Technology Innovation in Mechanical Engineering: Select Proceedings of TIME 2021 (Lecture Notes in Mechanical Engineering) 9811679088, 9789811679087
This book comprises select papers presented at the conference on Technology Innovation in Mechanical Engineering (TIME-2
128 66 28MB
English Pages 1070 [1028] Year 2022
Contents
About the Editors
Performance Enrichment of CI Engine Fueled with TiO2 Additive Blended Biodiesel Through Air Nanobubbles
1 Introduction
2 Material, Equipment, and Test Procedure
2.1 Hone Biodiesel Preparation
2.2 ANBs Production
2.3 Experimental Setup and Test Procedure
2.4 Error Analysis
3 Outcomes and Discussion
3.1 Brake-Specific Fuel Consumption (BSFC)
3.2 Brake Thermal Efficiency (BTE)
3.3 Hydrocarbon (HC) Exhaust
3.4 CO Emission
3.5 NOx Emission
4 Conclusion
References
Design and Optimization of NACA 0012, NACA 4412 and NACA 23,012 Aerofoils of Wind Turbine of Solar Updraft Tower Power Plant
1 Introduction
2 Methodology
2.1 Schmitz Model
2.2 Blade Element Momentum (BEM) Theory
3 Results and Discussion
3.1 Optimization of Angle of Attack (Α) and Number of Blades (N)
3.2 Estimation of Wind Flow Angle (Ø) and Blade Pitch Angle (Β) and Chord Length Distribution (C) of Blade at Each Segment
4 Conclusions
References
Experimental Analysis on Material Removal Modes and Mechanisms in Electrochemical Discharge Machining Process for Optical Glass
1 Introduction
2 Modes of Material Removal (MR) in ECDM
2.1 Studies on Gas Films in ECDM
3 Experimental Studies on Optical Glass
4 Tool Wear Studies
5 FESEM Studies on Tool Wear
6 Conclusions
References
Formulation of Empirical Correlation for Heat Transfer Coefficient, for Gases, in Terms of Fluid Properties, Tube Diameter and Mass Velocity; for Forced Convection Through Tubes
1 Introduction
2 Effect of Temperature on Coefficient of Heat Transfer for Helium–Argon Mixtures
3 Effect of Gas Properties
4 Heat Transfer Coefficients in Tubes Upto 5 mm
5 Heat Transfer Coefficients in Tube Diameters Above 5.0 mm
6 Discussion
7 Conclusions
References
Cost Analysis of PV–Wind Hybrid Energy System
1 Introduction
2 Modeling
2.1 System Architecture
2.2 Photovoltaic Details and Specification
2.3 Wind Turbine Detail and Specification
2.4 Battery Detail and Specifications
2.5 Converter Specifications
3 Result and Conclusion
3.1 Net Present Cost (NPC)
3.2 Cost Summary
3.3 Electrical Summary
3.4 Renewable Summary
3.5 Conclusion
References
A Review Paper: Study of Various Renewable Resources Polymer and Different Types of Nanocomposite Materials
1 Introduction
2 Literature Review
3 Conclusions
References
Self-directed Robot for Car Driving Using Genetic Algorithm
1 Introduction
2 Literature Survey
3 Proposed Work
4 Result and Discussion
5 Conclusion and Future Scope
References
Dynamic Analysis of Psychoacoustic Parameters to Evaluate Sound Quality of an Indian String Instrument Sitar
1 Introduction
2 Theoretical Background
2.1 Critical Band Rate
2.2 Loudness
2.3 Sharpness
3 Experimental Setup
4 Results and Discussion
4.1 Loudness
4.2 Sharpness
5 Conclusions
References
How Can Machine Tool Parameters Influence Tool Life and Wear Characteristics? An Experiment Design Approach
1 Introduction
2 Methods
2.1 Tool Life Calculation
2.2 Response Surface Methodology
3 Results
4 Conclusions
References
Experimental Evaluation of EDM Performance on EN8 Steel Using Taguchi Technique
1 Introduction
2 Literature Review
3 Methodology
4 Experimental Work
4.1 Work Piece Material
4.2 Tool Materials
4.3 Design Matrix and Observation Table
5 Results and Discussions
5.1 S/N Calculation Ratio
5.2 Influence on MRR
5.3 Influence on TWR
6 Conclusions
References
Design and Simulation of Smart Multipurpose Autonomous Ground Vehicle for Industrial Application
1 Introduction
2 Methodology
3 Hardware and Software
3.1 SLAM
3.2 Path-Planning
4 Implementation
5 Robot Modeling and Kinematics
5.1 CAD Modeling
5.2 Static Stress Analysis of the Robot
5.3 Kinematic Analysis of the Robot
6 Results and Discussions
7 Conclusion
References
Misfire Prediction on Spark Ignition Four-Stroke Engine Through Statistical Features Using Rough Set Theory Classifier
1 Introduction
2 Experimental Studies
3 Statistical Feature Extraction
4 Feature Selection Via J48
5 Rough Set Theory (RST)
6 Results and Discussion
7 Conclusion
References
Increasing the Wind Energy Production by Identifying the State of Wind Turbine Blade
1 Introduction
2 Experimental Studies
3 Statistical Analysis for Feature Extraction
4 Feature Selection Via J48
5 Fuzzy-Unordered Rule-Induction Algorithm (FURIA)
6 Results and Discussion
7 Conclusion
References
Characterization of Thin Film Over Vertical Fluted Tube: An Experimental Approach
1 Introduction
2 Setup Description
3 Result and Discussion
4 Conclusion
References
Analysis of Low-Power Cache Memory Design for Single Bit Architecture
1 Introduction
2 Literature Review
3 Cache Memory Design for Single Bit Architecture
3.1 Circuit of Write Driver
3.2 Six Transistor Static Random Access Memory Cell
3.3 Voltage Latch Sense Amplifier
4 Result Analysis
5 Conclusion
References
Cache Memory Design Analysis for Single Bit Architecture for Core Processor
1 Introduction
1.1 Power Reduction Techniques
2 Cache Memory Design for Single Bit Architecture
2.1 Circuit of Write Driver
2.2 Six Transistor Static Random Access Memory Cell
2.3 Differential and Latch Sense Amplifier’s
3 Results Analysis
4 Conclusion
References
A Review Paper: Breif Discussion on Power Generation by the Use of Various Technologies from Bio-renewable Resources
1 Introduction
2 Literature Review
3 Conclusion
References
Design of a Stand-Alone PV Powered Greenhouse Equipped with Distributed Evaporative Cooling
1 Introduction
2 Sizing of Cooling System Components
2.1 Cooling Pad
2.2 Pump and Sump
2.3 Fan
3 Sizing of Power System Components
3.1 Sizing of PV Array
3.2 Sizing of Battery Bank
3.3 Sizing of Inverter
3.4 Sizing of Charge Controller
4 Results and Discussions
5 Conclusion
References
Development of a Regression Model Through Variational Mode Decomposition for the Remaining Useful Life Assessment of a Gear Box
1 Introduction
2 Experimental Studies
3 Pre-processing Using VMD
4 Feature Selection via J48
5 Feature Selection
6 Multiple Regression
7 Results and Discussion
8 Conclusion
References
Influence of Successive Annealing on Mechanical and Wear Behavior of RCS-Processed Al2024 Alloy
1 Introduction
2 Experimental Study
2.1 Materials
2.2 RCS Processing and Successive Annealing
2.3 Characterization
3 Results and Discussions
3.1 Tensile Strength
3.2 Fractography
3.3 Wear Characteristics
3.4 Worn Surface Morphology
4 Conclusion
References
Experimental Investigation on Mechanical Properties of Sisal Fiber Reinforced Composite for Retrofitting Applications
1 Introduction
2 Experimentation
2.1 Sisal Fiber
2.2 Hardener and Resin Used
2.3 Chemical Treatment
2.4 Composite Fabrication
2.5 Characterization
3 Results and Discussions
3.1 Density
3.2 Water Absorption Test
3.3 Tensile Test
3.4 Flexural Test
3.5 Hardness Test
3.6 Morphological Analysis
4 Conclusions
References
A Low-Cost Selective Catalytic Reduction System for Diesel Engine Oxides of Nitrogen Control
1 Introduction
2 Materials and Methods
2.1 Preparation of Urea Solution
2.2 Decomposition of Urea
3 Experimental Methods and Testing
4 Results and Discussion
5 Conclusion
References
Performance Enhancement of Jatropha Methyl Ester by Utilizing Oxygen Enrichment in Diesel Engine
1 Introduction
2 Materials and Methods
2.1 Transesterification Process of Jatropha Oil
3 Experimental Setup and Its Procedure
4 Results and Discussions
4.1 Brake Specific Fuel Consumption
4.2 Brake Thermal Efficiency
4.3 Carbon Monoxide
4.4 Smoke Opacity
4.5 NOx Emissions
4.6 Hydrocarbon Emissions
5 Conclusion
References
Experimental Study on Utilization of Karanja Bio Oil in Diesel Engines and Performance Enhancement by Oxygenated Additives
1 Introduction
2 Oil Preparation and Experimental Procedure
2.1 Transesterification Process of Karanja Oil
3 Experimental Setup and Its Procedure
4 Results and Discussion
4.1 Specific Fuel Consumption
4.2 Brake Thermal Efficiency
4.3 Carbon Monoxide
4.4 Smoke Opacity
4.5 NOx Emissions
5 Conclusions
References
Experimental Assessment on Performance and Emission Characteristics of Calophyllum inophyllum (Tamanu) Seed Oil in Direct Injection Diesel Engines
1 Introduction
2 Materials and Approaches
3 Procedure of the Experiment
4 Results and Discussion
5 Conclusions
References
Investigating Outdoor Heat Stress Using Environmental Parameters and Selected Thermal Indices in Northern India
1 Introduction
2 Methodology
2.1 Considered Heat Stress Indices
3 Results and Discussions
3.1 Environmental Variables
3.2 Heat Stress Indices
3.3 Scatterplots and Correlation Analysis
4 Conclusion
References
Design and Reliability Study on Fixture for Normal and Underwater Friction Stir Welding
1 Introduction
2 Design Considerations
3 Development of the Fixture
3.1 Base Plate
3.2 Clamping Plate
3.3 Side Cover Plate
4 Feasibility and Reliability Testing of the Fixture
5 Conclusion
References
Parameters Affecting Design of Wind Turbine Blade—A Review
1 Introduction
2 Forces on Blade
3 Factors Affecting Blade Design
3.1 Tip Speed Ratio
3.2 Solidity
3.3 Angle of Twist of Blade
3.4 Loads on Blade
4 Optimization on Blade
4.1 Flaps on Blade
4.2 Vortex Generator
5 Conclusion
References
Comparative Study of Tensile Behavior Between Epoxy/Coir Fiber and Modified Epoxy/Coir Fiber Composite
1 Introduction
2 Materials and Methodology
2.1 Fabrication of Composite
3 Results and Discussions
3.1 Tensile Test
3.2 Block Copolymer
3.3 FTIR Spectroscopy Analysis
4 Conclusions
References
Validating Analytical and Numerical Predictions of Hydrodynamic Characteristics in Microchannel with Experimental Results
1 Introduction
2 Analytical Method
2.1 Darcy–Weisbach
2.2 Kandlikar Correlations
2.3 Fully Developed Laminar Flow
2.4 Fully Developed Turbulent Flow
3 Numerical Simulations
4 Results and Discussion
4.1 Axial Distance Versus Pressure Drop
4.2 Reynolds Number Versus Pressure Drop
4.3 Deviation Analysis
4.4 Sensitivity Analysis
5 Conclusion
References
Understanding the Logistics Services of Mumbai Dabbawallahs and Discussing the Factors Behind Its Success
1 Introduction
2 Understanding Logistics Services of Mumbai Dabbawallah’s
3 Literature Review
3.1 Organization: A Windup Design
3.2 A Regulatory Mechanism
3.3 Management
3.4 Process: Simplicity, Flexibility, and Rigor
3.5 Simple Codes
3.6 Buffer Capacity
3.7 Demanding Obedience to Processes and Standards
3.8 A Self-reinforcing System
4 Factors Behind Mumbai Dabbawallahs Success
4.1 Solution of Huge Social Problems and Customer Needs
4.2 Customer Support
4.3 Working Structure of Mumbai Dabbawallah
4.4 Standards and Quality Practices that Dabbawallahs Follows
5 Discussion
6 Conclusion
References
Experimental Investigation of Rail IRSM 41-97 Steel GTAW and GMAW Weldments Using ER70S-6 Filler
1 Introduction
2 Materials and Method
3 Result and Summary
3.1 Vickers Micro Hardness Survey
3.2 Macro Structure
3.3 Micro Structure
4 Conclusion
References
Sensorless Control of Reboost Converter for Grid-Connected WECS
1 Introduction
2 Block Diagram
3 Proposed Circuit Diagram
4 Simulation Results
4.1 Simulation Design
4.2 PIC Controller Unit
5 Hardware Kit
5.1 Reboost Converter
6 Conclusion
References
Intelligent Securing the Industrial IoT Data Based on Consensus Mechanism
1 Introduction
2 Related Work
3 Existing System
3.1 Disadvantages of the Existing System
4 Proposed System
4.1 Advantages of the Proposed System
5 Existing System
5.1 Disadvantages of the Existing System
6 Conclusion
References
Object Detection in Railway Track Using Industrial IoT (IIoT)
1 Introduction
2 Related Work
3 Proposed System
4 Results and Discussion
5 Conclusion
References
Construction of Hele Shaw Apparatus for Subsonic Flow Visualization
1 Introduction
2 Design of the Hele Shaw Apparatus
2.1 Design of Fluid Container Tank
2.2 Design of Structural Frame
2.3 Specifications of Glass Plate
3 Working of the Instrument
4 Conclusion
References
Impact of Acoustics Impingement on Proliferating Fires
1 Introduction
2 Experimental Setup and Solution Methodology
3 Results and Discussion
4 Conclusion
References
Kinematics and Dynamics Analysis of 5DOF 360 Degree Machining Robot
1 Introduction
2 Methodology
3 Design
3.1 Design in SOLIDWORKS
3.2 Modelling in RoboAnalyzer
4 Robot Kinematics
4.1 Forward Kinematics
4.2 Denavit-Hartenberg (DH) Parameters
4.3 DH Parameters Transformation Matrix
4.4 Inverse Kinematics
5 Workspace (ROM)
6 Trajectory
7 Static Torque Analysis
7.1 Calculation of Torque:
8 Results and Discussion
9 Conclusion
References
Effects of Geometry on the Stress Concentration Factor of an Isotropic Rectangular Plate with Central Elliptical Hole
1 Introduction
2 Physical Model and SCF
2.1 Validation of the Process
3 Methodology
3.1 Geometry
3.2 Meshing
3.3 Loads and Constraints
4 Results and Discussion
4.1 Derivation of the Formula
5 Conclusions
References
Thermodynamic Investigations of a Turbocharged Homogeneous Charge Compression Ignition (HCCI) Engine Running on Wet Ethanol
1 Introduction
2 Description of the Proposed Work
3 Thermodynamic Modelling
4 Result and Discussions
5 Conclusion
References
Effects of Thermally Induced Deformations and Surface Radiosity for 3D Heat Transfer and Its Applications
1 Introduction
2 Literature Survey
3 Research Methodology
4 Design and Simulation
5 Conclusion
References
Optimization of Cutting Zone Temperature in Machining of Magnesium Alloy Using Taguchi Method
1 Introduction
2 Methodology
3 Result and Discussion
3.1 Main Effects Plot
3.2 Response Table and ANOVA for Temperature
4 Regression Analysis
5 Conclusion
References
Design and Development of Smart Multipurpose Automated Guided Vehicle Implemented with SLAM and AMCL
1 Introduction
2 Methodology
2.1 Amcl and Hector Slam
3 Modeling and Analysis
3.1 CAD Model
3.2 Kinematic Modeling
4 Results and Discussion
5 Conclusion
References
Fully Automated Cricket Bowling Machine
1 Introduction
2 Proposed Model of the FAC-BM
2.1 Block Diagram
2.2 Flowchart
3 FAC-BM Design
4 Gear Design Calculation
5 Structural Simulation
6 Trajectory Simulation
7 Future Work
8 Conclusion
References
An Application of Computational Intelligence Techniques to Predict Biometal Deposition Characteristics in Metal Additive Manufacturing
1 Introduction
2 Proposed Adaptive Neural-Fuzzy-Based Computational Intelligence Techniques
2.1 Fuzzy and Adaptive Neural-Fuzzy System
2.2 Supervised Learning of the Proposed System
3 Results and Discussion
4 Concluding Remarks
References
Ranking of Critical Risk Factors in the Indian Automotive Supply Chain Using TOPSIS with Entropy Weighted Criterions
1 Introduction
2 Risk Factors Identified Through Literature Review and Industry Survey
3 Impact Ratings Obtained from Industry Experts
4 CRFs Ranking Using TOPSIS
4.1 Tabulation of Average Impact Ratings (AIR)
4.2 Normalization of Average Impact Ratings
4.3 Formulation of Weighted Normalization of Impact Ratings
4.4 Computation of Distance from Ideal and Unideal Solutions
4.5 Closeness Calculations Using Ratios Based on Computed Distances.
5 Conclusion
References
A Review on the Performance of Earth Air Heat Exchanger Coupled with Other Systems
1 Introduction
2 Literature Review on Coupled/Special Systems
2.1 Combination of EAHE, Trombe Wall, and Green Wall
2.2 Use of Coaxial Tubes
2.3 EAHE with Buoyancy-Driven Natural Ventilation System
2.4 EAHE with Water Jacket on the Outlet Pipe
2.5 EAHE with Air-to-Air Heat Exchanger
2.6 EAHE Controlled by “Internet of Things”
2.7 EAHE with Insulated Walls of the Building
2.8 EAHE with Heat Pipes
2.9 EAHE with Photovoltaic Cell
2.10 EAHE with Evaporative Cooler
2.11 EAHE Use with AHU of HVAC
3 Discussion
4 Conclusion
References
Beacon-Based Smart Shopping System Using IoT
1 Introduction
2 Related Works
3 Existing System
4 Proposed System
5 Modules
5.1 Location of Every Component
5.2 Data Collection Component
5.3 Data Filtering and Analyzing Component
5.4 Data Mining Component and Location-Based Suggestions
6 Comparison of the Radio Frequency (RF-Based) Technologies
7 Conclusion
References
An Modern Approach to Detect Person Wearing Mask Using Deep Learning
1 Introduction
2 Related Works
3 Existing System
4 Proposed System
5 Modules
5.1 Streaming Module
5.2 Face Detection
5.3 Human and Mask Detection
5.4 Distance Calculation
6 Result and Discussion
7 Conclusion
References
Machine Learning Based Predict Plant Growth and Yield in Greenhouse Environments
1 Introduction
2 Related Work
3 Existing System
4 Proposed System
5 Modules Description
5.1 Dataset Preprocessing
5.2 Data Mining for Crop Prediction
5.3 Association Rule
5.4 Crop Diseases and Pesticides
5.5 Data Visualization
6 Conclusion
References
Chatbot for Hospitality Service
1 Introduction
2 Related Work
3 Existing System
3.1 Disadvantages of the Existing System
4 Proposed System
4.1 Advantages of the Proposed System
5 Modules Description
5.1 User Login to System
5.2 Ask Some Questions
5.3 Age Based Medicine Dosage Details
5.4 Get Medicine Details on Medicine Name
6 Result and Discussion
7 Conclusion
References
Experimental Investigations of Process Variables on Wire Electrical Discharge Machining (WEDM) of AISI 52100 Steel
1 Introduction
2 Experimental Methodology
3 Result and Discussion
3.1 Model for Cutting Speed (CS)
4 Conclusions
References
Subsea Manifold with Mudmat Structure Design Evaluation Based on Performance of Stress Analysis
1 Introduction
2 Design Criteria
3 Manifold Structural Analysis
4 Result and Discussion
5 Conclusion
References
Fabrication and Experimental Investigation of Aluminum LM 25/h-BN/B4C Hybrid Composites for Automobile Applications
1 Introduction
2 Material and Manufacturing of Composites
2.1 Material
2.2 Manufacturing Method for Preparing Al-Based Hybrid Composite Samples
3 Preparation of Samples
4 Testing of Composites
5 Results and Discussion
5.1 Micro Structural Analysis
5.2 Hardness Test Analysis
5.3 Yield Test Analysis
5.4 Ultimate Tensile Strength Analysis
5.5 Percentage of Elongation (% OF E) Analysis
6 Conclusions
References
Productivity Improvement in a Manufacturing Industry by Using Man–Machine Chart Analysis
1 Introduction
2 Time Study
3 Identification of Gap and Problem Solving
4 Conclusion
References
Joint Impact of Carbon Emission and Partial Substitution on Inventory Model of Two Substitutable Products with Cost of Substitution
1 Introduction
2 Assumptions and Notations
2.1 Assumptions
2.2 Notations
3 Model Formulation
4 Solution Algorithm
5 Numerical Illustration and Sensitivity Analysis
6 Conclusions
References
Mechanical Characterization of Glass Fiber Metal Laminate
1 Introduction
2 Material
2.1 Core Material
2.2 Fiber Laminate
3 Methodology
4 Result and Discussion
4.1 Impact Testing
4.2 Flexure Testing
4.3 Tensile Testing
5 Conclusion
References
Herbivicus: A Full Stack Website with Chatbot and Google API
1 Introduction
1.1 Front-End Development
2 JavaScript
3 Bootstrap
4 Related Work
5 Proposed System
6 Results
7 Conclusion
References
Design of a Low-Speed Smoke Visualization Wind Tunnel
1 Introduction
2 Anatomy of a Wind Tunnel
2.1 Test Section Calculation
2.2 Contraction Section
2.3 Settling Chamber
2.4 Exit Diffuser Section
2.5 Driver System
3 Section Loss Coefficients and Pressure Loss
4 Power Required
5 Smoke Visualization Techniques
5.1 Development of Smoke Generating System
6 Conclusion
Appendix: Wind Tunnel with Stand
References
Comparison of Effects of Cross Sections of Twisted Inserts in a Concentric Tube Heat Exchanger
1 Introduction
2 Methodology
2.1 Creating a Model of the Concentric Tubes
2.2 Creating Boundary Conditions for the System
2.3 Input Variables
2.4 Running the Simulation
2.5 Simulation Giving Results
2.6 Results Making Realistic Sense
2.7 Accept and Generate Report
3 Mathematical Model
3.1 Heat Transfer Rate of Hot Fluid
3.2 Heat Transfer Rate of Cold Fluid
3.3 Logarithmic Mean Temperature Difference
3.4 Area of Interaction (A)
3.5 Number of Transfer Units (NTU)
3.6 Effectiveness (ε)
4 Results and Discussions
5 Conclusion
References
Experimental Investigation and Machinability Study of Ni–Cr-Based Super Alloy Using Powder Mixed EDM
1 Introduction
1.1 Electrical Discharge Machining
1.2 Need for Powder Mixed Electrical Discharge Machining (PMEDM)
2 Powder Mixed EDM
2.1 Mechanism of PMEDM
2.2 Experimental Setup PMEDM
3 Experimental Investigation for PMEDM
3.1 Design of Experiment
3.2 Material Selection
3.3 Experimentation for PMEDM
3.4 Analysis of Variance (ANOVA)
4 Conclusion
References
Performance Analysis of Single Cylinder Four-Stroke Diesel Engine
1 Introduction
2 Literature Review
3 Experimental Setup and Methodology
3.1 Specification of the Diesel Engine
3.2 Utilities Required
4 Result and Discussion
5 Conclusion
References
Kinematic Study of Collaborative Robot
1 Introduction
2 Methodology
3 Kinematic Modelling
3.1 Forward Kinematics
4 Results and Discussions
5 Conclusion
References
Novel Hybrid Bio Composite PLA Filaments Reinforced with Bio Fillers for 3D Printing
1 Introduction
2 Materials Used and Methods
2.1 Materials Selected
2.2 Preparation of New Composite Filaments
3 Testing of PLA Bio Filler Reinforced Filaments
4 Result and Discussion
4.1 Hardness
4.2 Density
4.3 Melting Point
4.4 Tensile Strength
4.5 Elongation
5 Conclusion
References
Study of Aerodynamic and Aerothermal Characteristics of Blunted Power Law Bodies
1 Introduction
1.1 Power Law Leading Edges
2 Computational Method and Procedure
3 Computational Results and Discussion
4 Conclusion
References
Optimizing Thermal Comfort for Office Room Using CFD Analysis
1 Introduction
2 Literature Review
3 Objective
4 Methodology
4.1 Computational Fluid Dynamics (CFD) Analysis
4.2 Algorithm Used for CFD Analysis
5 Result and Discussion
6 Conclusion
References
Exploring Classification Models for COVID-19 Novel Coronavirus Disease
1 Introduction
1.1 Paper Organization
2 Related Works
3 Model Description and Methodology
4 Conclusion, Discussion and Future Scope
References
Smart E-waste Tracking and Monitoring Model: A Modern Approach to Counter E-waste Management Issues
1 Introduction
2 Related Works
3 WEEE Management Issues
3.1 Involvement of Informal Sectors
3.2 Insufficient Implementation of EPR
4 Illegal Trade of E-waste
5 The Need to Protect the Integrity of E-waste for Data Protection
5.1 Data Stored on End-Of-Life Devices
5.2 E-waste Being Recycled by Vulnerable Groups
5.3 Unauthorized Access to Storage Devices
6 Proposed Smart E-waste Tracking and Monitoring Model
6.1 Tracking of WEEE
6.2 Monitoring of E-waste
7 Conclusion and Future Directions
References
An Introduction of Water Desalination Exploiting the Waste Heat and Other Different Renewable Source of Energy
1 Introduction
2 Water Desalination
3 Conclusion
References
Analysis of Electromagnetic Aircraft Launching System for Naval Aircraft
1 Introduction
1.1 Overview of Indian Naval Aircraft Launching Technology
1.2 Overview of STOBAR Technology
1.3 Overview of CATOBAR Technology
2 Design and Review of EMALS System
3 Modeling of EMALS System
4 Results and Discussion
5 Conclusion
References
Second-Order Filter for Improving the Performance of the Multi-level 3 Phase Inverter Using SPWM
1 Introduction
2 Contribution of Work
3 SPWM Inverter
4 Review of Multilevel Inverters
5 Evaluations of Multilevel Inverter
6 Conclusions
References
Numerical Investigation of Product Capability and Enhancement Through Multi-hole Extrusion Process
1 Introduction
2 Numerical Procedure
2.1 Material Models
3 Results and Discussion
3.1 Numerical Investigation
4 Conclusions
References
Automotive Advanced Toggle Transmission System Using Dynamo and Dual-Clutch Mechanism
1 Introduction
2 Objective
3 Modeling of the System
3.1 Dynamo Acts as Anti-Break Model and also Controls the RPM
3.2 Toggle Switches and Dual-Clutch Transmission
4 Designing of the System
4.1 Processing of the Transmission
5 Conclusion
6 Future Scope
References
Al-Zn-Mn Nanocomposite Sintering by Mechanical Alloying and Characterization with the Help of SEM and XRD
1 Introduction
2 Experimental Procedure
2.1 Results and Discussion
3 Phase Analysis of Powder Material
3.1 SEM and EDS Results
4 Conclusions
References
Effect of Trace Elements on Hardness and Impact Characteristics of Carbon Steel
1 Introduction
2 Methodology and Materials
2.1 Selection of Samples
2.2 Test Specimen Preparation
3 Mechanical Tests
3.1 Hardness Test
3.2 Impact Strength
4 Results and Discussion
4.1 Hardness
4.2 Impact Strength
5 Conclusions
References
Flexible Manufacturing System Scheduling Through Branch and Bound Algorithm
1 Introduction
2 FMS Narrative
2.1 Purpose of the Study
3 Simultaneous Scheduling with B&B
3.1 Branch and Bound Algorithm
4 Route Plan for Vehicles
5 Execution of Branch and Bound Algorithm
6 Result and Discussion
7 Final Remarks
References
Implementation of Jatinder N. D. Gupta Algorithm for FMS Scheduling Problems
1 Introduction
2 FMS Narrative
2.1 Purpose of the Study
3 Simultaneous Scheduling—J N D Gupta
3.1 Jatinder N. D. Gupta
4 Route Plan of Vehicles
5 Execution of J N. D. Gupta
6 Result and Discussion
7 Final Remarks
References
Flexible Manufacturing System Scheduling with Relative Importance of a Work Item in a Workflow
1 Introduction
2 FMS Narrative
2.1 Purpose of the Study
3 Simultaneous Scheduling
3.1 First Come First Serve
3.2 Shortest Processing Time
3.3 Longest Processing Time
4 Route Plan for Vehicles
5 Execution of Priority Rules
5.1 Simultaneous Scheduling—First Come First Serve
5.2 Simultaneous Scheduling—Shortest Processing Time
5.3 Simultaneous Scheduling—Longest Processing Time
6 Result and Discussion
7 Final Remarks
References
Investigation of Combustion and Performance Characteristics of Waste Plastic Oil
1 Introduction
2 Materials and Methods
2.1 Fed Materials and Properties
2.2 Engine Test Procedure
2.3 Uncertainty of Experiment
3 Results and Discussion
3.1 Engine Torque
3.2 Brake Mean Effective Pressure
3.3 Volumetric Efficiency
3.4 Peak Cylinder Pressure
3.5 Smoke Emission
3.6 NO Emissions
4 Conclusion
References
Implementation of Campbell, Dudek, Smith Algorithm in Flexible Manufacturing System with Mean Tardiness
1 Introduction
2 FMS Narrative
2.1 Purpose of the Study
3 Scheduling with Campbell Dudek Smith
3.1 CDS (Campbell Dudek Smith) Heuristic Algorithm
4 Route Plan of Vehicles
5 Execution of Campbell Dudek Smith
6 Result and Discussion
7 Final Remarks
References
AGVs and Machines Scheduling with Campbell, Dudek, Smith Algorithm
1 Introduction
2 FMS Narrative
2.1 Purpose of the Study
3 Scheduling in FMS
3.1 CDS (Campbell Dudek Smith) Heuristic Algorithm
4 Route Plan of Vehicle
5 Execution of Campbell, Dudek, Smith
6 Result and Discussion
7 Final Remarks
References
Implementation of Branch and Bound Algorithm in FMS with Mean Tardiness
1 Introduction
2 FMS Narrative
2.1 Purpose of the Study
3 Simultaneous Scheduling with B & B
3.1 Branch & Bound Algorithm
4 Route Plan of Vehicle
5 Execution of B & B
6 Result and Discussion
7 Final Remarks
References
A Contemporary Assessment on the Development of Automated Guided Vehicle to the Current Trends and Requirements
1 Introduction
2 Types of AGVs
3 Navigation Technologies of AGVs
4 Advantages and Advancement in AGV Technology
5 Applications of AGV
6 Conclusion
References
Experimental Investigation of Performance and Emission Characteristics of Direct-Injection Compression-Ignition Engine Fuelled with Pond Water Algae Biodiesel
1 Introduction
2 Material and Method
2.1 Material
2.2 Engine Setup and Facilities
2.3 Error Analysis
3 Results and Discussion
3.1 Performance Investigation
3.2 Emission Analysis
4 Conclusions
References
ACO-Based Resource Allocation Hybrid Algorithm for Cloud Environment
1 Introduction
1.1 About Cloud Computing
1.2 The SPI Framework for Cloud Computing
1.3 Motivation of the Present Work
1.4 Historical Development of Ant Colony Optimization
2 Literature Survey
2.1 Job Scheduling in Grids
2.2 Job Scheduling Algorithm in Cloud Environment
3 Proposed Job Scheduling Algorithm in Cloud Computing
3.1 System Architecture
4 Implementation and Experimental Results
4.1 Implementation
4.2 Modules and Its Implementation
4.3 Simulation and Testing:
5 Conclusion
References
Operational Control Decisions Through Random Rule in Flexible Manufacturing System
1 Introduction
2 FMS Configuration
2.1 Purpose of the Study
3 Simultaneous Scheduling
3.1 Random Sequence
3.2 Routing of AGVs
4 Random Rule Implementation
4.1 Simultaneous Scheduling—Random Rule
5 Result and Discussion
6 Final Remarks
References
Experimental Investigation on FMS Environment with Operational Completion Time
1 Introduction
2 FMS Configuration
2.1 Purpose of the Study
3 Simultaneous Scheduling
3.1 Random Sequence
3.2 Routing of AGVs
4 Implementation of Random Rule
4.1 Random Rule-Scheduling
5 Result and Discussions
6 Final Remarks
References
Design of Aerial Top Dresser
1 Introduction
2 Design Considerations
3 Aerodynamic Analysis
4 Results and Discussion
4.1 Deflection and Bending Stress Under Static Conditions
4.2 Deflection and Bending Stress Under Dynamic Conditions
5 Conclusions
References
Edge Irregularity Strength of Graphs Produced Utilizing M-Super Subdivision of Stars and Double Stars
1 Introduction
2 Super Subdivision Technique
2.1 Definition
2.2 Theorem 1
2.3 Theorem 2
3 Conclusions
References
Implementation of Priority Rules in Flexible Manufacturing System Scheduling with Mean Tardiness
1 Introduction
2 FMS Narration
2.1 Purpose of the Study
3 Scheduling with Priority Rules
3.1 First Come First Serve
3.2 Shortest Processing Time
3.3 Longest Processing Time
4 Route Plan of Vehicles
5 Execution of Priority Rules
5.1 Simultaneous Scheduling-First Come First Serve
5.2 Simultaneous Scheduling—Shortest Processing Time
5.3 Simultaneous Scheduling—Longest Processing Time
6 Result and Discussion
7 Final Remarks
References
Mean Tardiness with Heuristic in Intelligent Manufacturing System
1 Introduction
2 FMS Narrative
2.1 Purpose of the Study
3 Scheduling with D. S. Palmer
4 D. S. Palmer Heuristic Algorithm
5 Route Plan of Vehicle
6 Execution of D. S. Palmer
7 Result and Discussion
8 Final Remarks
References
Simultaneous Scheduling with J N D Gupta Heuristic Algorithm with Mean Tardiness
1 Introduction
2 FMS Narration
2.1 Purpose of the Study
3 Simultaneous Scheduling—J. N. D. Gupta.
3.1 J. N. D. Gupta Algorithm.
4 Route Plan of Vehicle
5 Execution of J. N. D. Gupta
6 Result and Discussion
7 Final Remarks
References
Flexible Manufacturing System Simultaneous Scheduling Through Palmer Heuristic Algorithm
1 Introduction
2 FMS Narrative
2.1 Purpose of the Study
3 Scheduling with D.S.Palmer
3.1 D.S. Palmer Heuristic Algorithm.
4 Route Plan of Vehicle
5 Execution of D.S. Palmer
6 Result and Discussion
7 Conclusion
References
Design and Optimization of Engine Block Using Gravity Analysis
1 Introduction
2 Literature Review
3 Methodology
4 Dynamic Analysis of Engine Block
4.1 Analysis Results for Engine Block Made of Stainless Steel Material
4.2 Optimization Results and Redesign
4.3 Cost Analysis
5 Results and Discussion
5.1 Comparison of Results of Old and New Designs of Engine Block
6 Conclusions
References
Performance of an Evaporative Condenser: A Review
1 Introduction
2 Passive Technique
2.1 Numerical Simulation
2.2 Software Simulation
3 Experimental Investigations in Evaporative Condenser as a Comparison
4 Conclusion
References
Analytical Study of Fluid Pressure-Sensing Mechanism in Microchannel for Microfluidic Device
1 Introduction
2 Design Methodology
2.1 Selection Factor for Microcantilever Pressure Analysis
2.2 Simulation Consideration
3 Mathematical Modeling
4 Result and Discussion
4.1 Impact of Velocity on Microcantilever Displacement
4.2 Impact of Pressure on Microcantilever Displacement
5 Conclusion
References
Recommend Papers
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Lecture Notes in Mechanical Engineering
Prem Kumar Chaurasiya Abhishek Singh Tikendra Nath Verma Upendra Rajak Editors
Technology Innovation in Mechanical Engineering Select Proceedings of TIME 2021
Lecture Notes in Mechanical Engineering Series Editors Francisco Cavas-Martínez , Departamento de Estructuras, Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia Francesca di Mare, Institute of Energy Technology, Ruhr-Universität Bochum, Bochum, Nordrhein-Westfalen, Germany Francesco Gherardini , Dipartimento di Ingegneria, Università di Modena e Reggio Emilia, Modena, Italy Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia Vitalii Ivanov, Department of Manufacturing Engineering, Machines and Tools, Sumy State University, Sumy, Ukraine 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
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More information about this series at https://link.springer.com/bookseries/11236
Prem Kumar Chaurasiya · Abhishek Singh · Tikendra Nath Verma · Upendra Rajak Editors
Technology Innovation in Mechanical Engineering Select Proceedings of TIME 2021
Editors Prem Kumar Chaurasiya Department of Mechanical Engineering Bansal Institute of Science and Technology Bhopal, Madhya Pradesh, India
Abhishek Singh Department of Mechanical Engineering National Institute of Technology Patna Patna, Bihar, India
Tikendra Nath Verma Department of Mechanical Engineering Maulana Azad National Institute of Technology Bhopal, Madhya Pradesh, India
Upendra Rajak Department of Mechanical Engineering Rajeev Gandhi Memorial College of Engineering Nandyala, Andhra Pradesh, India
ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-981-16-7908-7 ISBN 978-981-16-7909-4 (eBook) https://doi.org/10.1007/978-981-16-7909-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Contents
Performance Enrichment of CI Engine Fueled with TiO2 Additive Blended Biodiesel Through Air Nanobubbles . . . . . . . . . . . . . . . G. Senthilkumar, S. Lakshmi Sankar, and M. Purusothaman
1
Design and Optimization of NACA 0012, NACA 4412 and NACA 23,012 Aerofoils of Wind Turbine of Solar Updraft Tower Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ramakrishna Balijepalli, Upendra Rajak, Abhishek Dasore, Anshul Raj, and Prem Kumar Chaurasiya
9
Experimental Analysis on Material Removal Modes and Mechanisms in Electrochemical Discharge Machining Process for Optical Glass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. S. Jawalkar Formulation of Empirical Correlation for Heat Transfer Coefficient, for Gases, in Terms of Fluid Properties, Tube Diameter and Mass Velocity; for Forced Convection Through Tubes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Narendra J. Giradkar, Vivek M. Korde, and Jayant Giri Cost Analysis of PV–Wind Hybrid Energy System . . . . . . . . . . . . . . . . . . . Pankaj Tripathi, Shashank Dadhich, and Abhishek Kumar Gupta A Review Paper: Study of Various Renewable Resources Polymer and Different Types of Nanocomposite Materials . . . . . . . . . . . . Pankaj Sonkusare, Pankaj Agarwal, S. K. Dhakad, and Ravindra S. Rana Self-directed Robot for Car Driving Using Genetic Algorithm . . . . . . . . Harivansh Prasad Sharma, Manisha Pant, Reshu Agarwal, and Shylaja Vinaykumar Karatangi
19
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Dynamic Analysis of Psychoacoustic Parameters to Evaluate Sound Quality of an Indian String Instrument Sitar . . . . . . . . . . . . . . . . . Beena Limkar and Gautam Chandekar How Can Machine Tool Parameters Influence Tool Life and Wear Characteristics? An Experiment Design Approach . . . . . . . . . Balaji Krushna Potnuru, Vasishta Bhargava Nukala, Satya Prasad Maddula, A. C. Uma Maheshwara Rao, Praveen Ronad, P. Chinmaya Prasad, and Suresh Akella Experimental Evaluation of EDM Performance on EN8 Steel Using Taguchi Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Kishan, B. Sudheer Prem Kumar, S. Gajanana, and N. Sunil Naik Design and Simulation of Smart Multipurpose Autonomous Ground Vehicle for Industrial Application . . . . . . . . . . . . . . . . . . . . . . . . . . Gokula Vishnu Kirti Damodaran, J. B. Greesh Pranav, V. Siva Naga Yaswanth, Amartya Reddy Ponaka, and Joshuva Arockia Dhanraj Misfire Prediction on Spark Ignition Four-Stroke Engine Through Statistical Features Using Rough Set Theory Classifier . . . . . . Joshuva Arockia Dhanraj, Jenoris Muthiya Solomon, Mohankumar Subramaniam, Meenakshi Prabhakar, Christu Paul Ramaian, Nandakumar Selvaraju, and Nadanakumar Vinayagam Increasing the Wind Energy Production by Identifying the State of Wind Turbine Blade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Joshuva Arockia Dhanraj, Meenakshi Prabhakar, Christu Paul Ramaian, Mohankumar Subramaniam, Jenoris Muthiya Solomon, and Nadanakumar Vinayagam Characterization of Thin Film Over Vertical Fluted Tube: An Experimental Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rahul Deharkar, Anurag Mudgal, Kishan Patel, Joban Patel, and Bhavya Mehta
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91
101
115
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149
Analysis of Low-Power Cache Memory Design for Single Bit Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reeya Agrawal
161
Cache Memory Design Analysis for Single Bit Architecture for Core Processor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reeya Agrawal
171
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vii
A Review Paper: Breif Discussion on Power Generation by the Use of Various Technologies from Bio-renewable Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pankaj Sonkusare, S. K. Dhakad, Pankaj Agarwal, and Ravindra S. Rana
185
Design of a Stand-Alone PV Powered Greenhouse Equipped with Distributed Evaporative Cooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Debajit Misra
197
Development of a Regression Model Through Variational Mode Decomposition for the Remaining Useful Life Assessment of a Gear Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Joshuva Arockia Dhanraj, Christu Paul Ramaian, Jenoris Muthiya Solomon, Nandakumar Selvaraju, Mohankumar Subramaniam, and Meenakshi Prabhakar Influence of Successive Annealing on Mechanical and Wear Behavior of RCS-Processed Al2024 Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. J. Manjunath, H. P. Thirthaprasada, A. Chandrashekar, and M. C. Manjunath Experimental Investigation on Mechanical Properties of Sisal Fiber Reinforced Composite for Retrofitting Applications . . . . . . . . . . . . D. P. Archana, H. N. Jagannatha Reddy, R. Prabhakara, M. U. Aswath, and A. Chandrashekar A Low-Cost Selective Catalytic Reduction System for Diesel Engine Oxides of Nitrogen Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jenoris Muthiya Solomon, Mohankumar Subramaniam, Joshuva Arockia Dhanraj, Nadanakumar Vinayagam, Christu Paul Ramaian, Nandakumar Seelvaraju, and A. Ramana Johannes Bachmann Performance Enhancement of Jatropha Methyl Ester by Utilizing Oxygen Enrichment in Diesel Engine . . . . . . . . . . . . . . . . . . . . Mohankumar Subramaniam, Jenoris Muthiya Solomon, Joshuva Arockia Dhanraj, Christu Paul Ramaian, Nadanakumar Vinayagam, Nandakumar Selvaraju, and A. Ramana Johannes Bachmann Experimental Study on Utilization of Karanja Bio Oil in Diesel Engines and Performance Enhancement by Oxygenated Additives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohankumar Subramaniam, Jenoris Muthiya Solomon, Nadanakumar Vinayagam, Nandakumar Selvaraju, Joshuva Arockia Dhanraj, Christu Paul Ramaian, and A. V. Sivabalan
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Experimental Assessment on Performance and Emission Characteristics of Calophyllum inophyllum (Tamanu) Seed Oil in Direct Injection Diesel Engines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jenoris Muthiya Solomon, Mohankumar Subramaniam, C. Dinesh Kumar, Joshuva Arockia Dhanraj, Nadanakumar Vinayagam, Christu Paul Ramaian, and A. V. Sivabalan Investigating Outdoor Heat Stress Using Environmental Parameters and Selected Thermal Indices in Northern India . . . . . . . . . Milap Sharma, Narendra Mohan Suri, Suman Kant, and Abhishek Charak Design and Reliability Study on Fixture for Normal and Underwater Friction Stir Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Muthu Vaidyanathan, Mebratu Markos Woldegioris, N. Sivaraman, Mahaboob Patel, and Tsegaye Alemayehu Atiso Parameters Affecting Design of Wind Turbine Blade—A Review . . . . . . P. R. Mehta and R. V. Kale Comparative Study of Tensile Behavior Between Epoxy/Coir Fiber and Modified Epoxy/Coir Fiber Composite . . . . . . . . . . . . . . . . . . . . Animesh Sinha, Arindam Sinha, and Rajesh Kumar Validating Analytical and Numerical Predictions of Hydrodynamic Characteristics in Microchannel with Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shanmugam Mathiyazhagan and Lakshmi Sirisha Maganti Understanding the Logistics Services of Mumbai Dabbawallahs and Discussing the Factors Behind Its Success . . . . . . . . . . . . . . . . . . . . . . . Gaurav Kumar, Sagar Dagar, Shaikh Sadi, Naveen Kumar Bidhan, Ashutosh Kumar, Saqib Farooq Bhat, and M. S. Niranjan Experimental Investigation of Rail IRSM 41-97 Steel GTAW and GMAW Weldments Using ER70S-6 Filler . . . . . . . . . . . . . . . . . . . . . . . J. R. Deepak, V. K. Bupesh Raja, N. Joseph Amrish Lobo, and K. S. Deepak Kumaresh
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Sensorless Control of Reboost Converter for Grid-Connected WECS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Santhi Mary Antony and D. Godwin Immanuel
365
Intelligent Securing the Industrial IoT Data Based on Consensus Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Nagarajan, R. I. Minu, and T. Sasikala
373
Object Detection in Railway Track Using Industrial IoT (IIoT) . . . . . . . L. Sujihelen, Kota Vinodh Kumar, Madhav Srinivas, and G. Nagarajan
381
Contents
Construction of Hele Shaw Apparatus for Subsonic Flow Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akhila Rupesh, P. Vinaykumar Doddamani, P. Umeshkumar, Amaresh Wavare, and M. B. Mahanthesh Impact of Acoustics Impingement on Proliferating Fires . . . . . . . . . . . . . Bhushan Thombare, Saumya Shekhar, and Vinayak Malhotra Kinematics and Dynamics Analysis of 5DOF 360 Degree Machining Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mihir H. Amin, Monil M. Bhamare, Japagna N. Agnihotri, and Dipal M. Patel Effects of Geometry on the Stress Concentration Factor of an Isotropic Rectangular Plate with Central Elliptical Hole . . . . . . . . Prafull Agarwal, Dhruv Mathur, Manoj Parassery, Aayu Bhardwaj, and S. S. Ghosh Thermodynamic Investigations of a Turbocharged Homogeneous Charge Compression Ignition (HCCI) Engine Running on Wet Ethanol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohd Asjad Siddiqui, Abdul Khaliq, and Rajesh Kumar Effects of Thermally Induced Deformations and Surface Radiosity for 3D Heat Transfer and Its Applications . . . . . . . . . . . . . . . . . Kaustubh Kumar Shukla, T. Muthumanickam, and T. Sheela Optimization of Cutting Zone Temperature in Machining of Magnesium Alloy Using Taguchi Method . . . . . . . . . . . . . . . . . . . . . . . . . A. Saravanakumar, Jana Suresh Babu, Alagala Harikrishna, L. Rajeshkumar, and V. Sathiyamoorthy Design and Development of Smart Multipurpose Automated Guided Vehicle Implemented with SLAM and AMCL . . . . . . . . . . . . . . . . D. Gokula Vishnu Kirti, J. B. Greesh Pranav, V. Siva Naga Yaswanth, Amartya Reddy Ponaka, and Joshuva Arockia Dhanraj Fully Automated Cricket Bowling Machine . . . . . . . . . . . . . . . . . . . . . . . . . C. R. Balaji, R. G. Pranav Raj, V. Harish, and S. K. Indumathi An Application of Computational Intelligence Techniques to Predict Biometal Deposition Characteristics in Metal Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ananya Nath and Shibendu Shekhar Roy Ranking of Critical Risk Factors in the Indian Automotive Supply Chain Using TOPSIS with Entropy Weighted Criterions . . . . . . Vinod G. Surange and Sanjay U. Bokade
ix
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395
411
429
439
449
459
469
483
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503
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A Review on the Performance of Earth Air Heat Exchanger Coupled with Other Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vaishali Goyal, Arun Kumar Asati, and Rajeev Kumar Garg Beacon-Based Smart Shopping System Using IoT . . . . . . . . . . . . . . . . . . . G. Nagarajan, Y. D. V. V. S. Jiyyaparaju, and Yagnik
521 533
An Modern Approach to Detect Person Wearing Mask Using Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Nagarajan, Shaik Adil Ibrahim, and S. Mohan Kumar
539
Machine Learning Based Predict Plant Growth and Yield in Greenhouse Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Nagarajan, Pavankumark, and K. Mahesh
551
Chatbot for Hospitality Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Nagarajan, S. Madhu Sudhan Reddy, and Ashok Kumar
559
Experimental Investigations of Process Variables on Wire Electrical Discharge Machining (WEDM) of AISI 52100 Steel . . . . . . . . P. Santhi Priya, Subramanyam Pavuluri, and Yogesh Madaria
571
Subsea Manifold with Mudmat Structure Design Evaluation Based on Performance of Stress Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . Tarang T. Lakhani and Vijay R. Panchal
581
Fabrication and Experimental Investigation of Aluminum LM 25/h-BN/B4 C Hybrid Composites for Automobile Applications . . . . . . . . Katla Rajendar and K. Eswaraiah
591
Productivity Improvement in a Manufacturing Industry by Using Man–Machine Chart Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Mahendran, V. Amarnath, P. Rajkumar, L. Nirmal raj, S. Karthikeyan, and L. Rajeskumar Joint Impact of Carbon Emission and Partial Substitution on Inventory Model of Two Substitutable Products with Cost of Substitution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saumya Singh, Rajesh Kumar Mishra, and Vinod Kumar Mishra
603
615
Mechanical Characterization of Glass Fiber Metal Laminate . . . . . . . . . R. Naveen, S. Bairavi, P. S. Vijayanand, N. Swetha, K. Mathivannan, and M. Surya Muneeshwaran
627
Herbivicus: A Full Stack Website with Chatbot and Google API . . . . . . Khushboo Kumari, Aishwarya Srivastava, and T. Sasikala
635
Design of a Low-Speed Smoke Visualization Wind Tunnel . . . . . . . . . . . . Samprada Kumbhare, H. Jeevan Rao, and Jigarkumar Sura
649
Contents
Comparison of Effects of Cross Sections of Twisted Inserts in a Concentric Tube Heat Exchanger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yasir Baig, Alok Choubey, and Mousam Sharma Experimental Investigation and Machinability Study of Ni– Cr-Based Super Alloy Using Powder Mixed EDM . . . . . . . . . . . . . . . . . . . R. S. Barot, Janak B. Valaki, Alpesh H. Makwana, and Hardik Beravala Performance Analysis of Single Cylinder Four-Stroke Diesel Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manish Singh Bharti and Alok Singh Kinematic Study of Collaborative Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . Poornaditya Mishra, Haripriya Jagadeesh, Kole Laxmi Shashikant, and Amit Talli
xi
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673
685 697
Novel Hybrid Bio Composite PLA Filaments Reinforced with Bio Fillers for 3D Printing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. V. Lohar, A. M. Nikalje, and P. G. Damle
705
Study of Aerodynamic and Aerothermal Characteristics of Blunted Power Law Bodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. S. Sanjay Krishna and Vinod Kotebavi
717
Optimizing Thermal Comfort for Office Room Using CFD Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rajnish Kumar Gautam and Neeraj Agarwal
727
Exploring Classification Models for COVID-19 Novel Coronavirus Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Richa Suneja
745
Smart E-waste Tracking and Monitoring Model: A Modern Approach to Counter E-waste Management Issues . . . . . . . . . . . . . . . . . . . Mohammad Usman Rais
757
An Introduction of Water Desalination Exploiting the Waste Heat and Other Different Renewable Source of Energy . . . . . . . . . . . . . . Keshavendra Choudhary, Mayank Agarwal, and Rajesh Kumar
767
Analysis of Electromagnetic Aircraft Launching System for Naval Aircraft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shreyas Maitreya, Sameer Soni, and Priyanka Paliwal
773
Second-Order Filter for Improving the Performance of the Multi-level 3 Phase Inverter Using SPWM . . . . . . . . . . . . . . . . . . . . Ramlakhan Patel and Ashish Kumar Singhal
783
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Numerical Investigation of Product Capability and Enhancement Through Multi-hole Extrusion Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. Solomon, D. K. Sinha, P. J. Ramulu, and S. S. Gautam Automotive Advanced Toggle Transmission System Using Dynamo and Dual-Clutch Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Savanth Chandra Shekhar, P. N. V. Bala Subramanyam, Moon Banerjee, and B. Lakshmana Swamy Al-Zn-Mn Nanocomposite Sintering by Mechanical Alloying and Characterization with the Help of SEM and XRD . . . . . . . . . . . . . . . Moon Banerjee, B. Lakshmana Swamy, P. N. V. Bala Subramanyam, and Tikendra Nath Verma Effect of Trace Elements on Hardness and Impact Characteristics of Carbon Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Puneeth Kumar and A. S. Srikantappa Flexible Manufacturing System Scheduling Through Branch and Bound Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Nageswara Rao, K. Gaya Prasad, I. Praneeth, G. Sai Prudhvi, E. Vineeth, and Upendra Rajak Implementation of Jatinder N. D. Gupta Algorithm for FMS Scheduling Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Nageswara Rao, K. Prakash Babu, Kiran Kumar Dama, Santosh Kumar Malyala, and Upendra Rajak Flexible Manufacturing System Scheduling with Relative Importance of a Work Item in a Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . V. Mohan Manoj, Katta Sai Sandeep, G. Durga Prasad, M. Nageswara Rao, and Upendra Rajak Investigation of Combustion and Performance Characteristics of Waste Plastic Oil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Simhana Devi, Ravinder Kumar, and Upendra Rajak Implementation of Campbell, Dudek, Smith Algorithm in Flexible Manufacturing System with Mean Tardiness . . . . . . . . . . . . . . M. Nageswara Rao, K. Prakash Babu, G. R. Sanjay Krishna, T. Vijaya Kumar, and Upendra Rajak AGVs and Machines Scheduling with Campbell, Dudek, Smith Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Nageswara Rao, K. Prakash Babu, Kiran Kumar Dama, Santosh Kumar Malyala, and Upendra Rajak
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Implementation of Branch and Bound Algorithm in FMS with Mean Tardiness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Nageswara Rao, Kiran Kumar Dama, T. Vijaya Kumar, K. Prakash Babu, and Upendra Rajak A Contemporary Assessment on the Development of Automated Guided Vehicle to the Current Trends and Requirements . . . . . . . . . . . . . Meenakshi Prabhakar, Joshuva Arockia Dhanraj, Valenteena Paulraj, Dhusyant Arumukam Karthi Kannappan, and Adithyaa Hariharan Experimental Investigation of Performance and Emission Characteristics of Direct-Injection Compression-Ignition Engine Fuelled with Pond Water Algae Biodiesel . . . . . . . . . . . . . . . . . . . . K. Murali Krishna Prasad, P. Sravani, Upendra Rajak, Sk. Mohammad Shareef, Prem Kumar Chaurasiya, Nitin Malviya, and Pawan Yadav ACO-Based Resource Allocation Hybrid Algorithm for Cloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bhavana Gupta and Nishchol Mishra Operational Control Decisions Through Random Rule in Flexible Manufacturing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Sai Sandeep, M. Nageswara Rao, K. Rakesh, D. Phanindra Kshatra, and K. M. V. Ravi Teja Experimental Investigation on FMS Environment with Operational Completion Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Durga Prasad, K. M. V. Ravi Teja, M. Nageswara Rao, D. Phanindra Kshatra, and K. Rakesh Design of Aerial Top Dresser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mada Rukmini Sai Rupa Sri, Ganta Vanya Sree, Sk. Mohammad Shareef, Upendra Rajak, Avula Gouse Peera, and Prem Kumar Chaurasiya Edge Irregularity Strength of Graphs Produced Utilizing M-Super Subdivision of Stars and Double Stars . . . . . . . . . . . . . . . . . . . . . James Githinji Muya, G. Sobhalatha, G. Charankumar, Upendra Rajak, and P. Raju Implementation of Priority Rules in Flexible Manufacturing System Scheduling with Mean Tardiness . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Nageswara Rao, T. Vijaya Kumar, K. Prakash Babu, G. R. Sanjay Krishna, and Sanjay Kumar Singh
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Mean Tardiness with Heuristic in Intelligent Manufacturing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Nageswara Rao, T. Vijaya Kumar, K. Prakash Babu, G. R. Sanjay Krishna, Prem Kumar Chaurasiya, and Anshul Raj
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Simultaneous Scheduling with J N D Gupta Heuristic Algorithm with Mean Tardiness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1001 M. Nageswara Rao, K. Prakash Babu, T. Vijaya Kumar, Santosh Kumar Malyala, and Sanjay Kumar Singh Flexible Manufacturing System Simultaneous Scheduling Through Palmer Heuristic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 M. Nageswara Rao, K. Prakash Babu, Kiran Kumar Dama, Santosh Kumar Malyala, and Prem Kumar Chaurasiya Design and Optimization of Engine Block Using Gravity Analysis . . . . . 1023 B. Indrakanth, S. Udaya Bhaskar, CH. Ashok Kumar, and N. Srinivasa Rajneesh Performance of an Evaporative Condenser: A Review . . . . . . . . . . . . . . . . 1035 Vivek M. Korde, Shivam N. Dekate, Yash A. Bais, and Chirag P. Raut Analytical Study of Fluid Pressure-Sensing Mechanism in Microchannel for Microfluidic Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045 Ankur Saxena, Mahesh Kumar, and Kulwant Singh
About the Editors
Dr. Prem Kumar Chaurasiya was born at Bhilai (C.G) in India. He received his Bachelor of Engineering (B.E.) degree in Mechanical Engineering with securing first division from CSVTU, Bhilai (C.G) in 2010. He did his master degree in Hydro Power Engineering in 2013 from the department of Civil Engineering, Maulana Azad National Institute of Technology Bhopal (M.P.). He joined Vidhyapeeth Institute of Science and Technology Bhopal as Assistant Professor in July 2013 to July 2014. He has obtained his Ph.D. degree from Maulana Azad National Institute of Technology Bhopal (Madhya Pradesh). He joined Sagar Institute of Science and Technology, Gandhi Nagar Bhopal, as Assistant Professor in January 2019. Presently working as an Assistant Professor (Grade–I) in Mechanical Engineering department at Basnal Institute of Science and Technology, Bhopal. His areas of interest include wind energy, computational fluid dynamics, heat and mass transfer, renewable energy and alternative fuels in internal combustion engines. He got more than 15 papers in SCI and Scopus Indexed Journal. He has published one book and two book chapters in reputed publishers. He also serves as a potential reviewer of reputed journal; Elsevier (Energy, Fuel, Energy Conversion and Management), International Journal of Thermal Engineering, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, Taylor & Francis and more. Dr. Abhishek Singh was awarded with Ph.D. in Mechanical Engineering in 2014 by Indian Institute of Technology Roorkee (IIT Roorkee). He joined National Institute of Technology Patna (NIT Patna) in 2013 in the capacity of Assistant Professor and is presently having an experience of over 07 years in Teaching and Research. He is the member of various National and International Professional Scientific Societies such as MRSI, PMAI, ISME, IAENG, ASR, ISTE etc. Dr. Singh’s research areas are Advanced and Hybrid Machining Processes, Machining of Advanced Materials, Processing and Characterization of Composite Materials, Metal Matrix Composites and Biomaterials. Presently two students have completed Ph.D. under his supervision. In addition to this, he has guided more than 35 B.Tech. and M.Tech. students. In this journey he has authored and published more than 40 Research papers in various reputed National and International Journals and 7 Book Chapters. Dr. Singh has also xv
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participated in over 21 National and International Conferences of Engineering held in India and overseas. He has travelled to countries like Singapore, Japan and Malaysia to present papers on various topics of manufacturing science. He has received few best Research Paper awards too for his quality research. He has also been invited to scientific events in India and abroad as an Invited Guest Speaker and Technical Committee member. In addition to that, He has also been serving NIT Patna in the capacity of Nodal Officer, Departmental Academic Monitoring Coordinator and as a member of various Administrative committees of the Institute. Dr. Tikendra Nath Verma was born at Bhilai (C.G.) in India. He received his Bachelor of Engineering (B.E.) degree in Mechanical Engineering with securing first division from Pt. R.S.S.U. Raipur (C.G.) in 2006. He did his master degree in Thermal Engineering in 2009 from the department of Mechanical Engineering, Maulana Azad National Institute of Technology Bhopal (M.P.). He has obtained his Ph.D. degree from National Institute of Technology Raipur (C.G.) He joined National Institute of Technology Manipur as Assistant Professor in September 2015—May 2020. Presently working as an Assistant Professor (Grade–I) in Mechanical Engineering department in Maulana Azad National Institute of Technology Bhopal. His present areas of interest include computational fluid dynamics, heat and mass transfer, renewable energy and alternative fuels in internal combustion engines. He got more than 65 papers in SCI and Scopus Indexed Journal. He has published ten book chapters in reputed publishers. He is an active member of ISHMT, FMFP, ISHRAE, ASME, IOI, and IAENG. He also serve as a potential reviewer of reputed journal; Elsevier (Energy, Energy Conversion and Management, Applied Energy, Fuel, Journal of Cleaner Production, Journal of Building Engineering, Waste Management, Journal of Hazardous Material etc.); Journal of Thermal Analysis and Calorimetry (Springer), International Journal of Ambient Energy, Springer Journals; Bagell House Journals, Journal of Thermal Engineering. Dr. Upendra Rajak was born at Morena (M.P.) in India. He received his Bachelor of Engineering (B.E.) degree in Mechanical Engineering with securing first division from RGPV, Bhopal (M.P.) in 2010. He did his master degree in Hydro Power Engineering in 2013 from the department of Civil Engineering, Maulana Azad National Institute of Technology Bhopal (M.P.). He joined Vidhyapeeth Institute of Science and Technology Bhopal as Assistant Professor in July 2013 to July 2016. He has obtained his Ph.D. degree from National Institute of Technology Imphal (Manipur). He joined Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal as Assistant Professor in February 2020. Presently working as an Assistant Professor (Grade–I) in Mechanical Engineering department in Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, Andhra Pradesh. His present areas of interest include computational fluid dynamics, heat and mass transfer, renewable energy and alternative fuels in internal combustion engines. He got more than 25 papers in SCI and Scopus Indexed Journal. He has published one book and two book chapters in reputed publishers. He also serve
About the Editors
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as a potential reviewer of reputed journal; Elsevier (Energy, Fuel); SN Applied Sciences, an interdisciplinary journal published by Springer Nature, International Journal of Thermal Engineering, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, Taylor & Francis, etc.
Performance Enrichment of CI Engine Fueled with TiO2 Additive Blended Biodiesel Through Air Nanobubbles G. Senthilkumar , S. Lakshmi Sankar, and M. Purusothaman
Abstract The current work is intended at investigating the performance of bench scale standard DI-CI engine fueled with the blend of hone oil and TiO2 nano-additive. Further, air nanobubbles (ANBs) are allowed to flow with fuel in the fuel pipe at 0.8% uniform volume rate. The performance enhancement is assessed by brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE), while the tail pipe emissions are examined by hydrocarbon (HC), carbon monoxide (CO), and oxides of nitrogen (NOx ) indices. The outcome of the study shows 11% diminution in BSFC, 3.1% improvement in BTE, and 14–19% decrement in tail pipe emissions. Keywords Hone oil · Nano-additive · Air nanobubbles · Brake-specific fuel consumption · NOx emission · CI engine
1 Introduction Earth sustainability depends on the usage of fossil fuel sources for various dayto-day applications [1, 2]. So, researchers are focused on the efficacy of different engineering devices. CI engine is of such device employed in automobile sector whose efficacy needs to be enhanced through transforming its working fuel [3, 4]. Adding of nanoparticles in CI engine fuel is one such emerging field of research [5]. Hence, numerous investigators focused on mixing of nano-additives in biodiesels. The rise in Al2 O3 nanoparticle in fuel mixture caused an intensification in its flash point and heating value [6], while the same particles in base fuel released more CO2 emissions [7, 8]. The combustion features of bio-fuel were enhanced with the zinc oxide additive [9]. The ANBs greatly influenced the performance of CI engine [10] due to their huge inner pressure. The attributes of ANBs in various liquid samples were investigated [11]. The exhaust through the manifold is the sole reason for low-level air contamination. They comprise gases which are hazardous to both humans and atmosphere. G. Senthilkumar (B) · S. Lakshmi Sankar · M. Purusothaman Department of Mechanical Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_1
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Hence, in this work, attention is focused on the diminution of emissions percentage. ANBs improve the thermo-physical properties of fuel through extended retaining and complete burning [12, 13]. The solidity of fuel was improved by adding ANBs in it, which further enhanced engine performance [14]. Blending of ANBs and hone biodiesel in turbulence accredited to the production of micro-dimensioned fuel bubbles [15, 16]. The objective of current assessment is to examine the behavior of CI engine fueled with hone oil and TiO2 blends in the presence of ANBs.
2 Material, Equipment, and Test Procedure 2.1 Hone Biodiesel Preparation Hone oil was produced by the technique of transesterification by employing high temperatures and homogeneous stirring. Finally, the attained mixture is thermally treated at 50 °C to separate glycerol from ester.
2.2 ANBs Production ANBs produced through copper devices are injected in the leak-proofed fuel lines at 0.8% volume proportion. The dimension of ANBs utilized in the present assessment is in the order of 90–100 nm. Test fuel properties are indicated in Table 1. Table 1 Thermo-physio characteristics of test fuels B10
B20
B10 + ANBs
B20 + ANBs
B0
ASTM technique
Density (kgm−3 ) at 22 °C
852
868
855
864
851
D4052
Kinematic viscidness (cSt) at 45 °C
0.15
0.18
0.143
0.171
0.286
D445
Lower heating value (MJ/kg)
37.4
35.9
40.6
38.5
45.7
D240
Flash point (°C)
109
107
104
102
64
D93
Cloud point (°C)
−6.4
−6.2
−6.4
−6.3
−41
D2500
Fuel blend Property
Performance Enrichment of CI Engine Fueled …
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Fig. 1 Experimental setup
2.3 Experimental Setup and Test Procedure Prepared fuel mixtures were employed in bench scale standard CI engine test rig under various engine loads to evaluate its working characteristics. Before each experimentation, test rig is operated on B0 fuel for 10 min for the accuracy of data to be found at uniform engine speed of 1500 rpm. RTD instruments were utilized in assessing temperature and RH of intake air. All the tail pipe emissions were attained through FGA in real-time technique. The test Engine photograph and its technical details are shown in Fig. 1 and Table 2 correspondingly.
2.4 Error Analysis The rate of fuel flow, torque, speed, and BSFC was found with measured errors of 0.3%, 0.3%, 0.2%, and 0.4% respectively, and 0.4% correspondingly. The data obtained through experiments were mean of values obtained for six repetitions.
4 Table 2 Apparatus technical details
G. Senthilkumar et al. Attributes
Range
Brand
Kirloskar
Cylinder count
1
Strokes per cyclic process
04
Chilling technique
Water cooling
Opening technique
Cold beginning
Ignition technique
CI
Cylinder dimension
8.75 * 11.0 cm
Indicated speed
1600 rpm
Indicated power
3.5 kW
Ergometer
Eddy current kind
CR
18.5:1
3 Outcomes and Discussion 3.1 Brake-Specific Fuel Consumption (BSFC) Figure 2 depicts the BSFC variation with engine load for various fuel mixtures. From figure, it is identified that the company of ANBs in fuel lessens BSFC. The lowest
Fig. 2 Changes in BSFC with load
Performance Enrichment of CI Engine Fueled …
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Fig. 3 BTE changes with load
BSFC is found for B10 and ANB pair. The reason for this can be accredited to the occurrence of rapid fuel air mixing in the CI engine during this blend.
3.2 Brake Thermal Efficiency (BTE) Figure 3 demonstrates the BTE change with engine load for various fuel blends. From figure, it is identified that the presence of ANBs in fuel enhances BTE. The highest BTE is found for B20 and ANB pair. The motive for this can be attributed to the occurrence of complete burning of fuel in the CI engine during this blend.
3.3 Hydrocarbon (HC) Exhaust Figure 4 demonstrates the HC emissions variation with engine load for various fuel blends. From figure, it is identified that the presence of ANBs in fuel diminishes HC exhaust. The lowest HC emission is identified for B20 and ANB pair. The reason for this can be attributed to the enhanced flame speed in the CI engine during this blend.
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Fig. 4 Changes in HC emissions with load
3.4 CO Emission Figure 5 depicts the CO emissions variation with engine load for various fuel blends. From figure, it is identified that the presence of ANBs in fuel diminishes CO exhaust. The lowest CO emission is identified for B10 and ANB pair. The reason for this can be attributed to the lowest enthalpy of the blend.
3.5 NOx Emission Figure 6 demonstrates the NOx emissions variation with engine load for various fuel blends. From figure, it is identified that the presence of ANBs in fuel diminishes NOx exhaust. The lowest NOx emission is noticed for B10 and ANB combination. The reason for this can be accredited to the whole combustion of fuel mixture in the CI engine.
4 Conclusion The subsequent inferences can be made from this study:
Performance Enrichment of CI Engine Fueled …
Fig. 5 CO emissions changes with load
Fig. 6 Variation of CO emissions with load
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ANBs in the fuel pipe line enhanced the performance attributes and emission characteristics of CI engine fueled with hone oil and TiO2 additive. Engine BSFC was reduced by 15% for B10 and ANB blend. BTE of system was enhanced by 3.1% with B20 and ANB. HC emissions was diminished by 15.1% with B20 and ANB. CO and NOx emissions were lessened by 19% and 14%, respectively, with B10 and ANB blend.
References 1. Lin, C.Y., Chen, L.W.: Engine performance and emission characteristics of three phase diesel emulsions prepared by an ultrasonic emulsification method. Fuel 85, 593–600 (2006) 2. Senthilkumar, G., Kuruvilla, A., Joy, A.: Impact of second-generation alcohols on emission characteristics of biodiesel. Int. J. Ambient Energy 39(6), 547–550 (2017) 3. Senthilkumar, G., Sajin, J.B., Yuvarajan, T., Arunkumar, T.: Evaluation of emission, performance and combustion characteristics of dual fuelled research diesel engine. Environ. Technol. 41(6), 711–718 (2018) 4. Gaikwad, P.U., Gnanamani, S., Purusothaman, M., Lakshmi Sankar, S., Jeya Jeevahan, J.: Performance evaluation of variable compression ratio engine fueled with bio diesel from waste cooking oil. Indian J. Environ. Protection 40(1), 110–112 (2020) 5. Gaikwad, P. U., Senthil Kumar, G., Bobade, S.N.: Performance and emission analysis of waste cooking oil biodiesel added with Al2 O3 nanoadditive using VCR engine. Int. J. Adv. Sci. Technol. 28(20), 122–132 (2019) 6. Nadeem, M., Rangkuti, C., Anuar, M.R.U., Haq, I., Tan, I.B., Shah, S.S.: Diesel engine performance and emission evaluation using emulsified fuels stabilized by conventional and Gemini surfactants. Fuel 85, 2111–2119 (2006) 7. Selim, M.Y.E., Elfeky, S.M.S.: Effects of diesel/water emulsion on heat flow and thermal loading in a precombustion chamber diesel engine. Appl. Therm. Eng. 21, 1565–1582 (2001) 8. Gobinath, S., Senthilkumar, G., Beemkumar, N.: Air nanobubble-enhanced combustion study using mustard biodiesel in a common rail direct injection engine. Energy Sources Part A: Recovery Utilization Environ. Effects 41(15), 180–1816 (2018) 9. Kadota, T., Yamasaki, H.: Recent advances in the combustion of water fuel emulsion. Prog. Energy Combust. Sci. 28, 385–404 (2002) 10. Madhuri, C.R., Ramakrishna, K., Abhishek, D.: Heat transfer enhancement using hybrid nanofluids in spiral plate heat exchangers. Heat Transfer-Asian Res. 48, 3128–3143 (2019) 11. Rameshbabu, A., Senthilkumar, G.: Experimental investigation of variable compression ratio (VCR) diesel engine performance and characteristics of emission rate of cotton seed oil biodiesel with TiO2 as a biodiesel blend. Int. J. Mech. Prod. Eng. Res. Dev. (IJMPERD) 10(2), 1217–1226 (2019) 12. Dasore, A., Konijeti, R., Puppala, N.: Experimental investigation and mathematical modeling of convective drying kinetics of white radish. Front. Heat Mass Transf. 13(21), 1–8 (2019) 13. Konda, J.R., Madhusudhana Reddy, N.P., Konijeti, R., Dasore, A.: Effect of non-uniform heat source/sink on MHD boundary layer flow and melting heat transfer of Williamson nanofluid in porous medium. Multidiscip. Model. Mater. Struct. 15(2), 452–472 (2019) 14. Dasore, A., Konijeti, R., Polavarapu, T., Puppala, N.: Convective hot air-drying kinetics of red beetroot in thin layers. Front. Heat Mass Transf. 14(23), 1–8 (2020) 15. Gaikwad, P.U., Gnanamani, S., Subramani, N.: Experimental and Numerical Simulation of the Piston Engine Fueled with Alternative Fuel Blends: CFD Approach (2021) 16. Singh, T.S., Upendra, R., Dasore, A., Muthukumar, M., Verma, T.N.: Performance and ecological parameters of a diesel engine fueled with diesel and plastic pyrolyzed oil (PPO) at variable working parameters. Environ. Technol. Innov. 22, 101491 (2021)
Design and Optimization of NACA 0012, NACA 4412 and NACA 23,012 Aerofoils of Wind Turbine of Solar Updraft Tower Power Plant Ramakrishna Balijepalli, Upendra Rajak, Abhishek Dasore, Anshul Raj, and Prem Kumar Chaurasiya Abstract This work concentrates on the dimensional design and performance assessment characteristics of a wind turbine blade for a small-scale solar updraft tower power plant. Energy extraction from wind mainly relies on turbine blade design. Horizontal wind turbine blade with NACA 0012, NACA 4412 and NACA 23,012 profiles was designed and analysed by using a design method called blade element momentum (BEM) theory. This theory was applied to turbine blade to optimize various design factors like angle of attack (α), wind flow angle (Ø) and blade pitch angle (β), chord length of every sectional part of blade (c) and number of blades (N) and also to increase the smooth functioning of wind turbine and accordingly, increasing the power production. The power generation from wind turbine was also estimated for three different kinds of aerofoil blades (NACA0012, NACA4412 and NACA23012), and these are 0.59 W, 0.624 W and 0.61 W, respectively. The coefficients of lift and drag were estimated and analysed. Keywords Solar updraft tower · Wind turbine · Blade element momentum theory · NACA 0012 · NACA 4412 · NACA 23012
1 Introduction With a rapid depletion of fossil fuels and the raise in worldwide environment pollution, environment-friendly alternative energy sources (renewable energy sources) are the future. Among several types of alternative energy sources, solar and wind energy shares a large proportion. Nowadays, most of the energy utilisation in world is taken out from fossil fuel reserves and these energy resources come to an end in future. Then again this form of energy generation causes global warming, melting glaciers R. Balijepalli (B) · U. Rajak · A. Dasore Department of Mechanical Engineering, RGM College of Engineering and Technology, Nandyal, Andhra Pradesh 518501, India A. Raj · P. K. Chaurasiya Department of Mechanical, Bansal Institute of Science and Technology, Madhya Pradesh, Bhopal 462036, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_2
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and environmental contamination. Therefore, it appears that the use of conceptually never-ending energy source which is solar power; necessary. Solar updraft tower (SUT) power plant is a novel method to generate electricity and has been increased in the past few decades. Construction and fabrication materials for such a power plants are comparatively economical and easily available which implies that they can be developed in any country [1]. According to the literature review, it is seen that a couple of reduced scale SUTs have been built up [2–9]. Few studies have been focused on numerical (CFD) studies of fluid flow behaviour inside the small-scale SUT power plant [10–13]. Some studies were focused on the airflow behaviour and also assessed several flow parameters like air pressure distribution and velocity [14, 15] at various locations inside the chimney (tower) and solar collector [14–16]. Air temperature, collector covers temperature and absorber plate temperature of the SUT setups were estimated through experiments [2, 3, 15, 17]. But no study has concentrated on the design of wind turbine blade of reduced scale SUT plants for the production of electricity. And, still no other study was carried out on evaluation of turbine blade angle optimization, number of blades and chord length distribution of turbine blade of SUT plant. Hence, the essential goals of present research work are, (1) to build up a sophisticated design procedure and performance parameters evaluation of a wind turbine blade using BEM theory, (2) to optimize the various design parameters such as angle of attack (α), wind flow angle (Ø) and blade pitch angle (β), chord length (c) of each segment of blade and number of blades (N) under constant wind speed condition (3) to estimate the lift coefficient and drag coefficient to turbine blade, (4) to determine the power output of each aerofoil turbine blade and make a comparison.
2 Methodology The purpose of this section is to design a wind turbine blade which operates underflow speed conditions (2–5 m/s). In the process of design of turbine blade, the first and foremost step is to obtain the best power performance by doing aerodynamic analysis of fit. The key parameters which influence the blade design include blade radius (R),number of blades (N), type of aerofoil, radial distribution of chord (c) and blade pitch angle (β). A simple and an easy model introduced by Betz [18] can be utilized to find out the total energy extraction from a wind turbine. This theory can also helpful to estimate various aerodynamic forces like lift force, drag and axial thrust forces. This model can be used to see the impact of working of turbine blades on nearby wind field. This model purely operates on the law of conservation of linear momentum theory which developed 100 years before, in order to design and study the working of ship propellers. Rotational (or) wake flow is not considered for the analysis of Betz model [18].
Design and Optimization of NACA 0012, NACA 4412 …
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2.1 Schmitz Model Schmitz [18, 19] presented detailed and sophisticated model to investigate the realistic wind flow behaviour across the wind turbine by taking into consideration of wake flow (Fig. 1). Conservation of angular momentum theory and aerodynamic laws has been utilized to build up Schmitz model. In this model, the complete. analysis of wind flow can be done by making it four divisions across the turbine. As shown in Fig. 1, the wind enters with an upstream velocity V1at the inlet to wind turbine (section-1). But the turbine blade goes through with relative velocity (W) in the rotor plane at Section 2 because of blade rotates with a velocity of u. The axial component (V 2) of W acts perpendicular to the rotational plane of turbine. The wind speed is almost constant at exactly before and after the plane of rotation (V 2 = V 3). In Section 4, the wind velocity is far decreased V4 due to most of the energy in the wind is extracted by turbine at rotor plane. Figure 2 presents the wind and blade velocities and the corresponding angles at a specified distance, r, from the rotor axis. In order to design the rotor blade, the β, angle of attack (α), angle of relative wind to rotor plane (φ) and c of the blade should be defined. α is the angle of attack. β is blade twist angle. ϕ is wind flow angle. As per Fig. 3, it is observed that a little growth in the tangential velocity component due to rotational wind flow decreases V 2. BEM theory helps stop redact the various aerodynamic forces (lift, drag, thrust and tangential) act on turbine blade elements
Fig. 1 Wind flow across the turbine
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Fig. 2 Angles and velocities
Fig. 3 Effect of wake flow at exit of turbine
and also to find out the optimum a and a’ for achieving maximum energy extraction from air.
2.2 Blade Element Momentum (BEM) Theory The primary goal of BEM theory is to maximize the lift force acts perpendicular to W on the blade and to minimize the drag force acts along the direction of wind on blade, which causes the force to act in rotational direction can be optimized. Momentum theory: With the help of this theory, one can estimate the total tangential force which is developed by the blade in the plane of rotation.
Design and Optimization of NACA 0012, NACA 4412 …
13
Fig. 4 Partition of blade into number of segments
Blade element theory: In the element theory, the turbine blade presented is divided into number of parts and very sectional part sweep an annular surface area at each rotation of turbine. The elements are considered separately. Under the assumption that there is no interference between the radial flows in one blade element to its adjacent blade elements. Consider a blade segment (element) with the thickness of dr and chord length of c at a distance r from the axis of rotation (Fig. 4). Based on the geometric relations (shown in Fig. 2), the relative flow angle at maximum energy extraction from wind is represented by Eq. (2.1),
3 Results and Discussion 3.1 Optimization of Angle of Attack (A) and Number of Blades (N) The coefficient of lift (CL) and drag (CD) was selected for different types of turbine blade aerofoil (NACA 0012, 4412 and 23012) under a particular Reynolds number (Re) and α, from NACA aerofoil data sheet are shown in Fig. 5. The maximum energy extraction rate from the turbine is obtained when glide ratio (CL/CD) is to be highest. The highest value of (CL /CD) for the different turbine blade aerofoil (NACA0012, 4412 and 23012) are 5.5°, 5° and 7°, respectively, which indicates that optimum angle of attack of respective blade aerofoil’s (Fig. 6). The power produced by different blade segments was estimated by Eq. (2.10) for various α and is shown in Fig. 6. Optimized power production of different blade aerofoil is shown in Fig. 6. NACA0012, NACA4412 and NACA23012 turbine blades produce maximum power of 0.59 W, 0.624 W and 0.61 W at α = 5.5°, α = 5°and α = 7°, respectively.
14
R. Balijepalli et al.
a
Wind flow angle Blade pitch angle
55 45
Angle, °
35 25 15 5 -50.00
0.05
0.10
0.15
0.20
0.25
0.30
Blade radius from centre of turbine, m
b Coefficient of drag
0.11
NACA 0012 NACA 4412 NACA 23012
0.09 0.07 0.05 0.03 0.01 -0.01
10
5
0
15
20
Angle of attack, °
Total power, W
Fig. 5 a Variation of lift coefficient, b Drag coefficient for different α 0.63 0.62 0.61 0.6 0.59 0.58 0.57 0.56 0.55 0.54 0.53 -1
NACA 0012 NACA 4412 NACA 23012
1
3
5
7
9
Angle of attack,° Fig. 6 Optimization of angle of attack (α)
11
13
15
Power output at each blade element (dP), W
Design and Optimization of NACA 0012, NACA 4412 … 0.12
Number of blades=2, Angle of attack=5 Number of blades=3, Angle of attack=5
0.10
Number of blades=4, Angle of attack=5
15
0.08 0.06 0.04 0.02 0.00 0.00
0.05
0.10
0.15
0.20
0.25
0.30
Blade radius from centre of turbine (r), m
Fig. 7 Optimization of number of turbine blades
The power output is evaluated at each blade segment (r/R) of turbine by varying numbers of blades (N) under the consideration of a constant α (5°). The kinetic energy extraction rate (dP) increases with the increase of N and is presented in Fig. 7. It is very difficult to balance the wind turbine by choosing two numbers of blades. In case, the total number of blades fixed to turbine is more than four, then the light and deadweight of entire system increases, which leads to decrease of angular velocity (ω) of shaft. And it is also observed that a very less quantity of air passing through the rotor plane when turbine having more than four number of blades. So that, turbine with four number of blades is considered as optimized one for effective energy extraction from wind.
3.2 Estimation of Wind Flow Angle (Ø) and Blade Pitch Angle (B) and Chord Length Distribution (C) of Blade at Each Segment The Ø and β were estimated for each and every partition of the blade, and the outcomes are presented in Fig. 8. It was observed that β is more than 30° near root section of the blade because maximum possible wind flow occurs at the central axis of the turbine (closer to blade root section). So that, the blade is twisted more at root section than at tip section to withstand incoming wind forces. Likewise, ϕ is also highest at the root section of blade and reduces linearly along with R for the same kind of reason justified above. β and ϕ were reduced with increased radius (in Fig. 8) because of the decrease of relative wind velocity along the radius of the blade (from root to tip) and enough time is accessible for wind flow to arrive at tip of the blade. Same trend of profile is observed for all three types of NACA series turbine blades.
16
R. Balijepalli et al. Wind flow angle Blade pitch angle
55
Angle, °
45 35 25 15 5 -50.00
0.05
0.10
0.15
0.20
0.25
0.30
Blade radius from centre of turbine, m
Fig. 8 Variation of ϕ and β
Chord length of blade, (m)
Chord length distribution (c) at each segment of turbine blade for various aerofoils is shown in Fig. 9. In Fig. 9, the maximum c is obtained at first sectional segment of the blade due to the c is directly proportioned to sine function, and also, the maximum bending and shear stresses are developed at this section only. This c is highest at root section of the blade and is lowest at tail end of the blade which causes to fabrication of narrow blade profiles. Subsequently, it leads to minimum usage of material and less operation and manufacturing costs. Out of all the three types of turbine blades, the NACA 0012 has more c (0.09 m) at root section due to symmetric aerofoil nature. NACA 0012 NACA 4412 NACA 23012
0.10 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0.00 0
0.05
0.1
0.15
0.2
Blade radius from center of turbine, (m)
Fig. 9 Chord length of blade at each segment
0.25
0.3
Design and Optimization of NACA 0012, NACA 4412 …
17
4 Conclusions Design of wind turbine blade is also a crucial aspect to extract more energy from air. Optimized angle of attack for various turbine blades (NACA 0012, NACA 4412 and NACA 23012) was estimated as 5.5°, 5° and 7°, respectively. In order to extract maximum kinetic energy from wind, the required number of blades of a wind turbine was optimized as four. The wind flow angle (Ø), blade pitch angle (β) and chord length distribution (c) of blade at each segment were estimated. The maximum values of Ø, β and c o fNACA4412 turbine blade were estimated as 54°, 49°and 0.063 m. The minimum values of Ø, β and c of NACA 4412 turbine blade were estimated as 11.8°, 6.8°and 0.019 m. Similar kinds of values were noticed for the remaining two turbine blades (NACA 0012 and NACA 23012). The maximum power produced by various wind turbines (NACA0012, NACA4412 and NACA23012) was estimated as 0.59 W, 0.624 W and 0.61 W, respectively.
References 1. Najmi, M., Nazari, A., Mansouri, H., Zahedi, G.: Feasibility study on optimization of atypical solar chimney power plant. Heat Mass Transf. 48, 475–485 (2012) 2. Zhou, X., Yang, J., Xiao, B., Hou, G.: Experimental study of temperature field in a solar chimney power setup. Appl. Therm. Eng. 27, 2044–2050 (2007) 3. Kasaeian, A.B., Heidari, E., S., Nasirivatan: Experimental investigation of climatic effects on the efficiency of a solar chimney pilot power plant. Renew. Sustain. Energy Rev. 15, 5202–5206 (2011) 4. Kasaeian, A., Ghalamchi, M., Ghalamchi, M.: Simulation and optimization of geometric parameters of a solar chimney in Tehran. Energy Convers. Manage. 83, 28–34 (2014) 5. Gholamalizade, E., Chung, J.D.: Analysis of fluid flow and heat transfer on solar up draft tower power plant coupled with a wind turbine using computational fluid dynamics. Appl. Therm. Eng. 126, 548–558 (2017) 6. Asnaghi, A., Ladjevardi, S.M., Kashani, A.H., Izadkhast, P.S.: Solar chimney power plant performance analysis in the central regions of Iran. J. Solar Energy Eng. Trans. ASME 135, 111–117 (2013) 7. Ramakrishna, B., Chandramohan, V.P., Kirankumar, K.: Performance parameter evaluation, materials selection, solar radiation with energy losses, energy storage and turbine design procedure for a pilot scale solar updraft tower. Energy Convers. Manage. 150, 451–462 (2017) 8. Schlaich, J., Bergermann, R., Schiel, W., Weinrebe, G.: Design of commercial solar updraft tower systems–utilization of solar induced convective flows for power generation. J. Solar Energy Eng. Trans. ASME 127, 117–124 (2005) 9. Prabhukhot, P.R., Prabhukhot, A.R.: Computer analysis of S822 Aerofoil section for blades of small wind turbines at low wind speed. J. Solar Energy Eng. Trans. ASME 139, 051008-1-4 (2017) 10. Koonsrisuk, A., Chitsomboon, T.: Partial geometric similarity for solar chimney powerplant modelling. Sol. Energy 83, 1611–1618 (2009) 11. Lal, S., Kaushik, S.C., Hans, R.: Experimental investigation and CFD simulation studies of a laboratory scale solar chimney for power generation. Sustain. Energy Technol. Assess. 13, 13–22 (2016) 12. Sangi, R., Amidpour, M., Hosseinizadeh, B.: Modeling and numerical simulation of solar chimney power plants. Sol. Energy 85, 829–838 (2011)
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13. Mohiuddin, A., Uzgoren, E.: Computational analysis of a solar energy induced vortex generator. Appl. Therm. Eng. 98, 1036–1043 (2016) 14. Ayadi, A., Driss, Z., Abdullah, B., Abid, M.S.: Experimental and numerical study of the impact of the collector roof inclination on the performance of a solar chimney power plant. Energy Build. 139, 263–276 (2017) 15. Kalash, S., Naimeh, W., Ajib, S.: Experimental investigation of the solar collector temperature field of a sloped solar updraft power plant prototype. Sol. Energy 98, 70–77 (2013) 16. Ghalamchi, M., Kasaeian, A., Mehrad, G.: Experimental study of geometrical and climate effects on the performance of a small solar chimney. Renew. Sustain. Energy Rev. 43, 425–431 (2015) 17. Ghalamchi, M., Kasaeian, A., Mehrad, G., Alireza, H.M.: An experimental study on the thermal performance of a solar chimney with different dimensional parameters. Renew. Energy 91, 477–483 (2016) 18. Soren, G.: A text book of Wind Turbines, 2nd edn. University College of Arhaus, Arhaus (2009) 19. Gasch, R., Twele, J.: Wind power plants, 2nd edn. Springer, Heidelberg, Dordrecht, London (2012)
Experimental Analysis on Material Removal Modes and Mechanisms in Electrochemical Discharge Machining Process for Optical Glass C. S. Jawalkar
Abstract The paper discusses key material removal and tool wear modes while electrochemical discharge machining on optical glass, a key material used in making lens and instruments. The parametric effects and experimental results obtained using Taguchi’s methodology are discussed in detail. The applied voltage, electrolyte concentration, feed rate, electrode spacing along with some innovative factors like material density and electrode immersion depth were studied. The experimental results are illustrated that applied voltage (22%) was the most significant factor in the material removal (MR) studies; however, in the tool wear (TW) studies, its material density (18.36%) and applied voltage (16.45%) were the significant factors. Surface roughness, Ra values on the fabricated channels were obtained in the range of 0.1 to 1.2 μm. Identification of some prominent material removal modes and its practical correlation was done as an innovative approach through microstructural studies using field emission scanning electron microscopy (FESEM) approach. The MR and TW were correlated with microchipping, cratering, and thermal effects, similar to those found in EDM; these were further analyzed in getting insights and parametric optimizations. Keywords ECDM · Electrode immersion depth · Material removal rates
1 Introduction The electrochemical discharge machining (ECDM) process has similarities to electrochemical and electric discharge machining. In the electrochemical machining process, there is a confined electrochemical dissolution of the anode material [1], while in the electric discharge machining there is a thermoelectric phenomenon associated with the erosive effects produced, when spatially and discrete discharges occur between two electrical conductive materials. The material removal phenomenon of C. S. Jawalkar (B) Department of Production and Industrial Engineering, Punjab Engineering College, Chandigarh 160012, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_3
19
20
C. S. Jawalkar
Tool gas enve-
Plasma channel
lope Work surface
Work surface
(a)
Plasma channel
(b)
Collapsed plasma
Vapor bubble Discharge phase
Ejected material
Crater
(c)
(d)
Recast layers
Fig. 1 Schematic illustration of the stages in the discharge process
ECDM resembles that of EDM and ECM. The major change is the use of an electrolyte in place of the dielectric fluid used in EDM process. The basic four stages of EDM process [2] are as follows: The first stage, Fig. 1a, is the ignition phase which represents lapse, corresponding to the occurrence of breakdown of the high open-circuit voltage (U i ) applied across the working gap having fairly low discharge voltage (U e ). The second phase is the formation of a plasma channel surrounded by a vapor bubble (Fig. 1b). The third phase is the discharge phase, when high energy and pressurized plasma channel sustains for a period of time (Fig. 1c). The last phase is the collapse of plasma channel caused by turning off the electric energy, which causes molten metal to violently eject (Fig. 1d). This causes material removal and a part of it gets deposited as the recast layer, if it is not flushed out immediately. All of these stages are schematically as shown in Fig. 1. Analogues to this, in the ECDM process, the reported studies conducted through rapid photography, SEM, and EDX reveal four stages: first being the pure ECM reaction stage, prior to sparking, second is ECM + spark discharge (resembling the first stage of EDM), third is the critical discharge which produces a compact gas layer (resembling the stage two in EDM), and the final stage comprises of continuous discharges [3]. The craters formed in ECDM process are almost the same as those formed in the EDM process, along with some recast effects [4]. The ECDM process is preferred in micromachining [5, 6] preferably for nonconducting and brittle materials like glass, ceramics, granite, and composites; its typical applications are in the field of microfluidics, microreactors, biomedical, electronic circuitry, artistic and decorative applications (through etching). The concept
Experimental Analysis on Material Removal Modes …
21
of electrode discharge was first initiated by Karafuji and Suda [7]. The academic concept of the process was earlier reported by Fizeau and Foucault [8, 9]. It has been reported that the surface finish obtained in ECDM was as lower as, Ra = 0.08 μm (micrometers), and the material removal rates (MRR) [3] achieved is upto 1.5 mm3 / min, which are much better than its nearest similar processes [10, 11].
2 Modes of Material Removal (MR) in ECDM The most prominent MR modes in ECDM among all of the earlier stated modes are: • Melting and vaporization phenomenon [12]: At a critical point (around 27 °C), the gas films collapse and sparking starts between the tool and the electrolyte. The MR takes place, when the workpiece is in the near vicinity of the sparking region. • Chemical etching effect: It occurs at high temperatures: The formation of gas bubbles on the tool is due to chemical activity; due to the presence of electrolyte and electric potential, chemical etching effect [13] also exists to some extent. • Microcracking and spalling: Due to random thermal stresses under specific conditions [14]. • Mechanical shock: Due to expanding gases and electrolyte movement [15]. The predominant material removal action in this process is through sparking (thermal mode) discharge through collapse of gas film, along with some etching action (ECM).
2.1 Studies on Gas Films in ECDM Wuthrich and Hof have presented a critical and precise study on gas films [16] and its thickness. A theoretical model for the estimation of the thickness of gas film was presented. Decreasing the gas film thickness and changing the wettability characteristic of the tool electrodes resulted in significantly higher machining rates along with considerable increase in repeatability. A schematic of the formed gas film after bubble coalescence is shown in Fig. 2, and key findings on earlier researches in this field are shown in Table 1. In another study by Liang et al. [17], the authors have experimented on gas films formed during the ECDM process while making ultra-white glass micro-array holes. The relation between process parameters, forming quality, and MRR under the conditions of gas film stability and breakdown characteristics have been reported. In order to reduce the gas film thickness, several strategies were discussed such as, use of: 1.
Hydrodynamic fluxes and tool rotation effect. It was demonstrated that the machining quality and MR improved by the use of rotation effect.
22
C. S. Jawalkar
Fig. 2 Schematic of the gas film formed after bubble coalescence
Tool electrode
Gas bubbles before coalescence
2.
3.
Gas film formed after coalescence
Influencing wettability of tool or capillary forces at electrode–electrolyte gas interface [18]. (it is theoretically possible by adding surfactant like liquid soaps in electrolyte to control and reduce gas film thickness). Influencing the density of gas bubble nucleating sites. (controlling the mean distance of activated bubble and its nucleation sites appear more promising but that again was not been tried actively by researchers).
3 Experimental Studies on Optical Glass In the presented experimental study, scott optical (SO 3111e) glass was used as the work material, on which microchannels were prepared. The composition of optical glass was tested through energy dispersive spectrometry (EDS); it contained silicon 40.25 wt. % as the major element, along with others. The composition and properties of this material are indicated in Table 2 [27]. It has unique properties, and it is the preferred material for higher-grade applications in defense and aerospace; prominently used in imaging devices like digital still-video cameras, lens, binoculars, digital projectors, etc. The review of previously published research indicated that specific studies on mechanism of “tool wear” along with studies on MR with reference to immersion depths, material density, and understanding the wear mechanisms were scarce and needed further research. The key results of trail experiments using NaOH electrolyte for MR and TW are shown in Figs. 3 and 4; wherein the voltage was initially varied from 45 to 80 V, and it was found that with an increase in the applied voltage the material removal increased, almost linearly (Fig. 3a). This is attributed to the increase in the heat input in the form of thermal energy of the sparking process, which increased the MR. If the value was increased beyond 80 V, then large thermal cracks began to appear and at some instances and workpiece failed due to multiple crack propagations. Tool wear increased almost linearly with an increase in the applied voltage (upto 60 V) (Fig. 3b), due to rise in the heat input; however, beyond 60 V, instability in the MR and TW characteristics were clearly observed as shown in Fig. 3a, b, with unusual undulations.
Experimental Analysis on Material Removal Modes …
23
Table 1 Key findings and approaches used in recent studies on ECDM Processing conditions/techniques
Key findings
Machining microchannels on quartz
Microchannels were machines on quartz, using Taguchi’s standard OA (L 9 ) and grey relational analysis (GRA) approach. Optimal parametric conditions were reported for increasing MRR and reducing width of overcut. [19]. In another study on micromachining quartz, using GRA technique, electrolyte concentration, voltage and inter-electrode gap [20] were crucial
Micromachining on ultraclear glass using innovative tooling
Using rotary helical tools, microholes, microchannels, microslits, three-dimensional structures, and complex closed structures with fine dimensional accuracies on side gaps (~10 μm) were successfully created on glass [21]
Use of response surface methodology and SEM Relations between input and response parameters have been established for minimizing tool wear using RSM technique. SEM was used for closely investigating heat-affected zone [22] Use of varied tool shapes, coated electrodes, and mixed electrolytes
Authors have reported the absence of experimental studies on the use of combined effects of: machining materials with coated electrodes, conductive particles mixed electrolytes and with varying tool shapes, thereby creating new opportunities [23]
Advancements in micro- and nanotools
Advancements in micro- and nanotools are manufactured through unconventional and hybrid machining techniques [24]
Studies on fume particles, revealing its structures and risks
Fume particle morphology was studied, and its chemical composition indicated the presence of toxic and carcinogenic elements, and hence, its associated risks were reported [25] In another study, the authors extracted fume particles (≤100 nm). The presence of amines, methane, NO2 , So2 , thio compound disulphades, and halogenated hydrocarbons was also revealed [26]
In order to understand the effect of electrolyte concentration and its range, experiments were conducted on optical glass (6 mm thickness) using similar process as illustrated earlier. Each of the experiments was repeated thrice, and the average values of MR and TW were considered. The electrolyte concentration was varied from 10 to 25% in steps of 5% each. It was observed and seen through Fig. 4a shows that MR increased with an increase in electrolyte concentration upto a value of 20%; after this, there was reduction in MR and the curve dropped down further. At electrolyte concentration values beyond 20%, saturation occurred resulting in lesser MR. The optimum level of MR (higher the
24
C. S. Jawalkar
Table 2 Composition and features of optical glass used in experiments
Element
Wt. %
At. %
C
11.48
19.21
O
32.63
41.00
Na
08.18
07.15
Si
40.25
28.80
K
07.46
03.84
Other properties
Value
Knoop Hardness (H K )
520
Refractive index
1.527
Transformation temperature (°C)
559
Young’s modulus, E (103 N/mm2 )
81
b
Tool Wear (mg)
Material Removal (mg)
a
Applied Voltage (V)
Applied Voltage (V)
Fig. 3 a Graph of MR versus applied voltage, b Graph of TW versus applied voltage
b Tool Wear (mg)
Material Removal (mg)
a
Electrolyte Concentration (%)
Electrolyte Concentration (%)
Fig. 4 a MR at varying electrolyte concentrations, b TW at varying electrolyte concentrations
Experimental Analysis on Material Removal Modes …
25
better) was seen around the value of 20% electrolyte concentration. Figure 4b shows TW at different electrolyte concentration values (10 to 30% in range of 5% each). The trend was similar to that seen in case of MR. The maximum tool wear occurred at around 20% value of electrolyte concentration. The reason for this decline was due to the saturation effect. At higher concentrations, the sparking was partially reduced and white-colored intense bubbling phenomenon was seen at the electrode/ electrolyte interface, which blocked the free movement of ions and partially decreased the freedom for free sparking activity. The same effect was observed at higher concentration values. In case of tool wear (lower wear is preferred), the optimum range of electrolyte concentration was around 10% (at the electrolyte concentration levels of below 10%, the sparking effect was extremely low, without any meaningful results). Using the pilot studies on applied voltage and electrolyte concentration as an input; trial experiments were further conducted for electrode immersion depths. The optimum values of MR and TW were seen when the cathode immersion depth was less than 1 mm and anode immersion depth was more than 2 mm, respectively. Using the DOE approach, Taguchi’s standard L18 orthogonal array (OA) was chosen [28], 18 experiments at different combinations are done and were repeated twice. Two tools were used: a wedge-shaped stainless steel (SS-304 having 0.6% Ni and 0.5% Cr, thickness 0.3 mm) and fine copper rod ( 0.3 mm, tip diameter). MR and TW were selected as response parameters. Other parametric settings were decided through trial studies as shown in Fig. 5 [28, 29]. In material removal study, orthogonal array L18 was used (parameters as per Table 3). All other parameters chosen were at three levels each, and studying the interaction effect of columns 1 and 2 was possible at column 3; therefore, column 2 was allocated as “applied voltage,” and interaction of columns 1 and 2 was reserved for column 3. Out of the maximum 8 columns, the last column was left vacant. In the
Linear feed actuator Feed
230 V AC Tool holder
micro-
controller unit Clamping fixture
Cathode (Tool)
AC-DC Convertor 0-150V Anode Work piece
Electrolyte
Base (Milling machine bed) Fig. 5 Schematic of the fabricated ECDM setup
26
C. S. Jawalkar
Table 3 Parameters used and their values Variables
Parameters
Units
Level 1
Level 2
Level 3
P1 = Tool–material (density)
g/cm3
Cu (8.9)
SS-304 (7.9)
–
P2 = Applied voltage
Volts, V
60
70
80
P3 = Interaction effect (column 3) P4 = Elect. concentration
%,salt in H2 0
10 (100 g/L)
15 (150 g/L)
20(200 g/L)
P5 = Tool feed
mm/ min
0.15
0.3
0.75
P6 = Electrode initial spacing
Mm
50
100
150
P7 = Anode immersion depth
Mm
2
3
4
study, two repetitions were done for each combination and its average response value was considered for further analysis. A calibrated digital weighing machine (make: SHIMADZU, AUW220D, least count 0.01 mg) was used in the MR studies. The obtained average values, their variance, and signal-to-noise ratio (S/N) are given in Table 4. The analysis of variance (ANOVA) for MR studies is given in Table 5. The average channel dimensions obtained were 0.5 mm width, 0.2 mm depth and 6–10 mm length. As evidenced in Fig. 6a, the material removal using stainless steel (SS) tool was marginally more as compared to the copper tools. The logical reason for this was the lower wear rate of stainless steel material as compared to copper, which created the adequate tool–material gap (of few microns), enabling steady and continuous material removal action. On the contrary, in case of copper tools, due to quicker wear, the tool–material gap increased and it reduced the MR process. This was considered as a useful methodology, in understanding the MR process in ECDM. The material removal increased with an increase in the applied voltage (Fig. 6b), which was primarily due to the increase in thermal inputs from sparking. During pilot experimentation, the values of electrolyte concentration were tried from 5 to 25% (50–250 g/L) and the effective range noted was 10–20% (100–200 g/L), which was taken up for further optimization. As evidenced in Fig. 6c, the electrolyte concentration had a significant effect and more of the material removal was at the 10% concentration (100 g/L) level. At the other levels, there was a marginal dip in the MR; primarily, due to the influence of other factors like saturation of the electrolyte, thermal effects, and chemical reactions taking place at the electrodes. The maximum MR was seen at the third level (0.75 mm/min) of the feed rate. At lower feeds, the presence of more recast layers could be attributed for the marginal reduction in MR (Fig. 6d). The slight quicker feed enabled quick abrasion of the immediate recast layers and thus improved the MR phenomenon to some extent.
Experimental Analysis on Material Removal Modes …
27
Table 4 Average MR values and S/N ratios No
Trial-1 MR, mg
Trial-2 MR, mg
Average MR, mg
Variance (ν)
Sum of sq. of reciprocal
S/N ratio
1
2.53
3.79
3.160
0.794
0.113
9.472
9.994
2
3.85
2.86
3.355
0.490
0.095
10.229
10.514
3
0.99
1.03
1.010
0.001
0.981
0.081
0.086
4
3.28
3.8
3.540
0.135
0.081
10.910
10.980
5
1.41
1.02
1.215
0.076
0.732
1.354
1.692
6
2.38
2.53
2.455
0.011
0.166
7.789
7.801
7
4.17
3.14
3.655
0.530
0.079
10.998
11.258
8
3.98
4.28
4.130
0.045
0.059
12.302
12.319
9
2.81
2.91
2.860
0.005
0.122
9.123
9.127
10
4.74
3.25
3.995
1.110
0.070
11.574
12.030
11
2.25
2.26
2.255
0.000
0.197
7.063
7.063
12
2.02
2.1
2.060
0.003
0.236
6.272
6.277
13
4.54
3.58
4.060
0.461
0.063
11.988
12.171
14
3.83
3.47
3.650
0.065
0.076
11.214
11.246
15
5.33
4.58
4.955
0.281
0.041
13.826
13.901
16
4.26
3.52
3.890
0.274
0.068
11.681
11.799
17
4.84
4.68
4.760
0.013
0.044
13.548
13.552
18
5.24
5.16
5.200
0.003
0.037
14.319
14.320
−10 log (Rˆ2)
Mean 10*logMRˆ2
Table 5 ANOVA for MR studies Source
SS
dof
V
F ratio
SS
P(%)
Tool-material (density)
47.46
2
23.73
11.36
43.28
16.4
Applied voltage
62.19
2
31.1
14.89
58.01
22
Interaction
20.49
4
5.123
2.45
12.13
4.6
(2)
–
Pooled (P = 1.87)
Electrolyte concentration
9.11
Tool feed
27.83
2
13.92
6.66
23.65
9
Electrode spacing
44.67
2
22.34
10.69
40.49
15.4
Immersion depth
20.87
2
10.44
4.99
16.69
6.3
39.69
19
2.089
68.94
26.3
35
–
Error Total
263.2
–
263.2
SS = Sum of squares, dof = degree of freedom, V = variance, P = % effect
100.0
28
C. S. Jawalkar
Fig. 6 Effect of material removal on different parameters
As seen from Fig. 6e, the MR was marginally higher at the second level of electrode gap (=150 mm). The intermediate gap (150 mm) gave an optimum space for the chemical reactions to take place and for gases to dispense out without interference with the surrounding tools and accessories. At the higher electrode spacing (200 mm), the travel time of the ions toward its opposite end increased and thereby it marginally reduced the MR. The immersion depth had a significant effect on the MR activity. It was practically evidenced that, at lower immersions, (lesser than 1 mm depths of anode and cathode inside the electrolyte) the sparking phenomenon at both the electrodes was very unique and it is illuminated the work environment. This action, however, reduced the MR, as the stability of ion transfer between electrodes was getting disturbed. In the experimentation process, the cathode depth was kept constant (1 mm) while the anode depth was varied. At the higher depth of anode, higher MR was observed. The optimum value of MR was noticed at the immersion depth values of 3 mm for anode and 1 mm for cathode (which was kept constant) respectively, as shown in Fig. 6f. These values are attributed to the chemical reactions taking place between the electrodes. Interaction effect is shown in Fig. 6g; the interaction effect between tool–material and applied voltage was strong and significant, and it contributed to 4.6%. The error factor predominated with 26.3% effect, which could be attributed to the typical characteristics of the process, which has been earlier reported to be an unrepeatable process due to the influence of various parameters like physical, chemical, electrochemical, fluid mechanics, and material science beyond the actual control and immediate feasibility of the researcher; such errors are evident in complex processes involving multidisciplinary problems [30], whose exact contribution, method of assessment, and scientific phenomenon are not adequately established. The FESEM micrographs (taken on machine FEI Quanta 200, FEG-SEM, make: Czech Republic) are shown in Fig. 7 (at 50×) and Fig. 8 (at 150×). The average
Experimental Analysis on Material Removal Modes …
29
Fig. 7 FESEM micrograph of fabricated channel (50×) Micro cracks and heat affected zone Channel width ~ 500 µ.m. and SF
Fig. 8 FESEM micrograph of fabricated channel (at 150×)
Heat affected zone
Tool feed direction and profile marks
Craters formed by erosion Microchips
fabricated channel width was in the order of 500 μm. The surface roughness as measured (Equipment make: Taylor Hobson, Gauge range: 300 μm, Resolution: 0.01 μm) and was found to be in the range of 0.1–1.2 μm (Ra). In normal EDM with fine sparking, the surface finish (Ra values) reported earlier for Al-Si MMC were 3.71 μm, [31] and in case of ECM, it has been reported earlier as 1.2–1.6 μm (Ra) [32]. Line marks seen on the fabricated microchannels (Fig. 8) indicate the direction of cutting; additionally, fine microcracks and heat-affected zones were seen at both the edges along with some powdered microchips. The primary mechanism of cutting in ECDM has been earlier attributed to melting and evaporation due to the sparking action; additionally, the other reported mechanisms are chemical etching and some amount of mechanical spalling. Most of these MR modes are evidenced in Fig. 8. Some craters are highlighted on the micrographs along with etching marks at the boundaries. The surface finish of channels could be further improved through optimization of process parameters, through some hybridization and by using a pulsed DC power source and maintaining “lower surface finish” as the prime criteria.
30
C. S. Jawalkar
In the MR analysis in ECDM, due to the complex nature of the process, it is very difficult to establish, as to exactly at what temperature and energy; the glass becomes viscous. If the discharges can give the required energy, then glass could be made to flow. The viscosity of glass decreases progressively with an increase in temperature, until it reaches a steady flow state. The increase in temperature (heat input) thus increases the material removal, which has been proven experimentally. The chemical mechanism is very well-explained by the smoothing of the surfaces, as noticed in the surface roughness study; which is primarily due to the etching phenomenon; which causes removal at the protrusions. Electrochemical discharges heat up the work locally; which results in lowering the viscosity of glass and enhances chemical etching, through the release of OH radicals from the electrolytes. This chemical effect can be directly co-related to the glass molecular structure (Fig. 9), wherein the Si, O, and Na atoms react with the electrolyte atoms to give the etching effect. Using the NaOH electrolyte, workpiece gets etched through complexation of silicate [33] as shown in Equation (i). 2NaOH + SiO2 → Na2 SiO3 + H2 O
(1)
The sodium silicate thus formed is removed by the electrolyte. This chemical reaction gets strongly enhanced through the increase in temperature. In the conducted experimental study, the rise in electrolyte concentration has shown an increase in MR value upto a saturation point (which was found ≈ 25% during pilot experimental studies). With increase in machining depths, the possibility of electrolyte to enter the gap reduces; also, the formation of a stable film and discharge reduces. Machining at Fig. 9 Molecular structure of glass
Ca
Experimental Analysis on Material Removal Modes …
31
increased depth is always difficult; therefore, MR is also a function of the same, which was further practically evidenced and proved.
4 Tool Wear Studies In a similar manner, as shown in the preceding section for MR, the TW of cathode was studied and the findings are further illustrated. The tool weights were measured accurately before and after each experiment. Table 6 shows the average TW results, taken from two trials along with the variance and S/N ratios. The ANOVA was performed and its results are shown in Table 7. As evidenced through Table 6, all the factors tested were significant. Major parametric contribution resulted from the tool–material (18.36% effect), followed by applied voltage (16.45%), immersion depth (12.11%), electrode initial spacing (10.38%), electrolyte concentration (9.6%), tool feed (6.14%). The individual effects and their influences were further analyzed, as shown through Fig. 10. The TW was more for copper tools as compared to stainless steel Table 6 Tool wear values and signal to noise (S/N) Trial
Trial-1 TW (mg)
Trial-2 TW (mg)
TW Avg. (mg)
Variance (ν)
Sum of square of reciprocal
S/N ratio
S/N Ratio Mean
1
0.12
0.11
0.095
0.0004
119.45
20.772
0.095
2
0.14
0.16
0.155
0.0005
42.811
16.316
0.155
3
0.18
0.19
0.195
0.0005
26.770
14.276
0.195
4
0.18
0.21
0.205
0.0012
24.884
13.959
0.205
5
0.38
0.39
0.395
0.0004
6.437
8.087
0.395
6
0.38
0.35
0.410
0.0018
6.045
7.814
0.410
7
0.22
0.24
0.230
0.0002
19.011
12.790
0.230
8
0.21
0.23
0.220
0.0002
20.790
13.178
0.220
9
0.31
0.34
0.325
0.0005
9.528
9.790
0.325
10
0.44
0.41
0.520
0.0008
3.715
5.699
0.520
11
0.40
0.38
0.400
0.0008
6.297
7.991
0.400
12
0.20
0.16
0.190
0.0018
29.862
14.751
0.190
13
0.18
0.15
0.165
0.0005
37.654
15.758
0.165
14
0.38
0.35
0.405
0.0012
6.167
7.901
0.405
15
0.28
0.26
0.230
0.0018
19.896
12.988
0.230
16
0.38
0.35
0.550
0.0002
3.309
5.197
0.550
17
0.32
0.35
0.465
0.0004
4.639
6.665
0.465
18
0.38
0.34
0.560
0.0008
3.201
5.053
0.560
32
C. S. Jawalkar
Fig. 10 Effect on tool wear on different parameters
(SS) (Fig. 10a). This phenomenon was primarily due to the material characteristic, i.e., higher melting and evaporation in case of Cu as compared to stainless steel. The FESEM micrograph of the tool (Fig. 11), clearly shows the excess tool wear in copper. Considering “low wear characteristic” as the criteria for the tool, the SS tools were preferred, since Cu melted away at a faster rate, thereby reducing the work-tool gap, in the process. As shown in Fig. 10b, it can be seen that, an increase in the applied voltage, results in an increase in tool wear, due to increased heat input, which is an established phenomenon [34, 35]. At the electrolyte, concentration of 15% (150 g/L), the tool wear was found to be minimum (Fig. 10c), the higher concentration resulted in marginally higher wear due to the chemical reactions and other effects. At the second level of the feed (feed = 0.3 mm/min), the tool and workpiece had maximum interaction time. The intermediate (tool–work) gap being minimum, almost all phases in the discharge process could get completed, thereby resulting in marginally increased wear. As the tool feed increased, the TW was marginally reduced, since the melting and evaporation cycle were inadequate. The higher feed rate thus resulted in more TW due to chemical, sparking, and other effects resulting in the formation of rough channels, due to inadequate time interval for tool and material interaction to take place. The TW was minimum (criteria considered was lower the better) at the second level (Fig. 10d). As seen in Fig. 6 e, the tool wear was lowest at the spacing of 50 mm (initial distance between electrodes), which yielded maximum sparking and heat energy. The immersion depth of 4 mm inside the electrolyte, resulted in minimum tool wear, as minimum sparking occurred at this depth (Fig. 10f). Interaction effect between tool material density combined with voltage was weak and not significant, 1.89% (Fig. 10 g).
Experimental Analysis on Material Removal Modes …
33
Excess tool wear
(a) FESEM micrograph of “Cu tool: (before use)
(b) FESEM micrograph of worn out “Cu tool” (after 36 cycles) Micro-pores and craters
Crater and erosion wear formed due to sparking
(c) FESEM micrograph of “SS tool”
(d) FESEM micrograph of ‘SS tool”
(after 18 cycles)
(after 36 cycles)
Fig. 11 FESEM micrographs of tool wear at different cycles Table 7 ANOVA for tool wear Source
SS
dof
V
F ratio
SS
P
Tool material (Density)
67.98
2
33.99
15.59
63.62
18.36
14.07
57
16.45
33.26
9.6
Applied voltage
61.36
2
30.68
Interaction
17.11
(4)
Pooled (P = 1.89%)
Electrolyte concentration
37.62
2
18.81
8.63
Tool feed
25.65
2
12.83
5.89
21.29
6.14
Electrode initial spacing
40.32
2
20.16
9.25
35.96
10.38
Immersion depth
46.32
2
23.16
10.62
41.96
12.11
Error SST
50.14 346.5
19
2.18
–
93.41
26.96
n = 35
–
–
346.5
100
SS = Sum of squares, dof = Degree of freedom, V = Variance, P = % effect
34
C. S. Jawalkar
5 FESEM Studies on Tool Wear Some studies on tool wear were conducted and illustrated further. The FESEM micrographs (Fig. 11) were taken and reported for the tools (for copper, Fig. 11a, b and for stainless steel, Fig. 11c, d), before and after experiments. The micrographs indicate immense microcraters and etching marks along with some large craters. The previously reported material removal modes in ECDM were melting and evaporation [34], chemical etching [33], spalling [14], and percolation [36]. The key modes being melting and evaporation (caused by thermal effect of sparking) and chemical etching effects have been further elaborated and presented in this paper along with their correlation to FESEM micrographs.
6 Conclusions The paper discusses the MR and TW results obtained through experimental studies conducted for microchanneling through ECDM process using six different process parameters, two tools, and two response parameters, on optical glass. Some innovative findings are as follows: • The material removal modes in ECDM have been illustrated and correlated with those obtained in the EDM process and the microstructural and material properties are also discussed. • The results on tool-immersion depths and its co-relation with MR and TW have been reported. The optimized parametric effects were as follows. • In MR studies, the most influencing factor was the applied voltage (22%). • The tool material and applied voltage (18.4% and 16.5%, respectively) were the more significant parameters reported in the TW studies. • The optimum parametric values for material removal studies were found at the following experimental conditions: the use of SS tool, with an applied voltage = 80 V, electrolyte concentration = 10% (100 g/L), tool feed rate = 0.75 mm/min, intermediate electrode spacing = 150 mm and immersion depth ratios = 3 mm; yielded best MR results. • Optimum parametric values in the tool wear studies were found at the following experimental conditions: using SS tool, applied voltage = 60 V, electrolyte concentration = 15% (150 g/L), tool feed rate = 0.30 mm/min, inter-electrode spacing = 50 mm and anode immersion depth = 4 mm, yielded the best results for in TW studies. • The surface finish (Ra value) obtained on optical glass specimens was in the range of 0.1–1.2 μm.
Experimental Analysis on Material Removal Modes …
35
• It was further seen that the stainless steel tools contributed to increased MR rates and reduced the tool wear; whereas, the copper tools were better in terms of electrical conductivity and thermal characteristics. The experimental data and FESEM reports supported theoretical findings; detailed parametric effects, its logical scientific reasoning shall help researchers to understand the process better. In the micro-ECDM domain, the reported optimal parametric settings can further aid the researchers in precise experimentations.
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19. Goud, M., Sharma, A.K.: On performance studies during micromachining of quartz glass using ECDM. J. Mech. Sci. Technol. 31(3), 1365–1372. https://doi.org/10.1007/s12206-017-0236-8 (2017) 20. Rajput, V., Pundir, S.S., Goud, M., Suri N.M.: Multi-response Optimization of ECDM Parameters for Silica (Quartz) Using Grey Relational Analysis, Silicon. https://doi.org/10.1007/s12 633-020-00538-7 (2020) 21. Liu, Y., Zhang, C., Li, S., Guo, C., Wei, Z.: Experimental study of micro ECDM of ultra-clear glass with a rotating helical tool. Processes 7, 195 (2019) 22. Charak, A., Jawalkar, C.S.: Experimental Studies in Micro Channelling on Borosilicate Glass Using RSM Optimization Technique, Silicon https://doi.org/10.1007/s12633-019-002 69-4 (2019) 23. Antil, P.: Modeling & multi-objective optimization during ECDM of SiC reinforced epoxy composite. SILICON 12, 275–288 (2020) 24. Kumar, N., Mandal, N., Das, A.K.: Micro Machining through ECDM process: a review. Mater. Manuf. 363–404 (2020) 25. Manpreet, S., Singh, S.: Sustainable ECDM process: characterization of emission products & occupational risks to operator. Mach. Sci. Tech. https://doi.org/10.1080/10910344.2020.175 2238 (2020) 26. Singh, M., Singh, S.: Environmental aspects on various electrolytes used in ECDM process. J. Braz. Soc. Mech. Sci. Eng. 42, 395 (2020) 27. Ramulu, M., Paul, G., Patel, J.: EDM surface effects on the fatigue strength of a 15 vol. % Si Cp/Al metal matrix composite material. J. Compos. Struct. 54, 79–86 (2001) 28. Jawalkar, C.S., Sharma, A.K., Kumar, P.: Parametric study while micro channeling on optical glass using microcontroller driven ECDM process. J. Adv. Mater. Process. 585, 417–424 (2012) 29. Jawalkar, C.S., Sharma, A.K., Kumar, P.: Experimental investigations on performance of ECDM using design of experiment approach. J. Mech. Eng. (i-manager) 24-29 (2011) 30. Fascio, V., Wuthrich, R., Bleuler, H.: SACE in the light of electrochemistry. Electrochimica Acta 49, 3997–4003 (2004) 31. Bhattacharya, B., Doloi, B.N., Sorkhel, S.K.: Experimental investigation into EDM of nonconductive ceramic materials. J. Mater. Process. Technol. 95, 145–154 (1999) 32. Clifton, D., Mount, A.R., Jardine, D.J., Roth, R.: Electrochemical machining of gamma titanium aluminide inter-metallic. J. Mater. Process. Technol. 108, 338–348 (2001) 33. Melcher, M., Wiesinger, R., Schreiner, M.: Degradation of glass artifacts: application of modern surface analytical techniques. Acc. Chem. Res. 43(6), 916–926 (2010) 34. Jain, V.K., Singh, M., Agarwal, D.C., Sidpara, A.: Investigation into machining of alumina ceramics using ECSM process. In: Proceedings of the 3rd International and 24th AIMTDR Conference, Vishakapatnam, India, pp 235–240 (2010) 35. Jawalkar, C.S., Sharma, A.K., Kumar, P.: Micromachining with ECDM: research potentials and experimental investigations. Int. J. Mech. Aerosp. Eng. 6, 7–12 (2012) 36. Wuthrich, R., Bleuler, H.: A model for electrode effects using percolation theory. ElectroChimica Acta 49, 1547–1554 (2009)
Formulation of Empirical Correlation for Heat Transfer Coefficient, for Gases, in Terms of Fluid Properties, Tube Diameter and Mass Velocity; for Forced Convection Through Tubes Narendra J. Giradkar, Vivek M. Korde, and Jayant Giri Abstract The present investigation explains the mechanism of heat transfer for gases in forced convection through tubes. The experimental data from number of investigations for gases is correlated for gases in terms of basic variables (measured) and to obtain the empirical correlation. The above phenomenon is explained with the help of the concept of overall turbulence which is a combined effect of mobility of fluid in sublayer and eddy turbulence in core. The heat transfer rate, in tube diameter around 5.0 mm, is maximum for gases. Keywords Heat transfer coefficient · Gases · Internal forced convection · Turbulence
1 Introduction The heating and cooling of fluids flowing inside conduits are among the most important convective heat transfer processes in engineering. The design and analysis of all types of heat exchangers require the accurate evaluation of heat transfer coefficients between the inner surface of tube and fluid. Since the heat transfer coefficients are evaluated with the help of empirical correlations, it is essential that the correlation should represent the applied heat transfer phenomenon precisely. Forced convection in tubes involves many independent variables like D, u, ρ, μ, Cp, k; many of which cannot be manipulated individually without disturbing others while conducting experiments. To tackle this complex situation, investigators in this field have taken the help of a mathematical tool called the dimensional theorem, which reduced the above-mentioned six variables into two. For example, Nu = f (Re, Pr). Because of this conversion of independent variables into dependent, it became necessary to study the effect of only two variables since the effect of the remaining four was then fixed automatically without experimental verification. N. J. Giradkar (B) · V. M. Korde · J. Giri Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Wanadongri, Hingna Road, Nagpur, Maharashtra, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_4
37
38
N. J. Giradkar et al.
McAdams [1], Knudsen and Katz [2], Gnielinski [3], Rice [4] and Petukhov [5] have given an excellent survey of heat transfer for internal forced convection. The summary of experimental results presented by various investigators for gases is given in Table 1. The following conclusions are drawn from these investigations. 1.
2.
3. 4.
5.
6.
The effect of the diameter, viscosity, specific heat and conductivity on coefficient of heat transfer is not individually determined. The results are presented in terms of dimensionless numbers. Most of the authors have introduced the ratio of wall temperature to bulk temperature with some exponent to compensate for the property variation across the tube. This exponent varies from −0.7 to −0.185. In most cases, the properties are evaluated at film temperature, and some at bulk temperature. As fluid viscosity changes with temperature and to account for its variation, a viscosity ratio correction is introduced in the equation. However, the properties can be evaluated at film temperature rather than using a viscosity ratio correction. And so some authors have indicated that the ratio of wall temperature to bulk temperature has no effect on coefficient of heat transfer, if properties are calculated at film temperature. As indicated by Pickett [6], Taylor [7] and Thompson and Geery [8], there is no effect of pressure on coefficient of heat transfer for gases. To compare the correlations suggested by various investigators, the heat transfer coefficients were evaluated by using these correlations for the following conditions by taking air as a motive fluid, G = 200 kg/m2 -s, T w = 375 K, T b = 330 K and D = 4 mm ID. The variation in coefficient of heat transfer was observed to be quite wide; for example, Petukhov’s [5] correlation gave the value of 604.4 W/m2 -K, whereas that of Humble [9] 1027.6 W/m2 -K. If Reynolds number is increased by increasing tube diameter, these correlations show a decrease in coefficient of heat transfer; and if Reynolds number is taken as an index of turbulence, this decrease in coefficient of heat transfer with increased values of Reynolds number creates confusion; and necessitates critical examination of experimental results. Therefore, it was decided to correlate the experimental results in terms of basic measured variables. McAdams [1] has given the velocity and temperature profiles for air and water in tubes from where it is clear that the velocity and temperature fields for gases are identical, whereas they deviate considerably from each other in case of liquids. Similarly, the resistance for heat transfer in case of water lies mostly in the laminar sublayer while the resistances due to sublayer and turbulent core are comparable for gases. This indicates that the mechanism of heat transfer in two cases might be different. Therefore, it was decided to study the data of liquids and gases separately.
Author
Barnes and Jackson [10]
Bialokoz and Saunders [11]
Cholette [12]
Colburn [13]
Delpont [14]
Dittus–Boelter [15]
Evans and Sarjant [16]
Fowler and Warner [17]
Humble et al. [9]
McCarthy and Wolf [18]
McEligot et al. [19]
S/No
1
2
3
4
5
6
7
8
9
10
11
Tw Tb
m
0.4 Nub = 0.021Re0.8 b Pr b
Tw Tb
0.4 Nub = 0.023Re0.8 b Pr b
−0.3
−0.5 −0.7 Tw 1 + Dx Tb
D
Nuf = 0.034Re0.8 f Pr f
0.4 L −0.1
He, N2 , Air
H2
Air
0.7 ≤ Pr ≤ 1
Tw Tb
Tw Tb
< 120
= 1.0 to 2.4
= 1.5 to 2.8
L D
= 1.17 to 2.47 30
63
< 63
0.023Re−0.2 f
St = 0.0265
2/3 Stb Pr f
x D
= 1.08 to 1.73
= 1.0 to 2.4
10.5
40
= 1.1 to 27.6
= 1.1 to 1.4,
x D
Tw Tb
= 1.1 to 16.5
Tw Tb = 1.78 to 2.38, x D = 2.2 to 52.4
Tw Tb
Tw Tb
> 20
n = 0.11 for T w > T b n = 0.25 for T w < T b n = 0 for constant heat flux or for gases
Tw Tb upto
Remarks
40 N. J. Giradkar et al.
Formulation of Empirical Correlation for Heat Transfer …
41
2 Effect of Temperature on Coefficient of Heat Transfer for Helium–Argon Mixtures The experimental results of Pickett et al. [6] are of prime importance in determining the effect of viscosity, specific heat and thermal conductivity on coefficient of heat transfer. They have conducted experiments for helium–argon mixtures, in turbulent flow condition inside a circular vertical tube, which is electrically heated, with inner diameter of 3.12 mm and with 0.56 mm thickness of wall. The heated section length was 98 diameters and calming section length 92 diameters. Chromel–alumel thermocouples (0.13 mm diameter) were used to measure wall temperature. Uniform axial heat flux condition was obtained by using electrical resistance heating for test section. The experiments were conducted for the mixtures having molecular weight of 15.3 and 29.7. The pressure range was 4.7–9.7 atmospheres. Table 2 shows the range of variables. They have correlated their results by the following equation,
0.55 Nub = 0.021 Re0.8 b Prb
Tw Tb
−0.4
D +0.85 x
(1)
and have mentioned that the above equation predicts 92% of the helium–argon data to within 10% for the range 2.1 < x/D < 81.6. As shown in Table 1, the numerous authors working on gases have presented their results in form of correlations using dimensionless numbers, viz. Nusselt number, Reynolds number and Prandtl number, and when they found that their results could not be correlated satisfactorily by considering only these three groups they have incorporated the factor TTwb as has been done by Pickett et al. in Eq. (1). As a matter of fact, the wall temperature can affect the heat transfer process in the following manner. 1. 2.
When the fluid inside the tube has considerable emissivity, then the heat can be transferred between inner surface of tube and fluid by radiation. When a low boiling liquid is being heated and if the inner surface temperature is greater than the boiling point of liquid, then the vapour bubbles may form
Table 2 Range of variables of helium–argon mixtures S/No
Property
Mol. Wt. 15.3
Mol. Wt. 29.7
1
Specific Heat (J/kg-K)
1357.0
699.13
2
Film Temperature (K)
311.0–704.7
310.8–734.2
3
Mass Velocity (kg/m2 -s) 232.5–403.3
228.9–600.0
4
Viscosity (N-s/m2 )
2.38 × 10–5 –4.49 × 10–5
2.41 × 10–5 –4.67 × 10–5
5
Thermal Conductivity (W/m–K)
0.0759–0.1348
0.0341–0.0633
42
N. J. Giradkar et al.
near the wall disturbing the laminar sublayer even though the bulk temperature of the fluid may be quite low.
Heat Transfer Coefficient h (W/m2-K)
Heat Transfer Coefficient h (W/m2-K)
Heat Transfer Coefficient h (W/m2-K)
In case of gases other than carbon dioxide and water vapour, there is no question of wall to gas heat transfer by radiation. The tube wall, which acts as a source of heat, transfers its energy to the bulk of the gas by forced convection through the laminar sublayer, which acts as a medium for heat transfer. And therefore, the thermal properties of fluid in sublayer along with its dynamic condition will control the process of heat transfer. The fluid properties like viscosity, thermal conductivity definitely depend upon temperature, and therefore, the coefficient of heat transfer will also depend on the film temperature but when the effects of these properties are included in the correlation then the wall or bulk temperature should not be a parameter. To critically study the effect of temperature on coefficient of heat transfer the experimental data of Pickett et al. were examined more critically. As mentioned earlier, run they had measured heat transfer coefficient at 13 loca in a particular tions Dx ratio by keeping mass velocity and heat flux constant. Similar runs were taken at various heat fluxes, by keeping mass velocity constant, which varied wall temperatures and consequently bulk and film temperatures. A few of the results thus obtained are shown in Fig. 1a, b for molecular weights 15.3 and 29.7, respectively. For example, four points on line AB in Fig. 1a represent data taken at four heat fluxes resulting in variation in wall temperature from 366.4 K to 712.1 K at Dx ratio of 40.8, keeping mass velocity constant at 232 kg/m2 -s. In this case, the variation in TTwb is reported to be from 1.14 to 1.61 and in film temperature as 343.3–577.1 K. The similar results are represented by lines CD, EF, GH, IJ and KL for two mass velocities, viz. 232 and 402 kg/m2 -s at three locations. These results show that the 2000 1500 1000
C
D
G 402
X/D 40.8
A
B
232
40.8
2000
D
C
1000
X/D 40.8
232
40.8
G
X/D
598
65.0
232
65.0
G
X/D
598
81.4
800
800 600
600
400
400
400
300
500
600
700
2000 1500 1000
H
G
F
E
800
G 402
X/D 65.0
232
65.0
B
A 400
300
500
600
700
2000
800
1500
H
G
1000 800
800
400
300
500
600
700
2000 1500 1000
L
K I
800
G 402
X/D 81.4
232
81.4
J
400
F
E
600
600 400
G 598
1500
400
300
500
600
700
2000
800
1500
L
K
1000 800
800 600
600
400
400
300
400
500
600
FILM TEMPERATURE
700
Tf (K)
800
232
I 300
J 400
500
600
FILM TEMPERATURE
700
81.4
800
Tf (K)
Fig. 1 Variation of heat transfer coefficient with film temperature for Helium Argon mixture. a Molecular Weight: 15.3. b Molecular Weight: 29.7
Formulation of Empirical Correlation for Heat Transfer …
43
coefficient of heat transfer is independent of film temperature, wall temperature or ratio of wall to bulk temperature. The effect of temperature on coefficient of heat transfer for molecular weight 29.7 is represented in Fig. 1b for mass velocities 232 and 598 kg/m2 -s at three locations by lines AB, CD, EF, GH, IJ and KL. In this case coefficient of heat transfer is not affected by either the film temperature, wall temperature or wall-to-bulk temperature ratio. For gases, both viscosity and thermal conductivity increase with temperature and these two parameters have opposing effect on coefficient of heat transfer. The heat transfer rate increases with the increase in thermal conductivity, whereas the viscosity hinders the movement of gas molecules. Since the coefficient of heat transfer remains unchanged with film temperature in these cases of gas mixtures, it seems that the effect of viscosity and thermal conductivity on heat transfer coefficient is equal but opposite. It is imperative that these two gas mixtures will give the same value of coefficient of heat transfer at the same mass velocity and x/D ratio. The comparison of results shown in Fig. 1a, b indicates at mass velocity 232 kg/m2 -s and x/D ratio 40.8, that the coefficient of heat transfers for molecular weights 15.3 and 29.7 is about 1134 W/m2 K and 563 W/m2 -K, respectively. This reduction in coefficient of heat transfer with increase in molecular weight is due to variation in thermal conductivity, viscosity and specific heat of two gas mixtures. The similar results are shown in these figures for the same mass velocity of 232 kg/m2 -s at x/D ratios 65 and 81.4 by lines EF and IJ, respectively.
3 Effect of Gas Properties To begin with, the experiments were performed by Kripalni [24] to study the effect of gas density on coefficient of heat transfer. Accordingly, the experimentations were performed in a 6.65 mm copper tube to get threefold variation in gas density with motive fluid as air, at different pressures. It was observed that, at constant mass velocity, coefficient of heat transfer is not a function of density. The effect of pressure is mentioned in the literature also. Thomson and Geery [8] conducted experiments for hydrogen with pressures varying from 47 to 96 atmospheres and that pressure has no effect on coefficient of heat transfer. Taylor [23] also conducted experiments for hydrogen with pressures up to 175 atmospheres, and no effect of pressure on coefficient of heat transfer is mentioned. Similarly, Pickett et al. [6] whose data is used to study the effect of gas properties also did not mention the effect of pressure on coefficient of heat transfer. Therefore, the coefficient of heat transfer in terms of turbulence and thermal behaviour controlling parameters can be represented by a relation,
D a b c d f (2) h = g D G μ k Cp 1 + q x
44
N. J. Giradkar et al.
Table 3 Experimental results for forced convection in tubes for gases from 3.12 mm to 4.82 mm diameter S/No Author
Fluid
Heat Exchanger Experimental correlation D (mm) Lc Lh (mm) (mm)
Experimental correlation normalized
1
3
4a
7
2
4b
4c
6
01
Pickett [6]
He-Ar 3.12 Mol. Wt. 29.7
287
306
h= 0.0757
G 0.71 k 1.34 µ1.29 Cp0.63
h= 0.0569
G 0.76 k 1.34 µ1.29 Cp0.63
02
Pickett [6]
He-Ar 3.12 Mol. Wt. 15.3
287
306
h= 0.1227
G 0.63 k 1.34 µ1.29 Cp0.63
h= 0.0573
G 0.76 k 1.34 µ1.29 Cp0.63
03
Kriplani Air [24]
4.00
710
680
h= 0.00837
G 1.16 k 1.34 µ1.29 Cp0.63
h= 0.0707
G 0.76 k 1.34 µ1.29 Cp0.63
04
Cholette Air [12]
4.82
303
310
h= 0.06751
G 0.81 k 1.34 µ1.29 Cp0.63
h= 0.0838
G 0.76 k 1.34 µ1.29 Cp0.63
Pickett et al. [6] had conducted experiments at only three mass velocities, viz. 232.0, 402.0 and 598 kg/m2 -s, and therefore, while processing their data the viscosity, which had wide variation, was taken as an independent variable. As already mentioned, the variation in gas properties is given in Table 2 and details of heat exchanger in Table 3. Equation (2) can be presented as, h = pμc D 1 + q x G b k d Cpf
(3)
The constant ‘p’ includes the effect of tube diameter. Their data on helium–argon mixtures were processed for the various assumed values of the coefficient of D/x ratio q, and the exponents of mass velocity, thermal conductivity and specific heat. The optimum value of these constants was evaluated by using method of least squares. The exponent of specific heat was varied from −2.5 to 1.0 and thermal conductivity from 0 to 2.5. The regression was carried out in increments of 0.1. As usual, the criteria followed in determining the optimum values of constants were regression coefficient, standard deviation, number of points with positive and negative error, average positive and negative error for total data as well as for individual gas mixtures. The best results were obtained with the coefficient of D/x ratio as 0.85 and the exponents of mass velocity, specific heat, thermal conductivity and viscosity as 0.69, −0.63, 1.34 and −1.29, respectively. The final equation obtained was:
D G 0.69 k 1.34 h = 0.0849 1 + 0.85 x µ1.29 Cp0.63
(4)
Formulation of Empirical Correlation for Heat Transfer …
45
with standard deviation and regression coefficient as 4.22% and 0.98, respectively for 221 data points. This equation is valid only for 3.12 mm diameter tube. The fluid properties used in the above equation were evaluated at film temperature. The specific heats of molecular weight 15.3 and 29.7 were taken as 1357.0 J/kg-K and 699.13 J/kg-K, respectively. The values of viscosity and thermal conductivity were taken from Pickett’s [6] paper, and their dependence on temperature is as follows: For molecular weight 15.3, k = 0.13655 × 10–2 T 0.7 W/m-K μ = 0.0322 × 10−5 T 0.75 N-s/m2 and for molecular weight 29.7, k = 0.05469 × 10–2 T 0.72 W/m-K μ = 0.02902 × 10−5 T 0.77 N-s/m2 When these values are substituted in the property group of Eq. (4), the following results are obtained. For molecular weight 15.3, k 1.34 = 365.3T −0.0295 µ1.29 Cp0.63
(5)
and for molecular weight 29.3, k 1.34 = 186.1T −0.0285 µ1.29 Cp0.63
(6)
Two observations are worth noting from Eqs. (5) and (6) 1.
2.
The exponents of temperature for both the gases are nearly the same, and their very low value suggests that the coefficient of heat transfer should not change with temperature. We have already observed that at constant mass velocity and x/D ratio, the coefficient of heat transfer does not change with film temperature as shown in Fig. 1a, b for molecular weights 15.3 and 29.7, respectively. −0.0295 The ratio of property groups as given by Eqs. (5) and (6) is 365.3T = 186.1T −0.0285 1.963T −0.001 ; meaning thereby that at constant mass velocity and x/D ratio, the coefficient of heat transfer in case of mixture of molecular weight 15.3 should be almost 2 times that of mixture with molecular weight 29.7. This result is confirmed by experimental values of coefficient of heat transfers shown in Fig. 1a, b for two mixtures. For example at mass velocity 232 kg/m2 -s and x/d ratio 40.8, the coefficient of heat transfers shown by lines AB in these figures is 1134 W/m2 -K and 563 W/m2 -K for molecular weights 15.3 and 29.7, respectively; the former being exactly two times the latter. It can be concluded from these observations that the property group in Eq. (4) predicts the experimental results very precisely and that the consideration of temperature group (T w /T b ) is not necessary if the properties of gas are evaluated at film temperature.
46
N. J. Giradkar et al.
4 Heat Transfer Coefficients in Tubes Upto 5 mm The overall turbulence depends upon tube diameter, mass velocity and viscosity and to be precise also on parameter x/D ratio. Cholette [12] conducted experiments with varying lengths of heat exchangers and has shown that the coefficient of heat transfer becomes independent of tube length after about 10 diameters. The data the summary of which is given in Tables 3 and 4 and processed for determining the effect of the diameter on the coefficient of heat transfer were taken from heat exchangers with large L/D ratios, and therefore, it was not necessary to consider its effect on coefficient of heat transfer. Coefficient of heat transfers obtained by Pickett et al. [6] was at constant heat flux and gave the point values. While considering their results, these point values of coefficient of heat transfers were divided by parameter, (1 + 0.85 D/x) to eliminate the effect of x/D ratio and then were processed along with the results of other authors taken at constant wall temperature conditions. It is observed in the literature, that the authors have presented experimental results by forming the empirical correlations or plotting the graphs and since the objective of the present investigation is to process the experimental data in terms of measured variables, such results available in the form of correlations or graphs were not of any use. The detailed experimental results available for processing were those of Pickett et al. [6], Cholette [12], Bialokoz and Saunders [11], Kolar [25] and Evans and Sarjant [16] taken, respectively, in 3.12, 4.82, 11.0, 26.0 and 76.2 mm tubes. The data taken Table 4 Experimental results for forced convection in tubes for gases from 6.65 mm to 76.2 mm diameter S/No Author
Fluid Heat Exchanger
1
3
2
Experimental correlation D (mm) Lc Lh (mm) (mm)
Experimental correlation normalized
4a
7
4b
4c
6
01
Kriplani [24]
Air
6.65
600
970
h= 0.412
G 0.91 k 1.34 µ1.29 Cp0.63
02
Bialkoz Air and Saunders [11]
11.0
605
792
h= 0.130
G 0.67 k 1.34 µ1.29 Cp0.63
h= 0.080
G 0.75 k 1.34 µ1.29 Cp0.63
03
Kriplani [24]
Air
12.0
780
960
h= 0.054
G 0.83 k 1.34 µ1.29 Cp0.63
h= 0.072
G 0.75 k 1.34 µ1.29 Cp0.63
04
Kolar V. [25]
Air
26.0
1020
800
h= 0.0428
h= 0.054
G 0.75 k 1.34 µ1.29 Cp0.63
05
Evans and Sarjant [16]
Air
76.2
–
2438
h= 0.054
h= 0.052
G 0.75 k 1.34 µ1.29 Cp0.63
Lc = Calming section, Lh = Heating section
G 0.82 k 1.34 µ1.29 Cp0.63
G 0.72 k 1.34 µ1.29 Cp0.63
h= 0.082
G 0.75 k 1.34 µ1.29 Cp0.63
Formulation of Empirical Correlation for Heat Transfer …
47
in these tubes were not adequate to determine the effect of diameter, and therefore, experimentations were done using 4.0, 6.65 and 12.0 mm copper tubes, the details of which are given elsewhere [24]. The exchangers were constructed for heating air by steam and had sufficient lengths of heating and calming sections as given in Tables 3 and 4. Copper-constantan thermocouples along with 41 /2 digit millivoltmeter were used for precise measurement of wall temperature. Orifice meter, calibrated by water displacement method was used for airflow measurement and the outlet and inlet temperatures of air were measured by mercury in glass thermometers with least count of 0.1 °C. During these studies, it was observed that the coefficient of heat transfer increases with diameter up to about 5 mm and then falls. Therefore, the experimental results for these two diameter ranges have been processed separately. The summary of these results is given in Tables 3 and 4, respectively, for these two diameter ranges. Having determined the effect of properties of gas on coefficient of heat transfer, the Eq. (4) can now be written as, h
µ1.29 Cp0.63 = pG b k 1.34
(7)
where the coefficient ‘p’ includes the effect of tube diameter. Taking the left-hand side as dependent variable and mass velocity G as independent variable the data of Pickett et al. (3.12 mm diameter), Kripalani (4.0 mm diameter) and Cholette (4.82 mm diameter) were processed by using the method of least squares to determine constants p and b. The results obtained are shown in column (6) of Table 3. Even though the exponent of mass velocity was supposed to be constant in Eq. (7), it was found to vary from experiment to experiment, as shown in column (6). In Pickett’s data (molecular weight 15.3), it was minimum as 0.63, while maximum as 1.16 in the Kripalani’s for 4.0 mm diameter. The constant ‘p’ which includes the effect of diameter also varied in these experimental correlations. To study the effect diameter from the correlation constants in Eq. (7), it was necessary to normalize the correlation constants reported in column (6) for an average exponent of mass velocity, which was found to be 0.76 for the complete data reported in this table. The correlations in column (7) of Table 3 represent the experimental results for this average value of exponent of mass velocity as 0.76. It was observed that the correlation constant increased from 0.0569 to 0.0838 when the tube diameter changed from 3.12 mm to 4.82 mm. These results are shown in Fig. 2 where correlation constant ‘p’ reported in column (7) of Table 3 is plotted as a function of tube diameter and is represented by line AB which follows the equation, p = 7.28D 0.84 The generalized correlation thus can be written as,
(8)
48
N. J. Giradkar et al. 0.84
AB, p=7.28 D (Equation: 08) -0.3 CD, p=0.0188 D (Equation: 10)
0.10
CONSTANT `p'
0.09
B
0.08
PICKETT et. al. [18] KRIPALANI [24] CHOLETTE [08] BIALKOZ AND SAUNDERS [07] KOLAR V. [25] EVANS ANDSARJANT [12]
C
0.07 0.06 0.05 0.04 3
A D 4
5
6
7
8
9 10
20
30
40
50
60
70 80
DIAMETER (mm)
Fig. 2 Effect of tube diameter on coefficient of heat transfer for gases
h= 7.28
G 0.76 D 0.84 k 1.34 µ1.29 Cp0.63
(9)
with regression coefficient of 0.98 and standard deviation of 8.57% for 261 data points. The experiments reported in Table 3 were carried out at constant wall temperature conditions except that of Pickett et al. [6] where resistance heating was used to maintain constant heat flux.
5 Heat Transfer Coefficients in Tube Diameters Above 5.0 mm The experimental correlations for the available data for tube diameters 6.65, 11, 12, 26 and 76.2 mm are given in column 6 of Table 4. In this case, the exponent of mass velocity was found to vary from 0.67 to 0.91. The normalized correlations for the average exponent of mass velocity as 0.75 are given in column (07) of Table 4. In this case, the correlation constant was found to decrease from 0.082 to 0.052 when tube diameter increased from 6.65 to 76.2 mm. Figure 2 shows the results, where correlation constants reported in column 7 of Table 4 are plotted as a function of diameter of tube and are represented by the line CD which follows the equation, p = 0.0188D −0.3
(10)
The generalized correlation becomes, h = 0.0188
G 0.75 k 1.34 µ1.29 Cp0.63 D 0.3
(11)
Formulation of Empirical Correlation for Heat Transfer …
49
with regression coefficient of 0.99 and standard deviation of 4.01% for 109 data points. Comparing Eqs. (8) and (10), it can be seen that the coefficient of heat transfer increases with tube diameter upto 4.82 mm, and for larger diameters, it decreases as shown in Fig. 2.
6 Discussion The transfer of heat from inner surface of tube to fluid in core takes place through the laminar sublayer by conduction as well as by convection. The sublayer acts as a medium of energy transfer, and therefore, the heat transfer rate depends on the thermal and dynamic behaviour of the same. In transfer processes may be mass transfer or heat transfer, the basic transport property is diffusivity; and specifically in k . As such the exponents of specific case of heat transfer, the thermal diffusivity ρCp heat and thermal conductivity in Eq. (4) as −0.63 and 1.34, respectively, properly represent the thermal behaviour. The overall turbulence in the tube depends upon mobility of fluid insublayer and and energy the turbulence in core which are functions of Reynolds number Duρ μ 2 dissipation uD , respectively, and is expressed by relation,
Overall Turbulence = f 1
Duρ µ
+ f2
u2 D
(12)
As per Eq. (12), in case of small tube diameters the eddy turbulence represented by second term of RHS is high and the overall turbulence is controlled by mobility of fluid in sublayer and is a positive function of tube diameter. In case of large diameters, the sublayer mobility is high and the overall turbulence depends upon eddy turbulence and thus decreases with increase in tube diameter. Since the heat transfer depends upon overall turbulence, the coefficient of heat transfer is found to increase up to 5 mm tube diameters and then fall as shown in Fig. 2.
7 Conclusions 1. 2. 3.
Equations (9) and (11) should be used for heat exchanger design. In case of gases, coefficient of heat transfer is maximum around 5 mm tube diameter. Up to 5.0 mm the sublayer mobility, which depends upon Reynolds number Duρ controls the heat transfer process; whereas in case of large diameter μ
50
4. 5.
N. J. Giradkar et al.
2 tubes, the eddy turbulence, which depends upon energy dissipation uD , is a controlling factor. The data on gases and liquids should not be intermixed while correlating experimental results. Dimensional analysis a purely mathematical tool which converts independent variables into the interdependent variables and hence does not represent a complex highly irreversible physical phenomenon of heat transfer in its true form
References 1. McAdams, W.H.: Heat Transmission. McGraw Hill Book Co. Inc., New York, pp. 206–208 (1954) 2. Knudsen, J.G., Katz, D.L.: Fluid Dynamics and Heat Transfer. McGraw Hill Book Company Inc., pp. 391–406 (1958) 3. Gnielinski, V.: New equations for heat and mass transfer in turbulent pipe and channel flow. Int. Chem. Eng. 16, 339–368 (1976) 4. Rice, W.: Forced convection of heat in gases and liquids–2. Ind. Eng. Chem. 16(5), 460–467 (1924) 5. Petukhov, B.S.: Heat transfer and fluid friction in turbulent pipe flow with variable physical properties. Adv. Heat Transf. 6, 503–564 (1970) 6. Pickett, P.E., Taylor, M.F., McElgott, D.M.: Heated turbulent flow of helium argon mixtures in tubes. Int. J. Heat Mass Transf. 22, 705–719 (1979) (Pergamon Press, Great Britain) 7. Taylor, M.F.: Correlation of Local Heat Transfer Coefficients for Single Phase Turbulent Flow of Hydrogen in Tubes with Temperature Ratio upto 23, Lewis Research Centre, NASA, pp. 1–27 (1968) 8. Thompson, W.R., Geery, E.L.: Heat Transfer through Cryogenic Hydrogen at Super Critical Pressures, Report No. 1842 (AFFTC-TR-61–52, DDC no AD-263465) Aerofet, General Corporation Huly (1960) 9. Humble, L.V., Lowdermilk, W.H., Desmond, L.G.: Fluid Dynamics and Heat Transfer, NACA Report 1020 (1951) 10. Barnes, J.F., Jackson, J.D.: Heat transfer to air, carbon dioxide and helium flowing through circular tubes under condition of larger surface/gas temperature ratio. J. Mech. Eng. Sci. 3(4), 303–314 (1961) 11. Bialokoz, J.E., Saunders, O.A.: Heat transfer in pipe flow at high speeds, General Meeting Report, Inst. Mech. Eng., pp. 389–399 (1958) 12. Albert, C.: Heat Transfer—Local and Average Coefficients for Air Flowing Inside Tubes. Lavel University, Quebec (1948) 13. Colburn, A.P.: A method of correlating forced convection heat transfer data and a comparison with liquid friction. Trans. AIChE 29, 174–210 (1933) 14. Delpont, J.P.: Influence Du Flux Dechaleur ET De La Nature Du Ga Sur Les Coefficients D’echangeDansVn Tube CylenderiqueLisse. Int. J. Heat Mass Transf. 7, 517–526 (1964) (Pergaman Press, Great Briton) 15. Dittus, F.W., Boelter, L.M.K.: Heat transfer in automobile radiators of tubular type. University of California, Publication, Eng 2, 443–461 (1930) 16. Evans, S.I., Sarjant, R.S.: Heat transfer and turbulence in gases flowing inside tubes. J. Inst. Fuel 216–227 (1951)
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17. Fowler, J.M., Warner, C.F.: Measurement of Heat Transfer Coefficient for Hydrogen Flowing in A Heated Tube, Jet Propulsion Centre Purdue University, Lafayette, Ind. ARS Journal 266–267 (1960) 18. McCarthy, J.R., Wolf, H.: Forced convection heat transfer to gaseous hydrogen at high heat flux and high pressure in a smooth, round, electrically heated tube. Rocketdyne Division, North America Aviation, Inc., Canoga Park California, ARS J. 423–425 (1960) 19. McElgott, D.M., Magee, P.M., Leppert, G.: Effect of large temperature gradients on convective heat transfer: the down stream region. J. Heat Transf. 67–76 (1965) 20. Perkins, H.C., Worsoe-Schmidt: Turbulent Heat and Momentum Transfer for Gases in a Circular Tube at Wall to Bulk Temperature Ratios to Seven, Stanford University, Report no. SV-247(7) (1964) 21. Rozhdestvenskii, V.I.: An experimental investigation of heat transfer during turbulent flow of air in a circular tube for the case of cooling in large temperature differences. Int. Chem. Eng. 10(2), 279–282 (1970) 22. Sieder, E.N., Tate, G.E.: Heat transfer and pressure drops of liquids in tubes. Ind. Eng. Chem. 28, 1429–1436 (1936) 23. Taylor, M.F., Bauer, K.E., McEligot, D.M.: Internal forced convection to low prandtl nunber gas mixtures. Int. J. Heat Mass Transf. 31(1), 13–25 (1988) (Pergaman Press, Great Britain) 24. Kripalani, V.M.: Forced Convection in Tubes for Gases, PhD Thesis, Nagpur University, Nagpur (1997) 25. Kolar, V.: Heat transfer in turbulent flow of fluids through smooth and rough tubes. J. Heat Mass Transf. 8, 639–653 (1965) (Pergaman Press, Great Britain) 26. Patil, P.D.: Mechanism of Forced Convection in Pipes, Ph.D thesis, Nagpur University, Nagpur (1989) 27. Korde, V. M.: Effect of Tube Diameter and L/D Ratio on Heat Transfer Coefficient in Forced Convection, M. E. Thesis, Nagpur University, Nagpur (2000)
Cost Analysis of PV–Wind Hybrid Energy System Pankaj Tripathi, Shashank Dadhich, and Abhishek Kumar Gupta
Abstract Energy is a pre-eminent input for the progress as well as the fiscal development of any country. The fossil fuel price is increasing day by day, whereas the cost of systems used for non-conventional energy moderately declines with the development of technology. In the last forty-year (1960–2020), 80 percent of fossil fuel is used. Approximately, 20 percent of whole energy resources come from a renewable source. This paper outlines the modeling and cost analysis of the PV–wind hybrid energy system for the institutional area using the Hybrid Optimization Model for Electric Renewable (HOMER). The complete analysis is carried out by the software HOMER. HOMER is a type of powerful software that can be used for different aspects of HPS such as their optimization, strategy of their control, size, and structure. Keywords Off-grid · Hybrid Renewable Energy System (HRES) · Net present cost (NPC) · Cost of energy (COE) · HOMER pro
1 Introduction A grid that operates separately or cooperation with any power grid is known as a microgrid. This type of microgrid is known as a small-scale power grid [1]. If a microgrid is combined with the leading power grid of any zone, then that microgrid becomes hybrid microgrid. A microgrid can be used as standby for the main supply grid at the time of heavy demand. Classification of microgrid is not always easy because microgrids come in many different permutations, often customized to the trade, business unit, health center or medical center, institution, community, or other operation they serve [2, 3].
P. Tripathi (B) Department of Electrical Engineering, Invertis University, Bareilly 243123, India S. Dadhich · A. K. Gupta Department of Electrical Engineering, Jaipur National University, Jaipur 302017, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_5
53
54
P. Tripathi et al.
However, we can classify the microgrid based on the type of connection of grid, holder, developer, and fuel, as well as motive, function, and sophistication. A microgrid is classified mostly by their grid interface, which is two types; i.e., a microgrid is either grid-connected or remote (not linked to the grid). Grid-connected microgrid works with the central grid. These grids have the option of taking help from the grid or implementing their self-internal generators to serve their customers [4]. The remote microgrid may be located in isolated areas where people do not have facilities or options of a central grid. Operation of a remote microgrid is independent, i.e., their total operation dependent on their generators to keep the power flowing to customers. But there are many types of microgrids which categorized on different applications such as military microgrids, campus microgrids, and community microgrids [5, 6]. For the generation of power, different sources of energy are used. These resources may be classified into conventional and non-conventional types. Solar, wind, biomass, etc., can be categorized as a non-conventional source of energy [7]. There are so many reasons going for the renewable source of energy. The main reason is the expansion of the global population which is responsible for the exhaustion of finite fossil fuel resources. Climate change is also a factor that is responsible for opting renewable as compared to finite fossil fuel resources [8]. Using renewable energy sources depends upon so many factors like location and environmental conditions. These resources have some unique features like a system which consists of solar as well as wind energy, that is, clean, unlimited, and ecofriendly. A stand-alone system cannot always fulfill the load requirement so this limitation is overcome by the combination of two energy resources [9], and it is known as a hybrid power system where the system has the advantage of two sources. A hybrid system that is considered in this paper has two sources, i.e., solar and wind. Reliability and generation cost are the two main factor which makes the superiority of the hybrid system [10]. In this paper, HOMER Pro software is used for the data analysis related to solar and wind resources. Parameter related to modeling of microgrid is explained.
2 Modeling 2.1 System Architecture Figure 1 represents the architecture of HRES which is considered and simulated in HOMER software. This HRES comprises wind turbines, PV panels, batteries, etc. For backup, the storage system inverter is used [11]. The system is designed for an off-grid system for an institutional area. Specification description of components like photovoltic, storage, system converter is shown in table. A microgrid that consists of photovoltaic–wind hybrid system is modeled and simulated, and after simulation in HOMER Pro software, the best result architecture
Cost Analysis of PV–Wind Hybrid Energy System
55
Fig. 1 80 kW microgrid configuration in HOMER
Table 1 Architecture of system Element
Name
Size
Unit
Photovoltic (PV)
Photovoltic (Generic flat plate)
79.5
Kilowatt
Storage
SSIG 12 V 120AH (Trojan)
179
Strings
System converter
MGS100 (ABB)
30.0
Kilowatt
Dispatch strategy
HOMER cycle charging
displays in Table 1. As per the simulation result, 80 Kilowatt microgrid is suggested by software based on load demand.
2.2 Photovoltaic Details and Specification For the photovoltaic power solar radiation and PV cell temperature is a decisive aspect. The relation between photovoltaic module and solar radiation is directly proportional. The costing constraints conferring to stipulations have been represented by Table 2 which is shown [12].
2.3 Wind Turbine Detail and Specification A device where the conversion of the wind’s kinetic energy takes place into electric energy is known as a wind turbine. In this energy conversion process, we obtain mechanical energy or electrical energy from the wind’s kinetic energy [13]. The speed of the wind plays an important role in receiving a suitable quantity of electrical power output. Table 3 represents the costing constraints according to wind turbine specifications [14].
56 Table 2 Costing constraints for PV
Table 3 Costing constraints and details of wind turbine
P. Tripathi et al. Item
Detail
Size (kW)
1 kW
Capital cost (in Rupees.)
25,000
Replacement (in Rupees.)
22,500
Operation & Maintenance (in Rupees.)
500
Span time (in Years)
20
Derating factor
80
Temp. Coefficient of Power (%/°C)
0.41
Efficiency at STC (in percentage)
95
Ground reflectance (in percentage)
20
Item
Detail
Size (kW)
1
Capital Cost (in Rupees.)
128,000
Replacement (in Rupees.)
115,200
O & M (in Rupees.)
9600
Span Time (in years)
20
Hub Height (in meter)
17
Type
DC
2.4 Battery Detail and Specifications Storage batteries stand as the main component of any hybrid system. The general requirement and cost constraints of the battery are given in Tables 4 and 5 [15]. Table 4 General detail of battery
Item
Detail
Name
SSIG12V 120AH
Manufacturer
Trojan
Nominal capacity (in Ampere hour)
120
Nominal voltage (in Volt)
12
Round trip efficiency (in percentage)
80
Minimum state of charge (in percentage)
20
Float life (Years)
47.9 year
Span time throughput (in kilowatt hour)
70,168
Suggested value (in kilowatt hour)
1.42
Cost Analysis of PV–Wind Hybrid Energy System Table 5 Costing parameters of battery
Table 6 Specifications detail and costing parameters of converter
57
Item
Detail
Rating (in Ampere hour)
120
Capital Cost (in Rupees.)
14,105
Replacement (in Rupees.)
11,284
O & M (in Rupees.)
70.52
Life Time (in Years)
7
Nominal capacity (in kilowatt hour)
1.42
Span Time throughput (in kilowatt hour)
392
Item
Detail
Size (in kW)
50
Capital Cost (in Rupees.)
319,500
Replacement (in Rupees.)
191,700
Operation & Maintenance (in Rupees.)
3195
Span Time (in Years)
10
Efficiency (in percentage)
97
2.5 Converter Specifications A device that is used for processing and controlling the flow of electric energy is known as converter. In rectifier, conversion of AC to DC takes place, whereas inverter is used for converting DC into AC. Converter specifications and costing parameters are shown in Table 6 [16].
3 Result and Conclusion HOMER permits the user to express different probable dimensions of the photovoltic system, wind System, bank of battery, converter, and alternator. In this paper, we use the HOMER simulation model to consider HRES. The overall result of the hybrid energy system is described below in detail by five points:
3.1 Net Present Cost (NPC) Objective function is to optimize net present cost of the designed system, and it is shown in Eq. 1.
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min CNPC,i =
−R0,i +
All Elements
T t=0
Rt,i (1 + x)t
(1)
Cost of each element is calculated by Eq. 2. Celement,i =
Ccapital ,i + CO&M,i + Creplacement,i + Cfuel,i
(2)
Detail analysis of NPC is represented in given tabular form. Net present cost Table 7 includes component, i.e., ABB MGS100, Generic flat plate PV, Trojan SSIG 12 120. Capital cost, operating cost, replacement cost, and salvage are represented for each component in the terms of Indian rupees. Table 7 Costing parameters of each components Name
ABB MGS100
Generic flat plate Photovoltaic
Capital
|162,703
|1.99 M
|2.52 M
|4.68 M
Operating
|20,879
|510,284
|161,991
|693,154
Replacement
|61,925
|0.00
|0.00
|61,925
Salvage
|0.00
|0.00
−|473,469
−|473,469
Resource
|0.00
|0.00
|0.00
|0.00
Total
|245,507
|2.50 M
|2.21 M
|4.96 M
Fig. 2 Net present cost versus different components
Trojan SSIG 12 120
System
Cost Analysis of PV–Wind Hybrid Energy System
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3.2 Cost Summary The graphical summary is shown in Fig. 2. The graphical representation includes components, i.e., ABB MGS100, Generic flat plate PV, Trojan SSIG 12 120, and their cost in the terms of capital cost, operating cost, replacement cost, salvage.
3.3 Electrical Summary The electrical summary is represented in a tabular form. The first table is used for the production summary while the second table is used for the presentation of consumption summary. The component used in Table 8 is generic flat plate PV where total production is 138,760 kWh/year. While element used as primary load (AC, DC, Deferrable) is shown in Table 9, total consumption by the load is 29,025 kilowatt-hour per year.
3.4 Renewable Summary Figure 3 shows that the total generation percentage of renewable output, as well as total load, is represented by the below graph (Fig. 4).
3.5 Conclusion In the present paper, optimization and simulation of solar and wind HRES for the supply of electrical power at selected load for Arya College of Engineering Jaipur have been conducted with the help of HOMER Pro software. The COE of solar wind HRES has been obtained to be |13.31(|/kWh), and total NPC is |4,957,290.00.
Table 8 Simulation result of energy production
Table 9 Simulation result of energy consumption
Element
Generic flat plate PV
Total
Production (kWh/year.)
138,760
138,760
Percent (%)
100
100
Element
AC primary load
DC primary load
Total
Consumption (kWh/year)
29,025
0
29,025
Percent (%)
100
0
100
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Fig. 3 Total generation percentage of renewable output
Fig. 4 Total load percentage of renewable output
References 1. Dadhich, S., Meena, P., Singh, S., Gupta, A.K.: A feasibility study of microgrids in India. In: 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), Moradabad, India, pp. 343–347 (2019). https://doi.org/10.1109/SMART46866. 2019.9117339 2. Dalton, G.J., Lockington, D.A., Baldock, T.E.: Feasibility analysis of stand-alone renewable energy options for a large hotel. Renew. Energy 33, 1475–1490 (2008) 3. SANDIA (Sandia National Laboratories): Stand alone Photovoltaic Power Systems, A Handbook of Recommended Design Practices. Sandia National Laboratories, USA (1991) 4. Del Carpio Huayllas, T., Ramos, D., Vasquez-Arnez, R.: Microgrid systems: current status and challenges. In: Transmission and Distribution Conference and Expo.: Latin America (T&DLA). IEEE/PES, Nov. 2010, pp. 7–12 5. Mahapatra, S., Chanakya, H.N., Dasappa, S.: Evaluation of various energy devices for domestic lighting in India: technology, economics and CO2 emissions. Energy Sustain. Dev. 13(4), 271–279 (2009) 6. Government of India: National electricity policy. Government of India (2005) 7. Singh, C., Sharma, R., Gupta, A.K., Singh, M.S.: Assessment and scope of decentralised power generation using renewable energy resources. In: International Conference on Innovative Advancement in Engineering and Technology , Elsevier SSRN Conference Proceedings Submission 25 March 2020 8. Adaramola, M.S., Paul, S.S., Oyewola, O.M.: Assessment of decentralized hybrid PV solardiesel power system for applications in Northern part of Nigeria. In: Energy for Sustainable Development (2013) 9. Schmidt, T.S., Blum, N.U., Wakeling, R.S.: Attracting private investments into rural electrification—a case study on renewable energy-based village grids in Indonesia. In: Energy for Sustainable Development (2013) 10. Awerbush, S.: Investing in photovoltaics: risk, accounting and the value of new technology. Energy Policy 28, 1023–1035 (2000)
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11. Howlader, A.M., Izumi, Y., Uehara, A., Urasaki, N., Senjyu, T., Yona, A., Saber, A.Y.: A minimal order observer based frequency control strategy for an integrated wind-battery-diesel power system. Energy 46(1), 168–178 (2012) 12. https://kenbrooksolar.com/adani-solar 13. Lasseter, R.H.: Microgrids and distributed generation. J. Energy Eng. Am. Soc. Civil Eng. 133(3), 144–149 (2007) 14. www.solarrooftop.gov.in 15. https://www.solaris-shop.com/trojan-signatur-essig-12-120-flooded-12v-107ah-battery/ 16. https://kenbrooksolar.com/price-list/abb-off-ongrid-tiesolarinverters#:~:text=The%20star ting%20price%20of%20ABB,19%2C500%20for%2050KW%20solar%20system
A Review Paper: Study of Various Renewable Resources Polymer and Different Types of Nanocomposite Materials Pankaj Sonkusare, Pankaj Agarwal, S. K. Dhakad, and Ravindra S. Rana
Abstract This paper audits ongoing propel the polymer mixed with composite by renewable resources and presently expected practical applications may be composed or constructed. So, defeat the burdens, for example, low-quality mechanical polymer by inexhaustible resources, or counterbalance significant expenses are manufactured biodegradable polymers with composite to be created throughout previous research work. There are three different types of polymers from renewable resources to be used (1) natural polymers, for example, starch, protein, cellulose, etc., (2) manufactured polymer has different characteristics of monomer and (3) polymerize by microbial ageing. The hydrophilic types of natural polymers are added to the fruitful improvement in favourable composites. The different types of polymers from renewable resources are combined with the nanocomposite material to prepare the new polymer materials. In case of environmental prospects, these polymer materials from renewable resources were to increase the growth due to neglecting the emission. So, we have recently used fossil fuels to generate more harmful gases and should be avoidable for environmental aspects. The most suitable products to prepare the new polymer-based nanocomposite material from the available in renewable resources. The various types of nanofiller and reinforcement matrix like graphene, CNT, layered silicate reinforced are to be used for the better improvement of mechanical, electrical, thermal and physical properties of polymer bio-nanocomposites material. Keywords Polymer · Renewable resources · Biodegradable · Bio-nanocomposites · Green materials · Biopolymer · Graphene · Carbon nanotubes (CNT) · Layered silicate reinforced · POSS
P. Sonkusare (B) · P. Agarwal · S. K. Dhakad Department of Mechanical Engineering, S.A.T.I Engineering College, Vidisha, MP 464001, India R. S. Rana Department of Mechanical Engineering, MANIT, Bhopal, MP 462003, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_6
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1 Introduction The advancement of elite inexhaustible materials is one significant factor for supportable development of the bundling business. Likewise, fibre-based bundling has the upside of lower weight, which is good from a transportation viewpoint, and the last items can, by and large, be reused. Different examinations show that purchasers favour fibre-based bundling material since it apparently is earth-friendly. Biobased polymers are applied as scattering coatings on paper and paperboard for bundling applications and bioplastics with a similar expected use furnish adequate hindrance properties concerning fats, yet are typically just moderate water fume boundaries. Different shortcomings can incorporate second-rate mechanical properties, inadequate warmth resilience and high dampness affectability comparative with oildetermined plastics. Moreover, to be serious it is significant that new biobased bundling arrangements ought to be economically feasible and ought to be effectively fused in present modern assembling measures. The analysis of polymer has great contribution on environmental aspects of characterizing the polymer. They are very important in order to set up the relationship between strong structured property of polymer and to designated polymer-based system like polymer, copolymer, micro- and nano-polymer composites material. It can potentially lead to the vast knowledge of about the field of specific applications. Then, it also characterizes the polymerization mechanism, polymers analysis, polymer additives and polymeric materials. This polymer system is to be characterized by some advanced methods that will be used. They are also measurements of polymers that have such parameters and to analyse should be checked the various behaviours produced in polymers such as morphological characterization, mechanical behaviour, thermal behaviour, rheological behaviour. They are strong bonding between surface structured of polymers, copolymers and nanocomposites materials. These PNCs have a place with the classification of multistage frameworks (MPS, viz. mixes, composites and froths) that devour about 95% of plastic creation. These frameworks require controlled blending/intensifying, adjustment of the accomplished scattering, direction of the scattered stage and the aggravating techniques for all MPS, including PNC is comparable. Then again, polymer can be invaded into 1D, 2D and 3D perform making high substance polymer nanocomposites. Polymer nanoscience is the investigation and utilization of nanoscience to polymer nanoparticle networks, where nanoparticles are those within any event one component of under 100 nm. The progress from smaller scale to nanoparticles leads to change in its physical just as substance properties. Two of the main considerations in this are the expansion in the proportion of the surface zone to volume and the size of the molecule. The expansion in surface region to volume proportion, which increments as the particles get littler, prompt and expanding strength of the conduct of molecules on a superficial level region of molecule over that of those insides of the molecule. This influences the properties of the particles when they are responding to different
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particles. In light of the higher surface region of the nanoparticles, the collaboration with different particles inside the blend is more and this builds the quality, heat opposition and so forth and numerous components do change for the blend.
2 Literature Review The current use of characteristic assets cannot be continued for eternity. The greater part of the fuel used in our social orders originates from non-renewable energy resources, for example, oil that, other than being exposed to value changes, should inevitably be drained. Rising barometrical CO2 stage from burning of petroleum products is believed to be expanding worldwide warmth. Fuel deficiency and waste gathering in nature are producing an overall enthusiasm for elective assets and especially for the utilization of inexhaustible assets both as a vitality source and vegetable oil for polymer materials. In this chapter, this is great achievement to increase the efficiency and properties of newly developed polymers from renewable polymers. Also, they have reduced the emission of hazardous gases from the environment by the polymers. Another aspect to see the huge amount of waste products approximately 1.5 billions tonnes to generated in all over the world. Wastage incineration like plastic is to create more hazardous gases to effect the environment. They are disposing the waste products of endanger the people life and environment. A broad expansion of materials from renewable resources is always available in potential. The resources are most important and are maintained to provide affluent variety of building structured, and polymers are presently available from industries of fossil fuel. The four major groups of renewable polymers are classified into the basis of new technology of synthesis and resources. (1) (2) (3) (4)
It is available in vegetables resources like cellulose and starch. It is available in animal resources like chitin and chitosan. It is available from the characteristics of micro-organism or microbial action. It is also available from monomers which are chemically and conventionally synthesized.
