
Fig. 3. “Preserve geometries” Volumes to be preserved.
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Figure 3 presents the partial assembly model of the chassis, which has been used to determine the volumes that must be retained. It can be seen that the chassis is a complex structure consisting of many parts to create a suitable interface. In this case, these models are formed through traditional modelling, which is not an automatic process. This process can serve as the creative part of the design, as the designer can significantly interfere with the specificities of emerging geometry (Fig. 4).
Fig. 4. “Obstacle geometries.“ Portion of space occupied by related adjacent components.
Tt the current design stage, it is necessary to perform the inverse of the previous process, so it is essential to define volumes that limit the design space of the program. Care must be taken in the process, as with these constraints, the maximum or even the smallest material thickness of a critical section can be limited, which can hinder the optimal design. In the case of the complete assembly, the best solution is to define the limitations using the results of a kinematic simulation parameterized with real movements. 4.2 Definition of Loads and Constraints Loads and constraints are specified on the volume parts to be retained with the following functions.
Fig. 5. Definition of constraints and loads.
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In the next step, as Fig. 5 represents, the individual constraints and load forces were placed on the connection points. The individual forces and force components are of the same magnitude as those for which the original part was inspected. These forces have been determined by experience, it always depends on the mass and engine power of the construction of a particular age, so these parameters can vary from year to year. The loads for the design are summarized in the below table, which can be considered to be maximum values (Table 1). Table 1. Loads affecting wheel hub Name of the load
Extent of the load
Surface affected by force
Fx
2000 [N]
Abutment surface of wheel bearing
Fy
3000 [N]
Abutment surface of wheel bearing
Fz
2400 [N]
Abutment surface of wheel bearing
Ft,braking
6000 [N]
Surface of the calliper mounting points
During the test, the moment with the highest load is taken into account when the forces summarized in the table occur simultaneously. The generative design module creates the optimal geometry based on the von Mises voltage formed in the material of the part. It is not possible to take dynamic effects into account. 4.3 Definition of Design Objective Functions In the applied program, the “Objectives” function can be used to define optimization goals and limits. When specifying the design objective function, there are two choices that seek to maximize rigidity or minimize mass. Throughout the design process, a target function is selected to maximize rigidity, with a mass target of 0.7 [kg], taking into account the current design of the component and the minimum required safety factor, which is 2. By prescribing different production technologies, the certain system of conditions on the basis of which the results are developed can be further tightened. At least one raw material must be assigned to the solution in the program. The design module can only handle a linearly flexible material model. In the case of the design of the wheel hub, the “SLM” (Stereo Lithography) and EBM (Electron Beam Melting) processes were selected from the additive manufacturing technologies, for which the chosen materials are Aluminum (AlSi10Mg) and Titanium (6Al-4V), as these can be applied in case of milling. 4.4 Evaluation and Selection of Solutions Various filters and diagrams are available to review and compare solution variants. The tabular view helps the sorting of solutions by different criteria. The weighting of each aspect can be modified, such as weight, price, and degree of deformation.
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Fig. 6. Evaluation of different production technology solutions depending on mass and maximum displacement.
Fig. 7. Evaluation of solutions based on different raw materials as a function of mass and maximum displacement.
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Comparing Fig. 6 and Fig. 7, it can be stated that the lowest weight solutions can be achieved with additive manufacturing technology and the application of Titanium as the raw material, but in this case, the material suffers relatively large deformations under load. In the case of additive manufacturing, the most massive but rigid solution is obtained by using aluminium raw materials (Fig. 8 and Table 2).
Fig. 8. Selected solution I, solution II, solution III.
Table 2. Analysis of solutions. Solution I
Solution II
Solution III
Manufacturing technology
Additive Y +
5D milling
Additive Y +
Raw material
Ti 6Al-4V
Al 6061
AlSi10Mg
Mass
0.63
0.69
0.7
Deformation
1.78
0.28
0.36
Safety factor
2.39
6.46
2.6
The three versions indicated in the table reflect the characteristics of the solutions. The lightest and heaviest parts have been selected, and an ideal solution has also been selected from the middle field. The selected parts made of aluminium show well that traditional cutting technologies do not have a negative impact on individual solutions. The raw material of solution number two from the middle field is inexpensive, and the production technology is widely used in five-axis milling. It achieves the desired weight reduction and meets the strength requirements well. In the case of the lowest weight solution, it is necessary to work with expensive raw materials, which are accompanied by expensive and limited production equipment (Fig. 9).
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Fig. 9. Preliminary FEM analysis results for stress and deformation test.
The program provides an opportunity to show the results of preliminary FEM analysis, based on which the critical points of the component are revealed, from the aspects of solidity.
5 Summary The article reviewed traditional design procedures and the classical method, as well as the motivation for the development of the discipline, which forms the basis of Generative Design, the defining stages of which were introduced. The factors influencing the spread of the process and the development of the necessary technological processes are described. The article revealed the steps of the design process currently being explored in generative design, the accuracy and applicability of which were examined in the framework of a case study. The study presented the workflows of each step in a popular integrated CAD system. By going through all the necessary steps, the article could present effective solutions for the formulated design needs.
References 1. Takács, G., Zsiga, Z., Szabóné Makó, I., Heged˝us, G.: Methodical design of production tools. Nemzeti Tankönyvkiadó (2011). Miskolc (in Hungarian) 2. Heged˝us, G.: Application of Knowledge-Based Design in Computer Aided Product Development Lecture Notes in Mechanical Engineering, pp. 109–114 (2017) 3. Heged˝us, G. The application of methodical design to industrial measuring machine development, PhD Students Forum. In: 2002: Proceedings of Faculty of Mechanical Engineering (2002). Miskolc (in Hungarian) 4. Tóth, D.: Conceptual design of fatigue equipment. Multidiszciplináris Tudományok 10(4), 381–384 (2020). https://doi.org/10.35925/j.multi.2020.4.41
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5. Wohlers, T., Campbell, R.I., Huff, R., Diegel, O., Kowen, J.: Wohlers report 2019: 3D printing and additive manufacturing state of the industry. Wohlers Associates (2019) 6. Szabó, K., Heged˝us, G.: Brief overview of generative design support software. Design of Machines and Structures 10(2), 123–132 (2020). https://doi.org/10.32972/dms.2020.023 7. Szabó, K., Heged˝us, G.: Brief overview of generative design support software. Multidiszciplináris Tudományok (2020). (in Hungarian) https://doi.org/10.35925/j.multi.2020. 3.39 8. Fischer, X., Nadeau, J.-P.: Research in Interactive Design, vol. 3: Virtual, Interactive and Integrated Product Design and Manufacturing for Industrial Innovation. Springer, Berlin (2011) 9. Szabó, K., Heged˝us, G.: Steps of generative design in integrated CAD systems. Multidiszciplináris Tudományok (2020). (in Hungarian) https://doi.org/10.35925/j.multi.2020. 4.43 10. Szabó, K., Heged˝us, G.: Steps of generative design in integrated CAD systems. Design of Machines and Structures 11(1), 53–58 (2021). https://doi.org/10.32972/dms.2021.007 11. Gönczi, D.: Essential features of topological optimization tasks in abaqus final program system. Multidiszciplináris Tudományok 11(4 sz), pp. 177–187 (2021). (in Hungarian) https:// doi.org/10.35925/j.multi.2021.4.22 12. Frazier, W.E.: Metal additive manufacturing: a review. J. Mater. Eng. Perform. 23, 1917–1928 (2014) 13. Kiss, D.: Reverse engineering at the University of Miskolc. Design of Machines and Structures 4(2), 13–18 and 6 (2014)
CFD Analyses of the Pressure Distribution in Hydrostatic Journal Bearings with Different Recess Shapes Sándor Gerg˝o Tóth(B) Department of Machine Tools, University of Miskolc, Miskolc-Egyetemváros 3515, Hungary [email protected]
Abstract. The following paper deals with examining the pressure distribution of hydrostatic journal bearing at different rotational speeds. The application of hydrostatic bearings has a number of advantages, including contactless operation and damping ability, but also dispose limited operation condition at high speeds. With bearing design parameters and limit speed calculations, the required boundary conditions and FEM volume model were set up to analyze the bearing pressure distribution In the ANSYS Fluent system; severe CFD analysis was performed on the reference bearing to confirm the presence of the limit speed. Additional CFD simulations were run for different bearing recess parameters e.g. recess depth, corner radius and number of pockets. Keywords: Hydrostatic journal bearing · Limit rotational speed · CFD analysis
1 Introduction The application of hydrostatic bearings has several advantages. These bearings have large loading capacity, excellent stiffness and damping properties with nearly non-contact operation. Opposite to hydrodynamic bearings, where the separation of the sliding surfaces occurs due to relative motion of the surfaces, at the hydrostatic bearings, the separating lubricant film is induced by an external pressure, thus a good lubrication can be provided in the specified operating range. In hydrostatic journal bearings, the shaft displacement is compensated as follows. When external forces arise, the shaft shifts in the direction of one of the bearing pockets. As a result, the pressure in the bearing pocket increases while the volume flow decreases due to the restrictor element belonging to the recess. In the pocket on the opposite side, the pressure begins to decrease, causing the volume flow-rate to increase. The pressure difference in the bearing recesses will recenter the shaft in the bearing while the recess restrictors ensure that the pocket pressure remains nearly constant. For this reason, these bearing types are widely used as spindle bearings for high-precision machine tools and grinding machines. In addition, nowadays new theoretical research is being conducted about the usage of hydrostatic bearings in vehicles e.g., the elimination of gear-mesh frequency in the transmission of lorries [1]. However, their wide usage is currently severely limited by the limit speed associated with © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 592–603, 2023. https://doi.org/10.1007/978-3-031-15211-5_50
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the onset of hydrodynamic effects and whirl frequency. In addition, other undesirable flow phenomena can occur, including the formation of the Taylor vortex series and the effect of recirculation within the pressure recess. The number of new publications based on hydrostatic bearings have grown significantly in recent times. Zhifeng Liu summarized the recent research about hydrostatic bearings [2]. M. Michalec has conducted a comprehensive study on the design and optimization of large hydrostatic bearings [3]. O.J Bakker studied the high-speed operation of hydrostatic thrust bearings with different recess designs (circular and annular) [4]. It was found that the centripetal inertia effect should be taken into account in high-speed operation, and a significant temperature increase can also be expected. Compared to the design optimized for general application, the shallower recess depth is more optimal for high-speed operation. F. Shen also investigated the pressure pocket parameters of hydrostatic thrust bearings (bearing recess depth and radius of recess curvature, inlet channel diameter, and recess shapes) [5]. Numerical and experimental results underpinned the appearance of vortex within the bearing recesses. The magnitude of the depth of the recess has a significant effect on the flow structure, but does not fundamentally affect the pressure distribution. The smaller curvature radius increases the pressure in the recesses. The annular thrust bearing design achieves maximum pressure, while the circular pressure pocket provides maximum stiffness. S. K. Yadaw investigated the performance of tilted thrust bearings with different pocket designs when operated with non-Newtonian fluid [6]. The results show that the static and dynamic performance of the circular pressure chamber is highly sensitive to tilting; however, the volume flow requirement is lower compared to the annular design. S. C. Sharma investigated the bearing performance by varying the parameters of a circular, capillary-controlled pressure pocket [7]. H.Sawano examined a bearing construction fitted with a thin metal plate in its bearing recess that acts as a variable recess restrictor unit [8]. Yi-Ting Lin analyzed the flow resistance of circular and square shape pressure pockets by a finite volume method, which proved that the gap size and recess size influence the flow resistance [9]. M. Fesanghary created a new groove design optimized for load-bearing capacity using a numerical method, which has better performance characteristics compared to the traditional spiral groove design [10]. In the study, the optimization was done by changing the radius and groove width, taking into account the cavitation effect. As the groove numbers increase, the maximum pressure drops, but the load capacity remains almost constant. The number of studies on hydrostatic journal bearings is significantly lower due to the more complex bearing surface. V. K. Dwivedi studied the performance of hybrid bearings by varying the size and number of the pressure recesses [11]. As the L/D ratio increases, the load capacity decreases. Increasing the length and circumferential dimensions of the pressure pockets increases the load capacity, but also increases the fluid flow rate. Increasing pressure pocket numbers only slightly improve load capacity and increases oil demands. N. Singh also dealt with hydraulic bearings with similar diaphragm compensation as H.Sawano, where four different bearing recess designs were examined, including in square, circle, ellipse, and triangle configurations [12]. The study also confirms that the stiffness can be improved with a circular recess design, the gap size can be minimized, and the best speed stability under constant load can be achieved with the triangular recess design. E. Rajasekhar also examinated these configurations with
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micropolar non-Newtonian fluids [13]. The results are in line with the previous ones, and it was found that the damping ability can be further improved with the triangular shape. Peng Liang developed a new method for determining the static performance of hydrostatic journal bearings [14]. In addition to faster calculation, the new method already takes into account the shaft speed and attitude angle. S.C. Sharma also made a finite element analysis of a multi-recess capillary compensated conical hydrostatic bearing [15]. By increasing the half-cone angle, the axial load capacity can be improved while the volume flow is reduced. Optimum range for stiffness, damping and loadbearing capacity can be achieved at 40° half-cone angle. The Department of Machine Tools of the University of Miskolc has gained more and more experience in this field recently because departmental researchers deal with the examination of hydraulic circuits [16–18] and hydrostatic guides [19] in several articles.
2 Governing Equation 2.1 Basic Equations The Reynolds equation can be used to calculate the pressure distribution of hydrostatic bearings, but it contains several simplifications. The Reynolds equation derived for hydrostatic bearings is as follows [20]: ∂ 3 ∂p ∂h ∂ 3 ∂p h + h = 6ηRω (1) ∂x ∂x ∂ ∂y ∂x The curved bearing surface of hydrostatic journal bearings significantly complicates the calculation of the pressure distribution and can therefore only be solved with numerically methods for good accuracy. The widespread use of various simulation software can be facilitated for examination of the pressure pockets. ANSYS FLUENT is a general CFD simulation software that provides modelling capabilities for a wide range of studies of laminar and turbulent fluid flow problems. During these CFD analysis the conservation equations of mass and momentum for all flows are solved. Conservation equations of mass and momentum are shown below [21]: ∂ρ + ∇ · (ρv ) = Sm ∂t = ∂ (ρv ) + ∇ · (ρv v) = −∇p + ∇ · τ + ρ g + F ∂t
(2) (3)
The source Sm is the mass added to the continuous phase from the dispersed second phase, in these simulations this effect will not occur. The unloaded state will be analyzed during the simulation, so only the gravitational force (ρ g ) acts as an external force in = the system. The term τ is the stress tensor, and it is given by [21]: 2 = τ = η ∇v + ∇vT − ∇ · vI (4) 3 At Eq. (4), right hand side term describes the volume dilatation, which in this case also be neglected.
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2.2 Limit Speed Calculation The limit speed can be determined from two directions: dynamics and flow phenomena. From the dynamic side, a transient rotational speed can be determined as describing the onset of the state when the spindle shaft “climbs” onto the hydrostatic bearing housing and induced hydrodynamic force due to the wedge effect [22]. The Stansfield equation [23], which can be used to calculate the transient velocity, is as follows: Nt =
p1 h2r(avg) 31 · D2 η (1 + ξ )φ 2 Ea (1 − Ea )
(5)
Although the majority of the parameters are given when designing and selecting the working fluid, an initial input parameter can only be given empirically to determine the average gap size (hr(avg) ), so the equation is difficult to apply. Therefore, the unloaded film thickness (h0 ) size can be expected as a good approximation Bearing overdriving in this case does not mean the failure of the hydrostatic bearing, but when the shafts orbital motion frequency within the bearing reaches the half of the natural frequency of bearing, resonance appears, which can cause fatal failure [22]. When approaching from the flow side, the limit speed should be chosen, so the flow remains laminar. Rowe’s equation [24] can be used to calculate a limit speed. Beyond the limit speed, Taylor vortex can occur in the pressure pockets: 41, 1 2hD0 (6) NT = Dπ h0 ρη
3 Model Setup 3.1 Generating the Volume Model To prepare the volume model required for the simulation, the CAD format of bearing model must first be created. The reference hydrostatic journal bearing was provided by the rear spindle bearing of an ultraprecision NC lathe. Table 1 summarizes the bearing parameters decomposed from the technical documentation and bearing design equations. Figure 1 shows an exploded view of the CAD assembly of the reference bearing. The internal volume model can be subtracted using the journal bearing and shaft interfaces (yellow-coloured body model).
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S. G. Tóth Table 1. Summary of the reference hydrostatic journal bearing parameters.
Bearing parameter
Nomenclature (unit)
Input Value
bearing diameter
D [mm]
60
bearing length
L [mm]
60
bearing length/bearing diameter ratio
L/D
1
pocket length/bearing length
Ea
0,67
bearing shape factor
ϕ
1,27
pocket length
lp [mm]
40
number of pockets
z
4
axial outlet length
la [mm]
10
axial outlet length/bearing length
la /L
0,17
effective bearing length
le [mm]
50
axial outlet width
ba [mm]
95
circumferential outlet width
bu [mm]
100
film thickness at unloaded state
h0 [mm]
0,04
circumferential outlet length
lu [mm]
20
supply pressure
ps [MPa]
6
pocket pressure
pr [MPa] N C μm
2,75
static stiffness
342,88
pressure ratio
β
0,5
flow resistance ratio
ξ
1
lubricating film kinematic viscosity
2 ν [ mm s ]
15
lubricating film dynamic viscosity
η [ Ns2 ] m
0,01
flow rate
Q
lubricant density
ρ
mm3 s
kg m3
4,23·10–2
872
specific heat capacity of the lubricant
Cw KJ
191,93
lubricant temperature
T [°C]
35,23
temperature increase during operation
ΔT [°C] 1 N min
10,23
v ms 1 Nt min
13,51
nominal rotational speed shaft circumferential speed transient limit speed (Stansfield)
3000
4020 (continued)
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Table 1. (continued) Bearing parameter limit speed (Rowe)
Nomenclature (unit) 1 NT min
Input Value 1712
Fig. 1. Exploded view of the CAD geometric model of reference bearing including lubricant volume model.
3.2 Meshing and Boundary Conditions The internal meshing module of FLUENT has been used for meshing the volume model because the boundary domains can be clearly defined. The volume surface meshing is automatically applied with the tetrahedron element type, but this was modified at body meshing. From the surface meshing, the boundary type of surfaces required for the boundary conditions can be defined. To handle the significant difference in film thickness between the bearing surface and the pressure pocket, a polyhedra element mode method was chosen for the volume meshing. In addition, the size and growth rate of these polyhedra elements must be specified: the max cell length is set to 1 μm, the growth rate is set to 1.2. Using the Improve Volume Mesh option, the mesh was repaired at the junction edges between the pressure pocket and the bearing pad. Given the following conditions, the completed meshed volume model is shown in Fig. 2. When specifying the physical characteristics as well as the settings of the solution, the parameters necessary for the solvent must be set. The material qualities in the model and the external effects on the system must be specified. The default values for the previously selected boundary condition zones are also defined in this step. The characteristics required for the boundary conditions are as follows (Fig. 3):
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• • • • • • • • • •
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Working fluid: VG 15 hydraulic oil; Working fluid temperature: 40 °C; Laminar flow; Green surface: moving wall (spindle axis), material quality: X5CrNi189; Red surfaces: static wall, material quality: bearing metal; Blue arrows: pressure inlet, 20 bar; Red arrows (pocket): pressure outlet, 2 bar; Red arrows (edges of bearing pad): pressure outlet, 1 bar; Unloaded state (only gravitational force); Initial rotational speed: 1000 rpm;
Fig. 2. The completed meshed volume model with polyhedra elements.
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Fig. 3. Specifying boundary conditions and solution parameters in the volume model.
4 Results of the CFD Analysis 4.1 Reference Bearing The results of the simulations performed on the reference bearing are shown in Fig. 4. The simulations were run in 1000 rpm increments. At idle, the pressure pattern is uniform, and the pressure difference at the outlets is visible inside the pressure pocket. On the bearing pad, the pressure distribution of the lubricating film can be seen beyond the recess environment a uniform pressure drop at the outlet side edges (where atmospheric pressure is assumed). At 1000 rpm, the pressure in the vicinity of the outlet point of pockets decreases further, but the pressure distribution can still be considered uniform. However, the initial desegregation of uniform pressure distribution can be observed on the bearing pad. At 2000 rpm, the above-mentioned effects are further intensified. The direction of shaft rotation has a clear effect on the pressure distribution in the pressure pocket. The pressure distribution on the bearing pad continues to dissipate and rupture appears at the bearing ends. The appearance of negative pressure causes this display error, which can be linked to the appearance of cavitation. At 3000 rpm (which is also the maximum permissible spindle speed), the pressure difference in the pressure pocket between the opposite edges continues to increase, the pressure distribution of the bearing pad has disintegrated, and the cavitation regions are also increasing. At 4000 rpm (equals the transient speed calculated with Eq. 5), pressure peaks occur at the corners of the pressure pockets corresponding to the direction of rotation, but a significant pressure drop occurs in one-third of the pressure pocket. This significant pressure difference can cause the appearance of recirculation and Taylor vortexes. Regional pressure peaks also occur on the bearing pad. A significant pressure drop in the vicinity of the pressure peak led to the appearance of additional cavitation zones.
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a; Static state
b; 1000 rpm
c; 2000 rpm
d; 3000 rpm
e; 4000 rpm
Fig. 4. Evolution of the pressure distribution of the reference hydrostatic journal bearing at specified shaft rotational speeds.
4.2 Different Recess Configurations The simulations performed on the reference bearing provide a basis for examining different recess designs. Separate CFD analyses were performed, so the different variants during the high-speed operation can be determined by examining and comparing the alteration in the pressure field. The designs were tested at 3000 and 4000 rpm, the results of which are shown in Fig. 5. In the first case, the effect of increasing the depth of
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b; Recess depth – 3 mm 3000 rpm
a; Recess depth – 2 mm 3000 rpm
c; Recess with corner radius 3000 rpm
d; Recess with corner radius 4000 rpm
e; Six pocket design 4000 rpm
Fig. 5. Results of CFD analysis for different bearing recess designs.
the pressure pocket was increased from reference recess depth of 1 mm to 2 and 3 mm,
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respectively. Based on the simulation, increasing the depth of the recess adversely affects high-speed operation, since the uniform pressure distribution on the bearing pad disappears at the tested speeds, however, the pressure change inside the pressure pocket is not so significant as at the reference bearing. The effect of the corner radius is illustrated in the middle simulation figures of Fig. 5. Based on the result, the pressure field is centered inside the pressure pocket boundaries. Pressure peaks develop nearby on the bearing pad in the lower pressure regions of the pressure pocket, and at 4000 rpm the hydrodynamic effect occurs significantly. Finally, the simulation results of a bearing design with six pressure pockets are shown in the last line of Fig. 5. The recess distribution, maintaining the proportions of the pressure pockets, was achieved by reducing the size of the bearing pad. Simulations performed in the limit speed range show that regional pressure peaks and cavitation zones also occur in the design below, but the dimensions of the regions are smaller, which means that the expected hydrostatic stiffness of the bearing is maintained.
5 Conclusion This paper deals with the creation of a CFD analysis that can be used to examine the alteration of pressure distribution in hydrostatic journal bearings. The result of the CFD analysis for the reference bearing is consistent with the presence of physical phenomena at different transient velocity values. The direction of shaft rotation has a clear effect on the pressure distribution in the bearing pocket and pad. The simulation results could serve as a basis for the examination of different bearing recess designs. Based on simulation results, a shallower pressure pocket design is preferred over high-speed operation. In the case of the corner radius design, the pressure field is concentrated in the vicinity of the pressure pocket, and among of the examined design, the hydrodynamic effect is the most significant here. The six-pocket design ensures a relatively uniform pressure field at high speeds.
References 1. Seperamaniam, T., Abdul Jalil, N.A., Zulkefli, Z.A.: Hydrostatic bearing design selection for automotive application using pugh controlled convergence method. Procedia Engineering 170, pp. 422–429. Elsevier (2017) 2. Liu, Z., Wang, Y., Cai, L., Zhao, Y., Cheng, Q., Dong, X.: A review of hydrostatic bearing system: researches and applications. Advances in Mechanical Engineering 9(10), pp. 1–27. SAGE (2017) 3. Michalec, M., Svoboda, P., Kˇrupka, I., Hartl, M.: A review of the design and optimization of large-scale hydrostatic bearing systems. Eng. Sci. Technol. Int. J. 24(4), pp. 936–958. Elsevier (2021) 4. Bakker, O.J., van Ostayen, R.A.J.: Recess Depth Optimization for Rotating, Annular, and Circular Recess Hydrostatic Thrust Bearings. Journal of Tribology 132, 011103–1. ASME (2010) 5. Shen, F., Chena, C.-L., Liua, Z.-M.: Effect of pocket geometry on the performance of a circular thrust pad hydrostatic bearing in machine tools. Tribology Transactions 57(4), pp. 700–714. STLE (2014)
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6. Yadav, S.K., Sharma, S.C.: Performance of hydrostatic tilted thrust pad bearings of various recess shapes operating with non-Newtonian lubricant. Finite Elements in Analysis and Design 87, pp. 43–55. Elsevier (2014) 7. Sharma, S.C., Phalle, V.M., Jain, S.C.: Performance analysis of a multirecess capillary compensated conical hydrostatic journal bearing. Tribology International 44(5), pp. 617–626. Elsevier (2011) 8. Sawano, H., Nakamura, Y., Yoshioka, H., Shinno, H.: High performance hydrostatic bearing using a variable inherent restrictor with a thin metal plate. Precision Engineering 41, pp. 78–85. Elsevier (2015) 9. Lin, Y.-T., Liao, C.-C., Lin, T.-Y., Lin, C.-A.: Simulations of Flow Resistances in Circular and Square Hydrostatic Bearings 10. Fesanghary, M., Khonsari, M.M.: On the optimum groove shapes for load-carrying capacity enhancement in parallel flat surface bearings: Theory and experiment. Tribology International 67, pp. 254–262. Elsevier (2013) 11. Dwivedi, V.K., Chand, S., Pandey, K.N.: Effect of number and size of recess on the performance of hybrid (Hydrostatic/Hydrodynamic) Journal Bearing. Procedia Engineering 51, pp. 810–817. Elsevier (2013) 12. Singh, N., Sharma, S.C., Jain, S.C., Reddy, S.S.: Performance of membrane compensated multirecess hydrostatic/hybrid flexible journal bearing system considering various recess shapes. Tribology International 37(1), pp. 11–24. Elsevier (2004) 13. Rajasekhar Nicodemus, E., Sharma, S.C.: Orifice compensated multirecess hydrostatic/hybrid journal bearing system of various geometric shapes of recess operating with micropolar lubricant. Tribology International 44(3), pp. 284–296. Elsevier (2011) 14. Liang, P., Lu, C., Pan, W., Li, S.: A new method for calculating the static performance of hydrostatic journal bearing. Tribology International 77, pp. 72–77. Elsevier (2014) 15. Sharma, S.C., Phalle, V.M., Jain, S.C.: Performance analysis of a multirecess capillary compensated conical hydrostatic journal bearing. Tribology International 44(5), pp. 617–626. Elsevier (2011) 16. Fekete, T.: The alternating current synchronous hydraulic drive, annals of faculty of engineering hunedoara. Int. J. Eng. 12, 235–238 (2014) 17. Fekete, T.: Alternating current hydraulic drive the possibility of applying in the automotive industry. Lecture Notes in Mechanical Engineering 12, 49–57 (2017) 18. Fekete, T.: Analyzing the temperature of the alternating current hydraulic drive. Engineering and Applied Sciences (2020) 19. Tóth, S.G., Takács, y.: Examination of machine tool slideway combined with pressure chambers. Design Machines and Structures 10(2), pp. 160–164 (2020) 20. Ansys Fluent 12.0/12.1 Documentation: https://www.afs.enea.it/project/neptunius/docs/flu ent/index.htm last accessed 01 March 2022 21. Ghosh, M.K., Majumdar, B.C., Sarangi, M.: Fundamentals of Fluid Film Lubrication. McGraw-Hill Professional (2014) 22. Bassani, R., Piccigallo, B.: Hydrostatic Lubrication 1st edn. Elsevier Science (1992) 23. Stansfiled, F.M.: Hydrostatic bearing for machine tools and similar applications. The Machinery (1970) 24. Rowe, W.B.: Hydrostatic, Aerostatic and Hybrid Bearing Design, 1st edn. ButterworthHeinemann (2012)
Investigation of the Tribological Behaviour of Advanced TiAlN and CrAlN Hard Coatings Deposited on X153CrMoV12 Cold Work Tool Steel Pusta Jalalova(B) , Fruzsina Fülöp, and Maria Berkes Maros Institute of Materials Science and Technology, University of Miskolc, Miskolc, Hungary [email protected]
Abstract. Due to the recent developments and growth in the field of surface engineering, several new coatings and deposition techniques, such as PVD, CVD, and PACVD (Plasma Assisted Chemical Vapour Deposition) are increasingly applied in the industry. Duplex technologies, like the combined processes involving a PVD or PACVD coating and a preliminary plasma nitriding, enhance the mechanical, chemical, and physical performance of the surface layer in a new, efficient way. The objective of our research work is to investigate the tribological behaviour of two, industrially applied duplex treated advanced TiAlN and CrAlN hard coatings, which can efficiently be used to improve the lifetime of the X153CrMoV12 cold work tool steel. Instrumented scratch tests with constant and progressive loading were carried out to compare the resistance of the two hard coatings to scratching, while ball-on disc tests were accomplished to compare their wear resistance. The investigations were supplemented with morphological analysis of the scratch grooves and wear tracks using optical microscopy, while the coatings were characterised with their microVickers hardness and layer thickness determined by the ball cratering method. Results prove a significant improvement of the tribological performance of the tested coatings compared to the uncoated reference material and superior wear behaviour of the CrAlN coating in terms of both scratch and wear resistance. Keywords: Scratch test · Wear test · CrAlN and TiAlN coating
1 Introduction Surface engineering is the comprehensive field of study and technical activity targeted at the design, manufacturing, investigation, and use of surface layers, both technological and for end-users, with properties better than the core, such as anti-corrosion, antifatigue, anti-wear, and decorative properties. The probability of surface-related failures and damages can be efficiently decreased by surface technologies extending the product life. Two of the most widely used methods to improve the crack and wear resistance of tools are surface modification and coating technologies [1, 2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 604–619, 2023. https://doi.org/10.1007/978-3-031-15211-5_51
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Surface modification heat treatment means influencing the surface microstructure due to simply heat or combined heat and chemical effects, while coating technologies involve the addition of a new layer onto the surface. In recent decades, several new deposition technologies have been developed, and there are numerous tribological coatings available today [3, 4]. Duplex treatments are the purposeful combinations of two or more different surface technological processes to build a gradient, multi-layer, composite surface structure on a substrate material with the aim of improving the load-bearing capacity and durability of the component achievable by neither individual process alone [5, 6]. In our study, the applied duplex treatment consisted of a plasma nitriding of the substrate and depositing the hard ceramic coating on it. Nitriding is a thermochemical surface treatment during which nascent nitrogen is introduced into the surface of the practical iron-carbon alloys to increase the hardness, fatigue strength, the wear- or corrosion resistance of the material [7]. The studied wear-resistant ceramic coatings – produced by a Hungarian subsidiary of an internationally acknowledged, leading heat treatment company, Oerlikon – are compared from the point of view of their scratch and wear resistance. With this purpose, two practically important coating families, applied in metal forming of high strength metal sheets, die casting applications and for cold work tool steels, as well were chosen. The coatings are known in the industrial practice by their trade names, i.e., Balinit Formera advanced (CrAlN) and Balinit Lumena advanced (TiAlN) coatings that are produced by one of the PVD based processes, i.e., arc evaporation [8], combined with plasma nitriding. According to the producer data, the duplex treated condition results in 6–7 times longer lifetime to the advanced Formera coatings compared to other comparable coatings, accompanied with great abrasive wear resistance and excellent coating adhesion, ensuring this way high productivity during forming AHSS steels applied widely in the vehicle industry [9], while advanced Lumena coatings provide extremely high hardness, wear resistance, improved plastic flowability to injection moulds made of low hardness ( σ⊥ ). In fact, on average σ are higher than σ⊥ by 37%. Accordingly, a surface with inhomogeneous stress distribution occurs in high-feed milling. That fact can negatively affect part performance and wear resistance. How significant the influence of that 37%-inhomogeneity of stress distribution on part performance is, should be taken into consideration during part engineering. Finally, the deviation from the proposed trend curve does not exceed 12.5% for stress in feed direction σ and 16.8% for σ⊥ . The observed deviation can be caused by the inhomogeneity of workpiece material and dynamical process phenomena. Regarding the interpretation of the results, the measured residual stresses are in good agreement with known theory and existing models about residual stresses in milling. Moreover, in practical investigations, the known correlations between feed and residual stresses have been extended up to feed fz = 8 mm/tooth. It is to notice that the surface layer is stronger affected during high-feed milling than in conventional milling. In fact, compared to conventional milling of C50 and C60 in [8], up to 20 times higher surface tensile residual stresses were observed during the high-feed milling of C45. The reason for that fact is the nature of chip formation during high-feed milling. In particular, when undeformed chip thickness h is higher than undeformed chip width b, inverse chip formation occurs [16]. In that case, passive forces Fp are higher than feed forces Ff . It means that during material removal, the minor cutting edge affects the workpiece surface stronger than at conventional milling. The workpiece surface receives more heat as well as more mechanical deformation. That results in higher residual stresses. Further investigations should focus on the residual stress distribution beneath the surface in high-feed milling.
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4 Conclusions After investigation of the impact of feed-per-tooth on residual surface stresses in highfeed milling of carbon steel, the following statements can be made: • the surface layer is stronger affected by high-feed milling compared to conventional milling (up to 20 times higher residual stresses), • surface tensile residual stresses occur for high-feed milling, • higher feed-per-tooth leads to a significant increase of surface tensile residual stresses in the milling of carbon steels.
Acknowledgement. The authors would like to thank the German Research Foundation (DFG) for its support of the collaboration project “Inverse cutting technology - a new strategy in face milling” (316141494).
References 1. Brinksmeier, E., Cammett, J.T., König, W., Leskovar, P., Peters, J., Tönshoff, H.K.: Residual stresses — measurement and causes in machining processes. Cirp Ann. 31(2), 491–510 (1982) 2. Field, M., Kahles, J.F.: Review of surface integrity of machined components. Cirp Ann. 20(2), 153–163 (1971) 3. Masmiati, N., Sarhan, A.A.D., Hassan, M.A.N., Hamdi, M.: Optimisation of cutting conditions for minimum residual stress, cutting force and surface roughness in end milling of S50C medium carbon steel. Measurement 86, 253–265 (2016) 4. Fuh, K.-H., Wu, C.-F.: A residual-stress model for the milling of aluminum alloy (2014-T6). J. Mater. Process. Technol. 51(1–4), 87–105 (1995) 5. Sridhar, B.R., Devananda, G., Ramachandra, K., Bhat, R.: Effect of machining parameters and heat treatment on the residual stress distribution in titanium alloy IMI-834. J. Mater. Process. Technol. 139(1–3), 628–634 (2003) 6. Mantle, A.L., Aspinwall, D.K.: Surface integrity of a high speed milled gamma titanium aluminide. J. Mater. Process. Technol. 118(1–3), 143–150 (2001) 7. Kwong, J., Axinte, D.A., Withers, P.J.: The sensitivity of Ni-based superalloy to hole making operations: influence of process parameters on subsurface damage and residual stress. J. Mater. Process. Technol. 209(8), 3968–3977 (2009) 8. El-Khabeery, M.M., Fattouh, M.: Residual stress distribution caused by milling. Int. J. Mach. Tools Manuf. 29(3), 391–401 (1989) 9. Wyen, C.-F., Jaeger, D., Wegener, K.: Influence of cutting edge radius on surface integrity and burr formation in milling titanium. Int. J. Adv. Manuf. Technol. 67(1–4), 589–599 (2013) 10. Axinte, D.A., Dewes, R.C.: Surface integrity of hot work tool steel after high speed millingexperimental data and empirical models. J. Mater. Process. Technol. 127(3), 325–335 (2002) 11. Li, B., Jiang, X., Yang, J., Liang, S.Y.: Effects of depth of cut on the redistribution of residual stress and distortion during the milling of thin-walled part. J. Mater. Process. Technol. 216, 223–233 (2015) 12. Saptaji, K., Afiqah, S.N., Ramdan, R.D.: A review on measurement methods for machining induced residual stress. Indonesian J. Comput. Eng. Des. 1(2), 106–120 (2019)
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13. Borysenko, D., Karpuschewski, B., Welzel, F., Kundrák, J., Felh˝o, C.: Influence of cutting ratio and tool macro geometry on process characteristics and workpiece conditions in face milling. Cirp J. Manuf. Sci. Technol. 24, 1–5 (2019) 14. Zauskova, L., Czan, A., Sajgalik, M., Drbul, M., Rysava, Z.: Triaxial measurement of residual stress after high feed milling using x-ray diffraction. Procedia Eng. 192, 982–987 (2017) 15. Sajgalik, M., et al.: Triaxial analysis of residual stress in surface layers after high feed machining using x-ray diffractometer. MATEC Web Conf. 157(1), 1–7 (2018) 16. Karpuschewski, B., Kundrák, J., Emmer, T., Borysenko, D.: A new strategy in face milling inverse cutting technology. SSP 261, 331–338 (2017)
Investigation on 3D Printing Parameters of PLA Polymers for Gear Applications Ziya Mehdiyev1(B) , Csaba Felh˝o1 , and Kovács Péter Zoltán2 1 Institute of Manufacturing Science, University of Miskolc, Miskolc, Hungary
[email protected] 2 Institute of Materials Science and Technology, University of Miskolc, Miskolc, Hungary
Abstract. Due to Fused Deposition Modeling (FDM), Additive Manufacturing technology offers to produce complex parts, low cost and large material selection; this technique is increasingly used in several industries. There are several printing parameters that directly affect the mechanical and tribological properties of the final part. The objective of our research work is to investigate different fabrication parameters for Polylactic acid (PLA) polymers for gear applications by characterising the ball-on-disk and compression tests. Several FDM printing parameters were considered for experiments, such as temperature, layer height, printing speed, infill pattern and so on. All specimens for the experimental investigations were printed with different printing parameter combinations by using the “Ultimaker Original +” 3D printer. Ball-on-disk tests were performed to analyse wear performance factors of samples, while compression tests were carried out to compare their load-bearing capabilities. The experimental tests were performed several times for each sample and summarised by determining the average values. The obtained results demonstrate that the FDM 3D printing parameters highly affect the wear behaviour and load-bearing capability of the produced parts due to the various microstructural modifications during the manufacturing process. Based on the ball-on-disc laboratory test results, the difference between the highest and lowest average friction coefficient is characterised by about 57%, while this difference reached 46% for maximum compressive force in the case of compression test results. The current research work provides useful guidance for the selection of FDM printing parameters to get better material properties of PLA polymers for gear applications. Keywords: 3D printing · PLA · FDM · Printing parameters
1 Introduction Additive manufacturing (AM), also known as 3D printing technology, involves producing 3D parts by integrating materials layer-by-layer by using digital CAD modelling, as opposed to subtractive manufacturing techniques [1, 2]. Three-dimensional printing has recently emerged as a potent technology that is rapidly thriving to revolutionise industrial operations [3]. Furthermore, this technology is currently being used in several industries, including automotive, aerospace, biomedical, energy and agriculture to improve © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 654–664, 2023. https://doi.org/10.1007/978-3-031-15211-5_55
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design fabrication, reduce lead times, and minimise the tooling costs for components to be manufactured. In recent years, 3D printing has developed as a versatile and effective approach in advanced manufacturing [4–6]. In essence, there is a significant increase in the usage of plastics in the additive manufacturing sector, as several plastic products can also be used in AM processes, providing great flexibility in the creation of complex designs. In general, fused deposition modelling (FDM, commercialised in 1990) is one of the most common, cost-effective, and easy to use 3D printing technology that uses thermoplastic polymers that come in a filament form to print 3D parts. Thermoplastics are widely used in extrusion-based techniques since they have a minimal environmental effect, are recyclable and come in a wide range of materials [3, 7, 8]. Due to several benefits over metal gears, polymer gears are used widely in various industries and applications, such as the mechatronic and automotive industries, medical instruments, etc. Although the polymer gears offer several economic and technical advantages (ability to operate without grease or oil lubrication, internal damping capacity, low cost of production, reduced noise etc.), there are also have some limitations such as less load-carrying capacity, poor temperature limits and poor heat conduction properties, great dimensional changes due to temperature and humidity conditions, etc. [9–12]. Because gears are used as fundamental elements to transmit power and rotational movement in many mechanical systems, their potential failure can result in the deadlock of the entire system. Thus, the proper estimation of load-bearing capacity against failures under given loading conditions is a crucial factor. Another important factor is the amount of wear that results in the short fatigue life of the gear drive due to polymer gears usually running without lubrication [12, 13]. There are several factors that influence the quality of the printed parts such as mechanical properties, surface quality, dimensional accuracy and so on [14]. If the product is a prototype or is printed for a purpose in which it will not be subjected to any mechanical impact, this will not cause any problems. However, the impact of the printing parameters on the mechanical properties becomes a critical factor if the produced product is any kind of replacement part. The objective of the current research work is to investigate different production parameters for Polylactic acid (PLA) polymers for gear applications by characterising the ball-on-disk and compression tests. “CETR – UNMT1” (CETR, INC. CAMPBELL, CA) universal micro-nano materials tester and “MTS” universal testing machine were used to perform ball-on-disk sliding friction tests and compression tests in order to analyse the load-bearing capacities and wear performance of the printed samples. Layer height, temperature, printing speed, number of contours, base layers, top layers, and infill pattern were the printing parameters that were considered for experimental investigations.
2 Material Testing Methods 2.1 Ball-on-Disk Test The ball-on-disk wear test is generally used to analyse the wear and sliding behaviour of materials under diverse tribological conditions. The specimens should be cleaned
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and dried prior to testing and measuring processes. Non-chlorinated, non-film-forming cleaning agents and solvents were used to remove all dirt and foreign substance from the specimens. Then the experimental tests were started by setting the required parameters while the specimens were in contact under load (Fig. 1).
Fig. 1. Schematic diagram of a ball-on-disk test [15]
The samples (small discs) for wear tests were made from PLA thermoplastics by using an “Ultimaker Original +” 3D printer. The dimensions of printed specimens were: diameter: d = 30 mm, and thickness: t = 5 mm (Fig. 3).There are several printing parameters for Fused Deposition Modeling (FDM) printers, such as layer height, printing speed, number of base layers, top layers and so on. The base and top layers are part of the print and exposed to the outside of the object. While the base layers face the build plate, the top layers face upwards, towards the nozzle, and the surface quality on this surface is usually the best.
Fig. 2. Schematic diagram of “base layers” and “top layers” 3D printing parameters [16]
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The samples were produced with different printing parameters to analyse the influence of these parameters on the wear behaviour. The printing parameters that were used in the production of samples are shown in Table 1 (Fig. 2).
Fig. 3. a) 3D printed PLA samples, b) geometrical model of the samples for ball-on-disk test
The most purposeful testing parameters were defined according to the performed several preliminary tests to provide visible wear tracks and steady-state friction conditions. The ball-on-disc wear test parameters: • • • •
Force, P = 10 N; Time, t = 180 min; Sliding velocity, v = 25 mm/s; Rotational speed, u = 79.6 rpm.
Target tests were repeated two times for each sample with mentioned parameters, during which the test machine recorded the friction coefficient-sliding distance curves.
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Material
PLA
Sample Temp. No. (0C) 1 2 3 4 5 6
210
Layer Prinng Infill Number of Base Top height speed paern, Contours layers layers (mm) (mm/s) % 0.2 0.4 0.2 0.2 0.2 0.2
50 50 30 80 50 50
3
3
100
1 5
1 5
25 50
2
2.2 Compression Test Compression testing is widely used to investigate the peak force and failure behaviour of the specimens (Fig. 4). The samples (small cubes) for the compression test were printed from the PLA thermoplastics by using the same 3D printer with different printing parameters (see Fig. 5). Three samples were printed with the same production parameter combinations and material to determine the average test results. The dimensions of the printed samples were: 10 × 10 × 10 mm. The utilised production parameters of the specimens can be seen in Table 2.
Fig. 4. Schematic diagram of the compression test machine [17]
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Fig. 5. a) 3D printed PLA samples, b) geometrical model of the samples for compression test
Table 2. Printing parameters of the produced samples for compression test.
Material
PLA
Sample No.
Temp. (0C)
Layer height (mm)
Prinng speed (mm/s)
Infill Number of Base Top paern, Contours layers layers %
1. 1-1.1-1.2
190
0.2
2. 2-2.1-2.2
210
0.2
3. 3-3.1-3.2
220
0.2
4. 4-4.1-4.2
210
0.4
5. 5-5.1-5.2
210
0.2
25
6. 6-6.1-6.2
210
0.2
50
100 50
2
3
3
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3 Results and Discussion In order to understand wear characteristics, the change in friction coefficient with respect to the sliding distance graph given in Fig. 6 should be inspected. According to the carried-out test results, the friction coefficient values are continuously decreasing without stabilisation. This kind of behaviour is characteristic of materials of good lubricity, providing a surface film that decreases friction and wears. Typical friction coefficient diagrams according to the ball-on-disk test results are shown in Fig. 6. Based on the collected diagrams, the final friction coefficient values (µfin ) were determined as the average of the last 5% of the total sliding distance. The characteristics of the final friction coefficient values determined in this way are shown in Table 3. Analysing the wear test data presented in Table 3, it can be seen that the variation coefficient is extremely high in two cases of the tests. These test results are unreliable, and the friction coefficient values of the specimens given in orange (i.e. 0.118 and 0.038) are neglected in the further analysis.
Fig. 6. Characteristic friction coefficient diagrams recorded during ball-on disc tests
By analysing the experimental test results, the following conclusions can be made concerning the effect of the production parameters on the friction behaviour of the PLA plastic samples: The lowest friction coefficient values were obtained for sample PLA_1 as a result of two tests (PLA_1_1 and PLA_1_2) with µfin1 = 0.201 and µfin2 = 0.213 respectively. The used 3D printing parameters for the sample (PLA_1) that were demonstrated the lowest friction coefficients are listed below:
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Table 3. Final friction coefficient values for PLA obtained from the performed ball-on-disk test. Ball-on-disc test 3D prinng parameters Material
PLA
• • • • • • •
File name (wear track)
PLA_1_v1 PLA_1_v2 PLA_2_v1 PLA_2_v2 PLA_3_v1 PLA_3_v2 PLA_4_v1 PLA_4_v2 PLA_5_v1 PLA_5_v2 PLA_6_v1 PLA_6_v2
Test results Layer height (mm)
Prinng speed (mm/s)
Base layers
Top layers
0.2
50
3
3
0.4
50
3
3
0.2
30
3
3
0.2
80
3
3
0.2
50
1
1
0.2
50
5
5
Final fricon Standart coefficient Average deviaon, SD μfin 0.201 0.213 0.118 0.266 0.378 0.344 0.316 0.332 0.038 0.309 0.295 0.327
Var. coeff. %
0.207
0.008
3.949
0.192
0.105
54.488
0.361
0.024
6.708
0.324
0.011
3.288
0.174
0.192
110.548
0.311
0.023
7.428
Temperature: T = 210 °C Layer height: h = 0.2 Printing speed: v pr = 50 mm/s Number of Contours: 2 Base Layers: 3 Top Layers: 3 Infill pattern: 100%
Regarding the applied printing parameters, the most inconvenient friction conditions occurred when the print speed was the lowest, i.e. v pr = 30 mm/s. To sum up the performed ball-on-disc test results, the friction behaviour of printed PLA parts is significantly influenced by printing parameters. The difference between the highest and lowest average friction coefficient can be characterised as about 57%. The coefficient of friction of the samples decreases uniformly and monotonically during the total wear distance. In addition, according to the performed experimental compression tests, the maximum compressive forces were determined for each specimen and were summarised by determining the average values (Table 4). Based on Table 4, sample group 2 (2; 2.1; 2.2) indicated the highest average compressive force (for 0.5 mm displacement or first peak point) which was F = 7100 N.
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The following figure (Fig. 7) is the typical diagram illustrating typical force vs. displacement diagrams to analyse the influence of the production parameters. Table 4. Maximum compressive force values for PLA materials obtained from compression test.
3D printing parameters have a significant influence on the load-bearing capacity of PLA materials and the difference between the highest and lowest maximum compressive force was reached 46%. It has been clearly seen that the temperature and layer height parameters directly affected the load-bearing capability of the specimens, but the strongest influence was observed in the case of the infill pattern parameter. The lower amount of infill pattern parameter makes the printed parts more sensitive against the compression loads. According to the performed experimental compression tests, the best results were observed where the processing parameters were 210 °C “Temperature”, 100% “Infill pattern” and 0.2 mm “Layer height”. The following parameters show the remaining processing parameters, which were the same for all sample groups: • • • •
Printing speed: v pr = 50 mm/s Number of Contours: 2 Base layers: 3 Top layers: 3
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Fig. 7. The effect of the printing parameters for PLA specimens
4 Conclusion The objective of the current study was to analyse different printing parameters for PLA thermoplastics for gear applications by performing different experimental tests. According to the working conditions of gears, the evaluation of load-bearing capability under the applied loading and the amount of wear are significant factors that directly influence the lifetime. In our current research work, we used ball-on-disk and compression experimental tests to characterise the wear performance and the load-bearing capacity of the analysed samples. All the tested specimens were printed from the PLA thermoplastics by using the “Ultimaker Original +” 3D printer. The printing parameters that were considered for the investigation were temperature, layer height, printing speed, number of contours, base layers, top layers, and infill pattern. For the ball-on-disc test, six specimens were printed with different parameter combinations and analysed by using the “CETR – UNMT1” universal micro-nano materials testing machine. The experimental tests were performed on each sample, and results were analysed using data that were collected by the test machine. As a result of wear tests, the lowest friction coefficient values were obtained for sample PLA_1 with µfin = 0.207 as average. In addition, the compression tests were performed on 36 specimens which were produced using six different printing parameter combinations. The force vs. displacement curves was created based on the collected test data to analyse the influence of applied printing parameters. Based on the compression test results, sample group 2 (2; 2.1; 2.2) represented the highest average force which was F = 7100 N.
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As a result of carried out both experimental tests, we can consider that the wear performance and load-bearing capability of the printed PLA parts are highly influenced by the printing parameters – the differences between the highest and lowest test results were 57% and 46% respectively. To sum up, from the viewpoint of the possibility of producing PLA thermoplastics with low friction and high load-bearing capacity – i.e. 210 °C temperature, 0.2 mm layer height, 50 mm/s printing speed, 2 contours, number of bases and top layers of 3 and 100% infill pattern can be regarded as the optimum printing parameters, among those, analysed during current research work. Acknowledgement. This work has been supported by Project no. NKFI-125117, which has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the K_17 funding scheme.
References 1. Technologies, A.C.F.o.A.M. and A.C.F.o.A.M.T.S.F.o. Terminology, Standard terminology for additive manufacturing technologies. ASTM International (2012) 2. Paudyal, M.: A brief study on three-dimensional printing focusing on the process of fused deposition modeling (2015) 3. Zaneldin, E., et al.: Dimensional stability of 3D printed objects made from plastic waste using FDM: potential construction applications. Buildings 11(11), 516 (2021) 4. Gokuldoss, P.K., Kolla, S., Eckert, J.: Additive manufacturing processes: Selective laser melting, electron beam melting and binder jetting—selection guidelines. Materials 10(6), p. 672 (2017) 5. Kele¸s, Ö., Blevins, C.W., Bowman, K.J.: Effect of build orientation on the mechanical reliability of 3D printed ABS. Rapid Prototyp. J. 23, 320–328 (2017) 6. Pal, A.K., Mohanty, A.K., Misra, M.: Additive manufacturing technology of polymeric materials for customised products: recent developments and future prospective. RSC Adv. 11(58), 36398–36438 (2021) 7. Tiwari, K., Kumar, S.: Analysis of the factors affecting the dimensional accuracy of 3D printed products. Mater. Today Proc. 5(9), 18674–18680 (2018) 8. Ahmed, W., et al.: Implementing FDM 3D printing strategies using natural fibers to produce biomass composite. Materials 13(18), 4065 (2020) 9. Gears, K.: Deciding when to go plastic (2014) 10. Abdelbary, A.: Wear of Polymers and Composites. Woodhead Publishing (2015) 11. Bravo, A., et al.: Life and damage mode modeling applied to plastic gears. Eng. Fail. Anal. 58, 113–133 (2015) 12. Trobentar, B., et al.: Experimental failure analysis of S-polymer gears. Eng. Fail. Anal. 111, 104496 (2020) 13. Singh, P.K., Singh, A.K.: An investigation on the thermal and wear behavior of polymer based spur gears. Tribol. Int. 118, 264–272 (2018) 14. Lennert, J.R., Sárosi, J.: Investigation of 3D printing parameters affecting the impact strength. Ann. Fac. Eng. Hunedoara Int. J. Eng. 19(2), 133–139 (2021) 15. Cabral Miramontes, J., et al.: Wear resistance of thermal spray WC-Co-VC nanostructured coatings (2016) 16. HUBS. https://www.hubs.com/knowledge-base/selecting-optimal-shell-and-infill-parame ters-fdm-3d-printing/. Accessed 26 May 2022 17. Ishii, T., et al.: Development of a performance analysis code for vibro-packed MOX fuels. J. Nucl. Sci. Technol. 45(4), 263–273 (2008). https://doi.org/10.1080/18811248.2008.9711436
Optimization
Fitness Landscape Analysis of Population-Based Heuristics in Solving a Complex Vehicle Routing Problem Anita Agárdi(B) University of Miskolc, Miskolc 3515, Hungary [email protected]
Abstract. In this paper, a fitness landscape analysis of a complex Vehicle Routing Problem (VRP) is presented, and the effectiveness of population-based heuristic techniques is analyzed on this complex problem. The Vehicle Routing Problem is a common optimization task where vehicles deliver products to customers. The task is NP difficult; several heuristic algorithms have been involved in solving the problem. The objective is to select the right algorithm for the task, where the search space analysis provides an analytical answer. In this paper, the analysis of the population-based heuristics is presented. The paper presents an analysis of the following population algorithms: Ant System, Elitist Strategy of Ant System, Firefly Algorithm, Genetic Algorithm. In this paper, the results of the iterations of each population algorithm are analyzed in terms of the followings: fitness values, fitness distances, basic swap sequence distances, Hamming distances, the best solution, and filtered optima. Based on the test results, it can be concluded that the Ant System algorithm proved to be the most effective and the Firefly algorithm is not recommended to solve the presented complicated VRP. Keywords: Vehicle Routing Problem · Fitness landscape analysis · Heuristics
1 Introduction The objective of logistics is the cost-effective delivery of products and services to the corresponding place at the corresponding time. This problem is represented by the Vehicle Routing Problem, in the classic case in which vehicles transport products out of a depot and then return to the depot at the end of their route. Over the years, several variations of this task have emerged, which are presented below. Vehicles can be the same [1] or different [1]. The number of the depot can be one [2] or more [2]. The system may also contain customers and satellites [3]. Product types can be one [4] or more [4]. Values can be exact or given with fuzzy numbers [5]. Open VRP [6] means that vehicles will not return to the starting depot. According to Inter-Depot Routes [7], they can return to any depot. According to Pickup and Delivery [8], some products are shipped while others are collected. Periodic VRP [9] means that customers are not visited once but at certain intervals. It is added to each customer how many times they need to be visited in a time interval, e.g., weekly, twice a week, every day. Vehicles can also be electronic; © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 667–677, 2023. https://doi.org/10.1007/978-3-031-15211-5_56
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this case is called Electric VRP [10]. Vehicles can also be rented vehicles [11], in which case minimizing the rental fee can also be an objective. One of the simplest examples of VRP is the Traveling Salesman Problem (TSP) [12], in which a single vehicle visits customers once. After that, the vehicle returns to the starting customer. The objective function is the minimization of the length of the complete route. There is no freight here, so we don’t have to reckon with a vehicle capacity limit. In the case of fitness landscape [13] analysis, the problem to be optimized is analyzed, its complexity, such as the local optimums, global optima, the chances of getting to optima, the efficiency of algorithms and operators for a given problem or problem area. Most of the time, when scientists develop a new algorithm, they prove, based on the test results of benchmark data sets, that the given algorithm has effectively solved the problem. However, in the case of fitness landscape analysis, we use analytical method to prove whether a given algorithm or operator is effective for the problem. The article is structured as follows: first, the analyzed Vehicle Routing Problem is presented, followed by population-based optimization techniques such as Ant System, Elitist Strategy of Ant System, Firefly Algorithm, Genetic Algorithm. Then a detailed evaluation of test results and the conclusion are presented.
2 Vehicle Routing Problem In this chapter, the analyzed Vehicle Routing Problem (VRP) is presented, which can be read in more detail in this article [14]. The VRP is a Two-Echelon VRP, it includes a depot, satellites, and customers. The system includes a single depot, 10 satellites and 15 customers. Each level contains 2 vehicles. The system also takes into account the distance between nodes (customers, satellites, depot), the travel time and the route status. The system also includes several components, such as loading time, which also depends on the node, products, and vehicle. The system also contains temporality, so almost every element of the VRP depends on time. The system includes several products. The components closely related to the products are loading time, unloading time, quality control time, administration time, loading, unloading quality control, administration cost, and the product demand of the customer. The objective function consists of the following components and objectives: route length minimization, times and costs minimization, and route status maximization. I did not find such a complicated, multi-component Vehicle Routing Problem among the benchmark data sets. I created my own data set, in which the values were randomly generated. The coordinates of the depot are between 0 and 100, the coordinates of the first level satellites are from the interval (200; 300), the capacity constraints of the vehicles are from (10,000; 50,000), and the cost values are between 10 and 50.
3 Population-Based Optimization Algorithms In this chapter, the population algorithms for fitness landscape analysis is presented.
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3.1 Ant System The Ant System [15] models the behaviour of ants. The ants put hormones along their way. The sections of the route that will contain more hormones attract the ants. The initial step of the algorithm is setting the parameters. Then each of the ants creates their way and puts a pheromone on the road sections, so the pheromone upgrade is the next step. The algorithm repeats these steps until the stop criteria is not fulfilled. 3.2 Elitist Strategy of Ant System The Elitist Strategy of Ant System [16] is a modification of the Ant System. Here, only the best ant can put down a pheromone. The best ant is the one with the shortest path. The algorithm also takes into account the evaporation of the pheromone. The deposited pheromone evaporates after some time, during which time the amount of pheromone decreases in each iteration. The first step of the algorithm is setting the parameters. The ants then construct their way. Then the pheromone of the road sections is updated. The pheromone evaporates at each pathway, and the value of the pheromone increases as the best ant travels, as the best ant can only put down pheromone. Ants will choose routes that are short and high in pheromones. 3.3 Firefly Algorithm The Firefly Algorithm [17] was inspired by the attitude of the firefly. The shiny firefly attracts the others. First, the initial population (fireflies) is created. Then the intensity and absorption coefficient values are evaluated. After that, the locations of the fireflies are determined. Then the movement of the fireflies will begin, they will move towards the brighter one. After that, the parameters of the fireflies are updated. The above-mentioned instructions continued until the stop criteria of the algorithm is not fulfilled. 3.4 Genetic Algorithm The Genetic Algorithm [18] was inspired by the population. The algorithm performs operations on a population of solutions and then creates a new population. After initializing the initial population, the algorithm creates a new population with crossover and mutation. These instructions are repeated until the stop criteria is not fulfilled. During the crossover, the algorithm creates two new solutions, while during the mutation, one solution will be changed slightly. The proposed genetic algorithm uses 3 crossover techniques, which are the Order Crossover [19], Partially Matched Crossover [19], and Cycle Crossover [20]. The 2-opt [21] was used for the mutation.
4 Test Results The test results are presented in this chapter. The following distances were analyzed: fitness distances, Hamming distances (HamD), and basic swap sequence (BSS) distances. The test results were constructed based on the results of the iterations of each algorithm,
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which represented the set of solutions on which the analyses were performed. The average of fitness distances is also considered for the set of solutions, the average of the fitness distances of each solution. The average of the Hamming distances and the average of the basic swap distances are also the values of the distance between the solution set. The distances from the optimum of the solution set (fitness distances of the optimum, HamD of the optimum, BSS distances of the optimum) are also examined. The cost of density is also analyzed, i.e. how many solutions have different fitness values. An analysis of the distances of the filtered global optima is also presented, which was prepared by selecting the best of the randomly generated solutions. In the following, an example is presented, where the solution set contains three elements. Figure 1 illustrates the permutations (solutions) and their fitness values.
Fig. 1. Example solution set.
For the analysis we need to prepare the fitness, Hamming and BSS differences between each solutions. These distances are illustrated in Table 1, Table 2 and Table 3. Table 1. Fitness differences for the example solution set Fitness differences
Solution 1
Solution 2
Solution 3
Solution 1
–
400
500
Solution 2
400
–
100
Solution 3
500
100
–
Table 2. Hamming differences for the example solution set Hamming differences
Solution 1
Solution 2
Solution 3
Solution 1
–
4
6
Solution 2
4
–
7
Solution 3
6
7
–
The fitness values are between 2000 and 2500 in the example because the lowest fitness value is 2000 and the highest is 2500.
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Table 3. Basic Swap Sequence differences for the example solution set BSS differences
Solution 1
Solution 2
Solution 3
Solution 1
–
3
4
Solution 2
3
–
5
Solution 3
4
5
–
In order to determine the average fitness differences, the fitness value differences between the individual solutions shown in the table must be calculated. Then avg (diff (solution1, solutionX)) = 450, avg (diff (solution2, solutionX)) = 250, avg (diff (solution3, solutionX)) = 300. The lower bound will be 250 and the upper bound will be 450. The Hamming distances must be calculated for the average Hamming distances. The average Hamming distances are the followings: avg (hamD (solution1, solutionX)) = 5, avg (hamD (solution2, solutionX)) = 5.5, avg (hamD (solution3, solutionX)) = 6.5. The lower bound for the average Hamming distances is 5 and the upper bound is 6.5. For the average BSS differences, the BSS differences of the solutions must be calculated. The average values are the following: avg (BSS (solution1, solutionX)) = 3.5 avg (BSS (solution2, solutionX)) = 4, avg (BSS (soution3, solutionX)) = 4.5. The lower bound is 3.5 and the upper bound is 4.5. For fitness differences of the optimum, the best element of the solution set must be determined, this is the solution3 (in case of minimization). Then diff (solution1, soution3) = 500 and diff (solution2, solution3) = 100. The same should be determined when calculating HamD of the optimum, so hamD (solution1, solution3) = 6 and hamD (solution2, solution3) = 7. The BSS differences of the optimum values are BSS (soltuion1, solution3) = 4 and BSS (solution2, solution3) = 5. The cost density value is 1 because each solution has a different fitness value. For the following calculations, the filtered global optima solution must also be defined, let this be the solution illustrated in Fig. 2.
Fig. 2. Example filtered global optima solution.
Considering the filtered global optima illustrated in Fig. 2, Table 4 shows the different values.
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Filtered global optima differences
Solution 1
Solution 2
Solution 3
Fitness differences
700
300
200
Hamming differences
6
5
7
BSS differences
4
3
4
For fitness differences of filtered global optimum the lower bound is 200 and the upper bound is 400. The HamD values of the filtered global optimum are the following: the lower bound is 5, the upper bound is 7. The BSS differences of the filtered global optimum are the following: the lower bound is 3, and the upper bound is 4 (Table 5). Table 5. Results for the example solution set Type
Lower bound
Upper bound
Fitness values
2000
2500
Average fitness differences
250
450
Average HamD
5
6.5
Average BSS differences
3.5
4.5
Fitness differences of the optimum
100
500
HamD of the optimum
6
7
BSS differences of the optimum
4
5
Cost density
1
1
Fitness differences of filtered global optimum
200
400
HamD of filtered global optimum
5
7
BSS differences of filtered global optimum
3
4
4.1 Ant System The method provides rapid convergence. Taking into account the averages of the fitness values related to the solutions, it can be said that the higher the fitness, the greater the average fitness distance. The same result applies to Hamming and BSS distances, but the values here are relatively scattered in the coordinate system. The distance from the best solution increases linearly with the suitability value. The situation is similar for Hamming and BSS distances. Taking into account the average of the fitness distances, we obtain the fitness distances and the parabolic function from the best solutions and the other solutions. The distance between Hamming and the BSS is scattered in the coordinate system. In terms of cost density values, only some of the solutions obtained during the iteration are identical. The function of the fitness distance measured from the
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filtered global optimum solution shows a decreasing and then an increasing trend. This means that during the iteration, the algorithm managed to find a better solution than the filtered optima. The Hamming and BSS values are condensed into a node as a function of fitness value and there are small distances between the solutions (Table 6). Table 6. Ant System results. Type
Lower bound
Upper bound
Fitness values
≈110,000
≈140,000
Average fitness differences
≈6,000
≈17,000
Average HamD
14
26
Average BSS differences
11
21
Fitness differences of the optimum
≈500
≈4,000
HamD of the optimum
10
34
BSS differences of the optimum
8
28
Cost density
1
2
Fitness differences of filtered global optimum
≈1,500
≈16,000
HamD of filtered global optimum
32
38
BSS differences of filtered global optimum
24
32
4.2 Elitist Strategy of Ant System Depending on the fitness values, the suitability distances between the solutions are compressed into one point. But the averages of Hamming and BSS distance values are high. The fitness distance from the optimal solution is linear, this is also true of the Hamming and base swap sequence distances. Hamming and BSS distances range widely. The average distance from the optimal solution and the distance from the solutions describe a parabolic function. Hamming and BSS distances are scattered. In terms of cost density values, almost every solution has unique fitness values. The fitness distances from the filtered global optimum solution are small, but the Hamming and BSS distances are not compressed into a node (Table 7). Table 7. Elitist Strategy of Ant System results. Type
Lower bound
Upper bound
Fitness values
≈120,000
≈140,000 (continued)
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Lower bound
Upper bound
Average fitness differences
≈3,500
≈11,500
Average HamD
18
27
Average BSS differences
13
20
Fitness differences of the optimum
≈500
≈16,000
HamD of the optimum
13
40
BSS differences of the optimum
10
32
Cost density
1
3
Fitness differences of filtered global optimum
≈200
≈11,500
HamD of filtered global optimum
34
38
BSS differences of filtered global optimum
26
30
4.3 Firefly Algorithm During the firefly algorithm, all solutions in the iteration gave the same solution, so the algorithm could not improve on the initial value. This initial value is about 140,000 fitness values. Thus, the values for each distance (fitness, Hamming, BSS) are 0, and each iteration solution has the same fitness value (Table 8). Table 8. Firefly Algorithm results. Type
Lower bound
Upper bound
Fitness values
≈140,000
≈140,000
Average fitness differences
0
0
Average HamD
0
0
Average BSS differences
0
0
Fitness differences of the optimum
0
0
HamD of the optimum
0
0
BSS differences of the optimum
0
0
Cost density
11
11
Fitness differences of filtered global optimum
≈20,000
≈20,000
HamD of filtered global optimum
35
35
BSS differences of filtered global optimum
30
30
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4.4 Genetic Algorithm The difference in fitness values of the genetic algorithm solutions is slight. The averages of fitness distances are slight. The Hamming and the BSS distances are also small. Therefore, the fitness distances are short from the best solution. The distance values are also small in the case of Hamming and the BSS. According to the cost density, 7 solutions have the same fitness value, which is a high number. Fitness distances from the filtered global optimum solution are small. However, the Hamming and BSS distances from the filtered global optimums are no longer so small, the distance values are in a narrow range (Table 9). 4.5 Summary It can be concluded from the test results that the Firefly algorithm did not improve the solutions during the iterations, so it is not recommended to use this algorithm to solve the problem. The Ant System algorithm was the best, it gave the lowest value among the fitness values. The average of Hamming and basic swap sequence (BSS) distance values were also high, which is good, because the solutions are varied, the algorithm maps the search space. The cost density value was also 1–2, which means that the solutions are different. For the genetic algorithm, this value is between 1 and 7, which is much worse. The Elitist Strategy of Ant System gave slightly worse fitness results and cost density values than the Ant System, but this algorithm can also be used effectively for the VRP task. Hamming and BSS distances are large, so it maps the search space well. Table 9. Genetic Algorithm results. Type
Lower bound
Upper bound
Fitness values
≈120,000
≈130,000
Average fitness differences
≈200
≈1,500
Average HamD
4
10
Average BSS differences
3
8
Fitness differences of the optimum
≈100
≈1,800
HamD of the optimum
8
12
BSS differences of the optimum
5
10
Cost density
1
7
Fitness differences of filtered global optimum
≈1,200
≈3,000
HamD of filtered global optimum
30
36
BSS differences of filtered global optimum
26
32
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5 Conclusions In this paper, a fitness landscape analysis of a complicated VRP is performed in terms of the effectiveness of Ant System, Elitist Strategy of Ant System, Genetic Algorithm, and Firefly Algorithm. These algorithms maintain a population of solutions and iteratively improve the elements of the population. In this article, three distances between the solution sets were examined: fitness, Hamming, and basic swap sequence. The article presented how many types of VRP have evolved over the years, thus modelling logistics complex systems. The article investigates a complex system, a Multi-Echelon VRP, which contains a depot, satellites at two levels, and customers. In addition, several components such as loading, unloading, administrative cost, loading time, unloading time, administrative time, travel time, travel distance, capacity limit, etc. were also considered. The article presented that the Ant System algorithm is the most efficient to solve the complex VRP, but the Elitist Strategy of Ant System is also an efficient algorithm. The Firefly Algorithm did not show efficiency, and the algorithm failed to improve the results during the iterations, so it is not recommended to use this algorithm to solve the presented complex VRP.
References 1. Taillard, É.D.: A heuristic column generation method for the heterogeneous fleet VRP. RAIRO Oper. Res. 33(1), 1–14 (1999) 2. Ho, W., Ho, G.T., Ji, P., Lau, H.C.: A hybrid genetic algorithm for the multi-depot vehicle routing problem. Eng. Appl. Artif. Intell. 21(4), 548–557 (2008) 3. Vakili, R., Akbarpour Shirazi, M., Gitinavard, H.: Multi-echelon green open-location-routing problem: a robust-based stochastic optimization approach. Scientia Iranica 28(2), 985–1000 (2021) 4. Weerakkody, H.D.W., Niwunhella, D.H.H., Wijayanayake, A.N.: Solution approach to incompatibility of products in a multi-product and heterogeneous vehicle routing problem: An application in the 3PL industry. In: 2021 International Research Conference on Smart Computing and Systems Engineering (SCSE), pp. 149–153. IEEE (2021) 5. Mehlawat, M.K., Gupta, P., Khaitan, A.: Multiobjective fuzzy vehicle routing using Twitter data: Reimagining the delivery of essential goods. Int. J. Intell. Syst. 36(7), 3566–3595 (2021) 6. Zwiers, R.J.: A modified cheapest insertion heuristic for the multi-objective pickup and delivery open VRP with time windows for a homogeneous fleet with heterogeneous freights: a case study. Master’s thesis, University of Twente (2021) 7. Ramos, T.R.P., Gomes, M.I., Barbosa-Póvoa, A.P.: A new matheuristic approach for the multidepot vehicle routing problem with inter-depot routes. OR Spectr. 42(1), 75–110 (2019). https://doi.org/10.1007/s00291-019-00568-7 8. Lagos, C., Guerrero, G., Cabrera, E., Moltedo, A., Johnson, F., Paredes, F.: An improved particle swarm optimization algorithm for the VRP with simultaneous pickup and delivery and time windows. IEEE Lat. Am. Trans. 16(6), 1732–1740 (2018) 9. Baldoquin, M.G., Martinez, J.A., Díaz-Ramírez, J.: A unified model framework for the multiattribute consistent periodic vehicle routing problem. PLoS ONE 15(8), e0237014 (2020) 10. Ewert, R., Martins-Turner, K., Thaller, C., Nagel, K.: Using a route-based and vehicle type specific range constraint for improving vehicle routing problems with electric vehicles. Transp. Res. Procedia 52, 517–524 (2021)
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11. Kaewman, S., Akararungruangkul, R.: Heuristics algorithms for a heterogeneous fleets VRP with excessive demand for the vehicle at the pickup points, and the longest traveling time constraint: a case study in Prasitsuksa Songkloe, Ubonratchathani Thailand. Logistics 2(3), 15 (2018) 12. Xin, L., Song, W., Cao, Z., Zhang, J.: NeuroLKH: combining deep learning model with LinKernighan-Helsgaun heuristic for solving the traveling salesman problem. In: Advances in Neural Information Processing Systems 34 (2021) 13. Pitzer, E., Affenzeller, M.: A comprehensive survey on fitness landscape analysis. In: Fodor, J., Klempous, R., Suárez Araujo, C.P. (eds.) Recent Advances in Intelligent Engineering Systems. SCI, vol. 378, pp. 161–191. Springer, Heidelberg (2012). https://doi.org/10.1007/ 978-3-642-23229-9_8 14. Agárdi, A., Kovács, L., Bányai, T.: An attraction map framework of a complex multi-echelon vehicle routing problem with random walk analysis. Appl. Sci. 11(5), 2100 (2021) 15. Kacem, I., Sait, B., Mekhilef, S., Sabeur, N.: A new routing approach for mobile ad hoc systems based on fuzzy petri nets and ant system. IEEE Access 6, 65705–65720 (2018) 16. Jaradat, G.M., et al.: Hybrid elitist-ant system for nurse-rostering problem. J. King Saud Univ. Comput. Inf. Sci. 31(3), 378–384 (2019) 17. Aggarwal, D., Kumar, V.: Performance evaluation of distance metrics on firefly algorithm for VRP with time windows. Int. J. Inf. Technol. 13(6), 2355–2362 (2021) 18. da Costa, P.R.D.O., Mauceri, S., Carroll, P., Pallonetto, F.: A genetic algorithm for a green vehicle routing problem. Electron. Notes Discrete Math. 64, 65–74 (2018) 19. Wibisono, E., Martin, I., Prayogo, D.N.: Comparison of crossover operators in genetic algorithm for vehicle routing problems (2021) 20. Visutarrom, T., Chiang, T.C.: An evolutionary algorithm with heuristic longest cycle crossover for solving the capacitated vehicle routing problem. In 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 673–680. IEEE (2019) 21. Barma, P.S., Dutta, J., Mukherjee, A.: A 2-opt guided discrete antlion optimization algorithm for multi-depot vehicle routing problem. Decis. Mak. Appl. Manag. Eng. 2(2), 112–125 (2019)
Analysis of the Multi-Objective Optimisation Techniques in Solving a Complex Vehicle Routing Problem Anita Agárdi(B) University of Miskolc, Miskolc 3515, Hungary [email protected]
Abstract. The Vehicle Routing Problem (VRP) is a common logistics problem. The problem was first published in 1959 as the Truck Dispatching Problem. In the basic problem, vehicles deliver the products from a given depot and then return to the depot. The objective function is to minimise the distance travelled by the vehicles. Since the first published paper, a number of variants have been developed that adapt to real logistics demands. This article investigates the optimisation of a complex Vehicle Routing Problem. The following multi-objective optimisation techniques are investigated in the article: weighted-sum method, weighted-exponential sum method, weighted global criterion method, exponentially weighted criterion, weighted product method, bounded objective function method, pareto ranking, Non-dominated Sorting Genetic Algorithm II, Strength Pareto Evolutionary Algorithm, Niched Pareto Genetic Algorithm. The article provides a detailed analysis with the following heuristic algorithms: Ant Colony System, Genetic Algorithm, Tabu Search, Firefly Algorithm, Simulated Annealing. Keywords: Vehicle Routing Problem · Logistics · Multi-objective optimisation
1 Introduction The Vehicle Routing Problem (VRP) [1] is one of the best-known logistics optimisation tasks, first published in 1959 as the Truck Dispatching Problem [2]. During the basic VRP, vehicles deliver products to customers from a depot. The objective is to minimise the distance travelled by the vehicles. Over the years, many types of the problem have emerged due to the complexity of logistics systems. During Capacitated VRP [3], vehicles have a capacity limit. Vehicles can be homogeneous (Homogeneous VRP [4]) or heterogeneous (Heterogeneous VRP [5]). Depending on the number of depots, the system can contain one (Single Depot VRP [6]) or multiple depots (Multiple-Depot VRP [7]). The system may also contain intermediate depots, called satellites (Two-Echelon VRP [8]). At this point, the vehicles first transport the products from the depot to the satellites and then on to the customers. If the satellites also form levels, the problem is called Multi-Echelon VRP [9]. The components of the VRP may also have fuzzy values, this is the Fuzzy VRP [10]. The vehicles of the system can also be drones, this is the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 678–693, 2023. https://doi.org/10.1007/978-3-031-15211-5_57
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VRP with Drones [11]. If the system includes a time window, it is called VRP with Time Windows. The time window can be soft [12], hard [13], or multiple [14]. The purpose of the basic VRP is the minimisation of the length of the distance traveled by the vehicles. However, if the task is more complex and involves several components, it may include other objective functions, such as the number of served customers [15], time minimisation [16], and fuel minimisation [15]. If the problem has several objective functions, the problem is called Multi-Objective Optimisation. Due to the complexity of the VRP task, it is usually solved with heuristic algorithms. In this article, the problem is solved with the following heuristics: Ant Colony System, Firefly Algorithm, Genetic Algorithm, Simulated Annealing, Tabu Search. The rest of the article continues as follows. In the second chapter, the complex VRP is presented. The third chapter introduces heuristic algorithms, and the fourth chapter presents Multi-Objective Optimisation techniques. The test results are then presented.
2 Complex Multi-Echelon Vehicle Routing Problem The complex Multi-Echelon Vehicle Routing Problem (VRP), discussed in more detail in this article [17], consists of the following components: single depot, multiple satellites, multiple customers, and heterogeneous vehicles. The vehicles on the first level transport the products from the depot to the satellite. And the second level vehicles deliver it from the satellite to the customers. The system also includes the following components: travel distance, route status, travel time, capacity limit, fuel consumption, rental fee, unloading time, loading time, administration time, product demand, loading cost, unloading cost, and quality control cost, administration cost. The system has the following objective function components: fuel consumption, length of the route, vehicle rental fee, administration cost, loading time, route time, unvisited customers, unloading time, loading cost, unloading cost, route status, quality control cost. Figure 1 illustrates the complex Multi-Echelon VRP. The figure contains one depot (D1 ), two satellites (S1 , S2 ), and eight customers (C1 , . . . , C8 ).
Fig. 1. Complex Multi-Echelon Vehicle Routing Problem.
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3 Applied Heuristic Algorithm In this section, the applied heuristic algorithms are presented, which are the followings: Ant Colony System, Firefly Algorithm, Genetic Algorithm, Simulated Annealing, Tabu Search. 3.1 Ant Colony System The Ant Colony System algorithm is a biological algorithm that models the behaviour of the ants [18]. During the algorithm, the ants put a pheromone hormone on their journey. The other ants will choose paths, which are high in pheromones and short. First, the initial population is created. Then the ants are placed in a random location and each ant chooses its path. Then the pheromone levels are updated. The evaporation of the pheromone is also taken into account. The termination condition is then evaluated. 3.2 Firefly Algorithm The Firefly Algorithm [19] is also a biology-inspired algorithm. This algorithm models the behaviour of the fireflies. The fireflies will fly towards the brighter ones. The first step in the algorithm is to initialise the initial population (fireflies). Elements of the population are then evaluated and ranked. Then the best item (firefly) is selected and the other firefly will fly to the direction of the best firefly. The next step is to test the termination condition. 3.3 Genetic Algorithm The genetic algorithm [20] is also inspired by nature, population, genetics. The algorithm maintains a population of solutions during the iterations. It performs crossover and mutation on the individuals. The crossover is usually performed on two individuals and will result in two children individuals. The mutation is performed on an individual and results a small change. 3.4 Simulated Annealing The Simulated Annealing [21] algorithm operates on a single solution. The algorithm was inspired by the cooling of metals. After generating the initial solution, the algorithm creates a new solution. Then, if the solution is better than the current solution, the algorithm accepts it as the current solution and also updates the temperature. The generation of new solutions and updating of the temperature will continue until the termination condition is not met.
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3.5 Tabu Search Tabu Search [22] also operates on a single solution. The algorithm selects the best of the neighbours (candidates) of the current solution. The algorithm maintains a tabu list, if the best neighbour is in the tabu list, it removes that solution from the list of the candidates. If it is not included in the tabu list, it is accepted as the current solution and inserted into the tabu list. The creation of new solutions continues until the termination condition is not met.
4 Multi-Objective Optimisation If a problem consists of a single objective function, then we are talking about singleobjective optimisation. However, in most engineering optimisations, we have multiple objective functions. In many situations, the objective functions may conflict with each other. The Multi-Objective Optimisation (MOO) problem can be described mathematically with the following formulas [23]: min F(x) x∈X
Subject to the following constraints: gi (x) ≤ 0, i = 1, . . . , m hj (x) = 0, j = 1, . . . , p
4.1 Normalisation of the Objective Function Many optimisation methods involve making decisions about different objective functions. Even if we may give different weights and different orders of magnitude to functions, the comparison is different. It is usually necessary to transform the objective function to have similar orders of magnitude [23]. finorm (x) =
fi (x) − fimin fimax − fimin
The value will be converted to the range [0, 1] [24]. In normalisation, the utopia point is often replaced by an estimate to facilitate the calculation. In the following, we assume that the objective functions are normalised.
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4.2 Methods with a Priori Articulation of Preferences These methods allow the user to set preferences that show the importance of the various objective functions [23]. Preferences are therefore matched before optimisation. Before the optimisation process, we aggregate the objective functions and solve the resulting problem [25]. Weighted-Sum Method In multi-objective optimisation, a common solution is to combine different objective functions into a single scalar function. One of the simplest and most common methods is weighted-sum or scalarisation, which is defined as follows [23]: Minimise u(x) = x∈S
q
wi fi (x)
i=1
which is a new optimisation problem with a unique objective function (u(x)). The weights q (wi ) are given by the user such that i=1 wi = 1 and wi > 0∀i [23]. This method is easy to use. But it has several disadvantages. Preliminary weight determination does not always guarantee that the solution will be acceptable, we may get a better solution with other weight choices, so the relationship between the choice of weights and the resulting solution is unclear. The method is not suitable for finding solutions that are located on the concave Pareto front, so it is not possible to produce the Pareto front in all cases [23]. Weighted-Exponential Sum Method Improving the weighted-sum method, an exponential coefficient (p) is added to the objective function, as follows [23]: Minimise u(x) = x∈S
q
wi (f i (x))p
i=1
q where i=1 wi = 1, wi > 0∀i and p > 0. In this case, p can be thought of as a compensation parameter, which means that a higher p value results in a preference for solutions with very high and very low objective values rather than averaged values. In general, the value of p must be very large to find Pareto points from nonconvex regions [23]. Weighted Global Criterion Method This is a scalarisation method that converts all objective functions into a single objective function that will then be minimised. The most common method is as follows [26]: q p 1/p 0 wi (f i (x) − fi ) Minimise u(x) = x∈S
i=1
Solutions that use the global criterion method depend on the values of w and p. In general, p is proportional to the emphasis placed on minimising the function by the
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largest difference between fi (x) and fi0 . The power of 1/p can be omitted because with or without it we get the same result. In general, p and w do not change or cannot be determined unanimously. Instead, we usually choose a fixed value for p, and we choose the value of w either to express our preferences or to change it systematically to get different Pareto optimal points. Using the weighted global criterion method, increasing the value of p can increase its efficiency in providing the full Pareto optimal set. It is possible to use an approximation point instead of an utopia point (aspiration point) [26]. Its advantage is that it provides a clear evaluation of minimising the distance from the utopia point, allowing multiple parameters to be set to reflect preferences. When using the utopia point, Pareto always offers an optimal solution [26]. The disadvantage of this method is that it uses the utopia point, the definition of which is computationally intensive. Using an aspiration point does not guarantee a Pareto optimal solution. Setting parameters can be difficult for the user [26]. Exponentially Weighted Criterion This method eliminates the disadvantage of the weighted sum [24]: Minimise u(x) = x∈S
k (epwi − 1)epfi (x) i=1
Weighted Product Method Without transforming the objective functions, we can achieve that the objective functions of similar significance have similar significance in the optimisation task [24]: Minimise u(x) = x∈S
k
w fi (x) i i=1
where wi weights show the relative significance of each objective function [24]. Bounded Objective Function Method This method minimises the most important objective function (fs (x)), by treating the other objective functions as constants, li ≤ fi (x) ≤ εi ; i = 1, . . . , k; i = s. li and εi are the lower and upper bounds for fi (x). This allows the user to set a limit on the objective function values [26]. 4.3 Population-Based Techniques - Global Optimisation Techniques The methods presented so far have reduced multi-objective optimisation back to singleobjective optimisation. But the genetic algorithm can solve the problem with multipurpose optimisation right away [24]. The genetic algorithm can also be used to solve the multi-purpose optimisation problem by applying the above methods, i.e. by tracing it back to single-purpose optimisation [24]. In the solution of immediate multi-objective optimisation, first, only one objective function is evaluated for each element of the population, and then individuals are
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selected to be transferred to the next population (using genetic operators). The operation is performed in all objective functions [24]. Pareto Ranking Pareto ranking can be incorporated into the genetic algorithm by using Pareto rank instead of fitness value when evaluating individuals in a population. To do this, a dominance test is performed. Solutions with a ranking of 1 are non-dominated and those with an i + 1 are dominated by all solutions from 1 to i. First, we assign 1 rank to non-dominated solutions. These are removed. The other solutions are set to 2. This is continued until the entire population is ranked. The ranking is the value of fitness [27]. Non-dominated Sorting Genetic Algorithm II The Non-dominated Sorting Genetic Algorithm II (NSGA II) [28, 29] combines the following special operators: the fast-crowding distance estimation procedure, the simple crowding comparison operator and non-dominated sorting. In non-dominated sorting, solution A dominates solution B if solution A has no worse objective function component value than solution B. Crowded distance of an individual is the average distance of its neighbouring solutions. The initial step of the algorithm is the initialisation of the population. The next step is the non-dominated sorting process. This is followed with the selection phase, based on crowded distance. The next step of the algorithm is the application of genetic operators: crossover and mutation. These two steps create new individuals, and the population is expanding with these individuals. This is followed with the recalculation of nondominated sorting and crowded distance. The above mentioned steps are continued until the termination condition is not met. Strength Pareto Evolutionary Algorithm The Strength Pareto Evolutionary Algorithm (SPEA) [30, 31] combines the following specialties: main and archive population, non-dominated solution set, Pareto set, Truncation operator. The first step of the algorithm is the following: the main population will be the initial population and the archive population will be initially empty. The next step is the calculation of the fitness values, which is the strength Pareto values. The strength Pareto value is calculated with the Pareto dominance relation. The strength Pareto value of an individual is the dominance of one solution over the other. The following step is the calculation of the raw fitness value. Non-dominated individuals are selected from the union of the main and the archive population set. If the non-dominated solution set is larger than the archive solution set, the next step is the extending of the Pareto set using the Truncation operator. Otherwise the next step is the extending of the Pareto set with the dominant solutions. These solutions will be the part of the archive population. Selection, mutation and crossover are also performed in the next step, that is new individuals will be created, which will be the elements of the next main population. The above described steps are continued until the termination condition is not met. Niched Pareto Genetic Algorithm The Niched Pareto Genetic Algorithm (NPGA) [32] is also a population-based algorithm. The first step of the algorithm is the creation of the initial population. The next step is the
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calculation of the objective function values for the solutions. Then pareto domination rank is performed for the total population. This is followed with the step of tournament selection. If the element is a clear winner, than it will be placed in the new population. If the element is not a winner than a niche count step is performed. After that, the algorithm chooses the winner solution. The next step is the generation of the new solutions with crossover and mutation operators. The calculation of the objective function values is the following step. These steps are continued until the termination condition is not met.
5 Test Results The purpose of the test experiment is to examine the effect of the components on the objective function. The purpose of the measurement was to determine the best multiobjective optimisation technique and the best heuristic algorithm. During the evaluations, the paper examines which multi-objective optimisation technique was best on average (regardless of heuristics). Then the paper investigates which heuristic algorithm provided the best solution (independent of multi-objective optimisation methods). After that, the followings are examined: which combination of multi-objective optimisation technique and heuristic algorithm gave the best results. During the measurement, the results of each objective function component are evaluated. The objective is to minimise the most of the components, but for some components, the objective is maximisation. Table 1 and Table 2 present the values of the length of the route. During the presentation of the test results, the following Multi-Objective Optimisation techniques are used: Bounded Objective Function Method (BOFM), Pareto Ranking (PR), WeightedExponential Sum Method (WESM), Weighted Global Criterion Method (WGCM), Weighted Product Method (WPM), Weighted-Sum Method (WSM), Non-dominated Sorting Genetic Algorithm II (NSGA II), Strength Pareto Evolutionary Algorithm (SPEA), Niched Pareto Genetic Algorithm (NPGA). Results of the following optimisation techniques are involved: Ant Colony System (ACS), Firefly Algorithm (FA), Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS). The best result of the length of the route value is the result of the Bounded Objective Function Method and Tabu Search. In terms of mean values, the Bounded Objective Function Method was the Table 1. Results for the length of the route component ACS BOFM
FA
GA
SA
TS
Average
9063,36
10619,9
9499,9
8317,65
7488,19
10059,12
9868,3
10939,06
8906,54
10135,99
9981,802
WESM
9186,34
10042,59
9238,13
7688,95
8969,93
9025,188
WGCM
7910,2
10405,37
9404,56
8306,69
9087,84
9022,932
WPM
8590,76
10372,3
9545,06
8509,61
8735,05
9150,556
WSM
9369,03
9828,34
11059,99
11153,52
10204,98
Average
9029,8
10189,47
9947,78
8813,83
9103,66
PR
8997,8
10323,17
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best. The worst values were obtained with the Weighted-Sum Method. The worst is the SA and WSM combination. On average, the WSM technique proved to be the worst. WESM, WGCM, WPM, NSGA II, SPEA, NPGA have also proven to be effective methods. Among the heuristic methods, ACS, SA, and TS are effective. On average the best was the SA. The FA proved to be a worse technique. Table 2. Results for the length of the route component (NSGA II, SPEA, NPGA) Length of the route value NSGA II
7837,53
SPEA
8513,93
NPGA
9139,12
Table 3 and 4 present the results of the unloading cost component. Here, the Pareto Ranking + Genetic Algorithm result was the best. The worst was the WSM + TS combination. On average, the Bounded Objective Function Method gave the best results, while the Weighted Sum Method gave the worst results. The difference between the best and the worst solution is 9803. PR, WPM and SPEA methods have also proved to be effective methods. The best of the heuristic methods was GA, and TS was also effective. The worst results were given by the FA on average. Table 3. Results for the unloading cost component ACS
FA
GA
SA
TS
Average
BOFM
258910,3
241219,9
271485,4
217606,4
229427,9
243730
PR
263778,2
272682,7
204251,8
286833,1
224773,4
250463,8
WESM
249054,3
229416,9
263700,3
290409,9
263623,8
259241
WGCM
270863,8
288062,5
253742,8
232841,2
272307
263563,5
WPM
244144,8
265849,5
248659,1
247329,7
235589
248314,4 271633,3
WSM
257585,4
281167,9
265175,4
259562,3
294675,4
Average
257389,5
263066,6
251169,1
255763,8
253399,4
In terms of the administrative cost (Table 5 and 6), the BOFM + GA combination gave the best results. On average, the BOFM technique proved to be the best. WESM, WGCM, and WSM techniques have also been shown to be effective. The worst solution was the WPM + TS combination. The NSGA II, SPEA and NPGA methods did not prove to be effective. The difference between the best and worst solutions was 58,156. In the case of the heuristic algorithms, GA proved to be the best and ACS the worst.
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Table 4. Results for the unloading cost component (NSGA II, SPEA, NPGA) Unloading cost component value NSGA II
222419,22
SPEA
208036,36
NPGA
230362,99
Table 5. Results for the administrative cost component ACS
FA
GA
SA
TS
Average
BOFM
253783,6
256821,7
223539,1
240011,9
270871,3
249005,5
PR
264481,7
265904,7
250984,6
279079,8
243443,5
260778,9
WESM
269297,8
228853,2
245937,6
270400,8
243269
251551,7
WGCM
241694,4
258703,8
262375,4
274757,9
253508,5
258208
WPM
251609,9
258307,5
267780,7
280507,9
281695,4
267980,3 254913,1
WSM
267597,8
272397,8
247277,8
233880,7
253411,3
Average
258077,6
256831,5
249649,2
263106,5
257699,8
Table 6. Results for the administrative cost component (NSGA II, SPEA, NPGA) Administrative cost component value NSGA II
279411,35
SPEA
244983,27
NPGA
250272,52
In the case of fuel consumption (Table 7 and 8), the best solution was the combination of SA + WESM techniques, but NSGA II, SPEA, NPGA techniques were also efficient. The GA + PR combination gave the worst results. On average, the best results were given with WGCM, but BOFM, WESM, WGCM also proved to be effective techniques. The difference between the worst and the best solution is 275504. Of the heuristic algorithms, SA proved to be the best and FA the worst, but GA also gave poor results on average. Table 7. Results for the fuel consumption component
BOFM
ACS
FA
GA
SA
TS
Average
586049,7
700489,4
633882,3
553303
523841,7
599513,2 (continued)
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A. Agárdi Table 7. (continued) ACS
FA
GA
SA
TS
Average
PR
659992,5
651561
754559
570874
671616,4
661720,6
WESM
599742,4
662091
626920,5
479054,2
591563,2
591874,3
WGCM
494506,1
712294,6
644383,2
515474,8
555647,2
584461,2
WPM
610508,3
665942,3
645011,4
584858,6
587874,4
618839 672485,2
WSM
630401,2
633390,6
706700,5
724354,6
667579,3
Average
596866,7
670961,5
668576,2
571319,9
599687
Table 8. Results for the fuel consumption component (NSGA II, SPEA, NPGA) Fuel consumption component value NSGA II
560348,11
SPEA
564146,69
NPGA
583328,51
In the case of the vehicle rental fee component (Table 9 and 10) the best result was given with the WESM + SA combination. The worst result was the same with many algorithms. On average, the WESM technique proved to be the best, but even the BOFM and WGCM proved to be good techniques. The difference between the best and worst results was 244. Of the heuristic algorithms, SA proved to be the best and FA the worst. TS also gave good results on average. Table 9. Results for the vehicle rental fee component ACS
FA
GA
SA
TS
Average
BOFM
921,55
921,55
921,55
921,55
779,97
893,234
PR
921,54
921,55
921,55
921,55
921,55
921,548
WESM
861,61
921,55
921,55
677,13
861,61
848,69
WGCM
921,55
921,55
921,55
779,97
921,55
893,234
WPM
921,55
921,55
885,75
921,55
921,55
914,39
WSM
921,55
921,55
921,55
921,55
921,55
921,55
Average
911,56
921,55
915,58
857,21
887,96
The best value for the route status component (Table 11 and 12) is 23273 with WGCM + ACS combinations. The worst solution is obtained with WSM + GA combinations. The value of the difference between the best and worst solution is 5884. The WGCM
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689
Table 10. Results for the vehicle rental fee component (NSGA II, SPEA, NPGA) Vehicle rental fee component value NSGA II
921,55
SPEA
921,55
NPGA
921,55
method proved to be the best on average and the worst was WESM. The value of the difference between the best and worst solution is 5884. The best heuristic method was TS, the worst was GA. ACS and SA also gave good results. Table 11. Results for the route status component ACS
FA
GA
SA
TS
Average
BOFM
26440,03
27565,69
27520,88
26044,67
25527,66
26619,79
PR
28021
27034,78
26014,51
25905,2
24721,64
26339,43
WESM
27598,97
26082,03
27043,06
27658,95
26370,99
26950,8
WGCM
23273,08
27069,06
26501,02
27931,2
24087,79
25772,43
WPM
26035,56
25814,41
28275,36
26850,9
25991,23
26593,49 26598,52
WSM
26083,57
26411,73
29157,09
25663,08
25677,11
Average
26242,04
26662,95
27418,65
26675,67
25396,07
Table 12. Results for the route status component (NSGA II, SPEA, NPGA) Route status component value NSGA II
25891,91
SPEA
27618,35
NPGA
25997,58
For the route time component (Table 13 and 14), WGCM + TS gave the best result and WSM + TS gave the worst solution. SPEA and NPGA proved to be also effective. On average, WESM and BOFM provided a good solution. On average, the following methods gave poor solutions: WPM, WSM. The difference between the best and worst cases is 6518. Of the heuristic methods, the best method was SA and the worst was ACS. The other components are similar to the components detailed above. The summary of the results is presented in Table 15 for heuristics and Table 16 for Multi-Objective Optimisation. Based on Table 15, SA and TS proved to be good heuristics. The wrong heuristic is FA.
690
A. Agárdi Table 13. Results for the route time component
BOFM
ACS
FA
GA
SA
TS
Average
28928,21
24721,75
26140,3
25729,01
24356,34
25975,12
PR
24628,2
28224,89
26344,95
25414,01
27050,17
26332,44
WESM
25793,14
24016,68
27259,85
24604,78
23346,51
25004,19
WGCM
25584,52
28745,7
27049,37
25446,82
26453,31
26655,94
WPM
27131,45
26472,67
26587,3
27635,04
26879,61
26941,21
WSM
29142,06
25841,52
23721,83
27547,29
29864,68
27223,48
Average
26867,93
26337,2
26183,93
26062,83
26325,11
Table 14. Results for the route time component (NSGA II, SPEA, NPGA) Route time component value NSGA II
27215,35
SPEA
25797,16
NPGA
25807,56
Table 15. Summary results (heuristics)
Length of the route
Best method (in average)
Worst method (in average)
ACS
FA
Unloading cost
TS
FA
Administrative cost
GA
SA
Fuel consumption
SA
FA
Vehicle rental fee
SA
FA
Route status
TS
GA
Route time
SA
ACS
Loading time
TS
SA
Unloading time
TS
GA
Administrative time
FA
TS
Regarding the Multi-Objective Optimisation techniques, the BOFM, WGCM, WESM techniques were the best, however, the WSM, WESM, NPGA, SPEA techniques proved to be the worst in several cases. The PR technique also proved to be poor in two cases.
Analysis of the Multi-Objective Optimisation Techniques
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Table 16. Summary results (multi-objective optimisation methods) Best method (in average)
Worst method (in average)
BOFM, NSGA II
WSM
Unloading cost
BOFM, SPEA
WSM
Administrative cost
BOFM
WPM, NSGA II
Fuel consumption
WGCM, NSGA II
WSM
Vehicle rental fee
WESM
PR, WSM, NSGA II, SPEA, NPGA
Route status
WGCM, NSGA II, NPGA
BOFM
Route time
WESM, SPEA, NPGA
WSM
Loading time
WSM, SPEA
WESM, NSGA II
Unloading time
WSM
WGCM
Administrative time
WGCM, NPGA
PR
Length of the route
6 Conclusions In this article, an analysis of Multi-Objective Optimisation techniques is presented through a complex Vehicle Routing Problem. The article presents the analysed Vehicle Routing Problem, which is a Multi-Echelon task. The Multi-Objective Optimisation techniques have also been presented in detail, which is the following: weighted-sum method, weighted-exponential sum method, weighted global criterion method, exponentially weighted criterion, weighted product method, bounded objective function method, pareto ranking, Non-dominated Sorting Genetic Algorithm II, Strength Pareto Evolutionary Algorithm, Niched Pareto Genetic Algorithm. The article analysed the efficiency of the following heuristic algorithms: Ant Colony System, Firefly Algorithm, Genetic Algorithm, Simulated Annealing, Tabu Search. The article analyses the example Vehicle Routing Problem in detail with the mentioned methods. The test results are detailed, according to the Simulated Annealing and Tabu Search proved to be efficient heuristics. The Firefly Algorithm proved to be a bad heuristic. In terms of Multi-Objective Optimisation techniques, BOFM, WGCM, WESM, NPGA, SPEA techniques were the best. The future research plan is to analyse simple optimisation tasks (such as Traveling Salesman Problem, Parallel Machines Scheduling Problem) using the above-mentioned methods.
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Vehicle Routing for Municipal Waste Collection Systems: Analysis, Comparison and Application of Heuristic Methods Mohammad Zaher Akkad1(B)
, Yaman Rajab2
, and Tamás Bányai1
1 Institute of Logistics, University of Miskolc, Miskolc, Hungary
{qgezaher,alttamas}@uni-miskolc.hu 2 Faculty of Computer and Automation Engineering, Damascus University, Damascus, Syria
Abstract. Optimization refers to finding the optimal value or best possible option. With optimization, the resource utilization can be planned to be the most effective and cost-efficient, especially in the vehicles sector, where cost and quality are both important factors. However, when dealing with complex systems, findings the best solution is considered almost impossible due to the time and the resources consumed. Therefore, optimization algorithms are used to find an optimum solution as much as possible within a relatively short time. The optimization algorithms evolved from conventional mathematical approaches to modern developed methods that use heuristic and metaheuristic approaches. Within the frame of this paper, the authors present a study that describes the effectiveness of three metaheuristic algorithms and show a municipal waste collection case study in Miskolc. After an introduction and theoretical background about the optimization algorithms development, the authors describe three metaheuristic algorithms: genetic, particle swarm, and simulated annealing. Five benchmarks are used to compare the results and consumed time for the mentioned algorithms. A Traveling Salesman Problem case study is solved to find the shortest real route of twenty locations for a municipal waste collection system in Miskolc city center by using the analyzed three algorithms. After that. The results are compared with a random solution. Particle swarm showed the best results, while simulated annealing was the fastest algorithm in the average execution time. Keywords: Metaheuristic optimization · Algorithms benchmarks · TSP application
1 Introduction and Theoretical Background Optimum results detection is the main objective in the vehicles sector, particularly in problem-solving designs where it is attempted to reach the best value, such as minimizing energy consumption and cost or maximizing the performance, profit, and efficiency [1]. Time, resources, and money make essential limits for the vehicles and transportation area; therefore, optimization is very fundamental to be applied in reality [2]. The appropriate utilization of available assets of any type requires a paradigm adopt in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 694–708, 2023. https://doi.org/10.1007/978-3-031-15211-5_58
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logical thinking and designing invention. The mathematical optimization started with traditional approaches, for example, linear programming, sequential quadratic programming, Newton-Raphson, interior-point methods, fractional programming, and Lagrange duality. Subsequently, modern approaches were invented that are mainly going to be evolutionary or bioinspired. Some examples of modern approaches contain evolutionary algorithms, swarm intelligence (SI), artificial neural networks (ANNs), and cellular signalling pathways that are mainly classified as heuristic and metaheuristic algorithms. For instance, genetic algorithms (GA) and SI are being used in many applications [3]. Nevertheless, the transportation area has different direct and indirect applications that aim to optimize target solutions in a short time relatively, mainly by using modern digital technologies, such as cyber-physical systems and the Internet of Things (IoT) that are involved in newly developed models. These applications gain strong territory in industrial transformation fast recently [4]. Alongside Industry 4.0, the developed tools and applications open a side for optimization processes that improve the industrial process, for instance, lean operations, six-sigma, circular economy, and other smart manufacturing tools and systems [5]. These tools and processes are addressed in the context of improving sustainable manufacturing, including collaboration, transparency, flexibility, innovation, and capabilities [6]. Scientific research in the vehicles and transportation area has complex and multi objectives cases that are defined as NP-hardness (non-deterministic polynomial-time hardness). These cases are very hard or even impossible to be solved in the conventional methods, i.e., the optimization of vehicle routing problems [7], such as in the cyber-physical systems for waste collection [8] since it has multi variables come from measurements and predictions, and in the multi-objective optimization of city logistics [9], especially when it uses the multi-echelon system. Heuristic and metaheuristic algorithms (modern algorithms) are getting more widely used to reach the best optimization results within a short time. Other developments, such as using real distances between the locations, were researched [10]. Furthermore, hybrid algorithms that combine more than one type are also used for the same purposes since they may achieve better results. In an analytical review on modern optimization algorithms [11], accelerated progress in using the heuristic and metaheuristic algorithms was found in various applications. This work presents three metaheuristic algorithms: genetic, particle swarm, and simulated annealing optimization algorithms. Five benchmarks are used to compare both the results and consumed time for the mentioned algorithms. After that, Traveling Salesman Problem (TSP) is used for a case of 20 locations in Miskolc city center by using the analyzed three algorithms to show and compare the results of the shortest real route length.
2 Optimization Algorithms Description The selection of algorithms usually depends on the nature of the problem being solved. Next to consider the commonly used algorithms in the literature review, both genetic and particle swarm algorithms are population-based approaches. The last selected algorithm is the simulated annealing, which is a single-solution approach that helps introduce a slight variation for better comparative analysis.
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2.1 Genetic Algorithm (GA) The GA is a metaheuristic inspired by the evolution operation and belongs to the major class of evolutionary algorithms in informatics and computational mathematics [12]. These algorithms are extremely used to make high-quality solutions by optimization by focusing on bio-inspired operators such as selection, convergence, or mutations [13]. Starting with John Holland who developed the GA in 1988 based on Darwin’s evolutionary theory [14]. Afterward, in 1992, the GA was extended by him as well [15]. This algorithm is considered under the address of evolutionary algorithms, which are utilized to solve problems that are not already efficiently solved. This approach is used widely to solve logistics and supply chain optimization problems (scheduling, shortest path, etc.) that are considered NP problems, and in modelling and simulation, that heuristic approach is used [16]. Every possible solution has a group of characteristics (the phenotype or genes) that are evolved and changed; typically, solutions are encoded in the binary digits as strings of 0s and 1s, however, another codec is also possible. Evolution, in general, begins starting from a collection of random individuals as the consideration of an iterative process for finding the population in each reproduction. For each generation, the fitness of all the individual solutions in the population is measured. Then, with the fitness value, the objective feature is solved [17]. Afterward, the individuals’ fits are chosen sufficiently in probability way from the existing population, and the gene is modified to make a new generation cycle for all (recombined and randomly with mutated potential). A newer generation of possible solutions would be reached in the next generation of the process. The algorithm usually stops and considers the reached generation as the optimized solution when either a maximum number of generations has been generated or satisfaction has been met [18]. For that reason, every successive generation should be a more suitable solution within the population. GA (): Initialize the population Evaluate the initial population fitness while (termination criteria are not satisfied) do Select parents from the current population Perform crossover between parents with a probability pc Mutate the new population with a probability pm Evaluate the fitness of the new population Find the fittest (best) individual end while
2.2 Particle Swarm Algorithm (PSO) PSO is a metaheuristic algorithm and one of swarm intelligence optimization algorithms (SI), which use the power of collective collaboration to solve complex problems [19]. It is originally attributed to Kennedy and Eberhart in 1995 [20] where the inspiration often comes from nature, it mimics the behaviour of biological systems like fishes or a flock of birds. The social interaction concept is used to solve problems. Several particles (agents)
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constitute a swarm that moves around in search space, looking for the global best solution within the possible solutions in the search space gbest . . These particles communicate with one another using search directions (gradients) and each particle represents a potential solution to the problem and can remember the best position (solution) it has reached pbest [21]. The swarm of particles updates its velocity and position from iteration to iteration, based on Eqs. (1) and (2): vi (t + 1) = ωvi (t) + c1 r1 (pbest − xi (t)) + c2 r2 (gbest − xi (t))
(1)
xi (t + 1) = xi (t) + vi (t + 1)
(2)
where v is the velocity vector, x(t) is the current position of the particle, and x(t + 1) is the new position in the next iteration, pbest is the best solution this particle has reached; gbest is the global best solution of all the particles. ω is a constant (inertia weight), c1 and c2 are two constants’ weights and r1 and r2 are two random variables (acceleration coefficients) [22].
2.3 Simulated Annealing (SA) SA algorithm is one of the oldest and preferred meta-heuristics methods for solving optimization problems. Specifically, for approximating the global optimization in a large search space and avoiding local minima [23]. It is inspired by the annealing of solids, which refers to a concept in physics describing the cooling of a solid until reaching minimal energy. The algorithm starts from a higher initial temperature. When the temperature gradually decreases, the solution tends to be stable [24]. The annealing concept was first developed in statistical mechanics, inspired by the behaviour of physical systems in a heat bath [25]. Starting with Kirkpatrick et al. in 1983 [26] and Cerny in 1985 [27] who introduced the concept as a general solution approach for optimization problems. In General, the algorithm starts with an initial solution x, then generates a candidate solution y randomly or using some rule from the neighborhood of x. To decide whether the solution y is accepted or not, the Metropolis acceptance criterion is used, which shows how a thermodynamic system moves from an old state to another new
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state to minimize the energy [28]. The temperature cooling rate is defined as α, and the acceptance probability is given by the following: 1 if f (xnew ) < f (xold ) p= (3) f (xnew )−f (xold ) exp − if f (xnew ) ≥ f (xold ) T and the temperature cooling schedule is defined as follows: Ti+1 = αTi
(4)
3 Optimization Algorithms Benchmarks The investigated algorithms “GA, PSO, and SA” performance is compared by conducting experiments on five benchmark functions on two bases; the optimized cost (best cost) with considering the average cost that was reached by the algorithms for each function formula and the average consumed time for executing the code, taking into consideration that Python was used for coding the algorithms. The used benchmarks’ functions are as follows, where D is the number of dimensions. Ackley Function Formula ⎞ ⎛
D D 1
1 xi2 ⎠ − exp − cos(2π xi ) (5) f (x) = −20exp⎝−0.2 D D i=1
i=1
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Non-continuous Rastrigin Function Formula D
yi2 − 10cos(2π yi ) + 10
f (x) = yi =
(6)
i=1
xi
round (2xi ) 2
if |xi | < 0.5 else |xi | ≥ 0.5
(7)
Alpine Function Formula f (x) =
D−1
|xi sin(xi ) + 0.1xi |
(8)
i=1
Griewank Function Formula D D 1 2 xi f (x) = xi − cos √ + 1 4000 i i=1 i=1
(9)
Schwefel 2.22 Function Formula f (x) =
D D
|xi | + |xi | i=1
(10)
i=1
The global minimum values of the benchmark functions, the corresponding x vectors as well as the lower and upper boundaries of the search space are presented in Table 1. Table 1. Benchmark functions boundaries Function name
ximin
ximax
x∗
f (x∗ )
Ackley
−32
32
(0, 0, …, 0)
0
Non-Continuous Rastrigin
−5.12
5.12
(0, 0, …, 0)
0
Alpine
−10
10
(0, 0, …, 0)
0
Griewank
−600
600
(0, 0, …, 0)
0
Schwefel 2.22
−10
10
(0, 0, …, 0)
0
The three algorithms were run 20 times on each benchmark, and the results of the evaluations were averaged and the minimum evaluation value was also found. The domain space x of all benchmarks, in general, is continuous (belongs to a set of Real numbers) so a few adjustments were made for each algorithm. For GA, both mutation operators and crossover operators had to be replaced. For PSO, it is used with its default implementation. For SA, the method for generating the neighbour candidate was changed to fit the continuous domain space.
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The parameters of the applied three algorithms are as follows: GA. Number of iterations: 4000. Population size: 100. Elite size: 30. Mutation probability: 0.01 (1%). Crossover probability: 1.0 (100%). Crossover method: Simulated Binary Cross Over (SBX). Mutation method: Gaussian Mutation. Selection method: Fitness Proportionate Selection. PSO. Number of iterations: 3000. Number of particles (agents) in a swarm: 100. Cognitive constant c1: 0.5. Social constant c2: 0.2. Velocity inertia w: 0.98. SA. Number of iterations: 20000. Starting temperature: 1000. Stopping temperature: 10–14 . Temperature cooling rate α: 0.997 (Tables 2 and 3). Table 2. Benchmark cost results Function name Ackley Non-Continuous Rastrigin Alpine Griewank Schwefel 2.22
GA 30.8668 39.2308 31.0246 32.1173 29.5942
Average execution time (s) PSO 21.1457 36.8104 17.3847 24.835 14.7014
SA 6.2626 4.1272 6.246006 6.2817 6.2413
Table 3. Benchmark execution time results Function name Ackley Non-Continuous Rastrigin Alpine Griewank Schwefel 2.22
GA 0.05435/0.07113 16.25899/20.1494 0.19723/0.45004 0.0081/0.06693 0.2993/0.36172
Best Cost/Average Cost PSO 0.005506/0.03243 29.2752/57.0901 0.0095/0.80053 0.00202/0.04938 0.02221/0.1611
SA 5.28125/12.156907 212.4601/267.4628 26.83422/39.64438 0.82798/0.89061 28.67485/14655.573
Benchmarks results showed that PSO and GA gave the best results with relatively near values, then SA came after them with less optimized results. PSO showed the best results in four formulas while GA showed the most optimized result in the NonContinuous Rastrigin function. On the other hand, SA was the fastest in the average execution time in all the benchmarks then, PSO was second fastest then GA, which took the longest time among them.
4 TSP Application in Miskolc For having a numerical application for the discussed three algorithms, this chapter presents a TSP case study in Miskolc in Hungary for a municipal waste collection system.
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4.1 Case Study Data As a case study application to solve a TSP problem by the mentioned algorithms, twenty locations in Miskolc city center were used for finding the shortest route to visit all of them while starting and ending in a specific location. This case can be considered as a municipal waste collection application where the twenty locations represent the bins that the waste truck should visit starting from a specific location that should go back to as well. The three algorithms are used to find the optimized results next to a random route that is used as a comparison reference. The real routes are calculated by using the Open Route Service that was developed by HeiGIT gGmbH [29]. It gives the required real distances by vehicles to move among given locations. Table 4 states the used location in Miskolc city center where the ID 0 states the route start- and end-point. Table 4. Miskolc case study locations ID
Latitude
Longitude
ID
Latitude
Longitude
0
48.104500
20.792322
11
48.100542
20.789675
1
48.102439
20.788955
12
48.102063
20.789383
2
48.101865
20.787420
13
48.102104
20.790459
3
48.101852
20.786693
14
48.103304
20.791231
4
48.101265
20.786310
15
48.103188
20.793006
5
48.100182
20.787304
16
48.104120
20.794390
6
48.098837
20.786294
17
48.105830
20.793720
7
48.098384
20.786947
18
48.105156
20.785587
8
48.098272
20.788453
19
48.106049
20.787717
9
48.100108
20.788731
20
48.105392
20.786827
10
48.100719
20.788667 —
—
—
4.2 Case Study Results The results of the optimization are mentioned in Table 5. The results show that PSO achieved the shortest route than GA with a very near result to PSO then SA as a less optimized result among the three algorithms. PSO achieved a 69.3% save of the random route, GA achieved a 68.4% save, and SA achieved a 57.9% save. The results reflect the importance of using optimization for its effectiveness in reducing the required route. Moreover, the results are compatible with the obtained benchmarks in the last chapter.
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PSO
SA
Random route
Shortest route (km)
6.14917
5.97373
8.20097
19.46269
Shortest route time (min)
17.33216
16.91766
22.8899
49.6466
Code Exec. Time (s)
5.0876
4.09234
0.28272 —
% Saved route
68.4%
69.3%
57.9% —
The following Figs. 1, 2, 3 and 4 show the real route maps for the three algorithms next to a random route. Also, Figs. 5, 6 and 7 show the optimization curve for distance with the iteration progress for three algorithms.
Fig. 1. Actual route map for GA optimization
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Fig. 2. Actual route map for PSO optimization
Fig. 3. Actual route map for SA optimization
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Fig. 4. Actual route map for random route
Fig. 5. GA optimization curve for distance and iterations
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Fig. 6. PSO optimization curve for distance and iterations
Fig. 7. SA optimization curve for distance and iterations
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4.3 Conclusions This study focused on the metaheuristic optimization of the vehicle routing problem by analyzing three optimization algorithms benchmarks, presenting an application of a waste collection system in Miskolc city center, and comparing the results with a random route solution to show their effectiveness. Using the real distance routes gave this study special importance to its results. The results can be summed up in the following points: • The benchmarks showed that GA and PSO happen to give similar results that reflect very effective optimization. SA showed unstable results with showing big differences between the best and average costs. This can be solved by applying repeated runs for SA and selecting the best results when it is in use. These results could be very effective for choosing the algorithms in the next studies. • The benchmarks tackled the execution time of the three algorithms as well. SA was the fastest in the average execution time in all the benchmarks then PSO then GA, which took the longest time among them. The simplicity of the SA algorithm can explain its speed. Next to the previous point, this could help a lot in the algorithm selection process depending on every case priority. • The presented application tackled a waste collection system in Miskolc city center in Hungary, where twenty locations should be visited as a TSP case study. This case explained, in numbers, the three algorithms’ effectiveness. By comparing with the random route, long distances were saved up to 68.4%. Especially in the current energy crisis, the results gain an important effect on distance and energy savings. • The distance optimization progress for every iteration was presented as a curve for the three algorithms. GA reached the optimized result in iteration number 300 while PSO reached it in iteration number 110 approximately. SA needed more than 800 iterations to reach the best result with a noticeable vibration curve in its first third, which is explained by the nature of the SA algorithm. • Further research of this study can be in one of the following directions. First, expand the analyzed algorithms’ numbers and types to examine other possible effective ones. Second, use a more realistic reference for the comparison process instead of the random route such as the nearest item heuristic. Third, tackle more complex logistics applications where more constraints and conditions are to be applied.
5 Summary This work presented three metaheuristic optimization algorithms: GA, PSO, and SA. Five benchmarks were used to compare both the optimized cost and consumed time of code execution for the mentioned algorithms. After that, a TSP municipal waste collection system for twenty locations in Miskolc city center was tackled as a case study by using the analyzed three algorithms to show and compare the results of the shortest real route length. PSO showed the best results in both the benchmarks and TSP application, then GA with relatively near values then SA. In the TSP application, PSO achieved 69.3%, GA achieved 68.4%, and SA achieved 57.9% save compared with the random route. The results reflect the importance of using optimization algorithms because of their
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effectiveness in reducing the required distance and energy. On the other hand, SA was the fastest in the average execution time, then PSO then GA. In conclusion, this study confirms the optimization algorithms’ importance and effeteness within a relatively short time. Acknowledgement. This work is supported by the ÚNKP-21-3 new national excellence program of the ministry for innovation and technology from the source of the national research, development and innovation fund.
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Comparative Study of Different Metaheuristics on CEC 2020 Benchmarks Shaymaa Alsamia1(B) , Hazim Albedran1,2 , and Károly Jármai2 1 University of Kufa, Najaf, Iraq
[email protected] 2 University of Miskolc, Egyetemváros, Miskolc 3515, Hungary
Abstract. Metaheuristic algorithms have increased in usage in all the scientific fields during the last decades. Since no optimisation algorithm is valid for all optimisation problems, many metaheuristics have been developed for various applications. Accordingly, this paper presents a comparative study on CEC 2020 optimisation problems among different algorithms. The goal is to give an overall sight of selecting a specific metaheuristic algorithm for a particular application. The algorithms in this study are; dynamic differential annealed optimisation, particle swarm optimisation, fertilisation optimisation algorithm, grey wolf optimisation, whale optimisation algorithm, firefly algorithm, artificial bee colony, ant lion optimisation, harris hawks optimisation, and sine cosine optimisation algorithm. The results are discussed in the respective sections with a focus on the convergence behaviour of the algorithms. Keywords: Optimisation algorithms · Metaheuristics · Fertilisation optimisation algorithm · Artificial bee colony · Dynamic differential annealed optimisation
1 Introduction Optimisation algorithms [1] have increased usage for different scientific and engineering problems during the last decade. They were used for robotics [2, 3], structural design of robot arms [4], optimal drilling performance [5], temperature distribution on a tube with constant heat flux [6, 7], and many other applications. Feature selection [8] is also a promising field in using optimisation algorithms. Also, steel structure [9] is one of the most employing fields in the metaheuristics. Some works have employed metaheuristics for cost calculations [10] or to find the best truss shape [11]. Natural or human-made phenomena inspire metaheuristics, and some are targeted for single objective or multiobjective, or both. The performance of an optimisation algorithm depends on the type and nature of the optimisation problem itself. Therefore, it is crucial to investigate which application is suitable for an optimisation algorithm or vice versa. In this paper, a comparative study among ten metaheuristic algorithms is performed on ten constrained optimisation problems. The problems are applications to real-life engineering problems that put challenges to the competitive methods in this study. The competitive algorithms are Fertilisation optimisation algorithm (FO), Dynamic differential annealed optimisation algorithm (DDAO), Artificial bee colony (ABC), Ant lion © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 709–719, 2023. https://doi.org/10.1007/978-3-031-15211-5_59
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optimisation (ALO), Firefly algorithm (FA), Particle swarm optimisation (PSO), Whale optimisation algorithm (WOA), Sine cosine optimisation algorithm (SCA), Harris hawks optimiser (HHO), and Grey wolf optimisation (GWO). Also, this paper introduces a novel application for the FO algorithm on CEC2020 constrained benchmarks.
2 Cec 2020 This study uses ten non-convex constrained optimisation problems [8] to compare the competitive metaheuristics in Sect. 3. Table 1 shows the constrained problems with sizes ranging from low to large scale, and their formulations are mentioned in [8]. Table 1. Non-convex constrained optimisation problems Function
D
Description
F1
9
Heat exchanger network design (case 1)
F2
11
Heat exchanger network design (case 2)
F3
7
Optimal operation of alkylation unit
F4
6
Reactor network design
F5
9
Haverly’s pooling problem
F6
38
Blending-pooling-separation problem
F7
48
Propane, isobutane, n-butane nonsharp separation
F8
2
Process synthesis problem
F9
3
Process synthesis and design problem
F10
3
Process flow sheeting problem
D: No. of variables
3 The Metaheuristics This study employs ten optimisation algorithms to have a comparative study on the constrained problems presented in Table 1. 3.1 Fertilisation Optimisation Algorithm The fertilisation process of the egg inspired the fertilisation optimisation algorithm [9] with sperm during the reproduction process of mammal animals. This algorithm was only used for mathematical optimisation problems and was never used before for constrained problems. Thus, this paper is an excellent opportunity to examine the FO algorithm on complex problems and compare the results with other smart metaheuristics. The source code of the FO algorithm can be downloaded at: https://www.mathworks.com/matlabcentral/fileexchange/103970-fertilization-opt imization-algorithm-fo.
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3.2 Dynamic Differential Annealed Optimisation Dynamic differential annealed optimisation algorithm [1] was inspired by the production of the dual-phase steel to convert ordinary quality steel into super properties steel. The most impressive thing is that the DDAO algorithm is independent of population size and can solve problems efficiently. 3.3 Artificial Bee Colony The artificial bee colony optimisation problem [12] stimulates the foraging behaviour in the honey hive. ABC algorithm was firstly introduced with unbalanced exploration and exploitation processes, and many researchers have written papers with different ideas to correct the design. 3.4 Ant Lion Optimisation Ant lion optimisation [13] simulates the predation of ants by the ant lion insects. Its search engine consists of five steps random walk of ants, building traps, entrapment of ants, catching prey, and rebuilding traps. ALO algorithm was employed to solve different engineering and mathematical problems, and its performance is problem-dependent, the same as other metaheuristic algorithms. 3.5 Firefly Algorithm The firefly algorithm [14] was inspired by the flashing behaviour of tropical firefly insects. The FA search engine consists of random search combined with local search represented by monotonic functional reduction. 3.6 Particle Swarm Optimisation Particle swarm optimisation [15] mimics the movement behaviour of flocks of fishes and birds. It has a simple formulation and fast operation and has been used in various applications since its development [16]. 3.7 Whale Optimisation Algorithm The whale optimisation algorithm [17] is a nature-inspired metaheuristic optimisation algorithm that stimulates the social behaviour of humpback whales during hunting action. According to many statistical results, WOA has good convergence and performance over many problems. 3.8 Sine Cosine Optimisation Algorithm The sine cosine optimisation algorithm [18] initialises random possible solutions and forces them to fluctuate about the best solution using sine and cosine functions. SCA was initially designed to solve single-objective optimisation problems and modified by many other subsequent works.
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3.9 Harris Hawks Optimisation Harris hawks optimiser [19] is a swarm intelligence optimisation algorithm. It mimics the action and reaction of a hawk’s group during hunting in nature and prey escaping to explore the solutions of the single-objective problems. HHO algorithm has good convergence over many problems where its search engine has different function evaluation stages on a single iteration [20]. 3.10 Grey Wolf Optimisation Grey wolf optimisation [21] inspiration is the hunting techniques of the grey wolves to large size prey. GWO has received a lot of attention from researchers, and it was the base for a lot of hybrids and modified versions of metaheuristics.
4 Statistical Results This section presents the statistical results of the comparative study of the ten optimisation algorithms in Sect. 3 on the ten constrained optimisation problems, which are presented in Sect. 2. The experiment’s run conditions are the maximum number of function evaluations of 100, the population size of 20, and 51 independent runs. The results were evaluated based on the best solution, worst solution, average solution, and the standard deviation over the 51 independent runs. An exception for DDAO is that it is independent of population size; therefore, its population size was set to only 3. The statistical results are illustrated in Tables 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11, where we can notice impressive findings. The FO algorithm performs well on most of the ten constrained optimisation problems. On its development, it had been tested on CEC2015 benchmarks, small scale and large scale mathematical optimisation problems. Thus, this paper is the first try to examine the algorithm on constrained problems. Anyway, further experiments and discussions on the FO algorithm should be released, which can be a topic for future works. DDAO was designed firstly to return results in terms of the best solution near the global one, and it did its job on most of the problems in these experiments. The rest of the algorithms were discussed extensively in the literature, which is why this paper has special attention to FO and DDAO algorithms. Table 2. F1 benchmark Algorithm
Best
Worst
Mean
STD
FO
48.1636
245.4884
143.2627
47.9818
DDAO
0
178.2522
65.3030
41.6607 (continued)
Comparative Study of Different Metaheuristics on CEC 2020 Benchmarks Table 2. (continued) Algorithm
Best
Worst
Mean
STD
ABC
0
0
0
0
ALO
0
0
0
0
FA
60.5775
307.5768
142.3640
57.3581
PSO
0
96.2533
10.9188
25.7695
WOA
0
0
0
0
SCA
45.2743
236.7643
141.3223
47.1992
HHO
0
0
0
0
GWO
0
52.2746
3.1279
9.5609
Table 3. F2 benchmark Algorithm
Best
Worst
Mean
FO
1520.91
4020.4082
2912.86
624.52
DDAO
1068.60
4284.76
2501.72
726.84
2688.391
1229.385
577.109
5187.18
3490.7745
842.97
ABC ALO
398.799 1256.63
STD
FA
372.402
540.943
427.347
33.819
PSO
357.617
1888.852
483.633
318.709
WOA
357.617
3091.636
807.527
592.688
SCA
400.495
4302.103
1941.102
802.274
HHO
357.617
6878.085
3578.791
1511.343
GWO
403.512
2033.614
956.740
400.484
Table 4. F3 benchmark Algorithm
Best
Worst
Mean
STD
FO
−19877.10
−14314.16
−17633.36
1149.75
DDAO
−21701.70
−11547.21
−17510.08
2079.30
ABC
−22056
−18925.002
−20591.68
727.408
ALO
−19529.78
−11795.99
−16178.75
1826.65
FA
−22022.26
−19465.24
−21267.26
599.09
PSO
−22056
−19343.03
−21657.74
614.69 (continued)
713
714
S. Alsamia et al. Table 4. (continued) Algorithm
Best
Worst
Mean
STD
WOA
−22056
−18811.08
−21464.95
746.32
SCA
−21611.98
−16249.64
−19697.95
1418.70
HHO
−21834.05
−16001.07
3181.04
GWO
−21950.53
−21117.80
459
−9828 −19873.77
Table 5. F4 benchmark Algorithm
Best
Worst
Mean
STD
FO
−0.999
−0.882
−0.978
0.023
DDAO
−1
−0.828
−0.966
0.043
ABC
−1
−1
−1
0
ALO
−1
−0.751
−0.951
0.057
FA
−1
−0.899
−0.995
0.016
PSO
−1
−1
−1
0
WOA
−1
−1
−1
0
SCA
−1
−1
−1
0
HHO
−1
−0.88
−0.993
0.024
GWO
−1
−1
−1
0
Table 6. F5 benchmark Algorithm
Best
Worst
Mean
STD
FO
−3295.79
−1334.88
−2216.23
458.63
DDAO
−3481.673
−1023.12
−2175.56
498.9
ABC
−3803.99
−2406.1
−3168.99
313.52
ALO
−2675.77
−1008.26
−1874.66
417.68
FA
−3855.73
−3358.95
−3711.62
117.29
PSO
−3900
−2891.63
−3773.33
266.38
WOA
−3900
−2285.47
−3097.81
396.33
SCA
−3443.02
−1978.64
−2714.60
359.23
HHO
−3429.36
0
−1625.51
762.50
GWO
−3812.01
−2682.07
−3279.79
260.45
Comparative Study of Different Metaheuristics on CEC 2020 Benchmarks Table 7. F6 benchmark Algorithm
Best
Worst
Mean
STD
FO
1.003
1.154
1.062
0.04
DDAO
0.997
1.175
1.037
0.041
ABC
0.997
1.132
1.006
0.022
ALO
1.033
1.457
1.17
0.089
FA
0.997
0.9979
0.9979
3.36E−16
PSO
0.997
0.997
0.997
3.36E−16
WOA
0.997
0.9979
0.9979
3.36Ee−16
SCA
0.997
1.210
1.066
0.062
HHO
0.997
1.182
1.001
0.025
GWO
0.9979
1.036
0.999
0.006
Table 8. F7 benchmark Algorithm
Best
Worst
Mean
STD
FO
0.732
0.941
0.823
0.051
DDAO
0.681
1.062
0.928
0.068
ABC
0.553
1.048
0.856
0.118
ALO
0.818
1.264
1.066
0.077
FA
0.823
0.997
0.9775
0.04
PSO
0.485
0.9978
0.724
0.228
WOA
0.486
0.997
0.774
0.159
SCA
0.505
1.011
0.824
0.144
HHO
0.486
0.9978
0.958
0.083
GWO
0.558
1.012
0.837
0.148
Table 9. F8 benchmark Algorithm
Best
Worst
Mean
STD
FO
−0.841
0.218
−0.0134
0.202
DDAO
−1
0.14
−0.247
0.417
ABC
−1
0
−0.967
0.149
ALO
−0.899
0.984
0.209
0.2864 (continued)
715
716
S. Alsamia et al. Table 9. (continued) Algorithm
Best
Worst
Mean
STD
FA
−1
0
−0.98
0.14
PSO
−1
0
−0.96
0.196
WOA
−1
−1
−1
0
SCA
−1
0.245
−0.509
0.424
HHO
−1
0
−0.76
0.427
GWO
−1
1
−0.806
0.43
Table 10. F9 benchmark Algorithm
Best
Worst
Mean
STD
FO
0.5
0.5
0.5
0
DDAO
0.5
1.16
0.681
0.202
ABC
0.5
1.082
0.574
0.115
ALO
0.537
1.457
0.934
0.23
FA
0.5
0.5
0.5
0
PSO
0.5
0.5
0.5
0
WOA
0.5
1.018
0.517
0.078
SCA
0.5
1.054
0.57
0.136
HHO
0.5
1.5
0.719
0.363
GWO
0.5
0.5
0.5
0
Table 11. F10 benchmark Algorithm
Best
Worst
Mean
STD
FO
0.1
0.1023
0.1002
0.000552
DDAO
0.1
0.48
0.124
0.06603
ABC
0.1
0.102
0.100
0.000349
ALO
0.1
0.1011
0.10003
0.000169
FA
0.1
0.1
0.1
4.19E−09
PSO
0.1
0.1002
0.10001
4.72E−05 (continued)
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Table 11. (continued) Algorithm
Best
Worst
Mean
STD
WOA
0.1
0.118
0.1003
0.002643
SCA
0.1
0.115
0.1015
0.003
HHO
0.1
0.807
0.1295
0.1063
GWO
0.1
0.1003
0.10004
7.85E−05
5 Discussion on FO Algorithm In this section, the population size parameter of the FO algorithm will be discussed using the same optimisation problems and run conditions. For a maximum number of evaluations FE = 10, the FO algorithm can still return good results for F4, F6, F7, F9, and F10, where the standard deviation STD is low on these problems. On the rest of the problems, the FO algorithm has high values of STD, and that is why the algorithm still gives close results to the case FE = 100 because of the high rate of randomness (Table 12). Table 12. Statistical results of the FO algorithm with FE = 10, population size = 5, and maximum number of iterations = 2 Algorithm
Best
F1
Worst 48.0919
Mean
STD
461.039
230.114
102.254
F2
1425.933
10223.845
5376.306
1657.961
F3
−18807.884
−5478.750
−12081.596
3427.350
−0.9996
F4 F5
−2956.971
F6
1.0132
−0.496
−0.806
597.833
−1197.587
800.838
1.592
1.219
0.119
0.1314
F7
0.742
1.0709
0.956
0.074
F8
−0.290
1.143
0.327
0.329
F9
0.5
1.584
0.777
0.360
F10
0.1
0.8001
0.139
0.109
6 Conclusion Ten metaheuristic algorithms were examined on non-convex constrained optimisation problems defined under CEC2020 benchmarks. The results show that the optimisation algorithms have sovereignty conflict where their performance differs from one problem to another. The FO algorithm has shown considerable convergence on most of the
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problems. Thus, this paper recommends the FO algorithm for further investigations of different problems. Also, DDAO has converged perfectly to the global of most of the constrained optimisation problems. PSO has excellent convergence on all the employed testing problems. Acknowledgement. The research was supported by the Hungarian National Research, Development and Innovation Office under the project number K 134358, and by the NTP-SZKOLL20-0022 identifier “Focus’21-Focus on community by developing digital competencies” project, supported by the Ministry of Human Resources and Human Resources Support Manager.”
References s 1. Ghafil, H.N., Jármai, K.: Dynamic differential annealed optimisation: new metaheuristic optimisation algorithm for engineering applications. Appl. Soft Comput. 93, 106392 (2020) 2. Ghafil, H.N., Jármai, K.: Optimisation for Robot Modelling with MATLAB. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40410-9 3. Ghafil, H.N., Jármai, K.: Research and application of industrial robot manipulators in vehicle and automotive engineering, a survey. In: Jármai, K., Bolló, B. (eds.) VAE 2018. LNME, pp. 611–623. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75677-6_53 ISBN 978-3-319-75677-6 (eBook) 4. Ghafil, H.N., Jármai, K.: Kinematic-based structural optimisation of robots. Pollack Period 14(3) (2019). https://doi.org/10.1556/606.2019.14.3.20 5. Alsamia, S., Ibrahim, D.S., Ghafil, H.N.: Optimisation of drilling performance using various metaheuristics. Pollack Period 16, 80–85 (2021) 6. Habeeb, A.A., Hazim, A., Endre, K., Károly, J.: A new method to predict temperature distribution on a tube at constant heat flux. Multidiszcip. Tudományok 11(5), 363–372 (2021) 7. Hazim, A., Habeeb, A.A., Károly, J., Endre, K.: Interpolated spline method for a thermal distribution of a pipe with a turbulent heat flow. Multidiszcip. Tudományok 11(5), 353–362 (2021) 8. Khalid, A.M., Hamza, H.M., Mirjalili, S., Hosny, K.M.: BCOVIDOA: a novel Binary Coronavirus Disease Optimization Algorithm for feature selection. Knowl. Based Syst. 248, 108789 (2022) 9. Talatahari, S., Azizi, M., Toloo, M., Baghalzadeh Shishehgarkhaneh, M.: Optimization of large-scale frame structures using fuzzy adaptive quantum inspired charged system search. Int. J. Steel Struct. 22(3), 686–707 (2022). https://doi.org/10.1007/s13296-022-00598-y 10. Jármai, K., Farkas, J.: Cost calculation and optimisation of welded steel structures. J. Constr. Steel Res. 50(2), 115–135 (1999) 11. Azizi, M., Aickelin, U., Khorshidi, H.A., Shishehgarkhaneh, M.B.: Shape and size optimisation of truss structures by chaos game optimization considering frequency constraints. J. Adv. Res. (2022) 12. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Engineering Faculty, Computer Engineering Department, Erciyes University (2005) 13. Mirjalili, S.: The ant lion optimiser. Adv. Eng. Softw. 83, 80–98 (2015) 14. Yang, X.-S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bioinspired Comput. 2(2), 78–84 (2010) 15. Eberhart, R., Kennedy, J.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
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16. Ghafil, H., Jármai, K.: Comparative study of particle swarm optimisation and artificial bee colony algorithms. In: Multiscience XXXII. MicroCAD International Multidisciplinary Scientific Conference, Miskolc-Egyetemváros, Hungary, pp. 1–6. http://real.mtak.hu/84332/1/ D1_Hazim_Nasir_Ghafil.pdf 17. Mirjalili, S., Lewis, A.: The whale optimisation algorithm. Adv. Eng. Softw. 95, 51–67 (2016) 18. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimisation problems. Knowl. Based Syst. 96, 120–133 (2016) 19. Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimisation: Algorithm and applications. Future Gener. Comput. Syst. 97, 849–872 (2019) 20. Mirjalili, S.: HHO. Mathworks (2022). https://www.mathworks.com/matlabcentral/fileexcha nge/92140-hho. Accessed 23 May 2022 21. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Weight Optimization of All-Composite Sandwich Structures for Automotive Applications Mortda Mohammed Sahib1,2(B) , György Kovács1 , and Szabolcs Szávai1 1 Faculty of Mechanical Engineering and Informatics, University of Miskolc, Egyetemváros,
Miskolc 3515, Hungary [email protected], {altkovac, szavai.szabolcs}@uni-miskolc.hu 2 Basrah Technical Institute, Southern Technical University, Basrah, Iraq
Abstract. Lightweight composite sandwich structures are widely used in the automotive industry, particularly in vehicle body applications, due to their advantageous properties, e.g. low density, high stiffness and high strength-to-weight ratio. The goal of the research was the elaboration of an optimization method for a totally Fiber Reinforced Plastic (FRP) sandwich structure in order to construct a minimal weight structure. The all-composite sandwich panel consists of a hexagonal FRP honeycomb core and laminated FRP face sheets. The article investigates the optimization of the FRP composite layers of the cell wall of the honeycomb core and the face sheets simultaneously. The Classical Lamination Theory (CLT) with analytical expressions was adopted to calculate the stiffness and strength of the sandwich components. The minimization of the total weight of the sandwich structure was the main goal during the structural optimization. Therefore, the design variables were the following: the orientation of the FRP layer for the cell wall of the core; furthermore the number and orientation of the face sheets’ layers. During the optimization, 5 design constraints which related to structure strength criteria were considered. I-sight software was used in conjunction with Excel software to perform the optimization process. Some of the feasible design points were numerically modelled using Abaqus Cae software and showed good agreement with the optimization result. The main added value of the research is the elaboration of the single weight optimization method for a totally FRP sandwich structure. Keywords: Composite sandwich structure · Weight optimization · FRP honeycomb core · Laminated FRP face sheets
1 Introduction Designing lightweight vehicles is one of the efficient strategies of sustainable development, which is essential for both energy savings and environmental protection. Due to energy-saving and emission-reduction standards, such lightweight vehicles are in high demand today. The applications of alternative materials are considered as one of the most important ways to attain vehicles’ weight reduction. Considering their stiffness © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 720–733, 2023. https://doi.org/10.1007/978-3-031-15211-5_60
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and high strength-to-weight ratio, as well as thermal and chemical stability, composite materials are ideal for achieving this goal. Therefore, applying composite materials in the automotive industry gives a typical advantage in optimal design compared with conventional metal materials [1, 2]. Composite sandwich structures commonly consist of high-strength and stiff outer thin panels (face sheets) and a low density, thick core material in the middle, as shown in Fig. 1. The skins are usually made of solid materials such as metal or laminated composites, while the cores are mainly made of cellular materials such as balsa wood, man-made materials such as metal, aramid, ceramic, thermoplastic honeycombs, polymer and metal foams. The advantages of cellular cores include their excellent stiffness and strengthto-weight ratio, good thermal and acoustic insulation, and energy-absorbing properties [3–5].
Fig. 1. Main structural elements of sandwich construction.
Recently Fiber Reinforced Polimers (FRP) have been used for the fabrication of honeycomb cores due to their superior specific stiffness and strength. Ariel Stocchi et al. introduced a new core made of natural fibers (jute fibers) with vinylester matrix [6]. They investigated the mechanical properties and failure modes under transverse compression. They reported that the performance of natural fiber-vinylester cores is comparable to that of commercially available cores such as Nomex and aluminium cores. 3D printing technology offers the possibility to develop composite honeycomb cores in different topological cells and structural shapes. Compton et al. used 3D printing technology to fabricate honeycomb cores using epoxy resin with short fibers as the base material [7]. Sugiyama et al. have used continuous carbon fibers in conjunction with 3D printing technology to fabricate composite sandwich panels [8]. They printed various shapes of honeycomb sandwich structures and evaluated the performance by studying the structural geometry in three-point bending tests. Juan Pablo Vitale et al. have used composites with natural and synthetic fibers to make different types of sandwiches [9]. They proposed analytical models to predict the mechanical behaviour and failure modes of composite sandwiches. The proposed models were verified by three-point bending test. Xingyu Wei et al. used analytical expressions to determine the three-dimensional failure mechanisms for all composite honeycomb sandwiches under bending load [10].
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The theoretical results were validated by three-points tests. The failure map characterized different types of dominant failure mechanisms under flexural loading. Russell et al. used carbon fibers with the interlocking method in the fabrication of sandwich beams with square honeycomb cores [11]. They used two-dimensional failure mechanism maps to investigate the dominant failure modes for the sandwich under threepoint bending load. Xiong et al. fabricated sandwiches of carbon fiber composites with eggs and pyramidal honeycombs [12]. They constructed two-dimensional failure mechanism maps for studying the failure modes of the proposed sandwich under three-point loading. The Finite Element Method (FEM) is a common used technique for structural analysis [13, 14]. Chun Lu used the compression moulding technique to fabricate a honeycomb sandwich panel from a kind of carbon fiber/epoxy composite [15]. He investigated the mechanical response of the composite sandwich using Finite Element Analysis (FEA) and experimental three-point bending tests. He reported that the composite honeycomb sandwich exhibited higher flexural strength than the conventional aluminium and Nomex honeycomb sandwiches. Several algorithms are available for the optimization of sandwich structures, e.g. Particle Swarm Optimization (PSO) and Non-dominant Sorting Genetic Algorithm. Nian et al. used Non-dominant Sorting Genetic Algorithm II (NSGA-II) to optimize the graded honeycomb structure [16]. They optimized the energy absorption by designing a graded hexagonal honeycomb, which showed an increase in absorption energy compared to a uniform honeycomb. Qin et al. fabricated hexagonal honeycombs with a graded wall thickness [17]. They performed a multiobjective optimization to achieve a trade-off between the specific energy absorption and the maximum compressive force. The aim of the study is the elaboration of the weight optimization method for a totally FRP sandwich structure. In the article, the stiffness of the composite core’s cell wall, the number and layup of composite face sheets’ layers were studied. The investigated FRP sandwich structures can be mainly used as structural elements of lightweight vehicles’ bodies. The effects of composite materials’ properties and structure configuration were analyzed numerically and analytically. The optimal configuration of sandwich structure’s design parameters were defined to achieve lightweight sandwich construction. The article is organized as follows. Analytical models for the mechanical properties of the sandwich structure are introduced in Sect. 2. In Sect. 3, the optimization problem is formulated to minimize the weight of the sandwich when subjected to out-of-plane loading. In Sect. 4, the Finite Element model is established to numerically validate the optimization results. The optimization results and the required comparison with the result of FEM are performed in Sect. 5. The main conclusions are drawn in Sect. 6. The significance of the study is the elaboration of the single weight optimization method for a totally FRP sandwich structure which can be used for the optimization of structural elements of vehicles (e.g. vehicles’ bodies, vehicles’ chassis, and vehicles’ floors).
2 Analytical Model of the Investigated Sandwich Structure In this study, the same composite material is used for the hexagonal core and face sheets of the sandwich panel. Figure 2 shows the composite sandwich panel and a detailed schematic of the hexagonal core cell. The global coordinate system was labelled 1, 2,
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723
and 3, respectively. The local coordinates of the core cell wall are denoted by (x, y, z). The cell size (c), cell angle (θ ), wall thickness (t), width (b), and cell side lengths (a) and (h) are fully defined in Figs. (2-a) and (2-b). For this study, the cell has a size of (c = 6.06 mm).
Fig. 2. (a) Dimensions of the sandwich structure; (b) Geometry of the honeycomb core.
The out-of-plane elastic properties of the composite core were calculated by the analytical expressions developed below for the composite cell wall [18, 19]. E33 = Ey · ρ
(1)
where E33 is the out-of-plane modulus elasticity of the core, ρ is the relative density of the core. Relative density of the core can be calculated for the hexagonal cells by the following equation: t 2 (2) ρ= cosθ (1 + sinθ ) l
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The core shear moduli G13 and G23 can be calculated through the below equations: 2 t t t Ex G13 = 3.27 · + 0.577 · Gxy (3) (1 − νxy νyx ) tc a a h + asin2 θ Gxy · t G23 = (4) (h + asinθ )acosθ where: Ex , Gxy , νxy and νyx are the longitudinal modulus of elasticity, in-plane shear modulus, and in-plane poissons’ ratios of the core’s cell wall. The elastic properties for different orientations of the composite layers of the core and face sheets were calculated using Classical Laminate Theory (CLT). The detailed calculations for composite laminates are beyond the scope of this article. However, the reader can refer to reference [20] for more details. The mechanical properties of employed composite material (T300/N5208) are listed in Table 1 [21]. Table 1. T300/N5208 composite properties. Material properties
Value
Longitudinal modulus: E x (MPa)
181000
Transverse modulus: E y (MPa)
10300
In-plane shear modulus: Gxy (MPa)
7170
Major Poisson’s ratio: ν xy (-)
0.28
Density: ρf (kg/m3 )
1600
Lamina thickness: tL (mm)
0.127
Longitudinal tensile strength: Xt (MPa)
1500
Longitudinal compressive strength: Xc (MPa)
1500
Transverse tensile strength: Yt (MPa)
40
Transverse compressive strength: Yc (MPa)
246
In-plane shear strength: Sxy (MPa)
68
3 Weight Optimization of the FRP Composite Sandwich Structure The goal of the sandwich structure’s optimization is to calculate the minimal weight of the sandwich structure when subjected to a prescribed out-of-plane load. Design variables included the orientation of the composite layers in the FRP core and FRP face sheets, and the number of composite layers of the face sheets. The design constraints of the optimization problem included 1.) shear strength of the core, 2.) yielding strength of the face sheets, 3.) intra-cell buckling, 4.) wrinkling of the face sheets and 5.) deflection of the sandwich. The scheme of loading and boundary conditions is shown in Fig. 3. The design parameters of the sandwich structure are listed in the Table 2 [22].
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Fig. 3. Loading and boundary conditions of the sandwich panel. Table 2. Design parameters and coefficients for the FRP composite sandwich structure [22]. Length
Width
Load
Bending deflection coefficient
Shear deflection coefficient
Equivalent load
Maximum bending moment
Maximum shear force
L (mm)
b (mm)
q (MPa)
Kb
Ks
P (N)
M (Nmm)
F (N)
2000
1000
0.02
5/384
1/8
q·L·b
PL/8
P/2
3.1 Design Variables In this study, the optimization of core and face sheets has been conducted simultaneously. The design variables for sandwich weight optimization can be listed as follows: • Layer orientation angle (θ c ) in the core’s cell wall −90◦ ≤ θc ≤ 90◦
(5)
1 mm ≤ tc ≤ 100 mm
(6)
• Core thickness (t c )
• Face sheets layers’ numbers (N l ) 3 layers ≤ Nl ≤ 8 layers
(7)
• Layers orientation angles (θ s ) in the face sheets −90◦ ≤ θs ≤ 90◦
(8)
3.2 Weight Objective Function The total weight of the all-composite sandwich structure can be formulated as below: Minimize Wt = Wf + Wc = 2ρf Lbtf + ρc Lbtc
(9)
where: Wt is the total weight of the sandwich structure, Wf is the weight of the face sheets and Wc is the weight of the core.
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3.3 Design Constraints The failure criteria of the face sheets and the core are considered as constraints for the optimization of the sandwich weight. In the literature studies, different values of safety factors have been selected depending on the application, in this study a minimum safety factor with the value (2) was chosen for each constraint [23], which can be mathematically expressed as follows. • The constraint for core shear τcs ≥ 2.0 τc
(10)
where: τcs is the shear strength of the composite core; τc is the core shear stress. The shear strength of the composite core (τcs ) can be calculated by the following equation [10]: −1 2 t 2sin2 2∅ 2cos2 2∅ 2 + τcs = . 3a Xt Xc Sxy 2
(11)
where: Xt , Xc and Sxy are the tension, compression, and shear strengths of the cell walls, ∅ is the applied load angle. The deduced shear stress in the composite core can be calculated by the following equation [22]: τc =
F db
(12)
• The constraint for yield stress of face sheet The yielding of face sheet constrain is formulated by σfx ≥ 2.0 σf
(13)
The yield strength of the laminated face sheet (σfx ) can be calculated by the classical laminate theory (CLT) and Tsai-Wu failure criterion [20], but the face sheet stress (σf ) can be calculated using [22]. σf =
M dtf b
(14)
where the maximum bending moment (M) can be calculated as in the Table 2, and d is the distance between the centers of upper and lower face sheets and it can be calculated as below: d = t f + tc
(15)
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• The constraint for face wrinkling Face wrinkling is a local buckling wave phenomenon that occurs when the sandwich structure is under in-plane shear or compression. Wrinkling criterion in two in-plane directions can be formulated as follows [22]: σwr,x ≥ 2.0 (16) σfx (17) σwr,x = 0.5 3 Efx Ec G13
σwr,y
σwr,y ≥ 2.0 σfy = 0.5 3 Efy Ec G23
(18) (19)
where: σwr,x and σwr,y are the face sheets wrinkling stresses at in-plane directions. • The constraint for intra-cell buckling of face sheets It is kind of local buckling that occurs in some regions of the face sheet that has no supported by honeycomb walls. The constraints for this phenomenon have below expressions [22]: σin,cr ≥ 2.0 (20) σfx 2 2Efx 2t σin,cr = (21) 2 1 − υxy c • The constraint for the total defection of the sandwich structure The total deflection constraint of the composite sandwich structure includes the bending deflection and shear deflection [22]: δ=
kb PL3 ks PL + ≤ 30 mm D S
(22)
where P, kb and ks are defined in Table 2., the bending stiffness (D) and shear stiffness (S) can be calculated as below: D=
Ef tf d 2 b 2
S = bdG13
(23) (24)
The calculations of the effective properties of composite materials were performed using Excel software based on the Classical Laminate Theory (CLT) and Tsai-Wu failure criterion. Optimization calculations were performed in I-sight software by mapping the Adaptive Simulated Annealing (ASA) optimization algorithm onto an Excel worksheet.
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4 Numerical Model The optimal designs of the sandwich structure weight were modelled and analyzed using the Abaqus Cae Finite Element package. To reduce the computational cost of the sandwich panel, the honeycomb core can be modelled as a solid layer characterized by homogeneous mechanical properties of the detailed honeycomb core [24, 25]. The maximum deflection of the mid-plane of a simply supported sandwich structure was determined numerically and compared with the optimization results.
5 Results and Discussion The optimization problem was solved by performing a discrete optimization process with design variables and constraints developed in Sect. 3. The final results for each layup, representing the optimal design for a sandwich panel with minimum weight, are shown in Fig. 4. As it can be seen, the lowest weight was achieved with a face sheet consisting of 6 composite layers and a core thickness (t c = 51 mm).
Fig. 4. Minimum total weight versus number of face sheets layers and core thicknesses.
The most critical failures in this type of structure are face sheet yielding and core failure due to shear. Therefore, the factors of safety (S.F) for the composite face sheets and the core are investigated. Figures 5 and 6 show the evolution of the safety factors as a function of the sandwich weight and the number of layers of the face sheet. It can be seen that the optimal design (6 layers layup face sheets) provides a reasonable safety factor and lower weight.
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Fig. 5. Face sheet safety factors versus sandwich weight and the number of face sheet layers.
Fig. 6. Core safety factors versus sandwich weight and the number of face sheet layers.
The detailed design variables, which include the orientation angles for the face sheet layers and composite core are listed in Table 3. The finite element results for the considered optimization cases are shown in Figs. 7 and 8. Figure 7 shows the deflection response of the optimal sandwich structure where 6 composite layers were used as face sheets. However, Fig. 8 shows the comparison between the maximum deflection of the sandwich structures obtained from the solution of the optimization problems and the corresponding values obtained from the finite element solution. A good agreement between the optimization result and the finite element solution can be clearly seen.
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W t (kg)
4.37
18.88
18.61
2.99
6.13
22.22
16.44
(5 layers) 61.01 18.5; −29; 0; − 12; 6.5
2.6
16.02
26.35
14.97
90
(6 layers) 5; 8.5; 0.5; − 12.5; −0.5; 21.5
3.09
4.28
27.04
14.0
−88
(7 layers) 49.58 5.5; 1.5; 28.5; −25; −31; 6; 7
3.5
3.97
28.46
14.55
−13
(8 layers) 46.39 −9.5; −90; 15; −15; −1.5; 10; −50; −3.5
2.26
2.98
29.6
14.8
Core layer orientation
Number of face t c (mm) sheet layers and fiber orientation
Face sheet S.F
−49.5
(3 layers) 2; 10; −13
90.45
2.03
−90
(4 layers) 73.77 −6.5; −3; 34.5; −10.5
52.5
51.05
Core shear S.F
Fig. 7. Numerical modelling of a sandwich structure.
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Fig. 8. Comparison between optimization solution and FEM solution.
6 Conclusion The optimization problem for the totally FRP sandwich honeycomb structure was studied. The structure responsible for the out-of-plane loading condition was analyzed, considering the different combinations of the sandwich panel. The main findings of the study are the following: • The composite FRP materials can be used for the manufacturing of automotive structural elements. • The weight of all-composite sandwich structures could be reduced by using different layups of the composite cell wall and face sheets. As the weight reduced by (4.6 kg) when used composite face sheet with 6 layers compared with face sheet consist of 3 layers. Noteworthy that increasing face sheets’ thickness leads to reducing the core thickness and hence the weight reduction is obtained. • The weight and deflection of the structure have a strong correlation with the core thickness and number of face sheet layers. Therefore, the optimization algorithm plays a key role in setting the proper number/orientation of layers for both core and face sheets to achieve the optimum balance between the mechanical properties and weight reduction. The main added value of the research was the elaboration of the single weight optimization method for a totally FRP sandwich structure. The newly elaborated weight objective optimization method can be used in the automotive industry for the design of vehicles’ structural elements. Acknowledgements. The research was supported by the Hungarian National Research, Development, and Innovation Office - NKFIH under the project number K 134358.
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References 1. Pan, S.D., Wu, L.Z., Sun, Y.G., Zhou, Z.G., Qu, J.L.: Longitudinal shear strength and failure process of honeycomb cores. Compos. Struct. 72, 42–46 (2006) 2. Jármai, K.: Newer manufacturing technologies and their costs in automotive structures, a review. Lect. Notes Mech. Eng. 22, 478–485 (2021) 3. Sun, Z., Li, D., Zhang, W., Shi, S., Guo, X.: Topological optimization of biomimetic sandwich structures with hybrid core and CFRP face sheets. Compos. Sci. Technol. 142, 79–90 (2017) 4. Zhang, Q., et al.: Bioinspired engineering of honeycomb structure - using nature to inspire human innovation. Prog. Mater. Sci. 74, 332–400 (2015) 5. Banerjee, S., Battley, M., Bhattacharyya, D.: Shear strength optimization of reinforced honeycomb core materials. Mech. Adv. Mater. Struct. 17, 542–552 (2010) 6. Stocchi, A., Colabella, L., Cisilino, A., Álvarez, V.: Manufacturing and testing of a sandwich panel honeycomb core reinforced with natural-fiber fabrics. Mater. Des. 55, 394–403 (2014) 7. Compton, B.G., Lewis, J.A.: 3D-printing of lightweight cellular composites. Adv. Mater. 26, 5930–5935 (2014) 8. Sugiyama, K., Matsuzaki, R., Ueda, M., Todoroki, A., Hirano, Y.: 3D printing of composite sandwich structures using continuous carbon fiber and fiber tension. Compos. Part A Appl. Sci. Manuf. 113, 114–121 (2018) 9. Vitale, J.P., Francucci, G., Xiong, J., Stocchi, A.: Failure mode maps of natural and synthetic fiber reinforced composite sandwich panels. Compos. Part A Appl. Sci. Manuf. 94, 217–225 (2017) 10. Wei, X., Wu, Q., Gao, Y., Xiong, J.: Bending characteristics of all-composite hexagon honeycomb sandwich beams: experimental tests and a three-dimensional failure mechanism map. Mech. Mater. 148, 103401 (2020) 11. Russell, B.P., Liu, T., Fleck, N.A., Deshpande, V.S.: Quasi-static three-point bending of carbon fiber sandwich beams with square honeycomb cores. J. Appl. Mech. Trans. 78, 1–16 (2011) 12. Xiong, J., Ma, L., Pan, S., Wu, L., Papadopoulos, J., Vaziri, A.: Shear and bending performance of carbon fiber composite sandwich panels with pyramidal truss cores. Acta Mater. 60, 1455– 1466 (2012) 13. Szirbik, S., Virág, Z. Finite element analysis of an optimized hybrid stiffened plate. Matec Web Conf. 342, 1–6 (2021). ID: 06003 14. Kundrák, J., Varga, G., Nagy, A., Makkai, T.: Examination of 2D and 3D surface roughness parameters of face milled aluminium surfaces. Rez. I Instr. V Tekhnol. Sis. 88(1), 94–100 (2018) 15. Chun, L.: Stress distribution on composite honeycomb sandwich structure suffered from bending load. Procedia Eng. 99, 405–412 (2015) 16. Nian, Y., Wan, S., Li, M., Su, Q.: Crashworthiness design of self-similar graded honeycombfilled composite circular structures. Constr. Build. Mater. 233, 117344 (2020) 17. Qin, R., Zhou, J., Chen, B.: Crashworthiness design and multiobjective optimization for hexagon honeycomb structure with functionally graded thickness. Adv. Mater. Sci. Eng. 2019, 1–13 (2019) 18. Wang, R., Wang, J.: Modeling of honeycombs with laminated composite cell walls. Compos. Struct. 184, 191–197 (2018) 19. Wei, X., Li, D., Xiong, J.: Fabrication and mechanical behaviors of an all-composite sandwich structure with a hexagon honeycomb core based on the tailor-folding approach. Compos. Sci. Technol. 184, 107878 (2019) 20. Aboudi, J., Arnold, S., Bednarcyk, B.: Micromechanics of Composite Materials, 2nd edn. CRC Press, London (2006)
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21. Dababneh, O., Kipouros, T., Whidborne, J.F.: Application of an efficient gradient-based optimization strategy for aircraft wing structures. Aerospace 5, 1–27 (2018) 22. HexCel Composites: Honeycomb sandwich design technology. HexWeb Honeycomb Sandwich Design Technology, pp. 1–28 (2000) 23. Zenkert, D.: An Introduction to Sandwich Construction. Student Chamelon Press, Oxford, London (1995) 24. Barbero, E.J.: Finite Element Analysis of Composite Materials Using Abaqus TM, 2nd edn. CRC Press, London (2013) 25. Yuan, J., Zhang, L., Huo, Z.: An equivalent modeling method for honeycomb sandwich structure based on orthogonal anisotropic solid element. Int. J. Aeronaut. Sp. Sci. 21, 957–969 (2020)
Optimum Design for the Bottom Panel of a Heavy-Duty Truck by Using a Composite Sandwich Structure Mortda Mohammed Sahib1,2(B) , György Kovács1 , and Szabolcs Szávai1 1 Faculty of Mechanical Engineering and Informatics, University of Miskolc,
Miskolc-Egyetemváros 3515, Hungary [email protected], {altkovac, szavai.szabolcs}@uni-miskolc.hu 2 Basrah Technical Institute, Southern Technical University, Basrah, Iraq
Abstract. Improving the fuel efficiency of heavy-duty trucks is essential for sustainable energy supply and future economic development. Consequently, new technologies are needed to enhance energy security in the transportation sector. In this context, this study investigates the optimal design of a composite sandwich panel as a lightweight structure to replace the conventional materials in the bottom panel of a heavy truck. The composite sandwich structure generally consists of an aluminium honeycomb core with two Fiber Reinforced Plastic (FRP) composite face sheets. The goal of this study is to call for new optimum design strategies of the composite sandwich structures to reduce heavy trucks’ body mass. In this work, the orientation of the composite layers and the core thickness were set as design variables for the optimisation problem. At the same time, the total weight of the structure is considered as the optimisation objective. Moreover, the constraints of the optimisation problem are set to be related to the strength limits of the face sheets and the core. The Classical Lamination Theory and the failure equations of composite plates are formulated using Excel software. To solve the optimisation problem, a Multi-Island Genetic Algorithm is applied under the I-sight software environment interacting with Excel. The numerical model is built using Abaqus Cae software. A good agreement was found between the numerical and optimisation results in terms of the overall deformation of the sandwich for this study. It is worth mentioning that the weight of the bottom plate of a heavy truck can be significantly reduced if the proper sandwich face sheets layup and core thickness are determined. The main added value of the research is the elaboration of the optimisation method for the bottom panel of heavy-duty trucks in order to define the optimal combination of honeycomb core and composite face sheets. Keywords: Bottom panel of heavy-duty truck · Sandwich structure · Optimization · Numerical modeling
1 Introduction Increasing oil consumption in much of the world has prompted many car buyers to look at fuel efficiency. The automotive industry has responded with a variety of new strategies © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 734–746, 2023. https://doi.org/10.1007/978-3-031-15211-5_61
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to reduce vehicle weight and improve performance. Due to the inherent relationship between vehicle weight and fuel economy, weight reduction is one of the most logical ways to meet these demands [1, 2]. Honeycomb sandwich panels are becoming increasingly attractive in the automotive industry because they can provide higher stiffness and strength at relatively low density. Therefore, sandwich structures can withstand the applied loads such as shear and lateral normal forces [3]. Many researchers have presented their contributions in this field. Reddy et al. studied a method for predicting the failure of the first layer of composite laminates and it agreed well with the experimental results [4, 5]. D. Jiang et al. and Yuan et al. developed a model for the core of a sandwich panel as an equivalent continuum using frequency response analysis with finite elements [6, 7]. Soliman et al. considered different theories in the equivalent sandwich plate using static analysis with finite elements [8, 9]. Alaa et al. developed an elaborate design to optimise an all-FRP sandwich structure for helicopter floors [10]. Haibin Ning et al. designed a composite sandwich with a thermoplastic core and face sheets of glass fibers to reduce the weight of a bus body [11]. Chahardoli et al. experimentally investigated the quality of novel sandwich structures for absorbing crush energy for vehicles applications [12]. Craig W. Hudson et al. applied an Ant Colony Optimization (ACO) algorithm for weight and cost multi-objective optimisation for a rail vehicle floor sandwich panel. They achieved a weight reduction of up to 60% compared to conventional floor systems [13]. The Finite Element Method (FEM) is an often used technique during the optimisation [14, 15]. Lee and Park used finite element analysis to design the structure of sandwich composite structures for automotive floors. They fabricated the proposed sandwich structure and confirmed that the proposed floor structure is safe [16]. The goal of the article is to achieve new optimum design strategies of the composite sandwich structures that help to reduce heavy trucks’ body mass. In this study, an optimal design is developed for the bottom plate of a heavy truck. The paper is structured into several sections. Section 2 deals with the structural design, boundary conditions and load conditions. Section 3 covers the applied materials in this study and the sandwich panel, including the face sheets and honeycomb core, with the corresponding mechanical properties. Section 4 looks at the solution of the optimisation problem for the considered sandwich panel. Section 5 introduces the numerical model of the bottom panel in a heavy truck. Sections 6 and 7 present the main results and conclusions. The main significance of the study is the elaboration of the optimisation method for the bottom panel of heavy-duty trucks by using an optimal combination of honeycomb core and composite face sheets.