There are wide varieties of different components of polymer materials to be used from renewable resources. Then, all the polymers that have natural resources should be easily available from different categories such as different types of oils are extracted from oilseed plants, animals like chitin and chitosan, microbial action and monomers have conventionally synthesized. Some products have been used people in daily life based on the chemically structured in chains that have monomers and polymers. It is also avoidable of fossil fuel agents for the environmental prospects and to increase the efficiency of environment. They have reduced the emission to put in the bio-renewable resources based on the polymers material. It will perform to growing task in terms of plastic goods and medical products. Hence, the economically and environmentally advantages were to achieve the newly polymers material from the renewable sources. This chapter has decided as future prospects to the development
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of new polymer materials that have different categories for environmental conditions and totally avoidable of harmful gases by the fuels [1]. An overview in this chapter is performance analysis and properties of nanocomposites mainly based on the various kind of nanofiller and reinforcement. They are mainly focussed on the completely analysis of procedure and practical applications of bio-nanocomposite. Bio-nanocomposite mainly depends on various chemical components of biopolymer like polylactic acid, polyvinyl alcohol, polyhydroxybutyrate, poly (e-caprolactone) and chitosan, and the nanofiller is based on different types like graphene, carbon nanotubes, layered silicate reinforcement, sepiolite and halloysite. They are increased the mechanical and thermal properties of nanofiller reinforcement on the bio-nanocomposite. It can be an important role-play to increase the conformity between component distribution on bio-nanocomposite and level of nanofiller reinforcement. It audits forefront of bio-nanocomposite, cutting edge on the research successful utilization of nanofillers reinforcement or fortifications upgraded presentation on the cutting edge of mechanical properties and fabrication. It covers a wide scope of points, for example, nanocellulose, nanotubes, nanoplatelets and nanoparticles, just as their broad applications. The sections give nitty gritty data, and how fillers and fortifications are utilized in the creation, blend and portrayal of cutting edge nanocomposite to accomplish phenomenal execution of new materials and huge improvements in their mechanical, thermal, basic and multi-utilitarian properties. It additionally futures new advanced for the creation of cutting edge nanocomposites utilizing inventive electrospinning method. It expands the various kinds of properties like graphene, carbon nanotunes, reinforcement, etc., on the bio-nanocomposite. The comprehension of the strengthening components is, in this way, significant for the boost of execution. These polymers are added to fillers to provide the better improvement in properties of final product in terms of reinforcement fillers. High concentrations were to enhance the improvement of the mechanical properties and composite mixers on the nanocomposites. The various types of analysis in graphene are the part of nanofiller particles that have changed the nanocomposite material sample. It enabled a higher accuracy of particles scattered and less aggregate structure. In this process, the nanocomposite particles are size of particles and molecular weight should be reduced in nanotype and also increase the properties of mechanical and thermal. The carbon nanotube (CNT) is also important role-play in our academician and industrial sector. They are two different categories between single-wall carbon nanotube (SWCNT) and multi-wall carbon nanotubes (MWCNT). The CNT has a good performance in various sectors due to the excellent mechanical, thermal and electrical properties. The CNT is reinforced filler to see the epoxy matrix in the study on rheology-based nanometer scales. The layered silicate reinforced is important role-play in the nanomaterials and part of the nanoclay. It is to enhance the mechanical and physical properties of the polymer nanomaterial. It is used to prepare the different types, shapes and sizes of sheet arranged in nanoclay of used by the preparation of various types of polymer nanocomposites [2].
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In this section, an effort has been made to summarize the uncommon creative work related to cellulose nanocrystal-invigorated nanocomposites. A low down examination exhibited the disengagement of glasslike part of cellulose fibres using various engineered materials is represented. Additionally, exceptional utilitarian social events created since used engineered materials during imprisonment, steps are inspected and their hindrance during composite creation is represented (sway on dissipating, scattering, mechanical properties, etc.). In this section, an exertion has been made to sum up the exceptional innovative work identified with cellulose nanocrystal fortified nanocomposites. A point-by-point study indicated the segregation of glasslike part of cellulose filaments utilizing different synthetic compounds is accounted for. Moreover, extraordinary utilitarian gathering rose since utilized synthetics during disengagement steps are talked about and their impedance during composite creation is accounted for (impact on scattering, conveyance, mechanical properties and so forth). Different handling courses are additionally detailed on the creation of dimensional nanocomposites. Creators are attempted to represent the relative investigation in different preparing courses. They are picked the handling courses in some way or another influence the properties, in which some way to show the conceivable application later on. The major concentration is about the lignocellulose biomass and is mainly used for the production of functional and structural dimensional nanocomposites. Cellulose is the most important part of the wood and is easily available from the natural polymer on the renewable resources. Cellulose fibres are sequential form of pairs of microfibers from the plant cell wall. Nanocellulose are break down into different categories cellulose nanofibrills, cellulose nanocrystals also termed as nanocrystalline cellulose. Nanocomposites are several types of applications that should be maintained like automotive parts, packaging, biomedical and electronic sensors. Sulphuric acid hydrolysis process can be used to dissolve the amorphous part of the cellulose and should leave the crystalline structure. The cellulose nanocrystals are the most important applications of role-play in this sector such as morphological, thermal, mechanical, rheological and crystalline. It can be studied about the isolated CNC morphology obtained by the various types of microscopic test or process such as transmission electron microscopy (TEM), scanning electron microscopy (SEM) and atomic force microscopy (AFM). This technique can be used to evaluate the surface morphology and dimensions or aspect ratio. In this chapter, isolated CNC can be extracted from the various types of different sources to be evaluated of dimensions and crystallinity. The different sources are to be used such as wood, tunicin, ramie, garlic straw, groundnut shells and chilli leftover. The isolated process is directly proportional to the thermal stability. In polymeric lattices is to be expanded the nanomaterials support because of the greater mechanical properties with at low substance in filler. They are different methods that have been adopted for polymer nanocomposite process such as electrospinning, thermo-pressing, melt extrusion and solvent casting. They are also different factors of mechanical properties of nanocomposite which have considerable like dimension of CNC and morphology, processing methods and matrix in the microstructure. The polymer nanocomposite can also be used in various types of possible emerging
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and biomedical applications. The isolated CNC is evaluated and analysis of various parameters in nanocrystalline structure of reduced lower weight of different tiny particles of polymers. Thus, the polymer nanocomposites have the higher mechanical strength and to increase the various properties [3]. The new green polymer composite becoming from inexhaustible assets is to be utilized in various methods likes as pressure shaping procedures are incorporated. It can be synthesized from pine needles and phenolic matrix. In these techniques, they are inclusive of various contents of fibres weight in the polymer matrix. Also, it can be investigated in mechanical properties of polymer at what effect the various fibres content. Then we have studied about the various types of a technique that is to be used such as analysis, thermal stability and polymer composite morphology. The most important role of natural fibres part of polymer composite is from renewable resources. In the present day, these natural fibres are used as practical applications in various sectors. Most of the researchers and material scientist are main focussed on the natural fibres in reinforced polymer composite. Then, this natural fibresreinforced polymer composite is compared with the synthetic fibres. These synthetic fibres have several factors and mainly depend as compared to natural fibres. They are most effective utilization of natural fibres obtained from the renewable resources in polymer matrix as compared to conventional matrix. These polymers have importance of commercially driven in various field of applications in composite polymer. The final component of material properties is higher than as compared to individual component. In the present day, the fabrication of pine needles reinforced composite is utilized to make different types of material in different regions. These prepared pine needles reinforced composite material is used by the people’s in different seasons in different forms. The preparation of several materials according to the seasons was utilized in different ways because these prepared products of cost are low. They are additionally not impact to climate of a few items pine needles supported composite materials made by individuals. It can also be prevented by fire hazardous gases. In the present time, the preparation in laboratory is the fabrication of biobased cellulose pine needles in green composite and resorcinol formaldehyde is based on polymer bio-composite by compression moulding techniques. Also, these techniques were used to affect the various other factors of fibres weight content on the mechanical properties, thermal and morphological properties in polymer matrix. There are various techniques done in polymer composite matrix samples and analysis of mechanical properties of polymer. The various techniques are to be used such as tensile strength test, compressive strength test, wear test, flexural strength test, morphological studies and thermal properties. Thus, we have more study in this chapter about this pine needle and are maximum potential to ability to set up in composite reinforced polymer. They are higher weight of natural fibres content which is to increase the maximum strength of polymer matrix [4]. In this chapter, we have studied about the biobased component of polyurethane adhesion. We are focussing this topic in our field area and polyurethane adhesive is best suitable for the environment as compared to petroleum adhesives. The most important component in polyurethane adhesion was utilized the castor oil from
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vegetables oil. This is also range of castor oil between all the vegetables oil, and all the efficient properties such as adhesion, mechanical and thermal are suited on it. This component of castor oil by polyurethane adhesive from the vegetables oils was used in various industrial applications. Most effective factor related to beneficial for the ecologically security, eco-friendly environment, cost-saving, higher chemicalresistant and greater thermal stability should be done. They are different types of development of new polymer materials from the available renewable resources to obtain various types of plant oils or vegetable oils. The polymeric material is to achieve the new product by the renewable resources or biobased polyurethane adhesives. The natural phenomena of polymer material-based bio-composite polyurethane adhesive are used for various applications in several field sectors. The new technologies of coating and adhesives were put in our environment to develop and analysis of polyurethane adhesions in various field applications. In polymeric adhesives, there are holding between the permanently two parts by the bond form of substrate surface. They are strong bonding between the two dissimilar bodies by interfacial contact can be transferred across the adhesion properties. These adhesion properties have natural polymeric material of substrate and were utilized the various material such as wood–wood, aluminium, plastic–plastic, rubber–plastic and rubber–rubber. The substrate has inorganic and nonpolar polymeric material, and it can occur the deformation in inorganic polymeric material. If the failure occurs, the deformation between the substrate surface and inorganic polymer is to be failure. It is also termed as adhesive failure. These failures mainly depend on the various factors to control the interfacial forces of polymeric materials adhesives. There are various polymeric adhesives that were utilized in our field applications which have clinically operated. The polymeric adhesives are to be used as cyanoacrylates, furan adhesive, polyvinyl acryl, epoxy adhesive and polyurethane adhesives. The stoichiometric proportion based polyurethane applications. The first German scientist name is so-called Otto Bayer. The Bayer was introduced the polyurethane, in other words, the father of polyurethane. The various chemical compounds are introduced by Bayer’s such as polyester and polyether and also prepared the polymeric polyisocyanate. There are various factors to suitable for pollution-free from environment, at low cost available, easily replaceable from renewable resources. Basically, the chemical compounds of the main ingredients involve in polyurethane and are polyol and isocyanate. The chemistry of polyurethane is between soft and hard segment. Then, both the combination of properties in different segments such as soft segment is rigid and elastomeric properties and hard segment to achieve the high tensile strength, high modulus and good high temperature. In this process, there are various types of techniques and methods are used to prepare the biobased polyurethane adhesives. We are main focussed on the renewable resources that depend on the biobased polyurethane as compared to petrobased polyurethane for environmental issues [5]. The main sources of chitin and chitosan are obtained by crustaceans, crab, insects, fungi and mollusos. Generally, the chitosan is obtained by chitin but it is modified type of natural carbohydrate polymer and they are the removal of acetyl group to
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make a natural polymer. The built-up of chitin has a linear chain of acetyl glucose amino group. These types of resource materials are most suitable properties considering biocompatibility, biodegradability, adsorption and capable of form films and to chelate metal ions. The modifications of chitosan in terms of chemically react under the heterogeneous conditions due to the lack of solubility. The chitin or chitosan has derived in three different ways that are substitution, chain elongation and depolymerisation. The chitosan has several applications used in biological sectors due to high molecular weight and high viscosity. In this process of depolymerize, the chitosan by various methods is like chemical method, physical and enzymatic method, graft copolymerization and chitosan crosslinking. The various applications of chitin and chitosan in different ways are biorenewable, biocompatible and biodegradable and biofunctional. These applications are used in several sectors like food/nutrition, microbiological, immunological and miscellaneous [6]. The expanded usage of inexhaustible assets in the compound business is chiefly determined by natural resources. Be that as it may, as a fairly disparaged reaction of this turn of events, new substance building squares have opened up, but there are financially not available from petroleum resources. In this chapter, the itaconic corrosive is increased extensive consideration, as it is biotechnologically created from sugars on a mechanical property. The structure of trifunctional is taken into consideration organization to establish new polymer materials such as polyitaconic related to copolymer; it is widely concentrated previously. Polyesters got from this unsaturated dicarboxylic corrosive then again have as of late began to pick up enthusiasm, not withstanding bunch expected applications; for example, UV-relieving gums, cements or thermal restoring structures give some examples. The fundamental goals of this short diagram of biobased epoxy sap and unsaturated polyester got from itaconic corrosive were to stress the extraordinary capability of this sort of materials to supplant oil-based polymers. The intriguing synthetic structure of the dicarbonic corrosive possessing α, β-unsaturated exo-twofold security and two carboxylic gatherings has been demonstrated that it is appropriate organization of new biobased polymer material. For example, biopolymer sap, unsaturated polyesters with excellent properties and greater biopolymer substance, supplanting great structure squares got by the petroleum-based resources like acrylic or methacrylic corrosive is acceptable for wide range applications [7]. Creative reusing, corruption, or removal alternatives are probably going to turn out to be considerably more significant for forestalling new materials from adding to existing plastic waste issues, and there may likewise be an open door for supporting approach and enactment to shape the result. Despite the fact that the immediate measurement and examination of feasible polymers with petrochemical determined counterparts are at a beginning phase, there have been adequate examinations to exhibit that much of the time; the effects of creation are diminished, especially on ozone harming substance discharges and the exhaustion of fossil assets. Studies ought to likewise think about the life of the item past production and the effects related to removal. Up until this point, barely any polymers have been intended to be both completely bioderived and biodegradable, albeit aliphatic polyesters; for example, polylactic are remarkable victories.
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Presently, we have used the renewable resources to grow the production of new polymer materials. There are more particulars used different variety of manageable polymer materials monomers for manufacturing purpose and also other products like plastic, elastomers, hydrogels, polymers and composite materials. It is required for better fermentation to developed monomers, by selective methods of polymerizations and also involving the waste materials should be recycled. All the opportunities are to manage new polymers in large areas and different applications. They are widely used to life cycle assessment to achieve the better suitable for environmental purpose. We are developed and established the new biobased polymer materials to use for environment to free from pollution and better suited to environmental conditions [8]. In this chapter, we have recently studied or mainly concentrated beginning of biobased thermoset epoxy resins. They are used as a managerial material in various fields and its applications. Then, the renewable resources are to be used of sorbitol and castor oil in different ratios which are obtained, and it is required to analysis of newly developed hyperbranch epoxy resins. These polymers are acquired chemically structured by some techniques of FTIR and NMR. The epoxy resins of biobased thermoset have a good quality, better mechanical properties to achieve such as high tensile strength, high thermal stability, better elongation properties, greater scratch hardness moderation and better chemically resist under the environmental condition and biodegradable will also be considered. Hence, the hyperbranch epoxy thermosets of castor oil are the modification of sorbitol that has a good property as comparable with epoxy resins in the absence of sorbitol from biobased renewable polymer materials. It is widely used as managerial polymer materials and different applications. The epoxy thermosets are widely used in engineering applications that have some advantages and some disadvantages. The advantages of epoxy thermosets such as good stiffness, tensile strength, simple procedure, better electrical strength, greater chemical or thermal resistance. Then, some disadvantages of epoxy thermosets are low toughness, available at high cost and high brittleness. They are used some techniques to be prepared the hyperbranch epoxy resins by high solubility and low viscosity. We are found to be in some issues of epoxy resins deal with biobased feedstock’s like cardanol, tannin, lignin, glucose, vegetable oil. The vegetable oils are easy to acceptable and better suited for environmental conditions. Recently, we are studied the great combination between the aromatic and aliphatic moieties and hyperbranch structure to be prepared the epoxy thermosets. This analysis is to be result in better mechanical, electrical, thermal and microbial biodegradation to be achieved as compared to linear epoxy resins [9]. The statistical analysis and experimental design have a certain three levels is to achieve more information by the central composite design (CCD). We are study of morphological that is to be treated the fibres by using different techniques like scanning electron microscopy (SEM). This technique of SEM is an important roleplay in extraction process and selectively parameters. The analysis of such factors and parameters in different categories is such as cellulose microfiber, cellulose semi-nanoparticles and cellulose nanoparticles. Then, these results were obtained by the extraction process in kenaf fibre. They can be performed over some testing
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of quadratic polynomial model and two-factor interaction model by the analysis of variance. Thus, there are such techniques that were used at certain level in statistical extraction process which is best-suited for kenaf fibre and also prepared to finalize the product by the removal of non-cellulose constituents. Green composites were utilizing cellulose strands as a fortification material and give a supportable and sustainable option in contrast to oil-based polymer. Be that as it may, controlling the utilization of synthetic compounds and handling boundaries to extricate the cellulose could be in some cases troublesome. Thus, this examination intends to streamline the conditions for removing the microcellulose from kenaf fibres utilizing central composite design (CCD) and a factual device in structure of investigations. Three components and three levels were picked for doing the examination. The plan is depended on sodium hydroxide measurements, sodium chloride dose and sonication time as free factors, while subordinate factor was the fibre size and debasement point [10]. We have studied about the topic of rosins which are also used in construction field as wooden naval vessels. It is the new transformations and applications from the natural renewable resources. This application is suitable for synthesis polymer, and a fossil resource has been depleted. The main source of rosin from monomers or additives for biopolymer composite material and most of rosin available from pine trees. This type of pine resin is dependent on it and differentiates into three categories: (1) gum naval stores, (2) sulphate naval stores and (3) wood naval stores. The first categories are obtained by living trees, second categories are obtained by tall rosin, and third categories are obtained by solvent extraction of hardened wood. The production of gum rosin is evaluated to be affected into two differentiating factors such as petroleum-based counterparts and manpower cost are included in tree tapping and resin recovery. There are three various acids used in this process such as pimaric, isopimaric and sandara copimaric acids. The rosin quality mainly depends on the four parameters that are the acid number, the saponification number, the colour and the softening point. The major applications are used under the derivatives such as paper sizing, emulsification, adhesive tack, polymer chemistry and processing, printing inks and miscellaneous applications. Thus, these types of technologies are used in process of renewable resources and to replace the fossil resources for the environmental prospects [11].
3 Conclusions Biodegradable polymer nanocomposites (BPN) have gigantic highlights and properties, which are significant in the part of utilizing them as superior materials over a scope of mechanical fields. Particularly, biopolymers will assume a key job later on when there may be the shortage of the shortage of manufactured polymers because of its non-organic starting point and non-biodegradable nature. Thus, in the most recent decade, numerous scientists have concentrated on biodegradable polymers and adjustment of their highlights. In such manner, utilization of natural and inorganic
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nanomaterials is end up being an all the more persuading course for the planning of nanocomposites of biodegradable polymers. So as to satisfy the developing interest of the world market, these eco-friendly nanocomposites with improved properties, for example, mechanical properties, electrical properties, thermal properties, crystallization rate, debasement rate and soften quality contrasted with that of slick polymer, are rising as an aid to humanity. The new polymers are obtained in different cases and in the form of chemical compounds from the biopolymer renewable resources. We are mainly focussed on that to give eco-friendly environment free from petroleumbased products. All the researchers to give in future prospects to prepare the natural biopolymer products in different forms are combined with nanocomposites materials.
References 1. Sharif, A., et al.: Renewable Resource-Based Polymers: Preparation, Processing, Properties &Performance. Springer Nature Switzerland AG, pp. 1–28 (2019) 2. Shrivastava, N.K., et al.: Fillers and Reinforcements for Advanced Nanocomposites. Springer Nature Switzerland AG, pp. 29–48 (2019) 3. Nagalakshmaiah, M., et al.: Cellulose Nanocrystals-Based Nanocomposites. Springer Nature Switzerland AG, pp. 49–66 (2019) 4. Thakur, V.K., et al.: Renewable resource-based green polymer composites: analysis and characterization. Int. J. Polym. Anal. Charact. 15, 137–146 (2010) 5. Sahoo, S., et al.: Biobased polyurethane adhesive over petroleum based adhesive: use of renewable resource. J. Macromolecular Sci. Part A 55(1), 36–48. https://doi.org/10.1080/10601325. 2017.1387486 6. Sahoo, D., et al.: Chitosan: The Most Valuable Derivative of Chitin Biopolymers, P L Nayak Research Foundation, pp. 129–166 (2011). https://doi.org/10.1002/9781118164792.ch6 7. Kumar, S., et al.: Itaconic acid used as a versatile building block for the synthesis of renewable resource based resins and polyesters for future prospective: a review. Polym. Int. 66(10), 1349– 1363. https://doi.org/10.1002/pi.5399 8. Zhu, Y., et al.: Sustainable polymers from renewable resources. Nature 540(7633), 354–362 (2016). https://doi.org/10.1038/nature21001 9. Saikia, A., et al.: Renewable resource based thermostable tough hyperbranched epoxy thermosets as sustainable materials. Polymer Degradation Stability 135, 8–17. https://doi.org/10. 1016/j.polymdegradstab.2016.11.014 10. Ketabch, M.R., et al.: Sonosynthesis of Cellulose Nanoparticles (CNP) from Kenaf Fiber: effects of processing parameters. Fibers Polymers 17(9), 1352–1358 (2016). https://doi.org/ 10.1007/s12221-016-5813-4 11. Armando, J.D., et al.: Rosin: major sources, properties and applications. Monomers Polymers Compos. Renew. Resources 67–88 (2008). https://doi.org/10.1016/b978-0-08-045316-3.000 04-1
Self-directed Robot for Car Driving Using Genetic Algorithm Harivansh Prasad Sharma, Manisha Pant, Reshu Agarwal, and Shylaja Vinaykumar Karatangi
Abstract The big issue with a human driving car is traffic, with the current continuous growth in the world population. The second big issue with the growing population is creating huge chaos, which leads to accidents. Every year nearly 1.35 million people lose their lives due to traffic crashes, and 20 to 50 million face serious injuries with some untreatable disability as of their road injury. Over 80% of accidents happen due to driver error. Other issues are the efficiency of the car as we are slowly transforming into the electric car. This paper introduced car with the self-driving feature using genetic algorithm to reduce the traffic with route optimization, and by reducing traffic, so that many problems related to driving can be solved. It minimizes the rate of an accident and also maximizes the efficiency of the car. Keywords Genetic algorithm · Path planning · Car driving · Robot · Artificial intelligence
1 Introduction Genetic algorithm is the process of selecting the fittest from the given population or set of data based on its immunity from previous test results—the result narrows down the selection chosen from the population/set of data for survival in the future. In the genetic algorithm, four stages are viewed. First, at the population, second at selecting dataset from the available considered solution, third at selecting fittest by evaluating the considered population or the dataset using the fitness function, fourth, at the modification in solution according to the situation.
H. P. Sharma (B) · M. Pant · R. Agarwal Amity Institute of Information Technology, Amity University, Noida, India M. Pant e-mail: [email protected] S. V. Karatangi G L Bajaj Institute of Technology and Management, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_7
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Further, fitness function takes the dataset as its source and as its product is the possible solution concerning the problem we want to solve. The fitness value is measured periodically in a genetic algorithm, and thus, it should be sufficiently fast. The inefficient fitness measurement process will have an adverse impact on the genetic algorithm (GA) and makes it too sluggish. Modification of dataset is done by changing some of the characteristics of an individual entity in the dataset to yield efficient results. Generally, the modification is used to optimize the dataset to produce the different optimal solutions and create a variance in the dataset. The termination of the algorithm happens when the dataset starts producing the same output as the last generations, which leads to random modification or mutation in data to produce the noticeably different output from the last generations. By using a GA, we create a situation for AI as human faces to learn. It will increase the accuracy of AI to handle the traffic jams and to avoid the accidental scenario. The system with the route optimization technology and genetic algorithm to train the model creates the nearly perfect self-driving car. The autonomous car’s efficiency increases by modification in route optimization dataset by training scenarios like traffic jams, red light, and parking. Training model with accidental scenario by genetic algorithm maximizes the tackling accuracy of accidents compared to drivers. It reduces the rate of accidents globally. The path planning issue of a car driving robot can be expressed as given (beginning area, objective area, 2-D guide of work environment including static deterrents), plan an impact freeway between two indicated points in fulfilling shortest path achieved with obstacles. Probably the most concerning issue confronted when attempting to upgrade a car direction in a circuit by methods for GAs is the manner by which to pick the principal design utilized. As it is exceptionally hard to acquire programmed driving development from people that have been created by some coincidence, we chose to build up an algorithm to acquire a base direction which can finish a lap. The component of this calculation is basic, it attempts for each portion, all potential combinations of steering and acceleration, looking for the pair of qualities which keeps up the vehicle closer to the focal point of the track. When all segments have been assessed we have acquired the individual taken as a basis for the GA, at that point the driving learning process can begin. Genetic algorithm does not ensure the accuracy is sought, but the outcome is typically approximate to the equilibrium globally. Although the solution is seen as probability-based, specific optima are not included. This paper concludes that we can maximize the selection process’s speed by creating the optimal fitness function. Then we change the speed of evolution of artificial intelligence by sufficient time. Then we are close to creating the AI closer to the human functions. Evolution will take real-world datasets to optimize the process of learning and adapting to the world. The genetic algorithm is currently widely used in the car industry for autopilot driving, which works on a fuzzy control system. In today’s world, the live example is autonomous driving which is used in the car. Big companies like Tesla and GM are focusing on the revolution of AI technology in system. Tesla is dominantly using artificial intelligence in their tesla model. Tesla AI is a mix of GA and unsupervised learning. Companies like Tesla, Argo Ai, Ford
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Motor Company, and General Motors Company are these big giants which are using AI in their car for autonomous driving. Robotics companies like Boston Dynamics, Neural, and OpenAI all these companies are creating robots.
2 Literature Survey Mobile robot is used in various types of situations, and it is essential for them to move in places with objects and deterrents. So as to explore the robot in an impact freeway, path planning calculations have been introduced. The principal objective of path planning is to decide the ideal conceivable way among the underlying point and the characterized objective situation in the least possible time. To obtain the goal, a new technique is proposed by using genetic algorithm [1]. However, it may be difficult to collect data from far-off destinations or from exceptionally unfriendly conditions. An algorithm is proposed that allows mobile robots to find the ways free of human intervention [2]. The mobile robot encloses the estimation devices and records the information at that point either sends it or takes it back to the administrator. Sensors are needed to identify obstructions in the path, and machine intelligent is needed for the robot to design a way nearby these deterrents. Further, an approach based on genetic algorithm is used to construct a path planner that considers both accurateness and speed as parameters [3, 4]. Moreover, a distributed model was proposed for autonomous robot navigation [5]. An algorithm was proposed based on fuzzy inference system (FIS) which finds a response to the route issues of a self-ruling automated vehicle [6, 7]. This self-ruling automated vehicle is viewed to be navigating in unpredictable conditions involving various stationary items. In realistic environment, there are certain planning problems related to motion like indoor application comprise of number of rooms, lobbies, different entryways with numerous static and dynamic impediment in between. To solve this navigation issue, an algorithm was proposed that uses adaptive path planning [8]. A comparison model based on parallel elite genetic algorithm (PEGA) was developed to compare the working of robots in different environments [9]. This model was further enhanced to work in dynamic environments also [10]. An algorithm based on hybrid metaheuristic genetic algorithm was suggested to discover ideal distance among beginning and final point [11]. Researchers focus on the fact that in certain situations, and they have to take in account conditions like temperature and pressure for gathering data from remote sites. Considering this fact, certain models for robots are developed to explore a situation in which no human is there [2, 12]. Further, a model was proposed to develop reconfigurable robot based on genetic algorithm to achieve path planning [13, 14]. However, sometimes performance varies with different parameters and environments, and to overcome this problem, more robust algorithm considering dynamic paths is proposed for navigation task [15, 16]. Table 1 shows the contribution of different authors.
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Table 1 Contribution of different authors Authors
Connected System
Localization
Manikas et al. [2]
✓
Shamsinejad et al. [3]
✓
Di Gesu et al. [5]
✓
Patial et al. [6]
Planning and Control
✓
✓
Tsai et al. [9] ✓ Huang and Tsai [11]
✓
Cheng et al. [12]
✓
✓
Datasets and Software
Implementation
✓
✓
✓ ✓
✓
Pol and Murugan [8]
✓ ✓
✓
✓ ✓
✓
Tanev et al. [14] Proposed work
Assessment
✓
✓
✓
✓ ✓
✓
✓
✓
✓ ✓
✓
3 Proposed Work Car simulator used the fuzzy control system, GA, and particle swarm optimization to simulate the movement of the autonomous car on the map. Input contains the car’s three distance sensors (front, 45° left and right), which can be achieved from the defined equation of motion, the car’s position and the angle between the car and the horizontal axis taken as ∅(t). Output is steering wheel rotation angle. The goal is to hit the end line without hitting the wall and output the trajectory of motion (including the position of each point in time, the value of the sensor, and the angle of rotation of the steering wheel) as a text file, then display on the graphical interface. The equation of motion is given from Eqs. (1) to Eq. (4). x(t + 1) = x(t) + cos[∅(t) + θ (t)] + sin[θ (t)] sin[∅(t)]
(1)
y(t + 1) = y(t) + sin[∅(t) + θ (t)] − sin[θ (t)] cos[∅(t)]
(2)
2 sin[θ (t)] ∅(t + 1) = ∅(t) − arcsin b
(3)
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Where b is the length of the simulated car, x and y are the coordinates of the car, and the θ (t) is the angle of the steering wheel such that:
∅(t) ∈ [−90◦ , 270◦ ] θ (t) ∈ [−40◦ , 40◦ ]
(4)
Fuzzy control system uses the custom seven fuzzy rules and discrete center of gravity defuzzifier. It uses the following functions, i.e., fuzzy start, fuzzy control, discrete center of mass, fuzzy rules center, fuzzy rules right, fuzzy rules left. Car simulator uses the real-evaluated genetic algorithm (GA) to train the radial base function network (RBFN), and RBFN controls the vehicle. The gene is defined as three parameters of mixed-dimensional vector RBFN (w, m, σ ). The fitness function in the input case is the mean variance of the predicted output of the dataset and the RBFN value. The lowest fitness value is the best RBFN parameter. Car simulator can also use particle swarm optimization (PSO) for RBFN preparation as shown in Eq. 5. F(x) =
J
w j ϕ j (x) + θ
j=1
=
J
w j ϕ j (x)
(5)
j=0
The arrangement of the radial base function network is shown in Fig. 1. Figure 2 shows the working of the proposed model. In Fig. 1, x is the dataset input and uses the Gaussian base function given by Eq. 6.
Fig. 1 Arrangement of the radial base function network
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Fig. 2 Model for car simulator
x −mj ϕ j (x) = exp − 2σ j2
(6)
Where w, m, σ are parameters of optimization algorithm. The fitness function is given by Eq. 7. 1 (yn − F(xn ))2 2 1 N
E(n) =
where y is the expected output and F(x) is the RBFN output.
(7)
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Fig. 3 Comparative results
4 Result and Discussion In this paper, car simulator used particle swarm optimization (PSO) for RBFN preparation. The PSO coordinate is defined as the three mixed dimension vector parameters of RBFN (w, m, σ ). For the input case, the fitness function is the mean variance of the predicted value of the data collection and the RBFN value. The lowest fitness rating is a good parameter for RBFN. The proposed model has the following features: status button, control button, camera first person/third person switch, rotation angle of steering wheel and moving speed, start the fuzzy control system, RBFN parameters setting, GA parameters setting, PSO parameters setting, save the trajectory and data, operating instructions, and graphic interface. Figure 3 shows the comparison of distance and the iteration used by different algorithms. The results show that our algorithm reduces the optimal path distance.
5 Conclusion and Future Scope This simulator this fully automated could be a part of human interaction with steering of car and to reduce the time. This simulator will eliminate the human driver. The novelty of this paper is to save time and money. In terms of time, algorithm will require more practical world dataset to fully automate. There are many future challenges that we can implement to make our model better. Some of the factors that can be considered to make our model more effective are sprung mass bounce, wheel bounce, resistance levels, and engine braking system. Moreover, resistance levels can be increased to maintain the speeds of the simulated vehicles. The problems like for
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a vehicle, if surface and initial speed are given, various combinations of steer and braking will be provided in solution that will lead to rollover.
References 1. Samadi, M., Othman, M.F.: Global path planning for autonomous mobile robot using genetic algorithm. In: International Conference on Signal-Image Technology & Internet-Based Systems, Kyoto, pp. 726–730 (2013) 2. Manikas, T.W., Ashenayi, K., Wainwright, R.L.: Genetic algorithms for autonomous robot navigation. IEEE Instrum. Meas. Mag. 10(6), 26–31 (2007) 3. Shamsinejad, P., Saraee, M., Sheikholeslam, F.: A new path planner for autonomous mobile robots based on genetic algorithm. In: 3rd International Conference on Computer Science and Information Technology, Chengdu, China, pp. 115–120 (2010) 4. Geisler, T., Manikas, T.W.: Autonomous robot navigation system using a novel value encoded genetic algorithm. In: The 2002 45th Midwest Symposium on Circuits and Systems, Tulsa, OK, USA, pp. 1–4 (2002) 5. Di Gesu, V., Lenzitti, B., Lo Bosco, G., Tegolo, D.: A distributed architecture for autonomous navigation of robots, pp. 190–194. Proceedings Fifth IEEE International Workshop on Computer Architectures for Machine Perception, Padova, Italy (2000) 6. Patial, A., Mandalia, D., Nandoskar, N., Haldankar, G.T., Kasambe, P.V.: FIS based autonomous navigation system. In: 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India, pp. 1–5 (2017) 7. Atkinson, J., Gutierrez, F.: Autonomous robotics self-localization using genetic algorithms, In: 21st IEEE International Conference on Tools with Artificial Intelligence, Newark, NJ, pp. 167–170 (2009) 8. Pol, R.S., Murugan, M.: A review on indoor human aware autonomous mobile robot navigation through a dynamic environment survey of different path planning algorithm and methods. In: International Conference on Industrial Instrumentation and Control (ICIC), Pune, 2015, pp. 1339–1344 (2015) 9. Tsai, C., Huang, H., Chan, C.: Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation. IEEE Trans. Industr. Electron. 58(10), 4813–4821 (2011) 10. Bruno, D.R., Marranghello, N., Osório, F.S., Pereira, A.S.: Neurogenetic algorithm applied to route planning for autonomous mobile robots. In: International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, pp. 1–8 (2018) 11. Huang, H., Tsai, C.: Global path planning for autonomous robot navigation using hybrid metaheuristic GA-PSO algorithm. In: SICE Annual Conference, Tokyo, Japan, pp. 1338–1343 (2011) 12. Cheng, K.P., Mohan, R.E., Khanh Nhan, N.H., Le, A.V.: Multi-objective genetic algorithmbased autonomous path planning for hinged-tetro reconfigurable tiling robot. IEEE Access 8, 121267–121284 (2020) 13. Chen, Q., Ozguner, U., Redmill, K.: Ohio state university at the 2004 DARPA grand challenge: developing a completely autonomous vehicle. IEEE Intell. Syst. 19(5), 8–11 (2004) 14. Tanev, I., Joachimczak, M., Shimohara, K.: Evolution of driving agent, remotely operating a scale model of a car with obstacle avoidance capabilities. In: Proceedings of the Conference on Genetic and Evolutionary Computation, ACM, New York, pp. 1785–1792 (2006) 15. Wang, J., Ersoy, O.K., He, M., Wang, F.: Multi-offspring genetic algorithm and its application to the traveling salesman problem. Appl. Soft Comput. 43, 415–423 (2016) 16. Tuncer, A., Yildirim, M.: Dynamic path planning of mobile robots with improved genetic algorithm. Comput. Electrical Eng. 38, 1564–1572 (2012).
Dynamic Analysis of Psychoacoustic Parameters to Evaluate Sound Quality of an Indian String Instrument Sitar Beena Limkar
and Gautam Chandekar
Abstract In this study, the psychoacoustic parameters, loudness, and sharpness are studied to establish their correlation to the sound quality of an Indian string instrument Sitar. These parameters are extracted using audio feature extraction in the Simcenter Testlab software. The sound recording is carried in an anechoic chamber to reduce or eliminate the noise in the recorded audio signal. The musical sound quality evaluation has a lot of challenges as it involves subjective human evaluation. Besides, quantifying the evaluation parameters must account for the nonlinear perception of sound in the frequency as well as time domain. The psychoacoustic parameters become important for such evaluation. Three different Sitars are used in this study. The quality of these Sitars are predecided using opinions given by expert juries of ten Sitarists and is reported here as ‘Khuli Jawari’, ‘Gol Jawari’, and ‘Band Jawari’ Sitars. The relative values of the chosen psychoacoustic parameters correlate with the subjective evaluation of the sound quality of the Sitar as established by the team of juries. Thus, these parameters quantify the subjective evaluation of the musical sound quality of Sitar. Keywords Psychoacoustics · Audio feature extraction · Loudness · Sharpness · Sitar
1 Introduction Sound quality evaluation focuses on the feeling of human about an audio signal using psychoacoustic parameters. As against elimination of sound (noise) or making the physical system quieter, the sound quality evaluation reworks the sound quality to be B. Limkar (B) Department of Technology, Savitribai Phule Pune University, Ganeshkhind, Pune, Maharashtra 411007, India G. Chandekar MKSSS’s Cummins College of Engineering for Women, Karve Nagar, Pune, Maharashtra 411052, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_8
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comfortable and soothing. To cater to this requirement, psychoacoustic technology is used which is a blend of engineering, medical science, and psychology. Widmann [1] attempted the objective measurement of psychoacoustic annoyance. Later, this psychoacoustic technology was applied in the industry for sound quality evaluation. Johansson [2] applied this technology to study the relation between the psychoacoustic descriptors and annoyance of the sound created by home appliances. He found that loudness significantly affected the subjective evaluation of annoyance. Kuwano et al. [3] specifically focused on loudness, sharpness, and comfort index to study the sound quality of electric home appliances. Wang [4] used psychoacoustic parameters to study room acoustics in the perspective of annoyance due to sound. He mentioned that the loudness and sharpness are more sensitive parameters under continuous operation. Hu et al. [5] used sound pressure level, loudness, sharpness, and roughness to study noise inside a rail vehicle. Moravec et al. [6] developed psychoacoustic model for the automatic washing machine sound using roughness, sharpness, loudness, tonality, and fluctuation strength parameters. Kwon et al. [7] studied vehicle interior noise using roughness, sharpness, and tonality. Murovec et al. [8] used psychoacoustic parameters for finding faults in centrifugal pumps. Cambell [9] in his article mentioned that the evaluation of musical instruments is a grand challenge. He suggests using psychoacoustic evaluation for eliminating the player bias and inconsistency. Thus, sound quality evaluation based on psychoacoustic parameters aptly suit in evaluating the sound generated by musical instruments. In this study, the Sitar sound quality is evaluated using the psychoacoustic parameters, loudness, and sharpness. Three Sitars are selected for experimentation. The subjective evaluation of these Sitars is done by a team of ten juries who are renowned artists. These Sitars are categorized as ‘Khuli Jawari’, ‘Gol Jawari’, and ‘Band Jawari’ Sitars. This categorization refers to the bridge top surface curvature [10] which supports the strings. The objective evaluation is carried by studying the loudness and sharpness parameters. The Sitar sound is recorded inside an anechoic chamber to eliminate any noise adulteration. A comparison of subjective and objective sound quality evaluation is presented.
2 Theoretical Background This section gives a brief theory of critical band rate, loudness, and sharpness. The concept of critical band rate is used in the evaluation of loudness and sharpness.
2.1 Critical Band Rate Human ear combines sound having frequencies close to each other as a single band because it vibrates a particular basilar membrane inside the ear. This band is called
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as critical band. Zwicker defined a scale having 24 critical bands to cover the audible range. This scale is known as ‘Critical band rate’.
2.2 Loudness Loudness of a sound is subjective parameter. It is a collective evaluation of the human ear characteristics, phenomenon occurring along the frequency as well as time axis. Loudness is measured in ‘sone’. A pure tone with 40 dB of sound pressure level and 1 kHz of frequency is called as 1sone. Zwicker method [4] expresses the loudness as follows: 24Bark
N=
N · dz
(1)
0
where z is the frequency band scale in Bark number, and E 0.23 E TQ 0.5 + 0.5 −1 N = 0.08 ED E TQ
(2)
where E TQ is the excitation at threshold in quiet, and E D is the excitation at reference intensity I 0 = 10−12 W/m2 .
2.3 Sharpness Sharpness determines the level of high frequencies in a sound. Sharpness is thus the ratio of high frequency level to overall level. Sharpness is measured in acum, which is the band noise centering on 1 kHz, and the band width is 1 Bark. Sharpness [4] is given by the following equation: 24 Bark S=
0
N · g(z) · z · dz
24 Bark 0
N · dz
where g(z) = 1(z ≤ 16 Bark) = 0.066e0.171z (z > 16 Bark)
(3)
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Fig. 1 Test setup for measurement of Sitar sound inside anechoic chamber
3 Experimental Setup Three Sitars are selected for experimentation in this study. They are tune to base frequency of 138 Hz corresponding to C # scale. Sitar has seven main strings. The sound is recorded by playing all the possible notes on the first four strings which are called as Madhyam, Jod, Kharaj Pancham, and Kharaj strings. The various notes on each string are played by limiting the length of the string at each fret on the Sitar finger board. The sound recording is done inside an anechoic chamber to avoid any other noise adulteration. Five microphones are used for sound pickup as shown in Fig. 1.
4 Results and Discussion The data acquired for three Sitars is processed in Simcenter Testlab software, to obtain loudness and sharpness values for all the acquired signals. The three Sitars are evaluated by a jury panel of ten Sitarists. They categorized these Sitars are as ‘Khuli Jawari’, ‘Gol Jawari’, and ‘Band Jawari’, based on the sound quality. Jawari [10] refers to the curvature of the top face of the bridge that supports the strings. Rework of this curvature enhances the sound quality of the Sitar. As per the juries’
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opinion the ‘Khuli Jawari’ Sitar sound cannot be improved by modification in the Jawari. However, they recommended modification of the bridge curvature of ‘Band Jawari’ Sitar for enhancement of the sound quality.
4.1 Loudness Loudness values are calculated using Zwicker method. Loudness perception of the sound is not linear with respect to the frequency or power of the sound signal. The frequency and temporal parameters are fixed in the experimentation; thus, higher loudness values indicate higher sound intensity. Figure 2 shows the plot of the loudness values for all the three Sitars when the Madhyam, Jod, Kharaj Pancham, and Kharaj strings are excited. The average loudness values for each string and for all strings together are given in Table 1. The Gol Jawari Sitar has the largest average loudness values as compared to the other two Sitars, and the average loudness value for all the strings is 11.73sone.
a: Madhyam string
c: Kharaj Pancham string Fig. 2 Loudness values (sone)
b: Jod string
d :Kharaj string
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Table 1 Average loudness values (sone) String Sitar
Madhyam
Jod
Kharaj Pancham
Kharaj
All strings
Fine Sitar
13.4
13.5
10.8
9.2
11.73
Medium Sitar
10.0
9.7
8.0
7.8
8.88
Student Sitar
11.3
11.2
9.7
9.1
10.33
The average loudness value of the Band Jawari Sitar and the Khuli Jawari Sitars are 8.88sone and 10.33sone, respectively. As per the opinion from the juries, the Band Jawari Sitar sound can be improved by changing the bridge curvature [10]. Visual inspection of this Sitar showed less curvature of the top face. Thus, increasing the curvature will lead to enhancement of the sound quality. However, as per the juries’ opinion no further improvement in this sound quality of Khuli Jawari Sitar is possible. Visual inspection of the curvature showed clear curved bridge. Further, increase of the curvature results in uncomfortable playing condition due to higher pressure requirement for pressing the string to control the vibrating length.
4.2 Sharpness Sharpness focuses on the content of high frequencies in the sound. In general, the sharpness of sound is inversely proportional to pleasantness. However, Tsumoto et al. [11] in their study discuss that addition up to six higher harmonics enhance the sound quality from musical perspective. Various sound quality features such as brightness, timbre, hollowness, richness, nasal quality, and shrillness are added due to higher harmonics. Seventh harmonic onward dissonance is added. Thus, for the frequency range considered in this study, higher the sharpness value better is the sound quality considering the musical aspect. Figure 3 shows the plot of the sharpness values for all the three Sitars when the Madhyam, Jod, Kharaj Pancham, and Kharaj strings are excited. The average sharpness values for each string and for all strings together are given in Table 2. The Gol Jawari Sitar has the largest sharpness values as compared to the other two Sitars, and the average sharpness value is 1.52acum. The Band Jawari and Khuli Jawari Sitars have the average sharpness values as 1.22acum and1.29acum, respectively. These values correlate with the juries’ opinion.
Dynamic Analysis of Psychoacoustic Parameters …
a: Madhyam string
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b: Jod string
c: Kharaj Pancham string
d:Kharaj string
Fig. 3 Sharpness values (acum)
Table 2 Average sharpness values (acum) String Sitar
Madhyam
Jod
Kharaj Pancham
Kharaj
All strings
Fine Sitar
1.83
1.53
1.30
1.42
1.52
Medium Sitar
1.29
1.18
1.15
1.27
1.22
Student Sitar
1.48
1.21
1.18
1.28
1.29
5 Conclusions This paper discusses the sound quality of the Indian string instrument Sitar based on the psychoacoustic audio features, loudness, and sharpness. Three Sitars are used for experimentation. The subjective evaluation of the sound quality of these Sitars is done by a team of ten expert Sitarists. Based on their opinion, the Sitars are labeled ‘Khuli Jawari’, ‘Gol Jawari’, and ‘Band Jawari’ Sitars. According to them, the bridge of the ‘Band Jawari’ Sitar requires correction in the curvature of the top surface for
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better sound quality, whereas no such enhancement is possible for ‘Khuli Jawari’ Sitar. The average loudness value for the ‘Gol Jawari’ Sitar is 11.73sone as against 8.88sone and 10.33sone for the ‘Band Jawari’ and ‘Khuli Jawari’ Sitars, respectively. The ‘Gol Jawari’ Sitar thus have better loudness characteristics indicating good sound intensity. Similarly, the average sharpness value for ‘Gol Jawari’ Sitar is 1.52acum which indicate better sound quality features such as brightness, timber, hollowness, richness, nasal quality, and shrillness. The average sharpness values for ‘Band Jawari’ and ‘Khuli Jawari’ Sitars are 1.22acum and 1.29acum, respectively. These values are in agreement with the opinion of the juries. Thus loudness and sharpness parameters are good indicators of the sound quality from the musical perspective.
References 1. Widmann, U.: A psychoacoustic annoyance concept for application in sound quality. J. Acoustical Soc. Am. 101(5), 3078–3078 (1997) 2. Johansson, O.: Relationship between psychoacoustic descriptors and annoyance: regarding sound in home environments. In: Proceedings: Inter. Noise 2000: August 27–30, 2000, Nice, France, pp 4178–4186. SFA. Godka¨nd; 2000; 20061012 (biem) (2000) 3. Kuwano, S., Namba, S., Fastl, H., Putner. J.: Continuous judgment of sound quality of electric home appliances. In: 43rd International Congress on Noise Control Engineering, INTERNOISE, Melbourne, Australia, pp. 1–8 (2014) 4. Wang, W.-H.: Application of psychoacoustics and sound quality assessment in noise control in rooms. J. Temporal Design Architecture Environ. 12(1), 149–158 (2013) 5. Wang, Y., Guo, H., Hu, K., Chen, H.: Sound quality evaluation and optimization for interior noise of rail vehicle. Adv. Mech. Eng. 6, 820875 (2014) 6. Moravec, M., Iarkov, G., Liptai, P., Badida, M., Badidov, A.: Development of psychoacoustic model based on the correlation of the subjective and objective sound quality assessment of automatic washing machines. Appl. Acoust. 140, 178–182 (2018) 7. Kwon, G., Jo, H., Kang, Y.J.: Model of psychoacoustic sportiness for vehicle interior sound: excluding loudness. Appl. Acoustics 136, 16–25 (2018) 8. Murovec, J., Urovi, L., Novakovi, T., Prezelj, J.: Psychoacoustic approach for cavitation detection in centrifugal pumps. Appl. Acoustics 165, 107323 (2020) 9. Campbell, M.: Objective evaluation of musical instrument quality: a grand challenge in musical acoustics. In: Proceedings of Meetings on Acoustics, vol 19, p. 032003, June 2013 10. Khan, M., Rao, P.: Perceptual discrimination of tone quality associated with sitar jawari. In: Proceedings of Frontiers of Research on Speech and Music, pp. 1–6 (2017) 11. Tsumoto, K., Marui, A., Kamekawa, T.: The effect of harmonic overtones in relation to sharpness for perceived brightness of distorted guitar timbre. J. Acoustical Soc. Am. 140, 3380–3380 (2016)
How Can Machine Tool Parameters Influence Tool Life and Wear Characteristics? An Experiment Design Approach Balaji Krushna Potnuru , Vasishta Bhargava Nukala , Satya Prasad Maddula, A. C. Uma Maheshwara Rao, Praveen Ronad, P. Chinmaya Prasad, and Suresh Akella Abstract In this work, we implement a response surface method, for investigating the impact of machining parameters on tool life and wear characteristics. The influence variables also include the temperature on the degradation of tool material. Taylor’s tool life equation based on the cutting speed is evaluated for different tool lift exponents which represent the type of work piece material. Three different types of cooling mediums have been considered for the series of experiments, and hexagonal boron nitride with maximum of 1.25% by volume was used as nano particle lubricant material. The tool life for different materials has been compared to verify the accuracy of tool life predictions. The results showed that with increase in cutting speed, feed rate and depth of cut, the tool life and wear reduced exponentially for cutting speeds between 50 mm/min and 100 mm/min and the maximum tool life was found when the cutting speed was 70 mm/min and feed rate of 0.15 mm/rev. Despite low cutting speeds, the depth of cut increased the tool wear and temperature by 11.1% and 10.4% respectively. Keywords Tool life · Tool wear · Depth of cut · Feed rate · Cutting speed · Temperature
B. K. Potnuru (B) Department of Mechanical Engineering, Malla Reddy Engineering College (Autonomous)-Main Campus, Maisammaguda, Telangana State, India V. B. Nukala · A. C. Uma Maheshwara Rao · P. Ronad · S. Akella Department of Mechanical Engineering, Sreyas Institute of Engineering and Technology, Bandlaguda, Nagole, Telangana, India S. P. Maddula Department of Aerospace Engineering, GITAM Deemed to be University, Hyderabad, Telangana, India P. Chinmaya Prasad Department of Mechanical Engineering, GITAM Deemed to be University, Hyderabad, Telangana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_9
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1 Introduction In field of manufacturing technology, machining processes are usually a sequence of operations which determine how a work piece material can be transformed to a finished industrial product by using high strength cutting tools. Metal cutting is one of the foremost techniques used in industry to shape or remove unwanted material from work piece. The most important variables in metal cutting processes include spindle speed, temperature, feed rate, lubrication, cutting forces, power consumption, and vibration. During such processes, tool wear and tool life are important considerations which take account of the forces and rely on the feed rate, cutting speed, and depth of cut involved in a single operation. In metal cutting operation, tool wear becomes dominant when the process conditions require maximum productivity and is achieved at an economical cost consideration. According to [1–4] nature of any tool wear is not clear due to high contact temperatures and pressures formed at the tool-chip and tool-work piece interfaces. It is result of physical, chemical, and thermo-mechanical phenomenon caused due to adhesive, abrasive, diffusive, and oxidative properties. Research in metal cutting techniques vary with material characteristics and the condition of machining operation, steel alloys are by far the most multifunctional and adaptable materials in manufacturing processes. In the present study, we examine the effect of machining parameters such as the speed, feed rate and depth of cut on the cemented carbide tool insert for various cooling medium conditions. Hexagonal boron nitride nano particles are added to improve the lubrication properties during machining process. In Sect. 2, we discuss the tool life and wear characteristics using the basic and extended empirical tool life equations. The empirical constants in tool life equations are obtained using experimental data analysis for cemented carbide and steel alloys. A response surface methodology using regression-based equation has been implemented for the data obtained through a series of 16 experiments carried on turning operations on lathe bed and boring operations. Tool life and wear was determined for cutting speeds that ranged between 40 mm/min and 120 mm/min and for maximum value of 0.25 mm/rev and 0.6 mm, respectively. Finally, we present a conclusion on suitable tool life for range of machining parameters studied.
2 Methods 2.1 Tool Life Calculation Taylor, 1906 developed an empirical correlation between cutting speed and tool life through a series of experiments and found that tool life varied exponentially with cutting speed [5]. In metal cutting operations, tool deforms the work piece material and produces chips during the deformation process under the action of mechanical forces, temperature, and tribological characteristics. The basic Taylor model as given
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by Eq. (1) provides relatively quick results that can be evaluated manually, but the extended Taylor model takes significantly longer time to evaluate manually. Hence, in the present study we used MATLAB 2019b software to evaluate the extended tool life equation given by Eq. (3) for different machining conditions. vs · Tln = C
(1)
vs · h x · Tln = C
(2)
vs · Tln · d x · f y = C
(3)
T = C n · v− n · d n · f 1
1
x
y n
(4)
where vs is cutting speed expressed in m/min, T l is tool life expressed in minutes, C and n are constants, which will be based on work piece and tool material. The value of n selected for ceramic inserts as 0.4. Tool life equation of Taylor may be extended by the influence of feed as shown in Eq. (2) where, f is feed rate mm/rev, C, x, and y are empirical constants, which will be based on tool and work piece material characteristics. Another empirical formula for tool life commonly used in case of turning process is given by Eqs. (3) and (4). Equation (3) is dependent on two additional parameters, feed rate, f, mm/rev depth of cut, d, mm. Hexagonal boron nitride (HBN) is anisotropic refractory substance which consists of primarily boron and nitrogen and used essentially as dry lubricant. Due to good thermal and chemical stability they are applied in ceramics parts production which can resist high temperatures. The cubic form of boron nitride has exceptional adhesive properties and often used as binders.
2.2 Response Surface Methodology In the simulation of manufacturing systems, there is a widespread use of simulation to design and optimization. Response surface methodology (RSM) is one such technique in which optimized values for design variables are sought by using a possible set of input–output factor combinations in the design space. The search procedures for optimized values according to the optimization technique adopted and involves complex calculations for machine tool and manufacturing industry. Hence, design of experiment process consists of factor inputs from experiments which produce model response outputs that vary with simulation runs. Hence, factor responses are estimated using model simulation runs iteratively and depend on new values of independent variables [6, 7]. This method also consists of metamodel normally, is a firstor second-order polynomial equations to determine the responses for different model
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parameters. Using the metamodel we can obtain a set of factor values which optimizes the response [8]. The important objective of experiment design is to find the key parameters that have strongest influence on outcomes and require least amount of computation cost of simulation. This is sometimes known as factor screening or sensitivity analysis. Schruben and Cogliano [9] introduced a method for dynamic simulation in which values of inputs are varied to find simulated outputs at different frequencies. The output process parameters are examined whenever the input parameters frequencies variation changed. This is known as factor’s oscillation to determine the change in output response at a given frequency. As a result the test models can be obtained at a reduced cost of simulation which is substantially lower than conventional experiment design approaches. Additionally statistically obtained values correlate well with RSM simulation outputs [9–11]. It also means optimization time for whole simulation runs are small enough to evaluate factor inputs and corresponding model response outputs. In case of manufacturing systems the product data used for mass production of components is validated using parametric data obtained from the metamodel [12]. The statistical fitness of metamodel responses can be tested reliably using ANOVA approach. It is especially useful when the population means are computed for more than two sets of groups. For such cases, correlation coefficient, R square is used to check accuracy among the model responses for discrete simulation runs and interactions are sought among factor variables [13]. Hence interactions are never determined by single factor variable approach [11, 12]. After a metamodel is obtained, RSM can evaluate the relationship as well as interactions among the several parameters using quantitative data. Common approach to RSM implementation is as shown below. • Factor screening of parameters • Finding the optimum region of interest • Model the optimum response using polynomial or factor equations. The screening procedure consists of listing all the factor parameters and performing an initial guess towards the region of optimum. Next, a first order model is fitted using the least squares. The path of steepest ascent is related to models regression coefficients. The direction of ascent is shown by signs. Higher order models, such as second-order model, also consists of stationary point that shows a point of maximum or minimum response known as saddle point. A second-order model in experiment design method can be evaluated using one of following ways. • • • • •
Box Behnken Central Composite Design (CCD) Face centred design Equiradial design Hybrid design
Parameters or variables for optimization carried out through model equation [14, 14]. In the present study we develop model equations which represent response surface modelling. Using the regression-based curve fitting method to the data obtained from series of 16 experiments, we obtain a polynomial expression given by
How Can Machine Tool Parameters Influence … Table 1 Empirical constants used for different tool material inserts
95
C
n
Material
160
0.4
Ceramic
350
0.125
HSS (non-steel)
200
0.125
Steel
2700
0.25
Cemented carbide (non steel)
1500
0.25
Steel
10,000
0.6
Ceramic
Eq. (4) based on the extended tool life equation given by Eq. (3) which is a function of all three machining parameters discussed earlier. As mentioned before, the correlation coefficient, R2 is given by Eq. (5) and found to be 0.9704 which represents an excellent fit with the experiment data and those obtained from model simulation runs. The model response equations are usually a second-order polynomial which are fitted by means of regression approach. T = −0.0053 · V 3 + 0.322 · F 2 V 2 − 6.1279 · V · D + 41.471
(5)
R2 = 0.9704
(6)
Similarly, we can obtain the regression equation and its correlation coefficient based on basic tool life Eq. (1) and given by Eqs. (7) and (8) respectively. T = 0.0981 · V 2 − 2.8676 · V + 23.446
(7)
R2 = 0.9156
(8)
where V is Cutting speed, F is feed, D is depth. A general second-order equation for tool life as function of machine spindle speed, feed and cut depth can be written according to Eq. (9) (Tables 1 and 2) T (F, V, d) = To + T (F) · V (F) + T (D) · V (D) + T (F · D) · V (F) · V (D) + T (F) · V 2
3 Results See Figs. 1, 2, 3, 4, 5 and 6.
(9)
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Table 2 Computed actual and predicted tool life using Eqs. (1) and (2), actual and predicted chip thickness S. No
Tool life (min) (Actual)
Predicted Tool life [min] using Eq. (1)
Predicted tool Life (min) using Eq. (2)
Chip thickness, mm
Predicted Chip thickness, mm
1
25.3
24.93
46.49
0.182
0.20
2
21.5
24.93
41.29
0.191
0.20
3
16.4
24.93
38.04
0.210
0.20
4
11.9
24.93
35.71
0.216
0.20
5
33.2
10.75
19.70
0.184
0.17
6
36
10.75
18.11
0.181
0.17
7
14
10.75
16.28
0.173
0.17
8
11.23
10.75
15.51
0.169
0.17
9
34
5.74
10.41
0.157
0.14
10
25.4
5.74
9.33
0.153
0.14
11
32
5.74
8.99
0.147
0.14
12
13
5.74
8.36
0.145
0.14
13
37
3.47
6.26
0.142
0.13
14
29
3.47
5.69
0.139
0.13
15
23
3.47
5.35
0.138
0.13
16
15
3.47
5.15
0.140
0.13
Fig. 1 Contour graph depicting desing life of tool for changing rates in feed and speed of cutting
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Fig. 2 Response surface of life of cutting tool at multiple rates applied to feed and cutting speed
Fig. 3 Contour plot of tool wear for different feed rates and cutting speed values
4 Conclusions It is observed that the effect of machining parameters feed rate (mm/rev), depth of cut (mm), and cutting speed (mm/min) have a significant influence on the tool life of the cutting tool. A response surface methodology was adopted on experiment data
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Fig. 4 Temperature distribution on tool face for different values of depth of cut and cutting speeds Fig. 5 Comparison of tool life when the tool life exponent, n is 0.4 and C value of 185, x = 0.01 and y = 0.1 (HSS non-steel) material using Eqs. (1) and (2)
Fig. 6 Comparison of tool life for varying cutting speed values and at different tool life constants
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to determine the effects of machining parameters on tool life and wear for various cooling medium conditions. Few important observations from the present study are: 1.
2.
3.
4.
5.
It is observed that by plotting the graph between tool life, varying cutting speed and feed rates, at low cutting speed tool life will increase and a high feed velocity will reduce the cutting time per work piece. Graph of surface degradation of tool for changing feed rate, cutting speeds reveals that when cutting speed increases the cutting temperature also increases which will reduce the tool life. The tool wear for different feed rates and cutting speed values demonstrates that tool wear is affected lowest when the feed rate at 0.15 mm/rev. Smooth surface roughness was produced due to the high values of speed and low values for feed rate. The analyses were done on temperature distribution on tool face for different values of depth of cut and cutting speeds. It is observed that the temperature of tool flank (side face) during the machining process is varied directly with speed and caused due to abrasion while the land (top face) is caused due to adhesive wear. At a higher value of spindle speeds, the temperature recorded at the tool face increased exponentially. Temperature distribution on tool face is also affected when the depth of cut and cutting speeds was increased. Hardness of work piece material strongly influences cutting speed and tends to reduce tool life significantly. By increasing cutting speed to ~20% the expected tool life is reduced to half but when cutting speed is increased by 50% the expected tool life is reduced to 1/5th of original life.
Further research on the variable tool geometry provides better understanding of tool life and wears characteristics.
References 1. Talib, A.N.: Studying the effect of cutting speed and feed rate on tool life in the turning processes. In: First Engineering Scientific/Conference College of Engineering, University of Diyala, 22–23 Dec (2010) 2. Kale, S.A., Chaudhari, N.B.: Effect of cutting parameters on tool life. Int. J. Curr. Eng. Technol. 6(6), 2178–2182 (2016) 3. Ayodeji, O.O., Abolarin, S.M., Yisa, J.J., Olaoluwa, P.S., Kehinde, A.C.: Effect of cutting speed and feed rate on tool wear rate and surface roughness in lathe turning process. Int. J. Eng. Trends Technol. 22(4), 173–175 (2015) 4. Astakhov, V.P.: Effects of the cutting feed, depth of cut, and workpiece (bore) diameter on the tool wear rate. Int. J. Adv. Manuf. Technol. 34(631–640). https://doi.org/10.1007/s00170-0060635-y (2007) 5. https://amtil.com.au/mathematical-models-effectively-calculate-tool-life/-:~:text=The%20e quation%20for%20Taylor%E2%80%99s%20basic%20model%20is%20vC,result%20in% 20a%20tool%20life%20of%20one%20minute. Mathematical models effectively calculate tool life - AMTIL, September 2, Industry news, Source: https://amtil.com.au/ (2016)
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6. Meilgaard, B.T., Civille, M., Carr, G.V.: Sensory Evaluation Techniques, 2nd edn. CRC Press, Boca Raton, FL (1991) 7. A.V.A. Resurreccion: Quantitative of quality attributes as perceived by the consumer. In: Consumer Sensory Testing for Product Development. Aspen Publishers, Inc., Gaithersburg, MD (1998) 8. Box, K.G., Wilson, G.E.P.: On the experimental attainment of optimum conditions. J. R. Stat. Soc. 13, 1–45 (1951) 9. Schruben, L.W., Cogliano, V.J.: An experimental procedure for simulation response surface model identification. Commun. Assoc. Comput. Mach. 30, 716–730 (1987) 10. Schruben, L.W., Margolin, B.H.: Pseudo random number assignment in statistically designed simulation and distributing sampling experiments. J. Am. Statist. Assoc. 73, 504–520 (1978) 11. Schruben, L.W.: Establishing the credibility of simulations. SIMULATION 34, 101–105 (1980) 12. Tan, C.-H., Ghazali, H.M., Kuntom, A., Tan, C.-P., Ariffin, A.A.: Extraction and physicochemical properties of low free fatty acid crude palm oil. Food Chem. 113, 645–650 (2009). https:// doi.org/10.1016/j.foodchem.2008.07.052 13. Moskowaitz, H.R.: Product optimization approaches and applications. In: Measurement Food Prefer. Blackie Academie & Professional, Glasgow, UK, pp. 97–136 (1994) 14. Schutz, H.G.: Multiple regression approach to optimization. Food Technol. 37, 46–48, 62 (1983) 15. Law, A.M.: Simulation Modelling and Analysis, 4th edn. McGraw Hill Publishers, New Delhi (2010)
Experimental Evaluation of EDM Performance on EN8 Steel Using Taguchi Technique B. Kishan, B. Sudheer Prem Kumar, S. Gajanana, and N. Sunil Naik
Abstract The main aim of the work is to evaluate the input and output constraints of Electrical Discharge Machining (EDM) process to attain the viability in machining of EN8 widely used in shafts, gears, studs, bolts and keys due to its high wear and tear resistance. The machining process is carried out by using copper, brass, Al 6061 low and high strength electrodes. The evaluation is done using MINITAB software and observed Taguchi signal–noise ratio are applied to improve the constraints such as MRR and TWR on EN8 and the ideal values of input constraints such as Pulse on Time, Discharge Current and Pulse off Time were obtained. Keywords Electrical discharge machining · EN8 · MRR · TWR
1 Introduction EDM process is widely used in Machining of forging dies, making injection mouldings, thread cutting, grinding brittle materials, preparation extrusion and blanking dies. EDM uses the thermoelectric process to erode the material from the workpiece by series of discharge spark. In this process, with the help of discharge spark between the electrode and workpiece immersed in the dielectric medium the material is removed, which creates the prescribed path for each spark towards fluid ionization. The space between the workpiece and electrode is controlled by servo control arrangement which melts the particles in such a way that the melted particles are
B. Kishan (B) · B. Sudheer Prem Kumar Department of Mechanical Engineering, JNTUH, Hyderabad, Telangana, India S. Gajanana Department of Mechanical Engineering, MVSR Engineering College, Hyderabad, Telangana, India N. Sunil Naik Department of Mechanical Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, Krishna (District), Andhra Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_10
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removed away by electrolyte. The optimum input and output parameters plays a critical role in terms of quality, cost and safety process. EDM is extensively used process in several machining processes where exactness and correctness are challenged. Several researchers have attempted to enhance these input and output constraints. These parameters are correlated with machining parameters. Optimum selection of constraints can result in highly accurate finish. So, EDM process is carried out to achieve optimum machining parameters.
2 Literature Review The performance of EDM process mainly depends upon the input and output parameters. A substantial work has been carried previously in this particular area. Navin et al. [1] studied the performance parameters of the En41b workpiece by using copper and brass electrodes. Using Taguchi method, they concluded that the optimum parameters are satisfactory. Bhaskar et al. [2] conducted experiments on AA6061/10%Al2 O3 to evaluate the cutting conditions by using Taguchi L27 orthogonal array and stated that MRR and TWR have shown a significant increase with the increase of input parameters. Surabjeet et al. [3] predicted the optimum EDM machining parameters SiC/A359 metal matrix composite by using the L9 orthogonal array and observed that MRR and TWR predicted responses are satisfactory and executed experimentally. Jaber et al. [4] reviewed the experimental and theoretical model studies on EDM process parameters and stated that machining parameters such as MRR and TWR are mainly influenced by the input parameters. Further, discussed outlines about the future work. Hadad et al. [5] carried out experiments on EDM machine with varied EDM process parameters to evaluate the cutting parameters. These results have shown a significant increase in TWR with a decrease in MRR is mainly due to change in surface finish. Payal et al. [6] applied Taguchi-Fuzzy approach for optimization of EDM with varied input parameters. An L36 orthogonal array is designed for conducting the experiments and found that significant increase in terms of MRR and TWR by using the proposed approach. Tripathy et al. [7] investigated to maximize input and machining parameters of EDM on steel workpiece and concluded that multi-objective optimization techniques has shown significant improvement in terms of performance parameters. Liang et al. [8] studied the effects on the Ti-6Al4V alloy. The machining parameters MRR and TWR are measured and stated that a significant increase in machining parameters is observed. Huang et al. [8] used Taguchi method to determine the milling characteristics and performance parameters of Ti-6Al-4V alloys. Further, compared the performance between different electrodes and confirmed that tungsten carbide electrode yielded highest machining parameters. Shukla et al. [9] studied the machining parameters on EDM process with Al-LM6/SiC/B4C hybrid composites by design of experiments and found that a significant increase in terms of MRR is observed with increase in discharge current whereas in case of TWR is mainly dependent on discharge current and pulse on time. Mathan et al. [10] enhanced new model to find out the outcome of the Al2618
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composite in an EDM machining. Taguchi’s DOE is used to analyze the input and performance parameters and stated that composites showed a significant increase in mechanical properties. Jeykrishnan et al. [11] calculated the parametric optimization of input parameters to boost MRR by using an L9 orthogonal array and stated that dielectric current has a major impact on MRR. Sidharth et al. [12] compared the experimental study of both input and machining parameters on AISI D2 workpiece. The experiment is carried by the L9 orthogonal array and concluded that the most instigation factors for both MRR and TWR are pulse off time and current respectively. Anil et al. [13] optimized the technique and found that the abrasive powder has shown significant behaviour on the input parameters and further can improve performance characteristics. Taweel et al. [14] investigated the performance behaviour of CK45 steel workpiece to plan the experiments. The experiment conducted was to evaluate the input and machining parameters by RSM and found that the difference between the forecast and trial values are satisfactory. Hourmand et al. [15] carried out EDM process using a new group of composites with copper electrodes as the tool and stated the significant factors affecting MRR. Whereas, on the other hand, the only pulse on time is the important factor affecting TWR. Baraskar et al. [16] developed new empirical model related to machining parameters for optimum selection of input parameters by using DOE technique. The developed new model is optimized but found that no best matching performance is obtained between both the parameters. Mohanty et al. [17] considered the influence on Iconel 825 with respect to machining parameters and stated that the MRR is the most influencing factor obtained by multi optimization technique. Nikalije et al. [18] used MDN 300 steel to determine the factors influencing while performing EDM process. The results stated that optimum level variation of TWR varied with respect to MRR and also stated that input parameters are main factors affecting the EDM performance. Patel et al. [19] developed a ceramic composite material (Al2 O3 -SiC-TiC) and investigated the machinability through EDM process. Experiments are carried out using input parameters and stated as significant factors affecting machining process. Sanchez et al. [20] carried out an inversion model to optimize the input parameters to ensure the partial fulfilment of performance parameters and predicted the output parameters of AISI 1045 steel with respect to the input parameters. Keeping in view of above points, the main focus of work is to optimize three numbers of input parameters of EN8 steel with two numbers of machining parameters and compared with the experimental results.
3 Methodology In this work, the Taguchi method which is one of the techniques of DoE is used to design the experiments required for investigation. These experiments were carried out by taking EN8 medium carbon steel as the workpiece and four electrodes as tool material. Three input parameters were considered to evaluate the two machining parameters (MRR and TWR) for each test run. Design of experiments optimizes the possible
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Fig. 1 RAM EDM setup
combination parameters and evaluates the control factors without conducting a large number of experiments. Based upon design, an orthogonal array has opted for the present work which minimizes the number of experiments [1]. In the present work total, 9 runs are performed with three input parameters.
4 Experimental Work 4.1 Work Piece Material The experiments are conducted on a RAM EDM setup as shown in Fig. 1. In the present work, EN8 steel is selected as work piece due to its good tensile strength and enhanced wear resistance used in applications like shafts, gears, studs, bolts and keys. The workpiece and composition of EN8 is shown in Fig. 2 and Table 1 respectively.
4.2 Tool Materials Four different types of tool materials have been used as electrodes for carrying experiments. T1 —Copper electrode, T2 —Brass electrode, T3 —Al6061 low strength electrode and T4 —Al6061 high strength electrode as shown in the Fig. 3 and the mechanical properties are shown in Table 2 (Fig. 4).
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Fig. 2 EN8 work piece
Table 1 Workpiece chemical composition Element
Carbon
Silicon
Manganese
Sulphur
Phosphorous
Composition %
0.36–0.44
0.10–0.40
0.60–1.00
0.05 Max
0.05 Max
Fig. 3 Electrode tool materials
Table 2 Mechanical properties of electrodes
Property/Electrode Al6061
Copper
Brass
Rockwell hardness 40 RHN
235–878 MPa 192-292 MPa
Ultimate tensile strength
310 MPa 220 MPa
338–469 MPa
Poisson’s ratio
0.33
0.31
Modulus of elasticity
68.9 GPa 117 GPa
0.355
97 GPa
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Fig. 4 Flowchart of the methodology
4.3 Design Matrix and Observation Table The experiments were carried out using Taguchi technique. The input parameters were optimized to carry 9 different experiments for calculation of output parameters [21] (Table 3).
5 Results and Discussions The response tables for different electrodes are shown in the following tables.
5.1 S/N Calculation Ratio It was broadly classified into two types “Larger is better” and “Smaller is better”. For MRR “Larger is better” and for TWR “Smaller is better” for optimum machining parameters [22] (Figs. 5 and 6) (Tables 4 and 5).
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Table 3 Weight of tool before and after machining process Electrode material
(A)
(T on )
(T off ) µs
EN8 wt. Before machining (g)
EN8 wt. After machining (g)
Tool wt. Before machining (g)
Tool wt. After machining (g)
Copper
4
100
50
18.2316
18.1817
119.096
119.094
Copper
6
150
75
19.6122
19.2501
119.094
119.093
Copper
8
200
100
19.0641
18.6752
119.093
119.090
Brass
4
150
100
19.0899
19.0195
97.4824
97.3845
Brass
6
200
50
18.8835
18.7435
97.3845
97.2317
Brass
8
100
75
18.2346
18.0963
97.2317
97.0051
Al6061 (low)
4
200
75
18.8652
18.8014
87.0722
87.0640
Al6061 (low)
6
100
100
18.9587
18.8835
87.0640
87.0590
Al6061 (low)
8
150
50
19.0840
18.9199
87.0590
87.0433
Al6061 (high)
4
200
75
18.0821
18.0185
86.3126
86.3044
Al6061 (high)
6
100
100
18.4275
18.3445
86.3044
86.2921
Al6061 (high)
8
150
50
19.4795
19.3066
86.2961
86.2845
Fig. 5 (Low strength) electrodes
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Fig. 6 (High strength) electrodes
Table 4 Response table for Copper, Brass and AL6061 (low strength) electrodes Run no Electrode
(A) T On (µsec) T off (µsec) MRR (mm3 /min) TWR (mm3 /min)
1
Copper
4
100
50
639.743
2
Copper
6
150
75
4642.307
0.000894
3
Copper
8
200
100
4985.89
0.00223
4
Brass
4
150
100
902.56
1.1252
5
Brass
6
200
50
1794.871
1.7563
6
Brass
8
100
75
1773.073
7
Al6061 (Low) 4
200
75
817.94
8
Al6061 (Low) 6
100
100
9
Al6061 (Low) 8
150
50
0.0257
2.02988 0.303
964.102
21.51
2103.84
20.24
Table 5 Response table for Copper, Brass and AL6061 (high strength) electrodes Run no
Electrode
(A)
Pulse on time (µsec)
Pulse off time (µsec)
MRR (mm3 /min)
TWR (mm3 /min)
1
Copper
4
100
50
639.743
0.0257
2
Copper
6
150
75
4642.307
0.00894
3
Copper
8
200
100
4985.89
0.00223
4
Brass
4
150
100
902.56
1.1252
5
Brass
6
200
50
1794.871
1.7563
6
brass
8
100
75
1773.076
10
Al6061 (high)
4
200
75
815.384
0.303
11
Al6061 (high)
6
100
100
1064.103
0.307
12
Al6061 (high)
8
150
50
2216.66
0.4296
2.02988
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5.2 Influence on MRR Figure 7 illustrates the main effects of Electrode (A), current (B), pulse ON and pulse OFF on MRR. As shown in the figure each parameter optimizes the responses for better results. Current plays an important parameter for MRR, which varies linearly with respect to MRR. This is due to advance action of spark which triggers the melting and vaporization process [11]. Most effective response parameters for MRR is (A1 B3 C2 D2 ), here A1 means electrode (1), B3 means current (8), C2 means pulse ON (150) and D2 means pulse OFF (75). The optimized combined response value of MRR is 3422.64 mm3 /min. Figure 8 illustrates the S/N plot with respect to MRR for the responses obtained from Table 5. It is clear from the fig, that the combination of plotted responses responsible for the optimized results. Here current parameter had shown a significant behaviour with respect to MRR which increases linearly with MRR. This is because of advancing the self-control in the spark plug [15]. The optimized response combination for MRR from the figure is (A1B3C2D2), here A1 means electrode (1), B3 means current (8), C2 means pulse ON (150) and D2 means pulse OFF (75). The optimized combined response value of MRR is 3422.64 mm3 /min. Main Effects Plot for SN ratios Data Means Pulse OFF
Pulse ON
Current
Electrode
Mean of SN ratios
68
66
64
62
60
58 1
2
3
4
Signal-to-noise: Larger is better
Fig. 7 MRR for Response from Table 4
6
8
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150
200
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Main Effects Plot for SN ratios Data Means Electrode
70
Pulse ON
Current
Pulse OFF
Mean of SN ratios
68
66
64
62
60
58 1
2
3
4
6
8
100
150
200
50
75
100
Signal-to-noise: Larger is better
Fig. 8 MRR for Response from Table 5
5.3 Influence on TWR Figure 9 shows the combined effect of input parameters with respect to S/N ratios. The influence of each input parameter plays a vibrant role to optimize the response results. Among the parametric analysis pulse ON is the most influencing parameter for TWR which varies linearly. From the figure, it is clearly shown that a significant increase of TWR is observed. This is due to the effective removal of metallic particles from the work piece [9]. Most optimized responses for TWR is (A3B3C1D1), here A3 means electrode (3), B3 means current (8), C1 means pulse ON (100) and D1 means pulse OFF (50), for this grouping optimized value for MRR is 0.009608 mm3 /min. Figure 10 shows the variation of Electrode (A), current (B), pulse ON and pulse OFF with respect to S/N ratio of TWR for response Table 5. It is clear from the figure each parameter behaviour is crucial in optimizing the output results. Pulse ON time has shown a significant linear behaviour with respect to TWR, this is due to high sparking behaviour observed between the inter-electrode gap arcing [19]. The optimized contribution of input parameters for TWR from the figure is (A2B1C1D1) here A2 means electrode (2), B1 means current (4), C1 means pulse ON (100) and D1 means pulse OFF (50), for this combination optimized value for MRR is 0.009608 mm3 /min. Regression equation for MRR using low strength Al6061 is MRR = −2322 − 1064 tool + 542 discharge current + 14.07 pulse ON time + 15.4 pulse OFF time. Regression equation for MRR using HIGH strength Al6061 is MRR = −2369 −
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Main Effects Plot for SN ratios Data Means Electrode
Current
Pulse OFF
Pulse ON
Mean of SN ratios
40
30
20
10
0
1
2
3
4
6
8
100
150
200
50
75
100
Signal-to-noise: Smaller is better
Fig. 9 TWR for response Table 4
Main Effects Plot for SN ratios Data Means Current
Electrode
Pulse OFF
Pulse ON
50
Mean of SN ratios
40 30 20 10 0 -10 -20 1
2
3
4
Signal-to-noise: Smaller is better
Fig. 10 TWR for response Table 5
6
8
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1029 tool + 551 discharge current + 13.73 pulse ON time + 15.3 pulse OFF time. Regression equation for tool wear rate using low strength Al6061 is TWR = 0.36 + 0.172 tool + 0.097 discharge current—0.00060 pulse ON time + 0.007 pulse OFF time. Regression equation for tool wear rate using high strength Al6061 is TWR = 0.38 + 0.167 tool + 0.077 discharge current—0.001pulse ON time—0.0046 pulse OFF time.
6 Conclusions The following conclusions for EN8 workpiece as per the responses obtained from the graphs plotted as follows: • The optimum combination of input parameters is explored using MINITAB software. • Investigation of EDM performance is carried out using different electrode materials such as Cu, Br and Al6061 using Taguchi technique. • Further comparing the performance of Al6061 electrode with Cu and Br electrode has shown a significant increase of 20.57%, 1.24% and 0.003%, 0.012% in terms of MRR and TWR respectively. • Hence the feasibility of machining Al6061 electrode material with Taguchi technique has shown satisfactory results in terms of the EDM performance rate.
References 1. Vincent, N., Kumar, A.B.: Experimental investigations into EDM behaviours of En41b using copper and brass rotary tubular electrode. Proc. Technol. 25, 877–884 (2016) 2. Kandpal, B.C., Kumar, J., Singh, H.: Optimization and characterization of EDM of AA 6061/10% Al2 O3 AMMC using Taguchi’s approach and utility concept. Prod. Manuf. Res. 5(1), 351–370 (2017) 3. Sidhu, S.S., Yazdani, M.: Comparative analysis of MCDM techniques for EDM of SiC/A359 composite. Arab. J. Sci. Eng. 43(3), 1093–1102 (2018) 4. Kumar, S., Singh, R., Batish, A., Singh, T.P.: Electric discharge machining of titanium and its alloys: a review. Int. J. Mach. Mach. Mater. 11(1), 84–111 (2012) 5. Hadad, M., Bui, L.Q., Nguyen, C.T.: Experimental investigation of the effects of tool initial surface roughness on the electrical discharge machining (EDM) performance. Int. J. Adv. Manuf. Technol. 95(5–8), 2093–2104 (2018) 6. Payal, H., Maheshwari, S., Bharti, P.S., Sharma, S.K.: Multi-objective optimisation of electrical discharge machining for Inconel 825 using Taguchi-fuzzy approach. Int. J. Information Technol. 1–9 (2018) 7. Tripathy, S., Tripathy, D.K.: Multi-response optimization of machining process parameters for powder mixed electro-discharge machining of H-11 die steel using grey relational analysis and topsis. Mach. Sci. Technol. 21(3), 362–384 (2017) 8. Liang, J.F., Liao, Y.S., Kao, J.Y., Huang, C.H., Hsu, C.Y.: Study of the EDM performance to produce a stable process and surface modification. Int. J. Adv. Manuf. Technol. 95(5–8), 1743–1750 (2018)
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9. Shukla, M., Agarwal, P., Pradhan, M.K., Dhakad, S.K.: Experimental investigation of EDM parameters on Al-LM6/SiC/B4C hybrid composites. In: Applied Mechanics and Materials, vol 877, pp. 149–156 (2018) 10. Kumar, N.M., Kumaran, S.S., Kumaraswamidhas, L.A.: An investigation of mechanical properties and material removal rate, tool wear rate in EDM machining process of AL2618 alloy reinforced with Si3N4, AlN and ZrB2 composites. J. Alloy. Compd. 650, 318–327 (2015) 11. Jeykrishnan, J., Ramnath, B.V., Felix, A.J., Pernesh, C.R., Kalaiyarasan, S.: Parameter optimization of electro-discharge machining (EDM) in AISI D2 die steel using Taguchi technique. Indian J. Sci. Technol. 9(43) (2016) 12. Gupta, S., Jain, S.K., Singh, G.: Experimental study of MRR, TWR, SR on AISI D2 Steel using Aluminium Electrode on EDM. Glob. J. Res. Eng. (2017) 13. Kumar, A., Maheshwari, S., Sharma, C., Beri, N.: A study of multiobjective parametric optimization of silicon abrasive mixed electrical discharge machining of tool steel. Mater. Manuf. Process. 25(10), 1041–1047 (2010) 14. El-Taweel, T.A.: Multi-response optimization of EDM with Al–Cu–Si–TiC P/M composite electrode. Int. J. Adv. Manuf. Technol. 44(1–2), 100–113 (2009) 15. Hourmand, M., Farahany, S., Sarhan, A.A., Noordin, M.Y.: Investigating the electrical discharge machining (EDM) parameter effects on Al-Mg 2 Si metal matrix composite (MMC) for high material removal rate (MRR) and less EWR–RSM approach. Int. J. Adv. Manuf. Technol. 77(5–8), 831–838 (2015) 16. Baraskar, S.S., Banwait, S.S., Laroiya, S.C.: Multiobjective optimization of electrical discharge machining process using a hybrid method. Mater. Manuf. Process. 28(4), 348–354 (2013) 17. Mohanty, A., Talla, G., Gangopadhyay, S.: Experimental investigation and analysis of EDM characteristics of Inconel 825. Mater. Manuf. Process. 29(5), 540–549 (2014) 18. Nikalje, A.M., Kumar, A., Srinadh, K.S.: Influence of parameters and optimization of EDM performance measures on MDN 300 steel using Taguchi method. Int. J. Adv. Manuf. Technol. 69(1–4), 41–49 (2013) 19. Patel, K.M., Pandey, P.M., Rao, P.V.: Optimisation of process parameters for multi-performance characteristics in EDM of Al2 O3 ceramic composite. Int. J. Adv. Manuf. Technol. 47(9–12), 1137–1147 (2010) 20. Sánchez, H.T., Estrems, M., Faura, F.: Development of an inversion model for establishing EDM input parameters to satisfy material removal rate, electrode wear ratio and surface roughness. Int. J. Adv. Manuf. Technol. 57(1–4), 189–201 (2011) 21. Singh, V., Bhandari, R., Yadav, V.K.: An experimental investigation on machining parameters of AISI D2 steel using WEDM. Int. J. Adv. Manuf. Technol. 93(1–4), 203–214 (2017) 22. Mazarbhuiya, R.M., Choudhury, P.K., Patowari, P.K.: An experimental study on parametric optimization for material removal rate and surface roughness on EDM by using Taguchi method. Mater. Today: Proc. 5(2), 4621–4628 (2018)
Design and Simulation of Smart Multipurpose Autonomous Ground Vehicle for Industrial Application Gokula Vishnu Kirti Damodaran, J. B. Greesh Pranav, V. Siva Naga Yaswanth, Amartya Reddy Ponaka, and Joshuva Arockia Dhanraj Abstract In this new era of smart sensors, the field of robotics has enormously grown to its next level, the automation process in the industrial sector increases the fast product development as well as cost reduction and the manpower requirement can be decreased. In industries, autonomous mapping and navigating robot will play a vital role for the large warehouse where multiple task can be implemented and done using the autonomous navigating general-purpose robot, in this project an autonomous navigating robot is developed based on the lidar system using SLAM methodology which has the ability to map the environment on its own and able to find the shortest / convenient path to the destination, this robot uses the lidar as a input sensor based on the input taken it creates a map and finds the path for navigation even in the partially observable environment, This robot model uses the model based reflex agent as its environment and uses the HECTOR SLAM (simultaneous localization and mapping) along with adaptive Monte Carlo localization (AMCL) on a robot operating system (ROS) platform deployed on Raspberry Pi, using the combination of HECTOR SLAM and AMCL both the dynamic and static environment can be handled by the robot due to the adaptiveness of the robot this is highly reliable for the use in the industrial environment, HECTOR SLAM technique eliminates the requirement of odometry as this HECTOR SLAM takes the lidar position as a feedback system unlike other SLAM algorithms. This work also features the implementation of both A* algorithm and AMCL based on the use cases and the user preference. By giving an add-on device to this robot which can accomplish the task given by the user like transportation of products and cleaning the floor of the industries, security and surveillance and much more activities. This kind of robot helps to reduce the manpower required in the industrial sector and to automate the industrial sector which paves the way for the next generation of development in the industry. Keywords HECTOR SLAM · Lidar · ROS · AGV · Path finding · Automation G. V. K. Damodaran (B) · J. B. Greesh Pranav · V. Siva Naga Yaswanth · A. R. Ponaka · J. A. Dhanraj Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai, Tamil Nadu 603103, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_11
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1 Introduction In the modern era, robots have been replacing humans in various areas and have been preferred for their efficient and effective execution of the task without any errors. One such area where the robots could be of high importance is in the industrial sector. An industry is an organization that produces or supplies goods, services, or sources of income. In these industries, there are several jobs to be carried out on a regular basis within the industrial region. Various autonomous operating machines are already being used in the industries for a lot of different applications, and now with the tremendous advancements in technology especially in the field of robotics in the past decades, it is now a necessity to use robots in place of humans for various time-consuming and stressful activities which also demands very high attention and concentration. The various applications where robots could be deployed in place humans include surveillance and security, goods transportation, packing, and various other manufacturing processes. In the industrial surveillance and security job, human workers are not the best always and hence require an additional technological support like the use of security camera, siren alert system, entrance scanning, etc. In the case of goods transportation, also carrying heavy loads is hard for human beings and doing it for multiple times a day becomes painful and leads to various physical problems. In every industrial application, using human workers requires more time and becomes stressful A robot is nothing but an automatic/semiautomatic electronic machine which can perform various pre-programmed tasks. Exercises show that no human-operated system can work efficiently for 24/7 without errors. These robots could be wheeled, walking, or even flying. To overcome the above-mentioned statements a general-purpose autonomous mobile robot which can map and navigate itself from a start point to the end point is developed using the robot operating system (ROS) platform which is given with an add-on device to accomplish various tasks. The methodology, implementation, studies, and results for developing and deploying the robot are discussed in this work. The main functionality and acquisition of the literature review are to learn and utilize the previously existing work which would enhance the new work to be developed and make analysis of the same technology and to fill the gap between the previously available work and the today’s technological world requirement. To name a few best works done on this field, Takamatsu et al. have presented and implemented [1] the mobile robot using the robot operating system (ROS) using 2D lidar and the RGB-D depth camera; in this work, in order to collect the 3D information for the robot depth sensor has been used which overcome the problem of high-cost 3D lidar by using 2D lidar, this work is conducted for two cases one is with lidar alone and other is with both lidar and RBG-D camera as results discussed both have passed the tests in all the cases for avoiding the obstacle and achieving the goal under both dynamic and static environment using the AMCL algorithms. Naigong et al. in his work proposed [2] an improved SLAM algorithm based on the information fusion has been done, his work includes the extending Kalman filtering (EKF) for fusing the information from the odometry provided using the IMU and laser sensor; this
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work has concluded and proved that the fusion of the odometry to the lidar data can provide accurate and distortion-free results when compared to the traditional SLAM approach. Filipenko et al. have conducted comparative study of the [3] different SLAM algorithms and also the visual-based mapping for various different algorithms and discussed in this work for this the prototyping and testing office-based environment has been used, and this work is carried out with the 2D lidar and the monocular and stereo cameras; based on the analysis in the work, it has proven and evaluated as the HECTOR and the cartographer algorithms provide similar results and both are better in the robustness when compared to the other SLAM algorithms such as GMapping and concluded results; based on the visual SLAM LSD SLAM, DSO provides dense map to make 3D object detection.
2 Methodology The methodology of this autonomous robot includes the integration of hardware and the software components and to deploy the controller to the robot that could have the intelligence to accomplish the tasks provided by the user by processing the flow of process for the task of path planning the robot utilizes the 2D lidar data and the MPU sensor data as an input to detect the obstacles and map the environment and the accelerometer and the gyroscope values to position and to maintain and detect orientation of the robot model in the environment. The control and integration of both the hardware model and the software model and control model are achieved using the Raspberry Pi 3 module in which the control algorithms are deployed on the ROS platform in addition to this, an Arduino UNO R3 controller is used as a controller to drive the motor and get the input from the sensors and the encoder, Arduino and the Raspberry Pi has established the connection using UART communication protocol. The main part of the methodology is to implement the SLAM [4] function and the path planning algorithms [9] along with the navigation stack for the robot to move autonomously in both the static and the dynamic environment with an adaptive motion of the robot according to the environment change. As part of the SLAM function HECTOR SLAM is used for the mapping and localization which is fused with the MPU sensor [5] which could build the error-free stable map without the distortion in the map which is the proved method for acquiring highly stabilized dense SLAM algorithms, the main function of the Odom is to provide the pose of the robot which optimize and utilize the features of the AMCL algorithm without and compromise, For the path planning and AMCL is used which is best for path planning which is provided by the ROS environment for building the robot and at the final part of the autonomous mobile robot implementation of the navigation stack is necessary to achieve the goal of navigation of the robot to follow the path planned and to reach the goal location without any collusion in the path ROS help by providing the libraries which is highly customizable for the user that if could adapt and use any [6] high level programming language and combine all the nodes to a single platform which help in the integration and achieving the desired goal of work. This work
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aims on providing and building the multipurpose ground vehicle for the industrial applications by providing the add-on to the main device. This work follow the stepby-step process of building the robot and simulating it on the real-world physics environment and implementing the same in real world for its operation, the building started with the designing of the mobile robot using Autodesk FUSION 360 CAD tool and conducting the stress analysis for the same design using the static stress analysis tool provided in the FUSION 360 software, as the next step of progress in the development kinematic modelling of the robot is done to check the robot design can move and follow assigned path without much deviation in its path. Then the work was carried out by creating and testing the environment in the Gazebo tool and visualizing the SLAM and the navigation outcome in the RVIZ toolbox. The entire process is carried out based on the sensors used such as 360 lidar, wheel encoder, 6 DOF MPU, Raspberry Pi controller with the Arduino Uno controller.
3 Hardware and Software This work consists of two modules hardware and software integration as part of the hardware (Fig. 1) represent the basic block diagram of the hardware integration, in
Fig. 1 Block diagram of hardware integration
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which Raspberry Pi plays a major role in the controlling and handling of the sensing and the actuator elements, RPLIDAR which could sample 6000 data per second and the MPU which has a 3 DOF of accelerometer and 3 DOF gyroscope works as the major sensing element and the vision system of the robot. When it comes to the software part of the robot operating system (ROS) is the heart of the robot which is deployed on the Linux-based OS; this ROS integrates various nodes and provides various packages for the robotic applications; ROS also provides Gazebo which mimics the real-world physics in the simulated setup, where the simulations are carried out using a simulated environmental setup; RVIZ is the visualization tool provided by the ROS where the SLAM and navigation of the robot are visualized and controlled; on the view of the robot designing and modeling, Autodesk FUSION 360 is used to carry out the designing and the stress analysis of the robot CAD model and to simulate the joint angles and the movement of the joints using motion study tool, and MATLAB with the robotic system toolbox is used for carrying out the kinematic study and simulation of its kinematics.
3.1 SLAM Simultaneous localization and mapping is used to localize the position of the robot in the environment and to map the environment in which the robot is working on simultaneously which provide the useful information for the robot to move or travel in the environment; this is also used to estimate the pose of the robot in the environment; SLAM has two types of visual SLAM and non-visual SLAM in this work non-visual SLAM, and using 2D lidar [10] data is focused and used. This work uses HECTOR SLAM which is the modern used for the mapping and localization. HECTOR SLAM [7] is one of the great packages provided by the ROS environment for the mapping of the world this could handle both the static and dynamic environment, and this HECTOR SLAM is one of the best-known mapping methods for building the stable and dense map with less error accumulation than other know mapping method. This method requires high-speed lidar with the high sampling rate which could position and localize itself in the environment when mapping without the additional requirement of the odometry data for the localization; instead this HECTOR SLAM uses its laser data to localize itself to the environment and able to build its own map which is unique compared to the other SLAM algorithms. These SLAM algorithms are comparatively fast than the traditional SLAM methods because of the closed looping ability exhibited by this method, this is cost-efficient and accurate by its results.
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3.2 Path-Planning Every robot which tends to be autonomous for the operation in an environment either static or dynamic must have the ability to plan a path and motion to move and operate in the robot’s environment. Path planning is that finding the waypoints which is collusion-free for the robot to operate without making damage to the objects in the environment of making damage to itself by identifying a path which is safe for both the cases. Motion planning is based on the kinematics of the robot by using the links and the joints the robot plans its motion by adjusting the link and joints of the robot based on the constraints given, and this helps the robot to follow the waypoints which are identified using the path planning algorithms. Path planning algorithms are of various types such as visibility graph, cell decomposition, and Voronoi graph.
3.2.1
AMCL
Adaptive Monte Carlo localization (AMCL) is a probabilistic localization [8] method for the system which is operating in the 2D space on an environment, and it is the advanced version of Monte Carlo localization; this method uses the particle filter to find the pose of the robot on the map; this is best known for its adaptiveness. This algorithm eliminates the object for the environment and project the free space which makes the robot collusion-free when moving on a dynamic environment; ROS navigation stack is the powerful tool provided by the ROS software which helps the robot to reach its destination without any collusion or damage. Computational capability of this algorithm is high as it frequently changes its particle filter based on the laser data which increases the processing speed and optimizes the usage when moving on the free space.
4 Implementation As a baseline of the implementation Raspberry Pi is loaded with the Ubuntu OS which is the LINUX distributor; this gives a smooth and clean experience, and then the Raspberry Pi is setup for the remote access using the non-GUI shell for faster processing and connection, then the remote desktop protocol (RDP) is setup, and the IP address of the Pi is identified and connected remotely then ROS is installed, and the workspace is created then the library is downloaded from the ROS repository. World for the robot to be simulated is created in the Gazebo, and the parameters are given, Robot model is converted into. STL format and imported to the .YMAL file and used into import the model in the world of the Gazebo and the constraints of the joints and the links are defined in the Gazebo environment. ROS nodes are built, and each nodes are linked to the single node; this node can be called to implement the entire functionality or even individual nodes can be called based on the requirement;
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once the setup part is completed, the first task is to map the environment using the SLAM algorithms by calling the RVIZ tool in the ROS platform; in this case, the HECTOR SLAM is used for the mapping and the localization; this does not require odometry, though using the MPU and the wheel encoder which give the pose of the robot could be built much stable and dense map this algorithm could work in both with and without Odom cases. Once the mapping is done, the map is saved as a .MAP file and used saved map to the AMCL algorithms file and the navigation stack file to find the path on the environment and plan the motion of the robot to achieve its goal. The entire process is given as a flowchart in Fig. 2. The output and results of this implementation are discussed in the following results and discussion section.
Fig. 2 Flowchart of the program
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5 Robot Modeling and Kinematics 5.1 CAD Modeling The modeling (Fig. 3) of the robot was done in “AUTODESK FUSION 360” which has many numbers of tools and is very convenient for designing. The robot has three layers on each of which different hardware components are boarded. It is designed in such a way that its CG acts exactly around the center point of the robot and the ergonomics and the generative design accounts for the robustness and weightless design which improves efficiency of the robot. We have used four-wheeled differential drive mechanism and hence have used sufficiently big wheels of diameter 43 mm each. The length, width, and height of the robot are 200 mm, 180 mm, and 160 mm, respectively, providing a track width of 80 mm. The robot is designed in such a way that it is highly stable to move around comfortably without any fumble.
Fig. 3 CAD model of robot
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5.2 Static Stress Analysis of the Robot The static stress analysis of the robot in (Fig. 4) indicates that the stress acting on the body is fully spread over the surface and is not concentrated over any single portion. The blue color indicates that the stress on the entire surface is well below the tolerance level, and hence, the robot can bear that load. The strain also indicates the same as the stress analysis that the strain on the robot is well under the tolerance level of the robot, and there is not accumulation of strain in any area; hence, it does not affect the robot’s functions. The displacement analysis indicates that the displacement toward the edges area a bit high compared to the center as its clearly shown in Fig. 7 which indicates that toward the edges the color slowly turns from light blue to yellow which means the displacement is high yet it is under the tolerance limit of the robot’s maximum displacement. The contact pressure is the ratio of the normal load to the true contact area, which is the sum of the front and rear areas. It may be called the scratch hardness only in the case of plastic contact. As it is clear from that the contact pressure of the robot is also indicated in blue which is the extreme safe limit and hence even if the contact pressure increases due to load near the screws and other contact points, the overall contact pressure will remain under
Fig. 4 Static stress analysis
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the robot’s maximum capability. This static analysis indicates and confirms that the robot design is feasible, and also, the design is under the safe limit which would not get fail.
5.3 Kinematic Analysis of the Robot Kinematic study is conducted based on the design of the robot model; the input parameters that we have taken into consideration are wheel radius, wheel speed range, tack width and defined the waypoints as in (Fig. 5) for the robot motion. We have given different set of waypoints and have obtained the two results and both the results indicate in (Fig. 6) that the robot’s kinematics is stable, and it could move on a given or the desired waypoints to reach its goal position. Fig. 5 Kinematic analysis
Fig. 6 Waypoints and robot movement
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Fig. 7 SLAM of simulated environment
6 Results and Discussions In this (Fig. 7), the mapping is done using the simulated environment in the Gazebo and the map is visualized using the RVIZ tool in the ROS where the entire map is created using the HECTOR SLAM by wandering around the environment using teleoperation or by just using an obstacle avoidance my moving in the random direction. Figure 8 represents the outcome of the SLAM made in the real time inside a building by operating the robot with the teleoperation of the robot, and the results are taken; the map is saved for the implementation of the autonomous navigation. With all the implementation in Fig. 9, the algorithms the navigation and autonomous driving have started, and all the nodes are initialized, and the pose of the robot is estimated and waiting to get the end location. Figure 10 represents the movement, and the navigation of the robot is achieved by reaching the goal location by avoiding the obstacles and completing the task which is given for the robot.
7 Conclusion The aim of the project was to create a multipurpose autonomous industrial robot that could perform various tasks in an industry that is currently carried out by humans with or without the assistance of technology. The objectives that were put front were to integrate lidar with Raspberry Pi (controller) and map the environment of the industry and its surrounding where the robot is to be deployed, to enable robot operating system (ROS) on the Raspberry Pi to take the input from the lidar and accelerometer to actuate the motor to reach the desired location, to map the complete
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Fig. 8 Real-world SLAM
Fig. 9 Implementation of AMCL
industrial working area using lidar in order to help the robot move around the industry where the work is allotted by implementing simultaneous localization and mapping (SLAM), to remotely monitor the robot movement and to record the visual of the environment in which the robot is moving and to estimate the position of the robot and to use a navigation algorithm to reach the goal in the shortest way possible. All the objectives that were put front were completed successfully and were tested using different simulation software for working and various other analyses such as kinematic analysis, stress, strain, displacement, and contact pressure analysis and motion study were carried out to ensure the efficiency and working of the robot in the expected way. Hence, the “Autonomous Industrial Robot” has been successfully
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Fig. 10 Implementation of navigation stack
simulated and analyzed, and thus, it could be implemented to improve the working speed and quality of an industry. The robot could reduce the physical and mental constraints of a human being working in any industry by assisting in several ways. It is also important to upgrade this robot further for better usage and to increase its area of applications. Also, different additional equipments (sensors) could be added to the robot for usage in specific applications.References [9, 10] are given in list but not cited in text. Please cite in text or delete them from list.cited in the text
References 1. Gatesichapakorn, S., Takamatsu, J., Ruchanurucks, M.: ROS based autonomous mobile robot navigation using 2D LiDAR and RGB-D camera. In: 2019 First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP) 2019 Jan 16, pp. 151–154, IEEE 2. Yu, N., Zhang, B.: An improved HECTOR SLAM algorithm based on information fusion for mobile robot. In: 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) 2018 Nov 23, pp. 279–284, IEEE 3. Filipenko, M., Afanasyev, I.: Comparison of various SLAM systems for mobile robot in an indoor environment. In: 2018 International Conference on Intelligent Systems (IS), 2018 Sep 25, pp. 400–407, IEEE 4. Chan, S.H., Wu, P.T., Fu, L.C.: Robust 2D indoor localization through laser SLAM and visual SLAM fusion. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018 Oct 7, pp. 1263–1268, IEEE 5. Goyal, J.K., Nagla, K.S.: A new approach of path planning for mobile robots. In: 2014 International Conference on Advances in Computing, Communications, and Informatics (ICACCI), 2014 Sep 24, pp. 863–867, IEEE 6. Kaoud, E., El-Sharief, M.A., El-Sebaie, M.G.: Scheduling problems of automated guided vehicles in job shop, flow shop, and container terminals. In: 2017 4th International Conference on Industrial Engineering and Applications (ICIEA), 2017 Apr 21, pp. 60–65, IEEE 7. Harik, E.H., Korsaeth, A.: Combining HECTOR SLAM and artificial potential field for autonomous navigation inside a greenhouse. Robotics 7(2), 22 (2018)
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8. Matias, L.P., Santos, T.C., Wolf, D.F., Souza, J.R.: Path planning and autonomous navigation using AMCL and ad. In: 2015 12th Latin American Robotics Symposium and 2015 3rd Brazilian Symposium on Robotics (LARS-SBR), 2015 Oct 29, pp. 320–324, IEEE 9. Lamini, C., Benhlima, S., Elbekri, A.: Genetic algorithm based approach for autonomous mobile robot path planning. Proc. Comput. Sci. 1(127), 180–189 (2018) 10. Ghorpade, D., Thakare, A.D., Doiphode, S.: Obstacle detection and avoidance algorithm for autonomous mobile robot using 2D LiDAR. In: 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017 Aug 17, pp. 1–6, IEEE
Misfire Prediction on Spark Ignition Four-Stroke Engine Through Statistical Features Using Rough Set Theory Classifier Joshuva Arockia Dhanraj, Jenoris Muthiya Solomon, Mohankumar Subramaniam, Meenakshi Prabhakar, Christu Paul Ramaian, Nandakumar Selvaraju, and Nadanakumar Vinayagam Abstract Misfire is one of the key challenges engines encounter because it adds to the power loss amid air pollutants such as CO and NOx from the exhaust gas. Due to a certain cylinder, discrepancy produces a special pattern of vibration. These patterns can extract useful properties and analyze them to detect misfire. This paper aims to use a machine learning method a misfire identification comprehensive framework. Vibration signals have been used in the present analysis (via piezoelectric accelerometer) as a form of misfire that is unique to each cylinders. Statistical features were then derived and used the J48 from the obtained features, and the feature selection is implemented. The roughest theory classifier was used in the classification of the misfire in the cylinder. In Maruti Suzuki Baleno, the experiment was tested and all cylinder misfire testing was carried out for individual cylinders separately. Through tenfold cross-validation in WEKA, the classifier output was validated. Keywords Misfire · Condition monitoring · J48 · Rough set theory · WEKA
J. Arockia Dhanraj (B) · M. Prabhakar Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India J. M. Solomon Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India M. Subramaniam Department of Automobile Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu 641049, India C. P. Ramaian · N. Selvaraju · N. Vinayagam Department of Automobile Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_12
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1 Introduction A common spark ignition (SI) system engine failure is misfire. This is owing to a, a blown-down head-gasket, a sleek combustible/air mix, faulty spark plug, cracked distribution cap, too high an engine ping, a failure to compress or even to exhaust gas recirculation problems, such as a closed or open valve that causes too much diffusion. The engine’s output can be reduced by 25% [1]. Some issues associated with this condition are the fuel economy and emissions from increased amounts of non-spending hydrocarbons in the exhaust gas. Owing to these issues, to reducing pollution and to increase fuel quality, it is necessary to address the issue of misfire [2]. Engine failures can be difficult to detect. Researchers have implemented different approaches in recent years to diagnose the faults. At the engine head or vibration transmission intensity signal of the shaft, a range of eminent techniques were established to diagnose misfire. In this area, several techniques have been used with features such as instantaneous exhaust multiple pressure, crankshaft rpm, instantaneous crank angles, instant angular velocity, cylinder divergence torque, and many more [3]. Rath et al. [4] performed a study of the autoregressive knock sensor coefficients in internal combustion engines for misfire detection. The vibration signal in this research is based on a process described in autoregression (AR) by PFC, and the results on these filter coefficients from misfires are assessed. The power of the vibratory signal is analyzed as a second method. From this study, one can predict that there is an analysis of cross-influences on the AR engine coefficients. Zhang et al. [5] suggested the realtime angular speed-based prediction of misfire by ANN. This paper introduces ANN to detect misfire occurring in an SI engine by analyzing the angular velocity of the shaft. Three accurate fault mechanisms are described in this study the first two approaches use the time domain and frequency domain strategies to extract the fault components, and MLP is the technique for the identification of the faulty cylinder. Misfire detection was conducted by Xu et al. [6] on the basis of standard engine center of gravity force identification. A modern error detection approach established on the description of generic force in the engine center of gravity is introduced in this study. The exact amplitudes and stage of the acceleration signals can be calculated using the engine speed signals on the stand using a different interpolation mechanism of frequency, and in the center of gravity, generalized force is evaluated. Song et al. [7] observed a misfire of the engine utilizing standard harmonics of angular acceleration. On the basis of the discrete Fourier transform of the angular shaft acceleration, a modern torsional vibration-based approach for detecting engine misfires was introduced. The typical harmonics and the characteristic Fourier transform were described through the study of the sensitivity of the discrete Fourier, which translated into variations in engine speed and load. Numerous solutions to the interpretation of signals are possible, including temporal and model-based evidence, model-based rationalization, heuristic logic,
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Fig. 1 Methodology for misfire detection
machine learning, etc. for the failure diagnostics method. Machine training is preferable because computing resources are relatively easy to access, they are more accurate and reliable, and also a system can be trained with this approach to change engine settings continually. The application of data on engine vibration is assisted so that tests are obtained with greater precision. In this case, the acceleration signal is measured with an accelerometer by the data acquisition device. Then the signal extracted statistical characteristics and selected the most contributing characteristics. Decision trees have the same features and are known for their family of deep machine learning algorithms [8]. The main advantage of decision tree algorithms is that many systems can easily apply the set of classification rules. Vibration signals for the fault diagnosis for the four-stroke ignition engine and classification by means of rough set theory (RST) classifier utilizing WEKA are used in the present study. Figure 1 displays the four-stroke spark ignition engine diagnostic solution flowchart.
2 Experimental Studies The experimental setup primarily involves the four-stroke SI Maruti Suzuki Baleno engine with the arrangements to produce a fault manually in a certain cylinder and by means of the data acquisition method, vibration data are recorded. Figure 1 shows the
132 Table 1 Engine specification
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Maruti Suzuki Baleno, Zeta model
Engine Type
1.2L VVT Petrol Engine
Engine Displacement
1197 cc
Number of Cylinders
4
Valves per Cylinder
4
Transmission Type
Manual
Max Torque (nm@rpm)
115 Nm@4000 rpm
Max Power (bhp@rpm)
83.1 bhp@6000 rpm
Rated Speed (manually controlled)
60 kmph
Gear Box
5 Speed
flow of the diagnostic phases. The experiment was conducted with a four-stroke spark ignition petrol engine (Maruti Suzuki Baleno). The engine requirements are shown in Table 1. Electrical supply to single spark plugs is cut in order to induce misfire. The accelerator is holding to capture an acceleration signal at a speed of 60kmph. A power distributor cup supplies each spark plug. Figure 2 shows the experimental setup. For measure vibration signals, a suitable sensor must be accessible for detect the vibration signals produced because of the misfire and to correspond to the working conditions of the engine. For this purpose, a single electric piezo accelerometer (KISTLER 8702B50, 50 g range, the sensitivity of 10 mV/g) was selected. The accelerometer’s output is wired to the NI9233 DAQ (with NI-USB-9162 hi-speed USB carrier) signal conditioning circuit, which transforms the signal from analog to digital (ADC) and integrates anti-aliasing filters. The digitized vibratory signal is saved on the computer (in the time domain).
Fig. 2 Experimental setup
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Fig. 3 Vibration signal patterns
The car starts and operates on the NH4 to monitor the vibration signals at a pace of 60 km/h. The DAQ was turned on, and the data is only obtained after the engine has been controlled when it has reached the approved rpm. These data have been processed in full load for 60kmph. Five occurrences, i.e., normal state, mechanical problem in cylinder one, two, third, and fourth, were listed for this analysis. 100 data points have been collected for each state. The vibration signal characteristics are shown in Fig. 3. The following conditions for signal recording were furnished below. 1. 2. 3.
Sample Length (2n ) ≈ 10,000 (arbitrarily chosen for data consistency) Sampling Frequency = 24 kHz (as per Nyquist sampling theorem) Number of Samples = 100
3 Statistical Feature Extraction For good and other engine misfiring state, the vibration signatures are obtained. The number of samples should be compatible if sampled signals time domain is
134 Table 2 Statistical features and their values
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0.67
Mean
6.76e−5
Median
6.99e−4 Mode
9.22e−5
Maximum
6.45e−2 Minimum
−7.13e−3
Range
1.35e−2 Skewness
3.96e−2
Standard Error
6.25e−6 Standard Deviation 6.25e−4
Sample Variance 4.71e−7 Kurtosis
22.66
defined explicitly as classification inputs [8]. The sample number obtained is the wheel frequency revolution function. Therefore, the input to the classifier cannot be used directly. However, before the classification process, certain features must be removed. The following are mathematical properties and their quantities (Table 2). Feature selection via J48.
4 Feature Selection Via J48 The practice of data mining is commonly used to retrieve useful information structures from libraries utilizing vibration knowledge. An essential information framework, which may emerge from the operations of data mining. Decision trees are recurrently developed after a top-down approach. The parameters in the decision tree nodes are of decreasing importance. The most useful parameter for identification can be used for the correct estimation criteria at each decision node of the decision tree [9]. The information gain and entropy concept are the principles used to categorize the superlative parameter. Two phases have been developed for the decision tree algorithm (J48). The construction phase is also referred to as the “development phase.” The main features of the wind turbine blade descriptors are usually available. Through J48, the important five features have been selected. The selected features are sample variance, skewness, sum, standard error, and kurtosis.
5 Rough Set Theory (RST) The rough set theory principle was proposed by Pawlak [10]. This is a conceptual principle that arises from computational modeling on the operating attributes of data systems. Rough theory is a database mining technique in relational databases or the exploration of information. It is very much associated with fugitive theory. It may be feasible to use an incorrect and optimistic method to explore relational interactions. Rough sets and soft sets compliment the standard collections. The rough-fit principle estimate spaces have multiple components, while fuzzy sets struggle with incomplete components. Soft computation, together with rough sets of applications, at least dynamic reasoning and principle of uncertainty, principles networks, comprises NN,
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chaos theory, probabilistic reasoning, and machine learning. The RST workflow is described in [11].
6 Results and Discussion Misfire identification with machine learning was addressed in this study. The classification of accuracy and confusion matrix was based on a rough set theory classifier [12–15]. Features with the most relevant classification accuracy were chosen from a large number of features based on a J48 tree algorithm and classification accuracy variations were recorded with changes in number of features [16–18]. From Table 3, relevant features such as standard error, skewness, sample variance, kurtosis and sum were selected. For choosing five dominant features in the J48, 50 instances per leaf are retained and the sum of data is used to mitigate error pruning. In RST, the standard specifications were batch size (100) and the “valid” debug equivalent [19– 21]. From Table 3, we can see that the overall accuracy of the top five features of the RST is better (97%) over the maximum accuracy of the J48 (90.50%). Table 3 reveals that RST performs better than J48 with a timeframe of 0.11 s for the computation. Figure 4 shows the RST tenfold cross-validation test [22, 23]. Figure 4 classifies diagonal components correctly and balance was misclassified instances. The results of the kappa from RST are 0.977 with an absolute mean error which was 0.0302, and the mean root square is about 0.167. The percentage of positive which are accurately classified as faults shall be estimated by TP [24–27]. FP is generally defined as a false alert that generates any failure if the true positive (TP) value isn’t already obtained for reliable diagnosis is close to 1, and the false positive (FP) incidence is almost zero. In Table 4, TP is almost 1 and FP would be almost Table 3 Impact of number of features versus accuracy of classification
Number of features
J48
RST
1
28.75
30.50
2
51.50
51.33
3
63.33
65.33
4
88.17
89.50
5
90.50
97.00
6
89.33
93.17
7
89.50
92.33
8
89.50
92.33
9
89.50
92.33
10
89.50
92.33
11
89.50
92.33
12
89.50
92.33
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Fig. 4 Confusion matrix of RST
Table 4 Classwise accuracy of RST Class
TP rate
FP rate
Precision
Recall
F-measure
ROC area
GOOD
1.000
0.004
1.000
1.000
1.000
1.000
MC1
0.960
0.032
0.968
0.960
0.961
0.970
MC2
0.980
0.027
0.989
0.980
0.983
0.989
MC3
0.950
0.041
0.957
0.950
0.956
0.967
MC4
0.970
0.025
0.977
0.970
0.979
0.988
0 in most categories. This results in an uncertainty matrix to validate the results in Fig. 4 [28–31].
7 Conclusion In order to prevent wasting fuel, reduce emissions of hydrocarbons and save energy, it is very necessary that misfire is discovered from four strokes, four-cylinder spark ignition engine. As a possible solution for misfire detection, the new method of rough set theory was introduced in this study. The use of engine block vibration signals, the experiment was conducted and thus enables great precision and reduced costs. Maruti Suzuki Baleno spark ignition four-stroke engine was undertaken for the experimental procedure. It can be inferred that in a four-cylinder ignition petrol engine, the rough set theory performs better than the J48 algorithm for error detection. The confusion matrices demonstrate quite strongly that a high classification accuracy (97%) in the distinguishing of faults from normal conditions is given by a rough set theory classifier.
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References 1. Bhadane, G., Jadhav, A.A., Bhong, V.S., Inamdar, S.A., Narsale, D.P.: Misfire detection of automotive engines with convolutional neural network. In: Techno-Societal 2018. Springer, Cham, pp. 121–131 (2020) 2. Li, S., Zhang, Y., Wang, L., Xue, J., Jin, J., Yu, D.: A CEEMD method for diesel engine misfire fault diagnosis based on vibration signals. In: 2020 39th Chinese Control Conference (CCC). IEEE, pp. 6572–6577 (2020) 3. Liu, Z., Wu, K., Ding, Q., Gu, J.X.: Engine misfire diagnosis based on the torsional vibration of the flexible coupling in a diesel generator set: simulation and experiment. J. Vibration Eng. Technol. 8(1), 163–178 (2020) 4. Rath, M., Wegleiter, H., Brasseur, G., Basso, R.: Analysis of autoregressive coefficients of knock sensor signals for misfire detection in internal combustion engines. In: 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, pp. 1–6 (2019) 5. Zhang, P., Gao, W., Song, Q., Li, Y., Wei, L., Wei, Z.: Real-time angular velocity-based misfire detection using artificial neural networks. J. Eng. Gas Turbines Power 141(6) (2019) 6. Xu, C., Li, S., Cao, F., Qiu, X.: Misfire detection based on generalized force identification at the engine centre of gravity. IEEE Access 7, 165039–165047 (2019) 7. Song, Q., Gao, W., Zhang, P., Liu, J., Wei, Z.: Detection of engine misfire using characteristic harmonics of angular acceleration. Proc. Inst. Mech. Eng. Part D: J. Autom. Eng. 233(14), 3816–3823 (2019) 8. Papacharalampous, G., Tyralis, H., Papalexiou, S.M., Langousis, A., Khatami, S., Volpi, E., Grimaldi, S.: Global-scale massive feature extraction from monthly hydroclimatic time series: statistical characterizations, spatial patterns and hydrological similarity. Science of The Total Environment 767, 144612 (2021) 9. Tsai, C.F., Lin, W.C.: Feature selection and ensemble learning techniques in one-class classifiers: an empirical study of two-class imbalanced datasets. IEEE Access 9, 13717–13726 (2021) 10. Pawlak, Z.: Rough set theory and its applications to data analysis. Cybern. Syst. 29(7), 661–688 (1998) 11. Tay, F.E., Shen, L.: Fault diagnosis based on rough set theory. Eng. Appl. Artif. Intell. 16(1), 39–43 (2003) 12. Dhanraj, J.A., Jayaraman, P., Ramanathan, K.C., Kumar, J.P., Jayachandran, T.: Statistical data mining through credal decision tree classifiers for fault prediction on wind turbine blades using vibration signals. In: IOP Conference Series: Materials Science and Engineering, vol. 988, no. 1, IOP Publishing, p. 012078 (2020) 13. Joshuva, A., Arjun, M., Adhithya, B.S., Akash, B., Wahaab, S.A.: Split-point and attributereduced classifier approach for fault diagnosis of wind turbine blade through vibration signals. In: IOP Conference Series: Materials Science and Engineering, vol. 923, no. 1. IOP Publishing, p. 012009 (2020) 14. Joshuva, A., Kumar, K.R., Gangadhar, G.S., Dhanush, S.S., Arjun, M.: Rough set theory based blade condition classification on wind turbine through statistical features. In: IOP Conference Series: Materials Science and Engineering, vol. 923, no. 1. IOP Publishing, p. 012010 (2020) 15. Joshuva, A., Kumar, R.S., Sivakumar, S., Deenadayalan, G., Vishnuvardhan, R.: An insight on VMD for diagnosing wind turbine blade faults using C4. 5 as feature selection and discriminating through multilayer perceptron. Alexandria Eng. J. 59(5), 3863–3879 (2020) 16. Joshuva, A., Aslesh, A.K., Sugumaran, V.: State of the art of structural health monitoring of wind turbines. Int. J. Mech. Prod. Eng. Res. Dev. 9, 95–112 (2019) 17. Joshuva, A., Sugumaran, V.: A lazy learning approach for condition monitoring of wind turbine blade using vibration signals and histogram features. Measurement 152, 107295 (2020) 18. Joshuva, A., Anaimuthu, S., Selvaraju, N., Muthiya, S.J., Subramaniam, M.: A Machine Learning Approach for Vibration Signal Based Fault Classification on Hydraulic Braking System through C4. 5 Decision Tree Classifier and Logistic Model Tree Classifier (No. 2020-28-0496). SAE Technical Paper (2020)
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19. Dhanraj, J.A., Ramanathan, K.C., Priyadharsini, S., Jayaraman, P., Sankar, M.J.: Boiler temperature and pressure monitoring system for thermal power plant through LabVIEW. In: IOP Conference Series: Materials Science and Engineering, vol. 988, no. 1. IOP Publishing, p. 012079 (2020) 20. Joshuva, A., Arjun, M., Murugavel, R., Shridhar, V.A., Gangadhar, G.S., Dhanush, S.S.: Predicting wind turbine blade fault condition to enhance wind energy harvest through classification via regression classifier. In: Advances in Smart Grid Technology. Springer, Singapore, pp. 13–20 (2020) 21. Joshuva, A., Sugumaran, V.: Fault diagnostic methods for wind turbine: A review. ARPN J. Eng. Appl. Sci. 11(7), 4654–4668 (2016) 22. Joshuva, A., Sugumaran, V.: Comparative study on tree classifiers for application to condition monitoring of wind turbine blade through histogram features using vibration signals: a datamining approach. Structural Durability Health Monit. 13(4), 399 (2019) 23. Joshuva, A., Sugumaran, V.: Crack detection and localization on wind turbine blade using machine learning algorithms: a data mining approach. Structural Durability Health Monit. 13(2), 181 (2019) 24. Mostafaeipour, A., Jahangard Rafsanjani, A., Ahmadi, M., Arockia Dhanraj, J.: Investigating the performance of Hadoop and Spark platforms on machine learning algorithms. J. Supercomputing 1–28 (2020) 25. Joshuva, A., Sugumaran, V.: Improvement in wind energy production through condition monitoring of wind turbine blades using vibration signatures and ARMA features: a data-driven approach. Progr. Industrial Ecol. Int. J. 13(3), 207–231 (2019) 26. Joshuva, A., Sugumaran, V.: Selection of a meta classifier-data model for classifying wind turbine blade fault conditions using histogram features and vibration signals: a data-mining study. Progr. Industrial Ecol. Int. J. 13(3), 232–251 (2019) 27. Joshuva, A., Sugumaran, V.: A machine learning approach for condition monitoring of wind turbine blade using autoregressive moving average (ARMA) features through vibration signals: a comparative study. Progr. Industrial Ecol. Int. J. 12(1–2), 14–34 (2018) 28. Moorthy, S.R.K., Prasanthi, A., Joshuva, A., Dinakaran, D.: Bearing fault diagnosis using fuzzy unordered rule induction algorithm through vibration signals. In: AIP Conference Proceedings, vol. 2311, no. 1. AIP Publishing LLC, p. 070006 (2020 29. Joshuva, A., Sugumaran, V.: A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: a comparative study. ISA Trans. 67, 160–172 (2017) 30. Joshuva, A., Sugumaran, V.: A comparative study of Bayes classifiers for blade fault diagnosis in wind turbines through vibration signals. Structural Durability Health Monit. 11(1), 69 (2017) 31. Mostafaeipour, A., Fakhrzad, M.B., Gharaat, S., Jahangiri, M., Dhanraj, J.A., Band, S.S., Issakhov, A., Mosavi, A.: Machine learning for prediction of energy in wheat production. Agriculture 10(11), 517 (2020)
Increasing the Wind Energy Production by Identifying the State of Wind Turbine Blade Joshuva Arockia Dhanraj, Meenakshi Prabhakar, Christu Paul Ramaian, Mohankumar Subramaniam, Jenoris Muthiya Solomon, and Nadanakumar Vinayagam
Abstract In environmental circumstances and high wind speed, the wind turbine blades are vulnerable to damage and causing performance deficiency. The key objective of this research is to conduct health inspection for working wind turbine (WT) blades. A wind turbine with three blades was used, by simulating the incidents such as pitch fault, hub-blade loose contact, crack, erosion, and blade bend faults. With the help of the accelerometer, the vibration signatures of these faults were collected, and it is supplied as input to the classifier for classification after the feature extraction and selection process. Descriptive statistical parameters were used as features, and the selection of features was performed with a J48 decision tree. This multiclass problem was classified using fuzzy-unordered rule-induction (FURIA) algorithm, and in order to assess whether the blade is in good or bad state, the total classification accuracy was found to be 87.5% over 0.63 s in time interval. Keywords Fault diagnosis · Wind turbine blade · Fuzzy-unordered rule-induction algorithm · Descriptive statistical features · J48 algorithm · Vibration signals
J. Arockia Dhanraj (B) · M. Prabhakar Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India C. P. Ramaian · N. Vinayagam Department of Automobile Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India M. Subramaniam Department of Automobile Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu 641049, India J. M. Solomon Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_13
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1 Introduction The spontaneous failure of the components of wind turbines may trigger disastrous damage and costly reconstruction and unattainability over time of the WT. This leads to higher operational and maintenance costs as well as electricity generation costs [1]. Consequently, early stage wind turbine condition tracking and failure detection is essential [2]. Status monitoring is also specified as the monitoring of a condition parameter in machinery that displays a major difference suggests a breakdown. Blades are core element of a wind turbine and are used for renewable energy harvesting from airflow [3]. The blades experience extreme shocks, resulting in damage to the tip, owing to the environmental conditions. Therefore, a wind turbine blade fault diagnostic device is required for tracking blade status [4]. Different tests have been performed to detect the WT blade state. Jiménez et al. [5] conducted a maintenance investigation focused continuous machine learning and nonlinear functionality in WTs. The study offers an innovative methodology towards delamination classification in WT blades for reliability control systems. It is consisted of the extraction of an input NARX and an AR model. NARX as a method for the elimination of features for the classification of WT blade delamination is unique in this article. In addition, NARX is shown that its AR function for bladder damage identification is considerably better than that of linear AR, and the intrinsic nonlinearity of blade delamination can be correctly described by NARX. Chen et al. [6] study on the detection of failures in wind turbines using algorithms for transfer learning. The TrAdaBoost analysis has shown an outstanding success with respect to data imbalance and distinct distributions and a novel transmission learning algorithm has been investigated. A new methodology is also being suggested for the calibration of data labels using algorithms that offer valuable insights into unmonitored learning for the failure diagnostics of wind turbine. Liu et al. [7] have carried out an acoustic emission and machine learning analysis on damage mode detection of the wind turbine blades under accelerated fatigue loads. The article experimentally investigates the impact behaviour, using an acoustic emission technique, of the 59.5 m broad composite blade of WT with accelerated fatigues. A clustering Kmeans study is done to classify multiple types of damage to acoustic emission signal sources. The whole work promotes safety and the protection of in-service blades in scientific and conceptual terms. Ti et al. [8] performed an analysis on wake simulation through machine learning for wind turbines. This paper presents a revolutionary system for the development of modern wake and turbulence model with high precision and high performance to boost the turbine wake predictions, using machine learning and CFD. The ANN model is programmed to establish the spatial relation between the input and threedimensional wake fluxes and established on the back-propagation set of rules. The discoveries of the ANN-based wake-model indicate that the numerical simulations and measurement data are well adapted, suggesting that the ANN will build the complex spatial link between the wake flow ANN. Xu et al. [9] have conducted a study to classify adhesive composite joints of AE and ML in a reliable WT blade destruction
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Fig. 1 Methodology
mode. The above paper describes a CFSFDP peak, where rigorous detection by the AE signal similarities aimed at numerous destruction types can be achieved. Centred on the matrix cracking and shear failure of the adhesion layer, a classification analysis has proved a fundamental and characteristic method for injury. Besides that, AE signals are addressed in detail in the subspace of AE functions for the resemblance of numerous destruction types (Fig. 1). A number of experiments were conducted using WT blade damage modelling analysis and strategy investigation, although some handful was experimentally performed [10]. A relatively small number of flaws were taken into account for evaluation. This is particularly factual in diagnoses of defective WT blades. A failure diagnostic framework, which can deal with many faults by using a machine learning method in wind turbine blades, is therefore highly required. In his study, the attempt was made through machine learning methods and mathematical testing to detect five distinct blade failure states. • Five defects in wind turbine blade defects have been investigated in this study. • Vibration signal statistics have been derived with the technique for extracting the characteristics needed. • The J48 decision tree algorithm for the set of dominant characteristics. • This analysis was modelled on the multiclass combination problem and the initiative to classify the fault through fuzzy-unordered rule-induction algorithm (FURIA).
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Fig. 2 Experimental setup
2 Experimental Studies The key goal of the study is to determine the health or deterioration of the cutting edge. The goal is to recognise the form of failure if the problem is poor. This illustrates the experimental structure and studies in [11]. The experimental arrangement is displayed in Fig. 2. The frequency of the sampling was 12,000 Hz with 10,000 data length per signal. The DYTRAN 3055B1 and the DAQ (NI-USB 4432) have been used to acquire the data.
3 Statistical Analysis for Feature Extraction For good and other faulty blade conditions, vibration signals were obtained. The number of collected samples that are blade velocity rotation functions. Therefore, the velocity varies. Furthermore, the number of data points that have been digitised in a signal is too high; classifiers cannot usually do this effectively. Therefore, before classification, a number of features must be extracted. The work of feature extraction processes was determined using descriptive statistical parameters [12].
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Fig. 3 J48 tree classification for feature selection
4 Feature Selection Via J48 This procedure was included when the user chooses features that correspond to a prediction feature or output most automatically or manually. Lack of appropriate data features minimises the accuracy of the model and allows the model to build on irrelevant features. As a tool to select features, the J48 was used. The method for selecting features was described in detail in [13]. The J48 tree split with selected features is displayed in Fig. 3.
5 Fuzzy-Unordered Rule-Induction Algorithm (FURIA) This is an extended learning algorithm of the advanced RIPPER rules, which has benefits including simple and readable fuzzy rules and new functions [14]. FURIA has three distinct RIPPER extension: (i) use fuzzy rules in lieu of narrow ones, (ii) use unordered ruleset in place of rule sets, and (iii) suggest a new rule stretching system for exposed examples.
6 Results and Discussion The vibration signals are registered for the correct state of the blade or other WT blades. 600 samples, 100 in good quality, and cumulative sets were essential. FURIA
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was used to derive the statistical parameters [15–19]. The relevant FURIA algorithm output will be the respective classified data state. The FURIA rules for obtaining the state of the wind turbine blade are as follows. • (Standard Deviation in [0.000719, 0.00072, inf, inf]) = > Condition = Good (CF = 0.94) • (Standard Deviation in [0.000591, 0.000593, inf, inf]) and (Kurtosis in [-inf, inf, 26.953833, 28.186533]) and (Range in [0.015555, 0.015589, inf, inf]) => Condition = Good (CF = 0.92) • (Standard Deviation in [0.000588, 0.000593, inf, inf]) and (Kurtosis in [-inf, -inf, 20.34688, 20.350079]) => Condition = Good (CF = 0.9) • (Standard Deviation in [0.000615, 0.000622, inf, inf]) and (Range in [0.01778, 0.017789, inf, inf]) => Condition = Good (CF = 0.87) • (Standard Deviation in [-inf, -inf, 0.00032, 0.000321]) and (Sum in [0.642842, 0.651451, inf, inf]) and (Kurtosis in [10.127781, 10.779418, inf, inf]) => Condition = Bend (CF = 0.97) • (Standard Deviation in [-inf, -inf, 0.000369, 0.00037]) and (Sum in [0.6555, 0.668664, inf, inf]) and (Sum in [-inf, -inf, 0.778651, 0.781816]) => Condition = Bend (CF = 0.95) • (Standard Deviation in [-inf, -inf, 0.000294, 0.000296]) and (Sum in [0.583748, 0.589149, inf, inf]) => Condition = Bend (CF = 0.97) • (Standard Deviation in [-inf, -inf, 0.000381, 0.0004]) and (Kurtosis in [27.976647, 27.993095, inf, inf]) and (Range in [-inf, -inf, 0.009885, 0.00991]) => Condition = Bend (CF = 0.91) • (Standard Deviation in [-inf, -inf, 0.000366, 0.00037]) and (Kurtosis in [15.354321, 16.391703, inf, inf]) and (Range in [-inf, -inf, 0.007374, 0.007389]) => Condition = Bend (CF = 0.91) • (Sum in [0.788863, 0.836332, inf, inf]) and (Standard Deviation in [-inf, -inf, 0.000478, 0.000485]) and (Standard Deviation in [0.000316, 0.000317, inf, inf]) and (Range in [0.008865, 0.00888, inf, inf]) => Condition = crack60 (CF = 0.97) • (Sum in [0.744036, 0.757496, inf, inf]) and (Standard Deviation in [-inf, inf, 0.000407, 0.000454]) and (Standard Deviation in [0.000325, 0.000327, inf, inf]) and (Range in [-inf, -inf, 0.008841, 0.008865]) and (Range in [0.006957, 0.006961, inf, inf]) => Condition = crack60 (CF = 0.94) • (Sum in [0.750127, 0.757496, inf, inf]) and (Standard Deviation in [-inf, -inf, 0.000534, 0.000548]) and (Standard Deviation in [0.000366, 0.00037, inf, inf]) and (Sum in [-inf, -inf, 0.823028, 0.851214]) => Condition = crack60 (CF = 0.91) • (Sum in [0.898144, 0.906595, inf, inf]) and (Standard Deviation in [-inf, -inf, 0.000388, 0.000397]) and (Kurtosis in [-inf, -inf, 14.937226, 17.054928]) => Condition = crack60 (CF = 0.88) • (Kurtosis in [39.8572, 63.753181, inf, inf]) and (Sum in [0.945039, 0.9515, inf, inf]) => Condition = crack60 (CF = 0.83)
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• (Sum in [0.885021, 0.888477, inf, inf]) and (Standard Deviation in [-inf, -inf, 0.000524, 0.000532]) and (Range in [0.013685, 0.013854, inf, inf]) => Condition = crack60 (CF = 0.89) • (Standard Deviation in [0.000507, 0.000517, inf, inf]) and (Standard Deviation in [-inf, -inf, 0.000657, 0.00066]) and (Sum in [0.691051, 0.694892, inf, inf]) = > Condition = Loose (CF = 0.8) • (Standard Deviation in [0.000478, 0.000485, inf, inf]) and (Sum in [0.868765, 0.871673, inf, inf]) and (Range in [-inf, -inf, 0.017726, 0.018115]) => Condition = Loose (CF = 0.8) • (Sum in [-inf, -inf, 0.524708, 0.531116]) and (Standard Deviation in [0.000397, 0.0004, inf, inf]) and (Kurtosis in [-inf, -inf, 58.311869, 62.957621]) => Condition = PAT (CF = 0.98) • (Sum in [-inf, -inf, 0.579786, 0.637104]) and (Standard Deviation in [0.000461, 0.000482, inf, inf]) => Condition = PAT (CF = 0.97) • (Sum in [-inf, -inf, 0.574277, 0.662438]) and (Standard Deviation in [0.000386, 0.000388, inf, inf]) and (Kurtosis in [-inf, -inf, 26.160404, 27.594062]) and (Kurtosis in [15.617673, 17.029925, inf, inf]) => Condition = PAT (CF = 0.96) • (Sum in [-inf, -inf, 0.6555, 0.659618]) and (Standard Deviation in [-inf, -inf, 0.000399, 0.0004]) and (Standard Deviation in [0.000308, 0.000311, inf, inf]) and (Range in [-inf, -inf, 0.009225, 0.009239]) => Condition = Erosion (CF = 0.98) • (Sum in [-inf, -inf, 0.583748, 0.589149]) and (Standard Deviation in [-inf, -inf, 0.000386, 0.000388]) => Condition = Erosion (CF = 0.98) • (Sum in [-inf, -inf, 0.671904, 0.700531]) and (Standard Deviation in [-inf, -inf, 0.000424, 0.000512]) and (Range in [0.010464, 0.010619, inf, inf]) and (Sum in [0.5393, 0.543234, inf, inf]) => Condition = Erosion (CF = 0.83) • (Sum in [-inf, -inf, 0.671904, 0.700531]) and (Kurtosis in [50.847486, 52.945241, inf, inf]) and (Standard Deviation in [-inf, -inf, 0.000461, 0.000477]) => Condition = Erosion (CF = 0.81) • (Kurtosis in [-inf, -inf, 7.417464, 7.821324]) and (Standard Deviation in [0.000282, 0.000307, inf, inf]) => Condition = Erosion (CF = 0.84) For FURIA, default settings such as scale factor (100) and the debug option “correct” were described, and total number of rules implemented is “25” [20–24]. The overall stratified cross-validation summary is presented in Fig. 4. Figure 5 displays the tenfold cross-validation finding for FURIA. For the 600 data points, 525 (87.5%) were correct and the remaining 75 were incorrect (12.5%) [25–28]. Figure 4 indicates other metrics such as mathematical Kappa (0.85), average mean error (0.0469), squared root error (0.1951), absolute relative error (16.8987%) and relative root square error (52.3636%). Figure 5 shows the complexity confusion matrix for FURIA. The lateral components are predicted correctly in the uncertainty matrix, in other cases wrongly classified. Figure 6 illustrations the class-wise accuracy of FURIA [29–32].
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Fig. 4 Stratified cross-validation summary
Fig. 5 Confusion matrix of FURIA
Fig. 6 Class-wise accuracy of FURIA
7 Conclusion The WT is an essential component in the production of existing wind energy. The machine learning algorithms were used to analyse and predict faults associated with the blade of a wind turbine. Early prediction of faults allows for predictive maintenance and possible avoidance of some failures. From the acquired vibration data, FURIA model was verified under tenfold cross-validation and provided a fault classification result of 87.5% with 0.63 s of computational time. FURIA’s approach can be clearly accepted and can be used to track the WT blade condition to minimise interruption and increase the output of wind power.
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Characterization of Thin Film Over Vertical Fluted Tube: An Experimental Approach Rahul Deharkar, Anurag Mudgal, Kishan Patel, Joban Patel, and Bhavya Mehta
Abstract Enhancement of heat transfer coefficient in evaporation processes using axially fluted surfaces compared to smooth surfaces is prominently displayed over several decades. Experimental research shows that with the help of a vertical tube evaporator, high heat transfer coefficient in the range from 10,000 to 20,000 W/m2 K can be achieved. Heat transfer coefficient enhancement is closely related to the formation of thin film over the vertical tubes. However, the lower thickness of thin film over vertical tubes causes dry patches during the evaporation process leading to significant salt depositions, thus decreasing the evaporator’s overall performance. Limited experimental studies are made to characterize the external evaporating thinfilm formation for variation in feed rate, temperature, and tube length. In this paper, an experimental setup is designed to study thin-film formation on a fluted aluminum pipe of 24 mm outer diameter with 21 flutes. A detailed experimental investigation is performed to study the thin-film characteristics with the use of digital imaging. The minimum flow rate to avoid trough flow with no external heat source is recognized as 1 l/m for feed temperature ranging from 22 to 70°C up to 350 mm tube length. This finding is vital for applying the fluted tube in a vertical tube evaporator for water distillation to avoid dry patches contributing to salt depositions. Keywords Vertical fluted tube · External · Evaporating · Thin film
1 Introduction It is well-established fact that the evaporator’s high heat transfer coefficient is directly proportional to the higher treated water production rate in thermal-based desalination systems. Fluted vertical tubes with simultaneous condensation and evaporation on either side have experimentally demonstrated heat transfer coefficient up to 20,000 W/m2 K [1]. The heat transfer can be enriched due to the structure of alternating troughs and the tube flutes’ crests. Gambaryan-Roisman et al. [2] and R. Deharkar (B) · A. Mudgal · K. Patel · J. Patel · B. Mehta Pandit Deendayal Energy University, Gandhinagar, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_14
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Pecherkin et al. [3] attempted an experiment in which they introduced a differently structured micro-region surface on a vertical tube to enhance heat transfer rate. Gambaryan-Roisman and Stephan [4] have developed a grooved wall model through which steady film flow and heat transfer are obtained during the evaporation of fluid film. Seouk Park [5] studied the film flow creation process. It also included the peripheral secondary flow motion in which the films initially flow with the finite film thickness over a vertical fluted tube cross section. The concentration process of a product majorly falling film evaporation process is utilized by removing its solvent and increasing its dry solid content. The usage of vertical tubes is the first choice in the desalination plant to reduce footage rather than traditional horizontal smooth tubes [6]. The characterization of film thickness has been considered crucial during the film flow heat transfer process. It is positively hooked into a flow regime modified with different flow behavior: Laminar, laminar wavy and then followed by transition and turbulent. Researchers have conducted several experiments to understand the relation between heat transfer, control parameters (temperature, pressure, feed rate), and film flow behavior. According to their respective applications, considering gravity as the driving force, the product of fluids falls along inside or outside of tubes. Film condensation of saturated steam provides the heat for the evaporative process on the tube’s opposite side. At the film’s free surface, the solvent evaporates by two significant phenomena. The first is named nucleate boiling evaporation, and the second is surface convective evaporation. Ernesto Mura [7] conducted an experiment to understand how heat transfer influences on the occurrence of the surface bubbles. In the early ‘90s, some researchers had shown their interest in developing techniques in vertical falling film. Yan [8] performed a numerical study to gain an idea about the amplification of the heat and mass transfer by transporting the latent heat, cooperation with the evaporation of a liquid film in laminar mixed convection channel flow by using the fluids primarily water and ethanol. Besides, the zero film and finite film thickness are compared and between the fluid mentioned before. His results conclude that it is not advisable to assume very thin liquid film formation apart from a minimal mass flow rate. He et al. [9] have studied both computational and experimental approaches to determine the behavior of the inspired-turbulent flow of air and water vapor. The experiment was accomplished on the long vertical tube, which was heated uniformly, along with the flow of water film inside the tube in the presence of passive cooling, especially in the nuclear reactor. Much of the literature is witnessed by different researchers in fluid falling film over vertical fluted tubes. Åkesjo et al. [10–12] have performed a numerical and experimental investigation of hydrodynamics and heat transfer on vertical falling films. In his research, he examined the fluid film’s behavior at different locations over a vertical tube. He also investigated the control parameters at the inlet conditions and their effects on the fluid film. Also, up to how much extent modification of surface can help improve heat transfer is examined in the paper. Huang et al. [13] experimented with studying at what distance the disperse of a thin falling film can occur over a uniformly heated vertical tube. And he also constructed an empirical expression for calculating the
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thickness of an evaporating liquid film and the corresponding wetting rate termination. Falling film evaporation in a vacuum was mathematically modeled by Pehlivan and Özdemir [14]. In their research, they have obtained the main factors that influence the performance of heat transfer in a vertical tube such as film thickness, sucrose concentration rate, pressure, and mass flow rate. A couple of studies for the improvement of heat transfer with the help of modified surfaces for the vertical or inclined falling film has been investigated by many researchers. Pecherkin et al. [15] studied film flow over the modified structured surface with different configurations such as smooth surface, horizontal ribs, and diamond-shaped knurling. Yang et al. [16] performed a numerical simulation in falling film evaporators with distinguished tube bundles’ parameters. The study mainly focused on heat transfer performance factors, such as liquid flow and maldistribution on tube bundles in falling film evaporators. The distributor improves heat transfer performance in the falling film tube. The essential data for the design structure of the evaporator and distributor can be provided by the paper written by Chao et al. [17]. The phenomena of heat and mass transfers taking place in this configuration can contribute to an evaporator’s simulation. In this research, thin-film formation over the fluted tube was subjected to variation in feed flow rate, and the temperature was recorded along with thin-film formation pattern over the fluted tube’s length’s (multieffect distillation) are used in large capacity plants to produce freshwater by treating seawater. But the major challenge is to construct a small-scale design, especially for the rural community where one can find the brackish water majorly. A design is made of a small-scale M.E.D. unit. To obtain the ideal yield for total distillate production investigation, many numerous effects have been done [18]. And in the second part of the paper, they emphasized how one can achieve the optimum product yield by varying the input variables has been examined with parametric studies—the crucial input parameters for the M.E.D operation. The steam pressure and total production of distillate water within an hour are input and output parameters, respectively, for the following experiment [19]. The third part is committed to understanding the physical science of the O.H.T.C., given parametric examinations on the input conditions for all the three frameworks: 9 + C, 6 + C, and 3 + C frameworks, and broad trials have been completed utilizing the above framework [1]. Another finding is that vertical tube evaporator (VTE) is the largest in industrial desalination processes worldwide with multi-effect systems. A parametric study was observed to find the optimum feed flow rate for ongoing film formation over the vertical tube at the different annular gaps. Deharkar [20], in his research, has considered a 1 mm annular gap, and the film was made outside the tubes with straight copper tubes. A modified design of VTE was then fabricated based on the results of the parametric study. Freshwater production with the vertical tube evaporator using fluted tubes to enhance heat transfer coefficient is a viable solution to increasing water scarcity. However, the salt deposition over the tubes invokes an additional challenge of maintenance and reduced performance. Dry patches over vertical thin film during the evaporator’s operation led to more significant salt depositions, thus decreasing the evaporator’s overall performance. The necessary conditions for continuous thin-film
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formation over vertical tube must be studied to implement the fluted tubes in vertical tube evaporator. In this paper, an experimental setup is developed for examining the thin-film formation characteristics over a vertical fluted aluminum tube. The conditions for continuous thin-film formation were analyzed for various feedwater flow rates, temperature, and tube length.
2 Setup Description For this research, a fluted aluminum tube of 24 mm outer diameter with 21 flutes having crest to trough distance of 2 mm was utilized. The end of the tube was fixed to a mild steel support frame for support and rigidity. The tube is passed through two tanks; the top tank is for the feed inlet and a 26 mm hole for the fluted pipe. An annular gap of 1 mm was maintained for this experimental study based on the study performed by Sen et al. [18, 19], Deharkar et al. [20] to achieve ongoing film formation. The feed inlet is provided with a temperature and mass flow rate sensor to monitor the feed flow conditions. Two, 2 kW heat were used for heating the feedwater in the insulated heating chamber. 800 l/h flow capacity, electrically controlled hot water pump was used for feed flow into the top tank. The bottom tank collected water that was re-circulated into the heating chamber using a secondary pump. The entire components were held on a mild steel support frame to provide support during experimentation. A digital camera with a tripod setup was used to imaging the thin film on markings provided on the tube at 100, 200, 300, and 350 mm from the annular gap. It is crucial to note that feedwater thin film is transparent and difficult to observe with imaging or the naked eye; thus, the additive was used to inhibit color in the water. The schematic diagram of the experimental setup is shown in Fig. 1. Figure 2 shows the reference images of the flute tube without the feed flow. An experiment was performed to study the thin-film characteristics over varying flow rates, temperature, and tube length. Feedwater was initially pumped at room temperature of 22 °C through the top tank at 3.5, 3, 2.5, 2, 1.5, 1, and 0.5 l/m, the flow pattern at 100, 200, 300, and 350 mm from the annular gap was captured using digital imaging, for a stable feed temperature. The feedwater temperature was maintained at 70, 60, and 50 °C in the heating chamber, and the experiment was repeated. Feedwater temperature was limited to 70 °C due to the pump’s thermal limitation. Figures 3, 4, 5, and 6 show the thin-film formation at 22, 50, 60, and 70 °C over the tube’s length and various feed flow rates.
3 Result and Discussion Thin film was experimentally formed over a fluted vertical tube and was recorded using digital imaging. The annular gap provided at the inlet of the tube is 1 mm, and thus, effective film thickness is 1 mm at the entrance. A coloring agent was added
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Aachment to MS frame Temperature Sensor T Top Tank
1 mm, Annular Gap Heang chamber with temperature regulator
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Fig. 1 Schematic diagram for the thin-film experimental setup
to the feed to recognize the film formation over the tubes quickly. Figure 7a shows a condition where we can observe the thin-film formation over the crest; similarly, Fig. 7b shows the condition with no film formation over the crest. Film thickness over the crest is less than 0.5 mm and is difficult to observe even with digital imaging. However, in case of no film or trough flow, the bare fluted tube is easily visible; thus, we can locate the points where the thin-film formation over the crest is broken into trough flow. Figure 8 shows the film formation at 3.5 l/m over tube length at varying temperatures. It must be noted that the variation in the lighting of the images is due to evaporation caused at higher temperatures. For all the temperatures, uniform thin film was formed for the length of the entire tube. The flow-through trough is visible due to the coloring agent in Fig. 8, and also a thin film is formed over the crest. It is vital to note that the overall film thickness is reduced with an increase in temperature, visualized with apparent fading in the color of film for the same flow rates. This can be attributed to the decrease in the density and surface tension at increased temperatures.
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Fig. 2 Fluted aluminum tube without thin film
Temperature Flow Rate (l/min)
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Furthermore, similar figures were made to examine the film formation at 3, 2.5, 2, 1.5, 1, and 0.5 l/m at varying temperatures and tube lengths. After close observations for flow condition, temperature and tube length development of trough flow were pinpointed for each condition. Figure 9 shows the trough flow formation over the fluted tube at various conditions. Figure 9a and b corresponds to 70 and 60 °C feed temperature, respectively, with a 1 l/m feed flow rate at 350 mm from the annular gap. Figure 9c and d corresponds to 50 and 22 °C feed temperature, respectively, with a 0.5 l/m feed flow rate at 350 mm from the annular gap. It should be noted
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(a)
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Fig. 7 Thin formation over the fluted tube. a Thin-film formation over the crest, b No film formation over crest or trough flow
that, for the given temperature in Fig. 9, trough flow was observed for all the lower values of flow rates. It can be stated that with an increase in temperature, the dry out of the thin film over crest happened at a higher flow rate.
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Fig. 8 Thin-film formation for at 3.5 l/m feed flow rate at various temperatures
4 Conclusion An experimental setup was designed and fabricated to investigate thin-film formation characteristics with varying feed temperature, flow rate, and tube length. The effect of these three parameters on film behavior over fluted pipe is experimentally examined. The main conclusions are as follows: • Uniform thin film was obtained for a flow rate above 1 l/m for temperature ranging from 22 to 70 °C. Thus, 1 l/m can be considered the lowest flow rate required for thin-film formation over a vertical fluted tube with an annular gap of 1 mm.
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(a)
(c)
(b)
(d)
Fig. 9 Trough flow formation on fluted tubes. a 70 °C with 1 l/m feed flow at 350 mm, b 60 °C with 1 l/m feed flow at 350, m, c 50 °C with 0.5 l/m feed flow at 350 mm, d 22 °C with 0.5 l/m feed flow at 350 mm
• Trough flow or no film over the crest was observed at 350 mm distance for flow rate lower than 0.5 l/m for all temperature; however at elevated temperature of 60–70 °C, the same is observed at 1 l/m. • Overall film thickness is reduced with an increase in the feed temperature; this is due to the increased evaporation rate caused by the higher temperature difference between feed temperature and ambient temperature. • With the absence of a heat source for the thin film’s evaporation, the dry out of the entire film was not observed for all parameters. This work can be further extended to find the film thickness over the fluted tube’s crest with the laser-triangulation method. Acknowledgements This study is sponsored by the Office of Research & Sponsored Program (O.R.S.P.) under the project “Study of vertical falling film over the vertical pipe to improve freshwaterproduction,” Project No.: ORSP/R&D/SRP/2019-20/1392/41. The authors are thankful to
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Pandit Deendayal Energy University, Gandhinagar, India, to provide research work infrastructure facilities.
References 1. Sen, P.K., et al.: A small scale multi-effect distillation (MED) unit for rural micro enterprises: Part-III Heat transfer aspects. Desalination 279(1–3), 38–46 (2011) 2. Wu, H., et al.: Engineering thermal and mechanical properties of multilayer aligned fiberreinforced aerogel composites. Heat Transf. Eng. 35(11–12), 1061–1070 (2014) 3. Pecherkin, N.I., Pavlenko, A.N., Volodin, O.A.: Heat transfer and critical heat flux at evaporation and boiling in refrigerant mixture films falling down the tube with structured surfaces. Int. J. Heat Mass Trans. 90, 149–158 (2015) 4. Gambaryan-Roisman, T., Stephan, P.: Analysis of falling film evaporation on grooved surfaces. J. Enhanced Heat Trans. 10(4) (2003) 5. Park, I.S.: Numerical analysis for flow, heat and mass transfer in film flow along a vertical fluted tube. Int. J. Heat Mass Trans. 53(1–3), 309–319 (2010) 6. Jin, W.X., Low, S.C., Quek, T.: Preliminary experimental study of falling film heat transfer on a vertical doubly fluted plate. Desalination 152(1–3), 201–206 (2003) 7. Mura, E., Gourdon, M.: Interfacial shear stress, heat transfer and bubble appearance in falling film evaporation. Exp. Thermal Fluid Sci. 79, 57–64 (2016) 8. Yan, W.-M.: Mixed convection heat transfer enhancement through latent heat transport in vertical parallel plate channel flows. Can. J. Chem. Eng. 69(6), 1277–1282 (1991) 9. He, S., et al.: Combined heat and mass transfer in a uniformly heated vertical tube with water film cooling. Int. J. Heat Fluid Flow 19(5), 401–417 (1998) 10. Åkesjö, A., et al.: Hydrodynamics of vertical falling films in a large-scale pilot unit–a combined experimental and numerical study. Int. J. Multiph. Flow 95, 188–198 (2017) 11. Åkesjö, A., et al.: Modified surfaces to enhance vertical falling film heat transfer–An experimental and numerical study. Int. J. Heat Mass Trans. 131, 237–251 (2019) 12. Åkesjö, A., et al.: On the measuring of film thickness profiles and local heat transfer coefficients in falling films. Exp. Thermal Fluid Sci. 99, 287–296 (2018) 13. Huang, K., Hu, Y., Deng, X.: Experimental study on heat and mass transfer of falling liquid films in converging-diverging tubes with water. Int. J. Heat Mass Trans. 126, 721–729 (2018) 14. Pehlivan, H., Özdemir, M.: Experimental and theoretical investigations of falling film evaporation. Heat Mass Trans. 48(6), 1071–1079 (2012) 15. Pecherkin, N., Pavlenko, A., Volodin, O.: Heat transfer and crisis phenomena at the film flows of freon mixture over vertical structured surfaces. Heat Trans. Eng. 37(3–4), 257–268 (2016) 16. Yang, L., Song, X., Xie, Y.: Effect of the dryout in tube bundles on the heat transfer performance of falling film evaporators. Procedia Eng. 205, 2176–2183 (2017) 17. Luo, C., Ma, W., Gong, Y.: Design of single vertical tube falling-film evaporation basing on experiment. J. Loss Prev. Process Ind. 24(5), 695–698 (2011) 18. Sen, P.K., et al.: A small scale Multi-effect Distillation (MED) unit for rural micro enterprises: Part I—design and fabrication. Desalination 279(1–3), 15–26 (2011) 19. Sen, P.K., et al.: A small scale multi-effect distillation (MED) unit for rural micro enterprises: Part II—Parametric studies and performance analysis. Desalination 279(1–3), 27–37 (2011) 20. Deharkar, R., et al.: Design challenges in vertical tube evaporator to reduce maintenance for small scale multi-effect desalination. ICTEA: International Conference on Thermal Engineering. vol. 2019 (2019)
Analysis of Low-Power Cache Memory Design for Single Bit Architecture Reeya Agrawal
Abstract This paper describes the analysis of low-power cache memory design for single bit architecture made up of six transistor static random access memory cell, write driver circuit, and voltage latch sense amplifier. At different values of resistance, consumption of power of cache memory design for single bit architecture has been analyzed. Process corner simulation and Monte Carlo simulation also have been done to check the robustness of the architecture. Conclusion arises that consumption of power decreases on increase in the value of resistance and 13.57 μW consumption of power done by cache memory design for single bit static random access memory cell voltage latch sense amplifier design with 13.02 ηs. Keywords Latch sense amplifier (LSA) · Write driver circuit (WDC) · Six transistor static random access memory (STSRAM) · Voltage latch sense amplifier (VLSA)
1 Introduction There are several memory elements within a system in contemporary computing systems, for example, main memory, cache memory, and register data [1–3]. With the latest development in the very large-scale integrated circuits (VLSI) technology, high-speed STSRAM is the industry’s prime desire. Memory is the key component of a chip, and STSRAM is used as a cache memory because it is a fundamental part of memory that is important in data execution [4–6]. STSTRAM is a volatile memory since the information persists once the power is usable, which is essential and cannot be ignored in terms of reducing capacity in STSRAM. In an STSRAM architecture, the LSA is one of the elements of the data line. LSA is used to detect the difference in voltage at the input of the bit lines and to create a complete voltage swing at the output during the read operation [7]. Instead of having to measure or
R. Agrawal (B) GLA University, Mathura, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_15
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wait for the swing voltage level, the memory will simply store ‘0’ or ‘1’ to save time while reading. The demand for mobile devices and a battery-operated embedded system is growing with greater breadth as VLSI industries grow. Cache memory design for single bit architecture is a central part of memory that plays a key role in data execution, cache occupying 60% to 70% of chip region [8]. As chip consumption increases rapidly, microprocessor velocity is then decreased. One million transistors also increase and degrade the efficiency of single-chip failure rates, so the industry is working to build a low-speed and low-power memory circuit, which keeps the development of the VLSI system informed. In current high-performance microprocessors, more than half of transistors are for cache memories, and in the future, this proportion is projected to increase [9]. STSRAM is usually the option for built-in stock because it is robust in such chips in a noisy environment. The device can use necessary memory cells by integrating them in SRAMC that are the right size for system requirements. Memories time for access and power consumption are calculated primarily by the configuration of LSA. LSA is one of the most important peripheral circuits in memory systems [10].
2 Literature Review Year
Author
Features
Sensing delay (ns)
Supply voltage (V)
2002
A Chrysanthopoulos et al.
Conventional sense amplifier
7.1
2.5
2002
A Chrysanthopoulos et al.
Clamped bit line sense amplifier
0.35
2.5
2002
K.-S. Yeo et al.
Low-power current sense amplifier
1.04
2.0
2002
A Chrysanthopoulos et al.
Simple four transistor 1.85 sense amplifier
2.5
2004
Chun-lung Hsu et al.
High-speed sense amplifier
0.51
1.8
2005
Z. H. Kong et al.
Ultralow-power
1.46
1.8
2007
Sandeep Patil et al.
Self-biased charge-transfer sense amplifier
0.723
1.8
2008
Ya-Chun Lai et al.
Latch type sense amplifier
0.33
1.8
2008
Anh-Tuan Do et al.
Fully current mode sense amplifier
0.38
1.8
2008
Do Anh-Tuan et al.
High-speed sense amplifier
0.26
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(continued) Year
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Alpha latch sense amplifier
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1
2011
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Decoupled sense amplifier
0.214
1
3 Cache Memory Design for Single Bit Architecture In this section, cache memory design for single bit architecture has been described with their design as shown in Fig. 1. Cache memory design for single bit architecture made up of WDC, STSRAM, and LSA [11, 12].
3.1 Circuit of Write Driver Figure 2 shows the circuit diagram of WDC. Each of the bit-lines in the STSRAM write driver circuit is quickly discharged from pre-charge stages to below the STSRAM write margin [13].
Fig. 1 Cache memory design for single bit STSRAM VLSA architecture schematic
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Fig. 2 Write driver circuit schematic
The write enable (WE) signal usually activates the WDC, which uses full-swing discharge to drive the bit-line from the pre-charge level to the ground. Five PMOS (PM1 , PM2 , PM3 , PM4 , and PM5 ) as well as five NMOS (NM1 , NM2 , NM3 , NM4 , and NM5 ) are used by WDC. When allowed by WE, the input data causes one of the transistors to become PM1 or NM1 through inverters, and a strong 0 is applied by discharging BTL and BTLBAR from the pre-charge level to ground level [14].
3.2 Six Transistor Static Random Access Memory Cell It is used for operations at low power, low voltage. Here, each bit is stored using bistable latching circuitry. Figure 3 shows the STSRAM cell schematic, the pull-up
Fig. 3 Six transistor static random access memory cell schematic
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transistors are M1 and M2 (PMOS ), while the driver transistors are M3 and M4 NMOS . These bit lines enhance the margin of noise. The value of measurable output voltage swings is given by differential circuitry. Logic 0 or 1 is stored as long as the power is on, but unlike DRAM cells [15, 16]; it does not need to be refreshed. In STSRAM architecture, the size of the transistors is most important for the proper operation of the transistors.
3.3 Voltage Latch Sense Amplifier The voltage latch sense amplifier schematics developed in this work are shown in Fig. 4. Internal nodes are pre-charged via the bit-lines in this design. The architecture of the circuit runs directly via input bit lines, based on its internal nodes [17–20]. If the word line is high pulled and followed by the amplifier sensor trigger, NM12 is OFF, and PM8 and PM9 are ON. If the voltage difference in the bit-lines increases, the random bit in the internal nodes of the LSA varies accordingly in voltage. When the LSA signal SAEN is claimed, the interlinking inverters consist of PM10 , NM10 , PM11 , and NM11 raise the voltage difference to the highest swing power [21–25] as shown in Fig. 5.
Fig. 4 Schematics of voltage latch sense amplifier
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Fig. 5 WDC output waveform
4 Result Analysis Figure 5 describes the output waveform of WDC, for cases arise: (a) when Bit = 0 V and WE = 0 V BTL = VDD and BTLBAR = VDD , (b) Bit = 0 V WE = VDD so, BTL = 0 V and BTLBAR = VDD /2, (c) Bit = VDD WE = 0 V so, BTL = 0 V and BTLBAR = VDD /2 and (d) Bit = VDD WE = VDD so, BTL = VDD and BTLBAR = 0 V. Figure 6 shows the output waveform of STSRAM which holds two operations:
Fig. 6 STSRAM cell output waveform
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Fig. 7 Output waveform of VLSA
1. 2.
Write Operation Hold Operation.
There are three types of transistors: (i) access transistors, (ii) pull-up transistor, and (iii) pull-down transistors. Figure 7 describes the read operation of VLSA when both SAEN = 1 and WL = 1 at that time sense amplifier works in read operation. Note: P = V2 /R as this voltage is constant on varying the R and analyzing the power consumption. Figure 8 shows the process corner simulation of cache memory design for single bit architecture at six different corners, whereas Fig. 9 shows the Monte Carlo simulation for cache memory design for single bit architecture. Table 1 depicts that consumption of power decreases as increase in value of resistance, whereas Fig. 10 shows the comparative analysis of different parameters of cache memory design for cache memory architecture using different values of resistance of Table 1 in form of a chart.
5 Conclusion In the proposed work, cache memory design for single bit architecture has been implemented, and on different values of resistance, different parameters of cache memory design for single bit architecture have been analyzed. To check the robustness of cache memory design for single bit architecture process corner simulation and Monte Carlo simulation also have been done. Furthermore, consumption of power of
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Y0 ()
Fig. 8 Process corner simulation
X0 (E-3) Fig. 9 Monte carlo simulation Table 1 Analysis of different parameter of single bit SRAMC VLSA design S. No. Parameters
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R = 42.3
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Fig. 10 Analysis of different parameter of single bit SRAMC VLSA design at different values of resistance
cache memory design for single bit architecture has been analyzed and the conclusion arises that consumption of power decreases on increasing resistance value (i.e., 14.32 μW). In the future scope, this work can be implemented in form of an array.
References 1. He, Y., Zhang, J., Wu, X., Si, X., Zhen, S., Zhang, B.: A half-select disturb-free 11T SRAM cell with built-in write/read-assist scheme for ultralow-voltage operations. IEEE Trans. Very Large-Scale Integr. (VLSI) Syst. 27(10), 2344–2353 (2019) 2. Fragasse, R., et al.: Analysis of SRAM enhancements through sense amplifier capacitive offset correction and replica self-timing. IEEE Trans. Circ. Syst. I Regul. Pap. 66(6), 2037–2050 (2019) 3. Gupta, S., Gupta, K., Calhoun, B.H., Pandey, N.: Low-power near-threshold 10T SRAM bit cells with enhanced data-independent read port leakage for array augmentation in 32-nm CMOS. IEEE Trans. Circ. Syst. I Regul. Pap. 66(3), 978–988 (2019) 4. Dounavi, H., Sfikas, Y., Tsiatouhas, Y.: Periodic aging monitoring in SRAM sense amplifiers. In: 2018 IEEE 24th International Symposium on On-Line Testing and Robust System Design (IOLTS), Platja d’Aro, pp. 12–16 (2018) 5. Ahmad, S., Iqbal, B., Alam, N., Hasan, M.: Low leakage fully half-select-free robust SRAM cells with BTI reliability analysis. IEEE Trans. Dev. Mater. Reliab. 18(3), 337–349 (2018) 6. Reddy, B.N.K., Sarangam, K., Veeraiah, T., Cheruku, R.: SRAM cell with better read and write stability with minimum area. In: TENCON 2019–2019 IEEE Region 10 Conference (TENCON), pp. 2164–2167. Kochi, India (2019) 7. Surkar, A., Agarwal, V.: Delay and power analysis of current and voltage sense amplifiers for SRAM at 180 nm technology. In: 2019 3rd International Conference on Electronics, Communication, and Aerospace Technology (ICECA), pp. 1371–1376. Coimbatore, India (2019) 8. Tripti, T., Chauhan, D.S., Singh, S.K., Singh, S.V.: Implementation of low-power 6T SRAM cell using MTCMOS technique. In: Advances in Computer and Computational Sciences, Springer, Singapore (2017) 9. Geetha Priya, M., Baskaran, K., Krishnaveni, D.: Leakage power reduction techniques in deep submicron technologies for VLSI applications. Int. Conf. Commun. Technol. Syst. Des. (2011)
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10. Sridhara, K., Biradar, G.S., Yanamshetti, R.: Subthreshold leakage power reduction in VLSI circuits: a survey. In: 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 1120–1124 (2016) 11. Gnana Deepika, K., Mariya Priyadarshini, K., David Solomon Raj, K.: Sleepy keeper approach for power performance tuning in VLSI design. Int. J. Electron. Commun. Eng. 6(1), 17–28 (2013). ISSN 0974-2166 12. Gomes Iuri, A.C., Cristina, M., Butzen Paulo, F.: Design of 16 nm SRAM architecture. In: South Symposium on Microelectronics (2012) 13. Sri Harsha Kaushik, C., Vanjarlapati, R.R., Krishna, V.M., Gautam, T., Elamaran, V.: VLSI design of low power SRAM architectures for FPGAs. In: 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), pp. 1–4 (2014) 14. Choudhary, R., Padhy, S., Rout, N.K.: Enhanced robust architecture of single bit SRAM cell using drowsy cache and super cut-off CMOS concept. Int. J. Ind. Electron. Elect. Eng. 3, 63–68 (2011) 15. Gajjar, J.P., Zala, A.S., Aggarwal, S.K.: Design and analysis of 32-bit SRAM architecture in 90 nm CMOS technology 03(04), 2729–2733 2016 16. Agrawal, R., Tomar, V.K.: Analysis of Cache (SRAM) Memory for Core I ™ 7 Processor. In: 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), p. 402 (2018) 17. Vanama, K., Gunnuthula, R., Prasad, G.: Design of low power stable SRAM cell. In: 2014 International Conference on Circuit Power and Computing Technologies (ICCPCT), pp. 1263– 1267 (2014) 18. Chandankhede, R.D., Acharya, D.P., Patra, P.K.: Design of high-speed sense amplifier for SRAM. In: IEEE International Conference on Advanced Communication Control and Computing Technologies, pp. 340–343 19. Wei, Z., Peng, X., Wang, J., Yin, H., Gong, N.: Novel CMOS SRAM voltage latched sense amplifiers design based on 65 nm Technology, pp. 3281–3282 20. Wang, Y., Zhao, F., Liu, M., Han, Z.: A new full current-mode sense amplifier with compensation circuit. In: 2011 9th IEEE International Conference on ASIC, Xiamen, pp. 645–648 (2011) 21. Eslami, N., Ebrahimi, B., Shakouri, E., et al.: A single-ended low leakage and low voltage 10T SRAM cell with high yield. Analog Integr Circ Sig Process (2020) 22. Bazzi, H., Harb, A., Aziza, H., et al.: RRAM-based non-volatile SRAM cell architectures for ultra-low-power applications. Analog Integr Circ. Sig. Proc. (2020) 23. Pal, S., Bose, S., Islam, A.: Design of SRAM cell for low power portable healthcare applications. Microsyst Technol. (2020) 24. Shah, A.P., Vishvakarma, S.K., Hübner, M.: Soft Error hardened asymmetric 10T SRAM cell for aerospace applications. J. Electron. Test 36, 255–269 (2020) 25. Singh, S., Mishra, V.: Enhanced static noise margin and increased stability SRAM cell with emerging device memristor at 45-nm Technology 61, 200–206 (2018)
Cache Memory Design Analysis for Single Bit Architecture for Core Processor Reeya Agrawal
Abstract This paper describe the performance analysis of low-power cache memory design for single bit architecture for core processors made up of six transistor static random access memory cell, write driver circuit and differential types and latch type sense amplifiers such as volatge differential sense amplifier and current latch sense amplifier. Furthermore, to reduce power consumption of cache memory design for single bit different kinds of power reduction techniques such as power reduction footer stack technique, power reduction sleep transistor technique and power reduction dual sleep technique are applied over different blocks of design and conclude that cache memory design for single bit architecture having volatge differential sense amplifier and six transistor random access memory cell with power reduction forced stack technique consume lowest power, i.e., 9.10 μW and 34 number of transistor in architecture. Keywords Six transistor static random access memory (STSRAM) · Voltage differential sense amplifier (VDSA) · Current latch sense amplifier (CLSA) · Latch sense amplifier (LSA) · Differential sense amplifier (DSA) · Write driver circuit (WDC)
1 Introduction As the very large-scale integrated circuit (VLSI) industry expands, so does the need for mobile devices and battery-powered embedded systems. Single Bit Cache Memory Design is a critical component of memory that plays an important role in data execution, with the cache occupying 60 to 70% of the chip space [1]. Microprocessor velocity decreases as chip consumption grows fast. Because one million transistors increases and degrades the efficiency of single-chip failure rates, the industry is trying to develop a low-speed and low-power memory circuit that maintains the VLSI system development informed. The sensory amplifier is the focus of this essay. R. Agrawal (B) GLA University, Mathura, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_16
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More than half of the transistors in today’s high-performance microprocessors are for cache memories, and this number is expected to rise in the future [2]. Because it is durable in such chips in a loud environment, SRAMC is generally the choice for built-in stock. As a result, the development of low-power, high-performance CPUs has garnered a lot of attention. The device can employ required memory cells by integrating them in SRAMCs of the appropriate size for system requirements. Speed and power lead to advancements in a field. SA is an essential ingredient that responds to high frequency in all SRAMC memory blocks. The setup of SA is largely responsible for calculating memory access time and power consumption. One of the most significant peripheral circuits in memory systems is the SA [3, 4]. SA is a power-operated circuit that shortens the time it takes for a signal to travel from a memory cell to a logic circuit on the memory cell periphery and converts arbitrary logical levels of peripheral Boolean circuits to digital logic levels [5]. Their output has a substantial impact on both memory access time and overall memory capacity depletion. Complementary metal–oxide–semiconductor (CMOS) memory, like other integrated circuits (ICs), are required to enhance speed, power, and maintain low energy dissipation. Typically, as memory space increases, so does the parasite space of the bit line. With increasingly energy-hungry memories, this bit line has constantly grown [6].
1.1 Power Reduction Techniques In the VLSI model, the designers suggested many strategies for reducing the circuit power consumption. The short descriptions of all the methods used in the circuits are given below [7].
1.1.1
Power Reduction Sleep Transistor Technique
The sleep transistor technique is the most commonly used power reduction technique. The diagram of the sleep transistor technique. In the sleep transistor procedure, and connection is made between V DD and pull-up network in the circuit PMOS , and (ii) a connection is made between pull-down network and GND in the circuit N MOS [8].
1.1.2
Power Reduction Forced Stack Technique
In the forced stack technique, instead of using voltage supply PM0 is used and in place of ground N M0 is used in the logic circuit. In this technique, both MOS have the same input. In this technique when PM0 is in the active region, N M0 is in the cut-off region [9].
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Power Reduction Dual Sleep Technique
The field requirements for this technique are maximized by using four transistors, two N MOS (N M0 and N M1 ) and two PMOS (PM0 and PM1 ). In sleep mode, in either an OFF/ON state, the dual sleep technique took advantage of the two additional pull-up and pull-down transistors. A particular logic circuit needs fewer transistors [10].
2 Cache Memory Design for Single Bit Architecture In this section, cache memory design for single bit architecture has been described with their design as shown in Figs. 1 and 2. Cache Memory Design for Single Bit Architecture made up of WDC, STSRAM, and sense amplifiers such as voltage differential sense amplifier and current latch sense amplifier [11, 12].
2.1 Circuit of Write Driver Figure 3 shows the circuit diagram of WDC. Each of the bit lines in the STSRAM write driver circuit is quickly discharged from pre-charge stages to below the STSRAM write margin. The write enable (WE) signal usually activates the WDC, which uses full swing discharge to drive the bit line from the pre-charge level to the ground. Five PMOS
Fig. 1 Cache memory design for single bit STSRAM VDSA architecture schematic
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Fig. 2 Cache memory design for single bit STSRAM CLSA architecture schematic
Fig. 3 Write driver circuit schematic
(PM1 , PM2 , PM3 , PM4 , and PM5 ) as well as five N MOS (N M1 , N M2 , N M3 , N M4 , and N M5 ) are used by WDC. When allowed by WE, the input data causes one of the transistors to become PM1 or N M1 through inverters, and a strong 0 is applied by discharging BTL and BTLBAR from the pre-charge level to ground level [13, 14].