2 Structural Design of Bottom Panel in a Heavy-Duty Truck The aim of this study is to save weight by using a composite sandwich with an optimal combination of design specifications. The bottom panel has dimensions of B = 2500 mm, L = 8000 mm and is supported by two beams to prevent failure due to concentrated loads. The truck carries up to 20,000 kg, which can be described as a uniformly distributed load. The schematic diagram of the bottom panel is shown in Fig. 1, while the design
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details and applied loads are shown in Table 1. The technical data and the scheme of Fig. 1 were taken from [17].
Fig. 1. Bottom panel of a heavy truck.
In the design of the bottom panel of a heavy truck, the fracture safety factors for the components of the sandwich structure were set at a minimum of 5 [17]. However, the maximum allowable deflection of the structure is 10 mm. Due to symmetry, the bottom plate can be divided into four equal parts, each part having a length (l) as it has shown Fig. 3. Since the aspect ratio of these plate parts is very large, they can be considered as cantilever beams with sufficient accuracy of the calculation. Therefore, a segment of the four parts is used to solve the optimisation problem [17]. Table 1. Design parameters of bottom panel of a heavy truck [17]. Length
Width
Maximal deflection
Load
Equivalent distribution load
L (mm)
B (mm)
δ max (mm)
W max (kg)
q (MPa)
8000
2500
10
20000
0.01
3 Structure of the Investigated Bottom Panel In this study, a composite sandwich structure was used in a bottom panel of the heavy truck to save weight. A composite sandwich structure consists of a hexagonal aluminium core with two composite face sheets. The face sheets are made of epoxy woven glass fiber and epoxy woven carbon fiber. The details of the sandwich structure are shown in Fig. 2. The mechanical properties of the core and face sheets are listed in Tables 2 and 3 [18].
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Fig. 2. Composite sandwich structure.
Table 2. Mechanical properties of hexagonal core. Density (kg/m3 )
83
Cell size (mm)
6
Compression
Core Shear Longitudinal direction Transverse direction
Strength (MPa)
Modulus (MPa)
Strength (MPa)
Modulus (MPa)
Strength (MPa)
Modulus (MPa)
4.6
1000
2.4
440
1.5
220
Table 3. Mechanical properties of composite face sheets. Face sheets material
Typical strength tension/compression (MPa)
Modulus of Elasticity Tension/Compression (GPa)
Poisson’s Ratio (–)
Ply Thickness (mm)
Ply Weight (kg/m2 )
Epoxy Woven Glass Fiber
600/550
20/17
0.13
0.25
0.47
Epoxy Woven Carbon Fiber
800/700
70/60
0.05
0.3
0.45
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4 Structural Optimisation The optimisation process typically involves finding the best solution from a set of potential nominated solutions [19, 20]. In the design of sandwich structures, a compromise between the weight of the structure and the strength is always required. In this regard, the optimisation process is an effective strategy used by designers to achieve this goal, since the strength of the sandwich structure decreases with the weight reduction. In this study, the optimisation process is to achieve the lowest possible weight for the bottom plate of a heavy truck while simultaneously achieving the required structural strength. The load and boundary conditions are shown schematically in Fig. 3. The design variables include the number of composite layers of the face sheets, the orientation of the composite layers, and the core thickness. The design is subject to six constraints, including face sheet failure in tension, face sheet failure in compression, core failure in out-of-plane shear, face sheet wrinkling in the longitudinal direction, and face sheet wrinkling in the transverse direction. The safety factors for failure of the composite face sheets and core were given as (5), while the safety factor for wrinkling of the face sheet was (2) [17]. The I-sight software in conjunction with the Excel software is used to solve the optimisation problem within the bounds of the given design variables and constraints by using the Multi-Island Genetic Algorithm (MIGA) [19].
Fig. 3. Load and boundary conditions of composite structure.
4.1 Design Variables In our research, the optimisation algorithm chooses the best feasible design from the pool of potential designs to conduct the weight saving of the sandwich structure. Three design variables are considered in this study which are the following: • Core thickness (t c ) 1 mm ≤ tc ≤ 100 mm
(1)
• Face sheets layers’ numbers (N l ) 1 layer ≤ Nl ≤ 8 layers
(2)
• Face sheets layers orientation angles (θ s ) ◦
−90 ≤ θs ≤ 90
◦
(3)
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4.2 Weight Objective Function The total weight of the sandwich structure is formulated below as an objective function for the optimisation problem: Minimise Wt = Wf + Wc = 2ρf L B tf + ρc L B tc
(4)
where: Wt is the total weight of the sandwich structure, Wf is the weight of the face sheets and Wc is the weight of the core. 4.3 Design Constraints The constraints of the optimisation process relate to the failure limits of the face sheets and the hexagonal aluminium core for the design of a lightweight sandwich structure. The minimum safety factors for constrains are expressed in the following equations: • Constraint for core shear τcs ≥ 5.0 τc
(5)
where: τcs is the typical shear strength of hexagonal core as it listed in the Table 2. While the shear stress in the core (τc ) can be calculated by [17, 18] τc =
F d
(6)
where: F is the shear force per unit width, and d is the distance between the centers of upper and lower face sheets and it can be calculated as below: d = tf + tc
(7)
• Constraint for yield stress of face sheet The yielding of face sheet constrain is formulated mathematically by σfx ≥ 5.0 σf
(8)
Tsai-Wu failure criterion and Classical Lamination Theory (CLT) were used to calculate the yield strength of the laminated face sheet (σfx ) [21], while the stress in the face sheet ( σf ) and the maximum bending moment can be calculated using [17, 18]. σf =
Mmax dtf
(9)
where: Mmax is the maximum moment per unit width and it can be calculated as below: Mmax = q ·
l2 8
(10)
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• Constraint for face wrinkling When the sandwich structure is subjected to in-plane shear or compression, local buckling waves may occur. This phenomenon is called face wrinkling. The criterion of in-plane wrinkling in two directions can be formulated as follows [17]:
σwr,x
σwr,x ≥ 2.0 σfx = 0.5 3 Efx Ec G13
σwr,y
σwr,y ≥ 2.0 σfy = 0.5 3 Efy Ec G23
(11) (12) (13) (14)
• Constraint for total defection of the sandwich structure The bending deflection, in addition to shear deflection are contributed in the total deflection [17, 18]: δ=
ql 2 ql 4 + ≤ 10 mm 8D 2S
(15)
The bending stiffness and shear stiffness can be calculated as below: D=
Ef tf d 2 2
S = dG13
(16) (17)
The failure criterion of the first layer (Tsai-Wu failure criterion) was used to estimate the strength of the face sheets. The effective properties of the composites were calculated using Excel software based on the Classical Lamination Theory (CLT). The optimisation process was performed in I-sight software by mapping the Multi-Island Genetic Algorithm (MIGA) optimisation algorithm to an Excel worksheet.
5 Numerical Validation Model The bottom panel in a heavy truck was numerically modelled using Abaqus Cae software. The composite face sheets were modelled with shell elements. The homogenised mechanical properties of the core were used with a solid layer to reduce the computation time [7, 22]. The total FEM elements of the structure are 48000 elements. The solid elements (C3D8R) are used for the core while the shell elements (S4R) are used for the face sheets. The optimal parameters, loads and boundary conditions were set in the numerical model to obtain a maximum deflection at the mid-plane of the designed panel. The deflection obtained from the numerical solution is compared with the optimisation results, as can be seen in Sect. 6.
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6 Results The weight of the sandwich structure depends mainly on the number of composite layers in the face sheets and the thickness of the core. A lower number of face sheets layers requires a higher core thickness to increase the flexural modulus of the sandwich structure against out-of-plane loads. However, this comes at the expense of the overall weight of the structure. On the other hand, increasing the number of composite layers in the face sheets and reducing the core thickness should be done in a controllable way. The optimisation algorithm plays a crucial role in determining a suitable value for the core thickness and the number of face sheet layers. Thus, the optimal values of the design variables (number of composite face layers, orientation of the layers, and core thickness) are selected depending on the predefined values of these variables along with the optimisation constraints and objective. Table 4. Optimisation results for using epoxy woven glass fiber materials in the face sheets. Face sheet layers (pcs) and orientation (o )
t c (mm)
δ (mm)
W t (kg)
(2 layers) 5; –5
61.98
9.99
140.48
(3 layers) –85; 5; 0
50.66
9.985
140.5
(4 layers) –10; 0; –15; –90
44.55
9.897
149.16
(5 layers) 0; 10; 5; 0; 5
38.62
9.982
158.11
(6 layers) 0; 10; –80; 65; -5; –5
35.88
9.98
172.36
(7 layers) –85; 80; –60; 85; –85; 0; 75
33.51
9.925
187.22
(8 layers) 0; 85; 5; 65; –35; 10; –80; –5
30.78
9.999
201.5
Table 5. Optimisation results for using epoxy woven carbon fiber materials in the face sheets. Face sheet layers (pcs) and orientation (o )
t c (mm)
δ (mm)
W t (kg)
(1-Layer) –80
80.89
4.387
152.29
(2 layers) –80; –5
33.73
9.995
92
(3 layers) 85; 90; –10
26.86
9.958
98.58
(4 layers) 10; 0; –5; 80
22.7
9.873
109.69
(5 layers) 15; 5; –20; 15; –80
19.99
9.957
123.19
(6 layers) 10; 0; 0; –70; 55
19.03
9.808
139.6
(7 layers) 30; –85; –30; –25; –80; –20; –5
18.73
9.325
157.1
(8 layers) –75; –35; –75; –80; –50; 80; 90; –15
18.43
8.112
174.6
Figure 4 and Fig. 5 show the evolution of the solution of the optimisation problem for the epoxy woven glass fiber and the epoxy woven carbon fiber materials, respectively.
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As it can be seen, the optimal values for the number of face sheets layers for both types are two layers. The details of the feasible and optimal designs for the epoxy woven glass fiber and epoxy woven carbon fiber sandwich structures are shown in Tables 4 and 5, respectively. From Fig. 6, it can be seen that the application of epoxy woven carbon fiber materials in the face sheets layer of the sandwich structure can make the sandwich structure lighter overall. On the other hand, the thickness of the sandwich can be significantly reduced compared to epoxy woven glass fiber material, as can be seen in Fig. 7.
Fig. 4. Weight of the sandwich structure versus core thickness and face sheets layer number in case of epoxy woven glass fiber face sheets.
To validate the optimisation results, the numerical model for the bottom panel of a heavy truck described in Sect. 5 was fed with the optimal design variables. The face sheets of the sandwich structure consisted of two layers of epoxy woven carbon fiber composites (second item in Table 5). The numerical results of the maximum sandwich deformation showed good agreement with the optimisation results, with the value of the deflection of the numerical model being 9.62 mm, as shown in Fig. 8. The value obtained from the optimisation process was 9.99 mm, as shown in Table 5.
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Fig. 5. Weight of the sandwich structure versus core thickness and face sheets layer number in case of epoxy woven carbon fiber face sheets.
Fig. 6. Weight compassion for the structures of epoxy woven carbon/glass fiber face sheets.
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Fig. 7. Total thickness compassion for the structures of epoxy woven carbon/glass fiber face sheets.
Fig. 8. The numerical results of the bottom panel.
7 Conclusions In this study, the optimal design of the bottom panel of a heavy truck was investigated. The weight reduction can be achieved by using composite structures. Therefore, the optimisation problem was solved by formulating the corresponding mathematical expressions in Excel and linked to I-sight software. The main conclusions from this study can be summarised as follows: • The weight of a sandwich structure mainly depends on the thickness of the core and facing materials, so weight reduction can be achieved by using different layups of composite facing materials.
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• In this study, two types of composite materials (epoxy woven glass fibers and epoxy woven carbon fibers) were used as face sheet materials with an aluminium honeycomb core. It can be concluded that the use of epoxy woven carbon fibers as face sheets results in lighter sandwich structures. • The optimisation algorithm plays a vital role in finding a compromise between the strength and weight of the sandwich panels. For this purpose, the design variables (number of layers in the face sheets, orientation of the layers, and thickness of the core) were changed in a continuous loop between Excel and the I-sight software until the optimal variables were reached. The optimal variables correspond to the sandwich structure with the lowest possible weight. The motivation goal of this study is to elaborate on new optimum designs of the composite sandwich structures that can achieve a mass reduction of the heavy trucks’ bodies. The main added value of the article is the elaboration of the optimisation method for the bottom panel of heavy-duty trucks in order to define the optimal combination of the honeycomb core and the composite face sheets. Acknowledgements. The research was supported by the Hungarian National Research, Development, and Innovation Office - NKFIH under the project number K 134358.
References 1. Joost, W.J.: Reducing vehicle weight and improving U.S. energy efficiency using integrated computational materials engineering. Jom 64, 1032–1038 (2012) 2. Czerwinski, F.: Current trends in automotive lightweighting strategies and materials. Materials 14(6631), 1–27 (2021) 3. Wang, J., Shia, C., Yanga, N., Suna, H., Liub, Y., Songa, B.: Strength, stiffness, and panel peeling strength of carbon fiber-reinforced composite sandwich structures with aluminum honeycomb cores for vehicle body. Compos. Struct. 184, 1189–1196 (2018) 4. Reddy, Y.S.N., Reddy, J.N.: Linear and non-linear failure analysis of composite laminates with transverse shear. Compos. Sci. Technol. 44, 227–255 (1992) 5. Reddy, J.N., Pandey, A.K.: A first-ply failure analysis composite laminates. Comput. Methods Appl. Mech. Eng. 25, 371–393 (1986) 6. Jiang, D., Zhang, D., Fei, Q., Wu, S.: An approach on identification of equivalent properties of honeycomb core using experimental modal data. Finite Elem. Anal. Des. 90, 84–92 (2014) 7. Yuan, J., Zhang, L., Huo, Z.: An equivalent modeling method for honeycomb sandwich structure based on orthogonal anisotropic solid element. Int. J. Aeronaut. Sp. Sci. 21, 957–969 (2020) 8. Kapania, R.K., Soliman, H.E., Vasudeva, S., Hughes, O., Makhecha, D.P.: Static analysis of sandwich panels with square honeycomb core. AIAA J. 46, 627–634 (2008) 9. Wang, W., et al.: Comparative application analysis and test verification on equivalent modeling theories of honeycomb sandwich panels for satellite solar arrays. Adv. Compos. Lett. 29, 1–15 (2020) 10. Al-Fatlawi, A., Jármai, K., Kovács, G.: Optimization of a totally fiber-reinforced plastic composite sandwich construction of helicopter floor for weight saving, fuel saving and higher safety. Polymers 13, 2735 (2021)
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11. Ning, H., Janowski, G.M., Vaidya, U.K., Husman, G.: Thermoplastic sandwich structure design and manufacturing for the body panel of mass transit vehicle. Compos. Struct. 80, 82–91 (2007) 12. Chahardoli, S., Tran, T.N., Marashi, S.M.H., Masoumi, F., Yu, S.T.: Experimental investigation of the crushing characteristics in sandwich panels in the application of light vehicles using three-point bending tests. Eng. Fail. Anal. 129, 105725 (2021) 13. Hudson, C.W., Carruthers, J.J., Robinson, A.M.: Multiple objective optimisation of composite sandwich structures for rail vehicle floor panels. Compos. Struct. 92, 2077–2082 (2010) 14. Kundrák, J., Karpuschewski, B., Pálmai, Z., Felh˝o, C., Makkai, T., Borysenko, D.: The energetic characteristics of milling with changing cross-section in the definition of specific cutting force by FEM method. CIRP J. Manuf. Techn. 32, 61–69 (2021) 15. Felh˝o, C., Varga, G.: CAD and FEM modelling of theoretical roughness in diamond burnishing. Int. J. Prec. Eng. Manuf. 23, 375–384 (2022) 16. Lee, H., Park, H.: Study on structural design and manufacturing of sandwich composite floor for automotive structure. Materials 14, 4–15 (2021) 17. Zenkert, D.: An Introduction to Sandwich Construction. Student Chamelon Press, Oxford, London (1995) 18. HexCel Composite:. Honeycomb sandwich design technology. HexWeb Honeycomb Sandw. Des. Technol., 1–28 (2000) 19. Virág, Z., Jármai, K.: Optimum design of stiffened plates for static or dynamic loadings using different ribs. Struct. Eng. Mech. 74(2), 255–266 (2020) 20. Velden, A.V., Koch, P.: Isight design optimisation methodologies. Appl. Met. Process. Simul. 22, 8–9 (2010) 21. Kaw, A.K.: Mechanics of Composite Materials, 2nd edn. Taylor & Francis, London (2005) 22. Barbero, E.J.: Finite Element Analysis of Composite Materials Using Abaqus TM, 2nd edn. CRC Press, London (2013)
Advanced Methods to Solve Multi-project Scheduling Problems Taking into Account Multiple Objective Functions Krisztián Mihály(B) , Mónika Kulcsárné-Forrai, and Gyula Kulcsár Department of Information Engineering, University of Miskolc, Miskolc, Hungary [email protected]
Abstract. Project-based planning and execution have an important role in the product lifecycle. Medium and large-sized companies are executing more than one project simultaneously, usually sharing common resources. Each project has its individual goals to achieve. Creating a company-wide optimal or near-optimal schedule in this complex environment is very difficult. Our paper presents a model to define the problem and a concept of a possible solver. A proof-of-concept of an advanced solver with experimental results is presented. Keywords: RCPSP · Multi-project scheduling · Multi-objective optimization · Management decision support
1 Introduction and the Scope of Our Research The paradigm of the cyber-physical manufacturing system and Industry 4.0 technologies play important roles in developing and managing production systems. These solutions can also be used to improve the processes of automotive manufacturing systems. To increase the efficiency of manufacturing and services, it is extremely important to schedule the activities as accurately as possible. This paper presents our research on developing an optimization model for solving extended multi-project scheduling problems. Different resource types must be considered with individual capacity constraints. These optimization problems appear in many designing, planning and control areas of the vehicle and automotive engineering. The initial model of our development is based on the well-known resource-constrained project scheduling problem (RCPSP). In our extended problem, the optimization aims to create detailed execution plans for the set of active projects and control loads of capacity-constrained resources. Allocating shared resources and scheduling tasks related to multiple projects are NP-hard problems. This multi-project scheduling problem is more difficult than scheduling a single project. This paper will describe the investigated problem’s characteristics, the solving approach, and the proposed new algorithms. Our results have been compared with known solutions of various special production scheduling problems. The effectiveness of the proposed algorithms will be demonstrated by numerical results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 747–755, 2023. https://doi.org/10.1007/978-3-031-15211-5_62
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2 Multi-objective Multi-project Scheduling Problems 2.1 The Initial Model of Our Research A classical resource-constrained project scheduling problem (RCPSP) consists of tasks to be executed on resources with predefined constraints. Our model extends the classical RCPSP problem with the capability to handle more than one project at the same time, sharing common resources and tasks. The scheduling problem has multiple objective functions to be considered at the individual project level. We modelled the extended problem and proposed an advanced method to solve it. 2.2 Mathematical Model of RCPSP First, the classical RCPSP problem is presented using the following formulation: • • • • •
T = {1, 2, 3,…,n}: set of tasks to be scheduled K = {1, 2, 3,…,m}: set of resource types Rk , k ∈ K: capacity constraint of resource type k Pj : immediate predecessors of task j(j ∈ T ) (rj,k ): resource requirement of task j(j ∈ T ), from resource type k(k ∈ K) Additional constraints:
• Resource types are renewable; after executing a given task, all blocked capacity will be free again and can be used to execute a different task. There is no decreasing or increasing effect on the capacity of the task execution process (amortization). • If Pj = ∅ then all task i(i ∈ Pj ) must be finished before execution of task j. • It is forbidden to have any circle in the task precedence graph. • It is forbidden to exceed the resource type maximum available capacity for any given resource need rj,k ≤ Rk , ∀k ∈ K, j ∈ T . 2.3 An Extended Mathematical Model The classical model is extended with the capability to define projects with individual project properties and project objective functions, sharing common resources and tasks. Definition of Projects. Each task is assigned to one or more projects. Each project may have individual goals represented by individual objective functions. Let us introduce a set of projects denoted by P. Each task is assigned to one or more projects, which is noted by the set TA. Each project has individual project properties, such as release time, due date, tardiness cost, and so on. The possible set of project properties is defined by set PPROP and it is known at design time. Definition of additional project properties is possible any time later; there is no modelled limitation.
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Definition of Project-Level Objective Functions. Various objective functions on the project level can be defined. Objective function value on the project level can be calculated by the problem definition and characteristics of the feasible schedule. As an example, three objective functions are presented: • Project completion time Project completion time is the maximum value of the task completion times in the project. • Number of projects with delay If a project has a defined due date and the project completion time is higher, then the project is delayed. The value of this objective function is the number of delayed projects. • The total cost of tardiness If a project has a defined tardiness cost and the project is delayed, then tardiness cost shall be counted. The total cost of tardiness is the summary of all tardiness costs of delayed projects. Definition of a Project Objective Functions. An individual set of optimization function definitions can be assigned to each project. Every element of a given set is built from pairs of the predefined objective function and its weight. The set of optimization function definitions for the project pl are denoted by POl .
3 An Advanced Solver 3.1 A Short Review of Related Research Multi-project extension of the classical RCPSP problem is an already researched domain. To solve the problem, classical optimization methods have been used. Pritsker et al. [1] proposed a zero-one programming approach to solve the problem. Deckro et al. used a general integer programming model and presented a decomposition approach to solve large size problems of multi-project problems [2]. Jolayemi proposed an integer programming approach for multi-project scheduling [3]. Proposed models are suitable for small-size problems and limited support of optimization goals. Researchers usually apply several heuristics and meta-heuristic methods for multiproject scheduling to deal with medium or large-size problems. In recent decades researchers developed many effective algorithms to generate feasible solutions for multiproject scheduling problems. The goal of this development was to increase the efficiency of the heuristic methods, to extend the scope of the problems with new methods and reduce the computation time. In the literature, we can find many papers in which artificial intelligence methods are presented to solve the multi-project resource-constrained project scheduling problems. Kim et al. presented a combined genetic algorithm to generate solutions [4]. The goal was the minimization of the completion time of the set of projects. Kumanan, et al. applied an advanced approach based on a genetic algorithm for solving multi-project scheduling
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problems [5]. They also tried to minimize the project completion time. Damak et al. also presented a new variant of the genetic algorithm with a local search strategy to solve the problem. They considered the project delay minimization as the optimization goal [6]. In the first phase of our research, we reviewed many related papers in the literature. As a result, we can conclude that there is a need to develop more flexible and efficient models and methods to improve the robustness and quality of the solutions for resourceconstrained solutions multi-project scheduling problems. 3.2 Compositional Structure Advanced solver is built up from generation schemes and search-based methodologies to combine the advantage of them. The high-level, compositional structure of the concept is depicted in Fig. 1.
Fig. 1. A conceptual overview of the advanced solver
Schedule Generation Scheme (GS). GS is a methodology to create a feasible schedule by applying predefined rules iteratively. The generation starts from an empty schedule, and in each iteration, a feasible semi-schedule is constructed until all tasks are scheduled. Search-Based Solver (SCH). Search-based solvers are working with a set of predefined rules to create new solution candidates. Each candidate is evaluated based on the defined goal functions. Evaluation of the candidate usually consists of two steps: simulating the schedule and calculating key performance indicators. Depending on the applied search methodology from the previous base and evaluated solution candidates, new candidates are constructed until a certain exit criterion is met. Key Performance Indicator Calculation (KPI). Key performance indicator calculation is evaluating a created feasible schedule based on defined project objective functions. Calculated values can be represented in a matrix for each project and each objective function.
3.3 Solver Behavioural Model The combined behaviour of the generation scheme and search-based solver is depicted in Fig. 2. The user defines the multi-project problem and sets up the multi-project objective functions. The user can calibrate the search components, for example defining exit
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conditions or defining used simulation variants. The search component is creating an initial sized, feasible schedule set and evaluating it. Based on the implemented search algorithm, new solution candidates are generated by altering existing solution candidates. Each new candidate is simulated by GS, and the results are compared by SCH. The main loop is iterated until the calibrated exit conditions are met. For example, the exit condition can be the maximum number of iterations or no more improvement of solution KPI after a predefined number of search iterations. User
Search-based Solver
Generation Scheme
Load problem set
Setup objective functions
Create initial schedule
Generate Scheduling
Evaulate scheduling
Adjust search parameters
Generate Scheduling
Evaulate scheduling
Exit condition meet? Yes
Provide best solution(s)
Fig. 2. Flowchart of the combined model
3.4 Injection of Search Variables into Scheduling Generation Scheme To use GS as a simulator for SCH, the GS core algorithm has been enhanced with search model injection parameters. Generation Scheme Core Algorithm. The core algorithm of GS is described with the following pseudo-code:
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First, the main state variables are initialized, such as scheduled tasks (TGEN), the schedule (SCH) and resource bookings (RB). In the main loop, the eligible tasks are calculated (D) based on the task precedence graph. A task is eligible if it is not yet scheduled and all prerequisite tasks are part of TGEN in the current iteration. One task from the eligible tasks is selected randomly, if not specified otherwise. The task is added to the scheduled tasks. The schedule is updated with calculated starting times; resource bookings are updated. Task Scheduling Priority. The basic algorithm selects a task from decision set D at random. We introduced a search scheduling priority value for each task, an importing parameter for the GS, defined by SCH. When the core algorithm selects a task from D, the task with the highest search scheduling priority is always selected.
4 Experimental Results We have implemented an advanced project scheduling (APS) module using SAP technologies to justify the presented concept. The high-level concept is depicted in Fig. 1 and the implementation level concept is depicted in Fig. 3.
Search Solver
Adjacent Pair Swap Neighbour Search
Random Pair Swap Neighbour Search
Multi Project Problem Definition and Task Search Priority Definition
Genetic Search
Heuristic or Genetic Search (Randomized)
Problem definition
Generation Scheme
Serial Generation Scheme
Task Selector
Parallell Generation Scheme
Generation Scheme (Deterministic)
Fig. 3. APS implementation concept
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4.1 Implemented Generation Scheme Generation scheme core algorithm pseudo-code is presented in Sect. 3.4. There are two main variants of generation schemes: serial and parallel. The main difference is the method of how the eligible tasks (D) are selected in each iteration. Serial Variant. A task is part of D when all its prerequisite tasks are part of TGEN and the task itself is not yet scheduled. Parallel Variant. A task is part of D if the serial generation scheme variant condition is met and the task’s earliest starting time is minimal.
4.2 Implemented Search Solvers All solver is enhancing the scheduling problem definition with a TsPrio attribute for each task j(j ∈ T ). TsPrio is an integer number and it is in range being equivalent with the number of tasks to be scheduled: TsPrio ∈ {0, 1, 2, . . . , n}. Task selection step of the generation scheme is enhanced to consume only the task search selection priority values. Neighbour Search. Neighbour search is initialized with generated search priority values at random. Based on user parameters, the maximum number of neighbours is generated from the best-known solution. All generated candidate is evaluated by project objective functions, and the best candidate is selected. The main loop is repeated until the exit condition is met.
Two neighbour generation operators have been implemented: • Select randomly a task and change its priority value with an adjacent task. • Select randomly two tasks and change their priority values. Genetic Search. An evolutional algorithm starts with a predefined size of the so-called population. A member of the population is a feasible schedule, represented by the task
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search selection priority vector. There is a predefined number of generations to be simulated. In each generation for each member of the population, a new candidate is generated, based on mutation function. Once the new population is generated the new population is selected based on existing and new populations.
4.3 Numerical Results The implemented solution was tested on known basic problems, and our numerical results are compared with the known results of the test problems. In the first case, we checked our scheduling method on special problems which have known optimum providing algorithms. The selected problem was mapped with special boundary conditions to our extended model. It is known that the Johnson algorithm gives optimal solution for permutation Flow Shop problem with makespan (C max ) objective function. Our heuristic-based solver found the optimal solution for small- and mediumsized problems in most cases (>95%). The benchmark RCPSP problem was mapped to our extended model in the second case. There are publicized lower and upper bound results for the benchmark problem dealing with project completion time (C max ) [7]. We compared our results with the benchmark problems’ best results. We observed that for small problems (up to 30 jobs) in most cases (>70%) the best-known solution was found by the scheduler.
5 Conclusion This paper presented our latest research focused on modelling and solving an extended resource-constrained multi-project scheduling problem. We investigated and modelled the detailed characteristics of the set of projects to be scheduled and the resourceconstrained execution environment. In addition, our model includes different resource types and their capacities, many projects with precedence constraints and individual project-dependent objective functions. We considered task-dependent individual resource requirements, processing times, and special tasks connected to many projects simultaneously. In our model, many objective functions can be specified to be optimized based on project-dependent arguments.
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In the experimental phase of our research, we developed new scheduling methods to solve the problems flexibly while all the constraints were met. Our solving method generate solutions by using a predictive searching algorithm and an advanced simulation algorithm. In the simulation, we use different constructive rule-based algorithms. The most efficient and flexible version of the generation scheme uses multiple control priorities simultaneously. We proposed a new approach to solve the extended problem. This approach combines two previous separate approaches. The framework of the solver engine is based on a searching method that generates control priority values, and in the searching iterations constructive methods are used as an embedded decision-making engine to build the complete schedule by considering the control priority values. This paper summarized our extended model and the solving approach. The effectiveness of the proposed methods are validated by using two different types of benchmark problems. The experimental results confirmed our assumptions. The proposed model, approach and algorithms are flexible and efficient. The research results inspire new directions of development and offer important practical opportunities. The model can support considering individual requirements of tasks and projects in practice. Many optimization goals can be taken into account simultaneously. These new features help to apply different management strategies and tactical and operational control policies.
References 1. Pritsker, A., Allan, B., Watters, L.J., Wolfe, P.M.: Multiproject scheduling with limited resources: A zero-one programming approach. Mngmt. Sci. 16, 93–108 (1969). https://doi. org/10.1287/mnsc.16.1.93 2. Deckro, R.F., Winkofsky, E.P., Hebert, J.E., Gagnon, R.: A decomposition approach to multiproject scheduling. Eur. J. Oper. Res. 51, 110–118 (1991). https://doi.org/10.1016/0377-221 7(91)90150-T 3. Jolayemi, J.K.: Scheduling of projects under penalty and reward arrangements: A mixed integer programming model and its variants. Acad. Inf. Mngmt. Sci. J. 15, 29–52 (2012). ISSN: 1524–7252 4. Kim, K.W., Yun, Y.S., Yoon, J.M., Gend, M., Yamazaki, G.: Hybrid genetic algorithm with adaptive abilities for resource-constrained multiple project scheduling. Comp. Ind. Eng. 56, 143–160 (2005). https://doi.org/10.1016/j.compind.2004.06.006 5. Kumanan, S., Jegan, J.G., Raja, K.: Multi-project scheduling using a heuristic and a genetic algorithm. Int. J. Adv. Manuf. Technol. 31, 360–366 (2006). https://doi.org/10.1007/s00170005-0199-2 6. Damak, N.J.B., Siarry, P., Loukil, T.: Differential evolution for solving multi-mode resource constraint scheduling problems. Comp. Oper. Res. 36, 2653–2659 (2009). https://doi.org/10. 1016/j.cor.2008.11.010 7. Kolisch, R., Sprecher, A.: PSPLIB - A project scheduling library. Eur. J. Oper. Res. 96, 205– 216 (1996). Project Scheduling Problem Library (2019). http://www.om-db.wi.tum.de/psplib/ library.html. Accessed 20 Mar 2022
Combination of GPU Programming and FEM Analysis in Structural Optimisation Szilárd Nagy1,2(B)
, Károly Jármai2
, and Attila Baksa2
1 Emerson Automation FCP Kft., Eger 3300, Hungary
[email protected]
2 University of Miskolc, Miskolc 3515, Hungary
{karoly.jarmai,attila.baksa}@uni-miskolc.hu
Abstract. GPUs no longer only support graphical applications and gaming. These are becoming cheap and powerful tools for scientific and general-purpose computations. They provide a massively parallel environment with the support of a single instruction multiple data (SIMD) programming model. Making finite element calculations is also a time-consuming process in some cases due to many elements or a large degree of freedom. The FEM simulation is essential to check the analytical or measured mechanical stresses, deformations, etc. In making structural optimisation, one needs several iterations and combining the optimisation with FEM, increasing the calculation time. GPU programming is a good solution for this. In the article, we show the applicability of the combination of GPU, optimisation, and FEM simulation. Keywords: Evolutionary optimisation · Finite element method · Parallel computation
1 Introduction Nowadays, graphic cards (video cards, GPU) are cheap and efficient hardware for general-purpose parallel computation. They are used for scientific computations, for topology optimisation [1] or structural optimisation [2], and for manufacturing technologies. They provide a massively parallel environment with the support of a single instruction multiple data (SIMD) programming model. Nowadays, larger software vendors – such as MathWorks – are increasingly developing frameworks based on the CUDA API (application programming interface) that offer more convenient and user-friendly tools than the original CUDA Runtime API. Nature-inspired, population-based, iterative, evolutionary algorithms – such as flower pollination algorithm [3], particle swarm optimisation [4], firefly algorithm [5], etc. – are powerful numerical optimisation methods. Their importance and effectiveness are underlined by the fact that they are used in several places in vehicle research to design optimal aerodynamics for UAVs (unmanned aerial vehicles) [6], for performance optimisation of formula vehicles [7], for optimising the manufacturing of vehicles [8] etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 756–767, 2023. https://doi.org/10.1007/978-3-031-15211-5_63
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The finite element method (FEM) is a universal tool for analysing structures and determines mechanical stress and deformations inside the structure. In this paper, we connect an evolutionary algorithm – differential evolution – with FEM. The method is presented through the optimisation of the truss structure. Computational capacity is demanded by both the evolutionary method and FEM. Therefore, we present a possible parallelisation using MATLAB software and the obtained results.
2 Differential Evolution Stron and Price introduced original differential evolution (DE) in [1]. DE improves the nD dimensional x individuals of np element population through a series of iteration steps T x = x1 x2 x3 · · · xnD ∈ S ⊂ RnD
(1)
where S is searching space. Ideally, the initial population randomly covers the entire search space. Each variable in an individual is a uniformly distributed random number in the search space. DE generates the new entity in each iteration step by performing three operations repeatedly. These are called mutation, crossover, and selection operations [9]. During the mutation operation, a G vi mutant is generated for each G xi individual of G generation using one of the following five strategies [10]: • DE/rand/1: G
vi = G xr1 + F
G
vi = G xb + F
• DE/best/1:
• DE/current to best/2: G
vi = G xi + F
• DE/best/2: G
vi = G xb + F
• DE/rand/2: G
vi = G xr1 + F
G
G
xr2 − G xr3
xr1 − G xr2
(2) (3)
xb − G xi + F G xr1 − G xr2
(4)
xr1 − G xr2 + F G xr3 − G xr4
(5)
xr2 − G xr3 + F G xr4 − G xr5
(6)
G
G
G
where r1 = r2 = r3 = r4 = r5 ∈ 1, np are random indices, F ∈ [0, 2) is the scaling factor, and G xb is individual with the best fitness value in each G generation.
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The mutation is followed by a “binomial” crossover operation that combines the newly created G vi mutant with the G xi individual G v U (0, 1) ≤ CR or j = jR G (7) uj,i = G j,i j xj,i otherwise where Uj (0, 1) ∈ [0, 1) is uniformly distributed random number, CR ∈ [0, 1) is the crossover rate, and jR ∈ [1, nD ] is a random index. During selection, if the fitness value of the newly generated G ui is better than that of the G xi , it will be included in the new generation; if not, the algorithm drops it G u F(G ui ) ≤ F(G xi ) G+1 (8) xi = G i xi otherwise The operation of differential evolution, and hence the success of the optimisation, is greatly influenced by the mutation strategy chosen, the value of the scaling factor F, and the CR crossing ratio.
3 Finite Element Model of Truss Structure The connection between members of tubular trusses is frequently modelled as pin connection inelastic analysis. The preferred value of eccentricities of the intersection of member’s center lines is [1, 12]. e ≤ 0.25D or e ≤ 0.25H0
(9)
where e is eccentricity, D is the outside diameter of a circular hollow section, and H0 is a typical size of rectangular hollow section. In this case, primary bending moments are produced by these eccentricities. Excessive moments are generated in brace members when rigid connections are considered. Usage of these is not recommended also for welded joints [1, 12]. The axial force distribution in a rigid joint is like pinned joint. The structure could be analysed with the pushed-pulled element model (shortly in the following rod or truss model) with finite element methods (FEM) if the condition of inequality (9) is met. In most cases, it is sufficient to examine the structure in a plane relevant to the load. If this was insufficient and the spatial analysis had to be performed, the presented method could be easily adapted to a spatial case. In this paper, we will only discuss planar problems.
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Fig. 1. Truss element model.
The model of the truss element is shown in Fig. 1. An approximation of the displacement within the rod element with a kinematically admissible function [13]
e u e ui e −ξ ξ e ξ −ξ i j e u(ξ ) = ie L e L (10) = Ni (ξ ) Nj (ξ ) e = e N e u e u u j j
where e L is the length of the rod element, e N is the matrix of shape functions, and e u is the vector of nodal displacement interpreted in element connected ξ coordinate system. In the global x − y coordinate system, nodal displacements could be described in the following form e
u=
e
uix e uiy e ujx e ujy
T
.
(11)
The transformation between the two-coordinate system could be made with the transformation matrix e T11 e T12 0 0 e (12) T= 0 0 e T23 e T24 where e
T11 = e T23 =
eu jx
eu − eu − e uix jy iy e e and T = T = 12 24 eL eL e
u = eTeu
(13) (14)
Elongation of truss element is e
ε=
1 d e u(ξ ) = e −1 1 e u dξ L
(15)
and stress in the axial direction is e
σ = Eeε =
E eL
−1 1 e u
(16)
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where E is elastic modulus. The strain energy of truss element with e A cross-sectional area is
1 1 e T e AE 1 −1 e 1 e e e e U = A σ εd ξ = u e u = eu T eK eu (17) 2 L 2 L −1 1 2
where e K is the stiffness matrix of the element. The work of external forces is
e e W = u(ξ )pd ξ = e u T e f
(18)
L
where e f is the vector of external forces reduced to nodes. The total potential energy of one element could be written in the following form e
p = e U − e W =
1 e T e e u K u − eu T ef 2
(19)
It could be rewritten with quantities, which are introduced in the global coordinate system e
p =
1e Te e u K u − e uT e f 2
(20)
where e
K = e T T e K e T and e f = e T T e f
(21)
Introducing the u all node displacement vectors and the f all node load vectors as the total potential energy of the whole structure is p =
1 T u (Ku − f ) 2
(22)
where K stiffness matrix of the complete structure according to the rules of element alignment, which is detailed described in [13, 14]. Many truss structures are built from different rods with different cross-sectional properties. These rods could be grouped by AE product. From the stiffens K matrix introduced initially in (22), these AE product can be extracted by cross-sectional groups nG Ai Ei K i K = A1 E1 K 1 + A2 E2 K 2 + · · · + Ai Ei K i + · · · + AnG EnG K nG = i=1
(23) where nG is the number of cross-sectional groups, and K i is stiffness matrix of ith group. If the unknown quantities of the optimisation are typical cross-section dimensions (for example, D outside diameter and t wall thickness for circular hollow section), preprocessing of FEM is enough to do it once before the first iteration step of optimisation. According to the principle of minimum total potential energy [15, 16], the δ first variation of total potential energy is zero. After applying boundary conditions, we get an algebraic equation system of FEM δp = δuT
∂p = δuT (Ku − f ) = 0 ∂u
(24)
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u = K −1 f .
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(25)
Post-processing of the result of Eq. (25) is necessary for further calculations. Axial stress of elements could be determined by e
σ =
eE eL
−e T11 −e T12 e T11 e T12 e u
(26)
4 The Optimisation Problem Optimisation of truss structures are constrained optimisation problem T min.f (x) x = x1 x2 · · · xD ∈ R gi (x) ≤ 1 1≤i≤q 1≤j≤r hj (x) = 0
(27)
where x is the vector of unknowns – in this paper, vector of typical dimensions of crosssection –, f (x) is the objective function to be optimised, gi (x) are inequality constraints, hj (x) are equality constraints, q and r are the numbers of constraints. In this paper, the target function of optimisation is the weight of the structure ne e e f (x) = ρ A L (28) e=1
where ne is the number of truss elements, where ρ is the density of steel. The structure must meet strength and stability requirements. In the present case, three criteria have been analysed. In the case of pulled rods, the resistance to tensile stress, and in the case of pushed rods, the buckling and finally the local buckling. The cross-sectional utilisation factor can well characterise these characteristics. A definition of an inequality condition can interpret the tensile and compressive strength of pushed-pulled rods if the stress from the load is interpreted as a sign. Negative tension means pressure, while positive means tension. γM 0 |e σ | ≤ 1 eσ < 0 (29) gIi = γMχ0f|ye σ | ≤ 1 eσ ≥ 0 fy where fy is yield strength, γM 0 is a safety factor according to [17], and χ is buckling factor also according to [17] ⎧ ⎨ λ ≤ 0, 2 1 (30) χ= 1 λ > 0, 1 ⎩ 2 φ+ φ 2 +λ
where φ is a factor 2 φ = 0, 5 1 + 0, 21 λ − 0, 2 + λ
(31)
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λ is a slenderness factor
A fy λ = π kL Ix E
(32)
where Ix is the second-order moment of the used cross-section, k is the deflection length factor, which is k = 1 for intermediate bars and k = 0.7 for the gripped bars. The limit of local buckling depends on the shape of the cross-section. A different formula should be used for a different shape [11]. Currently, we use local buckling of circular hollow section gIIi =
Dfy ≤1 21150t
(33)
This formula is valid only if the unit of fy yield stress is in MPa, and the unit of D diameter and unit of t wall thickness is mm. Using Eqs. (28), (29) and (33), the fitness function to be optimised ne ne nG e e F(x) = ρ A L+ p(gIi (x)) + p(gIIi (x)) (34) e=1
i=1
i=1
where p is the static penalty function p(x) =
0 g(x) ≤ 1 106 g(x) g(x) > 1
(35)
and x is the vector of unknowns (vector of independent variables). For example, in the case of a circular tube, un-knowns are characteristic dimensions of the cross-section T x = d1 d2 · · · dnG t1 t2 · · · tnG
(36)
5 Parallelisation with CUDA and MATLAB Nvidia corporation offers CUDA Driver API [18] and CUDA Runtime API [19] to program their graphics cards for general-purpose computation. There are many types of graphics cards on the market, with different computation capabilities and performance. The codec containing our unique calculation must be scalable [20], and it should automatically detect the used hardware capabilities [21]. Implementing this feature is sometimes more challenging than implementing our custom calculation. As an intermediate layer between CUDA and our code, MATLAB offers much simpler possibilities for implementing our parallel computation [22]. However, this ease of use comes at a price, so the computation speed increase will never be as great as using only native CUDA. MATLAB gives a reach toolset and many features to make operations with vectors and matrices. It offers many possible ways to rewrite original loop-based, scalar oriented operations to vector-matrix operations. This process is called “vectorisation”.
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To illustrate the differences between the two types of operations, let the population be given as follows for circular hollow section tubes ⎤ ⎡ D1,1 D1,2 · · · D1,j · · · D1,np ⎥ ⎢D ⎢ 2,1 D2,2 · · · D2,j · · · D2,np ⎥ ⎢ . .. .. .. ⎥ ⎥ ⎢ . . . . ⎥ ⎢ . ⎥ ⎢ ⎢ Di,1 Di,2 · · · Di,j · · · Di,np ⎥ ⎥ ⎢ .. .. .. ⎥ ⎢ .. ⎥ ⎢ . . . . ⎥ ⎢ ⎢ DnG ,1 DnG ,2 · · · DnG ,j · · · DnG ,np ⎥ ⎥= D ⎢ (37) X=⎢ ⎥ t ⎢ t1,1 t1,2 · · · t1,j · · · t1,np ⎥ ⎥ ⎢ t ⎢ 2,1 t2,2 · · · t2,j · · · t2,np ⎥ ⎢ . .. .. .. ⎥ ⎥ ⎢ . . . . ⎥ ⎢ . ⎥ ⎢ ⎢ ti,1 ti,2 · · · ti,j · · · ti,np ⎥ ⎥ ⎢ .. .. .. ⎥ ⎢ .. ⎣ . . . . ⎦ tnG ,1 tnG ,2 · · · tnG ,j · · · tnG ,np where Di,j is diameter of ith cross-sectional group for j th the individual in the population, ti,j is the wall thickness of ith cross-sectional group for jth the individual in the population. For example, the loop-based, scalar oriented implementation of Eq. (33) could be seen in Listing 1.
In contrast, the implementation in Listing 2 of the same equation covers a vectorised form.
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The striking difference between the two code snippets is that the latter is much shorter and more transparent. Sometimes, scalar-oriented operation vectorisation may not be formulated with element-wise operations (such as.*,./,.ˆ, etc.). In such cases, arrayfun() could be a good tool. The point is that the scalar operation inside the loop core must be organised into a separate function (see in Listing 3). arrayfun() will call this function one at a time as many times as many elements in the vector or matrix passed as a parameter.
Provide tools for vectorising operations performed on multidimensional matrices using “page-wised” functions and operations. These detailed descriptions could be found in [22] for length reasons; these are not detailed in this paper. MATLAB can always start the loop-based approach on only one thread, as illustrated in Listing 1. The situation is different with vectorised operations. It can automatically detect repetitive operations where only the data to be processed changes and automatically discover the capabilities of the runtime environment to perform them on multiple threads. In the simplest case, when using multi-core processors, it automatically – unless the opposite is set – takes advantage of the possibility of running on multiple cores in parallel. This automation also works for GPUs if the type of all variables in the expression is gpuarray. It automatically creates the required kernel functions based on the expressions and starts them on the required and possible number of threads, considering the capabilities of the GPU. All the expressions and functions presented in previous chapters are easy to vectorise. This allows complete optimisation – evolutionary algorithm, FEM solver and fitness function calculation – to be calculated using GPU in parallel. If all steps and operations are calculated with a GPU, the host machine only manages them; it is enough to move data between the host and GPU at the beginning and end of the optimisation.
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6 Comparison of Sequential and Parallel Optimisation The dimensionless computation speed up between sequential and parallel processing is defined as follows tseq ∇= (38) tpar where tseq is the average computation time of iteration steps using only sequential processing, tpar is the average computation time of iteration steps using only sequential processing. For measuring tseq computation time, we used 1 pcs CPU thread, and for measuring tpar computation we used as many as possible thread on Geforce GTX 1050 Ti type graphics card. The structure shown in Fig. 2 was optimised to determine the previously defined rate increase. This is a truss structure with deltoid-shaped stiffeners. Applied loads were F1 = 332.94 kN , F2 = 437.46 kN and F3 = 338.08 kN . Node 1 and 7 were fixed, that means any displacement in these points is not allowed. Cross-section of all rods was a circular tube, where we optimised of outside diameter and wall thickness of tubes according to Eq. (36). Rods of the structure were divided into three cross-sectional groups. The first group contains rods 1–10. Horizontal rods (11–16) are in the second group. Finally, rods in the third group are rods of deltoid shape (17–26). In our simulation, we have simulated optimisation with different numbers of individuals in the population which are used by SaDE. The dimensionless speed up achieved
Fig. 2. Sketch of optimised structure for comparison of sequential optimisation and parallel optimisation
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Dimensionless speed up
is illustrated in Fig. 3, with different np population sizes. We did not inspect the quality of optima in this paper; we inspected only the difference in computation time.
10.000
1.000
0.100 10
100
1000
10000
Population size Fig. 3. Dimensionless speed up with different population sizes and numbers of nodes.
7 Conclusion An evolutionary algorithm is presented in this paper, the differential evolution. This algorithm relates to the finite element method for optimising truss-like structures subject to static stresses, overall buckling and local buckling. This is a powerful approach for optimising any truss structure automatically. Evolutionary optimisation is a population-based iterative numerical method. That means the fitness function should be calculated many times; meanwhile, that could be a resource-demanding task and take a long time. One way to increase the speed of calculations is parallel computation with GPU. MATLAB offers user-friendly methods and tools for doing it. We have analysed dimensionless speed up of optimisation with tools of MATLAB. The available speed up depends on the size of the population Speed up increases approximately exponentially in the function of population size (see in Fig. 2). If the population size is small, there is no reason for parallelisation. In future exploration, it could be interesting to inspect speed up in the function of the number of elements and number of nodes with fixed and varied size populations. Acknowledgements. The research was supported by the Hungarian National Research, Development and Innovation Office—NKFIH under the project number K 134358.
References 1. Xia, Z., Wang, Y., Wang, Q., Mei, C.: GPU parallel strategy for parameterized LSM-based topology optimization using isogeometric analysis. Struct. Multidiscip. Optim. 56(2), 413– 434 (2017). https://doi.org/10.1007/s00158-017-1672-x
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2. Wang, J., Zhang, D., Luo, M., Zhang, Y.: A GPU-based tool parameters optimization and tool orientation control method for four-axis milling with ball-end cutter. Int. J. Adv. Manuf. Technol. 102(5–8), 1107–1125 (2018). https://doi.org/10.1007/s00170-018-2954-1 3. Yang, X.S.: Flower pollination algorithm for global optimisation. In: Durand-Lose, J., Nataša, J. (eds.) Unconventional Computation and Natural Computation, pp. 240–249. Springer, Berlin, Heidelberg (2012) 4. Xie, X.F., Zhang, W.J., Yang, Z.L.: Adaptive particle swarm optimisation on individual level. In: Proceedings of the 6th International Conference on Signal Processing, 2002, vol. 2, pp. 1215–1218 (2002). https://doi.org/10.1109/ICOSP.2002.1180009 5. Yang, X.S.: Nature-Inspired Optimization Algorithms, 2nd (edn). Academic Press, London (2021). https://doi.org/10.1016/C2019-0-03762-4 6. Lee, D.S., Gonzalez, L.F., Srinivas, K., Periaux, J.: Robust evolutionary algorithms for UAV/UCAV aerodynamic and RCS design optimisation. Comput. Fluids 37(5), 547–564 (2008). https://doi.org/10.1016/j.compfluid.2007.07.008 7. Tey, J.Y., Rahizar, R.: Handling performance optimisation for formula vehicle using multiobjectives evolutionary algorithms. Veh. Syst. Dyn. 58(12), 1823–1838 (2020). https://doi. org/10.1080/00423114.2019.1645861 8. Galván-López, E., Curran, T., McDermott, J., Carroll, P.: Design of an autonomous intelligent demand-side management system using stochastic optimisation evolutionary algorithms. Neurocomputing 170, 270–285 (2015). https://doi.org/10.1016/j.neucom.2015.03.093 9. Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimisation over continuous spaces. J. Global Optim. 11, 341–359 (1997). https://doi.org/10.1023/ A:1008202821328 10. Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimisation. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 2, pp. 1785–1791 (2005). https://doi.org/10.1109/CEC.2005.1554904 11. Wardenier, J., Kurobane, Y., Packer, J.A., van der Vegte, G.J., Zhao, X.-L.: Design Guide for Circular Hollow Section (CHS) Joints Under Predominantly Static Loading, 2nd edn. CIDECT, Zürich (2008) 12. Wardenier, J., Kurobane, Y., Packer, J.A., van der Vegte, G.J., Zhao, X.-L.: Design guide for rectangular hollow section (RHS) joints under predominantly static loading, 2nd edn. CIDECT, Zürich (2008) 13. Ferreira, A.J.M., Fantuzzi, N.: MATLAB Codes for Finite Element Analysis. Springer Cham, Heidelberg (2020). https://doi.org/10.1007/978-3-030-47952-7 14. Smith, I.M., Lee, M.: Programming the Finite Element Method, 5th edn. John Wiley and Sons Ltd, London (2013) 15. Páczelt, I.: Finite element method in engineering practice (in Hungarian). Miskolci Egyetemi Kiadó, Miskolc (1999) 16. Páczelt, I., Baksa, A., Szabó, T.: Fundamentals of the finite element method (in Hungarian). HEFOP jegyzet, Miskolc (2007) 17. EN 1993–1–1: Eurocode 3: Design of steel structures - part 1–1 General rules and rules for buildings. European Committee Standardization, Brussels (2009) 18. CUDA Drive API documentation. https://docs.nvidia.com/cuda/cuda-driver-api/index.html. Accessed 01 Mar 2022 19. CUDA Runtime API documentation. https://docs.nvidia.com/cuda/cuda-runtime-api/index. html. Accessed 01 Mar 2022 20. Cheng, J.: Professional CUDA C Programming. John Wiley & Sons, Hoboken (2014) 21. Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming. Pearson Education, Boston (2010) 22. Help center for MATLAB: Simulink and other MathWorks product. https://uk.mathworks. com/help/index.html?s_tid=CRUX_lftnav. Accessed 01 Mar 2022
Global and Local Cost Calculations at Welded Structures Károly Jármai(B) University of Miskolc, Miskolc 3515, Hungary [email protected]
Abstract. This article analyses cost estimations with different welding technologies using global and local methodologies. In structural optimisation these costs are the goal functions to be minimised. We consider the relationship between design and fabrication technology, as well as the economy, using optimisation. These three elements work together to assist us in determining the best option. Only those cost elements that are directly relevant to the structural dimensions are considered in the local approach. A real structure’s cost function may comprise material costs, assembly costs, and other fabrication costs, such as welding, surface preparation, painting, cutting, edge grinding, and creating geometry. Welding is the most common industrial procedure for combining metals, but it is also a significant producer of harmful fumes and gases. Depending on the welding technology, the amount of CO2 , CO, CH4 , NOx , etc., can be different. The global approach considers environmental issues such as global warming, ozone depletion, acidification, eutrophication, photochemical ozone production, and abiotic depletion. LCA is an objective approach for analysing and implementing alternatives to enhance the environment, as well as assessing the environmental consequences of a product, process, or activity. Keywords: Life Cycle Assessment · Welded structures · Cost calculation
1 Introduction This paper compares the cost calculations using global and local approaches with different welding technologies. In structural optimisation, these costs are the objective functions. We analyse the connection between design and fabrication technology, as well as the economy, using optimisation. These three factors work in tandem to help us identify the optimal solution. Cost-effectiveness is very important. The local approach’s cost estimations are based on material costs and are confined to fabrication expenses that have a direct impact on the structure’s sizes, dimensions, or shape. The cost function is built in accordance with the fabrication sequence and includes material, assembly, welding, surface preparation, painting, cutting, edge grinding, and forming expenses. Other expenses like as amortisation, investment, transportation, and maintenance are not taken into account by this approach. Design and inspection costs can be estimated, but in most cases, they are © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 768–789, 2023. https://doi.org/10.1007/978-3-031-15211-5_64
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proportional to the structure’s weight. Data on cost and manufacturing time comes from a variety of firms throughout the world. When comparing the same design in different nations, the differences in labour costs should be taken into account. If the production technology is identical, it has the most impact on the structure. When taking a global perspective, environmental problems such as global warming potential, ozone depletion potential, acidification potential, eutrophication potential, photochemical ozone creation potential, and abiotic depletion potential must all be considered.
2 The Local Approach Cost Elements In this approach, only cost elements that are directly proportional to structural dimensions are considered. Material costs, assembly expenses, and other fabrication costs like as welding, surface preparation, painting, cutting, edge grinding, and creating the geometry may all be included in the cost function of a real structure. Klansek & Kravanja [1], Farineau et al. [2], Farkas & Jármai [3–5] are some of the researchers who have worked in this field: Mela & Heinisuo [6] on high-strength steel and Kovács & Farkas [7] an optimization. 2.1 The Cost of Material Equation (1) contains the cost of material. KM = kM ρV ,
(1)
The specific material cost for steel may be k M = 1.0–1.5 $/kg depending on the thickness and grade of the material, but for aluminium, it can be k M = 3.2–4.2 $/kg [8]. The material cost is represented by K M [kg], the corresponding material cost factor is represented by k M [$/kg], and the structural volume is represented by V [mm3 ]. The density of steel is 7.85 × 10–6 kg/mm3 , while the density of aluminium is 2.7 × 10–6 kg/mm3 . If several different materials are utilised, several material cost factors may be employed in Eq. (1) at the same time. 2.2 The Cost of Manufacturing in General Fabrication costs are related to fabrication time and particular manufacturing costs, which vary by country. When employing the same technology, the manufacturing time is considered to be the same. Ti , (2) KF = kF i
where K F [$] denotes the fabrication cost, k F [$/min] denotes the fabrication cost factor, and T i [min] denotes the manufacturing time. For a given manufacturer, the value of k F is assumed to stay constant. If not, different fabrication cost factors can be applied simultaneously in Eq. (2).
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2.3 Fabrication Times for Welding When calculating the total cost of welding, numerous elements must be considered. The cost of consumables is the most evident factor (electrode and shielding gas or flux). The labour and overhead costs that can be assigned to the welding process are the less evident (and sometimes disregarded) costs. The choice of the most cost-effective welding procedure (FCAW Flux Cored Arc Welding, GMAW Gas Metal Arc Welding, and SMAW is for Shielded Metal Arc Welding., etc.) and welding electrode (wire or stick) for the available equipment is not as straightforward as it appears. If the welder’s maximum output power sources are 450 A, 60% duty cycle machines, the answer to electrode selection might not be as straightforward as “let’s utilise the widest wire diameter possible to attain the best deposition rates.” The welding time will be the principal basis of calculation in our approach. The major times involved with welding are preparation, assembly, tacking, welding time, electrode replacement, deslagging, and chipping. 2.4 Calculation of Preparation, Assembly, and Tacking Times To estimate the times for preparation, assembly, and tacking, apply the following approximation formula: [9] (3) Tw1 = C1 dw κρV , C 1 is a welding technology parameter (typically equal to 1), dw is a difficulty factor, and κ is the number of structural pieces to be assembled. The value of the difficulty factor is determined by the structure’s complexity (planar or spatial), as well as the type of members (flat, tubular). The suggested range of values is 1–4 [9]. 2.5 Actual Welding Time Estimation The formula below may be used to calculate the actual welding time.: 2 Tw2 = C2i awi Lwi , i
(4)
where awi is weld size, L wi is the length of weld for various welding methods, C 2i is constant. C 2 includes not only welding technology changes but also time disparities between positional (vertical, overhead) and typical welding in the down hand position [10–12]. On the internet, there are various documents that can be used to calculate welding speed [13, 14]. Table 1 shows the welding times for longitudinal fillet welds at SMAW, GMAW-CO2 and FCAW technologies. The new technologies’ acronyms are as follows: GMAW-CO2 stands for Gas Metal Arc Welding with CO2 , and FCAW-MC stands for Metal Cored Arc Welding. Based on the data collected on welding speeds, we could approximate the welding time as a function of welding technology and welded plate thickness using the TableCurve 2D software. The new technologies are laser and TIG/GTAW (Tungsten Inert Gas) welding, as shown in Fig. 1. As can be observed, the slowest welding method is SMAW (Shielded Metal Arc Welding), whereas the quickest is laser welding (assuming we ignore investment costs).
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Table 1. T w2 (min/m) welding times for longitudinal fillet welds in the down hand position as a function of weld size aw (mm). Welding technology
aw [mm]
103 Tw2
SMAW
1–6
2 0.7889aw
GMAW-CO2
1–6
2 0.3394aw
FCAW
1–6
2 0.2302aw
2.6 Calculation of Additional Fabrication Actions Time Change the electrode, deslagging, and chipping are some of the other fabrication operations to consider. A rough estimate of how long it will take is as follows: 20.0
SMAW
GMAW-CO2
FCAW
15.0 10.0 5.0 0.0 1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Fig. 1. T w2 (min/m) welding time for longitudinal fillet welds in the down hand position as a function of weld size aw (mm).
Tw3 = 0.3
2 C2i awi Lwi .
(5)
It has a proportionate relationship with T w2 . It accounts for around 30% of the total cost. The two time components are as follows: (Fig. 1): n C2i awi Lwi . (6) Tw2 + Tw3 = 1.3 For each technology, the welding times for 1/2 V, V, K, and X weldings varied.
3 Thermal and Waterjet Cutting Oxyfuel gas, plasma, laser, and abrasive waterjet are the four most popular non-contact metal cutting techniques. The first three cutting methods are thermal, whereas the waterjet method cuts through abrasive erosion. These four techniques are often used to create precise external and interior cuts in flat sheet and plate material [15–20].
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3.1 Plate Cutting and Edge Grinding Times Thermal cutting with oxyfuel gas, mainly acetylene gas, was originally the only option. The oxyfuel torch contains a pre-heating flame that heats the iron or carbon steels to their “kindling temperature,” which is roughly 480°C. After that, the steel is exposed to a stream of pure oxygen, which causes a quick combustion reaction between the steel and the oxygen. The molten material, known as slag, is blown through the metal by a stream of cutting oxygen, resulting in a relatively smooth and consistent cut. Cutting and edge grinding may be done with a variety of technologies, including Acetylene, Stabilised gas mix, and Propane at regular and high speeds. The following parameters were given by Farkas, Jármai [10]: Both the thickness and the cutting length are measured in millimeters. CCPi tin Lci , (7) TCP = i
Curve fitting calculations provide the value of n. Thermal processes and oxyfuel gas processes both have two drawbacks. The structure of the metal in “heat-affected zones” near the cut is first altered by heat. Some metallurgical characteristics may be degraded at the cut’s edge, necessitating pre-treatment or trimming. Second, with the exception of laser cutting, tolerances may be less exact than a machined cut. Metals and some non-metallic materials may be cut with remarkable accuracy using laser cutting, which is a relatively new technique. The laser beam has a diameter of 0.2 mm and a power of 1–2 kW. A high-density light beam is delivered via a small hole in the nozzle during the laser cutting process. When this beam meets the workpiece’s surface, the workpiece’s material is instantaneously sliced. Carbon and stainless steels are the ideal materials for lasers to work on. Because of their propensity to reflect laser light as well as absorb and transfer heat, metals like aluminium and copper alloys are more difficult to cut with a laser. Waterjet cutting of steel. Using an extremely high-pressure jet of water or a mixture of water and an abrasive substance, a water jet cutter can cut a broad range of materials [20]. Steel cutting with plasma. Plasma cutting involves cutting metal with a hightemperature, high-velocity jet of ionised plasma. Plasma temperatures range from roughly 5500°C to 28,000°C. Standard compressed shop air, oxygen, argon and hydrogen, or nitrogen and hydrogen are among the gases utilised, depending on the material to be plasma cut. A gas barrier can be made out of air, water, or carbon dioxide. Table 2 shows the cutting time for various cutting technologies. They were calculated using a variety of data and the TableCurve2D approximation software. The time is in minutes per meter, and the plate thickness t and weld size aw = 0.7*t for one-side welding) are in millimeters. Figure 2 compares the cutting times of several cutting systems. For thin plates (less than 1 mm), laser, plasma, and waterjet cutting are obviously the quickest, while laser cutting and high-speed acetylene cutting are the fastest for thicker plates (5–6 mm).
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Table 2. Plate cutting time, T CP (min/mm) for longitudinal fillet welds and T-, V-, 1/2 V butt welds as a function of weld size aw (mm). Cutting technology
Thickness t [mm]
103 TCP
Acetylene (normal speed)
1–6
1.1388t 0.25
Acetylene (high speed)
1–6
0.9561t 0.25
Stabilized gasmix (normal speed)
1–6
1.1906t 0.25
Stabilized gasmix (high speed)
1–6
1.0858t 0.23
Propane (normal speed)
1–6
1.2941t 0.24
Propane (high speed)
1–6
1.1051t 0.25
Laser
1–6
(0,144+0,452 t 0.5 )2
Waterjet
1–6
(0,511+0,251 t 0.5 Ln(t)2
Plasma
1–6
(0,447+0,384 Ln(t 2 ))2
4 Surface Preparation Time Surface preparation entails cleaning the surface using sand, ice, and other methods. The following formula may be used to calculate the surface cleaning time as a function of the surface area (As [mm2 ]): TSP = ds asp As ,
(8)
where ds is a difficulty parameter and asp = 2x10–6 min/mm2 .
5 Painting Time Making the ground- and topcoat is what painting entails. The following formula (9) can be used to calculate the painting time as a function of the surface area (As [mm2 ]): TP = dp agc + atc As , (9) where agc = 2x10–6 min/mm2 is the ground coat parameter, atc = 2.85x10–6 min/mm2 is the topcoat parameter, dp is the difficulty factor, dp = 1,2 or 3 for horizontal, vertical, or overhead painting
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2 acelen stab. Gasmix propan
3
4 acelen HS stab Gasmix HS propan HS
5
Fig. 2. Cutting time of plates, T CP (min/m) in the function of weld size aw (mm) for fillet for longitudinal fillet welds and T-, V-, 1/2 V butt welds
6 Total Cost (Local Approach) All of the previously specified cost categories are included in the total cost. K kF = ρV + Ti i kM kM
(10)
The cost of steel as a single material might be as high as k M = 1.0–1.5 $/kg. The fabrication cost is K f [$], and the fabrication cost factor is k F [$/min]. The production times are k F = 0 –1 $/min and T i [min]. For a given manufacturer, the value of k F is assumed to stay constant. The k F /k M ratio ranges from 0 to 2 kg/min. If k F /k M = 0, the smallest mass is obtained. If k F /k M = 2.0 it entails a significant labour expense (Japan, USA), k F /k M = 1.0 means a developed country labour cost, k F /k M = 0.5 is the cost of labour in underdeveloped country. Even though the production rates are equal in these situations, the cost differences are considerable owing to the varying labour expenses.
7 Global Cost Approach Welding is the most standard technique for joining metals in industry, but it also produces many toxic fumes and chemicals. Despite the processes’ mechanisation and automation, the number of welders exposed to welding gases is continuously rising as new welding methods and consumables are deployed. One of the most important criteria for choosing a welding method is its environmental friendliness, keeping in mind that toxic gases and chemicals may occasionally exceed their exposure limits. Welding has a variety of potential effects, and the industry is still researching the impact of welders’ exposure to regular welding fumes and gases and their impact on climatic conditions.
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Several vapours and gases can be created during welding. Welding fumes are metalcontaining aerosols composed of particles created during the welding process’s complex vaporisation, condensation, and oxidation processes. Many articles and standards deal with this topic [21–36]. Metal fumes’ health consequences vary depending on the metals present. Even so, there’s fear that they might cause everything from metal fume fever to long-term lung damage and neurological issues, including lung cancer and Parkinson’s disease. To determine the impact, microalloyed steel was welded with two distinct filler materials (metal-cored wire and self-shielded wire) [21]. During welding, dust emissions, CO, CO2 , Mn, SO, Al, Ni, Ca, Cr, Cr(VI), and P were all measured. When the results for both filler materials were examined, it was discovered that the high concentrations of manganese and CO in metal-cored wire, as well as the high concentrations of phosphorus and aluminium in self-shielded wire, require further attention. The volume of fumes and gases released is related to the welding technique used. Manufacturers and fabricators of flux cored wire are still looking for ways to improve the metal-cored welding process. To attain both high deposition efficiencies, they needed to overcome one last hurdle. The goal was to achieve high flux-cored wire productivity while maintaining high solid gas metal arc welding wire deposition efficiency [21]. The lowered smoke level demonstrated throughout a range of welding conditions is one of the most notable advances in the new generation of metal-cored wires. They can be utilised in applications where fumes must be decreased because this new generation of metal-cored wires produces fume levels that are 20 to 50% lower than comparable flux-cored wires. The primary distinction between flux-cored and self-shielded (FCAW-S) wires is that the former requires an external shielding gas while the latter does not. Although FCAW-S wires typically generate a more significant fume per welded joint, the welding zone can be kept safe by extracting the fumes. FCAW-S wires are ideal for outdoor use because they tolerate wind/weather, even though they typically generate a greater fume per welded joint. Metal cored wires and self-shielded wires are relatively novel filler materials, and sufficient experimental data haven’t supported their application. Although metal-cored and self-shielded wires are more expensive (by 20–25%) than flux-cored and solid wire equivalents, working with them allows for a stable arc, intense penetration, and minimal scattering. The higher CO2 concentration for self-shielded wire is most likely due to the increased carbonate and cellulose in flux, which provides gas protection during combustion. The concentration of CO released from a metal-cored wire is ten times that of a self-shielded wire and three times that of the maximum allowed level. Such high CO concentrations are most likely the result of inadequate flux combustion and the breakdown of CO2 as a shielding gas component. According to reasonable industrial hygiene procedures, engineering controls should be utilised to limit ambient concentrations to the acceptable exposure level. 7.1 Life Cycle Assessment (LCA) LCA is a scientifically validated method for estimating and assessing environmental effects throughout time. LCA analyses all processes from raw material to final disposal,
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K. Jármai
Fig. 3. The environmental effects’ system
including manufacturing, distribution, and use. An LCA can analyse consequences such as ozone depletion, eutrophication, implications for human health, and much more, in addition to greenhouse gas emissions (or a carbon footprint) (Fig. 3). LCA may be entirely automated and fully incorporated into the workflow without disturbing or slowing it down. This allows for cost-effective benchmarking. When multiple competing choices to acquire, own, operate, maintain, and eventually dispose of an object or process are equally acceptable to adopt on technical grounds, life-cycle cost analysis (LCCA) is a method to find the most cost-effective option among them. 7.2 Main Environmental Impacts to Be Considered General calculation method impactcat =
i
mi × charact_factorcat,i
(11)
where mi is the mass of the inventory flow i and charact_factor cat, i is the characterisation factor of inventory flow i for the impact category. 7.3 Calculation of Potential Environmental Impacts The goal of a life cycle assessment is to determine the potential environmental implications of recognised inputs and emissions. A quick introduction to the most prevalent environmental categories in LCA is given in the following paragraphs, along with the respective calculation method used in the simplified methodology outlined in this article. Normalisation is frequently required concerning the optional steps in LCA to illustrate to what extent an impact category contributes significantly to the total environmental impact. The normalised indicator findings for each effect category are allocated several factors based on their respective importance in the weighting step.
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7.4 Global Warming Potential (GWP) Infrared (IR) active gases naturally present in the Earth’s atmosphere (e.g. O3 , H2 O and CO2 ) absorb and reflect some of the terrestrial (infrared) energy (or radiation) departing the Earth, helping to warm the surface and lower atmosphere. As indicated in Table 3, the Intergovernmental Panel on Climate Change [26] calculated GWPs for three of the most major greenhouse gases and three time horizons of 20, 100, and 500 years for three of the most significant greenhouse gases. Table 3. GWPs for certain time periods (in kg CO2 eq./kg) [22]
Carbon Dioxide (CO2 )
20 years
100 years
500 years
1
1
1
Methane (CH4 )
62
25
7
Nitrous oxide (N2 O)
275
298
156
As a result of Eq. (11), the indicator “Global Warming” is determined as follows: Global Warming = GWPi × mi (12) i
where, mi is the mass of substance i released (in kg). This metric is measured in kilograms of CO2 equivalents. Only a 10-year time horizon is addressed in the selected strategy.
8 Ozone Depletion Potential (ODP) Ozone-depleting gases damage stratospheric ozone, or the “ozone layer,” by producing free radical molecules that break down ozone (O3 ). The worldwide loss of ozone due to a chemical is compared to the global loss of ozone due to the reference compound CFC11 to determine its ozone depletion potential. As a result, ODP has a reference unit of kg chlorofluorocarbon-11 (CFC-11) (Table 4). The World Meteorological Organization (WMO) established the characterisation model, which specifies the ozone depletion potential of various gases. Table 4. OPDs for some substances (in kg CFC-11 eq./kg) [23] Steady-state (t ≈:) CFC-11
1
CFC-10
1.2
Halon 1211
6.0
Halon 1301
12.0
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As a result, the indication Ozone Depletion is determined by, Ozone Depletion = ODPi × mi i
(13)
where, mi is the mass of substance i released (in kg). This indicator is expressed in kg of CFC-11 equivalents.
9 Acidification Potential (AP) The conversion of air pollutants (mostly ammonia (NH3 ), sulphur dioxide (SO2 ), and nitrogen oxides (NOx ) into acid compounds is known as acidification. Wind transports acidifying chemicals into the atmosphere, where they deposit as acidic particles, acid rain, or snow. Acidification potential may be calculated using a material’s capacity to generate H+ions, which is the cause of acidification, or an equivalent release of SO2 . The RAINSLCA model, which accounts for destiny, background depositions, and effects [24], was utilised to develop the characterisation variables in this work. As a result, Table 5 shows the average European characterisation factors for acidification. As a result, the indicator acidification is determined by, APi × mi (14) Acidification = i
where, mi is the mass of substance i released (in kg). This metric is measured in kilograms of SO2 equivalents. Table 5. Acidification potentials (in kg SO2 eq.) [24]
Api
Ammonia (NH3 )
Nitrogen Oxide (NOx )
Sulphur Dioxide (SO2 )
1.60
0.50
1.20
10 Eutrophication Potential (EP) Nutrients such as nitrates and phosphates are commonly provided to the soil through fertilisation to stimulate the development of plants and agricultural commodities. These nutrients are essential for life, but they may accidentally nourish sensitive natural water or land areas, resulting in an overabundance of plants or algae that suffocate other species when they die and disintegrate. Eutrophication is measured in kilos of nitrogen or phosphate equivalents. Eutrophication is caused by nitrogen compounds such as nitrates, ammonia, and nitric acid, as well as phosphoric compounds such as phosphates and phosphoric acid. The characterisation values for chosen compounds using phosphate as a reference component are shown in Table 6 [23].
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3− Table 6. Eutrophication potentials (in kg PO3− 4 PO4 eq.) [23]
EPi
Ammonia (NH3 )
Nitrogen Oxide (NOx )
Nitrate (N)
Phosphate (P)
0.35
0.13
0.10
1.00
As a result, the eutrophication indicator is, Eutrohication = EPi × mi
(15)
i
where, mi (kg) is the mass of substance i released to the air, water or soil. This metric is given in kilograms of PO3− 4 - equivalents.
11 Photochemical Ozone Creation Potential (POCP) In atmospheres containing nitrogen oxides (NOx), a common pollutant, and volatile organic compounds, ozone and other air pollutants can be generated in the presence of sunlight (VOCs). Although ozone is necessary in the upper atmosphere to defend against ultraviolet (UV) radiation, low-level ozone has been linked to a variety of negative effects, including crop damage and an increase in the prevalence of asthma and other respiratory problems. In the presence of NOx and sunlight, the POCP impact category measures a substance’s ability to produce ozone. The reference material ethylene is used to calculate POCP. POCP characterisation variables were created using the UN Economic Commission for Europe (UNECE) trajectory model. These two characterisation criteria are listed in Table 7 for a few selected substances. Therefore, the photo-oxidant formation indicator may be calculated, POCPi × mi (16) Photo − oxidant formation = i
where, mi is the mass of substance i released (in kg). This metric is measured in kilograms of ethylene (C2 H4 ) equivalents. Only the characterisation elements linked to a scenario with a high background concentration of NOx are taken into consideration in the chosen technique. Table 7. POCPs for various NOx concentrations and certain compounds (in kg C2 H4 eq./kg)[23] High-NOx POCPs
Low-NOx POCPs
Acetaldehyde (CH3 CHO)
0.641
0.200
Butane (C4 H10 )
0.352
0.500 (continued)
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K. Jármai Table 7. (continued) High-NOx POCPs
Low-NOx POCPs
Carbon monoxide (CO)
0.027
0.040
Ethyne (C2 H2 )
0.085
0.400
Methane (CH4 )
0.006
0.007
Nitrogen oxide (NOx )
0.028
no data
Propene (C3 H6 )
1.123
0.600
Sulphur oxide (SOx )
0.048
no data
Toluene (C6 H5 CH3 )
0.637
0.500
12 Abiotic Depletion Potential The goal of abiotic depletion indicators is to reflect the diminishing availability of nonrenewable resources due to exploitation and underlying scarcity. Two sorts of indicators are explored in this paper: Abiotic Depletion Elements deals with the extraction of rare elements (and their ores), whereas Abiotic Depletion Energy/Fossil Fuels deals with the use of fossil fuels as a source of energy or feedstock. The Abiotic Depletion Potential for elements (ADP elements) is computed for each element extraction based on residual reserves and extraction rate. The ADP is calculated using the equation Production/Ultimate Reserve and compared to the antimony (Sb) reference example [25]. The earth’s crust’s economic or ultimate reserve is employed in a variety of ways. As a result, the Abiotic Depletion Potential (Elements) of a resource (ADPi) is defined as the ratio of the amount of resource extracted to the recoverable reserves of that resource, expressed in kilograms of the reference resource, Antimony, and the characterisation factors for a few selected resources are listed in Table 8. Table 8. Potentials for abiotic depletion of certain elements (in Sb eq./kg) [25] Resource
ADP element
Aluminium
1.09E-09
Cadmium
1.57E-01
Copper
1.37E-03
Iron
5.24E-08
Lead
6.34E-03
As a result, the indication Abiotic Depletion (Elements) may be determined as follows, ADPi × mi (17) Abiotic Depletion = i
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where, mi is the quantity of resource i extracted (in kg). This metric is measured in kilograms of antimony (the reference resource). The indicator Abiotic Depletion Fossil is measured in megajoules (MJ).
13 Illustrative Example A short example is presented here to demonstrate the many phases of life cycle evaluation discussed in the preceding paragraphs. Assuming that different welding wires were used in the production [21], the following emissions (see Table 9) were collected during the inventory stage: Table 9. Emissions collected from the production using different welding wires [21] Metal core wire
Self-shielded wire
Emissions
Value (in kg/m3 )
Value (in kg/m3 )
carbon monoxide (CO)
0.00181
0.000172
carbon dioxide (CO2 )
0.03590
0.05760
sulphur dioxide (SO2 )
0.0000209
0.0000262
The selected environmental categories include, for example, global warming potential (GWP), acidification potential (AP), and eutrophication potential in the next phase, impact assessment (EP). Table 10 shows the characterisation parameters for each emission for each environmental category. Table 10. Characteristics of selected environmental groups GWP
AP
EP
(kg CO2 eq.) (kg SO2 eq.) (kg PO4 - eq.) carbon monoxide (CO) 1.53
-
-
carbon dioxide (CO2 )
1.00
-
-
ammonia (NH3 )
-
-
-
methane (CH4 )
-
-
-
nitrogen oxides (NOx )
-
-
-
phosphorus (P)
-
-
-
sulphur dioxide (SO2 )
-
1.20
-
As a result, each environmental category’s findings are determined by multiplying each contributing emission by its categorisation factor (e.g., for GWP: 0,00181 x 1.53 + 0,0359 x 1.00 = 0.03869 kg CO2 eq. For metal cored wire, 0,000172 x 1.53 + 0,0576
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K. Jármai Table 11. The chosen environmental indicators’ final findings GWP (kg CO2 eq.)