2.2 Six Transistor Static Random Access Memory Cell It is used for low-power, low-voltage activities. Each bit is stored using bistable latching circuitry in this case. The 6 T SRAMC design is shown in Fig. 4, with the
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Fig. 4 Six transistor static random access memory cell schematic
pull-up transistors M1 and M2 (PMOS) and the driver transistors M3 and M4 NMOS. These bit lines increase the noise margin. Differential circuitry determines the value of observable output voltage swings. Logic 0 or 1 is kept for as long as the power is turned on, however unlike DRAM cells [15, 16], it does not require refreshing. The size of the transistors is critical in SRAM design for effective transistor functioning.
2.3 Differential and Latch Sense Amplifier’s The sense amplifier amplifies a small analog differential voltage produced on the read-access bit lines. The amplification leads to a complete one-end digital output. Because of the length of the metal and because a lot of transistors take a long time to discharge the bit lines, bit lines have more power [16].
2.3.1
Voltage Differential Sense Amplifier
The power amplifier function is based on the differential voltage produced by the bit lines. The circuit consists of cross-connected inverters that convert the bit line voltage difference at its entrance to full swing output, as shown in Fig. 5. The cell columns integrate BTL and BTLBAR inputs with the cell column bit lines. P1 binds the memory cell to the P2 sensory boost and N 3 activates the sensation boost. The inner nodes of the sensory amplifier are separated by output inverters from the external load. The sensor amplifier voltage-mode is loaded in two steps. The sensor amplification is applied to the memory cell by returning the selected line during the evaluation process (SAen ) [17, 18].
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Fig. 5 Schematic of voltage differential sense amplifier
2.3.2
Current Latch Sense Amplifier
Figure 6 indicate the schematic of current latch sense amplifier. The operation of the circuit is as follows [19, 20]. The differential voltage on bit lines is transmitted to Fig. 6 Schematic of current latch sense amplifier
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CLSA inputs SA3 and SA4 . When SAEN is pulled high both outputs SA1 and SA2 start discharging. Suppose, SA3 = V DD and SA4 = V DD −V. Owing to its higher V gs, this results in higher current through NM12 than NM13. This triggers discharge of output V 3 faster than V 4 . When sense amplifier is low enough to turn ON PMOS device PM16, the powerful positive feedback loop is triggered, which causes SA2 to be charged back, its outputs are isolated from the inputs.
3 Results Analysis
Voltage (V)
Figure 7 describe the output waveform of WDC, for cases arise: (a) when Bit = 0 V and WE = 0 V BTL = V DD and BTLBAR = V DD , (b) Bit = 0 V WE = V DD so, BTL = 0 V and BTLBAR = V DD /2, (c) Bit = V DD WE = 0 V so, BTL = 0 V and BTLBAR = V DD /2 and (d) Bit = VDD WE = V DD so, BTL = V DD and BTLBAR = 0 V. Figure 8 describes the both write operation and hold operation of the SRAMC. There is a pull-up transistor (PM6 and PM7 ), pull-down transistor (NM6 and NM7 ), and access transistor (NM8 and NM9 ) which allows data to store and LSA to read the data. Note: P = V2 /R as this voltage is constant on varying the R and analyzing the power consumption. Figures 9 and 10 describe the read operation of VDSA and CLSA when both SAEN and WL are pulled high, during that time only the SA senses the data from the STSRAM at bit lines and gives output at V 3 and V 4 . Table 1 depicts that consumption of power decreases as increase in value of resistance whereas Fig. 11 shows the comparative analysis of different parameters of cache memory design for cache memory architecture using different values of resistance of Table 2 in form of a chart.
Case b
Case a
Case d
Case c Time (ηs)
Fig. 7 Output waveform of WDC
WE BLBBAR Bit BL
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Hold Write Operation Operation
Time (ηs)
WL BTL BTLBAR V2 V1
Fig. 8 STSRAM cell output waveform
Fig. 9 Voltage differential sense amplifier output waveform
Table 2 depicts that consumption of power decreases as increase in value of resistance whereas Fig. 12 shows the comparative analysis of different parameters of cache memory design for single bit STSRAM CLSA architecture using different values of resistance of Table 2 in form of a chart. Table 3 depicts that to reduce consumption of power, power reduction forced stack technique has been applied over VDSA and CLSA in cache memory design for single bit architecture with 39 number of the transistor whereas Fig. 13 shows the
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SAEN PCH YSEL WL V4 V5 Fig. 10 Current latch sense amplifier output waveform Table 1 Different parameter of cache memory design for single Bit STSRAM VDSA architecture S. No.
Parameters
Single bit STSRAM VDSA architecture Delay in sensing (ηs)
Transistors number
Consumption of power (μW)
1
R = 42.3
13.14
30
13.16
2
R = 42.3 K
13.14
30
11.34
Fig. 11 Different parameter of cache memory design for single Bit STSRAM VDSA architecture
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Table 2 Different parameter of cache memory design for single bit STSRAM CLSA architecture S. No.
Parameters
Single bit STSRAM CLSA architecture Delay in sensing (ηs)
Transistor number
Consumption of power (μW)
1
R = 42.3
13.51
35
73.92
2
R = 42.3 K
13.51
35
26.78
Fig. 12 Different parameter of cache memory design for single bit STSRAM CLSA architecture
Table 3 On applying power reduction techniques over sa different parameter of cache memory design for single bit architecture S. No.
Single bit STSRAM VDSA architecture
Single bit STSRAM CLSA architecture
Consumption of power (μW)
Transistor number
Consumption of power (μW)
Cransistor number
1
Sleep transistor
11.29
32
25.89
37
2
forced stack
11.29
32
13.4
37
3
dual sleep
11.03
34
25.9
39
comparison of power consumption of cache memory design for single bit architecture on applying power reduction techniques over SA of Table 3 in form of a chart. Table 4 depicts that when power reduction techniques are applied over STSRAM and SA consumes power upto 9.10 μW lowest as compared to others but the number of transistors increases whereas Fig. 14 shows the different parameter of cache memory design for single bit architecture when power reduction techniques are applied over STSRAM, LSA and DSA.
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Fig. 13 On applying power reduction techniques over sa different parameter of cache memory design for single bit architecture Table 4 On applying power reduction techniques over STSRAM and SA different parameter of cache memory design for single bit architecture Single bit STSRAM VDSA architecture
Single bit STSRAM CLSA architecture
Consumption of power (μW)
Transistor Number
Consumption of power (μW)
Transistor number
Sleep transistor
9.18
34
24.62
39
Forced stack
9.10
34
11.92
39
dual sleep
10.13
38
24.64
43
Fig. 14 On applying power reduction techniques over STSRAM and SA different parameter of cache memory design for single bit architecture
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4 Conclusion In this paper, cache memory design for cache memory architecture with different types of sense amplifiers such as voltage differential sense amplifier and current latch sense amplifier has been implemented, and compared on different values of resistance (R) with different parameters such as consumption of power, delay in sensing, and transistor number. Apart from it over different blocks of cache memory design for single bit architecture power reduction techniques are applied and results depicted that cache memory architecture for single bit architecture having voltage differential sense amplifier and six transistor static random access memory cell with power reduction forced stacked technique consumes 9.10 μW power.
References 1. Eslami, N., Ebrahimi, B., Shakouri, E., et al.: A single-ended low leakage and low voltage 10T SRAM cell with high yield. Analog. Integr. Circ. Sig. Process. 105, 263–274 (2020) 2. Bazzi, H., Harb, A., Aziza, H., et al.: RRAM-based non-volatile SRAM cell architectures for ultra-low-power applications. Analog. Integr. Circ. Sig. Process (2020) 3. Gupta, S., Gupta, K., Calhoun, B.H., Pandey, N.: Low-power near-threshold 10T SRAM bit cells with enhanced data-independent read port leakage for array augmentation in 32-nm CMOS. IEEE Trans. Circuits Syst. I Regul. Pap. 66(3), 978–988 (2019) 4. Dounavi, H., Sfikas, Y., Tsiatouhas, Y.: Periodic aging monitoring in SRAM sense amplifiers. In: 2018 IEEE 24th international symposium on on-line testing and robust system design (IOLTS), Platja d’Aro, pp. 12–16 (2018) 5. Ahmad, S., Iqbal, B., Alam, N., Hasan, M.: Low leakage fully half-select-free robust SRAM cells with BTI reliability analysis. IEEE Trans Dev Mater Reliab 18(3), 337–349 (2018) 6. Reddy, B.N.K., Sarangam, K., Veeraiah, T., Cheruku, R.: SRAM cell with better read and write stability with Minimum area. In: TENCON 2019–2019 IEEE region 10 conference (TENCON), Kochi, India, 2019, pp 2164–2167 7. Tripti, T., Chauhan, D.S., Singh, S.K., Singh, S.V.: Implementation of low-power 6T SRAM cell using MTCMOS technique. In: Advances in Computer and computational sciences, Springer, Singapore (2017) 8. Geetha Priya, M., Baskaran, K., Krishnaveni, D.: Leakage power reduction techniques in deep submicron technologies for VLSI applications. In: ELSEVIER, International Conference on Communication Technology and System Design (2011) 9. Sridhara, K., Biradar, G.S., Yanamshetti, R.: Subthreshold leakage power reduction in VLSI circuits: a survey. In: 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 1120–1124 (2016) 10. Gomes Iuri, A.C., Cristina, M., Butzen Paulo F.: Design of 16nm SRAM architecture. In: South Symposium on Microelectronics (2012) 11. Kaushik, C.S.H., Vanjarlapati, R.R., Krishna, V.M., Gautam, T., Elamaran, V.: VLSI design of low power SRAM architectures for FPGAs. In: 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), pp. 1–4 (2014) 12. Choudhary, R., Padhy, S., Kumar Rout, N.: Enhanced robust architecture of single bit SRAM cell using drowsy cache and super cut-off CMOS concept. Int. J. Ind. Electron. Electr. Eng. 3, 63–68 13. Gajjar, J.P., Zala, A.S., Aggarwal, S.K.: Design and analysis of 32 bit SRAM architecture in 90nm CMOS technology 03(04), 2729–2733 (2016)
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14. Agrawal, R., Tomar, V.K.: Analysis of cache (SRAM) memory for core I ™ 7 processor. In: 9th international conference on computing, communication and networking technologies (ICCCNT), 2018,402 15. Vanama, K., Gunnuthula, R., Prasad, G.: Design of low power stable SRAM cell. In: 2014 International Conference on Circuit Power and Computing Technologies (ICCPCT), pp. 1263– 1267 (2014) 16. Chandankhede, R.D., Acharya, D.P., Patra, P.K.: Design of high-speed sense amplifier for SRAM.In: IEEE International Conference on Advanced Communication Control and Computing Technologies, pp. 340–343 (2016) 17. Wei, Z., Peng, X., Wang, J., Yin, H., Gong, N.: Novel CMOS SRAM voltage latched sense amplifiers design based on 65 nm technology, pp. 3281–3282 (2016) 18. Wicht, B., Nirschl, T., Schmitt-Landsiedel, D.: Yield and speed optimization of a latch-type voltage sense amplifier. IEEE J. Solid-State Circ. 39(7), 1148–1158 (2004) 19. Kobayashi, T., Nogami, K., Shirotori, T., Fujimoto, Y., Watanabe, O.: A current-mode latch sense amplifier and a static power-saving input buffer for low-power architecture. In: 1992 Symposium on VLSI Circuits Digest of Technical Papers, Seattle, WA, USA pp. 28–29 (1992) 20. Kobayashi, T., Nogami, K., Shirotori, T., Fujimoto, Y.: A current-controlled latch sense amplifier and a static power-saving input buffer for low-power architecture. IEEE J. Solid-State Circ. 28(4), 523–527 (1993)
A Review Paper: Breif Discussion on Power Generation by the Use of Various Technologies from Bio-renewable Resources Pankaj Sonkusare, S. K. Dhakad, Pankaj Agarwal, and Ravindra S. Rana
Abstract In this research paper, we are concentrated to improve the environmental conditions by the use of non-conventional bio-catalysts. The production of chemical and new polymeric materials is catalyst agents from natural terms of bio-renewable resources. The various types of technologies were used to grow the environmental properties and to produce the fuels and energy from bio-renewable resources. It is very effective technologies to be improved and efficiency to enhance the better performance by the fermentation process. This fermentation of cell immobilized process to increase the cell immobilized and also improved the metabolism. Most of the polymer materials are major sources from bio-polymers or bio-renewable resources. While, the other technology will be used to improve the fermented process by the use of water–oil cultivation and there are most effectively technology will be helpful in operating factors for fermentation process. The carboxylic acids are used in fermentation process and they will be produced from the petroleum feedstocks. Those technologies were used for fermentation to be prepared the chemical structures and building blocks for industrial applications. In this process, the important role plays in fermentation of fungi for pharmaceuticals products. This review paper have been discussed on the various technologies be used for fermentation process in terms of industrial purpose. In this research, we are main focussed on that paper in fermentation process have been used the bio-processing for value added products from the renewable resources. Keywords Non-conventional bio-catalysts · Solid state fermentation · Bio-renewable resources · Carboxylic acid · Biomass · Biofuels · Fungal fermentation · Polymers · Polyester resins · Value added products P. Sonkusare (B) · S. K. Dhakad Department of Mechanical Engineering, S.A.T.I. Engineering College, Vidisha, M.P. 464001, India P. Agarwal Mechanical Engineering Department, S.A.T.I. Engineering College, Vidisha, M.P. 464001, India R. S. Rana Department of Mechanical Engineering, MANIT Bhopal, Bhopal, M.P. 462003, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_17
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1 Introduction We are introduced the various technologies to be used in fermentation process, there are natural raw material to be prepared by the bio-polymers from the renewable resources. Some of the various methods were used for industrial purpose in terms of chemicals and energy. The discussion in this chapter is very important role play of bio-catalysts for chemical production and enzymes technologies. We are studied most challenging topic can be applied in bio-catalyst act as chemical reactions. They are used through the medium of non-conventional resources to recognized be enhance after the bio-catalysts like as enzymes, cells transformations. The main advantageous were used as non-polar medium under the bio-catalysts as mentioned below: 1. 2. 3. 4.
It is greater stability of bio-catalysts. The solubility to be increased for substrates and products. It has been reduced the inhibition of substrates and products. In thermodynamic equilibrium, there are transformed to synthesis reactants by the shifting properties.
In this reviews, there are important techniques of immobilization cell used in fermentation process. This is the main part of the bio-processing in value added products from the bio-renewable resources. The techniques were used to protect the cells from such types of shear forces and partly to be stabilized the micro-organisms from the environmental conditions. One of the most excited and very effectiveness of cell immobilization to be improved the better performance or economical behaviour for the fermentation process. The main advantageous of immobilized cells have a varieties of bioreactors is to be developed the optimization of fermented process and presently were used in industrial products. The capabilities of bio-catalysts involving in various parameters such as rapid bio-catalytic trial and assessment, process development and optimized between the manufacturing scales. The carboxylic amino acid groups are generally obtained from the fossil fuels and it is applicable in various industries to be used significantly. Such type of process has some merits and demerits of carboxylic acid are valuable techniques for the separation and purification to prepare the value added products from the fermentation process. Recently, the food products and polyester resins were used in industrial applications while the biodegradable polymers and esterification are to be structured for synthesization. Most of the researchers were found the new drug from the fungi characteristics for several diseases or injuries of our human health. There are two types of categories in synthesized ranges of metabolities such as primary metabolities and secondary metabolities. The primary metabolities is to be treated as building blocks and secondary metabolities are related to simplify the inter-relationship. In secondary metabolities have five major resources from the fungi are as follows: amino acid, shikimic acid pathway, polyketide pathway, mevalonic acid and polysaccharides. Presently, we are discussed on the other technology of solid state fermentation is to be involved in various applications like as enzymes/antibiotic productions, bio-active agents and organic acids, ethanol and bio-diesel from the renewable resources. The
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work will be summarized in several applications to volarizes of solid state fermentation for alternative energy and environmental prospects. This chapter has been examined the power generated techniques available from bio-renewable resources under the bio-catalysts in biofuel cells. The various types of organic wastes are to be used for power generation and more efficient should be oxidized for complex chemicals. In this technology, we are focussed on enzymes-based biofuel cells. There are two parts of coupled electrodes such as cathode or anode is too used for the power generated techniques by the electrical circuits. These types of techniques to be enzymatic based on biofuels for power generation is involved have some reactions between the cathode and anode. The termed as energy is vital role play in our world demand energy. The main sources of energy from renewable resources for biological productions of hydrogen in chemical nature. The lot of micro-organisms have been included significantly between the taxanomic and physiological and they can be produced the molecular hydrogen. In this process, there is various fermentation processes to be examined of biological hydrogen productions. It stands to be used as energy sources for pressure of sufficient resources and also helpful for environmental conditions. This article has been used to propel such types of techniques or process to be used in such types of techniques or methods can be applied in limited applications. In thermodynamically, the reactions of microbial water gas shift are to be evolved in hydrogen due to the transformation of phase change during the mass transfer of carbon monoxide. The termed as bio-energy is to be produced by the biomass or biofuel but it is collected or absorbed the sunlight and storage the energy by the help of some devices to be prepared the chemical energy. The production of energy as compared to renewable resources to be calculated on the requirement based for energy production. In modern science, we are facing some related issues with natural materials on that physically and chemically. There are used as natural products in industrial technology and its derivatives. We are main focussed on that several products obtained from petroleum-based is to be replaced by some new polymer materials for to improve and efficiently growth of environmental conditions.
2 Literature Review In this chapter, we are discussed about the bio-catalysts of non-conventional for the generation of chemicals and polymers from the biomass. The petroleum is to consumed at peak high rate for the production of energy and raw materials. The new resources is to developed in naturally served by some new technologies of energy and raw materials are to grow the nature of environment. In industrial applications is to produce the ethanol and bio-diesel for power generation from the bio-renewable resources like as sunlight, nuclear reactors, wind, etc. They are also some alternatives sources from under the earth surface to be growing the fossil carbon sources. There are some different techniques are used to produce the chemicals and materials. This continues chain process of raw materials is to treated by the thermally and chemically
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are transformed. They are important role play in our bio-renewable resources to make the new developed products by enzyme technologies and are also introduced the microbial bio-processing. The enzymes catalyst is to regulate for all the biological transformed under the living organisms. The functions of enzyme catalyst is to be controlled the vitro-environment and to make a new valuable products. There are different categories of enzyme catalyst technologies for the production of chemical products by the biomass and biofuels. The first category of enzymatic is to treat some biomass components such as cellulose, starch, proteins, oils and fats from bio-renewable resources. While, the second category of enzymatic technologies are introduced the biotransformation of molecules from the biomass components like as free sugars, organic acids and alcohols. The enzymatic techniques are used to treat the biomass components involving the polysaccharides, proteins and oils. These components are decomposed into fine small particles in the process. There are various steps to synthesize the chemicals from biomass carbon sources and they are quickly replaced by the hydrolysis method. These methods are to quickly breakdown in the form of transformation of cellulose to glucose. Thus, the production of amino acid in large quantities by the microbial process while the hydrolysis process is not examined to produce the amino acid. However, there are multilateral used of various applications such as protein structural studies, food processing and personal care products. There are various process to enhance the polysaccharides enzymes used to make the products in large amounts for agriculture sectors like as starch. In this process, the lignocellulose biomass has various constituents are cellulose, hemicellulose and lignin and are also obtained from wood residues, agricultural residues and energy residues. We are focussed on the primary process on sugar fermentation and simple techniques of treatments to different polymers from bio-renewable raw materials and some different chemical compounds such as fatty acid, amino acid and monosaccharide acid. These enzyme technologies are used potentially to derive the value added products are to be introduced in various industrial applications [1]. In previous research, we have discussed on the topic of cell immobilization, these cell solidifies have fermented for the generation of some chemicals and fuels. The most interesting topic of cell immobilization is very important role in biotechnology. This fermentation process is to improve by some different effective methods and also improve the performance of this process. In this system, fermentation of solidifies the cell is to improve the performance have great advantages are as follows: 1. 2. 3. 4.
To provide the freely suspended cultures It is easy method to separate the biomass from liquid It is easy recoverable product It is easy improvements of specific metabolic and products on the cell solidify.
There are some process to involve in this system is to improved different parameters such as reduced the non-productive growth phase, yield strength and productive based volumetric on bioreactors. This cell immobilization has to apply the shear forces for cell protective and greater stabilization over environmental stresses. During the process, the main advantages of cell solidify, different variation have developed
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the solidifies cell bioreactors is to optimization of fermentation process. In recent scenario, they are widely used in industrial sector is to prepared the production of various acids such as vinegar, organic and amino acid also in waste water treatment. In this systems have different procedure of cell immobilization such as cell growth, production and process operation, they are also involved in different procedures have compared with free cell fermentation and immobilized cell fermentation. This solidifies techniques are used in industrial applications and also interested in various effects of cell immobilization in different parameters like as physiology, metabolism, genetics and fermentation behaviour. There are different techniques were used in cell immobilization is to support by included some various factors such as substrates, products and inhibitors. While the other parameters to be supported for cell immobilization like as stable, inexpensive, reusable and non-toxic. However, the procedure of cell immobilization techniques should be considerable such factors in fermentation process like as higher value of immobilization capacity, the substance at higher diffusion rate and greater mechanical strength for a long tenure. There are different techniques used in two categories such as carrier binding and entrapment. The first categories of carrier binding are related to physical adsorption and ionic or covalent bond, and second categories are related with polymer matrix and semi-permeable membrane. In this method there are basic needs of organic solvent in physical properties can be effect the different oil phases like as density, viscosity and oxygen solubility also be considerable. Finally, we are characterized in terms of terpolymers will be analysis through the different techniques were used for fermentation process such as GPC, size-exclusion HPLC, NMR and TGA. The different characterization techniques will be results to pertinent in W/O environment are as mentioned below: 1. 2. 3. 4.
We are study to structure the polymers through the chemically reacts. Both the combination of phase separation and pH-dependent emulsified. To evaluate the capability of polymers were used by the warring blender under emulsion of droplets sizes. It is used to make possible of w/o xanthan fermented process with pH sensible for polymer surfactants.
In fermentation process, we have added the polymeric materials will be affected by the rheological properties, therefore, these properties are affected the results in very high viscosity is accounted and aerobic micro-organism to be employed. The term of bio-polymer fermentation is very serious matter for viscosity problem. They are mainly broad range in several fields of applications under the cellular polysaccharides. In this research, we are used the different methods to make and improved the new development process for fermentation performance. There are different techniques were used for production of fermentation are as follows: (1) centrifugal fibrous bed bioreactors, (2) In stir-tank fermentation was used of mobilized cells, (3) to developed the water in oil cultivation technology. Thus, the whole studied have focussed on that technology were used in fermentation process for water–oil xanthan is to be improved and very effectively growth of high viscosity [2].
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The research article is focussed on the carboxylic acid and this carboxylic group is the main part of organic acid. This type of acid is combined with more carboxylic groups. They are significant in various properties such as carbon-chain length, molecular structure and functional groups. The main sources were used to produce carboxylic acid by the petroleum feedstocks from the synthesis and fermentation. These carboxylic acid is to produce by the biochemical processes in all industrial applications. In previous study, the petroleum-based products is to be replaced by carboxylic groups by the industrial technologies for production in several groups. There are various groups of carboxylic acids to be produced involving acetic, propionic, butyric, fumaric, malic and acrylic acids. Other, the different groups of carboxylic acid by the fermentation like as citric, gluconic and itaconic acids. They are generally produced in complex structures to be synthesized chemically will be difficult. The manufacturing items were used in industrial applications such as polyester resin and food acidulents. We can make the simple structures of polyester resin and bio-degradable polymer to be synthesized. The carboxylic acid for the production of micro-organism and chemically pathways mainly depends on it by the fermentation process. In microbial production under the carboxylic acids has several types of organic acid. The organic acid has two types of fermentation process are (1) aerobic fermentation and (2) anaerobic fermentation. The aerobic fermentation under the carboxylic groups such as acetic acid, citric acid, fumaric acid, gluconic acid, itaconic, lactic, malic, pyrunic acid. While, the anaerobic fermentation of carboxylic groups are under acetic, butyric, lactic, propionic, succinic acid. In this research, we are discussed the carboxylic acid to be produced by fermentation process will be recovered by various methods such as separation, distillation and solvent extraction. The distillation process was used by separation but these methods have volatile and non-volatile compounds. The non-volatile compounds are to be formed in esterification. There are various methods of separation were used for recovered in carboxylic groups such as precipitation, distillation, extraction, adsorption and electrodialysis. In this process, we have processed the extracted with the supported of liquid membrane under the follow fibre. The phase content in conventionally has created two phases are water solvent phases and incomplete phase separation. The important role play of supported liquid membrane (SLM) in fermentation process but they are also combined with extraction and stripped [3]. In modern research, the vital role played of fungi in biotechnological process, then it tends to macroscopic level of mushrooms. There are several applications to use of fungi for the development of antibiotics, alcohols, enzymes, organic acids and several pharmaceuticals products. The DNA technology is generated fungi were used carbon sources and proteins to be produced. In this review, the enzyme production to be produced microbial organisms as cell factories for biotechnological equipments to be limitations and scattered for food. Presently, we have discovered newly drugs by the fungi have analysed some diseases and it is also used for major diseases to solve the problems. They are effective process for fungi fermentation for medicinal items and a large amount of fungi were used in nutraceutical and food markets. The scientific reasons to achieve by the process of fermentation and therapic effects and healing mechanisms. The chemical nature of fungi to be synthesized in several
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metabolities. There are two types of metabolities in synthesize range as mentioned below: 1. 2.
In primary metabolities were used of citric acid and enzymes. In secondary metabolities were used of ergot alkaloids and antibiotics.
But the secondary metabolities have major distinguish between five categories of metabolic resources: amino acids, shikimic and pathway, polyketide pathway, mevalonic acid and polysaccharides. Generally, the mostly effects of fungi microbial under the fungi metabolities and humans to be separated compositions. In recent technologies, were used the therapatics from fungi of amino acid under the metabolities to describe the various drugs named and designed have each structure of amino acid. In this review, we are focussed on the pathway manipulation have included the genetic engineering in advanced technologies were used. They are greater sources of genetic process to develop the newly chains products or strains enabled to be modified pathway and to produce newly desirable metabolities. There are many tools and technologies were used in fungi engineering applications. The pathway engineering have a six major regulations are as follows: (1) it is improved metabolic pathway, (2) the rate limiting steps to be removed, (3) it includes allosteric regellation and transcriptional, (4) it clears the blockage of pathway, (5) to enhanced the metabolism pathway, (6) it required availability of enzymatic factors. The various fields of applications were used the advanced technologies in genetic pathway such as genetic manipulations to be efficiently, measurable methods and analytic tools in metabolism and proteins to be produced. There are various parameters to affect the morphological fungi and fermented process in bioprocess regulation such as substrate solutions, culture pH, inoculum density, temperature, shear rate and dissolved oxygen tension (DOT). Thus, the modern role play in genetic fermentation of fungi under the biotechnology pathway is scattered in several applications. So, it is also understand between the biological synthesized pathway and fungal physiology of desirable metabolism [4]. We are discussed on previous research of solid state fermentation process and it is also applied in industrial food products. The fermentation process helps to grown on steamed rice tends to shallow trays and it can be carried with low cost biomaterials such as cellulose, lignin, starch, hemicellulose and chitin. The microbial generation and metabolism events to provide put in microenvironments but this process is to use low energy as compared with submerged fermentation process. Presently, the solid state fermentation process is to generate newly products is to improved productivity than the submerged fermentation. In this field, we are greater availabilities of newly products and recovered cheaper items for future prospects a different range of agri-industry items and waste flow. It can be sustained the chemical products through the microbial production by the use of renewable resources. There are mainly generated to value added products because there are not produced the valuable items, so, it can be used biomass acts as wastage items or it may be required costly treatments related to disposal items. The western industry should not be satisfied with solid state fermentation (SSF) due to bioreactor technologies and lack of knowledge. Sometimes, we are need the suitable data which helps to production in
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kinetics formed, bioreactor design and process control is available on this process. The main aim is to achieve development of newly products by the use of technologies among the solid state fermentation and also applicable for industrial products under the microbial fermentations [5]. In this research, we have discussion on the power generation to be used with bio-catalysts under biofuels from the bio-renewable resources. It can also apply the microbial and enzymatic bio-catalysts by means of biofuel cells. It involves the biocatalysts from fuel cell to be used cathode and will be oxygen decreased. Most of the products will be used of biofuel cell in bio-renewable resources such as biological, biochemical, bio-catalytic or bio-electrochemical. Generally, we have prepared the fuels with different gases like as hydrogen and methane to make new products. The catalysts groups are categories into two different things of fuel cells such as primarily oxidized on inorganic groups at the electrodes surface for generation of electricity while the other related to biofuel cells. In advanced research, we are operated all the things to make a new development of new product including the genetic engineering, chemistry and material science fields as an opportunities for capability of bio-catalysts in fuel cells. In this review, the technologies were used on enzymatic biofuel cells depend on the mono layer functionalized electrodes. We are also focussed to construct the fuel cell in several applications for energy productions to examine the challenges of bio-catalyst under biofuel cells. In this nature to survival in the environment which helps to regulations of biotransformation. Most of the fuels are to be synthesized for generation of electricity have been analysed on noticeable efficiency such as methanol, ethanol, methane and hydrogen. We are categories the fuels production under the bio-catalysts such as fuels, feed biochemical, bio-catalysts and indirect biofuel cell. To evaluate and prepared biofuels with the combinations of biosynthesis reactor and fuel cell. The fuel cell is to be prepared in some form of chains reaction in termed as redox reaction for electricity generation. In present research, we can prepare the generation of electricity from biofuel cell is to be used with the mediators reported among the microbial and enzymatic biofuels. While, the other hands they are categories in two different path of biofuel cell such as direct and indirect. In case of bio-catalysts, the direct fuel cells are to be catalysing in redox reaction and indirect fuel cell for fuel to be synthesized. In this generation, they are various parameters to be analysed in biofuel cells are as follows: (1) greater electrochemical activity, (2) easy transport, (3) easily storage capacity, (4) its toxicity, (5) greater production cost. In this discussion, those fuels have been used for generation process in different categories of fuels are obtained by the renewable resources. Some electrons are transferred process in biofuel cells to be deposited electrodes. The biofuel cell is involved in such types of process are as follows; direct electron transfer, redox mediators and immobilizations between the bio-catalysts and mediators. In engineering aspects, we have used biofuels cells to idealize the electrodes in several parameters. In this process, they are generally involved the carbon-based electrodes and they have different types of carbon-based electrodes in biofuel cells. It can be summarize the various fields of applications based on carbon-based electrodes such as configurations, surface area and different types of electrodes. The bio-catalysts help to
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subject the mass transfer to be categories in three processes: bio-catalysts, electron transfer between the electrodes and proton transferred by the membrane. Although, this process of biofuel cell is to be enhanced the diffusion rate and to improve the polymer properties between the polymer backbone and redox centre. Presently, we have discussed the whole research about the biofuel cell is used in advanced technologies and genetic engineering is to evolve and better performed role played in dynamic field in energy sectors [6]. In industrial purposes, the hydrogen is biological production at atmospheric temperature and pressure. In this field, the several authors or researchers have been initiated the programmes of biological hydrogen process can be performed. We can perform in practically applicable of hydrogen production for transition by the fossil fuel resources. Thus, the bio-hydrogen process is very effectively and suitable for environmental conditions and also improved it as compared to fossil fuels based on hazardous components. In this process, the micro-organisms have a large quantity to involve in various types of taxonomic and physiologically to molecular hydrogen will be produced. The different types of biological hydrogen production are as follows: (1) direct biophotolysis, (2) indirect biophotolysis (3) photo fermentation, (4) dark fermentation. The comparison between the biological hydrogen production has four various types in this process with micro-organisms should be operated at atmospheric temperature and pressure. The improvements of some factors in photo-biological hydrogen production are to evolved by oxygen during the production process. Most of the phases have generated the problem due to overcoming the oxygen of enzymes. To maximize the solar conversion efficiency mainly depends on the mass conditions and enhance the hydrogen production with the engineering metabolism. We have new concepts were developed and strategies to produced bio-hydrogen from renewable resources is importance of energy to lack in availabilities of energy sources with used as new technologies. In biotechnological areas, we can use the new technology equipments for bio-hydrogen production is more challenging factors with respect in our environment and problems of energy sectors. The advancement of challenging the biological terms to encouraging in engineering sciences will grow some biological integration process for economically hydrogen production [7]. Generally, there are some derivatives to be used in chemical compositions of plant lipids such as triacylglycerides (TAGs) and glycerophospholipids (GPLs) and also used in industrial and domestically purpose. The chemical modifications to be changed in different fatty acids in polymer materials including a wide range of technologies between thermal and catalytic reactants. There are widely used as different methods to alter the crystalline properties and its chemical modifications. In this scenario, the natural oils have been used in industrial applications such as water proofing and developed the new polymeric materials and synthetic component by the use of different technology. The several types of applications were used in industrial sectors such as coatings and polymers, printing inks, lubricants, cosmetics/pharmaceuticals, leather processing, surfactants, solvents, hydraulic fluids, pesticide/herbicide adjuvants, glycerine and fuels. We are on-going research so many applications were studied to develop the new products with new technologies
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to be utilized. The important factors have some regulatory issues involving economic conditions and supporting bio-based products [8]. Recently, in advances to discussed on topic major sources available from renewable resources. The natural resources is to provide by the vegetable oils in different materials and their used to prepare of polymeric materials. Most of the contents are differentiate in different sections involved oils to be cross-linked by various parameters such as vinyl monomers, metal-catalysed reactions, interpenetrating networks formation, epoxidized and castor oils of products to be polymerized. Several types of oils to be used in polymeric materials and related properties are stressed out to replaced it at partially based petroleum products. In this reviews, the wide range of scientifically patents have been used of oils for the production such as coatings, inks, plasticizers, lubricants and agrochemicals. Presently, we are used of oils is major sources of polymer materials but they do not lend of structures on that applications. The alkyd resins is used to prepared the polymers by the esterification process between poly based alcohols and acids from the renewable resources. Then, the vegetable oils are physical and chemical properties based on their distribution of fatty acid. The mechanically extracted the solvent should be achieved vegetable oils are isolated, then the generation of heat in terms of mechanical is to be negative effect the proteins. But, there is a great advantageous in this mechanical isolation process consisting the low cost, low investment and safety views for environmental prospects. The solvent extraction is important role diffusion of solvent due to the use of organic solvents such as hexane. The main parameters of this process are solvent diffusion rate in polymeric materials such as body oils but they are more efficient than mechanical in this process. In this process, there are various parameters have been used to prepared polymers from vegetable oils are cross-linked by some various techniques and resulting the products is to modified epoxidized oils and polymerization. However, in this research paper are focussed on this synthesization of polymers bio-renewable resources. The chemical modifications in this process are to enhance the specific flow in variety of oils [9].
3 Conclusion In this review, we are main focussed on that to enhance and improve the efficiency of environmental conditions. Last twenty years, the main role plays in industrial productions of bio-catalysts from biomass. This research on discussed was used for various types of fermentation process for the productions of new polymeric materials. All types of technologies were used in industrial sector for better production to make new bio-polymer materials. Then, the main sources of raw materials from the renewable resources but in previous scenario, we have been used the products based on petroleum feedstock’s. The petroleum feedstock’s is very effectively on environmental conditions due to harmful components to generate by the petrochemical sources. However, this resources based on petroleum products is to be replaced by the use of some techniques were present in our bio-renewable resources. In this scenario,
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the mature or developed technology will be introduced in our industrial applications such as cell immobilizations, carboxylic acid, cell fermentation, fungal production and water–oil cultivation. All such types of techniques will be used to reduce the pollutions and improved the better environmental aspects. Most of the fermentation process in various kinds will also helpful for production of bio-polymer products and chemical agents over microbial micro-organisms.
References 1. Wang, P.: Nonconventional Biocatalysis for Production of Chemicals and Polymers from Biomass. In: Yang, S.-T. (ed.), Bioprocessing for Value-Added Products from Renewable Resources, © 2007, pp. 325–350. Elsevier, B.V., Wang, P. (2007). https://doi.org/10.1016/b978044452114-9/50013-x 2. Ju, L.-K.: Water-in-oil cultivation technology for viscous xanthan gum fermentation. In: Yang, S.T. (ed.), Bioprocessing for Value-Added Products from Renewable Resources © 2007, pp. 397– 419. Elsevier B.V., Ju, L.-K. (2007). https://doi.org/10.1016/b978-044452114-9/50016-5 3. Yang, S.-T., et. al.: Extractive Fermentation for the Production of Carboxylic Acids. In: Yang , S.-T. (ed.), Bioprocessing for Value-Added Products from Renewable Resources © 2007, pp. 421–446. Elsevier B.V. (2007). https://doi.org/10.1016/b978-044452114-9/50017-7 4. Shu, C.-H.: Fungal fermentation for medicinal products. In: Yang, S.-T. (ed.), Bioprocessing for Value-Added Products from Renewable Resources © 2007, pp. 447–463. Elsevier B.V., (2007). https://doi.org/10.1016/b978-044452114-9/50018-9 5. Wang, L., Yang, S.-T.: Solid state fermentation and its applications. In: Yang, S.-T. (ed.) Bioprocessing for value-added products from renewable resources, pp. 465–489. Elsevier B.V. (2007). https://doi.org/10.1016/b978-044452114-9/50019-0 6. Wang, P., Jia, H.: Power-generation from biorenewable resources: biocatalysis in biofuel cells. In: Yang, S.-T. (ed.) Bioprocessing for Value-Added Products from Renewable Resources © 2007, pp. 507–525, Elsevier B.V. (2007). https://doi.org/10.1016/b978-044452114-9/50021-9 7. Xu, Z.: Biological production of hydrogen from renewable resources. Yang, S.-T. (ed.) Bioprocessing for value-added products from renewable resources, pp. 527–557. Elsevier, B.V., https:// doi.org/10.1016/b978-044452114-9/50022-0 8. Tao, B.Y.: Industrial applications for plant oils and lipids. Yang, S.-T. (ed.) Bioprocessing for Value-Added Products from Renewable Resources © 2007, pp. 611–627. Elsevier B.V. (2007) 9. Naceur Belgacem, M., et al.: Materials from vegetable oils: major sources, properties and applications. Monomers, Polymers and Composites from Renewable Resources, pp. 39–66. https:// doi.org/10.1016/b978-0-08-045316-3.00003-x
Design of a Stand-Alone PV Powered Greenhouse Equipped with Distributed Evaporative Cooling Debajit Misra
Abstract This work delineates a design study for a stand-alone greenhouse system suitable for hot climatic regions. The greenhouse has been powered by PV and battery backup. This study is an extension of earlier research work where a detail thermal model of a ridge ventilated, distributed evaporative cooled greenhouse system had been illustrated. In this study, the sizing of the cooling system components like pump, sump, and forced-draft (FD) fan are suitably presented. Finally, a design strategy has been taken to run the cooling system components. It is found that to run 180 m2 greenhouse satisfactorily two sumps of 331.5 L each, two pumps 225 W each, six fans 112 W each and cooling pad having 18 m × 0.6 m are needed. The results depicts that a 5.4 kW PV array capacity of 18 modules, 28 (12 V, 200 Ah) batteries, 7.5 KVA, 48 V inverter, and 80 A, 24 V charge controller are required for supplying power to the electrical load of the greenhouse. Keywords Greenhouse · Evaporative cooling · PV powered · Ventilation
1 Introduction In hot and near-tropical regions, it is difficult to cultivate commercial plants in open field due to extreme climatic conditions. Most of the commercially cultivated plant species need protected environment to meet precise quality requirements. Greenhouse cultivation is one of the most appropriate methods in this context. It is an alternative production system involving artificial ventilation and cooling techniques to maintain greenhouse temperature within the required level. To sustain effective yield, evaporative cooling with mechanical ventilation systems such as fan-pad system have been mostly adopted. In the earlier work [1], a thermal model of a fan-pad ventilated greenhouse with longitudinally distributed evaporative cooling has been illustrated. This present paper revolves round the same greenhouse system as mentioned in earlier work [1]. However, some sizing features have been included. Kittas et al. [2] D. Misra (B) Department of Mechanical Engineering, Techno India Group, Kolkata, West Bengal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_18
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presented a work showing sensible and latent heat variation in a large evaporatively cooled greenhouse. They proposed a thermal model which evaluated the greenhouse temperature profile along the length. Another experimental work by Kittas et al. [3] showed that a large temperature variation (up to 8 °C) occurred between the pad and the fans due to the large length of the greenhouse (60 m) where rose plants were cultivated. In their experiment, Kittas et al. [4] found that air temperature inside the greenhouse was very much related to the ventilation rate and incident solar irradiation. Ganguly and Ghosh [5] presented a theoretical model to predict greenhouse temperature of a fan and pad type evaporatively cooled greenhouse. They also showed the greenhouse temperature was varied on the increment or decrement of shading and ventilation rate. Willits [6] conducted a modeling study considering to fan-ventilation as well as fan-pad ventilation for the same greenhouse. He took 14 days data for model validation and found a good agreement between measured and predicted results. Fuchs et al. [7] presented a strategy to estimate the latent heat cooling occurred due to plants’ transpiration and free water evaporation for a fan-pad greenhouse. It was noted that greenhouse cover material and ventilation rate play a significant role for effective cultivation of rose crops in summer. Misra and Ghosh [8] presented a performance study of a ridge ventilated greenhouse surrounded by shallow water bodies. They observed that greenhouse temperature could be maintained 3–4 °C lower than the ambient while saturation effectiveness remains 70%. In another work, Misra and Ghosh [9] carried out comprehensive evaporative cooling review studies which were applied for greenhouse cooling in hot climatic regions. After studying numerous research works they noticed some innovative cooling techniques where further research could be possible. Again, Misra and Ghosh [10] demonstrated a circular greenhouse, fitted with a 10 m high solar chimney. The greenhouse has been evaporatively cooled with fog nozzles placed below the canopy. They found that implementing a fogging cycle of 1.5 min spray time and 2 min spray interval, greenhouse temperature could be maintained 4–6 °C below the ambient temperature. It also showed that such kind of solar chimney greenhouse could provide 6–7.5 ACM during peak sunshine hours of a day in a hot summer month in India. Powering of greenhouse system has been suitably designed by Ganguly et al. [11]. They considered solar photovoltaic (PV) panel, electrolyzer, and fuel cell for the power management of the greenhouse. A yearlong energy generation and consumption data have been presented in their study to check its viability. Greenhouse microclimate in a hot and near-tropical region primarily depends on its design and cooling method applied to it. Generally, fan-pad greenhouse system is employed for hot climatic regions where FD fans are used to draw evaporatively cooled air via cooling pads. Very often cooling pads and fans are fitted on two distinct walls along the length of a greenhouse. Fans draw air through cooling pads and exclude at the exit which creates a temperature gradient along the length of the greenhouse. Thus, it is difficult to make long fan-pad greenhouse. Here, cooling pads are placed along the length of the greenhouse and fans are fitted on ridge (Fig. 1). The ambient air flows through the evaporative cooling pads and ventilates out through the roof. Thus, in this greenhouse system cooling can be maintained along the length of the greenhouse. A thermal model of such a greenhouse has been
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Shade net Cellulose pad Pump
Sump
Soil
Fig. 1 The general arrangement of the greenhouse cooling and ventilation system
developed and analyzed earlier [1]. In this work, the same greenhouse (30 m × 6 m) has been considered which is a stand-alone PV powered. In this work, cooling system components and PV power system components are suitably designed.
2 Sizing of Cooling System Components In this cooling system, air is drawn through wetted pad at the inlet of the ventilation system. In the earlier paper, it was mentioned that pads were longitudinally placed and fans were mounted on roof [1]. ID fans are provided for sufficient air flow across the surface area within the pad, in an evaporative cooling sufficient make up water must be provided by a reliable pump to maintained cold environment. Circulating water pump delivers require makeup water from the sump. A properly designed cooling system of a greenhouse can effectively cool the inner microclimate and minimize water losses. The main cooling system components of the greenhouse are cooling pad, ID fan, pump, and sump.
2.1 Cooling Pad In evaporative cooling system, outside air is drawn into the greenhouse through wet vertical pads. In the present greenhouse, 100 mm thick cross fluted cellulose pads are considered. To determined pad area, air ventilation through the greenhouse by ID fan has to be determined. In general, air flow rate per square meter of pad area for 100 mm thick cellulose pad is considered 77 m3 /min to design pad area [15]. In
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the present case, it is considered that air flow rate per square meter of pad area is 70 m3 /min.
2.2 Pump and Sump The pumps are sized for the system to supply water at least 6.2 L of water per minute per linear of pad system [14, 15]. The sump should be sized to have working capacity of at 30.56 L per square meter of pad area for cellulose pad to store water [14, 15]. The sump volumes are designed to operate at half the depth of the tank and will be provided room to accommodate water returning from the pad when the system is turned off [16]. The power requirement of circulating water pump can be evaluated knowing volume flow rate of water (Q pump ) and pressure drop to drive the pump (Ppump ). It is given by Ppump =
Q pump × Ppump ηpump
(1)
2.3 Fan Fans are provided for sufficient ventilation through the greenhouse as air is receiving heat by solar radiation. Fans are rated in terms of the volume of air flow per minute. Fan is sized at a static pressure in between 2.54 and 3.81 mm of water column to account for fan accessories and inlet restrictions [15, 16]. The static pressure loss depends upon the flow passage. As per design consideration of the greenhouse, here, it is considered 3.81 mm of water as fan static pressure and air change per minute (ACM) is 1.2, to provide sufficient ventilation. The power required for induced draft fan (Pfan ) can be determined by Pfan =
(V × ACM) × pfan 60 × ηfan
(2)
where, pfan is the pressure variation across the ID fans, ACM means of air change in a minute, ηfan is the efficiency of fans and V is the greenhouse volume (Table 1).
3 Sizing of Power System Components In this power system, PV array, charge controller, battery, and inverter are the main components. PV array receives solar energy and convert it into DC current which
Design of a Stand-Alone PV Powered … Table 1 Input parameters of the greenhouse cooling system components
201
Items
Values
Greenhouse volume
630 m3
Pad dimension
1500 mm × 600 mm × 100 mm
Pressure drop at pump
1 bar [11]
Drive efficiency of pump
0.75 [11]
Drive efficiency of fan
0.75 [11]
is supplied through inverter for converting AC current to run the greenhouse loads. The surplus DC current is supplied to the battery bank for future power backup. A charge controller is also incorporated to manage or regulate current which is sent to the battery as well as it prevents overcharging. A schematic is presented in Fig. 2 to show the detail of the power system. The PV power system design is based on incident solar irradiation and load requirement of the greenhouse. For load evaluation summer season has been considered when greenhouse requires the maximum numbers of connected loads to cool the inside air. Table 2 shows the appliances and their operating hours run in summer. The power or capacities of the individual appliances have been estimated using relevant equations and data, presented earlier.
DC
DC
Charge controller
Batteries
DC PV array Inverter
AC Greenhouse
Fig. 2 Schematic of the greenhouse PV power system
Table 2 Designed load and duty hours of the greenhouse electrical appliances
Appliances
Number
Duty hours
Pump
2
12
Fan
6
10
8 W LED light
10
10
202 7
Globalsolarradiation(KWh/m /day)
6
2
5 4 3 2 1
December
October
November
September
August
July
June
May
April
March
February
0
January
Fig. 3 Global solar radiation data for different months of the site [18]
D. Misra
An assessment of the solar radiation is the first criterion for initiating a plant related to any solar project. In the proposed site (Kolkata), annual average global solar radiation on a horizontal surface is 5.26 KWh/m2 /day [18]. Solar radiation is the minimum in December and February to June is the high solar radiation months and remaining months get almost average solar radiation. Figure 3 shows the solar radiation data for the site. In the proposed site, solar radiation is within acceptable range to run the greenhouse throughout the year.
3.1 Sizing of PV Array The power and working hours of individual loads (fans, pump, and light) are selected to find the total energy requirement in Ah which can be estimated by n It =
L i × n i × ti Vs × P.F
i=1
(3)
where, I t is the cumulative load current (Ah). ‘L’, ‘t’, ‘V s ’, ‘P.F’ are the load (W), the operating hour, the nominal system voltage and the power factor respectively. The inverter current (I I ) can be expressed by II =
IL ηinv × ηwire
(4)
The module/array current (I p ) can be given by Ip =
II tpeak × D f × ηcharge
(5)
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where, ‘Df ’ and t peak are the de-rating factor and the maximum time period of the peak sun in hours, respectively. For location of Kolkata its value is 6 [17]. The number of parallelly connected module can be given by np =
Ip Imp
(6)
where, I mp the maximum current of the module. The module in series can be evaluated by ns =
Vs Vmp
(7)
where, V mp is the maximum module voltage. The total connected modules in an array can be given by n m total = n s × n p
(8)
The PV system can be designed based on the maximum operating load for the greenhouse appliances as shown in Table 2. Table 3 shows some input parameters which are related to evaluation process of the PV array. Table 3 Brief of parameters to size PV array Particulars
Description
Value
SPV module (CEL –PM 300) [19]
Current at maximum power (I mp )
8.05 A
The maximum voltage (V mp )
37.3 V
Short circuit current (I sc )
8.65 A
Open circuit voltage (V oc )
45 V
Total cells in a module
72
Area of module
1965 × 990 mm2
Module efficiency
17.4%
Power factor (P.F) [12]
0.85
De-rating factor (D.F) [12]
0.9
Inverter efficiency [12]
0.88
Charge controller efficiency [12]
0.94
System voltage (V s )
48 V
Other particulars
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3.2 Sizing of Battery Bank In a battery bank, some batteries are connected in series and some batteries are connected in parallel to maintain a nominal system voltage. It is designed such that it could serve power during off sunshine or cloudy days (DoA). The total battery capacity (I bt ) can be evaluated by Ibt =
It × DoA DoD × ηb
(9)
where, ‘DoD’ is the depth of dischrage. ‘DoA’ is the days of autonomy and it is considered to be ‘3’ in Kolkata. The total parallel (N Bp ) connected batteries can be given by n Bp =
Ibt Ibh
(10)
where, I bh is the capacity of a single battery (Ah). The total series connected batteries are given by n Bs =
Vs Vb
(11)
where, V b is voltage in a battery. The total numbers of batteries in a bank is given by n Btotal = n Bs × n Bp
(12)
3.3 Sizing of Inverter Inverter can be designed either by the total load or by the total power generation through the PV array (PA ). A safe factor has often been considered to design it. The total inverter capacity (KVAin ) can be expresses by KVAin = (PA × 1.3)
(13)
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3.4 Sizing of Charge Controller Charge controller is always designed in such a way so that it could protect the short circuit current flow. It is evaluated by some suitable safe factor. Icc = 1.3 × Isc × n p
(14)
The total numbers of charge controllers is given by n cc =
Icc Iccsingle
(15)
where, I cc is the rating of charge controller (Ah). I sc is the PV module’s short circuit current. The data of Tables 3 and 4 are applied to evaluate the detail of power system appliances. The power generated by PV array is estimated using solar radiation (KWh/m2 day) data falling on PV array. It can be evaluated by the following equation E p = (PR × A p × n m total ) × (ηinv × ηwire × η p ) × H
(16)
where, PR is the performance ratio of PV system and its value has been considered as 0.78. ‘Ap ’, is the area of one module and ‘H’ is the incident of the solar radiation (KWh/m2 day).
4 Results and Discussions The total load estimated for the greenhouse power system design is estimated to be 12,920 Wh. The load estimation is based on the power consumption of the greenhouse electrical appliances on a peak summer day. In this section, the detail of the cooling system components are presented. Table 5 shows the required capacities of sump, pump, and fans to maintain proper cooling effect on greenhouse microclimate. Here, it is considered to be cooling pads dimension is 1500 mm × 600 mm × 100 mm which is available in market. It is found to be 10.8 m2 cooling pads are required for the greenhouse. Thus, pads can be equipped with six bay of the greenhouse maintaining Table 4 Brief of parameters for sizing battery, inverter, and charge controller Battery Voltage Current DoD Efficiency
Inverter 12 V 200 Ah 0.8 90%
7.5 KVA Efficiency
Charge controller 88–90%
MPPT type Current Voltage
40 A 48 V
206 Table 5 Summary of cooling system components capacities
D. Misra Items Pad
Pump
Sump Fan
Table 6 Summary of power system components capacities
values Area
10.8 m2
Height
0.6 m
Length
18 m
Total numbers
2
Discharge capacity
111.6 L/min each
Power
250 W each
Numbers
2
Capacity
331.5 L each
Discharge capacity
756 m3 /min
Numbers
6
Power
112 W each
Items
Description
Results
Load
Total estimated load
12,920 Wh
PV array
Capacity of PV array
5.4 KW
Numbers of PV modules in series
9
Numbers of PV modules in parallel
2
Battery bank
Total PV modules
18
Total capacity
5600 Ah
Numbers of batteries in series
7
Numbers of batteries in parallel
4
Total numbers of batteries
28
Charge controller
Total capacity
80 A
Total numbers
2
Inverter
Total capacity
7.5
Numbers of inverters
1
suitable gaps. It is also found that sump capacity should be 331.5 L with 250 W each pump capacity. Fans are also equipped on the ridge of the greenhouse in six bays to extract greenhouse air. 112 W each capacity would be required to maintain air circulation through the pads. Table 6 is the brief presentation of power system appliances used for the greenhouse. It is evaluated that 5.4 KW capacity would be require to supply the required power for the greenhouse when 300 Wp PV module is considered. 28 batteries are required for the battery bank. Inverter capacity must be 7.5 KVA if it imply singly.
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33
Load Generation
30 27
Energy (KWh)
24 21 18 15 12 9 6 3
December
November
October
September
August
July
June
May
April
March
February
January
0
Fig. 4 Daily average PV generation and load of the greenhouse for different months
Two 40 A charge controller are needed to regulate current flow through the electric circuit. The power estimation has been done using the Matlab software. Design strategy for the stand-alone PV array has been considered to meet 5.4 kWp power demand. Figure 4 shows greenhouse load and PV generation throughout the year. It is observed that surplus PV generation can be stored into battery bank as there are always excess generations. Thus, if there could be some worse days or non-sunny days battery bank could serve the purpose to meet the energy demand.
5 Conclusion The present study analyzes a stand-alone PV powered greenhouse for the fulfillment of proper cooling effect. The study deals with a strategy for the designing of cooling system components and power system equipments in a 180 m2 greenhouse located in a hot climatic area. The greenhouse cooling components and their capacities were suitably evaluated. The energy demand or load requirement for the greenhouse has also been estimated based on the cooling system components. The design strategy suggests that the greenhouse could meet suitable inside microclimate for the year round by providing power support through the stand-alone PV system.
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Development of a Regression Model Through Variational Mode Decomposition for the Remaining Useful Life Assessment of a Gear Box Joshuva Arockia Dhanraj, Christu Paul Ramaian, Jenoris Muthiya Solomon, Nandakumar Selvaraju, Mohankumar Subramaniam, and Meenakshi Prabhakar Abstract The gear trains were used as power transmission system in machines. Because of wear and tear operation, the gearbox gets defective and produce sounds and make the machine to vibrate. As wear and tear builds, the level of vibration and sounds additionally expands which makes the gears unfit for specific applications. Regularly, it is important to know the amount of time the gear can be utilized before it is replaced with new one. This paper makes use of the same ideas for remaining useful time of gears by building regression models. A set of examinations had been conducted to capture the vibration signals of gears at different stages. VMD is being utilized as an evaluation pre-treating technique. They have information on the statistical characteristics, and using the decision tree algorithm, J48 is chosen as the best feature. With the functionality selected, different regression models have been built and their performances were compared and found that for normalized, linear regression provided the maximum regression value of 0.9985 when compared to the discretized RBF of 0.8873. Keywords Fault diagnosis · Lifetime · Assessment · Gearbox · Vibration signals · Variational mode decomposition (VMD) · Statistical features · J48 algorithm · Multiple regression
J. Arockia Dhanraj (B) · M. Prabhakar Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India C. P. Ramaian · N. Selvaraju Department of Automobile Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India J. M. Solomon Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India M. Subramaniam Department of Automobile Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu 641049, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_19
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1 Introduction In power drive devices, gearbox is often used principally to generate speeds and transmission torques from spinning power sources into another system using equipment and gear trains. The faults are occurred on the tooth surface which it leads to machine failure. With the aid of condition detection, users can detect the life span of a gearbox. This lifespan forecast is used to repair the gearbox until it is wearing out, avoiding machinery damage [1]. Status monitoring is the method of examining a system status parameter such that an essential adjustment is recognized which indicates an inadequate development. It is an integral part of its prestigious aid. The use of stringent observation permits maintenance or other measures to be taken to deter and to prevent loss. Conditional control has a particular benefit in the situations where normal lifetime can be shortened until it forms into a big failure [2]. The primary state observing procedures are connected in the mechanical and transportation segments. They are vibration, oil investigation, AE, x-ray, etc. From this, we can analyse the fault in the rotating parts in the machine. Condition monitoring helps to predict the assessment where it can able to identify the exact fault which happened in the rotating part. This is one of the efficient ways to capture the faults in machines [3]. So many researchers researched error diagnostic equipment by pattern detection was used to decode vibration signals. Classification and regression are two main data collection techniques used in the failure process of diagnosis for the identification of flaws and their patterns. Regression analysis is a mathematical method used to approximate the patterns in the data. However, in this technique, it has more uniform convergence than that of classification, and hence, regression had been chosen. Multiple regressions are intended specifically to analyse the relationship between a metric-based and an actual metric or dichotomous. Regression has been commonly employed for contrast and recommendation for the optimum model in the literature analysis to extract machine-part errors. Pan et al. [4] proposed a work on sparse nonlinear fashion and its use in the diagnosis of planetary gearbox faults. This protocol suggests a new signal breakdown algorithm, NSMD. The fundamental of this NSMD is that under the operation of the single local linear operator, a local narrow-band signal is eliminated, so the single local linear operator is able to extract the local narrow-band portion of the signal being observed. Yu et al. [5] carried out a work on model analysis and analysis of signature signals for planetary gearbox error diagnosis in the resonance area. In leveraging the existence of the resonance independence of running conditions, an online method of resonance frequency detection is suggested with time varies. The IGD approach is used to efficiently classify time-variating side bands of the time–frequency domain to achieve high time–frequency resolution and to eliminate both internal and external interferences simultaneously. Via numerical simulation and experimental studies, the theoretical derivations and proposed approach are tested. Chen et al. suggested a dissertation on ASANN model for the diagnosis of planetary gearbox faults [6]. The ASANN model is proposed within instantaneous speed and offers an end-to-end learning mode with spinning speed information guidance.
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The proposed ASANN model allows the remarkable detection capability of the planetary gearbox defects under different operating procedures with instantaneous rotating speed information. An empirical analysis of the fault diagnosis in a planetary gearbox validates the validity of the ASANN model. Feng et al. [7] proposed a work on rotary encoder signal processing for planetary gearbox fault detection. They proposed to examine repeated time interval reciprocal order continuum in the angle domain between neighbouring encoder pulses. In particular, the task of diagnosing a planetary gearbox fault at varying speeds is evident from the computational complexity of instantaneous rated velocity or acceleration equations and addressed. It is substantiated under constant and time-varying conditions by experimental studies with a planetary gearbox. Sun, earth, and ring gear localized faults have all diagnosed successfully. Many of the works referred to above are based on analytical and theoretical modelling. The vibration signals were used by means of a criterion aimed at the lifetime measurement of the gearbox in this article. Statistical attributes have been used here. VMD is being utilized as an evaluation pre-treating technique for the assessment, and then, they serve as inputs for the statistical features. The good and various defective conditions with the assistance of an accelerometer take vibration signals. By analysing these set of signals, statistical analysis is carried out. For the functional selection method, J48 was used. Then, for regression analysis, multiple regressions were used. The techniques of the current work are shown in Fig. 1. The output of the research is • The successful and faulty gearbox with the aid of a microphone was taken from vibration signals in this article. • VMD was used as a predictive element pre-processing tool and a decision tree algorithm for J48 function selection was used. • The findings were evaluated by multiple regression. Different models of regression, and for the lifetime estimation of gearbox, the best one has been proposed.
2 Experimental Studies The experiment was conducted on a Triumph Herald laboratory setup (Fig. 2). The system has a stock 4-cylinder 948cc OHV SC motor with a 4-speed manual gear box and was used with synchronous shifting on top three gears. The vibration signals were acquired using a single-electric piezoelectric accelerometer (KISTLER 8702B50). Accelerometer was mounted near to the stationary place on top of the gearbox. The accelerometer’s output is wired to the NI9233 DAQ signal conditioning circuit with NI-USB-9162 hi-speed USB carrier, which transforms the signal from analogue to digital (ADC) and integrates anti-aliasing filters. The digitized vibratory signal is saved on the computer (in the time domain). The gearbox was made to run in the laboratory in the top gear to obtain the vibration of the gearbox. For the lifetime estimate, the gearbox is scheduled to run for 900 h, i.e. each day, nearly for 10 h, the setup is made to run to find the life assessment.
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Fig. 1 Methodology flowchart
3 Pre-processing Using VMD In the late 90s, Huang implemented the transformation from Hilbert–Huang, also known as the decomposition of empirical mode [8]. While there is a detailed computational model relating to this algorithm, today’s HHT/EMD algorithm is commonly used, and thus, the reliability of each variable and interrupting points are normally black. A few limitations are very obvious, but the algorithm is noise-and-samplessensitive. Consequently, EMD has difficulty in distinguishing relative intensity tones.
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Fig. 2 Experimental setup
There were a number of more numerical attempts, including synchronization, analytical waves and recursive variational degradation, into smooth signals and residues, to address this problem of decomposition. The vector model is optimized successfully by an alternative solution to the multiplier approach. The preliminary results demonstrate excellent performance with respect to existing modes of decomposition. In particular, a basic convergence tolerance condition is applicable for reconstruction of single harmonics irrespective of frequency and precision monitored. The output of the VMD has noted to be 95.556% which is used as the input to the statistical analysis where the feature extraction was carried out.
4 Feature Selection via J48 Statistical information [9] has many combinations of primary as well as secondary parameters. The statistical features that are included in the research are summarized as below: • Standard deviation:
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This is an important energy indicator or vibration signal strength content, suggesting a simple reduction in the state of the gear. For standard deviation estimation, the following formula has been used n x 2 − ( x)2 S= n(n − 1)
(1)
• Variance: The variance is a statistical measurable value of how broadly individuals in a category differ. The variation is good and vice versa where the individual findings differ considerably from the average population. It is also derived from the square of standard deviation (SD). S = 2
X−X N −1
2 (2)
• Kurtosis: “The term kurtosis is defined as spikiness or flatness of the signal”. The good performance signals are having very low kurtosis. K =
xi − x 4 n(n + 1) 3(n − 1)2 − Sd (n − 1)(n − 2)(n − 3) (n − 2)(n − 3)
(3)
• Mean: The phrases arithmetic, statistical and occasionally average are used to denote a central value for a discrete number set; in particular, the absolute value distributed by the possible values. mean =
n 1 ai n i=0
(4)
5 Feature Selection The algorithm is a tree consisting of a cross, multifaceted root [10]. Statistical characteristics suggest the algorithm that effectively creates the decision tree output as seen in Fig. 3. The decision tree displays the descending order characteristics of the gearbox with respect to the hours of run. This is the core function of the tree top node in the
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Fig. 3 Decision tree for statistical features
feature selection process. Table 1 shows the confusion matrix for the pre-processing technique where it is obtained for the VMD using MATLAB coding.
6 Multiple Regression Regression analysis is a mathematical method in the mathematics to approximate the relationships between variables. It depends on the treatment of a related variable with single or multiple different variables. The usefulness of regression evaluation techniques is the application be contingent to the output of the information and the implementation of the regression methodology. The effects of different describing variables on a Y response vector are concurrently considered E(Y ) = α + β1 X 1 + · · · + βk X k
(5)
where α = E(Y) when X 1 = X 2 = … = X k = 0. When a correlation happens, the calculation of the dependent variable values improves by using the knowledge in each of the variables. The above are the different ways of regression. Gaussian processes (GP): This framework is composed of times or ranges associated with random variables, which usually disperse any such random variable [11]. They are easy to use, scalable and fully probabilistic models
E exp i
k l=1
tl X tl
⎛ = exp⎝−
1 2
l, j
σl j tl t j + i
l
⎞ μl tl ⎠
(6)
AA (100 h)
10
1
0
0
0
0
0
0
0
Hours
AA
BB
CC
DD
EE
FF
GG
HH
II
0
0
0
0
0
0
0
9
0
BB (200 h)
Table 1 Confusion matrix of VMD
0
0
0
0
1
0
10
0
0
CC (300 h)
0
0
0
0
0
10
0
0
0
DD (400 h)
0
0
0
0
9
0
0
0
0
EE (500 h)
0
0
0
9
0
0
0
0
0
FF (600 h)
0
0
10
1
0
0
0
0
0
GG (700 h)
1
10
0
0
0
0
0
0
0
HH (800 h)
9
0
0
0
0
0
0
0
0
II (900 h)
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Least median squares (LMS): By solving the nonlinear minimization problem, they calculate the parameters. The optimization problem appears to be more systematic like min SSR = (Yi − (b0 + b1 X i ))2 b0 b1 n
(7)
i=1
Interception and sloping factors are conveniently measured using computational solutions. “Legendre named the least squares system, and the numbers have become a cornerstone. But this estimator is being increasingly criticized for its drastic lack of robustness considering its mathematical elegance and numerical simplicity”— Rousseeuw (1984) [12]. Isotonic regression (IR): Isotonic regression [13] under the L p for p > 0 is defined as min
n
|xi − ai | p
(8)
i=1
Pace regression (PR): When the number of coefficients is infinitely large, it is demonstrably optimum. It consists of a group of estimators which, under some conditions, is either overall optimal or optimal [14]. θiEB =
∫ θ f (xi |θ )dG k (θ ) ∫ f (xi |θ)dG k (θ )
(9)
Linear regression (LR): Data are modelled with vector predictor functions in linear regression and data projections of uncertain model parameters [15]. These models are referred to as linear models. yi = β1 xi1 + · · · + β p xi p + εi = X iT β + εi , i = 1, . . . .n
(10)
Radial basis function (RBF) network: It is a real value function which only influences on range from some other point or distance, which is called centre [16] y(X ) =
N
ωi ∅(X − X i )
(11)
i=1
Multilayer perceptron (MLP): It is a neuron linkage known as the perceptron. In 1958, Rosenblatt proposed the fundamental definition of a single perceptron [17]. The perceptron computes a single output by generating a linear combination based on its input weight from many inputs, which can then be used to trigger the output.
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y=ϕ
n
ωi xi + b = ϕ ω T x + b
(12)
i=1
Simple linear regression (SLR): A simple straight line regression matches the set of n points to the same degree as the sum of the square residue of the formula [18]. y = α + βx
(13)
Sequential minimal optimization (SMO) regression: The regression utilizing SVM and parameters can be calculated for different classifiers [19]. f (x, α, β) =
l
yi αi K (xi , x) + b
(14)
i=1
7 Results and Discussion In the current gearbox research, lifetime forecast was conducted utilizing the aid of different regression which was discussed in session 6 [20–24]. As explained in Sects. 3, 4 and 5, the statistical abstraction and important feature identification was conducted utilizing VMD as the pre-processing technique [25–28]. For important feature identification, J48 was utilized. The collection of features reduces the features and parameters which contribute to the easy diagnosis of the gearbox by a systematic strategy [29–33]. The following paragraph shows the regression analysis has been carried out by multiple regression. For the multiple regression process, both the normalization and the discretization are carried out where the output can be analysed for different modes in the regression model [34–36]. In Table 2, the different regression models give different correlation coefficient, but linear regression gives more about 0.9985 when compared to other model. Similarly from Table 3, radial basis function (RBF) gives the maximum correlation coefficient of 0.8873 when compared to other regression models [37]. The RMS error for RBF was 218.102, and 132.48 was the actual mean error. The comparative chart of both normalized and discretized is shown in Fig. 4 [38].
8 Conclusion From Tables 2 and 3, we can find different type of outputs from different models. For the lifetime prediction, normalized is preferred than that of discretized. The linear regression has the highest correlation coefficient of 0.9985 which is nearer to one.
Development of a Regression Model Through … Table 2 Regression table (Normalized)
Normalized
219 Correlation coefficient (CC)
GP
0.4519
SMO
0.8483
MLP
0.3683
IR
0.7854
RBF
0.8962
SLR
0.7274
LMS
0.6845
LR
0.9985
PR
0.7765
The bold indicates the maximum correlation coefficient in normalized and indiscretized mode
Table 3 Regression table (Discretized)
Discretized
Correlation coefficient (CC)
GP
0.4125
IR
0.7035
LMS
0.5586
LR
0.8723
MLP
0.3425
PR
0.7442
RBF
0.8873
SLR
0.7018
SMO
0.8364
The bold indicates the maximum correlation coefficient in normalized and indiscretized mode
The absolute error and RMS error is very low, and the optimal formula for estimation concerns is where the CC is nearest to 1. From this, one can be inferred that linear regression can estimate the gearbox residual life evaluation in contrast with various regression classifiers.
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Fig. 4 Normalized versus Discretized
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Influence of Successive Annealing on Mechanical and Wear Behavior of RCS-Processed Al2024 Alloy Y. J. Manjunath, H. P. Thirthaprasada, A. Chandrashekar, and M. C. Manjunath
Abstract In this work, mechanical characterization of Al2024 alloy subjected to repetitive corrugation and straightening (RCS) process has been carried out under the conditions of before and after annealing process also wear behavior of Al2024 alloy processed with optimized RCS parameters in annealed conditions was investigated. L9 orthogonal array was considered for selection of processing parameters to study the ultimate tensile strength. Specific wear rate was characterized in a pin-on disc tribotester under the varying conditions of sliding distance, load and sliding velocity. Results reveal that, ultimate tensile strength (UTS) of RCSed specimens at room temperature yields higher strength than the values under annealed conditions. It was observed that, the ultimate tensile strength of RCSed specimens are subsequently reduces an average of 5.38%, 11.84% and 25.46% under the treatment of subsequent annealing at 150 °C, 250 °C and 350 °C, respectively. Higher values of sliding velocity, applied load and sliding distance exhibit higher wear rate under all experimental conditions. Keywords Repetitive corrugation and straightening · Annealing · Tensile strength · Wear characterization
1 Introduction Severe plastic deformation (SPD) is a metal working process in which hydrostatic pressure is applied in higher level to achieve higher strain on a bulk metal without undergoing any change in the dimensions of the sample significantly along with an ability to achieve an excellent grain refinement [1]. SPD process alters the structural changes, which reflected in enhancement of mechanical properties [2]. The formation of structure is not only determined by the material itself, also by the working Y. J. Manjunath (B) · A. Chandrashekar · M. C. Manjunath Bangalore Institute of Technology, Bengaluru 560004, India H. P. Thirthaprasada VTU - PG Studies, Muddenahalli, Chickballapur 562101, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_20
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condition like temperature, strain rate and hydrostatic pressure, etc. General observation depicts that the grain refinement in SPD is mainly contributed by decrement in temperature, pressure increment and an addition of alloying elements [3]. Out of all available SPD techniques, repetitive corrugation and straightening (RCS) technique as a new processing route is employed to form porous-free nanostructured materials without any contamination and can be easily used for industrial production in large scale [4]. RCS technique is executed in two consecutive steps, namely corrugation (deformation of the sheet through corrugated rollers) and straightening of corrugated sheets through straight rollers [5]. Many researchers have investigated various base metals and alloys using RCS process [6–10]. Pandey et al. [11] found significant increase in hardness, degree of homogeneity and additional strengthening in Al–Cu and Al–Cu–Sc alloys with perpendicular rotation after each pass. Manjunath et al. [12] noticed improvement in wear resistance with increase in amount of alloying elements and decrement with increasing values of wear parameters such as load and sliding velocity with equal channel angular-pressed Al–Zn–Mg cast alloy. Similar work was conducted on high pressure torsion (HPT) processed Al-1050 alloy [13] compared with as-received alloy. HPT causes reduction in resistance to wear in an alloy, which results to impair the strain-hardening capacity after severe plastic deformation processing [14]. Dipankar Dey et al. [15] depicted that an addition of SiC to Al2024 alloy leads to reduction in volume loss. However, large surface contact between sliding faces increases the wear rate unconditionally in spite of presence with SiC content. Ashwin et al. [16] have investigated the effect of process parameters like sliding distance, sliding velocity and applied load on wear properties of Al2024T351 alloy using Linear–Radial Basis Function model. It has been observed that, an increase in sliding velocity increases wear rate and also causes reduction in wear rate with improvement in sliding distance. Increase in wear rate with applied load has been observed up to certain extent and showing decreasing trend later. Mohamed Ibrahim et al. [17] have varied the wear parameters on load and sliding distance by analyzing the influence of wear resistance on ECAP-processed Al–Cu alloy and noticed remarkable wear loss with number of passes. Also, it has been noticed that the wear rate was highly influenced by sliding distance than the other parameters. Mohamed Ibrahim et al. [18] worked on high pressure torsion process for Al-7%Si alloy and found improved wear resistance due to refinement of grains with uniform distribution of Si particulates. Study clearly reveals that the transformation of wear mechanism from delamination, adhesive, oxidization wear and plastic deformation bands has attributed to combination of adhesive and abrasive wear after the HPT process on the work material. Available literatures reveal that the various SPD processes on aluminium-based alloys improve the structural and mechanical properties. It is worth to focus some research activities on service and multifunctional properties (Thermal stability, electrical conductivity, wear and corrosion resistance, etc.) of nanostructured aluminium alloys due to wide gap in fundamental understandings. The present investigation aims the effect of process parameters on RCS cycles and influence of successive annealing on mechanical properties and wear characteristics of Al 2024 sheets.
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2 Experimental Study 2.1 Materials In this research work, Aluminum alloy Al2024 sheets were used as starting material. The chemical composition of material used in the present study is as shown in Table 1. The sheets have been cut into strips according to the dimensions of 150 × 100 mm.
2.2 RCS Processing and Successive Annealing Sheets are subjected to repetitive corrugation and straightening (RCS) in two independent stages as shown in Fig. 1. Design of experiments was defined to perform continuous corrugation and straightening of a sheet and was done accordingly as shown in Fig. 2a–c. RCS was performed as per the design of experiments (DOE) [19]. Then the RCS-processed sheets are subjected to successive annealing considering different annealing temperatures such as 150, 250 and 350 °C for 30 min and furnace cooled to eliminate the work hardening of the material after cold working.
2.3 Characterization The tensile test samples were prepared through wire cutting EDM process from the RCS-processed specimens before and after tensile successive annealing along rolling direction, following ASTM-E8 standard. The samples of tensile test were similar to the shape of dog-bone shape in appearance with a gauge length of 25 mm and a width of 8 mm. The specimens for tensile test were conducted at room temperature as per DOE shown in Table 2 and trail runs of tensile test is in Table 3 on a universal testing machine (make: TNE 5000-UTM-50KN). The tensile test specimens before and after testing are as shown in Fig. 3. The total elongation of the samples resulted with difference in gage length under the conditions of before and after testing. An average of three tensile test samples was considered under each condition for ensuring the repeatability of the results. Dry sliding wear test was done on a sheet processed with optimized parameters based on the tensile strength using a Pin-on disc wear testing machine (make: Ducom, model: TR-20LE) as per process parameters and their levels are as shown in Table 4 and trail runs (L27 orthogonal array) in Table 5. Fractography and wear track evaluation were investigated by using scanning electron microscope (Model: VEGA3 TESCAN).
0.1
Max. 0.1
Cr
3.8–4.9
4.2
Cu 0.3 Max. 0.5
Fe 1.2–1.8
1.6
Mg
Bold row indictes maximum percentage of alloying elements in Al2024 alloy
Wt. % (As per ASM)
Wt. % (actual)
Element
Table 1 Chemical composition of Al2024 alloy in the current study
0.3–0.9
0.6
Mn 0.25 Max. 0.5
Si
0.10 Max. 0.15
Ti
0.10 Max. 0.25
Zn
90.7–94.7
Rest
Al
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Fig. 1 Schematic illustration showing RCS processing and successive annealing of sheets
Fig. 2 a RCS set up b sheet is passed between pair of gears to corrugate c corrugated sheet is passing through rollers for straightening Table 2 Levels for design of experiments S. No.
Parameters
Levels
1
Number of passes (n)
2
Thickness (t, mm)
1
1.5
2
3
Room temperature (T, °C)
27
27
27
−1
0
+1
2
4
6
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Table 3 Trial runs (L9 orthogonal array) for the tensile test
Trial run order
Thickness (t, mm)
Number of passes (n)
Temperature (°C)
1
1
2
27
2
1
4
27
3
1
6
27
4
1.5
2
27
5
1.5
4
27
6
1.5
6
27
7
2
2
27
8
2
4
27
9
2
6
27
Fig. 3 Tensile test specimen before and after testing
Table 4 Parameters and their levels for wear test S. No.
Parameters
Levels −1
0
+1
1
Sliding distance (m)
2000
4000
6000
2
Load (N)
1
1.5
2
3
Sliding velocity (m/s)
1.45
2
2.66
3 Results and Discussions 3.1 Tensile Strength The tensile strength was evaluated for all nine sheets processed by RCS with varying parameters as per design of experiments shown in Table 3. All the specimens, later, subjected to successive annealing as per the standard procedure as explained in
Influence of Successive Annealing on Mechanical … Table 5 Trial runs (L27 orthogonal array) for the wear test after annealing
229
Trail run order Sliding distance Load (N) Sliding velocity (m) (m/s) 1
2000
9.81
1.45
2
2000
9.81
2
3
2000
9.81
2.66
4
2000
14.715
1.45
5
2000
14.715
2
6
2000
14.715
2.66
7
2000
19.62
1.45
8
2000
19.62
2
9
2000
19.62
2.66
10
4000
9.81
1.45
11
4000
9.81
2
12
4000
9.81
2.66
13
4000
14.715
1.45
14
4000
14.715
2
15
4000
14.715
2.66
16
4000
19.62
1.45
17
4000
19.62
2
18
4000
19.62
2.66
19
6000
19.81
1.45
20
6000
19.81
2
21
6000
19.81
2.66
22
6000
14.715
1.45
23
6000
14.715
2
24
6000
14.715
2.66
25
6000
19.62
1.45
26
6000
19.62
2
27
6000
19.62
2.66
the Sect. 2. Ultimate tensile strengths of RCS processed sheets are tabulated in table 6. Results were compared with the ultimate tensile strength before annealing. Results revealed that the ultimate tensile strength is greatly influenced by the process parameters. Prior to annealing, the highest tensile strength of 145.25 MPa was noticed for specimen number 7 (t = 2 mm, n = 2 at room temperature) strength was increased of about 19% (118.07 MPa) in comparison with the lowest. The lowest strength of 118.07 MPa was observed for specimen number 2 (t = 1 mm, n = 4 at room temperature). At initial passes, generation of dislocation and accumulations of grains leads to increase in strength. As increased number of passes due to higher strain imparted and formation of stable sub-grains results improvement in strength without losing
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ductility. However, further increase in number of passes imparted larger strains in the material and sub-grains with low-angle boundaries convert into stable grains with high-angle boundaries due to which possibility of decrement in strength and hardness [20]. Similar results are also exhibit because of anisotropy in mechanical behavior in higher passes [21]. The ultra-fine grains and nanostructured materials processed under SPD process are resulting with high density, non-uniform distribution of dislocations and non-equilibrium character of high- and low-angle grain boundaries [22]. After successive annealing with 150 °C specimen, number 7 showed maximum UTS of 142 MPa in comparison with rest of the annealing temperatures. Here, due to annealing UTS at 150 °C (142 MPa) is decreased by a marginal amount of 2.25% compared to the UTS before annealing (145.25 MPa) and found decrement with further improvement in annealing temperature attributing to improvement in ductility because recovery and recrystallization phenomena in the material. Highest UTS was recorded for annealing temperature of 150 °C, sheet processed with parameters like thickness 2 mm and number of passes 2, in comparison with all the combinations processed during the study ( Fig. 4). 160
0
150 C
2
Ultimate Tensile Strength (N/mm )
0
250 C
140
0
350 C
120 100 80 60 40 20 0 1
2
3
4
5
6
7
8
Specimen Number
Fig. 4 UTS of RCS-processed sheets with reference to annealing temperatures
9
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231
Table 6 Experimental and predicted tensile strength values for RCSened sheets UTS at annealing temperature S. No.
Thickness (mm)
No. of passes
Temperature (°C)
UTS
150 °C
250 °C
350 °C
1
1
2
27
137.05
130
119
68
2
1
4
27
118.07
127
113
70
3
1
6
27
126.6
115
85
74
4
1.5
2
27
131.55
120
106
73
5
1.5
4
27
120.6
114
100
66
6
1.5
6
27
119.1
112
98
62
7
2
2
27
145.25
142
137
129
8
2
4
27
142.3
138
124
107
9
2
6
27
132.55
131
117
101
3.2 Fractography Figure 5a, b shows SEM images of the fractured surfaces of RCS-processed Al 2024 alloy under annealed conditions highest tensile strength (Number of passes = 2, thickness = 2 mm, and temperature = 150 °C) and lowest tensile strength (Number of passes = 6, thickness = 1.5 mm, and temperature = 350 °C) recorded during tensile test. The shear ductile fracture were seen in all the tensile specimens attributing for dimples and gray fibrous in appearance. The results were also found to be constant in comparison with other works [16]. An increase in number of passes attributes to tiny and elongated dimples showing that the cause for failure is shear ductile fracture. The shear stress affects the fracture tending to elongated voids by resulting with fracture surface in order to form either elliptical or parabolic depressions.
Fig. 5 SEM showing Fractograph of RCS-processed sheet under the conditions a t = 2 mm, N = 2 and T = 150 °C b t = 1.5 mm, N = 6 and T = 350 °C
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3.3 Wear Characteristics The mechanical characterization studies reveal that, repetitive corrugation and straightened sheet, processed under conditions t = 2 mm, n = 2 and T = 150 °C showed highest tensile strength (142 MPa). Based on the tensile test experimental results, sheet processed under the above-mentioned configuration was considered for wear studies. Total 27 samples were taken from the sheet processed under above conditions and subjected to wear test as per the design of experiments shown in Table 5. Dry sliding wear test of RCS-processed samples was performed on pin-on disc wear testing machine at room temperature, under different process parameters. Wear was continuously monitored by measuring the weight losses, wear and frictional force of the specimen. The results of specific wear rate are tabulated in all experimental trials shown in Table 7.
3.3.1
Influence of Load and Sliding Distance on Specific Wear Rate at Various Sliding Distances.
Figure 6 shows the change in specific wear rate under different loads with respect to change in sliding velocity. It is seen that specific wear rate substantially improves with an increase in sliding velocity and applied load in all sliding distance conditions. Among all the tested samples, specimen worn at sliding velocity of 1.45 m/s and axial load of 9.81 N causes the least specific wear rate at 6000 m sliding distance and specimen worn at applied load of 19.62 N and sliding velocity of 2.66 m/s exhibited high rate in wear at 4000 m sliding distance.
3.4 Worn Surface Morphology Figure 7 shows the worn surface morphology of the RCS-processed sheets showing highest and lowest specific wear rate, for a sliding distance of 4000 m under the applied load of 19.62 N and sliding velocity of 2.66 m/s, and sliding distance of 6000 m, applied load 9.81 N and sliding velocity of 1.45 m/s, respectively. An increase in load attributed to increase in degree of adhesive wear and material has to be joined to the disc with the increase in contact area between the sliding surfaces with increase in applied load. The tendency of the welding shows an increase in delamination of the surface, which exhibited adhesive wear. Therefore, the mechanism of wear can be known with a combination of adhesion wear and plastic deformation bands [17]. The worn surface shows a combination of adhesion regions with plastic deformation bands in the direction of sliding and delamination areas.
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Table 7 Specific wear rate of RCS-processed sheet with varying wear parameters as per DOE Trail runs
Sliding distance (SD) (m)
1
2000
2
2000
3
Load (n) (N)
Sliding velocity (SV), (m/s)
Specific wear rate (mm3 /Nm) × 10–4
9.81
1.45
1.384
9.81
2
1.775
2000
9.81
2.66
2.077
4
2000
14.715
1.45
1.914
5
2000
14.715
2
4.088
6
2000
14.715
2.66
6.49
7
2000
19.62
1.45
4.339
8
2000
19.62
2
5.477
9
2000
19.62
2.66
7.798
10
4000
9.81
1.45
1.619
11
4000
9.81
2
1.897
12
4000
9.81
2.66
2.291
13
4000
14.715
1.45
4.23
14
4000
14.715
2
5.102
15
4000
14.715
2.66
6.844
16
4000
19.62
1.45
4.711
17
4000
19.62
2
6.905
18
4000
19.62
2.66
9.778
19
6000
9.81
1.45
0.914
20
6000
9.81
2
2.982
21
6000
9.81
2.66
3.301
22
6000
14.715
1.45
2.016
23
6000
14.715
2
4.094
24
6000
14.715
2.66
5.98
25
6000
19.62
1.45
2.982
26
6000
19.62
2
8.965
27
6000
19.62
2.66
9.337
4 Conclusion Aluminum alloy 2024 was successfully processed by repetitive corrugation and straightening technique and followed successive annealing. The following conclusions are drawn: 1.
The RCS technique has proven to be an effective technique by playing an important role in refinement of grains along with improvement in mechanical properties of Al2024 samples and influence of successive annealing on it.
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SD = 2000 m
SD = 4000 m
SD = 6000 m
Fig. 6 Variation in specific wear rate and sliding velocity for different loads
2.
3.
Successive annealing has showed its effect on mechanical properties of the processed material. Improvement in UTS was found at 150 °C annealing temperature. Then, strength was gradually decreased with increase in annealing temperature. The fractography of the fractured surfaces in tensile sample shows a mode of ductile in fracture.
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Fig. 7 SEM showing worn surface of RCS processed sheet tested under a SD = 4000 m, N = 19.62 N and SV = 2.66 m/s. b SD = 6000 m, N = 9.815 N and SV = 1.45 m/s
References 1. Langdon, T.G.: Processing by severe plastic deformation: historical developments and current impact. Mater. Forum Sci. Forum, 667–669, 9–14 (2011) 2. Rosochowski, A.: Processing metals by severe plastic deformation. Solid State Phenomenon 101–102, 13–22 (2005) 3. Valiev, R.Z., Islamgaliev, R.K., Alexandrov, I.V.: Bulk nano structured materials from severe plastic deformation. Prog. Mater. Sci. 45, 103–109 (2000) 4. Zhu, Y.T., Jiang, H., Huang, J., Lowe, T.C.: A new route to bulk nano structured metals. Metallurg. Mater. Trans. 32A, 1559–1562 (2001) 5. Huang, J., Zhu, Y.T., Alexander, D.J., Liao, X., Lowe, T.C., Asaro, R.J.: Development of repetitive corrugation and straightening. Mater. Sci. Eng. 371, 35–39 (2004) 6. Rajinikanth V., Arora. G., Narasaiah, N., Venkateswarlu, K.: Effect of repetitive corrugation and straightening on Al and Al–0.25Sc alloy. Mater. Lett. 62, 301–304 (2008) 7. Stobrawa, J., Rdzawski, Z., Głuchowski, W., Malec, W.: Ultrafine grained strips of CuCr0.6 alloy prepared by CRCS method. J. Achievements Mater Manuf. Eng. 33, 166–172 (2009) 8. Kwa´sny, W., Nuckowski, P., Rdzawski, Z., Głuchowski, W.: Influence of RCS process on the structure and mechanical properties of CuSn6 alloy. J. Achievements Mater. Sci. Eng. 62, 60–66 (2013) 9. Jenix, R.J., Balasivanandha, P.S., Padmanabhan, K.A.: On the influence of repetitive corrugation and straightening on the microstructure and mechanical properties of AA 8090 Al-Li alloy. Ach. Civ. Mech. Eng. 18, 280–290 (2018) 10. Avinash, J., Prabhakar, MB., Venkateshwaralu, K.: Microstructure and mechanical properties evolution of AA2024 alloy subjected to RCS. Ind. J. Eng. Mater. Sci. 26, 155–160 (2019) 11. Pandey, S.C., Joseph, M.A., Pradeep, M.S., Raghavendra, K., Ranganath, V.R., Venkateswarlu, K., Langdon, T.G.: A theoretical and experimental evaluation of repetitive corrugation and straightening: application to Al–Cu and Al–Cu–Sc alloys, Mater. Sci. Eng. A, 534, 282–287 (2012) 12. Manjunath, G.K., Udaya Bhat, K., Preetham Kumar, G.V., Ramesh, M.R.: Microstructure and wear performance of ECAP processed cast Al–Zn–Mg alloys. Trans. Ind. Inst. Met. 71, 1919–1931 (2018) 13. Wang, C.T., Gao., Wood R.J.K., Langdon, T.G.: Wear behaviour of Al-1050 alloy processed by severe plastic deformation. Mater. Sci. Forum 667–669, 1101–1106 (2011) 14. Gao, N., Wang, C.T., Wood, R.J.K., Langdon, T.G.: Wear resistance of SPD-processed alloys. Mater. Sci. Forum 667–669, 1095–1100 (2010)
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15. Dey, D., Sudhir Kumar, C., Bhowmik Abhijit Biswas, A.: Evaluation of wear performance of Al2024-SiC ex-situ composites. Mater. Today: Proc. (2020) 16. Ashwin, R., Hari Lakshman, B., Chand Swaroop, C.B.: Predicting the wear rate of aluminum alloy AA2024-T351 using hybrid linear function and radial basis function. IOP Conf. Ser.: Mater. Sci. Eng. 561, 012046 (2019) 17. Mohamed Ibrahim Abd El Aal, M. I., El Mahallawy, N., Shehata, F.A., Abd El Hameed, M., Yoon, E.Y., Kim, H.S.: Wear properties of ECAP-processed ultrafine grained Al–Cu alloys. Mater. Sci. Eng.: A, 527, 3726–3732 (2010) 18. Abd El Aal, M.I., Seop Kim, H.: Wear properties of high pressure torsion processed ultrafine grained Al–7%Si alloy. Mater. Des. 53, 373–382 (2014) 19. Manjunath, Y.J., Thirtha Prasada, H.P., Chandrashekar, A., Krishna Rao, T.: Effect of combined repetitive corrugation and straightening on the wear properties of Al2024 sheets. Int. J. Adv. Sci. Technol. 29, 9971–9986 (2020) 20. Thangapandian, N., Balasivanandha Prabu, S.: Effect of combined repetitive corrugation and straightening and rolling on the microstructure and mechanical properties of pure aluminum. Metallography, Microstruct. Anal. 6(6), 481–488 (2017) 21. Ghorbanhosseini, S., Fereshteh Saniee, F., Sonboli, A.: An experimental investigation on the influence of elevated temperature constrained groove pressing on the microstructure, mechanical properties, anisotropy and texture of 2024 Al sheets. J. Alloys Comp. 0925–8388(19), 34009–5 (2019) 22. Kang, D.H., Kim, T.W.: Mechanical behavior and microstructural evolution of commercially pure titanium in enhanced multi-pass equal channel angular pressing and cold extrusion. Mater. Des. 31, 54–60 (2010)
Experimental Investigation on Mechanical Properties of Sisal Fiber Reinforced Composite for Retrofitting Applications D. P. Archana, H. N. Jagannatha Reddy, R. Prabhakara, M. U. Aswath, and A. Chandrashekar Abstract The experimental study aimed on the fabrication of sisal fiber reinforced composites (SFRCs) and mechanical characterization such as tensile, hardness, and flexural strength. Three different kinds of samples of sisal fiber/epoxy composite were prepared by varied orientation of fiber and by considering the 30% of fiber– matrix weight ratio. Epoxy resin (LAPOX L12) and hardener (K-6) are taken as the matrix and binder. Alkali treatment was performed using 4% NaOH solutions. Test samples were prepared and characterized as per ASTM standards. The results exhibited the properties of treated SFRCs are superior to those of untreated SFRCs for same fiber loading. The unidirectional fiber orientation [0,0] of hybrid composites provides the maximum tensile and flexural strength as compared with [0,45] and [0,90] composites. Also, morphology is studied on fractured surfaces. Keywords Sisal fiber/epoxy composites · Chemical treatment · Flexural strength · Tensile strength
1 Introduction Today, strengthening of existing structures has gained importance, to improve their structural performances, particularly enhance in strength and stiffness of the member. Few of the conventional fiber reinforced composites (FRC), namely glass, carbon, aramid, etc. are being used as they increase strength and ductility of the structures. These FRC’s are expensive, un-environmentally friendly, and unsustainable. To promote sustainability, alternate FRPs made with natural fibers are necessary [1]. Natural fiber reinforced composites (NFRCs) are today’s interest for low to medium load structural applications as use of conventional FRCs have losing their interest [2]. Using NFRCs is a promising method due to their lightweight, relatively cheap, D. P. Archana (B) · H. N. Jagannatha Reddy · M. U. Aswath · A. Chandrashekar Bangalore Institute of Technology, Bengaluru, Karnataka 560004, India R. Prabhakara Bridavan College of Engineering, Bengaluru, Karnataka 562157, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_21
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improved corrosion resistance, and available locally. Nagaraj et al. [3] deliberate the importance of NFRCs. The behavior of NFRCs has been widely considered by many researchers in past [4–6]. Sivakandhan et al. [7] investigated on sisal and jute fiber hybrid composites loading 35% fiber in 65% resin. They were able to notice the enhancement of flexural strength with fiber loading. Harichandra Chandekar et al. [8] reviewed jute fiber reinforced polymer composites (JFRCs) owing to its processing and properties. They were discussed on properties of the fiber, surface treatment on them and their effect, properties of thermo-set and thermo-plastic-based jute fiber composites in detail. Volumetric instability was reduced and improves fibers affinity with a cementitious matrix by the several kinds of treatments on natural fibers [9, 10]. Cleaning of the fiber surface, modifying its chemistry, lowering of the moisture uptake, and increase of the surface roughness can be done by these procedures [11]. The various ways of improving composite’s properties by treatments have been considered by researchers [12], and their influence on mechanical properties of NFRCs has been studied. Basically, alkali treatment is used for cellulose fibers to remove wax, hemi-cellulose lignin, surface impurities, etc. before processing. The outcome is mainly dependent on the alkali concentration and duration of soaking [13]. Other than alkali treatments, researchers have explored other chemical treatments procedures like silane [13], potassium permanganate [14], benzoylation [15], acetylation [16], acrylation [17], eco-friendly treatment (i.e., NaHCO3 treatment) [12] etc. for the improvement of hydrophobicity and interfacial bonding of fiber–matrix bonding, thereby improving the mechanical properties. It is also noticed from the literature that the properties of the composite materials have significantly influenced by stacking sequence and orientation of fiber layers [18, 19]. Decrement in strength and modulus was recorded with fiber orientation and results in better mechanical properties when the fiber orientation is kept parallel to its loading direction [20, 21]. Literature reveals the use of composites when natural fibers are reinforced in the construction among other field’s places importance on studies related to the tensile and flexural studies of these composites. These properties are relevant, for instance, in the retrofitting of existing structures [22]. With this aim, here, methods were followed to measure the mechanical characteristics of natural SFRCs. The results help us to measure the suitability of natural fibers alternate to conventional fibers for structural repair and strengthening.
2 Experimentation Here, processing and characterization of sisal fiber reinforced composites were considered. Sisal fiber as reinforcing material, epoxy, and hardener is employed as matrix and binder for the preparation of composites. Three different fiber orientations were preferred for preparation of composites in the study.
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2.1 Sisal Fiber Sisal plant (Agave sisalana) contains around 220 leaves out of which fibers are extracted; every leaves will give about 1000 fiber bundles. Plant, fiber, and its crosswise section are shown in Fig. 1a–c. Tables 1 and 2 give properties and its chemical composition.
Fig. 1 a Sisal plant, b extracted sisal fiber, c crosswise section of a sisal leaf [23]
Table 1 Sisal fiber’s properties [16]
Sisal fibre
Density (g/cm3 )
Percentage elongation (%)
UTS (MPa)
Young’s modulus (GPa)
1.33.1.5
2.0–14
400–700
9.0–38.0
Table 2 Sisal fiber’s composition and water content [24–26]
Cellulose (%)
Hemicellulose (%)
Lignin (%)
Water content (%)
65–68
10–22
9.9–14
10–22
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Table 3 Details of resin and hardener Material
Trade name
Chemical name
Density (g/cm3 )
Resin
LAPOX L12
Diglycidyl Ether Bisphenol A (DGEBA)
1.12
Hardener
K-6
Triethylene Tetro Amine (TETA)
0.954
2.2 Hardener and Resin Used Epoxy resin is having an excellent strength and resistance to chemicals, so it is used in various applications. Matrix is prepared with epoxy resin (LAPOX L12), and hardener (K-6) was mixed in a mixing ratio of 10:1. Both were purchased from Yuje Enterprises, Bengaluru, India. Details of resin and hardener are given in Table 3.
2.3 Chemical Treatment 2.3.1
Alkali Treatment
Alkali treatment was performed using 4% NaOH solution. Fiber mats were soaked in the prepared concentration for 24 h. The solution was stirred at regularly during the treatment period and then rinsed in 1% acetic acid to neutralize excessive NaOH, and later, excess acid from the fiber surfaces is removed by gentle washing with water. Treated ones, then dried in a hot air oven at 50 °C for two hours.
2.4 Composite Fabrication 2.4.1
Steps Involved in the Fabrication of SFRCs
Figure 2 presents the alkali and heat treatment of sisal fiber. Figure 3 presents the fabrication steps of sisal fiber reinforced composite. Figure 4 depicts the processing of SFRCs.
2.5 Characterization 2.5.1
Density
The density was calculated by using the Archimedes principle with weight measurements in air and in water according to ASTM C830-00 considering masses of fiber
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Fig. 2 Alkali and heat treatment of sisal fiber a NaOH pellets, b alkali treatment, c treated fiber mat, d mat dried in hot air oven at 50 °C
in dry and wet conditions. Mass of dried fiber was weighed and fiber is immersed in water for a specific period and then the saturated weight of the fiber is weighed.
2.5.2
Moisture Absorption Test
The dimensional stability and the properties of the prepared composites were affected undesirably by poor resistance of the fibers to moisture absorption. Moisture absorption was measured using mass difference; the initial weight measurement of the specimen will be taken just before the immersion in water, and then after 12 h, final weight of the specimen is measured, following ASTM D570 standard. The moisture absorption percentage was estimated using a relation and is given by Moisture absorption (%) = ((Wt − Wo) / Wo) ∗ 100 Here, Wo—mass of specimen before immersion and Wt—mass of specimen after immersion.
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Fig. 3 Fabrication steps of sisal fiber reinforced composite, a resin and hardener used, b preparation of epoxy, c preparation of composite by hand layup, d static compression
Fig. 4 Details of tensile test specimen
2.5.3
Tensile Test
Specimens were prepared in accordance to ASTM D-638 and tested in a UTM (Model: KIC-2-1000-C, capacity 100kN) as per ISO 527-4:1997(E) Part-4. Figures 5 and 6 demonstrate the specimens are subjected to tensile and flexural loading, respectively.
2.5.4
Flexural Test
A bending test (three points) has been carried out on three different plates fabricated with different orientation namely [0, 0] (unidirectional), [0, 45] (angle ply), and [0,
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Fig. 5 Specimen subjected to tensile loading
Fig. 6 Specimen subjected to bending
90] (cross-ply); laminates are analyzed for the flexural behavior. Flexural strength was determined using the UTM (Model: KIC-2-1000-C 100 kN capacity) as per ISO 14125:1998. Figure 7 shows the layout of three point bending test. Fig. 7 Layout of three point bending test
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Table 4 Density of prepared sisal-based composite
2.5.5
Composite
Density (gm/cc)
Sisal fiber reinforced epoxy composite
1.34
Hardness Test
The specimen was cut, and hardness was recorded according to ASTM D-785. Initially, the gauge is set to zero, applying minor load, and then the major load is applied. Major load acts on specimen for 15 s and then removed. The hardness is recorded leaving specimen for 15 s to recover.
2.5.6
Morphological Analysis
Morphological analysis was conducted using scanning electron microscope (Model: VEGA3 Tescan) on fractured surfaces after tensile and flexural testing.
3 Results and Discussions 3.1 Density Particularly, prepared composite’s density depends on the relative proportion of matrix and the reinforcements. The density of sisal was calculated to be 1.34 g/ cm3 as shown in Table 4. The calculated density values for sisal were found to be in agreement with literature values (1.32–1.5 g/cm3 ) [27] for sisal.
3.2 Water Absorption Test Gradual increase in water absorption was noticed in accordance with increase in immersion duration (h) in all the combinations. The sisal/epoxy composite absorbs a larger amount due to its hydrophilic characteristic of fiber. A margin variation in the water absorption was noticed, and no notable variation was noticed in the composites.
3.3 Tensile Test Ultimate tensile strength values with fiber orientation of composite laminates are tabulated in Table 5. The bar chart showed in Fig. 8 provides data on ultimate tensile strength of the fabricated specimen. The results illustrate that the composite’s strength
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Table 5 Properties of composite laminates with orientation S. No.
Fiber orientation of composite
Ultimate tensile strength (MPa)
Hardness
Flexural strength (MPa)
1
Unidirectional (0°)
82.13
104
74.01
2
Angle ply (45°)
63.27
97
71.65
3
Cross-ply (90°)
55.61
85
60.88
Ultimate tensile strength, N/mm2
80 70 60 50 40 30 20 10 0 o
0
o
45 Fibre orientation, deg
90
o
Fig. 8 Tensile test of the composite laminates with orientation
has a reduced trend when the direction of fiber was altered from (0, 0) to (0, 45) and (0, 90). The maximum tensile strength was shown by the [0, 0] composite laminates (82.13 MPa) as compared to [0, 45] (63.27 MPa) and [0, 90] (55.61 MPa) laminates. The unidirectional composite laminates show highest resistance to the deformation to the applied force, since the force was parallel to the aligned continuous and long fibers when fibers were oriented unidirectional. The stress transfer from the matrix cannot take part if the fibers are placed in both [0.45] and [0.90] direction/orientation. As a result, ineffective stress transfer mechanism occurs in those laminates to the applied force direction. From the observations, it was noticed that the fiber orientation showed remarkable effect on the mechanical properties of the prepared SFRCs. Unidirectional [0, 0] composite demonstrates about 29% and 47% higher tensile strength compared with [0.45] and [0.90] directions, respectively. Figure 9 a, b demonstrates stress versus percentage strain recorded during the tensile test. Figure 10 shows details of test specimens: (a) tensile and flexural strength test specimens and (b) fractured specimen after tensile test.
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(a) Shows 0° epoxy composites
(b) Shows 45° epoxy composites
Fig. 9 Stress versus strain for sisal fiber reinforced composite laminates with different orientation
Fig. 10 Details of test specimens a tensile and flexural strength test specimens, b fractured specimen after tensile test, and c fractured specimen after flexural test
3.4 Flexural Test Flexural strength values with fiber orientation of composite laminates are tabulated in Table 5. Here, the failure occurs mainly due to compression, tension, and shear [28]. Figure 11 shows the composite’s strength at flexural failure with varied fiber orientations. The continuous and long fibers in the unidirection acted as the load carriers, and uniform, effective stress was transferred within the matrix when the force acts at mid span of the composites [18]. The composite’s failure is dominated by the fiber end impact because the applied force is received by the short, discontinuous fibers within [0, 45] and [0, 90] orientation. In case of [0, 45] orientations, half of the fibers are arranged normal to the applied load. As a result, these fibers could not endure a decent amount of force that imposes the impairing of flexural strength. Cross-ply composites illustrate 22% of lower flexural strength compared with the unidirectional composites. Figure 10 a, c shows the details of flexural strength test specimens before and after test.
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Flexural strength, MPa
70 60 50 40 30 20 10 0
0
o
45
o
90
o
Fiber orientation, deg
Fig. 11 Flexural strength of the composite laminates with orientation
3.5 Hardness Test The experimental hardness values for SFRCs are addressed in Table 5 and Fig. 12. The hardness measured is a function of modulus and depends on stiffness, wettability, ductility, plasticity, and interfacial bonding. Hence, it is clear that [0, 0] composites showed significant betterment in hardness than those of [0, 45] and [0, 90] composites.
100
Hardness, RHN
80
60
40
20
0
0
O
45
O
Fibre Orientation, deg
Fig. 12 Hardness of the composite laminates with orientation
90
O
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Fig. 13 a SEM micrograph of sisal composite laminate after tensile test. b SEM micrograph of sisal composite laminate after flexural test
The place of property of the prepared composites as follows: unidirectional > angle ply > cross-ply. Hardness value for [0, 0] laminates was 104 which is 7% and 18% higher than those of [0, 45] and [0, 90] composites, respectively. The maximum hardness was found for [0, 0] laminate may be because of better wettability and interfacial bonding of reinforcement with matrix.
3.6 Morphological Analysis Figure 13 a, b demonstrates the morphology of the unidirectional composites laminate by SEM failed under tensile and flexural loading, respectively. Images presented a void are evidence of weak interfacial bonding. In the case of flexural loading, voids were observed; the failure was may be due to debonding. Further, Fig. 13 depicts the fiber’s fracture after the flexural test and fiber bonding with resin.
4 Conclusions Studies on mechanical properties of SFRCs have been conducted successfully. Results revealed that. 1.
The NaOH solution treatment removes incompatible waxes from the surface of sisal fibers, provides a rough surface for better interlocking, and improves
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the matrix–fiber interfacial bonding and hydrophobicity, thereby improving the mechanical properties. The unidirectional fiber orientation of hybrid composites provides the maximum tensile and flexural strength as compared with [0, 45] and [0, 90] composites. The continuous unidirectional fiber’s composite supports in sustaining good amount of applied load compared with the rest considered. The fiber pull-out along with secondary failure mechanisms such as fiber breakage and delamination served key elements for tensile and flexure failure of composites.
There is a huge scope for research scholars to explore this natural reinforced polymer composite to substitute artificial fiber composites for retrofitting of existing structure. This work can be additionally reached out to examine different aspects of natural composites like utilization of other expected fillers for development of hybrid composites and assessment of their mechanical and physical behavior owing to use in construction field.
References 1. Archana, D.P., Jagannatha Reddy, H.N.: Potential of natural fibres for strengthening existing structures—a review. Int. J. Struct. Eng. Anal. 4(2), 38–46 (2018) 2. Tong, F.S., Chin, S.C., Doh, S.I., Gimbun, J.: Natural fiber composites as potential external strengthening material—a review. Ind. J. Sci. Technol. 10(2), 1–5 (2017) 3. Nagaraj, N., Balasubramaniam, S., Venkataraman, V., Manickam, R., Nagarajan, R., Oluwarotimi, I.S.: Effect of cellulosic filler loading on mechanical and thermal properties of date palm seed/vinyl ester composites. Int. J. Biol. Macromol. 147, 53–66 (2020) 4. Gomes Vieira, L.M., dos Santos, J.C., Panzera, T.H., Campos Rubio, J.C., Scarpa, F.: Novel fibre metal laminate sandwich composite structure with sisalwoven core. Ind. Crops Prod. 99, 189–195 (2017) 5. Easwara Prasad, G.L., Keerthi Gowda, B.S., Velmurugan, R.: Comparative study of impact strength characteristics of treated and untreated sisal polyester composites. Proc. Eng. 173, 778–785 (2017) 6. Sarikaya, E., Callioglu, H., Demirel, H.: Production of epoxy composites reinforced by different natural fibers and their mechanical properties. Compos. Part B (2019) 7. Sivakandhan, C., Murali, G., Tamiloli, N., Ravikumar, L.: Studies on mechanical properties of sisal and jute fiber hybrid sandwich composite. Mater. Today: Proc. (2019) 8. Chandekar, H., Chaudhari, V., Waigaonkar, S.: A review of jute fiber reinforced polymer composites. Mater. Today: Proc. (2020) 9. Ferreira, S.R., Lima, P.R.L., Silva, F.A., Toledo Filho, R.D.: Effect of sisal fiber hornification on the adhesion with Portland cement matrices. Rev. Mater. 17, 1024–1034 (2012) 10. Ferreira, S.R., Silva, F.A., Lima, P.R.L., Toledo Filho, R.D.: Effect of fiber treatments on the sisal fiber properties and fiber–matrix bond in cement based systems. Constr. Build. Mater. 101, 730–740 (2015) 11. Ton-That, M.T., Denault, J.: Development of Composites Based on Natural Fibers, National Research Counsil, Industrial Materials Institute, The Institute of Text. Sci., Ottawa, Canada (2007) 12. Sahu, P., Gupta, M.K.: Eco-friendly treatment and coating for improving the performance of sisal composites. Polym. Test. 93, 106923 (2021)
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13. Kabir, M.M., Wang, H., Lau, K.T., Cardona, F.: Chemical treatments on plant-based natural fibre reinforced polymer composites: an overview. Compos. Part B: Eng. 43(7), 2883–2892 (2012) 14. De Rosa, I.M., Kenny, J.M., Maniruzzaman, M., Moniruzzaman, M., Monti, M., Puglia, D., Santulli, C., Sarasini, F.: Effect of chemical treatments on the mechanical and thermal behaviour of okra (Abelmoschus esculentus) fibres. Compos. Sci. Technol. 71(2), 246–254 (2011) 15. Paul, S.A., Joseph, K., Mathew, G.G., Pothen, L.A., Thomas, S.: Influence of polarity parameters on the mechanical properties of composites from polypropylene fibre and short banana fibre, Compos. Part A: App. Sci. Manufac. 41(10), 1380–1387 (2010) 16. Li, X., Tabil, L.G., Panigrahi, S.: Chemical treatments of natural fiber for use in natural fiberreinforced composites: a review. J. Polym. Environ. 15(1), 25–33 (2007) 17. Chaitanya, S., Singh, I.: Sisal fiber-reinforced green composites: effect of eco friendly fibre treatment. Polym. Compos. 39(12), 4310–4321 (2018) 18. Sujon, Md.A.S., Habib, M.A., Abedin, M.Z.: Experimental investigation of the mechanical and water absorption properties on fiber stacking sequence and orientation of jute/carbon epoxy hybrid composites. J. Mater. Res. Technol. 9(5), 10970–10981 (2020) 19. Selver, E., Ucar, N., Gulmez, T.: Effect of stacking sequence on tensile, flexural and thermo mechanical properties of hybrid flax/glass and jute/glass thermoset composites. J. Ind. Text. 48(2), 494–520 (2018) 20. Ben Amor, I., Rekik, H., Kaddami, H., Raihane, M., Arous, M., Kallel, A.: Effect of palm tree fiber orientation on electrical properties of palm tree fiber reinforced polyester composites. J. Compos. Mater. 44, 1553–1568 (2010) 21. Norman, D.A., Robertson, R.E.: The effect of fiber orientation on the toughening of short fiber-reinforced polymers. J. Appl. Polym. Sci. 90, 2740–2751 (2003) 22. Sen, T., Jagannatha Reddy, H.N.: Strengthening of RC beams in Flexure using natural jute fibre textile reinforced composite system and its comparative study with CFRP and GFRP strengthening systems. Inter. J. Sust. Built. Envir. 2(1):41–55 (2013) 23. Lu, X., Zhang, M., Rong, M., Yang, G., Zeng, H.: Sisal reinforced polymer composites. Fuhe Cailiao Xuebao 19, 1–6 (2002) 24. Idicula, M., Joseph, K., Thomas, S.: Mechanical performance of short banana/sisal hybrid fiber reinforced polyester composites. J. Reinf. Plast. Compos. 29, 12–29 (2010) 25. Arthanarieswaran, V., Kumaravel, A., Kathirselvam, M.: Evaluation of mechanical properties of banana and sisal fiber reinforced epoxy composites: Influence of glass fiber hybridization. Mater. Des. 64, 194–202 (2014) 26. Bismarck, A., Mishra, S., Lampke, T.: Plant fibers as reinforcement for green composites. In: Mohanty, A.K., Misra, M., Drzal, L.T. (eds.), Natural Fibers, Biopolymers and Bio composites (2005) 27. Pickering, K.L., Aruan Efendy, M.G., Le, T.M.: A review of recent developments in natural fibre composites and their mechanical performance. Compos.: A 83, 98–112 (2016) 28. Nurazzi, N.M., Khalina, A., Chandrasekar, M., Aisyah, H.A., Rafiqah, S.A., Ilyas, R.A.: Effect of fiber orientation and fiber loading on the mechanical and thermal properties of sugar palm yarn fiber reinforced unsaturated polyester resin composites. Polimery 65(02), 115–124 (2020)
A Low-Cost Selective Catalytic Reduction System for Diesel Engine Oxides of Nitrogen Control Jenoris Muthiya Solomon, Mohankumar Subramaniam, Joshuva Arockia Dhanraj, Nadanakumar Vinayagam, Christu Paul Ramaian, Nandakumar Seelvaraju, and A. Ramana Johannes Bachmann Abstract Diesel-powered engines are the supreme efficient engines. But there is a major drawback in diesel engines which is the emission of NOx . The catalytic converter is ineffective in the best for the diesel engine. The inefficiency in the catalytic converter led to the introduction of the selective catalytic reduction (SCR) method. SCR is not implemented in Indian vehicles. Researches are going on to introduce the selective catalytic reduction system in Indian vehicles for Bharath stage 6. The catalytic layer present in the SCR is zeolite with titanium di oxide. The problem with this catalyst is the activity of this catalyst reduces when the temperature exceeds more than 700°. In this project, ceria and copper (II) oxide is used as a catalyst. This catalyst can retain its reducing property at 700 °C. The catalyst which is chosen for this project is cheaper than the existing catalyst. Urea concentration is also varied to check the performance of the catalyst. Aqueous urea solutions of concentrations 10, 20 and 30% are made to conduct experiments. The maximum reduction in NOx was up to 95% in the loaded condition. The reduction in smoke is found to an extent of 90%.The reduction of NOx efficiency in 30% concentration of aqueous urea. The reduction of smoke is found at all the concentrations of aqueous urea solution. Keywords Emission · Catalyst · Diesel · Exhaust · SCR
J. M. Solomon Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India M. Subramaniam Department of Automobile Engineering, Kumaraguru College of Technology, Tamil Nadu, Coimbatore 641049, India J. Arockia Dhanraj (B) Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India N. Vinayagam · C. P. Ramaian · N. Seelvaraju · A. R. J. Bachmann Department of Automobile Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_22
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1 Introduction Compression ignition engines are efficient engines which has high thermal efficiency and less fuel consumption. Fuel may be a mixture of hydrocarbons that throughout a perfect combustion method would turn out solely carbon dioxide (CO2 ) and steam or vapor (H2 O) [1, 2]. Diesel exhaust area unit predominantly composed of greenhouse emission. The volumetrical focuses of diesel exhaust area unit lie generally within the following ranges: CO2 : 1 to 10% H2 O : 2 to 12% O2 : 3 to 17% N2 : 59 to 97% The exhaust concentration increases or decreases depend on engine load. The incomplete combustion causes to produce more toxic emission in diesel engines. The emissions causes adverse health effects on all living organisms [3, 4]. The pollutants are originated throughout combustion, like incomplete combustion of fuel, poor fuel atomization and vaporization [5, 6]. The various emission from engines are hydrocarbons (HC), carbon monoxide (CO), oxides of nitrogen (NOx ) and particulate matter (PM) [7–9]. Figure 1 shows concentration of harmful gases in diesel exhaust. The different sources that may contribute to waste product emissions from burning engines sometimes in tiny concentrations. SCR is the best NOx reduction method to effectively reduce NOx emissions for diesel engines. The main drawback of SCR is its high cost. In this work, SCR is developed with low-cost catalyst. And it is tested with a single-cylinder diesel engine for emission reduction. Fig. 1 Concentration of harmful gases in diesel exhaust
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2 Materials and Methods In this method, the ceramic beads were splashed with concentrated water and dehydrated in a hot air oven for two hours [10, 11]. The cerium nitrate slurry was prepared using water and coated on the dried ceramic beads followed by calcination at 500 °C. The ceria-coated ceramic beads were impregnated with calculated quantities of copper oxide using isopropyl alcohol. Thus, impregnated ceramic beads were dried and used for evaluating the catalytic activity in controlling the NOx emissions from diesel engines (Fig. 2).
2.1 Preparation of Urea Solution In this work, the urea solutions were prepared with three different concentrations. Urea with 99% purity is mixed with distilled water to prepare an aqueous solution; 500-mL urea solution is prepared for all the concentrations. For 10% of urea solution, 50-g of urea is mixed with 500-mL of distilled water and stirred well until it dissolves completely. Then, the solution is filtered to separate the precipitates in the solution. For 20% of urea solution, 100 g of urea is mixed with 500 mL of distilled water and stirred well until it dissolves completely. Then, the solution is filtered to separate the precipitates in the solution. For 30% of urea solution, 150-g of urea is mixed with 500-mL of distilled water and stirred well until it dissolves completely. Then, the solution is filtered to separate the precipitates in the solution [1, 11]. Fig. 2 Ceramic beads coated with catalyst
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Fig. 3 SCR outer shell
2.2 Decomposition of Urea The effective thermal breakdown of urea and hydrolysis of urea solution is described as following [12] (Fig. 3). Urea solution evaporation: (NHx) × CO → (NHx) × CO(S) + HxO(g)
(1)
The thermal decomposition of urea: HNCO(g) + HxO(g) → NHx(g) + COx(g)
(2)
3 Experimental Methods and Testing In this research experiment, urea is sprayed into the manifold at a relentless pressure of 6-bar throughout experiment. And this can be attained by mistreatment of the float pump and pre-setting the injection pressure [13]. Liquid urea percentage varying with 10, 20 and 30% by weight should be prepared before starting the engine. The pump is powered by a 12-V battery. The experimental testing is performed in a diesel engine which will be fueled with diesel. Before starting the experimental testing, all the connections were checked and the calibration of all the equipment is made. Ammonia solution is introduced into the tail pipe and it converts into a gas once it is injected into a hot exhaust gas [14]. Ceramic beads coated with ceria *and copper oxide is used as a catalyst (Figs. 4 and 5). At first, engine is operated with diesel fuel and by changing the load by gradually increasing from 0 to 12. The concentration of urea in water solution is like 10, 20
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Fig. 4 Test engine
Fig. 5 SCR system fixed with the engine exhaust pipe
and 30%, and three readings are taken. The emission concentration of HC, CO, CO2 and NOx and smoke opacity were recorded for every load.
4 Results and Discussion The engine test was done on the SCR system with the help of a VCR engine. The emissions are checked for HC, NOx , CO, CO2 and smoke. The readings were taken for 0, 3, 6, 9 and 12-kg load [15, 16]. The experiment is also conducted by varying the level of the aqueous urea solution. Urea solutions were prepared with concentrations of 10, 20 and 30%. Tables and graphs were plotted concerning load and emissions from the engine. Base readings from the engine are taken without connecting the
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Load vs NOx
1600
NOx(ppm)
1400
base readings
1200 1000
10% aqueous urea
800 600
20% aqueous urea
400 200 0
0
5
10
15
30 %aqueous urea
Load (Kg) Fig. 6 Comparison of NOx with respect to engine load
SCR system with the engine to compare the reduction in emission after conducting experiments. After measuring the emissions from the test bench without any attachments, the selective catalytic reduction system is fixed at the exhaust of the VCR test bench. Then, the aqueous urea solution of concentration 10% is sprayed 3.3-ml for each load, and the urea solution was sprayed at five instalments. The number of emissions from the test bench varied according to the quantity of the liquid urea sprayed into the catalyst chamber. The graphs were plotted between different loads and emissions for different concentrations of aqueous urea solution [17–20] (Fig. 6). From the above graph, it is found that the conversion of NOx is efficient with the 30% of aqueous urea solution. NOx rises with increase in engine loads due to high in cylinder temperature [21, 22]. For other concentrations, there is a slight reduction in emission compared to 30%. But comparatively, the reduction is more in 30% of the urea solution. The reduction is efficient with 30% of urea solution because of the more availability of ammonia in exhaust stream (Figs. 7, 8, 9 and 10). From the above graphs, it was found that the reduction of emission was effective at 30% concentration of aqueous urea solution. The catalyst works effectively with the addition of 30% concentration of aqueous urea solution.
5 Conclusion It is concluded from the experiment that ceria and copper (II) oxide is effective in the decrease of emissions such as, HC, CO, NOx and smoke. The focus is to reduce NOx , and as expected, there was some reduction in NOx . The compression ignition
HC(ppm)
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Load Vs HC
base readings 10% aqueous urea 20% aqueous urea 30% aqueous urea 0
5
10
15
Load(Kg)
CO(% of concentration)
Fig. 7 Concentration of HC with respect to engine load
1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 -0.2 0
Load Vs CO
base reading 10% aqueous urea 20% aqueous urea 30% aqueous urea
5
10
15
Load (Kg)
Fig. 8 Concentration of CO emission with respect to engine load
Load Vs CO2
CO2 (% of concentration)
14 12 10 8
base readings
6
10 % aqueous urea
4
20% aqueous urea
2
30% aqueous urea
0
0
5
10
Load (Kg) Fig. 9 Concentration of CO2 with respect to engine load
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smoke ( % of opacity)
80 70 60 50
base readings
40
10% aqueous urea
30
20% aqueous urea
20
30% aqueous urea
10 0 0
5
10
15
Load (Kg) Fig. 10 Concentration of engine smoke with respect to engine load
engine has a large amount of emission of smoke. In this project, there was a drastic reduction of smoke. The catalyst which is used in this project is to mainly focus in the reduction of NOx , but there was a drastic reduction of smoke. The maximum reduction in NOx was up to 50% in the loaded condition. There was a 90% reduction in smoke, due to the oxidizing property of the catalyst.
References 1. Joseph, J., Pachamuthu, S., Solomon, J.M., Sathyamurthy, R.: Experimental investigation to enhance the low-temperature nitrogen oxide emission reduction in biodiesel exhaust using Scr with direct ammonia injection and manganese cerium zirconia catalyst. Environ. Prog. Sustain. Energy (2021) 2. Sathyamurthy, R., Balaji, D., Gorjian, S., Muthiya, S.J., Bharathwaaj, R., Vasanthaseelan, S., Essa, F.A.: Performance, combustion and emission characteristics of a DI-CI diesel engine fueled with corn oil methyl ester biodiesel blends. Sustain. Energy Technol. Assessments 43, 100981 (2021) 3. Subramaniam, M., Satish, S., Solomon, J.M., Sathyamurthy, R.: Numerical and experimental investigation on capture of CO2 and other pollutants from an SI engine using the physical adsorption technique. Heat Transfer. 49(5), 2943–2960 (2020) 4. Jenoris Muthiya, S., Amarnath, V., Senthil Kumar, P.: Carbon Capture and storage from automobile exhaust to reduce CO2 Emission. Int. J. Innovat. Res. Sci. Eng. Technol. 3(2), 33 5. Subramaniam, M., Solomon, J.M., Nadanakumar, V., Anaimuthu, S., Sathyamurthy, R.: Experimental investigation on performance, combustion and emission characteristics of DI diesel engine using algae as a biodiesel. Energy Rep. 6, 1382–1392 (2020) 6. Karuppan, D., Muthu Manokar, A., Vijayabalan, P., Sathyamurthy, R., Madhu, B., Mageshbabu, D., Bharathwaaj, R., Jenoris Muthiya, S.: Experimental investigation on pressure and heat release HCCI engine operated with chicken fat oil/diesel-gasoline blends. Mater. Today: Proc. (2020)
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7. Muthiya, S.J., Saravanan, I., Balachandran, G.: Experimental investigation in diesel oxidation catalyst by developing a novel catalytic materials for the control of HC, CO and smoke emissions (No. 2020–28–0458). SAE Technical Paper (2020) 8. Muthiya, S.J., Pachamuthu, S., Arulanandan, J.J., Thangavel, N., Sathyamurthy, R.: Electrochemical decomposition of NOx and oxidation of HC and CO emissions by developing electrochemical cells for diesel engine emission control. Environ. Sci. Pollut. Res. 1–10 (2019) 9. Jenoris Muthiya, S., Senthil Kumar, P.: Electrochemical NOx reduction and oxidation of HC and PM emissions from biodiesel fuelled diesel engines using electrochemically activated cell. Int. J. Green Energy 15(5) (2018) 10. Ramakrishnan, B., Elumalai, S., Mayakrishnan, J., Saravanan, I., Jenoris Muthiya, S.: Investigation on tribological performance of NanoZnO and mixed oxide of Cu−Zn as additives in engine oil. No. 2020-01-1095. SAE Technical Paper (2020) 11. Parthiban, K., Pazhanivel, K., Jenoris Muthiya, S.: Pollution control in multi-cylinder S.I Engines using Catalytic Converter. Int. J. Veh. Struct. Syst. 9(3), 134–138. ISSN: 0975-3060 (2017). 12. Jayanth Joseph, A., Jenoris Muthiya, S., Senthilkumar, P.: Reduction of NOx emissions in diesel engines by selective catalytic reduction using dual layer catalyst configurations. J. Chem. Pharm. Sci. 9(2), 789–793 (2016) 13. Padmanaba Sundar, S., Hema Kumar, M., Jenoris Muthiya, S.: Experimental investigation on performance combustion and emission characteristics of direct injection diesel engine using calophyllum inophyllum methyl ester. Ind. J. Environ. Protect. IJEP 39(9), 614–620 (2019) 14. Jenoris Muthiya, S., Senthil Kumar, P., Mohan Kumar, S., Jayanth Joseph, A.: Investigation of effective storage capacity of lean NOx trap coated with NOx storage materials. J. Chem. Pharm. Sci. 9(2), 794–797 (2016) 15. Subramaniam, M.K., Pachamuthu, S., Arulanandam, J., Jenoris Muthiya, S.: Simultaneous reduction of HC, NOx and PM by using active regeneration technique. SAE Int. J. on dio04/05/2016, 2016-01-0912 16. Jenoris muthiya, S., Mohankumar, S., Senthilkumar, P.: Effects of thermal barrier coating on single cylinder CI engine fuelled with diesel and biodiesel. J. Chem. Pharm. Sci. 9(2), 779–784 (2016) 17. Subramaniam, M., Jenoris Muthiya, S., Satish, S., Joshuva, A., Alexis, J.: Numerical investigation on various layouts of phase change materials based battery module used in electric vehicles. No. 2020-28-0499. SAE Technical Paper (2020) 18. Mageshbabu, D., Sathyamurthy, R., Madhu, B., Chandra Sekhar, S., Sreenivasulu, M., Jenoris Muthiya, S., Srihari Reddy, P.: Heat transfer and hydraulic characteristics of micro finned tube inserted with twisted tape inserts and hybrid nanofluid (CNT/Al2 O3 ) Advances in Materials and Processing Technologies (2021). https://doi.org/10.1080/2374068X.2021.1872245 19. Bhaskar, K., Solomon, J. M., Sathyamurthy, R., Vinayagam, N.K.: A review on PEM fuel cells used for automotive applications, models and hydrogen storage for hybrid electric fuel cell vehicle (No. 2020-01-5173) SAE Technical Paper. 20. Joshuva, J.M.S, Shridhar V.A, Nandakumar. S.: Wastage cost analysis of fillet welding on boiler. J. Adv. Res. Dynam. Control syst. 12(1), 5050–5511 (2020) 21. Vinayagam, N.K., Hoang, A.T., Solomon, J.M., Subramaniam, M., Balasubramanian, D., EL-Seesy, A.I., Nguye, X.P.: Smart control strategy for effective Hydrocarbon and Carbon monoxide emission reduction on a conventional diesel engine using the pooled impact of pre-and post-combustion techniques. J. Cleaner Prod. 127310 22. Solomon, J.M., Singh, K.P., Sawant, L.D., Dhanraj, J.A., M. Subramaniam, Samson, R.M.: Experimental investigation on effect of LHR in diesel engine fuelled with waste cooking oil biodiesel. In: IOP Conference Series: Materials Science and Engineering, vol. 1130, no. 1, p. 012083. IOP Publishing (2021)
Performance Enhancement of Jatropha Methyl Ester by Utilizing Oxygen Enrichment in Diesel Engine Mohankumar Subramaniam, Jenoris Muthiya Solomon, Joshuva Arockia Dhanraj, Christu Paul Ramaian, Nadanakumar Vinayagam, Nandakumar Selvaraju, and A. Ramana Johannes Bachmann Abstract Diesel engines play an important role in satisfying world power demand in both commercial and industrial sectors. However due to release of harmful gases and rapid depletion of fossil fuels for the last decades makes us shift to alternate fuels sources. In this work biodiesel extracted from the source jatropha oil and tested in diesel engine as an alternate fuel. Initially, biooil is extracted from Jatropha plant and it is changed into biodiesel with the aid of transesterification process. The corresponding fuel properties for biodiesel and its blends are tested and it is well matched with ASTM standards. Furthermore, experimental studies were performed in, naturally aspirated, mono cylinder, water cooled, direct injection diesel engine at various load conditions. Then the oxygen enrichment is done at the inlet with the percentage variation from 22 to 24%. Test results obtained reveal that BTE is raised by 8 to 10% for B20 blends with the increased oxygen concentration. Then increasing the inlet oxygen levels results in reduction of BSFC for both biodiesel and its blends. Regarding emission parameters, the gradual declination in smoke opacity and Carbon Monoxide is achieved at higher load conditions for biodiesel blends (B20). On the other hand, due to elevated oxygen concentration and rise in peak cycle temperature result in elevated NOx emissions for pure Biodiesel and its blends. Keywords Biodiesel · Alternate sources · Jatropha · Diesel engine oxygen enrichment M. Subramaniam Department of Automobile Engineearing, Kumaraguru College of Technology, Coimbatore, Tamil Nadu 641049, India J. M. Solomon Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India J. Arockia Dhanraj (B) Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India C. P. Ramaian · N. Vinayagam · N. Selvaraju · A. Ramana Johannes Bachmann Department of Automobile Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_23
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1 Introduction Fossil fuels plays a crucial role to satisfy the energy demand globally for the last hundred years. Diesel engines are considered as a potential one and are widely used as energy source for various domestic and industrial applications [1, 2]. Rapid depletion of fossil fuels makes us to go for alternate energy source for transportation and agricultural sector. If this scenario continues, it will lead to eradication of fossil fuels completely in the next coming 40 years [3]. On the other hand fossil fuels on its complete combustion release very dangerous pollutants like UHBC, Nitrous Oxide, Carbon Monoxide and Particulate matter into atmosphere [4, 5]. In the recent decades, even carbon dioxide which is considered as ideal product of combustion considered as potential source for global warming scenario [6, 7]. On the other hand due to the stringent emission norms, the adoption of post combustion devices like DPF and AdBlue technology to reduce NOx is made mandatory in current vehicles. These devices increase the overall cost of the vehicles and periodic replacement is required after several kilometres of operation. [8]. In order to reduce emissions with lower cost, the fuel modifications play a significant role in the past few decades. Biodiesel due to its easy availability considered as a potential alternate fuel source and gains its importance among researchers for the last several decades [9]. They are popularly known as vegetable oil methyl ester and is considered as potential source for replacement of diesel fuel. Vegetable oils can be made to run in diesel engine either directly as raw oil or after extracting using transesterification process. The major constraints in adapting straight vegetable oil in diesel engine are their higher viscous nature, low volatility and longer chain structure [10]. This may result in improper atomization of fuel droplets and it finally ended up in improper combustion of air fuel mixture. Various methods like transesterification, emulsification, pyrolysis and blending of biooil with diesel are utilized to make the vegetable oil to be used diesel engine [11]. Another major drawback lies in biodiesel is that due to its lesser calorific value leads to rise in consumption of fuel. In the present work, biodiesel was extracted from the source Jatropha curcas. It is found widely in tropical and subtropical regions especially in the areas of Mexico, India, Argentina and America [12]. Priyabarta et al. tested experimentally the preheated jatropha oil in diesel engine at various load conditions. Marginal rise in peak pressure, HRR was noted for biodiesel at elevated load conditions. The reductions in HC, NOx and Carbon dioxide emissions were observed for pure jatropha blends while compared with neat diesel [13]. Mohammed EL et al. tested jatropha oil in variable compression ratio engine under different load conditions. Among the blends tested B50 shows higher peak pressure and brake torque at 1750 rpm. HC, CO emission reduced greatly at peak compression ratio with the rise in NOx emission is observed at higher load conditions [14]. Mofijur et al. studied the ability of Jatropha blends as a significant feedstock for replacing diesel fuel. Test results obtained reveals that B10 and B20 blends show similar viscosity while compared with neat diesel. On the other hand, their oxidation stability after running 20 h shows promising results and it fulfils the requirements
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of European specifications (EN 590). Regarding BSFC it linearly increases as the blending percentage of jatropha oil in diesel rises. In case of emissions, there was about 3.84% and 10.25% decline in HC emission was noted down for 10 and 20% blend ratio while compared with pure biooil(B0) [15]. In the present work also B20 blend along the various oxygen enrichment ratio is used for conducting experimental studies. In recent years in order to increase the burning rate of fuel, oxygen enrichment is adopted. This technique enhances combustion rate of biodiesel also reduces Smoke and NOx emissions simultaneously [16]. Baskar et al. performed an experimental investigation in mono cylinder diesel engine by increasing oxygen content from 21 to 27%. They observed that SFC decreased by 5–12% and Brake Thermal efficiency increased by 4–8%. On the other hand HC,CO and Smoke reduced by 40, 55 and 60% with the increased oxygen content [17]. Chin et al. indicated that the input energy required to burn fuel inside combustion chamber reduces greatly with the increase in oxygen content. This also reduces combustion noise since the delay period reduces with the increased ignition oxygen concentration [18]. In this present work, an experimental study of emission and performance parameters of Jatropha with its blends and oxygen enrichment in four-stroke diesel engine is performed. In order to enhance the properties of pure jatropha oil, Transesterification process was performed. Once the oil is transesterified it is made to blended with neat diesel and further emission and performance parameters are analyzed by running in diesel engines. After that oxygen enrichment is done with bio diesel blends (B20) and the obtained results are compared and studied.
2 Materials and Methods 2.1 Transesterification Process of Jatropha Oil Raw oil extracted from the plant have higher viscosity and this makes them unsuitable to be used in C.I engine directly. The core aim of this process is to convert the heavier viscous nature of biodiesel into lighter mono alkyl structure. In this process, one mole of extracted biooil that is triglyceride is made to react with methanol with aid of catalyst. Initially, in the ratio of about 6:1 of methanol to oil is made to react with 10 ml of sulphuric acid at 60 °C in a magnetic stirrer for 45 min duration. Then the extract was collected using separate funnel overnight and then the extract is separated into three layers. Among the three layers top layer is pure biodiesel and it is collected for experimental studies. In the bottom layer impurities and glycerine is collected and used for other useful purposes. The extracted oil is mixed with diesel with a percentage of 80% diesel, 20% biodiesel and it is labelled as B20. Then the corresponding physical properties like fire point, flash point, viscosity and density are measured.
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3 Experimental Setup and Its Procedure Experimental investigation was performed on a mono cylinder naturally aspirated, vertical air-cooled four-stroke engine. The complete configuration of engine test setup is given in the Fig. 2. Test engine was plugged into to a Eddy current dynamometer for giving load at various operating conditions. Then the fuel consumption with the aid of mass air flow sensor connected at intake manifold. Rotational speed of the engine is acquired using tachometer which is mounted in the engine shaft. Emission parameters like CO, HC and Nitrous oxides were found with the aid of AVL five-gas analyzer. The tailpipe measuring probe is connected in the tailpipe and the other end is connected to analyzer for measuring emissions. In this study, the Karanja oil was mixed with diesel in the ratio 30:70. Many previous works made in biodiesel found that B20 blend shows optimum performance in all aspects while running in diesel engines [18, 19]. Then the test engine was run with neat diesel and B30 the corresponding emission and performance parameters were noted down. After this process oxygen enrichment is done with the aid of oxygen cylinder which is connected in intake manifold. The oxygen concentration is varied with the help of flow control value which is attached in oxygen cylinder supply hose pipe [20, 21]. Then the engine is run with B30 blend, and the oxygen is mixed with the concentration varying from 22 to 24%. After mixing oxygen with the inlet air once again fuel consumption and emissions readings were recorded (Fig. 2).
Fig. 1 Experimental setup layout
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4 Results and Discussions 4.1 Brake Specific Fuel Consumption Figure 2 gives the trend of BSFC at various brake power conditions. The plot obtained clearly pointed out that blends of biodiesel show marginally higher BSFC while compared with neat diesel. It is also inferring that SFC decreases gradually as the applied brake power increases for biodiesel blends and diesel. This happens because as brake power elevated the fuel injected quantity also rises which in turn increases peak pressure developed inside the combustion chamber. This scenario ends up in higher combustion chamber temperature with the reduced ignition delay and it results in enhanced burning rate of fuel [22]. However slight decrease in SFC is observed for biodiesel after enrichment since added oxygen enhances combustion rate. It was about nearly 3–14% reduction in BSFC is observed with the linear rise in oxygen concentration for B20 blend.
4.2 Brake Thermal Efficiency Figure 3 shows the BTE obtained with respective to various load conditions at different oxygen concentration. The plot obtained reveals that for both neat diesel and biodiesel blends BTE linearly along with applied load. The maximum of about 28% BTE is achieved for neat diesel and it declines for biodiesel blends. This may due to lesser energy content and high viscous nature of biodiesel results in lesser energy release. However due to oxygen enrichment, the b20 blends show maximum of around only 3% lesser BTE while compared to neat diesel. This scenario occurs
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since the oxygen enrichment results in increased burning rate of biodiesel blend with reduced ignition delay and it contributes to higher heat release rate [23].
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Figure 4 gives the CO emissions with respective to different brake power for neat diesel and biodiesel blends. CO is formed due to low oxygen availability and insufficient temperature inside the combustion chamber. The plot obtained reveals that Carbon monoxide emission declines with the rise in inlet oxygen concentration. The
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core reason is that biodiesel has 10% oxygen concentration in its elemental composition and thus enhances combustion rate [24]. Another reason for reduction is that due to oxygen enrichment at inlet makes the air fuel leaner and it increases oxidation reactions further. CO reduction percentage of around 40% is shown for B20 (24%) at elevated load conditions due to rise in cylinder temperature at this condition.
4.4 Smoke Opacity Figure 5 shows the variation of smoke while compared to different brake Power. Particulate matter in the presence of sunlight react results formation of smoke. The plot obtained clearly pointed out that as brake power rises the smoke opacity also rises for both B20 and neat diesel. However, for biodiesel blends with rise in oxygen concentration the smoke opacity also declined linearly while compared with neat diesel. The particulate matter mainly formed due to insufficient oxygen concentration at elevated temperature. This enriched oxygen concentration inside the combustion chamber results in complete oxidation of soot particles. Maximum of around 40% reduction is achieved for 24% oxygen enrichment for B20 blends at full load conditions.
4.5 NOx Emissions NOx is considered as potential treat to diesel engine and it is mainly formed due to oxygen availability and higher peak cycle temperature inside the combustion chamber. Figure 6 gives the NOx emission with respect to the various applied load conditions. The plot shows that NOx emission rises with the brake power for neat
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diesel and B20. This may due to enhanced oxidation reactions at higher applied load results elevated combustion chamber temperature and it leads to higher NOx concentration [25]. However, the NOx emission is reduced for biodiesel blends while compared with neat diesel. This occurs since biodiesel has higher viscosity and it results in improper fuel atomization which results in decline of peak cycle temperature of combustion chamber.
4.6 Hydrocarbon Emissions The UHBC emission variation with the different applied load conditions is shown in Fig. 7. The graph obtained clearly pointed out that UHBC emissions reduces greatly
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for biodiesel blends at all applied brake power. There was about 60% reduction is shown for B20 blends with maximum oxygen enriched condition of 24%. This may due to higher burning ability of biodiesel blends with the increased oxygen concentration results in lesser premixed combustion duration with reduced ignition delay. This factor enhances ignition quality of biodiesel thereby it prevents incomplete combustion of fuel [26, 27]. Then also as mentioned earlier biodiesel has significant oxygen molecules in fuel composition [28–30]. Another reason is that biodiesel contains nearly 10% per cent oxygen in its molecular structure [31, 32]. It improves the oxidation reactions of the fuel and thereby reduces UBHC significantly.
5 Conclusion Biodiesel from the Jatropha oil and experiments was performed in mono cylinder diesel engine at various oxygen enrichment levels. The important conclusions obtained are summarized as follows. • SFC decreases gradually as the brake power increases for both neat diesel and B20 blend. Nearly about 2–14% reduction in BSFC is observed for B20 blend with raised oxygen concentration linearly from 22 to 24%. • The BTE of the engine was elevated with an average increment of 8–10% when oxygen gas was supplemented with biodiesel. BTE of the B20 blend with 24% oxygen enrichment remains higher with 28.3% efficiency at peak load. • Regarding reduction ratio of UHBC maximum of around 60% is achieved for A20 blends with 24% oxygen addition. • The CO emission was reduced for biodiesel blend with the increase in oxygen enrichment percentages, an average decrement of 18% is observed. • The Nitrous Oxide emission is higher for the 20% blend ratio with the increase in oxygen enrichment percentage of about 24% is observed at higher load operating conditions
References 1. Mohankumar, S., Senthilkumar, P.: Particulate matter formation and its control methodologies for diesel engine: a Comprehensive review. J. Renew. Sustain. Energy Rev. 80, 1227–1238 (2017) 2. Subramaniam, M.K., Pachamuthu, S., Arulanandan, J., Muthiya, J.: Simultaneous reduction of HC, NOx and PM by using active regeneration technique. No. 2016-01-0912. SAE Technical Paper (2016) 3. Nautiyal, P., Subramanian, K.A., Dastidar, M.G.: Kinetic and thermodynamic studies on biodiesel production from spirulina platensis algae biomass using single stage extraction– transesterification process. Fuel, 135, 228–234 (2014)
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4. Subramaniam, M., Pachamuthu, S.: Two zone thermodynamic model for prediction of particulate matter emission from direct injection diesel engine. Therm. Sci. 20(suppl. 4), 1017–1028 (2016) 5. Muthiya, S.J., Saravanan, I., Balachandran, G.: Experimental investigation in diesel oxidation catalyst by developing a novel catalytic materials for the control of HC, CO and smoke emissions (No. 2020-28-0458). SAE Technical Paper (2020) 6. Muthiya, S.J., Amarnath, V., Senthilkumar, P., Mohankumar, S.: Experimental investigation and controlling of CO2 emission from automobile exhaust by CCS technique. Int. J. Appl. Engg. Res. 10, 36–46 (2015) 7. Subramanian, M., Satish, S., Solomon, J.M., Sathyamurthy, R.: Numerical and experimental investigation on capture of CO2 and other pollutants from an SI engine using the physical adsorption technique. Heat Transfer. 49(2020), 2943–2960 8. Muthiya, S.J., Pachamuthu, S.: Electrochemical NOx reduction and oxidation of HC and PM emissions from biodiesel fuelled diesel engines using electrochemically activated cell. Int. J. Green Energy 15(5), 314–324 (2018) 9. Subramaniam, M., Solomon, J.M., Nadanakumar, V., Anaimuthu, S., Sathyamurthy, R.: Experimental investigation on performance, combustion and emission characteristics of DI diesel engine using algae as a biodiesel. Energy Rep. 6, 1382–1392 (2020) 10. Xue, J., Grift, T.E., Hansen, A.: Effect of biodiesel on engine performances and emissions. Renew. Sustain. Energy Rev. 15, 1098–1116 (2011) 11. Pramanik, K.: Properties and use of Jatropha curcas oil and diesel fuel blends in compression ignition engine. Renew. Energy 28(2003), 239–248 (2003) 12. Ejaz, M.S., Jamal, Y.: A review of biodiesel as vehicular fuel. Renew. Sustain. Energy Rev. 12(9), 2484–2494 (2008) 13. Pradhan, P., Raheman, H., Padhee, D.: Combustion and performance of a diesel engine with preheated Jatropha curcas oil using waste heat from exhaust gas. Fuel 115(2014), 527–533 (2013) 14. EL-Kasaby, M., Nemit-allah, M.A.: Experimental investigations of ignition delay period and performance of a diesel engine operated with Jatropha oil biodiesel. Alexandria Eng. J. 52, 141–149 (2013) 15. Mofijur, M., Masjuki, H.H., Kalam, M.A., Atabani, A.E.: Evaluation of biodiesel blending, engine performance and emissions characteristics of Jatropha curcas methyl ester: malaysian perspective. Energy 55(2013), 879–887 (2013) 16. Senthilkumar, M., Arul, K., Sasikumar, N.: Impact of oxygen enrichment on the engine’s performance, emission and combustion behavior of a biofuel based reactivity controlled compression ignition engine. J. Energy Inst. 92(1), 51–61 (2019) 17. Baskar, P., Senthilkumar, A.: Effects of oxygen enriched combustion on pollution and performance characteristics of a diesel engine. Eng. Sci. Technol. Int. J. 19(1), 438–443 (2016) 18. Banapurmath, N.R., Tewari, P.G., Hosmath, R.S.: Experimental investigations of a four-stroke single cylinder direct injection diesel engine operated on dual fuel mode with producer gas as inducted fuel and Honge oil and its methyl ester (HOME) as injected fuels. Renew. Energy 33(9), 2007–2018 (2008) 19. Ghodasara, P., Ghodasara, M.: Experimental studies on emission and performance characteristics in diesel engine using bio–diesel blends and EGR (Exhaust Gas Recirculation). Int. J. Emerg. Technol. Adv. Eng. 2(2) (2012) 20. Tsaousis, P., Wang, Y., Roskillya, A.P., Caldwell, G.S.: Algae to energy: engine performance using raw algal oil. Energy Proced. 61, 656–659 (2014) 21. Arora, A., Singh, P.K.: Comparison of biomass productivity and nitrogen fixing potential of Azolla spp. Biomass Bioenerg. 24(3), 175–178 (2003) 22. Sathyamurthy, R., Balaji, D., Gorjian, S., Muthiya, S.J., Bharathwaaj, R., Vasanthaseelan, S., Essa, F.A.: Performance, combustion and emission characteristics of a DI-CI diesel engine fueled with corn oil methyl ester biodiesel blends. Sustain. Energy Technol. Assessments, 43, 100981 (2021)
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23. Solomon, J.M., Pachamuthu, S., Arulanandan, J.J., Thangavel, N., Sathyamurthy, R.: Electrochemical decomposition of NOx and oxidation of HC and CO emissions by developing electrochemical cells for diesel engine emission control. Environ. Sci. Pollut. Res. 27(26), 32229–32238 (2020) 24. Karuppan, D., Manokar, A.M., Vijayabalan, P., Sathyamurthy, R., Madhu, B., Mageshbabu, D., Bharathwaaj, R., Muthiya, S.J.: Experimental investigation on pressure and heat release HCCI engine operated with chicken fat oil/diesel-gasoline blends. Mater. Today: Proc. 32, 437–444 (2020) 25. Ramakrishnan, B., Elumalai, S., Mayakrishnan, J., Saravanan, I., Jenoris Muthiya, S.: Investigation on tribological performance of NanoZnO and mixed oxide of Cu–Zn as additives in engine oil. No. 2020-01-1095. SAE Technical Paper (2020). 26. Sundar, S.P., Kumar, M.H., Muthiya, S.J.: Experimental investigation on performance combustion and emission characteristics of direct injection diesel engine using Calophyllum inophyllum methyl ester. Indian J. Environ. Prot. 39(7), 614–620 (2019) 27. Parthiban, K., Pazhanivel, K., Muthiya, S.J.: Emission control in multi-cylinder spark ignition engines using metal-oxide coated catalytic converter. Int. J. Veh. Struct. Syst. 9(2), 134 (2017) 28. Jayanth Joseph, A., Jenoris Muthiya, S., Senthilkumar, P.: Reduction of NOx emissions in diesel engines by selective catalytic reduction using dual layer catalyst configurations. J. Chem. Pharm. Sci. 9(2), 789–793 (2016) 29. Jenoris Muthiya, S., Senthil Kumar, P., Mohan Kumar, S., Jayanth Joseph, A.: Investigation of effective storage capacity of lean NOx trap coated with NOx storage materials. J. Chem. Pharm. Sci. 9(2), 794–797 (2016) 30. Jenoris Muthiya, S., Mohankumar, S., Senthilkumar, P.: Effects of thermal barrier coating on single cylinder CI engine fuelled with diesel and biodiesel. J. Chem. Pharm. Sci. 9(2), 779–784 (2016) 31. Vinayagam, N.K., Hoang, A.T., Muthiya Solomon, J., Subramaniam, M., Balasubramanian, D., EL-Seesy, A.I., Phuong Nguyen, X.: Smart control strategy for effective Hydrocarbon and Carbon monoxide emission reduction on a conventional diesel engine using the pooled impact of pre-and post-combustion techniques. J. Cleaner Prod. 127310 (2021) 32. Solomon, J.M., Pratap Singh, K., Damodar Sawant, L., Arockia Dhanraj, J., Subramaniam, M., Manoj Samson, R.: Experimental investigation on effect of LHR in diesel engine fuelled with waste cooking oil biodiesel. In: IOP Conference Series: Materials Science and Engineering, vol. 1130, no. 1, p. 012083. IOP Publishing (2021)
Experimental Study on Utilization of Karanja Bio Oil in Diesel Engines and Performance Enhancement by Oxygenated Additives Mohankumar Subramaniam, Jenoris Muthiya Solomon, Nadanakumar Vinayagam, Nandakumar Selvaraju, Joshuva Arockia Dhanraj, Christu Paul Ramaian, and A. V. Sivabalan Abstract Rapid growth of industrialization and depleting resources of fossil fuel coupled with air pollution caused by the emissions released by engines have been a threat for the future generations. In this work biodiesel extracted from the source Pongamia Pinnata (Karanja) and tested in diesel engine. Initially biooil is extracted from karanja plant and it is converted into biodiesel by transesterification process. The corresponding physical properties for biodiesel and its blends are tested and it is well matched with ASTM standards. Furthermore, experimental studies were performed in naturally aspirated, mono cylinder, water-cooled, DI diesel engine at various load conditions. Then the oxygen enrichment is done at the inlet with the percentage variation from 21 to 27%. Test results obtained reveals that BTE is increased by 8–9% for B20 blends with the increased oxygen concentration. BSFC reduces for B30 blends with the rise in inlet oxygen levels. Regarding emission parameters the gradual reduction in CO and smoke opacity is achieved at higher load conditions for biodiesel blends (B30). On the other hand, due to increased concentration in oxygen and high combustion chamber temperature, NOx emissions increases for pure biodiesel and its blends. Keywords Biodiesel · Nonedible source · Pongamia Pinnata · Karanja · Oxygen enrichment M. Subramaniam Department of Automobile Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu 641049, India J. M. Solomon Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India N. Vinayagam · N. Selvaraju · C. P. Ramaian · A. V. Sivabalan Department of Automobile Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India J. Arockia Dhanraj (B) Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_24
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1 Introduction The rapid consumption of fossil fuels and its price hike in recent days makes us to explore alternate energy sources to satisfy our energy demands [1, 2]. Fossils like petrol and diesel on its combustion release very dangerous pollutants like UHBC, Nitrous oxide, Particulate matter and CO into ambient environment [3, 4]. In the recent decades even carbon dioxide which is considered as ideal product of combustion is considered as potential source for global warming scenario [5, 6]. On the other hand due to the stringent emission norms the adoption of post combustion devices like DPF and AdBlue technology to reduce NOx is made mandatory in current vehicles. These devices increase the overall cost of the vehicles and periodic replacement is required after several kilometers of operation [7]. In order to reduce emissions with lower cost the fuel modifications play a significant role in the past few decades. Biodiesel due to its easy availability considered as a potential alternate fuel source gained its importance among researchers for the last several decades [8]. Biooil can be derived from both non-consumable oil source and edible or consumable oil source. In the past few decades extracting biooil from consumable oil sources such as rapeseed oil, sunflower oil, soya bean, palm oil, and cotton oil etc. have been carried out [9–11]. On the other hand rapid consumption of edible sources leads to food scarcity issues in countries like India. In this regard various non edible oil extracted from the sources like Jatropha, Pongamia, Mahua and Tamuna oil has greater ability to replace fossil fuels [12–14]. In the present study biooil is derived from Karanja oil commonly known as and tested in diesel engine experimentally. Pongamia pinnata majorly has its origins in tropical regions like Japan, India, China and Australia [15]. Karanja is, usually known as drought resistant tree usually grows with the height of about 20 m long [16]. Lohith et al. performed experiments by running CI engine with the biodiesel blends. Study reported that mechanical efficiency of nearly 66% is achieved for Karanja oil with 50% blend. BMEP is higher for biodiesel with 10% addition of Karanja oil into diesel while comparing with other blends [17]. Prajapati et al. experimentally tested the Karanja oil biodiesel blends in mono cylinder Direct injection diesel engine. In case of BSFC it declines for all blends while the applied load increases. Regarding emission parameters significant reduction in smoke and UHBC with the slight rise in CO emission is observed [18]. In another work they concluded that Karanja oil has ability to run in diesel engines successfully without preheating upto 50% blending ratio. Test results confirms that Karanja oil gives lower emissions with higher engine performance [19]. Bhatt et al. conducted experimental study and concluded that Karanja oil upto blends B40 can produce similar brake powers and BTE trends while comparing with diesel fuel [20]. Raheman et al. also confirmed that Karanja oil with 40% blended ratio has greater ability to replace biofuel with producing lesser emissions [21]. In recent years in order to increase the burning rate of fuel oxygen enrichment is adopted. This technique enhances combustion rate of biodiesel that also reduces smoke and NOx emissions simultaneously [22]. Baskar et al. performed study by running biodiesel blends in mono cylinder diesel engine by increasing oxygen content
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from 21 to 27%. They observed that SFC decreased by 5–12% and Brake Thermal efficiency increased by 4–8%. On the other hand hydrocarbons, Carbon Monoxide and smoke reduced by 40, 55 and 60% with the increased oxygen content [23]. Chin et al. indicated that the input energy required to burn fuel inside combustion chamber reduces greatly with the increase in oxygen content. This also reduces combustion noise since the delay period reduces with the increased ignition oxygen concentration [24]. In this present work study investigation of performance and emission parameters of Karanja with its blends and oxygen enrichment in four-stroke diesel engine are performed. In order to enhance the properties of pure jatropha oil transesterification process was performed. Once the oil is trans esterified it is made to blend with neat diesel and further emission and performance parameters are analyzed by running in diesel engines. After that oxygen enrichment is done with biodiesel blends (B30) and the obtained results are compared and studied.
2 Oil Preparation and Experimental Procedure 2.1 Transesterification Process of Karanja Oil Raw oil extracted from the plant have higher viscosity and this makes them unsuitable to be used in diesel engine directly. Initially the Karanja oil is reacted with sulfuric acid and methanol and in the flask and it is stirred continuously at different temperature. This process prolongs until the acid value falls between 0.1 and 0.5. After the solution attains the prescribed acid value entire process is stopped and it is cooled further. Then the distillation process was carried out to get the unreacted methanol from the mixture. Remaining products further undergoes esterification process once again to obtain methyl ester. After this process the by-product Glycerol deposited at the bottom of the flask and the remaining Karanja oil is extracted for blending process. The extracted oil is mixed with diesel and the corresponding physical characterization study were carried out.
3 Experimental Setup and Its Procedure Experimental investigation was performed on a mono cylinder water-cooled, naturally aspirated, DI four-stroke diesel engine. The complete configuration of engine test setup is given in Fig. 2. Engine testing was connected to a dynamometer for giving brake power at various operating conditions. Then the fuel consumption with the aid of mass air flow sensor connected at intake manifold. Rotational speed of the engine is acquired using tachometer which is mounted in the shaft of the engine. Various pollutants like Nitrous oxides Carbon Monoxide and HC were captured with
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Fig. 1 Experimental setup layout
the aid of emission gas analyzer. The exhaust gas measuring probe is connected in the tailpipe and the other end is connected to analyzer for measuring emissions. In the present work the Karanja oil was blended with diesel in the ratio 30:70. Many previous works made in biodiesel found that B20 blend shows optimum performance in all aspects while running in diesel engines [18, 19]. Then the engines were made to run with neat diesel and B30, the corresponding emission and performance parameters were noted down. After this process oxygen enrichment is done with the aid of oxygen cylinder which is connected in intake manifold. The oxygen concentration is varied with the help of flow control value which is attached in oxygen cylinder supply hose pipe. Then the engine is tested with B30, and the oxygen is mixed with the concentration varying from 22 to 24%. After mixing oxygen with the inlet air, once again fuel consumption and emissions readings were recorded. In this experiment the oxygen enrichment ratio 22, 23 and 24% is labeled as B30(1%), B30(2%) and B30(3%) in the graphs (Fig. 2).
4 Results and Discussion 4.1 Specific Fuel Consumption Figure 2 highlights the trend of BSFC at different load conditions for various brake power. The plot obtained clearly pointed out that blends of biodiesel gives marginally elevated BSFC while compared with neat diesel. It is also inferring that SFC decreases gradually as the brake power increases for diesel and blends of biodiesel. Present situation happens because as brake power rises, the fuel injected amount also rises which in turn increases peak pressure developed inside the combustion chamber. This scenario ends up in higher combustion chamber temperature with the reduced ignition
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Fig. 2 Specific fuel consumption versus brake power
delay and it results in enhanced burning rate of fuel [22]. However slight decrease in SFC is observed for biodiesel after enrichment since added oxygen enhances combustion rate. It was about nearly 4% to 15% reduction in BSFC is observed with the linear rise in oxygen concentration for B20 blend.
4.2 Brake Thermal Efficiency Figure 3 shows the BTE obtained with respect to various load conditions at different oxygen concentrations. The plot obtained reveals that for both neat diesel and biodiesel blends BTE linearly increases along with applied load [25, 26]. BTE for B30 blends slightly declines at higher load conditions with the magnitude of 18.4%. Whereas in case of diesel BTE of around 23.2% is shown at similar conditions. When the addition of oxygen (22, 23, 24%) the BTE increases due to the complete combustion. The BTE for B30(2%) is higher 27.3%, while comparing with the diesel the BTE of B30(2%) is increased to 4% which results in efficient combustion and higher BTE. This scenario occurs since the oxygen enrichment results in increased burning rate of biodiesel blend with reduced ignition delay and it contributes to higher heat release rate [23].
4.3 Carbon Monoxide Figure 4 gives the CO emissions with respective to different brake power for blends and neat diesel. CO is formed due to low availability of oxygen and insufficient
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Fig. 3 Brake thermal efficiency versus brake power
Fig. 4 Carbon monoxide versus brake power
temperature inside the combustion chamber. The plot obtained indicates CO reduces in align with the rise in inlet oxygen concentration. This happens since biodiesel have 10% concentration of oxygen in its composition and thus enhances combustion rate [27, 28]. Another reason for reduction is that due to oxygen enrichment at inlet makes the air fuel leaner and it increases oxidation reactions further. CO reduction percentage of around 57% is shown for B30(3%) at higher load conditions due to higher in cylinder temperature at this condition.
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Fig. 5 Smoke opacity versus brake power
4.4 Smoke Opacity Figure 5 gives the smoke pollutant trend with respect to various brake power. Particulate matter in the presence of sunlight react results formation of smoke. The plot obtained clearly pointed out that as brake power rises the smoke opacity also rises for both B20 and neat diesel. However, for biodiesel blends with rise in oxygen concentration the smoke opacity also declined linearly while compared with neat diesel. The particulate matter mainly formed due to insufficient oxygen concentration at elevated temperature. This enriched oxygen concentration inside the combustion chamber results in complete oxidation of soot particles. Maximum of around 45% reduction is achieved for B30(3%) oxygen enrichment for B30 blends at full load conditions (Fig. 5).
4.5 NOx Emissions NOx is considered as potential treat to diesel engine and it is formed due to availability of oxygen and higher temperature inside the combustion chamber. Figure 6 indicates the NOx pollutant variation for different brake power conditions. The plot shows that NOx rises while the applied brake power increases for B30 and neat diesel. This may be due to enhanced oxidation reactions at higher applied load results higher in cylinder temperature and it results in higher NOx concentration [31, 32]. However NOx emission is reduced for biodiesel blends while compared with neat diesel [33, 34]. This occurs since biodiesel have higher viscosity and it results in improper fuel atomization which results in decline of peak cycle temperature of combustion chamber.
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Fig. 6 Nitrous oxide versus brake power
5 Conclusions In this work biodiesel extracted from the Karanja oil and experiments were performed in mono cylinder diesel engine at various oxygen enrichment levels. The important conclusions obtained are as follows. 1
2
3 4
The Thermal Efficiency got raised with an average increment of 8–10% when oxygen gas was supplemented with biodiesel when compared to straight blends but the brake thermal efficiency of the B30(2%) remains higher with 27.3% efficiency at elevated load conditions. The BSFC remained low for oxygen supplementing biodiesel an average decrement of 4–10% when compared to biodiesel blends but the curve remained low for diesel an average decrement of 6% when compared to biodiesel and high oxygen enriched. The CO emission were reduced for biodiesel blend with the increase in oxygen enrichment percentages, an average decrement of 15% is observed. The NOx emission is higher for the B20 blend with the increase in oxygen enrichment percentage of about 24% is observed at higher load operating conditions.
References 1. Subramaniam, M., Pachamuthu, S.: Two zone thermodynamic model for prediction of particulate matter emission from direct injection diesel engine. Therm. Sci. 20(suppl. 4), 1017–1028 (2016) 2. Subramaniam, M.K., Pachamuthu, S., Arulanandan, J., Muthiya, J.: Simultaneous reduction of HC, NOx and PM by using active regeneration technique. No. 2016-01-0912. SAE Technical Paper, (2016)
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3. Mohankumar, S., Senthilkumar, P.: Particulate matter formation and its control methodologies for diesel engine: a Comprehensive review. J. Renew. Sust. Energy Rev. 80, 1227–1238 (2017) 4. Subramaniam, M., Jenoris Muthiya, S., Satish, S., Joshuva, A., Alexis, J.: Numerical investigation on various layouts of phase change materials based battery module used in electric vehicles. No. 2020-28-0499. SAE Technical Paper (2020) 5. Muthiya, S.J., Amarnath, V., Senthilkumar, P., Mohankumar, S.: Experimental investigation and controlling of CO2 emission from automobile exhaust by CCS technique. Int. J. Appl. Engg. Res. 10, 36–46 (2015) 6. Subramanian, M., Satish, S., Muthiya Solomon, J., Sathyamurthy, R.: Numerical and experimental investigation on capture of CO2 and other pollutants from an SI engine using the physical adsorption technique. Heat Transfer 49(2020), 2943–2960 7. Sathyamurthy, R., Balaji, D., Gorjian, S., Muthiya, S.J., Bharathwaaj, R., Vasanthaseelan, S., Essa, F.A.: Performance, combustion and emission characteristics of a DI–CI diesel engine fueled with corn oil methylester biodiesel blends. Sustain. Energy Technol. Assess. 43, 100981 (2021) 8. Subramaniam, M., Muthiya Solomon, J., Nadanakumar V., Anaimuthu S., Sathyamurthy, R.: Experimental investigation on performance, combustion and emission characteristics of DI diesel engine using algae as a biodiesel. Energy Rep. 6, 1382–1392 (2020) 9. Lipase M.M.: Lipase-catalyzed alcoholysis of sunflower oil. J. Am. Oil Chem. Soc. 67, 168170 10. Clark, S.J., Wagner, L., Schrock, M.D., et al.: Methyl and ethyl soybean esters as renewable fuels for diesel engines. J. Am. Oil Chem. Soc. 61, 1632–1638 (1984) 11. Ilgen, O., Dincer, I., Yildiz, M., et al.: Investigation of biodiesel production from canola oil using Mg-Al hydrotalcite catalysts. Turk. J. Chem. 31, 509–514 12. Ong, H.C., Mahlia, T.M.I., Masjuki, H.H., et al.: Comparison of palm oil, Jatropha curcas and Calophyllum Inophyllum for biodiesel: a review. Renew. Sust. Energ. Rev. 15, 3501–3515 13. Singh, S., Singh, D.: Biodiesel production through the use of different sources and characterization of oils andtheir esters as the substitute of diesel; a review. Renew. Sust. Energ. Rev. 14, 200–216 14. Dinesh, K., Tamilvanan, A., Vaishnavi, S., Gopinath, M., Raj Mohan, K.S.: Biodiesel production using Calophyllum inophyllum (Tamanu) seed oil and its compatibility test in a CI engine, Biofuels, 10(3), 347–353 (2019). https://doi.org/10.1080/17597269.2016.1187543 15. Panigrahi, N., et al.: Non-edible Karanja biodiesel-A sustainable fuel for CI engine. Int. J. Eng. Res. Appl. 2(6), 853–860 (2012) 16. Bobde, S.N., Khyade, V.B.: Detail study on properties of Pongamia pinnata (Karanja) for the production of biodiesel. Res. J. Chem. Sci. 2(7), 16–20 (2012) 17. Lohith, N., et al.: Experimental investigation of compressed ignition engine using Karanja oil methyl ester (KOME) as alternative fuel. Int. J. Eng. Res. Appl. 2(4), 1172–1180 (2012) 18. Prajapati, V.V., et al.: Performance and emission analysis of diesel engine fuelled with Karanja oil and Diesel. Int. J. Adv. Mech. Eng. 7(1), 15–29 (2017) ISSN 2250-3234 19. Agarwal, A.K., Das, L.M.: Bio-diesel development and characterization for use as a fuel in C.I. engines. J. Eng. Gas Turb. Power, ASME, vol. 123 (2001) 20. Bhatt, Y.C., Murthy, N.S., Datta, R.K.: Use of mahua oil (Madhuca indica) as a diesel fuel extender. J. Inst. Eng. (India): Agricul. Eng. Div. 85, 10–14 (2004) 21. Raheman, H., Phadatare, A.G.: Diesel engine emissions and per-formance from blends of Karanja Methyl ester and diesel. Biomass Bioenergy, 27(4), 393–397 (2004). https://doi.org/ 10.1016/j.biombioe 22. Senthilkumar, M., Arul K., Sasikumar, N.: Impact of oxygen enrichment on the engine’s performance, emission and combustion behavior of a biofuel based reactivity controlled compression ignition engine. J. Energy Inst. 92(1), 51–61 23. Baskar, P., Senthilkumar, A.: Effects of oxygen enriched combustion on pollution and performance characteristics of a diesel engine, Engineering Science and Technology. Int. J. 19(1), 438–443 (2016) 24. Banapurmath, N.R., Tewari, P.G., Hosmath, R.S.: Experimental investigations of a four-stroke single cylinder direct injection diesel engine operated on dual fuel mode with producer gas as
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Experimental Assessment on Performance and Emission Characteristics of Calophyllum inophyllum (Tamanu) Seed Oil in Direct Injection Diesel Engines Jenoris Muthiya Solomon, Mohankumar Subramaniam, C. Dinesh Kumar, Joshuva Arockia Dhanraj, Nadanakumar Vinayagam, Christu Paul Ramaian, and A. V. Sivabalan Abstract Rapid depletion of fossil fuels biodiesel comes into play in satisfying the current energy demands. Biodiesel extracted from the non-edible source gains an attention due to food scarcity issues faced by current increasing world population. In the present work an oil is extracted from the source of tamanu seed and tested in diesel engine. Initially biooil is extracted from tamanu seed and it is converted into biodiesel by transesterification process. The corresponding physical properties for biodiesel and its blends are tested and it is well matched with ASTM standards. Experiment is performed in a diesel engine. Then the oxygen enrichment is done at the inlet with the percentage variation from 21 to 27%. Test result shows efficiency is improved by 7 to 9% for B20 blends with the increased oxygen concentration. For both biodiesel and its blends the brake specific fuel consumption reduces with the rise in inlet oxygen levels. Regarding emission parameters the gradual reduction in CO and smoke opacity is achieved at higher load conditions for biodiesel blends (B20). On the other hand, due to more oxygen concentration and rise in cylinder temperature NOx emissions increases for biodiesel and its blends. J. M. Solomon Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India M. Subramaniam Department of Automobile Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu 641049, India C. Dinesh Kumar Department of Automobile Engineering, BS Abdur Rahman Crescent Institute of Science and Technology, Chennai, India J. Arockia Dhanraj (B) Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India N. Vinayagam · C. P. Ramaian · A. V. Sivabalan Department of Automobile Engineering, Hindustan Institute of Technology and Science, Padur, Chennai 603103, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_25
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Keywords Biodiesel · Non-edible source · Calophyllum inophyllum · Tamanu seed · Oxygen enrichment
1 Introduction The global energy demand is rising exponentially due to rapid rise in industries and transportation sector in developing countries. The world’s energy demands are heavily relied on conventional fossils fuels such as petrol and diesel for the past several decades [1, 2]. Due to stringent emission norms, rapid rise in price of fuels and global warming scenario leads to search of substitute fuels. Potential source for alternate fuels is the biodiesel in addressing these energy demands and emission norms. They can be produced from various sources and it shows significant amount of reduction in emissions like UHBC, CO and particulate matter while tested in diesel engine [3, 4]. Another major advantage is that this biodiesel has no sulphur content in its fuel composition this makes them as suitable fuel to be used for BS VI engines with no further fuel modifications [5]. Biodiesel may be derived from each edible or expendable oil and non-edible sources or non-consumable oil. In the past few decades extracting biooil from eatable sources such as soybean and cotton oil have been carried out [6–8]. On the other hand rapid consumption of edible sources leads to food scarcity issues in countries like India. In this regard various non edible oil extracted from the sources like jatropha, pongamia, mahua and tamanu oil has greater ability to replace fossil fuels [9–11]. In the last few years biooil extracted from the source tamanu seed (Calophyllum inophyllum) has gained its attention among researchers. This non-edible seed has found widely in tropical climate especially in Australia, southern Asia and centralized pacific region. It has ability to develop to 8–20 m with an inexperienced stone fruit of 2–4 cm diameter [12]. Dinesh et al. experimentally tested tamanu oil under different load in engine. Biodiesel blends show reduction in HC, CO and NOx emissions [12]. In another work they tested various blends of tamanu oil in variable compression ratio engine. Test results obtained shows that peak pressure obtained for biodiesel blends shows more similar trend while compared with diesel. Regarding emission parameters not much reduction in HC, CO and NOx emission is noted for biodiesel blends while compared with diesel [13]. Rahman et al. conducted experimental study by running the engine with C. inophyllum blends at high idle conditions. They found that the various physical properties like density, viscosity and calorific valve for biodiesel blends is within acceptable limits as per ASTM standards. Fuel consumption increases for pure biooil and all blends while compared with diesel. Gradual reduction in HC, CO and decrease in NOx is observed for biodiesel and all blends [14]. Ayyasamy et al. examined the combine effect of adding tamanu biodiesel with biogas and tested in diesel engines at various load conditions. Test results obtained reveals that the blends B20, B40 and B100 shows better mechanical efficiency, SFC and BTE. Regarding emissions UHBC
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reduces greatly and the NOx emissions increases at all applied loads conditions for biodiesel blends. In recent years in order to increase the burning rate of fuel oxygen enrichment is adopted. This technique enhances combustion rate of biodiesel also reduces Smoke and NOx emissions simultaneously [15]. Baskar et al. conducted an experimental study in diesel engine by increasing oxygen content from 21 to 27%. They observed that SFC decreased by 5–12% and Brake thermal efficiency increased by 4–8%. On the other hand UHC, CO and smoke reduced by 41, 55 and 60% with the increased oxygen content [16]. Chin et al. indicated that the input energy required to burn fuel inside combustion chamber reduces greatly with the increase in oxygen content [17]. In this experimental work performance and emission parameters of tamanu oil biodiesel and oxygen enrichment in four-stroke diesel engine. Transesterification process was carried out to enhance the properties of biooil. Then the esterified oil is blended with conventional diesel and the performance and emission characteristics of biodiesel blends is analysed. After that oxygen enrichment is done with biodiesel blends (B20) and the obtained results are compared and studied.
2 Materials and Approaches Extraction of Oil The tamanu fruit is collected and sees are dried and beneath daylight for 3–4 days. The yield for seeds after drying was 150 seeds/kg and also the liquid content in the seeds were 10 (Fig. 1). Esterification Process of Tamanu Oil Usually the esterification method is employed once the FFA content of oil is larger. C. inophyllum oil quantity was measured to be forty one.74 mg KOH/g oil. To convert crude C. inophyllum oil (CCIO) into C. inophyllum alkyl organic compound (CIME) an acid–base catalyzed transesterification was implemented. After transesterification process the by-product glycerol deposited at the bottom of the flask and the remaining tamanu oil is extracted for blending process. The extracted oil is mixed with diesel and the corresponding physical characterization studies were carried out.