AP (kg SO2 eq.)
metal cored wire
0.03869
0.000025
self-shielded wire
0.05786
0.0000314
x 1.00 = 0.05786 kg CO2 eq. For self-shielded wire) resulting in the outcomes shown in Table 11. The harmful fumes and gases during welding are also essential to be considered. Depending on the welding technology, the amount of CO2 , CO, CH4 , NOx , etc., can be different. We have different wires, metal-cored and self-shielded wire. The compared two wires provide an additional amount of carbon monoxide (CO), carbon dioxide (CO2 ) and sulphur dioxide (SO2 ). Their real value on global warming potential and acidification potential depends on the time they have used. Also, the energy usage can be great considering longer time, like decades.
14 Optimisation of Stiffened Plates On a welded stiffening plate, the cost optimisation is displayed. Flat stiffeners are used, and they are fillet welded to the cover plate. Steel has an E = 2.1*105 MPa modulus of elasticity, a material density of 7.85*10–6 kg/mm3 , a Poisson’s ratio of 0.3, yield stress of f y = 235 MPa, and a plate width of bo = 1500 mm and a length of L = 1500 mm. In the axial direction, the compressive force is. N = fy bo tfmax /10 = 235 × 1500 × 5/10 = 1.7625 × 105 [N] The variables to be optimised are as follows (Fig. 4): the thickness of the base plate t f , the sizes of stiffeners hs and t s and the number of stiffeners φ = bo /a.
Fig. 4. Compressed stiffened plate
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The overall buckling constraint is calculated as follows: N ≤ χ fy A
(18)
where the buckling factor χ is given in function of the reduced slenderness λ χ = 1 for λ ≤ 0.5
(19a)
χ = 1.5−λ for 0.5 ≤ λ ≤ 1
(19b)
χ = 0.5/λ for λ ≥ 1
(19c)
where b0 λ= tf
12 1 − ν 2 fy Eπ 2 k
k = min (kR , kF );
(20)
kR = 4ϕ 2
L (1 + α 2 )2 + φγ when α = ≤ 4 1 + ϕγ α 2 (1 + φδP ) b0 √ 2 1 + 1 + φγ kF = when α ≥ 4 1 + φγ 1 + φγ
kF =
δP =
Etf3 h3 tS hS tS EIS ; IS = S ; D = ; γ = b0 tf b0 D 3 12(1 − ν 2 )
(21) (22) (23)
(24)
so γ = 4(1 − ν 2 )
h3S tS b0 tf3
= 3.64
h3S tS b0 tf3
(25)
D is the flexural stiffness of the base plate and I s is the moment of inertia of one stiffener about an axis parallel to the plate surface at its base. According to EC3, the limiting slenderness ratio is used to determine the limitation on the local buckling of a flat stiffener. hS 235 1 ≤ = 14 (26) tS βS fy The computational results are summarised in Table 11.
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15 Optimisation Method and Results for the Local Approach When the input values (or decision variables) change, the Generalised Reduced Gradient technique looks at the gradient or slope of the objective function. It concludes that it has arrived at an optimal solution when the partial derivatives equal zero. The GRG method’s core premise includes using the Taylor expansion equation to linearise the nonlinear goal and constraint functions at a local solution. The implicit variable elimination notion is utilised to express the basic variable by the non-basic variable, as well as the reduced gradient approach to divide the variable set into two subsets of basic and nonbasic variables. Finally, the restrictions are removed, leaving just non-basic variables in the variable space. The approximated problem should be solved using the established efficient approach for non-constraints NLP problems, and then the next best solution for the approximated problem should be discovered. The technique is repeated until the ideal circumstances are met. The disadvantage is that the solution obtained using this technique is very reliant on the beginning circumstances and may not be the global optimum. The solver will almost definitely end at the local optimal value that is closest to the original conditions, resulting in a solution that is either globally optimised or not. There is a significantly better likelihood that the solution obtained is the global optimum if you start from various initial circumstances numerous times. The method generates a population of randomly dispersed starting values, each of which is assessed using the conventional GRG Nonlinear algorithm. Table 11 shows the optimum values for laser welding and laser cutting for various k F /k M values. Table 12. Computational results for different fabrication costs kF /kM
0
1
2
tf
2.000
2.167
2.167
hs
32.617
33.588
33.588
ts
2.330
2.399
2.399
ϕ
6.000
5.000
5.000
Total cost
40.7
111.8
180.6
Mass
40.7
43.0
43.0
Welding
0.0
19.7
39.3
Cutting
0.0
14.9
29.7
Surface cleaning
0.0
10.0
20.0
Painting
0.0
24.3
48.5
Figures 5 and 6 show the cost distribution in case of k f /k m = 1 and 2 considering laser welding, laser cutting.
Global and Local Cost Calculations at Welded Structures Painti ng 22% Surfa ce cleani ng 9%Cutti ng 13%
Paintin g 27%
Mass 38%
Surfac e cleani ng 11%
Weldi ng 18%
Fig. 5. The cost distribution in case of, k f /k m = 1, laser welding, laser cutting
785
Mass 24%
Cutting 16%
Weldi ng 22%
Fig. 6. The cost distribution in case of k f /k m = 2, laser welding, laser cutting
Figures 7 and 8 depicts the cost distribution for SMAW, GMAW CO2 and SAW welding, as well as laser cutting, where kf /km = 2. Due of the long production time, manual arc welding (SWAW) has a considerably higher welding cost. When compared to laser welding (22%), GMAW CO2 welding (33%), and SAW welding (46%), it accounts for 46% of the overall cost (29%). Figures 9 and 10 depicts the cost distribution for laser welding, as well as laser cutting, and acetylene cutting, where kf/km = 2. When we alter the cutting technology, the cost of cutting changes as well. When compared to laser cutting, where it accounts for 16% of the overall cost, standard speed acetylene cutting accounts for 28% (Fig. 6, 10). Painti Surfac ng e 19% cleani ng 8% Cuttin g 11%
Mass 16%
Weldi ng 46%
Fig. 7. The cost distribution in case of, k f /k m = 2, GMAW CO2 welding, laser cutting
Pain ng 23% Surfa ce cleani ngCun 10% g 14%
Mass 20%
Weldi ng 33%
Fig. 8. The cost distribution in case of k f /k m = 2 SMAW welding, laser cutting,
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K. Jármai Mass 22%
Painti ng 24% Surfac e cleani ng 10%
Cutting 15%
Mass 20%
Painti ng 23% Surfac e cleani ng 10%
Weldi ng 29%
Fig. 9. The cost distribution in case of k f /k m = 2, SAW welding, laser cutting
Cutting 28%
Weldi ng 19%
Fig. 10. The cost distribution in case of k f /k m = 2, laser welding, acetylene cutting
16 Optimisation Method and Results for the Global Approach We can anticipate LCA indicators using various welding methods. In this situation, we take into account not just the structure’s mass but also the energy consumed. The energy consumption of producing one ton of steel is estimated to be between 11–24.5 GJ [37– 40] in the literature. Welding energy consumption is measured in kWh. If we assume four hours of labour every day, the total amount of time under a decade is 14600 h. For the mass, we used 18 GJ/t energy, which is equivalent to 5 kWh/t. MIG and hybrid (laser+MIG) welding are two distinct welding methods that we consider. The speed and the energy consumption are visible in Fig. 11 [41]. The amount of energy utilised may be estimated using the formula below. Etotal = Ttime Especific vspeed
(27)
Table 13. The main parameters of the two different welding wire Hybrid (laser+MIG)
MIG
Ttime (hour)
14600
14600
Especific (J/mm)
109
129
vspeed (mm/min)
4
1.5
Etotal (GJ)
381.936
169.506
Table 12 shows, that the used energy during welding is comparable in 10 years’ time to the energy of producing 1 ton steel, or even greater. The main parameters of the two different welding wires are visible on Table 13.
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Fig. 11. Parameters of the MIG and the hybrid Laser-MIG processes
17 Conclusion On a local level, the study explained how to compute the costs of welded structures that are directly related to structural sizes. On a compression welded stiffening plate, the cost optimisation is illustrated. Even for thinner plates, these figures indicate that the cost is highly dependent on welding and cutting technologies. One can identify the most significant technology and the lowest cost by using modern welding and cutting processes. TIG and laser welding are both fast, and if the investment cost is not included, they can be cost-effective. Also, the speeds of laser, plasma, and waterjet cutting are highly dependent on the thicknesses, but through optimisation, the most cost-effective option may be found. In this case, utilising laser welding instead of SMAW one can save 24% in overall costs, and using laser cutting instead of acetylene cutting can save 12%. The hazardous fumes and gases produced by welding must also be considered. The amount of CO2 , CO, CH4 , NOx , and other pollutants produced varies depending on the welding method used. We compared two types of wire: metal-cored and self-shielded wires. They produce more carbon monoxide (CO), carbon dioxide (CO2 ), and sulphur dioxide (SO2 ). The true worth of their global warming and acidification potential is determined by the amount of time they have spent on the planet. Also, when looking at energy use over a longer period of time, such as decades, it may be rather high. Acknowledgements. The research was supported by the Hungarian National Research, Development and Innovation Office under the project number K 134358.
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26. Loukas, C., et al.: A cost-function driven adaptive welding framework for multi-pass robotic welding. J. Manuf. Process. 67, 545–561 (2021). https://doi.org/10.1016/j.jmapro.2021. 05.004 27. ISO 14040: International Standard 2006–07–01, Environmental management — Life cycle assessment — Principles and framework, p. 41. International Organization for Standardisation (2006) 28. ISO 14044: International Standard, First edition, 2006–07–01, Environmental management — Life cycle assessment — Requirements and guidelines, p. 7. International Organization for Standardisation (2006) 29. Vuorinen, P.: CEN standardisation on sustainability of construction works. ETSI 14–15 May 2012 Finnish Association of Construction Product Industries, CEN/TC350/WG6 “Civil engineering works” (2012) 30. Mistry, P.K.J.: Impact of welding processes on environment and health. Int. J. Adv. Res. Mech. Eng. Technol. (IJARMET), 1(1) (2015). ISSN: 2454–4736 31. Chang, Y.-J., et al.: Environmental and social life cycle assessment of welding technologies. In: Procedia 12th Global Conference on Sustainable Manufacturing CIRP 26, pp. 293–298 (2015) 32. Golbabaei, F., Khadem, M.: Air Pollution in Welding Processes — Assessment and Control Methods, p. 33 (2021). https://doi.org/10.5772/59793. Accessed 12 Jun 2021 33. Sproesser, G., et al.: Sustainable technologies for thick metal plate welding. In: Proceedings of the Sustainable Manufacturing, pp 71–84. Sustainable Production Springer Verlag Life Cycle Engineering and Management (2017) 34. Jenkins, N.T., Eagar, T.W.: Chemical analysis of welding fume particles. Weld. J. 84(6), 87s–93s (2005) 35. Fard, M.H., Fard, M.H.: Impacts of welding on environmental problems and health and solutions to overcome these problems. https://www.semanticscholar.org/paper/Impact-ofWelding-Processes-on-Environment-and-Kumar-Mistry/d0848b8c3bc952fdfbd21ce3bf2a4f 03f5d6caf1. Accessed 12 Jun 2021 36. de Souza, N.: Total Fume Emissions and Emission Factors Applicable to Gas Total Fume Emissions and Emission Factors Applicable to Gas Metal Arc Welding, University of New Orleans. Dissertations and Theses Spring, p. 89 (2019) 37. Fruehan, R.J., et al.: Theoretical Minimum Energies to Produce Steel. Energetics, Inc. (2000). file:///C:/Users/Me/Downloads/fruehan-mar00.pdf 38. Martelaro, N.: Energy Use in US Steel Manufacturing (2016). http://large.stanford.edu/cou rses/2016/ph240/martelaro1/ 39. He, K., Wang, L., Li, X.: Review of the energy consumption and production structure of China’s steel industry: Current situation and future development. Metals 10(3), 302 (2020). https://doi.org/10.3390/met10030302 40. Németh, B.: Industrial Technologies. University of Pécs, Hungarian. https://regi.tankonyvtar. hu/hu/tartalom/tamop412A/2011_0025_kor_3/ch05s02.html 41. Dilthey, U.: Multi material car body design - a challenge for welding and joining. Regional Congress on Welding and Related Inspection Technologies (2006). Stellenbosch/South Africa
Vibration and Noise
Vehicle Dynamics Modelling of the Mercedes-Benz REFORM 501 LE Urban Bus by Using AVL Cruise Software Dániel Nemes1(B)
and Sándor Hajdu2
1 Doctoral School of Informatics, University of Debrecen, Kassai Road 26, Debrecen 4028,
Hungary [email protected] 2 Department of Mechanical Engineering, Faculty of Engineering, University of Debrecen, Ótemet˝o Street 2-4, Debrecen 4028, Hungary [email protected]
Abstract. The goal of the research presented in this article is to create a vehicle dynamics model, with which can simulate the emissions and consumption data of buses. During the research we created a vehicle dynamics model with AVL Cruise software. This is a longitudinal vehicle model that can be used to analyze the behavior of the bus in the direction of travel, considering different losses, resistances, inertial effects of rotating and traveling masses, and wheel grip properties and rolling resistances. Our future goal is to explore the development possibilities of the vehicle and to make a proposal for the development and possible structural transformation of the powertrain elements. Keywords: Urban bus · Dynamics modelling · AVL Cruise · Simulation · Emission · Consumption
1 Introduction The simulation can be used to model the driving dynamics, consumption and emissions of the Mercedes-Benz REFORM 501 HP bus in Debrecen. There are currently several developments and researches in the city that target this vehicle. With the model it is possible to simulate the effect of a possible modification on the consumption and emission values. The reduction in consumption also means a reduction in emissions, and if more people use public transport, the city’s emissions will be reduced indirectly. The model can be used to optimize vehicle parameters in the future to operate in the better efficiency range, thus directly reducing emissions. When optimizing the simulation of city buses, it typically focuses on urban transport, but in our case the optimum will have to be found over a wide range of operating parameters. It is a flight to a neighboring town, so the driving cycle includes not only urban but also road sections. The simulation of buses equipped with an internal combustion engine is of great importance because, due to the nearly identical driving cycles and load intensities, the possibility of hybridization can © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 793–798, 2023. https://doi.org/10.1007/978-3-031-15211-5_65
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be tested without the result being as uncertain at the end of the parameter optimization as in a passenger car. One of the great challenges when optimizing passenger cars is to find the most common operating conditions due to the different modes of use. The aim of the research [1] is to simulate and compare electric and hybrid buses using AVL Cruise and TruckMaker software. The criteria for comparison were fuel consumption, acceleration and maximum speed. This article describes the steps for coordinating the two programs, as well as the information and settings required for communication. Describes the structure of the trajectory model and how to measure the results. When simulating hybrid buses, fuel consumption was improved by 50%. The result was found to be sensitive to mains charging frequency, driver style and terrain. The used internal combustion engine did not perform well, will be optimized later. In the research [2], Y. Yang et al. Performed simulations of parallel-powered hybrid buses using AVL Cruise and Matlab Simulink software. The tests were performed in China. The aim was to compare the consumption of hybrid and conventional internal combustion buses. The driving cycle of the bus includes many starts and stops, so a supercapacitor was used as an energy store instead of a battery. The control strategy is contained in a Matlab Simulink file (*.DLL) that can be created through the interface component of the AVL Cruise model. The controller selects one of five operating modes according to power requirements and vehicle parameters. The article also introduces AVL and Matlab. The result of the simulation is that the hybrid vehicle accelerates better and has lower fuel consumption. The aim of the study [3] is to simulate serial hybrid city buses using AVL Cruise. City buses were chosen because they operate a lot during the day in high braking and acceleration conditions. The simulated results were compared with real Madrid data. PCAN Explorer®, GPS and Dewetron meters were used for the measurements. The simulated and measured results (CO2 emissions, fuel consumption, voltage, electricity) differed by only 5%. This research points out that with due care in parameterization, the simulation is able to provide precision data that can already serve as a basis for prototype experiments or fleet development investments.
2 Model Structure During the construction of the model, the aim was to create the simplest possible model. This helps us to focus on accurate tracking of key parts of vehicle behavior during future validation without the error of other blocks with unverified data. The main data of the bus is given in Table 1. As several parameters are required to simulate the engine, only the type, the fuel consumption curve, the CO2 emission datas, the speed-torque curve and the power are reported. For similar reasons, the inertia and resistances of the rotating masses of the transmission are not covered. Table 1. Main data of the simulated city bus Name
Value
Vehicle name
Mercedes-Benz REFORM 501 LE (continued)
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Table 1. (continued) Name
Value
Engine type
Mercedes-Benz OM 936 LA.6–1
Engine power [kW]
260
Engine euro grade
6
Transmission type
ZF EcoLife AP 6 + R
Maximum vehicle weight [kg]
18 600
Given that the simulation in the research also focuses on fuel consumption and emissions, only the longitudinal dynamics was simulated. The characteristic fields required to determine these are the input values of the fuel consumption shown in Fig. 1 and the emission shown in Fig. 2, where the different curves are the different contour lines of the map. In both cases, the horizontal axis shows the speed of the internal combustion engine, while the vertical axis shows Brake Mean Effective Pressure (BMEP). With the help of BMEP, it is possible to plot the values indirectly as a function of torque without having to consider the variable pressure in the cycle typical of an internal combustion engine. These inputs well reflect the property of internal combustion engines that high fuel consumption and emissions are priced at higher power consumption at higher loads in the higher speed range.
Fig. 1. Fuel consumption map of vehicle
The motor torque curve is shown in Fig. 3. The torque plateau characteristic of diesel engines can be clearly seen, which gives the vehicle a particularly popular feeling of acceleration in passenger cars. Although the engine is listed here as input, it is important
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Fig. 2. CO2 emission map of vehicle
to note that the plateau part is provided by the turbocharger, but it is not necessary to simulate its detailed operation, it is sufficient to enter the basic parameters of the engine to take into account the effect of these additional elements.
Fig. 3. Speed-torque curve of vehicle
The suspension is rigidly simulated, in which case the footprint forces will be more inaccurate, but since the vehicle does not operate in the wheel-adhesion limit states, it does not cause a significant difference in consumption. The generated AVL bus model is shown in Fig. 4. The model includes the vehicle body, a Mercedes-Benz OM 936 - EURO 6 internal combustion diesel engine, a ZF EcoLife AP 6 + R automatic transmission, transmission, wheels and their control and regulation.
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Fig. 4. Structure of the AVL bus model
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The simulation was tested on the driving cycle of the bus, which was prepared by the staff of the research team responsible for measuring and creating the driving cycle using the method they have already published [4, 5]. Figure 5 shows the change in speed and cumulative fuel consumption over time.
Time s Velocity
Cumulated fuel consumpion
Fig. 5. Simulated bus speed and cumulative fuel consumption as a function of time
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3 Summary At the end of the 7.674 km driving cycle, the cumulative fuel consumption is 2.05 L, which corresponds to a consumption of 26.7 L per 100 km. CO2 emissions were 853.34 g/km. It is much clearer when we look at CO2 emissions per so-called passengerkilometer. As the total number of passengers that can be transported without a driver is 85, the former value is 10.03 g/pkm. If we consider the bus to be halfway, as in many cases only a few passengers use the flight, and in many cases it is full, we get an average CO2 emission of around 24 g/pkm [6]. The output values of the simulation do not contain any outliers that would indicate the collapse of the simulation model or a singularity error, so the future goal is to refine the input data with detailed measurements and validate it. Another goal is to explore the development possibilities of the vehicle and to make a proposal for the development and possible structural transformation of the powertrain elements. Acknowledgment. The research was supported by the Thematic Excellence Programme (TKP2020-NKA-04) of the Ministry for Innovation and Technology in Hungary, within the framework of the (Automotive Industry) thematic program of the University of Debrecen. The authors are thankful to AVL List GmbH for providing licenses to. AVL CRUISE under AST-University Partnership Program with University of Debrecen, Debrecen, Hungary.
References 1. Varga, B.O., Iclodean, C.: Advanced Research Methods of Hybrid Electric Vehicles’ Performances 56(1), 111–116 (2015) 2. Yang, Y., Zhao, H., Jiang, H.: Drive train design and modeling of a parallel diesel hybrid electric bus based on AVL/cruise. World Electr. Veh. J. 4(1), 75–81 (2010). https://doi.org/10. 3390/wevj4010075 3. Zamora, D.R., Martínez, J.M.L., Carrasco, C.J.L., Vaca, J.J.D.: Development of an in-series hybrid urban bus model and its correlation with on-board testing results. World Electr. Veh. J. 6(2), 405–415 (2013) 4. Vámosi, A., Czégé, L., Kocsis, I.: Development of bus driving cycle for debrecen on the basis of real-traffic data. 2019, 1–7 (2022) 5. Vámosi, A., Czégé, L., Kocsis, I.: Comparison of bus driving cycles elaborated for vehicle dynamic simulation. Int. Rev. Appl. Sci. Eng. 12(1), 86–91 (2021). https://doi.org/10.1556/ 1848.2020.00153 6. Maheshwari, M., Jana, A., Bandyopadhyay, S.: Optimizing the modal split to reduce carbon dioxide emission for resource-constrained societies. Transp. Res. Procedia 48(2019), 2063– 2073 (2020). https://doi.org/10.1016/j.trpro.2020.08.266
An Approach for Hierarchical Clustering of Road Vehicle Vibration Spectrums László Róbert Hári(B)
and Péter Földesi
Széchenyi István University, Egyetem tér 1, Gy˝or 9026, Hungary {hari.laszlo,foldesi}@sze.hu
Abstract. Research on the non-stationary nature of road vehicle vibrations (RVV) led to advances in simulating such processes. Contemporary methods introduced for the analysis of RVV primarily aimed at partitioning the signal in the time- or time − frequency domain, providing differing segments of a signal. However, a degree of dissimilarity, or conversely similarity, is still challenging to find. Hereunder we argue that in some cases, merely a statement of dissimilarity between neighbouring segments within a signal might be well-enough, though from a broader perspective, the assessment of the similarity of discrete Fourier transforms (DFT) may be the next practical step forward. For this reason, the current paper presents the hierarchical clustering of elements of the short-time Fourier transform (STFT) plane from an RVV measurement; secondly, it introduces a clustering validation metric to arrive at an optimum distance metric and a threshold to use in binary hierarchical clusters. Keywords: Clustering spectrums · Distance probability · Hierarchical clustering · Road vehicle vibration
1 Introduction Every product transported should withstand vibrations during transportation. Road vehicle vibrations (RVV) are commonly studied in time-, frequency- and probability domains. Recent theoretical developments have revealed that, most often, time-frequency domain representations can contribute to a more detailed understanding of the phenomena. With emerging interest in the study of non-stationarity in RVV, different signal segmentation approaches have been introduced, previously reviewed by the first author in [1]. That is, changepoints can be present in an RVV acceleration signal recorded, e.g., on the truck bed. This way, a recording can be segmented by different changepoint-detection algorithms to find homogeneous sections in-between the changepoints and sufficiently different segments from adjacent ones. Although similar considerations (reviewed in [1]) provide significant improvements in understanding the non-stationarity in RVV, they can only say little about the spectral characteristics, especially spectral similarities. It might be enough to find change points in RVV using any signal representation domain; still, no investigations are provided on how to relate segments of the signal to each other, be it represented in the time-domain or equivalently in the time − frequency domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 799–811, 2023. https://doi.org/10.1007/978-3-031-15211-5_66
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Let us suppose that an STFT is appropriately segmented, each segment consisting of one or more DFT profiles. Let us further assume that the segments are characterised by one representative profile, e.g., the average DFT profile of that segment. We wish to investigate the similarities of representative DFT spectra of segments. Therefore, the current article provides a methodology for measuring DFT vector similarity from the STFT of RVV. Instead of figuring out completely artificial examples of representative DFT profiles, we provide a recording in Fig. 1.a) serving as a population; only its elements are ordered in time, apparently. The so-called representative samples are drawn randomly from the population. The field of clustering is maturing with a wealth of procedures and algorithms. Several approaches and their different taxonomies are available. A comprehensive review of methods would escape the possibilities of the current section. However, a common taxonomy is discussed in [2], defining hierarchical and partitional clustering families at the first grouping level. Hierarchical clustering techniques provide a hierarchical sequence of divisions according to a similarity-based criterion for merging or dividing clusters. Partitional clustering approaches identify the partition that optimises a clustering criterion. While hierarchical techniques create a nested series of partitions, partitional clustering simply creates one. Other introductions may distinguish first the families of hard- and soft clustering. In hard clustering, an object can belong only to one cluster; in soft (or fuzzy) clustering, objects can belong to more than one cluster. In the latter case, a fuzzy clustering may be transformed into a hard clustering by allocating each pattern to the cluster with the highest membership. Standard techniques for hard clustering are hierarchical clustering and k-means; and Gaussian mixture models or fuzzy C-means for soft clustering. The primary benefit of hierarchical clustering methods is that the number of clusters does not have to be specified a priori. Dendrograms provide appealing representations assisting the clustering; however, they also introduce the typical dilemma of defining a threshold of distances by which cluster boundaries can be determined. The first and most subjective solution to this question is a visual examination, followed by a subjective decision. While this might not provide a proving ground, the visual assessment is more or less unavoidable in clustering, which can be assisted by other qualitative figures, e.g., silhouette plots [3]. Thus, one might classify subjective decisions as manual evaluation. Researchers also introduced dozens of metrics to validate clustering results by boiling down the problem to single metrics. The external evaluation covers the comparison of the given results to ground truth, if available. Some of the often-favoured internal evaluation metrics follow here [4]. The Variance ratio criterion [5] balances the within-cluster variation against the between-cluster variation in Euclidean spaces. The Dunn family of indices [6] formalises the idea of a ratio between the between-cluster separation and within-cluster compactness for general dissimilarity data. The Average silhouette width criterion [7] is a trade-off concerning the between-cluster separation and the withincluster homogeneity. The CDbw-index [8] assesses the separation and compactness of clusters. The Clustering validation index based on nearest neighbours [9] proposed an alternative idea to measure separation, which does not excavate dissimilarity values; instead, it is based on how many of the k nearest neighbours of each observation are in
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the same cluster. The reader is referred to [10] for a more extensive list of validation metrics. The authors address a projected question in this paper. Once a generally accepted segmentation technique is adopted, we speculate that the similarity of DFT or Power spectral density (PSD) profiles from segments need to be analysed. While we cannot be sure about the future, we are still confident that clustering methods offer prominent techniques in this setting. The process introduced here is based on the idea of defining a threshold, which maximises the number of clustered spectrums beneath an overall low spread. Although one of the above validity indices could have been adopted; instead, the present technique gives a straightforward and more tactile measure.
2 Methods The technique is shown first on a random sample from the STFT of an RVV measurement to illustrate the clustering process, yielding an optimum distance metric beneath an optimum parameter setting. Then, the application is repeated many times for a probabilistic appraisal of arriving at such optima. Section 2.1 introduces the establishment of DFT vectors to be clustered. A broad introduction to dendrograms follows it, and various distance metrics are discussed (Sect. 2.2). Next, the distributions of distances by various metrics are illustrated (Sect. 2.3); afterwards, the optimality criterion follows (Sect. 2.4). 2.1 Source Data A DC MEMS accelerometer measured the vertical acceleration of a passenger car with a sampling frequency of 1024 Hz, placed in the coin toss behind the handbrake of a Suzuki Swift Sedan 1.3 GLX (2002). The journey took place in everyday traffic conditions. The obtained series is high-pass filtered with a 1 Hz cut-frequency; however, the spectrogram is accounted only up to 100 Hz, as let through an ideal low-pass filter, in order to reduce computational needs. The resulting STFT matrix without overlapping of one-sec-long timeframes is plotted in Fig. 1.a). Twenty-five timepoints, tk , are randomly selected from the STFT matrix, yielding the first sample. 2.2 On the Use of Dendrograms A brief introduction to dendrograms follows, whereby the frequent notations are illustrated in Fig. 2. Objects may have one or more dimensions, e.g., single points on an axis, coordinate pairs in a plane, coordinates in a three-dimensional space, etc. Here, each x itself is a DFT vector from the STFT plane. Let us imagine a tree upside down as in Fig. 2. The tree (a dendrogram) is built up from branches, each one ending in one leaf or object. Currently, the k-th DFT vector xk = ak,f is the k-th leaf comprising f = 1, 2, . . . , F frequency bins.
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Fig. 1. Sample DFT vectors are randomly selected from the same population in a) denoted by “×” at f1 = 1; f101 = 100 Hz. Pane b) shows the selected vectors in the frequency domain.
Distance [-]
A dendrogram
DS,T cD ds,t xk=ak,f e.g., a1,f =
a 1,1 a 1,2 . .. a1, F
Leaf [-] Ck=1; n1 = 2 C2 NC = 2; NΣ = 4
Lk=1
Fig. 2. A dendrogram showing five leaves combined into two multi-element clusters, Ck , and one lone branch, Lk , at the utilised cutting height, cD . The number of leaves in the k-th multi-element cluster is nk , the number of multi-element clusters is NC , while the number of clustered objects is N .
Here, defining a cutting height at cD (viz. a threshold for distance,) some branches fall, yielding clusters with multiple leaves as collectives, Ck and a lone branch holding only one leaf, Lk . (Singleton is a common term for a stand-alone leaf, but the notion s is spared for source later.) The number of leaves in the k-th multi-element cluster is denoted by nk , and the number of multi-element clusters is NC , while the number of clustered objects is represented by N . As the first step, one calculates the similarity between the pair of objects given a distance measure [11, pp. 3925–3926], called here metrics. Common similarity metrics are Euclidean-, standardised Euclidean-, Chebyshev-, cosine-, correlation-, and Spearman distances, respectively Mm = {EUC, SEU, CHE, COS, COR, SPE} (1–6). For xs source- and xt target objects in general, each representing a row vector, the Euclidean distance is 2 ds,t = (xs − xt )(xs − xt )
(1)
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the square root of the scalar product of the difference vector (xs − xt ). The standardised form of the Euclidean distance is
2 ds,t = (xs − xt )V−1 (xs − xt ) ,
(2)
where V is the n × n diagonal matrix whose j-th diagonal element is Sj2 . The term S is the vector of scaling factors per dimension. The Chebyshev distance, (3) ds,t = max xsj − xtj j
expresses the maximum of the absolute of difference vector between source and target objects. The cosine distance,
xs xt ds,t = 1 − xs xs xt xt
(4)
is one minus the cosine of the included angle between points (treated as vectors). Correlation distance is defined as
ds,t = 1 −
(xs − xs )(xt − xt ) , (xs − xs )(xs − xs ) (xt − xt )(xt − xt )
(5)
which is one minus the sample correlation between the objects, where xs = 1 n j xsj and xt = 1 n j xtj . At last, the Spearman distance,
ds,t
(rs − rs )(rt − rt ) =1− , (rs − rs )(rs − rs ) (rt − rt )(rt − rt )
(6)
which is one minus the sample Spearman rank correlation between observations (treated as sequences of values), where rsj is the rank of xsj taken over x1j , x2j , ..., xmj ; rs and rt are the coordinate-wise rank vectors of xs and xt , i.e., rs = rs1 , rs2 , ..., rsn ; rs = (1/n) j rsj = (n + 1)/2, and rt = (1/n) j rtj = (n + 1)/2. The second phase is the clustering of objects into the binary, hierarchical cluster tree. Objects that are in proximity are linked. As objects are linked into binary (two-object) clusters, the newly generated clusters are grouped into larger clusters until a hierarchical tree is constructed. Different methods can be used for calculating linkages [11 pp. 3087–3088]. Single linkage, also known as nearest neighbour, uses the shortest distance between objects in the two clusters. The maximum distance between objects in the two clusters is used in complete linkage, in other terms, the farthest neighbour. Average linkage uses the average distance between all pairs of objects in any two clusters. The Euclidean distance between the centroids of the two clusters is used in centroid linkage, while the Euclidean distance between the weighted centroids of the two clusters is used in median linkage. Further options are, e.g., Ward’s linkage and Weighted average linkage.
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Fig. 3. Probability mass function (PMF) along with Kernel-PMF and Kernel cumulative distribution function (K-CDF) show that the E-CDF (referred to later p(D)) and the theoretical K-CDF are in close proximities for distances obtained by metrics: a) Euclidean-; b) standardised Euclidean-; c) Chebyshev-; d) cosine-; e) correlation- and f) Spearman distances. Note that the Kernel distributions do not utilise positive support.
The average linkage (7) method is used in the current paper, which can be expressed for S source- and T target clusters as: DS,T =
nS
nT 1
d (xS,i , xT ,j ) nS nT
(7)
i=1 j=1
where xS,i is the i-th object in the source cluster S, having a total number of included objects nS ; similarly, xT ,j is the j-th object in the target cluster T with nT included objects. Note how any metrics from the first step (1–6) can be used in the operator d (· · · , · · ·). At each stage of hierarchical clustering, the clusters S and T , for which DS,T is the minimum, are combined. In summary, given the objects xk , the pair-wise differences d are calculated by metrics (1–6), and the average linkage method (7) yielding D constitutes the clustering. In the following, an optimal threshold for D is sought. We highlight the following notations, P := cp the set of cp , since C is reserved by collectives. Thus, the M × P parameter space describes the M metric types and P denotes the various thresholds cp .
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Fig. 4. Evaluations in the M × P parameter space, where M indicates metrics and P denote the cp thresholds. Results from {m, p} settings: a) the number of clusters, NC ; b) the number of clustered objects, N (m, p); c) the centre of mass, G; and d) benefit-to-cost ratio, R.
2.3 The Distribution of D Given a cD threshold as in Fig. 2. the clustering is complete, i.e., a cluster identification number is ordered to each sample DFT profile. The empirical cumulative distribution functions of distances p(D) are obtained in each metric type, as in Fig. 3. The panes show that kernel cumulative distribution functions are close to the empirical distributions. The cut-off values for probabilities cp = {k · 0.10} for k = 1, 2, . . . , 9 are available in the ECDF values vertically, from which the cD cut-offs for distances can be obtained on the horizontal axis. Whenever p(D) = cp , inverse linear interpolation obtains cD . 2.4 A Benefit-to-Cost Ratio There are many proposed methodologies for defining a threshold on dendrograms; nevertheless, the lack of a golden rule does not ease the circumstances. However, it is hypothesised that the following are often aimed at (a) as many clustered elements as possible, beneath (b) as low spread within clusters as possible. To characterise the variability within collectives, Ck , among its elements, xi , over the frequency domain, f , the following measure is used nk
F
2 vk = xi,f − xi,f , (8)
i=1
f =1
xi,f being the average DFT profile in the i-th cluster. Thus, the square root of the squared deviations between cluster elements, x, and cluster mean, x, is summed into one number,
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vk , in the k-th collective cluster. However, it might be favourable to represent this error from many clusters in a single measure characterising an {m, p} setting. Therefore, the centre of mass for one setting is defined as Gm,p =
NC
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N
C (m,p)
v(m,p)k n(m,p)k
v(m,p)k ,
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k=1
vk . A possible representation of a benefit-to-cost ratio appin short Gk = vk nk roach is implemented, such as the more objects are clustered, the more benefits are provided as of condition (a), and the higher error term is provided by an {m, p} setting, the higher costs are to be paid in line with condition (b); together defined as (10) Rm,p = N m,p G m,p equivalently R = N/G, shown in Fig. 4.d), which offers the optimum at its maximum in the M × P parameter space of trial clusterings. The following section presents the clustering of the above given random sample and Sect. 3.1. Investigates the approach by repeating the procedure 100 times per different sample size.
3 Results Figure 4 presents the outcomes from clustering setups collocated by the distance metrics, m, and cut-off probabilities, cp , for 25 randomly selected vectors from Fig. 1. Beginning by Fig. 4.a), it shows that the number of clusters at low and high cp settings is likely to be minor, while middle regions of cp are likely to produce maxima in NC .
Fig. 5. Dendrogram by cosine metric, where alteration of lighter and darker grey colours shows ∗ optimal cut value. different clusters, black denoting lone objects below the cD
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∗, Fig. 6. Results of clustering in the frequency domain by cosine distance metric at optimum cD objects denoted by grey and their mean by black.
Fig. 7. Sample clustering yielded a few lone leaves (grey), which are overlayed by the mean of multi-element clusters (black).
This is expected since a low cD might cut all leaves, yielding only a few multielement clusters. Conversely, a high cD might also surpass the largest distance in a dendrogram, generating one group comprising every branch. Pane b) supports the above observations since an increase in cp , consequently in cD , yielded an increasing number of clustered objects in N (m, p) regardless of the type of metric. Pane c) depicts a nonlinear proportionality between the centre of mass, G, in clustering settings {m, p} as of cp ; however, it also shows a promising dale in the middle regions, especially at the correlation and cosine metrics leading to the suggestion in pane d). That is, benefiting from the number of clustered objects, N (m, p), but at the cost of within-cluster-variability, vk , the benefit-to-cost ratio, R expresses the optimum at its maxima, which in the above-given ∗ = 0.24. sample led to m∗ =’COS’ at cp∗ = 0.40, which meant cD Figure 5 shows the dendrogram of the first sample using m∗ and cp∗ yielding a seemingly sensitive clustering as a function of cD since distances are dense around the ∗ . Figure 6 shows each cluster with its objects and the mean of objects in threshold cD the frequency domain, producing a qualitatively nice well-alignment in Ck , for k = 3, 4, 6, 12, 14. The last cluster, C15 , is seemingly more spread within. Finally, Fig. 7 shows that the means from multi-element clusters tend to be separated from singletons.
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3.1 Replications The procedure was repeated 100 times utilising random samples of sizes Ss = {25, 50, 75, 100}. The M × P parameter space was evaluated in each random sample. Figure 8 left column depicts that an optimum metric based on BCM maxima is foremost likely the be the cosine-, second-most the correlation distance metrics. It also shows in ∗ is not of particular the right column that cp∗ is most often around 0.40–0.50, whereas cD interest. The left panes also report cases where multiple Mn optima were found in a trial. The benefit-to-cost ratio surfaces (R) tended to have typically one global maximum, which eases the choice by the current methodology.
Fig. 8. The optimum choice on the metric (left column) and corresponding swarmchart of optimum cut-off for probability (right column). Each row represents 100 trials, according to Ss sample sizes. Mn denotes the cases where various optima had been found in a trial.
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4 Discussion Hennig argues that “indices used for finding an optimal number of clusters by optimisation should not systematically prefer lower or higher numbers of clusters” [4]. However, considering the number of clustered objects, we believe the presented approach should avoid such a conflict. Though the long-maintained interest in clustering has led to “a possibly three-digit number of algorithms” [12], the current procedure can be beneficial in the spectral clustering of DFT vectors obtained in RVV. Finally, we note that the above investigation could be extended to different linkage methods, as well. The idea of clustering can be imagined in a completely different use case. The clustering may be applied to the whole STFT itself in Fig. 1.a) given a choice of metric ∗ . Now, if the and either an omnibus threshold or a procedure to arrive at optimum cD series of leaves in a dendrogram (see upper horizontal axis in Fig. 5) is constrained to the original appearance in time, the dendrogram may not be easily readable. However, apart from aesthetics, the similarity of consecutive DFT vectors could be evaluated. It might be rightly supposed that some of the neighbour DFT vectors get into the same cluster, while borders of consecutive clusters are also present. The consecutive leaves being in the same cluster would yield a coherent segment different from neighbouring segments, which would yield a novel segmentation. We have argued in this initial study that the next advantageous step following the segmentation of the time − frequency domain can be the quantification of similarity among segments. The clustering of representative DFT vectors could be accompanied by the collection of exogenous data in order to relate such a segmentation (via clustering). For instance, road profile surveys, internal and external dashcam recordings could be used to identify the scenarios contributing to the clusters.
5 Conclusion The current paper presented a validation criterion for clustering road vehicle vibration spectrums in an automatable procedure. The choice of a threshold for distances, cD , in a dendrogram has been investigated by thresholds on the empirical cumulative distribution probabilities of distances, cp , allowing the unified treatment of distance metrics. In hierarchical clustering, a benefit-to-cost ratio surface, Rm,p , can be defined as a function of metric types, m and cut-offs, cp . The scheme was repeated in 100 replications as of the four random samples, consisting of {25, 50, 75, 100} elements. It showed that the cosine distance metric is the most likely optimum metric. However, the corresponding optimum thresholds for ECDF produced a wide range, primarily found in the middle regions. In fact, cp has a secondary interest in the current demonstration since a finer resolution of P can yield a more consequent estimate. Acknowledgements. The first author was supported by the ÚNKP-21-3-II-SZE-24 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.
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Nomenclature
C cD
Frequent variables Multi-element cluster (collective)
CDF
Frequent abbreviations Cumulative distribution function
Cut value for distances
CHE
Chebyshev distance metric
cp
Cut value for probabilities
COR
Correlation distance metric
d D G L M nk
Distance according to a metric COS Distance according to a method DFT Centre of mass at {m, p} parameter pair EUC
Cosine distance metric Discrete Fourier transform
Singletons (lone objects) Set of metric types Number of objects in the k -th cluster
ECDF PMF RVV
Empirical cumulative distribution function
Probability mass function Road vehicle vibration
NC
Number of multi-element clusters
SEU
Standardised Euclidean distance
NΣ P
Number of clustered objects Set of probabilities for c p
SPE
Spearman distance metric
STFT
Short-time Fourier transform
R
Benefit-to-cost ratio on parameter space Variability in a cluster
v
Euclidean distance metric
References 1. Hári, L.R.: Event detection algorithms in packaging vibration testing: an analytic review. In: Business Logistics in Modern Management, Osijek, Croatia, pp. 453–472, October 2021 2. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999). https://doi.org/10.1145/331499.331504 3. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). https://doi.org/10.1016/0377-042 7(87)90125-7 4. Hennig, C., Meila, M., Murtagh, F., Rocci, R. (eds.) Handbook of Cluster Analysis, 0 ed. Chapman and Hall/CRC (2015). https://doi.org/10.1201/b19706 5. Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Comm. Stats. Theory Methods 3(1), 1–27 (1974). https://doi.org/10.1080/03610927408827101 6. Dunn, J.C.: Well-separated clusters and optimal fuzzy partitions. J. Cybern. 4(1), 95–104 (1974). https://doi.org/10.1080/01969727408546059 7. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, Hoboken (2005) 8. Halkidi, M., Vazirgiannis, M.: A density-based cluster validity approach using multirepresentatives. Pattern Recogn. Lett. 29(6), 773–786 (2008). https://doi.org/10.1016/j.pat rec.2007.12.011 9. Liu, Y., Li, Z., Xiong, H., Gao, X., Junjie, W., Sen, W.: Understanding and Enhancement of Internal Clustering Validation Measures. IEEE Trans. Cybern. 43(3), 982–994 (2013). https:// doi.org/10.1109/TSMCB.2012.2220543 10. Desgraupes, B.: Clustering Indices: Package clusterCrit for R. Lab Modal’X. https://cran.rproject.org/web/packages/clusterCrit/vignettes/clusterCrit.pdf. Accessed 10 October 2021
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11. Statistics and Machine Learning Toolbox User’s Guide: Natick. The MathWorks Inc., MA (2020) 12. Peters, G., Crespo, F., Lingras, P., Weber, R.: Soft clustering – Fuzzy and rough approaches and their extensions and derivatives. Int. J. Approximate Reasoning 54(2), 307–322 (2013). https://doi.org/10.1016/j.ijar.2012.10.003
Evaluation of a CUSUM-Type Changepoint Detector Applied in the Time-Frequency Domain of Synthetic Road Vehicle Vibrations László Róbert Hári(B)
and Péter Földesi
Széchenyi István University, Egyetem tér 1, Gy˝or 9026, Hungary {hari.laszlo,foldesi}@sze.hu
Abstract. Non-stationary random vibrations gained increasing interest in vibration testing. Often, a changepoint detection procedure handles the decomposition of Road vehicle vibrations (RVV) when analysing the recorded series. Unfortunately, only subjective justifications support the proposed methods, and mainly the validation of the non-stationarity of simulated signals is concerned. Thus, if a detector is inherent to the procedure, it is also recommended to calibrate it. The current paper concerns the Receiver operating characteristics (ROC) of a CUSUMtype algorithm and supplies contextual support by Segment length distributions (SLD). Keywords: Segmentation · Spectrogram · Receiver operating characteristic · Road vehicle vibration
1 Introduction Road vehicle vibrations (RVV) undergo state changes; thus, a non-stationary character is present. An RVV might be dissected into segments for analysing purposes, which segments can be separately simulated and then concatenated to a non-stationary signal in total. While simulations inherit such segmentation prior to the synthesis, only a few presented detectors are calibrated. This paper uses the Receiver operating characteristic (ROC) and the Segment length distribution (SLD) to assert the applicability of a CUSUM-type changepoint detector (CPD). A short introduction follows the previously developed detectors for RVV segmentation. The most straightforward approaches exploit the sensitivity of Moving statistics (MS) in the presence of non-stationary disturbances, which can be directly deployed in the time-domain representation of a signal. The MS detectors are typically moving mean, -RMS, -crest factor, or -kurtosis [1, 2]. Bayesian algorithms offer a probability-based framework, for instance, presenting on-road profile data [3]. The underlying assumption of stationarity of a single Power spectral density (PSD) profile has been addressed in a multitude of cases, leading to the following logical step of the application of multiple PSD profiles by the Split spectra method [4]. Nevertheless, the cut-off values separating events into low- and high RMS groups need to be justified. Railcar vibration environment had been studied in [5], proposing among others, the filtering for demarcating © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 812–823, 2023. https://doi.org/10.1007/978-3-031-15211-5_67
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the structural high-frequency bursts from an underlying rigid-body motion. Also, for railcar vibrations, the previously mentioned rigid-body and structural vibrations might be separated by the number of Intrinsic mode functions from Empirical mode decomposition [6]. The International roughness index series of roads had been subjected to wavelet transformation, [7] presenting the wavelets detection capabilities. RVV series had been iteratively decomposed in the translation-scale domain of complex wavelets in the Wavelet-based Gaussian decomposition [8]. Later, Cumulative sum (CUSUM) schemes received increasing attention. For instance, the instantaneous magnitude by Hilbert-transform had been subjected to such an algorithm in [9] to find change points in RVV. The particular usefulness of machine learning classifiers excels in detection or classification tasks due to being able to simultaneously consider several predictors [9, 10]. The growing toolbar of detectors is ideally calibrated, which has well-established methodologies in the discipline of machine learning. Such prior verifications increase the trust in proposed algorithms and mind the necessity of heuristics.
2 Methods 2.1 Test Samples Preliminary investigations on the simulation of RVV showed promising results, the method itself being in the publishing process as of writing the current paper. It suffices to say for current purposes that each segment is defined a priori; hence all true changepoints are on hand. The method relies on data-driven methods to fit the distribution of Fourier coefficients at each frequency bin through time indices of segments to mimic realworld spectrograms of RVV. For this purpose, four samples with different densities of changepoints—hence different segment lengths—are prepared, being on view in the Appendix. 2.2 The Detector The current changepoint detector operates on one-dimensional vectors; therefore, the two-dimensional short-time Fourier transform (STFT) matrix should be described in a different way. For this reason, the spectral centroid, -spread, -skewness, and -kurtosis [11] pp. 371, 281, 299, 317, respectively μik , for i = 1, 2, 3, 4 each tk are calculated, being on view in Fig. 1. The four spectral descriptors of a sample are introduced to the algorithm in Fig. 2. The resulting four series of changepoints are concatenated and handled as the final segmentation in the time-frequency domain. In a CUSUM scheme, the cumulated sum of differences, μik − μk i at fixed i, such as Sik =
K
(μik − μk i )
(1)
k=1
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Fig. 1. The short-time Fourier transform (STFT) plane in sample A with a prevalence (PR) of 4.5%. The “ +” symbols at f = 0; 100 Hz denote condition positives (CP); the spectral descriptors overlay the surface. Note that μ3 and μ4 are near to f = 0 Hz.
The following step excavates resampling techniques to assess the significance of Cpc , such that an actual subseries μik , k ∈ [Lk ; Rk ] is rearranged as of k, an N number of times. Every time a permuted sample produces a more extreme test statistic than the observed one from the original sample Ext(Sik ), the approximated p-value increases by 1/N starting from p = 0. Given that the approximated p-value stays below the threshold, α, the changepoint is considered positive, i.e., Cpc → Cp not necessarily a valid condition positive CP. Finally, each subsection is subjected to the same steps until any of the exit conditions is met. 2.3 Operational Surface It can be seen that a candidate changepoint preserves its initially devoted significance during the resampling if the number of more extreme statistics—than the observed one— remains under SR = α/(1/N ). Formally, we introduce the term Significance reserve, SR, as a function of α and N , illustrated in Fig. 3. Discrete parameter settings as Points of interest (POI) are defined at the cross-sections of α = 0.01, 0.05, 0.25, 0.50 significance limits and N = 200, 1000, 4000 permutations. Indeed, our choices of α greater than 0.10 might sound atypical, but it is aimed to explore a broader range on the operational surface. Furthermore, all N are above a minimum number of permutations, such that SR > 1. In order to avoid exponential burden by permutations, the upper limit N = 4000 is considered here sufficient. 2.4 Receiver Operating Characteristic A brief introduction to statistical terms follows. It is recommended to note the subtle difference between changepoint Cp and condition positive CP. Also, some variables are expressed from abbreviations, and thus, it shall be noted that, for example SR is not a product of quantities S · R; but is merely a two-letter abbreviation and variable.
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Fig. 2. A CUSUM-type changepoint detection framework. Let Lk and Rk denote respectively the left and right boundaries of a sample of length T or its subsample. Also, let dt denote the minimum segment length here 1. The CUSUM points to a candidate changepoint Cpc , which becomes Cp if considered significant. The procedure runs until no more changepoint can be found or an exit condition is met.
Let prevalence PR, denote the percentual presence of changepoints in a series. A changepoint (Cp) being true is a condition positive (CP). Hence the real absence of a changepoint is condition negative (CN ). It can be expressed that PR = CP/(CP + CN ), since the denominator gives the population size. Unfortunately, the real presence of a condition is most often unknown. By testing a member of the population, i.e., deciding whether the individual does have the condition or does not have the condition, the following four cases can occur. If it is
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Fig. 3. The Significance reserve surface as a function of the “number of permutations per candidate changepoint,” N ,and the significance limit α. The points of interest can be found in the parameter pairs. Note that each axis is depicted on semi-log scales.
decided about the member to have the condition, whereas it actually has the condition, the solution is considered true positive (TP). Similarly, if a negative condition is deduced from the test of a member actually not having the condition, it is considered a true negative (TN ). However, given a decision of negativity on an actually positive member, a false negative (FN ) decision is incurred; and deciding by positivity on an actually negative member leads to a false positive (FP) conclusion. The current paragraph is also summarised in Table 1 for the binary response case. Table 1. The confusion matrix in the binary response case Decision Condition
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Standard statistics of the decisions are the sensitivity, in other terms, the true positive rate TPR = TP/CP; and the specificity of a test, viz. true negative rate TNR = TN /CN . From this occurs the false positive rate FPR = 1 − TNR, or directly FPR = FP/CN . A convenient appraisal method illustrates TPR as a function of FPR, which function is called the Receiver operating characteristic, ROC curve. Due to computational efficacy, the four samples are only investigated in distinct parameter settings at twelve POIs. Hence, only a few points of the theoretical ROC curve are available in Fig. 6, called snapshots. It might be difficult for the first time to refer to curves from snapshots; thus, an example ROC curve is introduced in Fig. 4.
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Fig. 4. An illustrative example for calculating Receiver operating characteristic (ROC). Pane a) shows 250 records of condition negatives, CN , and 100 statistic-value of condition positives CP, and the fitted binary logistic model. An arbitrary threshold is chosen at the rhombus, ♦, indicating that Negatives by Decision DN , and Positives by Decision DP, are demarcated; the consequence of this threshold on the true positive rate TPR, and the false positive rate FPR, can be seen in pane b). The ROC curve can be obtained for various thresholds (solid), which is evidently better than guessing in the example (dashed).
2.5 Segment Length Distribution The development of the current paper has explored the distances between changepoints. The Segment length distribution, SLD, by condition positives and the SLD by decision positives offers a practical and easily referrable depiction of the algorithm’s capability. The distributions are introduced in Fig. 7 for further analysis.
3 Results The schematic in Fig. 5 also illustrates the process flow behind the analysis. To summarise prior preparations, twelve POI were chosen from the SRS in Fig. 3 to investigate the effect of distinct parameter settings on the ROC curves of the CPD. In addition, four samples of different prevalence had been prepared, being on view in the Appendix. The ROC snapshots are to be found in Fig. 6 and SLD results are summarised in Fig. 7. The collection of ROC snapshots allows the following conclusions to be drawn: 1. The low prevalence sample contributed to consistently better TPR compared to high prevalence, cf. symbols in any pane. The algorithm may be more beneficial in the presence of a few changepoints. 2. The prevalence has a more dominant effect on ROC statistics at low significance limits compared to the high significance limits; observe the contraction of scattering by symbols from row to row of panes. 3. At a given significance limit, the ROC statistics only show minor differences at different permutations, cf, any three panes in a row. 4. Even though the significance limit is set to unreasonably high α = 0.50 in the 4th row, the FPR statistics safely stayed below 50%; see any pane.
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Fig. 5. Illustration of workflow, describing the calculation for one sample Samps = A from Samps : {A, B, C, D} at one POI {Nn , αm } from n = 1, 2, 3 and m = 1, 2, 3, 4. Pane a) shows that a sample STFT profile is chosen first, whereas CP true changepoints are known in advance also the distance between them, namely, the segment length, sl(CP) are accounted. Pane b) shows that one parameter-pair of Nn for replications and αm significance level is chosen, being forwarded to the CPD. Pane c) shows that changepoint detection is accomplished. Note, the changepoints Cp are indicated at different timepoints. Pane d) shows that condition positives (CP) and found changepoints (Cp) are compared, and one “snapshot” at symbol “◯” is obtained in the ROC curve. Pane e) shows that the true segment length from a) and the obtained segment length from c) are collected and visualised in histograms, SLD. That is, Fig. 6 will introduce s = 4 snapshots per pane in total of n · m panes; and Fig. 7 will introduce the true distribution and n = 3 obtained distributions per pane, in total of s · m panes.
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Fig. 6. Receiver operating statistic snapshots according to points of interest per pane, in each pane, four samples of different prevalence and the mean coordinates, as of the legend.
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Fig. 7. Segment length distributions (SLD) at different samples per column (A, B, C, D), at various α thresholds per row, in each pane three different permutations as of the legend, compared to the true distributions. Each pane is repeated in semi-log vertical scales in the insets.
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SLD offers a tactile illustration, as in Fig. 7. Panes of grey background are considered sub-optimal solutions by visual judgement, which allows the further conclusions: 5. Increasing the permutation limit N , yielded minor differences in the obtained distributions. See the overlay of lines in any pane. 6. Below the white-background-diagonal, an excessive number of short segments can be seen in the obtained distributions compared to the true distribution. That is, corresponding panes illustrate over-segmented results. 7. Above the white-background-diagonal, moderate to severe discrepancies in the short segment bins are found. That is, corresponding panes show the under-segmentation phenomena typically. 8. There must exist a significance limit for every prevalence, which minimises the discrepancy of the real and the obtained segment length distribution. See the white background diagonal panes.
4 Discussion The FPR of 50% is hard to achieve, said the exclamation point 4. It might seem strange at first sight, but a vital conclusion follows, such that the algorithm is likely to avoid enormously FPR. From FPR = FP/CN occurs that either FP does not increase limitless or CN show an increase. Since the number CN is a constant per sample, the only possible variable to investigate remains the FP. Hence, false positives are often avoided, meaning not every point μik can be positive due to the exit conditions. This satisfactory result supports the current procedure. Furthermore, the SLD plots occurred to be a particularly useful tool in the analysis, which facilitates a more direct indicator of performance compared to ROC curves or other meta-statistics. Therefore, optimal operating points in a changepoint detector can be directly asserted by the segment length distributions, offering a more direct appraisal platform compared to ROC. As shown in Fig. 6, the ROC statistics are likely to scatter at a low significance limit as of prevalence, while the contraction of estimates is observable at larger thresholds. While we cannot estimate PR in advance, it has been shown that optimal thresholds can increase our trust in detection in a wide range of PR. Thus, if it can be rightly supposed that only a few changepoints will be present, for instance, in the RMS series of RVV recorded on a highway, a low threshold might be sufficient. However, e.g., the prevalence can be higher on a poorly maintained and old road, necessitating larger thresholds.
5 Conclusion The current paper has introduced a CUSUM-type changepoint detection (CPD) applied to artificial samples of different prevalence. The CPD shows robust TPR and FPR estimates at α = 0.50 a significance limit, more consequent than at a low threshold 0.05. The CPD is robust at low permutations; thus, further investigations can lead to faster results. The segment length distributions (SLD) showed to be easing the appraisal of detectors under
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the study of RVV. It is shown that optimum threshold levels exist for different prevalence classes, which minimises the deviation of the obtained SLD from the true SLD. Acknowledgements. The first author was supported by the ÚNKP-21-3-II-SZE-24 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.
Appendix
Fig. 8. Four simulated road vehicle vibration signals are depicted by short-time Fourier transform (STFT) with a different number of condition positive (CP) changepoints, hence, with various prevalence (PR).
Nomenclature Cp CP CPD CN CUSUM DFT FN FP
Change point Condition positive Changepoint detection Condition negative Cumulative sum scheme Discrete Fourier transform False negative False positive
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False positive rate Prevalence Receiver operating characteristic Road vehicle vibration Segment length distribution Significance reserve (surface) Short-time Fourier transform True negative True negative rate True positive True positive rate
References 1. Bruscella, B., Rouillard, V., Sek, M.: Analysis of road surface profiles. J. Transp. Eng. 125(1), 55–59 (1999). https://doi.org/10.1061/(ASCE)0733-947X(1999)125:1(55) 2. Bruscella, B.: Analysis and Simulation of the Spectral and Statistical Properties of Road Roughness for Package Performance Testing. Master of Engineering in Mechanical Engineering, Victoria University of Technology, Victoria (1997) 3. Thomas, F.: Automated road segmentation using a bayesian algorithm. J. Transp. Eng. 131(8), 591–598 (2005). https://doi.org/10.1061/(ASCE)0733-947X(2005)131:8(591) 4. Kipp, W.I.: Random Vibration Testing of Packaged-Products: Considerations for Methodology Improvement, pp. 1–12. Thailand, Jun, Bangkok (2008) 5. Rouillard, V., Richmond, R.: A novel approach to analysing and simulating railcar shock and vibrations. Packag. Technol. Sci. 20(1), 17–26 (2007). https://doi.org/10.1002/pts.739 6. Rouillard, V., Sek, M.A.: The use of intrinsic mode functions to characterise shock and vibration in the distribution environment. Packag. Technol. Sci. 18(1), 39–51 (2005). https:// doi.org/10.1002/pts.677 7. Wei, L., Fwa, T.F., Zhe, Z.: Wavelet analysis and interpretation of road roughness. J. Transp. Eng. 131(2), 120–130 (2005). https://doi.org/10.1061/(ASCE)0733-947X(2005)131:2(120) 8. Griffiths, K.R., Hicks, B.J., Keogh, P.S., Shires, D.: Wavelet analysis to decompose a vibration simulation signal to improve pre-distribution testing of packaging. Mech. Syst. Signal Process. 76–77, 780–795 (2016). https://doi.org/10.1016/j.ymssp.2015.12.035 9. Lepine, J., Rouillard, V.: Evaluation of shock detection algorithm for road vehicle vibration analysis. Vibration 1(2), 220–238 (2018). https://doi.org/10.3390/vibration1020016 10. Lepine, J., Rouillard, V., Sek, M.: Evaluation of machine learning algorithms for detection of road induced shocks buried in vehicle vibration signals. Proc. Inst. Mech. Eng. Part D: J. Automob. Eng. 233(4), 935–947 (2019). https://doi.org/10.1177/0954407018756201 11. G. The MathWorks, Inc., Audio Toolbox Reference Guide, R2021a ed. Natick, MA: The MathWorks, Inc. (2021). https://www.mathworks.com/help/pdf_doc/audio/audio_ref. pdf. Accessed 17 April 2021
Investigation of the Vibrational Behavior of a Quarter-Car Model László Rónai(B) University of Miskolc, Miskolc-Egyetemváros, Miskolc 3515, Hungary [email protected]
Abstract. This paper deals with the formulation of the governing equations of a quarter car model with an energy-based approach using Extended Hamilton’s principle. The derivations are performed with generalized displacements and generalized momenta for a 2 DoF quarter-car model. The latter description of the problem is rarely discussed in the literature. The formulated ordinary differential equations are solved in Scilab software with Sundials/CVODE solver kind. The state-space representation of a 3 DoF system, which has active suspension, is extended with a Linear-Quadratic Regulator (LQR) theory in order to reduce the unwanted vibrations of the road. The main aim of the formulation of the governing equations with generalized momenta compared to the generalized displacements is to directly provide the forces of the system, which gives the ability to establish a control loop easier with the force measurement of the suspension of a vehicle. Since force measurement for the case of suspension of a vehicle is more feasible compared to displacement measurement. Keywords: Energy-based modelling · Generalized momenta · Simulation · Quarter car model
1 Introduction Nowadays, in the automotive industry, due to the development of informatics in the recent decades, there is an increasing focus on performing simulations. Modelling and simulation seem to be versatile tools in several fields, e.g., for the design phase of a vehicle. In addition to the field of modelling and simulation, the Institute of Machine Tools and Mechatronic at the University of Miskolc also deals with several other areas [1–4]. Numerous paper deals with the modelling and simulation of a quarter car [5–9]. The model is useful in vehicle suspension analyses [5]. Passive, semi-active, and active quarter car models are available to analyze the dynamic behaviour of a vehicle. In contrast to passive suspensions, active suspensions help to increase comfort by reducing unwanted vibrations in the passenger compartment due to the unevenness of the road [7]. With an active element, e.g., a hydraulic cylinder, a control loop can be set up in order to decrease the vibrations. A number of papers deal with control [9, 10]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 824–834, 2023. https://doi.org/10.1007/978-3-031-15211-5_68
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The derivation of the mathematical model with free-body diagrams in the case of simple systems can be useful in producing the equations of force equilibrium for the masses. Furthermore, the creation of a model with energy-based approach [11] with extended Hamilton’s principle using generalized displacement coordinates is also well-known in the literature. However, the application of energy-based method utilizing generalized momenta instead of generalized displacement coordinates (dual description) is less commonly applied in the literature. A dual-mixed variational formulation is applied to make dynamic analyses of thin, linearly elastic shells of revolution in [12]. Furthermore, the dual-Hamilton’s principle is derived in [13], which is the base of the dual dynamical description. This paper deals with the modelling and simulation of a quarter car problem. Energybased method with extended Hamilton’s principle is used to produce the mathematical model of the system, i.e., the differential equations of the quarter car. Different derivations of the governing equations are detailed in this article with the use of generalized momentum and displacement coordinates for the quarter car problem. The formulation of the equations with generalized displacements is very common in modelling but using generalized momenta is less well known. Therefore, the main aim of this paper is to show the formulation also with the second method. The paper is organized as follows: Sect. 2 describes the production of the mathematical model of a quarter car with the set up of Lagrangian and co-Lagrangian in order to construct the governing equations. In Sect. 3 the model parameters are discussed. A special-purpose program is developed in Scilab software, within this, the XCOS environment is used to build the state-space representation of the model. The derived systems of differential equations are solved numerically with the Sundials/CVODE solver kind. Section 4 presents an example of active suspension. The governing equations are formulated with the use of generalized momenta. A Linear Quadratic Regulator (LQR) is applied in order to make a control loop. The implementation of force measurement is easier than measuring the displacement difference of a suspension unit. Therefore, the description of the quarter car model with generalized momenta can be more efficient since it provides the force values directly. At the end of the paper, the concluding remarks will be detailed.
2 Model of the Quarter Car The energy-based method with extended Hamilton’s principle is well applicable to produce the differential equations of the system, i.e., the mathematical model. A mechanical, electrical, or electromechanical system usually contains energy conservative and nonconservative elements. A nonconservative element means that it can only generate or dissipate the energy, e.g., damper, or resistor in an electromechanical system. The stored energy of conservative elements can also be specified with the so-called complementary energy, depending on whether the variable flow or effort is chosen to be unknown [11]. The creation of Lagrangian function is essential, which consists of energies and co-energies. The formulation of virtual work of the nonconservative elements is also needed to write the Lagrange equation of the second kind. The two degrees of freedom model of a quarter car is shown in Fig. 1. The unsprung and sprung masses are denoted with mu and ms , respectively. The further symbols of
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Fig. 1 are the following: y1 is the displacement of the wheel, y2 is the displacement of the mass of the quarter car, ru , rs are the damping ratios of the suspension and the wheel-tire assembly, respectively. The stiffness ku belongs to the wheel-tire part, ks is the stiffness of the suspension system. The movement of the wheel on a bumpy road results in a displacement excitation ut .
ms rs
ks mu
ru
y2
ku
y1
ut
Fig. 1. The model of the quarter car with passive suspension
2.1 Displacement Formulation of the Problem The Lagrangian function with generalized coordinates is the following: L(y, y˙ ) = T ∗ − Vp =
1 1 1 1 mu y˙ 12 + ms y˙ 22 − ku (y1 − ut )2 − ks (y2 − y1 )2 . 2 2 2 2
The virtual work of the nonconservative elements can be written as: δW nc = −rs (˙y2 − y˙ 1 ) δy2 − δy1 − ru (˙y1 − u˙ t )δy1 ,
(1)
(2)
where δ means the variation of the generalized displacement coordinates. The derivation of the governing equations can be found in more detail [8]. The two second-order ordinary differential equations (ODEs) of motion are: mu y¨ 1 + (ru + rs )˙y1 + (ku + ks )y1 − rs y˙ 2 − ks y2 = ku ut + ru u˙ t ,
(3)
ms y¨ 2 + rs y˙ 2 + ks y2 = rs y˙ 1 + ks y1 .
(4)
The state-space representation is well applicable if the system has more than one degree of freedom: x˙ = Ax + Bu, y = Cx + Du,
(5)
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where A is the state matrix, x contains the state variables, B is the input matrix, u is the control vector, y is the output vector, C is the output matrix and D is the feedforward matrix. The matrix form of the system in Fig. 1 is the following: ⎤⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ 0 0 1 0 0 0 x1 x˙ 1 ⎢ 0 0 ⎥ ⎢ x˙ 2 ⎥ ⎢ 0 0 0 1 ⎥ x2 ⎥ ⎢ ⎥⎢ ⎥ ut ⎢ ⎥ ⎢ ⎥=⎢ , (6) +⎢ ⎥ k +k k r +r r ⎥ ⎣ x˙ 3 ⎦ ⎢ ⎣ − umu s msu − umu s msu ⎦⎣ x3 ⎦ ⎣ mkuu mruu ⎦ u˙ t ks x˙ 4 x4 − mkss mrss − mrss 0 0 ms where x1 = y1 , x2 = y2 , x3 = y˙ 1 , x4 = y˙ 2 . The output vector for the unknown displacements and velocities can also be constructed: ⎡ ⎤⎡ ⎤ ⎡ ⎤ 1000 00 x1
⎢ 0 1 0 0 ⎥⎢ x2 ⎥ ⎢ 0 0 ⎥ ut y1 ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ =⎣ . (7) + y2 0 0 1 0 ⎦⎣ x3 ⎦ ⎣ 0 0 ⎦ u˙ t x4 0001 00
2.2 Momentum Formulation The second derivation of the differential equations is performed with the use of generalized momenta. This method is rather rarely used in practice. According to the extended Hamilton’s principle, the co-Lagrangian function [11] with generalized momenta can be written as: L∗ (p, p˙ ) = Vp∗ − T =
2 p˙ ku p˙ 2 p2 p2 + ks − mu − ms , 2ku 2ks 2mu 2ms
(8)
where Vp∗ is the potential co-energy, T is the kinetic energy, p˙ ku , p˙ ks are the spring forces, pmu and pms are the momentums of the masses. The momentum of mass mu and ms can be determined by the consideration of Newton’s second law: p˙ mu = ku ut + ru u˙ t − p˙ ru − p˙ ku + p˙ rs + p˙ ks ,
(9)
p˙ ms = −˙prs − p˙ ks ,
(10)
where p˙ ku = ku y1 , p˙ ks = ks (y2 − y1 ), p˙ ru = ru y˙ 1 , and p˙ rs = rs (˙y2 − y˙ 1 ). Taking the integral of (9), (10) the momenta can be provided: (11) pmu = ku ut dt + ru ut − pru − pku + prs + pks , pms = −prs − pks .
(12)
Since the momenta pmu , pms are not independent from the other momenta therefore, the potential co-energy and kinetic energy can be formulated as:
2 2 2 ru ut + ku ut dt − pru − pku + prs + pks p˙ ku p˙ ks (−prs − pks )2 ∗ Vp = + ;T = − , 2ku 2ks 2mu 2ms (13)
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the co-Lagrangian thoroughly:
2 2 2 ru ut + ku ut dt − pru − pku + prs + pks p˙ ks p˙ ku (−prs − pks )2 ∗ L (p, p˙ ) = + − − . 2ku 2ks 2mu 2ms (14) The virtual work of nonconservative elements coming from the dampers: δW nc = −
p˙ ru p˙ rs δpru − δprs . ru rs
(15)
The mathematical model will contain four ODEs; in order to provide the minimum number of independent momenta, the number of ODEs can be reduced by applying the following consideration to the equality of velocities p˙ ru = kruu p¨ ku , p˙ rs = krss p¨ ks . The first equation is written for generalized momentum pku : ∗ ∂L d ∂L∗ − = 0. (16) dt ∂ p˙ ku ∂pku After performing the partial derivatives and then the substitution of pru = prs = krss p˙ ks the differential equation will be as follows:
ru rs r u u + k dt − p ˙ − p + p ˙ + p u t u t ku ku ks ks ku ks p¨ ku − = 0. ku mu The second differential equation for pks : ∗ ∂L d ∂L∗ − = 0. dt ∂ p˙ ks ∂pks
ru ˙ ku , ku p
(17)
(18)
Performing the mathematical operations:
ru ut + ku ut dt − kruu p˙ ku − pku + krss p˙ ks + pks (− krss p˙ ks − pks ) p¨ ks + − = 0. (19) ks mu ms Equation (17), (19) provide the compatibility equations, i.e., the velocity of change of the spring ku length is equal to the velocity of the mass mu in (17), while the rate of change of the length of the spring ks is equal to the difference in velocities between the masses mu and ms in (19). In order to use the state-space representation, introducing new variables are essential: pku = x1 , pks = x2 , p˙ ku = x3 , p˙ ks = x4 . After expressing the highest derivatives and substituting new variables, the state-space representation in matrix form can be given as: ⎡
⎤ ⎡ 0 0 x˙ 1 ⎢ ⎥ ⎢ 0 0 x ˙ ⎢ 2⎥ ⎢ ku ⎢ ⎥ = ⎢ − ku ⎣ x˙ 3 ⎦ ⎣ mu mu ks ks x˙ 4 mu − mu +
ks ms
1 0 − mruu ks ru ku mu
⎤ ⎡ 0 0 x1 ⎥⎢ ⎥ ⎢ 0 0 x ⎥⎢ 2 ⎥ ⎢ ku rs ⎥⎢ ⎥ + ⎢ ku ru ku2 ⎦ ⎣ ⎦ ⎣ x k m 3 s u mu mu x4 − mrsu + mrss − rmu kus − kmu kus 0 1
⎤⎡
⎤
⎥ ⎥ ut . ⎥ ⎦ ut dt
(20)
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Table 1. The data of simulation Name
Symbol
Value
Dimension
Stiffness of the wheel-tire
ku
220000
[N/m]
Stiffness of the suspension
ks
25000
[N/m]
Damping factor of the wheel-tire
ru
1800
[Ns/m]
Damping ratio of the suspension
rs
1500
[Ns/m]
Sprung mass
ms
320
[kg]
Unsprung mass
mu
42
[kg]
3 Numerical Analysis The parameters of a quarter car model are given in Table 1. The vehicle is moving with 12 km/h constant velocity through an assumed speed bump [14]. The speed bump has 60 mm maximum height, and it is 400 mm long. Therefore, the displacement excitation ut is given with a triangular pulse function as follows: ⎧ 0 0s ≤ t < 0.5s, ⎪ ⎪ ⎨ (t − 0.5) 0.5s ≤ t ≤ 0.56s, ut (t) = (21) ⎪ −(t − 0.62) 0.56s ≤ t ≤ 0.62s, ⎪ ⎩ 0 t > 0.62s. The ut is performed with the use of RAMP and SATURATION blocks in XCOS (see Fig. 2). The state-space representation is made in Scilab software (see Fig. 3a, 3b). The determination of displacements y1 , y2 is performed with the help of p˙ ku = ku y1 and p˙ ks = ks (y2 − y1 ) for the generalized momenta derivation. Sundials/CVODE –BDF (Backward-Differentiation-Formula) solver kind is selected for solving the ordinary differential equations. CVODE is a general-purpose solver for IVPs, which is based on two FORTRAN packages [15].
Fig. 2. Performing the displacement excitation
The displacements y1 , y1 and ut versus time are shown in Fig. 4. The calculated displacements of the mass mu , and ms in time are the same for the two derivations. The applicability of the generalized momenta is a more time-consuming procedure than the use of generalized displacement coordinates. However, it has an advantage, the derivation
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Fig. 3. a) State-space representation with generalized coordinates, b) with generalized momenta
Fig. 4. Displacements versus time
directly provides the force values. Therefore, the installation of load cells can be resulted in feedback to create a control loop.
4 Controlling the Vibration with the Use of Feedback The advantage of the generalized momenta description, feedback can be established easily with force sensors. A model of a 3 DoF active suspension can be seen in Fig. 5. y3
mp kp
rp
y2
ms rs
Fa
A mu
ru
ku
Fa
ks
y1
ut
Fig. 5. The model of an active suspension
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The parameters are equal with an example of passive suspension, the mp = 80 kg is the weight of the passenger, the damping ratio rp = 50 Ns/m, and kp = 15696 N/m is the calculated stiffness of the seat, if 50 mm deflection of the seat occurs. The active element is a hydraulic cylinder, which can generate Fa force to reduce the vibrations caused by bumpy roads. The governing equations with generalized momenta formulation can be written as:
ru ut + ku ut dt − kruu p˙ ku − pku + krss p˙ ks + pks − Fa dt p¨ ku = , (22) m ku
u
r ru ut + ku p¨ ks =− ks
ut dt −
− pku +
rs ˙ ks ks p
+ pks −
Fa dt
mu
p¨ kp =− kp
ru ˙ ku ku p
− krss p˙ ks
− pks + pkp + ms
Fa dt +
+
− krss p˙ ks − pks + pkp +
rp ˙ kp kp p
Fa dt +
ms
−
(−pkp −
˙ kp kp p p
,
(23) rp ˙ kp ) kp p
mp
.
(24)
where pkp is the momentum of the spring kp and Fa is the actuator force. The state-space representation can be written with new variables: pku = x1 , pks = x2 , pkp = x3 p˙ ku = x4 , p˙ ks = x5 and p˙ kp = x6 : ⎡ ⎤ ⎡ ⎤ ⎤ 0 0 0 1 0 0 x˙ 1 ⎢ 0 ⎥ x1 0 0 0 1 0 ⎥⎢ x ⎥ ⎢ x˙ ⎥ ⎢ ⎥⎢ 2 ⎥ ⎢ 2⎥ ⎢ 0 0 0 0 0 1 ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ x3 ⎥ ⎢ x˙ 3 ⎥ ⎢ ku k r k r u u s ⎥⎢ ⎥ ⎢ ⎥ =⎢ − m 0 − muu 0 m k m u u ⎥⎢ x4 ⎥ ⎢ x˙ 4 ⎥ ⎢ s u ⎥⎢ ⎥ ⎢ ⎥ ⎢ ks ks rp k k k k r r r s s s s u s s ⎥⎣ x5 ⎦ ⎣ x˙ 5 ⎦ ⎢ m − m + m − + m m m ku mu kp ms u s s u s ⎣ u ⎦ kp kp kp kp rs rp rp x˙ 6 x6 0 − mp + ms − ms + mp 0 ms ks ms ⎡ ⎤ ⎤ ⎡ 0 0 0 ⎢ 0 ⎥ ⎢ 0 ⎥ 0 ⎢ ⎥ ⎥ ⎢ ⎢ ⎢ ⎥ ⎥ 0 0 0 ⎢ ⎥ ⎥ ⎢ u t ⎥ ⎥ ⎢ 2 +⎢ + k ku ⎥ ⎢ ku ru ⎢ − mu ⎥ Fa dt → x˙ = A3DoF x + bu uu + bf uf . u dt t ⎢ mu ⎥ ⎥ ⎢ u ⎢ ru ks mkuuks ⎥ ⎢ ks + ks ⎥ ⎣− m − m ⎦ ⎣ ms mu ⎦ u u k − mps 0 0 ⎡
(25)
An optimal controller is used in order to reduce the occurring vibrations of the passenger’s seat during the disturbance of the speed bump with the active element. The first step is to determine the controllability of the system, i.e., checking the rank of the control matrix CC (A, bf ): rank(CC ) = n → controllable, rank(CC ) < n → notcontrollable,
(26)
where n is the number of the state variables. This practically means that det(CC ) = 0. The rank of the control matrix is checked in Scilab console, and the value is the maximum, it means that the system is controllable. The task of the LQR method is to find the minimum of the quadratic cost function: 1 t T x Qx + Ruf2 dt, (27) J x, uf = 2 0
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where Q is a positive semidefinite matrix, which penalizes the vector x of the state variables, if it differs from zero. In practice Q is usually selected to an identity matrix. A large value in Q means that the user tries to make stable the system without changing the state variables excessively. The penalty parameter of the control signal uf is denoted with R, if the control output has a limit, the value R can be selected to a large number. The method of searching the minimum of (27) leads to the so-called Control Algebraic Riccati Equation (CARE): AT P + PA − Pbf R−1 bf T P + Q = 0,
(28)
where P is a positive definite matrix and its solution can be achieved in Scilab with the call of riccati() function. The values of Q and R can be user defined. Since the spring force and its integral are measured below mp , and measured also p˙ ks , pks as the feedback signals, the matrix Q can be defined as: ⎡ ⎤ 000000 ⎢0 0 0 0 0 0⎥ ⎢ ⎥ ⎢ ⎥ ⎢0 0 1 0 0 0⎥ (29) Q=⎢ ⎥. ⎢0 0 0 0 0 0⎥ ⎢ ⎥ ⎣0 0 0 0 0 0⎦ 000001 The penalty parameter of the feedback is selected to R = 10−3 . The solution of CARE can provide the vector of gain parameters kT directly: (30) uf = Fa dt = −k3 x3 − k6 x6 − k2 x2 − k5 x5 . The results of the displacements y1 , y2 and y3 are shown in Fig. 6, Fig. 7, and Fig. 8. The dotted lines represent the uncontrolled system, the solid lines show the behaviour of the controlled system.
Fig. 6. The displacement y1 of the uncontrolled and controlled system
The results showed that the application of generalized momenta to formulate the governing equations could be a good tool when force measurement is applicable in
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practice to set up a control loop. The loop provides the elimination of the unwanted vibrations to ensure higher comfort for the passengers during the motion of the vehicle.
Fig. 7. The displacement y2 with LQR and without control
Fig. 8. The effect of the LQR controller in the displacement of mp
5 Conclusion This paper dealt with the modelling and simulation of a 2 DoF passive suspension and a 3 DoF active suspension of a quarter car model. The mathematical model was derived with two ways by the use of the energy-based method. The formulation of the differential equations with generalized displacement coordinates is very common. In contrast, the method of writing with generalized momenta is not so well known.
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The main advantage of the use of generalized momenta is to make directly available the forces of the system. For a suspension, force measurement can be more feasible compared to displacement measurement. Therefore, with force measurement a control loop can be set up easily. A so-called LQR controller was applied in order to control the suspension of the quarter car with an active element. The simulation results showed that from the side of a passenger, the comfort was improved. The method described in this article can be well applicable to establish a control loop based on force measurement since the description of the system with generalized momenta can provide the forces directly. In the future, a test system will be set up to make a control loop, which will contain load cells to perform force measurement.