3 Procedure of the Experiment Experimental investigation was administered on one cylinder naturally aspirated four-stroke vertical cool engine. The engine was attached to a DC ergometer as a loading device. The entire layout of experimental setup is shown within Fig. 2. The engine speed was measured victimization progressive encoder associated an orificecoupled pressure gauge was for the activity of intake air. AVL five-gas analyzer was
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Fig. 1 Kernels before removal, seeds cake and also the extracted oil
accustomed live HC, CO, NOx . This analyzer is fitted within the pipe of the engine. Tamanu oil was mingling with diesel in 2 hundredth and half-hour mix. Performance and emission analysis were conducted altogether the higher than blends with diesel because the base initial the engine is formed to run with B20 (20% bio diesel eightieth diesel). Many previous works made in biodiesel found that B20 blend shows optimum performance in all aspects while running in diesel engines [18, 19]. Fuel O2 gas alongside B20 mix was created to supplement with intake air and so performance and emission are characteristics noted down.
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Fig. 2 Experimental setup layout
4 Results and Discussion Specific Fuel Consumption Figure 3 shows the BSFC at varied load conditions for B20 blends. For a hard and fast H to carbon molar quantitative relation (H/C), the ratio air–fuel quantitative relation decreases once the atomic number 8 concentration in air will increase. The BSFC increases at full load in B20 and shows gradual reduction at idle and part load conditions. This occurs since at higher load conditions more total of fuel got injected into combustion chamber. Due to the addition of oxygen the BSFC decreases for the
Fig. 3 Specific fuel consumption versus brake power
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Fig. 4 Brake thermal efficiency versus brake power
blends B20 (1% 2% 3%) since it improves oxidation reactions inside the combustion chamber [20]. Brake Thermal Efficiency Figure 4 shows the BTE of the fuel tested in the engine. The thermal efficiency is stricken by compression quantitative relation and therefore the thermodynamical properties of the operating mixture. A rise in O2 concentration increases the mixture quantitative relation of specific heats, that successively will increase the mixtures thermal energy to figure energy [21]. For B20 blend the BTE decreases at full load (18.4%) whereas the BTE of diesel remains higher with 23.2% at full load when comparing with biodiesel (B20) the BTE decreases. when the addition of oxygen (22% 23% 24%) the BTE increases due to the complete combustion. The BTE for B20 (2%) is higher 27.3%, while comparing with the diesel the BTE of B20 (2%) is increased to 4% which results in efficient combustion and higher BTE. Carbon Monoxide Figure 5 shows the variation of CO emissions with variable load conditions. CO emission decreases with the rise in recess O2 concentration. This state of affairs happens since biodiesel contains 10% O2 concentration in its fuel composition and so enhances combustion rate [22, 23]. Another reason for reduction is that due to oxygen enrichment at inlet makes the air fuel leaner and it increases oxidation reactions further. CO reduction percentage of around 60% is shown for B20 (3%) at higher load conditions due to higher in cylinder temperature at this condition.
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Fig. 5 Carbon monoxide versus brake power
Smoke Opacity Soot formation is powerfully obsessed on the ratio, temperature and pressure and intermixture [23, 24]. O2 enrichment additionally reduces the ignition delay, which means higher burning rate and shorter combustion period, that more reduces soot formation [25–27] (Fig. 6). NOx Emissions NOx formed at higher level in diesel combustion due to high in cylinder temperature. By increase of O2 24% the NOx level increases for B20 blends (1% 2% 3%) while compared with biodiesel. While using biofuel with oxygen enrichment NOx increases compared with diesel [28]. Since oxygen enrichment improves oxidation reactions and thereby increase in cylinder temperature greatly [29, 30] (Fig. 7).
Fig. 6 Smoke opacity versus brake power
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Fig. 7 Nitrous oxide versus brake power
5 Conclusions In the present work biodiesel extracted from the tamanu seed is carried out in compression ignition engine at various oxygen enrichment levels. The important conclusions obtained is summarized as follows 1.
2.
3.
4.
Increase in efficiency of the engine by an average increment of 7–9% when oxygen gas was supplemented with biodiesel when compared to straight blends but the brake thermal efficiency of the B20 (2%) remain higher with 27.3% efficiency at full load. The fuel consumption remained low for oxygen supplementing biodiesel an average decrement of 5–10% when compared to biodiesel blends but the curve remained low for diesel an average decrement of 6% when compared to biodiesel and high oxygen enriched. The CO emission were both biodiesel and low for oxygen enriched when compared to diesel there was an average decrement of 20%. But in terms of biodiesel and oxygen enriched biodiesel the emission were low for oxygen enriched with an average decrement of 8–10%. The NOx emission was observed to be high oxygen enriched biodiesel and low for biodiesel and high on. There was a low in NOx emission in both biodiesel and oxygen enriched biodiesel and average increment of 10 and 20% when compared to diesel. This is due to rise in operating temperature.
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Investigating Outdoor Heat Stress Using Environmental Parameters and Selected Thermal Indices in Northern India Milap Sharma, Narendra Mohan Suri, Suman Kant, and Abhishek Charak
Abstract Heat stress is often an unacknowledged occupational health hazard especially in developing countries. Climatic zones like tropical and subtropical regions having higher air temperature, humidity and radiant temperature values may impose greater risks of heat-related illness and safety threats to users employed in developing countries having low and medium incomes. Present study aimed at assessing the environmental variables followed by evaluating the heat stress exposures levels at different time periods during the hot summer season utilizing widely used indices, i.e. wet bulb globe temperature (WBGT), discomfort index (DI), humidex (HD), heat index (HI), tropical summer index (TSI); so to have better insights of the stressful thermal climatic conditions experienced by the users engaged in outdoor work activities. Results revealed higher heat stress exposure levels attributable to noon and afternoon periods, indicating stressful climatic conditions, with respective indices exceeding the threshold limit values (TLVs). For WBGT, strong relationship was observed with TSI (R2 -value = 0.918) and DI (R2 -value = 0.891) indices, although least association with HI (R2 -value = 0.644) and HD (R2 -value = 0.566) indices. However, highest association was observed among HI and HD indices (r-value = 0.995; p-value < 0.01). From results, it may be concluded that noon and afternoon time periods could impose excessive heat strain on user’s work capabilities as compared to morning and evening periods, which could cause greater risks of heat-related morbidities and safety threats to workers/users employed, with consequent negative impacts on productivity and financial burden. Keywords Environmental variables · Negative impacts · Heat stress indices · Exposure assessment
M. Sharma (B) · N. M. Suri · S. Kant · A. Charak Department of Production and Industrial Engineering, Punjab Engineering College (Deemed to be University), Chandigarh 160012, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_26
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1 Introduction India is a diverse country with extreme climatic conditions ranging from tropical to subtropical regions, and there is huge unorganized sector [1]. Climatic zones like tropical and subtropical regions having higher air temperature and humidity values may cause a greater risks of heat-related illness and safety threats to users employed in developing countries having low and medium incomes. In developing countries like India, heat stress is often considered as an ignored occupational health hazard. A prolonged period of heat exposure could affect the production level, and at the same time, it will negatively impact the performance of the user [2]. There are six main agents of thermal comfort (four environmental factors; air temperature, radiant temperature, air speed and relative humidity, and two individual factors; metabolic rate and clothing worn) that accord to the heat stress exposure experienced by the user. Several indices have been developed for the assessment of heat stress exposure, but each varies depending on the considered environmental and personal factors [3]. Previous studies reveal that wet bulb globe temperature (WBGT) index could be used as an optimal heat stress index, due to its applicability under hot work environments [3–6]. But, merely relying on a single index may generate inappropriate results. So, it could also be used in combination with other widely used heat stress indices like discomfort index (DI), humidex (HD), heat index (HI), tropical summer index (TSI), etc., which will provide better detailed estimation of the thermal strain experienced by the targeted audience. According to a previous study in India, during the hot summer season, millions of poor workers are affected by excessive workplace heat with consequent health risks, which ultimately affects their productivity and daily incomes [5]. Ahmed et al. (2020) analysed thermal stress exposure among workers engaged in outdoor construction work activities in UAE, by evaluating the WBGT, HSI and TWL indices for comparative analysis, with WBGT exceeding the threshold limit values (TLVs). The authors suggested that more in-depth analysis can be done using alternative indices in locations where the WBGT index is high [6]. From previous research findings, it is evident that these indices could effectively be used for analysing the heat stress exposure levels under the targeted thermal work ambience. Several assessment studies have been conducted under various work sectors in developed countries, but there is still a lot of research work required to be executed in developing countries. As the geographical conditions differ from one country to another; there is need to explore various environmental factors affecting thermal comfort in a rigorous manner. The present study aimed at assessing the environmental variables followed by evaluating the heat stress exposures levels during the hot summer season utilizing widely used indices, so to have better insights of the stressful thermal climatic conditions experienced by the users engaged in outdoor work activities, followed by descriptive and inferential statistical analysis for the evaluated heat stress parameters. A comparative analysis has also been performed between the summer and winter season under the same geographical location.
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2 Methodology In present study, five different widely used heat stress indices have been considered for evaluating the heat stress exposure level under outdoor climatic conditions during the hot summer season (June–July, 2020) in Chandigarh union territory of India (30° 44 14 N; 76° 47 14 E) and additionally during the winter season (January, 2021) also. The environmental measurements were monitored using Kestrel 5400 Heat stress tracker Pro (Nielsen-Kellerman Co.; USA) [7], placed on a tripod at 1.1 m floor surface height as per standards [4]. The equipment was allowed to stabilize for period of 15–20 min, after which the monitored readings were considered for evaluation purposes as shown in Fig. 1. The environmental variables were monitored during four different time periods throughout the day (i.e. morning: 9.30 A.M. to 10.30 A.M.; noon: 11 A.M. to
Fig. 1 Monitoring heat stress environmental variables at different time periods
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12:30 P.M.; afternoon: 1:00 P.M. to 3:30 P.M.; evening: 4:30 P.M. to 6:00 P.M.). From the monitored variables, respective heat stress indices were evaluated to analyse the associated risk exposure levels. Further descriptive and inferential statistics have been performed on the evaluated variables. IBM SPSS Statistics 26 have been used for analysing the evaluated variables; scatterplots and regression lines were plotted for the considered heat stress indices and further Pearson product moment correlation was also performed for analysing the respective associations.
2.1 Considered Heat Stress Indices 2.1.1
Wet Bulb Globe Temperature (WBGT)
WBGT is an empirical index, which is a widely used and validated heat stress index [4], for assessing hot work environments considering the combined effects of air temperature, humidity, air velocity and radiation by measuring natural wet bulb temperature (T nw ), dry bulb temperature (T a ) and radiant temperature using Globe temperature (T g ) for both indoor and outdoor work conditions.
2.1.2
For indoor work environment : WBGT = 0.7Tnw + 0.3Tg
(1)
For outdoor environment : WBGT = 0.7Tnw + 0.2Tg + 0.1Ta
(2)
Tropical Summer Index (TSI)
TSI is an empirical heat stress index based on the Indian climatic conditions [8]. It gives an equivalent temperature of still air at a constant relative humidity of 50%, which provides the similar thermal sensation experienced by a user as the actual environment under consideration. It is expressed by a mathematical relation involving (T wb ) wet bulb temperature (°C), (T g ) globe temperature (°C), and (V ar ) air speed (m/s) as under: (3) TSI = 0.308 ∗ Twb + 0.745 ∗ Tg − 2.06 Var + 0.841.
2.1.3
Heat Index (HI)
Heat index (HI) involves the use of a regression equation based on two environmental factors, i.e. (RH) relative humidity in per cent and (T a ) air temperature (in degree Fahrenheit) [9]. The developed heat index regression equation was developed by Rothfusz and is given by:
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HI = −42.379 + 2.04901523 ∗ Ta + 10.14333127 ∗ RH − 0.22475541 ∗ Ta ∗ RH − 0.00683783 ∗ Ta2 − 0.05481717 ∗ RH2 + 0.00122874 ∗ Ta2 ∗ RH + 0.00085282 ∗ Ta ∗ RH2 − 0.00000199 ∗ Ta2 ∗ RH2 2.1.4
(4)
Humidex (HD)
Humidex developed by Canadian meteorological service department is a direct index (involving dry bulb temperature (T db in °C), vapour pressure (V p in hPa)) for evaluating, how hot the thermal environment feels to an average person, when combining the effect of heat and humidity (dew point temperature) [10].
2.1.5
Humidex = Tdb + [0.5555(Pa − 10)]
(5)
Pa = 6.11 ∗ e[5417.7530∗((1/273.16)−(1/Tdp in Kelvin))]
(6)
Discomfort Index (DI)
The development of a direct indices tool called the “discomfort index” based on (T wb ) wet bulb temperature (in °C) and (T db ) dry bulb temperature (in °C) with some correction factor, which relates the thermal degree of discomfort perceived by the user in a work environment [11]. DI = 0.5(Tdb + Twb )
(7)
3 Results and Discussions 3.1 Environmental Variables The monitored variables during different time periods (relative humidity, dry bulb temperature, globe temperature, wind speed, natural wet bulb temperature) were further analysed using statistical analysis to draw logical conclusions. During the hot summer season, it was observed that RH values rise during the evening and morning periods as compared to pre-noon and afternoon time periods. Highest values of globe temperature (50.35 °C), dry-bulb/air temperature (37.61 °C) and air velocity (1.528 m/s) were observed during afternoon period followed by pre-noon period, indicating harsh climatic conditions. Although higher values of natural wet bulb temperature were found during the pre-noon period (31.05 °C) followed by afternoon
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Table 1 Average values for summer outdoor environmental parameters (mean (SD)) Parameters
Relative humidity (%)
Air temperature (in Celsius)
Globe temp. (in Celsius)
Air velocity (in m/s)
Natural wet bulb temp. (in Celsius)
Morning
63.26 (4.563)
29.59 (1.668)
41.34 (2.716)
0.462 (0.465)
26.24 (0.944)
Pre-noon
58.91 (2.225)
35.73 (0.769)
47.99 (1.987)
1.134 (0.727)
31.05 (0.469)
Afternoon
42.31 (2.095)
37.61 (0.641)
50.35 (1.648)
1.528 (0.867)
29.24 (0.424)
Evening
68.08 (3.432)
31.72 (0.895)
33.49 (2.770)
0.643 (0.463)
27.04 (0.653)
Day period
Table 2 Range values for outdoor environmental parameters (summer) Parameters
Relative humidity (%)
Air temperature (in Celsius)
Globe temp. (in Celsius)
Air velocity (in m/s)
Natural wet bulb temp. (in Celsius)
Morning
52.5–71.9
26.6–34
35.4–47.3
0–2.3
22.7–28.5
Pre-noon
54.2–65.8
34.1–37.3
39.1–50.7
0–3.8
29.7–31.8
38–50.4
36–39.8
42.6–52.8
0–6.1
27.4–30.4
62.6–79.8
30.3–34.2
29.9–39.1
0–2.4
26.3–29
Day period
Afternoon Evening
(29.24 °C), evening (27.04 °C), and morning (26.24 °C) period as described in Tables 1 and 2 depicts the range values for outdoor environmental variables during summer season. Similar trend was observed with respect to variations among the environmental variables throughout the day during winter season also; however, monitored values (as depicted in Tables 3 and 4) were significantly lower than the summer, as expected.
3.2 Heat Stress Indices From the analysed variables, considered heat stress indices (i.e. WBGT, HD, HI, DI, TSI) were evaluated as shown in Tables 5 and 6. During summer season, higher heat stress exposure levels were accountable to the noon and afternoon time periods as compared to morning and evening time periods indicating stressful climatic conditions; exceeding the threshold limit values (TLVs). Figure 2 depicts the bar-graphs, comparing evaluated heat stress indices mean values during the summer and winter seasons. Results revealed that respective heat stress indices were exceeding the TLVs during the summer season, indicating hot stressful climatic conditions, which could cause greater risks of heat-related morbidities and safety threats to workers/users employed with consequent negative impacts on productivity and financial burden. Figures 3 and 4 show the variation among evaluated indices (average values) during different time periods throughout the day with respect to summer and winter seasons
84.93 (4.363)
70.24 (3.171)
60.71 (1.587)
92.57 (1.657)
Pre-noon
Afternoon
Evening
Relative humidity (%)
Morning
Day period
Parameters
9.88 (0.392)
18.60 (0.427)
16.78 (0.894)
12.78 (1.304)
Air temperature (in Celsius)
10.20 (0.382)
26.07 (0.847)
25.93 (0.858)
21.64 (4.067)
Globe temp. (in Celsius)
Table 3 Average values for winter outdoor environmental parameters (mean (SD))
0.47 (0.456)
1.38 (0.918)
1.22 (0.813)
0.97 (0.595)
Air velocity (in m/s)
9.10 (0.220)
15.54 (0.287)
15.39 (0.473)
13.01 (1.328)
Natural wet bulb temp. (in Celsius)
8.73 (0.166)
10.86 (0.366)
11.32 (0.337)
10.29 (0.661)
Dew point temp. (in Celsius)
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Table 4 Range values for outdoor environmental parameters (winter) Parameters
Relative humidity (%)
Air temperature (in Celsius)
Globe temp. (in Celsius)
Air velocity (in m/s)
Natural wet bulb temp. (in Celsius)
Dew point temp. (in Celsius)
Morning
73.8–92.3
10.7–16.4
16.5–27.6
0–3.9
11.3–15.2
9.1–12.3
Pre-noon
64.3–77
14.4–18.7
23.5–27.5
0–4.9
14–16.2
10.3–12.2
Afternoon
55.3–65.8
17.3–20.4
24.5–28.1
0–7.8
14.8– 16.2
9.7–12.1
Evening
89.4–95.7
9.1–10.8
9.7–11.4
0–2.9
8.7–9.6
8.2– 9.4
Day period
Table 5 Evaluated heat stress indices during the summer season Indices→ WBGT
HI
HD
DI
TSI
Day period↓ Morning
29.46 (1.2678) 33.39 (2.9307) 38.62 (2.3291) 27.91 (1.1795)
Pre-noon
34.82 (0.7628) 47.87 (1.774)
36.57 (2.4052)
49.53 (1.2185) 33.40 (0.51133) 42.47 (1.7413)
Afternoon 34.22 (0.5939) 45.09 (1.2509) 47.36 (0.9937) 33.42 (0.3870)
43.39 (1.3735)
Evening
28.83 (1.1665) 39.73 (1.7578) 43.99 (1.1398) 29.38 (0.7076)
30.79 (2.2323)
Overall
31.56(2.8474)
37.57 (6.0240)
41.63 (4.8125) 45.02 (3.5156) 31.09 (2.3766)
Table 6 Evaluated heat stress indices during the winter season Indices→
WBGT
HI
HD
DI
TSI
12.73 (1.171)
14.13 (1.582)
12.90 (1.263)
17.39 (3.463)
Day period↓ Morning
14.64 (1.825)
Pre-noon
17.59 (0.5708) 16.04 (0.7389) 18.62 (1.0114) 16.09 (0.6424) 21.15 (0.8476)
Afternoon 17.90 (0.3578) 17.44 (0.5082) 20.21 (0.5352) 17.07 (0.2506) 21.20 (0.8645) Evening
9.40 (0.2470)
10.06 (0.3568) 10.53 (0.4526) 9.49 (0.2931)
8.08 (0.4831)
Overall
15.45 (3.423)
14.72 (3.017)
17.74 (5.394)
16.71 (3.965)
14.50 (3.044)
separately, with highest values observed during noon and afternoon session. From results, it may be concluded that noon and afternoon time periods could impose excessive heat strain on user’s work capabilities as compared to the evening and morning work periods. During summer season, for noon and afternoon periods; WBGT values exceeded the TLVs; humidex indicated dangerous discomfort experienced; HI classified the risk category as danger zone, with more chances of heat cramps, heat exhaustion and possible heat cramps with prolonged work activity; TSI also exceeding the TLVs, and DI indicating serious discomfort observed during the respective work periods. However, morning and evening periods were slightly less distressing in terms of thermal ambience as compared to harsh noon and afternoon periods during the summer season.
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Fig. 2 Variations among considered indices between two different seasons
Fig. 3 Indices mean values during different time periods (summer)
3.3 Scatterplots and Correlation Analysis Further, Pearson product moment correlation was performed using IBM SPSS 26.0 software package for the evaluated heat stress indices. Highest association was observed among HI and HD indices (r-value = 0.995; p-value < 0.01); for WBGT, highest correlation was observed with TSI (r-value = 0.958) and DI (r-value = 0.944), whereas lowest association was found for TSI with HD (r-value = 0.581)
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Fig. 4 Indices mean values during different time periods (winter)
and HI (r-value = 0.647) indices (as shown in Table 7). During winter, correlation analysis signified slight variations among the respective heat stress indices (Table 8) as compared to summer results; for WBGT, highest positive association was observed with TSI (r-value = 0.989) and DI (r-value = 0.980); however, strong Table 7 Bivariate correlation analysis for considered indices (summer period) WBGT
HI
DI
TSI
Humidex
WBGT
1
0.803**
0.944**
0.958**
0.752**
HI
0.803**
1
0.935**
0.647**
0.995**
DI
0.944**
0.935**
1
0.850**
0.905**
TSI
0.958**
0.647**
0.850**
1
0.581**
Humidex
0.752**
0.995**
0.905**
0.581**
1
** Correlation
is significant at the 0.01 level (2-tailed)
Table 8 Bivariate correlation analysis for considered indices (winter period) WBGT
HI
DI
TSI
Humidex
WBGT
1
0.947**
0.980**
0.989**
0.953**
HI
0.947**
1
0.991**
0.915**
0.999**
DI
0.980**
0.991**
1
0.953**
0.994**
TSI
0.989**
0.915**
0.953**
1
0.921**
Humidex
0.953**
0.999**
0.994**
0.921**
1
** Correlation
is significant at the 0.01 level (2-tailed)
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Fig. 5 Scatterplot and regression lines depicting relationship between WBGT and other respective indices
positive relationship was found between HI and HD (r-value = 0.999). Figure 5 depicts the scatterplots and regression lines for the relationship among WBGT and other respective indices, i.e. HI, DI, TSI, HD. From results, strong relationship was observed among WBGT and TSI (R2 -value = 0.918); WBGT and DI (R2 -value = 0.891) indices. Although least association was observed among WBGT and HD (R2 -value = 0.566); WBGT and HI (R2 -value = 0.644). From analysed results; it may be concluded that noon and afternoon time periods were prone to stressful thermal conditions, with respective heat stress indices exceeding the TLVs. Remedial control measures could be considered beneficial in improving the thermal ambience, which may include adequate prevention control policies, work-rest periods, regular fluid intake, and even simulation-based control studies; also, engineering control design interventions may be taken as an effective and important control measure in reducing the exposure levels up to the desired permissible limits [12–14].
4 Conclusion During hot summer season, it was observed that RH values rise during the evening and morning periods as compared to pre-noon and afternoon time periods. Highest
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values of globe temperature, dry bulb temperature were observed during afternoon and noon period, indicating harsh climatic conditions. Variations were observed among respective heat stress parameters during different time periods throughout the day. Strong positive association was observed for WBGT, with TSI and DI indices, whereas lowest association was found for TSI with HD and HI indices. Scatterplot and regression lines also showed better relationship among WBGT and TSI, DI indices, however least with HD and HI, but strong relationship was observed between HD and HI. Results indicated that WBGT index could effectively be used in conjunction with other widely used heat stress indices like DI, TSI, which may provide better estimation of the harsh thermal work conditions. Higher exposure levels were accountable to noon and afternoon time periods as compared to morning and evening periods, with respective heat stress indices exceeding the threshold limit values during the summer season, indicating hot stressful climatic conditions, which could cause greater risks of heat-related morbidities and safety threats to users’ employed.
References 1. Venugopal, V., Chinnadurai, J.S., Lucas, R.A., Kjellstrom, T.: Occupational heat stress profiles in selected workplaces in India. Int. J. Environ. Res. Publ. Health 13(1), 89 (2016) 2. Kjellstrom, T.: Climate change, direct heat exposure, health and well-being in low and middleincome countries. Global Health Action (2015) 3. Epstein, Y., Moran, D.S.: Thermal comfort and the heat stress indices. Ind. Health 44(3), 388–398 (2006) 4. International Organization for Standardization (ISO), ISO 7243: Ergonomics of the thermal environment—assessment of heat stress using the WBGT (wet bulb globe temperature) index (2017) 5. Krishnamurthy, M., Ramalingam, P., Perumal, K., Kamalakannan, L.P., Chinnadurai, J., Shanmugam, R., Venugopal, V.: Occupational heat stress impacts on health and productivity in a steel industry in southern India. Saf. Health Work 8(1), 99–104 (2017) 6. Ahmed, H.O., Bindekhain, J.A., Alshuweihi, M.I., Yunis, M.A., Matar, N.R.: Assessment of thermal exposure level among construction workers in UAE using WBGT, HSI and TWL indices. Ind. Health 58(2), 170–181 (2020) 7. Product Specifications for Kestrel 5400 Heat Stress Trackers. https://kestrelinstruments.com/ mwdownloads/download/link/id/41/. Last accessed 2020/09/22 8. Sharma, M.R., Ali, S.: Tropical summer index—a study of thermal comfort of Indian subjects. Build. Environ. 21(1), 11–24 (1986) 9. Rothfusz, L.P., Headquarters, N.S.R.: The heat index equation (or, more than you ever wanted to know about heat index). National Oceanic and Atmospheric Administration, National Weather Service, Office of Meteorology, 9023, Fort Worth, Texas (1990) 10. Masterton, J.M., Richardson, F.A.: Humidex: A Method of Quantifying Human Discomfort Due to Excessive Heat and Humidity. Environment Canada, Atmospheric Environment (1979) 11. Tennenbaum, J., Sohar, E., Adar, R., Gilat, T.: The physiological significance of the cumulative discomfort index (Cum. DI). Harefuah 60(10), 315–19 (1961) 12. Sharma, M., Kataria, K.K., Kant, S., & Suri, N.M.: Ergonomic assessment of a fettling operation in foundry based on digital human modeling and statistical analysis. In: Optimization Methods in Engineering, pp. 481–502. Springer, Singapore (2020) 13. Varghese, B.M., Hansen, A., Bi, P., Pisaniello, D.: Are workers at risk of occupational injuries due to heat exposure? A comprehensive literature review. Saf. Sci. 110, 380–392 (2018) 14. Sharma, M., Kataria, K.K., Suri, N.M., Kant, S.: Monitoring respirable dust exposure in fettling work environment of a foundry: a proposed design intervention. Int. J. Saf. Secur. Eng. 10(6), 759–767 (2020)
Design and Reliability Study on Fixture for Normal and Underwater Friction Stir Welding R. Muthu Vaidyanathan , Mebratu Markos Woldegioris , N. Sivaraman , Mahaboob Patel , and Tsegaye Alemayehu Atiso
Abstract In a non-fusion joining procedure, friction stir welding is a relatively modern approach to join material without melting by the non-consumable uniquely tuned tool. During the welding, a rigid workpiece clamping is the most significant aspect of preventing lifting and dispersing. This poses several timely clamping issues. These problems are strongly interlinked with weld strength. Suitable work holding device is essential for normal and underwater friction stir welding. A fixture is designed to overcome the existing problems of the convention clamping systems. A fixture is made of D2 steel and 12050 steel material. The experimental validation found that this approach achieves superior weld on the visual inspections. The Autodesk Fusion360 package is adopted to model the fixture design. The job changing time was significantly reduced. The developed fixture is reliable on both welding conditions for making the proper weldment. Keywords UFSW · Underwater friction stir welding · Fixture · NFSW · FSW · Friction stir welding
1 Introduction Over two decades, friction stir welding plays a significant part in joining various metals in sound conditions. In the present scenario, welding the different materials is essential to achieve quantitative weld in high strength and low weight application [1, 2]. Friction stir welding has vast potential for the dissimilar materials joining process. The FSW is discovered and established at The Welding Institute (TWI) of the UK in December 1991 by W. Thomas. This method is widely engaged as an interesting procedure to manufacture light-weight devices in the arena being aerospace, automobile, aviation, marine, and railway, for about a decade. The method can bond diverse sorts of materials, including metals, polymers, and ceramics [3]. The FSW has three significant zones on the welded material; the stirred zone (SZ) present at R. Muthu Vaidyanathan (B) · M. M. Woldegioris · N. Sivaraman · M. Patel · T. A. Atiso Department of Mechanical Engineering, Wolaita Sodo University, Wolaita Sodo, Ethiopia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_27
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the intermediate of the weld; besides, it is named a thermo-mechanically affected zone (TMAZ). Finally, halfway between base metal (BM) and TMAZ, the heataffected zone (HAZ) was observed [4, 5]. The researchers categorize the welding operation into three stages: the plunging, dwelling, and transition stages. Experimentally noticed that maximum forces exerted in the plunging stage than the translation stage [6]. The pin geometries, rotational speed, and welding speeds influence the mechanical strength. They experienced a rise in temperature on minor variations in the rotational speed and transverse speed [7]. Non-destructive rotating tool plunges into the mating materials. The friction between the shoulder surface and materials produces frictional heat. This heat makes it easier for further tool movement. The forward action produces the joint on the mating materials. Underwater friction stir welding (UFSW) remains an ideal modern approach of friction stir welding procedure that’s the novel and developing method in current years. The authors reported that liquid cooling is causing grain modification and increasing UFSWed material strength associated with normal friction stir welding (NFSW). The rise of rotational speed causes a higher temperature and the same absorbed by the water in UFSW. In Heat treatable aluminum alloys, the temperature change causes the increase in mechanical properties similar to tensile strength, fatigue, and hardness of the UFSW weldments correlated to the NFSW. Hence, the cooling medium affects the hardness; the diminishing hardness is the sign of an increment of the strength in UFSW [3, 8–10]. A fixture has been developed for the FSW of titanium material. The tungsten insert is placed below the weld portion to resist the downward force and process temperature. A circular cooling channel is made on the steel base plate. The channel assists in taking off the process heat for better performance [11]. In the EBSD study of dissimilar UFSW on AA7003 and AA6060, the examiners experienced fine equiaxed grain on the HAZ. The partial dynamic recrystallization was seen on the TMAZ. The average grain size of 5.2 µm gained on the stirred zone. This grain size is finer than the grain size of 11.1 µm on air cooling. More cooling with dislocation causing fine dynamic recrystallization (DRX) [12]. Researchers reported UFSW temperature is 40% lesser than the NFSW. Thermal degradation of the process increases the transverse force. Though the percentage of elongation is minimal on the UFSW exhibits a brittle fracture, tensile strength is significantly higher than NFSW [7]. The authors found that UFSW material has more ductile than NFSW. Also, they noticed the grain refinement on the UFSW [13]. The position of the weld material on the backplate is a challenging aspect. The misalignment of weld materials may lead to the wrong weld line [14]. The weld material has to be clamped rigidly on the backplate to avoid the expelling due to the axial force act from the tool [15]. An appropriate clamping system produces a defect less weld. A continuous clamping procedure was employed on the welding and offers sound quality and high strength weld. The authors obtained the greater clamping forces on the material. This force leads to less deformation, and the residual stress is dispersed to the whole depth of the materials [16]. In dissimilar NFSW of AA2024 with AA7075, the finer grain size of 5.2 µm was achieved on SZ by 600 rpm over the 950 rpm and 1650 rpm. During the tensile examination, the fracture has occurred on the lower hardness region of
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HAZ on the advancing and retreating sides. Though the 950 rpm reports a higher tensile of 411.11 MPa, this is very close to the 650 rpm strength of 403.8 MPa [17]. A researcher obtained an ultimate tensile strength of 13% in UFSW over FSW. In FSW joint, fractured reported on the stirred zone. Similarly, in UFSW, stirred zones became harder due to this fracture has occurred on HAZ. The authors employed a versatile stainless steel fixture for UFSW. Also, a gate is provided to supply and drain the water on the Plexiglas box [18]. Effects of pin profile on dissimilar UFSW joint, triangular pin produces a fine grain size than the cylindrical threaded pin. This triangular pin results in higher strength [19]. Tensile strength is increased during the rise in tool transverse speed of UFSW. Also, the cooling effect of water exhibits brittle fracture [20]. A threaded taper profile has a greater UTS of 13.38 and 19.06% than a taper and cylindrical threaded profile [21]. Numerous researchers stated that the UFSW joint exhibits a greater tensile strength over the FSW [18, 22]. This work aims to design and develop the fixture for normal and underwater friction stir welding conditions. In recent days, very limited literature is available on UFSW or SFSW. It is essential to explore more about the results of water effects on friction stir welded material. Further, the developed fixture experiences validation for understanding the reliability.
2 Design Considerations The authors studied the mechanical and metallurgical characteristics of AA 6063. Due to improper alignment of weld material, they have obtained the inclined weldment. Figure 1 illustrates improper welding [23]. In general, this poses several practical challenges in existing FSW fixtures. The potential alternatives are explored due to the difficulties in implementing this in practice. Proper welding is most important for examining welding strength. Materials misaligned may lead to the nonuniform
Fig. 1 Improper FSW [23]
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welding transverse length from the weld line. Table 1 demonstrates the cause and effects of the process. Several factors to be considered for designing a suitable and easy fixture for different welding circumstances. Part list of the fixture is presented in Table 2. For simplicity production, the fixture is modeled in Fusion 360. The factors to consider for fixture design • • • • • •
Simple and easy to handle. Ease of positioning the fixture on the machine table. Ease of aligning the material on the fixture. Uniform clamping on the entire weld material to avoid buckling. Versatility for various sizes and thickness of materials. Adaptability for normal and underwater FSW.
Table 1 Cause and effects of fixture Cause
Effects
Locating the fixture
Improper location of the fixture may lead to the wrong welding line
Positioning of weld materials
Misalignment or spread out of mating faces
job changing
Conventional clamping takes more time to set every job
Thermal deformation
The weld quality may affect by thermal distortion of the backplate,
Productivity
Vast job setting time affect the productivity
Complexity
The aligning and material setting may be more difficult
Clamp
Uniform pressure may not be act on the materials by several clamps
Buckling
Improper clamping may lead to material buckling
Weld gap
If the materials not clamped rigidly may cause the weld gap
Table 2 Part list and material type
Part name
Quantity
Material
Base plate
1
D2 steel
Clamping plate
2
D2 steel
Side cover plate A
2
12050 steel
Side cover plate B
2
12050 steel
Guide
2
12050 steel
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Fig. 2 Base plate
Table 3 Chemical composition of D2 steel Element
C
Cr
Cu
Mn
V
Si
Ni
Mo
Co
P
S
%Weight
1.4
11.0–3.0
0.25
0.6
1.10
0.6
0.30
0.7–1.20
1.0
0.03
0.03
3 Development of the Fixture 3.1 Base Plate The base plate is a significant part of this fixture and is also known as a backplate or pressure plate. During the process, the base plate withstands the axial downward force of the tool. Also, the plate assisting in producing the sound weld. The plate made up of high carbon high chromium steel with 400 × 350 × 30 mm is demonstrated in Fig. 2. Table 3 expresses the chemical composition. The plate’s top surface through the pocket with 200 mm wide and 2 mm depth machined to place the weld materials. The maximum size of each weld material is 100 mm wide and 160 mm in length. The pocket is designed for welding. It has three rows, namely row A, row B, and row C, to fasten the clamping plate. Row B is in the middle, assisting in overcoming the clamping plate’s bending fastening by the other two rows. The M8 threaded holes are also made on the sides of the plate to fix the cover plate for underwater friction stir welding. The entire setup fastened on the machine table through the diameter 20 mm hole.
3.2 Clamping Plate The plate is made up of D2 material with 250 mm length, 160 mm wide, and 20 mm depth, as illustrated in Fig. 3. It has three slots to fastening the weld materials by a M14 bolt on the base plate. A slope of 45° provided at the front side of the plate for
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Fig. 3 Clamping plate
Table 4 Chemical composition of 12050 steel Element
C
Cr
Cu
Mn
Si
Ni
P
S
%Weight
0.4–0.5
0.25
0.30
0.5–0.8
0.17–0.37
0.30
0.04
0.04
the unrestricted tool movement. Table 4 demonstrated the chemical composition of 12050 steel. A guide assists in locating the fixture on the table slot. It is essential to ensure the alignment between weld material and tool. The dimension is14.9 × 14.9 × 40 mm and fabricated by 12050 steel. The guide is fastened at the bottom of the base plate using a M8 bolt.
3.3 Side Cover Plate The side cover plate’s primary purpose is to facilitate sufficient water for the UFSW process. There are two sets of cover plates, namely A and B, used for the UFSW process. For the water supply and drainage, a hole is provided on the side cover plate A. Also, it ensures the freshwater supply during the process of heat rejection. The dimensions of cover plates A and B are 350 × 200 × 15 mm and 430 × 200 × 15 mm and are produced by the 12050 steel material. Figures 4 and 5 illustrate a fixture assembly. NFSW contains a base plate, clamping plates, and guides. Similarly, UFSW additionally has side cover plate A and B.
4 Feasibility and Reliability Testing of the Fixture The proposed technique relies on having substantial knowledge about FSW. The fixture backplate is fixed at the machine table, and readily weld materials were
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Fig. 4 NFSW fixture setup
Fig. 5 UFSW fixture setup
placed on the slit. Then, the materials are clamped using the clamping plate. Fixture assembly is illustrated in Fig. 6. The advantage of the fixture demonstrated in recent trials. Figure 6 described our approach with experiments on NFSW and UFSW. It was found that a system produces good quality results in both welding circumstances. The fixtures offer uniform clamping and alignment on the material. Also, no lifting and the dispersed issue occurred during the process. The validation run of UFSW and NFSW is demonstrated in Fig. 7.
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Fig. 6 Developed fixture
Fig. 7 Welded material on NFSW and UFSW
5 Conclusion The design offers a new way to overcome the usual limitations and difficulties. The fixture is made up of D2 steel and 12050 steel. Validation was performed in order to provide confidence in the results of the developed model. Overall, this work offers a successful approach to UFSW and NFSW. In terms of quality of results, this approach delivers well. • Autodesk Fusion360 was adapted to model the fixture design.
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• This fixture has typically demonstrated satisfactory performance on NFSW and UFSW conditions. • A guide on the fixture base plate offers a proper and comfortable location on the machine table to achieve a sound weld. • The slit on the top of the fixture offers an appropriate alignment for the straight welding line. • Clamps offer a rigid holding on the weld material. • During the validation, no lifting or misalignment issues occurred on the mating weld material. Acknowledgements Funding for the present work was provided by Wolaita Sodo University, Ethiopia (Ref no. WSU41/16/403). The authors also acknowledge Ethio Engineering Group Addis Machine and Spare Part Manufacturing Industry at Addis Ababa, Ethiopia, for a grant (Ref no. EEG3/AT28/4137/2021) for the support in carrying out the research.
References 1. Mahto, R.P., Gupta, C., Kinjawadekar, M., Meena, A., Pal, S.K.: Weldability of AA6061-T6 and AISI 304 by underwater friction stir welding. J. Manuf. Process. 38, 370–386 (2019) 2. Dong, J., Zhang, D., Zhang, W., Zhang, W., Qiu, C.: Microstructure and properties of underwater friction stir-welded 7003–T4/6060-T4 aluminum alloys. J. Mater. Sci. 54, 11254–11262 (2019) 3. Awang, M.: The Advances in Joining Technology. Springer Singapore (2019) 4. Balaji, S., Aadithya, S., Balachandar, K.: Conventional and underwater friction stir welded AA2024-T351 aluminium alloy—a comparative analysis. World J. Eng. 17(6), 795–801 (2020) 5. Dewangan, S.K., Tripathi, M.K., Manoj, M.K.: Effect of welding speeds on microstructure and mechanical properties of dissimilar friction stir welding of AA7075 and AA5083 alloy. Mater Today Proc. (2019) 6. Trimble, D., Monaghan, J., Donnell, G.E.O.: Force generation during friction stir welding of AA2024-T3. CIRP Ann. Manuf. Technol. 61, 9–12 (2012) 7. Papahn, H., Bahemmat, P., Haghpanahi, M., Sommitsch, C.: Study on governing parameters of thermal history during underwater friction stir welding. Int. J. Adv. Manuf. Technol. 78, 1101–1111 (2015) 8. Babu, K.T., Muthukumaran, S., Bharat Kumar, C.H., Narayanan, C.S. A study on influence of underwater friction stir welding on microstructural, mechanical properties and formability in 5052-o aluminium alloys. Mater. Sci. Forum 969 MSF, 27–33 (2019) 9. Hajinezhad, M., Azizi, A.: Numerical analysis of effect of coolant on the transient temperature in underwater friction stir welding of Al6061-T6. Int. J. Adv. Manuf. Technol. 83, 1241–1252 (2016) 10. Wahid, M.A., Khan, Z.A., Siddiquee, A.N.: Review on underwater friction stir welding: a variant of friction stir welding with great potential of improving joint properties. Trans. Nonferrous Metals Soc. China (English edn.) 28, 193–219 (2018) 11. Fratini, L., Micari, F., Buffa, G., Ruisi, V.F.: A new fixture for FSW processes of titanium alloys. CIRP Ann. Manuf. Technol. 59, 271–274 (2010) 12. Dong, J., Zhang, D., Luo, X., Zhang, W., Zhang, W., Qiu, C.: EBSD study of underwater friction stir welded AA7003-T4 and AA6060-T4 dissimilar joint. J. Mater. Res. Technol. (2019) 13. Mabuwa, S., Msomi, V.: Comparative analysis between normal and submerged friction stir processed friction stir welded dissimilar aluminium alloy joints. J. Mater. Res. Technol. 9, 9632–9644 (2020)
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14. Ramnath, B.V., Elanchezhian, C., Rajesh, S., Prakash, S.J., Kumaar, B.M., Rajeshkannan, K.: Design and development of milling fixture for friction stir welding. Mater. Today Proc. 5, 1832–1838 (2018) 15. Kumar, P., Kumar, R., Hembram, B.K., Murugan, M., Arif, A., Veerababu, M.: Study of microstructure and mechanical properties of aluminium alloy (AA-6351-T6) using friction stir welding. Mater. Today Proc. 27, 1733–1737 (2020) 16. Richter-trummer, V., Suzano, E., Beltrão, M., Roos, A., Santos, J.F., De, C.P.M.S.T.: Influence of the FSW clamping force on the final distortion and residual stress field. Mater. Sci. Eng. A 538, 81–88 (2012) 17. Zhang, C., Cao, Y., Huang, G., Zeng, Q., Zhu, Y., Huang, X., Li, N., Liu, Q.: Influence of tool rotational speed on local microstructure, mechanical and corrosion behavior of dissimilar AA2024/7075 joints fabricated by friction stir welding. J. Manuf. Process. 49, 214–226 (2020) 18. Talebizadehsardari, P., Musharavati, F., Khan, A., Sebaey, T.A., Eyvaziana, A., Derazkola, H.A.: Underwater friction stir welding of Al-Mg alloy: thermo-mechanical modeling and validation. Mater. Today Commun. 26, 101965 (2021) 19. Msomi, V., Mabuwa, S., Muribwathoho, O., Motshwanedi, S.S.: Effect of tool geometry on microstructure and mechanical properties of submerged friction stir processed AA6082/AA8011 joints. Mater. Today Proc. (2021) 20. Sabry, I., Zaafarani, N.: Dry and underwater friction stir welding of aa6061 pipes—a comparative study. IOP Conf. Ser. Mater. Sci. Eng. 1091, 012032 (2021) 21. Banik, A., Saha, A., Deb Barma, J., Acharya, U., Saha, S.C.: Determination of best tool geometry for friction stir welding of AA 6061-T6 using hybrid PCA-TOPSIS optimization method. Meas. J. Int. Meas. Confed. 173, 108573 (2021) 22. Mistry, H.J., Jain, P.S., Vaghela Tinej, J.: Experimental comparison between friction stir welding and underwater friction stir welding on Al6061 alloys BT. In: Kalamkar, V.R., Monkova, K. (eds.) Advances in Mechanical Engineering, pp. 169–177. Springer Singapore, Singapore (2021) 23. Xu, A.: Properties of high speed friction stir welded 6063-T6 aluminum alloy. J. Phys. Conf. Ser. 1676 (2020)
Parameters Affecting Design of Wind Turbine Blade—A Review P. R. Mehta and R. V. Kale
Abstract Wind energy is a promising sector in renewable sources of energy in India. The power generated from a wind turbine depends on wind speed and wind density for a given blade radius. The wind speed is an uncontrollable factor, but the blade’s design should be such that it gives better output in all types of wind conditions. This paper presents parameters affecting the blade’s design in the wind turbine and includes a study on various factors like tip speed ratio, solidity, and twist in the blade. Loads acting on the blade are gravitational, bending and edge-wise, and centrifugal. Loads set critical limits of the design. To sustain load endurance in modern wind turbine blades uses specific optimization. Paper presents two such optimizations, which include vortex generators and flaps. Flap increases the blade’s efficiency by 5–12%. They reduce the load on blades, leading to more energy dissipation at low wind speed conditions. According to the vortex generator’s industrial usage, it brings a rise in the AEP of the wind power plant by 24%. Keywords Blade design · Wind turbine · Tip speed ratio · Vortex generator · Flaps
1 Introduction Wind power plants have become the fastest enhancing renewable energy resource for electricity generation. Overall installing capacity has reached above 500 GW. Optimization of the blade for a wind turbine is needed to increase the city’s wind power technologies. There are two types of wind turbines: horizontal axis wind turbine (HAWT) and vertical axis wind turbine (VAWT). There is a discontinuity in the development and large-scale usage of VAWT designs in the industry. HAWT has increased rotor control, and it produces enough torque at regular intervals of time and self-starting. It has widespread acceptance among large-scale production as it has better efficiency and energy output. It has become a dominant configuration design in the industry. The wind velocity continuously varies in its intensity, and a practical P. R. Mehta (B) · R. V. Kale Department of Mechanical Engineering, MCT’s Rajiv Gandhi Institute of Technology, Mumbai 400053, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_28
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wind turbine should withstand all intensity of the wind. Blade designing involves solidity, tip speed ratio, angle of twist, and forces on the blade. Load analysis is also a significant criterion for designing blades. The blade’s load capacity also signifies the blade’s endurance limit and failure limit. Because of today’s fast-growing technology from a standard simple design blade, they have reached an optimized blade with vortex generators and flaps on them. According to the latest development in the wind power plant sector, an innovative wind turbine blade, Sweep-Twist Adaptive Rotor, has shown an enormous increase in energy output by 12%; the main characteristic of the blade is a curved tip, which is designed to take maximum advantage of all wind speeds [1]. There is a scope in the study of optimizing the blade of a wind turbine, which researchers are still exploring.
2 Forces on Blade The usage of appropriate airfoils increases the lift of the blade. Two airfoil families highly being used are NERL and NACA. NREL airfoil families are 23–35% used for stall regulated turbines, 8–20% used for variable-pitch turbines, and 8–10% for variable rpm turbines. Table 1 presents the criteria for selecting NACA and NERL airfoil [2]. The lift-to-drag ratio is also an important consideration while choosing a rotor blade design. Rotor design includes a two-dimensional coefficient for a different angle of attack, Reynolds number, blade speed, and blade’s pitch angle. When it comes to relating the structural stability of blades, the thickness of the airfoil in the ratio of its chord to hub plays a role. A thick airfoil section is supposed to have a reduced lift-to-drag ratio. Graphical presentation of thickness and camber of different airfoil is presented in Figs. 1 and 2. A term known as the stall is also an essential factor for a better design; stall occurs at a higher angle of attack according to airfoil design. Here, there is a separation of the boundary layer at the tip of the blade, which causes the formation of wake on the blades’ upper surface. Stall decreases the blade’s lift capacity and increases the drag factor, which lowers the turbine’s efficiency level. Table 1 Comparison of average values of four criteria between selected NACA and NREL airfoils [3]
Criteria
NACA
NREL
Average max. GR at low Re
46.8
39
Average max. GR at high Re
79
75.2
Average max. difference of angle of attack
2.51
2.33
Average percent deviation of GR
4.66
2.63
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Fig. 1 Different airfoil and its thickness
Fig. 2 Different airfoil and its camber
3 Factors Affecting Blade Design Blade designing involves specific parameters which have a significant impact on blade performance. Paper presents the following factors: tip speed ratio, solidity, blade geometry, and twist angle. The efficiency of the blade is dependent on these factors.
3.1 Tip Speed Ratio The tip speed ratio is defined as the relationship between the tangential velocity at the rotor tip and relative wind. Parameters to decide the tip speed ratio of the blade include the amount of torque generated by it, the allowable mechanical stress, and the noise control. The blade’s tip speed ratio depends on the total number of blades used. The fewer blades help to get the faster motion of turbines and give a better output. As shown in Table 2, designs with two and three blades will have a tip speed ratio of range 5. Four to seven blades design will have a range of 3 tip speed ratio. An optimized airfoil rotor blade design increases the tip speed ratio by a 20–25% increase in the rotor’s speed motion and gives better power output [5]. A design of 3 blades will have a tip speed ratio of 6–7 [6].
318 Table 2 The relation between number of blades and tip speed ratio [4]
P. R. Mehta and R. V. Kale No. of blades
Tip speed ratio (TSR)
6–7
2
4–7
3
2–3
5
According to the literature survey, a high tip speed ratio results in decreased chord widths, leading to a slender blade profile but causes an increase in aerodynamic forces and centrifugal forces. When designed with a high tip speed ratio, blades generate minimum torque at a lower speed, creating the self-starting problem. HAWT designs have TSR between 5 and 7. Table 2 illustrates the relation of TSR with the number of blades [7].
3.2 Solidity Solidity is defined as the ratio of area of the number of blades present in the turbine to the air area that passes through the rotor of design. The solidity factor depends on the change in rotor diameter and the number of blades in design. Solidity depends on the material, and high solidity gives high material usage. Increase in the number of rotor blades leads to increase in rotor blade material, which increases solidity of wind turbines proportionally [8]. An increase in the number of rotor blades leads to an increase in rotor blade material, which increases the solidity of wind turbines proportionally. In Fig. 3, graph depicts the change in solidity, which leads to the change in the number of blades. As the number of blades increases, solidity also increases. It concludes that it is preferable to have fewer blades in the wind turbine. The ideal number, according to survey, is 3. Lower angular velocity must obtain maximum output from a high-solidity blade. Increasing the solidity ratio gives less efficiency rate in design. Some design considerations illustrate how the change in blade number affects the solidity of design. Fig. 3 Blade number and solidity graph
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High solidity has enormous torque. It results in maximum speed at extreme wind conditions and faces difficulty while self-starting. It also leads to coarse blade angles. Also, low solidity results in higher efficiency [9]. It requires less material, generates low torque, and does not have a self-starting issue. In any design, the solidity ratio’s value varies according to designs. These factors for determining the effects of solidity are constant.
3.3 Angle of Twist of Blade The study indicates that twisting blades toward the stall gives a considerable increase in wind turbine’s annual energy output. Adopting the blade’s twist toward stall helps increasing energy output by 5–10% [10]. The curved blade has faster air moving above the curve than the air moving below the curve’s surface. It makes a lower pressure area on top, and hence, as a result, it creates better lift—the greater the wind velocity, the greater the lift, which gives faster rotation. Hence, in comparison to flat, we can always prefer curved and twisted blades [11]. The overall angle gives us the signification of the angle of twist in a blade. Using twisted blades is needed to design the blade’s wind turbine optimization efficiently. The twisting of the blade changes the angle of the wing. The effect of twisting and tapering helps improve the attack angle, increases the speed, increases efficiency, and reduces drag. These blades are structurally enhanced and, at the same time, reduces the bending stress on blades. The angle of twist is the difference in the blade’s angle at its hub section. It is dependent on two parameters angle of attack and tip speed ratio [12]. The lift generated by the airfoil depends on the angle of attack of the inflowing airstream. Moreover, an inflow of air depends on the wind’s velocity and rotational velocity with its specific radius. The airfoil sectioned near the blade’s hub is preferred to be angled than the tip section of the blade. The hub sections face more vital wind conditions and high-velocity wind than the wind’s tip section. For more substantial blade profile, angle of twist is provided; it makes the hub region stronger than the tip.
3.4 Loads on Blade The turbine blade faces loads like aerodynamic load, centrifugal load, bending load, and edge-wise bending. Industries use Software like Ansys and SOLIDWORKS to verify the blade’s loading sections. Gravitational force is proportional to mass and increases in the cube’s proportion with an increase in diameter. As per the latest industry models, it is preferable to have a diameter below 10-m. It reduces the inertial load on the blade. Centrifugal force acts in the radial outward direction, and it is a product of mass and velocity (rotational) squared [13]. It increases the load requirement of high-speed velocity. These two loads, that are centrifugal and
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Fig. 4 Pie chart on load distribution on blade
gravitational, are both superimposed. It gives positive displacement, which equates to blades one revolution in an alternating condition. The direction of this force is always from root to tip. Figure 4 explains load distribution on the wind turbine blade through a pie chart. It depicts the blade faces an enormous amount of edgewise bending force. The maximum pressure is on the hub section and then gets distributed to the tip section. So if the centrifugal force is exceptionally high, it might create a critical region in the blade’s hub section. Higher gyroscopic loads and centrifugal force exist in smaller blades. Bending and edge-wise bending occur by the combined factors of mass and gravity [14]. These forces depend on the shear force on the blade. Bending moment is a crucial factor in signifying the limits of fatigue failure. It is the maximum limit force beyond which the blade can undergo fatigue failure. When this force gets unevenly distributed, it faces its maximum pressure on the blade’s tips, which is known as an edge-wise bending moment. When the velocity of wind is exceptionally high, rotation of the blade increases rapidly and, it has reached its maximum bending moment value soon, then critical edge bending on the blade will take place. To prevent structural failure of blade, the modern wind turbine has some optimization on the surface of blades. This optimization includes placements of vortex generator and flaps on the blades’ surface. They reduce the load and enhance the blade’s lift capacity and reduce drag faced by the blade. Optimization helps increase the AEP percentage rapidly and generate electricity for larger areas [15, 16].
4 Optimization on Blade Modern designs of the wind turbine are more effective and efficient. Due to optimization, the average generating capacity of the turbine is increasing. 3D printing is introduced for wind turbines in industries and saves resources during manufacturing
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and time. Blade in a wind turbine undergoes various loads and moments. Like loads due to gravitational force, bending load, edge-wise bending load, gyroscopic load, and structural load, which causes its failure and to prevent it and make our design safe; we use some optimization on the blade. Optimization of design involves increasing its output efficiency, increasing the blade’s lift criteria, generating more power from wind, and more utilization of wind energy [15, 16]. In today’s time, placements of vortex generator and flaps are some of the blade optimizations for an efficient blade design. This paper further illustrates these two optimizations.
4.1 Flaps on Blade As colossal growth in the wind turbine sector has taken place in recent decades, there are goals to achieve immense load control. Many advanced techniques have been implemented, among which flaps are the most useful devices. Figure 5 presents a graphical view of flaps placement on the blade. According to the survey, this result is of the S818 series of airfoil used for blades. It demonstrates co-efficient of lift increases with flaps usage, which helps to get maximum lift. Some devices like flaps increase wind turbine efficiency outcomes and maintain flow control. Flaps are placed on the edges of the blade. They bring change in the camber’s structure of the airfoil and its chord length. The camber’s role is to generate better lift by providing the required curve to the airfoil. Flaps increase the camber in the airfoil and provide a better lift to the blades [17]. Flaps also change the blade’s planform area as there is an extension on the blade’s tip, increasing the blade’s effective area and increasing the lift. When the wind conditions are not extreme, and there is low wind velocity, flaps help the turbine to self-start in low wind conditions. Location of flaps is at a distance of 70% from the root of the blade covering 25% of the blade span, which increases blade load elevation [18]. The flap’s design depends upon the shape and design of the blade. Flaps are the trait borrowed from aerospace industries; it increases the blade’s efficiency by 5–12%. It reduces the load on blades, leading to more energy at low wind speed
Fig. 5 Graphical analysis of placement of flaps
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conditions. As the blade gets exposed to higher loads, there is a reduction in flap angle to reduce the blade’s overall load stress. Hence, it explains that flaps parallelly act with loads and simultaneously reduce the blade’s load.
4.2 Vortex Generator In the aerospace industry, vortex generators are part of planes, but vortex generators are now part of wind turbine blades due to our technological development. For better efficiency of wind turbine blades, larger dimensions blade production is preferable. This airfoil thickness leads to more separation of flow in the blade’s tip section [19]. A vortex generator is an additional structure added to the blades, and there are many shapes of vortex generators. It improves the blade’s performance by increasing the flow of energy around it, which delays the flow separation of wind. Delay in flow separation increases the blade’s efficiency in terms of power, load, lift, and performance. Figure 6 gives a graphical analysis of the usage of vortex generator. Design with VG has a higher tip speed ratio and a high AEP percentage. In all, VG enhances the efficiency output of blades in wind turbines. It reduces the drag and also helps to give the better rotational velocity of the turbine. Placements of VG are an essential part of designing optimization. Their location is at 20% to the blade’s chord position at its edges. Another essential part is the acceptable range of distance between two vortex generators. Preferable is the short spacing between them, as it acts as an additional catalyst to delay flow separation for the blade. Extending VG length can harm the flow separation; hence, we prefer a short and precise vortex generator length. According to the vortex generator’s industrial usage, it brings a rise in the AEP of the wind power plant by 24%. Hence, there is a 24% rise in wind turbine’s output using a vortex generator [20]. Factors to consider while designing a vortex generator on a wind turbine blade are its shape, the distance
Fig. 6 Graph on design with VG and without VG
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between two adjacent vortex generators, and their angle of attack for the wind. The vortex generator’s thickness is always 10–15% of the boundary layer thickness [21]. The two main factors for the placement of vortex generators are counter and co-rotational rotating configuration. Vortex generator placed in a counter-rotating pattern has equal but in a direction opposite incidence angle while co-rotating has an equal incidence angle concerning flow, which helps the vortex generator produce both counter and co-rotating vortices. Essential steps for designing a vortex generator are choosing rotational or counter-rotational. Fixed-size in the VG is desirable for simplicity and reduced cost of installation.
5 Conclusion HAWT designs are the dominant design in the industry. Overall installing capacity has reached above 500 GW. In designing a blade, airfoil selection is a crucial criterion. Two airfoil families highly used are NREL and NACA. This paper illustrates the criteria of selection between these airfoil families. Airfoil’s stability depends on airfoil’s thickness. The paper depicts how thickness and camber vary with different airfoil through graphical analysis. Thick airfoil has a low lift-to-drag ratio. The factors which need an essential consideration for a blade design include the type of airfoil, loads that act on blades, the tip speed ratio of the blade, angle of twist, and blade geometry. In the process of blade design, the tip speed ratio of the blade depends upon the number of blades used, the amount of torque generated, and the allowable mechanical stresses on the blade. As per the study, for three blades HAWT, 5–7 is the tip speed ratio range. The blade design’s solidity must be low as it provides higher efficiency, consistent torque generation. A significant change in the blade’s design is the angle of twist in the blade; it offers less bending stress on the blade, provides the required angle of attack, increases the lift, and makes the blade’s hub region stronger. Loads from forces that a wind turbine blade has to sustain for an efficient design include gyroscopic force, centrifugal force, gravitational force, and edge-wise bending force. Load generated by the centrifugal and gravitational force is superimposed, which further results in positive displacement and equates to the blade’s one revolution. This force’s direction is from root to tip, so the load must not exceed the endurance limit, which if exceeded results in the generation of critical sections at the hub. The too-high wind velocity also generates a force that results in edge-wise bending. High velocities rapidly increase the blade’s moments, further increasing the edge-wise bending load in a wind turbine blade. Some optimizations include a vortex generator and flap placement on a wind turbine blade to prevent critical failures. A flap on the blade helps reduce overall pressure on the blade acted upon by different loads. Flaps increase the camber in the airfoil and increase the planform area of blades, increasing lift and helping the blade self-start when the wind velocity is low. They are placed at a distance of 70% from the blade’s root and cover 25% of the blade span. The airfoil thickness leads to more separation of flow in the blade’s tip section. Placement of vortex generator
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helps to increase the flow energy around the blade and delays the flow separation of wind. Vortex generator’s placement is 20% to the blade’s chord position at its edges. The vortex generator’s thickness should always be 10–15% of the boundary layer thickness. Usage of them brings a rise in AEP of the wind power plant by 24%. This way, a conceptual study of blade design is illustrated in this paper to design an efficient wind turbine blade.
References 1. https://www.energy.gov/eere/next-generation-wind-technology 2. Tangler, J.L., Somers, D.M.: NREL airfoil families for HAWTs (No. NREL/TP-442-7109). National Renewable Energy Lab., Golden, CO (United States) (1995) 3. Islam, M.R., Bashar, L.B., Saha, D.K., Rafi, N.: Comparison and selection of airfoils for small wind turbine between NACA and NREL’s S-series airfoil families. Int. J. Res. Electr. Electron. Commun. Eng. 4, 1–12 (2019) 4. Schubel, P.J., Crossley, R.J.: Wind turbine blade design. Energies 5, 3425–3449 (2012). https:// doi.org/10.3390/en5093425 5. Ragheb, M.: Optimal rotor tip speed ratio. Lecture notes of course no. NPRE, 475 (2014) 6. http://www.reuk.co.uk/wordpress/wind/wind-turbine-tip-speed-ratio/ 7. Hansen, A., Butterfield, C.: Aerodynamics of horizontal-axis wind turbines. Ann. Rev. Fluid Mech. (1993) 8. Kumar, R., Baredar, P.: Solidity study and its effects on the performance of a small scale horizontal axis wind turbine. Impending Power Demand Innov. Energy Paths 290–297 (2012) 9. Mazarbhuiya, H.M.S.M., Biswas, A., Sharma, K.K.: Low wind speed aerodynamics of asymmetric blade H-Darrieus wind turbine-its desired blade pitch for performance improvement in the built environment. J. Braz. Soc. Mech. Sci. Eng. 42, 326 (2020) 10. Schubel, P.J., & Crossley, R.J.: Wind turbine blade design. Energies 5(9), 3425–3449 (2012) 11. Stäblein, A.R.: Analysis and design of bend-twist coupled wind turbine blades. In: Ostachowicz, W., McGugan, M., Schröder-Hinrichs, J.U., Luczak, M. (eds.) MARE-WINT. Springer, Cham (2016) 12. Yang, K.: Geometry design optimization of a wind turbine blade considering effects on aerodynamic performance by linearization. Energies 13(9), 2320 (2020) 13. El Mouhsine, S., Oukassou, K., Ichenial, M.M., Kharbouch, B., Hajraoui, A.: Aerodynamics and structural analysis of wind turbine blade. Procedia Manuf. 22 (2018) 14. Söker, H.: Loads on wind turbine blade. In: Advances in Wind Turbine Blade Design and Materials. Woodhead Publishing Series in Energy. Woodhead Publishing (2013) 15. Rehman, S., Mahbub Alam, M., Alhems, L.M., Mujahid Rafique, M.: Horizontal AxisWind turbine blade design methodologies for efficiency enhancement a review. Energies 11(3) (2018) 16. Jureczko, M., Pawlak, M., M¸ezyk, A.: Optimisation of wind turbine blades. J. Mater. Process. Technol. 167(2–3), 463–471 (2005) 17. McWilliam, M., Barlas, T., Madsen, H., Zahle, F.: Aero-elastic wind turbine design with active flaps for AEP maximization. Wind Energy Sci. 3, 231–241 (2018). https://doi.org/10.5194/ wes-3-231-2018 18. Andersen, P.B.: Advanced load alleviation for wind turbines using adaptive trailing edge flaps: sensoring and control (2010) 19. Froese, M.: How vortex generators boost wind-turbine performance and AEP (2017). https:// www.windpowerengineering.com/vortex-generators-boost-wind-turbine-performance-aep/ 20. Skrzypi´nski, W., Gaunaa, M., Bak, C., Junker, B., Brønnum, N.B., Kruse, E.K.: Increase in the annual energy production due to a retrofit of vortex generators on blades. Wind Energy 23(3), 617–626 (2020) 21. Li, X.K., Liu, W., Zhang, T.J., Wang, P.M., Wang, X.D.: Analysis of the effect of vortex generator spacing on boundary layer flow separation control. Appl. Sci. 9(24), 5495 (2019)
Comparative Study of Tensile Behavior Between Epoxy/Coir Fiber and Modified Epoxy/Coir Fiber Composite Animesh Sinha , Arindam Sinha , and Rajesh Kumar
Abstract Natural fiber composite plays significant role in recent development of manufacturing industry. Owing to the high modulus, diligent strength, and decreased carbon footprint on the atmosphere, the use of natural fiber composite is greatly enhanced over the years. This research provides an understanding of recent awareness of coir fiber composites and coir fiber reinforced composites updated with reactive diluents. Varying concentration of fiber (1, 3, 5, 7) wt% in epoxy and fix diluent concentration 5 wt% being used for this experimental study. Tensile behavior has been observed for both epoxy system where modified epoxy/coir fiber shows comparatively good result and enhancement of ~14% in tensile strength, where in case of epoxy/coir fiber composite shows the maximum enhancement of ~10 wt%. Natural fiber with high cellulose content gives better formation in polymeric chain. Increasing the amount of block co-polymer formation improves the strength of the epoxy system. Availability of functional group present in the system has been observed by Fourier Transformation Analysis (FTIR) which shows presence C=C and N–O bond justifies the enhancement the property of epoxy/coir and modified epoxy/coir system. This study shows that the use of diluent not only affects the viscosity of epoxy resin, but also increases the strength of polymeric composite material. Keywords DGEBA · TETA · PEG · FTIR
1 Introduction Due to the significant improvement in properties, NFCs are essential. Mechanical characteristics that could allow them to be in functional implementations, and useful practical applications [1]. Coir fibers have many beneficial characteristics such as
A. Sinha (B) · R. Kumar CVR College of Engineering, Hyderabad 501510, India A. Sinha National Institute of Technology Agartala, Agartala, Tripura 799046, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_29
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low cost, elevated content of lignin, low density, availability, high break elongation low elastic modulus, and low elastic modulus [2]. Natural fibers have some inherent properties that may serve as drawbacks in their polymer composite reinforcement, including low resistance to microbial attacks, weak moisture resistance, and poor adhesion between surface fibers of the fiber matrix have some advantageous properties such as low cost, high lignin content, low density, availability, high break elongation, and low elastic modulus [3]. To enhance adhesion between the fiber surface and the polymer matrix, chemical fiber treatment will not only alter the fiber surface, but also increase the strength of the fiber [4]. When used to strengthen hydrophobic matrices, the large amount of hydroxyl group in cellulose offers natural fiber hydrophilic properties; the result is a very weak interface and poor resistance to moisture absorption [5]. One way to improve a matrix’s plasticity is to change it with different additives, such as rubbers, thermoplastics, or active diluents [6]. For the most part, the tensile strength of the binder decreases monotonically after adding the active diluent. Since the intensity appears to increase with a content of DEG-1 to 10 wt%, this growth is commensurate with the data dispersion [7]. The lack of connection between the strength of a composite in tension and compression and the strength of the matrix is very normal, as it is understood that fibers make a fundamental contribution to the strength of a composite in these stress states [8]. Glass and other synthetic fiber-reinforced plastics have high specific strength, their applications are limited due to their higher production costs. Natural fibers are not only sturdy and lightweight, but they are also comparatively less in price [9]. It is observed that increasing the fiber content reduces the thermal stability of Coir-Polymer composites due to the lower degradation temperature of the coir fibers. Untreated coir composites are more vulnerable to high water absorption than treated coir composites, which have a direct influence on the material’s properties [10]. The mechanical properties of coir fiber composite allow for applications in a variety of fields, including aeronautical applications. Fiber processing, fiber propagation and direction, and composite material production technology all can enhance the properties of coir/epoxy composites [11]. Coconut fiber has more lignin and less cellulose and hemicellulose than other common natural fibers, which, combined with its high micro-fibrillar angle, provides a variety of valuable properties such as durability, strength, and damping, wear, resistance to weathering, and high elongation at split. Coir fiber is used to make ropes, blankets, mattresses, brushes, as well as in the upholstery industry, agriculture, and construction [12, 13]. Previously, coir fibers were used to make geotextiles (biodegradable fabrics) that were mostly used to control erosion caused by rain or dams on rivers and beaches. However, refined coir from mature coconuts has also been used in automobiles, most notably as the material for the seat base [14, 15].
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2 Materials and Methodology Resin epoxy-Lapox L-12 resin. It is a distinctive industrial epoxy resin that is synthesized in the presence of a simple catalyst by reacting with epichlorohydrin bisphenolA. The properties of DGEBA resins are based on the n value, which is the number of repeating units generally referred to as the degree of repeating units. For polymerization. It is essentially an unmodified medium viscosity epoxy resin that can be used to produce fiber-reinforced composites with several hardeners. Lapox K6/Triethylenetetramine epoxy hardener—(TETA). It is a mixture of four compounds of ethylene amine that has fewer impurities than other amines. With an ammonia-like odor, it exhibits sticky, yellow liquid behavior at room temperature. It is soluble in water as well as in organic solvents (Table 1). Polyethylene glycol (PEG) is a polyether compound that, depending on its molecular weight, is often referred to as polyethylene oxide (PEO) or polyoxymethylene (POE). PEG’s structure is generally expressed as H–(O–CH2 –CH2 ) n–OH. Owing to the strong hydrophilicity and biocompatibility of these polymers and their subsequent hydrogels, PEG is one of the most used synthetic polymers. Resulting hydrogels. Coir is the coconut shell’s fibrous husk. The coir, being strong and naturally immune to seawater, protects the fruit enough to survive months floating on ocean waves to be washed up on a sandy shore where it can sprout and grow into a tree if it has enough fresh water, since the seed has been brought along with all the other nutrients it requires (Fig. 1).
2.1 Fabrication of Composite Calculate the weight of epoxy resin (DGEBA) as per the quantity needed in a digital weighing machine. Mix the coir fiber with varying concentration of fiber with epoxy Table 1 Sample preparation table with different code Sample code
Sample preparation Epoxy
Hardener
Coir fiber (wt%)
Diluent (wt)
Neat epoxy
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Fig. 1 ASTM D-638 type-5 sample design
and stir the system for 2–3 min. Measuring the diluent Polyethylene glycol (PEG400) is added to the 5 wt% epoxy/coir system and repeat the stirring again. After stirring for 2–3 min, hardener must mixed to the system epoxy/coir/PEG at 10:1 ratio. Final stirring must be done after adding hardener and allow it to solidify.
3 Results and Discussions 3.1 Tensile Test Material’s tensile strength which is observed from the experiment is shown in Table 2. Test result of the tensile test shows the resistance, the material is applying before failure against the external loading. The most used specimen is the dog-bone form for the tensile measurement. Using an Instron universal test machine that followed ASTM D638, the mechanical properties were tested. At both ends, the sample was clamped, with one end linked to the machine and the other end to the flexible clamp. Due to increased hemi-cellulose content, where coir fiber has more than 40% of Table 2 Tensile properties of various epoxy system Sl. No.
Tensile strength Sample code
Tensile strength (MPa)
1
NE
59.01 ± 1.50
2
Epoxy + fiber (1 wt%)
60.34 ± 2.11
3
Epoxy + fiber (3 wt%)
62.29 ± 1.97
4
Epoxy + fiber (5 wt%)
66.87 ± 2.23
5
Epoxy + fiber (7 wt%)
63.21 ± 1.34
6
Epoxy + fiber (1 wt%) + PEG (5 wt%)
61.97 ± 2.21
7
Epoxy + fiber (3 wt%) + PEG (5 wt%)
64.12 ± 1.51
8
Epoxy + fiber (5 wt%) + PEG (5 wt%)
69.52 ± 2.34
9
Epoxy + fiber (7 wt%) + PEG (5 wt%)
65.24 ± 1.68
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Fig. 2 Tensile strength versus epoxy with coir fiber with varying concentration
lignin and cellulose and PEG is hydrophilic in nature. Thus, with the hydrophilic PEG and coir fiber interacted well, resulting in greater tensile strength and tensile modulus (Fig. 2). When specimens were measured with fiber (1, 3, 5, 7) wt% and PEG 5 wt%, the tensile strength improved gradually and then a sudden decrease in properties for the consecutive variations. The epoxy/coir/PEG composite has shown better tensile strength than the epoxy/coir composite. Compared to other formulations, PEG is believed to show better tensile properties due to good fiber/matrix bonding, leading to an even and efficient distribution of stress between fibers. When measuring samples of 5 wt% of fiber and 5 wt% constant diluents, the tensile strength has been reported as 69.52 MPa. The dispersion of the fiber with PEG and the interfacial adhesion between the epoxy matrix and the fiber are due to these changes, thereby limiting the mobility of the chain matrix under loading (Fig. 3). While using PEG, the interfacial communication and rigidity between the fiber and the polymer matrix have indeed been enhanced, and the mechanical properties of the composites have been improved. For this variation (Epoxy + PEG + coir), tensile strength increased steadily from (1–5) wt% and decreased at 7 wt%. This could be due to less uniform dispersion at higher fiber and PEG concentration.
3.2 Block Copolymer Coir fibers are primarily composed of lignin, hemicellulose, and from cellulose. Average chemical composition of coir fibers varies between 32 and 50%. Cellulose, hemicellulose at 0.15–15%, lignin at 30–46%, and about 3–4% Pectin. Figure 4 shows the expected chemical reaction between epoxy and cellulose forms block
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Fig. 3 Tensile strength versus epoxy with coir fiber and PEG with varying concentration
Fig. 4 Chemical reaction between epoxy and cellulose
co-polymer. Because of its quality of cellulose, hemicellulose, and lignin, raw coir fiber has several functional groups, such as hydroxyl, were found to contain (OH), Carboxyl (COOH), Methylene, Carbonyl (C=O) (CH2 ). C–O–C and C–O are one of the main functional group which reacts with resin and hardener and helps in forming of block polymer and enhances the chemical chain reaction between the system. Due to the higher bond strength of C–O–C bond, it encourages the strength enhancement which effects on overall improvement in the property of the system. C–O and C–H group present in lignin also simultaneously reacts with epoxy system as the double bond present in the DGEBA resin breaks in to two single bonds with different chemical compound. Figure 5 shows the structural image of epoxy/coir (7 wt%)/PEG (5 wt%) system where number of polymeric reactions is enhanced due to addition of PEG diluent.
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Fig. 5 Chemical reaction between epoxy and cellulose and PEG diluent
Reactive diluent PEG must involve in the chemical reaction formed between fiber and epoxy. Enhancement in tensile strength proves the number of strong bond in the system has been increased. PEG diluent reacts with resin, whereas fiber and hardener also react simultaneously with DGEBA.
3.3 FTIR Spectroscopy Analysis Fourier transform infrared spectroscopy (FTIR) analysis has been performed to explain the effect of coir fiber and PEG. It has been observed that all characteristics peaks of three combinations fall within the same wavelength ranges which reflect that they are of similar chemical constituents. Very few differences were observed in addition of PEG and coir. Intensity at 1611 cm−1 , 1510 cm−1 indicates presence of C=C and N–O stretching of epoxy resin with strong bond strength where 1254– 1035 cm−1 indicates the presence of amine group C–N group with medium bond strength. Peak at 828 cm−1 shows the C–H aromatic stretching. The strong intensity of has been found in epoxy/PEG/coir system which is mainly attributed to the formation of effective cross-linking density. These results indicated that the reactive diluent PEG has strongly integrated with epoxy and fiber (Fig. 6).
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Fig. 6 FTIR spectroscopy analysis of epoxy/coir/PEG system
4 Conclusions A detailed analysis was performed on the epoxy/coir system’s tensile behavior with distinct weight percent of fiber reinforcement. Coir fiber-reinforced epoxy resin developed by hand lay-up technique. Tensile test has been conducted, and it observed that in epoxy/coir system the strength value is increased by almost 12% at 5 wt% of fiber content as compared with neat epoxy. With increasing concentration of coir fiber, strength of composite has shown gradual enhancement intent till 7 wt% of which shows the number of free volumes also has been increased. Formation of block co-polymer due to addition of fiber and reactive diluent enhances the flexible chain reaction in the epoxy system which shows the property enhancement. Using other natural fibers and their behavior based on the same criteria used here, it is possible to study composite properties based on different manufacturing techniques other than hand lay-up techniques such as spray process, compression molding technique, method of filament winding, etc. Tribological, electrical, physical, thermal properties, etc. measurement and optimization. And the experimental results can be analyzed.
References 1. Sanal, I., Verma, D.: Construction materials reinforced with natural products. In: Martínez, L., Kharissova, O., Kharisov, B. (eds.) Handbook of Ecomaterials. Springer, Cham (2018). https://
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doi.org/10.1007/978-3-319-48281-1_75-1 2. Muensri, P., Kunanopparat, T., Menut, P., Siriwattanayotin, S.: Effect of lignin removal on the properties of coconut coir fiber/wheat gluten biocomposite. Compos. A Appl. Sci. Manuf. 42(2), 173–179 (2011) 3. Mohammed, L., Ansari, M.N.M., Pua, G., Jawaid, M., Saiful Islam, M.: A review on natural fiber reinforced polymer composite and its applications. Int. J. Polym. Sci. 15 pp, Article ID 243947 (2015) 4. Abdelhak, B., Noureddine, M., Hacen, M.: Improvement of the interfacial adhesion between fiber and matrix. Mech. Mech. Eng. 22(4), 885–894 (2018) 5. Ali, A., Shaker, K., Nawab, Y., et al.: Hydrophobic treatment of natural fibers and their composites—a review. J. Ind. Text. 47(8), 2153–2183 (2018) 6. Fernández, A.M., Barriocanal, C., Díez, M.A., Alvarez, R.: Influence of additives of various origins on thermoplastic properties of coal. Fuel 88(12), 2365–2372 (2009). ISSN 0016-2361 7. Doshi, D., Ravis, W., Betageri, G.: Carbamazepine and polyethylene glycol solid dispersions: preparation, in vitro dissolution, and characterization. Drug Dev. Ind. Pharm. 23, 1167–1176 (2008) 8. Jollivet, T., Peyrac, C., Lefebvre, F.: Damage of composite materials. Procedia Eng. 66, 746–758 (2013). ISSN 1877-7058 9. Harish, S., Peter Michael, D., Bensely, A., Mohan Lal, D., Rajadurai, A.: Mechanical property evaluation of natural fiber coir composite. Mater. Charact. 60(1), 44–49 (2009). ISSN 10445803 10. Adeniyi, D.G., Onifade, D.V., Ighalo, J.O., Adeoye, A.S.: A review of coir fiber reinforced polymer composites. Compos. Part B Eng. 176, 107305 (2019). ISSN 1359-8368 11. Van Quy, H.O., Nguyen, S.T.T.: Experimental analysis of coir fiber sheet reinforced epoxy resin composite. IOP Conf. Ser. Mater. Sci. Eng. 642, 012007 (2019) 12. Al-Oqla, F.M., Sapuan, S.M.: Natural fiber reinforced polymer composites in industrial applications: feasibility of date palm fibers for sustainable automotive industry. J. Clean. Prod. 66, 347–354 (2014) 13. Verma, D., Gope, P.C.: The use of coir/coconut fibers as reinforcements in composites. In: Faruk, O., Sain, M. (eds.) Biofiber Reinforcements in Composite Materials, pp. 285–319. Elsevier Ltd. (2014) 14. Prambauer, M., Wendeler, C., Weitzenböck, J., Burgstaller, C.: Biodegradable geotextiles—an overview of existing and potential materials. Geotext. Geomembr. 47, 48–59 (2019) 15. Salazar, V., Leão, A.L., Rosa, D., Gomez, J., Alli, R.: Biodegradation of coir and sisal applied in the automotive industry. J. Polym. Environ. 19, 677 (2011)
Validating Analytical and Numerical Predictions of Hydrodynamic Characteristics in Microchannel with Experimental Results Shanmugam Mathiyazhagan and Lakshmi Sirisha Maganti
Abstract In this article, the hydrodynamic performance of microchannel cooling systems has been predicted analytically. The microchannel have a high surface area to volume ratio, due to that it has high heat transfer coefficients. The microchannel cooling systems have received prompt attention from researchers to address the cooling challenges of electronic components. However, due to the diameter of the order of microns, as the pressure drop is inversely proportional to the channel diameter, it leads to more pressure drop in the microchannel. Such that the investigation of flow characteristics in the microchannel is tremendously on-demand to understand hydrodynamics. Unfortunately, the applicability of conventional theories (Darcy pressure drop equations) in microchannel flows is still under debate. Kandlikar has come up with an expression for predicting pressure drop in microchannels by considering the Poiseuille number and aspect ratio of microchannels. This paper concentrated on validating the predictions of the Kandlikar pressure drop equation and Darcy pressure drop equation with experimental work taken from literature. The results show that available analytical methods are under-predicting as those will not consider the surface roughness and uncertainty present while conducting experiments. Among the analytical models, the Kandlikar equation predictions are better than the other methods, and the results of the prediction are well in agreement with experimental results. Keywords Microchannel · Pressure drop · Validation · Analytical method
1 Introduction The use of microchannel in electronic cooling applications is getting increased which requires the high heat flux [1]. Especially, the multicore processor is generating high heat flux with non-uniform heating due to the densely packed transistors [2]. Microchannel has a larger surface area to volume ratio, which makes them more S. Mathiyazhagan · L. S. Maganti (B) Department of Mechanical Engineering, SRM University-AP, Amaravati, Andhra Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_30
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attractive applications like a micro heat exchanger and is suitable for many applications in the industrial sectors such as automobile, aerospace and medical equipments. The microchannel is more suitable for miniature heat exchanger applications [3]. To enhance the hydraulic and thermal performance of microchannel, for the past few decades, numerous studies on both experimental and numerical works have been conducted. There is plenty of research happened for the single-phase and two-phase flow in the microchannel to study the performance of heat transfer and hydrodynamic characteristics. A two-phase flow system has a better heat transfer performance than a single-phase flow system but pressure drop is high. Therefore, it is important to analyze the pressure drop in the microchannels, commonly used method for finding the pressure drop is experimental and numerical work; both of which require more computation time and cost. It is important to predict the pressure drop by analytical methods; to design the supporting equipments such as a pump is needed more accurate pressure drop value. But, the conventional flow theories are not suitable (not accurate) for predicting the pressure drop on the microchannel flow system. So, Kandlikar et al. [4] has derived the analytical equation based on the experimental work and this analytical method applicable up to 1 µm hydraulic diameter of microchannel. The most commonly used pressure drop correlations are the Darcy–Weisbach equation. This formula has used the Darcy friction factor to aid in predicting the pressure drop [5]. But the friction factor mainly depends on the viscous effect of fluid. But in a microchannel, the frictional and viscous effects play an important role in increasing the pressure drop, and additionally, other parameters such as entry effect, roughness factor and the temperature of fluid are causing the pressure drop [6]. The reason for the experimental and conventional theory results having more deviations is maybe the surface roughness in microchannel reported by Wu and Cheng [7]. Kandlikar [8] has presented the friction factor value based on the literature database with experimental results of single-phase flow in the microchannel. Kandlikar has concluded that conventional Poiseuille and stokes flow theories are still applicable to the microchannel. But the experimental result and predicted theoretical results are not matching; maybe the entrance and exit region friction factor should be carefully assessed. Kandlikar introduced the Hagenbach factor in improving analytical predictions. The literature reviews clearly show that conventional analytical theories have to be validated, and this is one of our main goals of this paper. Predicting the pressure drop by analytically and numerically, then predicted results are validated with experimental literature data. The most commonly used analytical methods are Kandlikar and Darcy pressure drop correlations methods; in this paper discussed the error deviation between these two correlations system.
2 Analytical Method Pressure drop predictions in the microchannel by using the analytical method is very important, and it necessary to evaluate the results. The most commonly used
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expression in present-day practices in common applications is the Darcy–Weisbach equation [5]. But Kandlikar [8] has developed the modified correlation, specifically for microchannel applications based on the experimental results. It helps to predict the pressure drop more accurately. The expression is shown in the following sections.
2.1 Darcy–Weisbach A vast amount of research has conducted to determine the friction factor based on the expression of the experimental results developed. The pressure drop mainly depends on the friction factor, hydraulic diameter, velocity and viscosity of liquid [5] P =
f LρUm2 Dh 2g
(1)
where P is the pressure drop and f , L, Dh are friction factor, length and hydraulic diameter of the channel. Um is velocity, ρ is the density of fluid and g is gravity. The friction factor for turbulent flow heavily depends on the surface roughness of the pipe and the Reynolds number of the fluid. While laminar flow (Re < 2000) condition, the friction factor depends on the Poiseuille number certainly not the surface roughness of pipe. The Darcy friction factor expression is f =
64 Re
(2)
2.2 Kandlikar Correlations Kandlikar [4, 8] comes up with a new analytical expression for predicting the microchannel pressure drop. Most of the microchannel is rectangular because it is easy to fabricate, so the aspect ratio (αc ) of channel is defined in Eq. (3) and for non-circular channels, the hydraulic diameter Dh is calculated by using Eq. (4). a b
(3)
2ab a+b
(4)
αc = Dh =
where a and b is channel width and height of rectangular channels, Dh is the hydraulic diameter for rectangular channels.
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2.3 Fully Developed Laminar Flow If it is a single-phase flow in the microchannels, the pressure drop can be calculated using Eq. (5) for flow laminar conditions. In microchannel, the slip boundary conditions, compressibility effects and rarefied flow are not valid at single-phase flow conditions. But in diabetic conditions, fanning friction factor (f ) will be used to calculate the pressure drop. P =
ρUm2 ρUm2 ρUm2 2( f Re)μUm L + K + K + K (∞) c e 2 2 2 Dh2
(5)
The μ is the dynamic viscosity of fluid, K c , K e are contraction and expansion loss, respectively. For laminar flow, the Poiseuille number is constant (Po = f Re). When f Re for the circular pipe section is 16, but in the case of the rectangular section, Eq. (6) shows the Poiseuille number equations. f Re = 24(1 − 1.3553αC + 1.9467αC2 − 1.70112αC3 + 0.9564αC4 − 0.2537αC5 )
(6)
The Hagenbach factor K (∞) depends on the channel aspect ratio, and it is shown in Eq. (7) K (∞) = 0.6796 + 1.2197αC + 3.3089αC2 − 9.5921αC3 + 8.9089αC4 − 2.9959αC5
(7)
2.4 Fully Developed Turbulent Flow If the flow is turbulent and the channel surface is smooth, then the following Blasius equation is f app = 0.0791Re−0.25
(8)
4 f app L ρUm2 P = (K c + K e ) + 2 Dh
(9)
The f app is an apparent friction factor that combined the effect of frictional losses and other additional forces.
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3 Numerical Simulations The three-dimensional numerical simulations of a single-phase flow in a rectangular microchannel have analyzed using Ansys FLUENT software. The grid independence test is carried out to predict more accurate results. In the Fluent, using SIMPLE algorithm to solve the governing equation of conservation of mass and momentum has adopted, and a second-order upwind scheme is used to discretize the momentum equations. The residual size is less than 1 × 10−6 for predicting more accurate results. The whole simulation is carried out under the laminar flow conditions as per the experimental literature [9] results. The rectangular cross-section microchannel channel dimensions and working fluid details are as shown here: channel hydraulic diameter is 438 µm, channel width and height is 500 µm and 390 µm, respectively, the channel aspect ratio is 0.78, the channel length is 62,000 µm and working fluid is water. The geometry dimensions and fluid properties are taken from the literature [9]. The specific boundary conditions are considered in the simulation inlet as uniform velocity in x-direction, and pressure outlet atmospheric conditions and reaming all body section are specified as boundary wall conditions.
4 Results and Discussion The hydrodynamic characteristics of a single microchannel have investigated in numerical and analytical method; predicted results are compared with experimental literature results. The experimental literature paper Mirmanto et al. [9] has shown the result of laminar and turbulent flow medium. But in our case, numerical and analytical results are carried out under a laminar flow medium. The results sections are classified into three categories, which are discussed in the following sections.
4.1 Axial Distance Versus Pressure Drop The presented results are obtained from the numerical simulation without applying heat flux load; the pressure drop results are shown in Fig. 1. The pressure drop along the axial distance of the microchannel has compared the numerical results with experimental literature results [9]. The literature model and numerical model have the same axial distance of the microchannel inlet to outlet sections. When the Reynolds number is 573, experimental results and numerical results are similar results. The numerical method is predicting similar results if Reynolds numbers are less than 1079, but when the Reynolds number is increased, then the change in pressure drop also increased. The numerical simulations are showing better accurate results when there is a low Reynolds number. In the simulations model, surface roughness is not an important factor for low Reynolds number range. When
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Fig. 1 Pressure drop versus axial distance
Reynolds number is increased, then surface roughness plays an important factor in the experimental work, whereas the numerical simulation might not be considered as the roughness factor.
4.2 Reynolds Number Versus Pressure Drop This section presents the analytical and numerical pressure drop results, and those results are shown in Fig. 2. The analytical methods are Darcy and Kandlikar correlations used to calculate the pressure drop, and predicted results are compared with experimental data. The Darcy pressure equations are always under-predicting even with very low Reynolds number. So the conventional pressure drop equations (Darcy method) are not suitable for microchannel applications.
Fig. 2 Reynolds number versus pressure drop
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The Kandlikar has developed new pressure drop correlations based on experimental results of the microchannel. Numerical results and Kandlikar laminar correlations are almost linearly predicted, but all of them are under-predicting with experimental results. When Reynolds number is less than 1300, numerical and Kandlikar are predicting acceptable pressure drop value, but if Reynolds number increases, then pressure drop difference also increased. But interestingly, the Kandlikar turbulent equations are predicting the pressure drop data very well matching at Reynolds number greater than 1300. In the microchannel, the turbulent flow may start triggering early, but in the conventional channel, it happened at a high Reynolds number. In microchannel, the surface roughness is a very important factor for finding the hydrodynamics characteristics.
4.3 Deviation Analysis In this section the pressure drop predicted by the considered methods against experimental results have been presented in terms of deviation. The Fig. 3 shows the deviation of pressure drop predicted by numerical and analytical models from experimental results in terms pf percentage. When the error deviation is close to 0%, it means, the predicted results are matching well with experimental results. For low Reynolds number (up to 1079) the numerical model predicated pressure drop accurately compared with other methods, which has the deviation up to 10% only. Whereas the error is observed to be increasing in case of Kandlikar laminar method (based on Eq. 5) with increase of Reynolds number. Similarly, the Darcy weisbach Eq. 1 has also followed the same patterns. It has been observed from the figure that the Darcy model is over predicting has when compared with the kandlikar (laminar model). It is due to, the viscous effects and frictional effects are assumed to be neglected, whereas in case of flows in microchannels, those effects play a major role.
Fig. 3 Reynolds number versus deviations (%)
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Table 1 Sensitivity analysis of Reynolds number to match the Exp. pressure drop results Actual Reynolds number
Kandlikar_laminar sensitivity (%)
Kandlikar_turbulent sensitivity (%)
573
12.2
–
1079
12.3
12.3
1578
21.6
6.7
2016
26.4
5.1
In the Fig. 3 the Darcy and numerical methods has considered the laminar flow conditions. But Kandlikar analytical equation are taken as both laminar and turbulent flow equation which is shown in the figure. From the figure, initial Reynolds number conditions the Kandlikar turbulent (Eq. 9) model is observed to have maximum deviation in results compared with experimental results, which is expected. But the deviation has started to decrease gradually while Reynolds number is increasing. This could be due to triggering of turbulent flow regimes at low Reynolds number (Re = 1578) in microchannel, unlike to the conventional macro channels.
4.4 Sensitivity Analysis Based on the analytical method, predicted results are mainly get deviated because of the Reynolds number. The sensitivity analysis on Reynolds number reveals the robustness of the Kandlikar pressure drop equations. First, varying the Reynolds number on Kandlikar laminar and turbulent equations. If the Reynolds number lies in the range of 500–1100, the Kandlikar laminar equation shows that sensitivity of ± 12.2% to match the experimental pressure drop results. Interestingly, Kandlikar turbulent flow equations also have the same sensitivity error as shown in Table 1. The sensitivity analysis indicating the Kandlikar turbulent equation has less error at high Reynolds number compared with laminar equations.
5 Conclusion The analytical and numerical results are presented, and those results are compared with experimental literature results of single-phase flow of water in rectangular microchannels. The whole simulation and analytical calculation are done under the laminar flow region. The prediction of pressure drop in microchannel by using numerical modeling and Kandlikar laminar (analytical equations) method has the reasonable agreement with experimental results at low Reynolds number, if fully developed laminar flow conditions exists. If Reynolds number is more than 1300, Kandlikar turbulent equations are predicting much better than the Kandlikar laminar equation.
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The friction factor plays an important role in accurate pressure drop predictions using the analytical method in the microchannels. In the microchannel, turbulent flow got triggered at an early Reynolds number, not like conventional flow-through channels. The Darcy pressure drop has predicted with high error deviation from the experimental results, and it indicates that conventional theory is not valid for microchannel applications.
References 1. Mudawar, I.: Assessment of high-heat-flux thermal management schemes. IEEE Trans. Compon. Package Technol. 24(2), 122–141 (2001). https://doi.org/10.1109/6144.926375 2. Pfund, D., Rector, D., Shekarriz, A.: Pressure drop measurements in a microchannel 46(8) (2000) 3. Koszali´nska, P., Chłodnictwa, K.T.C.: Single phase pressure drop in minichannels. Politechnika 121, 17–32 (2008) [Online]. Available: https://www.imp.gda.pl/files/transactions/121/17-32.pdf 4. Satish, K., Srinivas, G., Li, D., Colin, S., King, M.R.: Heat Transfer and Fluid Flow in Minicahnnels and Microchannels, 2006th edn. Butterworth-Heinemann is an imprint of Elsevier, USA (2005) 5. Westaway, C.R., Loomis, A.W.: Cameron Hydraulic Data (1984) 6. Rosa, P., Karayiannis, T.G., Collins, M.W.: Single-phase heat transfer in microchannels: the importance of scaling effects. Appl. Therm. Eng. 29(17–18), 3447–3468 (2009). https://doi.org/ 10.1016/j.applthermaleng.2009.05.015 7. Wu, H.Y., Cheng, P.: An experimental study of convective heat transfer in silicon microchannels with different surface conditions. Int. J. Heat Mass Transf. 46(14), 2547–2556 (2003). https:// doi.org/10.1016/S0017-9310(03)00035-8 8. Steinke, M.E., Kandlikar, S.G.: Single-phase liquid friction factors in microchannels. Int. J. Therm. Sci. 45(11), 1073–1083 (2006). https://doi.org/10.1016/j.ijthermalsci.2006.01.016 9. Mirmanto, Kenning, D.B.R., Lewis, J.S., Karayiannis, T.G.: Pressure drop and heat transfer characteristics for single-phase developing flow of water in rectangular microchannels. J. Phys. Conf. Ser. 395(1) (2012). https://doi.org/10.1088/1742-6596/395/1/012085
Understanding the Logistics Services of Mumbai Dabbawallahs and Discussing the Factors Behind Its Success Gaurav Kumar, Sagar Dagar, Shaikh Sadi, Naveen Kumar Bidhan, Ashutosh Kumar, Saqib Farooq Bhat, and M. S. Niranjan
Abstract Mumbai dabbawallah’s is a great example of a Six Sigma certified worldclass logistics service. All of this is possible without the use of advanced technologies such as navigation services, GPS, mobile applications and tracking services, and even without the use of a car. So, looking at the case of dabbawallah in Mumbai, it is very important to know how they are doing this, even if they give their workers very little salary. This white paper details Mumbai’s dabbawallah logistics services and the most important factors for success without the intervention of advanced technology in the system. In this paper, detailing of the factors that contribute to the success of dabbawallah’s supply chain network or logistics services in Mumbai have been done. It is also confirmed population density, land distribution, urban freight transportation in Mumbai, Mumbai Suburban Railway System are Mumbai dabbawallah’s facing factors in operation. Keywords Mumbai dabbawallah’s · Supply chain management · Logistics service · Food delivery · Success · Sustainability · Dabbawallah · Mumbai · India · Case study
1 Introduction Dabbawallah in Mumbai constitutes a bento delivery and return system that delivers warm (even if not hot) lunches from your home in Mumbai to people at work. The first thing to clarify is that dabbawallah’s main business model in Mumbai is not to provide catering services, but to provide logistics services, a lunch box from the serviceman’s home to his office. It is to deliver (called dabbawallah in Mumbai) return to his house. Lunch boxes are picked up late in the morning and delivered by
G. Kumar (B) · S. Dagar · S. Sadi · N. K. Bidhan · A. Kumar · M. S. Niranjan Department of Mechanical Engineering, Delhi Technological University, New Delhi, Delhi, India S. F. Bhat Institute of Technology, University of Kashmir, Srinagar, J&K 190024, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_31
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bicycle, train, or even on foot. And in the afternoon, after lunch, their responsibility is also to return the lunch box home from the subscriber’s office. In late 1800, small villages supplied labor and had to travel to cities because agricultural activities could not support them. At the time, there was no culture of eating food outside, so the idea of delivering homemade food was born for them. Today, dabbawallah in Mumbai is a decentralized, flat, self-governing network organization with approximately 5000 associates/workers. In a three-tier structure, all workers are waged similarly. Administrative culture is categorized as discipleship, not followership. This philosophy is known to foster individual client care, joint preparation, and application. They take pride in strong co-operation and severe time management [1]. These particular economic conditions also predetermine the position of the influence of fresh logistical machineries on goods investment [2]. In 2010 survey by Harvard Business School, it was rated “Six Sigma”. This means that dabbawallah makes less than 3.4 errors per million dealings. With deliveries to about 200,000 clients daily, we end up with over 400 delays or missing daba in a year.
2 Understanding Logistics Services of Mumbai Dabbawallah’s Each dabbawallah (deliveryman) serves about 30 clients each day and works under the control of “Mukadam” (supervisor). They have been testing and perfecting the core and spoke thought for many years. Each spoke is autonomously handled by a group of 20–25 people. Over while, this “friendliness system” has changed into a perfect organization with Six Sigma performance ratings [3]. Dabbawallah (lunch boxes) are picked up from home by dabbawallah. After collecting all the bento boxes, they all go to the sorting area, where the bento boxes are grouped built on the target or region where they will be delivered. And this sorting location can be a sidewalk, depending on the location selected by the group members of the group. The grouped boxes are taken to the train coach. At the destination station, dabbawallahs find some dabbawallahs for picking and delivering these groups. This happens at almost every major station. Later that day, empty lunch boxes are returned to their original location in the same process, but vice versa. The mixture of shades and signs helps dabbawallahs classify the appropriate delivery orders. The lunch box has the subsequent identifiers: The starting point of daba. The name of the abbreviated location. The name and color coding of the closest to the starting point. The name and color coding of the place closest to your destination. And for the advantage of the target dabbawallah to handle the lunch box, the destination building, and floor identifier [4]. Their organization makes a matchless condition of circulation with suppliers, and what is provided frequently not distinguishable. It’s like a nil-order shut-loop source series. All dabbawallah use less expensive train passes and you can travel as much as you need to deliver dabbawallah. There is no separation of employment,
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and all dabbawallah are unbroken parts of the entire [5]. The whole sorting and shipping procedure takes place at a pace most of us can’t imagine, without the use of innovative automation methods. It is calculated that there is only single error for each one million transactions they perform. Dabbawallahs does not use GPS routing, barcodes, or RFID. Dabbawallahs might not say English or may not be in school. But, they are not uneducated about their work [6]. Their non-mainstream mastery is part of their unparalleled achievement and reasonable gain (Fig. 1).
Fig. 1 Flowchart of dabbawallah’s operations
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3 Literature Review Before understanding the decisive reasons for Mumbai dabbawallah’s success, we first need to understand Mumbai’s history, population density, land distribution, suburban rail systems, and Mumbai’s urban freight transport. The operation was initiated in 1890 by a group of people of the similar ethnic background in Pune. Businesses are categorized by organized community ties and can be associated to fresh guilds where effort and social character, dedication and financial interests, common aid, and shared objectives are not distinguishable from each other 19. A youth from Pune, Havji Bakke, arrived Mumbai in the late 1880s. A Parsis financier went to his house on Grant Road in Mumbai and hired a bucket to collect tiffins and deliver them to the Ballard Pier office. The system began in 1890 when the British were colonized in India. Parsis women started cooking as a cafeteria similar business. Inexperienced employees can be situated at any crossroads, and their tops are sitting there wearing hats. One day a woman told one of them to deliver food, and he gladly said yes. He started taking 20–25 tiffins from Girgaon to VT Station 19. Bucke decided to employee his corresponding villagers for delivery services in pursuit of making a planned working group. Since then, both systems and business models have suffered [7]. About 5000 dabbawallah in the town have an amazing facility record. Each day, they ship more than 130,000 bento boxes all over Mumbai, the fourth most crowded city in the world. This requires more than 260,000 transactions to be performed six hours in a day, six days in a week, 52 weeks in a year (excluding off days), but errors are tremendously occasional. Surprisingly, dabbawallah (mainly a semiliterate worker who manages himself) has that level of performance in an eco-friendly way, at a very low cost, without even using IT systems or cell phones.
3.1 Organization: A Windup Design The key to the procedure of dabbawallah is the Mumbai Suburban Railway. It is one of the greatest wide-ranging, difficult, and regularly used urban traveler trains in the world. Its simple design allows delivery staffs with bicycles and wheelbarrows to travel small spaces between the location and the buyer’s home or workplace. It is part of the railway organization that makes demand in the first place. Jampacked trains make it hard to carry daba, and office employees do not eat out on a regular basis. This is due to the cost, the taste of home cooking, and the bad quality of some agency cafeterias that exist. As a result, agency staffs prepare lunch at home and are delivered by dabbawallah after the morning rush time. One day, daba changes hands numerous times. In the morning, workers pick it up from the client’s house, take it to the nearest station (along with other daba), sort
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it there, and put it in a crate according to their destination. Then, take the train to the station nearby to your destination. There it is reclassified, assigned to another employee and delivered to the appropriate agency before lunch. In the afternoon, the procedure is reversed and daba is returned to the client’s home. To achieve their work most professionally, dabbawallah planned themselves into approximately 200 units of approximately 25 persons each. These minor groups have local independence. Such a flat administrative structure is ideal for providing little cost delivery services. (dabbawallah customers only pay about 400 or 500 rupees a month, or $7 to $9.) There are other high-priced delivery facilities for native groups, but I know. As far as dabbawallah is, there are no big rivals at that price. The service has been in operation for over 100 years, but no one was able to duplicate it [8, 9].
3.2 A Regulatory Mechanism The rail organization sets the pace and rhythm of work. Your daily program decides when specific tasks need to be performed and when each is allowed. For example, employees take 40 s to load a daba box on a train at a major station and only 20 s at an intermediate stop.
3.3 Management Dabbawallah basically manages itself in terms of employment, logistics, customer acquisition and retention, and dispute resolution. This allows you to operate efficiently, keep costs low, and maintain high quality of service. All workers contribute to charitable trusts that provide insurance and occasional financial assistance. For example, if you need to replace a stolen or irreparably broken bicycle.
3.4 Process: Simplicity, Flexibility, and Rigor For dabbawallah, implementing the right process is more than just implementing an efficient workflow. You also need almost everything in your organization, including how you manage your information, use built-in buffers, and adhere to strict standards.
3.5 Simple Codes To convey information, dabbawallah relies on a very basic system of symbols. There are three important marks on the lid of the daba. (See the “Decoding the daba Code”
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exhibit.) The first is the large bold number in the center, which indicates the area where the daba needs to be delivered. The second is a group of letters on the edge of the lid. The Dabbawallah number for delivery, the office building alphabet code (2 or 3 letters), and the floor number. The third is a combination of color and shape, sometimes a motif, which indicates the origin. Customers provide small bags to carry the doves, and the bag shape and color variations help workers remember which doves belong to which customers.
3.6 Buffer Capacity Even with an efficient coding system, workers still have a small margin of error for certain tasks. For example, the time allotted to pick up a dubber at home can be as little as 30–60 s, with any number of slight delays causing a cascading effect and delaying thousands of deliveries. There is sex. Therefore, to keep the schedule, each group has two or three additional workers, fill in where needed, and all members have a variety of collection, sorting, transportation, finance, customer relationships, etc. We are receiving mutual training on various activities.
3.7 Demanding Obedience to Processes and Standards This minimizes any fluctuations that could cause the wrench to be thrown into the piece. For example, Daba are all about the same size and cylindrical. Additional charges will be incurred if the container is too large and requires special treatment to facilitate customer conformance. Anomalous containers that interfere with delivery operations are simply unacceptable. This uniformity allows the dough to be quickly packed into a crate. The crate is also standard size, so it can be efficiently loaded on the train. Dabbawallah adheres to certain rules. For example, do not eat until all deliveries are complete. Workers are fined or dismissed for repeated negligence or negligence. Customers are also expected to follow this process. Those who do not respond to the warning will be dropped, repeatedly delaying that they are not ready to pick up daba. This system allows frontline workers to take action, as Toyota does in manufacturing plants. There, workers who find a problem pull an “Andon code” to shut down the production line so they can take immediate action.
3.8 A Self-reinforcing System The individual pillars help explain certain aspects of dabbawallah’s success. But to truly understand what they are doing, you need to look at the whole thing and
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Fig. 2 Mumbai population by year
consider how the columns reinforce each other. Take the coding system. Simple and visual, semi-illiterate workers can quickly classify daba.
3.8.1
History of Mumbai, Population Density, and Land Distribution in Mumbai
This city is on the west coast of the country and has a profound natural port. According to the 2001 census, the population of the area of Mumbai Metropolitan city was 11.91 million. The city’s population is predictable to increase to 14.8 million by 2011, with an annual growing rate of 2.2% and an usual density of population of 22,000 per km2 . As the latest census was done in 2011, based on that fact, it is the latest data (Fig. 2).
3.8.2
Urban Freight Transport in Mumbai
Transportation of millions of Mumbai citizens and goods takes place in train, road, and water modes. More than 88% of Mumbai commuters and a small portion of their goods are transported by public transport (suburban trains and busses).
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Mumbai’s Suburban Rail System
Mumbai’s suburban rail services are used not only for passenger movement but also for freight movement. The main framework of Mumbai’s rail network was laid in 1925 with a 1500-V direct current traction system, initially connecting Mumbai with the neighboring town.
4 Factors Behind Mumbai Dabbawallahs Success The Mumbai dabbawallahs also known as for its negligible number of transactional errors. We are now going to discuss in detail, “what is behind such an outstanding performance”.
4.1 Solution of Huge Social Problems and Customer Needs 4.1.1
Infrastructure
Local Trains: The black bones of dabbawallah in Mumbai are the high frequency of suburban train services in Mumbai. Trains run almost every two minutes. The entire operating area of dabbawallah in Mumbai is maintained by a well-developed rail infrastructure. In addition, train services are cheap. And its huge rail network covers almost every important part of Mumbai. Dabbawallahs use a monthly train pass, which allows you to use the train service as many times as you need. Transport Economics: Dabbawallahs deliver lunch boxes using only a network of bicycles, wheelbarrows and public transport. This keeps costs low and affordable for consumers/customers. Lower prices bring more customers and economies of scale. And the economics of this transportation bring low-cost supply chain logistics, making it more affordable for customers. Mumbai Topography: (Arrangement of natural and man-made physical features of the region). The dabbawallah service has evolved in the context of the city of Mumbai, where the terrain is nearly linear.
4.2 Customer Support Members of dabbawallahs will not wait for a lunch box at the pickup level when they arrive for collection at their place of residence if the lunch box is not ready. Customers also understand that if they wait, they will not be able to provide the best
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possible service. Therefore, they support dabbawallah and prepare their lunch boxes before they arrive at their location.
4.3 Working Structure of Mumbai Dabbawallah Flexibility: Each route is assigned to an individual member. However, the information on this route is known to everyone else on the team. If you need to change members on either route, you can easily do this. Independent teams and team leaders: Groups have 10, 20, or 30 dabbawallah based on the strength of their customers in the area. The oldest person in the group is mukadam (group leader). He manages all happenings of the group and associates and does not make extra money, but then he also manages all happenings, why? Because he had the chance to become a group leader. And due to the fact that he is the oldest person, based on Indian culture, team members respect them (Fig. 3).
4.4 Standards and Quality Practices that Dabbawallahs Follows • Six sigma: By choosing a Six Sigma methodology such as—(Quality is the responsibility of everyone). When the coding gets rough, dabbawallah writes it with fingers and paint; he can’t say, it’s not my job, because everyone in our organization is responsible. • Quality training: All dabbawallah are required to receive one month of training before attending. In this training, they also taught even the simplest ideas like how to ride a bike in traffic. • Codification system The codification system is a core part of dabbawallah’s work to track within the system. It is homemade, but it works well for the intended purpose. A combination of alphabets, symbols, and colors. This code will give you a hint of your home address, office address, and even the name of the person with whom the lunch box (dabba) is involved.
5 Discussion Dabbawallah in Mumbai plays an important role in delivering bento boxes at low prices. Here are some of the differences. 1.
The only logistics service network in the world with accuracy far exceeding Six Sigma without the intervention of the latest technology.
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Fig. 3 Mumbai suburban railway network map [10]
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3.
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This study proves that technological progress is not only a key factor in the success of a business, but that simplicity, discipline, determination, and even low wages for workers can make the system robust. Mumbai’s dabbawallah does not have its own scientific and/or technological progress or capabilities. This is a great business model based on an innovative approach for meeting the needs of real customers. This paper sought to consider a dabbawallah case study within the context of logistics operations and their success factors.
6 Conclusion Here, this paper details the factors that contribute to the success of dabbawallah’s supply chain network or logistics services in Mumbai. We also confirmed population density, land distribution, urban freight transportation in Mumbai, Mumbai Suburban Railway System, and operation in Mumbai dabbawallah. 1.
2.
3.
4.
5.
Mumbai’s dabbawallah has logistics system currently processes more than Rs. 40,000 transactions per day with much higher accuracy than Six Sigma. This study proves that not only are technological advances a key factor in the success of a business, but simplicity, discipline, determination, and even low wages for workers can make the system robust. This study shows that the informal sector can manage multipart urban logistics systems as efficiently and effectively as other large logistics companies systematically do. This treatise highlights a number of unique and culturally related success factors. And this is when dabbawallah’s logistics services are completely environmentally friendly, as they do nothing technically to cause pollution. Lunch boxes can also be reused. And this is all as low cost as possible. If other giants want to enter this business, they need at least dozens of warehouses and hundreds of light trucks. And this also prevents them from delivering their lunches on time due to traffic problems in Mumbai. And after all, their prices will also be high. Therefore, as a result of this overall research, dabbawallah adds a very unique twist to logistics services. This treatise sought to examine a dabbawallah case study in the context of the factors responsible for dabbawallah’s success, without the intervention of state-of-the-art technology. Dabbawallah in Mumbai does not have its own scientific and/or technological advances or capabilities. This is a great business model based on an innovative approach to meeting the needs of real customers.
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References 1. North, K., Kumta, G.: On the way to a knowledge society. In: Knowledge Management, pp. 1– 29. Springer, Cham (2014) 2. Kisperska-Moro´n, D.: Relativity of the “new logistic technologies” and of their inventory aspects. Int. J. Prod. Econ. 26(1–3), 181–186 (1992). https://doi.org/10.1016/0925-527 3(92)90061-b 3. Isher, A.S., Bhal, H.: Factor study of human reliability and industrial productivity: comparison of food delivery system. In: ASQ World Conference on Quality and Improvement Proceedings, vol. 59, p. 495. American Society for Quality (2005) 4. Ghodake, S.T.: Transcending life through romance: Mumbai tiffinwalas and the lunch box. Adv. Soc. Sci. Res. J. 3(12), 8–15 (2016) 5. Pathak, G.: Delivering the nation: the dabbawallas of Mumbai. South Asia J. South Asian Stud. 33(2), 235–257 (2010) 6. Chopra, R., Sharma, H.: Corporate to cooperative entrepreneurial leadership in emerging economy lessons from Indian enterprises. J. Organ. Human Behav. 1(4), 12–28 (2012) 7. Ganapathy, V., Mahadevan, P., Ravikeerthi, J.V.: An empirical study of the feasibility of introducing the Mumbai Dabbawallah food delivery system in Bangalore (2016) 8. Knorr, D., Augustin, M.A.: From value chains to food webs: the quest for lasting food systems. Trends Food Sci. Technol. 110, 812–821 (2021) 9. Janjevic, M., Winkenbach, M.: Characterizing urban last-mile distribution strategies in mature and emerging e-commerce markets. Transp. Res. Part A Policy Pract. 133, 164–196 (2020) 10. https://www.localsofmumbai.com/making-of-mumbai-rail-map/ 11. MCGM: Greater Mumbai city development plan (2005–2025) (2005). Retrieved on 18 Mar 2011 12. Pathak, G.: Delivering the nation: the Dabbawallahs of Mumbai. South Asia J. South Asian Stud. 33(2), 235–257 (2010) 13. Behrens, A., Singh, P., Bhandarker, A.: View from practice: managing effectively in collectivist societies: lessons from Samba Schools and Dabbawallahs. Thunderbird Int. Bus. Rev. 57(1), 37–51 (2016) 14. Sriraman, S., Venkatesh, A., Karne, M.: Competition issues in the road goods transport industry in India with special reference to the Mumbai metropolitan region. Transport Econ. 148 (2006) 15. Baindur, D., Macário, R.M.: Mumbai lunch box delivery system: a transferable benchmark in urban logistics 16. Lea: “Comprehensive transportation study of Mumbai” executive summary. Genesis 1–63 (2007) 17. Sehgal, P., Surayya, T.: Innovative strategic management: the case of Mumbai suburban railway system. Vikalpa J. Decis. Makers 36(1) (2011)
Experimental Investigation of Rail IRSM 41-97 Steel GTAW and GMAW Weldments Using ER70S-6 Filler J. R. Deepak, V. K. Bupesh Raja, N. Joseph Amrish Lobo, and K. S. Deepak Kumaresh
Abstract Rail IRSM 41-97 steel is a corrosion resistance steel used in railway industries to manufacture wagon body due to it high resistance to corrosion. During rail coach manufacturing, IRSM 41-97 steel plates are welded using ER70S-6 filler wire. Porosity, spatter, lack of fusion, improper weld bead profile, and lack of penetration are some of the problems that occur during GTAW and GMAW welding of IRSM 41-97 rail steel plates. When these rail wagons are exposed to extreme climatic conditions for a longer period of time, (i.e.,) for more than 10–12 years, catalytic corrosion initiates in the WZ which leads to catastrophic failure. Hence, this research aims to prepare a defect free rail IRSM 41-97 steel GTAW and GMAW single pass butt weld joints using ER70S-6 solid filler wire. Further, the integrity of rail IRSM 41-97 steel butt joints is investigated using Vickers hardness testing machine and optical microscope. Keywords Rail IRSM 41-97 steel plate · ER70S-6 filler wire · GTAW · GMAW welding
1 Introduction Rail IRSM 41-97 steel is an Indian railway specification steel which is a high strength and corrosion resistance steel, used in rail industries to manufacture wagon coaches. These steels are known in the industry for its low price, high quality, high strength, highly resistance to extreme climatic condition [1, 2], and resistance to very high temperatures. The chemical composition of the rail IRSM 41-97 steel is shown in Table 1. When these steels are exposed to atmosphere forms an adhesive protective tight oxide layer on the surface which is reddish brown in color. This is due to the presence of chromium and copper present in the steel which reacts with atmospheric oxygen. These steels can be used for various applications without painting. Hence, these J. R. Deepak (B) · V. K. Bupesh Raja · N. Joseph Amrish Lobo · K. S. Deepak Kumaresh School of Mechanical Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_32
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Table 1 Chemical composition of rail IRSM 41-97 steel plate Elements
Cr
Ni
Cu
Mn
Si
V
P
S
C
Fe
%
0.546
0.218
0.302
0.391
0.298
0.10
0.087
0.013
0.120
Reminder
S
Fe
Table 2 Chemical composition of copper coated ER70S-6 filler Elements
Cu
C
Ni
Cr
Mo
Mn
Si
P
V
Composition 0.50 0.15 0.15 0.15 0.15 1.85 1.15 0.025 0.03 0.035 Reminder %
steels are also used for various other industrial applications like frame structures in construction, shipping containers, storage tanks, construction of heavy vehicles, exhaust pipe structures, etc., [3–5]. For these applications, the IRSM 41-97 plates are welded using GTAW and GMAW welding technique. ER70S-6 solid filler wire is used as electrodes to prepare this weld rail IRSM 41-97 steel joints. The chemical composition of premium copper coated ER70S-6 solid filler an electrode is shown in Table 2. During GTAW and GMAW welding process, excess heat generated on the plate affects the WZ and a produces a wider HAZ. This results in decreasing metallurgical and mechanical property of the weldment. Common defects like porosity, spatter, lack of fusion, improper weld bead profile, and lack of penetration are some of the problems that occur during GTAW and GMAW welding of IRSM41-97 rail steel plates [6–10]. When these rail wagons are exposed to extreme climatic conditions for a longer period of time, (i.e.,) for more than 10–12 years, catalytic corrosion also initiates in the WZ leading to catastrophic failure. Figure 1 shows the defects of rail wagons at WZ which occurs over a period of time [11, 12]. The objective is to improve the life and quality of weld. Hence, this research aims to prepare a defect free rail IRSM 41-97 steel 2 mm thick GTAW and GMAW single pass butt weld joints using ER70S-6 solid filler wire. The mechanical and metallurgical properties, viz., micro hardness, macrostructure, microstructure investigation of the rail IRSM 41-97 steel butt joints weld joint are investigated in this research work [13, 14].
2 Materials and Method Rail IRSM 41-97 steel 2 mm thick plate is used for this research. These plates are cut into 300 × 150 × 2 mm dimension using MD NC HVR-1030 (shear cutting hydraulic machine). Then, the plates are wiped with acetone to get rid of the grease and dust particles on the surface. GTAW and GMAW single pass butt weldments are prepared using copper coated ER70S-6 solid filler wire. The parameters used for the GMAW and GTAW welding technique are shown in Table 3.
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Fig. 1 Defects of rail wagon at WZ Table 3 Process parameters of GTAW and GMAW welding process
Description
IRSM 41-97 square butt joints of 2 mm with 2 mm thick
Weld type
GTAW weld
GMAW weld
Filler wire
ER70S-6
ER70S-6
Filler rod dia (mm)
1.6
0.8
Shielding gas
Argon + CO2 (80:20)
Argon + CO2 (80:20)
Gas flow rate (lpm)
21
21
Root gap (mm)
2
2
Penetration (mm)
2
2
Voltage (V)
18
21
Current (A)
174
170
Weld length (mm)
150
150
Weld width (mm)
4.25
3.25
Weld speed (mm/s)
2.34
3.06
Time taken (s)
64
49
Heat input (Q) J/mm
1338.46
1174.34
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Using (electronica EcoCut) WEDM machine, samples are parted from the welded rail IRSM 41-97 steel plates for mechanical and metallurgical properties studies using Wilson Wolpert hardness test machine and Zeiss Axio Scope optical microscope [15].
3 Result and Summary 3.1 Vickers Micro Hardness Survey The Vickers hardness survey is done with 0.01 kg HRC load of with a dwell time of 5 s at a regular interval of 160 µ. At the traverse section, twelve survey points have been studied from the BM to the WZ of the GTAW and GMAW single pass butt weld joints. Vickers micro hardness of GMAW and GTAW welded rail IRSM 41-97 steel butt weld joint is shown in Table 4. Average Vickers hardness of GMAW weldments at BM, HAZ, and WZ is 179 HV, 262 HV, and 164 HV, respectively, whereas, for GTAW weldments at BM, HAZ, and WZ are 187 HV, 259 HV, and 204 HV, respectively. The increase of hardness in GMAW weldments from 179 to 262 HV is due to Widmannstatten massive structure, viz., martensite formed at the WZ due to high heating and quick cooling during welding process. The raise in hardness from 187 to 259 HV from the BM to WZ is due to Pearlite formation on the WZ due to high heat input followed by slow cooling during GTAW welding process (Fig. 2). Table 4 Vickers micro hardness values of GMAW and GTAW welding
Location
Weld metals toward left side
Weld center
Weld metals toward right side
Distance in (mm) 6 5 4 3 2 1 0 1 2 3 4 5 6
Base metal – – – – – 164 182 174 – – – –
GTAW weld 187 176 183 194 226 235 259 251 243 240 236 204 172
GMAW weld 179 186 181 185 158 230 262 248 237 226 214 178 164
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Fig. 2 Vickers micro hardness survey of GTAW, GMAW welded rail IRSM 41-97 steel
When comparing the Vickers micro hardness values at the WZ of the IRSM 41-97 rail steel GTAW and GMAW welding process, GMAW welding process at the WZ has an improved hardness of 262 HV than hardness of GTAW welding which is 265 HV.
3.2 Macro Structure The cross sectional macro structural imaging of GTAW and GMAW single pass butt weld joints is captured using optical microscope (Zeiss Axio Scope). The macro structure images that are observed is detailed using CAD software for calculating the cross sectional area of the WZ and HAZ. Figure 3 shows the cross sectional
Fig. 3 Cross sectional macro structure and CAD detailed diagram of rail IRSM 41-97 steel plate a BM without weld, b GTAW welded, and c GMAW welded
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macro structure and CAD detailed diagram of rail IRSM 41-97 steel plate at the BM without weld, GTAW, and GMAW welded plates. The weld width and area of cross section of the WZ and HAZ during GTAW welding process are 8.75 mm and 17.5 mm2 , respectively. Similarly, width and cross sectional area of the WZ and HAZ during GMAW welding process are 6.5 mm and 13 mm2 , respectively. Weld width and cross sectional area of WZ and HAZ are associated with heat input during the GTAW and GMAW welding. The cross sectional area of the GMAW weld is reduced by 75% while comparing GTAW weldment. The weld width of the GMAW weld is increased by 60% while comparing GTAW weldment. From the above macro structure and CAD detailed diagram, it is evident that GMAW process has narrow HAZ compared to GTAW welding process due to high energy heat input in GTAW welding technique.
3.3 Micro Structure Optical microscope (Zeiss Axio Scope) is used to capture the cross sectional micro structural imaging of GTAW and GMAW single pass butt weldments. The cross sectional micro structure of the BM, HAZ, and WZ of GTAW welded rail IRSM 4197 steel plate is shown in Fig. 4, and Fig. 5 shows the cross sectional micro structure BM, HAZ, and the WZ of the GMAW rail IRSM 41-97 steel weldment.
Fig. 4 Cross sectional micro structure of GTAW welded rail IRSM 41-97 steel plate
Fig. 5 Cross sectional micro structure of GMAW welded rail IRSM 41-97 steel plate
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The cross sectional micro structure of the BM clearly shows that the elongated grains are observed along the rolling direction during manufacturing process of the plate. The ferrite of grain size 25–30 µ is clearly evident in the BM. During GTAW welding process due to the higher heat input, a wide HAZ is formed having uniform fine grain of ferrite with precipitate of pearlite is formed. The grain size of 15–30 µ is formed at the HAZ. In the WZ dendritic grains, 30–40 µ with varying ferrite pools are formed. This is due to the quick solidification of ER70S-6 solid filler at the WZ. At the center of the weld, large dendrites of 40–50 µ are observed due to the slow cooling process. As the fusion is complete clear zone, a clear bifurcation could not be resolved. Similar to that of the micro structure of the BM cross section area of the GMAW weldment, the micro structure of the GTAW BM cross sectional of rail IRSM 41-97 steel clearly shows that the elongated grains along the direction of rolling. The grain size 25–30 µ of ferrite is clearly evident in the BM. Re-crystallized uniform fine grains of 15–20 µ are formed in the HAZ due to the rapidly cooling. Dendrite patters of varying orientation and varying amount of ferrite pools being formed in the WZ due to the rapid heating and solidification. As the fusion is complete and compatible with ER70S-6 solid filler material, the distinct boundary could not be resolved.
4 Conclusion In this research work, a defect free rail IRSM 41-97 steel 2 mm thick GTAW and GMAW single pass butt weldments using copper coated ER70S-6 solid filler wire is prepared. • The Vickers micro hardness of GMAW welded plate at WZ has improved from 179 to 262 HV due to Widmannstatten massive structure formed in the WZ due to quick cooling. The Vickers hardness of GTAW welded plate at the WZ has greater than before from 187 to 259 HV. Raise in hardness is due to pearlite formation at the WZ due to high heat input followed by slow cooling rate. • The weld width and area of cross sectional of the WZ and the HAZ are associated with heat input during the GTAW and GMAW welding technique. The cross sectional area of the GMAW weld is reduced by 75% compared to GTAW weldment. The weld width of the GMAW weld is increased by 60% compared to GTAW welding technique. • During GTAW welding process due to the higher heat input, a wide HAZ is formed having uniform fine grain of ferrite with precipitate of pearlite is formed. The grain size of the HAZ is 15–30 µ. WZ dendritic grains of 30–40 µ with pools of ferrite are observed in GTAW weldments. This is due to the quick solidification of ER70S-6 solid filler at the WZ. At the center of the weld 40–50 µ, large dendrites are observed due to slow cooling process. During the GMAW welding, fine recrystallized uniform grains of 15–20 µ are formed in HAZ zone due to quick
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cooling. Dendrite patters of varying orientation and varying amount of ferrite pools are formed in the WZ due to rapid raise in temperature and solidification.
References 1. Narayanareddy, V.V., Srinivasa Rao, D., Krishnaveni, M.N.V., Amareswarireddy, M.: Finite element modelling of TIG welding for 316L stainless steel plate using Sysweld (2015). ISSN (Online): 2250-0758, ISSN (Print): 2394-6962 2. Casalino, G., Contuzzi, N., Minutolo, F.M.C., Mortello, M.: Finite element model for laser welding of titanium (2014). CIRP 33, 434–439 (2013) 3. Yilbas, B.S., Arif, A.F.M., Abdul Aleem, B.J.: Laser welding of low carbon steel and thermal stress analysis. Opt. Laser Technol. 42, 760–768 (2010) 4. Lu, F., Yao, S., Lou, S., Li, Y.: Modelling and finite element analysis on GTAW arc and weld pool. Comput. Mater. Sci. 29, 371–378 (2004) 5. Sahaa, D.C., Westerbaan, D., Nayak, S.S., Biro, E., Gerlich, A.P., Zhoua, Y.: Microstructureproperties correlation in fibre laser welding of dual-phase and HSLA steels. Mater. Sci. Eng. A 607, 445–453 (2014) 6. Hafez, K.M., Ramadan, M., Fathy, N., Ismail, M.: Microstructure and mechanical properties of laser welded dual phase and mild steel joints for automotive applications (2017). ISSN: 1662-7482 7. Zhen, S., Duan, Z., Sun, D., Li, Y., Gao, D., Li, H.: Study on microstructures and mechanical properties of laser–arc hybrid welded S355J2W+N steel. Opt. Laser Technol. 59, 11–18 (2014) 8. Yan, J., Gao, M., Zeng, X.: Study on microstructure and mechanical properties of 304 stainless steel joints by TIG, laser and laser-TIG hybrid welding. Opt. Lasers Eng. 48, 512–517 (2010) 9. Gu, X.Y., Duan, Z.Z., Gu, X.P., Zhang, X.H., Xie, Y.L., Sun, D.Q.: Microstructure and mechanical properties of laser-MAG hybrid welded thick-section weathered steel joint (2015).https:// doi.org/10.1007/s00170-015-7286-9 10. Dong, C., Zhao, A., Wang, X., Pang, Q., Wu, H.: Microstructure and properties of 1100 MPa grade low-carbon hot-rolled steel by laser welding. J. Iron Steel Res. Int. (2018). https://doi. org/10.1007/s42243-018-0025-3 11. Deepak, J.R., Bupesh Raja, V.K., Jeswin Arputhabalan, J., Ra, Y.K.G., Thomas, S.K.: Experimental investigation of corten A588 filler rod for welding weathering steel. ICAMMAS17 16, 1233–1238 (2019) 12. Deepak, J.R., Bupesh Raja, V.K., Viswanatha Reddy, P., Lakshmi Venkata Sai, L., Ashok Kumar Reddy, G.: Investigation of microstructural and metallurgical properties of corten A588 grade steel GTAW joints. IJMPERDOCT2019111 2249–6890 (2019). ISSN(E): 2249-8001 13. Deepak, J.R., Bupesh Raja, V.K., Riaz Basha, M., Ganga Dharani Kumar, M., Mahesh, R.G.M.: Improvement of weldment by post weld heat treatment of low carbon steel. IJMPERDOCT2019110 2249–6890 (2019). ISSN (E): 2249-8001 14. Deepak, J.R., Bupesh Raja, V.K., Guptha, M.J., Prasad, P.H.D., Sriram, V.: Experimental investigation of mechanical properties of welded corten steel A588 grade plate using ER70S-6 filler material for construction application. Mater. Sci. Eng. 197, 012067 (2017) 15. Deepak, J.R., Bupesh Raja, V.K., Kaliaraj, G.S.: Mechanical and corrosion behavior of Cu, Cr, Ni and Zn electro plating on corten A588 steel for scope for betterment in ambient construction applications. Results Phys. 14, 102437 (2019)
Sensorless Control of Reboost Converter for Grid-Connected WECS A. Santhi Mary Antony and D. Godwin Immanuel
Abstract WECS for brushless DC motor using a reboost converter has been proposed using sensorless concept of electromotive force. Wind output power is erratic in nature; hence, sensorless control is proposed by using a reboost converter with the varied firing angle of power switches at ‘90-α’ and ‘150-α’ which is introduced to operate the brushless DC motor with various ranges of speed. A closed loop control is attained with the help of PI controller. The PI controller provides feedback compensation to regulate the speed of the BLDC motor for varying wind speed. The proposed reboost converter is controlled in the closed loop by using PI Controller. Keywords Sensorless BLDC MOTOR · Reboost converter · PI controller · Wind energy conversion system · Back EMF · Commutation signals
1 Introduction The position sensors are used for the proper positioning of rotor for obtaining the speed of the motor [1]. Few factors like cost, efficiency, etc., have to be considered for sensorless control of the DC motor [2]. Sensorless controls are widely used for wide range of speed. In a wide range of speed, sensorless commutation signals should be the first one followed by compensators [3, 4]. If the BLDC motor runs at very high speed, it needs a large phase angle delay, which in turn affects the efficiency of the machine [5–8]. Alternate method can be done by using the power converters for the elimination of harmonics. This in turn led the motor drive at a variable speed control operation [9]. The power converters should be operated with a freewheeling diode, which in turn ensures a continuous flow of current [4]. Nowadays, artificial neural networks have been implemented in sensorless control of motors [10, 11]. But they cannot be applied for varying speed control. Even operated at low speed, torque estimation is difficult. A sensorless closed loop speed control is proposed. The proposed EMF techniques are illustrated for the A. Santhi Mary Antony (B) · D. Godwin Immanuel Department of Electrical and Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_33
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Fig. 1 Proposed block diagram
WECS with a reboost converter [12]. This is implemented by PIC microcontroller. Sensorless method can make the BLDC motor to be operated from low range of speed to higher range [13, 14].
2 Block Diagram In this proposed system, a three-phase AC supply is considered as an input. AC input is rectified into DC voltage by bridge rectifier. DC voltage is bucked using buck converter. Once again, DC supply is converted into AC supply using inverter. The position angle of the motor is sensed without sensor. The errors are eliminated by commutation error compensation. Figure 1 reflects the proposed reboost converter with a feedback controller.
3 Proposed Circuit Diagram In the proposed circuit, the sensorless control is carried out by using line-to-line back EMF. It had been implemented effectively by using power converters. The converter used is the reboost converter. The switches have been turned ON/turned OFF by using PWM techniques [6]. Sinusoidal PWM technique is used to switch on/off the MOSFET switches. In addition to it, a PI controller is used in the feedback loop. The PI controller is enriched with PIC controller.
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Fig. 2 Output current waveform
4 Simulation Results 4.1 Simulation Design The MATLAB SIMULINK is used for simulation design system; with the help of switches and voltage sources, we get desired output voltage. The motor position is sensed by line-to-line back EMF. The voltage source inverter converts ac to dc. The Sinusoidal PWM techniques are used for firing sequences of power switches in the DC–DC reboost Converter. The six switches are been switched ON, so that the voltage and current are in-phase with each other. By using power factor module in the MATLAB, power factor is measured. The total harmonic distortion is also calculated. Based on these values, wide range of speed can be controlled. The converter fed motor drive with position sensor to sense the position of the motor which increases the cost of the system and also increase in harmonic effect. Here, we removed the sensor as shown in Fig. 2, and the motor position is sensed by line-to-line back EMF. The output of the WECS is fed to a buck converter using a variable DC link. The switching of the motor is given by the sensorless control of brushless DC motor. The waveform of output current and voltage are illustrated in Figs. 2 and 3.
4.2 PIC Controller Unit The architecture of PIC controller is shown in Fig. 4. It consists of twenty pins which perform as an integrated circuit. An integrated circuit is a monolithic form of
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Fig. 3 Output voltage waveform
Fig. 4 PIC Microcontroller
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ICs which can be used for its desirable applications. The IC is programmed for the sensorless control for grid-connected WECS with variable speed.
5 Hardware Kit 5.1 Reboost Converter Figure 5 shows the reboost converter with four MOSFET switches which are used as switching devices. The switching sequence of the MOSFET is done by the sinusoidal pulse width modulation technique. The hardware driver circuit is shown in Fig. 6. The driver circuit uses a PIC microcontroller for reliable operation of sensorless control of WECS.
Fig. 5 Reboost converter
Fig. 6 Hardware driver circuit
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Fig. 7 Sensorless control of brushless DC motor
The complete hardware setup of reboost converter for variable wind speed is shown in Fig. 7. Sensorless control of reboost is done by back EMF commutation technique by using a buck converter. The power MOSFFET switches are turned ON and OFF by using sinusoidal pulse width modulation techniques. A closed loop control of speed operation is obtained by using PI controller.
6 Conclusion A novel sensorless closed loop for BLDC motor had been achieved by line-toline back EMF method. The closed loop control for wide range of speed is done by varying the firing angle of reboost converter (DC–DC converter). The closed loop of wind energy conversion system is implemented by using a conventional PI controller. By using the two controllers, an effective sensorless closed speed control for BLDC motor can be attained. Further, these methods of control also can be
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extended for various power converters for getting the desired speed with respect to desired applications.
References 1. Kim, N.H., Yang, O., Kim, M.H.: BLDC motor control algorithm for industrial applications using a general purpose processor. J. Power Electron. 7(2), 132–139 (2007) 2. Lin, C.-T., Hung, C.-W., Liu, C.-W.: Sensorless control for four-switch three-phase brushless DC motor drive. In: Conference Record. IEEE IAS Annual Meeting, vol. 4, pp. 2048–2053 (2006) 3. Shen, J.X., Iwasaki, S.: Sensorless control of ultrahigh-speed PM brushless motor using PLL and third harmonic back-EMF. IEEE Trans. Ind. Electron. 53(2), 421–428 (2006) 4. Santhi Mary Antony, A.: Closed loop control of three port converter with high voltage gain. Int. J. Eng. Technol. (IJET) 7(4), 1224–1235 (2015). ISSN No. 0975-4024 5. Babul Reddy, K., Santhi Mary Antony, A.: A single sensor based PFC SEPIC converter fed BLDC motor drive for fan application. Int. J. Appl. Eng. Res. 10(5), 12187–12196 (2015). ISSN No. 0973-4562 6. Nagarajan, G., Thanigaivel, K.: An implementation of SSSC-based cascade H-bridge model series compensation scheme. In: 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT), pp. 147–151. IEEE (2013) 7. Govidan, N., Rajasekaran Indra, M.: Smart fuzzy-based energy-saving photovoltaic burp charging system. Int. J. Ambient Energy 39(7), 671–677 (2018) 8. Nagarajan, G., Ravi, C.N., Vasanth, K., Godwin Immanuel, D., Sundarsingh Jebaseelan, S.D.: Dual converter multimotor drive for hybrid permanent magnet synchronous in hybrid electric vehicle. In: Proceedings of the International Conference on Soft Computing Systems, pp. 237– 249. Springer, New Delhi (2016) 9. Santhi Mary Antony, A.: Cascaded multilevel inverter of 11 levels for RL load with reduced distortion. Indian J. Sci. Technol. (INDJST) 08(19) (2015). ISSN No. 0974-5645 10. Park, J.S., Lee, K., Lee, S.G., Kim, W.: Unbalanced ZCP compensation method for position sensorless BLDC motor. IEEE Trans. Power Electron. 34(4), 3020–3024 (2019). https://doi. org/10.1109/TPEL.2018.2868828 11. Santhi Mary Antony, A., Godwin Immanuel, D.: Implementation of Self-regulating Controller for Integrating DFIG-based Grid System with Load Interruption. Springer, Environment, Development and Sustainability, ISSN 1387-585X (2021). https://doi.org/10.1007/s10668-021-017 95-1 12. Zhou, Y., Zhang, D., Chen, X., Lin, Q.: Sensorless direct torque control for saliency permanent magnet brushless DC motors. IEEE Trans. Energy Convers. 31(2), 446–454 (2016). https:// doi.org/10.1109/TEC.2015.2505326 13. Lai, Y., Lin, Y.: Novel back-EMF detection technique of brushless DC motor drives for wide range control without using current and position sensors. IEEE Trans. Power Electron. 23(2), 934–940 (2008). https://doi.org/10.1109/TPEL.2007.915048 14. Santhi Mary Antony, A., Godwin Immanuel, D.: An Enhancement Approach of Re-Boost Converter for Unification of Disparate WECS with the Utility Grid System. Elsevier, Environmental Technology and Innovation, vol. 24, pp. 101953. ISSN 2352-1864 (2021).https://doi. org/10.1016/j.eti.2021.101953
Intelligent Securing the Industrial IoT Data Based on Consensus Mechanism G. Nagarajan, R. I. Minu, and T. Sasikala
Abstract Industrial Mechanical Internet of Things (IIoT) assumes a crucial job for Industry 4.0, individuals are focused on actualizing a general, versatile and make sure about IIoT framework to be embraced across different ventures. Be that as it may, existing IIoT frameworks are powerless against single purpose of disappointment and noxious assaults, which can not offer stable types of assistance. Because of the versatility and security guarantee of block-chain, consolidating block-chain and IoT increases extensive intrigue. Be that as it may, block-chains power-serious and have low-throughput, that are not reasonable power-obliged IoT gadgets. We propose a block-chain-empowered productive information assortment and secure sharing plan consolidating Ethereumblock-chain and deep reinforcement learning to make a dependable and safe condition. Right now, is utilized to accomplish the most extreme measure of gathered information, and the block-chain innovation is utilized to guarantee security and dependability of information sharing. The proposed framework exploits block-chain innovation as far as its straightforwardness and sealed nature to help reasonable products trade among dealers and providers. Also, the decentralization and pseudonymity property will assume a critical job in safeguarding the security of members in the block-chain. Keywords Industrial mechanical internet of things · Block-chain · Credit-based · Proof of work
G. Nagarajan · T. Sasikala Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India R. I. Minu (B) Department of Computing Technology, SRM Institute of Science and Technology, Kattankulathur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_34
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1 Introduction Bitcoin and Ethereum (a well known brilliant agreement upheld stage) are, maybe, the two most broadly perceived executions of block-chain. The last is a cryptographically connected and constantly developing rundown of permanent information records. Inside the block-chain, an (open) record is utilized for recording the information, just as the data of every exchange. Data about each finished exchange is put away in a disseminated record, shared over all the taking part hubs of the blockchain arrange. As examined in the writing, block-chain is able to do proficiently recording exchanges between at least two included gatherings on an appropriated shared (P2P) arrange, with the put away information co-possessed by all individuals from the system, and for all time unmodifiable [1]. At the end of the day, they gives an unchanging, trusted and make sure about stage for various substances (the two people and associations) to trade information/resources, team up and perform exchanges. Notwithstanding, security assaults and disappointments could raise extraordinary ruckus against worldwide IIoT organize, which may exceed any one of its advantages. For instance, the focal server farm powerless against one point disappointment and malevolent assaults, for example, DDoS, Sybil assault [2], which can not ensure administrations accessibility. What’s more, sensor information put away in a server farm are at the danger of divulgence. Likewise, information capture may happen in interchanges between IoT gadgets, which can not guarantee the credibilities of gathered information. Lately, with the rise of block-chain, consolidating block-chain and Internet of Things has increased extensive intrigue. By utilizing highlights of the sealed and decentralized agreement component of block-chain, we get the opportunity to comprehend the previously mentioned reliability issues in IIoT frameworks. The exchange off among effectiveness and security: We realize that accord calculations in block-chain can successfully assist with shielding malignant assaults, and PoW is the most generally utilized agreement calculation, which powers hubs to run high intricacy hash calculations to confirm exchanges. Be that as it may, it is overburden for power-compelled IoT gadgets. While taking out the PoW instrument can conceivably increase productivity of exchanges, framework security issues occurs by this. Therefore, we have to make the exchange among security and proficiency on accord instruments is main test of the work. The conjunction of straightforwardness and security: Block-chain highlights of straightforwardness, that is a significant trademark in fund fields. Be that as, it might turn into a downside for some IIoT frameworks, where they gathered touchy information require privacy and just open by approved one. Itis a thusly critical to structure an entrance regulation plot in straightforward framework. The contentions between large simultaneousness and small throughput: Internet of Things gadgets reports information constantly in IIoT frameworks, prompting a high simultaneousness. Sadly, complex cryptographic-based security systems to a great extents the limit of throughput of block-chain. Also, the synchronously accord models in chain organized block-chains can not utilize data
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transfer capacity in Industrial IoT frameworks. To address these type of difficulties, we propose a block-chain framework with credit-based agreement system for Industrial IoT. So as to diminish force utilization agreement system, we are presenting a self versatile PoW-calculation power constrained Internet of Things gadgets. It is used to alter the trouble of PoW-dependent on hubs’ conduct, that can diminish the trouble of legitimate hubs while expanding for pernicious hubs. We are additionally presenting an entrance regulation conspire dependent on symmetric cryptography in straightforward block-chain framework, that gives an adaptable information authority the executives strategy for clients. Our framework foundation is assembled dependent on the DAG structured block-chain, that improves the framework throughput by utilizing the offbeat accord model. Actualize a solid framework of Raspberry Pi for savvy manufacturing plant situation. Broad examinations and investigation results show the proposed credit-based PoW system of information authority the board technique will ensure effectiveness and the reliability all the while. Our primary commitments of the paper are depicted as follows: Distinguish the three principle tasks incorporating block-chain innovation in Industrial Internet of Things and have proposed relating three answers for handling the difficulties. Proposed a general, adaptable that make sure about block-chain framework of Industrial Internet of Things, to plan the moderate cost credit based Pow component with a proficient control on accessing plot of power-obliged gadgets. Likewise, unique in relation to past work, he use a dag-organized block-chain as the foundation to assemble the framework which accomplish the large throughput. The plan that execute a proposed framework of the brilliant production line situation. Analyses results show that the credit-based PoW instrument with information control of the board technique has the decent presentation in gadgets.
2 Related Work In mechanical IoT framework, these are basic specialized difficulties [1] expected to handle, for example, adaptability, constancy, security, get to control, and so forth. Right now, audit related work completed for explaining these difficulties and examine the inadequacies of them quickly. There are some current arrangements which are not founded on block-chain advances. For instance, Kaed et al. present the asemantic principles motor of modern IoT doors which permits actualizing vigorous with adaptable guideline based regulation systems, that are defenseless against one point disappointment, pernicious assaults because of the concentrated engineering. M. Shamem Hassain et al. present the Health IIoT-empowered observing system to gather social insurance information from cell phones and sensors, which likewise faces similar dangers. Also, social insurance information put away in focal servers might be helpless against protection exposure. These are additionally some experimentation joining block-chain having IoT for fathom with previously mentioned
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problems. For instance, A. Dorri proposed the Block-chain based keen house structure to accomplish reliability objectives for classification, trustworthiness accessibility. Be that as it may, they dispense with the idea of PoW to accelerate proficiency of exchanges, that increases framework security dangers. Additionally, K. Christidis embrace the comparable execution, utilize a white list conspire, of drop accord components in personal systems, subsequently this face the equivalent secured problems. Z. Shae proposed the block-chain stage of clinical preliminary, accuracy medication, that despite everything struck in the idea arranged an absence for assessment. Attempt to coordinate low-control IoT gadgets for the block-chain based framework, however a framework as executed with Ethereum block-chain, that are over-burden for Internet of Things gadgets. What’s more, the less throughput for Ethereum blockchain can not fulfill a request for Industrial Internet of Things framework. Di Pietro portray the circulated belief structure of a Internet of Things which spans these to make start to finish belief between Internet of Things gadgets with no outsider, which essentially apply block-chain innovation with Internet of Things framework but do not present the nitty gritty execution. These are few current investigation on these points, for instance, now propose an entrance regulation framework dependent of block-chain innovation with oversee Internet of Things gadgets. Be that as it may, the framework is not completely based on a dispersed design in view of the use of the focal administration center point. When the administration center is fizzled or assaulted, IoT gadgets associated with that becomes inaccessible. Z. Li misuse of consortum block-chain innovation which proposes the safe vitality exchanging framework. Be that as it may, they do not consider security issues, for example, the delicate information exposure hazard, and in this manner it can not ensure touchy information secrecy. The previously mentioned frameworks all receive chain-organized block-chains in Internet of Things frameworks, which are over-burden of potential-obliged Internet of Things gadgets. Z. Xiong present edges registering the versatile block-chain applications that presents the Stackelberg game structure of proficient edges asset the board of portable block-chain. They decrease computational necessities of cell phones by utilizing edges figuring. What’s more, there are some different difficulties that additionally got the interim while presenting the novel plan of block-chain into IIoT frameworks.
3 Existing System On the inventory aspect, humans can provide matters, as an instance, brief leases of their inactive vehicles, or more rooms in their condos or homes. At the interest facet, buyers can income through leasing products at decrease fee or with lower price-based totally overhead than shopping or leasing thru a traditional provider [1–3]. The sharing financial system has made various open doors for clever towns areas concerning enhancing resource use and effectively lessening change charges [4–6]. Enhancing the usage of advantages infers various nice results, for example, vitality
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sparing and blockage diminishing. Whilst sharing products and ventures in business facilities has a long records, and antiquated vis-à-vis sharing still happens in networks throughout, Internet center people might now be able to assist those exchanges and in shape market interest continuously on an huge scale [7–10]. The sharing economy is pushed by using empowering advancements of computerized network, which offer the established order of these traits as in it lets in promptness [11, 12]. Steady statistics and information assembled through [13] human beings are critical to dealing with the wasteful usage of beneath-used assets and creating a metropolis “keen.“ citizens, items communities can interface up flawlessly by making use of commonplace advances to essentially improve information sharing with respect to the status and trade of inert assets. With advanced availability, people can hire save rooms and cellars, retain parking spots full, experience an inert motorcycle in the road, and take a mutual taxi with a extra atypical heading a comparable way. For addressing the reliability and effectiveness problems for gigantic Industrial IoT information, block-chains are generally considered to be promised answer for empower information putting away/preparing/partaking in safe or proficient ways. To overcome high throughput necessity, the paper proposed the novel DRL-based execution advancement structure of block-chain empowered Industrial IoT [14] frameworks, objectives of that are four-overlay: Provides procedure of assessing framework for the parts to adaptability, decentralization, inactivity and reliability; Improvising the versatility of basic block-chain with out influencing framework’s decentralization, dormancy and reliability; Design the modulable block-chain for Industrial IoT frameworks, that the square makers, agreement calculation, square size and square interim can be chosen/balanced utilizing the DRL strategy. The adaptability issue alludes to capacity of block-chain frameworks for processing exchanges. For making block-chain innovation progressively inescapable, framework ought to have the option to deal with the expanding sound for exchanges in vast scope for utilizations decentralization mirrors the fracture of power over the entire framework, which permits the framework to accomplish different objectives: restriction opposition, open support, insusceptibility from specific assaults, and disposal of single purposes of disappointment.
3.1 Disadvantages of the Existing System • • • • •
Security hazard in Data. High operational expense. Frequency delay. High interest for processing assets. Centreless Trust.
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4 Proposed System The proposed is of block-chain framework of credit-based accord component of Industrial IoT. Propose the credit-based PoW instrument of Internet of Things gadgets, that can ensure framework reliability that exchange productivity all the while. So as to secure touchy information privacy, we plan an information authority the board strategy to direct the entrance to sensor information. What’s more, our framework is constructed dependent on coordinated noncyclic chart (DAG)organized block-chain, that are most productive then satoshi style block-chain of execution. Block-chain highlights of straightforwardness, which is a significant trademark in the fund field. In any case, it might turn into a disadvantage of few Industrial IoT frameworks, that the gathered touchy information requires classification are just available for approved one. That are in this manner critical to structure an entrance control conspire in a straightforward framework (Fig. 1). Our convention ensures reasonableness by depending on shrewd agreements among providers and vendors over decentralized cryptographic money. A legit provider must guarantee that the dealer will pay in the wake of getting the products and a legitimate trader is guaranteed that he possibly pays if the provider conveys the merchandise requested by the vendor. In instances of difference, in the proposed framework, the agreement plays the job of an appointed authority. As such, the shrewd agreement goes past a conventional agreement that speaks to an understanding between the provider and vendor, and furthermore goes about as an appointed authority to determine questions between the provider and dealer.
4.1 Advantages of the Proposed System • Accomplishes higher throughput. • Provides higher exactness on estimation.
Fig. 1 Overview of the proposed system
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• High-speed information transmission. • Low correspondence inertness. • High versatility.
5 Existing System Haze processing framework is a developing engineering for giving registering, stockpiling, control, and systems administration abilities for acknowledging Internet of Things (IoT). In the haze figuring framework, the cell phones (MDs) can offload its information or computational costly errands to the haze hub inside its nearness, rather than far off cloud. Despite the fact that offloading can decrease vitality utilization at the MDs, it might likewise acquire a bigger execution delay including transmission time between the MDs and the mist/cloud servers, and pausing and execution time at the servers. Consequently, how to adjust the vitality utilization and defer execution is of research significance. Broad reproduction considers are directed to show the adequacy of the proposed plan and the better execution more than a few existed plans are watched.
5.1 Disadvantages of the Existing System • This sets aside more effort to process. • This furnish just reproduction result not with ongoing procedure.
6 Conclusion Proposes the block-chains based Industrial IoT framework for enforced situations for keen production line for addressing previously mentioned difficulties in industrial IoT. A propose PoW component, that diminishes potential utilization of genuine hubs for expanding figuring intricacy of noxious hubs, assists with making the DAG organized block-chain increasingly reasonable for IIoT frameworks. Additionally, the information authority the board technique can secure data protection with out influencing framework for execution, that are likewise down to earth in IIoT framework. Explicit accentuation is put on the acknowledgment of a reasonableness convention in the keen agreement. As a result, decency property for each side is characterized and a procedure for how to accomplish reasonableness in the proposed plot is depicted.
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References 1. Lu, Y., Xu, L.D.: Internet of things (IoT) cybersecurity research: a review of current research topics. IEEE Internet Things J. 1–1 (2018) 2. Yu, H., Gibbons, P.B., Kaminsky, M., Xiao, F.: Sybillimit: a near-optimal social network defense against sybil attacks. In: IEEE Symposium on Security and Privacy (S&P), May 2008, pp. 3–17 3. Dorri, A., Kanhere, S.S., Jurdak, R., Gauravaram, P.: Blockchain for IoT security and privacy: the case study of a smart home. In: IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Mar 2017, pp. 618–623 4. Novo, O.: Blockchain meets IoT: an architecture for scalable access management in IoT. IEEE Internet Things J. 5(2), 1184–1195 (2018) 5. Yang, Z., Yang, K., Lei, L., Zheng, K., Leung, V.C.M.: Blockchain based decentralized trust management in vehicular networks. IEEE Internet Things J. 1–1 (2018) 6. Li, Z., Kang, J., Yu, R., Ye, D., Deng, Q., Zhang, Y.: Consortium blockchain for secure energy trading in industrial internet of things. IEEE Trans. Industr. Inf. 14(8), 3690–3700 (2018) 7. Xiong, Z., Zhang, Y., Niyato, D., Wang, P., Han, Z.: When mobile blockchain meets edge computing. IEEE Commun. Mag. 56(8), 33–39 (2018) 8. Swan, M.: Blockchain: Blueprint for a New Economy. O’Reilly Media, Inc. (2015) 9. Karlsson, K., Jiang, W., Wicker, S., Adams, D., Ma, E., van Renesse, R., Weatherspoon, H.: Vegvisir: a partition-tolerant blockchain for the internet-of-things. In: IEEE 38th International Conference on Distributed Computing Systems (ICDCS), July 2018, pp. 1150–1158 10. Zheng, Z., Xie, S., Dai, H., Chen, X., Wang, H.: An overview of blockchain technology: architecture, consensus, and future trends. In: 2017 IEEE International Congress on Big Data (BigData Congress). IEEE (2017), pp. 557–564 11. Wang, X., Zha, X., Ni, W., Liu, R.P., Guo, Y.J., Niu, X., Zheng, K.: Survey on blockchain for internet of things. Comput. Commun. (2019) 12. Iwanicki, K.: A distributed systems perspective on industrial IoT. In: IEEE 38th International Conference on Distributed Computing Systems (ICDCS), July 2018, pp. 1164–1170 13. Huang, J., Kong, L., Chen, G., Wu, M.Y., Liu, X., Zeng, P.: Towards secure industrial IoT: blockchain system with credit-based consensus mechanism. IEEE Trans. Industr. Inf. 15(6), 3680–3689 (2019) 14. Rathee, G., Balasaraswathi, M., Prabhu Chandran, K., Gupta, S.D., Boopathi, C.S.: A secure IoT sensors communication in industry 4.0 using blockchain technology. J. Ambient Intell. Humanized . 12(1), 533–545 (2021)
Object Detection in Railway Track Using Industrial IoT (IIoT) L. Sujihelen, Kota Vinodh Kumar, Madhav Srinivas, and G. Nagarajan
Abstract In railway track, more accidents occur due to the objects in the railway track. In train, everyday getting to thousands of passengers traveling by trains. Consequently, the protection of the travelers has to be safeguarded. The proposed system is used to sense the object in the railway track and intimate information to the control office and engine driver. So, this proposed work can observe the object using sensor, and the information about the object is transferred before the 10 min of the arrival of the train. The railroad is totally automated using Bluetooth, RFID, Wi-Fi, GPS, live video streaming, and GPS. The live video stream is transmitted to the cloud from the mobile application. To avoid the accidents, the train may be stopped. Keywords IoT · GPS · Obstacle · Ultrasonic sensor · Object detection
1 Introduction The railway system is one of the capable ways of itinerant from one place to another place. The valuation of cost is very less and can be affordable by all levels of people. For many people, the transportation is done using the railways because it is the cheaper way to travel and many of us choose railways [1, 2]. Nowadays, many train accidents have been occurring. The majority of the train accidents occur due to objects on the railway tracks. By object detection on the railway tracks, the train safety process will be easily instigated. Currently, many accidents are occurring in railways, the reason for the accidents is the object on the railway tracks and typically human intervention. Due to the recognition of the objects on the railway tracks, we can avoid accidents of the train. For objects in track causes more damage in railroad [3, 4]. To detect the object on the railway track, we use sensors for detecting. When the object is nearer to the sensor, then the sensor will detect the object. For any object detected on the railway track means using ultrasonic sensor to calculate the distance between the object and train. Support the information received, starting L. Sujihelen · K. V. Kumar · M. Srinivas · G. Nagarajan (B) Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_35
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actions will be performed. Uncertainty the distance is extremely less victimization react and trigger choice of thing speak, automatic message that consists of latitude and the longitude of the object is sent to the control station and police station [5, 6]. Due to the detection of the object on the railway tracks, we can avoid many of the accidents. We can get the latitude and longitude details of the object [7]. Due to this, we can remove the objects on the railway tracks such that the accidents due to these objects can be avoided. When the object is detected too close to the train, then the automatic message is sent to the pilot such that we can avoid the accidents. Hence using the sensors on the railway tracks, we can avoid the accidents.
2 Related Work The authors of the paper have shown the different ways for tracking of the train using Ethernet and GPS. The proposed system detects the latitude and longitudes of train by using GPS, and also, the status of the pilot is known whether the pilot is taken alcoholic or not. The paper shows the detection of the obstacle on the railway level crossing using curved laser scanner gained with pointed clouds [8]. This presented system allows the detection of the small obstacles such as rocks which are lying near the railway tracks by using highly dense and correct point clouds. The author [9] has presented an algorithm to detect an obstacle and tracking of the object on the railway track. The tracking of the object is based on matching of the template and adding their absolute differences. The method that was proposed is verified on real-time scenario which consists of two railway crossings. To detect the cracks in the tracks in the running path by using the basic principles of the electromagnetic tomography [10]. To classify the given the track image whether it is cracked or normal has proposed a linear backpropagation algorithm. They proposed an algorithm which is applied to validate the experimental results. The paper [11] develops a random forest classification algorithm for efficient rail crack detection system. From the real-time track images, the proposed system extracts some integral features to detect the defect or crack in rail track [12]. Using wireless protocol ZigBee, they propose an idea for conflict detection by controlling the train’s speed [13].
3 Proposed System In this proposed system, a new computer vision-based method is proposed for analyzing the object on the railway tracks of railway systems. There are image processing-based several techniques in the literature. The rails are determined by means of image processing techniques using the existed method. This proposed system also concentrates on the use of ultrasonic sensors which are used to detect humans or any object being pursuing on the track. If any crack or objects occur in the track means longitude and latitude of the place are messaged to the nearest control
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station or police station. The recording and sending of coordinates are done by GPS and WIFI module as shown in Fig. 1. When the train is started on the track, the ultrasonic sensor which is placed in front of the train will sense the object. When the ultrasonic sensor finds an object in its radius or detects an object on the railway, it will calculate the distance between the object and the train. If the distance between the object and train is very less, then the location details of the object will be sent by using the GPS module. The GPS module will send the latitude and the longitude details to the nearby control station or police station. Due to this, the object will be removed and the collision with the train can be avoided.
Fig. 1 Process for proposed system
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4 Results and Discussion The proposed system converses the serious protection techniques for high-speed train procedure situation based on the train control system. The proposed system replaces the labor-intensive inspection of the track section, by automatic inspection. This will help to detect the object immediately and reduce the possibilities of accidents. When an object is there in the railway track, it will predict the object very fast and reported to the controller room. There are more methods are there to predict the object in the railway track. The detection of the object in the railway track is very less. The proposed system will detect the object very fast. The detection accuracy is very high compared with the existing method. The detection accuracy is calculated by the equation, and the figure is shown in Fig. 2. Detection Rate =
No. of Objects detected × 100 Total No. of Objects
Time Consumption The time consumption will calculate the time to observe the object and communication as shown in Fig. 3. Time = Observe the object + communication. In Fig. 4, there is an object on railway track. The object is a stationary object which is unmovable. There is an ultrasonic sensor near railway track. The ultrasonic sensor will detect the object (solid object such as stone and rock) which presents in the radius of the sensor. When the sensor detects the object within its radius using GPS tracker, the latitude and longitude position has sent to the server. With the position of the object, the authorized persons will remove the object from the track. If the train
Fig. 2 Detection rate
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Fig. 3 Time computation
Fig. 4 Object detection on railway track
is nearer to the object, then the message is sent to the engine pilot such that to avoid the collision with object as shown in Fig. 5. Figure 5 shows the GPS location of the object on the track. The object is detected on the railway track, and the location details of the object can be get by the GPS module. Due to this GPS position, we can find the object which can be removed.
5 Conclusion It is necessary for the safety of the people while traveling through the railways. Many people are traveling from one place to another place using railways. The proposed system will find any objects that were detected on the railway tracks. The engine driver can control the train when the object is detected by the ultrasonic sensor
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Fig. 5 GPS location of the object
and the distance between the sensor and the train is less, then the train will be stopped. Ensuring safety features using reliable and fast communication system and the digitalization of the railways make a better mode of transport than the others. The proposed work ensures the safety of passengers in railway transportation, and it helps to manage the geological risks such as landslides. The work can be extended to work with various lighting and various climatic conditions.
References 1. Aboelela, E., Edberg, W., Papakonstantinou, C., Vokkarane, V.: Wireless sensor network based model for secure railway operations (2006) 2. Guoqiang, C., Limin, J., Liming, Z., Yu, L., Xi, L.: Research on rail safety security system 1405–1410 (2010) (World Academy of Science Engineering and Technology) 3. Pitchai Ramasamy, R., Praveen Kumar, M., Sarath Kumar, S., Raghu Raman, R.: Avoidance of fire accident on running train using ZigBee wireless sensor network. Int. J. Inf. Comput. Technol. 3(6), 583–592 (2013) 4. Teja, K.W., Angadi, S.: Fire detection and notification system in trains. Int. J. Innov. Res. Sci. Eng. Technol. 2(4), 32–35 (2014) 5. Amaral, V., Marques, F., Lourenco, A., Barata, J., Santana, P.: The detection of an obstacle at the railway level crossing using lasers. J. Sens. 2 (2016) 6. Bharathan, S., Shameem, B.P., Savitha, T., Vimal Kumar, P.: Advanced security system in trains using RF module. Int. J. Adv. Res. Electron. Commun. Eng. 4(3), 543–547 (2015) 7. Immanuel Rajkumar, R., Sankaranarayanan, P.E., Sundari, G.: Real time wireless based train tracking, track identification and collision avoidance system for railway sectors. Int. J. Adv. Res. Comput. Eng. Technol. 3(6), 2172–2177 (2014)
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8. Silar, Z., Dobrovolny, M.: The detection of the objects on the railway tracks at crossing levels. In: Computational Collective Intelligence, pp. 245–255. Springer International Publishing, Berlin (2015) 9. Ze, L., Wen, L., Junyan Xiafang, X., Bu, B., Zheng, Y.: Rail defect inspection using electromagnetic tomography. IEEE Trans. Magn. 15(5), 1 (2015) 10. Yong, S., Limeng, C., Zhiquan, Q., Fan, M., Zhensong, C.: Using random structured forests automatic road crack detection. IEEE Trans. Intell. Transp. Syst. 17(12), 3434–3445 (2016) 11. Haripriya, K., Harshini, G., Sujihelen, L.: Survey on efficient automated toll system for license plate recognition using open CV. In: 2018 International Conference on Emerging Trends and Innovations in Engineering and Technological Research (ICETIETR), pp. 1–6 (2018) 12. Sharma, K., Kumawat, J., Maheswari, S., Jain, N.: Railway security based on the wireless sensor networks. Int. J. Comput. Appl. 96(25), 32–35 (2014) 13. Nagarajan, G., Minu, R.I.: Wireless soil monitoring sensor for sprinkler irrigation automation system. Wirel. Pers. Commun. 98(2), 1835–1851 (2018)
Construction of Hele Shaw Apparatus for Subsonic Flow Visualization Akhila Rupesh , P. Vinaykumar Doddamani, P. Umeshkumar, Amaresh Wavare, and M. B. Mahanthesh
Abstract Most of the flows encountered in engineering applications are complex in nature to enable and understand such flow Hele Shaw is the technique means of obtaining such quantitative pattern of flow. Fluid dynamics is a vast subject for the study of fluid behavior, also there are lot of improvements in modern physics and mathematics of fluid dynamics even there exist a lot of tools like computational fluid dynamics for simulation and huge flow visualization methods like analytical method, graphical method and electrical method but to determine the flow parameters is difficult task for inviscid flow analysis, under various conditions flow parameters for any object keeps on varying. Even a small dust particle exists in lab may change the density of the flow and there by changes flow patterns, etc., hence to study and analyze this unpredictable nature hele Shaw method is the best method considered and most convenient for drawing flow patterns this method is based that pass the flow of fluid between two parallel plate with narrow gap. To conduct this experiment, it is necessary to design and construct a suitable Hele Shaw apparatus. Keywords Streamlines · Aerodynamics · Subsonic flow
1 Introduction Foundational fluid mechanics studies that what we have known is only very little, and the most of the fluid flow phenomena are extremely complicated, and with currently acquired knowledge, even providing a realistic representation of them is not feasible. To understand and solve such issues, the science world still searches for more advanced techniques. We found that flow visualization, by the way, contributes to prepare a path for the development of essential techniques for solving complicated topics, though, like separated flows, jets, and so forth. In reality, the data given by flow visualization could not be provided in several flow situations collected through A. Rupesh (B) · P. Vinaykumar Doddamani · P. Umeshkumar · A. Wavare · M. B. Mahanthesh Department of Aeronautical Engineering, Mangalore Institute of Technology and Engineering, Moodbidri, Karnataka, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_36
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any other technique or method. These visualizations, though, lack genuine mathematical processes [1]. They actually act as a means for simple comprehension of such complex fields of flow. To resolve this visualization flaw, several systems were proposed to research the visualization problems of fluid flow without really getting through the difficulties encountered [2]. They are known as analog techniques. Fluid flow concerns in analog techniques by setting up another physical structure, problems are solved. A process, such as an electric field, for which are of the same shape with corresponding simple governing equations boundary conditions, such as fluid flow conditions. The initial solution the problem can be experimentally derived from the calculation on the analogous method. Some of a very well fluid flow analogy technique such as the Hele Shaw analogy [3]. Explaining profile drag over an object and boundary layer effects is very difficult, mathematical theories of boundary layer motion can explain the boundary layer motion but because of complexity it should develop even more to describe it in simple manner, also some of the theoretical studies made on subsonic compressible fluid flow are incomplete and are not enough up to the mark, it needs some experimental results. Experiments should provide knowledge of fundamental nature to crack the eddies before to complete the theoretical work. Experiment should be easy to design and modify quick, safe and inexpensive [4].
2 Design of the Hele Shaw Apparatus The Hele Shaw cell contain two parallel plates with thickness 6–10 mm, and the plates are separated by a narrow gap 1–3 mm wide that was field with layer, the plates placed in vertical direction and connected with a rectangle zones in the top edge and bottom edge is connected with rubber tubes. The tanks are connected on the top side of the glass plates, and the tanks are having small slits which is passage for the space between the glass plates. The fluids are fixed in the tanks one of the tank contain water and other within fluid (potassium permanganate). Valve is allow water to flow from source, and it is key to make stream line flow.
2.1 Design of Fluid Container Tank Tanks are connected on the top side of the glass plates, the tanks are having small slits which is passage for the space between the glass plates. The specification of the tank is designed to be 100 mm × 220 mm × 50 mm. It consists of 10 exit channels of diameter 3.82 mm which enter into the narrow gap between two plates as shown in Fig. 1.
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Fig. 1 Design of fluid container tank
2.2 Design of Structural Frame One small exit value for fluid to flow out of boundary working space for which controller nob will fitted so has to control the fluid flow in such that flow is laminar stream lines as shown in Fig. 2.
2.3 Specifications of Glass Plate The two glass of size 200 mm × 280 mm × 5 mm (width × length × thick). Both the glasses are made of acrylic transparent glass plates will fitted to frame of thickness 5 mm and same boundary as glass plate. So that their leaving vacant of space about 190 mm × 275 mm × 5 mm working space for the flow of streamlines as shown in Fig. 3.
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3 Working of the Instrument The Hele Shaw apparatus is held in vertical position (mount the Hele Shaw apparatus in vertical position). The fluid will flow once the passage is opened, the flow of water and dyes flow through vertical passage between the transferred plates. The water and
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dyes will flow according to gravitational force, the uniform flow fluid shown in the rectangular slit of the Hele Shaw apparatus is used as the test section of the flow visualization for the test model. Inserting the test model in the test section between two parallel plates. Tanks are closed with pitch coke, connect the drain tube apparatus to end of the test section. The opening the pitch cock slowly water and dyes will start flowing through slits as around the narrow gap in between two parallel plates. The flow fluid with dye touch the streamlines flowing around the rectangular model is photographed or sketched on paper as a light pass-through other side of the model Shadow will capture on the plain paper then the procedure is repeated for the flow visualization of another model. Clean the reservoir properly with water and insert the given object or model between the plates. Take dye in dye reservoir and put water in reservoir and allow the flow through gap between parallel plates. Remove entrapped air if any and adjust flow control valve to adjust dye flow and want to get a well-designed pattern of stream line around object. Fix tracing paper on the plate surface and place lamp on other side of the apparatus. Sketch the pattern of streamline and the geometry of body on the tracing paper. Repeat the same procedure for all different strapped object.
4 Conclusion Using the designed apparatus, stream lines can be traced around different objects placed in fluid stream by injecting dyes in Hele Shaw apparatus. Finally, the fundamentals of fluids flow can be studies using the designed Hele Shaw apparatus. Further the data obtained in the experimentation with Hele Shaw apparatus can be validated using a subsonic wind tunnel.
References 1. Paul, A.R., Upadhyay, R.R., Jain, A.: A novel calibration algorithm for five-hole pressure probe. J. Flow Meas. Instrum. 3(2), 89–95 (2002). https://doi.org/10.4314/ijest.v3i2.68136 2. Lien, S.J., Ahmed, N.A.: An examination of suitability of multi-hole pressure probe technique for skin friction measurement in turbulent flow. J. Flow Meas. Instrum. 22, 153–164 (2011). https://doi.org/10.1016/j.flowmeasinst.2011.01.004 3. Rupesh, A., Muruga Lal Jeyan, J.V., Uthaman, S.: Design and analysis of five probe flow analyser for subsonic and supersonic wind tunnel calibration. IOP Conf. Ser. Mater. Sci. Eng. 01(715), 1–7 (2020). ISSN 1757-899X. https://doi.org/10.1088/1757-899X/715/1/012083 4. Rupesh, A., Muruga Lal Jeyan, J.V., Lal, K.: Dynamic characterization of single lap joints in composite laminate over experimental and computational approach. Int. J. Eng. Technol. IJET 07(03), 1062–1070 (2018). ISSN 2227-524X. https://doi.org/10.14419/ijet.v7i3.12.17633
Impact of Acoustics Impingement on Proliferating Fires Bhushan Thombare, Saumya Shekhar, and Vinayak Malhotra
Abstract Forest fires are one of the major ultimate to natural and human established environment and have shown a tendency to rise in recent years. One of the crucial issues for a forest fire is associated with the combustion and propagation process. Researchers have been appraising new methods to control and diminish the repercussion of concurrent fires such as forest fires, building fires, and various space fires, but no captivating approach has been deduced from their studies so far. The key sources of these kinds of fires scrutinize the unstable nature of the flames and the strong unpredictability connected with them. Through proper experimentation, the effect of sound on the spreading of flames is investigated in the aid of spread rate. The present work is motivated by the superior standards of fire safety from practical and functional significance as it covers a wide range of engineering and industrial applications. Appreciable work had been carried out; however, the effect of sound on flames in a purely natural convective environment is an aspect yet to be thoroughly understood. The main objectives of the project are to study the fire propagation phenomenon in presence of acoustics and to fundamentally understand the role of key controlling parameters. The present work attempts physical insight into the effect of sound frequency, sound source distance, and a number of acoustic sources on the spreading of flames in opposed and concurrent configurations. An experimental setup was upraised comprising of a sound source with essential controls. The frequency of sound source and number of external sources are systematically varied keeping the ignition front fixed. The result advocates the significant effect of acoustics on the fire propagation phenomenon in distinct modes. Keywords Fire propagation · Forward heat transfer · Compression · Rarefaction
B. Thombare (B) · S. Shekhar · V. Malhotra Department of Aerospace Engineering, SRM Institute of Science and Technology, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_37
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1 Introduction The systematic exploitation of fire has progressed in a spectacular manner in the disciplines of engineering, industrialization, practical world, functional and operational systems in various sectors. While fire is a fairly regular event in which nearly everyone uses its area of engineering, industrialization, practical world, functional and operational systems in various disciplines, it also has the potential to get out of control, posing a risk to human lives and property. Fire is also the cause of mankind’s greatest calamity. Nowadays, as a result of climate change and the desire to acquire more barren territory, mankind is suffering a massive loss in the form of forest fires. According to National Fire Prevention Association (NFPA) data, forest fires have resulted in the loss of 8.5 million acres of land, a loss of 10.7 billion dollars (USD), a loss of residential, industrial, educational institutions, and wildlife states totalling 10,700 million dollars (USD), and an annual rate of increase of 77%. Recent forest fires in the Amazon, Australia, and California have wreaked havoc on human and animal lives, property, and the ecosystem. The current fire suppression technology is incapable of meeting the current societal demand resulting from a more wealthy society and expanding technology. Due to the unique features of these places, existing extinguishers can be inefficient in putting out flames in tight areas such as aircraft, vehicle cockpits, and below decks on ships. The opposing direction of airflow to that of flame propagation is characterized as the opposed flame spread rate. Flame propagation is the movement of the reaction zone or combustion wave through a combustible mixture. The current effort focuses on flaring combustion. The flame spread, which is the rate of flame movement across the fuel surface, is noticed in this kind of combustion (Figs. 1, 2 and 3). Fig. 1 Arizona wildfire
Fig. 2 Space shuttle explosion
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Fig. 3 BOAC flight 712
From the conventional work done by Clarke and McChesney [2] suggested that when the wave drives the non-equilibrium component of the flow itself, then wave attenuation may occur in the dissociating mixture. Appreciable scientific work had been done and reviews can be found in [1–10]. The main motive of work is standards of fire safety from functional and practical significance. Appreciable work has been done, but certain aspects are yet to be studied. The specific objectives of the work are: 1. 2. 3.
To study fire propagation phenomenon in presence of acoustics. To study the effect of the number of external heat sources in the form of distinct configurations on the pilot fuel. To fundamentally understand the role of key controlling parameters.
2 Experimental Setup and Solution Methodology A small experimental setup was built to investigate fire propagation (Figs. 4 and 5). It was done so that additional parameters may be changed and examined as a result. The setup consists of (a) a mellow mild steel plate, (b) a rod to change the plate’s orientation, (c) a matchstick as a fuel source, (d) a protractor, and (e) speakers. A 0.5 cm division marking was followed by three 1 cm marks on the coordinate sticks. To settle the coordinate sticks opposite the base, a mild steel plate was utilized. The distance between two matchsticks is 0.5 cm. To produce sound, a computer multimedia speaker 2.1 was used. To create the sound of shifting frequencies, NCH tone generator computer software was utilized. To investigate the various effects of Fig. 4 Mild steel plate
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Fig. 5 External heat sources
Fig. 6 Experimental setup with acoustic source
acoustic life on heat energy. Matchsticks were fixed in four different configurations: (a) unilateral, (b) bilateral, (c) ‘Y ’, and (d) ‘+’. For all four situations, the speaker was placed 100 cm away from the pilot fuel. The main goal was to conduct tests in the absence of an auditory source. In each example, the fire spread rate was calculated by recording the time it took to burn 1 cm of the matchstick marking. These fire spread rate statistics serve as the baseline for comparison. Following that, a speaker was positioned at 100 cm perpendicular to the pilot fuel, and changing recurrence noises were generated using the NCH Tone generator computer software (Fig. 6). All experiments were guaranteed to be repeatable to the third order. Forward Transfer Theory From the Second Energy Equation we know that: Energy Change = Energy produced − Energy lost So, ρs Cs V (dT /dt) = q p − q L
(1)
q p = Hc V Ci A ∗ e(Ea /RT )
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where
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Hence, according to classical forward heat transfer theory, the flame spread rate (r) is given by: Measurement The rate at which the surface burns is called the flame spread rate which is linearly calculated as: r=
Distance burnt Time taken to burn that region
(5)
The term qnet is the difference between the energy generated and the energy lost. Where ρs = Density of the solid fuel, τs = Thickness of the solid fuel, cs = Speed of sound in that medium, TSurface = Temperature of the surface, T∞ = Temperature of the surroundings, dT /dt = Change in temperature, qp = Energy produced, qL = Energy lost, A* = Pre-exponential factor, E a = Activation energy, R = Ideal gas constant, T = Temperature in kelvin, H = Thermal Conductivity constant, A = Cross-sectional area of the material, T a = Ambient temperature, qnet = Total Energy, cs = Speed of sound in the material, T ∞ = Temperature of the surroundings, r = Flame spread rate. The fire spread rate was calculated in each case where a speaker was utilized to radiate sound of distinctive frequencies. The experiment was conducted at normal room temperature and readings were taken properly guaranteeing efficiency and congruity in each case. It is vital to note that each information displayed here represents the repeatability and reproducibility of the third order.
3 Results and Discussion It was first tested to determine the base case flame spread rate. The flame spread rate of the pilot fuel was tested in all directions, first without and subsequently with the acoustic enclosure. The acoustic frequency was 8000–10,000 Hz. Figure 7 shows a non-monotonic trend for all instances, with a maximum burn rate of 163.16% at 900 for 10,000 Hz. These data are used to compare flame rate between different setups with external heat source. The propagating front expands via heat feedback (forward heat transfer) from the burning to the unburned solid fuel upstream, according to conventional heat transfer theory over thin solid fuels. Regression rates rise or decrease as a result of this. Convective buoyant flow, localized velocity, and temperature fields emerge around the fuel surface owing to convective buoyant flow. High-temperature smoke, which conveys heat flowing parallel to the fuel surface, provides additional energy and preheating. High regression rates can be ascribed to high cumulative heat transfer from burned to unburned fuel. The remaining results were split into two groups.
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Fig. 7 Acoustic fire interaction by varying frequency
Stabilizing Effect: In this effect, the flame flickering stops and the flame becomes stable in the influence of acoustic. Destabilizing Effect: In this case, the flames start flickering due to the influence of acoustic, and hence, the regression rate changes accordingly. These effects further divide the result into three zones. (1)
(2)
(3)
Heat Sink Zone: The heat transfer in this regime drops due to the decrease in the localized temperature around the pilot fuel. The reason is not enough oxygen gets to enter to fume the pilot fuel due to acoustic. Hence, the regression rate for this case is less compared to the pilot fuel. Neutralizing zone: Because the heat transmission is constant with the pilot fuel in this situation, there is no acoustic impact. In this scenario, the regression rate is the same as in the pilot fuel base case. Heat source zone: Because of buoyancy, which transports heat from burning to unburned fuel, owing to the external impact of sound, cumulative heat transfer dominates in this region. As a result, the regression rate for this scenario is higher than for the pilot fuel.
The fire stimulation number is the ratio of the flame spread rate at a given orientation for a given number of external sources in the presence of acoustics to the flame spread rate at the same orientation for the same number of external sources without acoustics (FSN). These figures are split into several regimes. The heat sink zone is FS1, the heat source zone is FS > 1, and the neutralizing zone is FS = 1. A non-dimensional integer was calculated for each direction. It was done to verify the configuration’s effectiveness and to investigate the impact of different configurations, orientations, acoustics, and external sources as compared to when there were no acoustics.
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Fig. 8 Effect of 8000 Hz frequency
FSN =
Spread rate in presence of acoustic at particular orientation(cm/ min) Spread rate in absence of acoustic at particular orientation(cm/ min)
Based on the FS number, new regime is formed. 1. 2. 3.
FS-I: Heat Sink Potential—Here, the ratio of FS numbers is less than 1 (FS1). Pilot fuel absorbs energy from external sources in this effect. Neutralizing Effect (FS-II)—The FS number ratio is 1 in this case. (FS = 1). It is not affected by either positive or negative heat sources. Heat Source Effect (FS-III)—The FS number ratio is larger than 1 in this case. (FS > 1). Pilot fuel is used to supply energy to external sources in this effect.
From the graph (Fig. 8) for 8000 Hz when the number of external sources was fixed at 1. The result was compared for all four configurations with the single fuel case. The highest value of flame spread was obtained in Y-lateral with a massive increase of 284.61% (FS > 1) at 90° orientation as were the least flame spread rate was also obtained in Y configuration at 45° with a decrease of −38.60% (FS < 1) (refer Table 1). In this case, blow off (the phenomenon in which fire extinguishes momentarily or permanently) was observed. An acoustic speaker was placed at a distance of 100 cm from the pilot fuel. It was observed that blow off was more prevalent in this case, especially in the bilateral and ‘Y ’ configuration. The FS number was less then1 for most of the cases; the reason is heat transfer is dominating in this region because of buoyancy that carries the heat from burning to the unburnt fuel. It is interesting to note that neutralizing effect was seen for bilateral configuration at 75° orientation. For the case of 8500 Hz, from the graph (Fig. 9), it can be seen that all configuration follows the non-monotonic trend. The highest regression rate was obtained for the ‘+’ configuration when n = 1. The rise of around 257.22% (FS > 1) was seen at 90° orientation. The maximum drop was seen for the ‘Y ’ configuration of
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Table 1 FS number variation for unilateral configuration when N = 1 Orientation
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Fig. 9 Effect of 8500 Hz frequency
around −51.62% (Fs < 1) at 0° orientation. Blow off was dominant for the bilateral configuration. The FS number was less than1 for most of the cases; the reason is heat transfer in this regime drops due to the decrease in the localized temperature around the pilot fuel. It is interesting to note that neutralizing effect was seen for bilateral configuration at 90° orientation. In the case of 9000 Hz (Fig. 10), the maximum rise in flame spread rate was seen for ‘Y ’ configuration with an increase of 212.5% (FS = 1) for 90° orientation whereas the maximum drop was observed for bilateral configuration of around −27.07% (FS < 1) at 0° orientation. It was interesting to note that for Bilateral and ‘Y ’ configuration, many cases of blow off were seen except for (75° and 90° orientation). It is interesting to note that neutralizing effect was seen for the ‘+’ configuration at 60° and 90° orientations. In most of cases, the FS number is less than 1. For the case of 9500 Hz, from the graph (Fig. 11), it can be seen that all configuration follows a non-monotonic trend. The highest regression rate was obtained for the ‘+’ configuration when n = 1. The rise of around 233.33% (FS > 1)
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Fig. 10 Effect of 9000 Hz frequency
Fig. 11 Effect of 9500 Hz frequency
was seen at 90° orientation. The maximum drop was seen for the ‘Y ’ configuration of around −21.07% (FS < 1) at 0° orientation. The FS number was equal to 1 at the ‘Y ’ configuration for 90° orientation and show neutralizing effect. In the case of 10,000 Hz (Fig. 12), the maximum rise was seen for ‘Y ’ configuration with an increase of 284.611%(FS > 1) for 90° orientation where the maximum drop was observed for unilateral configuration of around −100%, where the fire extinguished completely before reaching the first 1 cm mark. It was interesting to note that for
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Fig. 12 Effect of 10,000 Hz frequency
this case, no flame condition was observed. Also blow off was dominant for unilateral, bilateral, and ‘Y ’ configurations for 0°–30° of orientation. In most cases, the regression rate comes under FS < 1. The heat transfer in this regime drops due to the decrease in the localized temperature around the pilot fuel. The interesting thing to note here is none of the cases show neutralizing effect. For ‘Y ’ configuration at 8000 Hz (Fig. 13), it can be seen that the massive increase in regression rate of about 316.66% (FS > 1) was seen for 90° when pilot fuel was accompanied by two external sources on each side due to the fact that cumulative heat Fig. 13 ‘Y ’ configuration at 8000 Hz
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transfer is dominating in this region because of buoyancy that carries the heat from burning to the unburnt fuel. The maximum drop can be observed at 30° of around − 42.88% (FS < 1) when the number of external sources is 1 on each side. For unilateral configuration at 8500 Hz (Fig. 14), the massive increase of around 257.22% was seen for regression rate at 90° and when n = 5. A maximum drop of −37.5% was observed at 0° orientation when the number of external sources is 4 on each side. The FS was greater than 1 for both cases due to fact that the buoyancy that carries the heat from burnt to the unburnt fuel. Blow off was only seen for 0° orientation for N1 and for 15° orientation for N2 , respectively. The neutralizing effect can be observed at 90° and when N = 2 and for 0° when N = 5. For bilateral configuration at 9000 Hz (Fig. 15), it is interesting to note that flame spread rate changes with a massive increase of 140% (FS > 1) were seen at 30° orientation when 4 external sources are attached on either side of pilot fuel whereas the maximum drop was obtained at 45° orientation of around −38.60% (FS < 1) when 3 external sources were present on either side. Cases of blow off were observed when one external source is present for 0°–45° orientations. The neutralizing effect can be observed at 75° when N = 3 and for 30° when N = 5. Here, acoustic does not affect the flame spread rate. For the ‘+’ configuration at 10,000 Hz (Fig. 16), it was interesting to note that the maximum rise in regression rate is of 400% was observed at 90° orientation for N = 3, 4, and 5. The FS number was greater than 1 for all of the cases due to fact that cumulative heat transfer is dominating in this region because of buoyancy that carries the heat from burning to the unburnt fuel. The maximum drop of around 20.77% (FS < 1) was observed at 30° orientation when three external sources are attached on all four sides of pilot fuel. The neutralizing effect can be observed at 0° and 15° orientations when N = 4 and at 60° when N = 5. The graph (Fig. 17) is comparing Fig. 14 Unilateral configuration at 8500 Hz
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Fig. 15 Bilateral configuration at 9000 Hz
Fig. 16 ‘+’ configuration at 10,000 Hz
the flame spread rate of all the configurations when the number of external sources is fixed at 1. The massive rise of around 284.61% (FS > 1) at 90° orientation for ‘Y ’ configuration and a drop of around −42.88% (FS < 1) at 30° is seen. For unilateral configuration, the rise of around 194.16% at 90° and a drop of −14.4% (FS < 1) at 30° was observed. For bilateral configuration, maximum rise of 177.77% (FS > 1) at 90° and a drop of −25.53% (FS < 1) at 45°, whereas for ‘+’ configuration, the rise of 194.66% (FS < 1) at 90° and drop of −10.15% (FS < 1) at 45° was obtained, respectively. The plot (Fig. 18) represents the data for regression rate when
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Fig. 17 For N = 1 at 8000 Hz frequency
Fig. 18 For N = 3 at 8000 Hz Frequency
the number of external sources is fixed to 3 for all configurations. From the plot, it is seen that a massive rise of 177.77% (FS > 1) at 90° and a drop of −25% (FS < 1) was seen for unilateral configuration at 0° orientation. For bilateral configuration, maximum rise of around 118.56% (FS > 1) was obtained at 75°, whereas a drop of − 34.8% (FS < 1) was obtained at 0° orientation. For ‘Y ’ and ‘+’ configurations, rise of 233.33% (FS > 1) and 316.66% (FS < 1) was observed at 90° and a drop of around −14.29% (FS < 1). No rise was obtained at 30° and 45° orientations, respectively. For all the above cases, most of the cases come under the heat source zone because of
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Fig. 19 For N = 5 at 8000 Hz Frequency
buoyancy which carries the heat from burning to the unburnt fuel due to the external influence of acoustic. For the condition when the number of external sources was fixed at 5, from the graph (Fig. 19), it can be seen that the rise of around 233.33% (FS = 1) at 90° and a drop of −18.05% (FS < 1) were obtained at 15° for unilateral configuration. For bilateral and ‘+’ configuration massive rise of 127.25% (FS > 1) and 257.22% (FS = 1) was seen at 90°, whereas a drop of −20.45% (FS < 1) and 42.77% (FS = 1) was observed at 45° and 15° orientations, respectively. For the ‘Y ’ configuration, rise of 220% (FS > 1) was observed at 75°, whereas a drop of −6.25% (FS < 1) was obtained at 0°.
4 Conclusion From the experimental investigation, it can be deduced that acoustics plays a significant role on fire propagation. The variation within the reaction zone indicates potential energy interaction zone between thermal and acoustic energy. The formation of an energy interaction zone points to a relation between thermal and acoustic energy. Based on the results, following conclusions may be drawn: 1.
A massive rise of around 400% was observed for ‘+’ configuration when the number of external sources is varied from 3 to 5 for frequency range 9000 to 10,000 Hz, respectively, at 90° orientation. This shows that acoustic energy shows an outstanding result on flame unfold rate, so the effect of forward heat transfer and hearth propagation.
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The maximum drop of 100% was observed in the unilateral configuration for 10,000 Hz with n = 1 − 3. Here, the fire extinguishes fully before reaching the 1 cm mark. From the experimentation done and results obtained, it can be deduced that presence of acoustic affects the fire propagation. Cases of blow off and reappearance were observed for unilateral, bilateral, and ‘Y ’ configurations when orientation is varied from 0° to 60° in the presence of acoustics. The determining physics of this development includes (a) modification of the reaction zone (b) formation of associate energy interaction zone (c) interaction between thermal and acoustic energy.
Applications of work: Based on physical intuition, new pointers within the fireplace safety on the traditional and additional territorial atmosphere will be framed. The work carries a wide selection of applications which include Engineering viz. combustion and propulsion, arms validation, testing and up-gradation of missile systems, industrial and power generation systems, practical, purposeful, and scientific applications. Acoustics can be used in case of fire accidents to minimize losses. Acknowledgements We dedicate our research work to the firefighters for their hard work and incessant service they have been providing for the past many decades. Their fast reaction time has helped in saving thousands of lives in forest fires, aircraft, and rocket fires, building and compartment fires, industrial fires, and domestic fires.
References 1. Elaine, S.O., Gardener, J.H.: Chemical-acoustic interaction in combustion systems. Prog. Energ. Combus. Sci. 11 (1985) 2. Clarke, J.F., McChesney, M.: The Dynamics of Real Gases. Butterworths (1964) 3. Toong, T.Y.: Chemical effects on sound propagation. Combus. Flame 18 (1972) 4. Oran, E.S., Gardner, J.H.: Chemical-acoustic interactions in combustion systems. Prog. Energ. Comb. Sci. II, 253–276 (1985) 5. Niegodajew, P., Łukasiak, K., Radomiak, H., Musiał, D., Zajemska, M., Poskart, A., Gruszka, K.: Application of acoustic oscillations in quenching of gas burner flame. Combust. Flame 194, 245–249 (2018) 6. Friedman, A.N., Stoliarov, S.I.: Acoustic extinction of laminar line-flames. Fire Saf. J. 93, 102–113 (2017) 7. Yamazaki, T., Matsuoka, T., Nakamura, Y.: Dynamic response of non-premixed flames subjected to acoustic wave. In: 12th Asia-Pacific Conference on Combustion, vol. 4, July 2019 8. Mckinney, D.J.: Acoustically driven extinction in a droplet stream flame acoustically driven extinction in a droplet stream flame, p. 2202 (2007) 9. Pratap, S., Andrei, N.L., Jerzy, C.: Effects of flame development and structure on thermoacoustic oscillations of premixed turbulent flames. Japan Soc. Mech. Eng. C8–4 (2004) 10. Shafiq, R.Q., Waqar, A., Robert, P.: Behaviour of a premixed flame subjected to acoustic oscillations. PLoS ONE 8(12), e81659 (2013)
Kinematics and Dynamics Analysis of 5DOF 360 Degree Machining Robot Mihir H. Amin, Monil M. Bhamare, Japagna N. Agnihotri, and Dipal M. Patel
Abstract Industries are moving towards automation looking for application of robotics for performing flexible machining operations with high accuracy and precision. Industries require such robotic configuration which covers multiple domain within a single robotic system. The current work proposes for such robotic configurations to achieve such flexible 360° machining operations such as drilling. A threedimensional computer aided design of the 5DOF robot manipulator robotic structure is done with the help of SOLIDWORKS software and it is visualized using RoboAnalyzer and then is fabricated accordingly. Kinematic analyzes such as forward kinematics and reverse kinematics of 5DOF robot manipulator with five revolute joints of proposed model are represented in a simplified manner. The forward kinematics are developed from Denavit-Hartenberg parameters and homogenous transformation matrix. The inverse kinematics are further obtained using algebraic solution method. The workspace is shown for the proposed robotic configurations from RoboAnalyzer software. The path planning is created and evaluated for the desired orientation and position of end effector manipulator. The calculation for the static torque is done. Finally, the results of kinematic analysis are validated with the help of RoboAnalyzer and MATLAB-Simulink to obtain desired output. Keywords Robotics · Forward and inverse kinematics · SOLIDWORKS · Mathematical modelling · DH parameter · Trajectory · RoboAnalyzer · Range of motion (ROM) · Drilling robot · MATLAB-Simulink
M. H. Amin (B) · M. M. Bhamare · J. N. Agnihotri · D. M. Patel Mechanical Engineering Department, Chandubhai.S.Patel Institute of Technology, CHARUSAT University, Nadiad-Petlad Road, Changa, Gujarat 388421, India D. M. Patel e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_38
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1 Introduction The current scenario in industries is of automation, there are several machining operations that are done using robots nowadays for precise and accurate machining operations. There is need of automation for better result of work carried out with desirable output as per the required applications and variations. The machining processes involving assembling, painting, welding, drilling, etc., can be done with the help of 5R manipulators. For better and flexible motion of the robot for the above-mentioned jobs, the hereby mentioned robotic configurations of a 5R manipulator robot are given in a simple and understandable manner for future scientists and industry person of a 5R manipulator robot which can cover 360° motion for various machining operations. The configuration of robot is of a structure designed in designing software, and then robot is designed using Roboanalyzer and robotic configuration are given with the help of it. Very flexible, accurate and precise machining operations such as drilling and welding can be obtained using such robotic configurations which are required when automation is applied. With the help of user-friendly, MATLAB guide, end effector and poisoning matrices calculation of SCARA robot (3DOF TO 6DOF) were focussed for industrial application such as drilling, welding, material handling, assembly and pick and place operation [1–4]. A simple 5DOF Pravak robot with all revolute joint in the application of research and laboratory training was investigated by using forward kinematics study. Positioning and orienting of the end effector were analyzed with the help of mathematical model and MATLAB programming [5–8]. Tarun et al. [9] were prepared forward and inverse kinematics approach for PUMA 560 (5DOF and 6DOF) robot manipulator successfully. Result of DH transformation was compared with experimental result by using RoboAnalyzer and MATLAB software. Possible of all joint values for end effector was analyzed with RoboAnalyzer graph and tabulated properly. In addition, surface plot for end effector was examined by graph. Performance of drilling process has done with analytical and experimental on alloy materials [10, 11]. High precision 6 axis industrial robot ABB-IRB1410 and CNC machine compassion have done properly with parametric study of different parameter like drilling machine speed, size of drill bit and feed rate. Performance optimization was achieved by various parameters on surface roughness, time taken, entry and exit of circular portion [12–14]. With all view factor, the current aim of the research work is to provide with kinematics study of a 5DOF robot manipulator for a flexible motion and robotic configuration to achieve 360° machining operations such as drilling. The modelling of the design is done using SOLIDWORKS and validation of robotic configuration is done using RoboAnalyzer software. The forward dynamics of proposed model are done using RoboAnalyzer with the help of Denavit-Hartenberg parameters and homogenous transformation matrix for the given robotic system. For the inverse kinematics of the proposed model, algebraic solution approach is used. Static torque analysis is done for the model to obtain the values of various forces and torque to manipulate the transmission system required for automation.
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Fig. 1 SOLIDWORKS design of 360° drilling machine
2 Methodology In this present work, a robotic structure for drilling mechanism was developed which is shown in the figure. This present study of robot arm is made of multiple serial links attached together with help of revolute joints from the given base frame to EE manipulator. Each component was designed, assembled and analyzed using SOLIDWORKS. For the proposed design, tabulation of DH parameters is done. Thereafter, transformation matrix was obtained for the five joints. With the resultant effect of homogeneous transformation matrix, the end effector position was obtained.
3 Design 3.1 Design in SOLIDWORKS Initially, the boundary interface of the design was required to define well. This is further divided into roughly around eleven components. First, the frame to mount the horizontal and vertical arm such that it is capable to hold the drill bit and motor and setup of the structure is as shown in Fig. 1. The construction of drilling robot is as follows; the structure consists of base plate, supporting links, drill bit and arm holder. The arms are fixed on base using hinge to rotate about the base. The arms can perform such as moving upwards and downwards and rotate about its axis as shown in Fig. 2.
3.2 Modelling in RoboAnalyzer The given Fig. 3 shows the robotic structure created using RoboAnalyzer. The following structure consists of 5DOF with 5 revolute joints and 2 links. Joint 1 is kept at 90°, joint 2 is kept at 0°. Between joint 2 and joint 3, first link is kept and joint 3 is kept at −90°. Joint 4 is kept at 90° having joint offset of d. Second link is
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Fig. 2 Drawing of robotic structure in SOLIDWORKS
Fig. 3 Skeleton model of 5DOF robot manipulator in RoboAnalyzer
kept between joint 4 and joint 5, where joint 5 is kept at 0° w.r.t. previous joint. Joint 5 is the end effector manipulator as shown in Fig. 3.
4 Robot Kinematics Kinematic analysis of robot provides us the defined relationship between links and joints w.r.t position and orientation of the manipulator. The kinematics study consist of forward kinematics and inverse kinematics.
4.1 Forward Kinematics Forward kinematics deal with determining the position and orientation of the robot end effector as a function of its joint angles. Denavit-Hartenberg (DH) method utilizes
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Fig. 4 Joint axes of 5DOF 360 degree machining robot
Table 1 DH parameters Joint type Joint No Link length (a)m Twist angle (α) ° Joint offset (d) m Joint angle (θ) ° θ1
Revolute
1
0
90
0
Revolute
2
l1
0
0
θ2
Revolute
3
0
−90
0
θ3
Revolute
4
l2
90
D
θ4
Revolute
5
0
0
0
θ5
the four parameters such as ai−1 , α i−1 , d i and θ i ; which are the link length, link twist, link offset and joint angle, respectively, and joint axes are as mentioned in Fig. 4.
4.2 Denavit-Hartenberg (DH) Parameters The matrix T i i−1 is known as a Denavit-Hartenberg (DH) convention matrix. In the matrix T i i−1 , the quantities of α i−1 , ai−1 ,i are the constants for the given link and parameter θ i for revolute joint is the variable (Table 1).
4.3 DH Parameters Transformation Matrix ⎤ cθi −cαi sθi sαi sθi ai cθi ⎢ sθi cαi cθi −sαi cθi ai sθi ⎥ ⎥ =⎢ ⎣ 0 sαi cαi di ⎦ 0 0 0 1 ⎡
Tii−1
(1)
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where cθ = cos θ cα = cos α sθ = sin θ sα = sin α ⎡
⎤ cθ1 0 sθ1 0 ⎢ sθ1 0 −cθ1 0 ⎥ ⎥ T10 = ⎢ ⎣ 0 1 0 0⎦ 0 0 0 1 ⎤ ⎡ cθ2 −sθ2 0 l1 cθ2 ⎢ sθ2 cθ2 0 l1 sθ2 ⎥ ⎥ T21 = ⎢ ⎣ 0 0 1 0 ⎦ 0 0 0 1
(2)
(3)
where l1 = link 1 l2 = link 2 ⎡
⎤ cθ3 0 −sθ3 0 ⎢ sθ3 0 cθ3 0 ⎥ ⎥ T32 = ⎢ ⎣ 0 −1 0 0 ⎦ 0 0 0 1 ⎤ ⎡ cθ4 0 sθ4 l2 cθ4 ⎢ sθ4 0 −cθ4 l2 sθ4 ⎥ ⎥ T43 = ⎢ ⎣ 0 1 0 d ⎦ 0 0 0 1
(4)
(5)
where d4 = d ⎡
⎤ cθ5 −sθ5 0 0 ⎢ sθ5 cθ5 0 0 ⎥ ⎥ T54 = ⎢ ⎣ 0 0 1 0⎦ 0 0 01 T50 = T10 . T21 . T32 . T43 . T54 ⎤⎡ ⎡ ⎤⎡ ⎤ cθ3 0 −sθ3 0 cθ1 0 sθ1 0 cθ2 −sθ2 0 l1 cθ2 ⎢ sθ1 0 −cθ1 0 ⎥ ⎢ sθ2 cθ2 0 l1 sθ2 ⎥ ⎢ sθ3 0 cθ3 0 ⎥ ⎥⎢ ⎥⎢ ⎥ =⎢ ⎣ 0 1 0 0 ⎦.⎣ 0 0 1 0 ⎦.⎣ 0 −1 0 0 ⎦ 0 0 0 1 0 0 0 1 0 0 0 1
(6)
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⎤⎡ ⎤ cθ5 −sθ5 0 0 cθ4 0 sθ4 l2 cθ4 ⎢ sθ4 0 −cθ4 l2 sθ4 ⎥ ⎢ sθ5 cθ5 0 0 ⎥ ⎥.⎢ ⎥ .⎢ ⎣ 0 1 0 d ⎦ ⎣ 0 0 1 0⎦ 0 0 0 1 0 0 01 ⎡ cθ1 cθ4 cθ5 c(θ2 + θ3 ) − sθ1 cθ4 cθ5 − s(θ2 + θ3 )cθ1 sθ5 ⎢ sθ1 cθ4 cθ5 c(θ2 + θ3 ) + cθ1 sθ4 cθ5 − sθ1 sθ5 s(θ2 + θ3 ) =⎢ ⎣ cθ4 cθ5 s(θ2 + θ3 ) + sθ5 c(θ2 + θ3 ) 0 ⎡
−cθ1 cθ4 sθ5 c(θ2 + θ3 ) + sθ1 sθ4 sθ5 − cθ1 cθ4 s(θ2 + θ3 ) −sθ5 sθ1 cθ4 c(θ2 + θ3 ) − cθ1 sθ4 sθ5 − sθ1 cθ5 s(θ2 + θ3 ) −cθ4 sθ5 s(θ2 + θ3 ) + cθ5 c(θ2 + θ3 ) 0 cθ1 sθ4 c(θ2 + θ3 ) + sθ1 cθ4 sθ1 sθ4 c(θ2 + θ3 ) − cθ1 cθ4 sθ4 s(θ2 + θ3 ) 0
⎤ l2 cθ4 cθ1 c(θ2 + θ3 ) − l2 sθ1 sθ4 − dcθ1 s(θ2 + θ3 ) + l1 cθ1 cθ2 l2 cθ4 sθ1 c(θ2 + θ3 ) + l2 sθ4 cθ1 − dsθ1 s(θ2 + θ3 ) + l1 sθ1 cθ2 ⎥ ⎥ ⎦ l2 cθ4 s(θ2 + θ3 ) + dc(θ2 + θ3 ) + l1 sθ2 1 (7)
where T50 is the final matrix and T50 = Te ⎡
nx ⎢ ny T50 = Te = ⎢ ⎣ nz 0
ox oy oz 0
ax ay az 0
⎤ px py ⎥ ⎥ pz ⎦ 1
where n x = cθ1 cθ4 cθ5 c(θ2 + θ3 ) − sθ1 cθ4 cθ5 − s(θ2 + θ3 )cθ1 sθ5 n y = sθ1 cθ4 cθ5 c(θ2 + θ3 ) + cθ1 sθ4 cθ5 − sθ1 sθ5 s(θ2 + θ3 ) n z = cθ4 cθ5 s(θ2 + θ3 ) + sθ5 c(θ2 + θ3 ) ox = −cθ1 cθ4 sθ5 c(θ2 + θ3 ) + sθ1 sθ4 sθ5 − cθ1 cθ4 s(θ2 + θ3 ) o y = −sθ5 sθ1 cθ4 c(θ2 + θ3 ) − cθ1 sθ4 sθ5 − sθ1 cθ5 s(θ2 + θ3 ) oz = −cθ4 sθ5 s(θ2 + θ3 ) + cθ5 c(θ2 + θ3 ) ax = cθ1 sθ4 c(θ2 + θ3 ) + sθ1 cθ4 a y = sθ1 sθ4 c(θ2 + θ3 ) − cθ1 cθ4 az = sθ4 s(θ2 + θ3 ) px = l2 cθ4 cθ1 c(θ2 + θ3 ) − l2 sθ1 sθ4 − dcθ1 s(θ2 + θ3 ) + l1 cθ1 cθ2 p y = l2 cθ4 sθ1 c(θ2 + θ3 ) + l2 sθ4 cθ1 − dsθ1 s(θ2 + θ3 ) + l1 sθ1 cθ2 pz = l2 cθ4 s(θ2 + θ3 ) + dc(θ2 + θ3 ) + l1 sθ2
(8)
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⎡
⎤ px The end effector position is given by ⎣ p y ⎦. pz
4.4 Inverse Kinematics The method of inverse kinematics is used for the calculation of joint angles which will accomplish desired position and orientation of the end effector which are relative w.r.t. base. The robot in this present study has two joint offsets (on X and Z axis). Thus, depending on the configuration of the robot problem of inverse kinematics results in sets of joint angles. ⎤ n x ox ax px ⎢ n y oy ay py ⎥ ⎥ T50 = Te = ⎢ ⎣ n z oz az pz ⎦ 0 0 0 1 T50 = T10 . T21 . T32 . T43 . T54 ⎡
−1 ∴ T10 .T50 = T21 . T32 . T43 . T54 ⎡
⎤ ax sθ1 + n x cθ1 n x sθ1 − ax cθ1 ox px ⎢ a y sθ1 + n y cθ1 n y sθ1 − a y cθ1 o y p y ⎥ ⎢ ⎥ ⎣ az sθ1 + n z cθ1 n z sθ1 − az cθ1 oz pz ⎦ 0 0 0 1 ⎡ c(θ2 + θ3 )cθ4 cθ5 − s(θ2 + θ3 )sθ5 ⎢ s(θ2 + θ3 )cθ4 cθ5 + c(θ2 + θ3 )sθ5 =⎢ ⎣ −sθ4 cθ5 0
−c(θ2 + θ3 )cθ4 cθ5 + s(θ2 + θ3 )cθ4 −s(θ2 + θ3 )cθ4 sθ5 + c(θ2 + θ3 )cθ5 sθ4 sθ5 0 ⎤ c(θ2 + θ3 )sθ4 l2 c(θ2 + θ3 )cθ4 − ds(θ2 + θ3 ) + l1 cθ2 s(θ2 + θ3 )sθ4 l2 s(θ2 + θ3 )cθ4 − ds(θ2 + θ3 ) + l1 cθ2 ⎥ ⎥ (9) ⎦ cθ4 −l2 cθ4 0 1 T50 = T10 . T21 . T32 . T43 . T54 −1 ∴ T21 .T50 = T10 . T32 . T43 . T54
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⎡
n x cθ2 − ox sθ2 n x sθ2 + ox cθ2 ⎢ n y cθ2 − o y sθ2 n y sθ2 + o y cθ2 ∴⎢ ⎣ n z cθ2 − oz sθ2 n z sθ2 + oz cθ2 0 0
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⎤ ax px − l1 n x a y p y − l1 n y ⎥ ⎥ az pz − l 1 n z ⎦ 0 1
⎡
cθ1 cθ3 cθ4 cθ5 − sθ1 sθ4 cθ5 − sθ5 cθ1 sθ3 −cθ1 cθ3 cθ4 sθ5 + sθ1 sθ4 sθ5 − cθ1 sθ3 cθ5 ⎢ sθ1 cθ3 cθ4 cθ5 + cθ1 sθ4 cθ5 − sθ1 sθ3 sθ5 −sθ1 cθ3 cθ4 sθ5 − cθ1 sθ4 sθ5 − cθ5 sθ1 sθ3 ⎢ ⎣ sθ3 cθ4 cθ5 + sθ5 cθ3 sθ3 cθ4 sθ5 + cθ3 cθ5 0 0 ⎤ sθ4 (cθ1 cθ3 + sθ1 ) l2 cθ4 (cθ1 cθ3 + sθ4 sθ1 ) − dcθ1 sθ3 sθ1 cθ3 sθ4 − cθ1 cθ4 l2 cθ4 (sθ1 cθ3 − sθ4 cθ1 ) − dsθ1 sθ3 ⎥ ⎥ (10) ⎦ sθ3 sθ4 l2 sθ3 cθ4 + dcθ3 0
1 T50 = T10 . T21 . T32 . T43 . T54 −1 ∴ T32 .T50 = T10 . T21 . T43 . T54
⎡
⎤ n x cθ3 − ax sθ3 ax cθ3 + n x sθ3 −ox px ⎢ n y cθ3 − a y sθ3 a y cθ3 + n y sθ3 −o y p y ⎥ ⎢ ⎥ ⎣ n z cθ3 − az sθ3 az cθ3 + n z sθ3 −oz pz ⎦ 0 0 0 1 ⎡ cθ1 cθ5 c(θ2 + θ4 ) + sθ1 sθ5 −cθ1 cθ2 cθ4 sθ5 − cθ1 sθ2 sθ4 sθ5 + sθ1 cθ5 ⎢ sθ1 cθ5 c(θ2 + θ4 ) + cθ1 sθ5 −sθ1 cθ2 cθ4 sθ5 + sθ4 sθ5 sθ1 sθ2 − cθ1 cθ5 =⎢ ⎣ cθ5 s(θ2 + θ4 ) −sθ5 s(θ2 + θ4 ) 0 0 ⎤ s(θ2 + θ4 )cθ1 l2 cθ1 c(θ2 + θ4 ) − dsθ1 + l1 cθ1 cθ2 s(θ2 + θ4 )sθ1 l2 sθ1 sθ2 (cθ4 − sθ4 ) − dcθ1 + l1 sθ1 sθ2 ⎥ ⎥ (11) ⎦ −c(θ2 + θ4 ) l2 s(θ2 + θ4 ) + dl1 sθ1 0
1
−1 In T10 .T50 = T21 . T32 . T43 . T54 from (3, 3) oz = cθ4 ∴ θ4 = c−1 (oz )
(12)
2 ∴ θ4 = A tan 2 ± 1 − (oz ) , oz
(13)
−1 In T21 .T50 = T10 . T32 . T43 . T54 from (3, 3)
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az = sθ3 sθ4 ∴ θ3 = s ⎛
−1
az sθ4
az ,± 1 − ∴ θ3 = A tan 2⎝ sθ4
(14)
az sθ4
2
⎞ ⎠
(15)
−1 In T10 .T50 = T21 . T32 . T43 . T54 from (1, 3) ox = c(θ2 + θ3 )sθ4
−1 ox − θ3 ∴ θ2 = c sθ4 ⎛ ⎞ 2
o o x x ⎠ − θ3 , ∴ θ2 = A tan 2⎝± 1 − sθ4 sθ4
(16)
(17)
−1 In T32 .T50 = T10 . T21 . T43 . T54 from (2, 3) − o y = s(θ2 + θ4 )sθ1
−1 −o y ∴ θ1 = s sθ24
(18)
where, sθ24 = s(θ2 + θ4 ) ⎛ ∴ θ1 = A tan 2⎝
−o y ,± 1 − sθ24
−o y sθ24
2
⎞ ⎠
(19)
−1 In T32 .T50 = T10 . T21 . T43 . T54 from (3, 1) n z cθ3 − az sθ3 = cθ5 s(θ2 + θ4 )
−1 n z cθ3 − az sθ3 ∴ θ5 = c sθ24 where sθ24 = s(θ2 + θ4 )
(20)
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⎛ ∴ θ5 = A tan 2⎝± 1 −
n z cθ3 − az sθ3 sθ24
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2
⎞ n z cθ3 − az sθ3 ⎠ , sθ24
(21)
5 Workspace (ROM) The work envelope is the workspace of robot manipulator defined as the set defined by the points which the end-effector can reach to perform the motion. It is the space which shows the working of mechanism and the envelope formed by the manipulator or end effector [17, 18]. The envelope or the range of motion shows the shape created when a manipulator reaches forward, backward, up and down, showing the enclosed space for the workspace. Every axis has its ROM. From the Fig. 5 for the given 5R manipulator, joint axes positions and link lengths, the work envelope is defined and illustrated for the given robotic configurations which show the workspace for several machining operations such as drilling. Figure 6 shows front of the workspace in half of the plane, and Fig. 7 shows the top view.
Fig. 5 Workspace of 5DOF 360 degree drilling robot
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Fig. 6 Front view of ROM
Fig. 7 Top view of ROM
6 Trajectory Path generation of robotic configuration from initial position to final position of each joint or end effector is called trajectory planning. By taking four constraints, with the help of cubic polynomial analysis, robotic cubic trajectory is used to determine angular displacement, angular velocity and angular acceleration q(t) = c0 + c1 t + c2 t 2 + c3 t 3
(22)
The velocity can be given as from differentiating Eq. (22), q(t) ˙ = c1 + 2c2 t + 3c3 t 2
(23)
The acceleration can be given as from differentiating Eq. (23) q(t) ¨ = 2c2 + 6c3 t where c0 , c1 , c2 and c3 are independent coefficients t: time q: angular displacement q: ˙ angular velocity
(24)
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q: ¨ angular acceleration The four constraints or conditions are t0 : initial time t f : final time ˙ 0 )): initial velocity (q(t q˙ t f : final velocity Applying constraints in equation (22) and (23), q(t0 ) = u 0 = c0 + c1 t0 + c2 t02 + c3 t03
(25)
q t f = u f = c0 + c1 t f + c2 t 2f + c3 t 3f
(26)
q(t ˙ 0 ) = w0 = c1 + 2c2 t0 + 3c3 t02
(27)
q˙ t f = w f = c1 + 2c2 t f + 3c3 t 2f
(28)
For obtaining the values of c0 , c1 , c2 and c3 , initial conditions can be applied in Eqs. (25), (26), (27) and (28). And further substituting the values of independent coefficients in Eqs. (22), (23), (24) values of q(t), q(t) ˙ and q(t) ¨ are obtained, respectively.
7 Static Torque Analysis A torque which does not produce angular acceleration is known as static torque. To find out the magnitude of static torque, the lever arm is multiplied with the force applied or weight, where lever arm is the perpendicular distance to the force applied. Static torque can be used to calculate the efficiency of the gearbox. The static torque calculations for the proposed designed are done as mentioned in Fig. 8. Here, Wi = weight of the component li = length of the link Ti = torque from the given point For the given design, the dimensions and values are obtained as given, l1 = 35.5 cm l2 = 38.1 cm W A+D = 1.34 kg W B = 1.00 kg Wc = 2.22 kg
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Fig. 8 Configuration of 5DOF 360 degree drilling robot for static torque
Wl1 = 1.00 kg Wl2 = 1.14 kg
7.1 Calculation of Torque: T A = (W A+D + W B + WC + Wl1 + Wl2 ).(l1 + l2 ) = (6.7)(73.6)kg cm T A = 493 kg cm
(29)
TD = (W B + WC + Wl1 + Wl2 ).(l1 + l2 ) = (5.36)(73.6)kg cm TD = 395 kg cm
(30)
TC = (WC + Wl2 ).(l2 ) = (3.36)(38.1)kg cm TC = 128 kg cm
(31)
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8 Results and Discussion For present machining (drilling) robot system, the homogenous transformation matrix can be obtained by entering suitable DH parameter which is given in RoboAnalyzer. Figure 9 shows the end effector position for given robot. At instantaneous value of θ, obtaining the value end effecter position, joint velocity, joint acceleration, force/torque at given time as shown in the given figure for corresponding value of θ (°) as shown in Figs. 10, 11, 12, 13, 14 and 15. Figure 11 shows all five joint value properly which unit is in degree. From Fig. 12, velocity (°/s) of each five joint for drilling robot shown with every change of second. Figure 13 describes acceleration (°/s2 ) of each and every joint properly. Using the concept of inverse dynamics, Fig. 14 shows instantaneous torque value (in N m) for 360 drilling robot effectively by RoboAnalyzer software. Figure 15 provides the MATLAB model generated using Simulink of the 5DOF robot manipulator at the given variables of joint angle (°) and joint velocity (°/s). This MATLAB/Simulink code validates the given 360° drilling robot successfully and effectively. Fig. 9 End effector configuration matrix
Fig. 10 EE positions of X, Y and Z
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Fig. 11 Joint values
Fig. 12 Velocity of each joints
Fig. 13 Acceleration of each joints
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Fig. 14 Torque of each joints
Fig. 15 Robotic configuration using MATLAB
9 Conclusion The kinematic analysis plays an important role in creating a robotic manipulator which is presented in a simplified form to obtain desired motion of manipulator. The work presented in this study tries to present using a single platform for kinematic analysis, workspace, trajectory planning and static torque analysis for each component for attaining 360° machining operations. The 360° machining robot is designed to execute the purpose of drilling and various machining processes. In addition, various machining operations can be done as per requirement by changing end effector. By using forward and inverse kinematics developed in this work, any manipulator having same type of variable structures can be controlled and more accuracy can be provided to the motion of end effector. The theoretical validation is performed using RoboAnalyzer. From the obtained results, it can be concluded for 5DOF 360 degree
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drilling robot, drilling can be performed with smooth movement and high precision within the given workspace. With the help of MATLAB-Simulink, interactive model can be obtained at the calculated parameters.
References 1. Shrivastava, S.: MATLAB guide for forward kinematic calculation of 3 to 6 DOF SCARA robots 2. Alshamasin, M.S., Ionescu, F., Al-Kasasbeh, R.T.: Kinematic modeling and simulation of a SCARA robot by using solid dynamics and verification by matlab/simulink. Eur. J. Sci. Res. 37(3), 388–405 (2009) 3. Ibrahim, B.S.K.K., Zargoun, A.M.: Modelling and control of SCARA manipulator. Procedia Comput. Sci. 42, 106–113 (2014) 4. Mariappan, S.M., Veerabathiran, A.: Modelling and simulation of multi spindle drilling redundant SCARA robot using SolidWorks and MATLAB/SimMechanics. Revista Facultad de Ingeniería Universidad de Antioquia 81, 63–72 (2016) 5. Shah, J.A., Rattan, S.S., Nakra, B.C.: End-effector position analysis using forward kinematics for 5 DOF pravak robot arm. IAES Int. J. Robot. Autom. 2(3), 112 (2013) 6. Deshpande, V., George, P.M.: Kinematic modelling and analysis of 5 DOF robotic arm. Int. J. Robot. Res. Dev. (IJRRD) 4(2), 17–24 (2014) 7. Shabeeb, A.H., Mohammed, L.A.: Forward analysis of 5 DOF robot manipulator and position placement problem for industrial applications. Eng. Tech. J. 32(3), 617–628 (2014) 8. Lu, J., Xu, D., Wang, P.: A kinematics analysis for a 5-DOF manipulator. In: The 26th Chinese Control and Decision Conference (2014 CCDC), pp. 1495–1499. IEEE (2014, May) 9. Singh, T.P., Suresh, P., Chandan, S.: Forward and inverse kinematic analysis of robotic manipulators. Int. Res. J. Eng. Technol. (IRJET) 4(2), 1459–1468 (2017) 10. Bi, S., Liang, J.: Robotic drilling system for titanium structures. Int. J. Adv. Manuf. Technol. 54(5–8), 767–774 (2011) 11. DeVlieg, R., Sitton, K., Feikert, E., Inman, J.: ONCE (one-sided cell end effector) robotic drilling system (No. 2002-01-2626). SAE Technical Paper (2002) 12. Garnier, S., Subrin, K., Waiyagan, K.: Modelling of robotic drilling. Procedia Cirp 58, 416–421 (2017) 13. Liang, J., Bi, S.: Design and experimental study of an end effector for robotic drilling. Int. J. Adv. Manuf. Technol. 50(1–4), 399–407 (2010) 14. Akhil, A.A., John, M.S.: Comparison between simulation and experimental results for drilling process in robot drilling and normal drilling machine. IOP Conf. Ser. Mater. Sci. Eng. 402(1), 012003 (2018). IOP Publishing 15. Othayoth, R.S., Chittawadigi, R.G., Joshi, R.P., Saha, S.K.: Robot kinematics made easy using RoboAnalyzer software. Comput. Appl. Eng. Educ. 25(5), 669–680 (2017) 16. Gupta, V., Chittawadigi, R.G., Saha, S.K.: RoboAnalyzer: robot visualization software for robot technicians. In: Proceedings of the Advances in Robotics, pp. 1–5 17. Tsai, Y.C., Soni, A.H.: Workspace synthesis of 3R, 4R, 5R and 6R robots. Mech. Mach. Theor. 20(6), 555–563 (1985) 18. Zeeshan, S., Aized, T.: Kinematic analysis and simulation of an orange harvesting robot (2020)
Effects of Geometry on the Stress Concentration Factor of an Isotropic Rectangular Plate with Central Elliptical Hole Prafull Agarwal, Dhruv Mathur, Manoj Parassery, Aayu Bhardwaj, and S. S. Ghosh
Abstract With the help of the Roark formula, the stress concentration factor for a rectangular plate with a central elliptical hole can be evaluated. However, the effect of length was missing in the formula and the literature as well. Although its effect has been noticed by researchers. The effects of length and other geometrical factors on SCF are studied in this paper. The influences of length on SCF between transition length and critical length are identified. Effects of length on SCF for other parameters are drawn to get a value of SCF quickly. Finally, a formulation of SCF with different geometrical parameters is established. The deviation of results from the formulation and simulation is within ±7%. Keywords Stress concentration factor · Elliptical hole · Critical length · Transition length · FEM
1 Introduction A lot of efforts are being made to find the effect of different form of discontinuities on the stress concentration factors of rectangular plates. Primarily, studies have been performed on the isotropic plates to extend the results for composite and orthotropic plates. The resurgence in the research on stress concentration factor (SCF) can be credited primarily to the developments taking place in the field of machine design. Inventors and researchers now are constantly searching to achieve better, safer, and greener designs to replace the existing systems while improving its performance. Research on stress concentration has started long back. Stresses in an infinite plate with cracks and sharp corners was first presented by Inglis [1]. The SCF for finitewidth long isotropic rectangular plates with a central circular opening subjected to tension was presented by Howland [2]. Peterson [3] compiled the works of various P. Agarwal (B) · D. Mathur · M. Parassery · A. Bhardwaj · S. S. Ghosh Department of Mechanical Engineering, Manipal University Jaipur, Jaipur, India S. S. Ghosh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_39
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researchers on rectangular plates with holes, notches, and discontinuities, subjected to tension, bending, or torsion. R J Roark’s work in connection with stress concentration related to plates, beams, shafts, etc. has been compiled by Young et al. [4]. Roark’s equations are very much convenient and well accepted by engineering community. The problem of stresses around the elliptical hole on an infinite rectangular strip subjected to tension were theoretically solved by Isida [5]. Tan [6] proposed a finite-width correction (FWC) factor for an orthotropic plate to get the stress field for a finite-width plate containing an elliptical hole subjected to uniaxial tension. Assuming that the length of the plate to be infinite, a ratio of SCF for infinite width and finite width was presented. Extending Tan’s work for isotropic plate also, Wang [7] proposed that the SCF in a finite plate with different openings can be calculated multiplying a finite-width correction (FWC) factor with the SCF of an infinite plate. Research on the effect of length is, however, low. The concept of transition length was first presented by Pollonais et al. [8]. The effect of length on SCF was observed by his experiments on stepped flat tension bars with shoulder fillets. He found that the SCF increased rapidly after certain length of the member. The transition length was then defined as the length of the bar for which the calculated SCF varies 1.0% + 0.05% with respect to the corresponding established values for long bars. The same definition of transition length was later adopted for plates with a circular, elliptical hole. Other researchers [9, 10] also worked on SCF with geometrical parameters. Transition length value between long and short plates was identified by Bakhshandeh et al. [10] using Tan’s equation and shown that the magnitude of transition length might be taken as L/B = 2.5. Troyani et al. [11] have reported some expressions by which the SCF values could be estimated for finite-width rectangular plates using the SCF solution curve presented by Howland long ago for isotropic finite-width plates with central circular holes. The effect of length on SCF was presented by geometrical scaling. Recently, the effect of plate length on the stress distribution on a finite plate with central hole was also studied by Bakhshi et al. [12] to check the level of accuracy of Tan’s model. From the literature review, it is clear that the transition length is the important geometrical criteria which divides plates into long and short ones. For L/B > L t , there is very little change of value for K t and is almost independent of L/B, whereas for L/B < L t , K t becomes a function of L/B [11]. However, for a low value of L/B (say, L/B < 1), the K t value becomes unstable, and this length can be called a critical length. Within this range of values of L/B, K t is a function of L/B, along-with other geometrical factors, i.e. a/b and a/B. Any relation between these is not available in the literature. An attempt is made in this work to get a relation of K t with these geometrical parameters.
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2 Physical Model and SCF A finite flat isotropic rectangular plate of length L, width B with a central elliptical hole of dimension (2a ✕ 2b) is subjected to in-plane force P, as shown in Fig. 1. The maximum principal stress will occur at the edge ‘e’. The stress concentration factors (K t ) can be calculated using the formula: K t = σmax /σnom The nominal stress, i.e. σ nom is calculated from the relation: σnom =
P t(B − 2a)
Stress concentration factor (SCF) equations for an elliptical hole in a rectangular plate has been given by Roark [4]: K t = C1 + C2 (2a/B) + C3 (2a/B)2 + C4 (2a/B)3 The SCF around elliptical holes has been analysed for different sizes by ANSYS and the process is validated by the Roark formula. Roark formula is valid for 0.5 < a/b < 10. For a/b > 10, a new formulation was presented by Agarwal et al. [13].
2.1 Validation of the Process For achieving the desired relation, firstly, the entire process for the research needs to be validated. To validate the process, we verify the theoretical stress concentration factor with the SCF derived from Roark’s formula. For validation, we consider a plate of 500 mm ✕ 250 mm ✕ 5 mm in length, width, and thickness, respectively. Central elliptical cavities with varying eccentricity ratios have been cut out on the plate. This implies that the only varying dimensional parameter for the setup is the eccentricity ratio of the elliptical hole. The setup is modelled and fed to the finite element method (FEM) Ansys solver to calculate the results. The theoretical values Fig. 1 Finite rectangular plate with a central elliptical hole subjected to in-plane loading
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Table 1 Roark formula verification S. No
2a/B
a/b
K t (Theoretical)
K t (Ansys)
1
0.2
2
4.118368
4.3857
Error
2
0.2
3
5.71544
6.097
6.675951458
3
0.2
4
7.32099
7.816
−6.761517226
4
0.2
5
8.93116
8.836
1.065483095
5
0.25
2
3.94746
4.1646
−5.500752383
6
0.25
3
5.46612
5.7457
−5.114779771
7
0.25
4
6.99715
7.4324
−6.22038973
8
0.25
5
8.53453
9.105
−6.68425795
6.491212053
are calculated by substituting the values of the ratio in the Roark’s formula and the values compared. Since the errors obtained between the experimental values and the values obtained from the Roark formula are within ±7%, it can be said that the process is verified and can be utilized for further use.
3 Methodology Primarily, the use of three packages has been done to arrive at the solutions. These include SOLIDWORKS 2019, Ansys 19.2 premium, and Microsoft Excel. SOLIDWORKS 2019 was used to model the parts, while the analysis was performed on Ansys 19.2 Premium and Excel was used to process the results and arrive at the relations. Primarily the ratios considered during the experiment were L/B, 2a/B, a/b. For performing the various simulations, six values of L/B ratios have been considered: 1.0, 1.35, 1.7, 2.0, 2.25 and 2.5. For every six values of L/B, four values of a/b have been considered for which simulations have been performed on four values of a/B for every value of a/b. The thickness of the plate has been kept constant at 10 mm, and a constant tensile force of 20 N has been applied on the top face of the plate.
3.1 Geometry The modelling of the plates was done on SOLIDWORKS 2019. The primary reason behind choosing the package was that SOLIDWORKS enables us to modify the sketches without needing to sketch the entire model again.
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3.2 Meshing Once the modelling of the part was complete, it was saved as a Step file of STP A214 format. The Step file was imported onto Ansys and structural analysis was performed on it. On the Ansys Mechanical, the part was pre-processed. The part was meshed with tetra elements, keeping a threshold of 215,000 nodes and tolerance of ±10,000. To comply with the limit, the element size was varied according to the size of the plate. The mesh around the elliptical cavity was refined with refinement set at 3. Refining the mesh allowed us to get better results for principal stress. A good enough mesh is finally achieved.
3.3 Loads and Constraints After achieving the mesh, the necessary loads and constraints were applied to the model. The bottom face of the plate was fixed to restrict any moment of the face. A uniaxial load of 20 N was applied on the middle face of the plate allowing the plate to deform in only one direction which is the direction of the load. After applying the load, the model was simulated to calculate the values for maximum principal stress and deformation.
4 Results and Discussion More than 100 simulations were performed to arrive at the final relations. A sample case for stress distribution by Ansys is shown in Fig. 2. The analysis has been performed on an isotropic plate of structural steel material. The plates used for analysis are uniform except for the elliptical cavity at the centre. The semi-major axis a of the plate is constant, while the minor axis b of the plate has been varied according to the ratio a/b. The material properties of structural steel are as follows: Fig. 2 Result of simulation by ANSYS
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Fig. 3 K t versus a/b
Density = 7850 kg/m3 , E = 2e11 Pa, K = 1.667e11 Pa, G = 7.69e10 Pa, υ = 0.3, Y = 2.5e8 Pa The change in length has been depicted not exactly changing the length of the plate but by changing the ratio L/B. A constant value of length equal to 700 mm has been assumed for all cases. From the ratio L/B, the value of width has been calculated, which comes out to be constant for a particular value of a/b. As discussed in Sect. 1, the L/B ratio varies between 1 (L c ) and 2.5 (L t ). Variation of a/B is performed between 0.03 and 0.25, while that of a/b is done between 1.5 and 3. From the results, following graphs can be plotted to depict the relation between SCF and different geometrical parameters. The above graph (Fig. 3) is a plot between the stress concentration factor (K t ) and the ratio a/b for different values of a/B keeping a constant value of L/B (=1). The above graphs are a plot between the stress concentration factor (K t ) and the ratio a/B. For the other two parameters, the value of parameter a/b has been plotted for a constant value of L/B (=2), in Fig. 4, while in Fig. 5, a/b is kept constant (=1.5) for different values of L/B. The above graph is a plot between the stress concentration factor (K t ) and the ratio L/B. For the two parameters, the value of parameter a/B has been plotted for a constant value of a/b (=1.5). This graph has been plotted using the Bezier curve template using two end control points, and the cure is plotted between the two control points while obeying the convex hull property. Fig. 4 K t versus a/B plotting a/b
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Fig. 5 K t versus a/B plotting L/B
Fig. 6 K t versus L/B
From the graphs plotted, it can be concluded that the relation between K t and a/b is like what is obtainable from Roark’s formula. From Fig. 3, on increasing the value of a/b, K t also increases; additionally, decreasing the value of a/B, K t also increases. When K t is plotted against a/B for different values of a/b, keeping L/B constant, K t increases with the increase of a/b but decreases with the increase of a/B. The graph in Fig. 5 shows the behaviour of a/B with respect to K t for different values of L/B keeping a/b constant. On increasing the value of L/B and a/B, K t decreases. The value of K t is directly related to a/b and inversely to L/B and a/B. SCF can be obtained from the graph for a particular combination of a geometrical parameter.
4.1 Derivation of the Formula From the results obtained, attempts are now made to get a relation between the stress concentration factor (K t ) and the three ratios: a/b, L/B, a/B. A general equation of the following form is assumed: p
y = cx1n x2m x3
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where y = K t , x1 =
a L a , x2 = , x3 = b B B
Taking the logarithm on both the sides we obtain the following relation: log(y) = log(c) + n log(x1 ) + m log(x2 ) + p log(x3 ) The equation has been solved using Microsoft Excel using available data. The process of derivation of the equation is based on curve fitting where a curve is fitted about the general equation of the curve. For the different values of the ratios obtained from the analysis, the natural log values were calculated and fed in the correct form to obtain the relation. The final equation is expressed as: K t = (1.51969125)
a 0.808032627 L −0.009254072 a −0.204389026 b
B
B
The value of r obtained after deriving the relation is approximately equal to 0.9983 which is extremely close to 1, indicating the fact that the equation derived is true to simulation values. The equation is valid for 1.5 < a/b < 3, 0.03 < a/B < 0.25 and 1 < L/B < 2.5.
5 Conclusions A lot of research works have been done related to SCF for circular as well as an elliptical hole on rectangular isotropic and orthotropic plate subjected to uniaxial loading. However, the effect of length on SCF was not readily available for a finite rectangular plate with an elliptical hole. The present study tries to fill that gap. Effects of geometry on SCF of a rectangular plate with an elliptical hole are studied in this work. From the attached graphs, the SCF value for different geometry will be obtained readily. A new formulation for SCF with the geometry of finite isotropic rectangular plate with a central elliptical hole is established. The maximum deviation between simulation results and formulation results is within ±7%. The formulation is valid for 1.5 < a/b < 3, 0.03 < a/B < 0.25 and 1 < L/B < 2.5. Further research for the extension of the zones is kept for future work.
References 1. Inglis, C.E.: Stresses in a plate due to the presence of cracks and sharp corners. Trans. Inst. Naval. Archit. 55, 219–241 (1913)
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2. Howland, R.: On the stresses in the neighbourhood of a circular hole in a strip under tension. Philos. Trans. Royal Soc. London. Series A, Containing Papers of a Mathematical or Physical Character 229(670–680), 49–86 (1930) 3. Peterson, R.E.: Stress concentration design factors: charts and relations useful in making strength calculations for machine parts and structural elements. Wiley, New York (1953) 4. Young, W., Budynas, R.: Roark’s formulas for stress and strain, vol. 7. McGraw-Hill, New York (2002) 5. Isida, M.: On the tension of a strip with a central elliptic hole (Part 1). Trans. Jpn. Soc. Mech. Eng. 21(107), 507 (1955) 6. Tan, S.C.: Finite-width correction factors for anisotropic plate containing a central opening. J. Compos. Mater. 22(11), 1080–1097 (1988) 7. Wang, Q.: Simple formulae for the stress concentration factor for two-and three-dimensional holes in finite domains. J. Strain Anal. Eng. Des. 37(3), 259–264 (2002) 8. Pollonais, Y., Gomes, C., Troyani, N.: Factor de concentración de esfuerzos para placas cortas con entallas en U simétricas sometidas a tensión. Memorias IV Congresolberoamericano de lngeniería Mecánica, ClDlM 99 (1999) 9. Troyani, N., Gomes, C., Sterlacci, G.: Theoretical stress concentration factors for short rectangular plates with centered circular holes. J. Mech. Des 124(1), 126–128 (2002) 10. Bakhshandeh, K., Rajabi, I.: Orthotropy and geometry effects on stress concentration factors for short rectangular plates with a centred circular opening. J. Strain Anal. Eng. Des. 42(7), 551–555 (2007) 11. Troyani, N., Sánchez, M.: Howland’s isotropic Kts curve for plates with circular holes as a master curve for Kts in orthotropic plates with elliptical holes. J. Strain Anal. Eng. Des. 52(3), 152–161 (2017) 12. Bakhshi, N., Taheri-Behrooz, F.: Length effect on the stress concentration factor of a perforated orthotropic composite plate under in-plane loading. Int. J. Compos. Mater 1, 71–90 (2019) 13. Prafull, A., Dhruv, M., Ghosh, S.S.: Formulation of stress concentration factor of a finite plate with an elliptical hole of high eccentricity ratio. Paper Presented at ICAST 20, LPU, Phagwara, India, 6–7th November (2020)
Thermodynamic Investigations of a Turbocharged Homogeneous Charge Compression Ignition (HCCI) Engine Running on Wet Ethanol Mohd Asjad Siddiqui, Abdul Khaliq, and Rajesh Kumar
Abstract To study the performance of a turbocharged wet ethanol operated homogeneous charge compression ignition (HCCI) engine-based thermal system at various operating parameters, thermodynamic investigations were conducted. The goal of this thermodynamic analysis is to see how the compressor pressor ratio, ambient temperature and equivalence ratio affect the thermal and exergetic efficiencies of a wet ethanol powered turbocharged HCCI engine. According to a detailed performance analysis of a wet ethanol powered HCCI engine-based system using the above discussed operating parameters, raising the compressor pressure ratio and equivalence ratio increases thermal and exergetic efficiencies. On the other hand, as the ambient temperature increases, the energetic and exergetic efficiency of the turbocharged HCCI engine operating on wet ethanol decrease. Keywords HCCI engine · Wet ethanol · Energy analysis · Exergy analysis
1 Introduction The two most difficult global energy and environmental issues in the twenty-first century are emissions and consumption of fuel. Public transport has become one of the primary components of both consumption of fuel and emissions in the world, due to the large number of vehicles produced worldwide. Today’s transport needs are primarily dependent on traditional fossil fuels; however, these fuels are environmentally risky as they add to the impact of gas emissions and global warming. One significant approach for reducing emissions is to consider sustainable power for decreasing greenhouse gas emissions and enhancing air quality, and minimizing the M. Asjad Siddiqui (B) · R. Kumar Department of Mechanical Engineering, Delhi Technological University, Shahbad Daulatpur, Bawana Road, Delhi 110042, India A. Khaliq Department of Mechanical Engineering, College of Engineering at Yanbu Taibah University, Yanbu Al Bahr 41911, Saudi Arabia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_40
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level of dependence on high-polluting traditional fossil fuels to meet strict emission requirements and resolve environmental issues. Ethanol is one of the attractive solutions amongst cleaner resources, because it offers a high rate of combustion efficiency and reduces carbon monoxide, carbon dioxide, NOx and unburned hydrocarbon [1]. Agricultural products such as corn and sugarcane can be used to make ethanol. In emerging nations, ethanol is increasingly being blended with gasoline, but the barriers to its use as a main fuel in traditional IC engines have yet to be overcome. Corn is assumed one of the most frequent sources of production of ethanol in the United States and it has been reported, after dehydration and distillation, that about 37% of total energy input has been spent on water suppression from ethanol [2]. By minimizing the amount of energy expended extracting the water from ethanol, direct utilization of wet ethanol in modified combustion technology will significantly increase the net energy gain from 21 to 55% [3]. Researchers [4, 5] have concluded that internal combustion engines that are not fitted with wet ethanol in traditional combustion technologies such as spark ignition (SI) and compression ignition (CI), and therefore, a changed method of combustion mode is needed in automotive engines for the utilization of water–ethanol mixture. The homogeneous charge compression ignition (HCCI) engine concept brings together different characteristics of CI engine and as well as SI engine system. Fuel is premixed homogeneously with air, to reduce particulate matter (PM) pollutants, as in a SI engine. As in a CI engine, the mixture is compressed ignited, with high compression ratios, a short combustion time and no throttling losses, all of which contribute to high performance. The principle for homogenous charge compression ignition (HCCI) combines aspects for SI and CI combustion and gives superior thermodynamic and environmental engine performance. HCCI engines used a premixed and lean fuel–air mixture at a high compression ratio, resulting in substantially reduced PM and NOx emissions whereas achieving a higher thermal performance [6, 7]. During a compression operation, HCCI engines can use wet ethanol more effectively than conventional engines because the low-quality fuel utilized auto-ignited, and can therefore be assumed as an acceptable way of combusting the wet ethanol directly as a primary fuel [8]. The traditional first-law approach that normally includes the thermodynamic advantages of fuel energy process in internal combustion engines; ignores system losses and easily provides for overall efficiency that demonstrates a considerable difference in the theoretical and effective performance of the system [9]. Exergy analysis has been found to be sufficient to explain the mechanism of thermodynamic losses occurring during the energy conversion process, the application of the second law analysis is along with the energy balance method. The part of the system with the worst output can be identified using exergy analysis, and the research efforts that achieve greatest efficiency improvements could be evaluated as well. Reduced thermodynamic losses in the components improve the system’s overall energy performance whilst minimizing its effect on the environment [10, 11].
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2 Description of the Proposed Work A commercial engine similar with the heavy-duty truck engine is used in this study except for a regenerator for the heating of the exhaust intake gases and a fuel vaporized for the evaporation of wet ethanol. The turbocharged and ethanol-fuelled engine’s key specifications are described in Table 1. As shown in Fig. 1, a clear outline of the proposed system is as follows: The proposed system includes an HCCI engine, a compressor, a regenerator, a turbine, a fuel vaporizer and catalytic converter. The compressor receives ambient air, which raises its temperature as well as pressure, and the compressed air is heated by the regenerator at a constant pressure. The heated air is injected into the fuel–air mixer with wet ethanol, where it vaporizes and creates an air–water-ethanol homogeneous mixture. This homogeneous mixture reaches the HCCI engine cylinder and combines with residual gases, and engine waste particles join the catalytic convertor after combustion, where the temperature rises. Because of heat release during combustion of carbon dioxide and unburned hydrocarbons that were not burned in the combustion chamber, the mixture of least harmful chemical species leaves the convertor at a higher temperature. The turbine generates power to drive the turbocharger by Table 1 Operating parameters and specification of engine for wet ethanol operated turbocharged HCCI engine [4]
Environment temperature (K)
290–310
Environment pressure (P0 ) (bar) 1.01325 Effectiveness of regenerator
0.78
Turbocharger efficiency (%)
80
Speed of engine
1800 rpm
Compression ratio of engine
16:1
Fuel of engine
35% ethanol in water by volume
Catalytic converter
(Pd-loaded SiO2 –Al2 O3 )
LHV of ethanol (MJ/kg)
26.9
Chemical exergy of ethanol (MJ/kg)
29.6
Volume swept per cylinder (cm3 )
2400
Cylinder diameter (cm)
13.7
Piston displacement (cm)
16.5
Length of connecting rod (cm)
26.2
Compressor pressure ratio
2.5–3.5
Equivalence ratio
0.3–0.7
Residual gases fraction
0.03
Engine volumetric efficiency (%)
100
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1 7
Turbine
Compressor 2
Catalytic Converter
Regenerator 8
3
9
Exhaust gases
4
Fuel Vaporizer
6
5
Wet-Ethanol
HCCI Engine
Fig. 1 Schematic of the proposed turbocharged system based on HCCI engine running on wet ethanol
allowing less harmful gases from the catalytic converter to deliver at an elevated temperatures. In the regenerator, the higher temperature waste gases transfer heat with compressed air before eventually exiting the device at a higher temperature and pressure.
3 Thermodynamic Modelling The energy conversion system performance evaluation criteria associated with conventional energy balance approach include no details on power or resource deterioration throughout a procedure and are silent regarding internal loss estimates. The exergy analysis based on the thermodynamics second law offers a simple evaluation of the different losses during the process of power conversion and can measure the cause and position of thermal irreversibility in the power transition process in quantitative terms. Exergy analysis may also be sufficiently taken into consideration to identify the elements of the system that have the greatest potential for development. The concept of mass conservation and the first thermodynamic law applying in steady-state conditions to any control volume and ignores the variation in kinetic and
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potential energies which provide m˙ i − m˙ o = 0
(1)
Q˙ − W˙ = m˙ o h o − m˙ i h i
(2)
The energy balance equations for HCCI engine running on wet ethanol according to its P–V diagram shown in Fig. 2 was formulated after applying the above Eqs. (1) and (2). Table 2 shows the equations that have been formulated. The highest possible useful work that could be achieved during a cycle that brings the system into equilibrium with a high temperature reservoir, generally the environment, is known as exergy. Under steady-state operating conditions, the exergy rate balance equation that can be applied to any control volume is as follows: E˙ x,Q − W˙ + m˙ i exi − m˙ o exo − E˙ x,D = 0
(3)
Fig. 2 The P–V layout of wet ethanol powered turbocharged HCCI engine
Table 2 Component wise energy balance equations Component Air compressor Regenerator Fuel vaporizer HCCI engine (compression) process, 1´–2´ Heat addition process, 2´–3´ Expansion process, 3´–4´ Blowdown process, 4´–5´ Catalytic convertor
Balance equations W˙ AC = m˙ a (h 2 − h 1 ) m˙ a (h 3 − h 2 ) = m˙ exh (h 8 − h 9 ) m˙ a h 3 + m˙ f h 4 = m˙ a + m˙ f h 5 m˙ a + m˙ f + m˙ rg (h 2 − h 1 ) = W˙ comp − Q˙ surr m˙ a + m˙ f + m˙ rg (h 3 − h 2 ) = Q˙ H m˙ a + m˙ f + m˙ rg (h 3 − h 4 ) = W˙ exp + Q˙ surr m˙ exh h 4 = m˙ exh h 5 + Q˙ surr ◦ ◦ n e h f + h = n i h f + h Product
Turbine
e
W˙ exh,T = m˙ exh (h 7 − h 8 )
Reactant
i
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T0 ˙ E˙ x,Q = 1 − Qi Ti
(4)
where E˙ x,Q , W˙ is the exergy transfer through heat and work during a cycle. The flowing mass rate of a material is denoted by m. ˙ The exergy destruction rate during the process is denoted by E˙ x,D . ex is the unit mass flow exergy of a substance that is categorized into the physical and chemical exergy and is provided by ex = exph + exch
(5)
A gaseous species’ change in enthalpy and loss in entropy during the transition betweena given state and the restricted dead state is accounted for by physical specific ph exergy ex . It is expressed in the following exph = (h − h 0 ) − T0 (s − s0 )
(6)
In the chemical exergy exch , the composition differences of the substance from the substances used in the reference environment are taken into account. It can be stated as follows: p (7) xi μi∗ − μ0 = −gi0 + RT0 xi ln exch = pref where gi is the Gibbs function and μi is the chemical potential of the ‘i’ species, the pressure p and temperature T are to be calculated. pref and T0 are the reference environment pressure and temperature, respectively. The pressure and temperature of the reference environment shall be pref = 1.013 bar and T0 = 298 K for engine applications. Taking into account, the concept of exergy in accordance with the assumptions and balances listed earlier, the exergy calculations which were destroyed during the process are developed and presented in Table 3 in respect of the key components of the proposed configuration of combined energy system.
4 Result and Discussions A detailed performance assessment of a HCCI engine that runs on a wet ethanolbased system is presented here. The system’s thermal and exergetic performance at a compressor pressure ratio, ambient temperature and equivalence ratio have been calculated using acceptable operating condition using the energetic and exergetic balance method. This HCCI engine, which runs on wet ethanol, has been conducted
Thermodynamic Investigations of a Turbocharged … Table 3 Equations of exergy balance for the components of proposed system
Component Air compressor
445 Equations of exergy balance ex,D,AC = ex,1 − ex,2 + wAC
Regenerator
ex,D,HE = ex,8 − ex,9 + ex,2 − ex,3
Fuel vaporizer
ex,D,FV = ex,3 − ex,5 + ex,4 ex,D,Comp = ex,1 − ex,2 + wcomp − qsurr 1 − TT0
HCCI engine (compression) process, 1´–2´ Heat addition process, 2´–3´
Expansion process, 3´–4´
Blowdown process, 4´–5´
Catalytic converter Turbine
ex,D,Heat addition = ex,2 − ex,3 + q H 1 − TTH0 ex,D,exp = ex,3 − ex,4 − wexp − qsurr 1 − TT0 ex,D,exh and blowdown = ex,4 − ex,5 − qsurr 1 −
T0 T
ex,D,converter = ex,6 − ex,7 + ech ex,D,T = ex,7 − ex,8 − wexh,T
in steady-state conditions and the main operational parameters of this system seen in Table 1. The combined influence of ambient temperatures and compressor pressure ratio on the thermal and exergetic efficiency of the wet ethanol powered HCCI engine-based system is depicted in Figs. 3 and 4. It has been discovered that as the compressor pressure ratio is increased, the thermal and exergetic efficiency of the HCCI engine increase result of an increase in the density of the fuel–air mixture, that raises the mixture’s temperature and improves the useful work output by the engine. The thermal and exergetic efficiency of the system are similarly increased, although the system’s exergetic efficiency is considerably smaller than the thermal efficiency because of the fuel’s exergy more than the fuel’s energy. In addition, increasing Fig. 3 Influence of thermal efficiency with ambient temperature and compressor pressure ratio
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Fig. 4 Influence of exergetic efficiency with ambient temperature and compressor pressure ratio
Fig. 5 Influence of equivalence ratio on the thermal and exergetic efficiency
the ambient temperature means decreasing the system’s thermal and exergetic efficiency as the ambient temperature increases require more fuel consumption resulting in decreased work performance and thus system’s efficiency. From an energy and exergy perspective, the HCCI engine that runs on a wet ethanol-based system is found to be more appropriate for lower ambient temperatures. The influence of the equivalence ratio on the thermal and exergetic efficiency of HCCI engine that runs on wet ethanol-based system is depicted in Fig. 5. It has been discovered that as the equivalence ratio increases, the thermal and exergetic efficiency of the HCCI engine increase resulting in increased cylinder inside pressure and temperature, that results in more energy heat release and an enhances the engine’s useful work performance.
5 Conclusion Thermodynamic investigations were conducted of a turbocharged HCCI engine that runs on wet ethanol. Numerical methods have been used for the determination and
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analysis of system performance in operation of the ambient temperature, compressor pressure ratio, equivalence ratio and compression ratio of engine equivalent to 16. A detailed operational assessment of an HCCI engine that runs on a wet ethanol-based system using the operating parameters described above revealed that increasing the equivalence ratio and compression pressure ratio, thermal efficiency as well as the exergetic efficiency also increases. On the other hand, as the ambient temperature increases, the thermal and exergetic efficiencies of the turbocharged HCCI engine running on wet ethanol decrease.
References 1. Flowers, D.L., Aceves, S.M., Frias, J.M.: Improving ethanol life cycle energy efficiency by direct utilization of wet ethanol in HCCI engines. SAE Paper 2007-01-1867 (2007). 2. Patzek, T.W.: Thermodynamics of corn-ethanol bio-fuel cycle. Crit. Rev. Plant Sci. 23(6), 519–567 (2004) 3. Frias, J.M., Aceves, S.M., Flowers, D.L.: Improving ethanol life cycle energy efficiency by direct utilization of wet-ethanol in HCCI engines. J. Energ. Res. Tech. ASME Trans. 129, 332–337 (2007) 4. Mack, J.H., Aceves, S.M., Dibble, R.W.: Demonstrating direct use of wet-ethanol in a homogeneous charge compression ignition engine. Energy 34(6), 782–787 (2009) 5. Yao, M.F., Zhang, Z., Liu, H.: Progress and recent trends in homogeneous charge compression ignition (HCCI) engines. Prog. Energ. Combust. Sci. 35, 398–437 (2009) 6. Christensen, M., Johansson, B., Einwall, P.: Homogeneous charge compression ignition (HCCI) using iso-octane, ethanol and natural gas—a comparison with spark ignition operation. SAE Paper 972874 (1997) 7. Mack, J.H., et al.: The effect of the di-tertiary butyl peroxide (DTBP) additive on HCCI combustion of fuel blends of ethanol and di-ethyl ether. SAE Paper 2005-01-2135 (2005) 8. Onishi, S., Jo, S.H., Shoda, K., Jo, P.D., Kato, S.: Active Thermo-Atmosphere Combustion (ATAC)—A New Combustion Process for Internal Combustion Engines. Society of Automotive Engineers, SAE 790501 (1979) 9. Alkidas, A.C.: The application of availability and energy balances to a diesel engine. J. Eng. Gas Turb. Power 110, 462–469 (1988) 10. Rokopoulos, C.D., Giakoumis, E.G.: Parametric Study of Transient Turbocharged Diesel Engine Operation from the Second Law Perspective. Society of Automobile Engineers, Inc. Warrendale PA, SAE Paper 2004-01-1679 (2004). 11. Caton, J.A.: Exergy destruction during the combustion process as functions of operating and design parameters for a spark ignition engine. Int. J. Energ. Res. 36(3), 368–384 (2012)
Effects of Thermally Induced Deformations and Surface Radiosity for 3D Heat Transfer and Its Applications Kaustubh Kumar Shukla, T. Muthumanickam, and T. Sheela
Abstract This research work is focusing on the different properties of heat transfer through 3D structures. There are four different level of analysis to find the facts of 3D heat transfer. In the first step, nature and effects of surface radiosity have been analyzed, and it is found that diffuse irradiance (I diff ) value is 100 [W/m2 ] for diffuse surface and 0.5 [W/m2 ] for diffuse mirror. It is also noticed that the effect of surface radiosity which is the same in both the cases, while surface emission (e) value is 0, and 1 is 519 w/m2 (max.). Second step explains about how to quantify the temperature variations which is very helpful for any micro-level devices for better retaining at higher level of temperature. It is also providing the knowledge of thermal stability for different applications like retaining of electronic devices for higher temperature and continuously for long time period. In third step, effort has been done to understand the thermally induced deformation of a plate, and finally, in fourth step, 3D heat transfer and effects of conductivity have been analyzed. After analyzing all these steps, one can use these properties to fabricate the micro- or nano-level devices for different kind of biomedical applications; these kind of devices are very sensitive, and all the parameters can be checked using simulation so that by using micromachining technique novel type of devices can be manufactured which may help to the society to solve many health or environment-related issues. Keywords Deformation · MEMS · Microheater · Multiphysics · Radiosity
1 Introduction Thin-film technology and lab on chip technologies are more popular because of its endless features and diversity. The base of silicon-based research is almost depends K. K. Shukla (B) Research Scholar, Department of ECE, Vinayaka Mission’s Research Foundation (Deemed to be University), Salem, Tamilnadu, India T. Muthumanickam · T. Sheela Department of ECE, Vinayaka Mission’s Kirupananda Variyar Engineering College (Vinayaka Mission’s Research Foundation), Salem, Tamilnadu, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_41
449
450 Fig. 1 Block diagram of work followed
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Multiphysics Environment
Thermally Induced Deformation
Surface Radiosity
Temperature Variations
3-D Heat Transfer
Analysis and Applications
on film technology, lab on chip, and micromachining. Actually, because of micro manufacturing technology, micro-electro-mechanical systems (MEMS) became so popular, and it has huge demands. In this paper also, several aspects have been considered. In this paper, at basic level very initially, the design of microstructure devices has been considered and by varying the sensing materials and by altering the geometrical parameters particular device performance has been checked in multiphysics environment. Through Fig. 1, which is shown below, one can understand the work projected in this paper. The purpose of using multiphysics software is design and compare of 2D and 3D devices. By using different physics, which is inbuilt in software different sensing materials have been tested, and even though mechanical parameters has been also analyzed. Apart from it after basic level of analysis next four stages like surface radiosity, thermally induced deformation, temperature variations, 3-dimensional heat transfer, and its analysis has been done step by step.
2 Literature Survey Popularity of gas sensor and microheater is growing each and every day because of social requirements and different health issues [1, 2]. A huge research is continuously going on, especially on thin-film technology [1, 3, 4] and micro manufacturing [5–7]. It is very necessary to understand the microfabrication techniques [8–10] to combine these both technologies for the purpose of analyzing metal oxide gas sensor [11– 14]. The strong reason of preferring the metal oxide gas sensor [15] is because of its simplicity, consumption of very less power as compare to other sensors. To understand the heat flow [16–18] through different kind of devices (2D or 3D), [19] it is very important to analyze the surface [20], material sensing ability [21–25], internal structure of the device, medium, and interfacing capability with different technology for better outcomes. There are different parameters [1–15] on which the performance of the microheater depends, so it can be checked throughout for better sensitivity [26, 27]. Actually, microheater plays a vital role [28–30] and treated as a key component due to its unique features. The main focus which has been considered here is temperature distribution in a controlled manner and high mechanical strength [30]. For doing so, it is always preferable to analyze the performance of microheater. The structure of the microheater can be analyzed by two different forms [27]; one
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is closed membrane type, and another one is suspended membrane type [2]. To understand the concept of both the structures of a microheater, Fig. 2a, b has been drawn. Closed membrane type and suspended membrane type have been fabricated from the back and front side, receptively. In the microheater, the membrane [8–19] is used as a supporting layer, and the main purpose of the resistor is whenever current passes through a resistor it will generate heat. The major role in microheater design is the selection of materials because by selecting more suitable materials power consumption can be reduced as well as mechanical strength and stability can be improved. Generally, it has been observed that Si, SiO2 , and Pt are few common materials that give the best results. Importantly, by using Pt thin-film temperature sensor, the temperature of the microheater can be controlled which is much useful in gas-sensing applications [4, 30]. From Figure 3, given below, it has been tried to provide hints on one of the major work that is done by Pt temperature sensor to control the temperature of microheater. Apart from this, one has to understand the optimization technique for the better improvement of micro-level devices. So, to optimize the performance of different devices [] like photonic and thermoelectric, it is very necessary to concentrate more on
Fig. 2 a Closed membrane type. b Suspended membrane type. Source Tie Li et al., microheaterbased gas sensors. Micro-electro-mechanical systems, 2018
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Fig. 3 Micro gas sensor using Pt thin-film temperature sensor. Source Jun-gu Kang et al. Temp. control of microheater using Pt thin-film temperature sensor embedded in the micro gas sensor. Springer 2017
controlling the thermal conductivity of semiconductors [15]. Parameters like surface radiosity, thermally induced deformation, and temperature variations analysis are much important to design a very sensitive device and heat flow characteristics.
3 Research Methodology According to the objective of this paper, main concentration is to showcase the heat flow at the micro-level in the multiphysics environment, the effects of self-healing, and the benefits of study and design of different kinds of microstructures. Figure 4 showing the methodology along with step-by-step work done. Several analyses have been done to understand the heat flow and its impact at the different applications level. The property of heat conduction, temperature effects, material properties, and heat propagation strategy has been thoroughly analyzed. Fig. 4 Working steps and methodology
(Work-Flowchart)
Step 1: Nature and Effects of Surface Radiosity
Step 2: Quantify the Temperature Variations
Step 3: Thermally induced Deformations of a Plate
Step 4: 3D Heat Transfer and Effects of Conductivity
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4 Design and Simulation Step I: Study of surface radiosity has been done for a block which width, depth, and height is equal to 10 µm shown in Fig. 5. In this simulation-based study, structural steel material has been taken, and the diffuse surface is the 6th surface of the block, as well as a diffuse mirror, which is 1st surface of the same block. Maximum inbuilt value from the nature and properties of the material has been considered except little things. Surface emission (e) value has been considered as 0, 1, and observed that surface radiosity is the same in both the cases 519 w/m2 (max.), but the surface temperature has been varied from 293 to 309 K (max.), respectively. Diffuse irradiance (I diff ) value is 100 [W/m2 ] for diffuse surface and 0.5 [W/m2 ] for diffuse mirror. Now, the effect of surface radiosity which is the same in both the cases, i.e., while surface emission (e) value is 0 and 1 is 519 w/m2 (max.) have been shown through Fig. 5. Further, it has been used by changing the materials and analyzes its effects on different parameters. Step II: In this step, joule effects have been studied, and it has been shown with the help of Fig. 6.
Fig. 5 Surface radiosity which is same in both the cases and its total internal energy
Fig. 6 Minimum and maximum stress
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Fig. 7 a Surface temperature due to heat source. b Temperature effects on surface
The main purpose is to quantify the temperature variations because of the flow of electricity through the electrical conductor. In this design, two different materials have been considered copper and silicon. During the simulation, while constructing geometry, six different blocks have been considered which unit is in mm, and with the help of these blocks, final geometry has been constructed, and the multiphysics concept has been applied. Step III: In this step, simulation has been done for the thermally induced deformations of a plate in a multiphysics environment. This effect has been shown with the help of Fig. 7a, b. Heat source has been considered on the top surface of the microstructure block for which one side has been fixed. Radiation has been considered from all six sides and due to circular heat flux power has been considered as 8000 W. Step IV: In this step, the nature of 3D heat transfer has been studied, tested, and simulated using COMSOL Multiphysics 5.3a through Fig. 8, which is given below. It has been noted if thermal conductivity is less then slower heat propagation will occur whereas, in case of greater thermal conductivity, faster heat propagation will take place which is extremely very useful for heat transfer applications.
5 Conclusion Finally, all the four major steps have been analyzed, i.e., the nature and effects of surface radiosity, quantify the temperature variations, thermally induced deformations of a plate, and 3D heat transfer and effects of conductivity. At the end, it has been observed that choosing a perfect suited membrane material is one of the key secrets apart from selection of sensing material which plays a vital role. On the other
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Fig. 8 Temperature effects and variations
hand, it is also noted that surface emission (e) value have been considered as 0, 1, and observed that surface radiosity is same in both the cases 519 w/m2 (max.), but the surface temperature has been varied from 293 to 309 K (max.), respectively. Diffuse irradiance (I diff ) value is 100 [W/m2 ] for diffuse surface and 0.5 [W/m2 ] for diffuse mirror. Through surface temperature analysis, it has been also noted if thermal conductivity is less, then slower heat propagation will occur, whereas in case of greater thermal conductivity, faster heat propagation will take place which is extremely very useful for heat transfer applications. As a whole, this analysis is much useful to design a micro- or nanolevel device for biomedical or as a gas sensor applications.
References 1. Hsu, T.-R.: MEMS and Microsystems Design and Manufacture. McGraw Hill Education (India) Private Limited. 26th Reprint (2017) 2. Wu, Z., Zhang, X., Song, X., Ma, C., Qi, Y., Chen, X.: Microstructure and properties of welded joint for T92 ferritic heat resistant steel. J. Alloys Compd. (2017) 3. Deng, S., Sumant, A.V., Berry, V.: Strain engineering in two-dimensional nanomaterials beyond graphene. Nano Today 22, 14–35 (2018) 4. Ramachandran, K.P., Vijayaraghavan, G.K., Balasundaram, M.S.: Mechatronics Integrated Mechanical Electronic Systems. Wiley (2016) 5. Nitaigour, P.: “MEMS” Mahalik-Published by McGraw Hill Education (India) Private Limited Eleventh reprint (2016) 6. Gu, T., et al.: Photonic and plasmonic guided modes in graphene–silicon photonic crystals. ACS Photonics 2, 1552–1558 (2015) 7. Timurdogan, E., et al.: An ultralow power athermal silicon modulator. Nat. Commun. 5, 4008 (2014) 8. Casalino, M., Iodice, M., Sirleto, L., Rendina, I., Coppola, G.: Asymmetric MSM sub-bandgap all-silicon photodetector with low dark current. Opt. Exp. 21, 28072–28082 (2013)
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9. Rai-Choudhury, P.: MEMS and MOEMS Technology and Applications. PHI Learning Private Limited (2012) 10. Ananthasuresh, G.K., Vinoy, K.L., Gopalakrishnan, S., Bhat, K.N., Aatre, V.K.: Micro and Smart Systems. Indian Institute of Science, Bangalore, India, Reprint, ISBN 978-81-265-27151, Wiley, India (2011) 11. Singh, A., Sharma, A., Dhull, N., Arora, A., Tomar, M., Gupta, V.: MEMS-based microheaters integrated gas sensors. Integr. Ferroelectr. 193(1), 72–87 (2018). https://doi.org/10.1080/105 84587.2018.1514877 12. Selvakumar, V.S., Sujatha, L., Sundar, R.: A novel MEMS microheater based alcohol gas sensor using nanoparticles. J. Semiconduc. Technol. Sci. 18, 445–453 (2018) 13. Li, T., Xu, L., Wang, Y.: Micro-heater-based gas sensors. Micro Electro Mech. Syst. 717–752 (2018) 14. Kang, J., Park, J.-S., Park, K.-B., Shin, J., Lee, E.-A., Noh, S., Lee, H.-J.: Temperature Control of Micro Heater Using Pt Thin Film Temperature Sensor Embedded in Micro Gas Sensor. Springer (2017) 15. Roy, S., Sarkar, C.K.: MEMS and Nanotechnology for Gas Sensors. CRC Press Taylor & Francis Group, New York (2016) 16. Tao, C., Yin, C., He, M., Tu, S.: Thermal analysis and design of a micro-hotplate for Sisubstrated micro-structural gas sensor. In: Proceedings of the 3 IEEE International Conference on Nano/Micro Engineered and Molecular Systems, Sanya, China, 6–9 January (2008) 17. Spannhake, J., Schulz, O., Helwig, A., Krenkow, A., Müller, G., Doll, T.: High-temperature MEMS heater platforms: long-term performance of metal and semiconductor heater materials. Sensors 6(4), 405–419 (2006) 18. Robinson, R.: Removing contaminants from silicon wafers; To facilitate EUV optical characterization, BSc thesis, Brigham Young University, Provo, UT, August (2003) 19. Velmathi, G., Ramshanker, N., Mohan, S.: 2D Simulations and electro-thermal analysis of micro-heater designs using COMSOLTM for gas sensor applications. In: Proceedings of the COMSOL Conference, Bangalore, India, 29–30 October (2010) 20. Zhao, D., Li, S., Wang, Y., Liu, F., Wang, X.: Investigation of ion irradiation hardening behaviors of tempered and long-term thermal aged T92 steel. J. Nucl. Mater. 511, 191–199. https://doi. org/10.1016/j.jnucmat.2018.09.016(2018) 21. Khanna, V.K., Prasad, M., Dwivedi, V.K., Shekhar, C., Pankaj, A.C., Basu, J.: Design and electrothermal analysis of a polysilicon microheater on a suspended membrane for use gas sensing. Indian J. Pure Appl. Phys. 45, 332–335 (2007) 22. Eun, Y., Na, H., Kim, J.: Bidirectional electro thermal electromagnetic torsional microactuators. In: IEEE 22nd International Conference, pp. 1039–1042 (2009) 23. Noh, S., Seo, J., Lee, E.: The fabrication by using surface MEMS of 3C-SiC micro-heaters and RTD sensors and their resultant properties. Trans. Electr. Electron. Mater 10, 131–134 (2009) 24. Xu, W., Song, K., Ma, S., Gao, B., Chiu, Y., Lee, Y.: Theoretical and experimental investigations of thermoresistive micro calorimetric flow sensors fabricated by CMOS MEMS technology. J. Microelectromech. Syst. 25, 954–962 (2016) 25. Ahmed, M.G.A., Dennis, J., Khair, M.H.M., Rabih, A.A., Mian, M.U.: Characterization of embedded micro-heater and temperature sensor in a CMOSMEMS resonator for gas sensing. Int. J. Appl. Eng. Res. 11, 4381–4386 (2016) 26. Cheng, Russell, J.L., Yu, S.-Y., Poilvert, N., Mahan, G., Mohney, S.E., Crespi, V.H., Mallouk, T.E., Badding, J.V., Foley, B., Gopalan, V., Dabo, I.: Achieving minimal heat conductivity by ballistic confinement in phononic metalattices. ACS Nano 14, 4235−4243 (2020) 27. Zhao, Q., Wang, T., Ryu, Y.K., Frisenda, R., Castellanos-Gomez, A.: An inexpensive system for the deterministic transfer of 2D materials. J. Phys. Mater. 3(1), 016001. https://doi.org/10. 1088/2515-7639/ab6a72(2020) 28. Molnára, D., Sun, X., Lu, S.: Effect of temperature on the stacking fault energy and deformation behavior in 316L austenitic stainless steel. Mater. Sci. Eng. A 759, 490–497 (2019)
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29. Taghavi, N.S., Gant, P.. Huang, P., Niehues, I., Schmidt, R., Michaelis de Vasconcellos, S., Bratschitsch, R., García-Hernández, M., Frisenda, R., Castellanos-Gomez, A.: Thickness determination of MoS2 , MoSe2 , WS2 and WSe2 on transparent stamps used for deterministic transfer of 2D materials. Nano Res. (2019) 30. Benzing, J.T., Liu, Y., Zhang, X., Luecke, W.E., Ponge, D., Dutta, A., Oskay, C., Raabe, D., Wittig, J.E.: Experimental and numerical study of mechanical properties of multi-phase medium-Mn TWIP-TRIP steel: influences of strain rate and phase constituents. Acta Materialia 177, 250–265 (2019)
Optimization of Cutting Zone Temperature in Machining of Magnesium Alloy Using Taguchi Method A. Saravanakumar, Jana Suresh Babu, Alagala Harikrishna, L. Rajeshkumar , and V. Sathiyamoorthy
Abstract Taguchi based approach was utilized in this research to predict the cutting zone temperature of AZ91D Magnesium alloy during turning operation. For this research turning parameters such as depth of cut, feed and spindle speeds were selected uniformly in three different levels. During machining, the heat measurement of cutting zone is being influenced by machining parameters, particularly in continuous operation. Since the cutting tool life and machined parts surface quality robustly depend upon temperature at cutting zone, it is vital to forecast heat generation in cutting zone. Outcome response has been analyzed and optimal cutting parameters obtained which generates minimum amount of temperature at cutting zone. Keywords Turning · AZ91D Magnesium alloy · Taguchi method · Temperature
1 Introduction Magnesium alloys are utilized in both the aerospace and automotive industries due to its high specific strength characteristics when compared with conventional material structures. Wrought and cast magnesium alloys are in high demand lately due to its inherent characteristics even though these alloys are of expensive in nature [1]. Additionally, density of magnesium alloys is as low as the other metals such that it is almost one-third of aluminum alloys and one-fourth of steel-based alloys. Even if fewer parts of any load bearing material are made of magnesium-based alloy, the overall weight of the structure decreases by a greater margin [2–4]. This is the primary reason for the wider scope of application of magnesium alloys in various fields like weapons, aerospace and automobiles. Some other inherent meritorious characteristics of magnesium alloys include high heat and electrically conductive A. Saravanakumar · J. Suresh Babu · A. Harikrishna · V. Sathiyamoorthy Department of Mechanical Engineering, K.S.R.M. College of Engineering, Kadapa, A.P 516003, India L. Rajeshkumar (B) Department of Mechanical Engineering, KPR Institute Engineering and Technology, Coimbatore, Tamilnadu 641407, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_42
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nature, better shielding performance towards electromagnetic waves and ability to absorb shock waves [5–9]. The machining operations can be performed by conventional manually operated machine tools, or purpose-built, automatized machine tools. The fact that magnesium alloys have good machinability allows for heavy cuts at high cutting speeds and feeds, which implies reduced operating times [10– 13]. Besides the previously stated facts, high thermal conductivity and low cutting pressure might let the generated heat dissipate quickly, and thus improve tool life [14–16]. Many researchers tried to address the above issues by the appropriate selection of process parameters for machining and suitable machining ad cutting tools selection which in turn minimized the machining complexity through proper selection of tools and parameters [17–20]. Usually, magnesium alloy AZ91D and their composites were fabricated by various solid and liquid state manufacturing methods like squeeze casting, powder metallurgy, spray deposition and disintegrated melt deposition method [21–23]. It was stated by many of the earlier researchers that the properties of the resultant composites were comparatively lower when the manufacturing method was selected inappropriately. Composites and alloys based on magnesium metals were manufactured through gravity casting, permanent mould casting or die casting in order to avoid the anomalies that happen as a result of formation of magnesium oxide which is highly toxic and reactive. High temperature sand or stir casting method was used to manufacture heavy components in controlled environment [24–26]. Machinability is a very significant belonging for the metal removal process. Amongst various material removal processes, the turning operation is one of the most essential and familiar material cutting procedure for machining element that is identified to be rapid and flexible [27, 28]. Nowadays modern industries are very much concerned about the excellence of their products interns of quality. They are paying more attention on manufacturing high-class products at minimum cost within specified time. Consequently, increasing the productivity and getting better quality components are amongst the main objective of the modern production industry [29, 30]. Due to requirements of the modern industries, in recent times extensive research is going on all the aspects of the machining process including surface finish of the finished parts, tool wear, cutting force and monitoring cutting zone temperature [31– 34]. In the current work magnesium alloy AZ91D magnesium alloy was turned in an CNC machine using a carbide insert tool. The main purpose of this experiment is to choose the most significant factors and accordingly decide on the optimum turning parameters setting that produce least amount temperature in cutting zone.
2 Methodology In the current experimentation, AZ91D magnesium-based alloy is selected as work piece in the form of cylinder of diameter 35 mm. Taguchi’s design technique was used to derive the combination of process parameters adopting L9 orthogonal array and the tests were performed. Process parameters considered for the machining of AZ91D
Optimization of Cutting Zone Temperature … Table 1 Upper and lower limit of tuning parameters
Levels
461 V (rpm)
F (mm/rev)
D (mm)
I
750
0.10
0.3
II
1000
0.20
0.6
III
1250
0.30
0.9
magnesium alloy were depth of cut (D), feed rate (F) and spindle speed (V ) within levels in uniform intervals. Table 1 enlists the factors and levels considered for the machining experiments. Machining studies were carried out using CNC horizontal centre (Model: Sprint 16 TC, Make: Batliboi) using a carbide inserted tools having higher hardness characteristics under dry cutting conditions at elevated temperatures. Figure 1 depicts the machining setup along with the insert used for machining. The experimentation has been performed according to L9 orthogonal array shown in Table 2 in order to minimize the cutting zone temperature. Cutting zone temperature is calculated by means of METRAVI MT-9 equipped with compact IR thermometer Fig. 1 Turning machine setup with insert
Table 2 Experimental results for cutting temperature Exp. No
V (rpm)
F (mm/rev)
D (mm)
Temp (°C)
S/N Ratio
1
750
0.1
0.3
58
−35.27
2
750
0.2
0.6
61
−35.71
3
750
0.3
0.9
65
−36.26
4
1000
0.1
0.6
71
−37.03
5
1000
0.2
0.9
74
−37.38
6
1000
0.3
0.3
69
−36.78
7
1250
0.1
0.9
94
−39.46
8
1250
0.2
0.3
87
−38.79
9
1250
0.3
0.6
82
−38.28
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for measuring the temperature using dual laser target system with temperature range between −50 and 1000 °C [27, 35–37].
3 Result and Discussion In Machining, better quality product can be derived if temperature generated is minimum at cutting zone. For that reason, “smaller the-better” model for the cutting zone temperature was utilized for acquiring optimal turning parameters. MINITAB 16 used to calculate S/N ratios to every experimental process parameter and response adopting smaller-the-better objective function since the temperature at the machining zone has to be maintained minimum for a better surface quality. The evaluation of factors by means of (i) main effect (ii) table of response values (iii) Analysis of variance (ANOVA) method of statistical analysis.
3.1 Main Effects Plot Plot for signal-to-noise ratio representing the main effect influence of the factors and levels on corresponding response were drawn. The process parameters levels that maximize the suitable S/N ratio are the optimum levels. Figure 2 illustrates the plot of process parameters for the temperature at the machining zone. Further it is noted that minimum value of temperature generated at lowest spindle speed, lower
Fig. 2 Main effects plot of signal-to-noise ratio
Optimization of Cutting Zone Temperature … Table 3 Output response table
463
Level
V
F
D
1
−35.74
−37.25
−36.95
2
−37.06
−37.29
−37.00
3
−38.84
−37.10
−37.70
Delta
3.10
0.19
0.76
Rank
1
3
2
feed rate and depth of cut. Also, it was observed that temperature of the cutting zone increases as the spindle speed increase and along with depth of cut increase. Every time increase in feed, it increases the cutting zone temperature slightly which is not much influencing when compared to depth of cut and spindle speed.
3.2 Response Table and ANOVA for Temperature The influence of turning process parameters on cutting zone temperature can be acquired using response Table 3. Response table illustrates the mean response characteristics for each one level of each factor in the design. The rank of the parameters is decided based on the value of delta in the table from the uppermost to the slightest effect depending on the response. Table 3 demonstrates the response table for temperature S/N ratio in which it is obviously visible that spindle speed is the main influencing parameter in view of the fact that it is ranked first followed by depth of cut and feed. ANOVA can be applied to examine and model the correlation between a response and independent variables. Further the analysis utilized to confirm the process parameters that are observed to be insignificant statistically and noteworthy at a confidence level of 95% with respect to P-value [6]. If the P-value is close to or equal to 0.05 shows the superior significance level for the considered process parameter. Percentage contribution of spindle speed was found to be maximum which was followed by the contributions made by depth of cut and feed rate as shown in the last column of Table 4.
4 Regression Analysis The relationship between the turning parameters and the measured temperatures were acquired by means of an equation in regression analysis [7, 8]. With this equation, the temperature was calculated for all the combination within selected limits and evaluated with the experimental results, and a good correlation was found between them which is evident from Fig. 4. The proficiency of the obtained regression equation was confirmed through the range of regression spread. From the statistical analysis,
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Table 4 ANOVA for temperature Source
Degree of freedom
Sequential sum of square
Adjusted sum of square
Adjusted mean square
F value
P value
Pc (%)
Spindle speed (V )
2
1060.22
1060.22
530.11
52.43
0.019
90.6
Feed (F)
2
9.56
9.56
4.78
0.47
0.679
0.8
Depth of cut (D)
2
80.22
80.22
40.11
3.97
0.201
6.9
Residual error
2
20.22
20.22
10.11
Total
8
1170.22
1.7
it was noted that the final R-sq value was at 94.7% whilst the factor adjusted R-sq value was about 91.6%. The regression equation is Temperature = 16.8 + 0.0527 Spindle speed − 11.7 Feed + 10.6 Depth of cut (1) The regression equation capability was examined through the evaluation of residuals. Figure 3 exhibits residuals of the normal probability plot that was generated through a straight line which shows that all responses very closer to the line and dispersed uniformly around the sides of the straight line depicting the accuracy of
Fig. 3 Residual plot of temperature—regression analysis
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the results. The residual becomes visible randomly scattered around zero in residual versus fit plot. The histogram plot confirms the uniform spread of temperature generated around the cutting zone and also around the zero residual sides. Versus plot of the residues shows that all the values were exactly falling on the reference line depicting the accuracy of observation distribution. Figure 4 describes the comparison between theoretically predicted and values of cutting zone temperature with the help of regression equation. Figure 5 shows the error percentage of regression equation nearly less than 5%. Meanwhile, it was noted that the calculated values are in good agreement with the experimental values. Fig. 4 Comparison of actual and regression equation response of cutting zone temperature
Fig. 5 Error percentage of actual and regression equation response
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5 Conclusion In the present research work, the process parameter which has the control the temperature around cutting zone whilst performing turning operation on AZ91D magnesium alloy is optimized using Taguchi’s L9 (OA) experimental design. The investigational outcomes are: • The cutting zone temperature of the turning operation increased at increase in spindle speed and other parameters not much influence on temperature of the cutting zone. • From ANOVA and Response table analysis shows that spindle speed is the most important leading factor influencing on the cutting zone temperature with highest percentage of contribution (90.6%). • Regression analysis was utilized to develop the model for the temperature around the cutting zone in circumstances of selected process parameters. Results that were theoretically predicted using the regression analysis were in good agreement with that of experimental values.
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Design and Development of Smart Multipurpose Automated Guided Vehicle Implemented with SLAM and AMCL D. Gokula Vishnu Kirti, J. B. Greesh Pranav, V. Siva Naga Yaswanth, Amartya Reddy Ponaka, and Joshuva Arockia Dhanraj
Abstract In this present era, where technological and strategical advancement in the field of automation and smart sensors, the role of robotics plays a vital act through its actions, the process of automation in industrial sector extends its limit in rapid product development as well as cost reduction. In industries, multipurpose autonomous robot which could be able to map the environment and navigate to the desired location will play an enormous role in the large industries and in warehouse where multiple tasks to be executed and accomplished. In this work, a multipurpose autonomous ground vehicle is developed based on the SLAM algorithms using a 360º LIDAR, which could map the environment in which it is deployed and uses the same for planning a path to navigate to its desired destination. This robot uses LIDAR and camera as a major source of input parameter for determining its environment to create a map and plan a path for navigation which gives the ability to operate in both static and dynamic environment. This robot is mainly featuring with the Hector simultaneous localization and mapping (SLAM) which eliminated the need of odometry and uses its laser data as a feedback for determining the environment and to localize the state of the robot in the environment. This robot utilizes the feature of adaptive Monte Carlo localization (AMCL) for its navigation, which is programmed on a robot operating system (ROS) deployed on a Raspberry Pi. By including various features and add-on devices, robot can accomplish the task given by the user such as transportation of products, cleaning the floor and maintenance, surveillance, and much more activities using various featured programs, this robot helps in the reduction of the manpower requirement, maintenance, cost, and improve the productivity by increasing the production rate and automate the industrial sector for the current and the future generation requirements. Keywords Hector SLAM · LIDAR · ROS · Mapping · Path finding · Raspberry Pi
D. Gokula Vishnu Kirti · J. B. Greesh Pranav · V. Siva Naga Yaswanth · A. R. Ponaka · J. Arockia Dhanraj (B) Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai, Tamil Nadu 603103, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_43
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1 Introduction In this technological era, robots are taking over different roles of a human in different domains due to their effective and efficient way of completing tasks. One of the domains where the need for robots is arising is in the industries. An industry is an organization that produces and supplies goods and other services and serves as a source of income. In educational perspective, industries are usually divided into primary, secondary, and tertiary industries. In these industries, there are several jobs to be carried out on a regular basis within the industrial region. Example of such jobs to be carried out within the industrial area includes surveillance and security, transportation of goods, and involving in other manufacturing processes. All the activities are generally carried out by human beings, which are tiring and boring and hence often leads to confusions. A robot is nothing but an automatic/semi-automatic electronic machine which can perform various preprogrammed tasks. Exercises show that no human-operated system can work efficiently for 24/7 without errors. In the industrial surveillance and security job, human workers are not the best always, hence requires an additional technological support like the use of security camera, siren alert system, entrance scanning, etc. In the case of goods, transportation also carrying heavy loads is hard for human beings and doing it for multiple times a day becomes painful and leads to various physical problems. In every industrial application, using human workers requires more time and becomes stressful. In order to overcome these problems stated above, an autonomous industrial robot has been proposed and tested using different simulation platforms and a prototype of the same has been built and successfully tested. The above stated industrial robot uses Raspberry Pi as its primary controller and uses an A1 RPLIDAR to map and navigate through the surroundings. The robot is developed on robot operating system (ROS) platform and uses Hector slam mapping technique. The functionality, development and implementation techniques, and working of the robot are discussed. The importance of literature survey is to identify the positives and the drawbacks of the similar works carried out before and to find the best possible solution for every challenge that comes while carrying out the work. To name a few of the best works carried out with a similar goal, Che-An Liao et al. had investigated [1] the indoor positioning and navigation of autonomous ground vehicle based on wireless distance measurement and its uncertainties. A linear and a curved trajectory for an autonomous ground vehicle were simulated and tested considering various parameters. Through their simulation and research studies, they were able to conclude that using greater number of sensors for wireless measurement and more number of trails for multiliterate proved to improve the accuracy of the AGV to a great extent. Sebastian Thrun et al. carried out a study on [2] “finding landmarks for mobile robot navigation,” wherein he presented studies related to ball, an algorithm which is used in mobile robots in order to identify and learn a set of landmarks which are used for localization and to know how to recognize those landmarks using artificial neural networks. The algorithm ball works based on statistical localization approach. This algorithm can be applied to a large number of different environments and sensors.
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The landmarks selected by the algorithm outperformed the landmarks selected by human experts in the conducted study. Computational complexity tends to be the only drawback of the approach. Alexander Zelinsky et al. proposed an algorithm [3], for navigation and path planning in his study “a mobile robot navigation exploration algorithm,” In this method, the robot uses tactile sensing for mapping simultaneously while the robot generates a path to the specified goal location. The mapping is only done till certain extent up to which is necessary for the robot. The paths are generated by assuming the unknown areas in the environment as free space. When obstacles in the unknown areas are encountered, the robot auto-updates the map and a new path is created and executed immediately. The paths that were generated initially are now considered negotiable paths. For the efficient execution of planned paths, different mechanisms have been provided to the robot. It can be noticed that by changing of generation of the distance transform, different behaviors could be induced from the navigation system, which includes exploratory, conservative, and finding optimal path behaviors.
2 Methodology The methodology of the multipurpose industrial ground vehicle is to enable robot operating system (ROS) on the Raspberry Pi to take the input from the LIDAR and accelerometer to actuate the motor to reach the desired location, the complete map of the industry is to be mapped using the LIDAR to help the robot move around the industry where the work is allotted by implementing simultaneous localization and mapping (SLAM) method and also by estimating the position of the robot and to use a navigation algorithm to reach the goal in the shortest way possible. Integrate LIDAR with Raspberry Pi (controller) and map the environment of the industry and its surrounding where the robot is to be deployed and to integrate the motors with Arduino Uno [4] for movement control. The block diagram Fig. 1 depicts the power transmission from the Li-Po battery (power supply) to the Raspberry Pi (controller) and to the 300 rpm DC motor with encoder (actuator) through the power distribution board. All the other sensors such as camera, RPLIDAR, accelerometer, gyroscope, and the Arduino Uno are connected to the Raspberry Pi. The Arduino Uno is used as it comes with an inbuilt pulse width modulator (PWM) and analog to digital converter (ADC) which are required to control the L298n motor drive which is connected with the DC motor. The flowchart Fig. 2 of the multipurpose ground vehicle depicts the flow of the operations step by step that takes place when our robot is put into work. Starting with the initialization of the robot till finding the goal position,
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Fig. 1 Block diagram
2.1 Amcl and Hector Slam Simultaneous localization and mapping (SLAM) is a technique that can be used for mapping the environment and also identifying the robot’s position at the same time. SLAM in robotics [5] is defined as construction or updating of a map of an environment while also having track of the agent’s location in the environment at the same time [6]. Within it, Hector SLAM contains ROS packages related to performing SLAM in unstructured environment. Hector mapping [7] is a technique under SLAM, which can be used without an odometry and also on different platforms that exhibit roll/pitch motion. Unlike other SLAM algorithms, Hector SLAM, which we are using, does not require any separate feedback as it takes its own laser data as feedback. Adaptive Monte Carlo localization (AMCL) [8] is an algorithm which uses a probabilistic localization system for robots moving in a 2D plane. When this algorithm is implemented, it uses a particle filter to track the pose of a robot with respect to a known map/pre-existing. This algorithm displays limited pose accuracy due to the non-convexity of the laser sensor model and the randomness of particle sampling and final pose selection problem. It can adapt to the environment easily and hence can be used for both static and dynamic environments.
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Fig. 2 Flowchart
3 Modeling and Analysis Modeling and analysis for the robot model are conducted and estimated to check the feasibility [9] of the robot in the environment which includes the CAD model, static stress analysis, and kinematic modeling done using Fusion 360 and MATLAB tools, and the results are discussed below.
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Fig. 3 Orthographic view
3.1 CAD Model The above image Fig. 3 represents the CAD model developed using the Fusion 360 tool with the exact dimensions of the robot built in the real time, this robot is of the dimensions of the robot is of the length 260 mm with the breath of 220 mm and the total height of 220 mm, this includes the fully assembled bot dimension. This model is designed in the way that the entire center of gravity (CG) is exactly at the center, and the robot is well balanced to completer the objectives of the robot.
3.2 Kinematic Modeling Consideration: Location coordinates frame—(a1, b1, c1) Coordinates of internal center of mass—(a, b, c) C is not considered due to the trajectory on a plane surface c = constant
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Linear velocity V = [V a V b 0]T Angular velocity ω = [0 0 ω]T State of general coordinate q = [a b θ ]T Generalized velocity = dq = [da db dθ ]T
da db
cos θ − sin θ = sin θ cos θ
va vb
(1)
As dθ = ω via = ri ωi
(2)
where via is the longitudinal velocity of the wheel, ri is the rolling radius of the wheel, considering all the wheels. Radius vector di = [dia dib]T and dc = [dca dcb]T based on the geometry by deducing the expression using Euclidean norm, vi/di = v/dc = |ω|
(3)
via/(−dib) = va/(−dcb) = vib/dia = vb/dca = ω via/ − dib = via/ − dib = via/ − dib = via/ − dib = ω
(4)
Instantaneous center of rotation(ICR) = (aICR, bICR) = (−dac, −dbc)
(5)
Rewriting Eq. 4 as Va Vb = =ω bICR aICR
(6)
Coordinate vector di satisfies the following relationship. ⎫ d1b = d2b = dcb + z ⎪ ⎪ ⎪ d3b = d4b = dcb−−z ⎬ d1a = d4a = dca−−x ⎪ ⎪ ⎪ ⎭ d2a = d3a = dca + y
(7)
Here, x, y, and z are positive kinematic parameter of robot, to obtain wheel velocity combine Eqs. 4 and 7
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⎫ VL = via = v2a ⎪ ⎪ ⎪ VR = v3a = v4a ⎬ VF = v2b = v3b ⎪ ⎪ ⎪ ⎭ VB = v1b = v4b
(8)
VL, VR, VF, and VB are the velocity of left, right, forward, and backward wheel. ⎡
⎤ ⎡ ⎤ VL 1 −c ⎢ VR ⎥ ⎢ 1 c ⎥ VL ⎢ ⎥=⎢ ⎥ ⎣ VF ⎦ ⎣ 0 −aIRC + y ⎦ ω VB 0 −aIRC − x
(9)
Using Eqs. 4 and 8, relationship between wheel velocity and robot velocity is obtained, assuming radius ri = r for each wheel wω = [ωL/V R] = 1/r
VL VR
(10)
Combining Eqs. 9 and 10, relationship between angular velocity of wheel and velocity of the robot is obtained. μ=
Va ω
=r
ωL+ω R 2 −ωL+ω R 2c
(11)
μ is the control input, from Eq. 6. V a + aIRC dθ = 0
(12)
[− sin θ cos θ aICR][da db dθ ] = A(q)q = 0
(13)
Generalized velocity dq is always in the null space A dq = S(q)μ
(14)
ST(q)AT(q) = 0
(15)
where
⎡
⎤ cos θ aIRC sin θ S(q) = ⎣ sin θ −aIRC cos θ ⎦ 0 1
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Fig. 4 Kinematic model of the robot
Fig. 5 Kinematic model simulation results
Considering the parameters such as wheel radius, angular and linear velocity, and the length and breadth of the robot, the above kinematic model in Fig. 4 of the robot is generated and simulated in MATLAB and the results are displayed in Fig. 5.
4 Results and Discussion Figure 6 shows the final image of the prototype built. The smart multipurpose industrial ground vehicle was successfully built exactly as shown in the CAD design with
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Fig. 6 Prototype of the robot model
accurate dimensions of 260 mm × 220 mm × 220 mm with three layers with each layer consisting of different sensors, actuators, and the controller. To be precise, the LIDAR is placed at the top having its sensing part at the center for accurate mapping position and the Raspberry Pi (controller) placed in the middle layer for feasibly connecting with all the other sensors and the motors are placed under the third layer attached with the wheels. The diameter of each wheel is about 120 mm, and the vehicle has a track width of 180 mm. The chassis have been assembled in such a way that the center of gravity of the robot falls very near to the actual center of the robot which enhances the stability of the vehicle and helps with the steady movement of the robot even in uneven surfaces. The powerful 300 rpm motor ensures that when supplied with required power can rotate the wheels at a decent speed even with a load up to 4 kg on it in addition to the weight of the pre-mounted components. Figures 7 and 8 represent the SLAM outputs of simulation and real time, respectively. It is clear from the images that both are similar and real-time mapping produces a similar map to that of the one used for simulation, but with minor deviations due to the actual environmental movements in the surroundings or disturbances. The simulation environment was created in Gazebo [10] and visualized using the Rviz tool in ROS. The real-time SLAM output was taken by operating the robot using a joystick controller and navigating the robot through the area to be mapped. The minimal deviations in the real-time mapping would not affect the vehicle’s performance much and hence proves to be feasible. The simulation result shown in Fig. 9 explains the autonomous navigation capability of the industry vehicle, as it was able to find an optimal path to travel from the starting point to the end point without colliding with any obstacles in its way and without getting deviated from the actual existing path in the area. In the figure above, the dotted lines represent the path taken by the robot and the map shows two different room or sectors of the industry mapped, an important things to note in this
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Fig. 7 Simulated SLAM
Fig. 8 Real-time SLAM
is that the path taken by the robot need not necessarily be the same every time even if the start point and end point or the goal location remain the same, the adaptive Monte Carlo localization (AMCL) algorithm does not consider any fixed landmarks and hence the path it takes varies according to the changes in the surroundings.
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Fig. 9 Navigation results
5 Conclusion This paper presents the different techniques, algorithms, and methods that were used to overcome the challenges that were faced during the construction of the smart multipurpose industrial ground vehicle’s prototype by completing all its objectives that were put front considering all the previous research works studies by various people across the globe in this area. The Hector SLAM techniques used proved to be highly effective as it used its own laser data as feedback and hence the map obtained was more precise. The same way adaptive Monte Carlo localization algorithm which does not consider any fixed landmarks for navigation made the autonomous navigation more efficient. These techniques were preferred over other similar once after a detailed comparison between the same by both literature reviews and real-time work experience. The objectives regarding the integration of several sensors like the LIDAR with the Raspberry Pi and integration of the DC motors in order to control the steering were all done successfully even though it was complex. LIDAR integrated with the Raspberry Pi (controller) enabled with robot operating system (ROS) that was used to map the environment of the industry and its surrounding where the robot is to be deployed, to remotely monitor the robot movement and to record the visual of the environment in which the robot is moving and to determine the position of the robot by using a navigation algorithm to reach the goal in the shortest way possible. Finally, the prototype was tested for the working of all its claimed functions which were compared with the different analysis like stress, strain, displacement and contact pressure analysis, motion study for the joints, and the kinematic analysis for the robot that were carried out and the working movement of the robot were also
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compared with the simulation results and verified. Hence, the robot’s prototype was successfully built and tested.
References 1. Lin, P.T., Liao, C.A., Liang, S.H.: Probabilistic indoor positioning and navigation (PIPN) of autonomous ground vehicle (AGV) based on wireless measurements. IEEE Access. 5(9), 25200–25207 (2021) 2. Thrun, S.: Finding landmarks for mobile robot navigation. In: Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No. 98CH36146), 20 May 1998, vol. 2, pp. 958–963. IEEE 3. Zelinsky, A.: A mobile robot navigation exploration algorithm. IEEE Trans. Robot. Autom. 8(6), 707–717 (1992) 4. De Simone, M.C., Guida, D.: Identification and control of a unmanned ground vehicle by using Arduino. UPB Sci. Bull. Ser. D. 80, 141–154 (2018) 5. Zheng, F., Tang, H., Liu, Y.H.: Odometry-vision-based ground vehicle motion estimation with se (2)-constrained se (3) poses. IEEE Trans. Cybern. 49(7), 2652–2663 (2018) 6. Ibragimov, I.Z., Afanasyev, I.M.: Comparison of ROS-based visual SLAM methods in homogeneous indoor environment. In: 2017 14th Workshop on Positioning, Navigation and Communications (WPNC), 25 October 2017, pp. 1–6. IEEE 7. Pfrunder, A., Borges, P.V., Romero, A.R., Catt, G., Elfes, A.: Real-time autonomous ground vehicle navigation in heterogeneous environments using a 3D LiDAR. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 24 September 2017, pp. 2601–2608. IEEE 8. Hiremath, S.A., Van Der Heijden, G.W., Van Evert, F.K., Stein, A., Ter Braak, C.J.: Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter. Comput. Electron. Agric. 1(100), 41–50 (2014) 9. Guerreiro, J., Sato, D., Asakawa, S., Dong, H., Kitani, K.M., Asakawa, C.: Cabot: designing and evaluating an autonomous navigation robot for blind people. In: The 21st International ACM SIGACCESS Conference on Computers and Accessibility, 24 October 2019, pp. 68–82 10. Rivera, Z.B., De Simone, M.C., Guida, D.: Unmanned ground vehicle modelling in Gazebo/ROS-based environments. Machines 7(2), 42 (2019)
Fully Automated Cricket Bowling Machine C. R. Balaji, R. G. Pranav Raj, V. Harish, and S. K. Indumathi
Abstract The automation process has entered into most of the domains, and this project is to further enhance the process in the sports domain in cricket. The cricket bowling machine is developed to hone the batting skills of the batsman by training and to reduce the injuries for the bowlers during the net-session. The existing bowling machines require a second person to operate it. So, the fully automated cricket bowling machine (FAC-BM) is developed to eliminate the second person to operate and to gain a match bowling experience during the training session. The FAC-BM is developed with four play modes as Training mode, Stroke play mode, Resistance mode, and Slog over mode to get a high-quality training session for the batsman. This machine is developed with an automatic line and length changing system with robotic actuation for more accuracy with gear assembly for more variations in line and length. This machine includes an automatic ball feeder system that will feed 33 balls per round with a servo actuator. The telescopic rods been used in the base stand adjust the height of the machine for the different age players. Keywords Automation · Arduino · Wireless control · Android application · Gear mechanism
1 Introduction The usage of bowling machines in cricket clubs, schools, and colleges increasing in recent years. The concept of cricket bowling machine is introduced to improve the batting skills like consistency, good shot selection, timing of the shot, and to reduce the injuries for the bowlers during the net-practice session. Sometimes, the batsman would not face the deliveries in which he needs to improve. The bowling machine C. R. Balaji (B) · R. G. Pranav Raj · V. Harish · S. K. Indumathi Centre for Automation and Robotics (ANRO), Department of Mechanical Engineering, Hindustan Institute of Technology and Science, Chennai, Tamil Nadu 603103, India S. K. Indumathi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_44
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will bowl the deliveries accurately which is chosen for numerous times until it is manually changed by the operator. There are three types of bowling machines were in existence as follows: (1) The pneumatically operated cricket bowling machine which uses the pneumatic action to bowl the required delivery, (2) Spring actioncontrolled bowling machine which uses two links and two joints with a ball holder at the free end of the link. (3) The machine that employs one or more wheels to bowl the prefixed delivery. The above three types of bowling machines will require a second person to operate it manually [1, 2]. In this project, the FAC-BM is developed which will eliminate the second person for operating the machine. This machine will be provided with four modes to operate, Training mode, Stroke play mode, Resistance mode, Slog over mode. This machine will be provided with a set of pre-programmed deliveries with variation in deliveries by changing the line and length with the help of stepper motor so that the number of deliveries attained is more and the user gets to have different and multiple number of deliveries to face as a batsman. This machine is designed to bowl 33 random deliveries at a time from the pre-programmed set of data.
2 Proposed Model of the FAC-BM The FAC-BM is designed to provide the real match experience to the user of the machine. The whole process is controlled by the Arduino Mega microcontroller. The user will control the machine with the mobile application. The mobile application will provide the user to select the different kinds of modes and to select the field set he wishes to play during the training session. For adjusting the line and length of the different kinds of deliveries the stepper motor is used. For driving or bowl, the balls the MY1016 E-bike motors are used. The automatic ball feeder system contains a servo motor that will feed the ball into the machine one by one when it receives a feedback signal from the proximity sensor. The NodeMCU is used for establishing the Wi-Fi connection between the machine and the mobile to access the machine through the Blynk mobile application. The FAC-BM circuit is split into modules for the designing of the circuit, Power source module, Sensory module, Data transmission module, System control module, and Driving module.
2.1 Block Diagram Arduino Mega is the brain of the machine and it controls the two E-bike motor controllers, two stepper motor controllers, proximity sensor and 360° servo motor (Fig. 1). The NodeMCU is used to establish a stable connection between the blynk application and the machine. The 360° servo motor was in the automatic ball feeder system to feed the ball into the machine. The proximity sensor output signal is used
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Fig. 1 Block diagram of the FAC-BM [3]
as a feedback signal for the servo mechanism. The MY1016 motor controller is used to control the speed of the MY1016 E-bike motor. MY1016 E-bike motor: The E80D18NK is the infrared proximity sensor switch used to count the number of balls been bowled during the practice session and also acts as the input feedback signal for the ball feeder mechanism. The A4988 is a micro-stepping motor driver which can be controlled by Arduino mega with two pins in which one for rotation direction control of the shaft and the other for controlling step signal. NEMA-23 stepper motor is used in this machine for the line and length adjustment. It has 18.9 kg cm torque with 200 steps per revolution actuation. The power supply for the machine will be provided with a special design printed circuit board (PCB). The Blynk application is interfaced with NodeMCU to control the machine operations.
2.2 Flowchart The flow of working of the machine is described below (Fig. 2). The FAC-BM is designed to get controlled wirelessly by an android application. When the machine is connected to the power supply and the Blynk application is connected with NodeMCU through a Wi-Fi connection, the machine will get initialized. At the initial stage, the E-bike motors will rotate at its high speed and the stepper motors will get energized and with its holding torque, and it will be at its home position. When the user selects the respective mode needs to play, the Arduino mega microcontroller will select any one set randomly among the numerous predefined set
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Fig. 2 Flowchart for working of the FAC-BM
values. Each set consists of the speed of the E-bike motor 1, E-bike motor 2, Angle of the stepper motor 1, stepper motor 2. When the user needs to play in training mode, the swing/spin levels, line value, length value, and speed of the motors should be selected. After the selection of the type of any play mode, the microprocessor will check for proximity sensor value as zero, if it is then the microprocessor will send pulse signals to the servo motor to rotate 120° from its home position. Then, the machine will check for the proximity sensor value. If the proximity sensor value increases by one, then the microprocessor will repeat its process. When the proximity sensor value increases to multiples of 33, then the buzzer will get powered to intimidate the user to refill the balls in the automatic ball feeder
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system. When the machine is turned off the total balls faced during the training session will be displayed in the indicator on the Blynk application.
3 FAC-BM Design The FAC-BM has been designed in Autodesk Fusion 360, which is a cloud-based CAD/CAM tool for product development. This machine is designed to produce more number possible deliveries with high accuracy and to produce real match experience batting. This machine has four different parts, Ball driving body, delivery adjustment body, base part and ball feeder system. The ball driving body contains the main frame in which the two E-bike motors with ball driving wheels is attached (Fig. 3). The main frame of the bowling machine is fixed with C-shaped handle to carry the ball driving body to training places. The main frame is designed with a rectangular bar with two diagonal cross members to withstand more loads with less deformation in structure of the machine. The ball driving body of the machine is connected with the delivery adjustment body at the position of the center of gravity point of the ball driving body. The base part is the body in which the delivery adjustment body is mounted. In the base part, six telescopic type cylindrical legs are used. Each telescopic leg consists of two cylindrical rods for the ball throwing nozzle height adjustment of the machine (Fig. 4). Fig. 3 a Isometric view of the FAC-BM. b Main frame of the FAC-BM
Fig. 4 Ball feeder system of the FAC-BM
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The ball feeder system is designed with three rows of semi-cylindrical pipe attached together with side edges. Each row can hold 11 balls, and totally the ball feeder system can hold 33 balls. Each ball is fed into the machine by the servo motor which is connected to the ball feeding rod by a coupling. The ball feeding rod is designed with six set of ball carrying curved strips. To drive or hold a ball, a set pair of ball carrying curved strips are used. Each set pair strips are separated 40 mm from the other pair. In each set pair, three curved strips will be attached to the rod with 120° splitted.
4 Gear Design Calculation The mathematical analysis is carried out using various formulas [4–8]. The maximum speed required for this application is 30 rpm. The stepper motor used has a torque of 18.53 kg/cm The output power of the stepper motor =
2∗∗ N ∗T 60
(1)
Given: P = 5.81 KW; i = 0.33; N = 30 rpm Step 1: Material selection For both pinion and wheel, the ‘15NiCr1Mo15 steel’ is selected by considering the gears need to occupy less space and need to transmit high torque and to withstand high-shear stress. Design surface stress, [σc ] = 9500 kgf/cm2 Design bending stress, [σb ] = 3200 kgf/cm2 Step 2: Calculation of minimum center distance, 0.74 2 E ∗ [Mt ] 3 a ≥ (i ± 1) ∗ i ∗ψ [σc ]
(2)
Step 3: Calculation of maximum module: Spur gear, m ≥ 1.26 3
[Mt ] Y ∗ [σb ] ∗ ψm ∗ Z 1
Dimension Correction Step 4: Calculation of corrected center distance
(3)
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a=m∗
Z1 + Z2 2
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Step 5: Calculation of face width: = b/a
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ψm = b/m
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Step 6: Calculation of design twisting moments: [Mt ] = Mt ∗ K d ∗ K
(7)
Step 7: Calculation of induced stress: σc = 0.74 ∗
i +1 a
i +1 ∗ ∗ E ∗ [Mt ] i ∗b
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Since σc < [σc ], the design is safe. Step 8: Calculation of bending stress: σb =
i +1 a∗m∗b∗y
∗ [Mt ]
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Since σb < [σb ], the design is safe. Step 9: Calculation of gear parameters: • Pitch diameter, d1 = m ∗ Z 1
(10)
d2 = m ∗ Z 2
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da1 = (Z 1 + 2 ∗ f 0 ) ∗ m
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da2 = (Z 2 + 2 ∗ f 0 ) ∗ m
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• Tip diameter,
• Root diameter,
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d f 1 = (Z 1 − 2 ∗ f 0 ) ∗ m − 2c
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d f 2 = (Z 2 − 2 ∗ f 0 ) ∗ m − 2c
(15)
5 Structural Simulation The computational structural simulation is the method to assess the design created with various mechanical factors. In this project, the Autodesk fusion 360 software is used for the structural simulation [4, 5]. Since the mainframe of the machine need to hold more weight, it is simulated. The structural simulation in the Autodesk software will provide the results like displacement of the part when respective amount of force applies on the body, stress induced on the part when respective amount of force applies on the body, strain generated on the part when respective amount of force applies on the body, safety factor for the component being inspected, contact pressure generated on the part and the reaction force on the component inspected (Fig. 5). The overall results of the simulation can be viewed as a pdf format by using the results option in the tool bar. The force acting on the frame of the bowling machine is calculated using newton’s law of gravitational force = 410 KN. The maximum displacement on the mainframe when 410 KN is applied on it is 0.008499 mm. The maximum stress induced on the mainframe when 410 KN is applied on it is 17.78 MPa. The maximum strain induced on the mainframe when 410 KN is applied on it is 1.206 × 10−4 .
Fig. 5 Structural simulation—a displacement, b stress, c strain
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Fig. 6 a Isometric view of environment and, b velocity graph obtained in MSC ADAMS
6 Trajectory Simulation The ball is been simulated in MSC ADAMS to analyze the velocity of the ball, while it’s in motion. An initial velocity will be assigned to the ball. The pitch will be grounded and the characteristics of ball stumps like mass, moment of inertia and coordinates where it need to positioned in the software environment. The contact forces have been created between the off stump, leg stump, middle stump, pitch and the ball to make the components realistic, while they get simulated. The base for each stump has been merged together with the respective stumps to make it as a single component (Fig. 6).
7 Future Work Since the quality in shot selection is directly proportional to the real-time experience. Thus, the future research will focus to develop an artificial intelligence system integrated with object tracking system to deliver different kinds of deliveries with respect to field settings and the movement of the batsman across the popping crease. The object tracking system will include an image processing system which will track the batting pads and with respect to the movement of the batsman across the popping crease to select the delivery within the predefined set of deliveries to make the batsman feel tough to face and to provide them the real match experience.
8 Conclusion The evolution of cricket from its initial stage to present many rules has been changed to make the game interesting. This makes the cricket players need to excel in every bit in their role of the game. Since the bowlers creating some innovative variations of deliveries to defend the batsman, the batsman needs to hone their batting skills.
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In this paper, we have summarized various methodologies used in cricket bowling machine from which it can be understood that the bowling machine which employs two or more Wheels to bowl the delivery is an effective option to obtain an automatic bowling of multiple number of deliveries at a predetermined delay time. Also, a unique design is proposed, which can bowl 33 balls per round automatically with the predefined delay time with randomly selected delivery with respect to the field set chosen. The structural simulation is done in the Autodesk fusion 360 to see how the component reacts when some force acts on it. The gears for the actuation have been designed using the design data book. And the trajectory analysis of ball is done mathematically and simulated to see how the ball moves in reality. Since the aim of the proposed concept is to develop it at low cost with high features, the Arduino microcontroller and E-bike motors were used. Future research of this project will be toward implementing the artificial intelligence in the bowling machine to bowl different deliveries according to the movement of the batsman across the popping crease.
References 1. Justham, L., West, A.: Design and development of a novel, integrated bowling machine for cricket. Proc. Inst. Mech. Eng. Part P J. Sports Eng. Technol. 223(4), 125–137 (2009) 2. McGrath, J.W., Neville, J., Stewart, T., Cronin, J.: Cricket fast bowling detection in a training setting using an inertial measurement unit and machine learning. J. Sports Sci. 37(11), 1220–1226 (2019) 3. Davaraj, F.D., Hassan, A.H.A.: Ball trajectory analysis of multimode cricket bowling machine. In: 2018 IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE), pp. 47–52 (2018) 4. Kumar, J., Singh, P., Sharma, S., Tewatia, V.: Design and experimental analysis of automatic bowling machine. MIT Int. J. Mech. Eng. 5(2), 88–92 (2015) 5. Noakes, T.D., Noorbhai, H.: An analysis of batting backlift techniques among coached and uncoached cricket batsmen. South Afr. J. Res. Sport Phys. Edu. Recreation 38 (3), 143–161, 2016 6. Yetirajam, M., Nayak, M.R., Chattopadhyay, S.: Design of fuzzy logic controller to enhance the operation of cricket bowling machine. IJCTA 3(5), 1662–1666 (2012) 7. Peploe, C., King, M., Harland, A.: The effects of different delivery methods on the movement kinematics of elite cricket batsmen in repeated front foot drives. Procedia Eng. 72, 220–225 (2014) 8. Davaraj, F.D., Faizal, M.I.N., Sunandar, M.R., Abu Hassan, A.H.: Performance difference with respect to the number of wheels on the cricket bowling machine. In: 9th International Conference on Robotic, Vision, Signal Processing and Power Applications, pp. 363–368 (2017)
An Application of Computational Intelligence Techniques to Predict Biometal Deposition Characteristics in Metal Additive Manufacturing Ananya Nath and Shibendu Shekhar Roy
Abstract In metal additive manufacturing process, deposition characteristics are very important to determine the quality of the deposition and the process efficiency. To achieve, this an adaptive neural-fuzzy-based computational intelligence (CI) technique is used to model the layer width and layer height in laser-based direct energy deposition technique as this is one of the most promising metal additive manufacturing methodologies. In this present study, neural network-based architecture was applied to develop an optimal neural-fuzzy system for modeling and predicting the width and height of the deposited layer for three controllable process variables such as feed rate, power, and scanning velocity of the laser. Hybridization of steepest descent and least squares method was used as a learning method in the proposed adaptive neural-fuzzy predictive system. To do the comparative study of prediction accuracy of biometal deposition characteristics, trapezoidal and Gaussian membership function were considered after careful study. The predicted deposition characteristics feature values predicted from proposed adaptive neural-fuzzy model have been compared with the real data. This comparative study indicates that the proposed methodology can design optimal data base and rule base of the fuzzy system for predicting the deposition characteristics in metal additive manufacturing process. Keywords Metal additive manufacturing · Direct energy deposition · Computational intelligence · Adaptive neural-fuzzy system · Deposition characteristics · Biometal
1 Introduction Laser-based direct energy deposition (DED) is an advanced metal 3D printing technology which has one of the most increasing interests in aerospace, automobile, and other engineering applications. This technique is also beneficial for its properties like maintainability, cost effectivity, etc., [1]. In recent years, DED is also becoming A. Nath · S. S. Roy (B) National Institute of Technology, Durgapur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_45
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most popular in the biomedical field because of the ability to build metals with patient specific porous architectures like joint implants, bone implants, or porous coatings which are now a hot topic in research [2]. Due to high biocompatibility, good mechanical properties like strength and corrosion resistance, titanium-based alloys are the most extensively used metallic biomaterials for orthopedic bone or implants in dental applications [3]. The good product quality, strength, or wear resistance can be acquired by good surface finish which indicates good deposition. Therefore, the desired deposition characteristics can only be obtained by the right selection of input parameters. In this present work, the input parameters are power of the laser, feed rate of the powder, and laser scanning velocity. But on the other hand, the deposition mechanism is dependent on numerous uncontrollable factors which make the process almost impossible to get a proper right solution. Therefore, modeling and prediction of deposition characteristics, namely layer width and layer height, are very important parameter to define the direct energy deposition (DED) process as DED has a vital role in the advanced manufacturing as it one of the promising additive manufacturing methods. However, this can be contend that the deposited feature depends on controllable inputs such as powder properties, substrate properties, type of carrier gas, together with laser properties, scan speed, and powder flow. But the exact contribution of each of the process variables on the response is difficult to evaluate. However, various researchers have tried to developed model empirically to predict the deposition performance in respect of process parameters. In this context, work of Sun and Hao [4], Cheikh et al. [5] are significant. Statistics-based response surface methodology (RSM) has been used for investigating the contribution of each process variables on deposition characteristics. This methodology was applied for layer width and layer height modeling of DED by Bhardwaj et al. [6]. Bhardwaj et al. [6] developed regression models in respect of power of the laser, laser scanning velocity, and feed rate of the powder for DED of biometal. This present study proposes an application adaptive neural-fuzzy-based CI technique for modeling and predicting the width and height of the depositing layer for three controllable process variables (feed rate of the powder power, and scanning velocity of the laser). The proposed method uses hybrid learning method for supervised learning of first-order Takagi–Sugeno-type fuzzy system. A number of membership functions are present in fuzzy interference system, among them two different MFs, namely trapezoidal and Gaussian, were used in order to compare the predicted response and experimental outputs of the biometal. Please note that the first paragraph of a section or subsection is not indented. The first paragraphs that follow a table, figure, equation, etc., does not have an indent, either.
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2 Proposed Adaptive Neural-Fuzzy-Based Computational Intelligence Techniques The emergence of computational intelligence (CI) techniques inspired by human reasoning and biological intelligence is one of the most fascinating areas in machine learning. It is widely expected that this technique will play a significant role in the design of smart and intelligent additive manufacturing system including additive manufacturing. In this study, an adaptive neural-fuzzy-based computational intelligence technique is proposed to model biometal deposition characteristics in direct energy deposition process.
2.1 Fuzzy and Adaptive Neural-Fuzzy System A fuzzy inference system [7] is operated based on some decision making rule base systems like “if-then” rule. Without applying precise quantitative evaluation, fuzzy system can model the reasoning process of the human being in qualitative form. This type of fuzzy modeling was explained by Takagi-Sugeno [7]. Fuzzification of the input parameters takes place before entering into the architecture, and defuzzification is done to the system outputs to convert it in a crisp set from a fuzzy set. The proper design of the number of fuzzy set of input parameters, the type of membership functions, and type and number of rules is very important to achieve the desired system. Though, it is often use trial and error procedure to work. The entry of fuzzy logic into neural network system makes it better for getting significant response from the network. The proposed neural-fuzzy system is based on Takagi–Sugeno fuzzy model. It uses the architecture of adaptive systems to facilitate the learning and adaptation [7]. Figure 1 shows the adaptive neural-fuzzy system architecture with two fuzzy rules. 1. 2.
1: If (p is P1 ) and (v is V 1 ) then g1 = r 1 p + r 1 v + t 1 . 2: If (p is P2 ) and (v is V 2 ) then g2 = r 2 p + r 2 v+t 2
where Pi and V i represent the input fuzzy sets, gi represents the outputs, r i , si , and t i are the decision parameters that will be estimated during the supervised learning. The architecture of the proposed method consists of five layers of neurons. 1st layer—Fuzzy layer: There is chance of adoption in every node of this layer. The nodes are defined as O1,i = μ Pi ( p) where i = 1, 2
(1)
O1,i = μVi (v) i = 3, 4
(2)
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Fig. 1 Basic adaptive neural-fuzzy architecture
where μ Pi ( p) and μVi (v) are membership value for different fuzzy membership function. If the trapezoidal membership function (MF) is considered, μVi (v) is given by di − v v − ai ,0 ,1 μVi (v) = max min bi − ai di − ci where ai , bi , ci , and d i represent the variables of the trapezoidal MF. For Gaussian membership function, μVi (v) is given by μVi (v) = e
−(v−ci )2 2σ 2
(3)
where {σ, ci } represents the premise parameters. 2nd layer—Product layer: Each node in the product layer is a fixed type node without any modification of the parameters. The output of the nodes of this layer is given as O2,i = wi = μ Pi ( p) × μVi (v) i = 1, 2
(4)
wi signifies the rule firing strengths. 3rd layer—Normalized layer: No adoption takes place in this layer. The fixed parameter (firing strength) values are normalized by following relations:
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wi w1 + w2
(5)
O3,i = wi =
4th layer—Defuzzy layer: In every node of this layer, the parameters will be adopted with node function as O4,i = wi gi = wi (ri p + si v + ti )
(6)
where r i , si , and t i are consequent parameters. 5th layer—Total output layer: The output of this layer can be written as O5,i =
2
wi gi
(7)
i=1
2.2 Supervised Learning of the Proposed System In the proposed adaptive neural-fuzzy architecture, two adaptive layers (first and fourth layer) are present. In 1st layer, the premise parameters are related to the input membership functions and can be modified by proper adoption. The consequent parameters in 4th layer, are to represent the first-order polynomial. The purpose of the learning method is to modify both premise and consequent parameters to obtain the optimal adaptive neural-fuzzy system. Supervised learning methodology is based on hybridization of the least squares and gradient descent method for tuning premise and consequent parameters. The consequent parameters are optimized by least square method with the premise parameters fixed. After obtaining the optimal consequent parameters, gradient descent method is used to obtain optimal value of the premise parameters.
3 Results and Discussion First, the proposed neural-fuzzy system for modeling the width and height of the deposited layer under a variation of operating conditions needs to be built. For that a relationship between different training process parameters and process performance needs to be established. The experiments were carried out by Bhardwaj et al. [6] on direct energy deposition setup using titanium molybdenum biomedical alloy. Power of the laser (P), laser beam scanning velocity (V ), and feed rate of the powder (F)
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were considered as the input process variables to analyze their contribution on the responses such as deposited layer width and layer height. The setting of power of the laser (P) includes 1700, 1800, and 1900 W; those of scanning velocity (V ) includes 300, 400, and 500 mm/min; the feed rate of the powder (F) is set at 3.0, 4.0, and 5.0 gm/min. RSM was used to analyze the interrelationship among three input process variables and responses. The quadratic regression equations for the width and height of the deposited layer were developed [6] as follows. Layer width(W ) = 85.3 + (−0.0927P − 0.0103V + 0.27F) + (0.026 × P2 − 0.009 × V2 − 53.1 × F2 ) + (0.9 × PV + 12.1 × PF + 4.4 × VF) × 105 Layer height(H ) = 17.87 + (−0.01541P − 0.00133V − 1.543F) + (0.003 × P2 − 0.005 × V2 − 7 × F2 ) × 103 + (0.3 × PV + 91.4 × PF − 15.6 × VF) × 105 After careful study, a training data set is used from the above regression equations in adaptive neural-fuzzy system for modeling deposited layer width and layer height. The flowchart for modeling the process performance using adaptive neural-fuzzy system has been shown in Fig. 2. In this study, power of the laser (P), scanning velocity (V ), and feed rate of the powder (F) are inputs whereas width and height of the deposited layer are the output. The membership function used for the input variables is trapezoidal and Gaussian shape. The fuzzy expressions for the power of the laser are low power (LoP); medium power (MeP); high power (HiP), for scanning velocity is slow velocity (SlV); medium velocity (MeV); high velocity (HiV) and for feed rate of the powder is low feed (LoF); medium feed (MeF); high feed (HiF). The number of fuzzy rules are fix by the numbers of levels of input parameters which are divided. This each level of input parameters is called fuzzy set of input parameter. In this study, three inputs such as power of the laser, scanning velocity, and feed rate of the powder are subdivided further into three fuzzy set. So maximum 27 number of rules can be exhibited in this proposed architecture. The ith rule for first-order Takagi–Sugeno fuzzy system will follows: IF power of the laser (Pi ) is high (HiP) AND laser beam scanning velocity (V i ) is high (HiV) AND feed rate of the powder (f i ) is low (LoF) THEN width of the layer is (qi Pi + r i V i + si f i + t i ), where qi , r i , si , and t i are the consequent parameters. Frist, 10,000 cycles and 500 cycles of learning of adaptive neural-fuzzy system are done. After that by hybrid learning, the proposed system is made. Tables 1 and 2 show the comparison between the predicted values and real experimental output of the width and height of the deposited layer. In almost all the cases, Gaussian membership function shows slightly more accurate value than trapezoidal MF. This is because of the non-linear behavior of the response parameter with respect to process parameters.
An Application of Computational Intelligence Techniques … Fig. 2 Flowchart of proposed adaptive neuro-fuzzy system
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Start
Training data input to proposed Structure Set the input parameters & MFs Adaptive Neural-Fuzzy Structure by Grid partition Supervised learning to proposed System Optimal parameters values of fuzzy system Feeding test data to the optimal adaptive neural - fuzzy system Testing of optimal adaptive neural-fuzzy System Test (Predictions)
Stop
4 Concluding Remarks The adaptive neural-fuzzy structure based on first-order Takagi–Sugeno fuzzy system is adopted to model and predict width and height of deposition in DED process. Hybridization of least squares and gradient descent method is used. An adaptive neural-fuzzy-based computational intelligence (CI) technique has been effectively generated optimal trapezoidal and Gaussian-shaped membership functions distribution and fuzzy rules. The predicted response of the proposed architecture is compared with real experimental response values. The adopted Gaussian-shaped MF
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Table 1 Predicted layer width for trapezoidal MF and Gaussian MF Power of the laser (watt)
Laser beam scanning velocity (mm/min)
Feed rate of Layer the powder width (gm/min) (mm)
Predicted value
1740 1740 1740 1780 1780 1780 1820 1820 1820 1860 1860 1860 1860 1900 1700
340 420 460 340 380 460 460 420 380 500 460 460 380 380 420
3.8 3.4 5 4.6 4.6 3.4 4.2 3.8 4.6 5 4.2 3 4.2 3.4 4.2
4.621 4.470 4.462 4.758 4.736 4.509 4.813 4.845 4.931 5.060 5.118 4.958 5.192 5.461 4.505
Trapezoidal MF
Gaussian MF
Layer width (mm)
Abs. % error
Layer width (mm)
Abs. % error
4.6206 4.4684 4.4626 4.7598 4.7324 4.5077 4.8139 4.8419 4.935 5.0613 5.1174 4.9575 5.1906 5.4621 4.5068
0.0116 0.0410 0.0179 0.0343 0.0816 0.0304 0.0260 0.0618 0.0745 0.0178 0.0073 0.0141 0.0230 0.0225 0.0295
4.6209 4.4699 4.4619 4.7586 4.7361 4.5092 4.8126 4.8445 4.9315 5.0611 5.1177 4.9587 5.1917 5.461 4.5057
0.0051 0.0074 0.0022 0.0091 0.0035 0.0028 0.0010 0.0082 0.0035 0.0138 0.0015 0.0101 0.0018 0.0023 0.0051
Table 2 Predicted layer height for trapezoidal MF and Gaussian MF Power of the laser (W)
Laser beam scanning velocity (mm/min)
Feed rate of Layer the powder height (gm/min) (mm)
Predicted value
1740 1740 1740 1780 1780 1780 1820 1820 1820 1860 1860 1860 1860 1900 1700
340 420 460 340 380 460 460 420 380 500 460 460 380 380 420
3.8 3.4 5 4.6 4.6 3.4 4.2 3.8 4.6 5 4.2 3 4.2 3.4 4.2
0.7613 0.7485 0.5738 0.7247 0.7124 0.6924 0.6839 0.7214 0.7418 0.6814 0.7179 0.6760 0.7663 0.7354 0.6911
Trapezoidal MF
Gaussian MF
Layer width (mm)
Abs. % error
Layer width (mm)
Abs. % error
0.7625 0.748 0.5741 0.7253 0.7113 0.6918 0.6806 0.7232 0.743 0.6816 0.7191 0.6757 0.7669 0.7358 0.6935
0.1529 0.0716 0.0523 0.0789 0.1578 0.0901 0.4889 0.2540 0.1618 0.0294 0.1677 0.0503 0.0767 0.0587 0.3479
0.7615 0.7483 0.5737 0.7245 0.7121 0.6925 0.6838 0.7215 0.7422 0.6817 0.718 0.6757 0.7664 0.7352 0.6913
0.0215 0.0315 0.0174 0.0315 0.0455 0.0110 0.0211 0.0183 0.0539 0.0440 0.0145 0.0503 0.0115 0.0228 0.0295
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in adaptive neural-fuzzy system has shown the most accurate output than trapezoidal membership function.
References 1. Shamsaei, N., Yadollahi, A., Bian, L., Thompson, S.M.: An overview of direct laser deposition for additive manufacturing; Part II: mechanical behavior, process parameter optimization and control. Addit. Manuf. 31(8), 12–35 (2015) 2. Geetha, M., Singh, A.K., Asokamani, R., Gogia, A.K.: Ti based biomaterials, the ultimate choice for orthopaedic implants e a review. Prog. Mater. Sci. 54, 397–425 (2009) 3. Almeida, A., Gupta, D., Loable, C., Vilar, R.: Laser-assisted synthesis of Ti-Mo alloys for biomedical applications. Mater. Sci. Eng. C 32, 1190–1195 (2012) 4. Sun, Y., Hao, M.: Statistical analysis and optimization of process parameters in Ti6Al4V laser cladding using Nd: YAG laser. Optic Laser. Eng. 50, 985–995 (2012) 5. Cheikh, H.E., Courant, B., Hascoet, J.Y., Guillen, R.: Prediction and analytical description of the single laser track geometry in direct laser fabrication from process parameters and energy balance reasoning. J. Mater. Process. Technol. 212, 1832–1839 (2012) 6. Bhardwaj, T., Shukla, M., Paul, C.P., Bindra, K.S.: Direct energy deposition—laser additive manufacturing of titanium-molybdenum alloy: parametric studies, microstructure and mechanical properties. J. Alloys Compounds 787, 1238–1248 (2019) 7. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybernet. SMC-15(1), 116–132 (1985)
Ranking of Critical Risk Factors in the Indian Automotive Supply Chain Using TOPSIS with Entropy Weighted Criterions Vinod G. Surange
and Sanjay U. Bokade
Abstract To survive and grow in today’s world characterized by fierce competition and surrounded by uncertain environments, manufacturing industries are forced to manage internal and external supply chain (SC) disruptions to achieve operational excellence. Automotive manufacturing industries have multiple collaborations at various operations levels, making a complex network of linked activities. Any unprecedented event of slight to severe magnitude adversely hampers various workday activities in the organization. Multiple researchers have cited the susceptibility of the automotive supply chain to numerous risks. Mitigation of risks is vital as complete elimination is impossible on many occasions. This research aims to find out risk factors through a literature review coupled with input from industry experts. After identifying risks, this article ranks the risk factors critical to the automotive SC based on the severity of adverse impacts by considering five different criterions using Technique for Ordered Preference and Similarity to Ideal Solution (TOPSIS). The weight of the evaluation criteria was calculated based on the entropy method. This study identifies thirteen critical risk factors (CRFs), and ranking tools prioritizes “Delay risks,” “Management risks,”, “Supplier risks,” “Employees risks,” and “Inappropriate tools and techniques risks” as the top five CRFs. These research findings will support managers and policymakers framing risk mitigation plans to achieve operational excellence in the entire SC and use the systematic modeling approach to identify CRFs with adverse impacts. Keywords Risk factors · Automotive manufacturing · MCDM · TOPSIS
V. G. Surange (B) Department of Mechanical Engineering, Research Scholar at Rajiv Gandhi Institute of Technology, Mumbai and Assistant Professor at St. John College of Engineering, Palghar, Maharashtra, India S. U. Bokade Principal, Rajiv Gandhi Institute of Technology, Mumbai, Maharashtra, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_46
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1 Introduction The economic progress of any country receives a significant contribution from the automobile industry [1], and this applies equally in the context of the Indian automobile sector as well [2]. According to the Society of Indian Automobile Manufacturers (SIAM), multiple vehicles are recalled due to potential defects observed by many automotive manufacturers [3]. Despite numerous exercises in managing interruptions throughout the supply chain, setbacks still exist in the Indian automobile industries, restricting organizations from achieving excellence [4]. The supply chain involves nexus of activities, and its effective management is the prime focus of supply chain managers [5]. Risk management in the supply chain context is defined as “the management of supply chain risks through coordination or collaboration among the supply chain partners to ensure profitability and continuity” [6, 7]. This research aims to identify factors critical to the Indian automotive manufacturing supply chain through a literature review accompanying by an actual survey of industry experts. Post identification, critical risk factors are prioritized according to their impact severity on five different criteria, namely “Financial Aspects,” “Operations/Activities,” “Brand/Reputation,” “Schedule/Timeline,” and “Product Quality and Reliability.” The Technique for Ordered Preference and Similarity to Ideal Solution (TOPSIS) [8] is adopted to rank risk factors. The weight of the evaluation criteria was calculated based on the entropy method [9–11]. The overall process of ranking thirteen critical risk factors considering five criteria is as shown in Fig. 1.
2 Risk Factors Identified Through Literature Review and Industry Survey Table 1 presents the aggregation of risk factors critical to the automotive manufacturing supply chain identified through literature review and confirmed after consultation with industry experts of reputed Indian automotive manufacturing.
3 Impact Ratings Obtained from Industry Experts The industry experts’ input was obtained on a 1-to-7-point Likert scale (very low– very high) to identify the severity of each risk factor’s adverse impact on criteria. CR denotes criteria, IE denotes industry experts, and AIS denotes the average impact score on each criterion. The adverse impact of CRF is severe as the rating approaches 7. Industry experts profile is presented in Table 2.
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Fig. 1 Process of ranking critical risk factors using TOPSIS
4 CRFs Ranking Using TOPSIS 4.1 Tabulation of Average Impact Ratings (AIR) Criteria arranged in rows (r, r = 1, 2, …, m, m = 5) and each CRF in column (c, c = 1, 2, …, n, n = 13). Impact rating table AIRrc is calculated below by taking average after obtaining impact ratings from industry experts (Refer to Table 3).
4.2 Normalization of Average Impact Ratings The distributive normalization table is computed using Eq. (1) below. (Refer to Table 4). AIRrc Nrc = . n 2 c=1 AIRrc
(1)
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Table 1 Critical risk factors to automotive supply chain Code
Risk factor
Description
Reference
CRF1
Natural disasters
External unforeseeable environmental risks such as flood, drought, tsunami, earthquake, hurricane, disease outbreak
[12, 13]
CRF2
Manmade disasters
Blunders, system failure with the intent of human, strikes, political unrest
[12, 13]
CRF3
Risks related to (ICT)
Cyber-attacks, virus intrusion, [12, 13] failure of IT systems, vulnerability due to complex network, data alteration/loss
CRF4
Risks related to Merger and acquisitions of competition/competitive risks significant brands, strategies of competitors
[14–17]
CRF5
Risks related to raw materials Scarcity of raw material, inferior quality of raw material, price fluctuations
[12, 13, 18, 19]
CRF6
Risks related to suppliers
Transactional relationship, [5, 12, 13, 18, 20, 21] poor process quality, improper supplier selection process, the bankruptcy of supplier, an inadequate response
CRF7
Risks due to delays
Complicated processes in obtaining clearance from regulatory bodies, strikes, delay because of suppliers’ internal issues, delay due to critical part/process failure
[12, 13]
CRF8
Market demand risks
Error in the forecast, market uncertainty, bullwhip effect
[2, 12, 13, 18]
CRF9
Economic risks
Tax laws changes, exchange rate variations, issues related to payment processing, untimely/ less than market standard payment to employees
[5, 18, 20, 22–25]
CRF10 Risks related to management
Lack of top management [13, 25–33] commitment, Strategic and tactical imbalance, inability to resolve conflicts, delay in decision making, lack of transparency (continued)
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Table 1 (continued) Code
Description
Reference
CRF11 Risks related to tools and techniques
Risk factor
Inability to adopt new technology, adhering to the traditional approach, no encouragement for advanced tools adoption
[13, 23, 34]
CRF12 Risks related to employees
Incompetence, low skill sets, no training, poor attitude
[13, 18, 31, 34, 35]
CRF13 Risks related to the impact of Pollution, waste creation, no [19] product/processes on the concern about the environment environment
Table 2 Industry experts profile Industry expert (IE) No
Designation
Total experience in years
Industry type
1
Design Engineer
5
Automobile
2
Supply chain analyst
5
Research associate
3
Sr. Project Manager
22
Steel, cladding, and framing industry
4
Sr. Project Manager
12
Technical consultancy
5
Manager
12
Compressors and pumps manufacturing
6
Senior Design Engineer 8
Automotive seating design and manufacturing
7
ISO Consultant (Risk Management)
Consultancy (certification)
10
4.3 Formulation of Weighted Normalization of Impact Ratings Criterion weight computation using the entropy method The weight of each criterion Wr is computed using entropy method as below (Refer Tables 5, 6, 7 and 8). Step (a) Average impact ratings of CRFs on decided criteria by industry experts. Table 5 (transpose of Table 3) contains average impact ratings from industry experts considering five different criteria (n) and thirteen risk factors (m). Step (b) Normalization of Table 5 cells is done by using equation below.
6.1429
5.2857
3.0000
5.7143
4.2857
CR1
CR2
CR3
CR4
CR5
CRF1
4.2857
5.2857
3.8571
5.1429
5.7143
CRF2
4.2857
5.5714
4.7143
5.7143
5.0000
CRF3
4.8571
4.5714
5.2857
3.7143
4.7143
CRF4
5.0000
5.4286
4.1429
5.1429
5.5714
CRF5
5.7143
5.8571
4.8571
4.7143
5.0000
CRF6
5.1429
6.0000
6.0000
5.2857
5.7143
CRF7
Table 3 Average impact ratings of CRFs on decided criteria by industry experts CRF8
3.8571
4.0000
4.1429
5.0000
5.7143
CRF9
3.2857
4.4286
4.8571
4.5714
5.8571
CRF10
5.0000
5.1429
5.8571
5.2857
5.8571
CRF11
5.2857
5.4286
4.8571
5.0000
5.0000
CRF12
5.1429
5.0000
5.1429
5.0000
4.2857
CRF13
4.7143
4.4286
5.2857
4.5714
4.4286
508 V. G. Surange and S. U. Bokade
0.3191
0.2945
0.1721
0.3061
0.2516
CR1
CR2
CR3
CR4
CR5
CRF1
0.2516
0.2832
0.2213
0.2865
0.2969
CRF 2
0.2516
0.2985
0.2705
0.3184
0.2597
CRF 3
Table 4 Normalization of impact ratings CRF 5
0.2936
0.2908
0.2377
0.2865
0.2894
CRF 6
0.3355
0.3138
0.2787
0.2626
0.2597
CRF 7
0.3020
0.3214
0.3442
0.2945
0.2969
CRF 8
0.2265
0.2143
0.2377
0.2786
0.2969
CRF 9
0.1929
0.2372
0.2787
0.2547
0.3043
CRF 10
0.2936
0.2755
0.3360
0.2945
0.3043
CRF 11
0.3103
0.2908
0.2787
0.2786
0.2597
CRF 12
0.3020
0.2679
0.2950
0.2786
0.2226
CRF 13
0.2768
0.2372
0.3032
0.2547
0.2301
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Table 5 Average impact ratings from industry experts CR1
CR2
CR3
CR4
CR5
CRF1
6.1429
5.2857
3.0000
5.7143
4.2857
CRF 2
5.7143
5.1429
3.8571
5.2857
4.2857
CRF 3
5.0000
5.7143
4.7143
5.5714
4.2857
CRF 4
4.7143
3.7143
5.2857
4.5714
4.8571
CRF 5
5.5714
5.1429
4.1429
5.4286
5.0000
CRF 6
5.0000
4.7143
4.8571
5.8571
5.7143
CRF 7
5.7143
5.2857
6.0000
6.0000
5.1429
CRF 8
5.7143
5.0000
4.1429
4.0000
3.8571
CRF 9
5.8571
4.5714
4.8571
4.4286
3.2857
CRF 10
5.8571
5.2857
5.8571
5.1429
5.0000
CRF 11
5.0000
5.0000
4.8571
5.4286
5.2857
CRF 12
4.2857
5.0000
5.1429
5.0000
5.1429
CRF 13
4.4286
4.5714
5.2857
4.4286
4.7143
Table 6 Normalization CR1
CR2
CR3
CR4
CR5
CRF1
0.089027
0.082040
0.048387
0.085470
0.070423
CRF 2
0.082816
0.079823
0.062212
0.079060
0.070423
CRF 3
0.072464
0.088692
0.076037
0.083333
0.070423
CRF 4
0.068323
0.057650
0.085253
0.068376
0.079812
CRF 5
0.080745
0.079823
0.066820
0.081197
0.082160
CRF 6
0.072464
0.073171
0.078341
0.087607
0.093897
CRF 7
0.082816
0.082040
0.096774
0.089744
0.084507
CRF 8
0.082816
0.077605
0.066820
0.059829
0.063380
CRF 9
0.084886
0.070953
0.078341
0.066239
0.053991
CRF 10
0.084886
0.082040
0.094470
0.076923
0.082160
CRF 11
0.072464
0.077605
0.078341
0.081197
0.086854
CRF 12
0.062112
0.077605
0.082949
0.074786
0.084507
CRF 13
0.064182
0.070953
0.085253
0.066239
0.077465
xi j N i j = m i=1
xi j
(2)
Step (c) The calculation of the degree of deviation for each criterion and the calculation of the criteria’s entropy weights Refer to Table 7, which presents the calculation of the criteria’s entropy weights.
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Table 7 Entropy weight calculations for each criterion CRF1
Equations
CR1
Ni j ln Ni j
−0.21534 −0.20514 −0.14654 −0.21022 −0.18685
CR2
CR3
CR4
CR5
CRF 2
−0.20631 −0.20179 −0.17278 −0.20062 −0.18685
CRF 3
−0.19019 −0.21486 −0.19591 −0.20708 −0.18685
CRF 4
−0.18335 −0.16450 −0.20990 −0.18343 −0.20177
CRF 5
−0.20319 −0.20179 −0.18080 −0.20388 −0.20532
CRF 6
−0.19019 −0.19134 −0.19951 −0.21331 −0.22212
CRF 7
−0.20631 −0.20514 −0.22600 −0.21635 −0.20881
CRF 8
−0.20631 −0.19837 −0.18080 −0.16849 −0.17484
CRF 9
−0.20937 −0.18772 −0.19951 −0.17981 −0.15760
CRF 10
−0.20937 −0.20514 −0.22290 −0.19730 −0.20532
CRF 11
−0.19019 −0.19837 −0.19951 −0.20388 −0.21223
CRF 12
−0.17260 −0.19837 −0.20650 −0.19393 −0.20881
CRF 13
m i=1
Entropy for each index
Ej = − ln1m
−0.17625 −0.18772 −0.20990 −0.17981 −0.19815 Ni j ln Ni j m
wr =
0.997661
0.998172
0.994395
0.997332
0.996324
0.002339
0.001828
0.005605
0.002668
0.003676
0.145116
0.113436
0.347782
0.165553
0.228113
i=1 Ni j ln Ni j
Degree of Dj = 1 − Ej deviation of essential information for each criterion Calculation of the criteria’s entropy weight
−2.55895 −2.56026 −2.55057 −2.55811 −2.55552
D n j j=1
Dj
Formulation of weighted normalization of impact ratings: Weighted normalization table (Refer to Table 8) is computed using Eq. 3 (multiplying each element of Table 4 by entropy weight of each criterion obtained in Table 7). W n rc = Nrc × Wr
(3)
0.0463
0.0334
0.0599
0.0507
0.0574
CR1
CR2
CR3
CR4
CR5
CRF1
0.0574
0.0469
0.0770
0.0325
0.0431
CRF 2
0.0574
0.0494
0.0941
0.0361
0.0377
CRF 3
0.0651
0.0405
0.1055
0.0235
0.0355
CRF 4
Table 8 Weighted normalization of impact ratings CRF 5
0.0670
0.0481
0.0827
0.0325
0.0420
CRF 6
0.0765
0.0519
0.0969
0.0298
0.0377
CRF 7
0.0689
0.0532
0.1197
0.0334
0.0431
CRF 8
0.0517
0.0355
0.0827
0.0316
0.0431
CRF 9
0.0440
0.0393
0.0969
0.0289
0.0442
CRF 10
0.0670
0.0456
0.1169
0.0334
0.0442
CRF 11
0.0708
0.0481
0.0969
0.0316
0.0377
CRF 12
0.0689
0.0443
0.1026
0.0316
0.0323
CRF 13
0.0631
0.0393
0.1055
0.0289
0.0334
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4.4 Computation of Distance from Ideal and Unideal Solutions Establishing ideal solution max max max Refer to Table 9 for ideal solution (W n max 1 , W n 2 , W n 3 , …, W n m ) by selecting a maximum weighted impact rating in each criterion, i.e.,
ISr = W n rmax =
max (W n rc )
c=1,2,...,n
(4)
Establishing an unideal solution min min min Refer to Table 10 for unideal solution (W n min 1 , W n 2 , W n 3 , …, W n m ) by selecting a minimum weighted impact rating in each criterion. i.e.
UiSr = W n rmin =
min
c=1,2,...,n
W n rc
(5)
The distance of each CRF from the ideal point The distance of each CRF from the ideal solution (Refer to Table 11) is determined by DistIS CRFc
=
(ISr − W n rc )2
r
Table 9 Ideal solution for each criterion
Table 10 Unideal solution for each criterion
Criteria
ISr
CR1
0.0463
CR2
0.0361
CR3
0.1197
CR4
0.0532
CR5
0.0765
Criteria
UiSr
CR1
0.0323
CR2
0.0235
CR3
0.0599
CR4
0.0355
CR5
0.0440
(6)
0.00000
0.00001
0.00358
0.00001
0.00037
0.06295
CR1
CR2
CR3
CR4
CR5
Dist. (IS) CRF
CRF1
0.04751
0.00037
0.00004
0.00183
0.00001
0.00001
CRF 2
0.03336
0.00037
0.00001
0.00066
0.00000
0.00007
CRF 3
0.02777
0.00013
0.00016
0.00020
0.00016
0.00012
CRF 4
Table 11 The distance of each CRF from the ideal point
0.03901
0.00009
0.00003
0.00137
0.00001
0.00002
CRF 5
0.02521
0.00000
0.00000
0.00052
0.00004
0.00007
CRF 6
0.00874
0.00006
0.00000
0.00000
0.00001
0.00001
CRF 7
0.04834
0.00062
0.00031
0.00137
0.00002
0.00001
CRF 8
0.04277
0.00106
0.00019
0.00052
0.00005
0.00000
CRF 9
0.01302
0.00009
0.00006
0.00001
0.00001
0.00000
CRF 10
0.02595
0.00003
0.00003
0.00052
0.00002
0.00007
CRF 11
0.02542
0.00006
0.00008
0.00029
0.00002
0.00020
CRF 12
0.02821
0.00018
0.00019
0.00020
0.00005
0.00017
CRF 13
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The distance of each CRF from the unideal point The distance of each CRF from the implied unideal solution (Refer to Table 12) is determined by DistUiS CRFc =
(UiSr − W n rc )2
(7)
r
4.5 Closeness Calculations Using Ratios Based on Computed Distances. The formula of closeness calculation for each CRF (Refer to Table 13) is as below. ClCRFc =
DistUiS CRFc UiS DistIS CRFc + DistCRFc
(8)
where 0 ≥ ClCRFc ≤ 1, and impact is severe as ClCRFc approaches toward 1.
5 Conclusion The obtained ranking of risk factors is presented in Table 14. Multiple risk factors impact adversely on various activities of the supply chain. Identifying CRFs is an essential task as proactive action can be taken if and only if risks are identified before its occurrence. The research article identified thirteen prominent risks critical to the Indian automotive manufacturing supply chain. Ranking tool TOPSIS uncovers “Delay Risks,” “Management Risks, “Supplier Risks,” and “Employee Risks” as top risks which have severe adverse impacts on multiple criteria considered in this study. Managers should proactively mitigate these risks to have a minimum disruption in the entire supply chain. This research is expected to assist decision-makers in modeling risk scenarios and building effective risk mitigation plans.
0.00020
0.00010
0.00000
0.00023
0.00018
0.02656
CR1
CR2
CR3
CR4
CR5
Dist. (UiS) CRF
CRF1
0.02827
0.00018
0.00013
0.00029
0.00008
0.00012
CRF 2
0.04162
0.00018
0.00019
0.00117
0.00016
0.00003
CRF 3
0.05059
0.00044
0.00003
0.00208
0.00000
0.00001
CRF 4
Table 12 The istance of each CRF from the unideal point
0.03719
0.00053
0.00016
0.00052
0.00008
0.00009
CRF 5
0.05264
0.00106
0.00027
0.00137
0.00004
0.00003
CRF 6
0.06878
0.00062
0.00031
0.00358
0.00010
0.00012
CRF 7
0.02758
0.00006
0.00000
0.00052
0.00007
0.00012
CRF 8
0.03946
0.00000
0.00001
0.00137
0.00003
0.00014
CRF 9
0.06418
0.00053
0.00010
0.00325
0.00010
0.00014
CRF 10
0.04844
0.00072
0.00016
0.00137
0.00007
0.00003
CRF 11
0.05090
0.00062
0.00008
0.00183
0.00007
0.00000
CRF 12
0.04991
0.00037
0.00001
0.00208
0.00003
0.00000
CRF 13
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0.2967
13
Relative Closeness of CRF
Rank
CRF1
11
0.3731
CRF 2 8
0.5551
CRF 3 6
0.6456
CRF 4 9
0.4881
CRF 5
Table 13 Closeness calculations using ratios based on computed distances CRF 6 3
0.6762
CRF 7 1
0.8873
CRF 8 12
0.3632
CRF 9 10
0.4799
CRF 10 2
0.8314
CRF 11 5
0.6512
CRF 12 4
0.6670
CRF 13 7
0.6389
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Table 14 Ranking of CRFs obtained using TOPSIS Code
Title
Rank
CRF 7
Delay risks
1
CRF 10
Management risks
2
CRF 6
Suppliers risks
3
CRF 12
Employees risks
4
CRF 11
Inappropriate tools and techniques risks
5
CRF 4
Competitive risks
6
CRF 13
The adverse impact of product/processes on environment risks
7
CRF 3
Information and communication technologies (ICT) risks
8
CRF 5
Raw materials risks
9
CRF 9
Economic risks
10
CRF 2
Manmade disasters risks
11
CRF 8
Demand risks
12
CRF 1
Natural disasters risks
13
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A Review on the Performance of Earth Air Heat Exchanger Coupled with Other Systems Vaishali Goyal , Arun Kumar Asati , and Rajeev Kumar Garg
Abstract Earth air heat exchangers (EAHE) are gaining importance worldwide due to very small amount of energy consumption. But, EAHEs alone cannot provide the comfortable conditions all around the year. So, they are coupled with other systems. Various systems that have been coupled with EAHE and studied by research scholars around the world are discussed in this paper. It is concluded that EAHE coupled with some additional system provides added advantages in the form of higher energy savings and comfortable environment. It is also pointed out that there should be some control mechanism that switches on/off the required equipment depending upon the ambient weather conditions. Keywords Earth air heat exchanger · Coupled systems · Energy savings · Co2 emission savings
1 Introduction The use of high grade energy is on the rise in building sector due to its use in ventilating, air conditioning, and heating systems. Non-renewable energy sources are depleting fast in generation of high-grade energy. It is high time that mankind should think of alternative systems that consume very little or no energy at all for cooling or heating systems. One such system is earth air heat exchanger (EAHE) that utilizes earth’s potential as heat sink in summer season as well as heat source in winter season. The concept behind this system is that the temperature below earth’s surface beyond certain depth becomes independent of ambient air temperature. At a particular depth, this temperature becomes constant and is called earth’s undisturbed V. Goyal (B) · A. K. Asati Department of Mechanical Engineering, Shaheed Bhagat Singh State University, Moga road, Ferozepur 152004, India e-mail: [email protected] R. K. Garg Department of Chemical Engineering, Shaheed Bhagat Singh State University, Moga road, Ferozepur 152004, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_47
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temperature (EUT). The EUT is greater than the ambient temperature in winter season and lower in summer season. When outside air is passed through the tubes buried underground at a considerable depth, the air that comes out is warmer in winter season and cooler in summer season than ambient air. So, it is considered to be a promising technique for energy savings. EAHE alone cannot provide the comfortable conditions all around the year. Thus, it should be coupled with other systems. This paper reviews the research work on various systems that have been coupled with EAHE by research scholars around the world.