References 1. Takács, Gy., Patkó, Gy., Heged˝us, Gy., Csáki, T., Szilágyi, A.: Development of mechatronic systems at the institute for mechatronics at the University of Miskolc. In: IEEE International Conference on Mechatronics, Budapest, pp. 326–331 (2006) 2. Heged˝us, G.: Application of knowledge-based design in computer aided product development. In: Jármai, K., Bolló, B. (eds.) Vehicle and Automotive Engineering. LNME, pp. 109–114. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51189-4_11 3. Fekete, T.: Alternating current hydraulic drive the possibility of applying in the automotive industry. In: Jármai, K., Bolló, B. (eds.) Vehicle and Automotive Engineering. LNME, pp. 49– 57. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51189-4_5 4. Tóth, D., Szilágyi, A., Takács, G.: Investigation of rolling element bearings using time domain features. In: Jármai, K., Bolló, B. (eds.) Vehicle and Automotive Engineering. LNME, pp. 3– 12. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51189-4_1 5. Ebrahimi-Nejad, S., Kheybari, M., Borujerd, S.V.N.: Multi-objective optimization of a sports car suspension system using simplified quarter-car models. Mech. Ind. 21(4), 1–12 (2020) 6. Hegazy, S., Sharaf, A.M.: Ride comfort analysis using quarter car model. In: 15th International Conference on Aerospace Sciences & Aviation Technology, pp. 1–11 (2013) 7. Koch, G., Pellegrini, E., Spirk, S., Lohmann B.: Design and Modeling of a Quarter-Vehicle Test Rig for Active Suspension Control. Technical Reports on Automatic Control, vol. TRAC-5, 1–28 (2010) 8. Bokor, J., Gáspár, P.: Control Technique with Vehicle Dynamics Applications (in hungarian), Typotex, Budapest (2018) 9. Virgala, I., Gmiterko, A., Kelemen, M.: Modelling and simulation of vertical position stability of quadrocopter. Appl. Mech. Mater. 816, 43–48 (2015) 10. Grossman, M., Gmiterko, A.: An n-link inverted pendulum modeling. Acta Mechanica Slovaca 13(3), 22–29 (2009) 11. Wellstead, P.E.: Introduction to Physical System Modelling. Academic Press Ltd., London (1979) 12. Tóth, B.: Natural frequency analysis of shells of revolution based on hybrid dual-mixed hp-finite element formulation. Appl. Math. Model. 98, 722–746 (2021) 13. Tóth, B.: Three-field dual-mixed variational formulation and hp finite element model for elastodynamic analysis of axisymmetric shells. Ph.D. dissertation, University of Miskolc (2012) 14. ECOBAM®: Speed Bump ECOMBAM RDV 1200/60, Product datasheet (2015) 15. Cohen, S.D., Hindmarsh, A.C.: CVODE, a Stiff/Nontiff ODE solver in C. Comput. Phys. 10(2), 138–143 (1996)
Influence of Kinematic Excitation on the Dynamic Load of Rotary Machines Bearings Mounted on a Rail Vehicle ˇ Stanislav Žiaran(B) , Ondrej Chlebo, and Lubomír Šooš Mechanical Engineering Faculty, Slovak University of Technology in Bratislava, Nam. Slobody 17, 812 31 Bratislava, Slovakia {stanislav.ziaran,ondrej.chlebo,lubomir.soos}@stuba.sk
Abstract. The increased dynamic loading of rail vehicles during the motion depends primarily on their kinematic excitation. For the rotary service units which are attached to railway wagons, the reliability and lifetime can be affected by the increased dynamic load generated by the motion. The article deals with the measurement of the vibration on the radial piston pump at different speeds from 60 km/h to 120 km/h. The dynamic load of the pump also depends on its size, the characteristics of the bogie which vibrates and the place of attachment of the pump to the wagon and/or on the aim for which the pump was designed. For almost all pumps, regardless of the type of bearings used, measuring the vibration acceleration and calculating the effective value of the broadband vibration velocity on components such as bearing boxes is generally sufficient to evaluate the operating conditions of rotating shaft components with respect to trouble-free operation and corresponding lifetime. The magnitude of the dynamic load of the pump is indicated mainly by the characteristic frequencies of the bearings, whose amplitudes are noticeably larger. The article also deals with a theoretical analysis of the vibration severity related to the conditions of mounting and attachment of rotating components, such as a pump and/or compressor, at a moving vehicle that is kinematically excited. It also analyses the need to extend the frequency range to determine the vibration severity of rotary machines towards the lower frequency range. Keywords: Wagon · Pump · Kinematic excitation
1 Introduction Experimental tests of the dynamic load during the motion of a radial piston pump mounted on the wheelset of the dump truck required a dynamic analysis of the pump and its attachment to the axle (bearing) box of the wheelset (Fig. 1). All finite structures have a series of resonances and associated natural frequencies. The extent to which these resonances characterise the response depends on the damping of the structure. Damping comes from a number of sources. Material damping of metal structures is generally very low and in built-up structures is usually of less importance than damping from joints, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 835–847, 2023. https://doi.org/10.1007/978-3-031-15211-5_69
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boundaries, etc. Modal analysis of the radial piston pump and some component of the bogie was carried. The aim of the modal analysis was to determine the natural frequencies of the natural modes of the mounted radial piston pumps with nine cylinders and bogie components in their immediate vicinity [1, 2]. The measurement of mechanical vibration focused on the dynamic loading of a radial piston pump mounted on an axle box and driven by the wheelset shaft of the railway dump wagon, i.e. to perform vibration acceleration measurements on the pump cower near its bearings and on the transmission path near the pump at speeds of 100 km/h and 120 km/h which was carried out on the test track in VUZ Velim in the Czech Republic. Among other things, the vibration acceleration parameter was transformed into a vibration velocity in mm/s, which represents the vibration severity, according to which the dynamic load of the machines was evaluated [12, 13]. The measurement was also carried out at a starting speed of 60 km/h. The objects of the measurement were two radial piston pumps with different fluid flow rates [2].
Fig. 1. Dump wagon type with the axle box of the bogie wheelset.
The wheelsets of the wagons and the construction of their bogies are, due to the superstructure of the freight railway wagon, mostly made of metal components which are a very good conductor of vibration energy from the primary source. The main source is the wheel-rail contact to the superstructure of the wagon, including the superstructure. In the analysed case, the superstructure of the damp casing and the vibration energy generated by the contact of the wheel with the rail is also transmitted to the metal casing of the hopper body, thus increasing the total vibration energy. Exciting natural frequencies of the chassis and superstructure are also transmitted to the radial piston pumps, via the ball joint and a bogie main cross member of the chassis, the longitudinal beam, the axle guide and the axle box [2–5]. The hopper shell is thus a secondary source of dynamic load in the axle box of the wheelset mounted radial pumps. The maximum values of the dynamic load are reached when the natural frequencies of the natural modes of the hopper casing coincide with the excitation frequencies. For each component of the wagon, from the wheel to the superstructure itself, the natural frequency values are characteristic, which in coincidence with the excitation frequency, cause the component to resonate and thus increase the dynamic load of the radial piston pump and components in their immediate vicinity. The analysis of the results of the vibration
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acceleration measurements under operating conditions will show the differences in the dynamic load of the pumps from the dump wagon itself. The reduction in vibration energy generated by rolling the wheel along the rail, transmitted to the bogie structure and the wagon body, depends on the transmission loss from the source through the main structural components to the dump wagon shell. The transmission of vibration energy through selected main components (structural blocks) of the wagon bogie depends on their construction, the material used and the method of application (modification of contact surfaces, mounting and fastening) [6–8]. The transmission loss in the frequency range up to 3.2 kHz from the excitation source by the modal hammer through the main components to the body of the radial piston pump was processed by the transmission of the power flow through the axle box [2]. By comparing the amplitudes of the frequency characteristics of the acceleration at the output with the input for the individual structural blocks, significant differences were recorded in the vibration acceleration (velocity). The difference represented the transmission loss of the vibration energy depending on the frequency. It should be noted that the dynamic load of the pump was mainly influenced by the undulation of the track and the wheel, the placement of the track, especially its vibration isolation, as well as the roughness of the track. To assess the dynamic load in terms of applicable standards, and based on the previous experience of the researchers, the acceleration of the mechanical vibration was transformed into a vibration velocity and an effective velocity representing the vibration severity was determined [2, 9].
2 Environment, Object, Goals, Description and Methodology of Vibration Measurement 2.1 Measurement Environment, Conditions and Subject of Measurements The Railway Testing Circuit (RTC) is a closed track on which railway vehicles are tested. According to the official permit, it is categorised as a siding. The large circuit is approximately 13.3 km long with a maximum permissible speed of 230 km/h for units with non-tipping boxes and 210 km/h for other railway vehicles. A RTC with a straight section of 1.98 km was used to measure the dynamic load of two radial piston pumps. The measurement of the vibration acceleration during the motion of radial piston pumps on the RTC with a variable operating speed from 120 km/h to 60 km/h was carried out. The meteorological conditions for this type of measurement were satisfactory. The measurement was carried out at two operating speeds, namely 100 km/h and 120 km/h. The necessary starting speed of 60 km/h was also used for the dynamic tests. In this way, a sufficient number of vibration parameters was obtained for the correct assessment of the dynamic load of the radial piston pumps and for the possible further dynamic analysis. The object of dynamic measurements on the track in RTC were two radial piston pumps (Fig. 2a). The measurements were carried out with their working load, i.e. with the tank pressure (active state) and without the tank pressure (passive state). The drive shaft mounted in ball bearings contained an eccentric bearing which drove nine pistons
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pushing the fluid through an axial bore into the outlet hose (Fig. 2b). The pump was driven by a wheelset shaft via a flexible gear coupling [2]. The pumps were installed on two wheelsets connected by a frame structure containing an axle box, a horn-plate, a longitudinal beam and a bogie main cross member (Fig. 2b). The bogie main cross member was connected to the front and rear of the hopper shell by means of a ball joint and two slides (see Fig. 1). Thus, the superstructure (shell) of the dump wagon was connected to the bogie with four wheelsets in six contact areas, namely by two ball joints and four slides [2–5]. These contact surfaces were the points where the vibration energy transmitted from the main source of excitation without an ideal rolling geometry and steel wheels placed on slightly uneven steel rails. These wheel and track irregularities generated a significant kinematic excitation of the dump wagon during the motion. The vibration energy transmission paths were also of the opposite direction, as the superstructure acted as a secondary source of vibration in a certain frequency range (see below) which was assumed to affect the dynamic load of the radial piston pump [5].
Fig. 2. Cross-section of a radial piston pump (a) and bogie with two wheelsets and two radial piston pumps installed.
2.2 Objectives, Equipment and Measuring Points Apart from obtaining very important vibration signals at different operating speeds, the main purpose of mechanical vibration measurement was to determine the dynamic load of two radial piston pumps in the natural frequency range of their natural modes and to analyse the corresponding magnitude of the wagon´s dynamic load from the real kinematic excitation as well as from the exciting vibration of the superstructure (shell) under the defined operating conditions of the dump wagon. The obtained results can be used to analyse the possible negative effects on the operation, reliability and lifetime of radial piston pumps fast mounted on the axle box of the wagon. The signals are usually generated by rotating machines, and their response on the mounting is investigated. To identify the energy dominant low frequencies more precisely, the fast Fourier transform (FFT) analysis was carried out using the FFT analyser PULSE (Fig. 3) [15]. The measurement of the investigated objects coincides with ISO 5348 guidelines for accelerometers and with respect to past experiences [2–5]. The goal was to ensure that the sensors would correctly reproduce the motion of the analysed components without interfering with the response. Apart from the frequency range,
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and with respect to the type of signal, it is also very important to select the appropriate type of averaging, the number of averages per unit time and a suitable time window [10]. The measured data were processed in the PULSE Reflex program. The methodology presented in the paper can also be applied to other type of excitation sources of low-frequency vibration including the sources generated by the technological process.
Fig. 3. Frequency analyser B & K PULSE 3053-B-120, used accelerometers and the modal hammer.
The selection of measuring points was determined on the basis of an analysis of the design of the pump in defined directions [11, 13] and inlet and outlet points of the measured adjacent components. The main aim was to determine the measuring points on the pump casing as close as possible to the bearings that were most dynamically loaded as well as at the axle box inlet and at the bogie main cross member near the pumps, where energetically strong vibrations were expected on the transmission path from the primary vibration source and from the secondary source, namely from the shell of the dump wagon. In addition to the standards of the ISO 10816 and ISO 7919 series, the experience of operators with the measurements made on other machinery and structures and especially with vibration measurements on the prototypes of tank wagons manufactured in Tatravagónka, was used in the selection of the measuring points on the wagon [2–4]. The acceleration sensors were attached to the surfaces of the pump casing and selected bogie components with a magnet, as well as near the source (only in measurements at rest – modal analysis). By attaching the sensors with a magnet, the used frequency range was reduced to approximately 4 kHz, which was sufficient for the planned measurements. The accelerometers were attached in three directions – a) to the pump casing, b) to the bottom of the axle box and c) to the bogie´s main cross member as closely as possible to the pumps (see Fig. 2b). Two variants of measuring the vibration of the dynamic load of radial piston pumps were carried out, from the excitation generated by the impact hammer in the hall and while moving on the test track at defined speeds [2].
3 Vibration Severity of the Radial Piston Pumps as an Indicator of Dynamic Load 3.1 Vibration Severity It is confirmed that the measurement of the effective value of the vibration velocity is very useful in characterising the vibration echo of a wide range of classified machines
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and is suitable for further use [9]. In simple time-varying waveforms, which consist of a number of discrete harmonic components of a given amplitude and phase that do not contain the components of significant random vibrations, the interrelationships of different fundamental quantities as displacement, velocity, acceleration, peak value, r.m.s. value, average value, are calculated. From the measured time history of the velocity, the effective value of the velocity can be calculated, which represents the vibration severity according to the relation T 1 v2 (t)dt (1) vef = T 0
where v(t) is the time dependence of the vibration velocity, (mm/s); vef – corresponding r.m.s. velocity value, (mm/s); T – sampling time that is longer than one period of any of the fundamental frequency components of which v(t) is composed, (s). The magnitudes of acceleration, velocity and/or displacement (aj , vj and sj ; j = 1, 2,…, n) can be determined for individual frequencies (f 1 , f 2 ,… f n ) from the analysis of the recorded spectrum. If either the vibration displacements of peak-to-peak values, s1 , s2 ,… sn , [in micrometers], or the vibration velocity r.m.s. values, v1 , v2 ,… vn , [in millimeters per second], or the vibration acceleration r.m.s. values, a1 , a2 ,… an , [in meters per second squared] are known, and so are the frequencies f 1 , f 2 ,… f n , [in hertz], then the associated effective speed value characterising the dynamic motion is given by 2 −3 1 2 2 2 vef = π • 10 (s1 f1 ) + (s2 f2 ) + · · · + (sn fn ) = v1 + v22 + · · · + vn2 2
2 2 2 a1 103 a2 an = + + ··· + (2) 2π f1 f2 fn In the case where the vibration is composed of only two significant frequency components forming the beats of the r.m.s. value, vmin and vmax , corresponding r.m.s. velocity vef can be roughly determined from the relation 1 2 2 v + vmax (3) vef = 2 min From the point of view of dynamic loading of the machinery and its individual parts, it is therefore very important to know quantitative and qualitative indicators of surface vibration of these components. The safety and quality of machinery in terms of dynamic loading and mounting can be assessed according to the intensity of vibration, which is characterised by the maximum effective value of the vibration velocity, measured at predetermined places in the system. It has been found that increasing the effective velocity 1.6 times from a very good operating condition of the machinery results in a change which is already reflected in the operation of the machine [9].
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There is a very close connection between operational safety and the magnitude of vibrations. The safety of operation is to a large extent ensured by the permissible operation of the machinery, which means that the vibration of the surface of the machinery is in accordance with regulations and standards. The vibration severity expresses the energy state of the vibration of the machinery. It therefore follows that any vibration increases the energy consumption of the machines. This difficulty will increase with the increasing vibration severity. 3.2 Vibration Severity and Dynamic Load As stated in 3.1, the vibration severity of the machines is an indicator of the dynamic load of the machines. A specific machine is a radial piston pump connected to a bearing box, the shaft of which is connected by a flexible coupling to the drive shaft of the wheelset of the dump wagon. It follows that the dynamic load of the pump will primarily depend on the kinematic excitation in contact with the wheel with the rail. As mentioned above, the primary excitation also generates the natural frequencies of the wagon’s natural superstructures and in particular the dump sheet part of the wagon [2]. Table 1 measures and calculates the vibration severity values of the two dynamically assessed radial piston pumps described above, with active pump load (increasing the pressure in the tank – yes) and passive pump load (no). The vibration severity (r.m.s. velocity) was measured by accelerating the vibration at three different damp speeds in all three pump-defined directions. In terms of dynamic loading, the vertical direction is decisive. Table 1. Dynamic load values of the pump are expressed by the vibration severity. Frequency range, Hz Speed km/h Pump load Measuring point pump no.1 and no. 2 0–3200 0–50
10–1000
Vibration severity, mm/s 60 100
Yes Yes
1 vertical direction (1VE)
10.36
10.03
3.80
2 vertical direction (2VE)
10.65
10.45
4.33
1 vertical direction (1VE)
12.51
12.03
8.42
2 vertical direction (2VE)
12.29
11.81
8.39
18.98
18.62
8.23 9.49
100
No
1 vertical direction (1VE) 2 vertical direction (2VE)
19.57
19.27
120
Yes
1 vertical direction (1VE)
20.25
19.76 11.34
2 vertical direction (2VE)
20.72
20.26 12.80
1 vertical direction (1VE)
17.05
16.55 10.85
2 vertical direction (2VE)
17.04
16.52 11.10
120
No
It is clear from Table 1 that as the speed of the wagon increases, i.e. as the rotational frequency increases, the intensity of the vibrations also increases in all three defined
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directions [2]. The highest values were measured at a speed of 120 km/h, in the vertical direction, which is natural with respect to the direction of excitation. According to the valid standard [15], the nominal value of the relative sensitivity of the machine is defined in the frequency range from 10 Hz to 1 kHz. For information, we have calculated the vibration severity for the frequency range from 0 to 3.2 kHz and from 0 to 50 Hz. It is clear from the table that low frequencies have the greatest influence on the vibration severity, which is logical. The evaluation of the dynamic load according to the standards shows that the highest value of the vibration severity was reached at a speed of 120 km/h with active operation of the pump, in the vertical direction, which is 12.8 mm/s. In passive operation, these values are approximately the same. Thus, the measured values show that the operation of the pump itself, whether active or passive, has a negligible effect on the dynamic load of the pump itself, unless it is severely damaged. This assignment can be confirmed by a stationary experimental test of the analysed pump, which is being prepared. According to ISO 10816-1, this pump is classified in class 1, which contains “individual parts of motors and machines integrally connected to the whole machine under normal operating conditions (typical examples of machines in this category are electric motors with power up to 15 kW)” and based on measured values of 12.8 mm/s vibration severity, the pump falls into a band D, where “vibration values in this band are normally considered unacceptable causing damage to the machine”. This dynamic load can cause the pump failure in a relatively short time. However, it should be emphasised that, in view of the applicable standards cited, the measured impermissible vibration severity was caused by the external drive unit, i.e. the dynamic load generated by the dump wagon in motion and not by the radial piston pump itself. The frequency distribution of the vibration velocity for the defined directions of the two radial piston pumps analysed at speeds of 100 km/h and 120 km/h is shown in Fig. 4 and Fig. 5, respectively. It is clear from both figures that the highest values of vibration velocity are achieved at low frequencies, which is also confirmed by high values of vibration severity in the vertical direction, for example at a speed of 120 km/h it is for pump no. 2 to 20.26 mm/s. The frequency analysis of the vibration velocity, which corresponds to the vibration acceleration, illustrates the bands with an increased amplitude. Significant amplitudes at higher frequencies are negligible in terms of the dynamic loading of radial piston pumps. This fact is also confirmed by the measured value of the vibration severity in the frequency range from 0 to 3.2 kHz, which is 20.72 mm/s. This value is only 0.46 mm/s higher than in the frequency range from 0 to 50 Hz. By comparing the frequency spectra at different speeds, the frequency coincidence of the vibrations of the individual components of the dump wagon can be seen. The difference is in the value of the amplitude at a given frequency. The frequency behaviour of the dynamic load of the pumps at a wagon speed of 60 km/h is shown in Fig. 6, from which a significant decrease in the amplitudes of the vibration velocity and thus also the dynamic load of the pumps can be seen. However, the frequency distribution is the same, which confirms that the natural frequencies of the
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Fig. 4. Frequency distribution of the velocity course in defined directions for active radial piston pump no. 1 and no. 2 at a speed of 100 km/h.
Fig. 5. Frequency distribution of the velocity course in defined directions for active radial piston pump no. 1 and no. 2 at 120 km/h.
natural modes of the dump wagon do not change with the change of the wagon speed and it is only the amplitudes of the monitored vibration parameter that change. Based on the document ISO 10816-1, when classifying a pump in Class 1 for a measured vibration severity value of 3.8 mm/s, band C is assigned, i.e. vibration severity values in this band are normally considered unsatisfactory for a long-term continuous operation. These machines can generally be operated for a limited period of time.
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The vibration severity is evaluated according to the standard in the range from 10 Hz to 1 kHz. However, it should be emphasised that the maximum dynamic load of the pumps is at frequencies up to 50 Hz (the maximum value was measured at 4 Hz), which indicates an extension of the frequency range below 10 Hz in the currently valid ISO documents (see Table 1). This dynamic load will most affect the lifetime of the pumps which are mounted on the bearing or axle box of the wheelset.
Fig. 6. Frequency distribution of the velocity profile in defined directions for active radial piston pump no. 1 and no. 2 at 60 km/h.
4 Discussion and Conclusion The analysis of the dynamic load of the radial piston pump of the dump wagon presented in this paper was based on vibration acceleration measurements carried out on two radial piston pumps mounted on the bearing (axle) box as well as the analysis of close surrounding components and the previous vibration measurements of railway wagons [2–4]. It seems that there is a frequency and amplitude correlation between the results of the vibration measurements of the bogie with pumps and the measurements without pumps because the values of vibration severity appear to differ only slightly from the measured values on the radial piston pumps. The measurement of the vibration of the radial piston pumps and the contact components of the bogie of the dump wagon during the kinematic excitation focused on the dynamic loading of the radial piston pumps attached to the axle box. During this kinematic excitation, the frequency transmission of the power flow to the pump body and its mounting point was also monitored. Experimental tests carried out at different speeds from 60 km/h to 120 km/h have confirmed that the dynamic load is significantly dependent on the speed of the dump wagon. The analysis of the carried out vibration
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acceleration measurements made during the motion of the dump wagon confirmed that the shell of its storage space also generated the natural frequencies in the area of medium frequencies reaching around 300 Hz and its values in this frequency interval changed only slightly depending on the wagon speed. This corresponds with the theory, since the body of the wagon vibrates at its natural frequencies of its natural modes that do not depend on the speed of the wagon, but on its design. The same is true for a bogie whose natural frequencies are around 1.2 kHz and higher [2–5]. The dynamic load of the pump also depends on its size, the characteristics of the vibrating bogie and the mounting of the pump and/or on the purpose for which the pump was designed. When determining the vibration measurement ranges of different types of machines, and therefore also of pumps, it is therefore necessary to take into account different aims and corresponding circumstances. For almost all the pumps, regardless of the type of bearings used, measuring the effective value of the broadband vibration velocity on components, such as axle box, will, in general, adequately characterise the operating conditions of the rotating shaft elements with respect to failure-free operation. For the radial piston pumps analysed, it is recommended to verify the risk of pump failure by experimental tests, as the measured dynamic load of the pumps can cause its failure in a relatively short time. This is also confirmed by the significant amplitudes of the characteristic frequencies of bearing damage (Fig. 7) [9, 10, 12].
Fig. 7. Frequency spectrum on the cover pumps in the vertical direction at the kinematic excitation and a speed of 120 km/h for the dynamic behaviour of the pump bearings.
The above analysis and conclusion were carried out on the basis of applicable standards and previous measurements [9, 10]. However, this technical problem requires a deeper dynamic analysis, including an experiment to confirm the analysis of the results obtained so far, since this is a specific case of propulsion and mounting of a radial piston pump on a dynamically active base plate (axle box). However, it should be emphasised that the maximum dynamic load of the pumps is at a frequency of 4 Hz. This dynamic load will have the highest negative effect on the lifetime of those pumps which are mounted on the axle box of the wheelset.
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It is essential to verify every measure taken to reduce the excessive external dynamic load of the pumps where the measures are carried out. Solving the problem also required some of the expertise mentioned in the publications, which can be found in the used reference literature. Acknowledgements. The research presented in this paper is an outcome of the project No. APVV19-0538 “Progressive hybrid high-speed spinning actuator” funded by the Slovak Research and Development Agency, and this contribution was elaborated within execution of the project “New generation of freight railway wagons” (Project code in ITMS2014+:313010P922), on the basis of support of the operational program Research and innovation financed from the European Regional Development Fund.
References 1. Thompson, D.: Railway-noise and vibration. Printed and bound in Great Britain (2009) 2. Žiaran, S., Chlebo, O.: Dynamic Load of Hydac Bieri Pumps During Motion of the Wagon. Research report, p. 35, FME STU in Bratislava (2021) 3. Žiaran, S., Chlebo, O., Pokusová, M., Šooš, L., Úradníˇcek, J., Maˇcák, L.: New Generation of Freight Railway Vehicles. Part 1: Measurement of Vibroacoustic Properties of a Tank Wagon. Research report of FME STU in Bratislava, p. 73, Trnava (2019) 4. Žiaran, S., Chlebo, O., Musil, M., Úradníˇcek, J., Maˇcák, L.: New Generation of Freight Railway Vehicles. Part 2: Frequency Analysis of Tank Wagon Bogie Sound. Research report of FME STU in Bratislava, p. 31, Trnava (2020) ˇ Maˇcák, L., Búry M.: New Generation 5. Žiaran, S., Chlebo, O., Petrák, P., Úradníˇcek, J., Šooš, L., of Freight Railway Vehicles. Part 3: Frequency Analysis of Vibration and Noise of a Tank Wagon in Motion with Proposed Measures. Research report of FME STU in Bratislava, Trnava, p. 166 (2020) 6. Ziaran, S., Chlebo, O., Cekan, M., Tuma, J.: Transmission of vibrations through vibration isolators, theory and application. In: Uhl, T. (ed.) Advances in Mechanism and Machine Science. IFToMM WC 2019. Mechanisms and Machine Science, vol. 73, pp. 3995–4004. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20131-9_398 7. Segla, S.: Modelling and optimization of passive and semi-active suspension of a 3 DOF seat platform. In: Uhl, T. (eds) Advances in Mechanism and Machine Science. IFToMM WC 2019. Mechanisms and Machine Science, vol. 73, pp. 4075–4084. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20131-9_406 8. Danko, J., et al.: Dynamic properties modelling analysis of the rubber-metal elements to electric driver. J. Mech. Eng. 71/01 (2021) 9. Žiaran, S.: Vibration and acoustics. Vibration and noise control in industry. Monograph, Issued by Slovak University of Technology in Bratislava, p. 330 (2006). (in Slovak) 10. Žiaran, S.: Technical diagnostics. Scientific monograph. Issued by Slovak University of Technology in Bratislava, p. 332 (2013). (in Slovak) 11. Darula, R., Žiaran, S. On experimental study of optimal measurement point location for gear wheel state-of-wear measurements by means of vibro-acoustic diagnostics. J. Mech. Eng. 62(2) (2011) ˇ Determination of bearing quality using frequency vibration 12. Žiaran, S., Chlebo, O., Šooš, L.: analysis. J. Mech. Eng. 71(02) (2021)
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13. ISO 10816-1 Mechanical vibration. Evaluation of machine vibration by measurements on non-rotating parts. Part 1: General guidelines 14. ISO 7919-1 Mechanical vibration of non-reciprocating machines. Measurements on rotating shafts and evaluation criteria. Part 1: General guidelines 15. ISO 2954 Mechanical vibration of rotating and reciprocating machinery. Requirement for instruments for measuring vibration severity
Malfunction or Normal Operation? Evaluation of the Subjectivity of Noise and Vibration Phenomena Accompanying the Operation of Motor Vehicles Balázs J. Kriston and Károly Jálics(B) Institute of Machine and Product Design, University of Miskolc, 3515 Miskolc-Egyetemváros, Hungary [email protected]
Abstract. In the age of modern automotive diagnostic instruments and automotive on-board computers, the human ear is still the benchmark for detecting and judging certain automotive noise phenomena. The human ear is an extremely sensitive, versatile “instrument,” but it does not provide information that can be stored and processed later. The sense of hearing it evokes is subjective, often different from person to person, even for the same noise/sound. Therefore, it is not possible to determine certain defects that provide a more differentiated acoustic pattern using the human ear alone. This publication deals with the comparison of measurable results and subjective impressions. For that purpose, noise measurements were performed on two different passenger vehicles, and psychoacoustic parameters (loudness, roughness, sharpness) and in addition the overall sound pressure level were evaluated. Based on the analysis, it can be stated that the evaluated psychoacoustic parameters can judge the perception of disturbing noise phenomena. So, the claims of the passengers can be strengthened through the psychoacoustic analysis. Keywords: Transmission · Diagnostics · Psychoacoustics · Noise · Vibration
1 Introduction In our days, the sales of passenger vehicles have steadily increasing tendency worldwide. Among other components, every car contains a powertrain, including the gearbox. Even electric cars can have gearbox, which is the most important and expensive parts of the cars. Depend on the type of failure the repair costs can be very expensive, especially in case of automatic transmissions (planetary, DCT, CVT). Regarding to older vehicles the needed repair could mean a financial loss. The investigation of the malfunction in the gearbox could also mean cost saving. Strange noise or unusual vibration that come from a certain component of the car may indicate a fault for the customer. For example, grinding/squeaking sound can indicate that the transmission fluid is on a low level or brake pads are worn out. However, an awkward noise does not mean necessarily that © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 848–860, 2023. https://doi.org/10.1007/978-3-031-15211-5_70
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something is wrong with the vehicle. The human sound perception strongly subjective. For some people, a specific noise is annoying, while the others do not even notice it. The sensation depends on many parameters, such as spatial distribution and time structure of sound, noise duration, sound level, subjectivity etc. The scientific study of sound perception and audiology is psychoacoustics. At this point arise a question what acoustic phenomena can be considered as a failure noise? Is it possible to make decision without objective measures? Last, but not least, what kind of relations are between the objective fundamental measurements used in diagnostics and psychoacoustical values? To answer these questions the human centered subjective concepts, like sharpness – roughness should be transformed into objective concepts, which are measurable and properly describe the human judgement of noise. That is what psychoacoustic partially do. After these establishments, the task is now that involve the psychoacoustic assessment criteria (loudness, harshness etc.) in failure diagnostics in order to make decision about a certain noise problem from customer point of view.
2 Psychoacoustics Psychoacoustics is a branch of science dealing with the perception of sound, the sensation produced by sounds and the problems of communication. The question of psychoacoustics is that what kind of relationship is between sensory perception and physical variables. Figure 1 shows the correlation between objective measurements based on physical parameters and subjective concepts. Strictly speaking, it represents the concept of psychoacoustics through one picture.
Fig. 1. The concept of psychoacoustic
Hearing is not purely a mechanical event but a perceptual phenomenon. Inside the inner ear the mechanical sound energy transforms into biological signals. In the physical world every sound is affected by the encoding and transmission characteristics of the auditory nervous system. High-level frequency – and temporal resolution is provided
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by the encoding process during transduction. The sound is transformed in many ways while it passes through the outer and middle ear (Fig. 2). In nutshell these events of the auditory system are responsible for our acoustic perception of the world around us. The field of psychoacoustics is much wider than we could discuss about it in few pages. Research and concepts as frequency selectivity and masking, perception of loudness, timbre, pitch, localization of sounds or temporal processing in the auditory system require more attention [0].
Fig. 2. Structure of the human ear: outer, middle, and inner ear [2]
The limitations of A-weighted sound level measurement became clear more than 30 years ago in the automotive industry, when the sound quality of interior and exterior noise in motor vehicles was to be improved. The A-weighted sound pressure level is only a very rough approximation of the loudness perception. Moreover, a low noise level does not equate to acoustic ‘well-being’. Noise can not only impair a product but also benefit it. The sound of an automobile door closing is a classic example that has been studied for many years. The aim of these investigations was to generate a door closing noise that suggests a solid car door. (Remark: the geographical region where the people live also have an effect on it.) Today, the term “noise quality” or “sound quality” summarizes the endeavour to use technical freedom in the development of a product in such a way that the resulting noise matches the product as well as possible. A hearing-friendly sound analysis with the help of psychoacoustic measurement technology is essential. A disproportionate number of high frequencies conveys a close and “aggressive” sound source that compels increased attention from the listener. Such a noise attribute, which is usually undesirable, can be measured using the psychoacoustic variable “sharpness”. If a low-frequency component is added to a “sharp” sound, the sharpness can be reduced. However, the newly created sound is louder, but is often perceived as “less annoying” or “less disturbing”. However, periodic fluctuations in the envelope of a signal or the frequency can also lead to a noise being judged as “dissonant” or “unpleasant”. In addition, periodic fluctuations in the envelope can also indicate disturbances in the smooth running of a machine. Psychoacoustic measurement technology is therefore not only suitable for
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optimizing noise quality, but also for acoustic quality control or system monitoring. However, psychoacoustic parameters can no longer be determined using an elementary sound level meter, but require a time-dependent, narrow-band signal analysis. In the present days a branch of “classic” psychoacoustic parameters is established, e.g., loudness, sharpness, roughness, tonality, fluctuation strength, in addition, numerous other hearing-related variables and parameters are known that are also often used. Next, we short introduce a few of them (based on [3]): a. Loudness: The psychoacoustic parameter loudness reflects the subjectively perceived loudness of sounds. The loudness scale is calculated using psychophysical ratio scaling, based on relative judgments. The loudness scale was developed to represent human perception of loudness on a linear scale. The reference signal, a sinusoidal tone with a frequency of 1 kHz and a sound pressure level of 40 dB, has a loudness of 1 sone. Since this is a ratio scaling, the loudness corresponds to twice as loud perceived signals 2 sone. E.g., if 40 phon = 1 sone, then 50 phon get the value 2 sone, 60 phon get the value 4 sone, 70 phon get the value 8 sone etc. Accordingly, the loudness indicates by how many times louder a sound event is perceived on average in comparison to another sound event. Loudness is considered an essential parameter in the area of sound quality b. Sharpness: The psychoacoustic parameter “sharpness” refers to the perception of sounds, whose energetic focus is in the high-frequency range. These are often described by listeners with attributes such as “sharp”, “shrill” or “bright”. A narrowband noise (Δf ≤ Δf G ≈ 160 Hz) with a center frequency of 1 kHz and a sound pressure level of 60 dB is therefore assigned a sharpness of 1 acum (acum = Latin for sharp). Sharpness is standardized in DIN 45692. c. Roughness: The psychoacoustic parameter roughness describes a sensation caused by modulations in a sound event. The impression arises if there is a time-variant envelope within a frequency group, i.e., if for example, tones have a temporal structure due to the permanent change in amplitude or frequency. A 1 kHz tone with a level of 60 dB, which is amplitude-modulated with a modulation frequency of f mod = 70 Hz and a modulation depth of m = 1 (reference sound), is assigned the roughness R = 1 asper (lat. rough). d. Fluctuation strength: The psychoacoustic parameter fluctuation strength describes the perception of “slow” modulations. The fluctuation reflects the perception experienced when the signal fluctuates at very low modulation frequencies. Sounds modulated at 20 Hz and below are perceived as “fluctuating”. e. Tonality: Tonality is another one-dimensional psychoacoustic parameter that can be perceived isolated. A noise is considered tonal if individual tones or tonal components are clearly perceptible. Narrowband noise is often perceived as having tonal content as well, but this effect diminishes significantly as the bandwidth increases.
3 Problem Description and Measurements In this paper, we focus on acoustic problems in two different vehicles, which show the difficulties of distinguishing between normal operation noise and failure noise. The outline of these acoustic problems will be described in this chapter.
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3.1 Problem 1: Impact/hit Noise of a Manual Gearbox It was the first time, when we met with the problem of human subjectivity regarding noise perception during diagnostics. An owner after 6 months of using a relatively new car (year of manufacture 2019, turbocharged direct injection, petrol engine, front-wheel driven, 6-speed manual transmission) complained about a specific noise during gear shift from gear 1 to gear 2, resp. from gear 2 to gear 3. In terms of frequency content, the noise can be described as impact or hit noise. During shifting the noise can be heard clearly inside and outside of the car. For the owner, the noise is very disturbing, while some of the passengers do not find the noise to be very annoying and someone do not even notice it. After discovering the problem several components were replaced: clutch, axle - shafts and the gearbox one after the other. Despite of the repair the noise was still existent. Therefore, our task was to analyze the problem without disassembling the gearbox [4]. To make sure the noise is existent and identify the noise characteristics a pass-by noise measurement was performed. The test could not meet the ISO 362 - 1 to 3 since we do not have the proper pass-by noise test facility and in addition a gear shift had to be performed during the acceleration. Acc. to standard a fixed gear must be used during the acceleration. Nevertheless, we managed to perform measurements as close as possible to the standard. The car was started from stillstand in gear speed 1 and was driven on an approximately 30 m long road with one lane while the driver shifted to gear 2. The pass-by sound pressure level was recorded during several measurements and the noise phenomena could be reproduced every time. To get closer to the malfunction a static state measurement of the car was also performed. The car moved onto a lifting platform and the engine-gearbox unit was moved back and forth by hand against the pendulum support. At this movement a sharp impact/hit noise could be heard, as something might be loose inside the gearbox. On the gearbox housing accelerometers were mounted to measure the vibration and a near field microphone to record the sound pressure. The resulted noise was very similar to that we observed during pass-by noise measurement. The measurements were taken separate for the measurement points, due to the lack of a multichannel DAQ system. 3.2 Problem 2: Whistling Noise of an Automatic Gearbox An upper-class second-hand AWD saloon car (mileage approx. 200.000 km) with diesel engine and automatic gearbox (with hydrodynamic torque converter) is given where the new owner complains about a whistling noise driving in reverse gear. The noise can be heard by accelerated ride, by creeping at idle engine speed and by braking from creeping to stillstand. The noise is like an electric motor noise, but there was no electric motor in the drivetrain of the car. Another possibility is the gear wine noise from the reverse gear wheels. The car was bought a short time before the investigations, it is not known if the noise problem was known before. The seller didn’t want to provide information about it. Nevertheless, the whistling noise was very annoying and worrying for the new owner, and if there is really a malfunction, it implies a warranty claim towards the seller. So, the problem had to be investigated systematically.
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For that purpose, interior noise measurement was performed several times at the front passenger head position, in driving conditions which represented the disturbing noise. The measurements contained the following driving phases (only in reverse gear): acceleration, rollout, creeping and braking. Later the microphone raw time data was post-processed and analyzed. Furthermore, we tried to obtain drawings, exploded view, the power flow scheme of the gearbox, resp. information about the gear types, number of teeth of the reverse gears, but only a single3D drawing could be found, which was unfortunately not particularly helpful for the analysis and in addition the gearbox could not be disassembled.
4 Analysis 4.1 Analysis of Impact/hit Noise For the first analysis the averaged spectrums and spectrograms were created by Fast Fourier Transformation from the pass-by and standstill measurements based on the raw time signals. The analysis aims to find frequency contents which may refer to the failure. Figure 3 shows the spectrogram of the pass-by noise [4].
Fig. 3. Colourmap of the pass-by noise measurement
The frequency range of the noise phenomenon is approx. 4000 Hz wide and there are only a few frequency peaks (at 2000 Hz and 3000 Hz) that might refer to a certain component of the gearbox. Overall based on the pass-by noise we could not identify any faulty component in the gearbox. Nevertheless, this measurement, respectively its replay and listening were able to highlight the existence of the disturbing noise phenomena. We did further investigations at standstill condition, which result can be seen in Fig. 4. The figure shows the raw time signal of standstill measurements. The time signals have almost the same curve progression, the distinct peaks clearly indicate the presence of impacts. However, the localization of the failure was also not possible based on this information. Next let us look on the human perception of that impact noise. As it was already mentioned, the owner finds that gear shift noise very annoying, but other persons did not
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Fig. 4. Time signals at the standstill measurements (top: sound pressure, mid: acceleration point 1, bottom: acceleration point 2). (The recording of the 3-time signals was not performed synchronous.)
even notice it. We investigate first the pass-by noise measurement regarding psychoacoustical parameters, which could explain the grade of annoyance of the listeners. We investigate the psychoacoustical parameters loudness (based on Zwicker), roughness and sharpness and as comparison the A-weighted overall sound pressure level (SPL(A)). In the following few paragraphs we give a short overview of the calculation of these three psychoacoustical parameters, and after that we introduce our calculation results based on the pass-by and stillstand noise measurements: • In addition to sound pressure level and frequency, loudness also depends on the bandwidth of a signal. An increase in bandwidth leads to an increase in loudness if the frequency range of the sound event exceeds the frequency group width (critical bandwidth). This is considered in the loudness calculation method of Zwicker, which schematic workflow is shown in Fig. 5. The excitation level and critical band rate (CBR) patterns are transformed into specific loudness in a first step. This transformation is based on Stevens power law by assuming a power function with a fixed exponent and a pitch-dependent basic excitation. Finally, the overall loudness is obtained by integrating the specific loudness over the frequency groups. The method is standardized and described in ISO 532B.
• According to v. Bismarck a single numerical value of the weighted loudness- critical band rate pattern (CBR) is calculated. Finally, the weighted total loudness is divided by the total loudness. The ratio is a measure of the perception of sharpness. The reference sound is marked by a cross in Fig. 6.
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Fig. 5. Schematic diagram of the various steps in Zwicker’s loudness model [5]
Fig. 6. The sharpness of narrowband noise (solid), low-pass noise (dotted), and high-pass noise (dashed) as a function of center frequency f m , upper cutoff frequency f go , and lower cutoff frequency f gu , respectively. The cross marks the reference sound with a sharpness of 1 acum [6]
• The perception of roughness is particularly pronounced in frequency and amplitude modulated tones. On the other hand, there is no strong dependency on the sound pressure level. Only an increase in the sound pressure level of around 40 dB causes the roughness to double. The figure shows the dependence of amplitude-modulated tones on the degree of modulation m, the modulation frequency f mod and the frequency f c of the tone Based on the methods shown above, and described in the related literature, we performed the calculations. The results are shown in Fig. 8 for the pass-by noise. As can be seen on Fig. 8 (top left/red) the trend of the overall SPL can be compared with the colourmap in Fig. 3. The loudness curve (top right/blue) also has similar trend to the overall curve (not surprisingly). In both cases, the maximum values are reached between 5 s and 7 s, because in this time range the vehicle is the closest to the microphone. The sharpness (bottom right/brown) shows that the noise is not particularly sharp, and the sharpness stays quite similar during the measurement time. The roughness (bottom
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Fig. 7. Roughness (R) of an amplitude modulated tone depends on the degree of modulation m (part a) and the modulation frequency f mod (part b). In part b the carrier frequency f c is varied as a parameter [7]
Fig. 8. Calculation results of 3 psychoacoustic parameters plus overall SPL based on the pass-by noise signal
left/green) shows in contrast that the noise is increasingly rough with the maximum value at approx. 4 s of time. This time value marks exactly the start of the gear shift process. So, the gear shift noise could really mean a certain disturbing effect on the human perception in some cases, where the persons are sensitive for that type of noise modulation. Figure 9 shows a similar diagram as Fig. 8, but now with the standstill noise measurement. In this case, the motor-gearbox unit (queer built-in) was rhythmically moved forwards and backwards by hand and that is shown in the raw time diagrams in Fig. 4 (top diagram). This signal characteristics can also be found in Fig. 9 top left (red) curve which is also representing the overall level during the measurement. Regarding loudness, sharpness and roughness can we state almost the same as already written in the latter
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paragraph. So, there is a good agreement of the conclusions between the pass-by and standstill measurements regarding the calculated psychoacoustical parameters. The gear shift noise by the pass-by noise measurements and the standstill noise measurements are both rough noises that are capable to cause disturbance by persons in certain amount.
Fig. 9. Calculation results of 3 psychoacoustic parameters plus overall SPL based on the standstill measurement signal
4.2 Analysis of Whistling Noise In the post-processing of the raw time signal the spectrums and spectrograms were produced with the help of Fast Fourier Transformation in the same manner. The goal is to find evidence which is refer to the failure. Figure 10 illustrates the frequency content of time signal of sound pressure in reverse gear speed. The red coloured markings show the changing of frequency content and sound pressure level in correlation with the motion of the car. In the low frequency range 3rd , 6 th and 9th order of the ignition noise appeared. After a certain speed of the car the noise originated from the ignition is not significant anymore, since the car is not accelerating any more, the load is reduced. In the higher frequency range between 500 Hz and 4000 Hz one can clearly see the increased level of sound pressure when the car is accelerating, then creeping and finally stopping. On the other hand, during rolling, the noise disappeared. After the car was accelerated for 8 s, due to the mechanical energy loss of the motor the velocity started to decrease. However, because of the momentum, the car was still moving for a while without energy investment, roughly for additionally 10 s. Near idle speed the car reached the creeping phase. After that the car was stopped with the brake pedal under 2 s. This chart
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shows us that, the whistling noise only occurs, when the transmission is under load, but only in reverse gear.
Fig. 10. Interior noise in reverse gear speed
The calculation of the psychoacoustic parameters was also done for the raw time signal similar as already described in chapter 4.1. Due to the longer time signal (approx. 30 s) we took relevant time ranges which were related to significant phases of the ride: • 0–8, 5 s: acceleration phase from stillstand • 10–18 s: rollout phase from max. speed to creeping speed • 20–26 s: creeping phase at idle engine speed In Fig. 11 the calculation results are shown for each calculated psychoacoustical parameters and for each significant ride phase. The results show on the top left (red and blue curves) and top right (red and blue curves) diagrams, that for the accelerating and for the creeping phase (in both is the whine/whistle noise present) the overall SPL and the loudness have lower levels as in the rollout phase where no whistle/whine noise is present. Regarding the roughness it can be stated that the noise in the investigated ride phases is not rough, and the three curves are almost on the same level. The sharpness is in case of accelerating (bottom right red curve) and creeping (bottom right blue curve) is distinctly higher than in roll-out phase. So, the results meet well with our expectations that a whine/whistling noise has higher frequency contents, thus it is sharp and could be disturbing for some people too.
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Fig. 11. Calculation results of 3 psychoacoustic parameters plus overall SPL based on the interior noise measurement for 3 ride condition
5 Conclusions Present article illustrated, based on real-life acoustic problems on motor vehicles the difficulties of the characterization and judging of noise phenomena. Due to the lack of detailed information about the gearbox components in both presented cases, unfortunately it was not possible to exactly define whether a failure or a normal operation noise occurred. We can only presume that in the first case (gear shift noise) the occurred noise did not result from a mechanical fault, the gearbox and the gear shifting mechanism worked well. In the second case (whine noise) we can rather interpret the occurred noise as a fault noise since upper class (almost luxury) car must not produce such a noticeable noise. With psychoacoustic investigations could be proved in both cases that the noises can be really perceived disturbing. In the first case the noise was rather rough, in the second case it was rather sharp. The claims of the owner were reasonable. Unfortunately, the objective of the investigations could not be fully reached due to the lack of important details about the gearboxes, nevertheless, the investigations will continue.
References 1. Hermann, T., Hunt, A., Neuhoff, J.G.: The Sonification Handbook, pp. 41–46. Logos Verlag, Berlin, Germany (2011) 2. https://blog.kiversal.com/en/the-ear-structure-and-functions/. downloaded on the 25.03. 2022 3. Möser, M., et al.: Messtechnik der Akustik, Springer Verlag, Heidelberg, Dordrecht, London. N. Y. (2010). https://doi.org/10.1007/978-3-540-68087-1
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4. Kriston, J.B., Jálics, K.: Benefits and limitations of acoustic methods in the vehicle transmission diagnostics – a case study, Design of Machines and Structures, vol. 11, No. 2 (2021), pp. 28–35. http://doi/org/https://doi.org/10.32972/dms.2021.012 5. Zwicker, E.: Psychoakustik, p. p84. Springer Verlag, Berlin, Heidelberg (1982) 6. Zwicker, E., Fastl, H.: Psychoacoustics - Facts and Models, 2nd edn., p. 285. Springer-Verlag, Berlin/Heidelberg/New York (1999) 7. Moore, B.C.J., Glasberg, B.R., Baer, T.: Model for the prediction of thresholds, loudness and partial loudness. J. Audio Eng. Soc. 45(4), 224–239 (1997)
Vibroacoustic Investigation of Automotive Turbochargers Focusing on the Effect of Lubricant Temperature and Bearing Conditions Márk Pesthy(B)
, Richárd Takács , Jan Rohde-Brandenburger , and Csaba Tóth-Nagy
Széchenyi István University, Gy˝or 9025, Hungary [email protected]
Abstract. The detailed mapping of the rotordynamic properties of a turbocharger is an important tool of turbocharger development, where one of the main focuses is lubricant condition. This is well understandable considering the trend of decreasing viscosity levels of engine lubricants. This article introduces rotordynamic investigations of turbochargers performed on a component testbench. The experiments were carried out with different inlet oil temperatures. The goal was to investigate the effect of oil temperature on rotordynamic properties. The component under investigation was a turbocharger of a 4-cylinder gasoline engine equipped with full-floating hydrodynamic bearings. The application of journal bearings can cause several rotordynamic phenomena at high rotational speeds that are typical of turbochargers. Experiments were conducted on new condition and used conditions (over 50.000 km, in urban traffic) of a turbocharger, where oil temperature varied between 20 °C and 120 °C in six steps. Lubricant temperature and bearing wear had a noticeable influence on both the synchronous and subsynchronous vibrational behaviour of the turbocharger. The present paper describes the applied testing processes and evaluation methods. Results contributed to ongoing comprehensive research regarding rotordynamic mapping of automotive turbochargers. Keywords: Turbocharger · Vibroacoustic analysis · Rotordynamics · Engine noise · Measurement techniques General Topic: Noise & Vibration
1 Introduction Turbochargers supported by hydrodynamic bearings are widely used in internal combustion engines in the era of continuously tightening emission standards. However, the development of the internal combustion engines resulted in an increased mechanical and thermal load on the core assembly of turbochargers. Therefore, the circumstances increased the importance of the detailed investigation and interpretation of rotordynamic © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 861–870, 2023. https://doi.org/10.1007/978-3-031-15211-5_71
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phenomena. This branch of research became essential for the lifespan optimization of turbocharger development. Turbochargers can reach exceptionally high rotational speeds. At high speeds, the rotor shaft is considered to be flexible. This flexibility leads to various bending modes of the shaft. These bending modes can have mutual effects on the bearings and the lubricant conditions at different ranges of the rotational speed [1]. The lubrication system of turbochargers is usually part of the lubrication system of the internal combustion engine. The rotor that is designed to spin up to 300.000 rpm, uses a lubricant affected strongly by the engine operation. Due to the appearance of worn particles in the oil, the constant change in the oil temperature, and the new trends of lubricant development (modification of viscosity and additives), the rotor of the turbocharger may face a number of challenges during its operation, that must be taken into account in future developments [2–4]. The application of hydrodynamic floating ring bearings became the conventional solution to support the rotor of an automotive turbocharger due to their low cost and high reliability. However, a disadvantage of hydrodynamic floating ring bearings is that the oil film can produce numerous self-excited oscillations that lead to subsynchronous vibration components emitted from the core assembly of the turbocharger. These subsynchronous vibration components are mainly generated by the inner/outer oil whirl/whip and are present at a wide range of rotational speeds [5]. This phenomenon can result in undesired noise generation and instability of the bearings [6]. Several researchers published papers aiming for a detailed physical understanding of the oil whirl/whip phenomenon. Kuma et al. [7] introduced experimental results that clarified the relationship between the bearing clearance and whirl behaviour. They showed that reduced inner, and increased outer clearances can decrease the magnitude of whirl vibrations. Deng et al. [8] investigated the effects of contaminated lubricant on subsynchronous vibrations. The results proved that constant tone noises could be identified if the floating ring bearings are damaged from oil debris. Chatzisavvas et al. [9] created a numerical hydrodynamic bearing model that enabled the investigation of thermal effects on subsynchronous oscillations and showed that the rotational speeds of floating ring bearings on the turbine side and compressor side differ, due to the nonlinear change of bearing behaviour. Following the findings of several research groups, the paper at hand aims to contribute to the topic of rotordynamic investigations, attempting to find the characteristic change of the subsynchronous (oil whirl/whip) oscillations under different lubricant (temperature/viscosity) and bearing (wear) conditions.
2 Measurement Methodology This chapter introduces the circumstances that are important regarding the measurement results, involving the test environment, running circumstances of the turbocharger, the devices and setup of vibroacoustic measurements, and the evaluation methodology used to comprehend the results from the measurement data.
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2.1 Test Environment and Turbocharger Testing Circumstances Measurements were implemented on a turbocharger testbench capable of hot-gas operation, and operation without burning gas but using an electric heater. The turbine inlet temperature ranged from room temperature to 120 °C. The testbench with the electric heater was used, since the aimed vibroacoustic testing did not require hot conditions, the aimed operations can be achieved with an appropriate gas mass flow of pressurized air. During the tests, the turbocharger was driven by pressurized air that was electrically pre-heated to 90 °C in order to avoid the high amount of condensation at the outlet of the turbine housing. The measurement series involved tests with an Otto engine’s automotive turbocharger equipped with full-floating hydrodynamic bearings. The turbocharger was newly installed on the test bench for the first set of measurements, where the turbocharger was used with different lubricant temperatures in six steps [between 20 °C and 120 °C with a 20 °C increment], while the lubricant pressure was set to constant 3 bar, and the type of lubricant was SAE 0W-20. On every temperature step, the turbocharger run ramp-up and ramp-down starting from 8.000 rpm, reaching 140.000 rpm, then slowing down back to initial rotational speed within 120 s. In the meantime, the most important parameters of the turbocharger (rotational speed, compressor outlet pressure), and lubricant parameters (in-, and outlet temperature and pressure, and volumetric flow) were recorded. After the completion of this first set of measurements, the turbocharger was endurance tested, which is out of the scope of this article but with appropriate conditions (proper oil pressure and temperature). This endurance test aged the turbocharger with 142.000 rpm rotational speed for 120 h, which is equivalent to 60.000–80.000 km in urban traffic. After finishing the endurance tests, the second set of temperature steps was measured, similarly to the first set. This second set aimed to investigate the vibroacoustic conditions, assuming some bearing wear and any sign of rotor condition change on the vibroacoustic results. The lubricant parameters were investigated between the two sets of measurements. The inlet pressure and temperature of the lubricant were set by the controller of the testbench, so these were kept constant. However, the outlet temperature and volumetric flow are values which are the output values of the core assembly of the turbocharger. It is generally assumed that the change of outlet temperature and volumetric flow can provide information on the condition of the core assembly. A comparison of the measurements revealed that the output temperature does not vary between the two sets of measurements. In all cases, the outlet temperature was a little lower than the inlet since the turbocharger is operating with relatively “cold” turbine inlet temperatures, but the difference of inand outlet temperatures are the same for the new and the used core assembly. However, the volumetric flow showed differences between the new and the used conditions. It is assumed that bearing wear was responsible for the increased volumetric flow of the lubricant at all temperatures. Figure 1 shows the volumetric flow of the lubricant at all inlet temperatures. The four columns show the volumetric flow of the new and the used conditions at the initial rotational speed (8.000 rpm) and the final rotational speed (140.000 rpm). It can be observed that the used condition allowed
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higher volumetric flows in all cases. The difference is smaller at high temperatures and high rotational speeds. However remarkable differences can be seen at the initial speed, at all temperatures. This is a certain indicator of the wear of the bearings. The lower speed range is important regarding lifespan since passenger vehicles spend a significant amount of their operation time in idle mode in the real-life conditions.
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Fig. 1. Volumetric flow of the lubricant as a function of the inlet temperature
2.2 Vibroacoustic Test Environment The tests were supplemented with vibroacoustic measurements. An accelerometer was installed on the center housing of the turbocharger to measure the structure-borne noise, and an industrial microphone was used to collect airborne noise. Both sensors were capable of measuring a frequency range of 20 Hz–23 kHz. Sensors were connected to an imc CRONOSflex data acquisition system, where the sampling rate was set to 50 kHz (Fig. 2).
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Fig. 2. Schematic model of hot-gas operating turbocharger testbench with an illustration of the positions of the microphone and accelerometer
2.3 Evaluation Techniques The turbocharger was exercised through ramp-like tests. The results of the vibration and noise measurements included the whole speed range up to a frequency of 23 kHz. Different evaluation methods can provide different aspects for data evaluation. The measurements can be visualized on different spectrums: time-based frequency spectrum, rotational speed-based frequency spectrum, and rotational speed-based order spectrum. Figure 3 shows these three spectrums. The first order (or synchronous) vibrations can be identified as a parabolic line on the time-based that reaches 2.35 kHz in the middle of the measurements time, as a slant line on the frequency spectrum that reaches 1 kHz on 60.000 rpm (2 kHz on 120.000 rpm), and as a vertical line on the order spectrum at order 1. The other important component is the subsynchronous vibration which is about half of the synchronous vibration in both frequency and order domains (or less than half on higher rotational speed). It is important to note that a certain range of the discussed components is in the dark range of the colour scale (700–800 mm/s2 ). However, the adjustment of the colour scale was necessary for the qualitative analysis of the weaker components. Section 3.1 discusses these quantitative results on 2-D diagrams. After processing the main results of the accelerometer and microphone, at different temperatures, it was concluded to work further with the data from the accelerometer because the microphone did not provide appropriate results in the investigation of subsynchronous components. However, the microphone showed an overview of higher frequency components that can be useful to infer the condition of the impellers of the turbocharger.
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Fig. 3. Data measured with the accelerometer
3 Results This chapter aims to introduce the most critical results of the new and used condition of the turbocharger. Since the measurements focused on investigating the effect of lubricant temperature and bearing condition on rotordynamics, the analysis compares results based on these two factors. 3.1 The Effect of Lubricant Temperature on Subsynchronous Vibrations The effect of lubricant temperature was more determining than the effect of aging, since the turbocharger was aged moderately. Figure 4 shows the results: the order range (0.3 – 0.6) of the subsynchronous vibrations were filtered, and then averaged root-meansquare (RMS) values were presented throughout the total rotational speed range. It can be observed that the higher temperature lubricant resulted in lower amplitudes of vibration, especially above 50.000 rpm. This phenomenon may be due to the temperature resulting viscosity change of the lubricant as well as the temperature resulting changes in the bearing clearance. 3.2 The Effect of Bearing Wear on Subsynchronous Vibration The turbocharger run 120 h between the two sets of measurements, which is moderate regarding the lifespan of turbochargers, even if this amount of time means more than 1 billion revolutions at high speed as 140.000 rpm. However, the hypothesis of the second set of measurements aimed to find the possible changes in the range of subsynchronous oscillations between the new and the used cases. The expected differences are most observable on the order spectrum. Figure 5 shows the results of the order spectrum of the new (left) and the used (right) condition of the turbocharger. The colour map range was set to similar for the comparison. Looking at the subsynchronous oscillations, synchronous sideband oscillations can be observed in the case of the used condition. These sidebands are just slightly observable in the case of the new condition. Although the increase of the amplitudes is not too significant, the sidebands may mean that the rotational speed of the two radial bearings changed, that influenced the stability of the rotor.
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Fig. 4. The root-mean-square values of the subsynchronous vibrations on the whole speed range at all measured temperatures
In order to get a more detailed qualitative analysis of the oil whirl/whip phenomenon, the exact positions of the subsynchronous lines were exported and visualized as rotational speeds. Figure 6 shows the calculated possible rotational speeds for the radial bearings. In the case of the new condition, only the main subsynchronous component is shown, and in the case of the used condition, the main subsynchronous component is supplemented with the sideband components since these are clearly identifiable. As it can be seen, the main subsynchronous components have nearly the same characteristics, but the maximal rotational speeds of the used condition are higher in the significant part of the rotor’s speed range. It may be a result of the change of bearing clearances.
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Fig. 5. Order spectrums of the new and used conditions of the turbocharger, measured with 100 °C lubricant temperature
Calculated bearing rotaonal speed (1/min)
The calculated lower and upper rotational speeds based on sidebands follow the shape of the main speed, but the difference increases with the rotational speed, it can reach ±25% difference. 54000 49000 44000 39000 34000 29000 24000 19000 30000
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4 Conclusions The results show that the introduced vibration measurement methods are capable of investigating the effect of the lubricant temperature and bearing conditions. Results were evaluated with a focus on subsynchronous oscillations of the vibrations. The oil whirl/whip phenomenon was observable in the range of vibration order 0.2 to 0.7. Analysis showed that increased lubricant temperatures caused decreased oscillation amplitudes in the subsynchronous range. The measurement of 20 °C and 40 °C resulted in the largest differences in oscillation compared to higher lubricant temperatures. Therefore, it can be assumed that lower lubricant temperatures may have an unfavourable effect on the rotordynamic behaviour, as they result in increased oscillations. Examination of the tested turbocharger in new and used conditions provided results where the characteristic change of subsynchronous oscillations was observable. Further observation is that the measurement of the used condition resulted in sideband components around the oil whirl/whip oscillations. The sidebands were presumed indicators of the change of the rotational speed of the two radial bearings.
5 Future Opportunities Further examination could provide more information to help understand the stability of turbochargers supported by hydrodynamic bearings. The following tests are planned: • Investigation of wider lubricant temperature ranges: Lower lubricant temperature could provide more information on subsynchronous (oil whirl) phenomena and is important regarding the simulation of cold-start experiments. A higher temperature lubricant could be interesting for endurance testing of the lubricant. • Investigation of lubricant quality: oil degradation or dilution may affect the oil whirling significantly. Therefore, the application of artificially aged oil samples on turbochargers is also an opportunity to simulate conditions of reaching the end of lifespan [10, 11]. • Investigations with the application of eddy-current displacement sensors to track shaft motion. The tracking of the orbit of the shaft can provide a step-change in the details of the rotordynamics. • Investigation of other bearing structures as semi-floating or ball-bearings. Ball bearing supported turbochargers are becoming widespread in the automotive market. However, several articles can be found that researchers facing challenges meanwhile rotordynamic optimization [12–14]. Therefore, the analysis of subsynchronous vibrations can be critically important in the case of ball bearing supported turbocharger rotors. In the long term, the detailed mapping and characterization of measurement data with the identified phenomena could contribute to a realization of a real-time condition monitoring of turbochargers in passenger vehicles.
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References 1. Nguyen-Schäfer, H.: Rotordynamics of Automotive Turbochargers. Springer Tracts in Mechanical Engineering, 2 edn. Springer, Cham (2015). https://doi.org/10.1007/978-3-31917644-4 2. Champagne, N., Obrecht, N., Gangopadhyay, A., Zdrodowski, R., Liu, Z.: Enhanced antiwear performance induced by innovative base oil in low viscosity engine oil. SAE Int. J. Fuels Lubr. 10(3), 822–830 (2017) 3. Nagy, A.L., Knaup, J.C., Zsoldos, I.: Investigation of used engine oil lubricating performance through oil analysis and friction and wear measurements. Acta Technica Jaurinensis 12, 237–251 (2019) 4. Yang, K., Fletcher, K.A., Styer, J.P., Lam, W.Y., Guinther, G.H.: Engine oil components effects on turbocharger protection and the relevance of the TEOST 33C test for gasoline turbocharger deposit protection. SAE Int. J. Fuels Lubr. 10(3), 815–821 (2017) 5. Nguyen-Schäfer, H.: Aero and Vibroacoustics of Automotive Turbochargers. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35070-2 6. Schweizer, B.: Oil whirl, oil whip and whirl/whip synchronization occurring in rotor systems with full-floating ring bearings. Nonlinear Dyn. 57, 509–532 (2009). https://doi.org/10.1007/ s11071-009-9466-3 7. Kuma, H., et al.: Development of reduction method for whirl noise on turbocharger. In: Powertrain & Fluid Systems Conference & Exhibition. SAE Technical Paper Series, Rosemont (2007) 8. Deng, D., Shi, F., Begin, L., Du, I.: The effect of oil debris in turbocharger journal bearings on subsynchronous NVH. SAE Technical Paper 2015-01-1285 (2015). https://doi.org/10.4271/ 2015-01-1285 9. Chatzisavvas, I., Nowald, G., Schweizer, B., Koutsovasilis, P.: Experimental and numerical investigations of turbocharger rotors on full-floating ring bearings with circumferential oilgroove. In: ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition (2017). https://doi.org/10.1115/GT2017-64628 10. Nagy, A.L.: Development of an artificial ageing process for automotive lubricants. Spring Wind 3, 98–104 (2019) 11. Nagy, A.L., Zsoldos, I.: Artificial aging of ultra-low viscosity lubricant samples on a programmable oil aging rig. In: Jármai, K., Voith, K. (eds.) VAE 2020. LNME, pp. 139–147. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9529-5_12 12. Biet, C., Baar, R.: Turbocharger test bench extension for acoustic measurements at cold environment conditions. SAE Int. J. Eng. 8, 1790–1797 (2015) 13. Brouwer, M.D., Sadeghi, F., Lancaster, C., Archer, J., Donaldson, J.: Whirl and friction characteristics of high speed floating ring and ball bearing turbochargers. J. Tribol. 135, 041102 (2013) 14. Conley, B., Sadeghi, F.: Experimental and analytical investigation of turbocharger whirl and dynamics. Tribol. Trans. (2020). https://doi.org/10.1080/10402004.2020.1827106
Comparative Testing of Vibrations in Vehicles Driven by Electric Motor and Internal Combustion Engine (ICE) József Zoltán Szabó1(B) and Ferenc Dömötör2 1 Bánki Donát Faculty of Mechanical and Safety Engineering, Institute of Mechatronics and
Vehicle Engineering, Óbuda University, Népszínház str. 8., Budapest 1081, Hungary [email protected] 2 Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Stoczek str. 4., Budapest 1111, Hungary [email protected]
Abstract. Electric vehicles are more and more popular nowadays. This is the reason, why the problem of noise, vibration and harshness of these vehicles is in focus of discussions. This paper presents the results of a study about vibration testing of vehicles driven by electric motor on the one hand and Internal Combustion Engine (ICE) on the other hand. During the tests, carried out on the test rig of the laboratory, vibrations of rolling element bearings have been measured at several rotational speeds in the horizontal, vertical and axial direction. For the measurements a four channels vibration analyser was used. Piezoelectric accelerometers were fixed to the bearing houses by magnets. Acceleration, velocity and displacement signals were recorded in several frequency ranges at various locations of the cars. Special attention was paid to the wheel bearing units. For processing raw vibration data different filters have been used. The authors analysed the condition of the rolling element bearings by using both time signals and vibration spectra. In order to increase the reliability of the failure detection, commercially available post processing methods have been used in the high frequency range. The measured signals of cars driven by electric motor and petrol/gasoline engines were compared on waterfall diagrams. The obtained results demonstrate that vibration measurement is a useful tool for monitoring the condition of rolling element bearings in order to avoid unexpected failures. Keywords: Electric vehicles · Vibration testing · Rolling element bearings
1 Introduction Vibration analysis has been used all over the world to detect rolling element bearing problems. Among the numerous methods wavelet transform is an especially useful tool in the field of condition monitoring. Deak, Menyhart and Czege [1] used five different wavelets to analyse the vibration signal caused by faults on the bearing elements. A. Szanto, T. Mankovits, G. A. Sziki provide an overview [2] of the various drivetrains © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 871–879, 2023. https://doi.org/10.1007/978-3-031-15211-5_72
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used in modern electric and hybrid vehicles. Duer, S.; Zajkowski, K.; Harnicarova, M.; Charun, H.; Bernatowicz, D. published a paper [3] about the diagnostic examination of the House on Water hybrid electric power system. Kriston J.B. and Jalics K. studied the problem localisation of failures of mechanical structures using vibro-acoustical methods [4]. The authors reviewed the state-of-the art monitoring procedures and diagnostic techniques based on vibrational and acoustical signals. Fault diagnosis in squirrel cage induction motors using vibration and current analysis was studied by Ladanyi Gabor and Ladanyi Gergo in their paper [5]. Vibrations of mechanisms designed to reduce the number of revolutions have been studied by M. Rackov, S. Kuzmanovic, I. Knezevic, M. Cavic and M. Pencic in their paper [6].
2 Test Rig and the Tested Vehicle Used for Analysis In the center of our research work there was a hybrid personal car. It has been tested in laboratory circumstances. The output power of the gasoline engine is 110 kW, while the power of the electric motor is 75 kW. Due to a traffic accident the left wheel and the body of the car was seriously damaged (see Fig. 1.).
Fig. 1. General view of the tested hybrid vehicle
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3 Tools and Instruments Used for Analysis In order to take the measurements a 4 channels vibration analyser Adash VA4 Pro has been used. (see Fig. 5). Vibration measurements were taken at the same time by 4 accelerometers, fixed by screws and magnetic pads. Location of the measurement points were as follows: 1. Right wheel (intact) bearing in horizontal, vertical and axial directions, 2. Left wheel (damaged) bearing in horizontal, vertical and axial directions, 3. Gear box output vertical direction only, 4. Electrical motor bearing horizontal, vertical and axial directions. The rotational speed of both left and right-hand side wheels was measured by a laser optical phase equipment using reflecting tapes fixed on the surfaces of the wheels (Fig. 2).
Fig. 2. Arrangement of the car, analyser and the accelerometers
Vibration measurement was carried out while the vehicle was lifted by a car lift in a service workshop. At first only the Internal Combustion Engine (ICE) and then only the electric motor was operating. Virtual speeds of the tested car were as follows: 40 km/h and 60 km/h and 80 km/h. At all measurement points and in all directions, the following parameter setup was used: Displacement: peak to peak, 0–200 Hz, overall and spectra Velocity: peak, 0–200 Hz and 0–1500 Hz, overall and spectra Time signal, HFD (High Frequency Domain) overall peak to peak and RMS Acceleration 0–3200 Hz, overall and spectra Enveloped acceleration spectra, Env2 filter, 0–1000 Hz Enveloped acceleration spectra, Env3 filter, 0–1000 Hz (Table 1)
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4 Measurements Carried Out During the Test
Table 1. Vibration measurements carried out during the test. Serial number
Virtual speed of the car [km/h]
Rotational speed of the wheel [RPM]
Which unit was in operation
1
40
295
Electric motor only
2
60
493
Electric motor only
3
80
643
Electric motor only
4
40
311
Electric motor only
5
60
492
Electric motor only
6
80
639
Electric motor only
7
40
323
Internal Combustion Engine
8
60
514
Internal Combustion Engine
9
80
643
Internal Combustion Engine
10
40
321
Internal Combustion Engine
11
60
491
Internal Combustion Engine
12
80
632
Internal Combustion Engine
13
40
332
Internal Combustion Engine
14
60
494
Internal Combustion Engine
15
80
643
Internal Combustion Engine
16
40
323
Electric motor only
17
60
522
Electric motor only
18
80
643
Electric motor only (continued)
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Table 1. (continued) Serial number
Virtual speed of the car [km/h]
Rotational speed of the wheel [RPM]
Which unit was in operation
19
40
347
Internal Combustion Engine
20
60
485
Internal Combustion Engine
21
80
638
Internal Combustion Engine
22
40
319
Electric motor only
23
60
492
Electric motor only
24
80
671
Electric motor only
Number of vibration measurement series taken altogether: 2x3x4 = 24 for this one car.
5 Results Obtained During the test 12 various parameter setups (overall and spectra) were used, and consequently 12 x 24 = 288 vibration measurements were available for analysis. Special attention was paid to the parameter setup in order to make a difference between the signals of damaged and intact bearing, and vibrations of electric motor and Internal Combustion Engine (ICE). Out of a large number of vibration records, only some of the typical ones containing relevant information are demonstrated here. The effects of the wheel rotational speed on the vibration severity can be seen in the Fig. 3. The amplitude of 1X vibration spectrum component increases when the virtual speed of the car passes 40–60–80 km/h values. The higher the speed, the higher are the vibrations.
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Fig. 3. Waterfall diagram of the vibration velocity spectra and the velocity spectrums are of the left (BK) wheel bearing (damaged) at various virtual speed values of the car (40 km/h and 60 km/h and 80 km/h).
Vibrations of vertical direction (VER) of both wheels at a virtual speed of 80 km/h are shown in the Fig. 4. Differences of the spectra between the intact and damaged bearings are obvious. In order to detect bearing defects demodulated signals of Envelope acceleration were used. Differences between the vibrations of the bearings can be seen on the Fig. 5. Records of the intact bearing (right-hand side wheel) are on the lower diagram, while vibration spectrum of the damaged bearing (left-hand side wheel) is on the upper diagram. These measurements were taken at a virtual speed of 80 km/h.
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Fig. 4. Waterfall diagram of the vibration velocity spectra of the left and right wheel bearing (damaged) at 80 km/ virtual speed values of the car (40 km/h and 60 km/h and 80 km/h).
Fig. 5. Waterfall diagram of the measured vibration spectra using Enveloped Acceleration signal on the damaged, and on the intact wheel bearings at 80 km/h wheel speed
There is a difference between the vibrations of operation by Internal Combustion Engine (higher amplitudes) and an electric motor (lower values). This means, that in case of electric motors, vibration testing enables a more accurate diagnosis (Fig. 6). Calculation of defect frequencies of rolling element bearings is a known practice, published in numerous books and papers. Vibration components of defect frequencies usually cannot be seen on vibration velocity spectra. However, they are easily to be observed on spectra of Envelope acceleration. The wheel bearings of passenger cars are usually double row angular contact ball bearings (Fig. 7.). However, the exact type and dimensions are not easy to identify due to commercial reasons. This is the reason why the defect frequencies can be calculated only roughly.
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Fig. 6. Vibration spectra of the damaged bearing (left-hand side wheel) were measured at 80 km/h virtual speed when the Internal Combustion Engine (upper diagram) and the electric motor (lower diagram) was operating.
In this case a commercial identification number of the car manufacturer was available only. Based on an educated guess the bearing was identified with high reliability as a double row, angular contact ball bearing. Using the vibration diagnostic data processing software, the peak values of the spectra could be identified as the bearing defect frequencies (see the Fig. 7.).
Fig. 7. Identification of the defect frequencies of the wheel bearings
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6 Conclusions In the focus of our research work was the early detection of bearing defects. Tests were carried out on a so called hybrid passenger car, driven by an Internal Combustion Engine (ICE) and electric motor. Due to a traffic accident, the left-hand side wheel of the car was seriously damaged. Measurements were carried out in two or three directions at the wheel bearings using several vibration measurement setups, including Enveloped acceleration at various virtual speed values of the car (40 km/h and 60 km/h and 80 km/h). After analysing the records the following conclusions were drawn. Bearing defects can be found using Envelope acceleration measurement without dismounting the car. Vibration spectra indicate the faults, especially at higher rotational speeds. In case of hybrid cars, it is recommended to use the mode of operation of electric motor only.
References 1. Deak, K., Menyhart, J., Czege, L.: Defect analysis of bearings with vibration monitoring and optical methods. Int. J. Eng. Manage. Sciences (IJEMS) 3(1), 1–12 (2018). https://doi.org/10. 21791/IJEMS.2018.1.1 2. Szanto, A., Mankovits, T., Sziki, G.A.: Review of Drive Systems Applied in Modern Vehicles (In Hungarian). Int. J. Eng. Manage. Sciences (IJEMS) 5(1), (2020). https://doi.org/10.21791/ IJEMS.2020.1.61 3. Duer, S., Zajkowski, K., Harnicárová, M., Charun, H., Bernatowicz, D.: Examination of multivalent diagnoses developed by a diagnostic program with an artificial neual network for devices in the electric hybrid power supply system “house on water.” Energies 14, 2153 (2021). https:// doi.org/10.3390/en14082153 4. Kriston, J.B., Jalics, K.: Localisation of failures with vibro-acoustical methods in case of mechanical structures, GEP, Volume LXXII., Nr. 1–2 of, 31–35 (2021) 5. Ladanyi Sr., G., Ladanyi Jr., G.: Fault Diagnosis in Squirrel Cage Induction Motors using Vibration and Current Analysis Jointly (In Hungarian) GEP, Volume LXIV., Nr. 3 of, 16–20 (2013) 6. Rackov, M., Kuzmanovic, S., Knezevic, I., Cavic, M., Pencic, M.: Analysis of conceptual solutions of universal helical geared reducers. International Journal of Engineering and Management Sciences (IJEMS) 5(2), 64–72 (2020). https://doi.org/10.21791/IJEMS.2020.2.8
Welding
Design and Manufacture Requirements of Welded Car Bodies and Components for Innovative Railway Vehicles István Borhy1 and László Bels˝o2(B) 1 TÜV Rheinland InterCert Kft., Gizella u. 51.-57., 1143 Budapest, Hungary 2 MÁV VAGON Kft., K˝orösi út 1.-3., 5000 Szolnok, Hungary
[email protected]
Abstract. Increasing the significance of railway transportation and developing economic and competitive services is one of the key priorities of the European Union’s transport policy. The demand to increase transport safety while minimising energy consumption and pollutant emission requires the design and deployment of innovative railway vehicles and rolling stock. The requirements for vehicle structures call for the design of structures that are adequate in terms of strength and fatigue, with the lowest possible weight, can be manufactured economically and are easy and cheap to maintain. In the manufacture of railway vehicle structures, the use of welding has great significance. The quality of welded joints significantly affects the reliability and safety of welded vehicle components; thus, learning and applying best industrial practices is essential. In our presentation, we would like to present the application of the requirements for the design, manufacture and conformity assessment of railway vehicle structures through the example of MÁV-START IC + type high-speed passenger coach family. Keywords: Railway vehicle · Design requirements · Weld performance class
1 Introduction As a result of the vehicle development process started by the MÁV GROUP in 2012, the design, production and conformity assessment of the IC + type vehicle family has gained momentum in recent years. The first two prototypes of the vehicle series have been on the tracks with the highest satisfaction from the travelling public since 2014. In the first phase of series production, 10 s-class wagons and 10 multi-purpose wagons (Fig. 1.), which can be used by people with reduced mobility and are also suitable for transporting bicycles, were built. Recently, the second phase of series production is nearing completion. In this phase, 35 multi-purpose coaches and 35 first-class saloon coaches with a buffet and business class compartments will be built in Szolnok. As a continuation of the project, the design of the next member of the vehicle family, the driving trailer car with a second-class compartment section, began in 2020. Information related to the design of the IC + family of vehicles has been presented in detail to © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 883–892, 2023. https://doi.org/10.1007/978-3-031-15211-5_73
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Fig. 1. IC + type multi-purpose coach (Photo: Sándor Czeglédi).
interested professional audiences in recent years [1], with a separate presentation on issues related to the welding of vehicle structures [2, 3]. In this presentation, we aim to review the requirements for the design, manufacture, and conformity assessment of welded structures for IC + type railway vehicles.
2 The Carbody Structures of Railway Vehicles The main construction part of railway vehicles is the car body structure, its task is to ensure the strength and stiffness against the outsider dynamical stresses, the same the reliable protection of passengers and intern area [4]. The forming of the car body structure – as the load-bearing element, is done according to the principle of lightweight construction. This lightweight construction decreases the weight of the coach; via this fact, it has a positive influence on operational costs and decreases the load of landing gears, which is important in the case of vehicles running over 160 km/h speed [5]. The engineering of railway vehicles is a complicated task; it demands a high degree of care a solid theoretical knowledge. Engineers have to accept and evaluate a lot of viewpoints of – each other, often conflicting – requirements in the aim to meet the requirements. To meet all requirements is a hard task; necessary to accept requirements of strength, functionality, reliability, aesthetics, etc., necessary to accept the requirements of manufacturability and controllability, and to minimise the costs of a full lifetime (LCC). The typical failure mode if welded structures of vehicles – in addition to the corrosion – appear and developing of cracks in a result of the exhausting stress; thus for these effects have to be accepted during the full process of engineering and make all necessary measures to avoid these. The requirements of the vehicle structure with sufficient rigidity and the wearing stress is also suitable but at the same time the lowest possible weight, economical to
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manufacture and easy respectively. The development to maintain a low-cost structure is required. The car body structures of the passenger coach are characterised by a rib stiffened hull plate that bears the chassis together with all the load acting on the vehicle. The individual structural elements plating themselves participate in the rigidity of the structure and design of the vehicle’s longitudinal axis parallel forces uptake. In case of a differentiated method of construction for each strengthen elements and panels are made out separately and are compiled into an assembly unit (the IC + passenger coach structure was constructed on the basis of a differentiated method (Fig. 2.).
Fig. 2. Cross-section of the car body structure and the welded structure.
The vehicle structures of the large element construction principle will be designed according to the principle by which the pre-built components (side panels, front- and roof elements, chassis, etc.) are installed in the cabinet assembler machine and welded together.
3 Design Requirements The rules for the interoperability of rail systems within the European Union are laid down in Directive (EU) 2016/797 of the European Parliament and of the Council of 11 May 2016 [6]. The technical requirements for interoperability set out in Commission Regulation (EU) No 1302/2014 of 18 November 2014 (hereinafter referred to as: the Regulation) shall apply to the “rolling stock - locomotives and passenger rolling stock” subsystem [7]. Pursuant to point 4.2.2.4 (3) of the Regulation, “The static and dynamic strength (fatigue) of vehicle bodies is relevant to ensure the safety required for the occupants and the structural integrity of the vehicles in train and in shunting operations. Therefore, the structure of each vehicle shall comply with the requirements of the specification referenced in Appendix J-1, index 7.” The requirements detailed above have been taken into account in the design phase of IC + type coach. During the design of the vehicle family, the platform conception was
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used (Fig. 3), and the following expectations were formulated: the full lifetime of the coach is 30 years, to reducing production costs and to increase the quality and reliability of the vehicles structure.
Fig. 3. Layout drawing of a driving car coach driving trailer car with a compartment second-class section (MÁV-START Zrt.)
The static strength requirements for the vehicle structure are detailed in the MSZ EN 12663–1 standard [8]. The standard classifies each railway vehicle into different structural categories and specifies the type and magnitude of test loads to be applied. The car body structure of IC + coaches belongs to category type P-I. Passive safety of railway vehicles (impact resistance) is regulated by clause 4.2.2.5 of the Regulation (Passive safety). Pursuant to point 4.2.2.5 (5) of the Regulation, “Passive safety is aimed at complementing active safety when all other measures have failed. The mechanical structure of vehicles shall provide protection of the occupants in the event of a collision.” Therefore, the structure of each vehicle shall comply with the requirements of the specification referenced in Appendix J-1, index 8 related to crashworthiness design category C-I. The requirements for the crashworthiness (dynamic strength) of railway vehicles are set out in standard MSZ EN 15227 [9]. The performed dynamic collision simulations confirmed the conformity of the vehicle structure. Figure 4 shows the results of the finite element simulation for the 3rd design collision scenario according to clause 5.3 of MSZ EN 15227. In this case, the train to be assessed is colliding with a large obstacle a level crossing or with common urban road traffic obstacles. This scenario is representative of a collision with an obstacle that can cause significant loss of survival space in the leading end of trains.
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Fig. 4. Finite element simulation of a collision at a railway crossing (eCon Engineering Kft.)
When dimensioning welded railway vehicle structures for fatigue, the relevant loads are specified in standard EN 12663–1. In addition to these requirements, it is essential to determine the value of the fatigue limit stress. In the absence of a standardised European specification, DVS 1612 (for steel structures) [10] and DVS 1608 (for aluminium structures) [11] are generally accepted for determining the limit fatigue stress value. The fatigue dimensioning of welded railway vehicle structures and the determination of the fatigue strength of welded joints are detailed according to Guideline DVS 1612 [12]. The value of the permissible normal stress in the vicinity of the point of the notch can be calculated according to the following equation: (1) min , the value of the exponent x for the different load cases can be where: Rσ = σσmax determined from Table 1.
Table 1. Determining the value of the exponent x based on the nature of the incision
The values of the permissible normal stresses calculated for the different welded joint designs are summarised in the so-called MKJ diagram (Fig. 5):
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Fig. 5. MKJ diagram: permissible normal stress for steel grade S355 [10]
Annex B of DVS Guideline 1612 - the so-called Bauformen-Katalog (Catalogue on building forms) - summarises the requirements for each welded joint design in tabular form (Fig. 6).
Fig. 6. Requirements for one-sided fillet weld and HY joints [10]
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4 Defining the Weld Performance Class of the Joints The Norm EN 15085–3 [13] contains requirements for the design of welded joints in railway vehicles and their components. The welds must be designed according to the loads acting on the structure and the safety categories. The safety category determines the consequences for the safety of life and property in case of the failure of each weld. The standard distinguishes the following safety categories (Fig. 7):
Fig. 7. Safety categories [13]
The welded joint performance class (CP A to CP D) is determined on the basis of the loads acting on the structure and the safety classification of the vehicle structure, which also determines the test requirements (test method and extent of testing) as well as the quality levels for imperfections (Fig. 8. - Fig. 9):
Fig. 8. Weld performance classes [13]
In addition to the fusion welding procedures, the requirements of the standard also specify the requirements for resistance spot-welded joints (Annex F of the Norm MSZ EN 15085–3). Spot welding is widely used in the manufacture of sheet metal vehicle structures (side and front walls, roof elements, etc.). We recommend the related literature on the application of resistance spot welding in the railway industry [14, 15].
5 Welding Technology The requirements for welded joints are specified in the drawing documents (Fig. 10.).
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Fig. 9. Correlation between weld performance classes and inspection classes [13]
Fig. 10. Welding requirements on the drawing (Nr.: 358-AA-140–10-00-b)
Welding technology specifications (for example: joint preparations, tacking welds, welding sequence, etc.) are detailed in the related technology documentation (Fig. 11.).
6 Further Possibilities for Development Plans to develop the IC + vehicle family also open up further opportunities for welded vehicle structures. The more effective method that reduces the mass and cost is to make thinner the plates and beams. The result is the thin-walled welded car body construction. The shrinkage of welded connection makes permanent tensions and warps, thin plates and shells reformate, and it needs to damp vibration. These structures are sensitive to inhibit warping. To avoid these effects, the thin plate walls should be ribbing is required.
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Fig. 11. Detail of welding technology of main cross girder with identification of Welding Procedure Specification (WPS) (Nr.: 140–10-00-T)
The optimal thickness of plates should be calculated. Our target to minimise the local and global deformation of railway vehicle structures. In addition to laser beam welding [16] the resistance spot welding one of the advanced technology of railway vehicle structure manufacturing is, which is proven by a lot of facts (for example, effective manufacturing, ease of automation, even weld quality, etc.). The engineering, economic and environmental factors and the permanent development of technology (periodic energy input, decried the specific heat input) ensure that this technology will be decisive for decades to come. The goal is to determine the optimal technological parameters (e.g., welding sequence plan and parameter window) during the production of spot-welded sheet metal structures. We also want to pay more attention to the economics of production. By economic production, in this case, we mean the pursuit of a minimum cost.
7 Summary In our presentation, we have shown that the role of welding is significant in the production process of railway vehicle structures. The quality of welded joints significantly affects the reliability and safety of vehicle components, so we have drawn attention to the application of best industrial practices. In our presentation, we described the application of the requirements for the design, manufacture and conformity assessment of railway vehicle structures through the example of the IC + type high-speed passenger coach family of MÁV-START Zrt. Finally, we drew attention to further development opportunities.
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References 1. István, M.: IC+ belföldi InterCity Vasúti Kocsik Tervezése És Fejlesztése (ISBN 978–963– 9852–20–4), MÁV-START Zrt., Budapest (2014) 2. Borhy, I., Kovács, L.: A lightweight design approach for welded railway vehicle structures of modern passenger coach. In: Jármai, K., Bolló, B. (eds.) Vehicle and Automotive Engineering. LNME, pp. 425–437. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51189-4_37 3. Erdei, A., Stósz, I., Kovács, L.: Welding aspects of the development of the IC+ vehicle family. In: Proc. of 6th European Conference JOIN-TRANS 2022 “Joining and Construction of Railway Vehicles”, pp. 11–18, Warsaw (2022) 4. István, Z.: Vasúttechnikai kézikönyv (ISBN 963 204 127 5), Magyar Államvasutak Zrt., Budapest (2006) 5. Guido, R.: Könny˝uszerkezetek a járm˝u- és gépiparban (ISBN 963 17 0765 2), Tankönyvkiadó, Budapest (1976) 6. Directive (EU) 2016/797 of the European Parliament and of the Council of 11 May 2016 on the Interoperability of the rail system within the European Union, https://eur-lex.europa.eu/ legal-content/en/ALL/?uri=CELEX%3A32016L0797 Accessed 04 Mar 2022 7. Commission Regulation (EU) No. 1302/2014 of 18 November 2014 concerning a technical specification for interoperability relating to the “rolling stock - locomotives and passenger rolling stock” subsystem of the rail system in the European Union, https://eur-lex.europa.eu/ legal-content/en/TXT/?uri=CELEX%3A32014R1302 Accessed 04 Mar 2022 8. MSZ EN 12663–1:2010+A1:2015 Vasúti alkalmazások. Vasúti járm˝uvek kocsiszekrényeinek szerkezeti követelményei. 1. rész: Mozdonyok és személykocsik (és a teherkocsik alternatív módszere), MSZT, Budapest (2015) 9. MSZ EN 15227:2020 Vasúti alkalmazások. A vasúti járm˝uvek ütközésállósági követelményei, MSZT, Budapest (2020) 10. Guideline DVS 1612:2014 Design and endurance strength analysis of steel welded joints in rail-vehicle construction, DVS e.V., Düsseldorf (2014) 11. Guideline DVS 1608:2011 Design and strength assessment of welded structures from aluminium alloys in railway applications, DVS e.V., Düsseldorf (2011) 12. Borhy I., Kovács L.: Hegesztett vasúti járm˝uszerkezetek fáradásra történ˝o méretezése. In: Gáti, J.: (eds.) Proceedings of 25. Jubileumi Hegesztési Konferencia (ISBN 978–615–5018–00–8), pp. 439–446, Budapest (2010) 13. MSZ EN 15085–3 Vasúti alkalmazások. Vasúti járm˝uvek és részegységeik hegesztése. Tervezési követelmények, MSZT, Budapest (2022) 14. Borhy I., Szabó P.: Possibilities of predicting the fatigue life of resistance spot welded joints, Hoorwood Publishing, Chichester (UK), K. Jármai, J. Farkas (eds.) (ISBN 978–1–904275– 28–2), pp. 201–210, (2008) https://doi.org/10.1533/9781782420484.4.201 15. Borhy, I.: Termomechanikusan hengerelt nagyszilárdságú acélok ellenállásponthegesztésének lehet˝oségei a vasúti járm˝uszerkezet gyártásban, In: Török, I.: (eds.) Proceedings of 29. Nemzetközi Hegesztési Konferencia (ISBN 978–963–358–160–5), pp. 169–180, Miskolc (2018) 16. Neubert, J., Keitel, S., Schuster, J.; Langner, C., Wendler, W.: Laserstrahlschweißen von Seitenwandsegmenten für den Schienenfahrzeugbau und deren prüftechnische Bewertung. In: Proc. of 5. Fachtagung „Fügen und Konstruieren im Schienenfahrzeugbau”, pp. 42–50, Halle (Saale) (2003)
The Effect of Multiple Flame Straightening on High-Strength Steels Applied in Vehicle Industry Marcell Gáspár1(B) , László Gyura2 , and Raghawendra P. S. Sisodia1 1 University of Miskolc, 3515 Miskolc, Hungary
{metgaspar,metraghu}@uni-miskolc.hu 2 Linde Gas Hungary Ltd, 1097 Budapest, Hungary [email protected]
Abstract. High strength structural steels (HSSS) in medium plate thickness are frequently used as the structural elements of trucks, railway wagons, cranes and earthmoving machines. Since large, welded components are produced in many cases, the use of flame straightening is unavoidable after welding to comply with the strict dimensional tolerances. Due to the not very concentrated heat source, the process can cause significant changes in the microstructure. Among the flammable gases (hydrocarbons), acetylene is typically used for flame straightening. To compensate for the welding deformations, heat source may pass through the part multiple times. Therefore, there is a relatively high risk of the repeated thermal effect reducing the heated area’s strength and toughness. In the present experimental work, the effects of multiple flame straightening were compared between S355J2 + N mild steel and an S960QL HSSS. The thermal cycles were determined by using thermocouples arrangement during real experimental circumstances. Three characteristic peak temperatures were selected for the investigations with the Gleeble 3500 thermophysical simulator: 675 °C, 800 °C, and 1000 °C. The applied specimen geometry was 10 × 10 × 70 mm. The simulated heated zones were analysed by optical microscopic analysis, hardness tests, and instrumented Charpy V-notch pendulum impact tests. Based on the results, using a maximum temperature above A1 during flame straightening is not recommended since a significant toughness reduction can occur in the intercritical temperature range. However, multiple heating in the same location typically does not cause further negative changes and may even slightly improve toughness. Keywords: Flame straightening · High Strength Structural Steels (HSSS) · Physical simulation · Instrumented charpy V-notch pendulum impact test · Multiple thermal cycle
1 Introduction Due to the weight reduction, there is an increasing demand for the application of high strength steels (HSS) in the vehicle industry. The different body parts of vehicles are generally connected by welding [1, 2], although the welding heat input results in a relatively © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 893–903, 2023. https://doi.org/10.1007/978-3-031-15211-5_74
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large deformation of the welded structure. The higher strength categories of structural steels are generally more sensitive to the welding heat input, softened and hardened zones form in the welded joints. Additionally the toughness and the fatigue resistance can be significantly reduced [2–5]. The impurity level fundamentally determines the toughness of the base material. Furthermore, the inclusions can have a detrimental effect on the heat-affected zone (HAZ) characteristics [6]. Since, in many cases, large, welded components are produced (e.g., trucks, cranes), the use of flame straightening is unavoidable after welding to comply with the strict dimensional tolerances. Due to the not particularly concentrated but very high temperature heat source, the process can cause significant changes in the microstructure, which can deteriorate the outstanding mechanical properties of these steels. This may be particularly true for the investigated quenched and tempered HSSs where the high yield strength and toughness is realised by alloying (e.g., Mo, Cr) and microalloying elements (Al, Ti, Nb, V), rolling technology followed by the application of water quenching and high temperature tempering. Among the flammable gases (hydrocarbons), acetylene is typically used for flame straightening [7, 10]. To compensate for the unfavourable deformation of the welded structure, the heat source often passes through the part multiple times. In real straightening tasks, it is often the case (especially in the production of the first parts, when the manufacturer does not yet have sufficient experience) that the heating carried out for straightening does not fully achieve the desired effect, and further treatments are required. Based on the principle of straightening, reheating the already heated and cooled part clearly further increases the total deformation. However, the question arises of what kind of effects can be expected on the reheated steel. In the present experimental work, the effects of multiple flame straightening were compared between S355J2 + N mild steel and an S960QL HSS. Thermocouples were used to measure the thermal cycles of flame straightening in real-experimental conditions. Three characteristic peak temperatures were selected for the investigations: 675 °C, 800 °C, and 1000 °C. Then, the determined thermal cycles were used for the physical simulation of flame straightening in a Gleeble 3500 thermophysical simulator. The simulated heated zones were analysed by optical microscopic analysis, hardness tests, and instrumented Charpy V-notch pendulum impact tests.
2 Review on Flame Straightening Experimental Research The effect of flame straightening on the mechanical properties of three base materials from different grades (S235JR, S460ML, S690QL) was presented [8]. Although the summary highlights the benefits of the acetylene-oxygen flame, the actual experiments were performed with a propane-oxygen burner. The heating was conducted along a straight line in the middle of the 500 mm wide and 600 mm long, 20 mm thick plates. The temperature was measured with 4 thermocouples placed 2 mm below the 600 mm long heating line, and put in evenly spaced holes from below. The velocity of the burner was 3.7 mm/s for the S690QL steel and 2.5 mm/s for the other two materials. Under these boundary conditions, the thermocouples measured an average of 794 °C for S235JR, 824 °C for S460ML, and 663 °C for the S690QL steel. These values correspond to the recommended straightening temperature for the material. The temperatures are described
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as above the Ac1 temperature of the steels (although this is questionable for the S690QL steel) but, in any case, below the Ac3 value. The heated plates were cooled in the open air. In the case of S235JR steel, the grain structure was remarkably roughened. A more significant amount of tertiary cementite at the grain boundaries was identified. In the medium-strength material, only slight modifications were observed in the grain structure. The flame straightening resulted in a more inhomogeneous, coarser grain structure at the higher strength category. The hardness of the low-strength material did not change significantly, contrary to the higher strength category steel. In the case of quenched and tempered steel, the heating could be considered an additional tempering process. According to the author’s proposal, changes in the properties of individual steels can be determined experimentally with greater certainty. While the lowest strength steel did not show a change in hardness under the influence of heat, the yield strength, elongation, and toughness of the material also deteriorated. The degree of hardness loss was significant for higher strength materials, and there was also a slight loss of strength. At the same time, the plasticity improved somewhat, and there was no significant change in the temperature dependence of the toughness, which may have been since the maximum heating temperature did not reach 700 °C. A similar but even more extensive study and its results are reported in the [9] literature. A total of five steels of different strengths were subjected to line heating with an acetylene-oxygen burner with slow air cooling on 20 and 50 mm thick plates. For steels with strength up to 460 MPa, the heating temperature was 800–850 °C, and for the upper two grades, it was 650–700 °C. Due to heat, there was no significant change in either the yield strength or the change in tensile strength for any of the steels. In the case of the yield point, there was a slight decrease at the longer heating time (20 mm plate) and a slight increase at the shorter heating time (50 mm plate). In both cases, the change in tensile strength was within 4% for all steels, which is practically negligible. There was a decrease in hardness for all materials, averaging 22% for more prolonged heating and 9.5% for thicker sheets. Overall, the most significant change was observed for the 20 mm plate thickness of the lowest strength steel, both in the impact energy value and the increase in the transition temperature. This is due to the precipitation of tertiary cementite at the grain boundary already mentioned above. For the two high strength materials, 50 mm plates with short heat resistance and a slight deterioration in hardness were observed, mainly due to a slight increase in the plastic-brittle transition temperature.
3 Applied Materials and Methods 3.1 Chemical Compositions and Mechanical Properties The experiments were performed on conventional 15 mm thick S355J2 + N fine-grained steel and a 10 mm thick S960QL high strength steel, which is often used to produce vehicle parts (trucks, railway wagons, cranes, and earthmoving machines). The chemical compositions and the mechanical properties of the examined 15 mm plate-thick are shown in Table 1 and Table 2.
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[%]
C
Si
Mn
P
S
Al
B
Cr
S355J2 + N
0.18
0.35
1.55
0.014
0.003
0.036
0
0.022
S960QL
0.16
0.20
1.22
0.011
0.001
0.055
0.001
0.20
[%]
Cu
Mo
Nb
Ni
Ti
V
Ce (IIW)
CET
S355J2 + N
0.01
0.005
0.005
0.045
0.003
0.005
0.451
0.341
S960QL
0.01
0.605
0.015
0.05
0.002
0.037
0.536
0.355
Table 2. Mechanical properties of base materials. Rp0.2 [MPa] S355J2 + N S960QL
Rm [MPa]
422
563
1027
1058
A5 [%]
KV [J]
HV10 [-]
26.8
166*
152
15
87**
348
* -20 °C, ** -40 °C
3.2 Temperature Profiles for the Physical Simulations To investigate the effects of flame straightening, temperature profiles were recorded by thermocouples under different conditions of flame straightening [10]. For our experiments, we chose one of the most common heating methods (line heating) for welded structures by moving the burner mechanically. The examined 300 × 300 mm plates (without weld line) were heated along their centerline with the appropriate power burners for the plate thickness. Based on the registered temperature profiles, thermal cycles 1200
1000
T [°C]
800
600
1000 °C 800 °C 675 °C
400
200
0 0
100
200
300 t [s]
400
500
600
Fig. 1. Generated flame straightening temperature profiles for the physical simulations.
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have been generated for the physical simulation experimental program (Fig. 1.): below (but close to) Ac1 temperature of 675 °C, between Ac1 -Ac3 temperature of 800 °C and above Ac3 (overheated) 1000 °C. The measured t8/5 cooling time was 82 s at 800 °C and 35 s at 1000 °C, which values are significantly longer than the typical cooling time range of gas metal arc welding (GMAW) despite the relatively characteristic fast heating and cooling of acetylene [10]. 3.3 Experimental Circumstances A Gleeble 3500 thermophysical simulator was applied for the physical simulation experiments of flame straightening [10]. The used specimen geometry was 10 × 10 × 70 mm. K-type (Ni-Cr/Ni-Al) thermocouples were welded to the samples for precise temperature control, and the tests were performed in the vacuum chamber of the equipment. The same specimens with different maximum temperature thermal cycles were loaded by one time, two times, and four times based on the determined temperature profiles for acetylene heating and simple air cooling.
4 Results and Discussion 4.1 Optical Microscopic and Hardness Tests After the successful simulations, the specimens were cut at the thermocouples, and the surface was prepared for microstructural analyses by grinding, polishing, and etching with 2% Nital (2% HNO3 ). A Zeiss Axio Observer D1m type microscope was used for the optical microscopic tests. The hardness tests (Fig. 4.) were performed by a Reicherter UH250 universal macro hardness tester. For all three steels, it can be observed that the microstructure and mechanical properties obtained during the production of the base material typically changed in an unfavourable direction under the influence of heat. For S355J2 + N, the characteristics of the rolled ferrite-perlite microstructure can be observed (Fig. 2. (a-f)). However the perlite starts to decompose above Ac1 (Fig. 2. (d-f)). At 1000 °C peak temperature, the rolled direction microstructure was transformed and recrystallised, and a mixture of ferrite and bainite can be identified (Fig. 2. (g-i)). At S960QL steel, a tempered martensitic microstructure can be observed under Ac1 (Fig. 3. (a-c), while the martensite-austenite islands form due to intercritical heating in Fig. 3. (d-f). At 1000 °C, the bainitic-martensitic microstructure can be observed (Fig. 3. (g-i). It is clear that 675 °C peak temperature had practically no significant effect on the hardness of both investigated steels. In the case of normalised steel, a small amount of grain roughness occurred at this temperature due to repeated heating. In the case of the S355J2 + N mild steel, the rolled structure characteristic of the base material remained even at temperatures below Ac3 . All this suggests that the cumulative holding time of multiple heating is not sufficient for the subcritical (simple) softening to take place completely. For the investigated HSS above Ac3 , quadruple heating caused a significant but not critical increase in hardness. The microstructure of the steels was virtually unchanged compared to single heating.
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Fig. 2. Optical microscopic images of the S355J2 + N steel microstructure at multiple acetylene heating and air cooling (2% Nital).
Overheating compared to the technological temperature of the flame straightening (above Ac3 ) already caused hardness increases even after single heating, which increased slightly further due to multiple heating. In the case of S960QL steel, the hardening can be identified more clearly; approximately a 10% increase was observed in quadruple heating. As a result of multiple heating, none of the steels still reached the allowable critical hardness values (Fig. 4.) of the governing standard (EN ISO 15614 Table 3. [11]), which is 380 HV10 for S355J2 + N (1st steel group according to ISO/TR 15608 [12]) and 450 HVA10 for S960QL (3rd steel group according to ISO/TR 15608 [12]). 4.2 Instrumented Charpy V-notch Pendulum Impact Tests Standard impact test specimens (10 × 10 × 55 mm) loaded by each thermal cycle were used for the Charpy V-notch pendulum impact tests performed with PSD 300/150 instrumented equipment. According to EN 10025–6 [13], the required minimum impact energy is 27 J at − 40 °C for S960QL steel, while EN 10025–2 [14] guarantees the same impact energy value at −20 °C for S355J2 + N. According to the material certificates, both steels have significantly higher toughness than the governing requirements: 87 J for S960QL and 166 J for S355J2 + N.
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Fig. 3. Optical microscopic images of the S960QL steel microstructure at multiple acetylene heating and air cooling (2% Nital).
HVmax, S960QL= 450
Hardness [HV10]
450 HVmax, S355J2+N = 380
400 350 300
348
250 152
200 150 100 50 0 1x
2x
4x
1x
675 °C
2x
4x
800 °C S355J2+N
1x
2x 1000 °C
S960QL
Fig. 4. Measured average hardness on the simulated samples.
4x
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Based on the results (Fig. 5.) relatively large standard deviation was observed. It can be concluded that repeated heating typically does not impair the toughness values formed after one heating. In the case of normalised steel, it can be clearly stated that multiple heating below the Ac3 temperature tends to improve the material’s toughness, originating from the decomposition of perlite (Fig. 2. (d-f)). In the overheated state (above Ac3 ), however, multiple flame straightening did not significantly increase the toughness of the steel, which has already hardened due to single heating. The observed brittle characteristics of the samples (Table 3–4 and Fig. 6.) during the instrumented impact tests clearly confirmed this toughness deterioration.
Impact energy [J]
300 250 200
166 J
150 87 J
100 27 J
50 0 1x
2x
4x
1x
675 °C
2x
4x
1x
800 °C S355J2+N -20 °C
2x
4x
1000 °C
S960QL -40 °C
Wi/KV [%]
Fig. 5. Measured impact energy values on the simulated samples during the instrumented Charpy V-notch impact tests.
100 80
60 40 20
0 1x
2x 675 °C
4x
1x
2x
4x
1x
800 °C S355J2+N -20 °C
2x
4x
1000 °C
S960QL -40 °C
Fig. 6. The percentage ratio of the energy of crack initiation to the total absorbed energy during instrumented Charpy V-notch impact tests.
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Practically below Ac1 , a similar increase in toughness can be observed for investigated HSS. At the intercritical temperature between Ac1 and Ac3 , where the toughness decreased significantly even after one heating, the measured values did not change significantly under the effect of multiple heating’s. The impact energy values are typically around the critical 27 J (see green line in Fig. 5.). The local brittle martensite-austenite (M-A) islands did not transform under the effect of multiple heating under relatively short-term heating loads in the examined temperature interval. Above Ac3 , however, a remarkable increase in toughness is observed due to multiple heating. The results of the instrumented impact tests showed that multiple heating at this temperature increases the energy for crack propagation (Fig. 6.). In our practical experience, many welders are reluctant to reheat areas that have already been heated and then cooled. Based on the simulation experiments presented, multiple heating at the same location in the examined steels does not cause a significant negative change in the material’s structure. It may even increase the toughness Table 3. The registered force-displacement diagrams during instrumented Charpy V-notch pendulum impact tests at S355J2 + N. Tmax [°C]
1x
2x
4x
Wi/KV [%]
Wt/KV [%]
Wi/KV [%]
Wt/KV [%]
Wi/KV [%]
Wt/KV [%]
55
45
46
54
30
70
70
30
77
23
36
64
67
33
53
47
63
37
675
800
1000
902
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of the material. If additional heating is required after the first heating to increase the deformation, the heating can be repeated in the same area. Table 4. The registered force-displacement diagrams during instrumented Charpy V-notch pendulum impact tests at S960QL. Tmax [°C]
1x
2x
Wi/KV [%]
Wt/KV [%]
Wi/KV [%]
24
76
21
61
39
38
62
4x Wt/KV [%]
Wi/KV [%]
Wt/KV [%]
79
19
81
74
26
70
30
30
70
27
73
675
800
1000
5 Conclusions Based on the performed physical simulation experiments and the related material tests, it can be stated that it is not recommended to use a maximum temperature above Ac1 during flame straightening. Not only hardening but also softening can occur in the heated zone due to the heat effect of flame straightening. It is important to note that even with the investigated highest strength steel, the degree of hardening did not reach the critical 450 HV10 value specified in the governing EN ISO 15614–1 [11] standard. If only a hardness test is used to check the effect of flame straightening, it can even be concluded that there has been no critical change in the heated zone for structural integrity. However,
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non-compliance with the proposed boundary conditions clearly causes microstructural changes in the heated area that leads to a decrease in the toughness of the steel. A series of experiments in the physical simulation of the thermal zone revealed that the normalised, or quenched and tempered microstructure resulting from careful metallurgical technologies are irreversibly changed by the thermal cycle of straightening. In the case of investigated S355J2 + N and S960QL, the toughness at the intercritical temperature can fall to the level of the required 27 J value. However, multiple heating in the same location typically did not cause further negative changes and may even slightly improve toughness.
References 1. Májlinger, K., Katula, L.T., Varbai, B.: Prediction of the shear tension strength of resistance spot welded thin steel sheets from high- to ultrahigh strength range. Periodica Polytechnica Mechanical Eng. 66(1), 67–82 (2022) 2. Lukács, J., Mobark, H.F.H., Dobosy, Á.: High cycle fatigue resistance of 700 MPa and 960 MPa strength categories high strength steels and their gas metal arc welded joints. Lecture Notes in Mechanical Eng. 22, 539–555 (2021) 3. Koncsik, Z., Lukács, J., Nagy, G.: Fracture mechanical analysis of gleeble simulated heat affected zones in high strength steels. Periodica Polytechnica Mechanical Eng. 66(1), 83–89 (2022) 4. Kovács, J., Lukács, J.: Effect of the welding thermal cycles based on simulated heat affected zone of S1300 ultrahigh strength steel. Key Eng. Mater. 890, 33–43 (2021) 5. Maurer, W., Ernst, W., Rauch, R., Vallant, R., Enzinger, N.: Evaluation of the factors influencing the strength of HSLA steel weld joint with softened HAZ. Welding in the World 59(6), 809–822 (2015). https://doi.org/10.1007/s40194-015-0262-z 6. Tervo, H., Kaijalainen, A., Pikkarainen, T., Mehtonen, S., Porter, D.: Effect of impurity level and inclusions on the ductility and toughness of an ultra-high-strength steel. Mater. Sci. Eng., A 697, 184–193 (2017) 7. Linde Group: Fundamentals of Flame Straightening, White Paper, 4–26 (2009) 8. Lacalle, E., et al.: Influence of the flame straightening process on microstructural, mechanical and fracture properties of S235 JR, S460 ML and S690 QL structural steels. Exp. Mech. 53, 893–909 (2013) 9. Schäfer, D., Rinaldi, V., Beg, D.: Optimisation and improvement of the flame straightening process. Research Fund for Coal and Steel, ISBN: 978–92–79–22426–3 (2012) 10. Gyura, L., Gáspár, M., Balogh, A.: Investigation of thermal effects of flame straightening on high-strength steels. In: Jármai, K., Voith, K. (eds.) VAE 2020. LNME, pp. 526–538. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9529-5_46 11. EN ISO 15614:2017 Specification and qualification of welding procedures for metallic materials. Welding procedure test. Part 1: Arc and gas welding of steels and arc welding of nickel and nickel alloys 12. ISO/TR 15608:2017 Welding. Guidelines for a metallic materials grouping system 13. EN 10025–6:2020 Hot rolled products of structural steels. Part 6: Technical delivery conditions for flat products of high yield strength structural steels in the quenched and tempered condition 14. EN 10025–2:2020 Hot rolled products of structural steels. Part 2: Technical delivery conditions for non-alloy structural steels
Bending Fatigue Characteristics of Butt Joints by Laser-Arc Hybrid Welding for Steel Bridge Members Gang Chen1 , Natsumi Sakai1 , Mikihito Hirohata1(B) , Kengo Hyoma2 , Naoyuki Matsumoto3 , and Koutarou Inose2 1 Graduate School of Engineering, Osaka University, Suita, Japan
[email protected] 2 IHI Corporation, Yokohama, Japan 3 IHI Infrastructure Systems, Tokyo, Japan
Abstract. To investigate the application of laser-arc hybrid welding to manufacturing steel bridge members, a series of experiments were carried out. One-pass full-penetration butt joints employing steels for bridge high-performance structures (SBHS500) were fabricated by the laser-arc hybrid welding with a thickness of 15 mm, which applied the specified welding condition based on the previous study to avoid generating cracks and defects. Subsequently, the same dimensional arc welded butt joints utilizing SBHS500 were fabricated. The four-point bending fatigue tests were conducted on the laser-arc hybrid welded butt joints and arc welded butt ones. In order to elucidate the bending fatigue characteristics of butt joints fabricated by the laser-arc hybrid welding, the fatigue strength of laser-arc hybrid welded butt joints were compared with that of arc welded butt joints. The experimental results revealed that the laser-arc hybrid welded butt joints had the same or higher fatigue strength than the arc welded butt joints under the stress range of 250 MPa or more, and no cracks were observed under the stress range of 200 MPa. The fatigue strength of the butt joints using laser-arc hybrid welding was improved compared with those using conventional arc welding, possibly because the hardening due to the rapid heating and cooling around the weld toe in laser-arc hybrid welded butt joints was larger than that in arc welded butt joints. Keywords: Laser-arc hybrid welding · Butt joints · Steel bridge · Fatigue
1 Introduction Laser-arc hybrid welding (so-called, hybrid welding), as an effective method for joining and assembling steel bridge structures, has been receiving considerable attention [1]. Hybrid welding simultaneously utilizes a high-power laser and a conventional arc integrated to provide primary and secondary heat sources. Due to the synergistic action of the laser beam and welding arc, hybrid welding offers many advantages over laser welding and arc welding alone [2], such as higher welding speed, deeper penetration, better weld quality with reduced susceptibility to defects and cracks [3], good gap bridging ability © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 904–916, 2023. https://doi.org/10.1007/978-3-031-15211-5_75
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[4], as well as process stability and efficiency. Therefore, it is expected to be applied to the fabrication of steel bridge structural members. Fatigue strengths of welded joints are regarded as one of the most important factors in determining the service life and safety of steel bridge structures. Japan has specially developed the SBHS (Steels for Bridge High Performance Structure), which features high strength, workability, and weldability by applying TMCP (Thermo-mechanical Control Process). Due to these merits, it was estimated that the weight of steel bridge structures can be reduced by 4% and the construction cost can be reduced by 2% compared with the conventional SM570 [5]. Hence, SBHS is expected to be widely utilized in steel bridge structures. The previous study investigated fatigue strengths of out-of-plane gusset welded joints using SBHS and revealed that the fatigue performances of welded joints using SBHS are almost the same as those of conventional structural steels [6]. In our previous study [7], a series of experiments and investigations has been conducted on the butt joints by hybrid weldings, such as weld cracking tests for identifying proper welding conditions, experiments for measuring distortion and residual stress, and bending tests and tensile tests for investigating the soundness of hybrid welded butt joints. However, the fatigue strengths of hybrid welded butt joints have yet to be tested. In this study, the fatigue properties of hybrid welded butt joints were compared to those by the conventional arc welding to elucidate the fatigue properties of butt joints by hybrid welding, and an in-depth investigation into the differences between the two types of welded joints was conducted. Eventually, the fatigue properties of hybrid welded butt joints were confirmed.
2 Fabrication of Welded Joints 2.1 Material and Specimen The material employed in this study was SBHS500 specified by Japanese Industrial Standards (JIS G 3140) [8]. The chemical compositions and mechanical properties of the material are illustrated in Table 1 (mill sheet values). In our previous study [7], for fabricating one-pass full-penetration butt joints by hybrid welding, the experimental specimens were fabricated to the shapes and dimensions shown in Fig. 1 (a) and (b). For fabricating welded joints where a misalignment due to tack welding does not occur, in reference to a previous study [9], the specimen was not assembled with two plates, but with only one plate with a groove in the center of it. The width and length of the plate were 500 mm and 300 mm, respectively. The length of the weld line was 340 mm because of the protruding tabs for the start and end of the weld line. A slit with 0.3 mm in Table 1. Chemical compositions and mechanical properties of SBHS500. Chemical compositions [mass %] C
Si
Mn
P
S
Mechanical properties Ni
Cr
0.10 0.24 1.57 0.008 0.003 0.02 0.12
Yield stress Tensile strength Elongation [MPa] [MPa] [%] 559
643
35
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width was set to perform welding. The plate thickness became 15 mm from the original thickness of 16 mm in that the mill scale with a range of 50 mm width centered on the slit was removed on the obverse and reverse surfaces of the specimen.
Fig. 1. Shape and dimension of specimen for hybrid welding and arc welding.
2.2 Fabrication of Welded Joints In our previous study [7], one-pass full-penetration butt joints by hybrid welding were fabricated, and six-pass butt joints by arc welding were done. The welding conditions for hybrid welding are shown in Table 2. The arc welding conditions were the current of 200 A, the voltage of 26–27 V and the welding speed of 0.15–0.25 m/min. The welding wire for hybrid welding and arc welding were G49AP3M16 (JIS Z 3312) and YGW11 (JIS Z 3312). The wire diameter was 1.2 mm. Cross-sectional macroscopic photographs of hybrid and arc welded joints are shown in Fig. 2. No welding defects such as cracks or under-fill were observed in hybrid welded joints. Similarly, no welding defects were observed in arc welded butt joints. Table 2. Welding conditions for hybrid welding. Steel grade
Laser power [kW]
Welding speed [m/min]
Arc current [A]
Arc voltage [V]
SBHS500
13.01
1.6
264
28.1
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Fig. 2. Macroscopic photographs of welded joints.
3 Vickers Hardness Test and Bead Shape 3.1 Vickers Hardness Test Vickers hardness tests were conducted on the welds of hybrid welded joints and arc welded joints. The test force was set to 0.98 N. The loading points for hybrid welded joints were set on the lines ➀ and ➁ shown in Fig. 3 (a). The lines ➀ and ➁ have gone through arc heat input area (z = 14 mm) and laser heat input area (z = 7.5 mm), respectively. The loading points on the lines ➀ and ➁ were 50 points spaced 0.2 mm apart, centered on the weld metal in hybrid welded joints, through the heat-affected zone on both sides, and down to base metal. The loading points in arc welded joints were set on the lines ➂, ➃ and ➄ shown in Fig. 3 (b). The loading points on the lines from ➂ to ➄ were 50 points spaced 0.4 mm apart, centered on the weld metal of arc welded joints, passing through the heat-affected zone on both sides, and down to the base metal. The results of Vickers hardness tests of the butt joints by hybrid welding and arc welding are shown in Fig. 4 and Fig. 5. It was confirmed that the weld metal for hybrid welded joints was approximately 1.3 times harder than the base metal in hybrid welded joints. Moreover, the heat-affected zone (approximately 1 mm) of the hybrid welded joints was narrow, and the dramatic change in hardness was confirmed within the heataffected zone. On the other hand, no difference in hardness was observed in the weld metal and heat-affected zone of arc welded joints compared to the base metal in arc welded joints. The maximum hardness of the weld depends on the hardness of 100% martensitic microstructure and martensite content, and its maximum hardness increases with an increasing cooling rate for the weld below the critical cooling rate at which the microstructure became 100% martensite [10, 11]. The reason for the increases hardness of the hybrid weld compared to the arc weld may be due to the higher cooling rate of the hybrid weld.
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Fig. 3. Hardness measurement position.
Fig. 4. Vickers hardness for hybrid welded joints.
Fig. 5. Vickers hardness for arc welded joints.
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3.2 Bead Shape The surface profiles of the weld beads of specimens for hybrid welding and arc welding were measured with a laser displacement meter. The x-axis and z-axis were set as shown in Figs. 6 (a) and 7(a). A 30mm wide area centered on the weld was measured. The number of measurement points was 201 at 0.1 mm intervals. The measurement results are illustrated in Figs. 6(b) and 7(b). For the hybrid welded joints, undercuts were observed with 0.14 mm at the left weld toe and 0.16 mm at the right weld toe. The depth of undercuts was 0.35 mm at the left weld toe and 0.33 mm at the right weld toe. However, no undercuts were observed in arc welded joints. Undercuts are notch-like defects generated at weld toes and caused by the lack of weld metal melted into grooves created by welding heat input. Undercuts are regarded as the main cause of high stress concentration and reduced fatigue strength. Moreover, lowering the fatigue strength grade by one grade is mandatory in a case where the depth of an undercut in a transverse butt welded joint exceeds 0.3 mm stated by the Fatigue Design Guideline for Steel Road Bridges [12] and is less than 0.5 mm, an allowable value indicated by the Specifications for Highway Bridges [13]. Although the undercut depth of the hybrid welded joints in this study exceeded 0.3 mm, hybrid welded joints were found to satisfy the tolerance value (0.5 mm) of undercuts required for bridge members.
Fig. 6. Bead shape for hybrid welded joints.
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Fig. 7. Bead shape for arc welded joints.
4 Fatigue Test 4.1 Four-Point Bending Fatigue Test To clarify the fatigue characteristics of hybrid welded joints, four-point bending fatigue tests were performed. The four-point bending fatigue tests were not only conducted on butt joints by hybrid welding but also on butt joints by arc welding because the results of the tests were investigated based on the comparison between the hybrid welded joints and arc welded joints. The specimens were extracted from the hybrid welded joints and arc welded joints. Figure 8 illustrates the shape and dimension of the specimen for four-point bending fatigue tests. Loading conditions are illustrated in Fig. 9. Four-point bending load was applied with a support span length of 240 mm and a region length subjected to uniform bending moments of 100 mm so that the weld zone was included in the center of the uniform bending region. The specimens were set on the supports so that the tensile stress by the bending loads was applied to the top surface of the hybrid welded joints at which the undercut was included. The arc welded specimens were set as well as the hybrid welded specimens so that the tensile stress was applied to the top surface of the joints. Figure 10 shows the relationship between stress and time in the fatigue test. The stress range was set based on the nominal tensile stress applied on the surface of the specimens of the uniform bending region, and the stress ratio was set to 0.1. Strain gauges were attached to the specimens at 5 mm from weld toes to monitor the strain amplitude during cyclic loading. As a fatigue crack was initiated and propagated at the weld toe, the strain amplitude decreased compared to the initial value. The fatigue life in this test was
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defined as the point in time when the rate of decrease from the initial value reached 5%. Previous studies had shown that a crack depth of roughly 1 mm (physical microcrack) was reached when the amplitude of a strain gauge at 5 mm from the weld toe decreased by 5% [14]. The test was terminated when specimens ruptured or the number of cyclic loadings exceeded 2.5 million cycles, and the number of cyclic loadings at which the strain amplitude decreased by 5% from the initial value was then calculated.
Fig. 8. Specimen for four-point bending fatigue test.
Fig. 9. Loading conditions.
4.2 Test Results Figure 11 shows the results of the fatigue test. The specimens by hybrid welding satisfied the design curve of the fatigue class (grade D) of similar arc welded joints (butt, without weld toe treatment) [15]. The test results of hybrid welded joints were plotted around the mean curve of the fatigue class (grade D). It is known that fatigue strength under bending load is roughly one grade (approximately 20% to 25%) higher than that under axial load. The Fatigue Design Recommendations for Steel Structures [15] (JSSC Guidelines) introduced by the Japan Society of Steel Construction states that the out-of-plane bending stress should be multiplied
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Fig. 10. Relationship between stress and time.
by 4/5 and added to the axial stress to obtain a normal stress range in a case where out-of-plane bending occurs in a welded joint. Accordingly, the fatigue test results with the nominal stress range multiplied by 4/5 are shown in Fig. 12. The test results of the arc welded joints did not satisfy the class D design curve at the high stress range when multiplying the factor of 4/5 for converting the bending loading condition to the axial loading condition. However, the test results of the hybrid welded joints satisfied the class D design curve even after the conversion.
Fig. 11. Fatigue test results.
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Fig. 12. Fatigue test results with stress range multiplied by 4/5.
Fig. 13. Ruptured specimens for hybrid and arc welded joint.
It was observed that the cracks in all ruptured specimens initiated at weld toes, shown in Fig. 13. From the original stress range (without multiplied by 4/5), the strain amplitude did not decrease by 5% from the initial value, and no cracks occurred in either the hybrid welded specimens or arc welded ones under the nominal stress range of 200 MPa. In the nominal stress range above 250 MPa, the hybrid welded specimens had similar or higher fatigue strength than the arc welded ones. 4.3 Investigation of Differences in Crack Propagation In Sect. 4.2, it was confirmed that the fatigue life of the hybrid welded joints was improved compared to those of the arc welded joints in the stress range above 250 MPa. Figure 14 shows the behaviour of decreasing strain amplitude for specimens in the stress range of 300 MPa. The hybrid welded joints had a smaller rate of decrease in strain amplitude, indicating that the rate of crack propagation was suppressed. A similar tendency was
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observed in the stress ranges of 275 MPa, 290 MPa, and 310 MPa. Furthermore, fatigue cracks initiated at the weld toe and propagated through the heat-affected zone and then through the base metal. Since the base metal properties of both hybrid and arc welded joints are the same, there is no difference in the rate at which the crack propagates through the base metal. Therefore, the fatigue life of the hybrid welded joints was considered to be improved due to the suppression of the crack growth rate in the initial stage of crack initiation. In Sect. 3.1, the lines ➀ and ➂ located near the surfaces of the weld metals of hybrid welded joints and arc welded joints, respectively. The Vickers hardness values ranged from 242 to 361 for hybrid welded joints and from 186 to 249 for arc welded joints, indicating that the hybrid welded joints showed greater hardening around the weld toe due to rapid heating and cooling. The hardening was regarded as one of the reasons for the increasing strength in the weld toes of hybrid welded joints, suppressing crack propagation in the initial stage of crack initiation. For the other reasons, the narrower hardened area in the weld bead of hybrid welded joints than arc welded joints might affect the crack initiation and propagation. The investigation should be continued as future work.
Fig. 14. Behaviour of decreasing strain amplitude (300 MPa).
5 Conclusions To investigate the fatigue performance of butt joints by laser-arc hybrid welding for steel bridge members, a series of experiments were performed. The following results were obtained. (1) One-pass full-penetration butt joints were fabricated by laser-arc hybrid welding and conventional arc welding using SBHS500. The weld metal of hybrid welded joints was approximately 1.3 times harder than the weld metal in hybrid welded
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joints. The area of the heat-affected zone was found to be as narrow as 1 mm. On the other hand, no difference in hardness was observed in the weld metal and heat-affected zone of arc welded joints compared to the base metal. (2) Bead shapes of hybrid and arc welded joints were measured. No undercuts were observed in the arc welded joints. On the other hand, undercuts of 0.14 mm to 0.35 mm were observed in the hybrid welded joints, but the depth of undercuts was within the limits required for bridge members. (3) Specimens extracted from hybrid and arc welded joints were subjected to four-point bending fatigue tests. The results showed that the hybrid welded joints had equal or higher fatigue strength than the arc welded joints in the nominal stress range of 250 MPa or higher. This is thought to be because the heat-affected zone around the weld toe of hybrid welded joints was hardened compared to that of the arc welded joints, and the increased strength suppressed the crack growth rate in the initial stage of crack initiation. The other influence factors on the crack initiation and propagation of hybrid welded joints should be investigated in future work. (4) The hybrid welded joints satisfied the fatigue strength grade D specified by the JSSC guideline when considering the conversion from the bending loading pattern to the axial loading pattern.
Acknowledgements. This research was partly funded by the Japan Iron and Steel Federation (JISF). Also, this work was supported by JST SPRING, Grant Number JPMJSP2138.
References 1. Inose, K., Owaki, K., Kanbayashi, J., Nakanishi, Y.: Functional assessment of laser arc hybrid welded joints and their application for bridge construction. Welding in the World 56, 118–124 (2012) 2. Campana, G., Fortunato, A., Ascari, A., Tani, G., Tomesani, L.: The influence of arc transfer mode in hybrid laser-MIG welding. J. Mater. Process. Technol. 191, 111–113 (2007) 3. Katayama, S., Uchiumi, S., Mizutani, M., Wang, J., Fujii, K.: Penetration and porosity prevention mechanism in YAG laser-MIG hybrid welding. Weld. Int. 21, 25–31 (2007) 4. Kim, Y.P., Alam, N., Bang, H.S.: Observation of hybrid welding phenomenon in AA5083 butt joints with different gap conditions. Sci. Technol. Weld. Joining 11(3), 295–307 (2006) 5. Takagi, M., Homma, K.: Creation of high quality and cost-effective bridges using newly developed steels for bridge high-performance structures (SBHS). IABSE-JSCE Joint Conference on Advances in Bridge Engineering-III, pp. 174–181 (2015) 6. Hanji, T., Tateishi, K., Ono, S., Danshita, Y., Choi, S.M.: Fatigue strength of welded joints using steels for bridge high performance structures (SBHS). In: Proceedings of the Thirteen East Asia-Pacific Conference on Steel Structural Engineering and Construction, C-4–5 (2013) 7. Hirohata, M., Sakai, N., Morioka, K., Matsumoto, N., Hyoma, K., Inose, K.: Application of laser-arc hybrid welding to thick steel plates for bridge structures. In: Jármai, K., Voith, K. (eds.) VAE 2020. LNME, pp. 489–496. Springer, Singapore (2021). https://doi.org/10.1007/ 978-981-15-9529-5_42 8. Japanese Standards Association: Higher yield strength steel plates for bridges, JIS G 3140 (2011, in Japanese)
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9. Lee, J.-Y., Kim, Y.-C., Inose, K.: Verification of validity and generality of dominant factors in high accurate prediction of welding distortion. Welding in the World 54, R279–R285 (2007) 10. Mohammad, H.F., Amir, K., Adrian, G.: Real-time monitoring and prediction of martensite formation and hardening depth during laser heat treatment. Surf. Coat. Technol. 315(15), 326–334 (2017) 11. Hirohata, M., Suzaki, M., Inose, K., Matsumoto, N., Abe, D.: Mechanical Investigation on Cold Cracking by Laser-arc Hybrid welding. Quarterly J. Japan Welding Soc. 35(4), 160–170 (2017, in Japanese) 12. Japan Road Association: Fatigue Design Guideline for Steel Road Bridges, (2002, in Japanese) 13. Japan Road Association: Specifications for Highway Bridges, (2012, in Japanese) 14. Al-Karawi, H., Karawi, R.U., Franz von Bock und Polach, Mohammad Al-Emrani: Crack detection via strain measurements in fatigue testing, Strain, 57(4), e12384 (2021). 15. Japan Society of Steel Construction: The Fatigue Design Recommendations for Steel Structures, (2012) (in Japanese)
An Experimental Study of the Gas Metal Arc Welding Ultraviolet Effect as a Function of the Distance Márton Schramkó1(B) , Abdallah Kafi2 , László Gyura3 , and Tünde Anna Kovács4 1 Doctoral School on Materials Sciences and Technologies, Óbuda University, Budapest 1081,
Hungary [email protected] 2 Doctoral School on Safety and Security Sciences, Óbuda University, Budapest 1081, Hungary 3 Linde Gas Hungary Ltd., Budapest 1097, Hungary 4 Bánki Donát Mechanical and Safety Engineering Faculty, Óbuda University, Budapest 1081, Hungary
Abstract. Arc welding produces several harmful health effects on the welder. The authors wanted to determine the intensity of ultraviolet (UV) radiation as a function of distance from the welding. The research focused on the UV radiation generated during the arc welding process, as it is a widely used process in industrial practice today. Several tests were performed on the gas metal arc welding process (GMAW) during the experiment. The arc welding UV effect investigation was made with three different shielding gases C1 (CO2 ), M21 (82% Ar-18% CO2 ) and M20 (10% CO2 - 30% He - 60% Ar) as a function of the arc distance. The used gas metal arc welding (GMAW) process is a common, easily automated technology. In the case of robot welding, the robot and the human operators have to be separated to assure safety. The research focuses on a collaborative workshop where the robots and the human operators are not separated; they are working together. In the collaborative workshop, it needs to assure the safety of the operators with virtual curtains. The danger zone determination is key to determining the virtual border around the welder robot. The goal of the research is the danger zone determination based on the health damage effects. The biggest health-damaging effect of arc welding is the UV effect. The result of the research is the danger zone determination made based on the UV radiation level. Keywords: Ultraviolet radiation · Arc welding · Shielding gas
1 Introduction 1.1 Ultraviolet Radiation (UV) Ultraviolet (UV) radiation is electromagnetic radiation. In this kind of radiation, there are two main types visible and invisible, the UV is in the part of the invisible spectrum seen in Fig. 1. The UV radiation is between 100–400 nm in the wavelength range in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 917–924, 2023. https://doi.org/10.1007/978-3-031-15211-5_76
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the electromagnetic radiation scale. This scale can be further subdivided into UV-A radiation from 315 to 400 nm, UV-B from 280 to 315 nm, and UV-C radiation from 100 to 280 nm [1]. In nature, the main source of UV radiation is from the sun. The largest amount of UV-A enters the earth’s surface; most of the UV-B, all the UV-C, is absorbed by the ozone layer in the stratosphere. During arc welding, the welding arc emits the full spectrum of UV [2]. In the case of arc welding, the UV-C radiation cannot be overlooked because there is no ozone shield present filtering the UV-C radiation, like in the case of sunlight entering the atmosphere. This is the reason why the UV-C radiation effect is not investigated; not a lot of published data are available on the topic. All spectrum of the UV radiation is dangerous; therefore, it needs to be measured, and the dangerous level determined to minimize the health risk of human operators.
Fig. 1. The electromagnetic spectrum [13]
1.2 Health Effects of the Ultraviolet Radiation UV radiation interacts strongly with molecules that make up living organisms, damaging them, so increased exposure to UV radiation poses a serious health risk. The well-known examples of its acute health effects include keratoconjunctivitis and erythema. UV-C radiation is absorbed by the cornea, the UV-B and UV-A radiation is also absorbed by the cornea, and the ocular lens, respectively, and only a small amount of UV radiation reaches the retina. Keratoconjunctivitis is an inflammatory condition of the cornea with unpleasant symptoms such as pain, foreign body sensation in the eye, blurred vision, photosensitivity, tearing and convulsions. The symptoms go away on their own within two days [3, 4]. Erythema (redness) caused by UV radiation results in increased blood flow in the surface capillaries of the skin. UV-B and UV-C radiation have direct damaging effects on the DNA. The body recognizes the damage and then initiates several defence mechanisms, including DNA repair to reverse the damage, apoptosis and exfoliation to remove irreparably damaged skin cells, and increased melanin production to prevent future damage [5]. UV radiation can also induce several chronic processes in the body. UV-B and UV-C radiation damage DNA directly or UV-A indirectly via creating reactive oxygen species. This causes premature ageing of the skin, loss of skin tone,
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the formation of wrinkles, and the induction of metalloproteinase, which cleave the structural protein that provides the skin’s elasticity, collagen [6]. Long-term exposure to UV radiation and the resulting DNA damage can have far more serious consequences than premature skin ageing. UV radiation is known to be carcinogenic, the damage and improper repair of specific DNA sequences called protooncogenes and tumour suppressant genes can lead to cancer [7]. UV radiation also induces immunosuppression, which exacerbates the course of infectious diseases and further increases the likelihood of developing skin cancer [8]. UV radiation to the eye is also a serious risk factor for the development of severe vision-loss disorders such as cataracts and macular degeneration [9]. 1.3 The Permissible Level of UV Radiation A daily maximum limit for UV radiation can be interpreted as the amount of time a given worker can stay in an area exposed to UV at a given intensity. This limit is given in mW/cm2 , and the daily highest value one person is able to handle is 3 mW/cm2 , more than that can already be harmful to the worker. One of our measurements, which was performed outdoors on an overcast winter day, where the amount of UV radiation from the sun was 0.001–0.002 mW/cm2 , may help to interpret this. It would follow that 30–60 min could be spent outdoors. This is because we also take into account the UV-C radiation when setting the limit value, which is filtered out to the full extent by the Ozone Layer, so the values change positively for us [10]. The radiation effect can be interpreted by considering the exposition time. In the case of UV radiation, the safety value interpreted for one day (UV radiation/day) was established. It can determine the UV radiation efficiency by the next Eqs. (1) and (2): Eeff =
400 180
tmax =
Eλ · S(λ) · λ
3mJ/cm2 Eeff
(1) (2)
where, Eλ (W/(cm·nm) is a special radiation, S (λ) (-) is the relative spectral efficiency, λ (nm) in the middle wavelength, the value of 3 mJ/cm2 corresponds to the daily allowable value [11]. The UV value is collected by the human body during one day and can quickly reach the limit without protection. It needs to remark that the UV radiation quantity decrease as a function of the distance from the emission source shielded by air. During the welding, the emitted wavelength of arc light varies as a function of the media (shielding gas) according to the range in which the medium emits invisible or visible light [10].
2 Methods In the case of arc welding, UV radiation is an inevitable source of danger. For this reason, protective equipment is currently defined, for example: covering skin surfaces with clothing or welding masks, shields or goggles. This protective equipment has been
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in use for many years and mainly focuses on masking and protecting only the immediate wearer from UV light. In the welding workshop, the welding workplaces are separated by curtains and screens, thus protecting the other welders and workers. In a collaborative welding workshop, the human welders and the welder robots are working together at the same time. To increase productivity and let the robot move between the workplaces without barriers requires a curtain free area. The safety of human welders is a key point of the workplace. In the collaborative welder, the workshop has required the minimization of the robot welder affected risk. This concept can be implemented using a virtual curtain. The virtual curtain is the boundary of the danger zone that the system prevents from being crossed. The virtual border should be determined based on human exposure to UV radiation. Outside of the virtual curtain, people can move without danger without safety equipment. 2.1 Measurement of the Welder Arc Emitted UV Radiation Several research has already been done to determine the arc emitted UV radiation as a function of different shielding gas. The goal of the research is to determine a danger zone around the gas metal arc welding welder robot to assure the safety of the human workers around in the workshop. The danger zone diameter depends on the welding parameters (shielding gas, current). The virtual border of the danger zone is flexible, always depends on the welding task. Artificial intelligence can determine the size of the danger zone as a function of the welding data and the UV daily allowable limit and let know the danger to the entering people. To determine the relationship between the welding parameters and the UV radiation level, the emitted radiation during the gas metal arc welding process was measured. The setup of the measurement is shown in Fig. 2.
Fig. 2. Sematic image of the used UV level measurement setup
For the measuring three different types of shielding gas C1 (CO2 ), M21 (82% Ar18% CO2 ) and M20 (10% CO2 - 30% He - 60% Ar) were used. The main goal of the test was to make clear how the ultraviolet radiation changes depending on the distance to the source (arc). The UV level was measured in five different distances from the UV source (0,5 m; 1 m; 1,5 m; 2 m; 2,5 m). The experiments were carried out using a fixed UV
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radiation sensor. During the measuring, a 0.5m length joint was welded. The measured ultraviolet radiation values are noted below as a function of the sensor distance from the welding arc. The experiments were performed using three different shielding gases and using different currents. The used experimental parameters show in Table 1. Table 1. The experimental parameters. Variable parameters Current
~240 A
~202 A
~167 A
Feed
13 m/min
10 m/min
7 m/min
Constant parameters Welder wire
SG2
Wire extension (k)
22 mm
Workpiece material
S235JR
Wire thickness
1 mm
3 Results and Discussion Figure 3, 4 and 5 diagrams showing the results of the experiments. In the case of all three different shielding gas, the UV radiation decreases as a function of the distance from the welding arc. The measured data and the UV daily allowable value can be the base of the virtual risk zone conception.
UV radiation (mW/cm2)
C1 shielding gas 6 5 4 3 2 1 0 0.5
0.7
0.9
1.1
1.3
1.5
1.7
1.9
Distance (m) 7 m/min
10 m/min
13 m/min
Fig. 3. UV radiation level as a function of the welder arc used the C1 shielding gas
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The most commonly applied gases in industrial applications were used in the experiments (C1, M20, M21). Figure 3 shows the CO2 shielding gas effect during the welding. Also, it concludes that the UV radiation level depends on the welding current, a higher current effects higher radiation. The measured maximal UV radiation level was 4.8 mW/cm2 in the case of 0.5 m distance from the welding arc.
M21 shielding gas UV radiation (mW/cm2)
14 12 10 8 6 4 2 0 0.5
0.7
0.9
1.1
1.3
1.5
1.7
1.9
Distance (m) 7 m/min
10 m/min
13 m/min
Fig. 4. UV radiation level as a function of the welder arc used the M21 shielding gas
Figure 4 shows the Ar and CO2 mixed gas effect during the welding. It can conclude that the Ar gas UV shielding ability is lower than the CO2 gas. Also, it can see the current effect too. The higher welding current cause higher UV radiation. The measured maximal radiation in the case of the used M21 shielding gas was 12.2 mW/cm2 in the case of 0.5 m distance from the welding arc. Figure 5 shows the Ar, He, CO2 mixed shielding gas effect during the welding. The current tendency is similar to in the case of the C1 and the M12 shielding gas results. The M20 shielding gas shows a higher UV shielding effect than the M21 gas but is lower than the C1 gas.
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UV radiation (mW/cm2)
M20 shielding gas 8 7 6 5 4 3 2 1 0 0.5
0.7
0.9
1.1
1.3
1.5
1.7
1.9
Distance (m) 7 m/min
10 m/min
13 m/min
Fig. 5. UV radiation level as a function of the welder arc used the M20 shielding gas
4 Conclusion UV radiation can be determined by measurement as a function of current and shielding gas. We can conclude that the increasing current elevates the UV radiation in the case of all tested shielding gases. It can conclude that the Ar and CO2 mix gas UV shielding ability is lower than the 100% CO2 shielding gas UV shielding ability. We can conclude that 0.5 m far from the UV source (welding arc), the measured UV radiation is highest in the case of M21 between the tested shielding gases. Based on the measurement results and the daily allowable radiation limit, a virtual danger zone can be defined. The size of the danger zone depends on the composition of the shielding gas and the welding current. By using artificial intelligence and measuring continuous UV values with a sensor, a virtual dynamically changing danger zone can be defined to ensure the protection of the person entering. Acknowledgement. The authors would like to thank the Hungarian State, the National Research, Development and Innovation Office and the European Union for their support in project No. 2020-1.1.2-PIACI-KFI-2020-00081.
References 1. Gallagher, R.P., Lee, T.K., Bajdik, C.D., Borugian, M.: Ultraviolet radiation. Chronic Dis. Can. 29, 51–68 (2010) 2. Dixon, A.J., Dixon, B.F.: Ultraviolet radiation from welding and possible risk of skin and ocular malignancy. Med. J. Aust. 181, 155–157 (2004)
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3. Takahashi, J.: Comprehensive analysis of hazard of ultraviolet radiation emitted during arc welding of cast iron. J. Occup. Health 62, 1–10 (2019) 4. Hussey, M., Wu, B., Moore, L.A., Ferrreira, J.T.: Review of photokeratitis: corneal response to ultraviolet radiation (UVR) exposure. Afr. Vis. Eye Health 69, 123–131 (2010) 5. Sklar, L.R., Almutawa, F., Lim, H.W., Hamzavi, I.: Effects of ultraviolet radiation, visible light, and infrared radiation on erythema and pigmentation: a review. Photochem. Photobiol. Sci. 12(1), 54–64 (2012). https://doi.org/10.1039/c2pp25152c 6. Berneburg, M., Plettenberg, H., Krutmann, J.: Photoaging of human skin. Photodermatol. Photoimmunol. Photomed. 16, 239–244 (2000) 7. Narayanan, D.L., Saladi, R.N., Fox, J.L.: Ultraviolet radiation and skin cancer. Int. J. Dermatol. 49, 978–986 (2010) 8. Norval, M., Halliday, G.M.: The Consequences of UV-Induced Immunosuppression for human health. Photochem. Photobiol. 87, 965–977 (2011) 9. Roberts, J.E.: Ultraviolet radiation as a risk factor for cataract and macular degeneration. Eye Contact Lens Sci. Clin. Pract. 37, 246–249 (2011) 10. Takahashi, J., Nakashima, H., Fujii, N., Okuno, T.: Comprehensive analysis of hazard of ultraviolet radiation emitted during arc welding of cast iron. J. Occup. Health 00, 1 (2019). https://doi.org/10.1002/1348-9585.12091 11. TLVs and BEIs Based on the Documentation Threshold Limit Values for Chemical Substances and Physical Agents and Biological Exposure Indices. ACGIH, Cincinnati (2020). ISBN: 978-1-607261-12-4 12. Otokpa, O.E., Usman, Y.B.: An assessment of ultraviolet radiation components of light emitted from electric arc and their possible exposure risks. Glob. J. Pure Appl. Sci. 19, 145–149 (2013). https://doi.org/10.43146/gjpas.v19i1.18 13. Electromagnetic spectrum. https://www.weuvcare.com/how-does-ultraviolet-light-disinfect ion/
Numerical Solution of the Aluminium Plate Welding Process by Friction Stir Welding Roland Janˇco1(B)
, Ladislav Écsi1
, and Pavel Éleszt˝os2
1 Faculty of Mechanical Engineering, Institute of Applied Mechanics and Mechatronics, Slovak University of Technology in Bratislava, Nám. slobody 17, 81231 Bratislava, Slovakia [email protected] 2 Faculty of Mechanical Engineering, Lovak University of Technology in Bratislava, Nám. slobody 17, 81231 Bratislava, Slovakia
Abstract. Friction Stir Welding (FSW) is one of the most efficient solid-state joining methods and can be used in many industries. The simulation process can provide the development of temperature fields, metallurgical phases, stresses and strains that can be easily measured during welding. Numerical modelling requires modelling of the interaction between thermal, metallurgical and mechanical phenomena. The aim of this paper is to describe the thermo-fluid and thermomechanical analysis of FSW using the finite element method. This paper presents the results of both numerical simulations for an aluminium alloy using the SYSWELD program. Keywords: Aluminium alloy · Thermos-fluid model · Friction stir welding
1 Introduction Friction stir welding (FSW) is a new joining technology which was patented in 1991 by The Welding Institute (TWI), in the United Kingdom [1]. The FSW process is suitable for creating high-quality welds [2, 3, 5–7]. In Fig. 1 scheme of the friction stir welding process is shown.
2 Modelling of Welding of Two Aluminium Plates Using FSW The aim of this chapter is a numerical solution of FSW of materials without phase transformations, e.g. AlMg4.5Mn0.7 alloy. The task was solved using SYSWELD (software), including the FSW module. The first part is devoted to thermo-fluid analysis, the results of which are then used in thermo-mechanical analysis. Numerical FSW simulation of two plates (in Fig. 2) made of AlMg4.5Mn0.7 material with the dimension of plate b = 40 mm, L = 150 mm and thickness h = 5 mm. We consider the heat transfer coefficient, which is 19 W/(m2 .K). The tool used for welding has the geometry of FIG. 3. The welding speed is 1 mm/s, and the tool rotation speed is 62.83 rad/s. Material parameters are in Table 1. The theoretical background and equation of FSW module are described in [4]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 925–932, 2023. https://doi.org/10.1007/978-3-031-15211-5_77
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Fig. 1. Principle and scheme of FSW [4].
Fig. 2. Geometry of modeled plates
Fig. 3. Geometry of tool.
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Table 1. Material properties [4].
2.1 Thermo-fluid Numerical Simulation The finite element model for thermo-fluid analysis is in Fig. 4. The welded plates are made of AlMg4.5Mn0.7 alloy, and the backing plate is made of steel S235JRC + N. All boundary condition is shown in Fig. 5 The results of the thermo-fluid analysis are presented in the following figures. In Fig. 6 is the temperature field on the welded plates at 225 s, where the maximum value is 525.28 °C.
Fig. 4. Mesh for thermo – fluid analysis
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Fig. 5. The boundary condition for thermo-fluid analysis
Fig. 6. The thermal field in the time 225 s
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Fig. 7. Experimental measurement of the thermal field by thermo camera.
The thermal field was measured by thermography using a FLIR® SC660 camera. The thermal field for experimental measurement is shown in Fig. 7. The result of measured with the camera and the numerical solution of the thermal field calculated with SYSWeld are in good agreement. Figure 8 is a comparison of the measured and simulated temperature profile at a distance of 20 mm from the center of the weld, and the welding length is 300 mm.
Fig. 8. Comparison of measured temperature and temperature in numerical simulation
2.2 Thermo - Mechanical Simulation Temperature fields with thermo-fluid analysis were considered in the mechanical calculation. Other boundary conditions are shown in Fig. 9. Phase transformations of the material are also considered in the simulation. The mechanical result was solved by
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program SYSWeld. Numerical model is without the backing plate. The result of von Mises stress field is shown in Fig. 11, and the displacement field is shown in Fig. 10. The maximum value of stress is 145.5 MPa. For time 225 s. Modelling and measurement of the temperature and stress evolution in the FSW of AlMg4.5Mn0.7 The alloy model with the proposed boundary conditions agrees with the experimental values.
Fig. 9. The boundary condition for thermo – mechanical analysis
Fig. 10. Field of displacement vector sum (mm) in the time 225 s
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Fig. 11. Field of Von Mises stress (MPa) in the time 225 s
3 Conclusion In this paper, the numerical solution of the aluminium plate welding process by friction stir welding is shown. The process of said wielding is dived to termo-fluid solution, a thermo-mechanical solution in which is included phase transformation. Our problems are solved without the phase transformation because we have aluminium alloy without the phase transformation. For the numerical solution was used the program SYSWeld with the FSW module. The thermal field during friction welding was measured with a FLIR® SC660 camera mounted on a welding machine. The image was taken every 3 s in the case of aluminium welding (see Fig. 7 at time 225 s). The measured results at a given time of 225 s were compared with a numerical calculation, where the comparison is shown in Fig. 8. Both in the process of thermo-fluid numerical simulation and in the process of experiment, aluminium plates supported by a backing plate were welded. The results obtained by the temperature field are in good agreement in Fig. 8. The results of the thermo-fluid analysis are presented in Fig. 6 on the welded plates at 225 s, where the maximum value is 525.28 °C. These results are valid for the boundary conditions in Fig. 5, material properties in Table 1 and the dimensions of the welding tool in Fig. 3. By a thorough analysis of the temperature field in Fig. 6 we find that the temperature field is not symmetric. The higher temperature is the advancing side (see Fig. 1), because the rotational speed of the tool and the welding direction have the same direction. On the opposite side (retreating side see Fig. 1), they have the opposite direction. For this reason, an asymmetry also occurs in the von Mises stresses in Fig. 10. The results of the thermo-mechanical analysis are presented in Fig. 10 and Fig. 11. The maximum value of the displacement vector is 1.19 mm, and von Mises stress 145.5 MPa at time 225 s.
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Based on the results presented in this article, it can be stated that the numerical simulation of the friction stir welding process in SYSWeld (software) with the FSW module is a suitable tool for the calculation of the welding process, which is also confirmed by the experimental results. Acknowledgements. This publication was funded by the VEGA 1/0271/20 grant. The resources are greatly appreciated.
References 1. Chen, C.M., Kovacevic, R.: Finite element modeling of friction stir welding - thermal and thermomechanical analysis. Int. J. Mach. Tools Manuf. 43, 1319–1326 (2003). ISSN 08906955. http://www.sciencedirect.com/science/article/pii/S0890695503001585 2. Frigaard, Ø., Grong, Ø., Midling, O.T.: A process model for friction stir welding of age hardening aluminium alloys. Metall. Mater. Trans. A. 32A, 1189–1200 (2001) 3. Feulvarch, E., Robin, V., Boitout, F., Bergheau, J.M.: 3D modelling of thermo fluid flow in friction stir welding including metallurgical and mechanical consequences. In: Mathematical Modelling of Weld Phenomena, vol. 8, pp. 1–24 (2007). ISBN 9783902465696. http://books. google.sk/books?id=7auqPgAACAAJ 4. Janˇco, R., Écsi, L., Éleszt˝os, P.: FSW numerical simulation of aluminium plates by SYSWeld - Part I. Strojnícky cˇ asopis J. Mech. Eng. 66(1), 47–52 (2016). ISSN 0039–2472. https://doi. org/10.1515/scjme-2016-0010 5. Janˇco, R., Écsi, L., Éleszt˝os, P.: FSW numerical simulation of aluminium plates by SYSWeld - Part II. Strojnícky cˇ asopis J. Mech. Eng. 66(2), 29–36 (2016). ISSN 0039-2472. https://doi. org/10.1515/scjme-2016-0016 6. Gužela, Š., Dzianik, F.: Correction factors for determining the mass transfer coefficients. Strojnícky cˇ asopis J. Mech. Eng. 71(2), 109–120 (2016). ISSN 0039-2472. https://doi.org/10.2478/ scjme-2021-0022 ˇ Danko, J., Milesich, T., Križan, P., Skyrˇcák, R.: FSW simulation of 7. Hanko, L., Magdolen, L, pedestrian detection in urban environment. Strojnícky cˇ asopis J. Mech. Eng. 71(2), 121–130 (2021). ISSN 0039-2472. https://doi.org/10.2478/scjme-2021-0023
Numerical Simulation of Laser Beam Welding of Stainless Steel and Copper Butt Joint Martin Hnilica, Erika Hodúlová(B) , Miroslav Sahul, Pavel Kovaˇcócy, Beáta Šimeková, and Ingrid Kovaˇríková Faculty of Material Science, and Technology in Trnava, Slovak University of Technology in Bratislava, Jána Bottu 2781/25, 91724 Trnava, Slovak Republic {martin.hnilica,erika.hodulova,miroslav.sahul,pavel.kovacocy, beata.simekova}@stuba.sk, [email protected]
Abstract. The paper aims to design a simulation of a butt weld made of stainless steel and copper by a laser beam. The concept of the problem solution is based on a non-experimental method of thermomechanical and stress-strain analysis of the laser welding process of dissimilar materials, which can largely predict suitable welding parameters for real experiments and thus effectively reduce welding defects. All simulation steps were performed using ANSYS simulation software. A Gaussian volumetric heat source was used for the simulation. The physical and mechanical properties of the materials are temperature dependent and must be defined in the simulation software. The ANSYS SpaceClaim module was used to create the geometric model. The geometric model is dimensionally identical to the sample used in the real experiment. The initial conditions in the numerical simulation were determined based on the initial state of the experimental sample. Real samples were welded using a disk laser; parameters were set based on simulation. The results of the thermal analysis are used to examine the temperature fields created in the welding process. They are used to optimize welding parameters. According to experimental and simulation results, there is a different maximum temperature in the weld metal on the copper side as well as on the steel side due to a higher coefficient of thermal conductivity of copper. The results of the stress-strain analysis consist of two parts. The first part examines the effect of clamping on the stresses arising in the welding process, and the second part is focused on the overall deformation. Due to the small differences in the values of the coefficient of thermal expansion of the metals used and the small dimensions of the test specimens, the residual stresses and strains are negligible. Keywords: Laser welding · Numerical simulation · Dissimilar butt joint · Eletro tough pitch copper · Stainless steel
1 Introduction Welded joints of dissimilar materials are widely used in the automotive, oil, aerospace and nuclear power industries. In this research work, two materials were chosen, stainless © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 933–945, 2023. https://doi.org/10.1007/978-3-031-15211-5_78
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steel 304 and pure copper. This combination of materials was mostly used in past as pipes or copper stave coolers, which are used in combination with their properties like corrosion resistance and thermal conduction [1]. In last decade combinations of welded thin sheets undoubted industrial interest due to its implications in e-mobility fields. In the automotive industry prevails application when cylindrical cells needs to be connected to any elements constituting the battery, such as printed circuit boards or busbars [2]. Usage of simulations before the welding process offers great potential for the prediction of temperature fields or stresses [3]. To perform finite element simulation, a 3D model needs to be prepared with boundary conditions. The whole simulation of laser beam welding is simulated via ANSYS R1 software, with the usage of APDL console for creating Gaussian volumetric heat source. All material properties were calculated with the help of JMatPro software based on their chemical composition as temperaturedependent variables. After performing simulations, results would be compared with real samples welded with welding parameters set in simulations. Many researchers have performed numerical and experimental studies on the laser welding process. Chandelkar et al. 2019 [4] present study, where he used software ANSYS to perform thermal simulation and software Minitab to optimize values of parameters. Akella et al. [5] 2014 performed a simulation of welding with Gaussian volumetric heat source using ANSYS software to study achieved temperature, distortion and residual stresses. Antony et al 2019 [6] focused in their work on the mechanical and metallurgical studies of dissimilar joints of stainless steel and pure copper produced with a laser welding technique. Chen et al. 2014 [7] investigate the influence of reflectivity and possibilities of copper-based absorbers to increase the welding effectivity of laser welding. Kuryntsev et al. 2016 [8] attempted to weld austenitic stainless steel and pure copper in butt joint configuration without any intermediate material. They focused laser beam to stainless steel and melt copper via heat conduction. Meng et al. 2018 [9] buttwelded AISI 304 and T2 copper together with laser-cold metal transfer hybrid technique to obtain benefits from the Cu-rich matrix to strengthen the weld. Mannucci et al. 2018 [10] created a parametric study of continuous Yb: YAG laser welding between copper and stainless steel 316L to study microstructures placed in the melted zone. Attar et al. 2020 [11] developed a mathematical model of dissimilar metal sheets of copper and stainless steel. For the simulation, they used six different schematics of heat flux distributions and the simulation was performed through Abaqus solver using the FORTRAN programing language. To validate the results, they compared them with results reported by other researchers. Vemanaboina et al. 2014 [12] created a numerical model to observe structural distortion and residual stresses in the laser beam welding process. For heat distribution, they used a cylindrical heat flux model.
2 Experimental Procedure 2.1 Materials The dissimilar welding of stainless steel and copper presents a series of problems. First, there are large differences between their physical properties, including melting point, thermal conductivity, and thermal expansivity. With non-experimental method, thermomechanical analysis of the welding process of dissimilar materials with laser beam
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welding is possible to eliminate errors created during welding. The main problem is to find a general solution of laser welding simulation of two dissimilar materials – stainless steel AISI 304 and eletropitching copper [13]. AISI 304. The AISI 304 stainless steel (SS) is being widely used in different industrial applications and is accounted for approximately 50% of the world’s SS production [14, 15]. It has been reported that because of AISI 304 SS superior aesthetic, mechanical, physical properties, weldability, chemical and corrosion resistance, it is one of the most preferred structural materials. The chemical composition of SS304 is listed in Table 1 [16–18]. Table 1. Chemical composition of AISI 304 C
Si
Mn
P
S
N
Cr
Ni
Fe
≤0,07
≤1,00
≤2,00
≤0,045
≤0,03
≤0,11
17,00–19,5
8,00–10,5
Balance
Cu ETP. Cu ETP is the most common copper. It is universal for electrical applications. Cu ETP has a minimum conductivity rating of 100% IACS and is required to be 99.9% pure. It has 0.02% to 0.04% oxygen content. Most common Cu ETP - C11000C is an electrolytic refined copper widely used for electrical and electronic applications. Cu ETP has the properties required in all applications with a hydrogen-free atmosphere. In the presence of H2 and heat, all oxygen-bearing coppers suffer from so-called hydrogen embrittlement. This is a chemical reduction of copper oxide by diffusing hydrogen leading to the formation of H2 O within the microstructure, resulting in embrittlement of the grain boundaries. The phosphorus of our copper content is very low, so electrical conductivity is comparable to the best performing materials. C1100 is an oxygen-containing copper which has a very high electrical and thermal conductivity. It has excellent forming properties. Due to its oxygen content, soldering and welding properties are limited. Chemical composition of Cu-ETP is listed in Table 2 [19–22]. Table 2. Chemical composition of Cu ETP Cu
O
Bi
Pb
Balance
≤0,04
≤0,0005
≤0,005
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2.2 Calculations of Thermal and Structural Properties Parameters needed for thermal simulation in time are important to define variables dependent on temperature: • • • •
density, melting temperature, isotropic thermal conductivity, specific heat.
Those variables are calculated using of JMatPro software based on the chemical composition of materials and they are used as material properties used for transient thermal simulation. The second group of material properties is used for transient structural analysis. The input parameters used for the structural analysis are the results of the thermal analysis. A different set of temperature-dependent parameters is needed in this analysis, namely: • Young modulus, • Poisson’s ratio, • coefficient of thermal expansion. 2.3 Definition of Heat Source Advanced welding techniques find profound applications in welding of thick plates to produce deep and narrow full penetration welds. Laser welding is one of the most acclaimed processes for joining thick plates which often utilizes high intense laser power to produce a through-thickness weld in a single pass. The phenomenon of keyhole mode welding is generally associated with such high-density laser power capable of achieving deep and narrow-shaped weld pool. Heat source fitting plays a vital role in performing thermomechanical analysis. There are a few types of heat distribution. The most common possibilities of laser beam distributions are shown in Fig. 1. From the examples given above, it is appropriate to select a circular model with a Gaussian heat input distribution. It is also appropriate to consider based on a physical experiment, that we will not rotate the beam and therefore the C - Gaussian volumetric heat source option is most appropriate.