2 Literature Review on Coupled/Special Systems 2.1 Combination of EAHE, Trombe Wall, and Green Wall A 3D unsteady state conjugated fluid flow model was developed by Dabaieh and Serageldin, to study a combination of three passive technologies like that of EAHE, Trombe wall, and green wall that has been used to account for cooling and heating loads. The heating demands were reduced to 7.9 KWh/m2 per annum and cooling demands to 2.8 KWh/m2 per annum. The total payback period of 7.4 years has been calculated with large energy savings, and Co2 reduction of 242.2 kg has been achieved. Thermal comfort conditions could be achieved 88% duration of the year [1].
2.2 Use of Coaxial Tubes A new concept has been taken by Dehina et al., where coaxial tubes, inner one with hot water and outer one have been taken to heat the air as shown in the Fig. 1. Effect of various parameters has been studied like ratio of inner and outer diameter, ratio of air to water velocity and thermal conductivity of inner tube. It has been found that if hot water is circulated in inner tube; then, the heating load gain of 78% is achieved during winter season, and if no water is circulated, then simple EAHE gives 16% gain of cooling load during summer season. Later, it has been applied to a building and found useful [2].
2.3 EAHE with Buoyancy-Driven Natural Ventilation System Wei et al., coupled the EAHE with buoyancy-driven natural ventilation systems through theoretical analysis to achieve passive ventilation thus dropping the need of any additional mechanical ventilation system and further endorsed the positive effect
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Fig. 1 Coaxial tubes [2]
of ventilation flow rate fluctuation on the cooling or heating capacity at the hottest and coldest times of the year. Later, the results were compared with numerical results using CFD [3].
2.4 EAHE with Water Jacket on the Outlet Pipe EAHE with water jacket shown in Fig. 3, at the exit section has been compared with no water jacket by Zaphar and Hameed, in Ethiopia. It has been concluded that using water jacket, the temperature difference increased by 7.3% and the effectiveness increased by 52.14% when compared with the system where no water jacket was used [4]. A coiled type pipe shown in Fig. 2 was used. Fig. 2 A coiled type pipe [4]
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Fig. 3 Water jacket [4]
2.5 EAHE with Air-to-Air Heat Exchanger Comparison of EAHE and air-to-air heat exchanger has been done by D’Agostino et al. [5]. EAHE is found to be performing well with 50% energy savings for summer season, whereas air-to-air heat exchanger is found to be working well for winter season with 100% energy savings. The combination of both technologies leads to 75% energy savings [5]. From a case study by Agostino et al., on the combination of both EAHE and air-to-air heat exchanger by simulation in Energy Plus and Design Builder [6], it has been observed that energy requirement is reduced by 74% in extreme climate. CO2 emissions and pay back periods have also been reduced. Payback period is 8.5 years for the first city and only 02 years for the second city.
2.6 EAHE Controlled by “Internet of Things” Basok et al., emphasized on control of EAHE by Internet of Things so that EAHE is operated only if the temperature difference between inlet and outlet temperature is more than 2 °C. This control system will automatically switch off the EAHE when it is consuming more energy than energy saving provided [7].
2.7 EAHE with Insulated Walls of the Building Rosti et al., have studied the effect of providing insulation layers on the building walls to protect heat transfer across walls which is being conditioned by EAHEs. They concluded that single-layer insulation on the outer side and double-layer insulation on outer side and at middle side on the building walls give the best performance with
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Fig. 4 Insulated wall [8]
Fig. 5 Heat pipes [9]
EAHE [8] as shown in Fig. 4. The amount of insulation is independent of type of EAHE as well as wall heat storage materials.
2.8 EAHE with Heat Pipes Zhelykh et al., studied the use of heat pipes (that contain evaporator, condenser, and transport section) along with EAHE as shown in Fig. 5. They concluded that this system improves the heat transfer rate between working fluid and the ground sink [9].
2.9 EAHE with Photovoltaic Cell Li et al., studied two different configurations using photovoltaic cell (Fig. 6) and EAHE for both heating and cooling systems, and reported that there is a huge energy saving for both the configurations. It was found that the total energy and exergy
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Fig. 6 Temperature difference achieved
Temperature in celcius
25 20 15 10 5 0 [16]
[10]
[1]
[12]
[4]
Citations
outputs of the optimum configuration ranges from 96,448 to 98,537 KWh and 10,015 to 9888 KWh [10]. Coupled system of both EAHE as well as photovoltaic cell has been used by Afrand et al., for heating as well as cooling requirements and also for generation of electricity. It was shown that both energetic and exergetic performances improve with increasing duct length and diameter and with decreasing mass flow rate and duct depth. Electrical energy gain, thermal energy gain, and thermal exergy gain are of the order of 5900 KWh, 3450 KWh, and 54 KWh, respectively [11].
2.10 EAHE with Evaporative Cooler A hybrid system with evaporative cooler and EAHE was studied by Nemati et al., and it was concluded that the energy consumption reduced by 62% and water consumption reduced by 45%. The desired thermal comfort was maintained and cooling performance was improved by the system. It was shown that the outlet temperature can be decreased by increasing pipe length and pipe diameter. Also in winters, the energy consumption may be reduced by 29% by preheating the air through EAHE. The system can reduce carbon-di-oxide emissions by 1.7 tons per year [12]. A combination of EAHE, air handling unit heat exchanger, and evaporative cooler has been used by Rey-Hernández et al., to provide comfortable conditions in a building that has been declared as the most safe building in Covid-19. All the three systems were controlled by a building management system. Very high energy recovery, minimum electricity consumption, large reduction in carbon emissions, and operational costs have been achieved [13].
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2.11 EAHE Use with AHU of HVAC D’Agostino et al., studied the EAHE as a component inserted in air conditioning system and reported the amount of reduction in the load on heating and cooling coils of air handling unit. In summer, the maximum reduction reached up to 56%, and in winter, it reached up to approximately 70%, considering the length of 140 m. Small diameters like 0.2 m and length of 80–100 m are recommended in the research work. The heat exchange efficiency achieved is 0.9, thus found to be highly effective [14]. Baglivo et al., confirmed that the technology of EAHE is important and that the designs must be optimized for the system. They have studied the effect of number of pipes, air flow rate and soil thermal conductivity, and different ventilation system requirements on the performance of EAHE. It has been concluded that EAHE cannot completely provide comfort conditions in terms of temperature and humidity but can definitely reduce the energy consumption. A higher flow rate of air is important for attaining comfort conditions [15]. The idea of coupling EAHE with the condenser of AC system has been explored by Benhamza et al., and it has been observed that it is effective in reducing overall energy consumption and when AC loses its effectiveness due to ambient temperature exceeding 50 °C, this system works efficiently. There is increase of 20%, 19%, and 21% in COP, EER, and energy saving, respectively [16]. EAHE with ventilation system of vapor-compression refrigeration system has been studied by Ghaith and Alsouda, in Dubai with two different refrigerants, i.e., R22 and R410a, and it has been found the efficiency increases by 47–49%. The optimal length of EAHE was found to be 55 m [17], and the payback period obtained and expected life are 2.5 years and 10 years, respectively. Estrada et al., studied the coupled system of EAHE with vapor-compression system and have analyzed the situation where not only sensible heat is transferred but also the latent heat exchange takes place due to wetness of pipe. So, they concluded that although the outlet temperature of air decreases but total enthalpy of air increases due to moisture pickup from the pipes. This leads to increased load on the air conditioning system and that is not a desirable condition [18].
3 Discussion Different researchers have studied the performance of EAHE when coupled with other systems both experimentally as well as numerically. The findings of all researchers as reported in their papers are presented for I. II. III. IV.
Cooling and heating performance in Table 1. Payback period in Table 2. Inlet–outlet temperature and effectiveness in Table 3. Energy and carbon dioxide savings in Table 4.
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Table 1 Cooling and heating performance
Table 2 Payback period
Citation
Cooling performance
Heating performance
[3]
56.3 KWH energy savings
111.1 KWH energy savings
[1]
Demand reduced to 2.8 KWh/m2
Demand reduced to 7.9 KWh/m2
[12]
Reduced by 62%
Reduced by 28%
Citation
Payback period (year)
[17]
2.5
[1]
7.4
[12]
5.5–6.4
[6]
8.5
Table 3 Temperature difference and effectiveness Citation
Inlet temp (°C)
Outlet temp (°C )
EUT (°C)
[16]
37 48
15 25
24 28
[10]
43.7 43.1
29.3 29.9
[1]
5.2 35.6
10.6 28.8
10.8 30
Temperature difference (°C)
5.3–7.2 in Winter
[15]
16.7
[14]
17
[8]
21.5
[12]
21.99
10
[11]
22.3
13.34
[17]
27
[5]
14.4 and 17.8 for two cities
[4]
Effectiveness
0.9
47–55% increase
Winter 8–10 and Increased by summer 12–13, 52.14% maximum temperature difference of 21.3 with water jacket
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Table 4 Energy and emissions savings Citation
Energy savings
[16]
21%
[5]
75%
[6]
74%
[14]
56% in summer, 70% in winter
CO2 savings
[11]
5900 KWh
[2]
78% in winter, 16% in summer
[10]
97 MWh
[13]
Energy recovery during 70% of working time, 112,740 KWh per year
21 ton
[12]
1.7 ton
[1]
242.2 kg
90
Percentage Energy Savings
Fig. 7 Energy savings achieved
80 70 60 50 40 30 20 10 [2]
[2]
[14]
[6]
[14]
[5]
[21]
[16]
0
Citations
Figures 6 and 7 show the graphical representation of temperature difference and energy savings achieved, respectively.
4 Conclusion From the discussion on literature review presented in preceding sections on large variety of coupled systems, it is concluded that EAHE coupled with some additional system provides many advantages in the form of higher energy savings and comfortable environment. The maximum energy savings and carbon dioxide emission savings achieved are 74% and 91%, respectively. Maximum temperature difference reached a value of 23 °C and effectiveness of 0.9 could be achieved. Minimum payback period has been found to be 2.5 years.
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It has been reported by various research scholars who have studied coupled systems that EAHE alone does not provide sufficient conditions for human comfort during most part of the year. Therefore, it is beneficial to couple EAHE with other heating or cooling systems along with a mechanism, which controls the on/off switching of the required equipment depending upon the ambient weather conditions.
References 1. Dabaieh, M., Serageldin, A.A.: Earth air heat exchanger, Trombe wall and green wall for passive heating and cooling in premium passive refugee house in Sweden. Energ. Convers. Manag. 209, 112555 (2020) 2. Dehina, K., Mokhtari, A.M., Souyri, B.: Energy modelling of a new co-current coaxial earth– water to air-heat exchanger. Case study: heating of house in Biskra, Algeria. Geothermics (2020) 3. Wei, H., Yang, D., Guo, Y., Chen, M.: Coupling of earth-to-air heat exchangers and buoyancy for energy-efficient ventilation of buildings considering dynamic thermal behavior and cooling/heating capacity. Energy 147, 587–602 (2018) 4. Zaphar, S., Hameed, S.M.A.: Experimental performance analysis of earth-air heat exchanger for energy efficient and eco-friendly HVAC systems. Int. J. Eng. Res. Technol. 6, 628–6 5. D’Agostino, D., Marino, C., Minichiello, F.: Earth-to-air versus air-to-air heat exchangers: a numerical study on the energetic, economic, and environmental performances for Italian office buildings. Heat Transf. Eng. (2020) 6. Agostino, D.D., Marino, C., Minichiello, F.: A publication of IIETA the use of earth-to-air and air-to-air heat exchangers for different Italian climates. 34, 287–294 (2016) 7. Basok, B., Novitska, M., Bozhko, I., Priemchenko, V., Tkachenko, M.: Smart geothermal ventilation system. In: 2020 IEEE 7th International Conference on Energy Smart Systems, ESS 2020—Proceedings (2020) 8. Rosti, B., Omidvar, A., Monghasemi, N.: Optimum position and distribution of insulation layers for exterior walls of a building conditioned by earth-air heat exchanger. Appl. Therm. Eng. 163, 114362 (2019) 9. Zhelykh, V., Savchenko, O., Matusevych, V.: Horizontal earth-air heat exchanger for preheating external air in the mechanical ventilation system. Sel. Sci. Pap. J. Civ. Eng. 13, 71–76 (2019) 10. Li, Z.X., Shahsavar, A., Al-Rashed, A.A.A.A., Kalbasi, R., Afrand, M., Talebizadehsardari, P.: Multi-objective energy and exergy optimization of different configurations of hybrid earth-air heat exchanger and building integrated photovoltaic/thermal system. Energ. Convers. Manag. 195, 1098–1110 (2019) 11. Afrand, M., Shahsavar, A., Sardari, P.T., Sopian, K., Salehipour, H.: Energy and exergy analysis of two novel hybrid solar photovoltaic geothermal energy systems incorporating a building integrated photovoltaic thermal system and an earth air heat exchanger system. Sol. Energ. 188, 83–95 (2019) 12. Nemati, N., Omidvar, A., Rosti, B.: Performance evaluation of a novel hybrid cooling system combining indirect evaporative cooler and earth-air heat exchanger. Energy 215, 119216 (2021) 13. Rey-Hernández, J.M., San José-Alonso, J.F., Velasco-Gómez, E., Yousif, C., Rey-Martínez, F.J.: Performance analysis of a hybrid ventilation system in a near zero energy building. Build. Environ. 185 (2020) 14. D’Agostino, D., Esposito, F., Greco, A., Masselli, C., Minichiello, F.: Parametric analysis on an earth-to-air heat exchanger employed in an air conditioning system. Energies 13, 1–24 (2020) 15. Baglivo, C., D’Agostino, D., Congedo, P.M.: Design of a ventilation system coupled with a horizontal air-ground heat exchanger (HAGHE) for a residential building in a warm climate. Energies 11 (2018)
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16. Benhamza, M.E., Brima, A., Houda, S., Moummi, N.: Contribution to the study of the reduction of energy consumption through the exchanger coupled conventional air-ground-air conditioner. Application to the building. Heat Transf. Asian Res. (2017) 17. Ghaith, F.A., Alsouda, F.J.: Enhancing the performance of the building’s vapor compression air cooling system using earth-air heat exchanger. In: ASME 2017 11th International Conference on Energy Sustainability collocated with the ASME 2017 Power Conference Joint With ICOPE17, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 (2017). https://doi.org/10.1115/ES2017-3200 18. Estrada, E., Labat, M., Lorente, S., Rocha, L.A.O.: The impact of latent heat exchanges on the design of earth air heat exchangers. Appl. Therm. Eng. 129, 306–317 (2018). https://doi.org/ 10.1016/j.applthermaleng.2017.10.007
Beacon-Based Smart Shopping System Using IoT G. Nagarajan, Y. D. V. V. S. Jiyyaparaju, and Yagnik
Abstract Localization is a key component of the Internet of things (IoT), where the location of everything (LOE) plays a critical role in the development of most IoT-related services. On the other side, data mining techniques and analyses are required when dealing with large amounts of data generated by IoT platforms. Indeed, combining location-based approaches with data mining analysis can result in a smart system service for IoT structure and applications. For this purpose, we created a smart shopping platform with four modules: component placement, component data collecting, component data analysis, and component data mining. Then, a precise localization technique known as “location orbital” is developed, which predicts the present position of mobile objects (users) based on both current and previous locations or regions. Keywords Internet of things · BLE—bluetooth low energy · Beacons · ID number
1 Introduction The Internet of things sparks the race to replace every small object in daily life of the mankind because this is an era which is filled with mass number of sensors. IoT is filled with the valuable equipment and machinery. IoT allows devices to communicate issues, and technicians can resolve the issues remotely without any contact to the equipment. In every domain of human concern like traffic navigation, smart security, smart agriculture, home automation, Ecommerce websites, and healthcare the IoT plays a vital role. Beacons are the best part of IoT. Beacons are small, wireless transmitters that use lower-energy Bluetooth technology to make signals to other devices which are close by. They are one of the fresh expansions in location technology and in hyperlocal marketing. Beacons are interaction between customers and providers in markets. A beacon is a simple device that contains a CPU, radio, and batteries, and it works by continuously broadcasting out an identifier. This is the device like G. Nagarajan (B) · Y. D. V. V. S. Jiyyaparaju · Yagnik Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_49
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mobiles and notifies the important area in the locality of the customers this identifiers has the specific ID numbers for every individual device of beacons so that customer can identify the place of the items and they can easily locate the product. Bluetooth beacons use Bluetooth low-energy proximity sensing to transmit a universal unique identifier (contains ID number) picked up by a certain app or OS that app will distribute messages to a certain areas like stores, malls, parking lots, and areas filled with public specific locations like a grocery or a furniture. Another app is using the indoor positioning system which simplifies our mobiles to locate the actual location of the products. With the help of a beacon sensor, a mobile phone’s software will track towards a beacon in a store and can make payments through point of billing.
2 Related Works These beacon sensors [1, 2] can be used in the places like hotels, resorts, apartments, private buildings, government and private offices, workspaces, healthcare sectors like pharmacy and hospitals. One beacon for a room will make a user can find a specific room location in a map of remaining rooms in that particular buildings, resorts, and hotels to navigate the place where they are. Buildings with more number of individual rooms need another beacon configuration to navigate and locate to user locality. With the help of single beacon in every room, users can utilize an app to locate the room where they are in, by using the algorithms to find the best route to navigate [3–5]. In the places like offices and workspaces, this beacons can be used to place at different locations like cafeteria or canteens and at receptions to simplify and specify the location to navigate to specific places. At consultancy and many places, these beacons can be used in pharmacy that beacons can be placed at racks to find the medicines [6, 7].
3 Existing System In existing thing, customers do not have any idea about the shopping place, people will find the item what they need in a random way, or they will search the products in the alphabetical order given by the store management that process will kills the lot of time. The major disadvantage is what if the product is not there in that which is searched by the customer, what if the product in that store is very costly than the product which can get in outside. If the user is new to that locality, that user might don’t know about any offers running in that locality shops. He/She might need to spend more time or spend more money to get a product without any offers [8, 9]. User can get notifications by passing beside the store, and when there is offer, they can get notified whether where they are in like in work or at home, they will get special offers and notifications [10, 11].
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Fig. 1 Overview of the proposed work
4 Proposed System Local positioning system is the method we use here in this to improve the IoT and its applications. This experimentation outlines on a smart shopping systembased beacon sensors on the given four modules that includes localizing the component, collection of components data, filtering of data and analyzing, and mining of components data (Fig. 1).
5 Modules • • • •
Location of Every Component Data collection Component Data Filtering and Analyzing Component Data Mining Component &Suggestions based on locations.
5.1 Location of Every Component The location of the staff, goods, devices, customers, and other objects is provided in this component that contains a specific role in this system. Locations of each and
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every devices are noted in the central server and by using the accurate localization mechanism system will make a track and navigate the location of the devices and all objects.
5.2 Data Collection Component The data will be gathered in two forms of groups in this component. Type 1 group will collects the users’ data (location, interest, social connections, etc.). Type 2 group will collect the data related to shopping mall (products, income, stocks, offers, discounts, etc.). By using the online central server, gathers the data from buyers’ phone crowd sourcing with IoT devices. By using Wi-Fi users can get some complementary notifications to get the best deals.
5.3 Data Filtering and Analyzing Component The collected data will undergoes the pre-processing process his component provides pre-processing to remove unusual data, for example, false signals through sensors. To get the best output, this undergoes analyzing random sampling and stratified sampling are tested to process different sampling methods. A distributed parallel analyzing system is set for enhancing various outputs. To address the analyzing challenges of the large amount of the data, no SQL database is used here. The data from the various sources are treated differently. To create profiles of shoppers and preserve their history of purchases for future recommendations is the main task. By this component, it will secure the privacy of the users.
5.4 Data Mining Component and Location-Based Suggestions A best data mining and machine learning technique is performed on all over data filtering in this component. So the output is for recommendation and prediction services for end users and advanced services for service providers. The scheme of localization in this is developed for smartphone sensors. In a mobile network, the location of every object in a specific time period is needed. At the location estimated, the time slot will be calculated.
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Table 1 Summary of signal properties [8, 12–14] Technology
Property
Indoor accuracy
Affected by multipath
Cost
Complexity
RSSI
Signal-based (received signal strength)
Medium
Yes
Medium
Low
Angle of Arrival Angle-based (AoA)
Medium
Yes
Expensive
High
Time of Arrival (ToA)
Distance-based
High
Yes
Expensive
High
Time Difference Distance-based of Arrival (TDoA)
High
Yes
Expensive
High
Fingerprinting
High
No
Medium
Low
Signal-based (received signal strength)
6 Comparison of the Radio Frequency (RF-Based) Technologies See Table 1.
7 Conclusion In this paper we are proposing a methodology of using RFID tags to simply the billing. Here, when the customers stand for the billing, the radio frequency identification (RFID), which allows the cardholder to show the RFID card in front of a payment terminal to make a payment transaction. Because contactless payments do not require a signature or ATM PIN to make any transaction. The beacons will help the user to make their purchase and their shopping as soon as possible. Like when there is any work for the customer or if they are new to the location or for that locality, they can get their own map to track and navigate the products in a particular location in time. Here, this process of beacon-based smart shopping simplifies the shopping and it is user-friendly to shop and search product by their own without wasting their time. They’ll get the special offers if that item which has been searched by the customers. This beacons-based will help the mankind and retailers to get their profits and also for time saving. By this type of technology, they can go with low number of the staff and both consumer and producer will get profits. This type of system does not need any repairs in contact; the technician can repair this by using the simple softwares by correcting the errors. This method of using beacons will make the system to simplify the shopping process.
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References 1. Hicks, D., Mannix, K., Bowles, H.M., Gao, B.J.: SmartMart: IoT-based in-store mapping for mobile devices. In: Proceedings of the 9th IEEE International Conference on Collaborative Computing: Networking, Applications, and Worksharing, Austin, TX, USA 2. Liu, Q.H., Yang, X.S., Deng, L.Z.: An Ibeacon-based location system for smart home control. Sensors 18, 1897 (2018) 3. Chen, D.Y., Shin, K.G., Jiang, Y.R., Kim, K.H.: Locating and tracking BLE beacons with smartphones. In: Proceedings of the 13th International Conference on Emerging Networking Experiments and Technologies (CoNEXT’17), Seoul/Incheon, Korea, pp. 263–275 12–15 December 2017 4. Huh, J.H., Seo, K.: An indoor location-based control system using bluetooth beacons for IoT systems. Sensors 17, 2917 (2017) 5. Jeon, K.E., She, J., Soonsawad, P., Ng, P.C.: BLE beacons for internet of things applications: survey, challenges, and opportunities. IEEE Internet Things J. 5, 811–828 (2018) 6. Faragher, R., Harle, R.: Location fingerprinting with bluetooth low energy beacons. IEEE J. Sel. Areas Commun. 33, 2418–2428 (2015) 7. Ke, C.K., Lu, C.C., Kuo, T.W.: Smart home power control via mobile device based on BLE Beacon multi-point positioning. In: Proceedings of the 24th Taiwan Academic Network Conference (TANET 2018), Taoyuan, Taiwan, 24–26 October 2018 8. Ke, C., Wu, M., Chan, Y., Lu, K.: Developing a BLE beacon-based location system using location fingerprint positioning for smart home power management. Energies 11(12), 3464 (2018) 9. Nagarajan, G., Minu, R.I., Jayanthiladevi, A.: Brain computer interface for smart hardware device. Int. J. RF Technol. 10(3–4), 131–139 (2019) 10. Nagarajan, G., Thyagharajan, K.K.: A machine learning technique for semantic search engine. Procedia Eng. 38, 2164–2171 (2012) 11. Nagarajan, G., Minu, R.I.: Wireless soil monitoring sensor for sprinkler irrigation automation system. Wireless Pers. Commun. 98(2), 1835–1851 (2018) 12. Zafari, F., Gkelias, A., Leung, K.K.: A survey of indoor localization systems and technologies. arXiv 2017, arXiv:1709.01015 13. Sakpere, W., Adeyeye-Oshin, M., Mlitwa, N.B.W.: A state-of-the-art survey of indoor positioning and navigation systems and technologies. South Afr. Comput. J. 29, 145–197 (2017) 14. Din, M.M., Jamil, N., Maniam, J., Mohamed, M.A.: Indoor positioning: technology comparison analysis. Int. J. Eng. Technol. 7, 133–137 (2018)
An Modern Approach to Detect Person Wearing Mask Using Deep Learning G. Nagarajan, Shaik Adil Ibrahim, and S. Mohan Kumar
Abstract In this paper, we are developing a system to constantly monitor people if they are wearing a mask and maintain social distancing. The human detection and mask verification from the live streaming is done using an object detection algorithm mobilenet SSD. The distance between two humans is calculated using the eucleidian distance between two bounding boxes of the humans to verify if they are maintaining social distancing. If two humans are not following social distancing or even if they are not wearing any mask the bounding boxes are marked red alerting them. Thus, helps to effectively monitor social distancing norms among the general public. Keywords Mobile face net · Neural network · Deep learning · SSD
1 Introduction Coronavirus 2019 (COVID-19) is an irresistible disorder. Little drops and compressed canned items containing the disease can spread from a defiled person’s nose and mouth as they breathe in, hack, or talk. Others are corrupted if the disease gets into their mouth, nose or eyes. The disease may moreover spread through polluted surfaces, but this is not accepted to be the central course of transmission. The particular course of transmission is rarely exhibited conclusively, yet defilement essentially happens when people are near each other for an impressive time allotment. Using the face cover can reduce the danger of transmissions. In this venture, we will be building up a framework to continually screen individuals on the off chance that they are wearing a cover and keep up social separating. The human discovery and veil check from the live streaming is finished utilizing an article location calculation mobilenet SSD. The distance between two people is determined utilizing the eucleidian distance between two jumping boxes of the people to check on the off chance that they are keeping up social removing. On the off chance that two people are not after social separating or regardless of whether they are not wearing any cover the G. Nagarajan (B) · S. A. Ibrahim · S. Mohan Kumar Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_50
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bouncing boxes are checked red alarming them. Subsequently, serves to adequately screen social separating standards among the overall population. Bluetooth beacons use Bluetooth low energy proximity sensing to transmit a universal unique identifier (Contains ID number) picked up by a certain app or OS, that app will distribute messages to a certain areas like stores, malls, parking lots, and etc. areas filled with public specific locations like a grocery or a furniture. Another app is using the indoor positioning system which simplifies our mobiles to locate the actual location of the products. With the help of a beacon sensor a mobile phone’s software will track towards a beacon in a store and can make payments through point of billing.
2 Related Works In [1], the author propose an amazing and capable structure for unconstrained videobased face affirmation, which is made out of modules for face/fiducial location, face connection, and face affirmation. Expansive tests on testing video datasets, similar to Multiple Biometric Grand Challenge (MBGC) are used for different shot chronicles, show that the proposed structure can exactly perceive and relate faces from unconstrained accounts and feasibly learn vivacious and discriminative features for affirmation. In [2], the author proposed a learning-based face descriptors have continually improved the face acknowledgement execution. Contrasted with the hand-made highlights, learning-based highlights are viewed as ready to misuse data with better discriminative capacity for explicit assignments. Inspired by the new accomplishment of profound learning, in this paper, we broaden the first ‘shallow’ face descriptors to ‘profound’ discriminate face includes by presenting a stacked picture descriptor (SID) [3]. With profound construction, more intricate facial data can be separated and the discriminate and smallness of highlight portrayal can be improved. The SID is learned in a forward improvement way, which is computational effective contrasted with profound learning. Broad examinations on different face data sets are led to show that SID can accomplish high face acknowledgement execution. In [4], the author proposed a new model to merge the two photos and diagrams to extend the open planning data, a strategy that is appeared to significantly help execution, and the UoM-SGFS informational index is loosened up to contain twofold the amount of subjects, as of now having 1300 portrayals of 750 subjects. A wide appraisal of standard and bleeding edge figuring is in like manner performed due to the shortfall of such information recorded as a hard copy, where it is shown that the proposed approach comprehensively beats top tier methodologies on all openly open composite sketch datasets. In [5], the researcher proposed a S2S to calculate the closeness with 2 sets to improve the exactness of face acknowledgement in genuine circumstances, for example, extraordinary stances or serious brightening conditions. This permit you to pick the fitting measurement relying upon the acknowledgement task to accomplish
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the best outcomes. To assess the proposed S2S distance, they direct broad examinations on the difficult set-based IJB-A face dataset, which exhibit that our calculation accomplishes the cutting edge results a disobviously better than the baselines including a few profound learning-based face acknowledgement calculations [6–8].
3 Existing System The existing system fails to recognize the image of person, the algorithm which is implemented earlier does not provide efficiency. The earlier used technique just represent an image and does not recognize the person [9, 10].
4 Proposed System In the proposed system, we have used deep learning with neural network to identify person who is wearing mask [11–13]. In this paper o, we have developed a system to constantly monitor people if they are wearing a mask and maintain so. The human detection and mask verification from the live streaming is done using an object detection algorithm mobilenet SSD. The distance between two humans is calculated using the eucleidian distance between two bounding boxes of the humans to verify if they are maintaining social distancing. If two humans are not following social distancing or even if they are not wearing any mask the bounding boxes are marked red alerting them. Thus, helps to effectively monitor social distancing norms among the general public (Fig. 1).
Fig. 1 System architecture
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5 Modules • • • •
Streaming module Face detection Module Human and mask detection Distance calculation.
5.1 Streaming Module Using Internet and camera we record the picture and video and utilize the video which in cooperates the audio and video in it streaming the video at client end side.
5.2 Face Detection The Face-Detector-1 MB hangs out regarding speed—the model’s default FP32 exactness (.pth) document size is 1.1 MB, and the deduction outline int8 is quantized to a size of 300 KB. As far as model computation, the information goal of 320 × 240 is simply around 90–109 MFlops. The Face-Detector-1 MB preparing measure utilized a VOC dataset created by the WIDER FACE dataset, a face recognition benchmark. More extensive face was delivered in 2015 and comprises of 32,203 pictures and 393,703 face bouncing boxes with a serious level of inconstancy in scale, present, appearance, impediment and enlightenment (Fig. 2).
5.3 Human and Mask Detection Human and mask detection is a process of finding humans and masks in images or videos, in real-time with utmost accuracy. Mobilenet SSD helps in the recognition, detection, and localization of multiple visual instances of humans and masks in an image or a video. For recognizing the face, mobileFaceNet has been used which is more accurate in classifying face. MobileFaceNet is a neural network and obtains accuracy up to 99.28% on labelled faces in the wild (LFW) dataset, and a 93.05% accuracy on recognizing faces in the Age DB dataset. MobileFaceNet architecture is partly inspired by the MobileNetV2 architecture. The residual bottlenecks proposed in MobileNetV2 are used as our main building blocks. The detailed architecture is mentioned in Fig. 3:
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Fig. 2 Ultralight face detector flow diagram
Fig. 3 MobileFaceNet architecture
5.4 Distance Calculation In this paper, we have proposed a new system to calculate the distance between two humans using eucleidian distance formula. A bounding box is constructed on a human and the eucleidian distance between two humans is calculated based on the bounding box.
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The box is a rectangular box that can be controlled by the xx and yy pivot facilitates in the upper-left corner and the xx and yy hub organizes in the lower-right corner of the square shape. Another generally utilized jumping box portrayal is the xx and yy pivot directions of the bouncing box place, and its width and tallness. Here, we characterize capacities to change over between these two portrayals, box corner to centre changes over from the two-corner portrayal to the middle width-stature introduction and box centre to corner bad habit stanza.
6 Result and Discussion In this paper, we have discussed the dataset collection which we have used to process the collecting the images of the people wearing a mask and not wearing a mask. Figure 4 states the people wearing the mask using deep learning and neural network and identifying the distance using ssd (Fig. 5). Figure 6 shows the model file generated by training the dataset. Figure 7 explains how they detect the person not wearing a mask is shown in a red bounding box is constructed around the persons face indicating he/she is not wearing a mask. The below image shows the response when a person not wearing a mask is detected:
Fig. 4 Dataset of people wearing mask
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Fig. 5 Dataset of people not wearing mask
Fig. 6 Model file generation
Figure 8 explains how a person wearing a mask is detected a green bounding box is constructed around the persons face indicating he/she is wearing a mask. The below image shows the response when a person wearing a mask is detected. When the camera is initiated bounding boxes are constructed on the faces and the body of two people. The bounding boxes are set to red when the two people do not wear mask and do not maintain social distancing (Fig. 9): Figure 10 shows the result when the two people do not wear mask but maintain social distancing:
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Fig. 7 Not wearing a mask
Fig. 8 Wearing a mask
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Fig. 9 Not wearing a mask and not maintaining social distancing
When both the people are wearing mask and maintain social distancing then the bounding boxes are set to green. Figure 11 shows the result when the two people wearing a mask as well as maintain social distancing: Thus, from the above results it is successfully noted that both social distancing and mask monitoring have been successfully implemented.
7 Conclusion In this paper, we used to detect people not wearing mask and not maintaining social distancing when moving in public places. This also help in maintaining efficient monitoring in a most cheap way and eventually reduce the spread of COVID-19 in public places. At present the social distance and mask monitoring is done manually which consumes more time and also involves human error rate. So, reduces the time required for manual classification and eliminates the human error rate by this project. We have also attached the dataset which we used to detect people wearing mask and identifying them. We have implemented ssd algorithm to calculate the distance between two people.
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Fig. 10 Not wearing a mask but maintaining social distancing
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Fig. 11 Both people maintaining social distancing and wear mask
References 1. Stuhlsatz, A., Lippel, J., Zielke, T.: Feature extraction with deep neural networks by a generalized discriminant analysis. IEEE Trans. Neural Networks Learn. Syst. 23(4) (2020) 2. Galea, C., Farrugia, R.A.: Matching software-generated sketches to face photographs with a very deep CNN, morphed faces, and transfer learning. IEEE Trans. Inf. Forensics Secur. 13(6), 1421–1431 (2017) 3. Liu, L., Zhang, L., Liu, H., Yan, S.: Toward large-population face identification in unconstrained videos. IEEE Trans. Circuits Syst. Video Technol. 24(11), 1874–1884 (2014) 4. Chiachia, G., Falcao, A.X., Pinto, N., Rocha, A., Cox, D.: Learning person-specific representations from faces in the wild. IEEE Trans. Inf. Forensics Secur. 9(12), 2089–2099 (2014) 5. Liu, X., Cheng T.: Video-based face recognition using adaptive hidden markov models. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I–I. IEEE (2003) 6. Zhao, J., Han, J., Shao, L.: Unconstrained face recognition using a set-to-set distance measure on deep learned features. IEEE Trans. Circuits Syst. Video Technol. 28(10), 2679–2689 (2017) 7. Lu, J., Wang, G., Zhou, J.: Simultaneous feature and dictionary learning for image set based face recognition. IEEE Trans. Image Process. 26(8), 4042–4054 (2017) 8. Lu, J., Hu, J., Tan, Y.-P.: Discriminative deep metric learning for face and kinship verification. IEEE Trans. Image Process. 26(9), 4269–4282 (2017) 9. Zhang, L., Liu, J., Zhang, B., Zhang, D., Zhu, C.: Deep cascade model-based face recognition: when deep-layered learning meets small data. IEEE Trans. Image Process. 29, 1016–1029 (2019)
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10. Dong, X., Yan Y., Ouyang, W., Yang, Y.: Style aggregated network for facial landmark detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 379– 388 (2018) 11. Nagarajan, G., Minu, R.I., Jayanthiladevi, A.: Brain computer interface for smart hardware device. Int. J. RF Technol. 10(3–4), 131–139 (2019) 12. Nagarajan, G., Thyagharajan, K.K.: A machine learning technique for semantic search engine. Procedia Eng. 38, 2164–2171 (2012) 13. Nagarajan, G., Minu, R.I.: Wireless soil monitoring sensor for sprinkler irrigation automation system. Wireless Pers. Commun. 98(2), 1835–1851 (2018)
Machine Learning Based Predict Plant Growth and Yield in Greenhouse Environments G. Nagarajan, Pavankumark, and K. Mahesh
Abstract Agriculture is a field that plays a critical role in the development of our country’s economy. The selection of each crop is crucial in the cultivation planning. Many experts studied crop production pace prediction, climate prediction, soil characterization, and crop grouping for agriculture planning using AI techniques. To alter modifications in our Indian economy, many advancements in the agriculture area are required. As of late, the ppl developing these items and such items are a lot of shaky to be delivered because of the abrupt weatherly natural reasons and absence of ground hydro assets. Now and again, ranchers don’t know about the crop which suits their dirt quality, soil supplements, and soil organization. This task means to anticipate winter wheat yields dependent on the spot and climate information. It is propelled by this information science challenge. The explanations for this incorporate climate conditions, obligation, family issues, and incessant change in Indian government standards. In this way, the framework centers around checking the dirt quality to anticipate the crop reasonable for development as per their dirt sort and amplify the crop yield with suggesting fitting manure. The work proposes to help ranchers check the dirt quality relying upon the investigation done dependent on information mining approach. In this way, the framework centers around checking the dirt quality to foresee the crop reasonable for development as indicated by their dirt sort and boost the yield with suggesting fitting compost. Keywords Agriculture · Crop · Prediction · Decision tree
1 Introduction Crop creation might be a convoluted improvement that is impacted by soil and natural condition input boundaries. Agribusiness input boundaries differ from field to field and rancher to rancher. Assortment such information on a greater space might be a debilitating errand. Notwithstanding, the natural condition data gathered in Republic G. Nagarajan (B) · Pavankumark · K. Mahesh Department of CSE, Sathyabama Institute of Science and Technology, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_51
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of India at each 1 sq. m space in various parts of the locale is organized by Indian fleeting department. The enormous such information sets might be utilized for foreseeing their effect on significant yields of that individual area or spot. There are totally unique anticipating procedures created and assessed by the specialists wherever the globe inside the field of agribusiness or related sciences. Various such investigations are as follows: Agricultural scientists in elective nations have shown that attempts of crop yield augmentation through favorable to pesticide state approaches have led to unsafely high substance use. These examinations have announced a connection between substance use and crop yield [1]. Agribusiness is partner exchange area that is profiting intensely from the occasion of finder innovation, information science, and AI (ML) methods inside the most recent years. These improvements get back to fulfill ecological and populace pressures round-looked by our general public, any place reports show a necessity for powerful worldwide agribusiness yield increment to create nourishment for a developing populace on a more sweltering planet. The vast majority of the work tired the area of yield predicting through cubic centimeter utilizes some sort of far-off detecting information over the homestead. Agribusiness looks to broaden and improve the crop yield and hence the nature of the yields to support human existence. Nonetheless, inside the current time, people will in general require a great deal of like a shot appreciated positions. There are less, and less people worried in crop development. Moreover, the consistent increment of human populace makes the development of the yields at the legitimate time and opportune spot even a great deal of crucial, in light of the fact that the environment is dynamic, and accordingly, the movements from conventional climate design are a ton of continuous than before make. Food weakness might be a downside that can’t be stayed away from, and people should construct utilization of most recent inventive advances to make utilization of existing soil, water, and cools to get bigger yields. The data hole between antiquated ways that of developing and new agrarian advancements might be survived if the PC code might be intended to show the intelligent effect of environment factors, especially the effect of greatest occasions (for example warmth, rainfalls, and overabundance water) happening at totally extraordinary developing periods of crops. The temperature change without a doubt influences the local and world food creation, along these lines arranging PC code to show crop forecasts needs new strategy for temperature change examines, circumstances for temperature change transformation, and policymakers which will restrict the overwhelming impacts of climate on food give. Test verification is utilized to shape ecological condition zones that have seen changes in climate and water, the two most hugest factors in ensuring an in crop. The dirt sort will adjust after some time due to climate and vermin, consequently crop the executives should deal with an extravagant amount of data, straightforwardly or by implication related with each other. It will along these lines by thinking about a worked on the real world, to allow a brisk appraisal of the effect of temperature change in agribusiness. Farming ought to adjust to those environment changes, and it will do accordingly by creating models which will in principle enhance the executives rehearses, augment the turns of the new yield to deal with the progressions of soil, novel rearing projects. By boosting the value of anticipating, the occasional environment changes might be discovered and recorded in an
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incredibly convenient way. Later on, by exploitation PC code upheld AI, one will conveniently evaluate the temperature change effect and check achievable circumstances that consolidate found out changes in climatic conditions and water dispersion. Information {processing} is that the way toward dissecting the test information gathered over a sum and changed areas from totally various perspectives, separate patterns, or examples {of information of information on info} and switch them into supportive data for clients. Clients will at that point also reason as well as sum up the connections found out from the gathered information, and normally anticipate what information to anticipate. Machine learning procedures are a piece of information handling and information investigation and spotlight only on trademark connections or examples among monstrous datasets or huge relative datasets. The examples, affiliations, or connections among this information will extra be reawakened into data that is offered to the client as recorded examples and future patterns. This data given by AI will encourage ranchers with crop development by foreseeing probabilities of crop misfortunes or stop misfortunes out and out. As per the insights with this heap number of ranchers and expanding selfdestruction rates, we need to assist ranchers with understanding the significance of earlier crop forecast, to thrive their essential information about soil quality, understanding area astute climate limitations, to accomplish high crop yield through our innovation arrangement. The vast majority of the current framework are equipment based which makes them costly and hard to keep up. Likewise, they need to give exact outcomes. A few frameworks propose crop succession relying upon yield rate and market cost. The framework proposed attempts to defeat these downsides and predicts crops by examining organized information [2]. The project being “Forecast of soil quality utilizing information mining approach” unquestionably centers around rural angles. Being an absolutely programming arrangement, it doesn’t permit support factor to be viewed to such an extent. Likewise, the exactness level would be high when contrasted with equipment-based arrangements, since segments like soil piece, soil type, pH esteem, and climate conditions all come into picture during the forecast interaction.
2 Related Work Virendra Panpatil et al. [1] had accomplished gigantic work for Indian ranchers by making productive yield proposal framework. They created framework utilizing classifier models like KNN, DT, and CNN. The suggested framework may be used to determine the optimal planting season, plant development, and plant harvesting. For instance, they used a unique classifier to get more precision: When a dataset has more variations, the precision of the decision tree decreases, but the exactness of the Naive Bayes algorithm outperforms that of the choice tree. The best favorable position of framework that it can without much of a stretch versatile all things considered/be utilized to test on various yields.
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Mayank et al. [3] have presumed that this paper fabricate extemporized framework for crop yield utilizing administered AI calculations and with objective to give simple to utilize user interface, increment the precision of crop yield forecast, investigate distinctive climatic boundaries. The primary favorable position of proposed framework is precision rate which is more than 75% on the whole the yields and areas chose in the examination. Shweta et al. [4] have extrapolated that this article would examine the many applications of AI in the agricultural fields. Furthermore, using these techniques, it is possible to pick the suitable crop, select land, and select the appropriate season. Naive Bayes and K-nearest neighbor are used in the computations. Precision of execution is used in the computations. Amit Kumar et al. [5] have presumed foreseeing crop arrangements and augmenting yield rates and making advantages to the ranchers. ML applications with farming in foreseeing crop sicknesses, examining crop copies, diverse water system designs. The calculations utilized are fake neural organizations. The serious issue with neural organization is that the proper organization which suits best for the arrangement is difficult to accomplish, and it incorporates experimentation. The second issue with neural organization is the equipment reliance as the calculation incorporates more calculations in reverse. The proposed framework likewise centers around crop determination utilizing natural just as financial variables. The framework likewise utilizes the monetary factor that is the cost of the crop which assumes a significant part on the off chance that if the yields with same yield yet unique yield cost. The proposed framework likewise centers around crop choice utilizing ecological just as financial variables. The framework likewise utilizes the monetary factor that is the cost of the crop which assumes a significant part on the off chance that if the crops with same yield yet unique yield cost. Manjula et al. [6] The k-means algorithm, bunching technique, and inferred affiliation rule mining have all been suggested. The major stumbling block is that the research relies on affiliation rule digging to predict crop production. The issue with affiliation decides mining is that it creates an excessive number of rules sometimes, and the exactness of the expectation decreases. Likewise, the principles will in general fluctuate according to dataset and the outcomes additionally and enormously. The proposed framework mostly centers around the issue of yield expectation of crop which assumes vital part in yield choice as rancher can choose crop with greatest yield. Rakesh Kumar et al. [7] and Nagarajan and Minu [8] ML has been described in detail. Bayesian computations, K-implies algorithm, clustering algorithm, and support vector machine are among the calculations offered. The stumbling block is that there may not be adequate precision and execution referenced in the study based on the stated computations. The article is a research paper that just suggests using the calculations, but there is no proof provided in the document. The crop decision approach used in this study is unusual in that it focuses on plants that may develop according to season. The suggested method settles crop (s) decisions based on predicted yield costs and supported by limits.
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Rajshekhar et al. [2] In this paper, the authors discuss how to utilize information mining techniques to extract data from horticulture records in order to estimate better crop yields for primary yields in India’s major areas. In our research, we discovered that ranchers in India will benefit from knowing the actual expectation of differential suggested crop yields across different locations. Ranchers in India will cultivate a variety of crops from this. From this, Indian ranchers will plant various crops in various district [2].
3 Existing System The financial growth of an agro-based united country is reliant on agribusiness. When a country’s population grows, so does its dependency on agriculture, and the country’s economic development is affected as a result. Crop selection method (CSM) is one of the current frameworks we identified for achieving a net yield pace of crops across the season [9, 10]. We have created an image of CSM to show how it aids ranchers in achieving higher yields. Seasonal crops, year-round crops, short-term estate crops, and long-term manor crops are all examples of crops. A combination of these crops can be chosen in an arrangement based on daily yield rate. Shows crop successions over the course of the season, along with the total yield rate. We have created a CSM graphic to show how it may assist ranchers in achieving higher yields. Seasonal crops, year-round crops, estate crops, and long-term manor crops are all examples of crops. A combination of these crops can be chosen based on daily yield rate. Shows crop successions during the season, along with their total production rate. The notion of the naive-likelihood model will be used to apply the Naive Bayes data mining technique for crop determination. It may be quite straightforward to prepare in a controlled learning environment. Boundary assessment for gullible Bayes uses the approach for guileless Bayes model with putting faith in Bayesian likelihood or any Bayesian methods in a few beneficial applications.
4 Proposed System The framework expects to assist ranchers with developing appropriate crop for better yield creation. To be exact and precise in foreseeing crops, the undertaking break down the supplements present in the dirt and the crop efficiency dependent on the spot. It tends to be accomplished utilizing solo and regulated learning calculations. The choice tree is strategy for choosing best root hubs until we get components of same class. We proceed to divide the tree into sections depending on characteristics. It is a type of controlled learning computation commonly used for grouping difficulties, with flexible features realizing both straight out and nonstop ward factors. This algorithm divides the population into at least two homogenous groups based on the major credits, ensuring that the gatherings are as distinct as possible. The choice tree
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Fig. 1 Overview of the proposed work
calculation will give us best split on various highlights for choice of most appropriate crop among the populace. The element determination approach of decision tree classifier makes it reasonable for forecast of appropriate crops. Dataset will at that point prepared by learning organizations. It thinks about the precision got by various organization learning methods and the most exact outcome will be conveyed to the end client. Alongside this, the end client is furnished with legitimate suggestions about manures reasonable for each specific crop (Fig. 1).
5 Modules Description 5.1 Dataset Preprocessing The informational indexes will be incorporated utilizing Microsoft Excel. The crude information gathered structure public assets will require broad pre-preparing for taking care of missing qualities and other information irregularities. The information should be standardized for grouping or affiliation rule investigation.
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5.2 Data Mining for Crop Prediction Different data mining characterization calculations will be assessed for better execution, for example, neural network, SVM. These information mining arrangement procedures will be utilized for anticipating the proper yield to the rancher as indicated by his field’s climatic and soil data accessible.
5.3 Association Rule Digging for social event, more data association rule mining will be done on the dataset to produce various relationship with crop yield, precipitation, soil type, and so forth. These affiliations will be valuable for choosing the proper crop.
5.4 Crop Diseases and Pesticides Required once the rancher has been proposed with the suitable yield, he needs to choose the costs needed all through the season. The most impacting part here is the yield sicknesses and pesticides required. The ranchers will be given data about the likely yield illnesses in his general vicinity and the pesticides required.
5.5 Data Visualization To build up an overall comprehension of the dataset, an information perception module will be planned. Perception of environment and other information will be completed. A visual examination of precipitation patterns across years and spatially across regions will be utilized for additional investigation of yield expectation.
6 Conclusion The suggested study uses classifier models to offer a useful yield recommendation framework. The framework is adaptable, since it may be used to evaluate a variety of crops. The optimal planting season, plant development, and harvesting of plants may all be found in the yield diagrams, as well as crop forecasts. Machine learning is used in the proposed system. When a dataset has a lot of variety, a choice tree performs well and produces superior results. As a result, the framework will assist ranchers in reducing the problems they face and preventing them from attempting suicide. It will
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go about as a medium to give the ranchers effective data needed to get high return and consequently augment benefits which thus will diminish the self-destruction rates and reduce his challenges.
References 1. Champaneri, M., Chandvidkar, C., Chachpara, D., Rathod, M.: Crop yield prediction using machine learning. Int. J. Sci. Res. (2020) 2. Borate.: Applying data mining techniques to predict annual yield of major crops and recommend planting different crops in different districts in India. IJNR 3(1), 34–37 (2016) 3. Patil, P., Panpatil, V., Kokate, P.S.: Crop prediction system using machine learning algorithms. Int. Res. J. Eng. Technol. (2020) 4. Medar, R., Shweta, Rajpurohit, V.S.: Crop yield prediction using machine learning techniques. In: 5th International Conference for Convergence in Technology (2019) 5. Bhange, T., Shekapure, S., Pawar, K., Choudhari, H.: Survey paper on prediction of crop yield and suitable crop. Int. J. Innovative Res. Sci. Eng. Technol. (2019) 6. Manjula, E., Djodiltachoumy, S.: A modal for prediction of crop yield. Int. J. Comput. Intell. Inform. (2017) 7. Jain, N., Kumar, A., Garud, S., Pradhan, V., Kulkarni, P.: Crop selection method based on various environmental factors using machine learning. Int. Res. J. Eng. Technol. (IRJET) (2017) 8. Nagarajan, G., Minu, R.I.: Wireless soil monitoring sensor for sprinkler irrigation automation system. Wireless Pers. Commun. 98(2), 1835–1851 (2018) 9. Nagarajan, G., Minu, R.I., Jayanthiladevi, A.: Brain computer interface for smart hardware device. Int. J. RF Technol. 10(3–4), 131–139 (2019) 10. Nagarajan, G., Thyagharajan, K.K.: A machine learning technique for semantic search engine. Procedia Eng. 38, 2164–2171 (2012)
Chatbot for Hospitality Service G. Nagarajan, S. Madhu Sudhan Reddy, and Ashok Kumar
Abstract To begin a decent life, medical care is vital. Be that as it may, it is exceptionally hard to the counsel the specialist if any medical problems. The proposed thought is to make a medical care Chatbot utilizing Natural Language Processing procedure it is the piece of Artificial Intelligence that can analyze the sickness and give essential. Chatbots can open up a doorway into the reality where Artificial Intelligence is getting progressed, particularly in the space of medical care, where patients will pass on their issues with a courier and get a discussion without burning through any time. The innovation at the center of the ascent of the Chatbot is natural language processing (“NLP”). The utilization of common language handling (NLP) strategies and their application to creating conversational frameworks for wellbeing analysis expands patients’ admittance to clinical information. The clinical visit bots working relies upon natural language preparing that encourages clients to present their concern about the wellbeing. The user can ask any close to home inquiry identified with medical care through the visit Bot without genuinely accessible to the emergency clinic. By Using Google API for voice-text and text-voice transformation. Inquiry is shipped off ChatBot and finds related solution and show answer on android application. The system’s significant worry behind building up this online stage is breaking down client’s feelings. Keywords Natural language processing · Medical Chatbot · Hospitality
1 Introduction A Chatbot is a framework that can connect with human clients with normal language. Presently a days, medical care is incredibly vital in our life. The present individuals are occupied with their works gathering, work environment works and extra dependent on web. They are not included in regards to their wellbeing. In this way, they stay away from to go in medical clinics for little issues. It may turn into a critical G. Nagarajan (B) · S. Madhu Sudhan Reddy · A. Kumar Department of CSE, Sathyabama Institute of Science and Technology, Chennai, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_52
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disadvantage. Because of the vast amount of data available on the internet, Chatbots can provide precise and useful information based on the client’s needs. Chatbots are used in areas such as customer service, virtual assistance, online training, and online reservations, as well as for casual conversations. The suggested medical Chatbot can converse with clients, providing them with a realistic experience of consulting with a medical professional. Siri, Google Chrome, and Cortana [1] are just a few examples of discourse-based web search tools and colleagues. Characteristic Language Processing (NLP) techniques, such as NLTK for Python, may be used to break down speech, and cunning replies can be discovered by programming a motor to respond in a human-like manner [2]. Some Chatbots act as an innovation demonstration, demonstrating how a suggested technology might assist a bot (a web administration). While a discovery approach is utilized, by controlling the correspondence structure, to and from the web-administration, the web-administration permits a wide range of customers to impart to the worker from any stage. With the advanced period of availability and mechanical development, cell phones have quickly acquired the notoriety and most clients have their cell phones on or close to them for the duration of the day. There is a developing number of emergency clinics, nursing homes, and even private facilities, presently utilize online Chatbots for medical care on their sites. These bots communicate with potential patients visiting the site, encouraging them discover specialists, booking their arrangements, and getting them admittance to the correct treatment. In this paper, nonetheless, the utilization of man-made brainpower in an industry where individuals’ resides could be in question, actually starts misgivings in individuals. It is an intelligent framework settle clients question with respect to medication. So they can get right direction for treatment The point of this paper is to examine the need and utilization of Chatbots in the medical services area. There are a great deal of existing Chatbots for medical care space serving various functionalities. To understand what the patient is experiencing, the framework ought to be cordial to the client, with the goal that the client can convey all the issues looked by him to the framework. The paper targets proposing a Chatbot framework, which is fit for setting up a keen correspondence. Along these lines, we will offer a musing is to make a medical care Chatbot framework utilizing AI that may ID the disease and supply essential data in regards to the ailment prior to counseling a specialist. Which assists the patients with capturing extra in regards to their ailment and improves their wellbeing. Client can do the all-sensibly disease data. The framework application utilizes question and answer convention inside the style of Chatbot to answer client inquiries. The reaction to the inquiry is answered upheld the client question. The critical watchwords are brought from the sentence and answer to those sentences. On the off chance that match is found or indispensable answer are offered or comparative responses are shown can recognizable proof which kind of disease you have upheld client side effects and moreover offers specialist subtleties of unequivocal sickness. It might scale back their medical issues by exploitation this application framework. The framework is created to downsize the tending cost and season of the clients since it isn’t potential for the clients to go to the specialists or experts once progressively required.
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2 Related Work Numerous individuals enduring with dementia holds quite a bit of their conversational capacities as their disease advances. Nonetheless, the disgrace and dissatisfaction that numerous dementia victims experience regularly make normal, ordinary talks with really close relatives testing. Li et al. [2] have examined that numerous individuals with Alzheimer’s illness battle with momentary cognitive decline. Accordingly, the Chatbot intends to distinguish deviations in conversational branches that may show an issue with prompt memory a serious goal-oriented specialized test for a NLPbased framework. The paper gives the data in regards to items which is helpful for purchasers to get what they need precisely. Question Answering (QA) frameworks can be recognized as data getting to frameworks which attempt to reply to common language inquiries by offering responses appropriate answers utilizing trait accessible in normal language strategies. Nuruzzaman et al. [1] This paper provides an overview of the tactics used to design Chatbots, as well as a comparison of various planning approaches from nine carefully selected publications based on the main methodologies utilized. These papers are illustrative of the huge enhancements in Chatbots in the most recent decade, the Chatbots intended for discourse frameworks in the chose examines are, all in all, restricted to specific applications. Universally useful Chatbots need enhancements by planning more exhaustive information bases. Bala and Kumar [3] The cycle of an online talk framework would follow a customer worker approach which gains the sign and streams it to a worker. The info voice is then handled and a reaction is produced. This cycle puts a huge preparing prerequisite on the worker’s processor and memory assets. This impediment is considerably more apparent when countless clients are to be at the same time obliged on the framework. Voice acknowledgment requires a two-section interaction of catching and examination of an information signal. Hoermann et al. [4] examine this evidence for the practicability and adequacy of online one-on-one mental state mediations that utilization text-based simultaneous visit. Simultaneous composed discussions are getting popular as web-based mental state mediations. This audit is predicated on partner examination of individual simultaneous web-based talk advances. A few of the common frameworks have live visits through writings and a couple of constraint like there’s no moment reaction given to the patients they need to go to for expert’s affirmation for an all-encompassing time. Some of the cycles could charge amount to gauge visit or telecom correspondence. Notwithstanding, the trouble of those advances is financially savvy in clinical practice stays an idea for future investigation considers. Mishra et al. [5] According to the company, the Chatbot would act as a virtual specialist, allowing the patient to collaborate with the virtual professional. For the advancement of this Chatbot, natural language processing and example coordinating calculations were used. It is written in the Python programming language. Based on the feedback, it was determined that the Chatbot provided 80% correct answers and 20% incorrect/uncertain answers. This product may be used for teaching and as a virtual specialist for mindfulness and vital contemplation, according to the results of this Chatbot review and analysis. Madhu and Jain [6] suggested a concept
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in which AI can predict illnesses based on symptoms and provide a list of treatments that are available. If an individual’s body is examined on a regular basis, it is possible to detect any potential problems before they do harm to the body. Exam and execution costs, as well as government rules for the proper use of personalized medicine, are two challenges that are not mentioned in the study. Hameedullah and Chowdhry [7] Depicts the development of a clinical understudy Chatbot built on the open source AIML-based Chatter bean. The AIML-powered Chatbot has been modified to convert common language enquiries into serious SQL queries [8]. A total of 97 inquiry tests were collected, and the inquiries were then divided into classes based on the type of inquiry. As indicated by the quantity of inquiries in every class the resultant classifications were positioned. Questions depended on queries, where 47% are of suggested conversation starters.
3 Existing System Ordinarily, users don’t know pretty much all the treatment or manifestations with respect to the specific sickness. For little issue, client need to go by and by to the clinic for registration which is additional tedious [9]. Additionally, dealing with the telephonic requires the grumblings is very rushed. A considerable lot of the current frameworks have live talks through writings and some limit, for example, there is no moment reaction given to the patients, they need to hang tight for specialist’s affirmation for quite a while [10]. A portion of the cycles may charge add up to live visit or communication correspondence. Be that as it may, the issue of these advancements is savvy in clinical practice stays a thought for future examination considers.
3.1 Disadvantages of the Existing System • • • •
We need to continually prepare the specialist. Complex interface. Only for crisis. This Chatbot isn’t give 100% treatment.
4 Proposed System The fundamental target of the plan is to construct the language hole between the client and wellbeing suppliers by giving prompt answers to the questions asked by the client [11]. Some Chatbots are reduced clinical reference books which are helpful for patients, specialists and so forth yet in addition for the individuals who need to
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Fig. 1 Overview of the proposed system
learn something about wellbeing. The client feels that they are joined during the time spent their wellbeing [12]. Patients who feel included, who are connecting through Chatbots with the medical services framework, will remain with the framework, and that is significant for them and the medical care supplier. The old Chatbot are customer correspondences frameworks and their best exertion is an inquiry and answer page on a site [13]. Bot can encourage to get the basic wellbeing related inquiry and expectation of sickness without a human impedance. This framework causes clients to present their objections and inquiries with respect to the wellbeing [14]. Client fulfilments the significant worry for building up this framework. Client can likewise see the accessible specialists for that specific infection (Fig. 1). This framework can be utilized by the different clients to get the directing meetings on the web. The information of the Chatbot put away in the data set as example layout. Bot will give analgesics and food proposals that implies which food you need to take dependent on the illness. The real government assistance of the Chatbot is the encourage individuals by giving legitimate direction with respect to the great and sound living. For the explanation that a considerable lot of individuals don’t have basic consciousness of state of being. A few people live for quite a long time with weakening however they don’t focus on indications essentially in light of the fact that they figure they don’t need a specialist.
4.1 Advantages of the Proposed System • • • •
Reduced holding up occasions Scalable client administrations Timely clinical guidance and improved inward interchanges Reduced expenses and all day, every day accessibility.
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5 Modules Description This framework causes clients to present their objections and questions in regards to the wellbeing. Client fulfilments the significant worry for building up this framework. The genuine Government Chatbot only gives legitimate direction for solid living. The working of the framework is as per the following. Clients need to first enroll with their name, mail, age, and address. Then bot asks about wellbeing related inquiries and furthermore offer answers to clients which are posed inquiries by user giving right data about medication. By utilizing Google dialog flow programming interface, we can fabricate this web application. Ease of use we furnished in the application with different controls utilizing google programming interface and dialog flow programming interface advancements.
5.1 User Login to System Client registers on Chatbot application. At that point ask inquiries in regards to the medical services and clinical subtleties.
5.2 Ask Some Questions You can pose a few inquiries in regards to some medical services. Also, it is identified with text discussion Using Google API.
5.3 Age Based Medicine Dosage Details You can ask clinical measurements related questions to this application in voice and framework gets yield for medication API and stand up and show all information. Get your age from enrollment information and give information identified with your information like age, territory, sex, etc.
5.4 Get Medicine Details on Medicine Name You can get some information about medication related subtleties based on medication names.
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7 Conclusion This clinical Chatbot is valuable for clinical organization or emergency clinics to assist the clients with openly asking clinical related questions by text and it will reply by voice. Framework gets yield for medication and show all medication names. Chatbot is incredible instrument for discussion language among human and machine. The application is created for getting a quick reaction from the bot which infers with none defer it gives the right outcome to the client. It’s finished that the use of Chatbot is easy to use and may be used by somebody who knows about the best approach to sort in their own language. Chatbot gives customized finding upheld indications. We are utilizing NLP since we need to a PC to speak with clients in their terms. So, by utilizing SVM, calculation and illness indications framework can anticipate sickness. Client can find related solution showed r on android application. Also, allude this response for examination.
References 1. Nuruzzaman, M., Hussain, O.K.: A survey on Chatbot implementation in customer service industry through deep neural networks. In: International Conference on eBusiness Engineering (ICEBE). https://doi.org/10.1109/ICEBE.2018.00019 2. Li, Y., David McLeanook.: Conversational bot for pharmacy: a natural language approach. In: IEEE Conference on Open Systems (ICOS) (2018). https://doi.org/10.1109/ICOS.2018.863 2700 3. Bala, K., Kumar, M.: An intelligent web based voice chatbots. Int. Res. J. Eng. Technol. (2019). https://doi.org/10.1109/EURCON.2009.5167660 4. Hoermann, S., McCabe, K.L., Milne, D.N.: Application of synchronous text-based dialogue systems in mental health interventions: systematic review. J. Med. Internet Res. (2017). https:// doi.org/10.2196/jmir.7023
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5. Mishra, S.K., Bharti, D., Mishra, N.: Dr.Vdoc: a medical Chatbot that acts as a virtual doctor. Res. Rev.: J. Med. Sci. Technol. 6(3), 16–20 (2017) 6. Madhu, D., Jain, C.: A novel approach for medical assistance using trained Chatbot. In: Conference: 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT). https://doi.org/10.1109/ICICCT.2017.7975195 7. Hameedullah, B.S., Chowdhry, Z.M.: MedChatBot: an UMLS based Chatbot for medical students. Int. J. Comput. Appl. 55(17), 1–5. https://doi.org/10.5120/8844-2886 8. Mathew, R.B., Varghese, S., Joy, S.E.: Chatbot for disease prediction and treatment recommendation using machine learning. In: International Conference on Trends in Electronics and Informatics (ICOEI). https://doi.org/10.1109/ICOEI.2019.8862707 9. Oh, K., Lee, D., Ko, B., Choi, H.: A Chatbot for psychiatric counseling in mental healthcare service based on emotional dialogue analysis and sentence generation. In: 18th IEEE International Conference on Mobile Data Management (MDM), Daejeon, pp. 371–375 (2017). https:// doi.org/10.1109/MDM.2017.64 10. Du Preez, S.J., Lall, M., Sinha, S.: An intelligent web-based voice chat bot. pp. 386–391 (2009).https://doi.org/10.1109/EURCON.2009.5167660 11. Nagarajan, G., Minu, R.I.: Fuzzy ontology based multi-modal semantic information retrieval. Procedia Comput. Sci. 48, 101–106 (2015) 12. Nagarajan, G., Minu, R.I.: Wireless soil monitoring sensor for sprinkler irrigation automation system. Wireless Pers. Commun. 98(2), 1835–1851 (2018) 13. Nagarajan, G., Thyagharajan, K.K.: A machine learning technique for semantic search engine. Procedia Eng. 38, 2164–2171 (2012) 14. Nagarajan, G., Minu, R.I., Muthukumar, B., Vedanarayanan, V., Sundarsingh, S.D.: Hybrid genetic algorithm for medical image feature extraction and selection. Procedia Comput. Sci. 85, 455–462 (2016)
Experimental Investigations of Process Variables on Wire Electrical Discharge Machining (WEDM) of AISI 52100 Steel P. Santhi Priya, Subramanyam Pavuluri, and Yogesh Madaria
Abstract Nowadays in the advanced manufacturing sector machining and metal cutting play a prominent role, when cost and quality act as two major parameters. WEDM is widely used for advanced machining processes due to its having capability of creating complex shapes with respective machining properties. WEDM process widely used in various industrial applications like die, automobile, sheet metal, aerospace industries. AISI 52100 Steel required non-conventional machining because of its superior material properties. In this paper in detail explain the effect of controlling parameters on the responses in WEDM of AISI 52100 Steel specimens by implemented central composite face design (CCFD) carrying outset of runs espouse for the response of RSM technique. Experiments are done as per CCFD has 25 sets of runs having 16 experiments and 8 axial points and 3 experiments from center point for ensuring analysis. In this connects to attempt made on empirical mathematical model error percentage and which enables optimum process parameters correlated with cutting speed experimental. Keywords Wire EDM · MRR · PP · Cutting speed
1 Introduction WEDM process is most important research investigations progress is going on machining variables like material removal rate and surface roughness by electrothermal process, it is used for prepare intricate shapes like two and three dimensional objects with the help of electrical conductivity material. In this process, brass wire is act as an electrode with the diameter of 0.1–0.3 mm and the job is mounted on the CNC work table. During this process, maintained the gap interface between wire and P. Santhi Priya (B) · S. Pavuluri · Y. Madaria Department of Mechanical Engineering, Malla Reddy Engineering College (A), Maisammaguda, Secunderabad, Telangana 500100, India e-mail: [email protected] Y. Madaria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_53
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Table 1 Literature has been gathered from various researcher Model prepared from the researcher
Findings
Nimonic90 [7]
↑ T on , I p and Discharge Energy, CS, Radial Overcut (ROC), T off, by ↓CS ↑ Ra
Inconel 706 [8]
↓CS ↓ Ra
Inconel 718 [9, 10]
ANOVA, Average recast layer thickness approx. 5.10–8.51 mm
Incoloy 800 [11], AISI D3 Steel [12]
↑ T on→ MRR, Ignition Current but its affects ↓ Surface Quality
Nimonic80A [13]
T on , T off is Sustainable factor for MRR and WWR
Al/SiC-MMC [14] and Stainless steel 304 [15]
T on Sustainable factor for Ra and Recast layer thickness depends on T on and I p
job is constant and the range is 0.025–0.05 mm through computer-controlled. Hence, the wire is continuously passed to the job and finally, intricate shapes are produced by the WEDM machining process under the supervision of a microprocessor. The main goal of the experimental investigation on this paper to present the optimal process parameters that are studied on AISI 52100 Steel. During this experiment design of CCF and means square of regression analysis is used to develop the model for deep learning, here present some important work is concluded. The optimization of the L16 orthogonal array by best selecting suitable values during the machining process [1]. Impact of WEDM process polar correlated with surface roughness and metallurgical structure through optical, SEM, microhardness, the intensity of process energies microcracks is affected on the recasting of surface roughness [2]. Correlated between various modes of Inconel 601 materials and explained by inventory the peak current value and dielectric pressure, leads to vary the volumetric removal rate [3]. The development and the results are based on gray process regression for HS-WEDM. Concluded to increase the machinability of an EDM through a selection of proper properties from geometric, physical, mechanical and electrical [4, 5] and applied of Taguchi method has studied on WC–Co material in die-sinking machining and reported by increases the duty cycle increases the material removal rate [6]. Some of the important research reviews of the literature have been gathered on various types of material and tabulated form is shown in Table 1.
2 Experimental Methodology See Tables 2, 3 and 4. Central composite design (CCD) has been used to conduct experiments for three factors and three levels. The standard CCD plan is chosen, which requires 26 experiments each involving different combinations of process variables. In this work, the
Experimental Investigations of Process Variables … Table 2 Material composition of AISI 52100
Table 3 Physical properties of AISI 52100
Table 4 Mechanical properties of AISI 52100
573
Element
Percentage (%)
Fe
96.5–97.32
Cr
1.33
C
0.957
Mn
0.418
Si
0.234
S
0.019
P
0.018
N
0.256
Mo
0.0981
Material
Electrical conductivity
Thermal conductivity
AISI 52100 steel
4.6 × 106 s/m 46.7 w/(m K)
Density (ton/mm3 )
Melting point 1424 °C
4.38e−9
Specific heat (mJ/ton °C)
0.75e9
Coefficient of thermal expansion (°C−1)
13.5e−6
Modulus of elasticity (MPa)
588,000
Poisson’s ratio
0.17
Room temperature (°C)
27
Electrical conductivity
4.6 × 106 s/m
Thermal conductivity
46.6 w/(m K)
experiments are based on 16 experiments from factorial design, 8 experiments from axial points and 3 experiments from the center point Table shows process parameters with their levels consider for this experimentation. The complete experimental investigation specimens are prepared with the dimensions of 35 × 35 × 12 mm, hydrocarbon oil is mixed with distilled water by the ratio of 1:40 carries on 5 axis WEDM machine (Figs. 1 and 2; Table 5 and 6). Central composite design (CCD) is used to examine the progress from the data to decide the optimal process variables. Table 7 shows the parameters such as surface roughness for the above-given table of input parameters.
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Fig. 1 CNC WEDM machine
Fig. 2 Shape of the workpieces obtained on WEDM for 26 experiments Table 5 Machining variables and their levels T on
T off
WP
WF
Units
µsec
µsec
Kg/cm2
m/min
Level-1
95
50
4
9
Level-2
100
55
6
12
Level-3
105
60
8
15
Experimental Investigations of Process Variables …
575
Table 6 Coded and real values of the variables S. No.
(T on )
(T off )
(w.p)
(w.f)
1
−1 [95]
−1 [50]
−1 [4]
−1 [9]
2
−1 [95]
−1 [50]
−1 [4]
+1 [15]
3
−1 [95]
−1 [50]
+1 [8]
−1 [9]
4
−1 [95]
−1 [50]
+1 [8]
+1 [15]
5
−1 [95]
+1 [60]
−1 [4]
−1 [9]
6
−1 [95]
+1 [60]
−1 [4]
+1 [15]
7
−1 [95]
+1 [60]
+1 [8]
−1 [9]
8
−1 [95]
+1 [60]
+1 [8]
+1 [15]
9
+1 [110]
−1 [50]
−1 [4]
−1 [9]
10
+1 [110]
−1 [50]
−1 [4]
+1 [15]
11
+1 [110]
−1 [50]
+1 [8]
−1 [9]
12
+1 [110]
−1 [50]
+1 [8]
+1 [15]
13
+1 [110]
+1 [60]
−1 [4]
−1 [9]
14
+1 [110]
+1 [60]
−1 [4]
+1 [15]
15
+1 [110]
+1 [60]
+1 [8]
−1 [9]
16
+1 [110]
+1 [60]
+1 [8]
+1 [15]
17
−1 [95]
0 [55]
0 [6]
0 [12]
18
+1 [110]
0 [55]
0 [6]
0 [12]
19
0 [105]
−1 [50]
0 [6]
0 [12]
20
0 [105]
+1 [60]
0 [6]
0 [12]
21
0 [105]
0 [55]
−1 [4]
0 [12]
22
0 [105]
0 [55]
+1 [8]
0 [12]
23
0 [105]
0 [55]
0 [6]
−1 [9]
24
0 [105]
0 [55]
0 [6]
+1 [15]
25
0 [105]
0 [55]
0 [6]
0 [12]
26
0 [105]
0 [55]
0 [6]
0 [12]
3 Result and Discussion See Tables 8 and 9.
3.1 Model for Cutting Speed (CS) The CCDs are exact fitting of 2nd order model and correspondingly CCF data is used for 2nd order model. The above procedure to develop cutting speed model by using CCF design. The inputs of the process are provided in coded form as −1 to +1 for
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Table 7 Values of output parameters
S. No.
CS
SR
1
1.097
0.981
2
1.067
0.991
3
0.960
0.876
4
0.868
0.867
5
1.114
1.120
6
0.922
0.980
7
0.869
0.790
8
0.867
0.680
9
2.117
1.100
10
1.697
0.989
11
1.877
1.230
12
1.644
1.110
13
1.445
1.350
14
1.319
1.020
15
1.310
1.005
16
1.404
0.769
17
0.856
0.978
18
1.680
0.912
19
1.251
0.898
20
1.036
1.001
21
1.161
1.012
22
0.994
1.015
23
1.171
0.897
24
1.187
1.320
25
1.115
1.278
26
1.115
1.121
Table 8 Mean response of cutting speed for factorial portion of CCF design A
B
C
D
Level −1
0.982
0.804
1.015
1.278
Level +1
1.040
0.963
1.002
1.101
Table 9 Mean response of cutting speed for single factor A
B
C
D
Level −1
0.979
0.804
1.005
1.218
Level 0
1.001
1.001
1.001
1.001
Level +1
1.037
0.963
1.020
1.101
Experimental Investigations of Process Variables …
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Table 10 Estimated variables Variable Constant
Coefficient 1.132
Std. err
t-value
Sig
0.033
34.658
0.000
A
0.326
0.020
16.027
0.000
B
−0.127
0.020
−6.235
0.000
C
−0.062
0.020
−3.066
0.007
D
−0.055
0.020
−2.690
0.015
A2
0.152
0.038
3.936
0.001
AB
−0.102
0.022
−4.735
0.000
SPSS Software. To be accurate, the factor in the Coded Scale =
(The real value factor − Middle value in the range) The difference of highest and mid values in the range
The estimated parameters from SPSS software are obtained in shown given Table 10. R2 = 0.951 Cutting speed = 1.132 + 0.326A − 0.127B − 0.062C − 0.055D + 0.152A2 − 0.102(A × B), The cutting speed model developed using CCF design is given by cutting speed = 1.132 + 0.326 pulse duration −0.127 pulse interval −0.062 dielectric presure-0.055 wire feed + 0.152 square of pulse duration −0.102 pulse duration × pulse interval. The value of R2 is 0.951 indicates that 95.1% of the variance in CS is determined by the model. The pulse duration, pulse interval, dielectric pressure has an indicative effect on cutting speed. But the interaction with dielectric constraints and electrode has no prominent effect on cutting speed except the interaction pulse duration with pulse interval and the square term of pulse duration is also included in the model equation which indicates the nonlinearity. From the model cutting speed values are calculated for each experiment and are compared with actual values. The percentage error associated with each experiment is plotted in the Fig. 3. From this graph the experimental error is within the minimum value.
4 Conclusions The observation made to get optimum machining variable of WEDM on AISI 52100 Steel GRA is used. It has been found that WEDM needs to fix at 120 ms, 60 ms, 3 Kgf for T on , T off and wire tension, respectively. Table 11 shows pulse duration < 0.010 which indicates it to be the most impact machining variable of the process. The interplay and the surface plots will be helpful to observe the effect of each processing
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Fig. 3 Percentage error plot for cutting speed
Table 11 Analysis of variance for the model Source
df
SS
MS
F
Sig
Model
6
2.616
0.436
58.383
0.000
Error
18
0.134
0.007
Total
24
2.751
variables over the evaluation of GFRG values. At least, a probatory test with obtained variables mix shows the progress of 1.5% of the acquired responses. A study carried has been made to note the impact of process variable parameters namely, T on , T off , WP and WF on CS in wire EDM. The impact of individual parameters pulse duration, pulse interval, dielectric pressure, wire feed, and interactions between the variable parameters has been identified on the cutting speed. The CCF gives better understanding of CS as a better replacement of other models. 1.
2.
The significance of individual parameters A, B, C, and D and interactions A with B has been identified on the cutting speed of A2 also included in the model which indicates the nonlinear behavior. The parameter A is observed as a more dominating variable for cutting speed followed by B, C and D. As A increases the CS increases from experiment run sl. 9–16.
References 1. Abinesh, P., Varatharajan, K., Satheesh Kumar, G.: Optimization of process parameters influencing MRR, surface roughness and electrode wear during machining of titanium alloys by WEDM. Int. J. Eng. Res. Gen. Sci. 2(4), 719–729 (2014). ISSN 2091-2730 2. Kumar, K., Ravikumar, R.: Modeling and optimization of wire EDM process. Int. J. Mod. Eng. Res. (IJMER) 3(3), 1645–1648 (2013). ISSN 2249-6645
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3. MalleswaraRaouthor, S.S., ParameswaraRao, C.V.S.: Optimization and influence of process parameters for machining with WEDM. Int. J. Innovative Res. Sci. Eng. Technol. 3(1), 8667– 8672 (2014). ISSN 2319-8753 4. Goswami, A., Kumar, J.: Int. J. Eng. Sci. Technol. 17’(2215–0986), 236–246 (2014) 5. Maan, V., Chaudhary, A: Optimization of wire electric discharge machining process of D-2 steel using response surface methodology. Int. J. Eng. Res. Appl. (IJERA) 3(3), 206–216 (2013). ISSN 2248-9622 6. Chalisgaonkar, R., Kumar, J.: Multi response optimization and modeling of trim cut wire EDM operation of commercially pure titanium (CPTi) considering multiple user’s preferences. Int. J. Eng. Sci. Technol. xxx, 1–10 (2014) 7. Kumar, V., Jangr, K., Kumar, V.: Effect of WEDM parameters on machinability of Nimonic-90. In: Proceedings of the National Conference on Trends and Advances in Mechanical Engineering (TAME), YMCA University of Science and Technology (2012) 8. Sharma, P., Chakradhar, D., Narendranath, S.: Evaluation of WEDM performance characteristics of Inconel 706 for turbine disk application. Mater. Des. 88, 558–566 (2015) 9. Newton, T.R.: Investigation of the effect of WEDM parameters on the formation of recast layer in wire-EDM of Inconel 718. Georgia Institute of Technology (2008) 10. Aggarwal, V., Khangura, S.S., Garg, R.: Parametric modeling and optimization for wire electrical discharge machining of Inconel 718 using response surface methodology. Int. J. Adv. Manuf. Technol. 79(1–4), 31–47 (2015) 11. Va, M.K., Ab, S.B., Rc, V., Md, R.: Optimization of the WEDM parameters on machining Incoloy 800 superalloy with multiple quality characteristics. Optimization 2(6), 1538–1547 (2010) 12. Ramakrishnan, R., Karunamoorthy, L.: Multi response optimization of wire EDM operations using robust design of experiments. Int. J. Adv. Manuf. Technol. 29(1–2), 105–112 (2006) 13. Lodhi, B.K., Agarwal, S.: Optimization of machining parameters in WEDM of AISI D3 Steel using the Taguchi Technique. Procedia CIRP 14, 194–199 (2014) 14. Goswami, A., Kumar, J.: Investigation of surface integrity, material removal rate and wire wear ratio for WEDM of Nimonic 80A alloy using GRA and Taguchi method. Eng. Sci. Technol. Int. J. 17(4), 173–184 (2014) 15. Kumar, H., Manna, A., Kumar, R.: Modeling of WEDM parameters for surface roughness and analysis of machined surface in WEDM of Al/SiC-MMC. Trans. Indian Inst. Met. 71(1), 231–244 (2018) 16. Durairaj, M., Sudharsun, D., Swamynathan, N.: Analysis of WEDM parameters in wire EDM with stainless steel using single objective Taguchi method and multi-objective grey relational grade. Procedia Eng. 64, 868–877 (2013) 17. Juhr, H., Schulze, H.-P., Wollenberg, G., Kunanz, K.: Improved cemented carbide properties after wire EDM by pulse shaping. J. Mater. Process. Technol. 149, 178–183 (2004) 18. Lauwers, B., Brans, K., Liu, W., Vleugels, J., Vanmeensel, K.: Influence of the type and grain size of electro-conductive phase on the Wire-EDM performance of ZrO2 ceramic composites. CIRP Annals-Manuf. Technol. 57, 191–194 (2008) 19. Hewidy, M.S., EL-Taweel, T.A., EL-Safty, M.F.: Modeling the machining parameters of wire electric discharge machining of Inconel 601 using RSM. J. Mater. Proc. Technol. 169, 328–336 (2005) 20. Aspinwall, D.K., Soo, S.L., Berrisford, A.E.: Workpiece surface roughness and integrity after WEDM of Ti-6Al-4V and Inconel 718 using minimum damage generator technology. CIRP Annals Manuf. Technol. 57, 187–190 (2008) 21. Ramakrishnan, R., Karunamoorthy, L.: Modeling and multi response optimization of Inconel 718 on machining of CNC WEDM process. J. Mater. Process. Technol. 207, 343–349 (2008) 22. Patil, P.A., Waghmare, C.A.: Optimization of process parameters in wire-EDM using response surface methodology. In: Proceedings of 10th IRF International Conference, Pune, India, pp. 110–115 (2014). ISBN 978-93-84209-23-0 23. Shandilya, P., Jain, P.K., Jain, N.K.: RSM and ANN modeling approaches for predicting average cutting speed during WEDM of SiC_p/6061 Al MMC. Procedia Eng. 64, 767–774 (2013)
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24. Sharma, A., Garg, M.P., Goyal, K.K.: Prediction of optimal conditions for WEDM of Al 6063/ ZrSiO_(4(p)) MMC. Procedia Mater. Sci. 6, 1024–1033 (2014) 25. Tilekar, S., Das, S.S., Patowari, P.K.: Parameter optimization of wire EDM on aluminum and mild steel by using Taguchi method. Procedia Mater. Sci. 5, 2577–2584 (2014) 26. Duan, C., Kong, W., Hao, Q., Zhou, F.: Modeling of white layer thickness in high speed machining of hardened steel based on phase transformation mechanism. Int. J. Adv. Manuf. Technol. 69, 59–70 (2013)
Subsea Manifold with Mudmat Structure Design Evaluation Based on Performance of Stress Analysis Tarang T. Lakhani and Vijay R. Panchal
Abstract The paper presents optimum configuration and behavior of subsea manifold structure during lifting and installation conditions. The subsea manifold structure analyzed using Bentley SACS Software v14.1. The member and plate unity check, principal stress, bending stress, von Mises, and displacement are evaluated and critical locations of the manifold structure identified. The result of the study shows that the lifting analysis found to be critical analysis for members and installation (case 2) analysis found to be critical analysis for plates. Also, the critical location of members is identified based on the performance of stress analysis as a column during lifting and installation conditions. Keywords Manifold · Subsea · Offshore · SACS · Lifting · Installation
1 Introduction Manifolds (in subsea systems) have been used in the production of oil and gas to simplify the system, minimize pipeline usage, and optimize the flow of hydrocarbon fluids. The set of pipes and/or valves for mixing, transmitting, regulating, and frequently monitoring the output. Different kinds of manifolds, such as pipelines, are used for large structures such as the subsea process system (PLEM/PLET) with piles or skirts. Usually, most subsea manifolds are intended for the following unique functions such as production and/or testing, monitoring of the pipelines’ individual hydrocarbon flow, injection of steam, pumping gas into the risers, for water injection, supply the valve with water prior to the shut-off valve to increase the production of oil, for service and efficiency management, etc. [1, 2]. T. T. Lakhani · V. R. Panchal (B) M. S. Patel Department of Civil Engineering, Chandubhai S. Patel Institute of Technology, Charotar University of Science and Technology, Changa 388421, India e-mail: [email protected] T. T. Lakhani e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_54
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Fig. 1 Geometry of manifold structure with mudmat foundation
The subsea technology used in the manufacture of hydrocarbons at sea is a very special operating system that imposes complex engineering requirements. Hence, a need to develop a more efficient and smarter way to withstand the permanent loads and reduce the various structural responses such as deflections, utilization of elements is highly expected [3, 4]. The purpose of the production manifold frame structure is to support the piping kit, instrumentation, well jumpers, flowline jumpers, and controls. Additionally, the dropped object panels protect the piping kit and instrumentation. It consists of the lower deck, the upper deck, columns, and bracings. It has manifold overall dimensions of 30 long, 21.2 wide, and 10.1 height and mudmat overall dimensions of 44 long, 44 wide, and 3 height. It is composed of wide flanged beams, pipes, and plates. The manifold is supported on a mudmat foundation as shown in Fig. 1.
2 Design Criteria In general, all structural components are designed to meet AISC, manual of steel construction allowable stress design’s allowable stresses. All structural beams, pipes, and plates for the manifold assembly are of 50 ksi yield strength. All allowable stresses are considered as per section 6.1 of API RP 2A WSD 22nd Edition [5, 6].
3 Manifold Structural Analysis Analysis of the manifold structure was performed using structural analysis and computational software (SACS) v14.1 [4, 7]. Figure 1 shows the SACS model consists of members and plate elements. The purpose of the lifting analysis is to ensure that the frame structure of the manifold and mudmat are adequate to withstand the forces to which it will be subjected during the lifting process. The lift hook point is located directly above the COG of the model. The lifting slings are at minimum angle of 60° with the horizontal plane. The slings are modeled such that they do not transfer end moments or compression loads.
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The lift hook point is located directly above the COG of the model. The lifting slings are at minimum angle of 60° with the horizontal plane. The slings are modeled such that they do not transfer end moments or compression loads, i.e., the end moments of the sling members are released. The lift point is fixed against both translation and rotation, i.e., 111,111. The piping system weight is considered as 47.21 kips is applied at the respective hubs and header supports location on the manifold structure. The manifold structural dead weight is combined with the piping system weight for the lifting analysis. The assumed total lifted weight of the structure is 178.5 kips. The purpose of deployment analysis is to ensure that the frame structure of manifold and mudmat is adequate to withstand the forces to which it will be subjected, during deployment through splash zone. Wave height (Hs) and peak period (Tp) have been considered as 1.5 m and 8.5 s, respectively. Deployment speed of 0.5 m/s is considered in the installation case 1 analysis. The static analysis is performed to evaluate stresses for the installation analysis using total of 0.1847 kips slamming force applied in upward direction per mudmat node. Deployment speed of 0.8 m/s is considered in the installation case 2 analysis. The static analysis is performed to evaluate stresses for the installation analysis using total of 0.2490 kips slamming force.
4 Result and Discussion Analysis has been carried out according to API RP 2A to check the structural integrity of the manifold with mudmat during lifting, installation case 1, and installation case 2 conditions. The results of member and plate unity check, principal stress, bending stress, von Mises, and displacement are shown in Table 1 are tabulated and graphically represented as under. The following are observations made after the comparison of the study: Table 1 Comparative performance of manifold under various conditions [8–10]
Result type
Lifting
Installation Case 1
Case 2
Member UC
0.78
0.32
0.43
Plate UC
0.19
0.46
0.62
5.53
−7.25
−9.77
Bending Y (ksi)
−12.18
−2.73
−3.67
Bending Z (ksi)
−18.59
−1.22
−1.65
Principal stress (ksi)
4.97
−11.25
−15.17
Von Mises (ksi)
4.91
10.68
14.39
Axial stress (ksi)
Displacement (Z) (in)
−0.028
0.465
0.627
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Fig. 2 Manifold member unity check under lifting, installation case 1, and installation case 2
• Max. member unity check ascertained as 0.78 during lifting analysis. • Max. plate unity check ascertained as 0.62 throughout installation (case 2) analysis. • Max. axial stress (compressive) determined as 9.77 ksi throughout installation (case 2) analysis. • Max. axial stress (tensile) determined as 5.53 ksi throughout lifting analysis. • Max. bending stress (compressive) in Y-direction and Z-direction square measure determined as 12.18 and 18.59 ksi severally throughout lifting analysis. • During lifting study, maximum principal stress (Tension) is observed as 4.97 ksi. • The maximum principal stress (compressive) during installation study (case 2) is 15.17 ksi. • Maximum von Mises stress was observed during the analysis of the installation (case 2) at 14.39 ksi. • Maximum upward displacement (Z) is found as 0.627 in. at throughout of installation (case 2) analysis. • Max. downward displacement (Z) is found as 0.028 in. during lifting analysis (Figs. 2, 3, 4, 5, 6, 7, 8 and 9).
5 Conclusion Following conclusions are drawn after comparison of the results and discussion:
Subsea Manifold with Mudmat Structure Design Evaluation Based …
Fig. 3 Manifold plate unity check under lifting, installation case 1, and installation case 2
Fig. 4 Manifold Y bending stress under lifting, installation case 1, and installation case 2
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Fig. 5 Manifold Z bending stress under lifting, installation case 1, and installation case 2
Fig. 6 Manifold Max. principal stress under lifting, installation case 1, and installation case 2
• The increase in max. member unity check is observed to be 59 and 45% in lifting over installation case 1 and installation case 2. • The reduction in plate unity check is observed to be 59 and 69% in lifting over installation case 1 and installation case 2.
Subsea Manifold with Mudmat Structure Design Evaluation Based …
Fig. 7 Manifold von Mises stress under lifting, installation case 1, and installation case 2
Fig. 8 Manifold axial stress under lifting, installation case 1, and installation case 2
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Fig. 9 Manifold displacement under lifting, installation case 1, and installation case 2
• Relative to the lifting analysis, the reduction in axial stress (compressive) is found 31% for installation case 1 and 77% for installation case 2. • The max. bending stress reduction in Y-direction is observed to be 78% in installation case 1 and 70% in installation case 2 relative to lifting analysis. • The max. bending stress reduction in Z-direction is observed to be 93% in installation case 1 and 91% in installation case 2 relative to lifting analysis. • Relative to the lifting analysis, the reduction in principal stress (compressive) is found 56% for installation case 1 and 67% for installation case 2. • The reduction in max. von Mises stress is observed to be 54% and 66% in lifting over installation case 1 and installation case 2. • The max. displacement reduction in Z-direction is observed to be 94 and 96% in lifting over installation case 1 and installation case 2. Relative to the installation case 2 analysis, the reduction in member UC, plate UC, axial stress, bending stress, principal stress, von Mises stress and displacement are found 26% for installation case 1. So, behavior of subsea manifold structure can be effectively analyzed during lifting, installation case 1 and installation case 2 condition, and optimum configuration can be made for design of manifold with mudmat based on the performance of stress analysis.
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References 1. Cao, C., Pan, Y., Jiang, T., Li, B., Li, W.: Structural integrity analysis for final design of ITER gas fueling manifold. Fusion Eng. Des. 133, 157–162 (2018) 2. Sharma, K.: PLEMs, PLETs, and manifolds. In: Encyclopedia of Maritime and Offshore Engineering. The Jukes Group, TX, USA (2018) 3. Shi, Q.: Some aspects of pipeline end manifold design in deepwater field development. J. Offshore Eng. Technol. 1, 59–70 (2017) 4. Chakrabarti, S.: Handbook of Offshore Engineering. Elsevier Ltd. (2005) 5. American Institute of Steel Construction: Steel Construction Manual (14th edn.) (2011) 6. API-RP-2A WSD: Recommended Practice for Planning, Designing and Construction Fixed Offshore Platforms, Working Stress Design (22nd edn.) (2005) 7. Robinson, R., Kenny J P Inc., Grass J.: BP: design challenges of a 48-inch pipeline subsea manifold. In: Offshore Technology Conference, Houston, Texas, U.S.A., OTC 15275 (2003) 8. Paula, M.T.R., Labanca, E.L., Paulo, C.A.S., Petroleo Brasileiro, S.A.: Subsea manifolds design based on life cycle cost. In: International Conference on Offshore Technology, Houston, Texas, U.S.A., OTC 12942 (2001) 9. Mason, P.G.T., Aker Omega, Upchurch, J.L., Phillips: Seastar-subsea cluster manifold system design and installation. In: International Conference on Offshore Technology, Houston, Texas, U.S.A., OTC 8130 (1996) 10. Paulo, C.A.S., Petrobras, S.A.: Subsea manifolds optimization—the combination of mature and new technologies. In: Offshore Technology Conference, Houston, Texas, U.S.A., OTC 8238 (1996)
Fabrication and Experimental Investigation of Aluminum LM 25/h-BN/B4 C Hybrid Composites for Automobile Applications Katla Rajendar and K. Eswaraiah
Abstract This paper investigates the effect of hexagonal boron nitride (h-BN) and boron carbide (B4 C) reinforcement percentages on mechanical properties of LM 25(A 356) aluminum-based hybrid composites using mixture design of experiments (MDOE) concept. As per mixture design, total ten different composite samples were prepared by stir casting procedure and investigated the micro structural and mechanical properties like hardness, yield stress, ultimate tensile strength (UTS) and percentage of elongation (% of E). Result shown that the reinforcement was distributed properly and good influence on the mechanical properties and these composites are good replacement for automobile components like cylinder head, cylinder blocks, wheels and other automobile components. Keywords Metal matrix hybrid composites · Mixture design of experiments · Hardness · Microstructure
1 Introduction Aluminum and its alloys play an important role in the modern society and they are used almost in every field because of its attractive physical, mechanical and corrosion resistance properties. The important asset of aluminum for manufacturers is that it can easily be cast and fabricated into any form [1]. For automakers, aluminum is the material of choice for mass market vehicles and luxury cars for lowering the weight, fuel efficiency and air pollution [2, 3]. Most of the vehicle parts such as wheels, cylinder blocks, radiators, bumpers, suspension parts, transmission system and other K. Rajendar (B) Department of Mechanical Engineering, Kakatiya University, Warangal, Telangana, India e-mail: [email protected] Department of Mechanical Engineering, Malla Reddy Engineering College, Hyderabad, Telangana, India K. Eswaraiah Department of Mechanical Engineering, KITS Warangal, Warangal, Telangana, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_55
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body parts are being manufactured with the use of aluminum alloys. The use of aluminum also reduces CO2 emission and improves fuel economy and enhances the performance of the vehicle. On an average 90% of the aluminum was recyclable at the end of a vehicle’s life [4]. In cast aluminum alloys, the highest volume usages are in 3xx.x group alloys and it has major applications in all the sectors. In this alloy silicon is the major alloying element. Along with silicon; another alloying element is magnesium and/or copper [5, 6]. Among all the aluminum cast alloys, AL LM 25/A 356 (Al–Si–Mg alloy) alloy having major applications, because of good mechanical properties and also can cast a complicated shapes and dimensions. Its applications are in transportation including engine cylinder blocks, heads and wheels [2, 7]. Aluminum-based composites (AL MMCs) are the most commonly used MMCs due to its distinct properties like improved stiffness, greater strength, low density, low thermal expansion, better temperature properties and high resistance to wear [8]. In aluminum-based MMCs, the major constituent element is aluminum or aluminum alloy and carbon, silicon carbide, alumina, boron, aluminum nitride, boron carbide and boron nitride are some of the reinforcing materials [9]. Composite materials have distinguished physical properties which are not being found in traditional base metals and also in alloys [10]. Properties of the composites can be tailored by properly mixing the reinforcing components in the matrix phase effectively. So many researchers consider the cast alloys as base metal for preparing the ‘metal matrix composites (MMCs)’ in their analysis, Radhika and Sai Charan [11] fabricated and investigated the Al LM 25 and TiC composites by stir casting procedure. Nwobi-Okoye and Ochieze [12] investigated and simulated the A 356 aluminum alloy which reinforced with particles of cow horn. Kumar et al. [13] prepared LM25 Al alloy and its composites by reinforcing with 15% weight percentages of tungsten carbide (WC), titanium diboride (TiB2 ), and zirconium oxide (ZrO2 ) individually using squeeze casting method. Stojanovic et al. [14] fabricated aluminum A356 based composites and investigated the tribological properties. Hegde et al. [15] prepared MMCs with LM 25 as base and WC as reinforcement with weight percentages of 0, 3, 6 and 9% using Stir Casting Method and hardness, tensile strength and wear tests were conducted. Surendran et al. [16] fabricated LM 25 aluminum alloy as base metal and reinforced with different percentages of nanoalumina (Al2 O3 ) by using stir casting procedure. Elango et al. [17] used the stir casting route for preparing the hybrid matrix composites with LM 25 as base metal with 7.5% of SiC and 2.5%TiO2 and LM25 as base with 7.5% TiO2 and 2.5% SiC and analyze the wear characteristics. Suresh et al. [18] investigated LM 25 hybrid MMC reinforced with iron oxide (Fe2 O3 ), boron carbide (B4 C) and graphite which is manufactured by stir casting. Ravi et al. [19] has evaluated the mechanical properties of mischmetal addition to LM 25(AI–7Si–0.3Mg) alloy and results showed that there is a significant improvement in the properties. Vijaya Ramnath et al. [20] investigated the mechanical characteristics of aluminum LM 25 alloy reinforced with boron carbide (B4 C) and alumina (Al2 O3 ), and these results have shown that there is a good influence on the mechanical properties.
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Table 1 Composition of aluminum LM 25 alloy Element name
Si
Mg
Zn
Fe
Mn
Al
Weight %
7.2
0.39
0.12
0.11
0.3
Balance
From the literature, it is observed that, the proportion of compositions of the mixtures for aluminum-based hybrid composites was not reported by the any author and very limited work has been reported on boron carbide (B4 C) and hexagonal boron nitride (h-BN) as reinforcements for aluminum alloys. The objective of this work is to prepare an aluminum LM 25 based hybrid composites reinforced with different proportions of B4 C and h-BN by using stir casting method. For the selection of different compositional combinations, mixture design concept was adopted and micro structural and mechanical properties test were conducted.
2 Material and Manufacturing of Composites 2.1 Material Aluminum LM 25 alloy was used as the matrix metal because of its excellent mechanical properties and applications in the automobile sector for producing cylinder heads, cylinder blocks, wheels, other engine and body part castings. The composition of the LM 25 alloy was given in Table 1. The reinforcements were used as hexagonal boron nitride and boron carbide and with compositional volume percentages of 0–10 as per the design plan. Hexagonal boron nitride is used to prepare the composites because of its excellent properties like low density, and self-lubricating properties [21]. Boron carbide powder was mainly used for its exceptionally high hardness and low density and applied for wear resistance and high hardness applications without increasing any density [1].
2.2 Manufacturing Method for Preparing Al-Based Hybrid Composite Samples The aluminum LM 25 matrix composite samples were prepared using stir casting method, which was one of the casting routes that distributed the reinforcing particles properly throughout the matrix phase. The ingots of aluminum LM 25 alloy were made into small pieces and these pieces were kept in the graphite crucible, and this crucible was placed into an induction type electric furnace shown in Fig. 1, and heated the pieces to an above-melting temperature of 760 °C. B4 C and h-BN powder particles were preheated up to a temperature of 250 °C and these preheated
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Fig. 1 Stir casting setup
reinforcement powders were added to the melt vortex which has created by a rotating stirrer. The stirring process was done at 250 rpm for 10 min for distribution of the reinforcement particles uniformly in the aluminum LM 25 liquid metal. The melt was poured into the specially designed die and allowed it for solidification and keep it to attain the room temperature, once the sample was solidified and attain the room temperature cast was removed from the die. The simplex design plot in amounts was shown in Fig. 2. Fig. 2 Simplex mixture design plot
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Table 2 Composition of the composites S. No.
Composite sample No.
LM25
h-BN
B4 C
Total percentage
1
C1
100
0
0
100
2
C2
95
0
5
100
3
C3
90
0
10
100
4
C4
95
5
0
100
5
C5
90
10
0
100
6
C6
90
5
5
100
7
C7
94
3
3
100
8
C8
96
2
2
100
9
C9
92
6
2
100
10
C10
92
2
6
100
3 Preparation of Samples Based on the simple lattices mixture design, total ten different combinations of specimens were prepared for analysis by using stir casting method, aluminum LM 25 was chosen in the range of 90–100 volume percentage h-BN and B4 C were chosen in the range of 0–10 volume percentage. Table 2 shows the different compositional combination of the composites.
4 Testing of Composites Analysis of the effect of reinforcement proportions on micro structural and mechanical properties like hardness, yield stress, percentage elongation (% E) and ultimate tensile strength (UTS) were chosen. Micro structural study was investigated for analyzes the distribution of the reinforcement on base alloy and for hardness, Brinell hardness number (BHN) was used to analyze the hardness value. To analyze the effect of reinforcement percentage on yield stress, UTS and percentage of elongation tensile test was conducted on the specimens using computerized UTM with the range of 0–100 ton as per ASTM E8/E8M–08 standard.
5 Results and Discussion Table 3 shows the results obtained from the composite samples for hardness, yield stress, ultimate tensile strength and percentage of elongation.
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Table 3 Compositions and measured responses of the 10 composite samples for mechanical properties S. No.
Composite sample name
AL LM 25
h-BN
B4 C
Hardness (BHN)
Yield stress (N/mm2 )
UTS (N/mm2 )
%E
1
C1
100
0
0
56.63
131.18
133.44
3.80
2
C2
95
0
5
68.13
136.68
153.03
3.76
3
C3
90
0
10
78.80
139.69
151.35
1.74
4
C4
95
5
0
57.13
140.05
158.18
3.06
5
C5
90
10
0
68.40
146.79
148.99
2.41
6
C6
90
5
5
76.83
131.51
134.21
2.60
7
C7
94
3
3
77.10
144.25
168.24
3.65
8
C8
96
2
2
74.12
148.83
159.80
3.82
9
C9
92
6
2
72.03
134.85
158.40
2.50
10
C10
92
2
6
76.03
135.22
160.13
3.30
5.1 Micro Structural Analysis Microstructure analysis is used to analyze the reinforcement particles distribution in the base metal. Figure 31–10 shows the microstructure of Al matrix reinforced with h-BN and B4 C of the different compositional samples of C1 to C10, respectively.
Fig. 3 Microstructures of the hybrid composite samples
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Fig. 4 Hardness of the samples
From this analysis, it was clear that mixing was done properly and distribution of reinforcements was uniformly in the matrix phase. It was observed that the reinforcement percentage low in the matrix the particle distribution was uniform and while increases the percentages there is a formation of pores and voids due to high variation of densities and melting temperatures of the alloy and reinforcements.
5.2 Hardness Test Analysis In all the cases BHN was more for composites compared to base alloy due to ceramic hard particles reinforced in the alloy shown in Table 3 and Fig. 4. As B4 C and hBN percentage increases in the single mixture, the hardness value was increased. Maximum hardness number was 78.8 BHN for the composite sample C3 of 90% LM 25 and 10% B4 C, and C4 having less hardness of 57.13 BHN, in hybrid reinforcement C7 having more hardness compared to other hybrid reinforcement and C9 having least hardness.
5.3 Yield Test Analysis In all the cases, yield stress was more for composites compared to base alloy due to high strength particles reinforced in the alloy which were thoroughly mixed up with the alloy and improved the yield stress of the composite. In composite C6 the yield stress was low, compared to the other composites because of the porosities formed
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Fig. 5 Yield stress of the samples
in the composites. The yield stress for different composite mixtures were shown in Table 3 and Fig. 5. As B4 C and h-BN percentage increases in the single mixture, the yield stress was increased. Maximum yield stress was 148.83 N/mm2 for the composite sample C8 of 96% LM 25 and 2% B4 C and 2% h-BN, and C6 having less yield stress of 131.51 N/mm2 .
5.4 Ultimate Tensile Strength Analysis In all the cases, UTS was more for composites compared to base alloy due to high strength and hard particles reinforced in the alloy which were obstacles for the crack propagation. The UTS for all the samples were shown in Table 3 and Fig. 6. As B4 C and h-BN percentage increases in the single mixture, the UTS was increased and then decreased. In all the cases of hybrid reinforcement UTS was more compared to single reinforcement except C5, because of hybridization the strength was increased but in C5 formation of porosity causes in decreasing the UTS. Maximum UTS was 168.24 N/mm2 for the composite sample C7 of 94% LM 25 and 3% B4 C, 3% h-BN, and C6 having less UTS of 134.21 N/mm2 .
5.5 Percentage of Elongation (% OF E) Analysis The percentage of elongation was decreased as the B4 C and h-BN increased from zero percentage to ten percentages because of the brittle ceramic particles insertion in
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Fig. 6 UTS of the samples
the alloy. The % OF E for all the samples was shown in Table 3 and Fig. 7. Maximum % OF E was 3.82 for the composite sample C8 of 96% LM 25,2% B4 C and 2% of
Fig. 7 % of E for the samples
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h-BN, and minimum OF 1.75 for the composite sample C3 with 90% LM 25,10% B4 C.
6 Conclusions Aluminum LM25 hybrid composites were successfully fabricated via stir casting and boron carbide (B4 C) and hexagonal boron nitride (h-BN) were chosen as the reinforcing material to improve the properties. Effect of reinforcement on each property was analyzed, mixture design of experiment (MDOE) was successfully implemented to analyze the mechanical properties, the following were the conclusions drawn from the study. • Hardness of the composites analyzed by Brinell hardness number (BHN), in all the samples hardness was more compared to the base alloy because of the hard ceramic particles mixing to the alloy, maximum hardness number was 78.8 BHN for the composite sample C3 of 90% LM 25 and 10% B4 C, and C4 having less hardness of 57.13 BHN. • All the composites have more yield stress compared to base alloy. Maximum yield stress was 148.83 N/mm2 for the composite sample C8 of 96% LM 25 and 2% B4 C and 2% h-BN, and C4 having less hardness of 131.509 N/mm2 . • All the composites having more UTS compared to base alloy, maximum UTS was 168.24 N/mm2 for the composite sample C7 of 94% LM 25 and 3% B4 C, 3% h-BN and C6 having less UTS of 134.21 N/mm2 . • Maximum % OF E was 3.82 for the composite sample C8 of 96% LM 25,2% B4 C and 2% of h-BN, and minimum OF 1.75 for the composite sample C3 with 90% LM 25,10% B4 C. These prepared composites may be good replacement for the LM 25 alloys in automobile and other applications and in automobile, these composites may be suitable for engine cylinder blocks, heads and wheels.
References 1. Davis, J.R.: Alloying: Understanding the Basics, pp. 351–416. ASM International (2001). www.asminternational.org, https://doi.org/10.1361/autb2001p351 2. Miller, W.S., Zhuang, L., Bottema, J., Wittebrood, A.J., De Smet, P., Haszler, A., Vieregge, A.: Recent development in aluminium alloys for the automotive industry. Mater. Sci. Eng. A. 280, 37–49 (2000) 3. Hirsch, J.: Aluminum alloys for automotive application. Mater. Sci. Forum 242, 33–50 (1997).https://doi.org/10.4028/www.scientific.net/MSF.242.33 4. Jesse wili paegle filho: Opportunities for Aluminum Components in Automotive Applications. JWP Engineering & Consultancy, Brazil 5. ASM hand book: Properties and Selection: Non Ferrous Alloys and Special-Purpose Materials, vol. 2. ASM International-The Materials Information Company
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6. Kaufman, J.G., Rooy, E.L.: Aluminum Alloy Castings: Properties, Processes, and Applications. ASM International (2004). www.asminternational.org 7. British standard, specification for aluminum and aluminum alloy ingots and castings for general engineering purposes. BS 1490:1988 Incorporating Amendment Nos. 1 & 2, UDC 669.71–412– 14:621 8. Haghshenas, M.: University of Waterloo, Waterloo, ON, Canada, Metal-Matrix Composites. Elsevier Inc., Amsterdam (2016) 9. ASM hand book: Composites, vol. 21. ASM International-The Materials Information Company 10. Kishawy, H.A., Hosseini, A.: Machining Difficult-to-Cut Materials, Materials Forming, Machining and Tribology. Springer International Publishing (2019). https://doi.org/10.1007/ 978-3-319-95966-5_5 11. Radhika, N., Sai Charan, K.: Experimental analysis on three body abrasive wear behaviour of stir cast Al LM 25/TiC metal matrix composite. Trans. Indian Inst. Met. https://doi.org/10. 1007/s12666-017-1061-6 12. Nwobi-Okoye, C.C., Ochieze, B.Q.: Age hardening process modeling and optimization of aluminum alloy A356/Cow horn particulate composite for brake drum application using RSM, ANN and simulated annealing. Defence Technol. 14, 336e345 (2018). www.elsevier.com/loc ate/dt 13. Kumar, R.K., Radhika, N., Sam, M.: Synthesis of aluminium composites using squeeze casting and investigating the effect of reinforcements on their mechanical and wear properties. Trans. Indian Inst. Met. https://doi.org/10.1007/s12666-019-01680-6 14. Stojanovic, B., Babic, M., Velickovic, S., Blagojevic, J.: Tribological behavior of aluminum hybrid composites studied by application of factorial techniques. Tribol. Trans. ISSN: 10402004 (Print) 1547-397X. http://www.tandfonline.com/loi/utrb20 15. Hegde, N.T., Pai, D., Hegde, R.: Heat treatment and mechanical characterization of LM25/tungsten carbide metal matrix composites. Mater. Today: Proc. www.elsevier.com/locate/ matpr, https://doi.org/10.1016/j.matpr.2019.08.136 16. Surendran, R., Manibharathi, N., Kumaravel, A.: Wear properties enhancement of aluminium alloy with addition of nano alumina. FME Trans. 45, 83–88 (2017). https://doi.org/10.5937/ fmet1701083S 17. Elango, G., Raghunath, B.K., Palanikumar, K., Thamizhmaran, K.: Sliding wear of LM25 aluminium alloy with 7.5% SiCp2.5% TiO2 and 2.5% SiCp7.5% TiO2 hybrid composites. J. Compos. Mater. 48(18), 2227–2236 (2014). https://doi.org/10.1177/0021998313496592, jcm.sagepub.com 18. Suresh, V., Vikram, P., Palanivel, R., Laubscher, R.F.: Mechanical and wear behavior of LM25 aluminium matrix hybrid composite reinforced with Boron carbide, Graphite and Iron oxide. Mater. Today: Proc. 5(14 Part 2), 27852–27860 (2018). https://doi.org/10.1016/j.matpr.2018. 10.023 19. Ravi, M., Pillai, U.T.S., Pai, B.C., Damodaran, A.D., Dwarakadasa, E.S.: A study of the influence of mischmetal additions to AI–7Si–0.3Mg (LM 25/356) alloy. Metall. Mater. Trans. A 27 (1996) 20. Vijaya Ramnath, B., Elanchezhian, C., Jaivignesh, M., Rajesh, S., Parswajinan, C., Siddique Ahmed Ghias, A.: Evaluation of mechanical properties of aluminium alloy–alumina–boron carbide metal matrix composites. Mater. Des. 58, 332–338 (2014). www.elsevier.com/locate/ matdes 21. Lipp, A., Schwetz, K.A., Hunold, K.: State of the art hexagonal boron nitride: fabrication, properties and applications. J. Eur. Ceram. Soc. 5, 3–9 (1989)
Productivity Improvement in a Manufacturing Industry by Using Man–Machine Chart Analysis R. Mahendran, V. Amarnath, P. Rajkumar, L. Nirmal raj, S. Karthikeyan, and L. Rajeskumar
Abstract This work on man–machine chart is a clear and systematic analysis of the man and machine resources available in an industry. It also deals with the routing them in an optimized way to have increased productivity. This method is proven to be better and superior than the existing procedure by without making huge leaps in technology and not investing much more in the project. It should be confirmed that the proposed method does not cause fatigue to the workers. The improvement methods may include improvement in the output with fixed man and machine power, maintaining the output by reducing the man power and by reducing the process time of process for fixed man power. While making the time study of the process, bottle neck process should be taken in to the consideration. Since the improvement in the process prior to the bottle neck process does not take much effectiveness in the productivity. Keywords Work study · Time and method study · Time reduction · Process layout
1 Introduction Lean manufacturing is a process that identifies and reduces the wastes of any type during the process of production in a production line. It focusses in reducing wastes like space of production, human utilization and machine usage along with inventory availability [1, 2]. Womack and Jones during the year 1990 invented the concept of mass production while this concept was replaced by the lean manufacturing system R. Mahendran · P. Rajkumar · L. Nirmal raj · S. Karthikeyan Department of Mechanical Engineering, Jay Shriram Engineering College, Tirupur, Tamilnadu 638660, India V. Amarnath Department of Mechanical Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu 641022, India L. Rajeskumar (B) Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu 641407, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_56
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implemented for the production of automobile by Toyota production system (TPS) during the later times. TPS emphasized that the lean gives its prime focus in maintaining the product value but at a lesser time of production. Few other industrialists later realized that lean works basically for achieving the customer satisfaction, and its out goals were based on the concept of work force that was self-directed and self-motivated. Lean is characterized by many advantages such as improvement in productivity, improvement in quality, work flow organization, reduced lead time of production, operational safety and minimal use of inventory. It was stated by few authors that wastes were generated due to many factors including errors, costs, processes and unwanted delay. There are seven types of wastes associated with lean such as transportation, overproduction, inventory (finished goods and raw material), process, motion, defects and waiting time [3–6]. Productivity is the ratio of output to the input for any process. It is a quantitative relationship of what has to be produced and what was spent for its production. It can also be termed as human efforts to produce more quantity with less or optimum resources input so that there will be maximum distribution of benefits in various terms [7, 8]. Production planning could be efficiently carried out by the utilization of suitable forecasting methodologies and the past record surfing which reduce the lean wastes and contributes for the enhanced productivity. Improving productivity means increasing the productivity of the man, machine or material with the help of using same quantity of technology, materials, machining time, land and labor to fetch higher output. In a production industry, productivity can be enhanced by various tools such as time study, value stream mapping, man–machine utilization, method study and demand forecast which renders the analysis of the existing practices and bring out various alternatives for improving the productivity on a wholesome basis [9, 10]. All these techniques aim to reduce the unnecessary operation by the labor or a machine which pulls down the productivity by increased time to do that unnecessary operation. These techniques also control and optimize the all the manufacturing operations to utilize men, machine and materials in an optimized way [11]. Time study is one of the commonly used methods of enhancing the productivity of the process, and it is also a traditional technique. In a manufacturing industry, time study is the most useful one among all other tools since it records the details of time pertaining to each and every process of manufacturing in almost all the possible aspects. History of time study runs back to the World War 2 times, and this is a fool proof and validated method of time measurement which not only renders the basic time of all operations but also the standard time for any sort of specific works and operations. Usually, time study specifies the time of operation of any process to a skilled operator by standardizing the working conditions for a definite working rate. This is a standard time measuring technique in which the commonly used tools include stopwatch with minute decimal, video camera and a computer assisted timing device [12–15]. These tools could be used to record the time elapsed for carrying out an operation and it also segregates the redundant operations that incurs shorter or longer time durations, and the elements that governs the process during the working cycle. Time study is usually converted into a work chart which contains the details such as delay, transportation of materials and human movement, inventory, operation
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details and inspection, from which the problem would be identified and solutions would be proposed [16, 17]. Machine efficiency is also considered to be a most important parameter in enhancing the productivity. If this has not been given suitable importance, it may lead to yield reduction of the machine and increase in machine maintenance. If machine maintenance is not properly carried out, it results in sub-standard production of parts and even in malfunction of the machine [18–20]. Machines are prone to work with least efficiency if the preventive maintenance of the machine has not been done properly. A machine can be made to work with its highest efficiency by means of proper planning of the preventive maintenance of the machine during the production process. Even preventive maintenance has to be done by trained and skilled personnel so that the unwanted wasted generated due to the stoppage of machine can also be eliminated thus rendering the optimum utilization of the machine [21–23]. It is a well-known fact that half of the better productivity lies in the hands of the humans working in the shop floor who helps greatly in target accomplishments, and such humans are categorized as workers/operators in the production line and the workers in the supporting department of administration [24]. Few industrialists proposed that VSM-based lean systems helped the Chinese production firms to utilize the lean tools with high rate of usage which rendered a higher total overall efficiency of the production system and make them realize the position of system and the road map to further improve the productivity. Authors discussed about the utilization of lean tools to reduce the wastes, improve the value additions to the product and enhance the competitiveness of Chinese firms in the global market [25, 26]. Few authors compared the complexities involved in the dynamic simulation software modeled production layouts in a production enterprise. Works were carried out in identifying the manufacturing layout for critical components in a valve manufacturing industry, and the changes in layout for better productivity were suggested to minimize the lead time of production. Many experimenters discussed about the identification of bottlenecks in detail. Witness statistical simulation software was used to collect and compile the statistical data and use the results in an efficient way to modify the layout of the production line to enhance the productivity in aerator manufacturing [27–29] Man–machine chart is yet another tool used to enhance the productivity of the manufacturing industry which comes under the lean manufacturing system tools, and this graphically depicts the interrelationship between the manual work carried out by one or more humans and the manufacturing process carried out by one or more machines. For a man–machine chart, the usual input data given would include the process involved in production such as loading, operating and unloading the components in the machine along with the time of processes for the above operations, and the output of the chart would help the manufacturing firms to accomplish higher level of productivity with the same available resources. Output of man–machine chart can be used to implement a modified work cycle and process layout, and the production system can be restarted which renders minimum downtime as much as possible [30–33]. Commonly, a man–machine chart is used for the detection of idle time of machine and the labors by recording each and every operations performed. After
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examining each and every activity, man–machine chart helps to organize the operations by man and machine for a mass production by creating a balanced work team. It also aids in deciding the number of man power required for a team and the optimum work distribution between man and machine within that group. In the current work, a man–machine chart analysis was performed to improve the productivity of a disk pad manufacturing industry by process study and layout optimization [34–38].
2 Time Study Before starting of any project, the complete process and the method of doing that project should be studied that helps to collect the data in clear and authentic way; thus, the result of the project also will be more accurate, and the implementation of further proceedings will become easier to implement. Here, the disk manufacturing operations and process study has been taken into analysis. The study is carried out clearly by having discussion with the industrial persons, and the detailed step-by-step procedure is shown in Fig. 1. The second step of the project is to study about the process and operations in which the project is to be carried over; the study of the process includes watching the motion of machines and workers in a clear manner and learning the standard operating procedure of the every process. The study of process will avoid the confusions and mistakes in the collection of data. In this project, after completion of above two steps, the timing of the motions of man and machine is recorded using the stopwatch. The timing of every process is taken for at least five trails, and the average value is calculated. Time study chart and its time taken for performing various operations are shown in Figs. 2 and 3, respectively. As the collected data should be compared and analyzed to get the best result, the data should be plotted as the graph for the better understanding purpose. It could be understood from Figs. 1 and 2 that the idle time of the operator was very high while waiting for the next cycle of operation. When the disk was machined in one machine and sent to the bonding process, the operator has to wait till the completion of bonding process, only after which the next disk for machining would be supplied. It was also noted from the layout that the machines are far apart by 2000 mm, and the movement of operator takes more time which was also considered as idle time. This reduced the productivity considerably, and this time has to be reduced as per the initial analysis. Layout of the existing process is shown in Fig. 4 from which it could be seen that the man and machine operation has been not synchronized, and the idle time of the operator is high when the component travels from one machine to the other. In this layout, two machines are operated by two different operators; thus, the work efficiency of the operator was around 35% only.
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Fig. 1 Time study analysis flowchart CYCLIC WORK ELEMENTS
ID
1 2 3 4 5 6
DESCRIPTION OF OPERATOR WORK MAN OPERATION 1 MAN OPERATION 2 MAN OPERATION 3 MACHINE OPERATION1 MAN OPERATION 4 MAN IDLE TIME TOTAL TIME BY CATEGORY==>
% OPERATOR TIME BY CATEGORY==>
ELEMENT TIME VA = Value Added, NVA = Non-Value Added (VA)
(NVA)
Manual Manual Operation Operation
Machine (NVA) (NVA) Time Walking Wait for Cycle
2.62 1.35 1.73 22.23 3.59 18.67 18.7
9.3 33%
67% TOTAL CYCLICAL TIME==>
Fig. 2 Cycle timing of existing layout
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Fig. 3 Time graph for existing time of machining
Fig. 4 Process layout for existing operations
3 Identification of Gap and Problem Solving This is the important step in the project where the data is analyzed, and the correct gap in the process is to be found out. The gap may be in balancing of the work or the higher idle time of operator. After gap identification, the feasible activity in the process which is suitable for improvement is to be selected. The feasible improvement
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activity in the process should be selected in such a way that the implementation should be very effective and that does not deteriorate over time. In this project, the idle time of operator time is found during the bonding process and was found to be very high, and the implementation of remedy for reducing this idle time was carried over. It was proposed that in the existing layout if an unloading tray is provided, the machined components could be loaded into that tray that enables the same operator to perform another operation continuously so that the operator idle time is reduced. After the implementation, the process motion timing should be again noted for the recording purpose and to set the benchmark. The new implemented improvement should be set as the standard operating procedure, and it should be make sure that it is followed properly. The following timing shows the time for motion after the optimization. After recording the time of operation, it was set as a benchmark and the future production plans can be made according to the set benchmark. The problem identified from various processes is the idle time of the operator and this problem is currently selected for optimization. The result of the time study conducted was compiled, and the process which can be easily suited for the modification was studied, and the bonding process is selected for the improvement in production by changing lay out. Figures 5 and 6 show the modified time study analysis for the modified process and the graph of time variation for each and very operation, respectively. Correspondingly, the change of layout which renders lesser idle time of the operator has also been drawn and is shown in Fig. 7. In this layout, the machine is relocated; thus, a single operator can simultaneously operate both the machines;
CYCLIC WORK ELEMENTS
ELEMENT TIME VA = Value Added, NVA = Non-Value Added
(VA) (NVA) Machine (NVA) (NVA) Manual Manual Time Walking Wait for Operation Operation Cycle MAN OPERATION 1 1 2.78 MAN OPERATION 2 2 1.53 3 MAN OPERATION 3 1.67 MACHINE OPERATION 1 4 22.53 MAN OPERATION 4 5 3.71 6 WALKING TIME 2.47 7 MAN OPERATION 5 2.75 MAN OPERATION 6 8 1.12 9 MAN OPERATION 7 1.87 10 MACHINE OPERATION 1 23.02 MAN OPERATION 8 11 3.86 12 IDLE TIME 4.62 TOTAL TIME BY CATEGORY==> 19.3 2.5 4.6 73% 9% 18% % OPERATOR TIME BY CATEGORY==> TOTAL CYCLICAL TIME==> 26.4 ID
DESCRIPTION OF OPERATOR WORK
Fig. 5 Cycle timing of modified layout
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Fig. 6 Graph for modified time of machining
Fig. 7 Process layout for proposed operations
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the distance between the two machines is as low as 800 mm. Here, since both the machines were operated by the same operator and the idle time was also minimum, the efficiency of operations increased to 67%.
4 Conclusion Man–machine chart analysis was performed for the disk manufacturing operations to improve the productivity, and the following conclusions could be obtained. • Disk manufacturing process study was carried over, and the data is plotted in graph, and the gap in the manufacturing process is identified. It was determined from the study that the idle time of operator remained as high as 18.67 min, and the efficiency was only about 35% before the implementation. • Idle time of the operator was reduced by increasing his working time by unifying the work of two operators. Man–machine chart analysis was used for the same, and this reduced the idle time to 4.62 min, and the efficiency increased to 67%. • Layout of the machines was also modified in such a way that the distance between the machines was reduced to 800 mm from 2000 mm which in turn reduced the movement of operators between these machines. Such reduction of movements and change in layout rendered better productivity which was the ultimate aim of the study. This project, upon completion, eliminated three man powers a day without affecting the production. Hence, man–machine chart analysis improved the productivity in disk manufacturing process by reducing the idle time of the operator and eliminated the time waste by workers’ mobilization.
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Joint Impact of Carbon Emission and Partial Substitution on Inventory Model of Two Substitutable Products with Cost of Substitution Saumya Singh, Rajesh Kumar Mishra, and Vinod Kumar Mishra
Abstract In today’s modern era, inventory management must be environmentally sustainable. So, during developing the inventory model, consideration of carbon emission has become essential. Further, practically, substitution may happen partially in situation of item stock-out. Yet, the traditional inventory models disregard these factors. In this paper, we examine a joint impact of carbon emissions and partial substitution on inventory model of two substitutable products. This model is developed under two practical cases: with substitution and no substitution. The proposed inventory model determines optimal order quantities so that total cost and total carbon emissions can be minimized. An algorithm is provided to obtain optimal solution. Convex nature of total cost is shown by graphical approach. Lastly, a numerical illustration as well as sensitivity analysis is provided to validate the model. The result shows a substantial cost reduction by using substitution policy. Keywords Optimal policy · Substitutable products · Carbon emissions · Partial substitution · Cost of substitution
1 Introduction In real-world, often, customers experience stock-out situation for the preferred product. In today’s busy life, mostly customers do not want to visit another retail store, due to which they purchase an alternate product in place of preferred product. This phenomenon is called substitution, and products under substitution are termed as substitutable products for example, different brands of coffee, tea, chocolate etc. Furthermore, substitution policy is categorized as full substitution and partial substitution in stock-out situation [1]. Since, in reality, not all customers choose substitutable products due to which, partial substitution occurs more than full substitution. In a market survey, only 12–18% of customers said they would not purchase a substitutable item on a shopping trip if their desired brand was stock-out [2]. Due to S. Singh (B) · R. K. Mishra · V. K. Mishra Department of Mathematics and Scientific Computing, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_57
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certain business motivations e.g., minimizing the cost, improving the availability of products, promoting the substitutable products, and enhancing the goodwill, retail companies arrange substitutable products. Further, to reduce the cost, joint ordering policy and zero-lead time are also assumed in this model. Global warming is a significant threat to our planet. The emission of greenhouse gases such as carbon dioxide, methane, and nitrous oxide is the cause of global warming. However, among all gases, carbon dioxide plays the main role for global warming. The most important challenge in combating global warming is to drastically reduce carbon emissions. Therefore, considering above practical situations, this model has been developed under partial substitution and carbon emissions. So, this model incorporates carbon emission. In this proposed paper, we study an inventory model of substitutable products considering partial substitution and carbon emission, which includes inventory cost components: ordering cost, carrying cost (holding cost), cost of substitution, cost of shortage and carbon emission cost, and objective of this model is to minimize the total inventory cost and total carbon emission and also to find optimal order quantities. To the best of our knowledge, there does not exist an inventory problem with carbon emissions and partial substitution policy. Initially, inventory model for substitutable items was studied by McGillivray and Silver [3] considering same unit shortage penalty and variable cost. For thorough analysis of inventory models for substitutable items, the review paper based on inventory problem for substitutable products given by Shin et al. [4] may be referred by the readers. We can find several research papers addressing substitutable items [5–10]. Concerning the effect of carbon emission, many researchers consider carbon emissions into their inventory model [11–14]. The remaining of the article is organizing as follows: Assumption and notations, model formulation, solution algorithm, numerical illustration and sensitivity analysis, and conclusions are described in Sects. 2, 3, 4, 5, and 6 respectively.
2 Assumptions and Notations The proposed inventory model is developed using the assumptions and notations listed below.
2.1 Assumptions 1. 2. 3. 4. 5.
Two substitutable products are considered and substitution is taken as partial substitution. Due to substitution policy, cost of substitution is considered. Products are non-deteriorating. Demand is taken as deterministic and constant. Shortage is permitted.
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Carbon emission is considered and this is due to warehousing.
2.2 Notations Di Qi hi t1 , T2 t2 , p 2 γ1 , γ2 π1 , π2 CS12 CS21 W CW TCU1 TCU2 TCUW CE1 CE2 CE3
Rate of demand for product i (i = 1, 2) (unit/year). Ordering quantity for product i (i = 1, 2) (unit) (decision variables). Carrying cost for product i (i = 1, 2) ($/unit/year). Time when inventory level of items 1 and 2 depleted (year). Time period during substitution in case of with substitution (year). Rates of substitution in case of with substitution. Unit shortage cost in case of with substitution ($). Unit cost of substitution for product 1 in case of with substitution. Unit cost of substitution for product 2 in case of with substitution. Average warehousing energy consumption per unit time (kwh/unit/year). Carbon emission from electricity generation (ton CO2 /kwh). Total cost function per unit time in first situation of case of with substitution ($). Total cost function per unit time in second situation of case of with substitution ($). Total cost function per unit time in case of no substitution ($). Total carbon emission in first situation in case of with substitution (ton CO2 /year). Total carbon emission in first situation in case of with substitution (ton CO2 /year). Total carbon emission in case of no substitution (ton CO2 /year).
3 Model Formulation In the proposed substitution inventory model, practically, there occur two cases: case of with substitution and case of no substitution. Case of with substitution: The case of with substitution has also two practical situations: ending of product 1 before product 2 and ending of product 2 before product 1 (shown in Figs. 1 and 2). Ending of item 1 before item 2 (t 1 ≤ T 2 ): In this situation, product 1 is partially substituted by product 2 with substitution rate γ1 (shown in Fig. 1). Inventory levels Ii (t), (i = 1, 2, 3) in intervals [0, t1 ], [0, t1 ], and [t1 , t1 + t2 ] respectively are governed by following equations. dI1 (t) = −D1 ; [0, t1 ], I1 (0) = Q 1 , I1 (t1 ) = 0 dt
(1)
618 Fig. 1 Inventory diagram, when ending of product 1 before product 2
Fig. 2 Inventory diagram, when ending of product 2 before product 1
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dI2 (t) = −D2 ; [0, t1 ], I1 (0) = Q 2 , I1 (t1 ) = z dt
(2)
dI3 (t) = −(D2 + γ1 D1 ); [0, t1 ], I3 (t1 ) = z, I3 (t1 + t2 ) = 0 dt
(3)
After solving above equations, we get I1 (t) = −D1 t + Q 1
(4)
I2 (t) = −D2 t + Q 2
(5)
I3 (t) = (D2 + γ1 D1 )(t1 + t2 − t)
(6)
+γ1 Q 1 1 Q 2 −D2 Q 1 ) and cycle length = t1 + t2 = QD22 +γ . Since, t1 = QD11 , T2 = QD22 , t2 = (D D1 (D2 +γ1 D1 ) 1 D2 Total cost per unit time involves total cost of products 1 and 2, cost of substitution, shortage cost, and carbon emission cost. So, total cost function per unit time is given by:
TCU1 =
D 2 + γ1 D 1 Q 2 + γ1 Q 1
A1 + A2 +
h 2 (D1 Q 2 − D2 Q 1 )2
h 1 Q 21 2D1
+
h 2 (2Q 1 Q 2 D1 − D2 Q 21 ) 2D12
CS12 γ1 (D1 Q 2 − D2 Q 1 ) π1 (1 − γ1 )(D1 Q 2 − D2 Q 1 ) + (D2 + γ1 D1 ) (D2 + γ1 D1 ) Q2 (2Q 1 Q 2 D1 − D2 Q 21 ) (D1 Q 2 − D2 Q 1 )2 +C x WCW ( 1 + + (7) 2 2D1 2D1 2D12 (D2 + γ1 D1 ) +
2D12 (D2 + γ1 D1 )
+
Total carbon emission is given by CE1 = WCW
Q 21 (2Q 1 Q 2 D1 − D2 Q 21 ) (D1 Q 2 − D2 Q 1 )2 + + 2D1 2D12 2D12 (D2 + γ1 D1 )
(8)
Ending of item 2 before item 1 (t 1 ≥ T 2 ): In this situation, product 2 is partially substituted by product 1 with substitution rate γ2 (shown in Fig. 2). Using approach analogous to first situation: ending of product 1 before product 2, total cost function per unit time is given by: TCU2 =
D 1 + γ2 D 2 Q 1 + γ2 Q 2
A1 + A2 +
h 1 (D2 Q 1 − D1 Q 2 )2
h 2 Q 22 2D2
+
h 1 (2Q 1 Q 2 D2 − D1 Q 22 ) 2D22
CS21 γ2 (D2 Q 1 − D1 Q 2 ) π2 (1 − γ2 )(D2 Q 1 − D1 Q 2 ) + (D1 + γ2 D2 ) (D1 + γ2 D2 ) Q2 (2Q 1 Q 2 D2 − D1 Q 22 ) (D2 Q 1 − D1 Q 2 )2 +C x WCW ( 2 + + (9) 2 2D2 2D2 2D22 (D1 + γ2 D2 ) +
2D22 (D1 + γ2 D2 )
+
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Total carbon emission is given by CE2 = WCW
Q 22 (2Q 1 Q 2 D2 − D1 Q 22 ) (D2 Q 1 − D1 Q 2 )2 + + 2D2 2D12 2D22 (D1 + γ2 D2 )
(10)
Case of no substitution (t 1 = T 2 ): In this case, products 1 and 2 end simultaneously (shown in Fig. 3). In this case, total cost per unit time involves ordering cost, holding cost, and carbon emission cost. So, total cost function per unit time is given by TCUW
2 Q1 Q 21 Q 22 D1 Q 22 A1 + A2 + h 1 (11) = + h2 + C x WCW + Q1 2D1 2D2 2D1 2D2
Total carbon emission is given by CE3 = WCW
Fig. 3 Inventory diagram for case of no substitution
Q 21 Q2 + 2 2D1 2D2
(12)
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4 Solution Algorithm To determine optimal policy, we will implement an algorithm which is given as follows: Step I: Initialize all input parameters. Step II: Solve min TCU1 subject to QD11 ≤ QD22 , Q 1 , Q 2 ≥ 0 and min TCU2 subject to QD11 ≥ QD22 , Q 1 , Q 2 ≥ 0. Step III: Solve min TCUW subject to QD11 = QD22 , Q 1 , Q 2 ≥ 0. Step IV: To determine optimal total cost (TC∗ ), we apply TC∗ = min (min TCU1, min TCU2). Optimal ordering quantities corresponding to TC∗ are Q ∗1 and Q ∗2 Step V: Total carbon emissions CE1, CE2,CE3 are determined by Eqs. (8), (10), (12). Step VI: Exit.
5 Numerical Illustration and Sensitivity Analysis To validate the model, numerical illustration is provided. Values of input parameters for numerical illustration are given as follows: (A1 , A2 ) = (200, 200), (D1 , D2 ) = (100, 50), (h 1 , h 2 ) = (6, 3), (γ1 , γ2 ) = (0.3, 0.3), (π1 , π2 ) = (1, 1), (CS12 , CS21 ) = (2, 2), W = 10, C W = 0.0005, C X = 50. Firstly, we will solve two non-linear constrained optimization problems in step 2 of the solution algorithm in Sect. 4 by using maple software. In both situations of case of with substitution, optimal solution is TCU1∗ = 576.84, Q ∗1 = 24.64, Q ∗2 = 130.10, and TCU2∗ = 793.58, Q ∗1 = 102.39, Q ∗2 = 47.27. Hence, optimal solution with substitution is TC∗ = 576.84$, Q ∗1 = 24.64 units, Q ∗2 = 130.10 units. Moreover, optimal total carbon emission is CE∗ = CE1∗ = 0.60 ton-CO2 . Further, optimal solution with no substitution is TCw∗ = 793.73$, Q ∗1 = 100.79 units, Q ∗2 = 50.40 units. Thus, the result shows a substantial cost reduction by using substitution policy (around 27%). Nature of convexity is shown by graphically in Figs. 4, 5 and 6. Now, we conduct a sensitivity analysis for optimal total cost, cost reduction, and total carbon emission with variations in key parameters (Table 1).
6 Conclusions This model studies a joint impact of carbon emissions and partial substitution on inventory model for two substitutable products with cost of substitution. In this model, we investigated two practical cases: with substitution and no substitution. During developing the model, we obtained the expressions for total cost functions, and total carbon emissions in each possible case. This article aims to find optimal
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Fig. 4 Convexity of total cost (TC)
Fig. 5 Convexity of total cost (TC) versus Q1
order quantities by minimizing the total cost and total carbon emissions. Due to considering carbon emissions, total cost also involves a new cost: carbon emission cost. Convexity of cost function have been shown graphically. Further, a numerical example is presented and an analysis of sensitivity is carried out. By using this model, retailer can reduce the total cost. An integrated model for vendor and buyer may be another future extension.
Joint Impact of Carbon Emission and Partial Substitution … Fig. 6 Convexity of total cost (TC) versus Q2
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Table 1 Sensitivity with respect to key parameters Parameter
Value of parameter
TC∗
TCw ∗
CR
CE1∗
D1
100
576.84
793.73
27.33
0.601
110
597.20
824.62
27.58
0.599
120
617.36
854.40
27.74
0.598
130
637.35
883.18
27.83
0.597
200
576.84
793.73
27.33
0.601
220
588.33
813.33
27.66
0.632
240
599.54
832.47
27.98
0.662
260
610.49
851.18
28.28
0.693
6.00
576.84
793.73
27.33
0.601
6.50
577.65
818.54
29.43
0.602
7.00
578.33
842.61
31.36
0.602
7.50
578.90
866.03
33.15
0.602
0.30
576.84
793.73
27.33
0.601
0.50
644.16
793.73
18.84
0.591
0.70
702.58
793.73
11.48
0.573
0.90
750.89
793.73
5.40
0.540
1.00
576.84
793.73
27.33
0.601
1.50
606.11
793.73
23.64
0.593
2.00
633.92
793.73
20.13
0.582
2.50
660.18
793.73
16.83
0.570
2.00
576.84
793.73
27.33
0.601
2.50
589.56
793.73
25.72
0.598
3.00
602.02
793.73
24.15
0.594
3.50
614.21
793.73
22.62
0.590
10
576.84
793.73
27.33
0.601
12
580.33
797.50
27.23
0.710
14
583.79
801.25
27.14
0.817
16
587.23
804.98
27.05
0.921
50
576.84
793.73
27.33
0.601
60
580.33
797.50
27.23
0.592
70
583.79
801.25
27.14
0.583
80
587.23
804.98
27.05
0.575
A1
h1
γ1
π1
CS12
W
CX
Joint Impact of Carbon Emission and Partial Substitution …
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References 1. Kim, S.W., Bell, P.C.: Optimal pricing and production decisions in the presence of symmetrical and asymmetrical substitution. Omega 39(5), 528–538 (2011) 2. Anupindi, R., Dada, M., Gupta, S.: Estimation of consumer demand with stock-out based substitution: an application to vending machine products. Mark. Sci. 17(4), 406–423 (1998) 3. McGillivray, A.R., Silver, E.A.: Some concepts for inventory control under substitutable demand. Inf. Syst. Oper. Res. 16, 47–63 (1978) 4. Shin, H., Park, S., Lee, E., Benton, W.C.: A classification of the literature on the planning of substitutable products. Eur. J. Oper. Res. 246(3), 686–699 (2015) 5. Drezner, Z., Gurnani, H., Pasternack, B.A.: An EOQ model with substitutions between products. J. Oper. Res. Soc. 46(7), 887–891 (1995) 6. Salameh, M.K., Yassine, A.A., Maddah, B., Ghaddar, L.: Joint replenishment model with substitution. Appl. Math. Model. 38(14), 3662–3671 (2014) 7. Krommyda, I.P., Skouri, K., Konstantaras, I.: Optimal ordering quantities for substitutable products with stock-dependent demand. Appl. Math. Model. 39(1), 147–164 (2015) 8. Maddah, B., Kharbeche, M., Pokharel, S., Ghoniem, A.: Joint replenishment model for multiple products with substitution. Appl. Math. Model. 40(17–18), 7678–7688 (2016) 9. Mishra, V.K., Shanker, K.: Optimal ordering quantities for substitutable items under joint replenishment with cost of substitution. Oper. Res. Decisions 27(1), 77–104 (2017) 10. Mishra, R.K., Mishra, V.K.: Impact of cost of substitution and joint replenishment on inventory decisions for joint substitutable and complementary items under asymmetrical substitution. WPOM-Work. Pap. Oper. Manag. 11(2), 1–26 (2020) 11. Hammami, R., Nouira, I., Frein, Y.: Carbon emissions in a multi-echelon production-inventory model with lead time constraints. Int. J. Prod. Econ. 164, 292–307 (2015) 12. Bazan, E., Jaber, M.Y., Zanoni, S.: Carbon emissions and energy effects on a two-level manufacturer-retailer closed-loop supply chain model with remanufacturing subject to different coordination mechanisms. Int. J. Prod. Econ. 183, 394–408 (2017) 13. Aljazzar, S.M., Gurtu, A., Jaber, M.Y.: Delay-in-payments—a strategy to reduce carbon emissions from supply chains. J. Clean. Prod. 170, 636–644 (2018) 14. Taleizadeh, A.A., Hazarkhani, B., Moon, I.: Joint pricing and inventory decisions with carbonemission considerations, partial backordering and planned discounts. Ann. Oper. Res. 290, 95–113 (2020)
Mechanical Characterization of Glass Fiber Metal Laminate R. Naveen, S. Bairavi, P. S. Vijayanand, N. Swetha, K. Mathivannan, and M. Surya Muneeshwaran
Abstract Fiber metal laminates (FML) are composite materials that can be constructed by fiber and plastics, with a layer of metal in the core. It plays a vital role in the aerospace and automation industry because of its specific mechanical properties which are far better than other lightweight materials. The main limitation of this type of composite is the low binding energy between metal and plastic due to the low level of roughness that tends to delamination. The main focus of this paper is to fabricate and compare the FML composites of different core types. The core was pre-treated to clean the foreign particles that present on the surface. Fiber metal laminates are fabricated with epoxy resin and hardener in the ratio of 10:1, with three layers of glass fiber and two layers of core. The specimens were machined by using CNC machining according to ASTM standards for experimental testing. Then, the specimens were subjected to mechanical testing such as impact test, tensile test, flexural test, and the results were compared. From the result, it was identified that mesh is stronger than the plate. Keywords Composite · Tensile test · Fiber metal laminate · Glass fiber · Matrix
1 Introduction In this modern world, upcoming technologies and innovations are introduced in the industries to rectify the drawback of the main parameters in material selection. Weight, strength, and cost are the parameters mainly involved in the selection process. From the twentieth century, the need for lightweight, durable, and strong structures have been increased [1]. In the 70s, the idea of using fiber and metal to form hybrid composites to overcome the disadvantage of both materials was discovered [2]. Fiber metal laminate (FML) composite are hybrid and thin layers, which are composed R. Naveen (B) · P. S. Vijayanand · N. Swetha · K. Mathivannan · M. Surya Muneeshwaran Bannari Amman Institute of Technology, Sathyamangalam, Erode 638401, India S. Bairavi Nehru Institute of Technology, Kaliapuram, Coimbatore 641105, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_58
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to form a sandwich structure [3]. The thin layer materials used in FML composite are aluminum, stainless steel, titanium, and magnesium. Comparing the other side of the fiber reinforcement are carbon, aramid, and glass fiber. The main advantage of FML composite is the formation of a hybrid structure with a combination of fiber and metal. This structure results in high strength and excellent corrosion resistance [4]. The thin layer of metal in FML is used to reduce the weight, so the density of the material is also reduced. FML is good in plastic behavior because the aluminum layer has plasticity. The moisture absorption content in the FML composite is low when compared to another polymer composite. This abundance of advantages of FML composite is involved in several industries like aerospace and automobile [5]. Due to this advantage, all the aerospace industries started to work in this field to give an ideal composite and to reduce the cost as well as assure the safety of the aircraft. In recent decades, low-weight aircraft and automotive structures not only reduces fuel utilization and pollution but it also improves the service life of key components [6]. The FML composite started the first application in the aviation industry. The aircraft freight container certified by the FAA to absorb and neutralize the explosion from the bomb, with the FML the pan am flight disaster was neutralized in 1988 [7]. In the bombardier Lear jet 45, the FML is used in bulkhead and radome. This composite is widely used in straps in airbus A400M aircraft for better high loading in military transportation [8]. The FML composite is divided into three groups depending upon the metal used. Composites are used in all aircraft and spacecraft from hot air balloon gondolas and gliders to passenger airliners, fighter planes, and space shuttle components [9]. Aluminum is a major element in FML. Again, the composite is split into three parts. GLARE (glass laminate aluminum-reinforced epoxy), ARALL (aramidreinforced aluminum laminate), and CARALL (carbon fiber-reinforced aluminum laminate) [10]. The GLARE made the application in the fuselage of aircraft, stringers, vertical, and horizontal stabilizers in aircraft. The application of GLARE concludes that the composite has excellent strength, stiffness, low density, and so on. The FLMS composite has better mechanical properties, but the problem of FML is not controlled. The delamination of the FML is not resolved. Since, in order to decrease the delamination of the FML composite, the surface roughness needs to be increased. To increase the roughness, the material undergoes nano-starches. These starches help to increase the grip in the matrix and reinforcement. The purpose of the paper is to investigate the delamination of the FML with an increase in the roughness in the plate surface.
2 Material 2.1 Core Material FML consists of metal and fiber which are stuck together with the help of resin in an alternative layer of metal and fiber. The aluminum was used in the FML composite
Mechanical Characterization of Glass Fiber Metal Laminate Table 1 Table of material specification
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Materials
Tensile strength
Compression strength
Aluminum 8011
120
280
Glass fiber
2050
5000
and also in several applications like aerospace and automobile industries because of its lightweight structure, corrosion resistance, and low density. Strength wise the aluminum was better compared to other materials, so the metal laminate was chosen as aluminum 8011, and it is well suitable for reinforcement and easy availability. The composition of aluminum 8011 is 97.3% of aluminum, 0.6% of iron, 0.5% of silicon, and 0.2% of manganese. Here, the aluminum 8011 was used as a plate and mesh because the mechanical properties of the mesh and plates are different. The wire mesh was used in different geometries like squares and diamonds. As research was carried out, it showed that the wire mesh has a better grip and higher tensile, impact ductility, and flexibility which hold the laminate better compared to the plate. The plate has higher corrosion resistance, durable, easily machined, and cast. This paper carried out the mechanical properties of both plate and mesh which are experimentally tested. Then, the result was compared and analyzed (Table 1).
2.2 Fiber Laminate Polymer fibers are used as a fiber laminate. This laminate was quite easy to laminate over the plate and withstand high temperature and low absorption. The composite material used in this paper was GLARE (glass laminate aramid-reinforced epoxy). SINCE, it is a new emerging material in aerospace applications due to low cost, easily available, low fabrication complication, and no heavy machining to fabricate. The GLARE was fabricated with a thin layer of aluminum and fibers with resin in unidirectional and bidirectional. This laminate gave good impact strength and blunt notch strength as well as low density because of the thin layer with a high bonded structure. The laminate was widely used in aircraft because GLARE has high-temperature resistance (Fig. 1).
3 Methodology The fiber metal laminate was fabricated with the layers of glass fiber and aluminum over each other. The laminate was composed of three layers of glass fiber and two layers of aluminum metal. These are impregnated with epoxy resin and hardener as a matrix in the ratio of 10:1. Then laminates were cured in an autoclave method for
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Fig. 1 Fiber metal laminate
45 min at 15 lbs/in2 of pressure and 121 °C of temperature. The above-mentioned flowchart describes the process of testing on fiber metal laminates.
4 Result and Discussion 4.1 Impact Testing To observe the material which exhibited a sudden shocking load and will cause deformation or fracture, then the test is evaluated that the material can absorb the energy in the collision period. Using this energy, to determine the impact strength, fracture-resistant of the material, and toughness also. Here, the testing specimen was kept in the holding fixture, and then the pendulum suddenly dropped in the known height at the sudden force that hit the specimen. In this impact test, the specimen was cut in ASTM A370 standard dimension of 55 × 25. Using this ASTM standard to get a better result of impact resistance, the value was taken in perpendicular orientation. The following values are calculated in the Charpy impact test (Fig. 2; Tables 2 and 3).
4.2 Flexure Testing The fiber and metal are adhesion together to form an FML. The main objective of using FML is good plasticity, so it is necessary to take flexure testing to determine
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Fig. 2 Specimen used for impact testing
Table 2 Impact values of diamond and square mesh
Table 3 Impact values of plate
Mesh
Orientation
Impact value (J)
Square
Perpendicular
20
Diamond
Perpendicular
6
Plate
Orientation
Impact value (J)
t = 0.28
Perpendicular
31
t = 0.38
Perpendicular
45
t = 0.5
Perpendicular
55
the deflection of the material. The interface bonding between the fiber composites is always influenced by the result of the flexure testing. It was carried out through a three-point load test in a flexural bend test (Figs. 3 and 4). The above graph represents the result of the flexural test, applied load in kg as X-axis and displacement in mm as Y-axis. From the graph, it can be identified that the deflection in plate is less compared to other laminates. The maximum withstands load for diamond mesh is about 69 kg with a deflection of 5.6 mm. The square mesh withstands a load of 60 kg with a deflection of 5.9 mm, and the plate withstands the maximum load of 43.3 kg with a deflection of 3.9 mm.
4.3 Tensile Testing This testing is carried out with the specimen undergoing tension till the specimen is failed. It has two shoulders gripped with a gage in between the specimen for testing the breaking strength of the material, and the deformation area of the material is only in the gage cross-section. The effect of the geometry changes on the laminate is found using this test (Figs. 5, 6 and 7; Tables 4 and 5).
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Fig. 3 Comparison of flexural result between plate and mesh FML
Fig. 4 Flexural testing setup
5 Conclusion From the literature survey, the FML is widely used in aerospace industries, and it has superior properties compared to aluminum alloy. In this paper, the plate and the mesh are undergone some of the mechanical testing like impact, tensile, and
Mechanical Characterization of Glass Fiber Metal Laminate
Fig. 5 Load versus displacement comparison between mesh and plate FML
Fig. 6 Stress versus strain between diamond and square mesh
Fig. 7 CNC machine cutting for tensile testing
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Table 4 Tensile results for plates Plate (mm)
Max. Disp. (mm)
Tensile strength (Mpa)
Elongation %
Reduction in area %
t = 0.28
32.50
62.125
9.375
66.667
t = 0.38
28.6
71.150
7.245
54.545
t = 0.5
21.7
79.189
4.867
44.345
Table 5 Tensile results for mesh Mesh
Maximum Disp. (mm)
Tensile strength (kN/mm2 )
Elongation %
Reduction in area %
Diamond
7.99
0.891
2.000
87.50
Square
7.96
0.818
2.000
87.500
flexural test. From the obtained result, it is concluded that the mesh gave the better result compared to plate because of bonding between the matrix and reinforcement. In the future development, the nanoparticles are used in the FML to improve the intermolecular bonding between the fiber and metal and micro-scratches are made to increase the shear strength. The metal plate and glass fiber are easily removed, so it is important to increase the intermolecular shear strength.
References 1. Abdullah, M.R., Prawoto, Y., Cantwell, W.J.: Interfacial fracture of the fibre-metal laminates based on fibre reinforced thermoplastics. Mater. Des. 66, 446–452 (2015) 2. Vogelesang, L.B., Vlot, A.: Development of fibre metal laminates for advanced. J. Mater. Process. Technol. 103, 1–5 (2000) 3. Linganiso, L.Z., Anandjiwala, R.D.: 1,2 CSIR Materials Science and Manufacturing, Port Elizabeth, South Africa; Faculty of Science, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa 4. Giasin, K., Ayvar-Soberanis, S.: Microstructural investigation of drilling induced damage in fibre metal laminates constituents. Compos. A Appl. Sci. Manuf. 97, 166–178 (2017) 5. Chen, Y., Wang, Y., Wang, H.: Research on progress on interlaminar failure behavior of fiber metal laminates. 2020, 3097839 (2020) 6. Naveen, R., Kumar, M., Shwetha, C., Rishekesh, R.: Sound absorbing performance of hybrid Fiber metal laminate Composites. In: AIAA 2020–4047. ASCEND 2020 (2020) 7. Easwara Prasad, N., Wanhill, R.J.H.: Aerospace materials and material technologies. Aerosp. Mater. Technol. 2 (2017) 8. Hyness, N.R.J., Vignesh, N.J., Senthamaraikannan, P., Saravanakumar, S.S., Sanjay, M.R.: Characterization of new natural cellulosic fiber from heteropogon contortus plant. J. Nat. Fibers 15(1), 146–153 (2018) 9. Mukesh, A.M., Rajesh Jesudoss Hynes, N.: Mechanical properties and applications of fiber metal laminates. AIP Conf. Proc. 2142, 100002 (2019)
Herbivicus: A Full Stack Website with Chatbot and Google API Khushboo Kumari, Aishwarya Srivastava, and T. Sasikala
Abstract Web development can range from creating fundamental static pages to compound web applications. It usually alludes to web designing, website architecture, customer/worker side scripting. As we probably are aware, software engineering joined with ecological science dominant part centres around topographical interaction affecting earth, climate, earth, woodlands and water bodies. Similarly, this venture centres after empowering individuals towards ranch and encouraging them by starting from their home itself. The venture is entitled as “Herbivicus”. The goal of “Herbivicus” is to illuminate various parts of plants. This time individuals have thought little of the force of vegetation. From expanding oxygen levels to lessening feeling of anxiety, they can do everything and change one’s home into green desert garden. The highlights associated with this undertaking are Front web-development, chatbot (Artificial knowledge), structures, APIs and UI planning. There are many people in the world who have colour blindness problem so to overcome that colour theory has been implemented. Last yet not the most un-this undertaking spreads a word to re-establish greenery as well as is a stunner for individuals to comprehend the value of house plants. Keywords Web development · Chatbot · House plants
1 Introduction A houseplant is a plant that is filled inside in spots like homes and workplaces, in particular for embellishing purposes, however, contemplates have likewise shown them to have positive mental impacts and just as help with indoor air cleaning, since certain species, and the dirt dwelling microorganisms related with them, decrease K. Kumari (B) · A. Srivastava Department of Computer Science, Sathyabama IST, Chennai, India T. Sasikala School of Computing, Sathyabama IST, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_59
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indoor air contamination by retaining unpredictable natural mixtures including benzene, formaldehyde and trichloroethylene. While by and large poisonous to people, such contaminations are consumed by the plant and its dirt dwelling organisms without hurt. Basic houseplants are typically tropical or semi-tropical epiphytes, succulents or desert plants. Houseplants need the right dampness, light levels, soil combination, temperature and mugginess. Too, houseplants need the legitimate compost and right measured pots. That is what inspired us to come up with this project, we have developed a webbased application that showcases all the information about house plants and its benefits. If you want to know about any house plants then there will be lots of information related to that such as its scientific and household name, pictures, amount of water and sunlight required for the growth of plant, from where to get that plant and many more. The main highlights of this project are chatbot, colour theory and Google location, where chatbot maintains the communication with the users and provide them with the necessary as per their requirement, colour theory implementation helps colour blind people to see the mixture of colours and google api helps the end-user to get the details about the nurseries where the house plants are currently available.
1.1 Front-End Development Front-end web development is the act of changing information over to a graphical interface, using HTML, CSS and JavaScript, so clients can see and collaborate with that information. Hyper Text Mark-up Language Hyper Text terminology (HTML) is that the backbone of any website development process, without which an internet page doesn’t exist. When a user clicks on a word or a phrase that features a hyperlink, it’ll bring another web page. A terminology indicates, text are often became images, tables, links and other representations. It is the HTML code that gives an overall framework of how the location will look. HTML was developed by Tim Berners-Lee. The latest version of HTML is named HTML5 and was published on October 28, 2014, by the W3 recommendation. This version contains new and efficient ways of handling elements like video and audio files. Following Fig. 1 is the sample HTML code which is used for “Footer” of website: Cascading Style Sheets (CSS) Cascading Style Sheets (CSS) controls the presentation aspect of the location and allows your site to possess its own unique look. It does this by maintaining style sheets which sit on top of other style rules and are triggered supported other inputs, like device screen size and determination. CSS represents Cascading Style Sheets. It
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Fig. 1 Footer HTML code
portrays how HTML components are to be shown on screen, paper or in other media. CSS saves a great deal of work. It can handle the design of different pages at the same time Outer templates are put away in CSS records. Following Fig. 2 is the sample Animation CSS code which is used for “About Us” of website:
2 JavaScript JavaScript is an event-based imperative programming language (as against HTML’s declarative language model) that’s wont to transform a static HTML page into a dynamic interface. JavaScript code can use the Document Object Model (DOM), provided by the HTML standard, to control an internet page in response to events, like user input. Using a technique called AJAX, JavaScript code also can actively retrieve content from the online (independent of the first HTML page retrieval), and also react to server-side events also, adding a very dynamic nature to the online page experience. Following Fig. 3 is the sample Animation JavaScript code which is used for “About Us” of website:
3 Bootstrap Bootstrap is a HTML, CSS and JavaScript Library that centres around improving on the improvement of enlightening pages (instead of web applications). The basic role
638 Fig. 2 Animation CSS code
Fig. 3 Animation JS code
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of adding it to a web project is to apply Bootstrap’s decisions of shading, size, text style and design to that project. In that capacity, the essential factor is whether the designers in control discover those decisions, however, they would prefer. Once added to a venture, Bootstrap gives fundamental style definitions to all HTML components. The outcome is a uniform appearance for writing tables and structure components across internet browsers. Also, engineers can exploit CSS classes characterized in Bootstrap to additionally modify the presence of their substance. For instance, Bootstrap has provisioned for light- and dull-shaded tables, page headings, more unmistakable force statements and text with a feature. Bootstrap likewise accompanies a few JavaScript parts as jQuery modules. They give extra UI components, for example, discourse boxes, tooltips and merry go rounds. Each Bootstrap part comprises of a HTML structure, CSS affirmations and now and again going with JavaScript code. They likewise broaden the usefulness of some current interface components, including for instance an auto-complete capacity for input fields. Illustration of a page utilizing Bootstrap structure. Back-end Development The back-end of a site comprises of a server, an application and a data set. A backend engineer constructs and keeps up the innovation that controls those parts which, together, empower the client confronting side of the site to try and exist in any case. Python Python is known for its straightforward linguistic structure and short code length. This, matched with the way that there is likewise broad documentation and instructional exercises accessible on its utilization, makes it genuinely simple to learn. Besides, Python is additionally very flexible and all-around planned. On the off chance that wasn’t sufficient, Python is a stage autonomous language, implying that product made utilizing Python can be utilized on a wide assortment of working frameworks with no need of a mediator. The entirety of this implies that software engineers can invest a ton of the energy they generally dedicate to getting the code to run or sorting out how it functions handling the more significant difficulties of their specific improvement project. Python is an ideal back-end language with regards to this territory of innovation. Since the language is basic and predictable, engineers can compose dependable frameworks, zeroing in on the issues of AI without stressing over any potential migraines usually connected with complex programming dialects. Django Django is a mainstream open-source Python web system worked by experienced designers. It targets utilizing robotization in any place conceivable to accelerate web improvement and let programming engineers centre around the significant work. It is quick, secure and adaptable. It is a significant level Python Web system that empowers quick turn of events and spotless pragmatic plan. Worked by experienced engineers, it deals with a large part of the issue of web improvement, so you can zero in on composing your application without expecting to waste time. It’s free and open source.
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Fig. 4 MVT architecture
Above Fig. 4 is the architecture of Django framework and it illustrates the working mechanism of back-end. Database: SQLite SQLite is a C library that gives a lightweight plate-based information base that doesn’t need a different worker measure and permits getting to the data set utilizing a nonstandard variation of the SQL inquiry language. Colour Theory: Shading hypothesis is both the science and specialty of utilizing shading. It clarifies how people see tone; and the enhanced visualizations of how shadings blend, match or differentiation with one another. Shading hypothesis likewise includes the messages tones convey; and the strategies used to repeat tone. Shading is insight. Our eyes see something (the sky, for instance), and information sent from our eyes to our minds reveals to us it’s a sure tone (blue). Items mirror light in various blends of frequencies. Our minds get on those frequency mixes and make an interpretation of them into the wonder we call tone (Fig. 5). In this website, we have used the following two mixture of colours: (i) (ii)
Forest Green, Olive Green, Lime Green and light grey (Fig. 6). Royal Blue, Prussian Blue, Azure and Turquoise (Fig. 7).
Chatbot: A chatbot is a product application used to lead an online talk discussion through text or text-to-discourse, in lieu of giving direct contact a live human specialist. A chatbot is a sort of programming that can mechanize discussions and interface with individuals through informing stages. Chatbots, additionally called chatterbots,
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Fig. 5 Colour theory
Fig. 6 Combo of green colour
Fig. 7 Combo of blue colour
is a type of computerized reasoning (AI) utilized in informing applications. This apparatus helps add comfort for clients—they are robotized programs that associate with clients like a human would and cost little to nothing to draw in with. Figure 8 is the mechanism of chatbot. This is how the chatbot works.
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Fig. 8 Architecture of chatbot
Fig. 9 Google map
Google Map The Google API has been used in this website in order to help the clients so that they can get the location details about the nearest nursery. They can get street view, satellite view and nursery information. It is styled in such a way that the users can see only nursery location in the map. They won’t get confused with other locations. The marker has been used to point the exact nursery (Fig. 9). Search Bar This is used to search anything directly without scrolling much. Following Fig. 10 is the sample code related to search bar:
4 Related Work There have been lot of full stack website project and chatbot features but not included all together. Nowadays, artificial intelligence is emerging rapidly and it is also very useful as people find it very easy to use. The work that we looked up is to combine the chatbot, google map and search bar in the full stack website. It is a one-stop
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Fig. 10 Sample code of search bar
destination for plant likers to get to know each and every detail about the house plants, its uses and also from which nearby nursery to buy them. If any end-user has any doubt then they can clarify the doubts with the chatbot. These applications had a few drawbacks therefore, the approach we focused on was making the chatbot and user interface more interactive and fix the drawbacks that the other chatbot and full stack websites had. The paper “Desirable Features of a Chatbot-building Platform” by Saurabh Srivastava et all [1] shows the desirable features of commercial platforms where chatbots can be created using different mechanisms. It could be implemented using both the IT professionals and Non-IT professionals. It is doing the relative comparison between the few professional platforms and their hierarchy. They assessed the stages for all the highlights at the each level. They evaluated the stages on a size of 0 to 1 for each include. In the event that the element was not accessible on the stage, they relegated an estimation of 0. In the event that the component was accessible and simple to utilize, we doled out an estimation of 1. For situations where a highlight was either mostly present, or, its use was definitely not simple, they allotted an incentive somewhere in the range of 0 and 1, with three diverse middle of the road levels—0.25, 0.5 and 0.75. In the paper “A Method of Optimizing Django Based On Greedy Strategy” by Jian Zhou et all [2], they provide an approach to optimize the Django framework using greedy strategy. It is focusing on the memory efficiency as the runtime of python is less efficient. It clarifies three flaws of Python VM’s integer management, and then boost its memory utilization efficiency. The paper “The New Era of Full Stack Development” by Akshat Dalmia et al. [3] mark-out the new technologies that can be used in the development of full stack website such as using cloud. Their study shows the advancement of an absolute implementation of the dynamic website, which we allude to as the customer side and the worker side. Because of the presentation of distributed computing there have been wide-ranging substitute in the realm of Full Stack Development and the way it has influenced the turn of events. Distributed computing comprises of different benefits and has numerous preferences. They have described so many network topologies
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and the importance of cloud computing in order to make the full stack website more advanced in the new era. The paper “A Django Based Educational Resource Sharing Website” by Adamya Shyam et al. [4] delineates a site prototype with the assistance of which scholars can have the option to get the notes, earlier year question bank, prospectus and put up old books for sale from a similar advanced stage too. The paper additionally portrays the part of programming in task improvement. The task is created on Django Framework; the server-side amelioration is in SQLite, Python and Jinja2. HTML, Javascript(es) and CSS altogether constitute the front-end. The venture is created utilizing the appropriate programming measure, following the Iterative Model of SDLC. The methodology utilized in the System Development Model can act as a guide for the improvement of comparable sorts of web applications proficiently. In the paper “The Influence of House Plants on Indoor CO2 ” by Hakan Sevik et al. [5], here, the investigation pointed towards deciding the impact of carbon dioxide (CO2 ) in the interior climate of various indoor plants as the new ascent in urbanization has prompted expanded populace thickness, especially in metropolitan zones. Thus, the number of individuals per unit zone has expanded. These days, metropolitan individuals spend in any event 80% of their lives in indoor conditions as a result of expanded lodging and evolving life conditions. The investigation was led in a plant development area that was kept away from the outside air and whose inner proportion was noted. The temperature and light states of the area were resolved autonomous of the outside climate. Their study also describes that how natural conditions impressively alter the rate of effect while the process of photosynthesis and also impacts on the amount of CO2 . Hence, consideration of variables, for instance, leaf structure, temperature, plant size and light in upcoming research are notable for figuring out what kind of plants are more successful in definite ecological conditions. The paper “Bootstrap Framework” by Suraj Shahu Gaikwad et al. [6] showcases the benefits of Bootstrap framework. They have also mentioned various tool of Bootstrap framework which is used to create the static website. Bootstrap takes into account quick, flexible upgradation that is predictable and can be very much validated by the outcome and local area planning. As the framework continues to make, the utilization of Bootstrap continues to ascend. The website “https://nurserylive.com/” [7] is developed only for the commercial purpose. The customers can see artificial plants live and order. It also has chatbot feature which requires a manual instruction. It is not automated and it doesn’t understand the user inputs properly. The website doesn’t provide any information about any plants, especially house plants. The website “https://www.gardeningknowhow.com/” [8] is all about the tips and tricks and gardening. It doesn’t have features like chatbot and google map. Here, user cannot get instant reply of their query and suppose if they want to buy that specific plants, they have to search somewhere else for that particular plant.
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5 Proposed System To overcome the limitations of the existing system that will provide the user the facility of using the chatbot and google map for the ease of communication and navigation. Features of Proposed system • User Interface is friendly. • It will maintain the information of the house plants which are essential in day to day life and can be accessed in nearby nurseries. • User interface is developed in such a way that layman can understand it very well. • It enables the search box through which the user can get their search results prompt and fast. • Colour theory has been implemented. The combination of few colours has been used in entire website such that the people who have colour blindness can also see the website and its content properly. • The exciting feature of the whole system is the chatbot. The chatbot enables online chat conversations with the bot. • The chatbot is simple and hassle free, the user doesn’t have to write sentences in the box they will all the inputs related to that website and can even download the plant guidelines. • The Google map feature was also enabled as in the existing system, users were not able to get the plant nursery’s location at single website. So, Map will help users to get the details of nearby location of them. • Above all, the existing system includes a huge amount of colours and graphics which looks clumsy. Hence, the proposed system helps in protecting our beautiful nature also as the work done is entirely software oriented.
6 Results Our main objective in doing this project was to organize each and every module effectively so that the UI/UX doesn’t become clumsy and hard to understand by the end-user. The most challenging part of this project was to train the chatbot as it needs lots of information for processing and giving the desired output. Following are some snapshots of the Herbivicus (Figs. 11, 12 and 13):
7 Conclusion The full stack website has been created successfully and all the tools and technologies are used appropriately. Few limitations which people had during the usage of chatbot has been cleared here. It’s a one-stop destination where people can easily get all the
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Fig. 11 Home page
Fig. 12 Featured page
details about the house plants, their uses and benefits. The search bar enables more features where user can search directly according to their requirement. The chatbot feature provides all the input which a user can ask a live chat agent. The chatbot has custom input and user can opt-in their options to get results. The google map helps the end-user to find the locations of various nurseries nearby with so many map views and marker points out the exact destination.
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Fig. 13 Chatbot
References 1. Srivastava, S., Prabhakar, T.V.: Desirable features of a Chatbot-building platform. In: IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI) (2020). https://doi.org/10.1109/HCCAI49649.2020.00016 2. Zhou, J., Chen, L., Ding, H., Tu, J., Xu, B.: A method of optimizing Django based on greedy strategy. In: 10th Web Information System and Application Conference (2013).https://doi.org/ 10.1109/WISA.2013.41 3. Dalmia, A., Chowdary, A.R.: The new era of full stack development. Int. J. Eng. Res. Technol. (IJERT) 9(04) (2020) 4. Shyam, A, Mukesh, N.: A Django based educational resource sharing website: Shreic. J. Sci. Res. 64(1) (2020) 5. Sevik, H., Cetin, M., Guney, K., Belkayali, N.: The influence of house plants on indoor CO2 . Pol. J. Environ. Stud. 26(4) (2017) 6. Gaikwad, S.S., Adkar, P.: A review paper on Bootstrap framework. IRE J. 2(10) (2019) 7. https://nurserylive.com/ 8. https://www.gardeningknowhow.com/
Design of a Low-Speed Smoke Visualization Wind Tunnel Samprada Kumbhare, H. Jeevan Rao, and Jigarkumar Sura
Abstract The wind tunnel is a useful apparatus to study the flow over the body. The visualization helps to understand the flow field qualitatively and provides information which can help to improve the performance of the aircraft. Among the various techniques used for the flow visualization, smoke visualization technique is less costly and useful for academic purpose. Here, in this paper, an attempt has been made to design a small wind tunnel for smoke visualization. The process of calculations have also been mentioned. Keywords Subsonic wind tunnel · Smoke flow visualization · Smoke rake · Pressure loss
1 Introduction The wind tunnel is an important experimental setup to investigate the flow field around the flying object. Since the invention of the airplane, different types of wind tunnels have been developed around the world for various purposes. The subsonic, supersonic, transonic, hypersonic, and icing tunnel are some of these types. The test setup helps to collect data for further investigation and understand the effect of various flow parameters such as Reynolds number and Mach number. The lowspeed subsonic wind tunnel is the simplest type of tunnel which is useful to carry out experiments at low speeds, which are related to the low speed UAVs and similar aircrafts. S. Kumbhare (B) B.Tech-Aerospace Engineer, Amity School of Engineering and Technology, Mumbai, Maharashtra 410221, India H. J. Rao Assistant Professor of Aerospace Department, Amity School of Engineering and Technology, Mumbai, Maharashtra 410221, India J. Sura Department Coordinator of Aerospace Engineering, Amity School of Engineering and Technology, Mumbai, Maharashtra 410221, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_60
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The flow visualization technique helps to understand the flow field qualitatively. Various methods such as tuft flow visualization, smoke flow visualization, laser sheet, surface oil, shadow technique, Schlieren photography, interferometer technique, and particle image velocimetry are used for this purpose. The smoke visualization technique has been chosen as it was found to be cost-efficient and more useful for the academic purpose. In the smoke flow visualization, the smoke, having the properties similar to the air, is inserted within the airflow. Here, in this paper, an attempt has been made to design a small-scale low-speed wind tunnel for smoke flow visualization.
2 Anatomy of a Wind Tunnel The low-speed wind tunnel [1] can be classified as blower type and suction type. The suction type tunnel provides undisturbed airflow inside the test section, while blower type tunnel needs arrangement of the flow straightener after the fan. In the current setup, the suction type tunnel has been designed. As shown in Fig. 1, the major components of a wind tunnel are honeycomb, settling chamber, screen, contraction section, test section, diffuser, and fan. The addition of the smoke generating setup is needed for smoke flow visualization tunnel. The calculations related to these sections have been discussed in the subsequent sections.
Fig. 1 Structure of a wind tunnel
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Fig. 2 Test section
2.1 Test Section Calculation The test section (see Fig. 2) houses the facility to mount the model and investigate the flow field. Most of the design criteria of wind tunnel depend on the choice of test section. To have uniform intake of airflow and no boundary layer separation at the end of the test section, the test chamber length should be in the range of 0.5–3 times the hydraulic diameter [2]. It is constant cross-section and sharp edges can lead to adverse pressure gradient, thus 1 cm chamfer of 45° is added on all edges. The test section dimensions, in current work, were taken as 10-cm by 10-cm. For this cross-sectional area, the length of the test section, based on the hydraulic diameter is 26-cm. To avoid increase in flow velocity over the test models and altering flow pattern, a blockage effect of 9% is calculated.
2.2 Contraction Section The contraction section of the wind tunnel accelerates the flow which allows honeycombs and screens to be placed in low-speed region. It reduces the fluctuating velocity variation to a small fraction of the average velocity as the total pressure remains constant throughout the contraction section and provides uniform flow velocity profile at the test section [3]. The design of this section should ensure that the flow is not separated. The exit cross-section is of the same shape and dimensions as that of the test section. To minimize the boundary layer migration in a contraction section whose cross-sectional aspect ratio changes along its length, chamfer of 45° is added. The contour smoothness of the section determines the control of boundary layer flow separation and flow uniformity. This region is prone to high total pressure loss in the upstream direction, i.e., screens; hence, the area ratio should be high and have maximum flow acceleration. Contraction is less efficient in suppressing longitudinal turbulence than the mean
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Fig. 3 Contraction section
velocity variation as they compress the cortex filament and stretch in the streamwise direction. The contraction area ratio should be between 6 and 10 in terms of constraints of space and cost for small, low-speed wind tunnels [3]. The area ratio of 8 has been chosen for the contraction section, which provides the inlet of the contraction section of 28-cm by 28-cm. The length of the contraction section is an important parameter as the short section can lead to flow separation while long section can increase the boundary layer thickness. The total length should be the ratio of total length and side of the inlet of the contraction section to achieve acceptable compromise. Therefore, in the current work, length of the section has been taken as 28-cm. The 3D modeling of the section has been shown in Fig. 3. Separation begins with small non-uniformities in the boundary layer due to the flow merging from the flow stabilizers upstream of the contraction [3]. Once the flow merges, the non-uniformities are amplified by Görtler vortices forming adverse lateral pressure gradient. In this design, the Bell-Mehta fifth-order polynomial has been used to design the contraction silhouette (see Fig. 4).