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Fig. 1. Volumetric heat source models (a) Conical (b) Goldak Double ellipsoidal (c) Volumetric Gaussian (d) Rotary Gaussian [23]
2.4 Creating Geometrical Model The creation of the geometric model was created based on of cut samples for physical verification of the simulation model. A solid measuring 140x100 mm and 1 mm thick was created. This solid was split on the longer side into two halves, creating two solids placed side by side as in a butt weld. Subsequently, the solids could be meshed and given material properties. 2.5 Thermal Transient Transient thermal simulation is used to simulate temperature fields over time. It sets the initial conditions such as the ambient temperature, the method of heat dissipation to the environment and the method of supplying heat input. An APDL bracket is used to create a moving heat source. The moving heat source is expressed by the final formula: Q(x, y, t) = A
B∗((x−C)2 +((y−D∗{t}−E))2 ) F2
(1)
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where: A – power density [W/m2 ] B – coefficient taking into account the direction of heat exposure (calculated by ANSYS) C – shift of the starting point of the beginning of the heat source movement along the x-coordinate [m] D – the speed of movement of the center of the circle along the y-coordinate [m/s] E – the expression of the beam rise along the y-coordinate [m] F – beam radius [m] Q – heat flux density [W/m2 ] x – x coordinate [m] y – y coordinate [m] t – time in which the welding takes place [s] The essence of the formula came from the combination of the analytical expression of the circle, thanks to which we can represent the movement of the center of the beam trace along the ypsilon coordinate in time and the power density which represents the heat input of the laser. The equation is compiled as follows (Fig. 2):
Fig. 2. Graphical representation of the analytical expression of the circle
A circle with a centre S[m, n] and a radius of r > 0 has the equation: (x − m)2 + (y − n)2 = r2 where: x and y –represent the defined coordinate system [m] m – stands for x-axis offset of the defined coordinate system [m] n – stands for y-axis offset of the defined coordinate system [m]
(2)
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In the next step, it was possible to replace values representing the movement on y-axis of the beam over time: n = D∗t−E
(3)
where: D – beam velocity [m/s] t – time [s] E – a value representing whether the start-up of the beam is considered [m] 2.6 Power Density Laser welding of metals with high power densities results in a comparatively small weld with another technology. For the correct calculation of used power density in simulations, Eq. 4 is used [24]. A=
f1 ∗ P π ∗ r2
(4)
where: A – power density [W/m2 ] P – power input [W] r – beam diameter [m] f – material reflectivity coefficient [m] The value of the variable f can be found experimentally or from engineering tables. Value found from engineering tables is shown in Table 3 [25]. Table 3. The reflectivity of pure copper and stainless steel Material
Reflection factor [%]
Copper
70
Stainless steel
50
Subsequently, it is possible to apply the final formula to the ANSYS Mechanical Function creator in a general form, which will create commands for us in the APDL console. We define variables representing the welding parameters of real samples in the resulting commands in the APDL console. 2.7 Initial Conditions The initial conditions in the numerical simulation were determined based on the initial state of the experimental sample. At the initial time, t = 0, the temperature of the experimental sample was 20 °C and the ambient temperature was also 20 °C, the heat being removed by convection. Convection is defined as a form of motion induced in a
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gas (or liquid) due to a tendency to heat. Its essence lies in the exchange of a warmer surrounding medium with a lower density for a colder surrounding medium with a higher density. The simulation does not consider inducing an airflow around the welded parts, so the possibility of non-moving air with temperature-dependent thermal properties can be used from the default library. 2.8 Welding Parameters Set for Simulation. The parameters for the simulation were based on the parameters used in the welding of the real samples and the parameters used are presented in Table 4. Table 4. Used welding parameters Primary laser beam power
1800 W
Welding speed
25 mm/s
Radius of laser beam
0,1 mm
Focusation of the beam
Edge between materials
3 Results and Discussion 3.1 Interpretation of Simulation Results As in reality, as in the case of welding simulation, the welding process is divided into three phases, which can be identified as: • welding start-up – the material with an initial temperature of 20 °C is gradually heated to the temperature during the welding process, • continuous welding process – this part of the welding process achieves approximately the same temperature over the entire duration, • beam catch-up – this phase exists only in the theoretical plane, manifested by an increase in temperatures at the endpoint of welding and was caused by the fact that the possibilities of heat dissipation using material were reduced (Fig. 3).
Fig. 3. Temperatures during the welding process
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Real samples for comparison were welded with parameters set in Table 4. The difference between the simulation and the welding of the samples was caused by a slight deflection of the welding source for safety reasons because of copper reflexivity. A proper comparison of the results of temperature fields is in the second stage, so that a cut was made in the welded sample and also in a simulated sample. Comparation of temperature fields are in Figs. 4 and 5. After macroscopic analysis, microscopic analysis was also performed using a Neophot 32 microscope. The result is shown in Fig. 7 (Fig. 6).
Fig. 4. Distribution of temperature fields in the real sample
Fig. 5. Distribution of temperature fields in the simulated sample
Based on the same predicted temperature fields, it is also possible to predict stresses and deformations in the welding process at any point in time. In Fig. 8 the deformations at a given welding moment are shown and the strains in the welding process are shown in Fig. 9.
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Fig. 6. Distribution of temperature fields in the simulated sample
Fig. 7. Microsopic analysís of the real sample
To obtain results in structural analysis, material clamps must be defined as closely as possible for real samples. The bottom surface and the Z-axis surfaces perpendicular to the welding direction were firmly fixed as a suitable representation used in real welding.
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Fig. 8. Deformation in the simulated sample
Fig. 9. Von Mises strain in the simulated sample
3.2 Conclusion This study investigated possibilities of prediction of stresses and deformations based on the conformity of simulation of temperature field distribution of welded sample and simulated model. The results obtained from the numerical analysis provide an objective picture of the behaviour of different base materials under the influence of the laser beam. Thermal conductivity and specific heat have a significant effect on the results of thermal analysis depending on the properties of the materials. Based on computer simulation, it is possible to optimize welding parameters, welding conditions and thus start and improve the whole process.
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Acknowledgement. This contribution was supported by the Agency for the Promotion of Research and Development under contract no. APVV-18-0116. The experiment was also solved within the diploma theses, which are part of the project outputs and by the Vedecká grantová agentúra VEGA grant agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic, project No. 1/0499/21.
References 1. Zhang, X., Pan, T., Flood, A., Chen, Y., Zhang, Y., Liou F.: Investigation of copper/stainless steel multi-metallic materials fabricated by laser metal deposition. Mater. Sci. Eng. A 811 (2021). https://doi.org/10.1016/j.msea.2021.141071 2. Sadeghian, A., Iqbal, N.: A review on dissimilar laser welding of steel-copper, steel-aluminum, aluminum-copper, and steel-nickel for electric vehicle battery manufacturing. Opti. Laser Technol. 146 (2022). https://doi.org/10.1016/j.optlastec.2021.107595 3. Incropera, F., DeWitt, D., Bergman, T., Lavine, A.: Fundamentals of Heat and Mass Transfer, vol. 112, 6th edn. Wiley, New York (2005) 4. Chandelkar, V., Pradhan, S.K.: Numerical simulation of temperature distribution and experimentation in laser beam welding of SS317L alloy. Mater. Today Proc. 27 (2019). https://doi. org/10.1016/j.matpr.2019.11.331 5. Akella, S., Harinadh, V, Krishna, Y., Buddu, R.K.: A welding simulation of dissimilar materials SS304 and copper. Procedia Mater. Sci. 5 (2014). https://doi.org/10.1016/j.mspro.2014. 07.490 6. Antony, K., Rakeshnath, T.R.: Dissimilar laser welding of commercially pure copper and stainless steel 316L. Mater. Today Proc. 26 (2019). https://doi.org/10.1016/j.matpr.2019. 12.043 7. Chen, H.C., Bi, G., Nai, M.L.S., Wei, J.: Enhanced welding efficiency in laser welding of highly reflective pure copper. J. Mater. Process. Technol. 216 (2015). https://doi.org/10.1016/ j.jmatprotec.2014.09.020 8. Kuryntsev, S.V., Morushkin, A.E., Gilmutdinov, A.K.: Fiber laser welding of austenitic steel and commercially pure copper butt joint. Opt. Lasers Eng. 90 (2017). https://doi.org/10.1016/ j.optlaseng.2016.10.008 9. Meng, Y., Li, X., Gao, M., Zeng, X.: Microstructures and mechanical properties of laserarc hybrid welded dissimilar pure copper to stainless steel. Opt. Laser Technol. 111 (2019). https://doi.org/10.1016/j.optlastec.2018.09.050 10. Mannucci, A., Tomashchuk, I., Vignal, V., Sallamand, P., Duband, M.: Parametric study of laser welding of copper to austenitic stainless steel. Procedia CIRP 74 (2018). https://doi.org/ 10.1016/j.procir.2018.08.160 11. Aghaee Attar, M., Ghoreishi, M., Malekshahi Beiranvand, Z.: Prediction of weld geometry, temperature contour and strain distribution in disk laser welding of dissimilar joining between copper & 304 stainless steel. Optik (Stuttg) 219 (2020). https://doi.org/10.1016/j.ijleo.2020. 165288 12. Vemanaboina, H., Akella, S., Buddu, R.K.: Welding process simulation model for temperature and residual stress analysis. Procedia Mater. Sci. 6 (2014). https://doi.org/10.1016/j.mspro. 2014.07.135 13. Chen, S., Huang, J., Xia, J., Zhang, H., Zhao, X.: Microstructural characteristics of a stainless steel/copper dissimilar joint made by laser welding. Metall. Mater. Trans. A Phys. Metall. Mater. Sci. 44(8) (2013). https://doi.org/10.1007/s11661-013-1693-z
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14. Agrawal, H., Sharma, P., Tiwari, P., Taiwade R.V., Dayal R.K.: Evaluation of self-healing behaviour of AISI 304 stainless steel. Trans. Indian Inst. Metals 68(4) (2015). https://doi.org/ 10.1007/s12666-014-0467-7 15. Mascaraque-Ramirez, C., Franco, P.: Experimental study of surface finish during electrodischarge machining of stainless steel. Procedia Eng. 132 (2015). https://doi.org/10.1016/j. proeng.2015.12.547 16. Khobragade, N.N., Khan, M.I., Patil, A.P.: Corrosion behaviour of chrome–manganese austenitic stainless steels and AISI 304 stainless steel in chloride environment. Trans. Indian Inst. Met. 67(2) (2013). https://doi.org/10.1007/s12666-013-0345-8 17. Balusamy, T., Kumar, S., Sankara Narayanan, T.S.N.: Electrochemical behaviour of surface modified AISI 304 grade stainless steel in Ringer’s solution. Trans. Indian Inst. Metals 64(4–5) (2011). https://doi.org/10.1007/s12666-011-0076-7 18. Chandrasekar, G., Kailasanathan, C., Verma, D.K., Nandagopal, K.: Optimization of welding parameters, influence of activating flux and investigation on the mechanical and metallurgical properties of activated TIG weldments of AISI 316 L stainless steel. Trans. Indian Inst. Met. 70(3) 2017). https://doi.org/10.1007/s12666-017-1046-5 19. European Copper Institute: Properties of Cu-ETP. Last adr 20. Knych, T.A., Smyrak, B., Walkowicz, M.: Selected aspects of evolution properties of oxygen free copper for high-advanced electrotechnical application. https://www.researchgate.net/pub lication/267411152_Selected_aspects_of_evolution_properties_of_oxygen_free_copper_ for_high-advanced_electrotechnical_application. Accessed 2011 21. Freudenberger, J., Warlimont, H.: Copper and copper alloys. In: Warlimont, H., Martienssen, W. (eds.) Springer Handbook of Materials Data. SH, pp. 297–305. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-69743-7_12 22. Segl’a, P., Miklos, D., Melnik, M.: Structures, Physico-Chemical Properties and Biological Activities of Copper (II) Pyridinecarboxylates (2010) 23. Unni, A.K., Vasudevan, M.: Determination of heat source model for simulating full penetration laser welding of 316 LN stainless steel by computational fluid dynamics. Mater. Today Proc. 45 (2021). https://doi.org/10.1016/j.matpr.2020.12.842 24. Nothdurft, S., Springer, A., Kaierle, S.: Influencing the weld pool during laser welding. Adv. Laser Mater. Process. (2018). https://doi.org/10.1016/b978-0-08-101252-9.00010-8 25. Engineering ToolBox. Materials - Light Reflecting Factors. https://www.engineeringtoolbox. com/light-material-reflecting-factor-d_1842.html
Electron Beam Welding of Overlapped Joints Copper - Stainless Steel Beáta Šimeková, Pavel Kovaˇcócy, Miroslav Sahul, Ingrid Kovaˇríková, Maroš Martinkoviˇc, and Erika Hodúlová(B) Faculty of Material Science and Technology in Trnava, Slovak University of Technology in Bratislava, Trnava, Slovak Republic {beata.simekova,pavel.kovacocy,miroslav.sahul,ingrid.kovarikova, maros.martinkovic,erika.hodulova}@stuba.sk
Abstract. The paper aims to investigate the weldability of similar overlapped joints of stainless steel (SS-AISI 304) and copper (Cu-ETP) with thicknesses of 2 and 1 mm in the form of sheet metal. The main criterion was the design of electron beam welding parameters, while several samples were made, varying the welding current in the range of 80–10 mA, welding speed (30–40 mm/s) and beam offset. The samples thus prepared were further analysed by macro and microstructure, the microhardness was measured, and the mechanical properties of the joints were determined, namely the shear strength test. The influence of individual parameters on the weld geometry was monitored. The joints are characterised by the heterogeneity of the weld metal with a large mixing of Cu and SS and the formation of partial porosity. The microhardness of the weld metal was measured in the range 131 - 156 HV0.1 . Shear strength was measured and calculated at a range of 552 - 639 MPa. The analysis of the created joints was performed, taking into account the overall quality of welded joints in order to achieve joints without defects and with acceptable mechanical properties. Keywords: Electron beam welding · Electron beam offset · Welding parameters · Dissimilar overlapped joint
1 Introduction Electron beam welding in vacuum ensures high-quality welds, as the surface oxides aren’t formed on the surface of highly heated materials. The created welds are uniform, smooth, have little thermal influence and deformation. The application of electron beam welding technology is used in various fields of engineering and energy, such as nuclear energy, aerospace and missile technology, electronics, the automotive industry and the welding of oversized components. In this way, it is possible to weld various types of materials, for example steel with copper. The combination of copper and stainless steel is currently in great demand, even though they are very different, but their combination is creating unique material properties. Copper has a very high thermal and electrical conductivity, but is relatively soft and shapeable. Stainless steel has higher hardness than © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 946–956, 2023. https://doi.org/10.1007/978-3-031-15211-5_79
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Cu and high tensile strength, but does not have good thermal and electrical conductivity. Also, stainless steel is usually a cheaper material than copper. With the right experience with welding technology, these two dissimilar metals can be joined together and ensure the cost-effectively of the high-strength welded joint [1, 2, 7]. Electron beam welding of copper and stainless steel is well feasible, it can be performed by several beam transitions or by one beam transition, while it is possible to define the beam offset - tensile strength relationship, where with correct parameter settings, it is possible to influence weld strength. By applying beam oscillation, it is possible to reduce the formation and size of porosity in welded joints. By heterogeneous welding samples of greater thickness, the presence of nonequilibrium phases can be expected. It can be causes changes in the mechanical and corrosive properties of the welded joint [3, 4, 8, 9].
2 Experiment 2.1 Experimental Material The dimensions of the technically pure copper Cu ETP used in the experiment were 150 × 50 × 1 mm. Copper is widely used for electrical and electronic applications. It has good corrosion resistance, which is due to its positive electrochemical potential, but also to the passivation effects of oxides and compounds, which form a protective layer on its surface [5]. The size of the AISI 304 steel used in the proposed experiment was 150 × 50 × 2 mm, and in the overlap joint it was at the top of Fig. 1. Austenitic chromium-nickel stainless steel has good corrosion resistance due to its high chromium and nickel content.
Fig. 1. Welding scheme of overlapped joint.
The chemical composition of Cu-ETP copper is given in Table 1 and the chemical composition of AISI 304 is given in Table 2. Table 1. Chemical composition (w%) of Cu-ETP material. Material
Cu
O
Bi
Pb
Cu-ETP
Min 99.9
Max. 0.04
Max. 0.0005
Max. 0.0005
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Material
C
Si
Mn
P
S
Cr
Ni
Ti
Fe
AISI 304
0.04
0.22
1.15
0.03
0.01
18.00
8.52
1.24
Bal
2.2 Electron Beam Welding Equipment and Welding Parameters The experiment was performed at the electron beam workplace PZ EZ 30 STU. The electron beam welding equipment consists of an electron gun, an energy source, a control system, a device ensuring the movement of the weld or the beam and a vacuum chamber with dimensions of 1800 x 2360 x 3150 mm with a volume of 13.4 m3 . The vacuum system allows a vacuum of 5 × 10–2 Pa to be reached within 25 min. The device is located on the ground of MTF STU in the Centre Excellency of 5-axis machining Fig. 2.
Fig. 2. Equipment PZ EZ 30 STU for electron beam welding.
An important step in the experiment was to clean the clamping device in the vacuum chamber, demagnetise and properly clamp the materials with clamps so that there was no gap between them. For all overlapped welded joints, the value of the accelerating voltage U = 55 kV and the heating current Ib = 260 mA. The five welds were made on each sample (No. 1, No. 2). The selected welding parameters are listed in Table 3. For series 1 of samples was used focused beam and for series 2 of samples, was used defocused beam. As can be seen in the macrographs. Table 3. Welding parameters. Samples
Welding current Focusing current Welding speed Heat input Q (J/mm) I (mA) If (mA) v (mm/s)
Sample no. 1 Weld 1 (EBW1)
70
895
30
128,33 (continued)
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Table 3. (continued) Samples
Welding current Focusing current Welding speed Heat input Q (J/mm) I (mA) If (mA) v (mm/s)
Weld 2
70
895
40
96,25
Weld 3 (EBW3)
70
895
50
77
Weld 4
80
895
50
88
Weld 5
90
895
50
99
Weld 1
110
930
50
121
Weld 2
Sample no. 2 110
930
30
201,67
Weld 3 (EBW2) 110
930
20
302,50
Weld 4
100
930
20
275
Weld 5
90
930
20
247,50
The aim of the experiment was to find parameters suitable for remelting the entire thickness so that the weld didn’t contain any cracks, spatter, the caterpillar was uniform along its entire length and the root of the weld had a continuous weld. A visual test was performed at the end of the experiment, on the basis of which three suitable settings of welding parameters were selected. Samples EBW1, EBW2 and EBW3 were subsequently welded with these parameters, on which metallographic analysis, microhardness measurement and tensile strength test were performed. 2.3 Preparation of Overlapped Welded Joints Preparation of samples for quality assessment of the welding process was performed in the laboratories of MTF STU. The procedure for making samples for metallographic analysis was performed according to the standard STN EN ISO 17639. The samples were cut transversely and pressed into the mixture (BUEHLER KonductoMet). The samples were etched to make the structure visible. As two different materials were welded and each reacted differently, etching in two different acids was required. Based on the experience from previous experiments performed in the Structural Analysis Laboratories, a combination was used: for copper - nitric acid, and for steel AISI 304 - oxalic acid. The macrostructures and microstructures of the interlaced joints were evaluated with a Neophot 32 light microscope. The surface and root part of the weld was examined by visual inspection Figs. 3, 4 and 5 using a Zeiss Stemi 2000-C stereomicroscope. The width of welds, pores, spatter and surface cracks resp. integrity of welds was observed. All welded joints had a remelted root and an even weld surface. In the EBW1 welded joint (Fig. 3), transverse cracks were observed on the surface and a slight spatter was observed at the root. The welded joint EBW3 (Fig. 4) was the narrowest on the surface and in the root without significant cracks and spatter.
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Fig. 3. Weld face and root surface of overlapped joint EBW1.
Fig. 4. Weld face and root surface of overlapped joint EBW3.
The welded joint was wider with used the higher heat input. Increasing the value of the welding current increased the width of the welds. The weld was narrower at the higher welding speed. The EBW2 welded joint (Fig. 5), which was realised by a defocused beam, had the largest width on the surface and in the root. Transverse cracks were observed on the surface.
Fig. 5. Weld face and root surface of overlapped joint EBW2.
3 Results 3.1 Macroscopic and Microscopic Analysis Macroscopic analysis evaluated errors, mixing and weld width at the base material interface. The shape of the welds and the large dynamic influence of the electron beam can be observed in the photos from the macroscopic analysis. The flat narrow profile characteristic after focused electron beam welding was observed on the macrostructures of samples EBW1 and EBW3 in (Fig. 6 and 7). The EBW1 welded joint had the most complete root with a slight elevation. Columnary crystals in the weld metal region are visible. The heat-affected zone is insignificant in the steel, in the case of copper a coarse grain can be observed, and the more pronounced, the higher the heat input.
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AISI
Cu-ETP a)
b)
Fig. 6. Cross-sections a) and detail b) of the interface of overlapped joints EBW1.
AISI
Cu-ETP a)
b)
Fig. 7. Cross-sections a) and detail b) of the interface of overlapped joints EBW3.
A detailed view of the base material interface and the measured width of the welds in the interface line are documented in Fig. 6b), 7b) and 8b).
AISI Cu-ETP a)
b) Fig. 8. Cross sections a) and detail b) of the interface of overlapped joints EBW2.
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In defocused electron beam we can see that the shape of the weld is V, the deformations are larger in these welds as seen on the copper pressed from the steel Fig. 8. At the root in the area beyond the weld boundary, the copper melted and formed a separate weld boundary, which is indicated by the yellow arrows in Fig. 8. Copper slip and deformation also occurred along this boundary. In addition the width of the EBW2 welds on the interface line was smaller than the thickness of the copper base material. A dominant steel content can be observed at the interface lines of all-welded joints due to the penetration of steel into copper. Based on the above, it can be assumed that the strength at the interface line will have higher values than the strength of copper.
b)
a)
c)
Fig. 9. Fusion zone a), interface between Cu and AISI 304 b), detail of globular formation c).
In Fig. 9b) shows the microstructure of the EBW1 welded joint. The weld metal is heterogeneous and a high proportion of steel is observable at the interface. In the left part marked by a black arrow, you can see the Cu melting area with a diffusion joint at the interface with the steel. The weld metal with a dominant steel content has a fine dendritic structure with different grain orientations (Fig. 9a)). The detail of the globular structure with the dominant Cu content is documented in (Fig. 9c)). 3.2 Microhardness Test Microhardness measurements for welded joints EBW1, EBW2 and EBW3 were made in a weld at the material interface line. The base material AISI 304 steel reached an average hardness of 208 HV0.1, the base material Cu ETP 88 HV0.1 , the hardness values on the measuring lines depend on the degree of mixing of the materials. The dominance of the steel can be observed on the interface lines, which is also confirmed by the measured high values of microhardness (Fig. 10).
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Fig. 10. Material interface lines and microhardness graph for EBW1, EBW2 and EBW3 welded joints.
3.3 Mechanical Test of Overlapped Welded Joints The tensile strength test is used to determine the mechanical characteristics of a given material under static load, which are then used to evaluate the quality of materials or to generally assess the suitability of materials for certain technological operations (quality of the welded joint). In order to be able to define the stress and strain characteristics of the material by static tensile test, it is necessary adhere to the shape of the testing material according to the standard STN EN ISO 6892–1. The three standard bodies cut with a water jet from a welded joint EBW3 are shown in Fig. 11a). Similarly, the three bodies were prepared from EBW1 and EBW2 welded joints. During the tensile strength test, a failure occurred in all cases of heat-affected zone of copper, which is documented in Fig. 11b). A typical force and elongation profile for EBW3 welded joint is documented in Fig. 11c). The place of failure in the tensile strength test also follows from the metallographic analysis, where coarsening of the grain was observed in the heat-affected zone of copper. This is the first critical area of the joint. The second critical area is the interface line, especially at the EBW1 and EBW3 welded joints, where the weld width is less than the thickness of the base material copper.
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Fig. 11. Samples for tensile test a), samples after the test b), graph of force course and elongation at tensile test of sample EBW3 c).
However, the measured microhardness values at the interface lines predetermine the copper heat affected zone as the place of failure. The tensile strength values for welded joint EBW1 were in the range of 242.4 to 244 MPa, for the welded joint EBW2 in the range of 233.6 to 236 MPa and for the welded joint EBW3 in the range of 233.6 to 237.6 MPa. As the tensile test results do not provide further information on the strength of the weld metal, a shear test was performed, using a patented method with jig [6]. Shear test specimens were taken from the test specimens after the tensile test. The calculated values of shear strength for welded joint EBW1 (samples EBW11, EBW12, EBW13) were in the range of 418.5 to 542 MPa, for welded joint EBW2 (samples EBW21, EBW22, EBW23) were in the range of 421.3 to 427.5 MPa and for welded joint EBW3 (samples EBW31, EBW32, EBW33) were in the range of 412.2 to 466 MPa. The largest differences were observed at the welded joint EBW1, where the EBW12 sample had the lowest value of shear strength. The reason is the presence of large imperfections in the weld, as documented by the fracture surface (Fig. 12a)). The fracture surfaces at EBW 2 and EBW3 samples were free of significant imperfections. Differences in strength can also be caused by different degrees of mixing of materials in the shear plane. Figure 12b) shows a characteristic force-displacement relationship for a shear strength test. The graph was recorded at EBW1, sample EBW13.
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Fig. 12. Fracture surface of sample EBW12 a), force-displacement relationship for a shear strength test of sample EBW13 b).
4 Conclusion As the focusing current increased, the width of the welded joints on the surface increased, which had to be compensated with the optimum value of the welding current in order for the weld root to be remelted [10, 11]. Weld geometry and integrity were monitored by macroscopic analysis. The higher the value of heat input, the greater the width. In all cases, steel dominated the interface line, which is confirmed by the microhardness values. From the results of the microscopic analysis, we can state that all welded joints were heterogeneous and globular copper formations in the steel matrix were observed. The heat-affected zone of the steel was insignificant. Grain coarsening was observed in the heat-affected zone of copper, which was the cause of the failure in the tensile strength test. The calculated shear strengths for EBW1, EBW2 and EBW3 samples ranged from 412 to 542 MPa. The strength value depends on the degree of mixing of the materials in the weld. Based on the results of the shear test, the proportion of copper and steel in the mixing area proved to be a significant factor in the strength of the welded joint. Foundation. This contribution was supported by the Agency for the Promotion of Research and Development under contract no. APVV-18-0116. The experiment was also solved within the diploma theses, which are part of the project outputs and by the Vedecká grantová agentúra VEGA grant agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic, project No. 1/0499/21.
References 1. Cheng, Z., Huang, J., Ye, Z., Chen, Y., Yang, J., Chen, S.: Microstructures and mechanical properties of copper- stainless steel butt welded joints by MIG-TIG double-sided arc welding. J. Mater. Process. Tech 265, 87–98 (2019). https://doi.org/10.1016/j.jmatprotec.2018.10.007 2. Kuryntsev, S.V., Morushkin, A.E., Gilmutdinov, A.K.: Fiber laser welding of austenitic steel and commercially pure copper butt joint. Opt. Lasers Eng. 90, 101–109 (2017)
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3. Guo, S., et al.: Effect of beam offset on the characteristics of copper/304stainless steel electron beam welding. Vacuum 128(2016), 205–212 (2016) 4. Jyotirmaya, K., Soumitra, K.D., Gour, G.R., Sanat, K.R., Prakash, S.: X-ray tomography study on porosity in electron beam welded dissimilar copper 304SS joints. Vacuum 149, 200–206 (2018) 5. European Copper Institute – CuETP. http://conductivity-app.org/alloy-sheet/33 6. Martinkoviˇc, M., Kovaˇcócy, P.: Equipment for measuring the mechanical properties of a welded joint in a tensile testing machine. Utility Model Number (2020). https://wbr.indprop. gov.sk/WebRegistre/UzitkovyVzor/Detail/238-2020 7. Munteanu, A.: The electron beam welding of dissimilar materials - case study. Conf. Ser.: Mater. Sci. Eng. 161, 012058 (2016). https://iopscience.iop.org/article/10.1088/1757-899X/ 161/1/012058 8. Tosto, S., Nenci, F., Jiandong, H., Corniani, G., Pierdominici, F.: Microstructure of copper– AISI type 304L electron beam welded alloy. Mater. Sci. Technol. 19(4), 519–522 (2003). https://doi.org/10.1179/026708303225010722 9. Kuryntsev, S.: Laser welding of dissimilar materials (Al/Fe, Al/Ti, Al/Cu)—methods and techniques, microstructure and properties. Materials 15(1), 122 (2022). https://doi.org/10. 3390/ma15010122 10. Kar, J., Roy, S.K., Roy, G.G.: Effect of beam oscillation on electron beam welding of copper with AISI-304 stainless steel. J. Mater. Process. Technol. 233, 174–185 (2016). https://doi. org/10.1016/j.jmatprotec.2016.03.001 11. Monem, A., Batahgy, E.: Laser beam welding of austenitic stainless steels – similar butt and dissimilar lap joints. In: Welding Processes (2012). https://doi.org/10.5772/48756
Laser Beam Welding of Overlapped Joints Copper - Stainless Steel Ingrid Kovaˇríková, Pavel Kovaˇcócy, Miroslav Sahul, Beáta Šimeková, Maroš Martinkoviˇc, and Erika Hodúlová(B) Faculty of Material Science and Technology in Trnava, Slovak University of Technology in Bratislava, Trnava, Slovak Republic {ingrid.kovarikova,pavel.kovacocy,miroslav.sahul,beata.simekova, maros.martinkovic,erika.hodulova}@stuba.sk
Abstract. The paper deals with the production of overlapped welded joints in a continuous and pulsed laser welding process with different welding parameters in order to examine the influence of changing parameters such as: power, focus, speed and length of the welding pulse. Laser welding achieves a high energy density, so there are no special requirements for the cleanliness of the welding surface because all impurities are evaporated before the material is melted. The overlap joint is mainly used for joining thin sheets and pipes. The aim was to find suitable parameters of the proposed technology for welding overlapped joints created of austenitic steel AISI 304 and copper Cu - ETP. In the experimental part, the created overlapped joint with power of 2000 W, welding speed of 15 mm/s and beam focus of 0 mm, where the total power input was 133.33 J/mm met the required criteria based on macro and microanalysis. The weld was realised by continuous welding. Satisfactory welding parameters were used for test specimens, to perform tensile and shear tests. During the tensile strength test, the failure occurred in the heat-affected zone (TOO) of copper due to the softening of the copper by grain coarsening. The strength reached values from 230–240 MPa. The calculated shear strengths were ranged from 409 to 411 MPa. The strength value depends on the degree of mixing of the materials in the weld. Based on the results of the shear test, the proportion of copper and steel in the mixing area proved to be a significant factor in the strength of the welded joint. Keywords: Laser welding · Continuous and pulsed laser welding process · Dissimilar overlapped joint
1 Introduction Laser welding is a high concentration of energy per unit area. For this reason, it is possible to use the laser beam for various tasks such as cutting, soldering, surface treatment and the like. The welding process is very efficient due to the high speed of laser welding. Low thermal effects, minimal deformation, and a neat weld surface reduce or completely eliminate the subsequent processing of the product. These advantages, as well as the ability to precisely control material heating, predetermine laser welding technology © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 957–965, 2023. https://doi.org/10.1007/978-3-031-15211-5_80
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for joining difficult-to-weld materials such as stainless steel and copper, titanium and nickel, stainless steel and aluminium, and the like. By successfully combining stainless steel and copper, it is possible to produce a component with excellent properties. These components will find application in a wide range of chemical, petrochemical, nuclear, automotive, aerospace, electrical engineering industries, etc. For example, Audi and Tesla for electric motors - induction motors in electric cars. Parts made of copper can be partially replaced by stainless steel, which will reduce the weight of the part and, at the same time, reduce copper consumption worldwide. For this reason too, considerable attention is paid to the welding of these two metals [1, 2, 3, 4, 5, 9].
2 Experiment 2.1 Experimental Material As part of the experiment, overlapped joints made of austenitic steel AISI 304 and copper Cu ETP by TruDisk 4002 disk laser. The effect of varying power, focus and welding speed on the joint was monitored. Subsequently, the parameters at which the weld should achieve the required properties such as: integrity of the welded joint (pores, cavities and cracks), geometrically uniform caterpillar width, and uniform weld depth were determined. The mechanical strength of the weld must be higher than the strength of the designed experimental material Cu ETP. The chemical composition of AISI304 is shown in Table 1 and the chemical composition of Cu-ETP copper is shown in Table 2 [6, 7]. Table 1. Chemical composition of AISI 304 material wt%
C
Si
Mn
P
S
Cr
Ni
Ti
AISI 304
0.04
0.22
1.15
0.03
0.01
18.00
8.52
1.24
Table 2. Chemical composition of Cu-ETP material wt%
Cu
O
Bi
Pb
Cu-ETP
Min 99.9
Max. 0.04
Max. 0.0005
Max. 0.0005
The scheme of the overlapped joint is shown in Fig. 1. The dimensions of the sample made of AISI 304 material are 2 mm thick, 50 mm wide and 150 mm high. The Cu-ETP material has the dimensions 1 mm thick, 50 mm wide and 150 mm high.
Laser Beam Welding of Overlapped Joints Copper - Stainless Steel
Steel AISI
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Weld
Fig. 1. Welding scheme overlapped joint.
Laser Equipment and Welding Parameters The experiment consisted of the preparation of a TruDisk 4002 welding machine from Trumpf with a power of 4 kW, a wavelength of 1030 µm and a welding head, where the required welding parameters were set before each weld. The specimens were clamped before welding to prevent unwanted displacements. Experimental samples were welded by continuous and pulse welding methods, at different welding parameters. During welding, the welding speed gradually changed: the welding speed in the range of 30– 10 mm/s, the focusing of the laser beam in the range of 0–−1 and the power remained constant at 2000 W. Pulse mode with a frequency of 30 Hz, energy of 60 J and a pulse length in the range of 25–30 ms was used for welds no. 1 and 3. Shielding gas argon with a flow rate of 20 l/min. Did not change during welding. The proposed welding parameters are given in Table 3. The aim of the experiment was to find parameters suitable for remelting the entire thickness so that the weld did not contain any cracks and spatters. The formed caterpillar was even along the entire length, and the root of the weld was completely remelted. Finally, the welds were evaluated by visual inspection. Table 3. Welding parameters Sample
Power P(W)
Welding speed (mm/s)
Focus S (mm)
Frequency(Hz)
Pulse length T (ms)
Energy (J)
Overlapped 1
2000
30
Overlapped 3
2000
30
0
30
25
60
0
30
30
60
Overlapped 2
2000
30
0
Overlapped 4
2000
20
0
Pulse mode
Continual mode
(continued)
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Sample
Power P(W)
Welding speed (mm/s)
Focus S (mm)
Overlapped 5
2000
15
0
Overlapped 6
2000
10
0
Overlapped 7
2000
10
−1
Overlapped 8
2000
20
−1
Overlapped for tensile test (L1,L2,L3)
2000
15
0
Frequency(Hz)
Pulse length T (ms)
Energy (J)
Preparation of Overlapped Joints The procedure of making samples for metallographic analysis was performed according to the standard STN EN ISO 17639 in the laboratories of Faculty of Material Science and Technology in Trnava. The samples were cut transversely and pressed into the mixture (BUEHLER KonductoMet). Based on experience from previous experiments performed at the Institute of Materials Science Structural Analysis Laboratories, a combination of two different acids was used to etch the samples. For copper - nitric acid, and for steel AISI 304 - oxalic acid. The macrostructures and microstructures of the lap joints were evaluated with a Neophot 32 light microscope. The surface and root of the weld are shown in Fig. 2 and 3 using a Zeiss Stemi 2000-C stereo microscope. Results Based on visual inspection, it was found that there was no spatter on and around the welds. Weld joint no. 1 and weld joint no. 3 were welded in pulse mode, Fig. 2. Due to the high welding speed of 30 mm/s, no root remelting (Cu) occurred. The root no
no. 3
no. 1
no. 8 Fig. 2. Weld face of overlap joints no. 1, 3 and 8.
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remelting occurred in weld joint no. 8, where the focusing of the weld was −1 and the welding speed was 20 mm/s. From the results of the visual test, we selected the most suitable welded joint no. 5 with a power of 2000 W welding speed 15 mm/s and focusing 0 mm, where the total power input was 133.33 J/mm. The weld joint was realised by continuous welding, the surface had a fine pattern, the created caterpillar was solid with the same width along the entire length and without spatter. The width of the root did not change significantly, there was a spray around the root Fig. 3. Mechanical tests to determine the tensile and shear strength were performed on the sample, where we applied the welding parameters of sample no. 5.
weld face no.5
root surface no. 5
Fig. 3. Weld face and root surface of overlapped joint no. 5.
Macroscopic and Microscopic Analysis The macroscopic analysis mainly assessed the occurrence of pores, internal cracks, weld width and weld depth. Weld joints no. 1 and no. 3 Fig. 4 a, b have a wineglass shaped weld joint in cross-section, the weld is the widest on the surface and gradually narrows to the depth of the weld. Duo to the welding parameters, the width of the weld changes significantly, especially in pulse mode. A common feature in pulse welding is the weld overhang a little more than in the continuous mode. Weld joint no. 5 does not contain cracks, it was formed over the entire thickness of both materials with the formation of the root part of Fig. 4c. The parameters used, welding speed 20 mm/s and focusing −1 mm in weld joint no. 8 caused that the weld joint occurred only to a depth of 0.19 mm of copper material Fig. 4d. Microscopic analysis revealed an inhomogeneous weld joint and a fine dendritic structure in the weld metal of Fig. 5a. Weld joint microstructure no. 5 Fig. 5b shows the high mixing of AISI 304 and Cu-ETP materials at the interface. On the steel side, the TOO was indistinct.
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a)
b)
AISI304
AISI304
Cu-ETP
Cu-ETP
c)
d)
AISI304
AISI304
Cu-ETP
Cu-ETP
Fig. 4. Cross-sections of overlapped joints a) no. 1, b) no. 3, c) no. 5, d) no. 8.
a)
b)
c)
Fig. 5. AISI304 - HAZ transition of weld a) no. 5, b) fusion zone of weld no. 5, c) interface between Cu and AISI 304.
Mechanical Tests of Interlaced Joints The tensile strength test is used to determine the mechanical characteristics of the material under static load, which are then used to evaluate the quality of materials or to generally assess the suitability of materials for certain technological operations (weld joint quality). In order to be able to define the stress and strain characteristics of the material by static tensile test, it is necessary to shape the material according to the standard STN EN ISO 6892-1. Samples after the tensile test can be seen in Fig. 6a. The strength reached values of 230, 228 and 237 MPa. From the measured test results Fig. 6b, it is clear that the violation occurred in the heat-affected zone (TOO) of copper. The reason was the softening of the copper TOO due to grain coarsening. The weld joint has a higher strength than copper,
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Fig. 6. a) samples before and after the test, b) graph of force course and elongation of tensile test of sample L1.
but the tensile test results do not provide more detailed information on the strength of the weld metal. For this reason, a shear test was performed, using a patented method and preparation [8]. After the tensile test, specimens were taken from the test specimens for shear testing. The shear test was according to the patent mentioned in literature no. 8.The sample L1 is documented in Fig. 7. The fracture on the steel side with the marked dimensions is shown in Fig. 7d.The weld joint had a length of 9.78 mm and an average width of 0.76 mm, the fracture on the copper side had a length of 9.63 mm and an average width of 0.74 mm. Fig. 7b. The details of the quarries are shown in Fig. 7c, e. The shear test was performed on all three samples and the measured values were used for the calculation. A graph of the force and shear length for the L1 sample is shown in Fig. 8. The resulting strengths in the weld metal of the samples reached values of 409 to 411 MPa. Compared to the strengths of the basic materials (steel 500–700 MPa and copper 200–250 MPa), it can be stated that the strength in the weld joint is higher than the strength of copper. Which corresponds to the results of the tensile test. The value of strength depends on the degree of mixing of materials in the weld joint. From this fact, it can be assumed that there was a larger proportion of steel in the weld metal - which must be confirmed by SEM analysis.
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Fig. 7. a) sample L1 for shear test, b) fracture of copper side with appropriate dimensions, c) detail of fracture copper, d) fracture of stainless steel side with appropriate dimensions, e) detail of fracture stainless steel.
Fig. 8. Graph of the course of force and length of shear.
3 Conclusion The paper investigates the welding of overlapped joints with laser technology. The aim was to find suitable parameters of the proposed technology for welding overlapped joints made of austenitic steel AISI 304 and copper Cu - ETP.
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In the experimental part, 8 weld joints were created. Overlapped joint no. 5 with a power of 2000 W, a welding speed of 15 mm/s and a focus of 0 mm was suitable based on macro and microanalysis. The total power input was 133.33 J/mm. The weld joint was realised by continuous welding. Tensile and shear tests were performed on test specimens with satisfactory welding parameters. The violation in the tensile strength test occurred in the heat-affected zone (TOO) of copper, due to the softening of the copper due to grain coarsening. The strength reached values from 230– 240 MPa. The calculated shear strengths for samples L1, L2 and L3 ranged from 409 to 411 MPa. The strength value depends on the degree of mixing of the materials in the weld joint. Based on the results of the shear test, the proportion of copper and steel in the mixing area proved to be a significant factor in the strength of the overlapped joint. Foundation. This contribution was supported by the Agency for the Promotion of Research and Development under contract no. APVV-18-0116. The experiment was also solved within the diploma theses, which are part of the project outputs and by the Vedecká grantová agentúra VEGA grant agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic, project No. 1/0499/21.
References 1. Meng, Y., Li, X., Gao, M., Zeng, X.: Microstructures and mechanical properties of laser – arc hybrid welder dissimilar pure copper to stainless steel. Opt. Laser Technol. 111, 140–145 (2019) 2. Mai, T., Spowage, A.: Characterisation of dissimilar joints in laser welding of steelkovar, copper-steel and copper-aluminium. Mater. Sci. Eng. A 374, 224–233 (2004) 3. Katayama, S.: Fundamentals and Details of Laser Welding. Springer, Singapore (2020). https:// doi.org/10.1007/978-981-15-7933-2. ISBN : 978-981-15-7932-5. Metallurgy of Welding. Woodhead Publishing 4. Laser welding fundamentals: Amanda Weld Tech Inc. Amada Weld Tech Inc. (2020) 5. Sun, Z., Ion, J.C.: Laser welding of dissimilar metal combinations. J. Mater. Sci. 30, 4205–4214 (1995). https://doi.org/10.1007/BF00361499 6. Stainless Steel 304L: Thyssenkrupp Materialas (UK) Ltd. (2018) 7. Besnea, D., Dont1, O., Avram, M., Spânu, A., Rizescu, C., Pascu, T.: Study on laser welding of stainless steel/copper dissimilar materials. Conf. Ser. Mater. Sci. Eng. 147, 012047. https:// iopscience.iop.org/article/10.1088/1757-899X/147/1/012047 8. Martinkoviˇc, M., Kovaˇcócy, P.: Equipment for measuring the mechanical properties of a welded joint in a tensile testing machine. Utility Model Number 270 (2020), https://wbr.indprop.gov. sk/WebRegistre/UzitkovyVzor/Detail/238-2020 9. Sahul, M., Sahul, M., Turˇna, M., Zacková, P.: Disk laser welding of copper to stainless steel. Adv. Mater. Res. 1077, 76–81 (2014). https://doi.org/10.4028/www.scientific.net/AMR.107 7.76. www.scientific.net
Design and Fabrication Factors for the Fatigue Assessment of Welded Structures Tuomas Skriko1(B)
, Antti Ahola2
, and Timo Björk2
1 Laboratory of Welding Technology, Lappeenranta-Lahti University of Technology,
Yliopistonkatu 34, 53850 Lappeeranta, Finland [email protected] 2 Laboratory of Steel Structures, Lappeenranta-Lahti University of Technology, Yliopistonkatu 34, 53850 Lappeeranta, Finland {antti.ahola,timo.bjork}@lut.fi
Abstract. Generally, the fatigue strength of a welded structure is characterized by certain factors, which are incorporated in the detail categories and S-N curves as a statistical variation. However, digitized welding production enables the precise control and evaluation of the fatigue-related design and manufacturing parameters at different levels; this can be utilized when applying modern fatigue assessment methods. The fatigue strength of a weldment is usually evaluated based on idealized and standardized factors, but the availability of measured parameters can improve the accuracy of fatigue analysis. In this study, the validity of general design simplifications is compared with actual measurable parameters. Different factors, such as loading, global structure, structural detail, local weld geometry, initial defects and residual stresses, are identified, and their effect on the total fatigue life is evaluated. To exemplify the outcomes of different factors, the fatigue strength of a mobile working machine is evaluated using idealized and measured parameters. The results indicate that the idealized model can produce distinguishing features compared to the real structure, whereby the scatters of individual factors have an essential effect on the deviation in the fatigue assessment results. However, the case study reveals that when the fatigue analysis is conducted using the effective notch stress (ENS) method, the actual weld shape has a minor effect on the results compared to the idealized geometry. Thus, the analysis and efforts must be focused on those factors that have a considerable impact on the reliability of theoretical results in practice and a substantial effect on the quality of weldments. Keywords: Fatigue life · Welded structures · Design and fabrication · Digitized production · Weld quality · Quality parameters
1 Introduction The fatigue life of welded structures predominantly depends on the applied cyclic stress, global and local geometry, and residual stresses (RSs) in the joint. These main effects are included as direct (as design parameters) or embedded (included in the design resistance) parameters in all main fatigue design methods, such as the nominal stress, structural hotspot (HS) stress, effective notch stress (ENS) and fracture mechanics approaches; see, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 966–980, 2023. https://doi.org/10.1007/978-3-031-15211-5_81
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e.g., the fatigue design standards [1–5]. In addition, the microstructure in the vicinity of the weld toe has an influence on fatigue strength in the case of high-quality welds made of high-strength steels, whereby the crack initiation (CI) period plays an important role in the total fatigue life of the joint. The stress range, joint geometry, and RSs have several sub-parameters that must be considered separately. Conventionally, the fatigue life of machine and engine components can be estimated as presented in Eq. (1), where the nominal design load σ is adjusted by the effects on the fatigue life of a given material with the fatigue strength of σ f at a given number of cycles. σeq = ki σ ≤ σf , (1) i
where the correction factor k i can include multiple factors, such as the type of loading and/or stress gradient, notch geometry and notch sensitivity, size of the specimen, surface roughness and RS. Alternatively, these factors (as inverse values) can be used to reduce the fatigue strength, and the equivalent stress range or amplitude is compared to the reduced fatigue strength, similar to as presented, e.g., by Juvinall and Marshek [6] and Shigley et al. [7]. However, as these factors are closely related to the characteristics of the applied load, this work applies these factors in association with the applied load. The same approach is applicable for the fatigue design of welded joints, i.e., adjusting the design load to consider the various factors inherent in the design and fabrication phases of a welded structure. For an effective and reliable evaluation of the assessed fatigue life, it is of paramount importance to understand and consider the effects of different sources of error and deviation as well as their contribution to the fatigue performance of welded structures. The proposed concept can be applied to an individual joint, a component or a whole structure. However, the focus of this paper is on single weld joints because structural details and joints are usually the most critical locations and are decisive from the fatigue performance viewpoint. The aim of this research is to highlight the importance of considering all essential factors that can affect the fatigue life of a structure before focusing attention on any individual factor. Otherwise, the design and analysis can yield a structure that is not efficient, competitive or safe.
2 Fatigue Parameters Conventionally, the fatigue strength assessment of welded joints is based on stressbased approaches or fracture mechanics-based crack propagation analysis. To consider the uncertainties involved in the fatigue load actions and fatigue strength characteristics, different safety factors are introduced. In addition, the fatigue assessments are carried out using characteristic values for fatigue design resistances, such as FAT classes and crack propagation coefficients. In most design standards, these refer to a survival probability of Ps = 97.7%. In this work, these uncertainties are approached with correction factors k i , which are categorized according to the source of the uncertainties. Table 1 describes the factors as per Eq. (1), and the following sub-Sects. 2.2–2.7 focus on each parameter in detail.
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From the designer and structural analyst’s viewpoint, one of the critical steps is selecting these factors if the proper design data are unavailable. Table 2 summarizes the key features of each parameter at the design stage, when the actual design data are not usually available, and at the actual stage if the designers can utilize the collected data in the analysis covering the different stages of a welded product’s lifecycle. Table 1. Fatigue parameters of welded structures. Symbol
Description
kL
Load correction factor considering the difference between actual and designed load actions
kG
Global correction factor considering the differences between the actual structure and the idealized model
kS
Structural correction factor considering the differences between the real and designed structural details
kw
Weld geometry correction factor, considering the differences between real and designed local weld shapes
ka
Initial crack size correction factor
k RS
RS correction factor, considering the differences between the real and assumed RS states at weld joints
kC
Correction factor for material properties, considering the differences between real and assumed crack initiation, propagation and critical crack size
2.1 Loading Cyclic loading is usually regarded as the most uncertain factor in fatigue analysis, as also demonstrated in the round-robin study undertaken by Haglund et al. [8]. The loading factor k L can be defined as follows: kL =
σeq,actual , σeq,d
(2)
where σ eq,actual and σ eq,d are the actual and designed equivalent stress ranges, respectively. This factor can be evaluated using on-site field measurements of real structures or by virtual simulations. However, the main obscurity involved in this factor is the user or operator of the device or machine. The method and conducted practice, as well as the user’s experience in controlling the device, can have a remarkable effect on the fatigue loading of the structure. Even similar working periods under the same working circumstances can produce a large scatter on fatigue loading depending on the user’s mindset, skills and experience. Increased automatization in the operation of devices and so-called smart structures can decrease fatigue loading. Consequently, if the fatigue and degree of damage are controlled parameters in a smart system, their use can be optimized without decreasing the efficiency and productivity of the work. The utilization of
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Table 2. Comparison of designed and actual fatigue parameters in welded joints.
1
Stage Loading
Symbol kL
Design Measured history or statistical spectrum
Actual Random
Global structure
kG
ε=F/S
{F} = [K]{δ} - Dimensions - Frictions - Contacts - Constraints
Structural detail
kS
Imperfections
Weld geometry
kw
Toe – Root
Initial defects
ka
Residual stresses
kRS
Crack initiation (CI) and propagation (CP)
kC
IIW [1] Initial crack - Toe: ai = 0.15 mm - Root: ai = t σres = +fy
Unknown
C and m given in the design standards
Nf = NCI + NCP (C, m)
X-ray diffraction (XRD) and neutron diffraction (ND)
Measured values (XRD, ND, hole drilling) 1
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smart technologies in the operation and control of devices can decrease peak stresses and redundant cycles and, simultaneously improve the working efficiency. The other means to improve operational efficiency, along with diminishing the loading factor k L , is to eliminate unnecessary vibrations or improve fabrication quality, making small stress ranges ineffective. In welded plate structures, vibration problems can involve the global or local behaviour of the structure. The elimination of global vibration modes requires the main dimensions of the structure to be resized to alter the global stiffness. However, local vibration modes can be most efficiently eliminated by adjusting plate boundaries, e.g., by adding stiffeners or by sub-dividing sheet plates into smaller and narrower (in the width direction) sections by cold forming. If the mass of the plate is the main cause of the local vibration, the eigenvalue or natural frequency is linearly dependent on the wall thickness (t) but the width of the plate (b) quadratically affects the critical frequency. It is also worth mentioning that in the case of vibrations, the magnitude of individual stress ranges due to vibrations can be minor, resulting in a negligible small contribution to the fatigue damage and total fatigue life, as per the Palmgren-Miner damage accumulation regardless of a high number of cycles. Usually, such vibrations take place at a higher frequency compared to normal use and the corresponding operating cycles. However, the vibration stress peaks are superposed onto the external cyclic loading without the vibration, including both maximum and minimum values, and thus, can also influence on the fatigue performance of a structure (Fig. 1). Nonetheless, in the exemplified structure (see Sect. 3), global vibrations are not a critical issue, and local vibrations are prevented by the compact cross-section of the structure.
Stress at critical detail
(a)
(b)
Δσvibr.
(c)
Δσi
Δσi,actual ≈ Δσi + Δσvibr. Time
Time
Time
Fig. 1. Minor cyclic stresses due to vibration can have a significant effect on the fatigue life of the structure: (a) vibrating stress, (b) external cyclic stress, and (c) superposed stresses at a critical detail.
2.2 Global Structure To determine the behaviour of a critical joint, either the whole structure can be modelled, or just its most interesting components. Both approaches require the modelling of hinges, etc., and consequently, the consideration of clearances, contacts, and frictions. If the whole structure is modelled, the global boundary conditions must also be taken into account. The nominal stresses of the critical part can be measured using strain gauges, which should be located in a highly loaded area far from structural discontinuities, as
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illustrated in Fig. 2. Correspondingly, the nominal stresses can be determined numerically using the finite element (FE) model at the same points where the strain gauges are located in the real structure. Thus, the global structural factor k G can be defined as follows: kG =
σtest , σFEA
(3)
where σ is the nominal stress range.
Strain gauge rtoe
ai
σres
Loading
Strain gauge Global structure
Structural detail
Weld geometry
Initial defects
Residual stresses
Fig. 2. Global structure stage and nominal stress determination by means of strain gauges.
2.3 Structural Details and Imperfections The structural stress concentration factor comprises two terms; structural shape (usually denoted with K s factors) and imperfections and misalignments due to fabrication (usually denoted with K m factors). The structural shape factor of an actual geometry is typically close to the theoretical one because the shapes of the parts are created using numerically controlled systems wherein the benefits of digitized fabrication can be fully utilized. In addition, actual structural dimensions, such as plate thicknesses and shape and the dimensions of the cross-sections of the details, typically match with the theoretical values reasonably well. Therefore, the structural correction factor resulting from imperfections in structural details is close to k S = 1. As an exception to this, fillet and butt welds can have a significant effect on the structural stresses in a regime where stiff structural ligaments exist, such as small gaps between the brace members of trusses [9]. In such details, the actual structural stiffness might differ from the idealized geometry depending on the accuracy and simplifications in the CAD or FE modelling technique. Furthermore, manual welding causes more variation in the weld shape compared to the robotic welding of tack-welded joints due to the inexact positioning of the detail. These aspects can have an effect on the k S factor if the nominal stress has a significant gradient in the direction of the detail’s changed position. Nevertheless, the highest impact on the k S factors is from the imperfections due to fabrication effects, such as angular or axial misalignments and the buckling of the plate near the weld joint. The main reason for these misalignments is the welding heat input or the combination of cold forming and its relaxation effects due to the elevated temperature during welding. In general, these imperfections are not considered in the CAD or FE models, although they can significantly increase the local structural stresses. Furthermore, it is also worth mentioning that the macro geometric bending stresses
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are superposed with the structural stress concentration factor for bending loads, K s,b , which can significantly increase the structural stress. In the nominal stress approach, K m values of 1.2–1.3 are typically included in the S-N curves depending on the detail category while the structural HS method and ENS concept only cover a K m value of 1.05 [1, 2]. Considering the actual K s factor and misalignments, the k S correction factor can be determined as follows: 1 + Ks,actual − 1 + Ks,b,actual (Km − 1) . (4) kS = Ks,idealized In real structures, the effect of the structural details and imperfections on the stress concentration can also be defined by strain gauges, as illustrated in Fig. 3.
Strain gauge
rtoe
ai
σres
Strain gauge Loading
Global structure
Structural detail
Weld geometry
Initial defects
Residual stresses
Fig. 3. Structural detail stage and determination of the structural stress concentrations by means of strain gauges.
2.4 Weld Geometry Designers and structural analysts are responsible for the sufficient throat thickness of welds, particularly in the case of fillet welds. However, designers are responsible for neither the preparation of a weld nor the number or sequence of welding passes since the groove type and required number of passes depend on the applied welding process and equipment, which can still be undefined at the design stage. This is particularly the fact if the design and analysis actions are part of sub-contracting. Naturally, if everything is planned at the design stage, the designer can include all weld design information, e.g., through welding procedure specification (WPS), but manufacturing parameters are not the designers’ responsibility. If no information about weld quality and shape is available, the standard weld marks mean normal workshop quality class C [10]. Furthermore, the upcoming revision of the EC3 standard for the use of FE analysis [11] guides designers to use certain weld profiles for fillet welds and butt welds, i.e., with flank angles of 45° and 30°, respectively. However, these can differ from the local weld shape in the critical part of the structure, as illustrated in Fig. 4. In the case where the designer can set the requirements for welding quality, e.g., weld toe radius, flank angle and profile, or if quantitative welding quality inspection systems are applied (see e.g., [12]), the actual weld geometry can be implemented in the fatigue analysis. The weld shape factor k w describes the ratio of the fatigue notch factors between the actual and designed ideal joint geometries as follows: kw =
Kf,actual , Kf,design
(5)
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Strain gauge ai
rtoe
σres
Strain gauge Loading
Global structure
Structural detail
Weld geometry
Initial defects
Residual stresses
Fig. 4. Weld geometry stage and local shape of the joint, which is usually neither constant nor ideal.
where K f is the fatigue notch factor. If the material characteristics are known for the base material or the heat-affected zone (HAZ) in the vicinity of a fatigue-critical region, they can be considered in the fatigue notch factor. The HAZ properties can be different from the base material properties and should be particularly considered for ultra-high-strength steels (UHSSs) in the as-welded (AW) and post-weld treated conditions as well as lowstrength strength steels subjected to post-weld treatments (PWTs) [13]. The fatigue notch factor can be obtained from the notch stress concentration factor, K t , considering the material characteristics using the Peterson equation [14]: Kf = 1 +
Kt − 1 1+
a∗ ρ
,
(6)
where a* is the material constant, and ρ is the actual notch radius. If the ENS concept is used as a fatigue assessment method, the actual weld toe radius is replaced by the fictitious radius to obtain the fatigue-effective stress at the notch. For a plate thickness of t > 5 mm, the reference radius of r ref = 1.0 mm is recommended. Alternatively, the fictitious radius can be obtained as ρ f = ρ + 1 mm if the actual weld toe radius is known and defined by measurements. A simplification in the local notch geometry with the fictitious radius significantly averages the notch stress. Figure 5a presents the weld toe radii of a robotized fillet weld measured and analyzed using the Winteria weld quality inspection system, and Fig. 5b shows the obtained notch SCFs and fatigue notch factors determined for ρ = 0–1 mm. The results highlight that even in the robotized welds, there are minor changes in the weld toe radii. The effect on the weld toe radius on the notch SCF is very high, as demonstrated by K t factors (Fig. 5b). However, particularly when employing the ENS concept with ρ f = ρ + 1 mm, the changes in the fatigue notch factors are not that significant due to the stress averaging. 2.5 Weld Imperfections and Cracks In welded components, initial cracks and flaws are detrimental to fatigue performance and require careful consideration in fatigue assessments. Various non-destructive testing (NDT) methods, such as liquid penetrant and magnetic particle inspection, exist to identify the presence of cracks in weldments. However, these can only identify the presence of initial cracks and are unable to quantitatively measure the length and depths of technical cracks, e.g., at the weld toe position. On the other hand, when evaluating the effects of a certain crack length on the fatigue strength in the stress-based approaches
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Prob. Dens. Function
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x
9 8 Kt
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Kf = Kt(ρf = ρ + 1 mm), ENS
1 0
0 0
0
0.4 0.8 1.2 ρmeasured (mm)
0.2
(a)
0.4 0.6 ρ (mm) (b)
0.8
1
Fig. 5. (a) Laser-scanned data of the weld toe radii in a robotized fillet weld (data extracted from [15]), and approximated (b) notch SCFs and fatigue notch factors based on Ushikorawa’s analytical equation for the K t factors [16].
based on crack propagation, the determination of a reference crack size, corresponding to the fatigue strength of the S-N curve, is challenging. Nevertheless, if such an evaluation can be done, the initial crack size correction factor can be introduced as follows: ka =
σf (ai,ref ) , σf (ai )
(7)
where ai,ref and ai are the reference and case crack sizes, respectively. In this case, the fatigue strength σ f refers to CP analyses, and with the one-stage Paris’ law, Eq. (7) can be reformulated as: af da ai,ref K(a) m m k = , (8) a
af da ai K(a)m
where m is the slope exponent of Paris’ law and the slope parameter of the S-N curve (assumed to be identical), and K(a) is the stress intensity factor range. Another way to consider weld defects, such as undercuts and initial cracks at the weld toes, via the k a factor is by applying the fictitious radius (e.g., r ref = 1.0 mm) at the deepest point of a dent or crack-like defect. As a result, the reduction factor can be identified as: ka =
Kf (ai = 0) , Kf (ai )
(9)
Figure 6a presents a comparison of linear elastic fracture mechanics (LEFM)-based and ENS-based analyses on the k a factors in the non-load-carrying fillet weld configuration (2D FE models). As can be seen, a reasonably good agreement with two alternative concepts can be identified in both the axial and bending load conditions. Another concern is the weld undercuts, e.g., caused by improper welding or PWTs such as TIG dressing, burr grinding or HFMI treatment. In a such case, a reduction in the net plate thickness is present. This can be analytically considered as follows: α t , (10) ka = tnet
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where α is the exponent of the net thickness reduction. As can be seen from Fig. 6b, undercuts are more detrimental under axial loads than bending loads, and α = 4–5, and α = 2–3, Eq. (10), seem to be applicable for axial and bending loads, respectively. 3.5
3.5 1 mm
Axial Bending r
3
3 ai
t
ka (-)
σb σm
2 1.5
ENS LEFM Axial Bending
1 0
0.05
0.1 0.15 Ratio ai/t (a)
0.2
0.25
1
3
6 α t
2.5
2.5
ka (-)
r (mm)
ENS model
d
2
σb σm
1.5 1 0
0.05
0.1 0.15 Ratio d/t (b)
0.2
Fig. 6. (a) Comparison of the ENS and LEFM analyses (ai,ref = 0.01 mm) on the k a factors in a fillet weld joint, data extracted from [17], and (b) undercut effects on the k a factors in the different weld toe radii, data extracted from [18].
2.6 Residual Stress The underlying concept in the fatigue assessment of welded structures is the assumption of high tensile RSs equal to the yield strength of the material in the joints in the AW condition. This is a valid assumption in engineering (large-scale) structures as well as in laboratory (small-scale) samples if weld metal contraction is prevented at the cooling stage, which depends on the joint configuration [19]. Small changes in the magnitude of welding RSs do not have a significant effect on the fatigue strength when they are close to the yield strength of the material. Nevertheless, if PWTs or special welding techniques are employed, changes in RSs usually occurs. In addition, overloads might significantly alter the local RS state at the fatigue-critical locations. Conventionally, PWTs and changes in RSs are embedded in the fatigue strength (S-N curve) with enhancement factors or FAT classes. To specifically correct the RS state in association with the applied stress ratio of cyclic loading, IIW Recommendations has an enhancement factor f (R), which can be introduced as the RS correction factor: kRS =
1 f (R)
(11)
This factor is based on three qualitative categories: high tensile RS, medium tensile RS, and low tensile RS. Recently, this enhancement factor was further-improved and extended by Hensel [20] who introduced the effective mean stress level, defined by the mean stress and stabilized RS. With such modification, the RS state can be quantitatively considered, and R is replaced by Reff . An alternative concept is to utilize the 4R method,
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in which the local cyclic notch stress is obtained considering the material strength (stressstrain behavior, e.g., determined based on the ultimate strength Rm ), RS (σ res ) and applied stress ratio (R). The theoretical background and applications of the 4R method have been introduced in detail in [19, 21, 22]. The essence of the 4R method is the consideration of the combined effect of the aforementioned factors. As per the 4R method, the RS correction factor can be written for a certain applied stress range (σ k,i ) as follows: 1 − Rlocal (+fy , Rm , R, σk,i ) kRS = . (12) 1 − Rlocal,i (σres , Rm , R, σk,i )
2.7 Crack Initiation and Growth Stress-based fatigue strength assessment approaches for welded joints incorporate both the CI period (presumably rather short) and CP. Crack growth parameters are almost identical for all steel materials, but changes can occur in the CI period particularly in high-quality weldments. In S-N curves, this can be identified by shallower slopes than the standard slope parameter of m = 3. In welded joints, the crack initiation is governed by the weld quality, which is, at least partially, related to one or more of the factors introduced in sub-Sects. 2.5–2.7, namely weld geometry, imperfections and RS state, and can be taken into account using those factors. However, if the CI and CP periods (N CI and N CP ) are distinguishable, a k C factor can be introduced as follows: kC =
NCI,ref + NCP NCI + NCP
1
m
.
(13)
It is also worth mentioning that the CI period, e.g., calculated through the MansonCoffin equation, is not constant for the low- and high-cycle regimes. In addition, different strength- and hardness-based approximations for the fatigue parameters have been suggested for welded joints.
3 The Case Structure – A Heavy Working Machine In this case, the structure under investigation consisted of booms, connected by hinges and hydraulic cylinders. The goal of the study was to determine whether the FE modelling and analysis could simulate the real loading and behaviour of the structure (Fig. 7). Because the structure was a mechanism, there were an infinite number of kinetic combinations for analysis. Therefore, the most typical working cases were chosen for comparison, and the positioning of the FE model was aligned with the loading of the real structure. In the FE analysis, the modelled boom structure was subjected to the same force as was used (and measured) in the corresponding experimental laboratory test. In the real test structure, the used loading was based on the measured values from field tests. In addition, the boundary conditions of the FE model simulated the laboratory test conditions. A detailed description of this case study and the related FE analyses can be found in [23].
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Fig. 7. The case study comprised numerical simulations and analyses as well as experimental laboratory tests and measurements performed on a heavy working machine.
The results showed that the simple FE model could not properly describe the behaviour of the real structure. The reason for the differences between the simulation and actual outcome may have been the uneven loading of the boom structures due to unbalanced clearances in the hinges, distortions, and imperfections of the booms caused by fabrication actions and effects. A welded-around cover plate was one of the fatigue-critical details in the boom structure. In the case study, a change in the orientation of the cover plate was investigated. Figure 8a presents the original structure, in which the end of the cover plate is aligned with the web plate, and Fig. 8b shows the boom with the rotating plate. As a result, the k S and k w factors were obtained using structural and notch stresses, respectively. In the k w factor, the local weld geometry can be considered. In this work, the actual weld geometry was measured and modelled using the reference radius of r ref = 1.0 mm, as per the ENS concept. Figure 8e presents a comparison of the ENSs determined for the idealized (Fig. 8c) and actual (Fig. 8d) weld geometries. Although the local weld geometry was different from the idealized one, no major change in the ENSs was found as the use of ENS averaged the stress at the weld toe position (see also Fig. 6). RSs were measured at the weld toe position using the XRD technique (Fig. 8f). The measured average value for the RSs at the weld toe position was approximately + 150 MPa, equal to 20% of the yield strength of the base material (S700 grade steel). Table 3 presents the k i factors for the different aspects covered in this case study. As can be seen, a minor change in the orientation of the cover plate significantly reduced the stresses, and the total effect on the factor was k S ·k w = 0.68, corresponding to an improvement factor of 3.1 in the fatigue life if using a slope parameter of m = 3. A consideration of the actual measured weld geometry did not have major effect on the ENSs in this case. Regarding the welding RSs, their effects on the result also depended on the applied stress ratio. At high-stress ratios, a small change in the welding RS did not majorly affect the fatigue strength, while at low-stress ratios, a higher contribution to the increased fatigue strength could be identified.
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Rotation of cover plate 8°
Ks,1, Kf,1
Cover plate
Fillet weld
Fillet weld
Flange
Flange
Web plate
Web plate (b)
(a) Max (Fig. 8e)
Max (Fig. 8e) s
s CAD geometry ENS Sub-model
Sub-model
(c)
ENS Δσk (MPa)
Ks,2, Kf,2
Actual geometry
ENS Sub-model
(d)
Sub-model
400 300 200 100
Idealized Actual Coordinate ‘s’ (e)
(f)
Fig. 8. Effects of changes in the structural detail: (a) original cover plate detail, (b) rotated cover plate with the stress concentration and with the fatigue-critical location at the end of the cover plate. (c) Idealized and (d) actual weld geometries, and (e) a comparison of the resulting ENSs. (f) Measurement of RSs using the XRD technique.
Table 3. Comparison of the designed and actual fatigue parameters in the welded joints of the case study. Factor Value
Notes and comments
kS
0.81
Reduction in the K s factor due to the rotated cover plate (K s,2 /K s,1 , Figs. 8a–b). NB: Potential increase in angular distortion (K m > 1.0) with the new position not considered
kw
0.84
Reduction in the K f factor due to the change in the boundary condition (K f,2 /K f,1 , Figs. 8a–b)
kw
1.07
Increase in K f considering the actual weld shape and geometry with the ENS concept (Figs. 8c–e)
k RS
0.97 (R = 0.5) According to the 4R method, Eq. (12) for the ENS level of σ k = 0.91 (R = 0) 500 MPa. RSs have a more significant effect on fatigue strength at 0.74 (R = −1) low-stress ratios
k RS
1.0 (R = 0.5) According to the IIW enhancement factor f correction, Eq. (11) 0.83 (R = 0) assuming ‘negligible’ RSs, σ res < + 0.2f y : f (R) = −0.4R + 1.2 0.63 (R = −1)
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4 Conclusions When analyzing the fatigue strength of a welded structure, it is necessary to consider all the essential factors from different stages instead of focusing only on a single parameter. Furthermore, it is worth mentioning that a single parameter can have a significant effect on the fatigue strength in one condition but a negligible effect in another condition. This work introduced different factors to be taken into account in the fatigue assessment of welded joints. Subsequently, a case structure, namely a heavy working machine (Fig. 7), was evaluated regarding the factors that were applicable in the case. In the case structure, changes in the k S and k w factors were observed (Table 3 and Fig. 8), while the RSs, measured clearly below the yield strength of the material, were found to contribute to the increased fatigue strength or minimized equivalent applied stress, as per Eq. (1), only with load conditions with low-stress ratios. It can also be concluded that the use of the ENS concept averages the stress at the weld position regardless of the local weld geometry, which can significantly differ from the idealized weld (Fig. 8 and Fig. 5). Even major changes in the weld toe radii or local weld profile do not have a major effect on the ENSs with the fictitious radius. Acknowledgements. The authors wish to thank Sandvik Mining and Construction Oy for supplying the materials and the case structure for the experimental testing and Business Finland (formerly Finnish Funding Agency for Innovation, Tekes) for funding the research work in the Fimecc/MANU and FOSSA programs. In addition, the authors express their gratitude to Antti Raskinen for the analysis of the heavy working machine, as well as to Tuomo Saksa and Ossi Rantomaa for their contribution regarding FE modelling and analysis.
References 1. Hobbacher, A.: Recommendations for Fatigue Design of Welded Joints and Components, 2nd edn. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-23757-2 2. EN 1993-1-9: Eurocode 3: Design of steel structures - Part 1-9: Fatigue (2005) 3. DNVGL-RP-C203: Fatigue Design of Offshore Steel Structures (2016) 4. BS7608:2014 +A1:2015: Guide to Fatigue Design and Assessment of Steel Products (2015) 5. BS7910:2013+A1:2015 Guide to methods for assessing the acceptability of flaws in metallic structures (2015) 6. Juvinall, R.C., Marshek, K.M.: Fundamentals of Machine Component Design. Wiley, New York (2000) 7. Shigley, J.E., Mischke, C.R., Budynas, R.G.: Mechanical Engineering Design. McGraw-Hill, Boston (2003) 8. Haglund, P., Khurshid, M., Barsoum, Z: Mapping of scatter in fatigue life assessment of welded structures - a Round Robin Study. IIW-document XIII-2827-19 (2019) 9. Björk, T., Tuominen, N., Lähde, T.: Effect of the secondary bending moment on K-joint capacity. In: Batista, E., Vellasco, P., Lima, L. (eds.) Proceedings of the International Symposium on Tubular Structures. CRC Press, Leiden (2015) 10. EN ISO 5817: Welding. Fusion-welded joints in steel, nickel, titanium and their alloys (beam welding excluded). Quality levels for imperfections, 38 (2014) 11. prEN 1993-1-14: Eurocode 3 - Design of steel structures - Part 1-14: Design assisted by finite element analysis, 9 (2017)
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12. Winteria, S.E.: Weld Quality Inspection (2022). https://winteria.se/. Accessed 1 Mar 2022 13. Ahola, A., Lipiäinen, K., Afkhami, S., et al.: Fatigue performance of the welded details of an old, demolished steel railway bridge. Eng. Struct. 256, 113966 (2022). https://doi.org/10. 1016/j.engstruct.2022.113966 14. Radaj, D., Sonsino, C.M., Fricke, W.: Fatigue Assessment of Welded Joints by Local Approaches, 2nd edn. Woodhead Publishing, Cambridge (2006) 15. Ahola, A., Björk, T.: Bending effects on the fatigue strength of non-load-carrying transverse attachment joints made of ultra-high-strength steel. In: Zingoni, A. (ed.) Proceedings of the 8th International Conference on Structural Engineering, Mechanics and Computation (SEMC 2022), Cape Town, South Africa, 5–7 September 2022 16. Iida, K., Uemura, T.: Stress concentration factor formulae widely used in Japan. Fatigue Fract. Eng. Mater. Struct. 19, 779–786 (1996). https://doi.org/10.1111/j.1460-2695.1996.tb01322.x 17. Saksa, T.: Consideration of fatigue crack in the effective notch stress concept. Bachelor thesis, LUT University (2022). (in Finnish) 18. Rantomaa, O.: Consideration of weld and post-weld treatment undercut in the determination of notch stress concentration for fatigue assessments. Bachelor thesis, LUT University (2022) 19. Ahola, A., Muikku, A., Braun, M., Björk, T.: Fatigue strength assessment of ground filletwelded joints using 4R method. Int. J. Fatigue 142, 105916 (2021). https://doi.org/10.1016/ j.ijfatigue.2020.105916 20. Hensel, J.: Mean stress correction in fatigue design under consideration of welding residual stress. Henry Granjon Prize Category C: Design and Structural Integrity. IIW-document XIII2795-19 (2019) 21. Ahola, A., Skriko, T., Björk, T.: Fatigue strength assessment of ultra-high-strength steel fillet weld joints using 4R method. J. Constr. Steel Res. 167, 105861 (2020). https://doi.org/10. 1016/j.jcsr.2019.105861 22. Mettänen, H., Nykänen, T., Skriko, T., et al.: Fatigue strength assessment of TIG-dressed ultra-high-strength steel fillet weld joints using the 4R method. Int. J. Fatigue 139, 105745 (2020). https://doi.org/10.1016/j.ijfatigue.2020.105745 23. Raskinen, A.: Digital manufacturing impact on the fatigue life of the welded structure (2015). (in Finnish). https://urn.fi/URN:NBN:fi-fe201504172745
Investigation of Resistance Spot Welded Joints Made on Ultra-high-Strength Steel Sheets Sahm alden Abd al al(B) and Ákos Meilinger Institute of Materials Science and Technology, University of Miskolc, Miskolc 3515, Hungary {metakos,sahm.alden}@uni-miskolc.hu
Abstract. In the automotive industry there is an increasing demand for the reduction of CO2 emissions, which requires new generations of high strength steels. The high yield strength is originated with the combination of alloying elements, rolling and heat treatment. In the ultra-high strength steel (UHSS) category the microstructure is contain more and more martensite which results higher strength but less elongation. During welding, these properties irreversibly changes by the heat cycle, for example in the heat-affected zone (HAZ) strength reduction can be expected. Because these problems necessary to investigate the weldability of these UHSS sheets. In the automotive industry the most frequently used welding process is the resistance spot welding for thin sheet welding (e.g., in case of chassis elements). This is a precise pressure welding process so it uses pressure force and welding current, which means it has a thermomechanical effect on the base material. Therefore, in this case it has more chance to optimise the parameters for these welding sensitive materials. The present research work aims to investigate the mechanical properties of resistance spot welded joints of 1200 and 1400 MPa tensile strength steel sheets. The heat-affected zone softening and other weldability problems are investigated too. Very important to know how can the mechanical properties improve by technological parameter optimisation, so in this article it is examined and the results are written too. Previously the resistance spot welded joints properties were investigated in case of lower strength steel sheets like DP1000, DP800 and DP600. In this article a comparison is made between resistance welded joints of steel sheets from 600 MPa to 1400 MPa joint properties. This comparison basically focuses on weldability, mechanical properties, and welding technological modifications, and according to these, some designing conclusions are written. Keywords: Welding · Resistance spot welding · Ultra-high-strength steel
1 Introduction Research continues to develop the steel sheets in the automotive industry in order to reduce weight by increase the strength thus reducing the energy expended to eliminate CO2 emissions and obtain excellent crashworthiness behaviour [1]. Several joining methods other than RSW can be applied on the metal sheets, such as the clinching joining technology applied on several types of metal sheets like DP600 [2]. Desirable © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 981–994, 2023. https://doi.org/10.1007/978-3-031-15211-5_82
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properties in Ultra-High Strength Steel (UHSS) are obtained from the microstructure and from the thermomechanical treatment process during manufacturing for this reason, it requires research on the weldability of UHSS [3, 4]. The most common method of joining automotive body structures is Resistance Spot Welding (RSW), which is characterised by its efficiency, lower cost, high operation and productivity [5]. Therefore, it is self-evident to develop the RSW method and evaluate the weldability of the UHSS by having a high quality of RSW to increase UHSS usage in the automotive industry. The difficulties that can face UHSS welding are hardly the same as the challenges facing welding of other steel grades such as High Strength Low Alloy steel (HSLA), but the possibility of changes in the microstructure and therefore mechanical properties may increase due to the presence of a higher percentage of carbon that may reach 0.3% in the chemical composition added to increase the strength of UHSS [6]. There are many types of UHSS that can be classified according to the treatment process during manufacturing of the microstructure and the mechanical properties. The most commonly used type in the automotive industry is Dual Phase Steel (DP), and the use of Martensitic Steel (MS) these days has begun to increase mainly by using the RSW welding method [7]. Therefore, it is necessary to know the relevant variables, how they interact, and how to ensure the welding procedure to obtain acceptable similar and dissimilar weld joints of DP and MS that meet the design requirements. It is also necessary to know the mechanical properties of the applied weld. These materials were developed based on obtaining weldable alloys with relative proportions of elements and carbon. The recent research examines UHSS welding by RSW, however there was a shortcoming in comparing the microstructure and mechanical properties of different welded grades of UHSS steels. The objectives of this study are to compare the microstructure and static mechanical properties of MS1200 and MS1400 UHSS welded by RSW. In martensitic steels (MS), ferrite forms the minority, while martensite forms the majority in the ferrite matrix, it is transformed through quenching austenite phase, see Fig. 1. MS is characterised by high strength up to 1700 MPa with relatively low elongation [8]. The applied post heat treatment process is to improve ductility where the additive elements such as manganese, silicon and boron in order to increase the hardness and improve other mechanical properties of the MS [9].
Fig. 1. A: Schematic of a martensitic steel microstructure in left and B: Microstructure of MS 1200 in right [10]
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RSW technology is a process in which plain sheets interfaces are joined together by the heat generated by resistance to the flow of electric current through plain sheets under force by electrodes. Where the weld nugget is formed at the highest electrical resistance, which is between the metal sheets [13]. In general, there are three important factors for evaluating the quality of RSW, which are the weld-nugget size, weld mechanical performance and failure modes. Shear-tensile test is very important for evaluating the mechanical properties of the spot weld. Characteristics of the interfacial failure mode (IF) and plug failure mode (PF) are very important for the assessment of RSW welding quality [11]. Changing the microstructure as a result of RSW in the Nugget Zone (NZ) and Heat Affected Zone (HAZ) plays an important role in changing the mechanical properties and may have a positive effect on the failure modes. A study that monitored softening in the heat-affected zone (HAZ) resulting from welding of different martensitic steel (MS) grades so due to allotriomorphic ferrite formation in the inter-critical HAZ and the tempering of martensite in sub-critical HAZ [12]. It was found that the softness in MS resistance spot-welded joints may be used to improve some mechanical properties of spot weld joints used in the automotive industry due to its direct proportionality with elongation, which leads to avoiding the brittleness and increasing the ability of impact loads absorption. The aim of this research is to study the performance of mechanical properties by conducting shear-tensile, optical macroscopic and micro hardness tests for MS1200 and MS1400 UHSS by applying different welding parameters. The absorption energy can be found by drawing the relationship between the displacement and the applied load in shear-tensile test as shown in Figs. 5 and 6, in order to improve the ductility of the MS1200 and MS1400 RSW joints and increase the ability to absorb applied stresses [11]. This paper shows the effect of welding time and cooling time in welding MS1200 and MS1400 by RSW. Interfacial failure (IF) is a critical problem in vehicle safety. Plug failure (PF), which occurs due to weld nugget take off from one of the metal sheets, gives an indicator of several important mechanical properties, such as the strain. Adjusting the welding parameters may work to obtain a required plug failure PF mode result that suits the desired mechanical properties in the automotive industry, such as impact absorption and strain energy dissipation in crush case. A study has shown that welding of UHSS by RSW has more ability to cause interfacial failure IF, this diversity in failures modes arises from transformation states resulting from RSW, such as martensite transformation in the fusion zone (FZ), softening in HAZ and segregation phenomena [14, 15]. The transformation IF and PF modes in UHSS RSW depends to a large extent on a complex relationship between weld geometry, fusion zone/HAZ/base metal properties, test geometry, and the stress state. Microstructural knowledge generated by UHSS RSW is very important to produce strong and reliable RSW joints as in the design requirements and can also predict the behaviour of these welded spot joints when exposed to applied stresses [16]. Modification of RSW welding parameters such as current, cooling time and number of pulses is very important to improve the mechanical performance of welding spot joints so that they comply with the requirements of the design of the automotive industry. RSW experiment have been conducted on different steel grades with double pulse welding parameter, where better results appeared in the context of metallurgical weldability and an improvement in several important mechanical properties such as reducing the liquid metal embrittlement and increasing the shear-tensile strength. It was
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found that the second pulse reduces the partially melted zone and works as post-weld heat treatment (PWHT), which leads to spot weld microstructural improvement [17].