2.3 Settling Chamber The settling chamber, honeycomb, and screen are used for flow conditioning. The flow is aligned in the desired direction with the reduction in the unsteadiness. The turbulence is reduced, and good quality of flow is provided to the test section. The settling chamber houses the honeycomb and the screens to reduce the turbulence in the flow. The length of the settling chamber is a compromise between desired flow quality such that it decays the wake formation and growth of the boundary layer before the test section.
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Fig. 4 Fifth-order polynomial curve
Honeycomb The honeycomb reduces the fluctuations in transverse velocity by removing swirls with the least pressure drop and negligible effect on velocity. The honeycomb can be made of different cross-sectional elements such as circles, squares, hexagons, and trapezoidal. The study shows that the hexagonal structure is the shape with the lowest pressure loss [4]. The cell size determines the quality of the flow downstream. The porosity of the honeycomb is the ratio of actual flow cross-section area to total cross-section area. The value of the porosity should be more than 0.8 to minimize flow chocking. Also, the length of the honeycomb should be from 6 to 8 times of cell hydraulic diameter. Based on these constraints, the length of the honeycomb has been taken as 4-cm while the cell diameter as 0.6-cm. Solidity of honeycomb is defined as the ratio between the cross-section area of the solid sheet and settling chamber crosssection area. Here, honeycomb solidity and porosity are complementary factors. See Fig. 5 for the annotations. In Table 1, the main honeycomb characteristics are reported. Screen The screens are designed to reduce the stream-wise velocity fluctuations. Generally, metal wire meshes are used to form square or rectangular meshes. They break up the wake formation into smaller wakes which further decay and reduces the regeneration of turbulence due to vortex shedding. To achieve the uniform laminar flow, more than one screen is used. The distance between them and the number of screens are dependent on distortion caused by the previous mesh. The flow reaching the next screen should be without any distortion, and the pressure drop should have been fully recovered. The porosity of the screen should be in the range of 0.58–0.8 [5].
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(b) 3D
(a) 2D Fig. 5 Honeycomb section a 2D; b 3D
Table 1 Honeycomb main calculated characteristics Description
Symbol
Value
Cell diameter
dhoney
0.6 cm
Sheet thickness
Shoney
0.04 cm
Solidity
σh
0.11
Length
Lh
4 cm
Honeycomb cell side
lhoney
0.346 cm
External cell size
l g honey
0.392 cm
Divisions
z honey
14.78
Porosity
βh
0.887
Cell hydraulic diameter
Dh
0.63 cm
Length-hydraulic diameter ratio
L h /Dh
6.35
Lower porosity causes the flow instability while higher porosity does not reduce the turbulence. The distance between honeycomb and the screens was taken as 6-cm, and therefore, the length of the chamber was found to be 23-cm. The calculated values for the screens are given in Table 2. Table 2 Calculated screen values Screens
Porosity
Solidity
Screen wire number
Screen 1
0.69
0.31
160
Screen 2
0.78
0.22
110
flow cm2
Screen mesh density kg/m3
Mesh division (cm)
551.83
565.77
0.05
623.81
388.96
0.07
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2.4 Exit Diffuser Section The flow should decelerate smoothly after the test section. This is done by recovering the static pressure in a way that the load on the driver system is reduced. The area ratio should be in the range of 2–3, thus 2 was chosen [3]. Barlow’s [5] subsonic wind tunnel testing culminated that the diffuse cone expansion should not exceed 6°. Losses in the diffuser section is comparatively higher because of sudden decrease in flow velocity, thereby a pressure force is added to the skin friction forces. 6° divergence was found to be a good compromise between the length of the diffuser and the manufacturing cost. For these parameters, the exit dimensions of the diffuser were found to be 14-cm by 14-cm, and the length of the diffuser was found to be 39-cm. The inlet area of the diffuser is same as test section exit area. Since the fan is mounted at the exit of the diffuser, the outlet dimensions are kept similar to the fan dimensions.
2.5 Driver System Being the nature of air to move from high pressure to low pressure, this pressure difference is created inside the wind tunnel with the help of an exhaust fan attached downstream of the wind tunnel. The total length of the wind tunnel is 137-cm. The selected exhaust fan’s technical specifications are tabulated in Table 3.
3 Section Loss Coefficients and Pressure Loss While operating a wind tunnel, there are certain pressure losses due to the design criteria in the subsequent given conditions. Based on the methods given in ref [6, 7], various losses have been calculated and tabulated in Table 4. In the test section, constant cross-section increases the boundary layer thickness which increases the velocity outside the boundary layer. This causes static pressure drop and draws the model downstream. In contraction section, heavy pressure loss occurs due to its contour, thus a contour with low pressure loss, i.e., 5th degree Table 3 Technical specifications of exhaust fan
Technical specifications
Values
Sweep (fan diameter)
15 cm
Voltage
230 V
Power input
30 W
Speed
1300 RPM = 20.42 m/s (21.67 Hz)
Air delivery
275 cm
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Table 4 Losses in different section Section
Loss coefficient
Test section
0.037766
Pressure Loss (Pa)
Head loss
Contraction section
0.005163
Exit diffuser section
0.264
Honeycomb
0.24
0.9583
0.0797
Screen 1 (β = 0.69)
0.8655
3.456
0.2875
Screen 2 (β = 0.79)
0.55
2.196
0.1827
9.6457
0.8026
0.0206
0.0017
17.0559
1.4192
polynomial equation is used to construct the silhouette. In diffuser section, the total loss coefficient is evaluated by the sum of friction loss coefficient and expansion loss coefficient. Honeycomb structure itself causes lower pressure loss and screen experiences pressure loss at the entrance while reducing the turbulence in the flow.
4 Power Required The power input to the fan to generate the flow should overcome the losses incurred in the flow path. The losses calculated above occur due to the kinetic energy dissipated by turbulence and vortices due to screens, honeycomb, smoke rake, and test models. This loss in kinetic energy is compensated by pressure increase by an exhaust fan placed downstream of the wind tunnel. Since, the actual losses must be more than predicted with the empirical methods, and there are other losses due to the fan section and smoke rake a safety factor of 25% was added to the power required [8]. Based on the loss estimate, the power required was found to be about 120 W.
5 Smoke Visualization Techniques Two techniques are used for the smoke lines in the wind tunnel: smoke wire and smoke rake. In the smoke wire technique, liquid paraffin- or mineral oil-coated stainless steel wire is placed in the air flow region. The oil vaporizes because of an electrical pulse or resistive heating. The method is costly and requires high maintenance. The other technique (smoke rake) is an aerodynamically shaped body with row of tubes which emit the smoke [9]. The introduction of the smoke can be done through non-hazardous source connected with pipe. For the current project, the smoke rake technique has been used with smoke generated from glycerin. Since, the introduction of the rake in the test section can cause the flow to be altered; the rake has been designed with NACA 0024 airfoil (see Fig. 6). The distance between the tubes is kept such that there is little disturbance due to interaction between flow around nearest tubes [10].
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Fig. 6 Smoke rake
In order to capture the smoke clearly, one side of the test section is covered with black screen.
5.1 Development of Smoke Generating System A cost-efficient handmade smoke generating system is made using glycerin as the smoke material. To construct this device, a circular wooden board is taken to fix the equipments. The components required to heat up glycerin to generate smoke are a DC Air Pump, tick-tock on–off switch, a normal glass jar with a metal lid, copper wire of 0.1-cm and 0.2-cm OD (outer diameter), connecting wires, a metal screw, 0.07cm OD transparent rubber pipe, rubber connector (for pipes), 9 V Battery, M-Seal, Cotton, and Soldering Iron. A 12 V DC Air Pump is connected with tick-tock on–off switch using connecting wires and glued to wooden board. Four holes are drilled on the metal lid, one for soldering iron, one for a screw to connect with copper wire, and two for rubber pipes. The 0.2-cm copper wire is wound up on a 1-cm diameter rod to form a copper coil and is attached to the screw such that the spring stays inside the jar and exits a hole at the center of the metal lid. A transparent rubber pipe is connected to the DC air pump which is linked to the metallic lid such that the pipe ends inside the jar. This pipe is sealed with M-Seal on the inner part of the lid. A 9 V Battery is connected to the tick-tock switch and glued to the wooden board. Another pipe is connected to another the hole and sealed with M-Seal on the inner part of the lid. This pipe is connected to the smoke rake. To control the smoke flow, a valve is connected in between the smoke and transparent rubber pipe. To carry glycerin, cotton is wrapped around the copper coil. To hold the cotton, a 0.1-cm copper wire is wound around the cotton. Cotton is damped with glycerin depending on the amount of smoke required. Then, a soldering iron is inserted inside the copper coil such that the hot rod stays inside the jar. Once the setup is done, the lid is closed and the setup is operated. Figure 7a shows the arrangement of electronic devices, while the smoke generating
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(a) Arrangement of electronic devices
(b) Smoke generating system
Fig. 7 a Arrangement of electronic devices; b smoke generating system
Table 5 Salient features of smoke rake
Specifications
Values on the basis of smoke rake design
Number of stream tubes
9
Outer diameter of the tube
0.45 cm
Length of tube
3 cm
Distance between each stream tubes attached
0.8 cm
system is given in Fig. 6b. It takes about 10 min to generate sufficient smoke to obtain a steady flow stream. The salient features of the smoke rake are given in Table 5 [6].
6 Conclusion Design of a wind tunnel depends on the test section and its requirements. To achieve the desired flow rate inside the test section, other components are manipulated. The complete wind tunnel was designed to obtain smoke flow visualization. The test section size is apt to be visualized. A 5th degree polynomial equation for contraction contour provides smooth accelerated flow in the test section. Figure 8 depicts pressure loss in different regions. It can be seen that the diffuser section has the highest pressure loss because boundary layer cannot be estimated. Contraction section has the lowest pressure drop of less than 1%. The pressure losses in honeycomb and screens combined are comparatively lower than the test section and diffuser.
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Fig. 8 Pressure loss pie chart
Acknowledgements I would like to express my sincere heartfelt gratitude to my mentors Prof. H. Jeevan Rao and Dr. Jigarkumar B. Sura, assistant professor of Aerospace Engineering department, Amity University Maharashtra for giving me practical advice and unceasing ideas, their insightful guidance and knowledge, directing me at every step which has helped me tremendously. The profound experience and professional expertise of the mentors has enabled me to complete my project successfully.
Appendix: Wind Tunnel with Stand
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References 1. Mehta, R.D., Bradshaw, P.: Design rules for small low speed wind tunnels. Aeronaut. J. 443–453 (1979) 2. Abdullah, N.B.: Design and Fabrication of Portable Smoke Tunnel for Flow Visualization. Tronoh (2009) 3. Bell, J.H., Mehta, R.D.: Contraction Design for Small Low-Speed Wind Tunnels. Joint Institute for Aeronautics and Acoustics, Stanford (1988) 4. Prandtl, L.: Attaining a steady air stream in wind tunnel. Natl. Advisory Committee Aeronaut. 4(2), 40 (1933) 5. Barlow, J.B., Rae, W.H., Jr., Pope, A.: Low-Speed Wind Tunnel Testing. Wiley-Interscience Publication, Canada (1999) 6. Arslanian, P.J., Matin„ P.: Undergraduate Research on Conceptual Design of a Wind Tunnel for Instructional Purpose, p. 21. American Society for Engineering Education (2012) 7. Eckert, W.T., Mort, K.W., Jope, J.: Aerodynamic Design Guidlines and Computer Program for Estimation for Subsonic Wind Tunnel Performance. NASA, Wasington D.C. (1976) 8. Panda, M.K., Samanta, A.K.: Design of low cost open circuit wind tunnel—a case study. Indian J. Sci. Technol. 9(30), 1–7 (2016) 9. Trinder, M., Jabbal, M.: Development of a smoke visualization system for wind tunnel laboratory experiments. Int. J. Mech. Eng. Educ. 41(1), 17 (2013) 10. Beck, B.T., Whitson, B., Payne, G., Heitman, T.: An Investigation of Wing Morphing Phenomena in the Educational Wind Tunnel, p. 12. American Society for Engineering Education (2009)
Comparison of Effects of Cross Sections of Twisted Inserts in a Concentric Tube Heat Exchanger Yasir Baig, Alok Choubey, and Mousam Sharma
Abstract In present research work the problem of deviation of heat transfer rate along the other performance parameters in simple concentric tube heat exchanger (generally used in practice) is compared with tube in tube heat exchanger having circular cross sectional inserts and tube in tube heat exchanger having rectangular cross sectional insert with the help of a computer simulation performed on an analysis software, namely ANSYS-Fluent 16.0. The inlet conditions of all the cases are kept constant for all three systems and variations in outlet parameters are noticed with the help of simulation results as provided by the software. An increase in performance is noted with the insertion of inserts in heat exchanger, variation of results is also seen with varying cross sections of inserts. The rate of heat transfer is increased by 13.29% in case of rectangular cross sectional insert and by 0.28% in case of inserts with circular cross section. As the outcome depict a noteworthy improvement in the performance of a simple heat exchanger so this may prove to be very efficient in chemical industries as well as other sectors in which a fluid to fluid heat transfer takes place. Keywords CFD · Effectiveness NTU · Heat exchanger · Inserts · Concentric tube Highlights: • CFD analysis is used to simulate concentric tube heat exchanger with and without twisted inserts. • Transfer of Heat flow rate, LMTD and effectiveness is compared for the virtual models of the three heat exchangers. • Two inserts of different cross sections but same pitch length are used for creating turbulence.
Y. Baig (B) · A. Choubey Department of Mechanical Engineering, UIT, RGPV, Bhopal, India M. Sharma Department of Mechanical Engineering, SISTec, Bhopal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_61
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1 Introduction Heat exchanger is an important component of any plant which deals with the transfer of thermal energy for generation of power. The utility of a fluid to fluid heat exchanger is to a great extent in many chemical and automotive industries as well. As there is a huge demand for increasing the performance of heat exchanger, a huge amount of research is undergoing in various research labs and industries for achieving better heat transfer rates while making the heat exchanger more compact at the same time. The research is undergoing both experimentally and numerically using CFD analysis. Huge numerical and computational data needs to be analyzed and interpreted while using this method, thus use of computers become necessary. Much software’s are available in the market which allows us to perform these simulations after creating a virtual model of the problem. The solutions are generated based on the physical and boundary conditioned provided to the software. ANSYS-Fluent is one the very best software available for performing such lengthy simulations with a high speed computer. As according to the literature survey heat flow rate is made to enhance in heat exchangers with the help of various methods. Out of the different methods used, the method of creating protrusions over the surface or changing the geometry of the system is the most famous one. The necessity of the work is due to industrial problem related to heat recovery and Simulation is used to analyze such issues of heat transfer for a very long time by using different geometry and different inserts such as fins [1, 2]. After studying the work as described above, it is clear that a better performance of the heat exchanger could be achieved after inducing turmoil in flow of either one of fluids. To induce turbulence in the flow though the inner tube, different inserts have been used in the past. Our work depends upon the passive method. Here in order to create turbulence we are using helically twisted inserts which will be inserted in tube with smaller diameter for creating turbulence in hot fluid, which will lead to the increase in Reynolds number and eventually the increase in Nusselt number. A greater Nusselt number will lead to increase in heat transfer coefficient by convection, thus it could increase the heat transfer rate and other performance parameters significantly [3–5]. The present work is to analyze the effect of turbulence caused by the introduction of inserts of different cross section and how they affect the various parameters virtually. With the help of computational fluid dynamics we want to analyze that whether different cross sections of insert will have an effect over the systems performance? The heat exchanger on which the simulation is performed is modeled by using ANSYS 16 software and CFD (computational Fluid Dynamics) analysis is applied over it. The heat exchanger used is a concentric tube heat exchanger popularly known as tube in tube heat exchanger [6–10].
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2 Methodology A complete flow chart of the process used while performing the simulation is shown in Fig. 1. The objective here is to create a model and an environment in which the system will run in actual practice and then calculate the results based on some variable which will depict the performance of the systems under considerations. Similar process will be followed for all the three systems for comparing their results. The process followed is of iterative nature and consists of following major steps.
2.1 Creating a Model of the Concentric Tubes Firstly model of simple concentric tube heat exchanger is to be made by using a design modeler tool. A tube with inner diameter 10 mm and outer diameter 12 mm and length 1500 mm respectively is concentrically placed into another tube of 14 mm inner diameter, 16 mm outer diameter and 1500 mm length respectively as shown in Fig. 2. Apart from the basic concentric tubes of heat exchanger two helically twisted tapes are also created in the same software of pitch length 2.5inches. One insert has the circular cross section of 1.5 mm diameter whereas the other insert is having a rectangular cross section of 4 mm*1.5 respectively as shown in Fig. 3a–b. Both the tubes are given Stainless steel material whereas the inserts are made up of aluminum. The virtual model is made using the Ansys design modeler tool. A total of three such concentric tubes systems are made for all the three cases. After which it is used in Ansys fluent module for performing the simulation. In Meshing module automatic meshing is done in order to have the combined suitable type of mesh sizes and shapes
Fig. 1 Flowchart of Methodology adopted
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Fig. 2 Cross sectional view
Fig. 3 a Circular sectional view b Rectangular inserts
and different location. These meshes are created finely in order to have accuracy of results.
2.2 Creating Boundary Conditions for the System After Meshing is completed suitable boundary conditions are given and fixed for the geometry. The outer surface of the system is made to be adiabatic and the system is made to run in counter flow combination. Convection heat transfer is allowed in between the inner wall of inner tube and hot fluid flowing through the inner tube and outer wall of outer tube with cold fluid flowing through the annulus. Conductive heat transfer is also allowed through the wall of εinner tube and the system is given a degree of freedom to create turbulence by using K-ε model. Apart from these boundary conditions, some other necessary setup as required by the software was provided [11–13].
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2.3 Input Variables All the other necessary parameters are set in the setup module of ANSYS 16 for initializing the problem. The system is inserted with the input variables such as the temperature of hot and cold fluid at inlet is given to be 350 k and 300 k respectively. The material properties of both the tubes are given to be exactly as that of stainless steel and the fluid properties are given of that of water at 450 C. The volume flow rate of hot fluid is firstly kept at a value of 65 LPH (Litre per hour) and that of the cold fluid is kept at 85 LPH for the first case.
2.4 Running the Simulation The problem is then allowed to solve and calculate the outlet temperatures of the fluid at steady state condition [14–16]. The system uses the fluent solver in order to numerically calculate the temperature of the hot and cold fluid at outlet sections. It is also an iterative calculation and the solver is allowed to run until it reaches to a steady state and provide the results.
2.5 Simulation Giving Results After the solvent stops then it may give results based on our input parameters but as shown in the process flow chart in Fig. 2 there is a possibility that the solver may give an error and will not show any results. At times like these the system is required to check the process as explained in Sect. 2.2 and 2.3 respectively in order to identify the probable cause of failure. On the other hand if the system is giving us results then the process may move to the next step.
2.6 Results Making Realistic Sense Temperatures of hot and cold fluid at outlet conditions are noted and taken for the calculation of the performance parameters such as Rate of Heat transfer, Logarithmic mean temperature difference, Coefficient of overall heat exchange and effectiveness [17–20]. The results are again checked and if identified to be making realistic sense then only is accepted otherwise, we again iterate the process by moving on to the process as explained in Sect. 2.2 and 2.3 respectively.
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2.7 Accept and Generate Report After the long iterative process of finding out the correct performance parameters the results are accepted and a report is generated. Similar analysis is performed after varying the flow rate of hot fluid flowing through the inner tube. The values 65, 75, 85, 98, 105 LPH are used for calculating the performance parameters whereas the value of cold fluid flow rate was kept constant at 80LPH. After calculating five sets of readings for simple plane tube heat exchanger, the same analysis and simulation is performed for the heat exchanger with circular cross sectional inert and then for rectangular cross sectional insert. The values of Inlet temperatures and flow rates of both the fluids are taken similar to that of plane tube in tube heat exchanger.
3 Mathematical Model After observing the results of Fluent software in the form of outlet temperature of both the fluids at steady state, We can calculate the following parameters [21, 22].
3.1 Heat Transfer Rate of Hot Fluid As the inlet and outlet temperature of both the fluids are known then, we can find out the heat lost by hot fluid by using following expression. Q = mh Cph(Thi−−Tho)
(1)
3.2 Heat Transfer Rate of Cold Fluid With the help of the temperatures of cold fluid inlet and outlet we can find out the heat taken away by cold fluid by the following expression. Q = mc Cpc(Tco−−Tci)
(2)
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3.3 Logarithmic Mean Temperature Difference At different section along the length of circular tubes the temperature difference between hot fluid and cold fluid will be different, as the hot fluid will continuously be losing heat to cold fluid. Logarithmic mean temperature difference is the equivalent temperature difference which can be used to calculate heat transfer rate of the heat exchanger if it is substituted in the general equation of heat transfer in Heat exchanger. We find out the logarithmic mean temperature difference for every case by using the following expression [23]. Tlm =
(T2 − T1 ) 2 I n( T ) T1
=
(T1 − T2 ) T1 I n( T ) 2
(3)
3.4 Area of Interaction (A) As the transfer of amount of heat rate is also a function of how much area is given to the fluids to interact with each other thus we also calculate the area of interaction based on inner diameter of inner tube for the full length of the pipe by the following expression. A = π ∗ Di∗ L
(4)
3.5 Number of Transfer Units (NTU) The quantity is called the Number of transfer units. It is a dimensionless parameter, which is given as NTU =
Heat capacity rate of exchanger(W/K) UA UA = = Cmin (mC p min ) Heat Capacity rate of fluid(W/K)
(5)
3.6 Effectiveness (ε) A dimensionless parameter having ratio of heat transfer rate ‘Qactual by heat exchanger to maximum possible heat transfer rate Qmax ‘ it is denoted by ‘ε’. The value of effectiveness of the heat exchanger can also be determined for each case by
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using the following expression. mhCph(Thi − Tho) Q act Tco − Tci = ε= Q max Q max Q max
(6)
where, Qmax = maximum heat transfer possible Q max = Cmin ∗ (Thi−−Tci)
(7)
4 Results and Discussions Figure 4a and b shows the simulation results of volume renderings done by Ansys on plain tube heat exchanger and tube in tube heat exchanger with rectangular inserts respectively. A clear variation of outlet temperature of hot fluid can be noticed by color coding of both the systems. The red color specifies a much higher value of exit temperature in Fig. 4a where as a light brown color specifies a much lower value of exit temperature of inner fluid domain in case of rectangular insert as shown in Fig. 4b. Figure 5 the variation of heat transfer rate for standard model is shown in comparison to other two cases and the maximum heat transfer rate is 2176.19 Watts which is achieved. With inserting tapes of circular cross section at volume flow rate of 105LPH which is 0.28% greater than rate of heat transfer in rectangular cross sectional inserts and 13.9% greater than heat transfer rate achieved for simple tube in tube heat exchanger without any insert. In Fig. 6 the variation of Coefficient of overall heat exchange for simple tube in tube heat exchanger is shown in comparison with other two cases and the maximum value of coefficient of overall heat transfer is 1576.610603(Watts/m2 -k) which is achieved with inserting tapes of rectangular cross section at the volume flow rate of
Fig. 4 Simulation results for volume rendering
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Fig. 5 Effect of volume flow rate over heat exchanged
Fig. 6 Effect of volume flow rate over coefficient of overall heat exchange (U)
105LPH which is 2.07% greater than Coefficient of overall heat exchange in circular cross sectional inserts and 21.7% greater than Coefficient of overall heat exchange achieved for simple tube in tube heat exchanger without any insert. In Fig. 7 the variation of LMTD for standard model is shown in comparison with other two cases and it can be easily seen that the maximum LMTD is 32.29 k which is achieved in the case of standard model at 85LPH which is 9.56% greater than LMTD in circular cross sectional inserts and 12.6% greater than LMTD achieved for tube in tube heat exchanger with rectangular insert.
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Fig. 7 Effect of volume flow rate over LMTD
5 Conclusion After considering the results shown by the present research work the following points can be concluded. (1)
(2) (3)
(4)
(5)
(6)
It was seen that at flow rate of 105 LPH heat transfer rate was found to be maximum in circular cross sectional inserts but other than that it was maximum for rectangular cross sectional insert. So, there could a unique profile which could result in maximum heat transfer rate at that particular volume flow rate of water. Rate of heat exchanged is increased by 13.29% in case of rectangular cross sectional insert and by 0.28% in case of inserts with circular cross section The above presented results show a great amount of increase in all the performance parameters with increase in mass flow rate as well as with introduction of inserts of various cross sections. The variation among the results of both the inserts also tells us that the profile of inserts also encourages variation in heat transfer rates, LMTD and effectiveness etc. even though we have kept the pitch length of twists as constant. The results shown by rectangular inserts also shows us that the variation in output parameters were not greatly affected by varying flow rate, whereas the variation of performance parameters is greatly affected by circular cross sectional twisted inserts. The value of effectiveness for all the cases kept on decreasing until the heat capacity of hot fluid was kept lower than the heat capacity of cold fluid, but as soon as it becomes lower than the other, the value of effectiveness started increasing.
After going through all the results it is safe to say that the introduction of turbulence in the flow of fluid with the help of inserts definitely has increased the Nusselt number
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of the flow and ended up increasing all the performance parameters of the system under consideration. Thus it is proving very efficient and useful results and may lead to economical running of heat exchangers in practice.
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17. Kharat, R., Bhardwaj*, N., Jha, R.S.: Development of heat transfer coefficient correlation for concentric helical coil heat exchanger. Int. J. Therm. Sci. 48, 2300–2308 (2009) 18. Pal, E., Kumar, I., Joshi, J.B., Maheshwari, N.K.: CFD simulations of shell-side flow in a shelland-tube type heat exchanger with and without baffles. Chem. Eng. Sci. 143(2016), 314–340 (2015) 19. Luo, X.J.: Parametric study of heat transfer enhancement on cross-flow heat exchangers. Chem. Eng. Process. 121(2017), 81–89 (2017) 20. Rios-Iribe, E.Y., Cervantes-Gaxiola, M.E., Rubio-Castro, E., Hernández-Calderón, O.M.: Heat transfer analysis of a non-Newtonian fluid flowing through a plate heat exchanger using CFD. Appl. Therm. Eng. 101(2016), 262–272 (2016) 21. Dvoˇrák, V., Vít, T.: CAE methods for plate heat exchanger design. Energy Proc. 134, 234–243 (2017) 22. Abou, T.M., Elmaaty, A.E., Mahgoub, K.M.: corrugated plate heat exchanger review. Renew. Sustain. Energy Rev. 70(2017), 852–860 (2017) 23. Chen, J., Jiajun, W., Xiaoyan Ji Xiaohua, L., Wang, C.: Mechanism of waste-heat recovery from slurry by scraped-surface heat exchanger. Appl. Energy 207(2017), 146–155 (2017)
Experimental Investigation and Machinability Study of Ni–Cr-Based Super Alloy Using Powder Mixed EDM R. S. Barot , Janak B. Valaki, Alpesh H. Makwana, and Hardik Beravala
Abstract The field of electrical discharge machining (EDM) is very vast and has a lot of applications and variations in its execution. The advent of powder mixed EDM and ultrasonic vibrating EDM has given the area of micromachining several new parameters to improve the machining output. However, the presence of a vast number of input parameters like gap voltages, on–off time, workpiece material, tool material, dielectric, powder material, etc. has resulted in difficulties in predicting output parameters like surface quality or machining rate. Higher nickel content induces more difficulty in machining as nickel, cobalt content, or titanium constituent gives tendency to work harden. In this paper, authors have made an attempt to study machinability and comparative investigation between the input and output parameters using powder mixed EDM for INCOLOY 800 containing high % of NI, Cr. For the experimentation, an isolated setup for powder mixed EDM is built within the already available EDM setup. Experiments are performed for various combinations of current, on time and concentration of powder. Also, the effects of all parameters for both the output variables are shown graphically and studied for confirmation with the theoretical behavior. Using PMEDM machining of enhances process mechanism results higher MRR with good quality characteristics, which saves energy directing sustainable manufacturing. Predictive modeling is also adopted for the process performance using ANN and regression analysis for PMEDM. Keywords Powder mixed EDM · Machinability of super alloy · Design of experiment · Sustainable manufacturing · Surface quality improvement R. S. Barot (B) · H. Beravala Birla Vishvakarma Mahavadhiyalaya, Vallabh Vidhaynagar, Gujarat 388120, India e-mail: [email protected] H. Beravala e-mail: [email protected] J. B. Valaki Government Engineering College, Bhavnagar, Gujarat 364001, India A. H. Makwana Government Engineering College, Dahod, Gujarat 389151, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_62
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1 Introduction 1.1 Electrical Discharge Machining EDM is an thermo-electric metal removal process, in which material is eroded by electrical energy resulting into thermal energy of the spark. EDM works on the principle of spark erosion. A dielectric or insulating medium envelops the entire workpiece surface, which acts as a negative electrode. A moving tool, which acts as a positive electrode, is set at a small distance from the workpiece surface.
1.2 Need for Powder Mixed Electrical Discharge Machining (PMEDM) In today’s era of high precision manufacturing, electrical discharge machining (EDM) has emerged as one of most popular and versatile machining methods. Due to advancements in manufacturing, nuclear and aerospace industries, the demands for high machining precision with low surface roughness at relatively high machining rates are increasing. EDM process is primarily used for the machining of complex and uneven geometrical shapes with excellent dimensional tolerances. Moreover, EDM is also suitable for surface modification of work materials [1, 2]. Mainly, the research works reported in literatures are related to improving process performance measures, optimizing the process variables, study of the sparking mechanism and sparking generation methods, simplifying the electrode design and manufacture [1]. Industrial survey on EDM performance under the influence of parameters such as Material types, machining types and surface quality of product demands critical analyses [3]. Furthermore for the improvement of the performance parameter like material removal rate and surface quality, powder mixed EDM is considered as an appropriate technique. It is reported that, increasing the current during EDM processing, both MRR and surface roughness increases. While, with increase in pulse on time, MRR and surface roughness decreases [4, 5]. Impact of tool rotation and different concentrations of external magnetic field on electrical discharge machining (EDM) performance is investigated such as tool wear increases when the machining of hole drilling with dielectric fluid as oil than that of dielectric fluid as a distilled water [6]. Many researchers have made attempt to improve EDM process performance using powder in dielectric fluid [7, 8]. To overcome MRR and poor finish issue in EDM, metallic powder is added in dielectric fluid due to which spark gap increases [9]. Powder size plays important role in PMEDM process; as powder size increases, gap increases resulting high surface roughness and reduction in MRR [10]. Addition of powder increases gap discharge due to which flushing of debris get improved; hence, spark frequency improves [11, 12]. INCOLOY 800 machinability is greatly influenced by variation of EDM process parameters [13]. Biomedical implant surface medication can be attempted using PMEDM [14]. It is important to
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vary process parameter and process mechanism to optimize process performance [15, 16]. Sustainability issues and challenges need to be critically analyzed through new machining environment improvements which increases EDM process adoptability [17]. Machinability of Inconel 718 high temperature alloy shows prominent results in high speed machining as compared to turning and milling operations [18]. Pulse duration and pulse interval critically affects response parameters such as straightness perpendicularity and surface roughness for machining of Incoloy 825, Inconel 603 XL, Monel K400 and Inconel 600 in Wire EDM [19]. Incoloy 800 is high strength material which can be considered as difficult to machine and can be used for exposed to high temperature environments machined successfully with Wire EDM [19].
2 Powder Mixed EDM 2.1 Mechanism of PMEDM Researches have reported that the presence of these debris particles in the dielectric fluid significantly reduces the breakdown strength of the dielectric fluid. The external powder particles added may facilitate the ignition process and increase the sparking gap size. It may help to normalize electric field charges in the gap and results into uniform sparking generation and thereby uniform and controlled material removal process. It shows machining spark region without and with suspended particles in which subsequently erodes material in the form of tiny crates in later mechanism as shown in Fig. 1.
2.2 Experimental Setup PMEDM It is aimed to develop low-cost PMEDM setup which do not alter original machine machining conditions and evaluate the effect of mixing of conductive metallic powders in the dielectric fluid on the output parameters like MRR and surface finish. The experimental setup as shown in Fig. 2 designed for powder mixed dielectric fluid so that the powder and debris do not get mix with the electric fluid in the main tank of machine and also do not affect the filters of the machines. A separate tank has been prepared in which the dielectric fluid mixed with the metallic powder is stored. The dielectric fluid from tank is taken to the work holding tapering vessel through a circulating pump. A control valve has been provided in the jet of submersible pump to control the flow of the dielectric fluid in the tapering vessel. A return path of dielectric fluid has been provided along with flow control valve from hopper to the tank. With the help of this flow control valve, the head of the dielectric fluid is maintained above the workpiece. Magnets are provided in the dielectric fluid tank in order to remove the debris of the workpiece which is ferrous in nature; in this
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Fig. 1 Comparison of EDM and PEDM material removal mechanism [20]
Fig. 2 Aluminum powder used for experimental investigation
course of action, the nonmagnetic metallic powder is not separated, and they can be re-circulated along with the dielectric fluid.
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3 Experimental Investigation for PMEDM 3.1 Design of Experiment Experiments are properly designed to get maximum information with minimum number of observations for a given limits of time using design of experiments (DOE). Process parameters and its optimization using response surface methodology help to reduce number of experiments [21]. Machining conditions used in PMEDM are as shown in Table 1. In current experimental analysis, multi-factor multi-level design for 2 factor-3 level and one factor with 4 level is selected for factorial design to find out significance of process parameter using analysis of variance (ANOVA) at 95% confidence level with Minitab software.
3.2 Material Selection It is aimed to experimentally investigate effect of PMEDM for wide range of machining conditions. INOCOLOY 800 material has widespread applications in high temperature exposed environments, aerospace and biomedical applications due to excellent mechanical strength and oxidation resistance, good surface stability and corrosion. Specimen prepared of equal size of 20*20 mm and polished using lapping machine to ensure uniform machining conditions. 30% Ni, 20% Cr-based super alloy INCOLOY 800 is selected for experimental investigation and machinability analysis using PMEDM. Tool (electrode): Pure Electrode Copper of 12 mm diameter is used as electrode material. Kerosene is the most suitable since distilled water induces crack propagation [8]. Aluminum has the better surface finish, whereas silicon and graphite yield superior microhardness and MRR [9, 11]. Powder size affects performance of EDM [22]. In current experimental work 400 mesh size aluminum powder is used for the evaluation PMEDM process performance.
3.3 Experimentation for PMEDM Experimental process parameter variation and its combination is shown with measured response values for MRR, and surface roughness for both materials is as shown in Table 1.
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Table 1 Experimentation for INCOLOY 800 Experimental output Sr. no Gap current (I) (A) On time Concentration MRR Surface roughness Ra (Ton) (µs) (C) (g/l) (mm3 /min) (µ) 1
9
4
2
3.7
7.27
2
9
6
2
15.7
10.24
3
9
7
2
15.69
10.28
4
13
4
2
7.36
7.22
5
13
6
2
26.2
12.65
6
13
7
2
22.67
9.58
7
17
4
2
9.03
3.44
8
17
6
2
29.77
11.64
9
17
7
2
30.53
15.24
10
9
4
4
3.71
6.03
11
9
6
4
13.14
8.09
12
9
7
4
14
8.32
13
13
4
4
12.98
8.97
14
13
6
4
17.18
9.86
15
13
7
4
23.33
16
17
4
4
21.03
8.97
17
17
6
4
24.07
12.07
18
17
7
4
27.4
14.84
19
9
4
6
10
20
9
6
6
44.41
21
9
7
6
8.73
22
13
4
0
8.5
11.8
23
13
6
6
24.62
10.58
24
13
7
6
18.66
25
17
7
0
11.8
26
17
4
6
15.88
6.65
27
17
6
6
14.14
12.03
28
13
4
6
19.66
7.01
29
9
6
0
3.0
8.1
30
13
6
0
10.8
12.8
31
17
7
6
24
12.18
32
9
7
0
4.4
10.0
11.2
6.6 12.25 6.68
2.06 12.4
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3.4 Analysis of Variance (ANOVA) As shown in Table 2, gap current, on time and powder concertation have significant effect on MRR which validates the theoretical analysis with experimental analysis. P value for concentration and on time is closer to 0.029 as shown in Table 2 which indicates that PMEDM has significant improved effect on process removal of material removal for INCOLOY800 which evident form main effect plot as shown in Fig. 4. It is important to carry out experimental investigation on EDM performance for machinability of high Ni-Cr based super alloys for higher productivity and better surface quality of product using design of experiment followed by development of correlation using suitable optimization and artificial intelligent technique [23–32]. MRR increases with increase in gap current and concentration as observed in Fig. 3. Interaction effect of on time and concertation has positive effect and further can be studied in detail. On time affects the MRR most, since its P value is closest to 0.0. It is followed by gap current. From ANOVA as shown in Table 3, it is visible that gap current and on time have significant effect on surface roughness. Presence of powder reduces Table 2 Analysis of variance for MRR for INCOLOY 800 Source
DF
SS
MS
F
P
I
2
347.1
173.54
3.29
0.055
Ton
2
618.0
309.1
5.85
0.009
3.56
0.029
C
3
563.5
187.82
Error
24
1267.4
52.81
Total
31
2716.4
Fig. 3 Main effect plot for MRR
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Fig. 4 Main effect plot for SR
Table 3 Analysis of variance for SR for INCOLOY 800 Source
DF
SS
MS
F
P
I
2
36.92
18.460
2.82
0.080
Ton
2
69.46
34.728
5.30
0.012
1.04
0.392
C
3
20.49
6.829
Error
24
157.29
6.554
Total
31
284.49
gap leading to raise of energy intensity for same sparking gap resulting in formation of larger crater which increases surface roughness. Single objective optimization is performed using Taguchi analysis for SR. Minimization of SR set as goal to perform optimization. Surface roughness is less when the current is around 17 A in PMEDM. Roughness decreases with the increase in on time up to certain level as observed in Fig. 4. Selection range for gap current respective diameter plays an important role either in roughing cut or finishing cut. Single objective optimization is performed using Taguchi analysis for MRR. Maximization of MRR and minimization of SR set as goal to perform optimization as shown in Tables 4 and 5, respectively. Combination high gap current 10 amp, medium on time (µs) and low concentration (g/l) of given range always results in lower SR while highest MRR is achieved at high current 17 amp, medium on time and high concentration. The suggested optimal concentration yields good Ra values, whereas higher concentration values decrease surface roughness.
Experimental Investigation and Machinability Study … Table 4 Response table for optimization of MRR for INCOLOY 800
681
Level
I
Ton
C
1
19.10
19.64
16.62
2
24.14
24.19
23.37
3
25.72
24.17
23.76
Delta
6.62
4.95
8.44
Rank
2
3
1
4
Table 5 Response table for optimization of SR for INCOLOY 800
20.06
Level
I
Ton
C
1
−18.42
−17.00
−20.72
2
−18.73
−20.67
−19.14
3
−20.15
−19.34
−19.58
Delta
1.72
3.67
3.16
Rank
3
1
2
−17.57
4
4 Conclusion Experimental investigation validates potential impact of used of powder in EDM which reduces energy intensity area at energy level and which enables working at higher current levels resulting in higher MRR. Powder particles work as path way for current; hence, energy between electrode and workpiece remains same but intensity reduces through powder particles, which results into small craters. Hence, working at higher current values in presence of powder gives higher MRR without deteriorating the surface quality. • INCOLOY 800 machinability using EDM is successfully experimented and analyzed. • The plots and response surface show that effect of powder versus conventional EDM without concertation of powder is indeed visible and positive. It needs detailed analysis to predict the exact variations. • Gap current looks to improve MRR and surface roughness. • On time improves MRR and sometimes reduces surface roughness. • Concentration improves both MRR and Ra values up to a certain point (4 g per liter) after which Ra values may deteriorate. • Experimental investigation depicts need of development of predicated range of process parameters with detailed investigation using predictive ANN modeling techniques.
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References 1. Ho, K.H., Newman, S.T.: State of the art electrical discharge machining (EDM). Int. J. Machine Tools Manuf. 43, 1287–1300 (2003) 2. Kumar, S, Singh, R., Singh, T.P., Sethi, B.L.: Surface modification by electrical discharge machining: a review. J. Mater. Process. Technol. 209, 3675–3687 (2009) 3. Abbas, N.M., Solomon, D.G., Bahari, M.F.: A review on current research trends in electrical discharge machining (EDM). Int. J. Machine Tools Manuf. 47, 1214–1228 (2007) 4. Yerui, F, Feng, G.Y., Feng, L.Z.: Experimental investigation of EDM parameters for TiC/Ni cermet machining. Procedia CIRP 42, 18–22 (2016) 5. Teimouri, R, Baseri, H.: Study of tool wear and overcut in EDM process with rotary tool and magnetic field. Adv. Tribol. 1–8 (2012) 6. Fu, Y, Miyamoto, T., Natsu, W., Zhao, W., Yu, Z.: Study on influence of electrode material on hole drilling in micro-EDM. Procedia CIRP 42, 516–520 (2016) 7. Kansal, H.K., Singh, S., Kumar, P.: Technology and research developments in powder mixed electric discharge machining (PMEDM). J. Mater. Process. Technol. 184, 32–41 (2007) 8. Wong, Y.S., Lim, L.C., Rahuman, I., Tee, W.M.: Near mirror finish phenomenon in EDM using powder mixed dielectric. J.Mater. Process. Technol. 79, 30–40 (1998) 9. Prihandana, G.S., Mahardika, M., Sriani, T.: Micromachining in powder-mixed micro electrical discharge machining. Appl. Sci. 10(11), 3795 (2020) 10. Bhattacharya, A., Batish, A., Kumar, N.: Surface characterization and material migration during surface modification of die steels with silicon, graphite and tungsten powder in EDM process. J. Mech. Sci. Technol. 27(1), 133–140 (2013) 11. Khosrozadeh, B., Shabgard, M.: Effects of simultaneous ultrasonic vibration of tool and addition of carbon nanotube into the dielectric in EDM process on machining outputs and surface integrity of Ti-6Al-4V alloy. Indian J. Eng. Material Sci 24, 45–56 (2017) 12. Fong, T.Y., Chen, F.C.: Investigation into some surface characteristics of electrical discharge machined SKD-11 using powder-suspension dielectric oil. J. Mater. Process. Technol. 170 (1–2), 385–391 (2005) 13. Barot, R.S., Desai, K.P., Raval, H.K.: Experimental investigations and monitoring eectrical discharge machining of incoloy800. J. Manuf. Eng. 12(4), 196–202 (2017) 14. Mohammad, P.M.: Surface modification for osseointegration of Ti6Al4V ELI using powder mixed sinking EDM. J. Mech. Behav. Biomed. Mater. 113, 104145 (2021) 15. Phan, N.H., Muthuramalingam, T., Vu, N.N., Tuan, N.Q.: Influence of micro size titanium powder-mixed dielectric medium on surface quality measures in EDM process. Int. J. Adv. Manuf. Technol. 109, 797–807 (2020) 16. Valaki, J.B., Rathod, P.P., Sidpara, A.M.: Sustainability issues in electric discharge machining. Innov. Manuf. Sustain. 53–75 (2019) 17. Arunachalam, R., Mannan, M.A.: Machinability of nickel-based high temperature alloys. Machining Sci. Technol. 4(1) 127–168 (2000) 18. Manikandan, K., Ranjith kumar, P., Raj Kumar, D., Palanikumar, K.: Machinability evaluation and comparison of Incoloy 825, Inconel 603 XL, Monel K400 and Inconel 600 super alloys in wire electrical discharge machining. J. Mater. Res. Technol. 9(6) 12260–12272 (2020) 19. Manikandan, N., Binoj, J.S., Thejasree, P., Abhishek, H., Karthik Goud, B.: Multi aspects optimization on spark erosion machining of Incoloy 800 by Taguchi grey approach. Mater. Today: Proc. 39(1), 148–154 (2021) 20. Joshi, A.Y., Joshi, A.Y.: A systematic review on powder mixed electrical discharge machining. Heliyon 5, 1–12 (2019) 21. Tzeng, Y.F., Lee, C.Y.: Effects of powder characteristics on electro-discharge machining efficiency. Int. J. Adv. Manuf. Technol 17, 586–592 (2001) 22. Ramesh, S., Jenarthanan, M.P.: Optimizing the powder mixed EDM process of nickel based super alloy. Proc. Instit. Mech. Eng. Part E: J. Process Mech. Eng. (2021) 23. Zhao, W.S., Meng, Q.G., Wang, Z.L.: The application of research on powder mixed EDM in rough machining. J. Mater. Process. Technol. 129, 30–32 (2002)
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24. Pecas, P., Henriques, E.: Effect of the powder concentration and dielectric flow in the surface morphology in electric discharge machining with powder-mixed dielectric (PMD-EDM). Int. J. Manuf. Technol. 37, 1120–1132 (2008) 25. Ming, Q.Y., He, L.Y.: Powder suspension dielectric fluid for EDM. J. Mater. Process. Technol. 52, 44–54 (1995) 26. Karthikeyan, R„ Lakshmi Narayanan, P.R., Naagarazan, R.S.: Mathematical modeling for electric discharge machining of aluminium-silicon carbide particulate composites. J. Mater. Process. Technol. 87, 59–63 (1999) 27. Singh, N.K., Upadhyay, R.K., Singh, Y., Sharma, A.: Intelligent hybrid approaches for ensuring better prediction of gas-assisted EDM responses. SN Appl. Sci. 2, 914 (2020) 28. Alduroobi, A.A.A., Ubaid, A.M., Tawfiq, M.A., Elias, R.R.: Wire EDM process optimization for machining AISI 1045 steel by use of Taguchi method, artificial neural network and analysis of variances. Int. J. Syst. Assur. Eng. Manage. 11, 1314–1338 (2020) 29. Tyagi, R., Kumar, S., Kumar, V., Mohanty, S., Das, A.K., Mandal, A.: Analysis and prediction of electrical discharge coating using artificial neural network (ANN). In: Lecture notes on multidisciplinary industrial engineering advances in simulation, product design and development, pp. 177–189. (2019) 30. Valaki, J.B., Barot, R.S., Karhadkar, G.D.: Development of a microcontroller based rotating electrode attachment for ram type electro discharge machine. In: Proceedings of National Conference on Emerging Trends in Mechanical Engineering (ETME’07), June 4–5, pp. 78. (2007) 31. Barot, R.S., Beravala, H.S., Patel, B.S., Tadvi, P.M.: Parametric optimization of EDM process using grey relational analysis based on Taguchi orthogonal array. Int. J. Appl. Eng. Res. Proc. Int. Conf. Emerg. Trends Eng. Technol. 6(18), 2787–2790 (2011) 32. Sahu, D.R., Mandal, A.: Critical analysis of surface integrity parameters and dimensional accuracy in powder-mixed EDM. Mater. Manuf. Process. 35(4) (2020)
Performance Analysis of Single Cylinder Four-Stroke Diesel Engine Manish Singh Bharti and Alok Singh
Abstract One thing is compression ratio has an extremely key influence on emission, fuel economy, as well as other internal combustion engine performances. In diesel engines, variable compression ratio applications have various benefits, such as extensive field of the best operating rule and limiting maximal within cylinder pressure to the most important requirements: power, consumption, noise, emission, along with multi-fuel ability. The document presents the patented method for usually modify engine compression ratio through two portion connecting rod. Alongside investigational research, a model of the diesel engine combustion process through straight injection has been carried out. In this paper, we study about the four-stroke single-cylinder diesel engine as well as its features with the investigation of fuel consumption, basic power, brake thermal efficiency, and specific fuel consumption. Keywords Brake power · Fuel consumption · Efficiency
1 Introduction This arrangement consists of a four-stroke, single-cylinder, and variable compression ratio (VCR) diesel engine associated with the current form dynamometer designed for loading. The compression ratio could be altered with nonstop engine along with changing the combustion chamber geometry through particularly considered leaning cylinder chunk collection. The plan is outfitted with essential instruments for start squeezing component and wrench point assessments. These signs are line to PC throughout the engine indicator for drawing. The plan is as well completed for interfacing wind current, temperatures, fuel stream, and load assessment. The model has a self-sufficient board box that has two petroleum tanks intended for duel fuel test, air box, fuel assessing M. S. Bharti (B) Department of Mechanical Engineering, RNTU, Bhopal, India A. Singh Department of Mechanical Engineering, MANIT, Bhopal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_63
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unit, manometer, fuel stream assessments, and transmitters for air, engine marker, and cycle pointer. Rota meters are obliged calorimeter and cooling water stream assessment [5, 6]. As bit by bit, surge causes air tainting and cause to achieve a perilous barometrical devotion, release rule is to get demanding. It is critical to diminish NOx from CI similarly to gas engine and PM; CI engine. Vapor gas spread from inward engines fundamentally affects human prosperity, animal, plant, and biological prosperity, and government help. This drove us to use assorted advancement mechanical methodology to control exhaust release from inside consuming engine. In case we will accept this technique to control transmission, it would allow reducing engine execution if unfriendly affects engine execution. EGR technique is a lot of exhibited system to decrease NOx. We have found out about an examination to find the effect of EGR on consuming miracle of CI engine.
2 Literature Review In IC engine, exhaust gas recirculation (EGR) is a strategy that diminishes the hazardous discharge, for instance, NOx in gasoline well as in CI/Diesel engine. EGR is a direct strategy to supply recently exhausted gas again back to the ignition chamber by some cooling and filter media to increase or to keep up the temperature of the chamber head [1]. Agarwal et al. [2] observe the EGR impact on show off ash stores, and crucial motor parts, particularly cylinder rings, and execution in air-cooled, and aside from discharges, two chambers, direct injection steady speed diesel engine, which is frequently utilized within decentralized captive power age as well as hardware horticultural homestead. Such machines are naturally not effective through EGR. These tests were completed to uncertainly evaluate the execution and emanations for a variety of the machine EGR paces. Discharges of NOX, smoke capacity, exhaust gas temperature, hydrocarbons (HC), carbon monoxide (CO) of the exhaust gas, and so on were considered. Performance constraints such as BSFC (brake-specific fuel consumption) and thermal efficiency were calculated. Diminution in the exhaust gas and NOX temperature was experiential other than emissions of CO, HC, and particulate matters (PM) was established to have augmented with EGR usage. The engine was activated for 96-h in the deposits on fundamental engine parts and standard running conditions were evaluated. The engine specification (see Table 1) was once more activated for 96-h with related observations and EGR were confirmed [6, 8]. Agarwal et al. [2] observe the EGR collision on show off machines fundamental parts, and residue stores, principally cylinder rings, away from execution and outflows in two-chamber, an air-cooled, direct injection with steady speed diesel machine, which usually operates in horticultural ranch hardware and decentralized hostage power age. Machines are normally not working with the EGR system. The analyses were completed to tentatively evaluate the discharges and presentation for a variety of
Performance Analysis of Single Cylinder Four-Stroke Diesel Engine Table 1 Technical specifications of the engine
Specification
687
Parameter
9 kW
Rated power
Direct injection, two Injection
Engine type
87.3/110 mm
Bore/stroke
1500 rpm
Rated speed
16.5:1
C. R
45°ATDC/35.5°BTDC
The exhaust valve closes/exhaust valve opens
210 bar
Fuel injection release pr
35.5°ATDC/45°BTDC
Inlet valve close/Inlet valve open
13,181
Total displacement volume
EGR the machine paces. Outflows of exhaust gas temperature, NOX, hydrocarbons (HC), carbon monoxide (CO), and smoke limit of the exhaust gas and were expected. Implementation boundaries similar to SFC (brake-explicit fuel utilization), as well as humid efficiency, was resolute. Saravanan et al. [4] study about hydrogen-sophisticated air as admittance indict in a diesel machine embracing EGR (exhaust gas recirculation) process through hydrogen stream velocity at 20 l/min. Examination is lead in a solitary chamber, direct-injection diesel motor, water-cooled, four-stroke, coupled toward an electrical producer. Execution limitations like energy utilization are resolved and explicit brake warm proficiency and outflows like hydrocarbon, PM (particulate matter), and carbon monoxide, exhaust, smoke gas temperature, and oxides of nitrogen, are expected. Hydrogen consumption within dual-fuel form by EGR method concerning bring down NOX, and particulate discharges, and smoke level. Table 2 represents the given experimental test ring specification. Table 2 Schematic of the experimental test rig
Parameters
Specification
Stroke
110 mm
Clearance volume
36.37 cm3
Swept volume
553cm3
C.R
16.5:1
Rated speed
1500 rpm
Rated output
3.7 kW @ 1500 rpm
Injection pressure
240 bar
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Saleh [3] look at JME (Jojoba methyl ester) has been operating as unlimited fuels in a variety of inspection assess its feasible exercise in diesel machines. These examinations illustrated that this fuel is satisfactory gas oil alternate through an augmentation in the nitrogenous oxides expulsion was a notice at every working circumstance. The end of this examination essentially was to estimate the EGR (exhaust gas recirculation) productivity when developing JME fuel into an entirely instrumented, two-chamber, normally suctioned, and four-stroke direct injection diesel engine. The testing was completed given three areas. • Firstly, the diesel machine exhaust emanations and deliberate presentation working with JME and diesel fuel at special velocity under-occupied burden are compared and resolved. • Secondly, tastings were executed at stable speed through two burdens toward research the EGR collision on exhaust outflows and motor execution together with nitrogenous oxides (NOX), carbon monoxide (CO), and unburned hydrocarbons (HC), and exhaust gas temperatures. • Thirdly, the cooled EGR effect far above the ground ratio at complete load on emissions and engine performance was observed. With the EGR method application, the HC and CO attentiveness within the engine is discharged get enlarged. For every working circumstance, an enhanced trade-off among not, CO, and HC emissions can be accomplished in a restricted EGR velocity of 5–15% through extremely modest economic consequence.
3 Experimental Setup and Methodology A VCR single cylinder, naturally four-strokes, diesel engine test ring with electric brake is taken. In this, various parameters are measured by an electric alternator type dynamometer used to measure the brake power, brake thermal efficiency, specific fuel consumption, and fuel consumption. This dynamometer arrangement consists of (Fig. 1). • Loading understanding • Understanding for measure the heat carries away through cooling water as of engine sheath. • Measuring arrangement of fuel input • An arrangement of panel board • Air ingestion gauge tool • Arrangement for measure the heat carries away through cooling water as of exhaust gases.
Performance Analysis of Single Cylinder Four-Stroke Diesel Engine
689
Fig. 1 Experiment set-up
Table 3 Experimental engine specification
Parameter
Specification
Bore diameter
80 mm
Related power
7.5 kW(10HP)
Connecting rod length
234 mm
Swept volume
562 cc
Rated torque
4.6 kg-m
Stroke length
110 mm
Arm length
150 mm
Rated speed
Rpm
Make
Kirloskar
3.1 Specification of the Diesel Engine Diesel engine which has some features combination like single-cylinder four-stroke water-cooled CI engine used for this test which is logically aspirated. There is Table 3 that represents the specification of our experimental setup engine.
3.2 Utilities Required There are various requirements to operate our diesel engine to perform any operation which is represented in given Table 4.
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Table 4 Utilities required Specification
Requirement
Calorimeter cooling
Exhaust gas
@10 LPM at 1Bar for engine
Water supply continuous
= 0.75) are blunted to study its characteristic and compare with power law bodies that are already blunted in nature (n< = 05). Shock standoff distance is also studied to. The flight conditioned used here for study is at an altitude of 70 km regime, as this is the most suitable for hypersonic flights. At these altitudes, the Knudsen number is of the range 10–2 or more, and so this study is focused on low density region. In this study, Ansys simulation is used with solution steering condition in hypersonic flow.
1.1 Power Law Leading Edges Power law is mainly known for its relationship between two variables, as a variable change in one quantity results in a proportional change in the other variable. It is not dependent on the initial size of quantity, it depends on the power of the quantity, for example, the body gets more blunter as n decreases. The power law body shape is characterized by this dimensional form that is given by y = Ax n
(1)
where n is known as the power law exponent and power law constant is A. For current modeling, the power law shapes are the same as done by W.F.N Santos, which are assumed based on the wedge where the radius R or the base height is tangent to the given wedge, the sharp leading edge has P as a half angle. The power law leading edges between the cylinder and wedge are also tangent to the abovementioned wedge, and they also have common point where slope angle is same. The diameter of the circular cylinder is used as reference for the required blunting of the leading edge. It is considered that P = 10°, the circular cylinder diameter is of 10–2 m [2]. The power law bodies with different values of n are plotted in (Fig. 1).
Fig. 1 Power law bodies [4]
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Fig. 2 Power law leading edge design (n = 0.8) and the domain used
Blunting the Power Law Leading Edge, the blunting is done only on sharp power leading edges, four radiuses where considered they are 0.01, 0.02, 0.0505 and 0.074 cm. These radii are arbitrarily taken, and the last two radii were considered to check the blunt leading-edge equation. The sharp power law bodies are considered to reduce the drag but blunting of the leading edge is done to reduce the stagnation heating of the leading edge, stagnation heating causes a great damage to the sensors embedded to the leading edge. Meshing: Meshing is done by two types, normal face meshing of 5e-5 m element size and edge sizing of 1e-5 m element size. Edge sizing is done to get the results of the particular section which is nose, two types of meshing are done to reduce the computational time and cost, and less memory is used up for this. Grid independence test is done on cone leading edge, three types of grids are considered, and after testing, it is seen that only a 2–3% variation was found hence took the finer grid of face sizing = 5e-5m and edge sizing = 1e-5 m which have elements = 133,116 (Fig. 2).
2 Computational Method and Procedure Density solver is considered as it provides with higher precision than the pressurebased solver. Since, the body is taken as axisymmetric that option is considered. Viscous effects are modeled by using SST k-w, because it provides the value near to the wall and with help of SST, the layer above the wall also gets the calculated and compressibility effects are considered. Ideal gas equation with Sutherland law is considered since the Sutherland law more precise for hypersonic flow solutions than power law (Table 1). Validation of simulation is done based on the work of Professor Wilson F Santos research where the design of the model used here is taken from the author’s work. The
Study of Aerodynamic and Aerothermal Characteristics of Blunted Power … Table 1 Parameters at 70 km altitude at Mach 10
Parameters
721
Value
Velocity
2972 m/s
Temperature
220 K
Pressure
5.582 Pa
Density
8.839363e-05 kg/m3
Viscosity
1.7894e-5 kg/m-s
coefficient of drag from simulation result is compared. It is seen that the coefficient of drag difference of 4–5%, hence this simulation is validated. Also using Chapman’s equation, the heat flux at stagnation point is almost similar, power law exponent n = 0.9 with radius 0.02 at Mach 10, which was calculated to be 2.8E06, and the solution value is 2.6E06.
3 Computational Results and Discussion Here, in Fig. 3, it is seen that as Mach increases the coefficient of drag reduces, this is due to the drag divergence theorem where the aerodynamic drag on an aerofoil increases rapidly as the velocity reaches Mach number 1.2 and then decreases. Also, from Fig. 4, it is seen the total heat transfer rate increases as Mach number is increased. The increases in heat transfer rate are because of the higher density ratio across the shockwave and with the reduced shock layer thickness. Heat transfer rate causes ionization of molecules at lower regions, hence the hypersonic flights are usually flown at 70-90 km altitude, to reduce the ionization as Knudsen number is in the range of 10–2 , also reduces stagnation heat flux, which is also studied, this heat transfer rate is taken from the fluxes option from the Ansys Fluent. There is need to study the stagnation heat flux as it constitutes the main local heating, and with focus of new design, this is studied and can be seen that stagnation heat flux increases with increase in Mach for n = 0.8 in (Figs. 5 and 6).
Cd
Fig. 3 Cd versus Mach
1.8 1.6 1.4 1.2 1 6 n=0.25 n=0.8
7
8 Mach n=0.5 n = 0.9
9
10 n=0.75 n =1
Fig. 4 Heat transfer rate versus Mach
M. S. Sanjay Krishna and V. Kotebavi
Heat Transfer rate
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60 40 20 0 6
7
8
9
10
Mach n=0.5
n=0.75
n=0.8
n= 0.9
n=1
StagnaƟon heat flux
Fig. 5 Stagnation heat flux versus Mach for power law exponent 0.8
n=0.25
1.20E+07 1.00E+07 8.00E+06 6.00E+06 4.00E+06 2.00E+06 0.00E+00 6
7
8
9
10
Fig. 6 Surface heat flux versus mach
Surface heat flux
Mach
200000 150000 100000 50000 0 6
7
8
9
10
Mach n=0.75
n=0.8
n=0.9
The blunting is done for bodies that are considered as sharp bodies (n> = 0.75) for power law leading edges so as to decrease stagnation point heating. Here, when compared with blunted cone leading edge of same radius, the power law bodies have lower drag and lower heat transfer rate and the power law bodies with n> = 0.75 have the least coefficient of drag, the power law body with n = 0.9 has the lowest heat transfer rate (Fig. 4), depending on the requirement, the leading edge can be designed based on drag or heat transfer reduction. Although, blunting the power law bodies does not reduce drag, it is seen that comparing to same power law body only 2–3% decrease in drag coefficient and 1–2 W decrease in total heat transfer rate. From Fig. 7, we can see that the stagnation heating rate decreases as the radius increases,
Study of Aerodynamic and Aerothermal Characteristics of Blunted Power …
723
W/m^2
4.00E+06 3.00E+06 2.00E+06 1.00E+06 0.00E+00 0
0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007 0.0008
Radius (m)
Stadoff Distance
Fig. 7 Stagnation heating point versus radius (m)
0.1 0.08 0.06 0.04 r = 0.01
r=0.02
r=0.0505
r=0.074
Radius(cm) n = 0.9
n=0.8
n=0.75
n=1
Fig. 8 Standoff distance versus radius
as seen in equation given by Fay and Riddle, Chapman’s equation for hypersonic flow, etc. The radius till 0.074 cm is studied because as the radius is increased, the drag coefficient increases after 0.074 cm. Also, the stagnation heating rate is much lower for blunted power law leading edges (n> = 0.75) than the power law bodies which are blunt in nature (n < = 0.5). From Fig. 8, we can see that for power law exponent n = 0.9 has the lowest surface heat flux which can be used for designing the waverider designs. Hence, it can be concluded blunting of power law bodies is better than blunting the cone leading-edge bodies. Heat transfer rate is lowest n = 0.9, blunted n = 0.9 leading edge which can be used for future studying as the heat transfer rate decreasing is the one the most important parameters in designing the aerodynamic bodies, also the heat transfer rate for blunt leading edge decreases to the same level as that of the n < = 0.5 power law bodies which are blunt in nature, but for n< = 0.5, leading edges the coefficient of drag are higher. Hence, the blunting of power law leading edges is better for designing the leading edge as it has lower coefficient of drag and heat transfer rate. One of the most important criteria for hypersonic waveriders is the shockwave detachment distance because high lift to drag ratio is obtained when the shock-wave detachment is least. The smaller shockwave standoff distance increases the heat load on the tip of leading edge hence the need for blunting of leading edges. The shockwave detachment distance is calculated using visual display, by using scale
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given in the solution section in Ansys Fluent and converting it to the required scale, the shockwave detachment is studied. This shockwave standoff distance can also be studied or measured by plotting density versus distance in chart and calculate the distance where there is sudden increase in density. The standoff distance is calculated for three power law bodies mentioned above and for cone also, and it has been plotted in (Fig. 8). From the calculation and plotting, the shockwave standoff distance is almost same except for radius of 0.0505 cm and it is seen that out of all the power law bodies, n = 0.9 and n = 0.75 are having the least standoff distance hence greater lift to drag ratio.
4 Conclusion This study of aerodynamic body is to get the perfect design with least drag coefficient and reduced heat transfer, hence work is done on sharp power law bodies. Ansys Fluent is used to study the aerodynamic and aerothermodynamic study of power law bodies with n = 0.25, 0.5, 0.75, 0.8, and 0.9. Studies are done at Mach numbers 6, 7, 8, 9, and 10 and as the Mach increases, the Cd decreases due to drag divergence theorem and total heat transfer rate increases. The stagnation heat flux and surface heat flux also increase as the Mach number increases. By blunting the sharp power law nose (n>= 0.75) at Mach 10, the drag and total heat flux are lower than that of same radius blunted cone nose. Stagnation heat flux reduces as the radius increases, and radius is increased till 0.074 cm, as anymore increase in radius causes increase in drag coefficient. The surface heat flux value is also studied, and for power law exponent n = 0.9, leading edge has the lowest surface heat flux comparing to all other geometry. Hence, it is better to study the blunting of power law bodies rather than blunting of cone. Shock standoff distance for Mach 10 is measured, and it is seen that as radius is larger the shock standoff distance becomes larger, but comparing with other nose design, the shockwave standoff distance is almost same with least detachment for power law exponent n = 0.9 and n = 0.75. Hence, it can be concluded that blunting of power law bodies is better than blunted cone nose.
References 1. Santos, W.F.N.: Aerodynamic heating performance of power law leading edges in rarefied hypersonic flow. J. Braz. Soc. of Mech. Sci. Eng. XXVII (2005). Author, F., Author, S.: Title of a proceedings paper. In: Editor, F., Editor, S. (eds.) CONFERENCE 2016, LNCS, vol. 9999, pp. 1–13. Springer, Heidelberg (2016) 2. Santos, W.F.N.: Power-law shaped leading edges in rarefied hypersonic flow. J. Spacecraft Rockets 43(5), (2004). Author, F.: Contribution title. In: 9th International Proceedings on Proceedings, pp. 1–2. Publisher, National Institute for Space Research, 12630–000 Cachoeira Paulista, Brazil
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3. Santos, W.F.N.: Leading-edge bluntness effects on aerodynamic heating and drag of power law body in low-density hypersonic flow. Combustion Propulsion Laboratory National Instit. Space Res. J. Spacecraft Rockets 43(5), (2005) 4. Peckham, D.H.: Measurements of pressure distribution and shock-wave shape on power-law bodies at a mach number of 6.85. Ministry of Aviation, Aeronautical Research Council (1967) 5. Anderson Jr, J.D.: In: Hypersonic and High Temperature Gas Dynamics. 2nd edn (2000) 6. Santos, W.F.N., Lewis, M.J.: Calculation of shock wave structure over power law bodies in hypersonic flow. National Inst. Space Res. Cachoeira Paulista J. Spacecraft Rockets 43(5), (2005) 7. Santos, W.F.N.: Aerothermodynamic performance analysis of hypersonic flow on power law leading edges. J. Spacecraft Rockets 43(5), (2006). National Institute for Space Research, 12630–000 Cachoeira Paulista-SP, Brazil and Mark J. 8. Visakh, P., Akhil, J., Nagaraja, S.R.: Effect of counter flowing jet on heat transfer and drag in hypersonic re-entry vehicle. In: International Conference on Mechanical, Materials and Renewable Energy, Department of Mechanical Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India (2018) 9. G. Gopala Krishnan, Akhil J. and Nagaraja S.R. 2015, Heat Transfer Analysis on Hypersonic Reentry Vehicles with Spikes, 1st International ISHMT-ASTFE Heat and Mass Transfer Conference IHMTC2015, Thiruvannanthapuram, India, December 2015. 10. Sreekanth, N., Akhil, J., Nagaraja, S.R.: Design and analysis of secondary spike on blunt head. Indian J. Sci. Technol. 9(45), (2016) 11. Rajesh, R., Rakesh, S.G.: Effect of dimensions of various spikes of a spiked cylinder on the buzz phenomenon subjected to hypersonic flow. Int. J. Fluid Mech. Res. 45(5), 377–388 (2018). Department of Mechanical Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India 12. Rajesh, R., Rakesh, S.G.: Effect of dimensions of sharp spiked cylinder on the buzz phenomenon subjected to hypersonic flow. Int. J. Fluid Mech. Res. 44(6), 469–485 (2017). Department of Mechanical Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India 13. Rajesh, R., Rakesh, S.G.: Effect on the drag coefficient of various spiked cylinders during buzz phenomenon subjected to hypersonic flows. J. Brazilian Soc. Mech. Sci. Eng. 42(6), (2020). Department of Mechanical Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India ISSN 1678–5878
Optimizing Thermal Comfort for Office Room Using CFD Analysis Rajnish Kumar Gautam and Neeraj Agarwal
Abstract Thermal comfort is an important factor to remember during the building construction process, since it can have a positive impact on the social and economic activities of those that use these places. Climates that are pleasant will cause people to relax, which can lead to a rise in commercial traffic. This study presents computational fluid dynamics analysis of an office room using ANSYS fluent to investigate the effects of better thermal comfort by changing the four-way cassette AC inlet position. For that four CAD model of office room is designed using the CATIA software with approximate dimension. The four models created were Single AC inlet in the office room from top, Double AC inlet from top, Double AC inlet from its side wall and Double AC inlet one from top and other from side wall. Results show that the design3 of the office room with double AC inlet from its side wall takes least time (44.93% compared with model-1 Single AC inlet and 5% as compared with model-2 Double AC inlet) to achieve the comfort temperature inside the office room. Keywords CAD · CATIA · ANSYS · Temperature · CFD
1 Introduction As indicated by gökhan güngör (2015) and Hoppe’s investigation (1998) the greater part of individuals living in metropolitan zones are spending over 90% of their time in cooled indoor spaces. Same examination likewise proposes that assessed expenses of an un-ideal thermal climate are higher than the energy cost which would be spent to improve the conditions to the ideal guidelines. Thermal comfort can be concentrated severally. Hypothetical examinations were generally founded on energy conditions which are worked in the middle of human and climate and required broad numerical work. Useful investigations then again were finished by testing people under different thermal conditions, which were tedious and could be deluding a result of the sincere beliefs of individuals in regards to comfort. R. K. Gautam (B) · N. Agarwal Department of Mechanical Engineering, IES College of Technology, Bhopal, MP, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_67
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Looking at those two techniques, estimating thermal comfort by utilizing reproduction programming gave the advantage of both by having the option to recreate numerous conditions without a moment’s delay and doing the colossal numerical work by the guide of the computer. Various meanings can be found in the ASHRAE handbook. • The condition of being comfortable with one’s current thermal climate. • Conditions for the comfort of the thermal environment. • Minimum attention requirement for the regulation of internal body temperature.
2 Literature Review Maykot et al. [1] inspects the impacts of gender on thermal comfort necessities in places of business. The information comes from 83 field considers directed in 2014 of every three places of business in Florianopolis, in southern Brazil. One of the structures is completely cooled and the other two utilize a blended mode system. The information was estimated by microclimate stations and the detainees’ suppositions were dictated by a survey. Jindal et al. [2] performed a field analysis during the storm and winter of 2015– 2016 to discuss the thermal climate and thermal comfort in the ventilated indoor homerooms of a school Ambala, India. The outcomes show that the warmth resilience of understudies is very high. The aftereffects of this examination ought to give rules to Indian thermal comfort principles so that schools can utilize energy effectively. Liˇcina et al. [3] analyze the ASHRAE Global Thermal Comfort II with 76,000 dataset. Derks bei et al. [4] did a mixed method study, with the nursing staff in hospital wards acting as participants. Ricardo et al. [5] Studies about thermal comfort in mixed-mode buildings. Shilei et al. [6] did a thermal environment test with a different room, showing that a high humidity environment did not have a significant impact on human comfort. Elena et al. [7] studied building thermal comfort and optimizing the HVAC. Vitor at el. [8] did the thermal comfort analysis for a running bus. Cho et al. [9] analyze the indoor-air conditioning system and Cosma et al. [10] analyze the human thermal comfort in transient conditions.
3 Objective This research examines the effects of better thermal comfort of an office room by changing its position which is exposed in the same thermal conditions. The following objectives have been set for this work. 1. 2.
Study of air conditioning systems for thermal comfort. Prepare the different models of office room and placement of AC outlets.
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Perform the computational fluid dynamic analysis for all above models Compare the various results such as temperature distribution and velocity distribution inside the office room and present the best model for better thermal comfort.
4 Methodology 4.1 Computational Fluid Dynamics (CFD) Analysis Computer analysis of fluid dynamics is performed with ANSYS fluent for office room. The input parameters were taken from the base paper. To perform this computational analysis, authoritative equations such as the continuity equation, momentum equation, energy equations and the K- ε equations are used.
4.2 Algorithm Used for CFD Analysis Four CAD office space models are designed using CATIA design software. The approximate size of the office space (6.8 × 3.8 × 3m with window size 3.4 × 2.4 m) was taken into consideration when creating the model (Fig. 1). The four models created were: 1. 2. 3. 4.
Single AC inlet in office room from top. Double AC inlet in office room from top. Double AC inlet in office room from its side wall. Double AC inlet in office room one from top and other from side wall.
Computational fluid dynamics analysis of office room model-1: CAD geometry: CAD model for office room of model −1 is created using CATIA software. For creating the model approximate dimensions of office room were considered and a three dimensional view is illustrated in Fig. 2. Meshing: After completing the CAD model, the office space is imported into the ANSYS workshop to perform the CFD analysis. In this process, appropriate meshing is done. The CAD model is divided into a large number of smaller components called meshes. There are 69,001 nodes and 358,336 elements used. Figure 3 shows the meshing of the office room. Boundary conditions: Boundary conditions are assigned to create a virtual environment of the real life working of the system. The boundary conditions at different locations of the office room model-1 are explained below and shown in Fig. 4. a.
Define the solver setting for pressure based transient and enable gravity option in y direction with the value of -9.81 m/s2.
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Fig. 1 Algorithm used for CFD analysis
Fig. 2 CAD model of office room model-2
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Working fluid set as air with density 1.22 kg/m3 , specific heat 1006.43 J/kg K, thermal conductivity 0.24 W/m2-K Set viscose model as K-epsilon realizable model with enhanced wall treatment Cold air inlet inside the room at inlet velocity 1 m/sec with 16 °C
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Fig. 3 Meshing of office room model-1
Fig. 4 Different boundaries of office room model-1
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To define the temperature distribution, there is a requirement for the energy equation. For the outlet boundary condition, the gage pressure should set as zero. Under discretization, select standard for pressure, and second order for momentum and energy equation.
Temperature distribution inside the room after CFD analysis office room for model-1: After performing CFD transient analyses with absolute velocity formulation using pressure based solver. The temperature distribution inside the room has been analyzed and the temperature contour diagram shown in below Fig. 14. Till 200 s each contour diagram is shown with an interval of 10 s, then after 200 s the contour diagrams are
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shown with an interval of 50 s, because for the first 200 s large variations on room temperature have been observed. Cooling time for this model is 905 s. Velocity distribution inside the room after CFD analysis office room for model-1: After performing CFD transient analyses with absolute velocity formulation using pressure based solver. The velocity distribution inside the room has been analyzed and the velocity contour diagram shown in below Fig. 15. Till 100 s each contour diagram is shown with an interval of 10 s, then after 100 s the contour diagrams are shown with intervals of 50 s. Computational fluid dynamics analysis of office room model-2: CAD geometry: CAD model for office room of model -2 is created using CATIA software. For creating the model, approximate dimensions of office room were considered and a three dimensional view is shown in Fig. 5. Meshing: After completing the CAD model, the office space is imported into the ANSYS workshop to perform another computer aided analysis of fluid dynamics, the next step is networking. In this process, meshing is an important operation in FEA. The geometry of CAD is divided into a huge number of small components called meshes. There are 113,410 nodes and 609,741 elements created. Figure 6 shows the meshing and Fig. 7 shows the model with boundaries conditions. Note: Material property and Boundary condition remain same as office room model-1. Temperature distribution inside the room after CFD analysis office room for model-2: After performing CFD transient analyses with absolute velocity formulation using pressure based solver. The temperature distribution inside the room for model-2 has Fig. 5 CAD model of office room model-2
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Fig. 6 Meshing of office room model-2
Fig. 7 Different boundaries of office room model-2
been analyzed and the temperature contour diagram shown in Fig. 16. Till 100 s each contour diagram is shown with an interval of 10 s, then after 100 s the contour diagrams are shown with an interval of 50 s, because for the first 100 s large variations on room temperature have been observed. Cooling time for this model is 602 s. Velocity distribution inside the room after CFD analysis office room for model-2: After performing CFD transient analyses with absolute velocity formulation using pressure based solver. The velocity distribution inside the room has been analyzed and the velocity contour diagram shown in Fig. 17. Till 100 s each contour diagram is shown with an interval of 10 s, then after 100 s the contour diagrams are shown with intervals of 50 s.
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Computational fluid dynamics analysis of office room model-3: CAD geometry: CAD model for office room of model -3( Fig. 8) is created using CATIA software. Meshing: After completing the CAD model, the office space is brought into the ANSYS workbench to perform another computer aided analysis of fluid dynamics, the next step is networking. Figure 9 depicts the meshing of model-3.In this process, CAD geometry is broken down into a large number of small parts called meshes.
Fig. 8 CAD model of office room model -3
Fig. 9 Meshing of office room model -3
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Fig. 10 Different boundaries of office room model-3
There are 67,286 nodes and 352,782 elements in the model. Figure 10 depicts the boundaries of office room model-3. Temperature distribution inside the room after CFD analysis office room for model-3: The temperature distribution inside the room for model-3 has been analyzed and the temperature contour diagram shown in below Fig. 18. Till 100 s each contour diagram is shown with an interval of 10 s, then after 100 s the contour diagrams are shown with an interval of 50 s, because for the first 100 s large variations on room temperature have been observed. Cooling time for this model is 573 s. Velocity distribution inside the room after CFD analysis office room for model-3: After performing CFD transient analyses with absolute velocity formulation using pressure based solver. The velocity distribution inside the room has been analyzed and the velocity contour diagram shown in below Fig. 19. Till 100 s each contour diagram is shown with an interval of 10 s, then after 100 s the contour diagrams are shown with intervals of 50 s. Computational fluid dynamics analysis of office room model-4: CAD geometry: CAD model for office room of model-4 ( Fig. 11) is created using CATIA software. Meshing: After completing the CAD model, the office space is imported into the ANSYS workshop to perform another computer aided analysis of fluid dynamics. His next step is networking. Meshing is an important operation in FEA. CAD geometry is broken into meshes. There are 351,526 nodes and 67,067 elements used in the study Fig. 12 depicts the meshing of office room model−4. Figure 13 depicts the boundary condition of moel-4.
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Fig. 11 CAD model of office room model-4
Fig. 12 Meshing of office room model-4
Temperature distribution inside the room after CFD analysis office room for model-4: The temperature distribution inside the room for model-4 has been analyzed and the temperature contour diagram shown in below Fig. 20. Till 100 s each contour diagram is shown with an interval of 10 s, then after 100 s the contour diagrams are shown with an interval of 50 s, because for the first 100 s large variations on room temperature have been observed. Cooling time for this model is 593 s.
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Fig. 13 Different boundaries of office room model-4 25
Comparative result of temperature distribution inside the room at different height for case-1
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Velocity distribution inside the room after CFD analysis office room for model-3: After performing computational fluid dynamic transient analyses with absolute velocity formulation using pressure based solver. The velocity distribution inside the room has been analyzed and the velocity contour diagram shown in below Fig. 21. Till 100 s each contour diagram is shown with an interval of 10 s, then after 100 s the contour diagrams are shown with intervals of 50 s.
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5 Result and Discussion Computational fluid dynamics analysis is performed for an office room using ANSYS fluent to investigate the effects of better thermal comfort by changing the AC inlet position. For all four designs of the office room, the following results with graphical and tabulated data have been explained.
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Comparative result of velocity distribution inside the room at different height for case-3
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From Fig. 22, it has been observed that model-3 (Double AC inlet from its side wall) takes 44.93% less time to achieve the comfort temperature as compared with model-1 (Single AC inlet from top). From Fig. 23, it has been observed that model-3 (Double AC inlet from its side wall) takes 5% less time to achieve the comfort temperature as compared with model2 (Double AC inlet from top) (Figs. 14, 15, 16, 17, 18, 19, 20, 21, 22 and 23).
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Fig. 23 Percentage difference for time taken to achieve the comfort temperature as compared with model-2
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6 Conclusion In this research computational fluid dynamics analysis is performed for an office room using ANSYS fluent to investigate the effects of better thermal comfort by changing the four-way cassette AC inlet position. The input parameters have been taken from the base paper. This computational work is carried out using ANSYS fluent. The working fluid is set as air with density 1.22 kg/m3, specific heat 1006.43 J/kg K, thermal conductivity 0.24 W/m2-K. Cold air inlet inside the room at inlet velocity 1.055 m/sec with 16 °C considered for calculation. The following conclusion is drawn from this research. Using CFD transient analyses with absolute velocity formulation using pressure based solver for design-1. The temperature distribution inside the room is analyzed with intervals of 10 and 50 s which shows the total cooling time of 905 s at the 0.8 m/sec air velocity. After performing CFD transient analyses for design-2. The temperature distribution inside the room is analyzed with intervals of 10 and 50 s which shows the total cooling time of 602 s at the 0.8 m/sec air velocity and takes 40.2% less time to achieve thermal comfort temperature as compared with model-1. After performing CFD transient analyses for design-3. The temperature distributions inside the room is analyzed with interval of 10 and 50 s which shows the total cooling time of 573 s at the 0.8 m/sec air velocity and take 44.93% less time to achieve thermal comfort temperature as compared with model-1 & 4.94% less time as compared with model-2. After performing CFD transient analyses for design-4. The temperature distributions inside the room is analyzed with interval of 10 and 50 s which shows the total cooling time of 593 s at the 0.8 m/sec air velocity and take 41.66% less time to achieve thermal comfort temperature as compared with model-1 and 1.5% less time as compared with model-2. It has been observed from the above conclusion that the design-3 (Double AC inlet from its side wall) take 44.93% less time to achieved the comfort temperature as compared with model-1 (Single AC inlet from top) and 5% less time to achieved the comfort temperature as compared with model-2 (Double AC inlet from top and) which is the least time to achieve the comfort temperature inside office room.
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4. Derks, M.T.H., Mishra, A.K., Loomans, M.G.L.C., Kort, H.S.M.: Understanding thermal comfort perception of nurses in a hospital ward work environment. Build. Environ. 140, 119–127 (2018) 5. Rupp, R.F., de Dear, R., Ghisi, E.: Field study of mixed-mode office buildings in Southern Brazil using an adaptive thermal comfort framework. Energy and Build. 158, 1475–1486 (2018) 6. Lu, S., Pang, B., Qi, Y., Fang, K.: Field study of thermal comfort in non-air-conditioned buildings in a tropical island climate. Appl. Ergon. 66, 89–97 (2018) 7. Barbadilla-Martín, E., Martín, J.G., Lissén, J.M.S., Ramos, J.S., Domínguez, S.Á.: Assessment of thermal comfort and energy savings in a field study on adaptive comfort with application for mixed mode offices. Energy and Build. 167, 281–289 (2018) 8. Cardoso, V.E.M., Nuno, M.M.R., Almeida, R.M.S.F., Barreira, E., Martins, J.P., Lurdes Simões, M., Sanhudo, L.: A discussion about thermal comfort evaluation in a bus terminal. Energy and Build. 168, 86–96 (2018) 9. Cho, H.-J., Jeong, J.-W.: Evaluation of thermal comfort in an office building served by a liquid desiccant-assisted evaporative cooling air-conditioning system. Energy and Buildings 172, 361–370 (2018) 10. Cosma, A.C., Simha, R.: Thermal comfort modeling in transient conditions using real-time local body temperature extraction with a thermographic camera. Build. Environ. 143, 36–47 (2018)
Exploring Classification Models for COVID-19 Novel Coronavirus Disease Richa Suneja
Abstract Coronavirus disease (COVID-19) is defined as a disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). Coronavirus has been declared a global pandemic in March 2020 by World Health Organization (WHO). The spread of coronavirus can be limited by early detection of the disease, for which RT-PCR and imaging studies are being used. The chest x-rays taken upon the arrival of the patient in the hospital can be used as the input source for early detection of disease with machine and deep learning algorithms. Even though, this is the most regular and supreme imaging modality, chest radiography is question to notable intraobserver variability and has almost minor sensitivity for major clinical findings. With advances in deep learning, convolutional neural networks (CNNs) not only improved chest radiograph evaluation but are also capable of staging radiologist-level performance. In this paper, we are applying CNN with PyTorch to train ResNet18 model as PyTorch is a lower-level application programming interface concentrated on direct work with the use of array expressions. This model implementation will be beneficial in rural areas where RT-PCR test results are delayed due to the geographical location, but portable chest x-ray machines are already installed. Here, we have collated different deep learning-based classification models at hand for identification of novel coronavirus. The results are present in tabular form. Keywords Convolutional neural networks · Deep learning · Image detection · PyTorch
1 Introduction Viruses are one of the first living organisms on earth, needless to say, not as alive as a human being. A virus needs to hijack other living cells to reproduce to replicate and survive themselves. It would be fair to compare a mass virus-outbreak to a missile or a bomb explosion, given the negative externality that it may cause, which can clearly R. Suneja (B) Department of Computer Science and Engineering, Jamia Hamdard, New Delhi 110062, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_68
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be seen with the current global pandemic known as coronavirus. With outburst of SARS in 2002 and MERS outburst in 2012, the world has already witnessed regional outbreaks and the possibility of a global pandemic was highly probable. The word corona is derived from the word ‘crown’, given its crown/spike-like appearance under microscopic images. The SARS virus was not as dangerous as COVID due to the fact that SARS virus would only spread when the person who is carrying the virus is sick, but in case of COVID, even an asymptomatic patient can spread the virus. The people with any medical conditions and elderly are more likely to get infected. The American Biological Society claims that this virus affects more men than women. The administration across the globe is taking every possible measure to slow down the growth rate by lockdown, social distancing, wearing masks in public places, etc. The health experts are also working on their toes to develop the vaccine as vaccines are developed over the years and it is a time-consuming process. As on 20 August 2020, daily confirmed cases across the globe rose to 23.4 million, 890 K deaths, 15.2 M recovered cases.1 With the current pandemic situation, there is always a need to develop a reliable solution to cope-up the consequences which get flouted when such kind of situation arises. More advanced techniques are required to achieve and analyze the realtime data on the growing network front. There are multiple sources of data which can be gathered from different platforms and can be analyzed to design a model using machine learning algorithms. Deep learning is an ambit of machine learning. Deep learning can be used in many different ways, such that detecting objects, realtime monitoring, translating languages, image segmentation, recognizing speech and taking necessary decisions. The key feature of deep learning is that it learns without human supervision, but functions like a human brain, one kind of deep learning algorithm is convolutional neural network (CNN) which can be used in prediction modelling, segmentation, etc.
1.1 Paper Organization The rest of the paper is organized as follows: Sect. 2 discusses the recent developments in field of deep learning algorithms in designing appropriate models to yield desired results and emerging trends in the field of machine learning. Section 3 discusses about model description and methodology which are being used to train the CNN model using PyTorch. It further provides description about ResNet18, training and testing. Section 4 highlights about the conclusion and discussion being drawn from the model, performance on test and train sets, dataset selection, the previous work carried out, comparison of accuracy being generated by different models, performance analysis. Figure 1 shows the overview of the paper organization.
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Fig. 1 Paper organization
2 Related Works Whilst machine learning uses rather simpler means to solve the problem, deep learning works on articular neural networks which are designed in such a way the humans think. The scholars around the world are using deep learning techniques to make lives simpler. The accuracy and cost effectiveness of models are also kept in mind before designing the same. Some of the works that already carried out are: Kieu et al. [1] discussed about multiple CNN model for detecting abnormal problem with 96% accuracy. They used digital chest x-ray as input dataset; they proposed fusion rules. Their model consisted of mainly three components, CNN 128F, CNN 64L and CNN 64R. Singh et al. [2] suggested that chest tomography is trustworthy, valuable and fast technique to evaluate and classify COVID-19 keeping in mind the outbreak by designing MODE algorithm-based CNN by taking out accuracy on four models. Their proposed model outperformed the competitive model as it gives finer, more congruous true positive and true negative values in comparison with other models. The proposed model yields marginal false negative and false positive values, which signify that the model proposed by them can efficiently detect the COVID-19 positive patient. Sethy and Behera [3] used support vector machine, ResNet 50 obtaining about 98.38% accuracy for 19 cases pandout of 25 cases. For this, they extracted the
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deep feature of nine pretrained CNN model and fed to SVM classifier individually. Statistical analysis was done to select the best classification model. Apostolopoulos and Mpesiana [4] used transfer learning approach with 96.46% accuracy keeping in mind the acute and mild symptoms. The proposed work outlines the possibility of cost-efficient, rapid automatic diagnosis of the coronavirus. They further suggested an investigation on feature extraction by the CNN, which constituted reliable biomarkers helpful in detection of COVID-19. Abiyev et al. [5] applied back propagation neural networks achieving 92.4% accuracy, frontal view x-rays taken into consideration. Abdulmajeed et al. [6] designed their model for Nigeria keeping in mind that medical help is not readily available in distant places of Nigeria, so, it would help the population staying there to be diagnosed early and treated accordingly, by applying ARIMA model and ensemble techniques, they could not calculate the percentage as there was no readily available data in hand. Milletari et al. [7] used Python Caffe framework, they designed this model keeping in mind the previous paper, but segmenting volume containing multiple regions was not taken into consideration. Panwar et al. [8] used CNN model with accuracy ranging from 93–97% particularly focussing on disease factors such as ground glass opacity and designing the model accordingly. Sousa et al. [9] carried out relative performance analysis of machine learning classifiers, namely KNN, SVM and Naïve-Bayes, with study focussing on childhood pneumonia using chest radiographs as cases of infant pneumonia are increasing. Oztutrk et al. [10] trained DarkNet-19 model using deep learning technique with 98.8% accuracy and compared the images with the pneumonia chest x-ray of children and infants, age range (1–5 years). Pardamean et al. [11] advocated transfer learning on pretrained CNN for learning mammogram data as the count of breast cancer cases are increasing every year, mostly prevalent in women. Wang et al. [12] discussed about supervised learning approach in predicting the common thorax disease using NLP applying on hospital database and benchmarks. Tuli et al. [13] built a model speculating the trend and growth of COVID-19 disease using machine learning algorithms and cloud computing techniques during the lockdown phase. It used FogBus framework, they also discussed the fact that there will be surge in cases once the lockdown gets uplifted and people returning back to their normal life. This model proved to be helpful in designing an approach during the lockdown period and to implement measures reducing the speed of the community transmission. Khan et al. [14] developed CoroNet with CNN with Xception using transfer learning techniques to diagnose COVID-19 using chest x-ray images, they also threw some light upon depth analysis, once more data are available in public domain, so, more in-depth research can be carried out and more accurate results can be produced faster. Hemdan et al. [15] took a random dataset and trained VGG19 and second version of Google MobileNet and also did comparison between CNN models that were already trained. It used XLA environment to train the model. Nature.com [16] proposed effectual deep network architectures that can be applied on chest x-ray for
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tuberculosis broadcasting and visualization by simple convolutional neural network to augment faster and more efficient detection than the previous models. They also suggested that the visualization potential of CNNs has not been fully explored. The research was carried out by nearing saliency maps and grad CAMs as tuberculosis visualization methods and discussed those from a radiological perspective. Majkowska et al. [17] in their article discussed about the challenges in deep learning having the capability to expand the utilization of chest radiography with poor generalizability, spectrum bias, etc. They also advanced deep learning models for chest radiography interpretation by making use of adjudicated labels as reference standard by taking into account a clinically representative dataset to produce more generalizable and comparable results to the results of the board-certified radiologist. Archie [18] discussed in her article about chest x-ray pneumonia detection using CNN by applying basic Tensorflow and Keras, the proposed study focussed on beginner level usage and learning. The proposed model was built up from scrape which consisted of five layers with a fully connected neural network. The trained model was evaluated by taking into consideration different unseen data to elude bias prediction. The accuracy on the test dataset touched 81.25%. Hsu et al. [19] proposed chest x-ray visualization by designing deep learning model. The performance of the DenseNet 121 model was done on different input sizes with image crop testing. The deep neural network classification model which promises good performance in interpreting the abnormality on the chest x-rays were also developed. The study provided a more practical approach that can be implement in the field of medical imaging classification. Shi et al. [20] talked about an sub-pixel convolutional neural network that is efficient by assessing super resolution video and real-time single image. By using single filter, they upgraded to high resolution space taking into consideration lowresolution input and coined it as bicubic interpolation.
3 Model Description and Methodology Imaging, namely chest and lung scans, is playing an important role than presumed in the coronavirus pandemic. It is helping in triaging and defining the aggression of the treatment. Imaging studies add insight by providing line of treatment and are indicative of the disease even when the RT-PCR test results show a false negative. The main drawback with RT-PCR is that it is expensive equipment, and therefore, it cannot be used in rural areas where spending power is not as compared to the urban areas. The possible solution for this problem is applying machine learning technique to better diagnose the disease and of course at a faster rate. One such algorithm is convolutional neural network (CNN). CNNs are a subsection of computer vision, which altogether is about applying computational methods to visual content. CNN algorithms are an amalgamation of many different algorithms that work well together. CNNs have two major fragments such as feature engineering and feature preprocessing where images are tuned in such a way that the model is able to interpret it easily and other one
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Fig. 2 Methodology adopted
is classification where the training the algorithm to map the images is done. Image classification with back propagation is used to identify to the COVID-19 cases with the help of PyTorch. An open source dataset on Kaggle2 with 3000 images in which 1341 images was normal, 1345 images were viral images and 219 images were COVID positive (dated 8 August 2020). The study around COVID-19 is continually evolving, so, we cannot say that the dataset is perfect. The other main limitation of this dataset is that it has low COVID positive images at this moment, which prohibits from testing more samples training the model. Figure 2 shows the methodology adopted. Random selection of images was done, selecting 30 images each from the three classes, the accuracy was different after each prediction, but after 80 epochs, it yielded 95%, going further up to the accuracy touched 96% afterwards beyond 80 epochs as shown in Fig. 3. Back propagation was handed down to fine tune the weights of a neural net based on the error rate (i.e. loss) obtained in the previous epoch. With each epoch, false positive and false negative rate decreased with validation loss of 1.00754.
2
https://www.kaggle.com/tawsifurrahman/covid19-radiography-database.
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Fig. 3 Epoch versus accuracy graph
4 Conclusion, Discussion and Future Scope According to the reports, asymptomatic patients have the highest potential of spreading the virus than symptomatic patients due to the fact that asymptomatic patients are unaware if they are carrying the virus or not. The symptoms of coronavirus are most commonly mistaken with flu symptoms. Extensive research is underway to prepare antidote of this virus and stop the spread of the same. Whilst it is one perspective to look at the pandemic as a biological accident, the other perspective is that humans were not ready to deal with this pandemic. It is really took an extraordinary act of political will to declare disease pandemic and infusing measures. The elimination of this virus is somehow impossible according to WHO reports. Biomedical sciences and technology shook hands together to deal with this situation by designing models in a way that the burden on the healthcare professionals is reduced with the help of machine learning algorithms. The basic premise of CNN model is that it would help in reducing the work of the radiologist as they have to scan numerous x-rays on a daily basis for the detection of COVID-19 positive cases. CNN with the help of PyTorch helps the radiologist to automatically diagnose COVID-19. PyTorch is one of the trending frameworks for designing deep learning methods that can easily be prototyped into any production ready software. It has gained popularity in the past year becoming a preferred solution of research, and applications of deep learning requiring optimizing traditional expressions. PyTorch is one of many frameworks, amongst popular ones like Tensor Flow and Keras that have been designed for this and work well with Python. PyTorch gives the liberty to simply print your model object. With the help of PyTorch, we tried to train a ResNet18 model for prediction of COVID-19. ResNet18 is relatively smaller CNN which gets train quickly and gives desired results.
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The ResNet18 is a type of convolutional neural network that is 18 deep layers. A pretrained version that is offered by the network is trained on more than a million images from the ImageNet dataset. The ResNet18 model relatively gets trains quickly. This model can be implemented in rural areas where medical aid is not readily available. The portable x-ray machines can be installed in such places and diagnoses can be made using this model. Early detection of disease can limit the community transmission of the disease. Comparison between different classifications models is as shown in Table 1. Whilst comparing the different classification models, there were numerous factors that governed the accuracy of the model, i.e. view of the images taken into consideration, dataset selection, disease classification, density consideration, etc. Tulin et al. achieved the accuracy of the model is 98.8%, Pardamean et al. have achieved 98.38% validation accuracy, Hsu et al. have achieved 87.50% accuracy, Harsh Panwar et al. have achieved 93–97% accuracy, Shreshth Tuli et al. have achieved 97% accuracy, Abiyev et al. have achieved 92.4% accuracy, Apostolopoulos and Mpesiana have achieved 96.46% accuracy, Singh et al. model achieved 93.2% and this model has achieved 95% accuracy on 80 epoch values and 96% accuracy after that. The basic idea behind this research is to ease the workload of the radiologists who are overburdened with the task of identifying COVID-19 positive disease. This model can be used in rural areas where immediate medical help is not available due to lack of resources. The basic limitation with the research work was there was less COVID positive samples available in the database. In the future, when more samples will be added to the dataset, the accuracy of the model will increase and less training time will be required to train the model. PyTorch is relatively easily to train and is comparatively lower-level API than other API available.
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Table 1 Comparison of existing approaches Author/year
Classification model used
Findings/ accuracy
Remarks/observation
Kieu et al. [1]
ConvNetJS
96%
The result of the model is normal/abnormal density in chest x-ray images
Singh et al. [2]
MODE
93.2%
Comparison was carried out with other classification models
Sethy and Behera [3] ResNet 50
95.38%
The accuracy was calculated ignoring MERS, SARS and ARDS
Apostolopoulos and Mpesiana [4]
MobileNet V2
96.46%
Differentiated between mild and acute symptoms for comparison
Abiyev et al.[5]
CNN
92.4
Comparison between supervised and unsupervised models was carried out
Abdulmajeed et al. [6]
ARIMA model, ensemble techniques
Due to lack of input, no percentage was calculated
Visualization of data was carried between South Africa and Nigeria
Sousa et al. [9]
KNN, SVM, Naïve Bayes
82%, 80%, 60%
SVM outperforms the diagnosis accuracy of medical resident’s doctors
Oztutrk et al [10]
DarkNet-19
98.8%, non-pneumonia cases 87.05%
Pneumonia chest x-rays used for comparison were of children and infant of 1–5 years
Pardamean et al. [11] Transfer learning
98.38% validation accuracy
Twelve layers were used to define the model, sixth layer proved to be the most efficient one
Tuli et al. [13]
97%
Predicted model stands good with lockdown being imposed
FogBus framework
(continued)
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Table 1 (continued) Author/year
Classification model used
Findings/ accuracy
Remarks/observation
Milletari et al. [7]
Python Caffe framework
They introduced a novel objective function that was enhanced during training based on the dice overlap
Image segmentation
Khan et al. [14]
CoroNet
89.6%
Precision and recall 93% and 98.2% for four-class cases
Panwar et al. [8]
nCOVnet
93–97%
PA views were taken into consideration
Hsu et al. [19]
DenseNet121
87.50%
They kept in the mind the position of the neural network model through gradient class activation mapping (Grad-CAM) method
References 1. Kieu, P.N., Tran, H.S., Le, T.H., Le, T., Nguyen, T.T.: Applying multi-CNNs model for detecting abnormal problem on chest x-ray images. In: 2018 10th International Conference on Knowledge and Systems Engineering (KSE), pp. 300–305. IEEE, (2018, November) 2. Singh, D., Kumar, V., Kaur, M.: Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks. Europ. J. Clinical Microbiol. Infectious Diseases 1–11 (2020) 3. Sethy, P.K., Behera, S.K.: Detection of coronavirus disease (covid-19) based on deep features. Preprints 2020030300 (2020) 4. Apostolopoulos, I.D., Mpesiana, T.A.: Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med. 1 (2020) 5. Abiyev, R.H., Ma’aitah, M.K.S.: Deep convolutional neural networks for chest diseases detection. J. Healthcare Eng. (2018) 6. Abdulmajeed, K., Adeleke, M., Popoola, L.: Online forecasting of Covid-19 cases in Nigeria using limited data. Data in Brief 105683 (2020) 7. Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016, October) 8. Panwar, H., Gupta, P.K., Siddiqui, M.K., Morales-Menendez, R., Singh, V.: Application of deep learning for fast detection of COVID-19 in X-rays using nCOVnet. Chaos, Solitons and Fractals, 109944 (2020) 9. Sousa, R.T., Marques, O., Soares, F.A.A., Sene, I.I., Jr., de Oliveira, L.L., Spoto, E.S.: Comparative performance analysis of machine learning classifiers in detection of childhood pneumonia using chest radiographs. Proc. Comput. Sci. 18, 2579–2582 (2013) 10. Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Acharya, U.R.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 103792 (2020)
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11. Pardamean, B., Cenggoro, T.W., Rahutomo, R., Budiarto, A., Karuppiah, E.K.: Transfer learning from chest X-ray pre-trained convolutional neural network for learning mammogram data. Proc. Comput. Sci. 135, 400–407 (2018) 12. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106. (2017) 13. Tuli, S., Tuli, S., Tuli, R., & Gill, S. S. (2020). Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing. Internet of Things, 100222. 14. Khan, A.I., Shah, J.L., Bhat, M.M.: Coronet: a deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine, 105581 (2020) 15. Hemdan, E.E.D., Shouman, M.A., Karar, M.E.: Covidx-net: a framework of deep learning classifiers to diagnose covid-19 in x-ray images (2020). arXiv preprint arXiv:2003.11055 16. Pasa, F., Golkov, V., Pfeiffer, F., et al.: Efficient deep network architectures for fast chest X-ray tuberculosis screening and visualization. Sci. Rep. 9, 6268 (2019). https://doi.org/10.1038/s41 598-019-42557-4 17. Majkowska, A., Mittal, S., Steiner, D.F., Reicher, J.J., McKinney, S.M., Duggan, G.E., Ding, A.: Chest radiograph interpretation with deep learning models: assessment with radiologistadjudicated reference standards and population-adjusted evaluation. Radiology 294(2), 421– 431 (2020) 18. Archie, C.N. Pneumonia detection using CNN (2020, June 6). https://towardsdatascience.com/ chest-x-rays-pneumonia-detection-using-convolutional-neural-network-63d6ec2d1dee. Date Accessed 25 July 2020 19. Hsu, W.H., Tsai, F.J., Zhang, G., Chang, C.K., Hsieh, P.H., Yang, S.N., Huang, E.T.: Development of a deep learning model for chest X-ray screening. Med. Phys. Int. 7(3), 314 (2019) 20. Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., ... Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874–1883. (2016)
Smart E-waste Tracking and Monitoring Model: A Modern Approach to Counter E-waste Management Issues Mohammad Usman Rais
Abstract It is widely assumed that electrical and electronic equipment (EEE) are safely disposed of. However, potential threats are introduced once these EEE turns into waste or generally known as e-waste. Several studies have shown the poor recycling rates of e-waste, raising a concern on a global level. E-waste is found to be ill-treated and even exported illegally to several countries. E-waste often contains valuable components such as hard drives where the personal data of individuals may be stored. Mismanagement of e-waste gives rise to a threat to the privacy of people. The illegal trade of such equipment may even lead to the compromise of the internal data of a country to other countries. Our study aimed to find such problems and threats and also discover the possible reasons for their existence. Further, we made use of smart services to counter the issues arising from the mishandling of e-waste and suggested an appropriate model that would help countries to build an efficient e-waste management solution. Keywords Data protection · Illegal trade · E-waste · Smart services
1 Introduction Electrical and electronic equipment (EEE) and as well as its components, which are no longer in working condition or suffered malfunctioning during its manufacturing, are known as Waste of Electrical and Electronic Equipment (WEEE) or in general terms “E-waste” [9, 14]. The total e-waste produced around the globe in 2016 was approximately 44.7 million tons and is predicted to reach 52.2 million tons in the year 2021 [3]. Approximately, 66% population of the earth is covered by e-waste regulation. However, the total amount of WEEE being recycled stands only about 20% [20, 21]. Countries with official e-waste statistics are very few, only 41 countries have managed to provide their statistics. Further, statistics of 16 countries were obtained M. U. Rais (B) Department of Computer Science and Engineering, School of Engineering Science and Technology, Jamia Hamdard, New Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_69
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from previous studies thus explaining the low collection rate for the e-waste leaving the 34.1 Mt majority WEEE unaccounted [3]. Although countries tend to provide e-waste data, there is no certainty that the data provided is accurate. Informal sectors have been a major influence in the recycling process of WEEE in many developing countries [1]. Thus, the e-waste collected and presented as official statistics may not contribute to a sustainable solution for e-waste disposal challenges. There is a strong need to prohibit the collection of WEEE by informal sectors. Developing countries with average or weak economic conditions have been a host for illegal e-waste from developed nations [10, 24]. This is either due to insufficient guidelines or inactive law enforcement involvement. Further, unaccounted e-waste can be a threat to an individual’s privacy. E-waste consists of storage devices which has various threats in terms of data security or information security [8]. The “International Telecommunication Union”, the “United Nations University”, and the “International Solid Waste Association” have collaborated to form the “Global E-waste Statistics Partnership” that will monitor the flow and enhance the global WEEE statistics [3]. So far, there is stress on global policy-making and collection of statistics of e-waste from various countries, but little or no research has been conducted in the technological sector to improve the e-waste collection from consumers. The emerging technologies may help in improving the statistics and provide better control of waste EEE across the globe. Our research aims to investigate e-waste management issues, WEEE produced by developed countries being illegally traded to underdeveloped nations, and data protection issues. Further, we aim to make use of smart services to counter the above-mentioned problems. The rest of the paper has been organized into seven sections. Section 2 discusses related works. Section 3 discusses the e-waste management issues; Sect. 4 discusses the illegal trade of e-waste. Section 5 discusses the need to protect the integrity of e-waste for data protection. Section 6 presents a smart model to counter the issues found. The last section concludes the paper.
2 Related Works Balance of trade between countries, ordinance, risks, waste management schemes, and recovering value using technical advancements for e-waste in an international context where present production of e-waste on a global level and current trends were discussed in [13]. There were two challenges stated in regards to the refurbishment of EEE. The first is the unsafe storage of the EEE, and the other is the data security of components like hard disks in WEEE. WEEE management exercises in GCC (Gulf Cooperation Council) countries were discussed in [2]. The article reviewed environmental effects and information security threats in GCC countries. Most of the GCC countries have little or no regulations for data protection, privacy, administration, and e-commerce. The need for research was expressed for collecting precise data on the amount of e-waste.
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Security concerns appearing with the process of reusing and recycling of waste electronic devices were discussed in [22]. E-waste is susceptible to reverse engineering causing serious issues like piracy and cloning. An “Electronic Component Testing Industry” was proposed that collects e-waste from consumers and tests its ECs to reuse and sell them to semiconductor manufacturing industry. However, some drawbacks limited the implementation of the industry. A study on data security and possible risks from the memory devices of the information technology assets, which are disposed of from medium-sized organizations, institutes, and individuals was done by Ceballos and Dong [8]. A proper “supply-chain network” (SCN) is the ultimate requirement for setting up a WEEE management system that will increase the recycling rates. The present SCN has several loopholes that make it difficult to trace e-waste. The study also included ISO/IEC 2700 guidelines to propose a framework for handling EOL devices.
3 WEEE Management Issues 3.1 Involvement of Informal Sectors The WEEE management system faces numerous challenges because WEEE in developing countries is operated by informal sectors at large which include collecting, recycling, refurbishing, reuse, and disposal of remains, where informal collectors purchase EEE from households and businesses and sell them to recycling facilities [1]. Numerous factors that indicate mismanagement of WEEE by informal sectors. Firstly, the informal sectors do not follow any specific guidelines throughout the process of collection and recycling. The chances are high that the e-waste collected was mishandled and not properly recycled. In the refurbishing industry, especially in the informal sector, EEE are kept in unsafe storage for a longer duration for repair or extraction of components for reuse [18]. Secondly, the informal recycling of WEEE lack in official records after collection. In such a condition, it is next to impossible to find accurate solutions for proper e-waste management as the amount of actual waste of EEE remains unknown.
3.2 Insufficient Implementation of EPR When a product reaches end-of-life (EOL), it is eventually considered as WEEE and can either be reused, disposed, or recycled to extract valuable materials. The e-waste is disposed of in two ways, either they are disposed of directly or indirectly (the remains from the recycled value) into standard landfills by municipal solid waste (MSW) management [13]. To restrict the mishandling of WEEE, the “Extended producer Responsibility” (EPR) scheme was introduced. This scheme allows the
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maker of a product to take charge of its complete life cycle [15]. The EPR scheme is designed to make sure that e-waste is properly managed and treated before its disposal [13]. Although the EPR schemes are well placed in developed countries, an estimated 0.7 Mt of waste EEE in the countries of the European Union is being disposed of in common bins [4].
4 Illegal Trade of E-waste Illegal export of waste EEE from developed nations to underdeveloped nations has been followed for economic advantages. It is observed that high volumes of e-waste generated across various countries may not be recycled in the countries they were generated. Rather, there is the illegal trade of WEEE to countries where less costly disposal facilities and poor enforcement of environmental standards and laws are present [6, 12, 19]. The “Basel Convention” was introduced in the year 1992 as the foundation of an international treaty that would restrict the cross-border transition of WEEE from developed nations to underdeveloped nations. However, the treaty allowed the export for repurposing and recycling [17], Basel Convention, [5]. Despite the treaty in place, an investigation in 2014–2016, Basel Action Network (BAN) revealed that the WEEE that involves computer manufacturers are exported from the United States to the countries that are developing (mostly China). Almost 90% of the WEEE, which was illegally exported to developing countries, was pretended as preowned equipment [11].
5 The Need to Protect the Integrity of E-waste for Data Protection 5.1 Data Stored on End-Of-Life Devices “Data protection is a broad spectrum that includes protection of stored data from loss/damage and safeguarding data from being accessed by unauthorized personnel both in persistent or streaming formats” [2]. Most of the devices used by individuals when reaching end-of-life (EOL) have some data in their storage. This data can be meaningful to some unauthorized entities. Although operating systems allow users to delete data with the help of specific commands, the data are not erased, but rather the space on the hardware the data is stored marked as “being free” [23]. This data need to be safeguarded from unauthorized people which poses threat to individuals’ privacy.
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5.2 E-waste Being Recycled by Vulnerable Groups People who are socially excluded or belonging to vulnerable groups are hired by the recycling industry. These people are mostly daily wagers, migrants, or prisoners [7]. A scrap facility dedicated to electronics was examined in the USA, and it was discovered the almost half its employees were migrants [16]. Such workers can lead to illegal activities such as stealing or reselling equipment to criminals. Hence, the data stored on these devices become vulnerable of being falling into the hands of bad people.
5.3 Unauthorized Access to Storage Devices Once the storage devices from e-waste fall into the hands of unauthorized people, there are high chances that the data may be compromised. Using modern techniques, all storage devices are prone to unauthorized access. Hence, devices must be processed appropriately before entering the recycling chain [8]. There are several threats such as reverse engineering of devices that could help in generating clones of products, but the biggest threat of all is to an individual’s private data [8]. When unauthorized entities get in reach of the e-waste, they get complete physical control to try out and test various methods and techniques that will give access to them to the data or recover if it was being deleted in the past [22].
6 Proposed Smart E-waste Tracking and Monitoring Model In this paper, we present a model to solve the e-waste management issues, limit the illegal trade, and provide data protection of e-waste. Our technique is based on smart services, developed using cloud technology. Bringing consumers, municipal authorities, law enforcement agencies on the same platform, which will lead to improved monitoring and better tracking of the e-waste (see Fig. 1). Table 1 provides the description of participants involved in the model. The consumers will be able to generate a request for an e-waste pick-up using a mobile application hosted by municipal authorities. Municipal authorities will manage the ewaste collection from consumers with the help of smart e-waste collectors and maintain a record of the generated e-waste with the help of collection data. Law enforcement authorities will be able to track the movement of e-waste from consumers till it reaches the evaluation centers, and if any misconduct is noticed, the agencies can intervene with ease as the exact location of the e-waste will be available. The evaluation centers will erase/destroy the data from the e-waste and categorize them further for reuse, recycling, or disposal. EPR scheme can be integrated at this stage where the producer can take liability for the further process and formal recycling facilities
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Fig. 1 Schematic representation of smart e-waste tracking and monitoring model Table 1 Description of participants in smart e-waste tracking and monitoring model S. no Participant
Description
1
Consumer
Source from which e-waste is generated
2
E-waste collectors
GPS-enabled smart e-waste collectors sharing location that receives the request from the cloud for WEEE, which will be collected from consumers and transported to e-waste evaluation centers
3
Municipal authorities
Responsible for managing requests and feedback for e-waste collection
4
Law enforcement agencies Responsible for tracking and monitoring the e-waste collection and transport
5
E-waste evaluation centers Entitled for erasing/destroying the data from the e-waste and categorize them further for reuse, recycling, or disposal
6
Cloud
Cloud services act as an intermediary between the consumers, municipal authorities, law enforcement agencies, and smart e-waste collectors
7
Mobile application
Responsible for sending/receiving the requests and feedback from the cloud
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can take charge. Feedback will be updated on consumers’ mobile applications about the evaluation process including the destruction of personal data from the e-waste.
6.1 Tracking of WEEE Tracking of WEEE will allow the law enforcement agencies to build a strong network, thereby increasing the accountability of the WEEE. The exact location of the WEEE can be determined to detect the illegal trade activities of e-waste. Tracking will reduce the intervention of informal sectors and also safeguarding the e-waste from threat actors who look to steal personal data from the e-waste as there will be a track record of the e-waste after being collected from the consumers till it enters the recycling chain.
6.2 Monitoring of E-waste Monitoring will allow the countries to improvise their statistics of WEEE collection and recycling. The municipal authorities will be able to monitor and record the data of WEEE collected from consumers. The actual amount of e-waste will be known to the municipal authorities, thereby guidelines for recycling can be created accordingly.
7 Conclusion and Future Directions In the present article, we investigated the issues in WEEE management arising from a large volume of WEEE being unaccounted for. Further, we investigated the illegal cross-boundary transition of WEEE and discovered loopholes in the regulations that were created to forbid illegal trade. We further went on to investigate the need to protect the integrity of e-waste for the data protection of individuals. Finally, we presented a “Smart collecting and tracking model” to counter the issues found. Smart services will enable the countries to centralize the WEEE collection and recycling, which will lead to the improvement of WEEE statistics. The network of law enforcement agencies will be strengthened, and no third party would be able to intervene until the e-waste is free from consumers’ data. Further research may extend this work by introducing smart segregating bins that would separate e-waste from general waste, which will help to improve the statistics of e-waste. Machine learning techniques can be combined to improve the data analysis and better prediction of future e-waste generation can be achieved, which will help in implementing better policies and guidelines.
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References 1. Ackah, M.: Informal E-waste recycling in developing countries: review of metal(loid)s pollution, environmental impacts and transport pathways. Environ. Sci. Pollut. Res. 24(31), 24092–24101 (2017). https://doi.org/10.1007/s11356-017-0273-y 2. Alghazo, J., Ouda, O.K.M., el Hassan, A.: E-waste environmental and information security threat: GCC countries vulnerabilities. Euro-Mediterranean J. Environ. Integrat. 3(1), 1–10 (2018). https://doi.org/10.1007/s41207-018-0050-4 3. Baldé, C.P., Forti, V., Gray, V., Kuehr, R., Stegmann, P.: The global e-waste monitor— 2017, United Nations University (UNU), International Telecommunication Union (ITU) and International Solid Waste Association (ISWA), Bonn/Geneva/Vienna (2017) 4. Baldé, C.P., Wang, F., Kuehr, R., Huisman, J.: The e-waste monitor—2014. United Nations University, IAS—SCYCLE Bonn, Germany (2015) 5. Basel Convention—Controlling transboundary movements of hazardous wastes and their disposal (2017). http://www.basel.int/TheConvention/Overview/tabid/1271/Default.aspx Accessed date 20 March 2021 6. Bisschop, L.: Governance of the illegal trade in e-waste and tropical timber: case studies on transnational environmental crime. Green Criminol. Series (2014) ISBN, p. 147241540X 7. Ceballos, D.M., Dong, Z.: The formal electronic recycling industry: challenges and opportunities in occupational and environmental health research. Environ. Int. 95, 157–166 (2016) 8. Debnath, B., Alghazo, J.M., Latif, G., Roychoudhuri, R., Ghosh, S.K.: An analysis of data security and potential threat from IT assets for middle card players. Institutions and Individuals. In Sustainable Waste Management: Policies and Case Studies. Springer Singapore, (2020). https://doi.org/10.1007/978-981-13-7071-7_36 9. Flavia, P.C.S., JimenezMMC, C.M.P.K., deMoraes, T.V., Espinosa, D.C.R., Tenório, A.S.J.: Printed circuit board recycling: physical processing and copper extraction by selective leaching. Waste Manag 46, 503–510 (2015) 10. Ghosh, S.K., Debnath, B., Baidya, R., De, D., Li, J., Ghosh, S.K., et al.: Waste electrical and electronic equipment management and Basel Convention compliance in Brazil, Russia, India, China and South Africa (BRICS) nations. Waste Manag. Res. 34(8), 693–707 (2016) 11. Hopson, E., Puckett, J.: Scam recycling: e-dumping on Asia by US recyclers. The e- Trash Transparency Project, Basel Action Network (2016). https://www.resource-recycling.com/ima ges/BANReportTwo.pdf, Accessed date 20 March 2021 12. Hotta, Y., Elder, M., Mori, H., Tanaka, M.: Policy considerations for establishing an environmentally-sound regional material flow in East Asia. J. Environ. Devel. 17, 26–50 (2008) 13. Ilankoon, I.M.S.K., Ghorbani, Y., Chong, M.N., Herath, G., Moyo, T., Petersen, J.: E-waste in the international context—a review of trade flows, regulations, hazards, waste management strategies and technologies for value recovery. Waste Manage. 82, 258–275 (2018). https://doi. org/10.1016/j.wasman.2018.10.018 14. Kumar, A., Holuszkoa, M., Espinosa, D.C.R.: E-waste: an overview on generation, collection, legislation and recycling practices. Resource Conserv Recycle 122, 32–42 (2017) 15. Lindhqvist, T.: Extended producer responsibility in cleaner production: policy principle to promote environmental improvements of product systems. AFR Rep. Lund University (2000). http://www.lub.lu.se/luft/diss/tec355.pdf 16. Page, E., Ceballos, D., Oza, A., Gong, W., Mueller, C.: Metal exposures in an electronic scrap recycling facility. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health Report No. 2013–0067–3228 (2015). https://www.cdc.gov/niosh/hhe/ 17. Schmidt, C.W.: Unfair trade e-waste in Africa. Environ. Health Perspect. 114, A232–A235 (2006) 18. Shamim, A., Ali, M.K., Rafiq, I.: E-waste trading impact on public health and ecosystem services in developing countries. Int. J. Waste Resour. 5, 1–12 (2015)
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19. Smith, T., Sonnenfeld, D.A., Naguib Pellow, D.: Challenging the Chip: Labor Rights and Environmental Justice in the Global Electronics Industry. Temple University Press, Philadelphia, PA (2006) 20. UNE: UN Report: time to seize opportunity, tackle challenge of e-waste (2019). https://www. unenvironment.org/news-and-stories/press-release/un-report-time-seize-opportunity-tacklechallenge-e-waste. Accessed 23 March 2021 21. WEF: The world’s e-waste is a huge problem. It’s also a golden opportunity (2019). https://www.weforum.org/agenda/2019/01/how-a-circularapproach-can-turn-e-wasteinto-a-golden-opportunity/. Accessed 23 March 2021 22. Roychowdhury, P., Alghazo, J.M., Debnath, B., Chatterjee, S., O. O. K. M.: Security threat analysis and prevention techniques in electronic waste. Waste Manage. Resource Effic. 853–866 (2019). https://doi.org/10.1007/978-981-10-7290-1_72 23. Krumay, B.: E-waste-privacy challenge. In: Lecture Notes in Computer Science: Vol. 9857 LNCS, pp. 171–189. (2016). https://doi.org/10.1007/978-3-319-44760-5_4 24. Someya, M., Suzuki, G., Ionas, A.C., Tue, N.M., Xu, F., Matsukami, H., et al.: Occurrence of emerging flame retardants from e-waste recycling activities in the northern part of Vietnam. Emerg. Contam. 2(2), 58–65 (2016)
An Introduction of Water Desalination Exploiting the Waste Heat and Other Different Renewable Source of Energy Keshavendra Choudhary, Mayank Agarwal, and Rajesh Kumar
Abstract The demand for freshwater is increasing exponentially with population, and the scarcity of potable water is also highlighted at various places due to lack of natural reservoirs and irregular rain availability, electricity shortage, and its reach and different factors which contribute to the water shortage in common man’s life, especially for less developed and low-level people living in remote areas who cannot have use of this natural commodity against which they have full right as a human being, so the best method as per the demand is replenishment of this water; many initiatives are being carried out like rain harvesting, proper drainage, but desalination is supposed to be the best among all; this work highlights the desalination process in detail with its necessary components as well as different drawbacks which if reduced can produce this method to be the best among all. Keywords Desalination plant · Solar collector · Waste heat recovery · Renewable energy resources
1 Introduction An important element for the survival of human being’s life is water which is about 71% on this blue planet Earth and almost 96% of this water is found to be in large ocean which is salty and unfit for drinking purpose although this ocean water can be used in power generation and for various industrial purposes. Water available from freshwater resources is less in quantity and the water available from ground water also leads to problems like water stress, water scarcity and water crisis [1]. So, to eliminate these problems, some alternative sources should be searched and developed to its best level. It is found that distillation is an appropriate solution K. Choudhary (B) Department of Mechanical Engineering, Sagar Institute of Science and Technology (SISTec), Gandhinagar, Bhopal, MP 462026, India M. Agarwal · R. Kumar Department of Mechanical Engineering, Delhi Technological University, Shabhad Daulatpur, Bawana Road, Delhi 110042, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_70
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for the present scenario. Distillation is the process to treat the saline water which is present in abundance. Desalination and distillation process occur in nature by the solar energy through which water from water bodies like lake and river gets evaporated and collected in the atmosphere, so distillation is a power-consuming process. Till date, thermal energy like coal and another fossil fuel is being used to produce heat which is used to produce water, which is neither environmentally nor economically good. As it produces large amount of co2 and other harmful gases which increases the aura of these gases in our atmosphere and hence increase the global warming effect. So, to overcome the problem of water shortage as well as to minimize the dependence on fossil fuel, efforts and techniques are being shifted to other sources of energy like energy from waste heat and other renewable energy like solar, bio-gas, geothermal, wind energy, LNG, etc. Among waste heat recovery system, we have Organic Rankine Cycle in which waste heat is used to run the turbine; it is based on working principle of Brayton cycle; although waste heat can be used for distillation process, system like heat recovery steam generation (HRSG) can be used to recover waste heat from different sources of energy like chemical industry, processing industry, food stuff, nonmetallic minerals, pulp, etc. The main cause of waste heat from these industries is thermodynamic deficiency and ineffectiveness of device and procedure being adopted [2]. So, waste heat with varying temperature range can be used in the distillation process. In addition to waste heat, other sources make use of solar and renewable energy resources which are sustainable as well as susceptible. In terms of renewable resources, we have geothermal, wind and bio-gas energy which produces freshwater as a primary product and also brine as a secondary or by-product [3]. Solar energy desalination is finding more attention due to its free availability in nature more over 1 kW/m2 amount of fall on the surface seems not to be utilized in any purpose [4]. Another factor due to which solar energy is found to be effective in comparison with other resources is its simplicity in operation as compared to other natural resources, less maintenance cost, low operation cost, and fewer work force for the operation of solar plant.
2 Water Desalination Water desalination is the process in which water is separated from the salt in saline water by evaporation using thermal energy as seen in the case of natural rain where evaporation and condensation phenomena are seen. When solar energy is used for desalination process, it is termed as solar desalination, and in solar desalination process, solar energy is used where solar radiation falls on the top surface of the solar still, i.e., cover plate from where heat is transferred to the basin where brackish water is available. Heat transfers through both convective and radiative modes, and radiative mode is found to be more effective [5]. Evaporated water gets collected at the top cover plate which is at low temperature relative to its basin, and condensation of droplet is seen, and these condensing droplets are being collected from the cover plate, which is pure distilled water. The reason behind blackened surface of basin is increase
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in the thermal performance as it absorbs maximum amount of heat as compared to reflecting the heat. Evaporation is totally dependent on heat, and hence, it gets increased with the hike in temperature and rate of radiation falls on the basin. Water desalination using direct solar radiation is effective as well as economic because freshwater collection and saline water for desalination are available at one place, and another solar desalination technique which is simple in use and effective in result is found to be humidification and dehumidification method. The solar desalination depends upon different factors in addition to solar radiation that includes the depth of water in the basin, intensity of radiation approaching to the basin, absorptivity of the basin, transmissivity of the plate at the top, velocity of wind at the place where desalination plant is set up. The improvement in these factors will increase the overall performance of the system. Types of Different Desalination Plants We are in the stage of desalination plant where desalination technique is undergoing day-by-day improvement, but still we are presently focusing on improving the thermodynamic efficiency and effectiveness of current process. The most commonly used distillation method and different solar distillation methods have been highlighted, respectively, below, but most research work and effort of scholars show that membrane technology is more preferable to the thermal desalination method as the energy requirement in membrane technology is less, and hence, more than 70% of the desalination plant operating in the world is running on this technology [6]; membrane technology accompanies reverse osmosis, forward osmosis, electrodialysis, and membrane distillation. Among all, reverse osmosis is found to be good as compared to others [7]. Another recent development seen in the type of desalination plant is the use of activated carbon in desalination; activated carbon seems to be in microporous form with pore structure having high internal surface area and high adsorption capacity. Activated carbon has been brought under water treatment and purification using adsorption mechanism as chemisorption and physisorption; in these mechanisms, impurities are forced to shift from liquid to solid surface. These activated carbons found more application in water treatment because of its high adsorption performance and usability, but this advance technique is limited in application due to it high cost of operation and maintenance [8]. Desalination process Thermal desalination
Membrane desalination
Distillation
Non-pressure-driven
crystallization
Pressure-driven
Distillation desalination process • Multi-effect distillation (MED) • Humidification and dehumidification
• Solar still distillation
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Crystallization desalination process • Freezing
• Hydration
Non-pressure-driven • Forward osmosis
• Electrodialysis
Pressure-driven • Reverse osmosis
• Membrane-driven
Next classification of solar desalination process is as under. Solar desalination Direct • Solar still
Indirect • Solar chimney
• Humidification and dehumidification
MED, MFD, VCD, RO, adsorption
Energy Consumption of Desalination Plant Energy consumption of different desalination plants depends upon various factors such as. • • • • • •
Operating capacity of desalination plant, i.e., small, medium, or large. Operating on thermal energy or electrical energy. Availability of feed water (brackish or sea water) Requirement of pre-treatment either mechanical or chemical. Use of renewable energy resources, i.e., either solar, wind, etc. Desalination method either thermal or membrane.
Energy source of different desalination plants As desalination chose evaporation and condensation so focusing on energy requirement, procurement and its availability becomes a priority. Reverse Osmosis and electrodialysis are the major phenomenon for desalination and in case of solar desalination solar energy is being used so at the beginning of this work the different solar energy sources for desalination is highlighted and then electrodialysis. Solar desalination deals with active and passive desalination technique which is part of direct desalination process and mostly used phenomenon; in case of passive solar desalination process, we make use of solar energy only, but the apparatus variation includes depth of water in basin, solar radiation incidence angle, and solar radiation travel categorizes passive solar desalination into various processes as follows: [9] • Single-slope solar still • Double-slope solar still
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• Wick-type solar still • Multi-basin solar still In [9] case of active solar still we are equipped with additional accessories as compared to passive solar. It includes solar flat plate collector, reflectors, condenser and concentrator and agitators all these accessories are helpful in adding energy reaching the basin water as well as it provides help in increasing the conductive surface required for condensation. So, for active solar desalination, we are using • • • •
Solar still with reflector Solar still with solar heater Solar still with condenser Solar still with concentrator
We use electrodialysis for the process of desalination and we have solar and wind as main source because it requires electricity for the desalination process. • PV—electrodialysis (conversion of solar to electricity for dialysis) • Photoelectrodialysis (direct use of solar energy) Energy sources for running the distillation plant till present were fossil fuel, but different desalination plants as per demand make use of different energy sources, so here are different desalination plants with the sources of heat [10]. Desalination 1. Thermal desalination and its energy sources
• Multi-stage flash desalination (MSF), in this desalination, occurs in multi-stage chambers at low temperature. It is operated in middle east where cheap and inexpensive • Multi-effect distillation (MED), it occurs in series of effect makes use of boiler steam of power plant for evaporator in desalination unit • Vapor compression distillation (VCD): in this, the heat source is compression of vapor for evaporating the water. It also makes use of mechanical compressor to generate heat for evaporation
2. Membrane desalination and its energy sources
• Electrodialysis and electrodialysis reversal take energy from electricity, and electric potential is used for removing salt from the freshwater which is not allowed to pass the membrane, and only salt is forced to pass the membrane leaving the freshwater • Reverse osmosis uses pressure to force the water to cross the semi-permeable membrane which is generally of two types, i.e., spiral wound and hollow fiber; RO makes use of electricity, so to reduce the dependence on electricity PV system, operated RO is used
Renewable energy resources for desalination plant
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K. Choudhary et al. Solar energy can be used directly and indirectly to produce desalination effect; in indirect desalination, solar energy is converted to electricity using PV cell which is used to run high-pressure pump
Geothermal energy Geothermal energy can be used in either high-pressure source which is used to drive shaft of mechanical-driven desalination or high-temperature source which can be used to produce electricity, and hence, the electricity may be used to run RO and electrodialysis Wind energy
Wind energy is high source of energy; it can be used at time when Sun is not available; wind energy can be used for the process of desalination either mechanical shaft-driven or electricity-driven based on RO
3 Conclusion The present work highlights the brief overview of desalination technique which focuses on different methods of desalination like solar desalination, activated carbon, electrodialysis. And the use and availability of different renewable resources for the desalination plant.
References 1. Mekonnen, M.M., Hoekstra, A.Y.: Sustainability: four billion people facing severe water scarcity. Sci. Adv. 2(2), e1500323 (2016) 2. Appavou, F., Brown, A., Epp, B., Leidreiter, A., Lins, C., Murdock, H., Musolino, E., Petrichenko, K., Farrell, T., Krader, T.: Renewables 2017 global status report. In: Renewable Energy Policy Network for the 21st Century. Paris: REN21, (2017) 3. Arunkumar, T., Raj, K., Dsilva Winfred Rufuss, D., Denkenberger, D., Tingting, G., Xuan, L. et al.: A review of efficient high productivity solar stills. Renew. Sustain. Energy Rev. Elsevier Ltd 101, 197–220 (2019) 4. NPTEL: Electrical Engineering—Energy Resources and Technology [Internet]. [cited 2020 Jun 6]. Available from https://nptel.ac.in/courses/108/105/108105058/ 5. Rashidi, S., Bovand, M., Esfahani, J.A.: Optimization of partitioning inside a single slope solar still for performance improvement. Desalination 3(395), 79–91 (2016) 6. Sadrzadeh, M., Mohammadi, T.: Sea water desalination using electrodialysis. Desalination 221, 440–447 (2008). https://doi.org/10.1016/j.desal.2007.01.103 7. Ali, E.S., Alsaman, A.S., Harby, K., Askalany, A.A., Diab, M.R., Ebrahim Yakoot, S.M.: Recycling brine water of reverse osmosis desalination employing adsorption desalination: a theoretical simulation. Desalination 408, 13–24 (2017). https://doi.org/10.1016/j.desal.2016. 12.002 8. Liu, D.Q., Xie, Q., Huang, X.Q., Wan, C.R., Deng, F., Liang, D.C., Liu, J.C.: Backwashing behavior and hydrodynamic performances of granular activated carbon blends. Environ. Res. 184, 109302 (2020a) 9. Sharon, H., Reddy, K.S.: A review of solar energy driven desalination technologies. Renew. Sustain. Energy Rev. Elsevier Ltd 41, 1080–118 (2015) 10. Krishna, H.: Introduction to desalination technologies (2011). Retrieved April 21, 2015,from https://www.twdb.texas.gov/publications/reports/numbered_reports/doc/R363/C1.pdf
Analysis of Electromagnetic Aircraft Launching System for Naval Aircraft Shreyas Maitreya, Sameer Soni, and Priyanka Paliwal
Abstract Electromagnetic aircraft launching system (EMALS) is being pursued by various navies around the world to enable assisted take-off but arrested recovery of naval aircraft from aircraft carriers. Currently, either short take-off but arrested recovery (STOBAR) or steam-driven catapult assisted take-off but arrested recovery (CATOBAR) is used. EMALS technology has the advantage of being able to provide smoother acceleration for the aircraft and reduced stress on their airframes along with being cheaper and requiring less maintenance in comparison with CATOBAR technology and unlike STOBAR technology, the aircraft is capable of carrying a full load of weapons, fuel, and additional avionics at the time of take-off. This paper presents a review by covering the various aspects such as design, cost, and reliability of EMALS system. Keywords After-burner · Aircraft carrier · Arrested recovery · Assisted take-off · CATOBAR · EMALS · MiG-29 K · STOBAR
1 Introduction 1.1 Overview of Indian Naval Aircraft Launching Technology The MiG-29 K fighter aircraft which is operated by the Indian Navy to carry out air operations at sea requires 122 m for take-off and 168 m for landing in a STOBAR configuration onboard aircraft carriers like the INS Vikramaditya [1], the technical specifications of which can be found in Table 1. The aircraft achieves this performance by the activation of its after-burners which increase the fuel consumption of the S. Maitreya (B) · S. Soni · P. Paliwal Department of Electrical Engineering, Maulana Azad National Institute of Technology, Bhopal, India P. Paliwal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_71
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774 Table 1 Technical specifications of MiG-29 K aircraft [2, 3]
S. Maitreya et al. Parameter
Value
Std. take-off weight
18,500 kg
Take-off thrust
2 × 88.51961 kN
Max. payload
3000 kg
Take-off distance (typical)
122 m
Landing distance
168 m
Table 2 STOBAR versus CATOBAR Parameter
STOBAR
CATOBAR
Moving parts (Y/N)
N
Y
Maintenance cost
Very low
High
Additional thrust to aircraft (Y/N)
N
Y
Max. aircraft weight
< 100% of rated load*
100% of rated load, “fully armed aircraft” can be launched
Thrust to weight ratio (T/W) of aircraft
Very high (>0.9)
Medium to high
aircraft. The increased demand for fuel in turn reduces the maximum payload that the aircraft can carry; as the maximum load (fuel + payload) is always constant. If more fuel is to be carried by the aircraft, it gets heavier and this limits the amount of payload (such as missiles and guided bombs), thereby limiting its combat capability. This problem can be solved by using assisted take-off systems such as CATOBAR [4] or EMALS [5]. These systems are discussed in detail in the following subsections. Indian aircraft carriers have used both STOBAR and CATOBAR technologies for carrying out assisted take-off—arrested recovery of naval aircraft. The comparison of these two technologies is given in Table 2. The design and overview of both CATOBAR and STOBAR-based carriers are elucidated further in following subsections.
1.2 Overview of STOBAR Technology STOBAR technology involves the take-off deck of the aircraft carrier to be angled upwards. The aircraft starts to pitch up without using its elevators, thereby allowing it to achieve a high rate of climb as soon as it leaves the bow of the carrier. As shown in Fig. 1, the curved bow of the carrier forces the aircraft to pitch up during the take-off roll itself without requiring any elevator input from the pilot. Once the deck run is complete and the aircraft overshoots the carrier, it enters the part-ballistic stage of its flight where the weight of the aircraft is balanced partly by
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Fig. 1 STOBAR-based take-off [4]
the thrust, it receives from its engines and partly by the lift generated by its wings, this is a critical stage of flight as the lift provided by the wings is still less than the weight of the aircraft and on account of its high pitch, it runs a risk of stalling which is why aircraft with T/W of 0.9 and above are preferred as these aircraft is less prone to stalling due to thrust itself being quite comparable to the weight of the aircraft.
1.3 Overview of CATOBAR Technology CATOBAR-based aircraft carriers generally use a high-pressure steam-driven catapult to get an aircraft up to speed before it takes off under its own power [5]. The basic design of CATOBAR technology is shown in Fig. 2. As shown in Fig. 2, the steam is first pressurized in the accumulator and then once the aircraft is given the go ahead, the launch valve is opened so that the high-pressure steam enters the cylinder where it hits the piston. The aircraft is held securely to the catapult by the shuttle, launch bar, and holdback bars, and this entire mechanism propels the aircraft forwards until it reaches take-off speed [5]. Since the flight deck of a CATOBAR-based carrier is flat, the aircraft has a much lower risk of stalling at the time of take-off and this allows for heavier, fully armed, and fully loaded aircraft to take-off increasing the combat potency of the aircraft carrier [6]. Further, this technology also allows a higher number of aircraft to take-off per minute [7] which further compounds the capabilities of the aircraft carrier. The key disadvantages of such a system as opposed to the STOBAR system are the high cost of both installation and maintenance, one of the key reasons why only a couple of blue-water navies in the world such as the US Navy and operate such carriers.
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Fig. 2 Basic design of CATOBAR [6]
2 Design and Review of EMALS System EMALS technology uses a simple linear electromechanical actuator [8] in place of a steam catapult and has all the benefits of CATOBAR technology. A schematic of EMALS technology is shown in Fig. 3. As evident from Fig. 3, EMALS technology does not require any complicated systems such as boilers, accumulators, and valves. to launch the aircraft; which allows it to be smaller and more efficient in comparison with steam-driven CATOBAR technology as seen in Table 3. Thus, as seen from Table 3, an EMALS-based aircraft launch system has all the advantages of the CATOBAR system with the added benefit of being cheaper and easier to maintain while being able to deliver more thrust and energy to the aircraft in comparison with an equivalent CATOBAR-based launcher. Figure 4 shows the basic working of an EMALS-based aircraft launch mechanism. From Fig. 4, it can be understood that EMALS technology allows a greater degree of precision in terms of adjusting the additional thrust that is to be provided to the aircraft that is going to take-off. Further, proven technologies such as linear induction motor and linear synchronous motor can be used as the necessary electromechanical actuator that provides the additional thrust to the aircraft [11, 12].
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Fig. 3 Schematic of EMALS-based aircraft carrier [8] Table 3 EMALS versus CATOBAR systems [9, 10]
Parameter
EMALS
CATOBAR
Peak thrust
120 kN
95 kN
Total kinetic energy
360.523 MJ
285.413 MJ
Aircraft load
100% of rated load
100% of rated load
T/W of aircraft
Medium to high
Medium to high
Maintenance cost
Medium (greater than STOBAR)
High
Fig. 4 Basic working of EMALS technology
778 Table 4 Simulation parameters
S. Maitreya et al. Parameter
Target value
Peak thrust
100 kN
Total kinetic energy
360 MJ
Peak speed
100 m/s
3 Modeling of EMALS System Any linear electrical machine can be used for the purpose of an EMALS launcher [9, 13], this paper, however, presents a model using a generic translational (linear) motor as the launcher. The values shown in Table 4 were taken as parameters in the MATLAB/SIMULINK environment, and the simulation was run for a period of 1 s. The following assumptions were made during the simulation: • • • •
Aircraft is extremely small in comparison with the carrier, i.e., point size. Thrust is provided exclusively by the launcher. All initial conditions (both electrical and mechanical) are zero. Forces of friction, rolling resistance, and air resistance are constant both in magnitude and in direction.
4 Results and Discussion The simulation was run for a period of 1 s, and the following results were obtained as shown in Table 5, and the results were plotted as a function of time as shown in Figs. 5, 6 and 7. As observed from Figs. 5, 6, and 7, the system delivers maximum thrust when the aircraft is at rest and the thrust reduces as its speed increases and becomes zero when it reaches peak value. The aircraft reaches target speed of 100 m/s within 0.2 s and hits a peak speed of 108.999 m/s within 0.4 s and continues to move with a uniform speed as the thrust acting on the aircraft has become zero. The aircraft reaches the targeted value of kinetic energy within 0.2 s and reaches peak energy of 427.7149 MJ and continues to move with this amount of kinetic energy as the thrust has reached zero value. Hence, it can be observed that the EMALS system provides a significant improvement of 9% for peak speed and 18% for peak kinetic energy while maintaining almost Table 5 Simulation results
Parameter
Achieved value
Peak thrust
99.2727 kN
Peak speed
108.999 m/s
Total kinetic energy
427.7149 MJ
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Fig. 5 Thrust provided by EMALS system versus time
Fig. 6 Speed of aircraft versus time
Fig. 7 Kinetic energy of aircraft versus time
at par value of peak thrust. Also, the aircraft attains peak kinetic energy and peak speed in a very short time, i.e., 0.4 s and 0.2 s, respectively, thereby allowing the launcher to launch a greater number of aircrafts in a given amount of time. This significantly reduces the time required by an aircraft carrier to engage with enemy targets and enhances its combat readiness.
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Additionally, since an EMALS system runs purely on electrical energy, it also has the added advantage of a reduced carbon footprint in comparison with a CATOBAR system which requires a continuous source of steam necessitating large quantities of water and fuel to produce and maintain adequate pressure in the launcher.
5 Conclusion In this paper, a review on various aspects such as design, cost, and reliability of EMALS system has been presented while comparing it with STOBAR and CATOBAR aircraft launching systems. Based on simulation results, it is clear that EMALS system has all the advantages of a CATOBAR system and comes with the added benefits of being cheaper and easier to maintain. Further, since all the controlling mechanisms of EMALS are electronic in nature, it is also possible to link it with IoT-based aircraft system which can directly exchange information with each other in order to automate the entire take-off process, thereby making the entire aircraft launch system a safer process. The safety, reliability, efficiency, and cost-effectiveness of the EMALS system make it a viable alternative to CATOBAR technology with an added benefit of sustainable development. Such a system is ideal for developing countries like India which require the necessary technology to have a blue-water navy but have significant financial and economic constraints.
References 1. Indian Navy official website (2020). http://indiannavy.nic.in/press-release/prime-ministersday-sea. Last Accessed 27 Dec 2020 2. Official website of Mikoyan and Gurevich (2020). http://www.migavia.ru/index.php/en/produc tion/the-mig-29-fighters-family/mig-29k-mig-29kub. Last Accessed 27 Dec 2020 3. Technical specifications of MiG-29K aircraft (2020). https://www.aircraftcompare.com/air craft/mikoyan-mig-29k-fulcrum/. Last Accessed 27 Dec 2020 4. Yangang, W., Weijun, W., Xiangju, Q.: Multi-body dynamic system simulation of carrier-based aircraft ski-jump takeoff. Chin. J. Aeronaut. 26(1), 104–111 (2013) 5. Qidan, Z., Lu, P., Yang, Z., Cui, Y.: Model research of steam catapult launch process for carrier-based aircraft. In: 2018 37th Chinese Control Conference (CCC), pp. 8519–8524. IEEE (2018) 6. Harper, H.J.C.: The development of the aircraft catapult. Royal United Serv. Instit. J. 81(523), 566–576 (1936) 7. Zhu, Q., Peng, L., Yang, Z., Ji, X., Han, Y.: Multi-parameter optimization for the wet steam accumulator of a steam-powered catapult. Energies 12(2), 234 (2019) 8. Doyle, M.R., Samuel, D.J., Conway, T., Klimowski, R.R.: Electromagnetic aircraft launch system-EMALS. IEEE Trans. Magn. 31(1), 528–533 (1995) 9. Bushway, R.R.: Electromagnetic aircraft launch system development considerations. IEEE Trans. Magn. 37(1), 52–54 (2001) 10. Bertoncelli, T., Monti, A., Patterson, D., Dougal, R.: Design and simulation of an electromagnetic aircraft launch system. In: 2002 IEEE 33rd Annual IEEE Power Electronics Specialists Conference. Proceedings (Cat. No. 02CH37289), vol. 3, pp. 1475–1480. IEEE (2002)
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11. Joao Murta, P., Martins, J.: Control of an electromagnetic aircraft launch system based on a superconducting linear synchronous motor. In: 2013 International Conference-Workshop Compatibility and Power Electronics, pp. 255–259. IEEE, (2013) 12. Kahlon, A.S., Gupta, T., Dahiya, P., Chaturvedi, S.K.: A brief review on electromagnetic aircraft launch system. Int. J. Mech. Prod. Eng. (IJMPE) 5(6), 58–67 (2017) 13. Xiaoming, Z., Li, H., Qu, X.:Modeling and simulation for dynamic system of electromagnetic aircraft launch. In: 2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE), pp. 125–129. IEEE, (2017)
Second-Order Filter for Improving the Performance of the Multi-level 3 Phase Inverter Using SPWM Ramlakhan Patel and Ashish Kumar Singhal
Abstract The uses of the multilevel inverters (MLI) have been significantly increased the MLI inverters gives the stepped approximation of the sine wave output. The Square wave output-based pulsating AC is sometimes harmful to electrical devices. All the equipment available in the consumer market is designed with the sine wave outputs. Therefore, in this paper, it is proposed to use the second-order low pass filter block to smoothen the multilevel output and to produce the pure sine wave in the system output. This may significantly minimize the harmonic distortion in the output of the system. This paper has improved SPWM modulation pass better performance of the THD. The FFT analysis is done to evaluate the comparison of THD performance. It is concluded that using the filtered output, the stamped AC is converted into the pure sine wave output. Keywords Three phase inverter · Three-level inverter · FFT aialysis · THD · SPWM · Modulation index
1 Introduction Power electronics (PE) is now forced to design and utilize the sources of renewable energy (RE) to serve the requirement of consumer’s daily life. An inverter is an essential part of such applications for converting the DC output of RE sources to the equivalent useful AC power to serve load at the line voltage. Any RE system requires the inverters at the front end to convert DC power to AC mains. The electrical equivalent of the process in Fig. 1. It is required to remain the inverting gain at a constant value relevant to all supply voltage fluctuations. Another way the output voltage may be varied by varying the gain of the inverter whether DC voltage is fixed and not controllable, which is normally achieved by
R. Patel · A. Kumar Singhal (B) Sagar Institute of Science, Technology and Engineering Bhopal, Bhopal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_72
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Fig. 1 Equivalent diagram of the inverting action
pulse-width-modulation (PWM) control within the inverter. The gain of the inverter may be defined as the ratio of the AC output voltage to DC input voltage.
2 Contribution of Work It is expected in many power applications that DC power may have lost over the long-distance applications and may offer higher attenuation loss. Thus for the longdistance application mostly AC power sources are preferred. The prime motivation for current research is that most of the existing inverting architecture suffers from harmonic distortions and fails to generate pure sine results. Thus the major motivation of the current work is to design the inverter with minimum possible Total harmonic distortion (THD) and to evaluate the performance of existing inverters for THD analysis. The prime contribution of the dissertation is that it has evaluated the performance at the high voltage application at the 400 V DC supply. The performance of the THD is minimized by applying second-order low pass filter block. It is observed that the filtered AC is a good approximation of the perfect AC voltage and current waves. The THD performance is significantly reduced with the uses of the proposed filtered design. Overall paper is a significant research contribution for next-generation Inverter design. Since huge inverting designs have been proposing past, thus this section begin to ease the review process by presenting the selective classification inverter designs as shown in Fig. 2. Although, there are a lot of higher multilevel configurations available, they require more components to design the system and may get bulkier for design. Thus, this research is the focus to limit design up to three levels only [1]. The major problem in such small multilevel designs is to mitigate the effect of the THD. As comparatively these inverters may offer higher THD losses. Thus, prime concern must be to evaluate the THD performance (Fig. 3).
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Fig. 2 The specific classification of three-phase inverters
Fig. 3 Proposed second-order filter based SPWM blokes, model for three-phase inverter
3 SPWM Inverter Usually, the sinusoidal signal is applied on the non-inverting, and the triangular reference signal is applied on the inverting terminal of the op-amp as a comparator. Based on the comparison, the comparator produces the PWM output with varying successive pulses of different widths as shown and respective output waves are shown in Fig. 4.
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Fig. 4 Process of PWM wave generation
4 Review of Multilevel Inverters Nabae et al. [2] have proposed a new approach of the Neutral-Point-Clamped for iproving the PWM performance of inverting operations. Hassaine et al. [3] have proposed to design the asymmetric inverter architecture for the specific application in grid connection. Ghalib et al. [4] proposed a design for pure sine wave-based inverter specifically to photovoltaic fields application. They have proposed a circuit for controlling the sine wave generation. The PIC microcontroller is used for control action using PWM. Further design is based on low-power electronic applications only. Maheshwari et al. [5] have proposed a goof simulation of the SPWM based single phase inverter design but was a basic one. Mathukiya et al. [6] have designed the three-phase inverter for the multiple conductions angles. They have considered the 180 and 120 degree angle of conduction for current cycles for design. There are huge numbers of applications of the inverters already available. The selective applications are described including uses of car battery-based design, [5]. The basic circuit diagram for the UPS design [7] and for the application of inverting operation in electric vehicle design [8] for improving the performance of the multilevel CHB inverters Namboodiri et al. [9] have designed the two-phase inverter based on the SPWM approach and presented a good comparison of the line and phase waveforms. The PWM is used for controlling the performance of switching. Namboodiri et al. [9] have designed an inverter using SPWM analysis and reported the FFT analysis of the THD achieved to 96%. Lee et al. [6] have opted for the modified PWM8 approach with phase-shifting to improve CHB inverting performance. The PWM is used for controlling the performance of cascaded H bridge inverter. Bajpayee et al. [2] have improved the triangular trigger pulse for the SPWM switching and reported improvement of THD up to 88%. A. Palanisamy et al. [10] have presented the good application of the PWM-based inverter for the solar PV cell
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s. of THD for various multilevel CHB inverters and concluded 23.54% for 9 level and 10.84% THD for 11 level CHB inverters. Gupta et al. [2] have presented the design of CHB inverter and presented minimum voltage THD of 28.9% for single-phase 4 level inverter.
5 Evaluations of Multilevel Inverter In this paper, to improve the performance of the existing inverter it is proposed to use the second-order low pass filter. The settings of the second-order low pass filter used for smoothening the output of multilevel three-phase waves Fig. 5 shown the settings of the proposed low pass filter block design. The performance of FFT is compared based on the Total Harmonic Distortion (THD), which defines the degree of closeness of the shape, between an output waveform and its fundamental FFT component. THD is mathematically defined as; (Figs. 6 and 7; Tables 1 and 2) ⎧ ⎫ ∞ ⎨ ⎬ (Von )2 THD = 1/Vo1 ⎩ ⎭ n=2,3
Fig. 5 Settings of the proposed low pass filter block design
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Selected signal: 4 cycles. FFT window (in red): 1 cycles 5
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(b) Fig. 6 a THD FFT analysis for filtered current waveform for proposed method b for voltage THD analysis in frequency axis
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Fig. 7 Inverter output voltage wave at the 400 V input DC supply
Table 1 Comparison of TSD for existing inverters and proposed methodologies
Table 2 Basic design summary report
S no
Without filter
3 phase inverter with filter
0.1
87.37
0.54%
S. no
Design component
1
Number of nodes:
Numbers 12
2
Number of branches:
21
3
Number of mutuals (inductive coupling):
Nil
4
Number of voltage sources:
8
5
Number of current sources:
18
6
Number of switches:
18
6 Conclusions This paper proposed to design the second-order low pass filter for improving the THD performance of the 3 levels 3 phase inverters. The reduction in harmonic distortion is a challenge in inverter design. In this paper, it is proposed to use the second-order low pass filter block to smoothen the multilevel output and to produce the pure sine wave in the system output [11]. This may significantly minimize the harmonic distortion in the output of the system. The FFT analysis tool is used for evaluation and the THD is plotted. As a case study, the inverting performance is evaluated for the higher input DC voltage range.
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The DC input voltage is raised to 400 V, and the FFT analysis is done; it can be observed that the output current amplitude scale is changed to 30 for 10. And the output voltage is also increased [12]. Acknowledgements Authors heartily acknowledge everyone who has supported the current research. The authors also highly acknowledge all referenced authors for indirect contributions to the current research.
References 1. Balamurugan, C.R., Natarajan*, S.P., Bensraj, R. :Control techniques for various bipolar PWM strategies of three phase five level cascaded inverter. J. Eng. Sci. Technol. 10(7), 878–897 (2015) 2. Nabae, A., Takahashi, I., Akagi, H.: A new neutral-point-clamped PWM inverter. IEEE Trans. Ind. Appl. 17(5), 518–523 (1981) 3. Hassaine, L., Olías, E., Haddadi, M., Malek, A.: Asymmetric SPWM used in inverter grid connected. Revue des Energies Renouvelables 10, 421–429 (2007) 4. Ghalib, M.A., Abdalla, Y.S., Mostafa, R.M.: Design and implementation of a pure sine wave single phase inverter for photovoltaic applications. In: American socoity for Engineering Education in 2014, (2014) 5. Kouro, S., Malinowski, M., Gopakumar, K., Pou, J., Franquelo, L., Bin, W. et al.: Recent advances and industrial applications of multilevel converters. IEEE Trans. Industr. Electron. 57(8), 2553–2580 (2010) 6. Bajpayee, A., Diwakar, N., Gautam, M., Shrivastava, M.: Analysis of the FFT performance of the bipolar SPWM inverter. Int. J. Eng. Innov. Technol. (IJEIT) 7(11), (2018) 7. Anubha, G.: Three phase inverter simulation using sinusoidal PWM technique. Int. J. Adv. Res. in Electri. Electron. Instrum. Eng. 6(5) (2017) 8. Ali, A., Nakka, J.: Improved performance of cascaded multilevel inverter. In: 2016 International Conference on Microelectronics, Computing and Communications (MicroCom), Durgapur, India, pp. 1–5. (2016) 9. Namboodiri, A., Wani, H.S. : Unipolar and bipolar PWM inverters. IJIRST–Int. J. Innov. Res. Sci. Technol. 1(7), (2014) 10. Zhang, B., Du, X., Zhao, J., Zhou, J., Zou, X.: Impedance modeling and stability analysis of a three-phase three-level NPC inverter connected to the grid. CSEE J. Power Energy Syst. 6(2), 270–278 (2020) 11. Ramteke, R.G., Patil, U.V.: Design and comparative study of filters for multilevel inverter for grid interface. In: 2014 International Conference on Power, Automation and Communication (INPAC), Amravati, India, pp. 39–44. (2014) 12. Oni, E.A, Oladapo, O.O., Ajayi Oluwatoyin, V.: Improving the output of cascaded five level multilevel inverter using low pass broadnband filter. Int. J. Model. Simul. Appl. (IJMSA) 1(1)
Numerical Investigation of Product Capability and Enhancement Through Multi-hole Extrusion Process Y. Solomon, D. K. Sinha, P. J. Ramulu, and S. S. Gautam
Abstract Increasing production efficiency while using the maximum effort to utilize energy and keeping the quality of products steady at the same time has been a complicated task for industries and manufacturing firms. The manufacturing process like extrusion plays a great role in improving product sustainability due to its near net shape fabrication character. But still, this process did not achieve its ultimate product enhancement capability. During the aluminium extrusion process, multihole extrusion dies are implemented to produce several extrusion products at a time, which maximizes the productivity of this process. But still, some improvements have been left to product quality enhancements. In this study, process enhancement has been taken care of to improve the productivity of the multi-hole extrusion process. A simulation of direct hot extrusion of AA6063 aluminium alloy is performed by using DEFORM-3D software at different extrusion process parameters, and the results were analyzed using the finite element method.Please confirm if the corresponding author is correctly identified. Amend if necessary.It is correct. No changes required. Keywords Extrusion process · AA6063 aluminium alloy · DEFORM-3D · Finite element method
1 Introduction An aluminium alloy of AA6063 which is produced by using extrusion process is mostly implemented for architectural applications due to their light in weight structure and high-strength properties and have much interest across the globe. An improvement in production efficiencies and process enhancement of aluminium alloy
Y. Solomon · D. K. Sinha · P. J. Ramulu School of Mechanical Engineering, Adama Science & Technology University, Adama, Ethiopia S. S. Gautam (B) Department of Mechanical Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_73
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processing’s have been the crucial task for aluminium extrusion industries. The extrusion of metals can be hot or cold, but for better product improvement hot extrusion will be implemented. Single-hole extrusion occurs when the extrudate passes through a single hole in the die; however, multi-hole extrusion occurs when the die has more than two holes. During the extrusion process of aluminium, multi-hole extrusion dies are implemented for the production of several extrusion products at a time, which maximizes the productivity of this process. Another merit of multi-hole extrusion die is the ability to reduce the reduction ratio. When a die of single hole is used then excess amount of force is required for the extrusion process. Therefore, when an excess stem force capacity is needed multi-hole extrusion process will be implemented in industries [1, 2]. In comparison to a single hole extrusion, Sinha et al. [3] found that during a multi-hole extrusion operation, half of the extrusion pressure is needed. When it comes to capability of extruded products, the detailed work of [1, 3–6] confirmed that more extruded products can be obtained with relatively minimum amount of press capacity during multi-hole extrusion process than single hole extrusion. An extensive review of past literature reveals that for better product quality and product efficiency pre-heating temperature, ram speed, extrusion ratio, cone halfangle, bearing length, etc. are the process parameters mostly needed to be optimized during extrusion processes. However, in most extrusion process, these sensitive processes parameters have been decided based on practice, the skill of workers and in most cases through trial and errors [7]. These leads to defects in extrudates such as; crack or surface burning, twist deformation, variation in material flow, wave, and bend appear, and actual production has to be stopped or postponed [7–9]. As stated by Saha et al. [10] die geometry and process parameters during multihole extrusion have a huge impact on the quality of extrudates. A study by Prabhu et al. [11] found that the process parameters during the extrusion of AA6061 aluminium alloy have a significant contribution to the quality of extrudates needed to be optimized. Different optimization technique will be applied to study the effects and relations of extrusion process parameters. In the study of Fang et al. [1] billet pre-heating temperature is the most affecting extrusion process parameter, which needs a critical balancing during the extrusion process. This is due to its higher possibilities to influence the quality of extruded products and the ram speed. An investigation on AA6061-T6 alloy of a twist extrusion process by Iqbal et al. [12] found that the mechanical properties can be improved up to 10% by increasing the number of passes and extrusion temperature during the twist extrusion process. During the pyramid die extrusion process, the increment in ram speed will increase welding pressure, extrusion force, and velocity distribution but, the length of transverse weld decreases simultaneously Chen et al. [13]. One of the complicated tasks for the multi-hole extrusion process is designing and analysis of die geometries. Die geometry determination, optimization and FEM analysis for multi-hole extrusion process have been investigated by many scholars [14–16], and they come up with better die designing and design considerations in their study.
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Microstructure investigations and numerical and computational modelling of manufacturing processes have a lot of potential to improve process efficiency and product quality. A simulation work and numerical relation of arbitrary Lagrangian– Eulerian (ALE) by He et al. [17] predicted the extrusion process defects, which are experimental verified in their result. The effect of steps in the die pocket on metal flow is investigated using 3D FEM simulation to create two chevron profiles with unequal thicknesses via two-hole dies [1]. Since, the simulation work and experimental results were in good agreement for their study [1], they suggested that 3D FEM demonstration is a powerful tool in optimizing die design and decreasing the number of trials in extrusion process. It is also found that the effective strain, which offers information about the material’s work hardening during the extrusion phase is dependent of the ram speed, extrusion load, and die pocket in their FEM analysis [2]. A numerical description made by authors [18] also formulated a mathematical relation for stress and velocity fields as well as strain rate of axisymmetric hot extrusion process using finite volume methods. The authors proposed a new numerical scheme for calculating stress and velocity fields of metal flows in the axisymmetric extrusion phase of aluminium alloy 6351 in steady state. There have been little investigations performed regarding to multi-hole extrusion process combined with capability and product enhancements. Therefore, in this study, the product capability enhancement through multi-hole extrusion die is investigated using numerical approach.
2 Numerical Procedure Therefore, the latest analysis of simulations performed using a metal forming simulation software DEFORM-3D 11.0 plat form, which incorporates with thermomechanical linking, automated meshing, and dynamic re-meshing into this program, both of which are essential for realistic extrusion process modelling. This simulation is performed for the multi-hole extrusion process of AA6063 aluminium alloy. DEFORM processing implements time-dependent non-linear problems, which creates a continuous finite element solution at distinct time increments. A moded plastic deformation behaviour of metal forming process implemented in this study uses a finite element analysis software DEFORM-3D. The fundamental equation of the finite rigid-plastic part are as follows: Equilibrium equation: σi j, j = 0
(1)
Compatibility and incompressibility equations ε˙ i j =
1 u i j + u ji 2
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ε˙ v = u i j = 0
(3)
Constitutive equations: (4)
Boundary conditions: σi j n i = F j on S F , u i = Ui on SU
(5)
where σi j , ε˙ i j , σ and ε˙ are the stress, strain rate, effective stress as well as strain rate, respectively. F j denotes force on boundary surface (S F ), and Ui as deformation velocity on boundary surface (SU ). By applying the variational method to Eqs. (1–4), the rigid-plastic FEM’s weak form to be calculated i.e.
σ δεdv + K ∫ εV δεV dV − V
Fi δu i dS = 0
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SF
where V and S are the volume and the surface area of the material, respectively, and K is the penalty constant. A Von Mises yield criteria is implemented in DEFORM-3D for solving the problems. Equations (7) and (8) show the effective strain and stress respectively. √ 2 ε= (ε1− ε2 )2 + (ε2− ε3 )2 + (ε3− ε1 )2 3 1 σ = √ (σ1− σ2 )2 + (σ2− σ3 )2 + (σ3− σ1 )2 2
(7) (8)
where εi and σi are the principal strain and stress in the direction of i respectively. The heat transfer coefficient between tool and billet was set as 11 N/s mm°C as per authors [5]. Table 1 shows the parameters and the necessary boundary conditions that were needed for the present simulations. The workpiece was allocated tetrahedron elements, which are simple to discrete an irregularly shaped object [1]. As per component descriptions in Table 1, the geometries of billet, ram, and the bottom die at various number of holes were produced in Solid works 2019, as shown in Fig. 1. Then, using Table 1, the boundary conditions for both dies and billets were set, and the billet was assumed to be plastic in a circular shape, with material properties assigned according to the material model. An even mesh distribution throughout the workpiece is performed for maximum data storage usage and utilization of computing time. Figure 2 depicts the meshed billet, die, and other extrusion tooling. The meshing models of billet and both dies are shown in Fig. 3.
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Table 1 Component description and boundary conditions Components Size
Boundary conditions
Billet
40 mm dia, 50 mm length
• Preheat temperature 450 °C • Meshing type Tetrahedral(a)
Container
50 mm outside dia, 40 mm inside dia, 60 mm length
• Preheat temperature 250 °C • Coefficient of friction 0.1
Ram
40 mm dia, 100 mm length
• Pre heat temperature 250 °C • Ram speed 6 mm/sec • Coefficient of friction 0.1
Bottom die
50 mm outside dia, 40 mm inside dia, 9 mm length • Pre heat temperature 1-hole, 2-holes, 3-holes and 4-holes with an equal 250 °C hole diameter of 5 mm • Coefficient of friction 0.1
Fig. 1 Model of dies. a 4-holes die. b 3-holes die. c 2-holes die
Fig. 2 Geometric models and meshing of a billet, die, and ram
By properly generating mesh, accurate simulations can be obtained. The material deformation is generally controlled by suitable reformulation of the mesh at each phase.
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Fig. 3 Meshing models. a Billet. b Ram. c Container. d 2-hole die
2.1 Material Models The hot deformation behaviour of billet materials has a huge contribution in determining extrusion process parameters during the hot extrusion process [19]. In the bulk metal forming process, the flow stress value and calculation will have high impact on the process of the operation. The Arrhenius hyperbolic sine function describes the material model of AA6063 aluminium alloy [20]. ε˙ = 8.99 × 1013 [sinh(0.01499σ )]7.28391 × exp
261848 8.314T
(9)
where ε˙ is strain rate of AA6063 aluminium alloy billet, σ flow stress, and T is the temperature in K.
3 Results and Discussion This section presents the results obtained from the numerical investigation of the multi-hole extrusion process parametric optimization for AA6063 alloy.
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Fig. 4 Billet material flow through 4-holes die (d = ram displacement). a d = 0 mm. b d = 3.32 mm. c d = 4.15 mm. d d = 5.41 mm. e d = 7.2 mm
3.1 Numerical Investigation 3.1.1
Material Flow
The ram displacement with the material flow characteristics during the multi-hole extrusion process is depicted in Fig. 4a–e. In the figure, the extrusion process from beginning up to the required displacement is shown. In Fig. 4b, the billet material is starting its plastic deformation and as a result, the materials about to exit the die holes, at this point the ram force will have a uniform characteristic. The influence of uneven velocity distributions exhibits a deflection of extrudates as shown in Fig. 4e.
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Numerical Analysis
Figures 5, 6, and 7 show damage velocity distribution, contact time, strain distribution, effective stress distributions, and load stroke curves during the multi-hole extrusion process. The simulation images are taken at the same boundary conditions as mentioned in Table 1 and the same extrusion process parameters except for the number of hole difference.
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Fig. 5 Simulation results of a damage, b velocity distribution, c contact time, d strain distribution, and e effective stress distribution and Load distribution for the 2-hole die extrusion process
The extrusion process parameters for Figs. 5, 6, and 7 are when ram speed is 6 mm/s, extrusion temperature is 450 °C and 2, 3, and 4 number of holes in extrusion dies is selected for discussions.
3.1.3
Damages
As it is observed from Figs. 5, 6 and 7 damage for each consecutive multi-hole die extrusions, the damage is decreased when the hole numbers in the die increase. For 2-hole and 3-hole die extrusion, the damage values are more than one which is an undesirable effect for extruded products. Whereas for the 4-hole die extrusion process the damage value is below one, this shows that the extrudate qualities for 4-hole die extrusion are relatively in safe condition than the 2-hole and 3-hole extrusions. But if
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Fig. 6 Simulation results of a damage, b velocity distribution, c contact time, d strain distribution, and e effective stress distribution and Load distribution for 3-hole die extrusion process
proper optimization is taken care of for 2-hole and 3-hole die extrusion, the damage of extrudates can be managed as well.
3.1.4
Velocity Distributions
The velocity distribution for 2-hole and 3-hole die extrusion is somewhat uniform which helps the extrudate to flow out uniformly. But for 4-hole extrusion die relatively none uniform velocity distribution is observed that why a relative tangled effect of extrudates is observed. As most of the works of literature are depicted the velocity distributions of extrudates can be optimized through an extrusion die geometry optimization.
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Fig. 7 Simulation results of a damage, b velocity distribution, c contact time, d strain distribution, and e effective stress distribution and Load distribution for 4-hole die extrusion process
3.1.5
Contact Time
If the contact time between billet and extrusion tools is small, there will be a higher chance of reduction to sticking condition of friction between material and tool interfaces. This will enhance the extrusion speed and improve die tool life. Figures 5, 6, and 7 of figure (c) show the contact time for 2-hole, 3-hole, and 4-hole extrusion die. The simulation result shows that for 2-holes and 2-holes extrusion die the contact time is higher than that of the 4-holes extrusion process, therefore, contact time improves at 4 number of holes during multi-hole extrusion.
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Stress, Strain and Load Distribution
The difference between simulation results, stress, and strain distribution for all three simulations doesn’t exhibit a huge difference. This is because the same extrusion process parameters have been implemented for all three simulations. When different extrusion process parameters are used it is observed that the effective stress and strain values fluctuate accordingly. However, the influence of these parameters can be seen in the load stroke distributions of three simulations. As shown in Figs. 5, 6, and 7, the ram force needed for multiple hole extrusion method diminishes as the hole number in the extrusion die rises, indicating that this study agrees with previous research that suggests implementing multi-hole die extrusion for better energy utilization.
4 Conclusions The numerical investigation on product capability and enhancement for multi-hole extrusions are investigated by using DEFORM-3D commercial software and as per the results obtained from the numerical approaches the following conclusions are drawn: • The enhanced damage value is obtained when 4-hole dies are used during multihole extrusion process. • A tangled effect observed during 4-hole die extrusion is as a result of geometrical relation of the extrusion die rather than the process parameter determinations. • The contact time between extrudates and extrusion components are improved in 4-hole die extrusion that will minimize the tribological effect between interfaces and have a huge contribution for tool life. • As compared to the smaller number of holes in the die, the ram force needed for a larger number of holes is minimal. Therefore, the investigation is in good agreement with previous investigations in multi-hole extrusion process.
References 1. Fang, G., Zhou, J., Duszczyk, J.: FEM simulation of aluminium extrusion through two-hole multi-step pocket dies. J. Mater. Process. Technol. 209, 1891–1900 (2009) 2. Das, R., Sarmah, A., Lakshmi, D.V.N., Sood, A.: A finite element analysis on the effect of location of holes, die pockets and extrusion speed in multi-hole extrusion process. Procedia. Eng. 97, 1247–1253 (2014) 3. Sinha, M.K., Deb, S., Dixit, U.S.: Design of a multi-hole extrusion process. Mater. Des. 30, 330–334 (2009) 4. Chahare, A.S.: Optimization of aluminium extrusion process using taguchi method. IOSR J. Mech. Civ. Eng. 17, 61–65 (2017)
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5. Jajimoggala, S., Dhananjay, R., Lakshmi, V.V.K.V.K., Shabana: Multi-response optimization of hot extrusion process parameters using FEM and Grey relation based Taguchi method. Mater. Today Proc. 18, 389–401 (2019) 6. Sinha, M.K., Deb, S., Das, R., Dixit, U.S.: Theoretical and experimental investigations on multi-hole extrusion process. Mater. Des. 30, 2386–2392 (2009) 7. Chen, Z.Z., Lou, Z.L., Ruan, X.Y.: Finite volume simulation and mould optimization of aluminum profile extrusion. J. Mater. Process. Technol. 190, 382–386 (2007) 8. Li, Q., Smith, C.J., Harris, C., Jolly, M.R.: Finite element investigations upon the influence of pocket die designs on metal flow in aluminium extrusion Part I. Effect of pocket angle and volume on metal flow. J. Mater. Process. Technol. 135, 197–203 (2003) 9. Zhang, C., Zhao, G., Chen, Z., Chen, H., Kou, F.: Effect of extrusion stem speed on extrusion process for a hollow aluminum profile. Mater. Sci. Eng. B Solid-State Mater. Adv. Technol. 177, 1691–1697 (2012) 10. Saha, P.: Aluminum Extrusion Technology. ASM International (2000) 11. Prabhu, R., Ganapathy, T., Venkatachalapathy, V.S.K.: Process parameters optimization on porthole-die hot extrusion of aluminium alloy tubes using taguchi method. Int. J. Mech. Mater. Eng. 6, 102–108 (2011) 12. Iqbal, U.M., Kumar, V.S.S., Gopalakannan, S.: Application of response surface methodology in optimizing the process parameters of twist extrusion process for AA6061-T6 aluminum alloy. Measurement 94, 126–138 (2016) 13. Chen, L., Zhao, G., Yu, J.: Effects of ram velocity on pyramid die extrusion of hollow aluminum profile. Int. J. Adv. Manuf. Technol. 79(2117), 2125 (2015) 14. Fang, W., Tang, D., Wang, H., Li, D., Peng, Y.: Optimization of die design for thin-walled flat multi-port tube with the aid of finite element simulation. J. Mater. Process. Technol. 277, 116418 (2020) 15. Chen, L., Zhao, G., Yu, J., Zhang, W., Wu, T.: Analysis and porthole die design for a multi-hole extrusion process of a hollow, thin-walled aluminum profile. Int. J. Adv. Manuf. Technol. 74, 383–392 (2014) 16. Kumari Sahu, R., Das, R., Dash, B., Routara, B.C.: Finite element analysis and experimental study on forward, backward and forward-backward multi-hole extrusion process. Mater. Today Proc. 5, 5229–5234 (2018) 17. He, Z., Wang, H.N., Wang, M.J., et al.: Simulation of extrusion process of complicated aluminium profile and die trial. Trans. Nonferrous Met. Soc. China (English Ed.) 22, 1732–1737 (2012) 18. Bressan, J.D., Martins, M.M., Button, S.T.: Analysis of aluminium hot extrusion by finite volume method. Mater. Today Proc. (2015). https://doi.org/10.1016/j.matpr.2015.10.007 19. Dong, Y., Zhang, C., Zhao, G., Guan, Y., Gao, A., Sun, W.: Constitutive equation and processing maps of an Al-Mg-Si aluminum alloy: determination and application in simulating extrusion process of complex profiles. Mater. Des. 92, 983–997 (2016) 20. Wang, G., Bian, D.W., Kou, L.Y., Zhu, X.J.: Hot deformation behavior and processing map of 6063 aluminum alloy. Mater. Res. Express (2019). https://doi.org/10.1088/2053-1591/ab2d07
Automotive Advanced Toggle Transmission System Using Dynamo and Dual-Clutch Mechanism Savanth Chandra Shekhar, P. N. V. Bala Subramanyam, Moon Banerjee, and B. Lakshmana Swamy
Abstract Toggle transmission is used in the automotive industry. In this paper, we are implementing the toggle transmission into the passenger and sports vehicles. The main objective of this toggle system is being able to change the gears at a faster rate with evicting the fear of losing the control while changing the gears without following any sequential format or H pattern. Toggle transmission doesn’t follow the traditional approach of moving gears in sequential order. Toggle gearing system in a way deploys the dual-clutch transmission for better traction control. While shifting the gears at a faster rate, there will need to be a constant for that instance we also deploy dynamo which will be used to control the car when the driver is shifting the gear from 1st to 5th gear directly without following the old school format. Gears in the manual and automatic transmission follow an ascending order to change the gears, but toggle transmission truly works on number game, you can directly shift from 1st gear and 5th gear by just toggling switches. Keywords Toggle switches · Dual clutch · Transmission system · Dynamo · Dash board
1 Introduction Gearing system usually runs the H pattern or paddle shifters, but in this system, there will be toggle switches on the dashboard, where in the gears are changed just by S. C. Shekhar (B) Department of Electronics and Communication Engineering, Koneru Lakshmaiah Educational Foundation, Hyderabad, Telengana 500075, India e-mail: [email protected] P. N. V. Bala Subramanyam · M. Banerjee · B. Lakshmana Swamy Department of Mechanical Engineering, Koneru Lakshmaiah Educational Foundation, Hyderabad, Telengana 500075, India e-mail: [email protected] M. Banerjee e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_74
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toggling the switches. When a switch is put to action, the gear clamp changes the alignment and withdraws from the rotating gear rod. Each gear will be in a semicircle shape; in neutral motion, the N gear clamp will get attached to the rotating rod. 1st gear toggled up and the neutral gear clamp will withdraw its position from the rod and the 1st gear clamp will be attached to rod. In this pattern, all the five gears + neutral gear and reverse gear will be aligned in the form of the switches on the dashboard of the car. The major question arises to the audience is, how the car be controlled when the gear is being shifted from one gear by just toggling the switches. We use a device called dynamo; this device is structured to do the reverse engine mechanism where car in be commanded to the stagnant rotating wheels, while the gear is being changed. Dynamo is deployed in the chassis part of the car where in the engine of the car is also stipulated. Toggle switches are directly connected to the engine. Toggle switches are present in the dashboard where in the main gear level is directly connected to the crankshaft of the engine for the movement of the car. At the last stage of the process, the crankshaft is initiates the process and there the car is moved. Dynamo takes the input from the engine and there acts as break pad for the wheels. Dynamo takes the input from the engine and also connected to hub where dynamo can do the reverse mechanism so that it can act as an RPM controller. This is how the stages of the toggle transmission work. This transmission system can be deployed in the existing technologies; also, dynamo can deploy in the existing chassis of the cars, just have to change the schematic of the existing chassis design. This will revolutionize the present automotive industry in such a way that the safety of the passenger and vehicle. Josephine Selle [1] mentioned about automatic and manual transmission system for the existing on-road vehicles. Singh [2] stated regarding the gear shift and its modeling procedures. Sudhabindu [3] conveyed about mechanical properties of materials used in manufacturing. Balasubramanyam [4] informed regarding the software’s required to perform the analysis and to select appropriate material basing on results. Mohan Phani Gopal [5] strength comparison was made between different materials. Bala Subramanyam [6] modal analysis performed to find out the vibrations occurred in the system when it is in function. Bala Subramanyam [7] design procedure was mentioned to finalize the exact shape of component.