2 Material and Method Docol MS1200 and Docol MS1400 steel sheets with a thickness of 1mm, were used as a base metal in our study of welding parameter modifications of RSW welding. The chemical composition and mechanical properties of MS1200 and MS1400 [20] are shown in Tables 1 and 2, respectively. RSW was carried out using of TECNA 8007 spot 50/60 Hz welder, 80 kVA single-phase AC controlled by TE550 microprocessor-based welding control unit. Two opposite copper chromium zirconium (Cu-Cr-Zr) electrodes with a 50 mm spherical tip curve radius and 5 mm cross-sectional diameter as shown in Fig. 2. moving perpendicular to the sheets with a 2.5 bar pressure equivalent 3 kN force applied by a pneumatic cylinder. Pressure turns into force as a result of pressure generated from the pneumatic system to the piston of a cylinder connected directly to the electrode [18]. An electric current pass through the clamped sheets by electrodes, which leads to joule heat. Highest resistivity is formed between steel sheets where the weld nugget constitutes [8].
Fig. 2. Schematic of Cu-Cr-Zr electrodes used in the experiments
Table 1. The chemical composition by maximum wt. % of the Docol MS1200 and MS1400 Grade
C
Si
Mn
P
S
Al
Nb + Ti
MS1200
0.14
0.40
2.00
0.020
0.010
0.015
0.10
MS1400
0.20
0.40
1.60
0.020
0.010
0.015
0.10
Table 2. The mechanical properties of the Docol MS1200 and MS1400 Grade
Yield strength Rp0.2 (MPa)
Tensile strength Rm (MPa)
Elongation A80 (min %)
Test direction
MS1200
> 950
1200 – 1400
3
T
MS1400
> 1150
1400 – 1600
3
T
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2.1 Samples Preparation After performing the RSW process of MS1200 and MS1400 steel joints, six joints of each grade were selected with different welding parameters as shown in Table 3 and cut in a cross-section that divides the weld nugget into two parts. The samples are placed in standing position inside crucibles, the direction of the cross-section of the weld nugget facing upwards. The crucibles are filled with acrylic resin for 24 h curing to solidify. The grinding process is carried out with an abrasive paper of P120, P400, P1000 and P1500 grit grades respectively. The samples were polished with a rotary pad polisher using distilled water and high-performance diamond DP-Stick P materialographic-polishing material. The polished samples were etched by nitric acid and alcohol solution (Nital) in rate of 2.5 vol.% HNO3 + 97.5 vol.% ethanol, respectively. 2.2 Macroscopic and Microhardness Tests Etched samples were examined by an optical macroscopic test conducted by Zeiss Stemi Stereo Microscope to identify the Heat affected zones HAZ and Base Metals BM. Intercritical Heat Affected Zone ICHAZ, Sub-critical Heat Affected Zone SCHAZ, Uppercritical Heat Affected Zone UCHAZ and Base Metal BM as shown in Fig. 3. Vickers hardness (HV0.2) was conducted after macroscopic test by Mitutoyo micro hardness tester for six samples for each of MS1200 and MS1400 as shown in Table 3, an average of 50 measurements of indentation were taken with a distance between measurements is 0.2 mm as shown in Fig. 8. For parameter determination, we used previously determined parameters for DP1000 steel sheets [19] as a base with slight modifications. Table 3. Welding parameters of Micro-hardness and optical macroscopic tests samples. Sample number
Sample grade
Current (I) kA
No. of pulses
Welding time (Cycle*)
Pulsation cooling time (Cycle*)
OPM01
MS1200
5.8
1
1 × 12
N/A
OPM02
MS1200
6.4
2
2×6
15
OPM03
MS1200
6.4
2
2×6
30
OPM04
MS1200
6.4
2
2×6
45
OPM05
MS1200
6.4
4
4×6
30
OPM06
MS1200
6.4
4
4×6
45
OPM07
MS1400
5.8
1
1 × 12
N/A
OPM08
MS1400
6.4
2
2×6
15
OPM09
MS1400
6.4
2
2×6
30
OPM10
MS1400
6.4
2
2×6
45 (continued)
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Sample number
Sample grade
Current (I) kA
No. of pulses
Welding time (Cycle*)
Pulsation cooling time (Cycle*)
OPM11
MS1400
6.4
4
4×6
30
OPM12
MS1400
6.4
4
4×6
45
*1 cycle = 20 ms
Fig. 3. Macrostructure photos for MS1200 in the left column and MS1400 in the right column
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2.3 Shear-Tensile Test Shear tensile test was carried out on samples with dimensions used previously in several scientific papers concerning RSW [18, 22, 23], see Fig. 4.
Fig. 4. Schematic Shear-tensile test samples
The shear-tensile test was carried out on seven samples of each grade as shown in Table 4 and Table 5. MTS 322 test frame machine was used with 0.2 mm/s loading speed, Figs. 5 and 6 show the relationship between displacement and the applied load.
Fig. 5. Load-displacement of the resistance welded MS1200 steel joints as Table 4.
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Fig. 6. Load-displacement of the resistance welded MS1400 steel joints as Table 5.
Table 4. Shear-tensile test results of the MS1200. Sample number
Current (I) kA
No. of pulses
Welding time (Cycle*)
Pulsation cooling time (Cycle*)
Peak load (kN)
Displacement (mm)
Failure mode
SA01
5.8
1
1 × 12
N/A
11.486
0.542
Interfacial
SA02
5.8
1
1 × 22
N/A
10.389
0.521
Interfacial
SA03
6.4
2
2×6
15
10.925
0.482
Interfacial
SA04
6.4
2
2×6
30
11.186
0.635
Interfacial
SA05
6.4
2
2×6
45
11.679
0.775
Interfacial
SA06
6.4
4
4×6
30
11.835
0.764
Plug
SA07
6.4
4
4×6
45
11.754
0.607
Plug
*1 cycle = 20 ms
3 Results and Discussion 3.1 Shear-Tensile Test Shear-tensile test results showed a slight difference in the shear-tensile strength as shown in Tables 4 and 5. The highest value among the samples is SA15 with 12.857 kN peak load, as the highest displacement value among all samples is 0.775 mm and 0.764 mm for samples SA09 and SA10 respectively, as shown in Table 4, this value is relatively low compared to the results of other studies [11, 16]. Two failures mode were observed as a result of the shear-tensile testing of MS1200 and MS1400 samples. The samples of MS1200 that were welded with four pulses were failed in the PF mode, and the rest was in the IF mode. As for MS1400, the samples that were welded by two and four pulses failed in the PF mode, as for the rest, failed in the IF mode. Every welding technique has identical failure modes, Table 4 and Table 5 shows the welding parameters
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Table 5. Shear-tensile test results of the MS1400. Sample number
Current (I) kA
No. of pulses
Welding time (Cycle*)
Pulsation Cooling Time (Cycle*)
Peak load (kN)
Displacement (mm)
Failure mode
SA08
5.8
1
1 × 12
N/A
11.408
0.504
Interfacial
SA09
5.8
1
1 × 22
N/A
12.857
0.631
Interfacial
SA10
6.4
2
2×6
15
12.750
0.496
Plug
SA11
6.4
2
2×6
30
12.283
0.640
Plug
SA12
6.4
2
2×6
45
11.320
0.440
Plug
SA13
6.4
4
4×6
30
12.116
0.519
Plug
SA14
6.4
4
4×6
45
12.034
0.480
Plug
*1 cycle = 20 ms
of failures mode, Figs. 5 and 6 shows the displacement of the samples of MS1200 and MS1400, respectively according to the welding parameters shown in Tables 4 and 5. When calculating the absorbed energy, which is the ability of the weld to absorb energy before failure start, it found that it is low compared to other studies of the same martensitic steel grades with a 1.5 mm thickness, where the current was raised up to 14 kA, and this explains the increase in displacement, as the increase in current has a great effect on increasing the amount of heat input, which means a greater effect on the microstructure recrystallisation. 3.2 Microstructural Evaluation and Hardness Profile No defects such as cracks were detected for the MS1200 and MS1400 RSW samples. Although martensite is the most important component of MS that guarantees high strength, the properties of the RSW in terms of energy absorption or rather ductility must be improved by softening the areas around the fusion zone FZ. The increase in the hardness and softening points were monitored as shown in Fig. 8. It is clear that the SCHAZ, UCHAZ and ICHAZ had a steep decrease in the hardness, reaching 120 HV lower. While hardness in FZ increased as it appeared in OPM10 sample. Welding parameters and results of conducted tests led to several reasonable explanations based on the study of metallurgy that explained the change in hardness and other mechanical properties as a result of extreme heat and fast cooling caused by RSW in several zones in the welding spots. Other experiment of RSW was carried out on single and double pulsed martensite-ferrite DP1000 steel. Single pulse microhardness test profile showed approximately 1.5-time higher hardness in the fusion zone FZ than in the base metal, while the hardness softened in the sub-critical HAZ outer boundary. Double pulse profile showed a hardness gradual rise from SCHAZ towards the FZ with a relative softening in the upper-critical HAZ compared with increased hardness in SCHAZ [19]. According to the study is that because of the steel sheet used in our study, it is MS cold formed
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Docol steel, as softening may occur in the HAZ’s as a result of recrystallisation caused by the sever temperature [11]. This phenomenon explains the occurrence of plug failure mode in seven samples as a result of the shear-tensile test, as shown in Tables 4 and 5. During of shear-tensile test, the stresses are generated in the vicinity of the weld nugget, as shown in Fig. 7.
Fig. 7. Schematic of weld nugget shear-tensile test stresses distribution
Since the percentage of carbon in MS1400 and MS1200 is 0.2% and 0.14%, respectively, the reaction during RSW solidification will be a peritectic reaction. As another study showed the effect of other alloys such as carbon on softening during the solidification process, so the liquid will solidify to delta ferrite first due to lack of the concentration of carbon in it, and since the solubility of carbon is higher in austinite than ferrite, the carbon will increase in its concentration in austenite as a result of the partitioning of carbon [21]. According to the isothermal transformation diagram, the cooling time in the RSW is considered very short, which leads to the transformation of the austenite into martensite as a result of the quenching, and this explains increasing the hardness of the Fusion Zone [11].
991
HV 0.2
HV 0.2
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Distance (mm)
HV 0.2
HV 0.2
Distance (mm)
Distance (mm)
HV 0.2
HV 0.2
Distance (mm)
Distance (mm)
Distance (mm)
Fig. 8. Microharness profile photos for MS1200 in the left column and MS1400 in right column
HV 0.2
S. a. Abd al al and Á. Meilinger
HV 0.2
992
Distance (mm)
HV 0.2
HV 0.2
Distance (mm)
Distance (mm) HV 0.2
HV 0.2
Distance (mm)
Distance (mm)
Distance (mm) Fig. 8. continued
4 Conclusion In this research RSW with different welding parameters have been conducted on MS1200 and MS1400 of martensitic ultra-high-strength steel samples. Shear-tensile, macroscopic, and microhardness tests were carried out. The macrostructural and the mechanical performance of the relevant samples are investigated too. The following conclusions can be drawn from this research:
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– Welded joints can be made by single, double and four pulses with different welding parameters on MS1200 and MS1400 types of martensitic steel samples. – In case of shear-tensile strength slight variation in the peak load observed as a function of pulse number and cooling time between pulses while the failure modes are different. During the shear-tensile tests the MS1200 four pulses and MS1400 two and four pulses samples plugged, the rest of MS1200 and 1400 samples failed in the interfacial mode. Additional mechanical testing (e.g., cross-tensile tests) requires because the shear-tensile tests cannot show significant differences. – A small displacement was observed in tensile-shear test compared to other high strength materials, so it may need to optimise the welding parameter to obtain better results. The best displacement was recorded for MS1200 samples welded with four pulses parameters. – Macrostructure test showed noticeable difference in the macrostructural evaluation between both of two and four pulses and single pulse. A “ring” formed in the fusion zone of the samples welded with two and four pulses. Softening occurs from the outside boundary of this ring to the HAZ. It is necessary to carry out examinations related to the microstructural tests such as SEM and EBSD to analyse the exact reason for this softening. – Using the two or four pulses technology the hardness increased in the weld nugget (compared with the base material hardness) especially in the case of MS1400 with four pulses. – From the applied parameter combinations, the pulse technologies show the best results. In case of MS1200 the four pulse was the optimal, while in the case of MS1400 the two and four pulses show the best with slight differences.
References 1. Zhao, Y., Wang, W., Wei, X.: Optimisation of resistance spot welding with inserted strips via FEM and response surface methodology. Materials 14(23), 7489: p. 1 (2021) 2. Jónás, S., Tisza, M., Felh˝os, D., Kovács, P.Z.: “Experimental and numerical study of dissimilar sheet metal clinching. In: AIP Conference Proceedings, vol. 2113. No. 1. AIP Publishing LLC, pp. 1–3 (2019) 3. Walp, M.S.: Impact dependent properties of advanced and ultra-high strength steels. SAE Transactions, pp. 30–43 (2007) 4. Hu, X., Feng, Z.: Advanced High-Strength Steel-Basics and Applications in the Automotive Industry. No. ORNL/TM-2021/2047. Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), p. 9 (2021) 5. Manladan, S.M., Abdullahi, I., Hamza, M.F.: A review on the application of resistance spot welding of automotive sheets. J. Eng. Technol. 10, 20–37: p. 22 (2015) 6. Feng, Z.: Challenges and opportunities in joining advanced high strength steels. In: Workshop On Addressing Key Technology Gaps in Implementing Advanced High-Strength Steels For Automotive Lightweighting, p. 2 (2012) 7. Tisza, M.: Three generations of advanced high strength steels in the automotive industry. In: Jármai, K., Voith, K. (eds.) Vehicle and Automotive Engineering 3, VAE 2020. Lecture Notes in Mechanical Engineering. Springer, Singapore (2020). https://doi.org/10.1007/978-981-159529-5_7
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8. Kekik, M., et al.: Microstructural evaluation and influence of welding parameters on electrode plunge depth in resistance spot welded dissimilar DP800HF/1200M steel joints. Academic Platform-Journal of Engineering and Science, p. 1 (2021) 9. Kimchi, M., Phillips, D.H.: Resistance spot welding: fundamentals and applications for the automotive industry. Synthesis Lectures Mech. Eng. 1(2), i-115: p. 9 (2017) 10. World auto steel Homepage. https://ahssinsights.org/tag/microstructure/ 11. Li, Y., Tang, H., Lai, R.: Microstructure and mechanical performance of resistance spot welded martensitic advanced high strength steel. Processes 9(6), 1021 (2021) 12. Rezayat, H., et al.: Correlation of local constitutive properties to global mechanical performance of advanced high-strength steel spot welds. Metall. and Mater. Trans. A. 51(5), 2209–2221 (2020) 13. Williams, N.T.: Resistance spot welding. ASM Handbook 6, 226–229 (1993) 14. Krajcarz, F., Gourgues-Lorenzon, A-F., Lucas, E.: Influence of carbon content on the primary solidification mode of high strength steels in resistance spot welding conditions. Scripta Materialia 120, 98–102 (2016) 15. Amirthalingam, M., van der Aa, E.M., Kwakernaak, C., M. Hermans, M.J., Richardson, I.M.: Elemental segregation during resistance spot welding of boron containing advanced high strength steels. Welding World 59(5), 743–755 (2015). https://doi.org/10.1007/s40194-0150250-3 16. Pouranvari, M., Sobhani, S., Goodarzi, F.: Resistance spot welding of MS1200 martensitic advanced high strength steel: microstructure-properties relationship. J. Manuf. Process. 31, 867–874 (2018) 17. Májlinger, K., Katula, L.T., Varbai, B.: Prediction of the shear tension strength of resistance spot welded thin steel sheets from high-to ultrahigh strength range. Periodica Polytech. Mech. Eng. 66(1), 67–82 (2022) 18. Resistance welding applications and controls production engineering Master Distributor – Tuffaloy, CMW, Entron, ACP 1-888-654-WELD (9353), p. 14 19. Prém, L., Bézi, Z., Balogh, A.: Development of resistant spot-welding technology for automotive ferrite-martensitic dual-phase steels with joint application of finite element modelling and experimental research. Advanced Materials Research, vol. 1138. Trans Tech Publications Ltd, p. 44 (2016) 20. SSAP Homepage, Martensitic steel: excellent formability-to-strength ratio. https://www.ssab. com/brands-and-products/docol/automotive-steel-grades/martensitic-steel 21. Lesch, C., Kwiaton, N., Klose, F.B.: Advanced high strength steels (AHSS) for automotive applications− tailored properties by smart microstructural adjustments. Steel Res. Int. 88(10), 1700210 (2017) 22. Gáspár, M., Dobosy, Á., Tisza, M., Török, I., Dong, Y., Zheng, K.: Improving the properties of AA7075 resistance spot-welded joints by chemical oxide removal and post weld heat treating. Welding World 64(12), 2119–2128 (2020). https://doi.org/10.1007/s40194-020-00988-y 23. Gáspár, M., Tervo, H., Kaijalainen, A., Dobosy, Á., Török, I.: The effect of solution annealing and ageing during the RSW of 6082 Aluminium Alloy. In: Jármai, K., Bolló, B. (eds.) Vehicle and Automotive Engineering 2, VAE 2018. Lecture Notes in Mechanical Engineering. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75677-6_59
Influence of Filler Metals on Microstructure and Mechanical Properties of Gas Metal Arc Welded High Strength Steel Judit Kovács(B)
and János Lukács
University of Miskolc, Miskolc, Hungary {metkjudit,janos.lukacs}@uni-miskolc.hu
Abstract. Nowadays, the automotive industry shows an ever-growing need for the application of high strength materials. The high strength steels are one of the most important materials used at present; these steels have excellent properties such as high yield strength and low weight. These properties are insured by their versatile and complex microstructures. The use of these steels is advantageous from the standpoint of economic application and fuel consumption. Furthermore, the weight loss of vehicles results in the reduction of pollutants and greenhouse gases emissions. Besides that, the application of high strength steels also causes an improvement in strength, stiffness, and other performance characteristic. In spite of the aforementioned advantages, there are still difficulties with their wider use due to their limited formability and weldability. The welding of high strength steels can be a great challenge because of cold cracking sensitivity, reduction of strength and toughness of heat affected zone and filler metal selection. To take advantage of the outstanding mechanical properties of high strength steels, it is very important to select the appropriate welding method with the exact welding parameters. In the present research work the effect of the filler metals on microstructure and mechanical properties of a high strength structural steel (Alform 1100M x-treme) having 15 mm thickness welded by gas metal arc welding was investigated. The chosen filler metals were the Böhler Union X96 (undermatching condition) and Böhler alform 1100 L-MC (matching condition). During the welding experiments, the t8/5 cooling time was regulated. Based on our former investigations (physical simulations on the examined material) and recommendations in the literature, the chosen t8/5 cooling time was 5 s. The welding parameters were determined based on the cooling time. After welding, different destructive and non-destructive investigations (optical microscope, hardness tests) were performed to compare the microstructure and mechanical properties of the joints made by different filler metals. Keywords: High strength steel · Gas metal arc welding · Mechanical properties
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 995–1005, 2023. https://doi.org/10.1007/978-3-031-15211-5_83
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1 Introduction Nowadays, the application ratio of high strength steel is continuously increasing. High strength steels have a more and more important role in engineering applications especially in the vehicle and transportation industry. With the increase in fuel consumption and CO2 and other greenhouse gas emission, environmental safety is becoming a social problem [1]. Thus, the automotive industry is under constant pressure because they need to face with the challenges of strict requirements for lower CO2 emission, need to improve fuel efficiency by reducing the weight of vehicles and there is customer demand for lower operational costs [2–10]. For the above-mentioned problems, the application of high strength materials, especially high strength steel may a possible solution because the use of these steels leads to building structures and components which are thinner, lighter, yet stronger. Besides, high strength steel may reduce the carbon footprint associated with its manufacturing by lowering the global steel’s net production volume and the carbon emission due to the transportation of such heavy materials. Cost-effectiveness for end users is also another benefit which could be gained by weight saving in the structure. The possibly thinner cross-section is a further advantage of high strength steels since they are material-saving and make possible the reduction of production time. Besides decreasing operational and production costs due to the energy-saving in mobile structures, in case of welding thinner plates and smaller cross-sections results savings in the amount of base materials and filler metals applied [3, 11–14]. Despite the good mechanical properties and potential benefits of high strength steels, there are still challenges in the case of their welding. The determination of the right welding technology, including the optimal process window, may still cause difficulties for welding engineers. Welding heat input and cooling rate are the key parameters affecting the joint’s capacity, ductility, and toughness. Due to the differences in the cooling rates at the vicinity of the joint, the local heating creates a range of materials with different characteristics depending on their distance from the weld fusion zone. Among these regions, the softening at the heat-affected zone (HAZ) is the most crucial. Due to the thermal cycles experiences during welding, these HAZs can exhibit significant losses in toughness. The degradation of material properties (such as toughness) in the weld HAZ, is required to remain tolerable. Besides that, the filler material selection (in other words, the matching) can also be a problem in case of higher yield strength, but the yield strength of commercial filler metals have already exceeded 1000 MPa [2, 3, 11, 15–19].
2 Materials and Methods In the present study Alform 1100M x-treme high strength steel plates produced by Voestalpine were used. Based on the material certificate, the mechanical properties – hardness (HV10), yield strength, tensile strength, elongation, and impact energy - and chemical composition are shown in Table 1 and Table 2, respectively.
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Table 1. Mechanical properties of the investigated base material. Thickness [mm]
HV10
Rp0.2 [MPa]
Rm [MPsa]
A [%]
CVN (at −40 °C) [J]
15
394
1193
1221
11, 6
88
Table 2. Chemical composition of the investigated base material [weight%]. C
Si
Mn
P
S
Cr
Cu
0.13
0.32
1.62
0.009
0.0015
0.63
0.047
Ni
Mo
V
Ti
Al
Nb
B
0.32
0.62
0.066
0.011
0.035
0.037
0.0014
The carbon equivalent value indicated in the certificate is CEV = 0.68%. The microstructure of the examined base material in the delivery state by Zeiss Observer D1 m optical microscope is shown in Fig. 1 (magnification = 200×), where it can be observed the characteristic fine-grained microstructure of the thermomechanically rolled steels. The specimen was etched by Nital (3% HNO3 ).
Fig. 1. Microstructure of the base metal in as-received condition, Nital etch
For the welding experiments Böhler Union X96 (Ø 1.2 mm) and Böhler alform 1100 L-MC (Ø 1 mm) filler metals were used. Table 3 and Table 4 contain the chemical composition of the filler metals, and Table 5 and Table 6 contain the mechanical properties of the filler metals based on the material certificates.
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C
Si
Mn
P
S
Cr
Mo
0.1
0.81
1.94
0.015
0.011
0.52
0.53
Ni
V
Cu
Ti
Al
Zr
2.28
< 0.01
0.06
0.06
< 0.01
< 0.01
Table 4. Chemical composition of the Böhler alform 1100 L-MC filler metal [weight]. C
Si
Mn
P
S
Cr
Mo
Ni
V
0.08
0.46
1.54
0.01
0.007
0.64
0.52
2.73
0.22
Table 5. Mechanical properties of the Böhler Union X96 filler metal. ReL or Rp0.2 [MPa]
Rm [MPa]
A [%]
CVN (at −50 °C) [J]
≥ 930
≥ 980
≥ 14
≥ 47
Table 6. Mechanical properties of the Böhler alform 1100 L-MC filler metal. ReL or Rp0.2 [MPa]
Rm [MPa]
A [%]
CVN (at −40 °C) [J]
≥1100
≥1140–1250
≥10
≥ 27
The welding parameters were determined based on the cooling time. The temperature range between 800 and 500 °C, t8/5 is important for identifying potential problems related to unfavourable mechanical properties caused by cooling. To determine the optimal t8/5 cooling time heat affected zone tests were performed with a Gleeble 3500 physical simulator in the Institute of Materials Science and Technology at the University of Miskolc. In order to simulate HAZ areas with the lowest toughness, the chosen parts of the HAZ were the coarse-grained (CG), the intercritical (IC) and the intercritically reheated coarse-grained zones (ICCG). To be able to simulate gas metal arc welding (GMAW) processes with low, medium, and high heat input, three different t8/5 cooling time, 5 s, 15 s and 30 s were set during the tests. After the simulations, optical microscope and hardness tests were performed. Based on the results (Fig. 2), increasing of the cooling time had a negative effect on the hardness of the steel. In case of higher cooling time even the hardness of the coarse-grained zone did not reach the hardness of the base material, the investigated high strength steel was softened on account of the welding heat cycles. Thus, as the result of physical simulations on the examined material and recommendations in the literature, the chosen t8/5 cooling time was 5 s. The welding parameters that were calculated based on the cooling time are shown in Table 7, respectively.
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Fig. 2. Result of the hardness tests.
Table 7. Welding parameters. Filler metal
Welding pass number
Welding current [A]
Welding voltage [V]
Welding speed [cm/min]
t8/5 cooling time [s]
Heat input [J/mm]
Böhler Union X96
1
180
19.1
24
5
688
2
190
19.7
27
666
3–4
260
25.1
50
624
5–8
280
28,7
61
632
180
19.1
24
688
190
19.7
27
666
3–4
240
22,7
42
623
5–8
260
25,1
50
624
Böhler alform 1 1100 L-MC 2
The plate dimensions were 350 × 150 × 15 mm for GMAW. The welding was performed in PA position using Daihen WB-P500L power source. A schematic illustration
Fig. 3. Schematic illustration of the X-groove and the welding passes.
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of the X-groove and the welding passes are shown in Fig. 3. The welding torch was moved by ESAB Miggytrac B5001 welding tractor. The preheating temperature was 100 °C and the interpass temperature during the welding experiments was about 130 °C. A shielding gas mixture of 80% Ar + 20% CO2 (M21) with a flow rate of 18 l/min was used.
Fig. 4. Optical microscopic images of the welded joints, Nital etch a), c) and e) joint with the use of Böhler Union X96 filler metal; b), d) and f) joint with the use of Böhler alform 1100 L-MC filler metal (ICHAZ = intercritical heat affected zone, FGHAZ = fine grain heat-affected zone, CGHAZ = coarse-grained heat-affected zone, WM = weld metal).
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3 Material Tests Firstly, all welded joints were X-ray tested according to the EN ISO 17636–1:2013 standard [16]. Based on the tests, the most common imperfection was gas porosity. After cutting, all the cross-sections were ground, polished and etched by Nital (3% HNO3 ) for optical microscopic investigations. The microstructure of the joints by Zeiss Observer D1 m optical microscope can be seen in Fig. 4. Based on the optical microscope images, there are no visible differences in the microstructure of the welded joints due to the similar heat input and cooling times applied. In Fig. 3 a) and b) the total width of the different heat-affected zones are also similar. Due to the different composition of the applied filler metals, some difference in the weld metals is visible. After optical microscopic investigations, Vickers hardness HV10 tests were performed. A Reicherter UH 250 universal macro-hardness tester was used for the measurements. In the case of both weldments, the hardness was measured in three different parts (lines) of the specimens (joints), in every location at three points in the base metal, in the HAZ and in the weld metal. The distribution of hardness points is presented in Fig. 5.
Fig. 5. Hardness points distribution.
The values of hardness in each measured points for the two applied filler metals are presented in Fig. 6 and Fig. 7, respectively. Thermo-mechanically controlled high strength steels belong to the 2.2 steel group, according to MSZ CEN ISO/TR 15608:2021 [21]. Based on the ISO 15614–1:2017(E) standard in the case of Vickers hardness testing with a load of HV10 the permitted maximum hardness values in the case of non-heat treated steels should be under 380 HV10 [22]. Based on the remark in the standard special values shall be specified for steels with minimum yield strength > 890 MPa. In the case of the examined steel most of the measured hardness values are higher than 380 HV10, even the average value of the base metal would be higher than the given amount in the standard. According to the hardness distribution diagram, the lowest hardness values were in the heat-affected zone of the root in
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Fig. 6. Hardness distribution for weldments with Böhler Union X96 wire.
Fig. 7. Hardness distribution for weldments with Böhler alform 1100 L-MC.
the case of the use of Böhler alform 1100 L-MC wire. Since the t8/5 cooling time was calculated to be around 5 s in all cases and the applied heat input was also similar, the differences caused by the different filler metals should be reflected in the weld metals hardness values. For a better comparison, the average, standard deviation, and standard deviation coefficient of the hardness of the weld metals are shown in Table 8.
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Table 8. The average, standard deviation, and standard deviation coefficient values of the hardness of the weld metals Filler metal
Place of the hardness tests
HV10
Standard deviation
Standard deviation coefficient [%]
Böhler Union X96
Line 1
401
24.10
5.99
Line 2
382
7.21
1.89
Line 3
402
22.90
5.70
Average
395
Line 1
399
18.08
4.53
Line 2
388
4.04
1.04
Line 3
388
13.08
3.37
Average
392
Böhler alform 1100 L-MC
According to Table 8, the results obtained show acceptable standard deviation and standard deviation coefficient of the hardness values. Based on the average hardness values measured in the welded joints, the two different (matching and undermatching) filler metals resulted in very similar weld metal hardness. The measured weld metal hardness was minimally higher for Böhler Union X96, but the difference was almost negligible.
4 Conclusion Based on the investigations and their results, the following conclusions can be drawn. 1. The coarse-grained heat-affected zone (CGHAZ), the intercritical heat affected zone (ICHAZ), intercritically reheated coarse-grained heat-affected zone (ICCGHAZ) of the investigated Alform 1100M x-treme high strength steel was successfully simulated for three technological variants of gas metal arc welding (GMAW) in the range of t8/5 = 5–30 s. The results of the hardness tests showed that, increasing of the cooling time has a negative effect on the hardness of the steel. In case of higher cooling time even the hardness of the CGHAZ did not reach the hardness of the base metal, the investigated high strength steel was softened on account of the welding heat cycles. 2. Thus, as the result of physical simulations on the examined material and recommendations in the literature, the chosen t8/5 cooling time was 5 s. 3. Based on the chosen cooling time the parameters were calculated and tested for GMAW. For the welding experiments two different filler metals were chosen: Böhler Union X96 (undermatching condition) and Böhler alform 1100 L-MC (matching condition). 4. According to the optical microscope images, there were no visible differences in the microstructure of the welded joints and in the total width of the different parts of the HAZs due to the similar heat input and cooling times applied.
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5. Based on the hardness tests the lowest hardness values were in the HAZ of the root in case of the use of Böhler alform 1100 L-MC wire. According to the average hardness values measured in the welded joints, the two different filler metals resulted in very similar weld metal hardness. The measured weld metal hardness was minimally higher in the case of Böhler Union X96, but the difference was almost negligible. 6. To obtain more comprehensive information of the mechanical properties of the joints, further tests (tensile, bending, instrumented impact test, as well as fracture mechanical investigations) will be required.
References 1. Net Zero by 2050 – A Roadmap for the Global Energy Sector. 4th revision, International Energy Agency October 2021. https://www.iea.org/reports/net-zero-by-2050. Accessed 09 Apr 2022 2. Gáspár, M.: Effect of welding heat input on simulated HAZ areas in S960QL high strength steel. Metals 9, 1226 (2019) 3. Gáspár, M., Sisodia, R.: Improving the HAZ toughness of Q+T high strength steels by post weld heat treatment. Mater. Sci. Eng. 426, 012012 (2018) 4. Cui, Q.L., et al.: Tensile and fatigue properties of single and multiple dissimilar welded joints of DP980 and HSLA. J. Mater. Eng. Perform. 26(2), 783–791 (2017). https://doi.org/10.1007/ s11665-016-2454-0 5. W˛eglowski, M. S., Zeman, M., Lomozik, M.: Physical simulation of weldability of weldox 1300 steel. Mater. Sci. Forum 762, 551–555 (2013) 6. Blacha, S.,Weglowski, M. S., Dymek, S., Kopyscianksi, M: Microstructural and mechanical char-acterization of electron beam welded joints of high strength S960QL and Weldox 1300 steel grades. Arch. Metall. Mater. 62(2), 627–634 (2017) 7. Kah, P., Pirinen, M., Suoranta, R., Martikainen, J.: Welding of ultra high strength steels. Adv. Mater. Res. 849, 357–365 (2014) 8. Weglowski, M. S., Zeman, M.: Prevention of cold cracking in ultra-high strength steel Weldox 1300. Arch. Civ. Mech. Eng. 14, 417–424 (2014) 9. Kurc-Lisiecka, A., Piwnik, J., Lisiecki, A.: Laser welding of new grade of advanced high strength steel Strenx 1100 MC. Arch. Metall. Mater. 62(3), 1651–1657 (2017) 10. Branco, R.: High-Strength Steels, New Trends is Production and Application. Mechanical Engineering Theory of Application. Nova Science Publisher, New York (2018) 11. Amraei, M., Ahola, A., Afkhami, S., Björk, T., Heidarpour, A., Zhao, X.-L.: Effects of heat input on the mechanical properties of butt-welded high and ultra-high strength steels. Eng. Struct. 198, 109460 (2019) 12. Tervo, H., Kaijalainen, A., Pikkarainen, T., Mehtonen, S., Porter, D.: Effect of impurity level and inclusions on the ductility and toughness of an ultra-high-strength steel. Mater. Sci. Eng. 697, 184–193 (2017) 13. Ban, H., Shi, G.: A review of research on high-strength steel structures. Struct. Build. 171(8), 65–641 (2018) 14. Gáspár, M., Balogh, A.: GMAW experiments for advanced (Q+T) high strength steels. Prod. Process. Syst. 6(1), 9–24 (2013) 15. Porter, D. A.: Weldable high-strength steels: challenges and engineering applications. In: 68 IIW Annual Assembly & International, Conference of the International of Welding, Helsinki, Finland (2015)
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16. Farrokhi, F., Siltanen, J., Salminen, A.: Fiber laser welding of direct-quenched ultrahigh strength steels: evaluation of hardness tensile strength, and toughness properties at subzero temperatures. ASME J Manuf. Sci. Eng. 137(6), 061012 (2015) 17. Amraei, M., Skriko, T., Björk, T., Zhai, X.-L.: Plastic strain characteristics of butt-welded ultra-high strength steel (UHSS). Thin-Walled Struct. 109, 227–241 (2016) 18. Tervo, H., et al.: Comparison of impact toughness in simulated coarse-grained heat-affected zone of Al-Deoxidized and Ti-Deoxidized offshore steels. Metals 11, 1783 (2021) 19. Tümer, M., Pixner, F., Vallant, R., Domitner, J., Enzinger, N.: Mechanical and microstructural properties of S1100 UHSS welds obtained by EBW and MAG welding. Welding in the World 66(6), 1199–1211 (2022) 20. EN ISO 17636–1: Non-destructive testing of welds – Radiographic testing – Part 1: X- and gamma-ray techniques with film (2013) 21. MSZ CEN ISO/TR 15608: Welding. Guidelines for a metallic materials grouping system (ISO/TR 15608:2017) (2021) 22. ISO 15614–1:2017(E) Specification and qualification of welding procedures for metallic materials. Welding procedure test. Part-1: Arc and gas welding of steels and arc welding of nickel and nickel alloys (ISO 15614–1:2017)
Examination of Absorbed Specific Fracture Energy and Notch Opening Displacement on S960QL Steel and Its Welded Joints Illés Sas1
and János Lukács2(B)
1 Elektro-Mont˝orING Ltd., Jászberény, Hungary
[email protected]
2 Institute of Materials Science and Technology, Faculty of Mechanical Engineering and
Informatics, University of Miskolc, Miskolc, Hungary [email protected]
Abstract. Nowadays, the safety and the economic perspective are given special attention in the production of welded structures made of high-strength steels (HSSs). One of the problems in welding of HSSs is the cold crack sensitivity, which is closely related to the residual welding stresses. These can be controlled by the use of preheating, the correct choice of welding sequence and the post-weld heat treatment (PWHT). Another problem in welding of HSSs is providing adequate toughness and hardness in the heat-affected zone. These can be controlled by heat input, more specifically by limiting the heat input. For these steels, the risk of reduced toughness from a manufacturing point of view is higher than the risk of cold cracking. Classical techniques of fracture toughness evaluation, such as determination of the plain-strain fracture toughness or the critical value of the crack-tip opening displacement, are complex methods. The necessity of fracture mechanical test is inevitable, applying notched and precracked specimens. The determination of the absorbed specific fracture energy (Wc ) and/or the notch opening displacement (NOD) is basically simpler. Notched cylindrical tensile specimens can be applied, characterised by different notch radii. S960QL high strength steel and its welded joints without preheating and with 150 °C preheating temperature were examined; NOD and Wc values were determined and compared. Conclusions belong to the effect of the preheating and the sensitivity of the HSS, as well as the reliability of the applied material characteristics were drawn. Keywords: High strength steel · Notch opening displacement · Absorbed specific fracture energy
1 Introduction The most commonly used structural material for the construction of different engineering structures is steel, especially structural or low alloyed steel. The most widely used © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 1006–1021, 2023. https://doi.org/10.1007/978-3-031-15211-5_84
Examination of Absorbed Specific Fracture Energy
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manufacturing technology is welding, especially arc welding. In our days, steel manufacturers develop continuously modern versions of high strength steels, both base materials and filler metals for welding. The yield strength values of these modern steels and filler metals are higher than 1000 MPa. Furthermore, high strength steel lightweight structures with low-cost weldments can be applied in many practical cases (e.g. fixed and mobile cranes, hydropower plants, offshore structures, trucks, earthmoving and timber (handling) machines), because of an extensive reduction in weight [1, 2]. The welded joints are sensitive parts of the engineering structures because the welded regions are in complex microstructural states and stress conditions. The yield strength to tensile strength ratio (Y/T) of the base materials and the weld metals are significantly higher than the modern high strength steels and the conventional mild steels. Therefore, both structural designers and welding technologists should attend to toughness and crack sensitivity of welded joints [2, 3]. During the welding process, the joining parts are affected by heat and force, causing inhomogeneous microstructure and mechanical properties in the different parts of the welded joints, as well as stress concentrator points can be formed. Both the inhomogeneity parts and the weld imperfections in the welded joints play an important role in case of different loading conditions. High cycle fatigue (HCF) and fatigue crack growth (FCG) phenomena are very common problems in the welded structures [2, 4–7], together with cold cracking sensitivity, and especially in high strength steels. Classical methods of fracture toughness evaluation, such as determination of the plain-strain fracture toughness (KIc ), or the critical value of the crack(-tip) opening displacement (C(T)OD) are complex methods, both for base materials and welded joints [8, 9]. The necessity of fracture mechanical test is essential, applying notched and precracked specimens [10]. The determination of the absorbed specific fracture energy (ASPEF), today Wc , and/or the notch opening displacement (NOD) is basically simpler; notched cylindrical tensile specimens should be applied, characterised by different notch radii. The aims of this article are to introduce shortly the notch opening displacement and the absorbed specific fracture energy quantities, and one of their application possibilities at welding of high strength steel and its welded joints.
2 Theoretical Backgrounds and Previous Applications 2.1 Specific Fracture Energy (ASPEF) It can be assumed that the ASPEF is the work of all external forces in an infinitesimal element in the crack point which is necessary for the propagation of crack. Thus the total energy (Wc ) is equal to the sum of energy of elastic deformation (We ), energy of plastic deformation (Wp ), and energy of formation of a new fracture surface, in other words for crack propagation (Ws ). The energy of elastic deformation (We ) will be released after the rupture; furthermore, the surface energy (Ws ) is negligible compared to the energy of plastic deformation (Wp ). If referring the energy of plastic deformation to the absorbing volume (V), then getting a physically correct value, and Wc is equal to Wp . The Wc defined in the previously described way can be determined by applying tensile
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tests [11–13]. Figure 1 shows a cylindrical tensile specimen under plastic deformation [14].
Fig. 1. Characteristics of the tensile specimen under plastic deformation [14]
The Wc value can be calculated using Eq. (1). lu Wc =
Fdl = V
l0
ϕu lu Fdl l0
Sl
σ d ϕ.
(1)
0
Several remarkable and interesting applications of Wc (ASPEF) can be found in the literature. Plastic strain and fracture were studied by testing the change in energy referred to as unit volume on unalloyed steel [15]; fracture toughness data measured on brittle materials (PMMA, glass, alumina and graphite) were re-analysed using ASPEF [16]. The relation between low cycle fatigue (LCF) data and ASPEF was studied on different materials, at different temperatures and after different in-service times [17, 18]. The embrittlement tendencies and the effect of neutron irradiation on low and medium strength steels and their weldments were investigated [19, 20]; furthermore, fatigue fracture of band-saw blades was studied by applying Wc values [21]. 2.2 Notch Opening Displacement (NOD) Figure 2 [14] shows a stress concentration place, in other words a notch, which can be considered a crack if the notch radius is sufficiently small.
Fig. 2. Stress concentration placed with its plastic zone and fictive tensile specimen [14]
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Applying tensile stress, a plastic zone will be formed in the root of the notch, in which considerable energy can be absorbed. The length of the fictive tensile specimen (L) is equal to the width of the plastic zone, and this length depends on the material characteristics and the sharpness of the notch. In the case of having a crack, the length value will be minimum, furthermore, the absorbed energy will be minimum, too. Regarding to the strain, the elongation (L) depends on the material characteristics and the length (L), accordingly if we have a crack, the elongation will be minimum. This minimum value should be equal to the COD value. Both minimum values can be determined by indirect methods [22, 23]. Different applications of NOD can be found in the literature, too. Determination of fracture toughness values on three different types of low-carbon structural steel were compared using conventional (JIc ) and non-conventional (NOD) methods [24]. NOD values of high, medium and low Cr-steels (nominal Cr content 12%, 9% and 2%, respectively) and their welded joints were tested and analysed at high temperature (500 °C) [25]. The measured NOD values and their main statistical characteristics (average, standard deviation (abbreviated as STD), and standard deviation coefficient (abbreviated as STD Coefficient)) were calculated and are summarised in Table 1 [25]. Table 1. Measured NOD values (in mm) and their main statistical characteristics [25] Notch radius, mm
Identification
High Cr-steel
Medium Cr-steel
Low Cr-steel
0.3
Specimen No1
0.57
1.02
1.15
Specimen No2
0.67
0.70
1.23
Specimen No3
0.7
0.72
1.20
Average
0.647
0.813
1.193
STD
0.068
0.179
0.040
STD Coefficient
0.105
0.220
0.034
Specimen No1
0.73
0.75
1.20
Specimen No2
0.70
0.60
1.20
Specimen No3
0.72
0.33
1.27
Average
0.717
0.560
1.223
STD
0.015
0.213
0.040
STD Coefficient
0.021
0.380
0.033
Specimen No1
0.77
0.67
1.32
Specimen No2
0.65
1.10
1.35
Specimen No3
0.92
0.75
1.30
Average
0.780
0.840
1.323
STD
0.135
0.229
0.025
STD Coefficient
0.173
0.272
0.019
0.4
0.6
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The standard deviation coefficient values cover wide range, excessively large values (0.380 and 0.272) can be found among the samples, too.
3 Investigations and Their Results 3.1 Preparation and Basic Investigations of Welded Joints S960QL type, quenched and tempered high strength steel (produced by Thyssen Stahl AG and designated as XABO 960) was applied for the experiments. The chemical composition and the mechanical properties of the 15 mm thick base material plate are summarised in Table 2 and Table 3, respectively. Table 3 contains the yield strength (ReH ), the tensile strength (Rm ) and the fracture elongation (A) values. Table 2. Chemical composition of the used base material (in weight%) C
Si
Mn
P
S
Al
Cr
Mo
Ni
V
0.17
0.28
0.7
0.01
0.001
0.031
0.62
0.37
1.69
0.05
Table 3. Basic mechanical properties of the used base material ReH , MPa
Rm , MPa
A, %
Charpy impact energy, CVN, J °C
No1
No2
No3
Average
1003
1077
15,0
−40
29
27
31
29
−60
22
20
19
20
The dimensions of the welded workpieces were 600 mm x 125 mm, the welding position was flat position (PA). V joint shape was applied, with 60° opening angle, 2 mm gap between the two plates (root opening), and 1 mm root face. The welding equipment was a MIG/MAG power source; 1.2 mm diameter solid wires in Union X96 (Böhler) types, and 18% CO2 + 82% Ar gas mixture (M21) were applied. Two welded joints were prepared, the first one without preheating, and the second one with preheating. Based on the chemical composition of the base material, the type of butt welded joint, and the applied welding process, the calculated preheating temperature [26, 27] was Tpre = 106 °C. Accordingly to the recommendation of the base material manufacturer (Thyssen Stahl AG), a higher preheating temperature was selected, which was Tpre = 150 °C. During the preparation of the welded joint without preheating, the interpass temperature was less than Tip = 50 °C. The welding technological parameters can be found in Table 4, separately for the root and the filler layers. The table introduces the welding current (I), the welding voltage (U), the wire-feed speed (vwire ), the welding speed (vw ) values, as well as the heat input (Q) calculated with arc efficiency (η = 0.85). Figure 3 shows the macrostructure of the welded joints.
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Table 4. The applied welding parameters for welded joints preparation Layer(s)
I, A
U, V
vwire, m/min
vw , mm/s
Q, kJ/mm
1 root
100–120
21–22
3.1–3.5
3.6–4.1
0.49–0.54
2–5 filler
240–275
29–31
10
9.2–13.0
0.49–0.72
6–10 filler
275–280
30–31
10
7.7–9.7
0.72–0.93
Fig. 3. The macrostructure of the welded joints: without preheating (left side) and with preheating (right side)
3.2 Basic Investigation of Welded Joints Welding procedure tests, non-destructive and mechanical investigations were accomplished on both welded joints; the detailed results (individual, average and range values) are summarised in Table 5. Table 5. Detailed results of the executed non-destructive and mechanical investigations Testing method
Welded joint without preheating
Welded joint with preheating (150 °C)
Visual testing (VT)
Appropriate
Appropriate
Magnetic particle testing (MT)
Appropriate
Appropriate
Ultrasonic testing (UT)
Appropriate
Appropriate
Tensile strength, Rm , MPa
1011; 1037
1013; 1047
Bending strain using non-standardized method with more rigorous criteria, %
38,25 (38,1; 38,3; 38,3)
38,35 (38,3; 38,3; 38,4)
Charpy impact energy in the weld 48 (47; 53; 45) metal (WM) at -40 °C, CVN, J
51 (45; 57; 51)
Charpy impact energy in the heat affected zone (HAZ) at -40 °C, CVN, J
54 (47; 53; 45)
63 (51; 79; 60)
Hardness, HV10
383–421
383–464
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Comparing the values of the welded joints without preheating and with preheating, as well as the comparable values of the base material and the welded joints, it can be drawn that there are no significant differences between the results. 3.3 Notch Opening Displacement and Specific Fracture Energy Investigations Specimen Characteristics. Cylindrical notched specimens with different notch radii (R) Were prepared to perform tensile tests. The different notch radii represent different stress concentration factors (Kt ). Figure 4 shows the characteristics of the un-notched and the notched specimens, where the diameter of the tested length of the un-notched specimens and the minimum diameter of the notched specimens at the notches were the same (d0 ). The larger diameter of the notched specimens (D0 ) was the same, too.
Fig. 4. Shape and geometry of the un-notched and the notched specimens
The stress concentration factor (Kt ) values were specified using a web calculator [28] developed based on formulas for stress and strain [29] and were controlled based on the well-known classical handbook [30]. The data belonging to the un-notched and differently notched specimens are summarised in Table 6. Table 6. Characteristic data of the applied un-notched and notched specimens D0 , mm
d0 , mm
R, mm
d0 /D0 , –
R/D0 . –
Kt , –
N/A
4
∞
N/A
N/A
1 (continued)
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Table 6. (continued) D0 , mm
d0 , mm
R, mm
d0 /D0 , –
R/D0 . –
Kt , –
6
4
1
0.667
0.167
1.67
0.5
0.083
2.12
0.3
0.050
2.60
0.2
0.033
3.07
0.1
0.017
4.15
Notch Opening Displacement (NOD) Investigations. Specimens cut from the base material and welded joints without preheating and with preheating (150 °C) were investigated. The notch locations of the specimens cut from welded joints were different, namely located in the weld metal (WM), in the heat-affected zone (HAZ), or in the boundary of the joint parts. These different positions allowed a statistical evaluation of the properties of the welded joints. To determine the notch opening displacement (NOD) values of the specimens, the contour lines of all different notch radii were projected before the tensile tests. After the tests, in other words, after the rupture, the two broken parts of all specimens were fitted carefully and the changed contour lines were projected again. Using the differences between the two contour lines, the NOD values were determined [19, 24, 25]. Table 7 summarises the results, the determined (also known as measured and calculated) NOD values. Table 7. The determined notch opening displacement (NOD) values (in mm) R, mm
Base material
Welded joint without preheating
Welded joint with preheating (150 °C)
1
0.35
0.72
N/A
0.50
0.66
N/A
0.5
0.35
0.52
N/A
N/A
N/A
0.57
N/A
N/A
0.42
N/A
N/A
0.42
0.3
N/A
N/A
0.29
0.2
0.29
0.25
0.25
0.28
0.31
0.28
0.36
0.31
N/A (continued)
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I. Sas and J. Lukács Table 7. (continued)
R, mm
Base material
Welded joint without preheating
Welded joint with preheating (150 °C)
0.1
0.29
0.22
0.18
0.22
0.36
0.17
0.15
0.26
0.13
Based on the shortly summarised theoretical background (see Subsect. 2.2), there is a connection between the notch opening displacement (NOD) and the notch radius (R) values; and the value of the function at R = 0 (at crack) is equal to the crack opening displacement (COD), as follows: NOD = kR + NODR=0 = kR + COD.
(2)
Figure 5 shows the measured values and the regression lines using the Least Squares Fitting (LSF) method, taking into account that all measured data can be found in Table 7. Because the measured notch opening displacement (NOD) values belong to different notch radii (R) in the three main groups (base material and two welded joints), and there are different numbers of specimens at a notch radii, mathematical-statistical values of the samples (NOD values at notch radius) were calculated. Using the average NOD values, NOD – R functions were imaged.
Fig. 5. The NOD – R functions taking into account all measured data
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The calculated statistical values are summarised in Table 8, and the NOD – R functions taking into account the average values of the measured data can be seen in Fig. 6. Table 8. The main statistical parameters of the investigated NOD samples R, mm
Statistical parameter
Base material
Welded joint without preheating
Welded joint with preheating (150 °C)
1
Average
0.400
0.633
N/A
STD
0.087
0.103
N/A
STD Coefficient
0.217
0.162
N/A
Average
N/A
N/A
0.470
STD
N/A
N/A
0.087
STD Coefficient
N/A
N/A
0.184
0.5
0.3
N/A
N/A
N/A
N/A
0.2
Average
0.310
0.290
0.265
STD
0.044
0.035
N/A
0.1
STD Coefficient
0.141
0.119
N/A
Average
0.220
0.280
0.160
STD
0.070
0.072
0.026
STD Coefficient
0.318
0.258
0.165
Table 9 summarises the calculated slope (k), COD (NODR=0 ) and correlation coefficient values of the approximated straight lines of the three material groups, belonging to both all data and average values. Correlation coefficient values demonstrate clearly that using the average values, the results are more reliable than using all data. It is remarkable that the NODR=0 = COD values of the base material and the welded joint without preheating were essentially the same, however the values belong to the welded joint with preheating were significantly lower. Absorbed Specific Fracture Energy (Wc ) investigations. The absorbed specific fracture energy values (Wc ) were calculated based on the data of the load – extension diagrams and the geometrical features of the specimens. Tensile strength (Rm ) and fracture strength (R’ u ) values were calculated using the appropriate loads (Fm and Fu , respectively) and specimen diameters (d0 and du , respectively), and the following equation was applied: Rm + Ru d0 . (3) Wc = 2ln 2 du
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Fig. 6. The NOD – R functions taking into account the average values of the measured data
Table 9. The parameters of the NOD – R functions NODR=0 = COD, mm Correlation coefficient
Group
Material / welded joint
k
All data
Base material
0.407 0.225
0.9354
Welded joint without preheating
0.166 0.238
0.7238
Welded joint with 0.756 0.091 preheating (150 °C)
0.9413
Average value Base material
0.166 0.238
0.9122
0.406 0.225
0.9970
Welded joint with 0.744 0.092 preheating (150 °C)
0.9869
Welded joint without preheating
Table 10 summarises the results, and the determined Wc values, furthermore, the calculated statistical parameters can be found in Table 11.
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Table 10. The determined absorbed specific fracture energy (Wc ) values (in MJ/m3 ) R, mm
Kt , –
Base material
Welded joint without preheating
Welded joint with preheating (150 °C)
∞
1
2210
871
1598
2243
1513
1573
1
0.5
1.67
2.12
2232
1352
1636
505
673
N/A
753
747
N/A
450
586
N/A
N/A
N/A
681
N/A
N/A
621
N/A
N/A
636
0.3
2.60
N/A
N/A
467
0.2
3.07
462
293
281
396
253
346
338
281
N/A
319
269
158
319
216
246
241
325
124
0.1
4.15
For analogous reasons to those described under the NOD investigations, both all Wc data and average Wc data were illustrated in Fig. 7 and Fig. 8, respectively. Table 11. The main statistical parameters of the investigated Wc samples R, mm
Statistical parameter
Base material
Welded joint without preheating
Welded joint with preheating (150 °C)
∞
Average
2228
1245
1602
STD
16.8
334.0
31.7
1
0.5
STD Coefficient
0.0075
0.2682
0.0198
Average
569
669
N/A
STD
161.4
80.6
N/A
STD Coefficient
0.2835
0.1205
N/A
Average
N/A
N/A
646
STD
N/A
N/A
31.2 (continued)
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I. Sas and J. Lukács Table 11. (continued)
R, mm
Statistical parameter
Base material
Welded joint without preheating
Welded joint with preheating (150 °C)
STD Coefficient
N/A
N/A
0.0483
0.3
N/A
N/A
N/A
N/A
0.2
Average
399
276
314
0.1
STD
62.0
20.5
N/A
STD Coefficient
0.1556
0.0745
N/A
Average
293
270
176
STD
45.0
54.5
63.0
STD Coefficient
0.1537
0.2019
0.3577
Fig. 7. The Wc – Kt functions taking into account all measured data
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Fig. 8. The Wc – Kt functions taking into account the average values of the measured data
4 Summary and Conclusions Based on the executed investigations and the calculated results, the following findings and conclusions can be drawn. The applied gas metal arc welding process and technological parameters are suitable for the production of welded joints of the investigated high strength steel with eligible quality. Based on the executed welding procedure tests and their results, the applied preheating temperature has no significant effect on the main characteristics (imperfections and mechanical properties) of the welded joint. Applying the notch opening displacement (NOD) and the absorbed specific fracture energy (Wc ) values, both the base material and the welded joints without and with preheating can be characterised from another point of view (in our case, cold cracking sensitivity), too. Because of different notch radii (R) were applied in the three main groups (base material and two welded joints), and a different number of specimens were investigated at a different notch radii, it was necessary analysing both all measured data and calculated average data. Both the notch opening displacement (NOD) and the absorbed specific fracture energy (Wc ) samples consist of a relatively small number of data (in other words, specimens), in several cases have high standard deviation coefficients. (See 0.318 and 0.258 values in Table 8, and 0.2835, 0.2682 and 0.3577 values in Table 11. These higher standard deviation coefficient values are in harmony with relevant values can be found in the literature [25] (see Table 1)) During the further investigations, both the element number of samples (in other words, the number of the tested specimens) and the number of tested notch radii should be increased.
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Based on notch opening displacement (NOD) and absorbed specific fracture energy (Wc ) investigations, and based on both all data and average data, the applied preheating temperature has no significant effect on NOD and Wc values, in other approach on cold crack sensitivity of the welded joints. Only COD (NODR=0 ) value of the preheated welded joint is an exception, however that is a consequence of the applied notch radii. It should be noted that the investigated welded joints were prepared without constraints; the deformations of the welded plates were free during the welding process. In that case when the welded structure can be produced without constraints, further investigations should be accomplished to study the welding technology without preheating temperature. In that case, when the welded structure can be only produced with constraints, further investigations should be accomplished to study the determination of the efficient preheating temperature. These investigations can be built upon notch opening displacement (NOD) and absorbed specific fracture energy (Wc ). Finally, the effect of the preheating temperature should be investigated deeper, analysing the correlations between the mechanical and microstructural characteristics.
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13. Gillemot, L.: Criterion of crack initiation and spreading. Eng. Fract. Mech. 8, 239–253 (1976) 14. Czoboly, E., Havas, I., Orbulov, I.: Törési vizsgálatok a BME Mechanikai Technológia Tanszéken. Anyagvizsgálók Lapja Válogatás 2012 – Jubileumi szám 43–45 (2012) 15. Kator, L., Gillemot, L.: Investigation of the plastic strain and fracture of metals by the method of the change of specific internal energy. Periodica Polytech. Mech. Eng. 15(3), 331–339 (1971) 16. Turner, C.E.: Fracture toughness and specific fracture energy: a re-analysis of results. Mater. Sci. Eng. 11(5), 275–282 (1973) 17. Gillemot, L.: Low-cycle fatigue by constant amplitude true mean stress. Periodica Polytech. Mech. Eng. 10(2), 77–94 (1966) 18. Czoboly, E., Havas, I., Ginsztler, J.: Relation between low cycle fatigue data and the absorbed specific energy. In: Faria, L. (ed.) Proceedings of ECF5, vol. I, pp. 481–491. EMAS, Warley (1984) 19. Gillemot, F.: Absorbed Specific Energy of Fracture, a Failure Criteria for Neutron Irradiated Materials. In: SMiRT 5, Structural Analysis of Steel Reactor Pressure Vessels, G3 - Fracture Mechanics: Elasto-Plastic. IASMiRT (1979) 20. Gillemot, F., Czoboly, E., Havas, I.: Fracture mechanics applications of absorbed specific fracture energy: notch and unnotched specimens. Theoret. Appl. Fract. Mech. 4(1), 39–45 (1985) 21. Dobránszky, J., Ginsztler, J., Lovas, J., Magasdi, A.: Szalagf˝urészlapok fáradásos törései http://real.mtak.hu/5756/1/1155684.pdf. Accessed 04 Jan 2022 22. Gillemot, L., Czoboly, E.: Generalized theory of fracture. In: II. Conference on Brittle Fracture 11, 1–21 (1970) 23. Czoboly, E., Havas, I., Gillemot, F.: The absorbed specific energy till fracture as a measure of the toughness of metals. In: Sih, G.C., Czoboly, E., Gillemot, F. (eds.) Proceedings of International Symposium on Absorbed Specific Energy and/or Strain Energy Density Criterion, pp. 107–130. Sijthoff and Noordhoff International Publishers, Alphen aan den Rijn (1981) 24. Bogucki, R.: The Evaluation of Resistance to Cracking in Structural Steels with the Use of the ASPEF Method. Arch. Metall. Mater. 54(4), 1073–1082 (2009) 25. Elarbi, Y. M.: Weldability of high Cr and 1% tungsten alloyed creep resistant martensitic steel. Dissertation of Ph.D., Budapest University of Technology and Economics (2008) 26. Winkler, F.: Nagyszilárdságú finomszemcsés szerkezeti acélok hegesztése. Hegesztéstechnika 3(2), 17–31 (1992) 27. EN 1011–2: Welding. Recommendations for welding of metallic materials. Part 2: Arc welding of ferritic steels (2001) 28. https://amesweb.info/stress-concentration-factor-calculator/u-groove.aspx. Accessed 04 Jan 2022 29. Pilkey, W.D.: Formulas for Stress, Strain, and Structural Matrices, 2nd edn. John Wiley & Sons (2005) 30. Pikley, W.D.: Peterson’s Stress Concentration Factors, 2nd edn. John Wiley & Sons (1997)
Comparison of Fatigue Life of K Joints with and Without Overlap Using 3D Fatigue FEM Analysis Shazia Muzaffer , Kyong-Ho Chang(B)
, and Zhen-Ming Wang
Department of Civil, Environmental and Plant Engineering, Chung-Ang University, Hekseouk-ro, Dongjak-gu, Seoul 0697, Republic of Korea [email protected]
Abstract. Tubular members are extensively nowadays used in the construction of many vehicles due to their excellent properties like high torsional rigidity, high strength to weight ratio, and less surface area. Also, it helps in reducing expenditure during the fabrication and installation of the big effective designs. The tubular members are subjected to different cyclic forces leading to plastic deformation of chords, material degradation, fatigue cracks due to which tubular members undergo severe damage, and finally, will lead to the failure of the whole structure. The overlapped tubular joints and simple gap joints can be used as the joint type in tubular members. In this study, the fatigue analysis of overlapped and non-overlapped K-type tubular joints was carried out using the 3D fatigue FE analysis method to investigate the fatigue performance. The 3D fatigue FE analysis based on continuum mechanics and elastoplastic cyclic hysteresis constitutive equations was carried out in 3 steps. Firstly, thermal load i.e., temperature histories, were calculated using thermal analysis secondly welding residual stress and welding deformation were estimated using thermal load as initial data in the mechanical analysis. In the 3rd step, the residual welding stresses and welding deformation were utilized as initial data to determine the fatigue life The S-N curve was drawn from 3D fatigue FEM analysis results. Keywords: 3D fatigue FEM analysis · Overlapped tubular joints · Simple gap joints · Crack initiation
1 Introduction Steel tubular members have been extensively used around the globe due to their excellent mechanical characteristics, very good weldability, geometric tolerances, lightweight, pleasing appearances, easy fabrication, and high load-bearing capacities. The tubular members are employed in a lot of structures due to inherent properties and possessing high torsional stiffness as well as the high compressive strength of tubular sections. The tubular members are connected by welding using different types of joints to form a whole structure. The joint is formed by welding the secondary member (the brace) onto the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 1022–1035, 2023. https://doi.org/10.1007/978-3-031-15211-5_85
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circumference of the main member (the chord). The joints being constantly exposed to cyclic load in a corrosive environment lead to material degradation, dynamic softening, dent, and damage which can weaken the tubular joints and the overall resistance, safety, service, and durability of the structure gets affected. The tubular joints are designed to withstand different kinds of basic loadings like axial loading, in-plane loading, and outof-plane which cause different types of stress concentration at the joint intersection of the structure. The simple non-overlapping joints are mostly used joints in structures or vehicles, but due to geometrical limitations in many design codes or unbalanced moments on the chord may cause the failure of joints. In the case of lightweight automotive structures, the fatigue behaviour is greatly influenced by the properties of its joints. Hence, partially overlapped joints were considered much better due to a more efficient load transfer mechanism and zero eccentricity. The load is transferred directly through the common weld between the two braces to achieve an efficient joint. The usage of partially overlapped joints reduces the cost of stiffeners and the thickness of chord walls. However, the overlapped K-type joints have been used during early times in structures but due to scarcity of research in some fields like fatigue performances, the effect of geometrical parameters, and the inability to inspect the hidden parts of weld diminished the usage of these joints. Enviyavan Subramanian carried out the estimation of fatigue life of welded joints using a vibration-fatigue computational model. Also, the effect of geometric changes on weld fatigue life was studied [1]. G. Marsel et al., investigated the effect of fillet welds on the initial rotational stiffness of welded tubular joints [2]. Johann Wanenburg carried out the study of fatigue loading on automotive and transport structures by using three methods first remote parameter analysis, second fatigue equivalent static load, and third Two Parameter approach [3]. Hanna Karlsson investigated the static and fatigue analysis of welded joints in thin-walled tubes with a thickness of less than 3 mm [4]. O.S. Ogbonna et al. have carried out a study on the application of MIG and TIG welding in the automobile industry [5]. K.M. Sharma investigated the fatigue of welded high-strength steels for automotive chassis and suspension applications. The approach used was considered useful in monitoring multiple failure mechanisms [6]. Jadav Chetan S. et al. reviewed different fatigue analysis techniques for different automobile frames [7]. Many experiments are expensive due to the complicated geometry, difficulties in manufacturing big models, etc. So, to tackle these difficulties, numerical analysis is being widely used to analyze the behaviour before construction.
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δD
α, β An D M
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total strain increment elastic strain increment plastic strain increment thermal strain increment increment of damage evolution increment of number of cycles mean stress maximum stress material constants of fatigue damage evolution amplitude of octahedral shear Stress damage variable material constants of new damage evolution
In this paper, the crack initiation and fatigue life of simple gapped K joint and overlapped K joint was studied using 3D fatigue FE analysis. The 3D fatigue FE analysis was carried out in two steps; firstly the welding condition was reproduced and, secondly, the cyclic load was applied to determine the fatigue life in overlapped and non-overlapped joints. The 3D fatigue FE analysis was based on continuum damage mechanics and cyclic elastoplastic constitutive hysteresis equations.
2 FE Welding Analysis Procedure of K-type Joint Steel for welding simulations, many models from the past years has been developed to reproduce the geometrical and material non-linearities during welding. The welding process involves a complex interaction of different mechanisms. With the continuous development of new welding methods, welding nowadays has become an efficient and reliable metal joining process. During the welding process, the area around the weld is heated up rapidly, resulting in high thermal gradients in the area. The material gets expanded, but the colder regions surrounding restrain the expansion leading to the rising of thermal stresses. The yield strength and ultimate strength of the material at elevated temperatures reduce leading to the development of plastic strains. The heated weld area gets reduced when it cools down. At cooling temperature, due to plastic, the material will retrieve its strength. The welding deformation and residual welding stresses are inevitable and can affect the geometrical and material properties of engineering structures like fatigue performances, dimensional stability, brittle fracture, and corrosion resistance. From the fatigue point of view, the compressive residual stresses are beneficial, whereas the tensile residual stresses are detrimental to structure. So, for accurate fatigue assessment, sound design, and safety of structure it is necessary to know about weld induced residual stresses and welding deformations. Over the past few years, various experimental methods have been used for the accurate assessment of residual stresses. Nowadays due to cost and time limitations, numerical simulation has become a valuable tool for analyzing structural behaviours. The welding process was simulated using the in-house-FE-code. The welding simulation process includes two steps, firstly the thermal hysteresis was
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calculated. Secondly, the thermal hysteresis is used as input data to calculate the residual welding stresses and welding deformation. The two analyses are sequentially coupled. 2.1 FE Welding Analysis Model (K-type Joint) The tubular joints are extensively used in the construction and automobile industry because of their better static strength, easy fabrication, and aesthetic appearance. The
(a) Overlapped K-type joint
(b) Non-overlapped K-type joint Fig. 1. Dimension of K-type tubular joints.
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tubular joints withstand different loadings like traffic, wave, and wind loads. As a result, fatigue damage occurs at transition parts of the tubular joint. A tubular joint is a connection between two or more tubular pipes of different diameters. The pipe with a large diameter is called the chord and the pipe with a smaller diameter is called a brace. Tubular joints are classified as simple, stiffened, or overlapped joints. In this paper we modelled two types of tubular joints overlapped K-type joints and non-overlapped K-type joints. The dimensions of both joints are the same as shown in Fig. 1. The two-brace members in the non-overlapped K-type tubular joint are connected to the chord. Similarly, the overlapped joints were interconnected to each other and then joined to the chord member. To reproduce the accurate stress gradients and a high temperature near the weld zone, a fine mesh was used in the HAZ zone. The eight-node isoparametric elements were used, as shown in Fig. 2. The meshed models of both K-type tubular joints are shown in Fig. 3. A mesh convergence study was carried out to select the suitable mesh density in both models. For data mapping in thermal and mechanical analysis, the same meshing with the same element type was used in both analyses.
Fig. 2. The eight-noded isoparametric solid element
2.2 Welding Temperature and Thermal Elastoplastic Analysis The transient heat transfer analysis is carried out based on heat conduction formulation. The temperature histories computed during heat transfer analysis for each time increment were used as initial data in mechanical analysis to calculate the residual stresses and welding deformation. SM355 is the type of steel used extensively in Korea, so it has been a material of choice. The temperature histories of both tubular joints are shown in Fig. 4. In thermo-mechanical analysis, the thermo-physical properties and thermo-mechanical properties were considered temperature-dependent for both base and weld metal, as shown in Fig. 5. The residual stress magnitudes in overlapped and non-overlapped Ktype tubular joints are shown in Fig. 6. As the metallurgical transformations have a negligible effect on residual stresses, therefore, the differential form of strain can be decomposed into three components, as follows: {d ε} = d εe + d εp + d εth (1)
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(a) Mesh view of overlapped K-type tubular joint.
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(b) Mesh view of non-overlapped K-type tubular joint.
Fig. 3. Mesh view
The isotropic Hook’s law was used to calculate the elastic strain with temperaturedependent Poisson’s ratio and young’s modulus. The thermal strain increment is calculated using the coefficient of thermal expansion. Rate-independent elastic-plastic constitutive equations with Von Mises yield criterion and the linear isotropic hardening rule were used to calculate the plastic strain. Newton Raphson’s iterative method was used to obtain a useful solution. The FE analysis results were validated by comparing with the experimental results, to confirm the authenticity of the results obtained from the FE analysis [8, 9].
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(a) Temperature distributions for overlapped K-type joint
(b) Temperature distributions for non-overlapped K-type joint Fig. 4. Temperature distributions.
3 Fatigue FE Analysis for K-type Joint Fatigue damage under cyclic loading is a major cause of structural failure. Fatigue failures in both low and high cycle follow the same path, first fatigue crack initiates, then propagates through the structure and ultimately lead to the failure of the structure. Fatigue failures under cyclic loading can be attributed to the presence of microscopic material imperfections, corrosion, temperature, and unpenetrated welds. Fatigue is expressed as the gradual reduction in the load-carrying capacity of structural elements to withstand cyclic loading. In this study, a nonlinear CDM model based on cyclic plasticity constitutive equations and continuum damage mechanics was used to calculate the fatigue life and crack initiation positions. The numerical procedure used to determine the total
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(a) Mechanical material properties
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(b) Physical constants
Fig. 5. Temperature-dependent mechanical material properties and physical constants.
fatigue life and crack initiation position in overlapped and non-overlapped K-type tubular joints are shown in this flow chart Fig. 7. Under the multiaxial stress state, the 3D damage evolution model was proposed as: δD = [1 − (1 − D)β+1 ]α(αMax ,αm )
A M (σm )(1 − D)
β
δN
(2)
The relationship between Damage growth (d), number of cycles (N), and fatigue life (Nf ) can be expressed from the below equation: D = 1− 1−
N Nf
1/(1−α) 1/(1+β) (3)
The number of cycles to failure is expressed as: Nf =
−β An 1 (β + 1)(1 − α) M (σm )
(4)
The analysis was carried out by using the in-house FE code, validated against experimental and numerical results found in the literature [10–13]. The other formulas used in this analysis have been in the above-published papers.
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(a) Residual stress distribution in overlapped K-type tubular joint.
(b) Residual stress distribution in non-overlapped K-type tubular joint Fig. 6. Residual stress distribution
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Fig. 7. Flow chart
3.1 Load and boundary condition of 3D Fatigue FEA To analyze the fatigue life and crack initiation cyclic loading was applied perpendicular to the weld line. The cyclic loads were applied as the distributed load on the top of both braces in overlapped and non-overlapped joints as shown in Fig. 8. The residual stresses and plastic strains calculated during thermo-mechanical are introduced as initial data. Similar geometry, material, and same FE mesh density were used during the analysis to facilitate data mapping between the structural models. In the FE models of both Ktype tubular joints, the nodes on one side of the chord were restrained in all (X, Y, and Z) directions while as nodes on another side of the chord were restrained in the X, Y directions and free in the Z direction (Roller type boundary condition) as shown in Fig. 9 (a) and (b).
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Fig. 8. Cyclic load type
(a) Overlapped joint
(b) Non-overlapped joint
Fig. 9. Boundary and loading conditions
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4 Results and Discussion: Comparison of Fatigue FEA Results in Overlapping and Non-overlapping Joints The fatigue Fem results are plotted using S-N Curve. The S-N Curve functions as a “lookup table” between alternating stress levels and the number of cycles to failure. In an S-N curve, the number of cycles is on the x-axis and the equivalent stress range is on the y-axis. As the stress decreases from high to low, the fatigue life of the structure also begins to increase slowly at first and then quite rapidly. During service, as most of the loadings are multiaxial, fatigue assessment of structure in a multiaxial state is cumbersome. In this study, to predict the fatigue life of overlapped and non-overlapped K-type tubular joints, the multiaxial stress state was reduced to a uniaxial stress state. At the specified number of loadings, the fatigue strength calculated by transformation is compared for both overlapped and non-overlapped joints. From the results, we found out that the fatigue life of overlapped joints is shorter than that of non-lapped joints. As the magnitude of tensile residual stresses in Z- the direction of the overlapped joint was high compared to non-overlapped joints, this high tensile residual stress being dominant and superimposed on internal stress developed by cyclic loading led to shorter fatigue of overlapped joints. The S-N curve calculated from 3D fatigue FEM analysis is shown in Fig. 10.
Fig.10. Comparison of results from 3D fatigue FEA.
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5 Conclusion 3D fatigue FEM analysis was carried out to study the fatigue life and crack initiation in overlapped and non-overlapped K-type tubular joints. The study was carried out in three steps, firstly the temperature histories were calculated, and second residual stresses along with welding deformation were estimated. And, then fatigue life was predicted under cyclic loading with residual stresses and welding deformation enforced as input. From the results following conclusions were drawn: 1. The welding residual stress and deformation were considered for fatigue analysis. 2. The fatigue life of overlapped and non-overlapped K-type tubular joints under cyclic loading was predicted. 3. 3D fatigue FEM analysis results predicted fatigue life of overlapped joint is shorter than that of the non-overlapped joint. 4. The fatigue FE analysis method is useful as a numerical tool for calculating the fatigue life.
Acknowledgments. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF2021R111A204584511). This research was also supported by Chung-Ang University research scholarship grants in 2019.
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10. Nguyen Van Do, V., Lee, C.-H., Chang, K.-H.: A nonlinear CDM model for ductile failure analysis of steel bridge columns under cyclic loading. Comput. Mech. 53(6), 1209–1222 (2014). https://doi.org/10.1007/s00466-013-0964-2 11. Vuong, N.V.D., Lee, C.H., Chang, K.H.: High cycle fatigue analysis in presence of residual stresses by using a continuum damage mechanics model. Int. J. Fatigue 70, 51–62 (2015) 12. Vuong, N.V.D., Lee, C.H., Chang, K.H.: A constitutive model for uniaxial/multiaxial ratcheting behavior of duplex stainless steel. Mater. Des. 65, 1161–1171 (2015) 13. Muzaffer, S., Chang, K.H., Wang, Z.M., Kang, S.U.: Comparison of stiffener effect on fatigue crack in KT-type pipe joint by FEA. Weld. world 66, 738-797 (2022). https://doi.org/10.1007/ s40194-022-01254-z
Author Index
A Abd al al, Sahm alden, 981 Abdullah, Mahir Faris, 374 Agárdi, Anita, 667, 678 Ahola, Antti, 966 Akkad, Mohammad Zaher, 694 Albedran, Hazim, 709 Alktranee, Mohammed, 521 Alsamia, Shaymaa, 709 Árpád, István, 330 Askar, Ali Habeeb, 357 Assmann, Tom, 444 B Baksa, Attila, 756 Bányai, Tamás, 428, 456, 694 Bellér, Gábor, 330 Bels˝o, László, 883 Bencs, Péter, 521 Benotsmane, Rabab, 11, 24 Bereczky, Ákos, 268 Berek, Lajos, 84 Berényi, László, 412, 636 Bihari, János, 223 Björk, Timo, 966 Bognár, Gabriella Vadászné, 103 Bolló, Betti, 357 Borhy, István, 883 Borysenko, Dmytro, 648 Botha, T. R., 237 Brinken, Julius, 444
C ˇ Cermák, Roman, 248 Chang, Kyong-Ho, 1022 Chen, Gang, 904 Chlebo, Ondrej, 835 Cservenák, Ákos, 50, 428 Csörnyei, Márk, 291 Czégé, Levente, 421 D Dadvandipour, Samad, 3 Denes, Takacs, 111 Dömötör, Ferenc, 871 Drágár, Zsuzsa, 179 E Écsi, Ladislav, 925 Éleszt˝os, Pavel, 925 Els, P. S., 237 Eze, Alagba Henry, 346 F Fekete, Tamás, 261 Felh˝o, Csaba, 648, 654 Ferencsik, Viktoria, 538 File, Máté, 549 Földesi, Péter, 799, 812 Fülöp, Fruzsina, 604, 620 G Gabriel, Popa, 165 Gadó, Krisztián, 126 Ganie, Aadil Gani, 3
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 K. Jármai and Á. Cservenák (Eds.): VAE 2022, LNME, pp. 1037–1039, 2023. https://doi.org/10.1007/978-3-031-15211-5
1038
Author Index
Gáspár, Marcell, 893 Gast, R., 237 Gharib, Hla, 190, 200 Gyura, László, 893, 917
Kulcsár, Gyula, 747 Kulcsárné-Forrai, Mónika, 747 Kumar, Baibhaw, 472 Kundrák, János, 648
H Hajdu, Sándor, 793 Hári, László Róbert, 799, 812 Hasan, Mustafa M., 302 Heged˝us, György, 151 Hirohata, Mikihito, 904 Hnilica, Martin, 933 Hodúlová, Erika, 933, 946, 957 Horváth, Csongor, 493 Hriczó, Krisztián, 302, 412 Huri, Dávid, 549 Hyoma, Kengo, 904
L Lakatos, Ákos, 346 Lakatos, Istvan, 35, 71 Lénárt, József, 568 Lu, Hangyu, 111 Lu, Jianwei, 111 Lukács, János, 995, 1006
I Ildiko, Száva Renata, 165 Inose, Koutarou, 904 Ioan, Száva, 165 J Jalalova, Pusta, 604 Jalghaf, Humam Kareem, 374 Jálics, Károly, 848 Janˇco, Roland, 925 Jármai, Károly, 709, 756, 768 Jónás, Szabolcs, 558 K Kafi, Abdallah, 917 Kállai, Imre, 549 Kamondi, László, 179 Kania, Malte, 444 Karpuschewski, Bernhard, 648 Katona, Mihály, 126 Kertész, József, 139 Kiss, Judit T., 330 Kiss, Péter, 126 Kocsis, Dénes, 330 Kocsis, Imre, 421 Kok, S., 237 Kondás, Béla, 505 Könecke, Björn, 444 Kovaˇcócy, Pavel, 933, 946, 957 Kovács, Endre, 357 Kovács, György, 190, 200, 720, 734 Kovács, Judit, 995 Kovács, Péter Zoltán, 558 Kovács, Tünde Anna, 139, 917 Kovács, Zoltán Péter, 505 Kovaˇríková, Ingrid, 933, 946, 957 Kriston, Balázs J., 848
M Makó, Dávid, 50 Mankovits, Tamás, 549 Marada, Imre, 223 Maros, Maria Berkes, 604, 620 Maros, Zsolt, 648 Martinkoviˇc, Maroš, 946, 957 Mašek, Václav, 248 Matsumoto, Naoyuki, 904 Mehdiyev, Ziya, 654 Meilinger, Ákos, 981 Mihály, Krisztián, 747 Muhssen, Hassan Sadah, 268 Muzaffer, Shazia, 1022 N Nagy, András Lajos, 575 Nagy, Jozsef, 71 Nagy, Szilárd, 756 Nemes, Dániel, 421, 793 Németh, Zoltán, 521 O Orosz, Tamás, 126 Orvai, Róbert, 291 P Pál, Gálfi Botond, 165 Pálinkás, Sándor, 157 Pesthy, Márk, 861 Petrik, Máté, 339 Porkolab, Laszlo, 35 R Rajab, Yaman, 694 Répás, József, 84 Rohde-Brandenburger, Jan, 861 Rónai, László, 824 S Sahib, Mortda Mohammed, 720, 734 Sahul, Miroslav, 933, 946, 957 Sakai, Natsumi, 904 Sarka, Ferenc, 212
Author Index Sas, Illés, 1006 Schramkó, Márton, 917 Shehab, Mohammed A., 521 Šimeková, Beáta, 933, 946, 957 Siménfalvi, Zoltán, 483 Sisodia, Raghawendra P. S., 893 Skriko, Tuomas, 966 Soltész, László, 636 ˇ Šooš, Lubomír, 835 Sorin, Vlase, 165 Stepan, Gabor, 111 Szabó, Ferenc János, 322 Szabó, József Zoltán, 871 Szabó, Kristóf, 582 Szamosi, Zoltán, 472 Szávai, Szabolcs, 720, 734 Szente, József, 103 Szepesi, Gábor L., 339, 472, 483 T Takacs, Agnes, 63 Takács, Richárd, 575, 861 Telek, Péter, 397 Tóth, Sándor Gerg˝o, 592
1039 Tóth-Nagy, Csaba, 861 Trohak, Attila, 11 Tugyi, Levente, 483 Tüske, István, 151 V Vámosi, Attila, 421 Varga, Gyula, 538 Várkuli, Miklós Gábor, 103 Vásárhelyi, József, 24 Vasile, Gheorghe, 165 Venczel, Tamás Bence, 412 W Wang, Zhen-Ming, 1022 Welzel, Florian, 648 Wilke, D. N., 237 Z Žiaran, Stanislav, 835 Zöldy, Máté, 268 Zoltán, Kovács Péter, 654 Zsoldos, Ibolya, 575 Zulkifli, Rozli, 374