2 Objective The main object is to build better monotony of driving the vehicles and focus on the safety issues of the 21st century vehicles. (1) (2) (3) (4) (5)
This transmission helps in the faster and safer commute between distances. Easier traction control of the car. Dynamo acting as a secondary brake in order to infuse the braking system. The RPM of car is also controlled due to this kind of technology. Dynamo as anti-clock wheeling system.
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Toggle transmission increases the efficiency of the vehicle.
3 Modeling of the System Gears in the manual and automatic transmission follow a sequential pattern, but toggle transmission truly works on number game. The transmission has mechanism where the gearing box work as on hop on hop off. 1st gear when toggled it will cling to the gear rod, and when 5th gear is toggled, the 1st gear hops of the gear lever, and the 5th gear gets clanged to the lever road. While shifting the gears, the driver might feel the edge of losing the control on the car; but due to installation of dynamo, we can get the control of the car and there is no verge of car being out of bounds so there is safety. When the engine is started and the driver moves the gear from neutral to first, instead of following the H pattern, the car driver presses one of the buttons on the dashboard. As soon the button is pressed, the neutral gear unclamps from the gear level which is long rod which is making the cars move. Gear clamps are the rods which clamp to the gear level, whenever the motion is initiated to the automotive. As the driver initiates the motion for the car, the neutral gear clamp gets clanked to the car. Unless and until there is another button is pressed on the dashboard. As soon next button is pressed, the neutral gear clamp gets unclenched from the gear lever. Dynamo although being an electric based operated motor plays a huge role in the controlling of the car. Dynamo helps in attaining the stability of the vehicle so that the driver doesn’t fear losing the control of the car. If the car is moving a certain speed in the highway and then there is a sudden loss of the control of the car, in such dynamo helps in getting the car back to the normal so that no causalities takes place.
3.1 Dynamo Acts as Anti-Break Model and also Controls the RPM (1)
As an anti-brake module—storage of energy in the main battery When we use the toggle transmission to setup the gearing system, we accelerate from 1st gear to 5st gear; in other transmission system, we face jerks and loss of car control; with toggle transmission coalescing both dual-clutch transmission and dynamo for rpm control, we can accelerate to the 5th gear without being able to face jerks and remain in the state of losing the car control. Once we inculcate the dynamo with dual-clutch transmission system, while a car is racing with an rpm and has a break fail toggle transmission not only helps the car to slow in the prospective way but also decreases the impact of the fatal cause. William W. Hurst mentioned in his survey that the fatal accidents due to brake failures constitute about 22% of the total accidents.
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(2)
As an RPM controller configuring dynamo as a reverse engine mechanism, the rpm of the car gets controlled when the gear is changed from the 1st gear to 5th as part of gear changing mechanism.
3.2 Toggle Switches and Dual-Clutch Transmission Dual-clutch transmission (insert Wikipedia link) is employed in most of cars in the current market of automotive. Laying the stone of establishment in 1950s had only become popular 1980s later making its way into the market. Dual-clutch transmission offers around 6% efficiency to the vehicle making it the most reliable transmission in the current market era giving a neck to competition for continuously variable transmission (CVT) This transmission will provide a better result in terms of providing efficiency to the vehicle as this part of gearing system also provides safety due to this, both toggle switches dual transmission would stand out among the other gearbox patterns to provide a better market value.
4 Designing of the System Toggle transmission tackles traction control in slightly different way, traction control although inducts the grip and stability of the car on the road during acceleration by measuring the wheel rotation it also gives a smoother control over the car. When the dynamo meets car during the emergency, the traction control process becomes much easier.
4.1 Processing of the Transmission (1) (2) (3)
The power attained from dynamo is connected to the engine directly for the operations need to perform in the transmission. The basic chassis model is presented to attain the power input; the dynamo is placed alongside the battery. Dynamo when attached to the chassis can act both like as generator as well as motor.
As shown in Fig. 1, the power attained from dynamo is connected to the engine directly for the operations need to perform in the transmission. The basic chassis model is presented to attain the power input; the dynamo is placed alongside the battery. Dynamo when attached to the chassis can act both like as generator as well as motor. This gives smooth operation of the transmission system better control over rollover of the vehicle, and passenger safety can be enhanced.
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Fig. 1 Schematic view of the modified chassis
The toggle switches are operated, as shown in Fig. 2, those in circular ones are switches when toggled the gear will be moved to that speed. These lines are the gear clamps, while these are attached to the main level which is the rectangular bar. Each time the button is pressed the clamp will get either clenched or unclenched to the gear lever and the car moves. Gear clamps are the rods which clamp to the gear level, whenever the motion is initiated to the automotive. As the driver initiates the motion for the car, the neutral gear clamp gets clanked to the car. Unless and until there is another button is pressed on the dashboard. As soon next button is pressed, the neutral gear clamp gets unclenched from the gear lever. Due to friction, the sudden change in gear there will be chance for clamps not getting unclenched at from the gear level. Tackling such speed densities, it is very much necessary to keep mind regarding the locking of the gears. These gear clamps are designed in a they depend on motion, and the user will able to know if a certain gear clamp from the order of “N R 1 2 3 4” doesn’t get unclenched from the gear level. This is very rare case because this depends on the excessive exposure to too much friction. In such cases, the engine slows and so does the speed so that there is no fear of losing the control of the car. Figure 3 is a depiction of the dual-clutch transmission; now, the crankshaft is connected to the gear lever; based on the input gear, the gear level will command the crankshaft; then, the transmission output will be obtained smoothly. Fig. 2 Toggle switches arrangement pattern
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Fig. 3 Schematic representation of dual-clutch mechanism used in this system
5 Conclusion We conclude that by implementing the toggle transmission in automotive vehicles we can rely on the faster commute of the journey and safer boulevard. Gearing system usually runs the H pattern or paddle shifters, but in this system, there will be toggle switches on the dashboard, where in the gears are changed just by toggling the switches. When a switch is put to action, the gear clamp changes the alignment and withdraws from the rotating gear rod. Each gear will be in a semi-circle shape; in neutral motion, the N gear clamp will get attached to the rotating rod. 1st gear toggled up and the neutral gear clamp will withdraw its position from the rod, and the 1st gear clamp will be attached to rod. In this pattern, all the 5 gears + neutral gear and reverse gear will be aligned in the form of the switches on the dashboard of the car. The major question arises to the audience is, how the car be controlled when the gear is being shifted from one gear by just toggling the switches. We use a device called dynamo; this device is structured to do the reverse engine mechanism where car in be commanded to the stagnant rotating wheels, while the gear is being changed.
6 Future Scope With advancement in technology in the automotive industry, we can assure the faster commute for the audience, wherein they can reach a destination from point A to point B in a much nimble way. With this kind of gearing technology and transmission type, we can indulge in much more creative space of being behind the wheel. This kind of transmission creates an enthusiasm among the professional when they realize they just must change the gears using switches.
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References Journal Article 1. Josephine Selle, J., Gokul Vasan, K. Devendran, V., Jeganadhan, K.: MyRio based automated gear transmission for manual gear cars. Int. J. Rec. Technol. Eng. (IJRTE) 8(4S2) (2019) 2. Singh, G., Sharma, M., Singh, A.P.: Novel automated manual transmission gear-shift map modelling based on throttle position. Int. J. Autom. Mech. Eng. 15(1), 5053–5073 (2018) 3. B.Sudhabindu ,Dr Md. P.V.Ramarao , Dr. C. Uday Kiran. “Effect of Opacity And Mechanical Properties On Jute/E-Glass Fiber Reinforced Polymer Composites When Subjected To Ultraviolet Radiation.” Journal of Advanced Research in Dynamical and control system. Issue-14, 2017, Pg No- 2087–2093, IF:-0.13. 4. Balasubramanyam, P., Nageswara Rao, B.: Impact analysis on Go-Kart chassis with variable speeds using ansys 19.0. Int. J. Eng. Adv. Technol. 8(6), 2614–2620 (2019) 5. Mohan Phani Gopal, N., Balasubramanyam, P.N.V.: Strength comparison of cylinder liner for different materials using ansys. Int. J. Innov. Technol. Explor. Eng. 8(8), 419–425 (2019) 6. Bala Subramanyam, P.N.V., Rao, B.N., Prakash, R.L.: Modal analysis on go-kart chassis. Int. J. Eng. Adv. Technol. 8(8), 1701–1705 (2019) 7. Bala Subramanyam, P.N.V., Nageswara Rao, B., Yashwanth Sai, Y.: Design and analysis of fsae chassis for safe conditions. Int. J. Innov. Technol. Explor. Eng. 8(5), 1–9 (2019)
Book 8. Rajput, R.K: A Text Book of Automobile Engineering. Laxmi Publications 9. Er. Gupta, S.K.: A Text Book of Automobile Engineering. S. Chand publication.
Chapter in a Book 10. Er. Gupta, S.K.: A Text Book of Automobile Engineering, vol. 2, pp. 174–297. S. Chand Publication.
Conference Proceeding 11. Marathe, S.R.: Former Director, Automotive Research Association of India (ARAI). In: Advanced Transmission Technologies for Future Automobiles” proceedings of The Auto Tech Review, Transmission.tech 2019 conference-cum-mini exhibition serves. held on 24th April, 2019
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Patent 12. Lepelletier, P.A.G.: Multispeed automatic transmission for automobile vehicles. U.S Patent US516352A 1989
Al-Zn-Mn Nanocomposite Sintering by Mechanical Alloying and Characterization with the Help of SEM and XRD Moon Banerjee, B. Lakshmana Swamy, P. N. V. Bala Subramanyam, and Tikendra Nath Verma
Abstract Sintering manufacturing method is used to prepare aluminium-based nanocomposite by the method of power blends in this research work. By considering the nominal elemental weight of aluminium, manganese and zinc, the alloying is performed with the help of planetary ball bearing mill. Further, analysis was carried out for studying the morphological characterization of the sintered nanocomposite. Different techniques were opted to study the properties of the nanocomposite by using SEM, XRD, EDS and FITR. The results shown by XRD technique predicts the behaviour of varying the milling parameters, crystalline size and lattice strain can be varied. Also, the results exhibit by SEM analysis shows the rich content of zinc (Zn). Lastly, FITR test confirms the percentage absorbance and transmittance of the crystalline structure. Keywords Alloying · Nanocomposite · Characterization
1 Introduction In the current scenario, nanomaterials are frequently used elsewhere for the sake of weight reduction, more strength, durability and long-term usage. From conventional size of the particle to nanosize, it leads to the requirement of high machineries. Thus, different sintering process is available like ball bearing milling machine which is M. Banerjee (B) · B. Lakshmana Swamy · P. N. V. Bala Subramanyam Department of Mechanical Engineering, Koneru Lakshmaiah Educational Foundation, Hyderabad, Telengana 500075, India e-mail: [email protected] B. Lakshmana Swamy e-mail: [email protected] P. N. V. Bala Subramanyam e-mail: [email protected] T. N. Verma Maulana Azad National Institute of Engineering & Technology, Bhopal, Madhya Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_75
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explained in the recent work. Nanomaterials are encountering a fast improvement as of late because of their current and additionally likely applications in a wide assortment of innovative zones like hardware, catalysis, pottery, attractive information stockpiling, primary segments and so forth to procure the innovative needs around there, the materials size ought to be diminished in a drastic scale. After procuring the nanometre size, materials start to exhibit change in the behaviour like instance extended mechanical strength, overhauled diffusivity, higher express warmth and electrical resistivity stood out from standard coarse-grained accomplices. Ravi et al. [1] performed the assessment of mechanical properties between Molyndic acid filled banana and nanoplay reinforced polyester composite. The composites are formed by maintaining fibre volume of 38% and remaining nanoclay matrix. Buddi et al. [2] developed and analyzed wood powder reinforced polymer matrix hybrid nanocomposite by using high density poly ethylene nano SiO2 . In this work, compression moulding process is used to mix wood powder and nano SiO2, which were further assessed for mechanical properties like strength and flexure strength. Arun et al. [3] analyzed the mechanical properties of carbon reinforced aluminium nanocomposite. Fabrication of carbon reinforced aluminium composite is performed by using powder metallurgy process followed by analysis of stress in a material just before it yields. Praveena et al. [4] had performed fabrication and analysis of silver nano-based composite system for antibacterial applications. Similar fabrication is performed by mixing new bio polymer named chitosan. Kumar et al. [5] had performed the characterization of mechanically synthesized MoO3 /TiO2 composite nanopowders. For the synthesis, fabrication is been performed by using MoO3 /TiO2 nanopowders by mechanochemical synthesis technique followed by morphological analysis by SEM, TEM and XRD. Baburaja et al. [6] have performed the fabrication and morphological analysis of aluminium bamboo leaf ash metal matrix composite by stir casting method. Sudhabindu et al. [7] have studied the optical and mechanical characteristics of Jute/E-glass fibre reinforced polymer composite subjected to radiations of different wavelength. This work predicts the ageing and opacity property of the jute fibre reinforced composite. Mufsir et al. [8] used waste cooking oil ZnCuO/N doped graphene nanocomposite for producing biodiesel. The synthesis was performed by facile mechanochemical procedure further tested with waste cooking oil. Pratibha [9] has performed the post assessment of laminar shear strength, drop load impact of the silane treated kelvar-reinforced nanosilicatoughened epoxy composite. Senthilanthan et al. [10] performed experimental investigation of polymer matrix composites gears with different fibre proportions. The present work emphasis on the fabrication of gears with the polymeric materials in spite of the conventional metallic approach. In the view of above literature review, it has been observed that nanomaterials were found to have widespread application in the field of mechanical and different fields of communication system. Thus, for the above reason, the size of the martials needs to be tailored in nanosize based on the requirement. Due to reduction in size, the composite nanomaterials have found to have change in mechanical as well as structural property change.
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2 Experimental Procedure The experimental procedure was carried out by synthesization of nanocomposite. The granules of aluminium (Al), manganese (Mn) and Zinc (Zn) are used in the powdered form mixed in the ball milling machining with high energy. The grinding media used under 500 rpm with the ball to powder ratio 9:1with xylene as a process control agent. After complete fabrication of the nanomaterials, the morphological analysis of the procured samples is executed by X-ray diffraction (XRD) analysis with the radiation of wavelength (λ = 1.43 nm) in an X-ray diffractometer. Sample of variable crystalline size and strains is obtained by Scherrer formula. Then, after SEM and TEM analysis, it is carried out by MSM LV 7860 scanning electron microscope. The ball milling machine grinding is carried out by final powder which was further compacted at 10–12 tonnes at different temperatures. The XRD analysis is further carried out for sintered compacts. Flexure rigidity, hardness and other mechanical properties were measured and compared by using BHN machine. X-ray powder diffraction identifies the composite development between aluminium, zinc and manganese. Further on exposure of XRD, it gives the better cell dimension of the nanostructure. The computations results are based on the varying size of small crystalline size and increasing grid size. The microstructure graphs show unmistakable Aluminium rich and combination rich of other components. There are various methods available for fabrication of nanocomposites of which the selected process for this research work is mechanical alloying of aluminium, zinc and manganese. The blending of nanocomposite takes place by opting for natural powder of aluminium, zinc and manganese by following particular by weight percentage and molecular size. The particular proportion of ball to powder in ball milling machine is kept as 10:1. After the formation of nanocomposite, further analysis is being performed for microstructural and morphological analysis by different machine-like scanning electron analysis (SEM), energy dispersive X-ray spectroscopy (EDS). The powder was further processed for ninety hours which were compacted for 14–15 tonnes of load. Then, the procured samples were further exposed to environment of nitrogen and hydrogen at different temperatures. The results are procured for different size of the nanocomposite through X-ray spectroscopy. The samples were further tested by Fourier transformed infrared (FITR) in which results of absorption peaks as per the varying crystalline size. It is observed that the samples exposed at 550 °C in the environment of nitrogen and hydrogen which exhibits high hardness of 135 BHN. As the fabrication process reached to 80%, the property of high green thickness reaches to 70%. The major role of aluminium powders later confirms the higher hardness.
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2.1 Results and Discussion Results affirm that the granules converted to powder of aluminium, Manganese and zinc whilst sintering processing and with subsequent increment arrangement intermetallic intensifies occurs. The increase in processing speed, peaks is not unmistakably noticeable because of widening. Thus, results were further isolated the plot into two different sections such as low point district and high point area which likewise affirm that pinnacle widening has occurred.
3 Phase Analysis of Powder Material As the size of the nanoparticles are varied, it is observed that reduction in the forces of the peaks. Further analysis of the samples revealed that a consistent increasing line is appearing as the processing continues. As a part of mechanical alloying process, it is found that different particles are not distinguished as they compacted into lattice structure. Finally, the compounds are being shaped (Figs. 1 and 2). The above Fig. 3 explains significance change in the dimensions of the Al granules as the milling time is varied. It very well may be seen that granular dimensions have reduced as processing time advances from the start and afterwards step by step levels out because of work solidifying.
3.1 SEM and EDS Results Figure 4 shows the microstructure analysis for different hours with the help of SEM and EDS machines. The images depict the splendid stage is high in aluminium and
Fig. 1 XRD spectra of the milled samples
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Fig. 2 Temperature versus intensity
Fig. 3 Plot for crystalline size versus milling time (hrs)
low stage show absence of the same. On further increase in processing time, oxygen content is getting widespread. All the above images ensure the reduction in granules size, increase in the ionic and reactivity character. The procured alloy of nanostructured is further compacted under the heavy loads of 11 to 13 tonnes. Further, the Table 1 shows the properties like mass, thickness, density and percentage densification of the compacts used in processing of nitrogen and hydrogen air at 550 °C. The average thickness of the compacts is maintained as 1.79 g/cc after many trails. Different mechanical properties like harness are been measured by Brinell hardness machine providing the normal green density as 30.1 BHN. A lot of hardness variation is observed when the samples are processed in variable temperature of 550 °C in the environment of nitrogen and oxygen. The above outcome is because of the increased thickness of 71% which on further processing reached to 81%.
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Fig. 4 Microstructure at different hours of processing
Table 1 Properties of green compacts for sintering process S No
Mass (g)
Thickness (mm)
Density (g/cc)
% Densification
1
0.79
1.9
1.76
71.4
2
0.81
2.1
1.77
72.3
3
0.80
2.3
1.84
72.1
4
0.82
2.4
1.79
72.5
5
0.79
2.6
1.82
72.6
4 Conclusions 1. 2. 3. 4.
The lattice structure observed confirms the homogenous behaviour of the nanoparticles selected. The microstructure shows the behaviour of crystalline dimensions which changes on further expanding of grid strain. Analysis confirms that aluminium particles exhibit special significance in combination with the other alloying particles. The samples tested under Fourier transformed radiation machine clearly depicts the significance of percentage transmission density on the corresponding lattice structure variation with different sintering time.
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The compacts obtained show less hardness with respect to higher temperature. Also, density-based variation is being observed in accordance with the varied temperature and hardness. Abrupt increase in the hardness is because the samples are exposed to atmosphere of hydrogen and nitrogen.
References 1. Teja, K.M.V.R., Naveen, J.: Comparison of the mechanical performance between nano clay and molybdic acid filled banana (empty fruit bunch) fiber reinforced polyester composites. Mater. Today: Proc. 4(2), 3861–3866 (2017) 2. Buddi, T., et al.: Development and analysis of high density poly ethylene (HDPE) nano SiO2 and wood powder reinforced polymer matrix hybrid nano composites. J. Exp. Nanosci. 13, S24–S30 (2018) 3. Kumar, G.A.: Investigation of filexur Al strength for carbon reinforced aluminium nano composite. Int. J. Mech. Eng. Technol. (IJMET) 8, 1083–1088 (2017) 4. Praveena, V.D., Kumar, K.V.: Synthesis and characterization of chitosan-based silver nano composite system for antibacterial applications. In: International Conference on Advanced Nanomaterials & Emerging Engineering Technologies. IEEE (2013) 5. Kumar, T.V., Ramana, K.V., Choudary, R.B.: Spectroscopic characterization of mechanically synthesized MoO3-TiO2 composite nano powders. Int. J. Mech. Eng. Technol. 8(5), 1051–1063 (2017) 6. Sai Chaitanya Kishore, D.: Synthesis and characterization of aluminium-bamboo leaf ash metal matrix composite by stir casting technique. Int. J. Mech. Eng. Technol. 8 (2017) 7. Sudhabindu, B., Ramarao, P.V., Uday Kiran, C.: Effect of opacity and mechanical properties on jute/e-glass fiber reinforced polymer composites when subjected to ultraviolet radiation. J. Adv. Res. Dyn. Control Syst. 14, 2087–2093 (2017). IF:-0.13 8. Kuniyil, M., et al.: Production of biodiesel from waste cooking oil using ZnCuO/N-doped graphene nanocomposite as an efficient heterogeneous catalyst. Arabian J. Chem. 14(3), 102982 (2021) 9. Dharmavarapu, P., Sreekara, M.B.S.: Failure analysis of silane-treated kevlar-reinforced nanosilica-toughened epoxy composite in laminar shear strength, drop load impact and drilling process. J. Failure Anal. Preven. 20(5), 1719–1725 (2020) 10. Senthilnathan, K., et al.: Experimental investigation of polymer matrix composites gears with different fiber proportions. Int. J. Vehicle Struct. Syst. 12(2), 212–216 (2020)
Effect of Trace Elements on Hardness and Impact Characteristics of Carbon Steel N. Puneeth Kumar and A. S. Srikantappa
Abstract The effects of trace elements on the mechanical properties (Hardness and Impact) of medium carbon steel were investigated in this research. Carbon steel is an alloy with a wide variety of uses, so it’s necessary to learn about its physical and mechanical characteristics. The factors like mechanical properties and SEM for two different variations of sulfur and phosphorous [Variation-1 (S%–0.006% and P%–0.013%) and variation-2 (S%–0.017% and P%–0.025%)] were investigated in this research. According to the results, mechanical properties such as hardness and impact increased marginally as the percentage of sulfur and phosphorous varied. Keywords Carbon steel · Hardness · Impact strength
1 Introduction The most popular type of steel is medium carbon steel. It is suitable for a wide range of engineering applications [1] because of superior mechanical properties at a low cost including strong tensile strength and endurance [2]. Steels with a medium carbon % are described as 0.25 to 0.65, whereas those containing less than 0.25% carbon are classified as low carbon. The high carbon content of steel typically ranges from 0.65 to 1.50% [2, 3]. Unfortunately, steels with low carbon content have some margins, which have limited their use in modern manufacturing processes. Steel with lower percentage of carbon, for example, has a carbon percentage ranging from 0.15 to 0.30%, rendering the surface strength inferior to that of other steel styles. Owing to its carbon content with lower percentage, it has a lower tensile strength, is neither brittle nor ductile, and is malleable. However, in applications such as wheels of railway, profile of gear N. Puneeth Kumar (B) Department of ME, CMR Institute of Technology, Bengaluru 560037, India e-mail: [email protected] A. S. Srikantappa Cauvery Institute of Technology, Mandya 571402, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_76
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N. Puneeth Kumar and A. S. Srikantappa Description
S (%)
P (%)
Standard value
0.045 max
0.045 max
Variation-1
0.006
0.013
Variation-2
0.017
0.025
tooth, wheels of crane, cable drum of crane and gear wheels lack ductility and become more difficult to weld. The metal becomes tougher and stronger as the carbon content increases [4]. The primary goal of this study is to determine how trace elements affect the mechanical properties of carbon steels [5] with two different percentage of sulfur and phosphorous. Even though many researchers were worked on the carbon steel material, very few researchers were reported on the properties of carbon steel [6]. Many researchers compared carbon steel by dipping (Quenching) in different oil or in water [7] and only with one chemical composition for its mechanical characteristics. But here in this work, two different samples of carbon steel were analyzed by varying percentage of trace elements of sulfur and phosphorous [8,9].
2 Methodology and Materials 2.1 Selection of Samples The samples were tested according to the research method ASTME 415: 2017/IS8811- 1998 at NABL certified metallurgical testing centers. Two samples were identified as a result of the experiments, the details of which are mentioned in Table 1. The composition, as per the standard is also given in Table 1.
2.2 Test Specimen Preparation From the as-received medium carbon steel samples of the two different variations as shown in table-1, a collection of samples for hardness and impact tests were prepared. Following a series of machining procedures, the specimens were prepared in compliance with the International Test Standard.
Effect of Trace Elements on Hardness …
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Fig. 1 Brinell hardness tester
3 Mechanical Tests 3.1 Hardness Test The sample hardness determined using Brinell hardness (HBW 10/3000) as shown in Fig. 1. According to Standard test reference IS: 1500–2013 Ball diameter −10 mm, load applied is 3000 kg and Average diameter of indentation is 4.43 for sample-1 and 4.37 for sample-2 of variation-1(S%–0.006% and P%–0.013%). The Average diameter of indentation is 4.36 for sample-1 and 4.38 for sample-2 of variation1(S%–0.017% and P%–0.025%).
3.2 Impact Strength Charpy effect is a useful tool for determining brittle fracture in metals. An impact test tests a material’s hardness or its ability to withstand energy during plastic deformation. The sample from the Charpy test dimensions are (10 × 10 × 55) mm3 45° V notch angle of 2 mm depth and 0.25 mm root radius will be hit by a pendulum attached at the opposite end of the notch as shown in Fig. 2.
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Fig. 2 Charpy test sample
4 Results and Discussion 4.1 Hardness The hardness of sample with lower percentage of sulfur and phosphorous and higher percentage of sulfur and phosphorous are determined using Brinell hardness testing machine (HWB). The object of this test is to compare the properties of hardness of medium carbon steel specimens that contain trace elements of sulfur and phosphorous. Figures 3a and b show the hardness values for variation-1 and variation-2 (Table 2).
Fig. 3 a Hardness values for variation-1 for two different samples 1 and 2. b Hardness values for variation-2 for two different samples 1 and 2
Effect of Trace Elements on Hardness … Table 2 Hardness values for different samples of two different variation of carbon steel
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Variation/sample
Hardness (HBW) Average hardness (HBW)
Variation-1 Sample-1 185
187.5
Sample-2 190 Variation-2 Sample-1 191
190
Sample-2 189
Table 3 Charpy Impact test values for different samples of two different variation of carbon steel
Sample/variation
Impact (energy-J)
Average impact (energy-J)
Variation-1
Sample-1
38
39
Sample-2
40
Sample-1
38
Sample-2
42
Variation-2
40
Fig. 4 a Impact Strength for variation-1 for two different samples 1 and 2. b Impact Strength for variation-2 for two different samples 1 and 2
4.2 Impact Strength The toughness of sample with lower percentage of sulfur and phosphorous and higher percentage of sulfur and phosphorous was determined using Charpy impact test (Table 3, Fig. 4).
5 Conclusions The following conclusion was drawn from the evaluation of the experimental results.
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The variation in the trace elements affects the mechanical characteristics of carbon steel material. • The hardness of the steel material increases with increase in percentage of trace elements. The hardness obtained for carbon steel with higher percentage of sulfur and phosphorus (S%–0.017% and P%–0.025%) is better and has good hardness value compare to lower percentage of sulfur and phosphorous (S%–0.006% and P%–0.013%). The average hardness value for variation-2 is 190 compare to variation -1 i.e., 187.5. • Similarly, the impact strength (toughness) of Carbon steel increases slightly with increase in the percentage of trace elements. Higher percentage of sulfur and phosphorous is slightly higher than that of variation-1 i.e., lower percentage of sulfur and phosphorous. It is understood from the above result that the material with higher percentage of sulfur and phosphorus (S%–0.017% and P%–0.025%) has better mechanical properties compare to lower percentage of sulfur and phosphorous (S%–0.006% and P%–0.013%).
References 1. Aziz, R.A., Ismail, M.: Effect of ferritic–martensitic constituent on mechanical property and corrosion behaviour of medium carbon dual phase (DP) steel. Saudi J. Eng. Technol. (SJEAT) ISSN 2415-6272 (Print) Scholars Middle East Publishers ISSN 2415-6264 (Online) 2. Mudasiru, L.O., Babatunde, I.A., Raheem, W.A., Lasisi, A.K.: Effect of immersion speed on the mechanical properties and micro-structure of oil quenched AISI 1020 steel. Article Number - CBCA8AD47531, 6(5), 68–74 (2014) 3. Odusote, J.K., Ajiboye, T.K., Rabiu, A.B.: Evaluation of mechanical properties of medium carbon steel quenched in water and oil. J. Min. Mater. Char. Eng. 11, 859–862 (2012) 4. Alias, S.K., Abdullah, B., Jaffar, A., Latip, S.A., Kasolang, S., Izham, M.F., Ghani, M.A.A.: Mechanical properties of paste carburized ASTM A516 steel. In: The Malaysian International Tribology Conference 2013, MITC (2013) 5. Ikubanni, P.P., Adediran, A.A., Adeleke, A.A., Ajao, K.R., Agboola, O.O.: Mechanical properties improvement evaluation of medium carbon steels quenched in different media. Int. J. Eng. Res. Africa 32, 1–10. ISSN: 1663-4144 6. Ankamma, K.: Effect of trace elements (Boron and Lead) on the properties of gray cast iron. J. nst. Eng. India Ser. D (January–June2014) 95(1), 19–26. https://doi.org/10.1007/s40033-0130031-3 7. Singh, R.R., Gaikwad, A., Singh, S.S., Singh, V.P.: Comparison of mechanical properties of medium carbon steel with dual phase steel. Int. J. Mech. Eng. (Ijme) Issn(P): 2319–2240; Issn(E): 2319-2259 Vol. 4, Issue 4, Jun - Jul 2015, 1–8. 8. Riaz, M., Atiqah, N.: A study on the mechanical properties Of S45c medium type carbon steel specimens under lathe machining and quenching conditions. Int. J. Res. Eng. Technol. eISSN: 2319-1163 | pISSN: 2321-7308 9. Ghali, S.N., El-, H.S., Eissa, M.: Influence of Boron Additions on Mechanical properties of carbon steels. J. Min. Mater. Charact. Eng. 11, 995–999 (2012)
Flexible Manufacturing System Scheduling Through Branch and Bound Algorithm M. Nageswara Rao , K. Gaya Prasad, I. Praneeth, G. Sai Prudhvi, E. Vineeth, and Upendra Rajak
Abstract The Adaptable Manufacturing System (FMS) is an amalgam framework containing of basics similar to work environments, mechanized putting away and recuperation frameworks, and material control gadgets like machines and AGVs. An endeavor is made to concentrate simultaneously the machine and AGVs arranging highlights in a FMS for decrease of the makespan. Branch and Bound algorithm is used to solve FMS simultaneous scheduling problems. 120 issues and their current arrangements with various methodologies are analyzed. Keywords FMS · Lower boundary · Makespan · AGVs
1 Introduction Need deals are utilized to choose which occupation will be prepared next grinding away focus, where a few positions are standing by to be handled. The positions hanging tight for preparing are sequenced utilizing one of numerous need sequencing rules. It is expected that the work place can deal with just each work in turn. An enormous number of sequencing rules are utilized in research and practically speaking to arrangement the positions hanging tight for preparing at a work place [1–5]. Later, Heuristic enhancement calculations (heuristics) look for great plausible answers for streamlining issues in conditions where the intricacy of the issue or the restricted time accessible for its answer doesn’t permit definite arrangement [6–8]. Further to build the opportunity of getting Global ideal arrangement with considering populace size through meta-heuristic calculation like Genetic Algorithm. The work can likewise be reached out by expanding the size of the populace through Meta-heuristic Algorithms. Booking of a FMS is a perplexing issue to address and consequently it has M. Nageswara Rao (B) · K. Gaya Prasad · I. Praneeth · G. Sai Prudhvi · E. Vineeth Department of Mechanical Engineering, K.L.E.F University, Guntur, AP, India U. Rajak Department of Mechanical Engineering, RGM College of Engineering and Technology Nandyal, Nandyala, Andhra Pradesh 518501, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_77
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made interest among the specialists [9–12]. Despite the fact that FMS planning issue is viewed as before, booking of material dealing with framework was not considered [4]. Among the individuals who decided on material taking care of framework, just scarcely any thought to be concurrent booking of machines and AGVs [13–16]. A painstakingly planned and oversaw material taking care of framework is imperative to accomplish the necessary coordination of FMS [17–23]. Henceforth, there is a requirement for booking both the machines and material taking care of framework all the while for the fruitful execution of a FMS.
2 FMS Narrative In FMS narration having four CNC machines with tool and pallet changer are shown in Fig. 1. The input data (i.e., traveling time matrix and machine sequence with processing time) [23]
2.1 Purpose of the Study Objective of FMS scheduling is to minimize the makespan
Fig. 1 Design plans in model issues
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Conclusion time of operation j and job i = Oi j = Ti j + Pi j Jobs completion time (Maximum) = (Ci ) =
n
Oi j
(1)
(2)
i=1
Makespan = max(C1, C2, C3, . . . , Cn)
(3)
T ij = traveling time, Pij = operation preparing time.
3 Simultaneous Scheduling with B&B Jobs are planned dependent on the activity grouping inferred by the calculations. The issue considered necessities booking of material taking care of framework alongside that of machines. In this work, B&B is altered to tackle concurrent planning issues which are talked below.
3.1 Branch and Bound Algorithm In this work, branch and bound calculation is adjusted to take care of synchronous booking issues which are talked about underneath. Branch and bound Algorithm was created by Ailsa land and Alison Doig in (1960) at London school of financial aspects. The means engaged with Branch and Bound algorithm is given beneath: Step1: Think about the work set and format. Step2: For equivalent occupation ascertain C1 (k), C2 (k) … Cn (k) and A1, A2, A3, …An. (Relies upon the quantity of occupation in the succession) Step3: Calculate A1 = C1(k) + P1j + min(P2j + P3j + P4j) A2 = C2(k) + P2j + min(P3j + P4j) A3 = C3(k) + P3j + min (P4j). A4 = C4(k) + P4j Step4: Calculate LB (k) = max (A1, A2, A3, A4). We assess LB (Lower Bound) first for n number of changes, for example, for these beginning with 1,2, 3,….n individually, having named the proper vertices of the planning tree by these qualities. Step5: Now investigate the vertex with LB. Assess LB to the (n-1) sub-classes. Beginning with this vertex and again focus on the most reduced name vertex. Proceeding with this path until we reach toward the finish of the tree portrayal by
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two single changes, for which we assess the absolute work term. In this manner, we get the ideal timetable of the positions. Step6: Stop.
4 Route Plan for Vehicles Stage 1: At the start, vehicles are positioned at Load and unload. Stage 2: For 1st and 2nd operations, use AGV1 and AGV2. Stage 3: From 3rd operation onwards, check which vehicle is suitable for next operation. Stage 4: make out the location of both vehicles with travel time. Stage 5: recognize the next operation machine number and job piece location. Stage 6: Calculate vehicle travel time like. Travel time of {AGV (Vehicle and Machine number) + (current location − job piece) + (job piece − next operation machine number). Stage 7: Select least travel time vehicle for next operation.
5 Execution of Branch and Bound Algorithm For execution of Branch and Bound calculation, work set 1 and design 1 are considered for instance. Branch and Bound Algorithm registers the lower limit for different occupations and the groupings are gotten dependent on the lower limit. Stage 1: work set 1 taking into consideration. Work set
Layout
Work set jobs
Operation numbers
Machine order
1
1
5
13
1: 1–2-4 2: 1–3-2 3:3–4-1 4:4–2 5: 3–1
Step2: Estimate completion time {C1 (k) to C 4 (k)}, A1 to A4 for job 1 (level 1). M1
[8, 8]
C 1 (k) = 8
M2
[16, 24]
C 2 (k) = 24
M3
[0, 24]
C 3 (k) = 24
M4
[12, 36]
C 4 (k) = 36
Flexible Manufacturing System Scheduling …
829 P2j + P3j + P3j + P4j P4j P4j
Jobs M 1
M2
M3
M4
1
8
16
0
12
2
20
18
10
0
28
10
0
3
15
0
12
8
20
20
8
4
0
18
0
14
32
14
14
5
15 0 10 0 10 P1j = 50 P2j = 36 P3j = 32 P4j = 22 Min = 10
10
0
A1 A2 A3 A4
= C 1 (k) + P1j = C 2 (k) + P2j = C 3 (k) + P3j = C 4 (k) + P4j
Min = 10 Min = 0
+ min(P2j + P3j + P4j ) = 8 + 50 + 10 = 68 + min(P3j + P4j ) = 24 + 36 + 10 = 70 + min (P4j ) = 24 + 32 + 0 = 56 = 36 + 22 = 58.
Step3: Calculate Lower Bound. LB (1) = max (A1 , A2 , A3 , A4 ) = max (68, 70, 56, 58) = 70. Step 4: Estimate completion time {C1 (k) to C 4 (k)}, A1 to A4 and lower bound for job 2. M1
[20, 20]
C1(k) = 20
M2
[18, 38]
C2(k) = 38
M3
[10, 48]
C3(k) = 48
M4
[0, 48]
C4(k) = 48
Jobs M 1
M2
M3
M4
P2j + P3j + P3j + P4j P4j P4j
1
8
16
0
12
28
12
12
2
20
18
10
0
3
15
0
12
8
20
20
8
4
0
18
0
14
32
14
14
5
15 0 10 0 10 P1j = 38 P2j = 34 P3j = 22 P4j = 34 Min = 10
10
0
Min = 10 Min = 0
A1 = C 1 (k) + P1j + min(P2j + P3j + P4j ) = 20 + 38 + 10 = 68 A2 = C 2 (k) + P2j + min(P3j + P4j ) = 38 + 34 + 10 = 82 A3 = C 3 (k) + P3j + min (P4j ) = 48 + 22 + 0 = 70 A4 = C 4 (k) + P4j = 48 + 34 = 82 LB (2) = max (A1 , A2 , A3 , A4 ) = max (68, 82, 78, 82) = 82. Similarly, calculate lower bound values for Job, 3, 4, 5, namely LB (3), LB (4) and LB (5) as shown in Fig. 2 From Fig. 2, it is observed that least boundary is 4th job hence from 4th job start the next branch ignore remaining jobs.
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Fig. 2 Branch and bound tree for all levels
Step 5: Estimate completion time {C1 (k) to C 4 (k)} A1 to A4 and lower bound for job 4, 1 (level 2). M1
[0, 0]
[8, 8]
C1(k) = 8
M2
[18, 18]
[16, 34]
C2(k) = 34
M3
[0, 18]
[0, 18]
C3(k) = 18
M4
[14, 32]
[12, 30]
C4(k) = 30
Jobs M1
M2
M3
1
8
16
0
12
2
20
18
10
3
15
0
4
0
18
P2j + P3j + P3j + P4j P4j
P4j
0
28
10
0
12
8
20
20
8
0
14
M4
(continued)
Flexible Manufacturing System Scheduling …
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(continued) Jobs M1 5
M2
M3
M4
P2j + P3j + P3j + P4j P4j
15 0 10 0 10 P1j = 50 P2j = 18 P3j = 32 P4j = 8 Min = 10
10
P4j 0
Min = 10 Min = 0
A1 = C 1 (k) + P1j + min(P2j + P3j + P4j ) = 8 + 50 + 10 = 68 A2 = C 2 (k) + P2j + min(P3j + P4j ) = 34 + 18 + 10 = 62 A3 = C 3 (k) + P3j + min (P4j ) = 18 + 32 + 0 = 50 A4 = C 4 (k) + P4j = 30 + 8 = 38. LB (6) = max (A1 , A2 , A3 , A4 ) = max (68, 62, 50, 38) = 68. Similarly, calculate For Job 4 and 2, Job 4 and 3 and Job 4 and 5 lower bound, namely LB(7), LB(8) and LB(9) as shown in Fig. 2 From Fig. 2, it is observed that least boundary is 4th and 2nd job hence from 2nd job start the next branch ignore remaining jobs. Step 6: Estimate completion time {C1 (k) to C 4 (k)}, A1 to A4 and lower bound for job 4-2-1(level 4). M1
[0, 0]
[20, 20]
[8, 28]
C1(k) = 28
M2
[18, 18]
[18, 36]
[16, 52]
C2(k) = 52
M3
[0, 18]
[10, 28]
[0, 28]
C3(k) = 28
M4
[14, 32]
[0, 28]
[12, 40]
C4(k) = 40
P2j + P3j + P3j + P4j P4j
P4j
20
20
8
10
0
Jobs M1
M2
M3
M4
1
8
16
0
12
2
20
18
10
0
3
15
0
12
8
4
0
18
0
14
5
15 0 10 0 10 P1j = 30 P2j = 0 P3j = 22 P4j = 8 Min = 10
Min = 10 Min = 0
A1 = C 1 (k) + P1j + min(P2j + P3j + P4j ) = 28 + 30 + 10 = 68 A2 = C 2 (k) + P2j + min(P3j + P4j ) = 52 + 0 + 10 = 62 A3 = C 3 (k) + P3j + min (P4j ) = 28 + 22 + 0 = 50 A4 = C 4 (k) + P4j = 40 + 8 = 48. LB(10) = max (A1 , A2 , A3 , A4 ) = max(68, 62, 50, 48) = 68. Similarly, calculate For Job 4-2-1, Job 4-2-3 and Job 4-2-5 lower bound, namely LB(11), LB(12) and LB(13) as shown in Fig. 2 From Fig. 2, it is observed that least boundary is 4th, 2nd and 1st job and 4th, 2nd, and 3rd job; hence, two branches are available from 1st job and 3rd job.
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Step 7: Estimate completion time {C1 (k) to C 4 (k)}, A1 to A4 and lower bound for job 4-2-1-3 (level 4) M1
[0, 0]
[20, 20]
[8, 28]
[15, 43]
C 1 (k) = 43
M2
[18, 18]
[18, 36]
[16, 52]
[0, 52]
C 2 (k) = 52
M3
[0, 18]
[10, 28]
[0, 28]
[12, 40]
C 3 (k) = 40
M4
[14, 32]
[0, 28]
[12, 40]
[8, 48]
C4(k) = 48
P2j + P3j + P3j + P4j P4j
Jobs M1
M2
M3
M4
1
8
16
0
12
2
20
18
10
0
3
15
0
12
8
4
0
18
0
14
5
15 0 10 0 10 P1j = 15 P2j = 0 P3j = 10 P4j = 0 Min = 10
10
P4j
0
Min = 10 Min = 0
A1 = C 1 (k) + P1j + min(P2j + P3j + P4j ) = 45 + 15 + 10 = 68 A2 = C 2 (k) + P2j + min(P3j + P4j ) = 52 + 0 + 10 = 62 A3 = C 3 (k) + P3j + min (P4j ) = 40 + 10 + 0 = 50 A4 = C 4 (k) + P4j = 48 + 0 = 48. LB (13) = max (A1 , A2 , A3 , A4 ) = max (68, 62, 50, 48) = 68. Similarly, calculate For Job 4-2-1-3 Job 4-2-1-5 Job 4-2-3-1 Job 4-2-3-5 lower bound, namely Lower Boundary 13 to 16 as shown in Fig. 2 From Fig. 2, it is observed that least boundary is 4-2-1-3 & 4-2-3-1. Step 8: Estimate completion time {C1 (k) to C 4 (k)}, A1 to A4 and lower bound for job [4, 2, 1, 3, 5] or [4, 2, 3, 1, 5]. As per the above procedure calculate Lower Boundary 17 and 18 as 68 as shown in Fig. 2 Hence, according to Branch and Bound algorithm, the job sequence is. 4 − 2 − 1 − 3 − 5 or 4 − 2 − 3 − 1 − 5 According to job sequences, the operation sequences are. 10-11-4-5-6-1-2-3-7-8-9-12-13 (or). 10-11-4-5-6-7-8-9-1-2-3-12-13. Step 9: according to the work request, grouping tasks in the line are performed the determined estimations of different boundaries for all activities are appeared in Table 1.
Flexible Manufacturing System Scheduling …
833
Table 1 Completion time with the help of B&B O No
M No
V No
Travel time
Job ready
Job reach
Make span
10
4
1
0
12
12
26
11
2
2
26
34
34
52
4
1
1
18
24
24
44
5
3
1
44
52
52
62
6
2
2
62
68
68
86
1
1
1
60
66
66
74
2
2
1
74
80
86
102
3
4
2
102
110
110
122
7
3
1
90
100
100
112
8
4
1
112
118
122
130
9
1
2
130
140
140
155
12
3
1
124
134
134
144
13
1
1
144
152
155
170
6 Result and Discussion The FMS work shop situation introduced here with digits that follow 10.10 exhibits the job set 10 and layout 1 with time travel half and run time is 2 times. Calculations for completing time for different blends of occupation sets and formats for B&B rule with different t/p ratios are done and tabulated in 2. In the ideal grouping of AGVS and machines are dictated by utilizing B&B for various t/p ratios are appeared in Table 2. In t/p > 0.25 Out of 40 issues, 21 issues give improved outcomes utilizing B&B in correlation with FCFS, LPT, and SPT (Nageswara rao et al., 2020). With t/p < 0.25, 18 issues give improved outcomes utilizing B&B in correlation with LPT, SPT, and FCFS out of 40 issues and in t/p < 0.25, 15 issues gives improved outcomes utilizing B&B in correlation with above rules out of 40 issues.
7 Final Remarks Intelligent Manufacturing framework is accepted as enhanced choice to confront the undertakings of worldwide challenge. Yet, for successful order compelling booking is significant. Scheduling of a FMS is an exceptionally convoluted issue on account of extra necessities like material dealing with. In this paper, an exertion has been influenced to take care of the NP difficult issues by B&B. Achievements of B&B rule are evaluated by examining 120 benchmark issues involving of various occupation sets and designs.
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Table 2 Execution examination t/p > 0.25
t/p < 0.25
t/p < 0.25
Job No
Layout
B&B
Job No
Layout
B&B
Job No
Layout
B&B
1
1
170
1
10
226
1
11
394
2
1
156
2
10
215
2
11
288
3
1
212
3
10
265
3
11
431
4
1
260
4
10
321
4
11
503
5
1
152
5
10
115
5
11
318
6
1
222
6
10
264
6
11
421
7
1
190
7
10
218
7
11
321
8
1
260
8
10
336
8
11
488
9
1
270
9
10
380
9
11
548
10
1
309
10
10
418
10
11
565
1
2
142
1
20
217
1
21
384
2
2
120
2
20
192
2
21
269
3
2
169
3
20
253
3
21
419
4
2
220
4
20
307
4
21
491
5
2
142
5
20
167
5
21
310
6
2
152
6
20
249
6
21
406
7
2
140
7
20
200
7
21
296
8
2
182
8
20
317
8
21
469
9
2
249
9
20
370
9
21
538
10
2
273
10
20
414
10
21
557
1
3
143
1
30
216
1
31
385
2
3
130
2
30
195
2
31
272
3
3
172
3
30
252
3
31
418
4
3
230
4
30
308
4
31
490
5
3
138
5
30
166
5
31
309
6
3
156
6
30
250
6
31
407
7
3
148
7
30
201
7
31
299
8
3
183
8
30
318
8
31
470
9
3
251
9
30
371
9
31
539
10
3
279
10
30
417
10
31
560
1
4
189
1
40
230
1
41
404
2
4
174
2
40
305
2
41
293
3
4
224
3
40
267
3
41
435
4
4
299
4
40
462
4
41
504
5
4
182
5
40
250
5
41
321 (continued)
Flexible Manufacturing System Scheduling …
835
Table 2 (continued) t/p > 0.25
t/p < 0.25
t/p < 0.25
Job No
Layout
B&B
Job No
Layout
B&B
Job No
Layout
B&B
6
4
237
6
40
268
6
41
424
7
4
227
7
40
221
7
41
332
8
4
285
8
40
305
8
41
493
9
4
295
9
40
386
9
41
554
10
4
348
10
40
428
10
41
578
Acknowledgements Shore up from DST-SERB, GOI, (Sanction No: SB/EMEQ-501/2014).
References 1. Viswanadham, N., Narahari, Y.: Performance Modeling of Automated Manufacturing Systems, Prentice Hall of India (1994) 2. Stecke, K.E.: Design, planning, scheduling and control problems of flexible manufacturing systems. Ann. Oper. Res. 3, 3–12 (1985) 3. Kusiak, A.: FMS scheduling: a crucial element in expert system control architecture. In: IEEE International Conference on Robotics and Automation, pp. 653–658 (1986) 4. Chang, Y., Matsuo, H., Sullivan, R.S.: A bottleneck-based beam search for job scheduling in a flexible manufacturing system. Int. J. Prod. Res. 27, 1949–1961 (1989) 5. Dasore, A., Ramakrishna, K., Kiran, N.B.: Evaluation of heat and mass transfer coefficients at beetroot-air interface during convective drying. Interfac. Phenomena Heat Trans. 8, 303–319 (2020) 6. Biegel, J.E., Davern, J.J.: Genetic algorithms and job shop scheduling. Comput. Eng. 19, 81–91 (1990) 7. Ulusoy, G., Bilge, U.: Simultaneous Scheduling of machines and automated guided vehicles. Int. J. Prod. Res. 31(12), 2857–2873 (1994) 8. Chen, C.L., Vempati, V.S., Aljaber, N.: Theory and methodology an application of genetic algorithms for flow shop problems. Euro. J. Oper. Res. 80, 389–396 (1995) 9. Cheng, R., Gen, M., Sujimura, Y.T.: A Tutorial survey of job shop scheduling problems using genetic algorithms-I, Representation. Comput. Ind. Eng. 4, 983–997 (1996) 10. Al-Hakim, L.: An analogue genetic algorithm for solving job shop scheduling problems. Int. J. Prod. Res. 39(7), 1537–1548 (2001) 11. Abdelmaguid, T.F., Nasef, A.O., Kamal, B.A., Hassan, M.F.: A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles. Int. J. Prod. Res. 42(2), 267–281 (2004) 12. Reddy, B.S.P., Rao, C.S.P.: A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS. Int. J. Adv. Manuf. Technol. 31, 602–613 (2006) 13. Metta, V.R., Ramakrishna, K., Dasore, A.: Thermal design of spiral plate heat exchanger through numerical modeling. Int. J. Mech. Eng. Tech. 9(7), 736–745 (2018) 14. Goncalves, J.F., Mendes, J.J.M.: A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem. AT&T Labs Research Technical Report TD-5EAL6J (2002) 15. Aytug, H., Khouja, M., Vergara, F.E.: Use of genetic algorithms to solve production and operations management problems: a review. Int. J. Prod. Res. 41(17), 3955–4009 (2003) 16. Nearchou, A.C., Omirou, S.L.: Differential evolution for sequencing and scheduling optimization. J. Heuristics 12, 395–411 (2006)
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17. Sreekara Reddy, M.B.S., Ratnam, C., Rajyalakshmi, G., Manupati, V.K.: An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem. Measurement 114, 78–90 (2018) 18. Patle, B.K., Parhi, D.R.K., Jagadeesh, A., Kashyap, S.K.: Matrix-binary codes based genetic algorithm for path planning of mobile robot. Comput. Electr. Eng. 67, 708–728 (2017) 19. Sachin, G., Narasimha, K.K., Ramakrishna, K., Dasore, A.: Thermodynamic analysis and effects of replacing HFC by fourth generation refrigerants in VCR systems. Int. J. Air-Cond. Ref. 26(1), 18500131–185001312 (2018) 20. Durga Rajesh, K.V., Chalapathi, P.V., Rao Nageswara, M., Krishna, C.E., Anoop, K., Neeraj, Y.: An efficient sheep flock heredity algorithm for the cell formation problem. ARPN J. Eng. Appl. Sci. 12(21), 6074–6079 (2017) 21. Rao Nageswara, M., Sai, B.C., Venkatesh, Y., Lokesh, K., Harish, V., Vara Kumari, S.: Simultaneous scheduling in FMS through priority rules. J. Adv. Res. Dyn. Control Syst. 9, 1995–2008 (2017) 22. Nageswararao, M., et al.: Scheduling of machines and automated guided vehicles in FMS using shuffled frog leap algorithm. Int. J. Mech. Eng. Technol. 8(5), 496–503 (2017) 23. Kumar, P.A., Nageswararao, M., Kumar, T.Y., Kumar, M.M., Bhanu, V.: Scheduling of machines and automated guided vehicles in FMS using scatter search algorithm. Int. J. Mech. Eng. Technol. 8(5), 471–481 (2017)
Implementation of Jatinder N. D. Gupta Algorithm for FMS Scheduling Problems M. Nageswara Rao , K. Prakash Babu, Kiran Kumar Dama, Santosh Kumar Malyala, and Upendra Rajak
Abstract The Adaptable Manufacturing System (FMS) is an amalgam framework containing of basics similar to work environments, mechanized putting away and recuperation frameworks, and material control gadgets like machines and AGVs. An endeavor is made to concentrate simultaneously the machine and AGVs arranging highlights in a FMS for decrease of the makespan. J N D Gupta is used to solve FMS simultaneous scheduling problems. 120 issues and their current arrangements with various methodologies are analyzed. Keywords FMS · Quasi equivalent sorting proble · Makespan · AGVs
1 Introduction Need deals are utilized to choose which occupation will be prepared next grinding away focus, where a few positions are standing by to be handled. The positions hanging tight for preparing are sequenced utilizing one of numerous need sequencing rules. It is expected that the work place can deal with just each work in turn. An enormous number of sequencing rules are utilized in research and practically speaking to arrangement the positions hanging tight for preparing at a work place [1–4]. Later, heuristic enhancement calculations (heuristics) look for great plausible answers for streamlining issues in conditions where the intricacy of the issue or the restricted time accessible for its answer doesn’t permit definite arrangement [5–7]. Further to build M. Nageswara Rao (B) · K. K. Dama Department of Mechanical Engineering, K.L.E.F. University, Guntur 522502, India K. Prakash Babu Department of Mechanical Engineering, VRSEC, Kanuru, Vijayawada, India S. K. Malyala Department of Mechanical Engineering, Acharya Institute of Technology, Bangalore, India U. Rajak Department of Mechanical Engineering, RGM College of Engineering and Technology Nandyal, Nandyal, Andhra Pradesh 518501, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_78
837
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the opportunity of getting global ideal arrangement with considering populace size through meta-heuristic calculation likes Genetic Algorithm. The work can likewise be reached out by expanding the size of the populace through meta-heuristic Algorithms. Booking of a FMS is a perplexing issue to address and consequently it has made interest among the specialists [8–10]. Despite the fact that FMS planning issue is viewed as before, booking of material dealing with framework was not considered [11–13]. Among the individuals who decided on material taking care of framework, just scarcely any thought to be concurrent booking of machines and AGVs [14–17]. A painstakingly planned and oversaw material taking care of framework is imperative to accomplish the necessary coordination of FMS [18–20]. Henceforth, there is a requirement for booking both the machines and material taking care of framework all the, while for the fruitful execution of a FMS.
2 FMS Narrative In FMS, narration having four CNC machines with tool and pallet changer is shown in Fig. 1. The input data (i.e., traveling time matrix and machine sequence with processing time) [21]. .
Fig. 1 Design plans in model issues
Implementation of Jatinder N. D. Gupta Algorithm …
839
2.1 Purpose of the Study Objective of FMS scheduling is to minimize the makespan Conclusion time of operation j and job i = Oi j = Ti j + Pi j Jobs completion time (Maximum) = (Ci ) =
n
Oi j
(1)
(2)
i=1
Makespan = max(C1, C2, C3 . . . .. Cn)
(3)
T ij = traveling time, Pij = operation preparing time
3 Simultaneous Scheduling—J N D Gupta Jobs are planned dependent on the activity assemblage contingent by the calculations. The issue considered necessities booking of material taking care of framework alongside that of machines. In this work, J N D Gupta is altered to tackle concurrent planning issues.
3.1 Jatinder N. D. Gupta In this work, J N D Gupta calculation is accustomed to take care of planning issues. Gupta Algorithm was created by Gupta, J. N. D. (1986). The means engaged with Gupta calculation are given. Stage 1: To consider a task set. Stage 2: To diminish ‘m’ machines and ‘n’ occupations issue to two machines and ‘n’ Jobs. Stage 3: After lessening to two machines and ‘n’ occupations issues the base interaction time is to establish for each work. Stage 4: To know ei esteem. (in the event that pi1 < pim , ei = 1; in any case ei = -1). Stage 5: To ascertain the slant record (S i ) an incentive for each work as per S i = ei /min (pi1 …. pim ). Stage 6: according to the slant record to mastermind the positions in diving request as si1 ≥ si2 ≥ … ≥ sin , Stage 7: Based on the slant record esteems the activity arrangement is to be acquired. Stage 8: According to the got succession to play out the tasks in the line.
840
M. Nageswara Rao et al.
4 Route Plan of Vehicles Stage 1: At the start vehicles are positioned at load and unload. Stage 2: For 1st and 2nd operations use AGV1 and AGV2. Stage 3: From 3rd operation onwards check which vehicle is suitable for next operation. Stage 4: Make out the location of both vehicles with travel time. Stage 5: Recognize the next operation machine number and job piece location. Stage 6: Calculate vehicle travel time like. Travel time of {AGV (Vehicle and Machine number) + (current location-job piece) + (job piece-next operation machine number). Stage 7: Select least travel time vehicle for next operation.
5 Execution of J N. D. Gupta For execution of J N D Gupta, Layout and Job set 1 and 5 are considered for instance. Gupta Algorithm registers the slant records for various positions and the successions are gotten dependent on the file esteems organized in the slipping request. The Gupta heuristic is clarified in the accompanying strides for the work set 5: Stage 1: work set 5 taking into considerations. Set number
Layout
Jobs
Operations
Machine order job wise
5
1
5
13
1: 1–2-4 2: 1–3-2 3: 3–4-1 4: 4–2 5: 3–1
Stage 2: Considering the interaction time esteems for each work as: Job
1
2
3
4
5
M(1)
6
18
12
0
9
M(2)
12
15
0
15
0
M(3)
0
6
9
0
3
M(4)
9
0
3
6
0
Stage 3: Reducing the ‘m’ machines and ‘n’ occupations issue to two machines and ‘n’ jobs.
Implementation of Jatinder N. D. Gupta Algorithm …
841
Job
1
2
3
4
5
M(1) + M(2)
18
33
12
15
9
M(3) + M(4)
9
6
12
6
3
Stage 4: For each work least interaction times is found as: Job
1
2
3
4
5
Min M(1) + M(2),M(3) + M(4)
9
6
12
6
3
Stage 5: For each work ei esteem is found (in the event that pi 1 < pim, ei = 1; in any case ei = −1). Job
1
2
3
4
5
pi1 < pim
6 0.25
t/p < 0.25
t/p < 0.25
Job. No
Layout
Gupta
Job. No
Layout
Gupta
Job. No
Layout
Gupta
6
4
237
6
40
270
6
41
380
7
4
212
7
40
240
7
41
335
8
4
285
8
40
343
8
41
493
9
4
295
9
40
388
9
41
554
10
4
348
10
40
430
10
41
618
Acknowledgements Shore up from DST-SERB, GOI (SB/EMEQ-501/2014).
References 1. Nageswararao, M., Narayanarao, K., Ranagajanardhana, G.: Simultaneous scheduling of machines and AGVs in flexible manufacturing system with minimization of tardiness criterion. In: International Conference on Advances in Manufacturing and Materials Engineering (ICAMME-2014) (2014) 2. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Scheduling of machines and automated guided vehicle in FMS using gravitational search algorithm. Appl. Mech. Mater. 867, 307–313 (2017) 3. Nageswara Rao, M., Vara Kumari, S., Praneeth, I., Gaya Prasad, K., Venkata Reddy, D., Vineeth, E., Maheshwar Reddy, D.: Simultaneous Scheduling of Machines and AGVs in FMS through Simulated Annealing Algorithm. Int. J. Innov. Technol. Explor. Eng. 9(4), 2235–2240 (2020) 4. Babu, K.P., Babu, V.V., Nageswara Rao, M.: Scheduling of machines and AGVs Simultaneously in FMS through hybrid teaching learning based optimization algorithm. Int. J. Eng. Adv. Technol. 9(2), 2048–2055 (2019) 5. Nageswara Rao, M., Lokesh, K., Harish, V., Sai Bharath, C., Venkatesh, Y., Vara Kumari, S.: Simultaneous scheduling through heuristic algorithm. Int. J. Eng. Technol. 7(2), 125–130 (2018) 6. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Integrated scheduling of machines and AGVs in FMS by using dispatching rules. J. Prod. Eng. 20(1), 75–84 (2017) 7. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Machines and AGVs scheduling in flexible manufacturing system with mean tardiness criterion. Int. J. Adv. Mater. Manuf. Char. 4, 100–105 (2014) 8. Subbaiah, K.V., Nageswara Rao, M., Narayanarao, K.: Scheduling of AGVs and machines in FMS with make span criteria using sheep flock heredity algorithm. Int. J. Phys. Sci. 4(2), 139–148 (2010) 9. Prakash Babu, K., Vijaya Babu, V., Nageswara Rao, M. (2018) Implementation of heuristic algorithms to synchronized planning of machines and AGVs in FMS. Manag. Sci. Lett. 8(6):543–554 10. Kanakavalli, P.B., Vommi, V.B. Nageswara Rao, M.: Fuzzy heuristic algorithm for simultaneous scheduling problems in flexible manufacturing system. Manag. Sci. Lett.8(12), 1319–1330 (2018) 11. Nageswara Rao, M., Dileep, K., Basha, S.K., Vara Kumari, S.: Modrak algorithm to minimize completion time for n-Jobs m-machines problem in flexible manufacturing system. Int. J. Emerg. Trends Eng. Res. 8(8), 4560–4566 (2020)
Implementation of Jatinder N. D. Gupta Algorithm …
845
12. Sachin, G., Narasimha, K.K., Ramakrishna, K., Dasore, A.: Thermodynamic analysis and effects of replacing HFC by fourth generation refrigerants in VCR systems. Int. J. Air-cond. Ref. 26(1), 1850013-1–18500131-2 (2018) 13. Jerald, J., Asokan, P., Saravanan, P., Rani, D.C.: Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm. Int. J. Adv. Manuf. Technol. 29, 584–589 (2006) 14. Metta, V.R., Ramakrishna, K., Dasore, A.: Thermal design of spiral plate heat exchanger through numerical modeling. Int. J. Mech. Engg. Tech. 9(7), 736–745 (2018) 15. Dasore, A., Ramakrishna, K., Kiran, N.B.: Evaluation of heat and mass transfer coefficients at beetroot-air interface during convective drying. Interfac. Phenomena Heat Transf. 8, 303–319 (2020) 16. Sreekara Reddy, M.B.S., Ratnam, C., Rajyalakshmi, G., Manupati, V.K.: An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem. Measurement 114, 78–90 (2018) 17. Patle, B.K., Parhi, D.R.K., Jagadeesh, A., Kashyap, S.K.: Matrix-binary codes based Genetic Algorithm for path planning of mobile robot. Comput. Electr. Eng. 67, 708–728 (2017) 18. Pappula, L., Ghosh, D.: Cat swarm optimization with normal mutation for fast convergence of multimodal functions. Appl. Soft Comput. 66, 473–491 (2018) 19. Durga Rajesh, K.V., Chalapathi, P.V., Nageswara Rao, M., Krishna, C.E., Anoop, K., Neeraj, Y.: An efficient sheep flock heredity algorithm for the cell formation problem. ARPN J. Eng. Appl. Sci. 12(21), 6074–6079 (2017) 20. Nageswara Rao, M., Sai Bharath, C., Venkatesh, Y., Lokesh, K., Harish, V., Vara Kumari, S.: Simultaneous scheduling in FMS through priority rules. J. Adv. Res. Dyn. Control Syst. 9, 1995–2008 (2017) 21. Nageswararao, M., et al.: Scheduling of machines and automated guided vehicles in FMS using shuffled frog leap algorithm. Int. J. Mech. Eng. Technol. 8(5), 496–503 (2017)
Flexible Manufacturing System Scheduling with Relative Importance of a Work Item in a Workflow V. Mohan Manoj, Katta Sai Sandeep, G. Durga Prasad, M. Nageswara Rao , and Upendra Rajak
Abstract The Flexible Manufacturing System (FMS) is an amalgam framework containing of basics similar to work environments, mechanized putting away, and recuperation frameworks, and material control gadgets like machines and AGVs. An endeavor is made to concentrate simultaneously the machine and AGVs arranging highlights in a FMS for decrease of the makespan. Priority rules are used to solve FMS simultaneous scheduling problems. 120 issues and their current arrangements with various methodologies are analyzed. Keywords FMS · Priority rules · Makespan · AGVs
1 Introduction Need deals are utilized to choose which occupation will be prepared next grinding away focus, where a few positions are standing by to be handled. The positions hanging tight for preparing are sequenced utilizing one of numerous need sequencing rules. It is expected that the work place can deal with just each work in turn. An enormous number of sequencing rules are utilized in research and practically speaking to arrangement the positions hanging tight for preparing at a work place [1–5]. Later, Heuristic enhancement calculations (heuristics) look for great plausible answers for streamlining issues in conditions where the intricacy of the issue or the restricted time accessible for its answer doesn’t permit definite arrangement [6–8]. Further to build the opportunity of getting Global ideal arrangement with considering populace size through meta-heuristic calculation likes Genetic Algorithm. The work can likewise V. Mohan Manoj · K. S. Sandeep · G. Durga Prasad Department of Mechanical Engineering, NRIIT, Vijayawada 521212, India M. Nageswara Rao (B) Department of Mechanical Engineering, K L E F, Guntur, AP, India U. Rajak Department of Mechanical Engineering, RGM College of Engineering and Technology Nandyal, Nandyal, Andhra Pradesh 518501, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_79
847
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be reached out by expanding the size of the populace through Meta-heuristic Algorithms. Booking of a FMS is a perplexing issue to address and consequently it has made interest among the specialists [9–11]. Despite the fact that FMS planning issue are viewed as before, booking of material dealing with framework was not considered [12–15]. Among the individuals who decided on material taking care of framework, just scarcely any thought to be concurrent booking of machines and AGVs [16–18]. A painstakingly planned and oversaw material taking care of framework is imperative to accomplish the necessary coordination of FMS [19–23]. Henceforth, there is a requirement for booking both the machines and material taking care of framework all the while for the fruitful execution of a FMS.
2 FMS Narrative In FMS narration having four CNC machines with tool and pallet changer is shown in Fig. 1. The input data (i.e., traveling time matrix and machine sequence with processing time) [24].
Fig. 1 Design plans in model issues
Flexible Manufacturing System Scheduling …
849
2.1 Purpose of the Study Objective of FMS scheduling is to minimize the makespan. Conclusion time of operation j and job i = Oi j = Ti j + Pi j Jobs completion time (Maximum) = (Ci ) =
n
Oi j
(1)
(2)
i=1
Makespan = max(C1, C2, C3 . . . .. Cn)
(3)
T ij = traveling time, Pij = operation preparing time
3 Simultaneous Scheduling Jobs are planned dependent on the activity confederacy anecdotal by the calculations. The issue considered necessities booking of material taking care of framework alongside that of machines. In this work, FCFS, SPT, and LPT are altered to tackle concurrent planning issues which are talked in below.
3.1 First Come First Serve The means engaged with FCFS are given beneath: Stage 1: To consider the work set. Stage 2: Adding jobi in the primary line.
3.2 Shortest Processing Time The means engaged with SPT are given beneath: Stage 1: To consider the work set. Stage 2: To discover each work complete handling times. Stage 3: According to handling times to orchestrate the positions in climbing request. Stage 4: According to work request grouping to play out the activities in the line.
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3.3 Longest Processing Time The means engaged with LPT are given beneath: Stage 1: To consider the work set. Stage 2: To discover each work absolute handling times. Stage 3: According to handling times to orchestrate the positions in plummeting request. Stage 4: According to work request grouping to plays out the activities in the line.
4 Route Plan for Vehicles Stage 1: At the start vehicles are positioned at Load and unload. Stage 2: For 1st and 2nd operations use AGV1 and AGV2. Stage 3: From 3rd operation onwards check which vehicle is suitable for next operation. Stage 4: Make out the location of both vehicles with travel time. Stage 5: Recognize the next operation machine number and job piece location. Stage 6: Calculate vehicle travel time like. Travel time of {AGV (Vehicle and Machine number) + (current location—job piece) + (job piece—next operation machine number). Stage 7: Select least travel time vehicle for next operation.
5 Execution of Priority Rules Created need rules to take care of NP-difficult issues emerge in the planning issues. The proposed priority rules are executed to the booking issues introduced in Bilge U, Ulusoy G (1).
5.1 Simultaneous Scheduling—First Come First Serve Layout and Job set 1 are conscientious for execution of FCFS as an illustration with movement time half and process time triple. The FCFS is clarified in the accompanying strides for the work set 1: Stage 1: Work set 1 taking into consideration. Stage 2: Adding position ‘I’ at first in the primary line. 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10 – 11 – 12 – 13 Stage 3: Identified the most extreme operational finish time. It addresses the conceivable fulfillment time (makespan) of given occupation set.
Flexible Manufacturing System Scheduling …
851
Table 1 Completion time with the help of FCFS O No
M No
V No
Travel time
Job ready
Job reach
Process time
Make span
1
1
1
0
3
3
24
27
2
2
2
27
30
30
48
78
3
4
1
78
82
82
36
118
4
1
2
35
38
38
60
98
5
3
2
98
102
102
30
132
6
2
1
132
135
135
54
189
7
3
2
106
111
132
36
168
8
4
2
168
171
171
24
195
9
1
1
195
200
200
45
245
10
4
2
174
180
195
42
237
11
2
2
237
241
241
54
295
12
3
1
206
211
211
30
241
13
1
1
241
245
245
45
290
The determined estimations of different boundaries for all activities are appeared in Table 1 Table 1 shows activity planning of through FCFS rule for work set 1 design 1 is appeared. For work set 1 and format 1, the operational culmination time (makespan) is 290.
5.2 Simultaneous Scheduling—Shortest Processing Time Layout and Job set 1 are painstaking for execution of SPT. Stage 1: Work set same as segment 5.1. Stage 2: Each work complete handling times are found. Job
1
2
3
4
5
Total route time
36
38
35
32
25
Stage 3: For distinguishing the greatest operational finish season of the above grouping, the means examined in 5.1 are executed. It addresses the conceivable fruition season of given occupation set. 12 -13 – 10 – 11 – 7 – 8 – 9–1-2 – 3 – 4 – 5 – 6 – Makespan-349.
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5.3 Simultaneous Scheduling—Longest Processing Time Layout and Job set 1 are painstaking for execution of LPT. Stage 1: Work set same as area 5.1. Stage 2: Each work complete preparing times are found. Job
1
2
3
4
5
Total route time
36
38
35
32
25
Stage 3: For distinguishing the most extreme operational consummation season of the above grouping, the means examined in 5.1 are executed. It addresses the conceivable consummation season of given occupation set. 4 – 5 – 6 – 1 – 2 – 3 – 7 – 8 – 9 – 10-11 – 12 – 13-makespan – 361.
6 Result and Discussion The FMS work shop situation introduced here with digits that follow 10.11 exhibits the job set 10 and layout 1 with time travel half and run time is 3 times. Calculations for completing time for different blends of occupation sets and formats for three need rules with t/p < 0.25 are done and tabulated in 2. In the ideal grouping of AGVS and machines are dictated by utilizing, LPT, SPT, and FCFS for T/P < 0.25 and appeared in Table no 2. From Table 2, out of 40 issue 28 issues gives improved outcomes utilizing FCFS in correlation with SPT and LPT, 16 issues gives improved outcomes utilizing SPT in examination with FCFS and LPT and 4 issues gives improved outcomes utilizing LPT in correlation with SPT and FCFS.
7 Final Remarks FMS framework is accepted as better choice to confront the undertakings of worldwide challenge. Yet, for successful order compelling booking is significant. Scheduling of a FMS is an exceptionally convoluted issue on account of extra necessities like material dealing with. In this paper, an exertion has been influenced to take care of the NP-difficult issues by dispatching rules. Achievements of dispatching rules are evaluated by examining 120 benchmark issues involving of various occupation sets and designs.
Flexible Manufacturing System Scheduling …
853
Table 2 Execution examination (t/p < 0.25) Job. No
Lay out
First come first serve
Shortest processing time
Longest processing time
1
11
290
349
361
2
11
299
299
316
3
11
366
473
411
4
11
426
467
448
5
11
215
262
271
6
11
443
398
433
7
11
325
334
379
8
11
488
488
508
9
11
560
521
509
10
11
652
617
641
1
21
280
339
358
2
21
276
276
297
3
21
350
457
399
4
21
407
450
429
5
21
205
252
268
6
21
432
377
420
7
21
299
315
364
8
21
469
469
495
9
21
550
509
497
10
21
645
612
638
1
31
279
340
357
2
31
279
279
300
3
31
349
458
400
4
31
412
453
430
5
31
204
253
267
6
31
433
378
421
7
31
302
318
365
8
31
470
470
496
9
31
551
510
498
10
31
648
615
641
1
41
296
356
363
2
41
307
307
319
3
41
370
476
411
4
41
434
471
451
5
41
218
269
270
6
41
445
405
433 (continued)
854
V. Mohan Manoj et al.
Table 2 (continued) Job. No
Lay out
First come first serve
Shortest processing time
Longest processing time
7
41
329
344
385
8
41
493
493
508
9
41
560
533
520
10
41
666
633
652
Acknowledgements Shore up from DST-SERB, GOI (Sanction No: SB/EMEQ-501/2014).
References 1. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Integrated scheduling of machines and AGVs in FMS by using dispatching rules. J. Prod. Eng. 20(1), 75–84 (2017) 2. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Machines and AGVs scheduling in flexible manufacturing system with mean tardiness criterion. Int. J. Adv. Mater. Manuf. Charact. 4, 100–105 (2014) 3. Subbaiah, K.V., Nageswara Rao, M., Narayanarao, K.: Scheduling of AGVs and machines in FMS with make span criteria using sheep flock heredity algorithm. Int. J. Phys. Sci. 4(2), 139–148 (2010) 4. Nageswararao, M., Narayanarao, K., Ranagajanardhana, G.: Simultaneous scheduling of machines and AGVs in flexible manufacturing system with minimization of tardiness criterion. In: International Conference on Advances in Manufacturing and Materials Engineering (ICAMME-2014) (2014) 5. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Scheduling of machines and automated guided vehicle in FMS using gravitational search algorithm. Appl. Mech. Mater. 867, 307–313 (2017) 6. Dasore, A., Ramakrishna, K., Kiran, N.B.: Evaluation of heat and mass transfer coefficients at beetroot-air interface during convective drying. Interfacial Phenom. Heat Transfer 8, 303–319 (2020) 7. Nageswara Rao, M., Vara Kumari, S., Praneeth, I., Gaya Prasad, K., Venkata Reddy, D., Vineeth, E., Maheshwar Reddy, D.: Simultaneous scheduling of machines and AGVs in FMS through simulated annealing algorithm. Int. J. Innov. Technol. Explor. Eng. 9(4), 2235–2240 (2020) 8. Babu, K.P., Babu, V.V., Nageswara Rao, M.: Scheduling of machines and AGVs simultaneously in FMS through hybrid teaching learning based optimization algorithm. Int. J. Eng. Adv. Technol. 9(2), 2048–2055 (2019) 9. Nageswara Rao, M., Lokesh, K., Harish, V., Sai Bharath, C., Venkatesh, Y., Vara Kumari S.: Simultaneous scheduling through heuristic algorithm. Int. J. Eng. Technol. 7(2.32), 125–130 (2018) 10. Nageswara Rao, M., Dileep, K., Basha, S.K., Kumari, V., Modrak, S.: Algorithm to minimize completion time for n-Jobs m-machines problem in flexible manufacturing system. Int. J. Emerg. Trends Eng. Res. 8(8), 4560–4566 (2020) 11. Metta, V.R., Ramakrishna, K., Dasore, A.: Thermal design of spiral plate heat exchanger through numerical modeling. Int. J. Mech. Engg. Tech. 9(7), 736–745 (2018) 12. Abdelmaguid, T.F., Nasef, A.O., Kamal, B.A., Hassan, M.F.: A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles. Int. J. Prod. Res. 42(2), 267–281 (2004)
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13. Reddy, B.S.P., Rao, C.S.P.: A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS. Int. J. Adv. Manuf. Technol. 31, 602–613 (2006) 14. Jerald, J., Asokan, P., Saravanan, P., Rani, D.C.: Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm. Int. J. Adv. Manuf. Technol. 29, 584–589 (2006) 15. Goncalves, J.F., Mendes, J.J.M.: A hybrid genetic algorithm for the job shop scheduling problem. AT&T Labs Research Technical Report TD-5EAL6J (2002) 16. Sachin, G., Narasimha, K.K., Ramakrishna, K., Dasore, A.: Thermodynamic analysis and effects of replacing HFC by fourth generation refrigerants in VCR systems. Int. J. Air-cond. Ref. 26(1), 1850013-1–1850013-12 (2018) 17. Nearchou, A.C., Omirou, S.L.: Differential evolution for sequencing and scheduling optimization. J. Heuristics 12, 395–411 (2006) 18. Sreekara Reddy, M.B.S., Ratnam, C., Rajyalakshmi, G., Manupati, V.K.: An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem. Measurement 114, 78–90 (2018) 19. Patle, B.K., Parhi, D.R.K., Jagadeesh, A., Kashyap, S.K.: Matrix-binary codes based genetic algorithm for path planning of mobile robot. Comput. Electr. Eng. 67, 708–728 (2017) 20. Pappula, L., Ghosh, D.: Cat swarm optimization with normal mutation for fast convergence of multimodal functions. Appl. Soft Comput. 66, 473–491 (2018) 21. Durga Rajesh, K.V., Chalapathi, P.V., Nageswara Rao, M., Krishna, C.E., Anoop, K., Neeraj, Y.: An efficient sheep flock heredity algorithm for the cell formation problem. ARPN J. Eng. Appl. Sci. 12(21), 6074–6079 (2017) 22. Nageswara, R.M., Sai, B.C., Venkatesh, Y., Lokesh, K., Harish, V., Vara, K.S.: Simultaneous scheduling in FMS through priority rules. J. Adv. Res. Dyn. Control Syst. 9, 1995–2008 (2017) 23. Nageswararao, M., et al.: Scheduling of machines and automated guided vehicles in FMS using shuffled frog leap algorithm. Int. J. Mech. Eng. Technol. 8(5), 496–503 (2017) 24. Kumar, P.A., Nageswararao, M., Kumar, T.Y., Kumar, M.M., Bhanu, V.: Scheduling of machines and automated guided vehicles in FMS using scatter search algorithm. Int. J. Mech. Eng. Technol. 8(5), 471–481 (2017)
Investigation of Combustion and Performance Characteristics of Waste Plastic Oil D. Simhana Devi, Ravinder Kumar, and Upendra Rajak
Abstract The main motive of this investigation is to maximize the engine performance and emission parameters of diesel engine fueled with waste plastic oil by experimentally. Due to exhaustion of traditional fuels, there is a scarcity of fuel supply to meet the demands also considering emission parameters and environmental characteristics. Many researchers investigated the effect of blending different types of oils with diesel. Many investigators focused on third-generation fuels include waste cooking oil, fish bones, microalgae, palm oil, vegetable oils, biomass, papaya seeds, soybean seed oil, cotton seed oil, etc. This research paper annotates an experimental investigation of maximizing the effect of emission characteristics and performance of diesel engine. The results showed that the reduction smoke emission and engine torque but slightly higher volumetric efficiency and NO emission with D80PPO20 blend as compared to diesel fuel. Keywords Energy from waste plastic feedstock · Emission characteristics · Performance characteristics
1 Introduction Production of plastics has been increased in recent years due to its tremendous applications in various sectors including household and industry needs having excellent properties such as light in weight, formation of rust and corrosion is low, manufacturing of plastic components is low and design flexibility. However, there are few challenges associated with the use of plastics in daily life. Non-degradability is the major problem associated with used plastics. Although the production and use of plastics have not been reduced due to its tremendous applications in almost all the D. Simhana Devi · R. Kumar (B) Department of Mechanical Engineering, Lovely Professional University, Punjab, Phagwara, Punjab 144001, India U. Rajak (B) Department of Mechanical Engineering, RGM College of Engineering and Technology Nandyal, Nandyal, Andhra Pradesh 518501, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_80
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fields [1, 2]. Degradability of waste plastics is less, therefore more chances for waste feedstock. Due to increase in rapid use of plastics, the waste feedstock has increased in past five to decades. There are tons of plastics that are not degradable on the earth’s surface that is the major root cause for depleting ozone layer. In order to reduce accumulation of plastics on the earth’s surface [3, 4], several alternatives are developed for recycling of plastics ranging from recycling process to reuse of plastics. Recycling and reuse of waste plastics fulfills the scarcity of fuel, non-degradability problem and also meets the fuel demand. The pyrolysis method is adopted for breaking large chemical compound to decompose by applying heat. Crude oil and paraffin are extracted from plastics by pyrolysis. However, proper conversion of plastics to oil results in low output. Fast pyrolysis is adopted to achieve proper quality of the fuel [5, 6]. There are various methods for converting waste plastics to oil: Pyrolysis, thermochemical treatment and catalytic conversion. Among all three, pyrolysis is the best productive method. Pyrolysis is another method of thermal degradation by applying heat to a range of 500–900 °C to break the chemical compound [7, 8]. Most of the investigators proved that physiochemical properties of the waste plastic oil are similar to diesel; hence, waste plastic oil can be used either alone or blended with diesel in diesel engine. Major products are oil, char and gas. Quantity of the oil produced from waste plastics depends on pyrolysis reactor temperature, type of reactor also the type of catalyst used. The present work focuses on the experimental investigation to study the thermo-physical characteristics and waste plastic oil-diesel fuel blends under different engine loads. The specifics of the experimental setup and processes are defined, and typical factors of diesel engine such as volumetric efficiency, pressure, NO emissions were used to evaluate the effect of loads.
2 Materials and Methods 2.1 Fed Materials and Properties Density in other words, the quantity of the fuel oil fed into the injection system, for completed combustion process. One of the main parameters that affects the density is fuel feedstock conversion. Range of third-generation fuels from 840–920 kg/m3. Flash point is the minimum temperature, at this temperature, fuel catches the fire and the fire gets shut off when source of fire is removed away from fuel surface. Flash point for Pyrolysis oil is 28 °C. Kinematic viscosity is defined as, dynamic viscosity times reciprocal of density. There is no force is involved in kinematic viscosity. The main problem with third-generation fuels is that it has higher viscosity compared to conventional fuels. In order to reduce the viscosity of waste plastic oil, catalyst is prefered. Cold fitter plugging temperature is always less than the cloud point temperature. Pour point: It is the temperature wherein the liquid loses flowing characteristics and becomes semisolid. The calorific value amount of heat energy liberated by combusting 1 kg of the fuel or m3 of fuel for gases. Compared to
Investigation of Combustion and Performance … Table 1 Fuel properties of tested fuel
859
S No.
Test parameters
Results
UOM
1
Gross Calorific value
10,800
Kcal/Kg
2
Moisture
0.21
% by mass
3
Sulphur
0.12
% by mass
4
Flash point
28
°C
5
Pour point
24
°C
6
Total sediments
0.087
% by mass
7
Specific gravity at 15 °C
0.8047
–
8
Viscosity at 50 °C
2.56
cSt
9
Density at 15 °C
801.3
Kg/m3
10
Ash content
Nil
conventional fuels third-generation fuels have low calorific value. The value of cloud point temperature at which wax or bio wax in fuel forms cloudy in appears. Cetane number defines the quality of the fuel. Cetane number has great influence on ignition delay time period. Cetane number is indirectly proportional to ignition delay. Higher the Cetane number lower will be the ignition time period [9, 10] (Table 1).
2.2 Engine Test Procedure Diesel is blended with the plastic oil in the proportions of 80% of diesel and 20% of plastic oil and denoted as D80PO20. The blend is tested on single cylinder diesel engine test rig as shown in Fig.1. The major technical details of single cylinder diesel engine are given in Table 2.
2.3 Uncertainty of Experiment Generally, errors occur in almost every experiments when performed. There are number of reasons for errors. The main reason is instrument errors that are employed for measuring the different parameters during experimentation. Let the weights be V1, V2, V3, V4, V5….Vn that are associated with the parameters, so that total uncertainty is estimated [9, 11, 12]. The uncertainties of parameters are shown in Table 3. 2 2 2 2 ∂R ∂R ∂R ∂R v1 + v2 + v3 + . . . . vn Uncertainty % (VR ) = ∂ x1 ∂ x2 ∂ x3 ∂ xn
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Fig. 1 Engine under test Table 2 Experimental engine specifications
Table 3 Uncertainties of parameters
Make
Kirloskar
“Model
TV1
Bore x stroke
87.5 × 110 mm
Swept volume
661.45 (cc)
Connecting rod length
234.00 (mm)
Compression ratio
18:1
Rate speed
1500 rpm
Cooling method
Water”
Name of instrument
Uncertainty of instrument (%)
“Crank angle encoder
± 0.2
Fuel sensor
± 0.5
Load sensor
± 0.2
Pressure sensor
± 0.5
Speed sensor
± 1.0
Smoke meter
± 1.0
Temperature sensor
± 0.15
Flue gas analyzer CO
± 0.3
CO2
± 1.0
HC
± 0.1
NOX
± 0.5
O2”
± 0.3
Investigation of Combustion and Performance … 40 35
861
D100 D80PPO20 PPO100
Engine torque (N.m)
30 25 20 15 10 5 0 -5 Low
Medium
High
Load (%) Fig. 2 Engine torque with loads
3 Results and Discussion 3.1 Engine Torque Engine torque is the twisting force with respect to engine rotation gives twisting force of that engine. Torque is measured with the help of dynamometer. In modern engines, sensors are placed at crank, with the help of Electronic Controlled Unit (ECU) connected to data encoder directly measures the torque. With the increase of load, it is observed that, pure diesel has highest torque [13, 14]. The variation of engine brake torque with load for the biodiesel blends and pure diesel fuel is shown in Fig. 2
3.2 Brake Mean Effective Pressure The variation of brake mean effective pressure with load for the biodiesel blends and pure diesel fuel is shown in Fig. 3. It is defined as the average effective pressure estimated from the brake dynamometer. With increase in load, pure diesel has highest brake mean effective pressure.
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D100 D80PPO20 PPO100
BMIP (bar)
10
5
0
-5 Low
Medium
High
Load (%) Fig. 3 Brake mean effective pressure with loads
3.3 Volumetric Efficiency The variation of volumetric efficiency with load for the biodiesel blends and pure diesel fuel is shown in Fig. 4. It is defined as the compressor cylinder efficiency to the compressed gas. With the variation of load, pure plastic oil has highest volumetric efficiency. The volumetric efficiency decreases with increase in engine load as shown in figure.
3.4 Peak Cylinder Pressure The variation of peak cylinder pressure with load for the biodiesel blends and pure diesel fuel is shown in Fig. 5. It is the pressure during four strokes of the engine, suction, compression, expansion and exhaust. With increase in load, pure diesel has highest peak cylinder pressure compared to pure plastic oil and blend [15, 16].
3.5 Smoke Emission The variation of smoke emission with load for the biodiesel blends and pure diesel fuel is shown in Fig. 6. Smoke emissions are commonly released from diesel engine,
Investigation of Combustion and Performance … 100
Volumetric efficiency (%)
98
863
D100 D80PPO20 PPO100
96 94 92 90 88 86 Low
Medium
High
Load (%) Fig. 4 Volumetric efficiency with loads 100
Peak cylinder pressure (bar)
90
D100 D80PPO20 PPO100
80
70
60
50
40
30 Low
Medium
Load (%) Fig. 5 Peak cylinder pressure with loads
High
864
D. Simhana Devi et al. 100
Smoke emission (%)
90
D100 D80PPO20 PPO100
80
70
60
50
40 Low
Medium
High
Load (%) Fig. 6 Smoke emission with loads
after combustion process. The main reason for emissions is incomplete combustion and sometimes because of poor quality of the fuel. In general, waste plastic oil releases more emissions because of high viscosity. However, in this experimental investigation, it is observed that pure diesel has highest smoke emissions. The main reason is impurities present in diesel fuel [17, 18].
3.6 NO Emissions The variation of NO emission with load for the biodiesel blends and pure diesel fuel is shown in Fig. 7. It stands for nitrogen oxides. Nitrogen oxides are released due to combustion if it takes place at highest temperature [19, 20]. As the load increases, pure plastic oil has the highest NO emissions.
4 Conclusion The retaining of plastic is amass commonplace concomitant with its attainability, primitive heft and frugality despite the waste plastic turned to be a severe affliction for earth’s ozone layer due to non-degradability of waste plastic oil, and hereby accumulation of waste plastic on the earth’s surface is more.
Investigation of Combustion and Performance … 20
865
D100 D80PPO20 PPO100
NO emission (g/kWh)
16
12
8
4
0 Low
Medium
High
Load (%) Fig. 7 NO emission with loads
• The transfiguration of waste plastic to fuel oil complies with the triple solution for the extant problems: Fuel undersupply, decadence and squandering. Critical analysis of innumerous techniques those employ waste plastic is performed. • However, many different fuel combinations could succeed either in enhanced performance of the engine or vectored in decrement of pollution causing elements; while few combinations of diesel and waste plastics blends mentioned poor performance from the engine. • Whereas the properties and chemical traits of the fuel blends had effect on the interpretation of the experiment besides the exhaust parameters of the engines to some better extent. • Engine exploration defined that D80PPO20 has exposition cylinder pressures in similar to the diesel range same range of diesel. • It degraded the rates of NO emission in the exhaust with D80PPO20 alternative in the blends augmented. Smoke emissions were reduced with the use of alternative fuel blend at 1500 rpm
References 1. Raja, A., Murali, A.: Conversion of plastic wastes into fuels. J. Mater. Sci. Eng. B 1, 86–89 (2011)
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2. Bezergianni, S., Dimitriadis, A., Faussone, G.C., Karonis, D.: Alternative diesel from waste plastics. Energies 10, 1750 (2017) 3. Singh, R.K., Ruj, B.: Time and temperature depended fuel gas generation from pyrolysis of real world municipal plastic waste. Fuel 174, 164–171 (2016) 4. Syamsiro, M., et al.: Fuel oil production from municipal plastic wastes in sequential pyrolysis and catalytic reforming reactors. Energy Procedia 47, 180–188 (2014) 5. Miandad, R., Barakat, M.A., Aburiazaiza, A.S., Rehan, M., Ismail, I.M.I., Nizami, A.S.: Effect of plastic waste types on pyrolysis liquid oil. Int. Biodeterior. Biodegrad. 119, 239–252 (2017) 6. UNEP, converting waste plastics into a resources (2009) 7. Nabi, M.N., Rasul, M.G., Brown, R.J.: Influence of diglyme addition to diesel-biodiesel blends on notable reductions of particulate matter and number emissions. Fuel 253, 811–822 (2019) 8. Anwar, M., Rasul, M.G., Ashwath, N., Nabi, M.D.N.: The potential of utilising papaya seed oil and stone fruit kernel oil as non-edible feedstock for biodiesel production in Australia—a review. Energy Rep. 131, 117–126 (2019) 9. Damodharan, D., Sathiyagnanam, A.P., Rana, D., Rajesh Kumar, B., Saravanan, S.: Extraction and characterization of waste plastic oil (WPO) with the effect of n-butanol addition on the performance and emissions of a DI diesel engine fueled with WPO/diesel blends. Energy Convers. Manag. 131, 117–126 (2017) 10. Tan, Y.H., Abdullah, M.O., Kansedo, J., Mubarak, N.M., Chan, Y.S., Nolasco-Hipolito, C.: Biodiesel production from used cooking oil using green solid catalyst derived from calcined fusion waste chicken and fish bones. Renew. Energy 139, 696–706 (2019) 11. Velmurugan, R., Mayakrishnan, J., Induja, S., Raja, S., Nandagopal, S., Sathyamurthy, R.: Comprehensive study on the effect of CuO nano fluids prepared using one-step chemical synthesis method on the behavior of waste cooking oil biodiesel in compression ignition engine. J. Therm. Sci. Eng. Appl. 11, 1–29 (2019) 12. Dinesha, P., Kumar, S., Rosen, M.A.: Combined effects of water emulsion and diethyl ether additive on combustion performance and emissions of a compression ignition engine using biodiesel blends. Energy 179, 928–937 (2019) 13. Tamilvanan, A., Balamurugan, K., Vijayakumar, M.: Effects of nano-copper additive on performance, combustion and emission characteristics of Calophyllum inophyllum biodiesel in CI engine. J. Therm. Anal. Calorim. 136, 317–330 (2019) 14. Hossain, A.K., et al.: Experimental investigation of performance, emission and combustion characteristics of an indirect injection multi-cylinder CI engine fuelled by blends of de-inking sludge pyrolysis oil with biodiesel. Fuel 105, 135–142 (2013) 15. Erdo˘gan, S., Balki, M.K., Aydın, S., Sayin, C.: The best fuel selection with hybrid multiplecriteria decision making approaches in a CI engine fueled with their blends and pure biodiesels produced from different sources. Renew. Energy 134, 653–668 (2019) 16. Venkatesan, H., John, G.J., Sivamani, S.: Impact of oxygenated cottonseed biodiesel on combustion, performance and emission parameters in a direct injection CI engine. Int. J. Ambient Energy 40, 1–46 (2019) 17. Chiatti, G., Chiavola, O., Palmieri, F.: Impact of waste cooking oil in biodiesel blends on particle size distributions from a city-car engine. J. Energy Inst. 91(2), 262–269 (2018) 18. Rachmat, D., Mawarani, L.J., Risanti, D.D.: Utilization of cacao pod husk (Theobroma cacao l.) as activated carbon and catalyst in biodiesel production process from waste cooking oil. In: IOP Conference Series: Materials Science and Engineering (2018) 19. Pandiyarajan, V., Chinnappandian, M., Raghavan, V., Velraj, R.: Second law analysis of a diesel engine waste heat recovery with a combined sensible and latent heat storage system. Energy Policy 39(10), 6011–6020 (2011) 20. Sorguven, E., Özilgen, M.: Thermodynamic assessment of algal biodiesel utilization. Renew. Energy 35(9), 1956–1966 (2010)
Implementation of Campbell, Dudek, Smith Algorithm in Flexible Manufacturing System with Mean Tardiness M. Nageswara Rao , K. Prakash Babu, G. R. Sanjay Krishna, T. Vijaya Kumar, and Upendra Rajak Abstract The adaptable manufacturing system (FMS) is an amalgam framework containing of basics similar to work environments, mechanized putting away and recuperation frameworks, and material control gadgets like machines and AGVs. An endeavor is made to concentrate simultaneously the machine and AGVs arranging highlights in an FMS for decrease of the tardiness. CDS (Campbell Dudek Smith) algorithm is used to solve FMS simultaneous scheduling problems. 120 issues and their current arrangements with various methodologies are analyzed. Keywords FMS · Johnson’s algorithm · Tardiness · AGVs
1 Introduction Need deals are utilized to choose which occupation will be prepared next grinding away focus, where a few positions are standing by to be handled. The positions hanging tight for preparing are sequenced utilizing one of numerous need sequencing rules. It is expected that the work place can deal with just each work in turn. An enormous number of sequencing rules are utilized in research and practically speaking to arrange the positions hanging tight for preparing at a work place [1, 2]. Later, Heuristic enhancement calculations (heuristics) look for great plausible answers for streamlining issues in conditions where the intricacy of the issue or the restricted time accessible for its answer does not permit definite arrangement [3, 4]. Further, to build the opportunity of getting global ideal arrangement with considering populace size through meta-heuristic calculation likes genetic algorithm. The work can likewise be M. Nageswara Rao (B) · G. R. Sanjay Krishna · T. Vijaya Kumar Department of Mechanical Engineering, K.L.E.F University, Guntur 522502, India K. Prakash Babu Department of Mechanical Engineering, VRSEC, Kanuru, Vijayawada, AP, India U. Rajak Department of Mechanical Engineering, RGM College of Engineering and Technology Nandyal, Nandyal, Andhra Pradesh 518501, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_81
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reached out by expanding the size of the populace through meta-heuristic algorithms. Booking of an FMS is a perplexing issue to address, and consequently, it has made interest among the specialists [5, 6]. Despite the fact that FMS planning issue are viewed as before, booking of material dealing with framework was not considered [7–9]. A carefully designed and managed material handling system is important to achieve the required integration of FMS hence there is a need for scheduling both the machines and material handling system simultaneously for the successful implementation of an FMS [10]. A painstakingly planned and oversaw material taking care of framework is imperative to accomplish the necessary coordination of FMS [11]. Henceforth, there is a requirement for booking both the machines and material taking care of framework all the while for the fruitful execution of a FMS.
2 FMS Narrative In FMS narration, having four CNC machines with tool and pallet changer is shown in Fig. 1. The input data (i.e., traveling time matrix and machine sequence with processing time) [12].
Fig. 1 Design plans in model issues
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2.1 Purpose of the Study The objective of FMS study is to minimize the mean tardiness. Finish time of operation j and job i = Oi j = Ti j + Pi j Job consummation time (maximum) = (Ci ) =
n
Oi j
(1)
(2)
i=1
Makespan = max(C1, C2, C3, . . . , Cn)
(3)
T ij = traveling time, Pij = operation preparing time Di = (C1 + C2 + C3 + . . . Cn)/n
(4)
Lateness value(Li) = Tardiness − Due date
(5)
Tardiness(Ti) = Max[Li, 0]
(6)
Mean Tardiness Value(T ) =
n 1 Ti n i=1
(7)
3 Scheduling with Campbell Dudek Smith Jobs are planned dependent on the activity grouping inferred by the calculations. The issue considered necessities for the booking of material, taking care of framework alongside that of machines. In this work, CDS algorithm is altered to tackle concurrent planning issues which are talked about underneath.
3.1 CDS (Campbell Dudek Smith) Heuristic Algorithm CDS heuristic algorithm was created by Campbell Dudek Smith (1970). The means associated with CDS heuristic calculation are given beneath: Stage 1: To consider a task set. Stage 2: To lessen ‘m’ machines and ‘n’ occupations issue to two machines and ‘n’ occupations issue.
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Stage 3: After diminishing to two machines and ‘n’ occupations issue the base cycle time is to establish for each work. Nonetheless on the off chance that both the positions have same estimations of cycle time, initial one is taken. Stage 4: To separate the all out positions in to two gatherings in particular U and V. Stage 5: To figure out the positions in U in climbing request. Stage 6: To figure out the positions in V in diving request. Stage 7: To acquire the work grouping by organizing V by the side of U. Stage 8: According to the got succession to play out the activities in the line.
4 Route Plan of Vehicles Stage 1: At the start vehicles are positioned at Load and unload. Stage 2: For 1st and 2nd operations use AGV1 and AGV2. Stage 3: From 3rd operation onwards check which vehicle is suitable for next operation. Stage 4: Make out the location of both vehicles with travel time. Stage 5: Recognize the next operation machine number and job piece location. Stage 6: Calculate vehicle travel time like. Travel time of {AGV (Vehicle and Machine number) + (current location - job piece) + (job piece – next operation machine number). Stage 7: Select least travel time vehicle for next operation.
5 Execution of Campbell Dudek Smith Layout 2 and job set 6 are painstaking in order for execution of CDS. The CDS heuristic is clarified in the accompanying strides for the work set 6: Stage 1: work set 6 taking into consideration. Set No
Layout
Jobs
operations
Machines sequence
6
2
6
18
1: 1-2-4, 2: 1-2-4 3: 2-3-4, 4: 2-3-4 5: 1-3-4, 6: 1-3-4
Stage 2: Considering the interaction time esteems for each work which are given. Job/machine
1
2
3
4
5
6
1
9
19
0
0
11
10
2
11
20
14
14
0
0 (continued)
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(continued) Job/machine
1
2
3
4
5
6
3
0
0
20
20
16
12
4
7
13
9
9
8
10
Stage 3: Reducing the ‘m’ machines and ‘n’ occupations issue to two machines and ‘n’ occupations issue as. Job/machine
1
2
3
4
5
6
1+2
20
39
14
14
11
10
3+4
7
13
29
29
24
22
Stage 4: For each work, least interaction times is found as: Job/machine
1
2
3
4
5
6
1+2
20
39
14
14
11
10
3+4
7
13
29
29
24
22
Stage 5: Dividing the absolute positions into two gatherings, specifically U and V as. Job/machine
1
2
3
4
5
6
1+2
20
39
14
14
11
10
3+4
7
13
29
29
24
22
U or V
V
V
U
U
U
U
Stage 6: Sorting out the positions in ‘U’ bunch as continues in rising request. From the above advance positions, 3, 4, 5 and 6 go under U gathering. Considering the least cycle time from (M1 + M2) and (M3 + M4) for occupations 3, 4, 5 and 6 are 14, 14, 11 and 10; individually, these preparing times are organized in climbing request 10,11,14 and 14. Then the work request is work 6, 5, 3 and 4. In the event that the interaction times are same, think about the first as need. Stage 7: Sorting the positions in ‘V ’ bunch as continues in diving request. From the above, advance Jobs 1 and 2 go under V gathering. Considering the least cycle time from (M1 + M2) and (M3 + M4) for occupations 1 and 2 are 7 and 13; individually, these preparing time are orchestrated in plummeting request 13 and 7; then the work is work 2 and 1. Stage 8: The work succession is gotten by organizing V by the side of U, as. J6 − J5 − J3 − J4 − J2 − J1
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Stage 9: From the above work succession, the operational grouping is discovered to be: 16 − 17 − 18 − 13 − 14 − 15 − 7 − 8 − 9 − 10 − 11 − 12 − 4 − 5 − 6 − 1 − 2 − 3 Stage 10: according to the work request arrangement activities in the line are performed. Stage 11: For distinguishing the most extreme operational fulfillment season of the above arrangement are determined estimations of different boundaries for all tasks are appeared in Table 1. Table 1 shows activity planning through CDS rule for work set 6 design 2 is appeared. For work set 6 and format 2, the operational culmination time (makespan) is 154. In the same way, calculate tardiness for left over 9 jobs, and tardiness values are. 144. 124, 171, 224, 140, 154, 142, 181, 249 and 273. Due date (Di) = 144 + 124 + 171 + 224 + 140 + 154 + 142 + 181 + 249 + 273/10 = 180. Lateness (Li) = 154–180 = −26. Tardiness (Ti) = Max (Li, 0) = Max (−26, 0) = 0. Table 1 Activities plan with the help of CDS O No
M No
V No
Empty travel
JRT
Job reach
Make span
16
1
1
0
4
4
14
17
3
2
14
18
18
30
18
4
1
30
32
32
42
13
1
2
24
28
28
39
14
3
2
39
43
43
59
15
4
2
59
61
61
69
7
2
1
36
42
42
56
8
3
1
56
58
59
79
9
4
1
79
81
81
90
10
2
2
65
71
71
85
11
3
2
85
87
87
107
12
4
2
107
109
109
118
4
1
1
85
89
89
108
5
2
1
108
110
110
130
6
4
1
130
134
134
147
1
1
2
113
117
117
126
2
2
2
126
128
130
141
3
4
2
141
145
147
154
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6 Result and Discussion The FMS work shop situation introduced here with digits that follow 6.4 exhibits the job set 6 and layout 4. Here, having a 0 or 1 as the last digit suggests that the run times are 2 times and 3 times, wherein the two cases travel times are half. In the ideal grouping of AGVs and machines are forbidden by utilizing CDS for t/p ratios > 0.25 are appeared in Table 2. From Table 2 it is pragmatic that CDS is to reduce the lateness of due date that means tardiness. In the ideal grouping of AGVs and machines are forbidden by utilizing B&B for t/p ratios < 0.25 are appeared in Table 3. From Table 3 CDS is to reduce the lateness of due date that means tardiness. In the ideal grouping of AGVs and machines are forbidden by utilizing B&B for t/p ratios < 0.25 are appeared in Table 4. From Table 4 CDS is to reduce the lateness of due date that means tardiness. Mean tardiness values are reported in Table 5 for various t/p ratios.
7 Final Remarks Intelligent manufacturing framework is accepted as improved choice to confront the undertakings of worldwide challenge. Yet, for successful order, compelling booking is significant. Scheduling of a FMS is an exceptionally convoluted issue on account of extra necessities like material dealing with. In this paper, an exertion has been influenced to take care of the NP difficult issues by CDS. Achievements of CDS algorithm are evaluated by examining 120 benchmark issues involving of various occupation sets and designs.
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Table 2 Execution examination Job
Layout
First come first serve
Shortest processing time
Longest processing time
CDS
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
1
1
221
−48
0
230
−37
0
224
−47
0
223
−50
0
2
1
221
−63
0
230
−72
0
224
−47
0
223
−65
0
3
1
221
−19
0
230
−6
0
224
−26
0
223
−9
0
4
1
221
42
42
230
37
37
224
40
40
223
41
41
5
1
221
−73
0
230
−66
0
224
−76
0
223
−67
0
6
1
221
10
10
230
10
10
224
3
3
223
2
2
7
1
221
−26
0
230
−20
0
224
−23
0
223
−31
0
8
1
221
40
40
230
31
31
224
42
42
223
38
38
9
1
221
49
49
230
47
47
224
44
44
223
50
50
10
1
221
87
87
230
78
78
224
86
86
223
89
89
1
2
181
−38
0
190
−17
0
185
−20
0
180
−36
0
2
2
181
−57
0
190
−66
0
185
−55
0
180
−56
0
3
2
181
−19
0
190
−2
0
185
−25
0
180
−9
0
4
2
181
36
36
190
33
33
185
39
39
180
44
44
5
2
181
−63
0
190
−46
0
185
−54
0
180
−40
0
6
2
181
−1
0
190
−21
0
185
−20
0
180
−26
0
7
2
181
−32
0
190
−30
0
185
−36
0
180
−38
0
8
2
181
0
0
190
−9
0
185
13
13
180
1
1
9
2
181
69
69
190
59
59
185
59
59
180
69
69
10
2
181
109
109
190
98
98
185
102
102
180
93
93
1
3
185
−40
0
194
−19
0
189
−22
0
183
−40
0
2
3
185
−55
0
194
−64
0
189
−53
0
183
−53
0
3
3
185
−25
0
194
−4
0
189
−27
0
183
−11
0
4
3
185
48
48
194
43
43
189
41
41
183
47
47
5
3
185
−65
0
194
−48
0
189
−56
0
183
−45
0
6
3
185
−3
0
194
−23
0
189
−22
0
183
−27
0
7
3
185
−30
0
194
−28
0
189
−38
0
183
−35
0
8
3
185
−2
0
194
−11
0
189
11
11
183
0
0
9
3
185
67
67
194
57
57
189
57
57
183
68
68
10
3
185
108
108
194
100
100
189
104
104
183
96
96
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Table 3 Execution examination (t/p < 0.25) with travel time double and travel time halves Job Layout First come first serve (Di) (Li)
Shortest processing time
(Ti) (Di) (Li)
Longest processing time
CDS
(Ti)
(Di) (Li)
(Ti)
(Di) (Li)
(Ti)
1
10
285
−78
0
297
−49
0
297
−45
0
278
−50
0
2
10
285
−68
0
297
−80
0
297
−72
0
278
−61
0
3
10
285
−28
0
297
30
30
297
−15
0
278
−11
0
4
10
285
18
18
297
31
31
297
20
20
278
45
45
5
10
285
−133 0
297
−107 0
297
−110 0
278
−161 0
6
10
285
19
19
297
−16
0
297
0
0
278
−12
0
7
10
285
−54
0
297
−57
0
297
−33
0
278
−58
0
8
10
285
53
53
297
41
41
297
50
50
278
60
60
9
10
285
105
105 297
70
70
297
62
62
278
104
104
10
10
285
167
167 297
132
132
297
147
147
278
142
142
1
20
269
−75
0
281
−43
0
286
−40
0
271
−52
0
2
20
269
−75
0
281
−87
0
286
−80
0
271
−77
0
3
20
269
−28
0
281
30
30
286
−16
0
271
−16
0
4
20
269
16
16
281
31
31
286
12
12
271
38
38
5
20
269
−127 0
281
−101 0
286
−102 0
271
−102 0
6
20
269
23
23
281
−21
0
286
−2
0
271
−20
0
7
20
269
−57
0
281
−63
0
286
−37
0
271
−69
0
8
20
269
37
37
281
38
38
286
48
48
271
48
48
9
20
269
111
111 281
74
74
286
61
61
271
101
101
10
20
269
176
176 281
142
142
286
153
153
271
145
145
1
30
271
−76
0
283
−44
0
287
−40
0
271
−53
0
2
30
271
−74
0
283
−86
0
287
−78
0
271
−74
0
3
30
271
−31
0
283
29
29
287
−16
0
271
−17
0
4
30
271
21
21
283
34
34
287
14
14
271
39
39
5
30
271
−130 0
283
−102 0
287
−104 0
271
−103 0
6
30
271
25
25
283
−22
0
287
−2
0
271
−19
0
7
30
271
−56
0
283
−62
0
287
−37
0
271
−68
0
8
30
271
36
36
283
37
37
287
48
48
271
49
49
9
30
271
110
110 283
73
73
287
61
61
271
102
102
10
30
271
177
177 283
143
143
287
155
155
271
148
148
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Table 4 Execution examination (t/p < 0.25) with travel time triple and travel time halves Job Layout First come first serve (Di) (Li)
Shortest processing time
(Ti) (Di) (Li)
(Ti)
Longest processing time
CDS
(Di) (Li)
(Di) (Li)
(Ti)
(Ti)
1
11
406
−116 0
421
−72
0
428
−67
0
404
−78
2
11
406
−107 0
421
−122 0
428
−112 0
404
−105 0
3
11
406
−40
0
421
52
52
428
−17
0
404
−18
0
4
11
406
20
20
421
46
46
428
20
20
404
52
52
5
11
406
−191 0
421
−159 0
428
−157 0
404
−161 0
6
11
406
37
37
421
−23
0
428
5
5
404
−22
0
7
11
406
−81
0
421
−87
0
428
−49
0
404
−98
0
8
11
406
82
82
421
67
67
428
80
80
404
84
84
9
11
406
154
154 421
100
100
428
81
81
404
144
144
10
11
406
246
246 421
196
196
428
213
213
404
204
204
1
22
391
−111 0
406
−67
0
417
−59
0
392
−75
0
2
22
391
−115 0
406
−130 0
417
−120 0
392
−116 0
3
22
391
−41
0
406
51
51
417
−18
0
392
−18
0
4
22
391
16
16
406
44
44
417
12
12
392
50
50
5
22
391
−186 0
406
−154 0
417
−149 0
392
−157 0
6
22
391
41
41
406
−29
0
417
3
3
392
−25
0
7
22
391
−92
0
406
−91
0
417
−53
0
392
−95
0
8
22
391
78
78
406
63
63
417
78
78
392
77
77
9
22
391
159
159 406
103
103
417
80
80
392
146
146
10
22
391
254
254 406
206
206
417
221
221
392
213
213
1
33
393
−114 0
407
−67
0
418
−61
0
393
−77
0
2
33
393
−114 0
407
−128 0
418
−118 0
393
−114 0
3
33
393
−44
0
407
51
51
418
−18
0
393
−20
0
4
33
393
19
19
407
46
46
418
12
12
393
50
50
5
33
393
−189 0
407
−154 0
418
−151 0
393
−159 0
6
33
393
40
40
407
−29
0
418
3
3
393
−25
0
7
33
393
−91
0
407
−89
0
418
−53
0
393
−95
0
8
33
393
77
77
407
63
63
418
78
78
393
77
77
9
33
393
158
158 407
103
103
418
80
80
393
146
146
10
33
393
255
255 407
208
208
418
223
223
393
215
215
0
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Table 5 Mean tardiness comparison Layout
FCFS
SPT
LPT
CDS
Mean tardiness (t/p < 0.25) 1
22.8
20.3
21.5
22
2
21.4
19
21.3
20.7
3
22.3
20
21.3
21.1
4
25.2
22.3
26.2
24.3
Mean tardiness (t/p > 0.25) 1
36.2
30.4
27.9
35.1
2
36.3
31.5
27.4
33.2
3
36.9
31.6
27.8
33.8
4
37
30.8
28.9
33.6
Mean tardiness (t/p > 0.25) 1
54
46.1
39.9
48.4
2
54.8
46.7
39.4
48.6
3
54.9
47.1
39.6
48.8
4
53.8
46.1
40.9
48.9
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10. Nageswara, M., Vara, K.S., Praneeth, I., Gaya, P.K., Venkata, R.D., Vineeth, E., Maheshwar, R.D.: Simultaneous scheduling of machines and AGVs in FMS through simulated annealing algorithm. Int. J. Innov. Technol. Explor. Eng. 9(4), 2235–2240 (2020) 11. Babu, K.P., Babu, V.V., Nageswara, M.: Scheduling of machines and AGVs simultaneously in FMS through hybrid teaching learning based optimization algorithm. Int. J. Eng. Adv. Technol. 9(2), 2048–2055 (2019) 12. Durga, K.V., Chalapathi, P.V., Nageswara, M., Krishna, C.E., Anoop, K., Neeraj, Y.: An efficient sheep flock heredity algorithm for the cell formation problem. ARPN J. Eng. Appl. Sci. 12(21), 6074–6079 (2017)
AGVs and Machines Scheduling with Campbell, Dudek, Smith Algorithm M. Nageswara Rao , K. Prakash Babu, Kiran Kumar Dama, Santosh Kumar Malyala, and Upendra Rajak
Abstract The Adaptable Manufacturing System (FMS) is an amalgam framework containing of basics similar to work environments, mechanized putting away and recuperation frameworks, and material control gadgets like machines and AGVs. An endeavor is made to concentrate simultaneously the machine and AGVs arranging highlights in a FMS for decrease in the makespan. CDS (Campbell Dudek Smith) Algorithm is used to solve FMS simultaneous scheduling problems. 120 issues and their current arrangements with various methodologies are analyzed. Keywords FMS · Johnson’s algorithm · Makespan · AGVs
1 Introduction Need deals are utilized to choose which occupation will be prepared next grinding away focus, where a few positions are standing by to be handled. The positions hanging tight for preparing are sequenced utilizing one of numerous need sequencing rules. It is expected that the work place can deal with just each work in turn. An enormous number of sequencing rules are utilized in research and practically speaking to arrangement the positions hanging tight for preparing at a work place [1–5]. Later, heuristic enhancement calculations (heuristics) look for great plausible answers for streamlining issues in conditions where the intricacy of the issue or the restricted time accessible for its answer doesn’t permit definite arrangement [6–8]. Further to build M. Nageswara Rao (B) · K. K. Dama Department of Mechanical Engineering, K.L.E.F. University, Guntur 522502, India K. Prakash Babu Department of Mechanical Engineering, VRSEC, Kanuru, Vijayawada, India S. K. Malyala Department of Mechanical Engineering, Acharya Institute of Technology, Banglore, India U. Rajak Department of Mechanical Engineering, RGM College of Engineering and Technology Nandyal, Nandyal, Andhra Pradesh 518501, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_82
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Fig. 1 Design plans in model issues
the opportunity of getting Global ideal arrangement with considering populace size through meta-heuristic calculation likes Genetic Algorithm. The work can likewise be reached out by expanding the size of the populace through Meta-heuristic Algorithms. Booking of a FMS is a perplexing issue to address and consequently it has made interest among the specialists [9–11]. Despite the fact that FMS planning issue are viewed as before, booking of material dealing with framework was not considered [12–16]. Among the individuals who decided on material taking care of framework, just scarcely any thought to be concurrent booking of machines and AGVs [17–19]. A painstakingly planned and oversaw material taking care of framework is imperative to accomplish the necessary coordination of FMS [20–23]. Henceforth, there is a requirement for booking both the machines and material taking care of framework all the while for the fruitful execution of a FMS.
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2 FMS Narrative In FMS, narration having four CNC machines with tool and pallet changer is shown in Fig. 1. The input data (i.e., traveling time matrix and machine sequence with processing time) [24].
2.1 Purpose of the Study The objective of FMS scheduling is to minimize the makespan Conclusion time of operation j and job i = Oi j = Ti j + Pi j Jobs completion time (Maximum) = (Ci ) =
n
Oi j
(1)
(2)
i=1
Makespan = max(C1, C2, C3 . . . .. Cn)
(3)
T ij = traveling time, Pij = operation preparing time.
3 Scheduling in FMS Jobs are planned dependent on the commotion grouping conditional by the calculations. The issue considered necessities booking of material taking care of framework alongside that of machines. In this work, CDS algorithm is altered to tackle concurrent planning issues.
3.1 CDS (Campbell Dudek Smith) Heuristic Algorithm CDS Heuristic Algorithm was created by Campbell Dudek Smith (1970). The means associated with CDS heuristic calculation are given beneath: Stage 1: To consider a task set. Stage 2: To lessen ‘m’ machines and ‘n’ occupations issue to two machines and ‘n’ occupations issue. Stage 3: After diminishing to two machines and ‘n’ occupations issue the base cycle time is to establish for each work. Nonetheless on the off chance that both the positions have same estimations of cycle time, initial one is taken.
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Stage 4: To separate the all out positions in to two gatherings in particular U and V. Stage 5: To figure out the positions in U in climbing request. Stage 6: To figure out the positions in V in diving request. Stage 7: To acquire the work grouping by organizing V by the side of U. Stage 8: According to the got succession to play out the activities in the line.
4 Route Plan of Vehicle Stage 1: At the start vehicles are positioned at Load and unload. Stage 2: For 1st and 2nd operations use AGV1 and AGV2. Stage 3: From 3rd operation onwards check which vehicle is suitable for next operation. Stage 4: make out the location of both vehicles with travel time. Stage 5: recognize the next operation machine number and job piece location. Stage 6: Calculate vehicle travel time like. Travel time of {AGV (Vehicle and Machine number) + (current location − job piece) + (job piece − next operation machine number). Stage 7: Select least travel time vehicle for next operation.
5 Execution of Campbell, Dudek, Smith Layout 2 and job set 6 are painstaking in order for execution of CDS. The CDS Heuristic is clarified in the accompanying strides for the work set 6: Stage 1: work set 6 taking into consideration. Set No
Layout
Jobs
operations
Machines sequence
6
2
6
18
1: 1-2-4, 2: 1-2-4 3: 2-3-4, 4: 2-3-4 5: 1-3-4, 6: 1-3-4
Stage 2: Considering the interaction time esteems for each work which are given. Job/machine
1
2
3
4
5
6
1
9
19
0
0
11
10
2
11
20
14
14
0
0
3
0
0
20
20
16
12
4
7
13
9
9
8
10
AGVs and Machines Scheduling with Campbell …
883
Stage 3: Reducing the ‘m’ machines and ‘n’ occupations issue to two machines and ‘n’ occupations issue as. Job/machine
1
2
3
4
5
6
1+2
20
39
14
14
11
10
3+4
7
13
29
29
24
22
Stage 4: For each work least interaction times is found as: Job/machine
1
2
3
4
5
6
1+2
20
39
14
14
11
10
3+4
7
13
29
29
24
22
Stage 5: Dividing the absolute positions into two gatherings specifically U and V as. Job/Machine
1
2
3
4
5
6
1+2
20
39
14
14
11
10
3+4
7
13
29
29
24
22
U or V
V
V
U
U
U
U
Stage 6: Sorting out the positions in ‘U’ bunch as continues in rising request. From the above advance positions 3, 4, 5, and 6 goes under U gathering. Considering the least cycle time from (M1 + M2)and (M3 + M4) for occupations 3, 4, 5, and 6 are 14, 14, 11, and 10 individually these preparing time are organized in climbing request 10, 11, 14, and 14 then the work request is work 6, 5, 3, and 4. In the event that the interaction times are same think about the first as need. Stage 7: Sorting the positions in ‘V ’ bunch as continues in diving request. From the above advance Jobs 1 and 2 go under V gathering. Considering the least cycle time from (M1 + M2) and (M3 + M4) for occupations 1 and 2 are 7 and 13 individually these preparing time are orchestrated in plummeting request 13 and 7 then the work is work 2 and 1. Stage 8: The work succession is gotten by organizing V by the side of U, as. J6 − J5 − J3 − J4 − J2 − J1 Stage 9: From the above work succession, the operational grouping is discovered to be: 16 − 17 − 18 − 13 − 14 − 15 − 7 − 8 − 9 − 10 − 11 − 12 − 4 − 5 − 6 − 1 − 2 − 3
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Table 1 Activities plan CDS calculation (for Job set 6 and layout2) O No
M No
V No
Empty travel
JRT
Job reach
Make span
16
1
1
0
4
4
14
17
3
2
14
18
18
30
18
4
1
30
32
32
42
13
1
2
24
28
28
39
14
3
2
39
43
43
59
15
4
2
59
61
61
69
7
2
1
36
42
42
56
8
3
1
56
58
59
79
9
4
1
79
81
81
90
10
2
2
65
71
71
85
11
3
2
85
87
87
107
12
4
2
107
109
109
118
4
1
1
85
89
89
108
5
2
1
108
110
110
130
6
4
1
130
134
134
147
1
1
2
113
117
117
126
2
2
2
126
128
130
141
3
4
2
141
145
147
154
Stage 10: According to the work request arrangement activities in the line are performed. Stage 11: For distinguishing the most extreme operational fulfillment season of the above arrangement are determined estimations of different boundaries for all tasks are appeared in Table 1
6 Result and Discussion The FMS work shop situation introduced here with digits that follow 6.4 exhibits the job set 6 and layout 4. Calculations for completing time for different blends of occupation sets and formats for CDS method with different t/p ratios are done and tabulated in 2. In the ideal grouping of AGVs and machines are forbidden by utilizing CDS for various t/p, ratios are appeared in Table 2. In t/p > 0.25 out of 40 issues 20 issues gives improved outcomes utilizing CDS in correlation with SPT, FCFS, and LPT (Nageswararao et al. 2020). With t/p < 0.25 out of 40 problems 19 issues gives improved outcomes utilizing CDS in correlation with SPT, FCFS, and LPT. In t/p < 0.25 out of 40 issues 20 issues gives improved outcomes utilizing CDS in correlation with SPT, FCFS, and LPT.
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Table 2 Execution examination t/p > 0.25
t/p < 0.25
t/p < 0.25
Job. No
Layout
CDS
Job. No
Layout
CDS
Job. No
Layout
CDS
1
1
173
1
10
228
1
11
326
2
1
158
2
10
217
2
11
299
3
1
214
3
10
267
3
11
386
4
1
264
4
10
323
4
11
456
5
1
156
5
10
117
5
11
243
6
1
225
6
10
266
6
11
382
7
1
192
7
10
220
7
11
306
8
1
261
8
10
338
8
11
488
9
1
273
9
10
382
9
11
548
10
1
312
10
10
420
10
11
608
1
2
144
1
20
219
1
21
317
2
2
124
2
20
194
2
21
276
3
2
171
3
20
255
3
21
374
4
2
224
4
20
309
4
21
442
5
2
140
5
20
169
5
21
235
6
2
154
6
20
251
6
21
367
7
2
142
7
20
202
7
21
297
8
2
181
8
20
319
8
21
469
9
2
249
9
20
372
9
21
538
10
2
273
10
20
416
10
21
605
1
3
143
1
30
218
1
31
316
2
3
130
2
30
197
2
31
279
3
3
172
3
30
254
3
31
373
4
3
230
4
30
310
4
31
443
5
3
138
5
30
168
5
31
234
6
3
156
6
30
252
6
31
368
7
3
148
7
30
203
7
31
298
8
3
183
8
30
320
8
31
470
9
3
251
9
30
373
9
31
539
10
3
279
10
30
419
10
31
608
1
4
189
1
40
232
1
41
330
2
4
174
2
40
221
2
41
307
3
4
224
3
40
269
3
41
388
4
4
299
4
40
331
4
41
464
5
4
182
5
40
186
5
41
252 (continued)
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Table 2 (continued) t/p > 0.25
t/p < 0.25
t/p < 0.25
Job. No
Layout
CDS
Job. No
Layout
CDS
Job. No
Layout
CDS
6
4
237
6
40
270
6
41
385
7
4
227
7
40
223
7
41
307
8
4
285
8
40
343
8
41
493
9
4
295
9
40
388
9
41
554
10
4
348
10
40
430
10
41
618
7 Final Remarks Intelligent manufacturing framework is accepted as enhanced choice to confront the undertakings of worldwide challenge. Yet, for unbeaten order compelling booking is significant. Scheduling of a FMS is an exceptionally convoluted issue on account of extra necessities like material dealing with. In this paper, an exertion has been influenced to take care of the NP difficult issues by CDS Heuristic. Achievements of CDS algorithm are evaluated by examining 120 benchmark issues involving of various occupation sets and designs. Acknowledgements Shore up from DST-SERB, GOI (SB/EMEQ-501/2014).
References 1. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Integrated scheduling of machines and AGVs in FMS by using dispatching rules. J. Prod. Eng. 20(1), 75–84 (2017) 2. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Machines and AGVs scheduling in flexible manufacturing system with mean tardiness criterion. Int. J. Adv. Mater. Manuf. Charact. 4, 100–105 (2014) 3. Subbaiah, K.V., Nageswara Rao, M., Narayanarao, K.: Scheduling of AGVs and machines in FMS with make span criteria using sheep flock heredity algorithm. Int. J. Phys. Sci. 4(2), 139–148 (2010) 4. Nageswararao, M., Narayanarao, K., Ranagajanardhana, G.: Simultaneous scheduling of machines and AGVs in flexible manufacturing system with minimization of tardiness criterion. In: International Conference on Advances in Manufacturing and Materials Engineering (ICAMME-2014) (2014) 5. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Scheduling of machines and automated guided vehicle in FMS using gravitational search algorithm. Appl. Mech. Mater. 867, 307–313 (2017) 6. Nageswara Rao, M., Lokesh, K., Harish, V., Sai Bharath, C., Venkatesh, Y., Vara Kumari, S.: Simultaneous scheduling through heuristic algorithm. Int. J. Eng. Technol. 7(32), 125–130 (2018) 7. Nageswara Rao, M., Dileep, K., Basha, S.K., Vara Kumari, S.: Modrak algorithm to minimize completion time for n-jobs m-machines problem in flexible manufacturing system. Int. J. Emerg. Trends Eng. Res. 8(8), 4560–4566 (2020)
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8. Nageswara Rao, M., Vara Kumari, S., Manohar, P., Madesh, B., Naveen Krishna, P., Suraj Krishna, R.: Simultaneous scheduling of machines and AGVs in FMS through ant colony optimization algorithm. Int. J. Eng. Adv. Technol. (IJEAT) 9(3), 1392–1397 (2020) 9. Nageswara, R.M., Vara, K.S., Praneeth, I., Gaya, P.K., Venkata, R.D., Vineeth, E., Maheshwar, R.D.: Simultaneous scheduling of machines and AGVs in FMS through simulated annealing algorithm. Int. J. Innov. Technol. Explor. Eng. 9(4), 2235–2240 (2020) 10. Dasore, A., Ramakrishna, K., Kiran, N.B.: Evaluation of heat and mass transfer coefficients at beetroot-air interface during convective drying. Interfacial Phenom. Heat Transfer 8, 303–319 (2020) 11. Al-Hakim, L.: An analogue genetic algorithm for solving job shop scheduling problems. Int. J. Prod. Res. 39(7), 1537–1548 (2001) 12. Abdelmaguid, T.F., Nasef, A.O., Kamal, B.A., Hassan, M.F.: A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles. Int. J. Prod. Res. 42(2), 267–281 (2004) 13. Reddy, B.S.P., Rao, C.S.P.: A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS. Int. J. Adv. Manuf. Technol. 31, 602–613 (2006) 14. Metta, V.R., Ramakrishna, K., Dasore, A.: Thermal design of spiral plate heat exchanger through numerical modeling. Int. J. Mech. Engg. Tech. 9(7), 736–745 (2018) 15. Goncalves, J.F., Mendes, J.J.M.: A hybrid genetic algorithm for the job shop scheduling problem. AT&T Labs Research Technical Report TD-5EAL6J (2002) 16. Sachin, G., Narasimha, K.K., Ramakrishna, K., Dasore, A.: Thermodynamic analysis and effects of replacing HFC by fourth generation refrigerants in VCR systems. Int. J. Air-cond. Ref. 26(1), 1850013-1-1850013–12 (2018) 17. Nearchou, A.C., Omirou, S.L.: Differential evolution for sequencing and scheduling optimization. J. Heuristics 12, 395–411 (2006) 18. Sreekara Reddy, M.B.S., Ratnam, C., Rajyalakshmi, G., Manupati, V.K.: An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem. Measurement 114, 78–90 (2018) 19. Patle, B.K., Parhi, D.R.K., Jagadeesh, A., Kashyap, S.K.: Matrix-binary codes based genetic algorithm for path planning of mobile robot. Comput. Electr. Eng. 67, 708–728 (2017) 20. Pappula, L., Ghosh, D.: Cat swarm optimization with normal mutation for fast convergence of multimodal functions. Appl. Soft Comput. 66, 473–491 (2018) 21. Durga Rajesh, K.V., Chalapathi, P.V., Nageswara, R.M., Krishna, C.E., Anoop, K., Neeraj, Y.: An efficient sheep flock heredity algorithm for the cell formation problem. ARPN J. Eng. Appl. Sci. 12(21), 6074–6079 (2017) 22. Nageswara, R.M., Sai, B.C., Venkatesh, Y., Lokesh, K., Harish, V., Vara, K.S.: Simultaneous scheduling in FMS through priority rules. J. Adv. Res. Dyn. Control Syst. 9, 1995–2008 (2017) 23. Nageswararao, M., et al.: Scheduling of machines and automated guided vehicles in FMS using shuffled frog leap algorithm. Int. J. Mech. Eng. Technol. 8(5), 496–503 (2017) 24. Kumar, P.A., Nageswararao, M., Kumar, T.Y., Kumar, M.M., Bhanu, V.: Scheduling of machines and automated guided vehicles in FMS using scatter search algorithm. Int. J. Mech. Eng. Technol. 8(5), 471–481 (2017)
Implementation of Branch and Bound Algorithm in FMS with Mean Tardiness M. Nageswara Rao , Kiran Kumar Dama, T. Vijaya Kumar, K. Prakash Babu, and Upendra Rajak
Abstract The Adaptable Manufacturing System (FMS) is an amalgam framework containing of basics similar to work environments, mechanized putting away and recuperation frameworks, and material control gadgets like machines and AGVs. An endeavor is made to concentrate simultaneously the machine and AGVs arranging highlights in a FMS for minimization of mean tardiness. Branch and bound algorithm is used to solve FMS simultaneous scheduling problems. 120 issues and their current arrangements with various methodologies are analyzed. Keywords FMS · Lower boundary · Tardiness · AGVs
1 Introduction Need deals are utilized to choose which occupation will be prepared next grinding away focus, where a few positions are standing by to be handled. The positions hanging tight for preparing are sequenced utilizing one of numerous need sequencing rules. It is expected that the work place can deal with just each work in turn. An enormous number of sequencing rules are utilized in research and practically speaking to arrangement the positions hanging tight for preparing at a work place [1, 2]. Later, heuristic enhancement calculations (heuristics) look for great plausible answers for streamlining issues in conditions where the intricacy of the issue or the restricted time accessible for its answer doesn’t permit definite arrangement [3–5]. Further to build the opportunity of getting Global ideal arrangement with considering populace size through meta-heuristic calculation likes Genetic Algorithm. The work can likewise M. Nageswara Rao (B) · K. K. Dama · T. Vijaya Kumar Department of Mechanical Engineering, K.L.E.F University, Guntur, AP, India K. Prakash Babu Department of Mechanical Engineering, VRSEC, Kanuru, Vijayawada, AP, India U. Rajak Department of Mechanical Engineering, RGM College of Engineering and Technology Nandyal, Nandyal, Andhra Pradesh 518501, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_83
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be reached out by expanding the size of the populace through meta-heuristic Algorithms. Booking of a FMS is a perplexing issue to address and consequently it has made interest among the specialists [6–8]. Despite the fact that FMS planning issue are viewed as before, booking of material dealing with framework was not considered. Among the individuals who decided on material taking care of framework, just scarcely any thought to be concurrent booking of machines and AGVs [9]. A painstakingly planned and oversaw material taking care of framework is imperative to accomplish the necessary coordination of FMS [10, 11]. Henceforth, there is a requirement for booking both the machines and material taking care of framework all the, while for the fruitful execution of a FMS.
2 FMS Narrative In FMS, narration having four CNC machines with tool and pallet changer is shown in Fig. 1. The input data (i.e., traveling time matrix and machine sequence with processing time) [11].
Fig. 1 Design plans in model issues
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2.1 Purpose of the Study Objective of FMS study is to minimize the mean tardiness Finish time of operation j and job i = Oi j = Ti j + Pi j Job consummation time (maximum) = (Ci ) =
n
Oi j
(1)
(2)
i=1
Makespan = max(C1, C2, C3, . . . , Cn)
(3)
T ij = traveling time, Pij = operation preparing time Di = (C1 + C2 + C3 + . . . Cn)/n
(4)
Lateness value(Li) = Tardiness − Due date
(5)
Tardiness(Ti) = Max[Li, 0]
(6)
Mean Tardiness Value(T ) =
n 1 Ti n i=1
(7)
3 Simultaneous Scheduling with B & B Jobs are planned dependent on the activity grouping inferred by the calculations. The issue considered necessities booking of material taking care of framework alongside that of machines. In this work, B & B is altered to tackle concurrent planning issues which are talked about underneath.
3.1 Branch & Bound Algorithm Branch and bound algorithm was created by Ailsa land and Alison Doig in (1960) at London school of financial aspects. Step wise procedure of branch and bound algorithm is given. Step1: Consider the work set and format.
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Step2: For equivalent occupation ascertain C1 (k), C2 (k) … Cn (k) and A1, A2, A3, …An. (relies upon the quantity of occupation in the succession). Step3: Calculate A1 = C1(k) + P1j + min(P2j + P3j + P4j) A2 = C2(k) + P2j + min(P3j + P4j) A3 = C3(k) + P3j + min (P4j) A4 = C4(k) + P4j. Step4: Calculate LB (k) = max (A1, A2, A3, A4) We assess LB (Lower Bound) first for n number of changes for example for these beginning with 1,2, 3,….n individually, having named the proper vertices of the planning tree by these qualities. Step5: Now investigate the vertex with LB. Assess LB to the (n-1) sub-classes. Beginning with this vertex and again focus on the most reduced name vertex. Proceeding with this path until we reach toward the finish of the tree portrayal by two single changes, for which we assess the absolute work term. In this manner, we get the ideal timetable of the positions. Step6: Stop.
4 Route Plan of Vehicle Stage 1: At the start vehicles are positioned at Load and unload. Stage 2: For 1st and 2nd operations use AGV1 and AGV2. Stage 3: From 3rd operation on check which vehicle is suitable for next operation. Stage 4: Make out the location of both vehicles with travel time. Stage 5: Recognize the next operation machine number and job piece location. Stage 6: Calculate vehicle travel time like. Travel time of {AGV (Vehicle and Machine number) + (current location-job piece) + (job piece-next operation machine number). Stage 7: Select least travel time vehicle for next operation.
5 Execution of B & B For execution of branch and bound calculation, work set 1 and design 1 are considered for instance. Branch and bound algorithm registers the lower limit for different occupations and the groupings are gotten dependent on the lower limit. Stage 1: work set 1 taking into consideration.
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Work set
Layout
Work set jobs
Operation numbers
Machine order
1
1
5
13
1: 1-2-4 2: 1-3-2 3:3-4-1 4:4-2 5: 3-1
Step2: Estimate completion time {C1 (k) to C 4 (k)}, A1 to A4 for job 1 (level 1). M1
[8,8]
C 1 (k) = 8
M2
[16,24]
C 2 (k) = 24
M3
[0,24]
C 3 (k) = 24
M4
[12,36]
C 4 (k) = 36
P2j + P3j + P4j
P3j + P4j
P4j
0
28
10
0
8
20
20
8
14
32
14
14
10
10
0
Min = 10
Min = 10
Min = 0
Jobs/machines
1
2
3
4
1
8
16
0
12
2
20
18
10
3
15
0
12
4
0
18
0
5
15
P1j =
50
A1 A2 A3 A4
= C 1 (k) + P1j = C 2 (k) + P2j = C 3 (k) + P3j = C 4 (k) + P4j
0
P2j =
36
10 32
P3j =
0 22
P4j =
+ min(P2j + P3j + P4j ) = 8 + 50 + 10 = 68 + min(P3j + P4j ) = 24 + 36 + 10 = 70 + min (P4j ) = 24 + 32 + 0 = 56 = 36 + 22 = 58.
Step3: Calculate Lower Bound. LB (1) = max (A1 , A2 , A3 , A4 ) = max (68, 70, 56, 58) = 70. Step 4: Estimate completion time {C1 (k) to C 4 (k)}, A1 to A4 and LB for job 2. M1
[20,20]
C 1 (k) = 20
M2
[18,38]
C 2 (k) = 38
M3
[10,48]
C 3 (k) = 48
M4
[0,48]
C 4 (k) = 48
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P2j + P3j + P4j
P3j + P4j
P4j
0
12
28
12
12
10
0 8
20
20
8
14
32
14
14
0
10
10
0
Min = 10
Min = 10
Min = 0
Jobs/machines
1
2
1
8
16
2
20
18
3
15
0
12
4
0
18
0
5
15
0
10
P1j =
38
3
P2j =
34
P3j =
22
P4j =
34
A1 = C 1 (k) + P1j + min(P2j + P3j + P4j ) = 20 + 38 + 10 = 68 A2 = C 2 (k) + P2j + min(P3j + P4j ) = 38 + 34 + 10 = 82 A3 = C 3 (k) + P3j + min (P4j ) = 48 + 22 + 0 = 70 A4 = C 4 (k) + P4j = 48 + 34 = 82 LB (2) = max (A1 , A2 , A3 , A4 ) = max (68, 82, 78, 82) = 82. Similarly calculate lower bound values for Job, 3, 4, 5 namely LB (3), LB (4), and LB (5) as shown in Fig. 2. From Fig. 2, it is observed that least boundary is 4th job hence from 4th job start the next branch ignore remaining jobs. Step 5: Estimate completion time {C1 (k) to C 4 (k)} A1 to A4 and lower bound for job 4, 1 (level 2). M1
[0, 0]
[8, 8]
C 1 (k) = 8
M2
[18,18]
[16,34]
C 2 (k) = 34
M3
[0,18]
[0,18]
C 3 (k) = 18
M4
[14,32]
[12,30]
C 4 (k) = 30
P2j + P3j + P4j
P3j + P4j
P4j
0
28
10
0
12
8
20
20
8
0
14
10 P3j = 32
0 P4j = 8
10
10
0
Min = 10
Min = 10
Min = 0
Jobs/machines
1
2
3
4
1
8
16
0
12
2
20
18
10
3
15
0
4
0
18
5
15 P1j = 50
0 P2j = 18
A1 = C 1 (k) + P1j + min(P2j + P3j + P4j ) = 8 + 50 + 10 = 68 A2 = C 2 (k) + P2j + min (P3j + P4j ) = 34 + 18 + 10 = 62
Implementation of Branch and Bound Algorithm …
895
Fig. 2 Branch and bound tree for all levels
A3 = C 3 (k) + P3j + min (P4j ) = 18 + 32 + 0 = 50 A4 = C 4 (k) + P4j = 30 + 8 = 38. LB (6) = max (A1 , A2 , A3 , A4 ) = max (68, 62, 50, 38) = 68. Similarly calculate For Job 4 and 2 Job 4 & 3 and Job 4 and 5 lower bound namely LB(7), LB(8) and LB(9) as shown in Fig. 2. From Fig. 2, it is observed that least boundary is 4th and 2nd job hence from 2nd job start the next branch ignore remaining jobs. Step 6: Estimate completion time {C1 (k) to C 4 (k)}, A1 to A4 and lower bound for job 4-2-1(level 4). M1
[0,0]
[20,20]
[8,28]
C 1 (k) = 28
M2
[18,18]
[18,36]
[16,52]
C 2 (k) = 52
M3
[0,18]
[10,28]
[0,28]
C 3 (k) = 28
M4
[14,32]
[0,28]
[12,40]
C 4 (k) = 40
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Jobs/machines
1
2
3
4
1
8
16
0
12
2
20
18
10
0
3
15
0
12
8
4
0
18
0
14
5
15 P1j = 30
0 P2j = 0
10 P3j = 22
0 P4j = 8
P2j + P3j + P4j
P3j + P4j
P4j
20
20
8
10
10
0
Min = Min = 10 10
Min = 0
A1 = C 1 (k) + P1j + min(P2j + P3j + P4j ) = 28 + 30 + 10 = 68 A2 = C 2 (k) + P2j + min(P3j + P4j ) = 52 + 0 + 10 = 62 A3 = C 3 (k) + P3j + min (P4j ) = 28 + 22 + 0 = 50 A4 = C 4 (k) + P4j = 40 + 8 = 48 LB(10) = max(A1 , A2 , A3 , A4 ) = max(68,62,50,48) = 68. Similarly calculate For Job 4-2-1 Job 4-2-3 and Job 4-2-5 lower bound namely LB(11), LB(12) and LB(13) as shown in Fig. 2. From Fig. 2, it is observed that least boundary is 4th, 2nd, and 1st job and 4th, 2nd, and 3rd job hence two branches are available from 1st job and 3rd job. Step 7: Estimate completion time {C1 (k) to C 4 (k)}, A1 to A4 and lower bound for job 4-2-1-3 (level 4). M1
[0,0]
[20,20]
[8,28]
[15,43]
C 1 (k) = 43
M2
[18,18]
[18,36]
[16,52]
[0,52]
C 2 (k) = 52
M3
[0,18]
[10,28]
[0,28]
[12,40]
C 3 (k) = 40
M4
[14, 32]
[0, 28]
[12, 40]
[8,48]
C 4 (k) = 48
Jobs/Machine
1
2
3
1
8
16
0
12
2
20
18
10
0
3
15
0
12
8
4
0
18
0
14
5
15 P1j = 15
0
10 P3j = 10
0 P4j = 0
P2j = 0
4
P2j + P3j + P4j
P3j + P4j
P4j
10
10
0
Min = Min = 10 10
A1 = C 1 (k) + P1j + min(P2j + P3j + P4j ) = 45 + 15 + 10 = 68. A2 = C 2 (k) + P2j + min (P3j + P4j ) = 52 + 0 + 10 = 62.
Min = 0
Implementation of Branch and Bound Algorithm …
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A3 = C 3 (k) + P3j + min (P4j ) = 40 + 10 + 0 = 50. A4 = C 4 (k) + P4j = 48 + 0 = 48. LB (13) = max (A1 , A2 , A3 , A4 ) = max (68, 62, 50, 48) = 68. Similarly calculate For Job 4-2-1-3 Job 4-2-1-5 Job 4-2-3-1 Job 4-2-3-5 lower bound namely lower boundary 13 to 16 as shown in Fig. 2. From Fig. 2, it is observed that least boundary is 4-2-1-3 and 4-2-3-1. Step 8: Estimate completion time {C1 (k) to C 4 (k)}, A1 to A4 and lower bound for job [4,2,1,3,5] or [4,2,3,1,5]. As per the above procedure calculate lower boundary 17 and 18 as 68 as shown in Fig. 2. Hence, according to Branch and Bound algorithm the job sequence is. 4-2-1-3-5 or 4-2-3-1-5. According to job sequences, the operation sequences are. 10-11-4-5-6-1-2-3-7-8-9-12-13 (or). 10-11-4-5-6-7-8-9-1-2-3-12-13. Step 9: Different boundaries for all activities are appeared in Table 1. Table 3 shows activity planning of through B & B Algorithm for work set 1 design 1 is appeared. For work set 1 and format 1, the operational culmination time (makespan) is 170. In similar way, calculate makespan for left over 9 jobs and makespan values are. 170, 156, 212, 260, 152, 222, 190, 260, 270, and 309. Due date (Di) = 170 + 156 + 212 + 260 + 152 + 222 + 190 + 260 + 270 + 309/10 = 210. Lateness (Li) = 170–210 = −40. Tardiness (Ti) = Max (Li,0) = Max(−40,0) = 0. Table 1 Completion time with the help of B & B O No
M No
V No
Empty travel
Ready
Reach
Make span
10
4
1
0
12
12
26
11
2
2
26
34
34
52
4
1
1
18
24
24
44
5
3
1
44
52
52
62
6
2
2
62
68
68
86
1
1
1
60
66
66
74
2
2
1
74
80
86
102
3
4
2
102
110
110
122
7
3
1
90
100
100
112
8
4
1
112
118
122
130
9
1
2
130
140
140
155
12
3
1
124
134
134
144
13
1
1
144
152
155
170
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M. Nageswara Rao et al.
6 Result and Discussion The FMS work shop situation introduced here with digits that follow 10.1 exhibits the job set 10 and layout 1. Here, having a 0 or 1 as the last digit suggests that the run times are 2 times and 3 times, where in the two cases travel times are half. In the ideal grouping of AGVs and machines are forbidden by utilizing B & B for t/p, ratios > 0.25 are appeared in Table 2. From Table 2, it is pragmatic that SPT is to reduce the lateness of due date that means tardiness. In the ideal grouping of AGVs and machines are forbidden by utilizing B & B for t/p, ratios < 0.25 are appeared in Table 3. From Table 3, LPT is to reduce the lateness of due date that means tardiness. In the ideal grouping of AGVs and machines are forbidden by utilizing B & B for t/p, ratios < 0.25 are appeared in Table 4. From Table 4, B & B is to reduce the lateness of due date that means tardiness. Mean tardiness values are reported in Table 5 for various t/p ratios. Table 2 Calculation of tardiness with given processing time and travel time Job
Layout
First come first serve
Shortest processing time
Longest processing time
B&B
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
1
2
181
−38
0
190
−17
0
185
−20
0
179
−37
0
2
2
181
−57
0
190
−66
0
185
−55
0
179
−59
0
3
2
181
−19
0
190
−2
0
185
−25
0
179
−10
0
4
2
181
36
36
190
33
33
185
39
39
179
41
41
5
2
181
−63
0
190
−46
0
185
−54
0
179
−37
0
6
2
181
−1
0
190
−21
0
185
−20
0
179
−27
0
7
2
181
−32
0
190
−30
0
185
−36
0
179
−39
0
8
2
181
0
0
190
−9
0
185
13
13
179
3
3
9
2
181
69
69
190
59
59
185
59
59
179
70
70
10
2
181
109
109
190
98
98
185
102
102
179
94
94
1
4
245
−56
0
256
−49
0
227
−38
0
246
−57
0
2
4
245
−71
0
256
−82
0
227
−53
0
246
−72
0
3
4
245
−25
0
256
−6
0
227
−15
0
246
−22
0
4
4
245
56
56
256
45
45
227
71
71
246
53
53
5
4
245
−74
0
256
−67
0
227
−56
0
246
−64
0
6
4
245
4
4
256
−4
0
227
10
10
246
−9
0
7
4
245
−28
0
256
−14
0
227
−76
0
246
−19
0
8
4
245
40
40
256
29
29
227
−27
0
246
39
39
9
4
245
47
47
256
55
55
227
63
63
246
49
49
10
4
245
105
105
256
94
94
227
118
118
246
102
102
40
40
40
40
40
40
40
40
40
4
5
6
7
8
9
10
20
8
3
20
7
2
20
6
40
20
5
1
20
4
20
20
3
20
20
2
10
20
1
9
Lay out
Job
292
292
292
292
292
292
292
292
292
292
269
269
269
269
269
269
269
269
269
269
(Di)
174
104
174
104
51
0
−53
51
18
0
18
−137
0
−31
23
0
−71
23
0
−79
111
176
176
111
37
0
−57
37
23
23
0
0
−28
−127
0
−75
16
0
−75
16
(Ti)
(Li)
First come first serve
305
305
305
305
305
305
305
305
305
305
281
281
281
281
281
281
281
281
281
281
(Di)
38 140
74
140
74
38
0
0
−54
0
−17
31
25
0
0
142
74
38
−108
31
25
−84
−50
142
74
38
0
0
−21 −63
0
31
30
0
0
(Ti)
−101
31
30
−87
−43
(Li)
Shortest processing time
Table 3 Calculation of tardiness with double the process time and halves the travel time
302
302
302
302
302
302
302
302
302
302
286
286
286
286
286
286
286
286
286
286
(Di)
153
68
47
−32
−3
−116
21
−20
−74
−48
153
61
48
−37
−2
−102
12
−16
−80
−40
(Li)
153
68
47
0
0
0
21
0
0
0
153
61
48
0
0
0
12
0
0
0
(Ti)
Longest processing time
312
312
312
312
312
312
312
312
312
312
269
269
269
269
269
269
269
269
269
269
(Di)
116
116
74
0
−7 74
0
0
0
150
0
0
0
145
101
48
0
0
0
38
0
0
0
(Ti)
−91
−44
−62
150
−45
−7
−82
145
101
48
−69
−20
−102
38
−16
−77
−52
(Li)
Branch & bound
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Table 4 Calculation of tardiness with triple the process time and halves the travel time Job
Layout
First first come serve
Shortest processing time
Longest processing time
B&B
(Di)
(Li)
(Ti)
(Di) (Li)
(Ti)
(Di) (Li)
(Ti)
(Di) (Li)
1
22
391
−111
0
406
−67
0
417
−59
0
414
2
22
391
−115
0
406
−130
0
417
−120
0
3
22
391
−41
0
406
51
51
417
−18
0
4
22
391
16
16
406
44
44
417
12
12
5
22
391
−186
0
406
−154
0
417
−149
6
22
391
41
41
406
−29
0
417
7
22
391
−92
0
406
−91
0
8
22
391
78
78
406
63
63
9
22
391
159
159
406
103
10
22
391
254
254
406
2
44
412
−105
0
3
44
412
−42
4
44
412
22
5
44
412
6
44
7
(Ti)
−30
0
414
−145
0
414
5
5
414
77
77
0
414
−104
0
3
3
414
−8
0
417
−53
0
414
−118
0
417
78
78
414
55
55
103
417
80
80
414
124
124
206
206
417
221
221
414
143
143
429
−122
0
431
−112
0
434
−141
0
0
429
47
47
431
−20
0
434
1
1
22
429
42
42
431
20
20
434
70
70
−194
0
429
−160
0
431
−161
0
434
−113
0
412
33
33
429
−24
0
431
2
2
434
−10
0
44
412
−83
0
429
−85
0
431
−46
0
434
−102
0
8
44
412
81
81
429
64
64
431
77
77
434
59
59
9
44
412
148
148
429
104
104
431
89
89
434
120
120
10
44
412
254
254
429
204
204
431
221
221
434
144
144
Table 5 Mean Tardiness comparison Mean tardiness (t/p < 0.25) Layout
FCFS
SPT
LPT
B&B
1
22.8
20.3
21.5
22.1
2
21.4
19
21.3
20.8
3
22.3
20
21.3
21.1
4
25.2
22.3
26.2
24.3
Mean tardiness (t/p > 0.25) 1
36.2
30.4
27.9
35.1
2
36.3
31.5
27.4
33.2
3
36.9
31.6
27.8
33.4
4
37
30.8
28.9
34
Implementation of Branch and Bound Algorithm …
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7 Final Remarks Intelligent manufacturing framework is accepted as improved choice to confront the undertakings of worldwide challenge. Yet, for successful order, compelling booking is significant. Scheduling of a FMS is an exceptionally convoluted issue on account of extra necessities like material dealing with. In this paper, an exertion has been influenced to take care of the NP difficult issues by branch & bound. Achievements of branch & bound algorithm are evaluated by examining 120 benchmark issues involving of various occupation sets and designs.
References 1. Durga Rajesh, K.V., Chalapathi, P.V., Nageswara, R.M., Krishna, C.E., Anoop, K., Neeraj, Y.: An efficient sheep flock heredity algorithm for the cell formation problem. ARPN J. Eng. Appl. Sci. 12(21), 6074–6079 (2017) 2. Nageswara, R.M., Sai, B.C., Venkatesh, Y., Lokesh, K., Harish, V., Vara, K.S.: Simultaneous scheduling in FMS through priority rules. J. Adv. Res. Dyn. Control Syst. 9, 1995–2008 (2017) 3. Nageswararao, M., et al.: Scheduling of machines and automated guided vehicles in FMS using shuffled frog leap algorithm. Int. J. Mech. Eng. Technol. 8(5), 496–503 (2017) 4. Kumar, P.A., Nageswararao, M., Kumar, T.Y., Kumar, M.M., Bhanu, V.: Scheduling of machines and automated guided vehicles in FMS using scatter search algorithm. Int. J. Mech. Eng. Technol. 8(5), 471–481 (2017) 5. Pundit, R., Palekar, U.S.: Job shop scheduling with explicit material handling considerations. Working paper, Dept. of M and IE, Univ. of Illinois at Urbana-Champaign, Urbana, IL61801 (1990) 6. Biegel, J.E., Davern, J.J.: Genetic algorithms and job shop scheduling. Comput. Eng. 19, 81–91 (1990) 7. Ulusoy, G., Bilge, U.: Simultaneous Scheduling of machines and automated guided vehicles. Int. J. Prod. Res. 31(12), 2857–2873 (1993) 8. Dasore, A., Ramakrishna, K., Kiran, N.B.: Evaluation of heat and mass transfer coefficients at beetroot-air interface during convective drying. Interfacial Phenom. Heat Transfer 8, 303–319 (2020) 9. Metta, V.R., Ramakrishna, K., Dasore, A.: Thermal design of spiral plate heat exchanger through numerical modeling. Int. J. Mech. Engg. Tech. 9(7), 736–745 (2018) 10. Sachin, G., Narasimha, K.K., Ramakrishna, K., Dasore, A.: Thermodynamic analysis and effects of replacing HFC by fourth generation refrigerants in VCR systems. Int. J. Air-cond. Ref. 26(1), 1850013-1-1850013–12 (2018) 11. Sreekara Reddy, M.B.S., Ratnam, C., Rajyalakshmi, G., Manupati, V.K.: An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem. Measurement 114, 78–90 (2018)
A Contemporary Assessment on the Development of Automated Guided Vehicle to the Current Trends and Requirements Meenakshi Prabhakar, Joshuva Arockia Dhanraj, Valenteena Paulraj, Dhusyant Arumukam Karthi Kannappan, and Adithyaa Hariharan Abstract In this paper, we discuss different types and applications of an automated guided vehicle (AGV). This paper gives an outline of various technologies of AGVs and recent mechanical advancements in the AGVs. Automated guided vehicle (AGV) frameworks are acquiring expanding acknowledgment in current assembling offices fundamentally due to the adaptability they offer. With this great feasibility and modularity, the AGV also has different mapping techniques suitable for each industry to fulfill their requirements. Besides discussing the recent advancement in AGVs, we also discuss the future development of autonomous mobile robots (AMR) in various sectors and the need for those innovations in the coming generation. Keywords AGV · Applicational advancement · Driverless systems · Advanced navigation · Innovation in AGV
1 Introduction An AGV is a portable machine which can work inside and outside the industries according to the needs of the users. Automated guided vehicles (AGVs) are utilized as a material dealing system in adaptable assembling frameworks. AGVs were generally utilized at assembling frameworks, yet as of now, different uses of AGVs are broadly evolved in different sectors, for example, stockrooms, import– export station, and transportation frameworks. The examination flourishes versatile advanced mechanics, and automated guided vehicle has started in India in the last 3–4 years. Before that time, AGVs were known as driverless systems. As the years progressed, propels in hardware have prompted propels in guided vehicles. These days, the innovation of AGV is generally utilized in the mechanical climate to play out an assortment of assignment that includes mechanization. Innovative improvements have given AGVs greater adaptability and capacity in playing out their assignments. In this day and age, automation is being presented as a M. Prabhakar (B) · J. Arockia Dhanraj · V. Paulraj · D. A. K. Kannappan · A. Hariharan Department of Mechanical Engineering, Centre for Automation and Robotics (ANRO), Hindustan Institute of Technology and Science, Padur, Chennai 603103, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_84
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fundamental prerequisite in each industry, as human blunder negatively affects security, effectiveness, and quality. Stockroom coordination is such a space where mechanization is executed utilizing automated guided vehicle (AGVs) [1]. AGVs were utilized such as a material-handling gadget in adaptable assembling frameworks. Customarily, AGVs were generally utilized at assembling frameworks; however, as of now different utilizations of AGVs are widely evolved in different zones, for example, stockrooms, holder terminals, and transportation frameworks. Fazlollahtabar et al. [2] talk about writing identified with various procedures to advance AGV frameworks for the two huge issues of planning and steering at assembling, conveyance, parcel, and transportation frameworks. They classified the strategies into numerical strategies (precise and heuristics), simulated examines, metaheuristic methods, and AI-based methodologies. The primary test in these systems is keeping up adaptability, having an effective steering system, and making a responsive crash shirking system. The primary focal point of this system is to make an AGV with a straightforward yet compelling directing system, decreasing expense, and expanding adaptability. Using a well-written survey technique, dos Reis et al. [3] aim to tackle the fundamental problem in discovery of which sensors and detection methods are used in AGVs locating control problems, as shown in the last five years of dispersed testing and their revolutionary effects. Three sub-issues are dealt with such as the sensor/detection approach that is associated with the AGV implementation area; the sensor/detection process applied to the problem identified with the control device and/or direct AGV; the sensor/detect technique identified by the required AGV accuracy and level of affectability. Yesilyurt et al. [4] analyzing potential effects of the unsettling floor mapping count reveal that a company should use AGV batteries to reduce their heavy loads as energy accumulation in plants that take social opportunity. Unmanned regular data was used to automatically guided cars to carry out goal progress and courses. It is a confusing challenge to deal with different environment data, but this data is crucial for enhancing their confirmation. This article therefore introduces RAIF, taking into account the importance of sensor data in this directed vehicle environment. The response sensor data for the selection of the performance standard for the led vehicle is seen in this scheme. The sensor data for multi-event was gathered from similar IoT devices built on the Internet of Things. With the expertise gained to improve the accuracy of the guideline cars, the ideal response sensors and their data are established. This strategy is based on the course of AI operation to detect dispatch and disclose activities which are subject to the data mix association. This aids in identifying and steering sensor data from combined to achieve the goal accurately, says AlZubi et al. [5].
2 Types of AGVs Automated guided carts (AGC) are an old kind of AGV with notable features. The systems can vary from structures to fundamental as attractive strips to a complex
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sensor-based system that uses AI to investigate their surroundings. They can move a range of materials, from little parts to assembled parts, and are consistently used in loading and delivery applications. Hansen et al. [6] have given the scope of utilizations for autonomous guided carts (AGC) which are progressively developing. Particularly, in mechanical conditions, guaranteeing high well-being guidelines in blend with high accessibility and adaptability are significant prerequisites. Consequently, information about its situation in the climate turns out to be especially significant. For AGC with low vehicle stature restriction approaches dependent on the shape, perceptions are inescapable. In any case, over the long-run changing conditions, the strength of these strategies is restricted. This paper proposes a methodology for refreshing the hidden guide continuously during activity. This guide update takes into consideration drawnout powerful confinement. A special research scenario using a cellular transport car is tested for the solution suggested. Forklift AGVs are the other regularly used sort of AGV. They are machined to work out comparable limits a human-worked forklift performs (dispatching things). Computerized guided vehicles (AGVs) and mechanical forklifts are logically transforming into a column in collecting workplaces and movement center exercises where works are especially standardized, dull, and helpfully refined without the requirement for incredible human organization. Speed-corrosion batteries are implemented for automated-driven vehicles and forklifts. Replacing the balance, battery with a complex hybrid energy source is central since long operating times cannot stimulate batteries, energy unit evaluation can be reduced, and energy recovery can be reduced during easing. This paper presents Artal-Sevil et al. [7] as the power of the leading unit of an active hybrid power system based on Li+ batteries, ultracapacitors, and PEM power units. Suryowinoto et al. [8] hope to develop an automatic forklift that can keep the products in the stacking racks which are controlled through an Android-based app. The AGV with this forklift-based robot follows a line that is present beneath the stacking rack positions. Thus, this forklift follows the course with a collision-avoidance system. It is operated through a Bluetooth connection. According to this paper, the robot has been completing the given task in a minimum of 43 s and for a maximum of 45.3 s from the starting point to the destination. Towing AGVs pull, at any rate, a non-powered, load passing on vehicles behind them in a cart-like turn of events. Every so often called driverless trains, controlled towing vehicles that travel on wheels. Towing automatic guided vehicles are consistently utilized for moving considerable weights over long distances. They may have a couple of drop-offs and get stops along a portrayed path through a stockroom or preparing plant. Heavy burden carriers—the load transporters are sort of AGV that are used in applications like large assembly parts, castings, and highly machined transports. Some heavy burden carriers make the load capacities more efficient and may have a standard rotate. Autonomous mobile robots (AMRs) are precise and advanced than various kinds of AGVs. Because of more refined development, AMRs can continuously investigate a stockroom or different zones and plan the most gainful ways. Give a comprehensive composing review that highlights what AMR inventive advances mean for organizing and control decisions. They have expounded on an
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AMR organizing and control framework to coordinate chiefs in the powerful cycle, thusly supporting them to achieve ideal execution. Finally, an arrangement for future investigation inside this field has been introduced by Fragapane et al. [9].
3 Navigation Technologies of AGVs A laser-guided vehicle (LGV) works with a laser pointed structure. Maybe the most standard track systems for laser-guided vehicles are laser navigation. A laser-guided vehicle uses a navigational laser light. They are mostly used in forklift trucks that can move around warehouses [10]. LGVs are attached with laser navigation fixed at the top of the post which can follow the laser source. In addition, several manual tests are necessary in a manual manner, which makes automated laser-guided guidance vehicles difficult to follow. Reinforcement learning is used in this paper to explain the arrangement of autonomous laser-driven vehicles by Du et al. [11]. Magnetic tape navigation AGVs installed with magnetic sensors follow a unique track with an attractive tape. The direction is magnetic tape which is placed on the surface of the floor. It detects the enticing area of the tape and drives the AGV along the way. It is easier to use magnetic tape. For laying enticing paths, high-bond glue is used. There are standard 1 mm-thick and 5 cm-wide sizes, so the tape is not intrusive. Proposed AGVs are worked at a cost of not exactly 50% of the current AGVs using a standard magnet synchronous engine for the road. Its plan is native, with a significant proportion of its pieces. With a pulling limit of 1.300 kg, it has a top speed of 0.83 m/s. It is operated by the battery and supports battery operations for up to four hours for a full mission. Anand et al. [12] claim that it has been debated and found to be a useful replacement for towing trucks. In magnetic spot navigation with the little circular and hollow magnetic spots on the surface, AGVs can travel about. Magnetic points with dimensions equal to 20 mm × 10 mm regularly are barrel-shaped magnets (08 × 04 inch). Every 250–500 mm (about 15 feet) magnetic spots are inserted in a simulated form. The AGVs start from one point to the next using sensors and controls using the impacts of sensors, encoders, counters, gyro sensors, and various kinds of encoders in order to match themselves with guide points. Natural navigation AGV technologies are being created and publicized by advanced manufacturers. AGV manufacturers may buy or develop their own navigation technology from the navigation technology supplier. The SLAM navigation is mostly suitable for handheld independent robots. It essentially implies that an AGV with SLAM navigation can detect its current surroundings and detect where it is on account of the data got from the particular readings taken. The AGVs will design the path through different sensors such as vision cameras, LIDAR sensors, or lasers for safety purposes. In order to characterize and recalculate the genuine AGV location, all of the details would be accompanied by the inertial measurement unit (IMU). These are made by an exceptionally unpredictable calculation called SLAM. Along these lines, SLAM is to a greater extent an idea than genuine innovation. A new structure of VSLAM based on a stereo camera from Wang et al. [13] is included in
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this study. The solution suggested combines the immediate and circuitous technique to restrict a self-managed elevator in a non-organized stockroom on a continuous basis. The suggested advanced strategic approach makes use of photometric errors to plan for knowledge association and stance evaluation, distinguishes points from key frames, and compares them to get the position refreshed. The tactic achieves a greater pace of equal accuracy to a state-of-the-art strategy by consolidating the competence of the immediate plan and the high precision of the roundabout technique. In addition, the technique of extraction of a two-stage dynamic edge mostly reduces work time.
4 Advantages and Advancement in AGV Technology AGVs are largely utilized in industries these days. One of the essential focal points ranges from the adaptability of the innovation. At the point when fixed frameworks are excessively obtrusive or oppressive for the climate, AGVs offer an adaptable, unique way to deal with the necessary activity. AGV and AMR also improved the efficiency of intralogistics and material-handling orders in the current circumstances for a number of years. However, improved, sensitive, powerful correspondence, and control devaluations for these automatic vehicles remain a difficult task for system integrators and for realistic operations. Correspondence points of interest for AGVs and AMRs allow a number of far-off developments to meet the severe criteria for idleness and dependability of correspondence [14]. Tests are the constant way to organize big, large, grid-based AGV frames, for example to organize packages. Any of the methodologies described in the paper cannot be quickly controlled or blocked constantly. This paper presents a unique methodology utilizing a chart portrayal of the grid framework format with vertex loads that are refreshed over the long run. By methods for a broad discrete-occasion reproduction, the proposed way arranging approach fundamentally builds the throughput contrasted with existing methodologies. Moreover, it empowers recuperation from stop circumstances [15]. PID controller and the impact of every boundary on the system and the advancement between employed techniques on AGV robots are recently in trend. Different PID tune strategies are utilized dependent on system necessities [16]. Moreover, components including security, exactness, and expanded efficiency play an efficient factor in the role of AGV in the process of a completely automated material dealing with the arrangement.
5 Applications of AGV A portion of the numerous advantages of AGVs incorporate diminished labor cost, decreased harm to items, expanded stockroom safety, and the moderately lower cost than some other more convoluted automated frameworks. Also, most AGVs can be actualized without a new framework, giving less interruption to the current activity
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at the particular place. AGVs can be utilized in a wide way which includes moving a wide range of materials including pallets, carts, racks, and containers. They are utilized for raw material dealing with work-in-progress shipment, finished product handling, hazardous materials handling, and so on. These days, the two principal arms of the transportation industry and assembling industry should be redesigned and developed a lot. The framework of these ventures is outfitted with new and technological advances of automated guided vehicles [17]. QR codes are used to detect the platform they are in as fake achievements for AGVs. The distance and point between the cameras attached to an AGV are used to identify the effective route planning for the relevant field of AGV [18] with QR codes that are intentionally located around one office. AGVs are even used in hospitals which may be very useful for doctors and nurses in the pandemic period. An automated guided vehicle that can follow a line map has been proposed by Prabhakar et al. [19]. They have developed an AGV that can serve the affected patients inside the hospital with the required essentials which can reduce the spread of the COVID-19 virus or any other deadly virus. Autonomous mobile robots (AMR) in medical centers are getting progressively significant [20]. Scheduling of storage and retrieval of unit loads from exceptionally thin passageways utilizing automated guided vehicles (AGVs) in the warehousing act is one of the primary utilizations of AGVs [21–25].
6 Conclusion In this review article, the AGVs with their form and navigation scheme are also clearly reviewed. AGVs are widely seen day after day. Industries are purely dependent on automation for increasing their production as the demand goes up too. The scope of AGVs has raised exponentially in recent years. All online shopping sectors like Amazon, Flipkart, and much more are using AGVs in their stockrooms. The most advanced AGVs which use SLAM navigation and flexible for use at any workspace are the most advanced one. In the future vision, navigation using artificial intelligence (AI) will be very thoughtful which can make the industries more revolutionary with modernized technologies. Also developing intelligent path planning can avoid dynamic obstacles through which a self-organized navigation system can be achieved.
References 1. Khedkar, A., Kajani, K., Ipkal, P., Banthia, S., Jagdale, B.N., Kulkarni, M.: Automated guided vehicle system with collision avoidance and navigation in warehouse environments. Sensors 7(05) (2020) 2. Fazlollahtabar, H., Saidi-Mehrabad, M.: Methodologies to optimize automated guided vehicle scheduling and routing problems: a review study. J. Intell. Rob. Syst. 77(3), 525–545 (2015) 3. dos Reis, W.P.N., Junior, O.M.: Sensors applied to automated guided vehicle position control: a systematic literature review. Int. J. Adv. Manuf. Technol. 1–14 (2021)
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4. Yesilyurt, O., Bauer, D., Emde, A., Sauer, A.: Why should the automated guided vehicles’ batteries be used in the manufacturing plants as an energy storage? In: E3S Web of Conferences, vol. 231, p. 01004. EDP Sciences (2021) 5. AlZubi, A.A., Alarifi, A., Al-Maitah, M., Alheyasat, O.: Multi-sensor information fusion for Internet of Things assisted automated guided vehicles in smart city. Sustain. Cities Soc. 64, 102539 (2021) 6. Hansen, C., Fuerstenberg, K.: Enabling robust localization for automated guided carts in dynamic environments. In: Advanced Microsystems for Automotive Applications, pp. 47–57. Springer, Cham (2018) 7. Artal-Sevil, J.S., Bernal-Agustín, J.L., Dufo-López, R., Domínguez-Navarro, J.A.: Forklifts, automated guided vehicles and horizontal order pickers in industrial environments. Energy management of an active hybrid power system based on batteries, PEM fuel cells and ultracapacitors. Renew. Energ. Power Q. J. (RE&PQJ) (15) (2017) 8. Suryowinoto, A., Wijayanto, M.: The prototype of A forklift robot based on AGV system and android wireless controlled for stacked shelves. Int. J. Artif. Intell. Robot 2(1), 1 (2020) 9. Fragapane, G., de Koster, R., Sgarbossa, F., Strandhagen, J.O.: Planning and control of autonomous mobile robots for intralogistics: literature review and research agenda. Eur. J. Oper. Res. (2021) 10. Liu, Y.T., Sun, R.Z., Zhang, T.Y., Zhang, X.N., Li, L., Shi, G.Q.: Warehouse-oriented optimal path planning for autonomous mobile fire-fighting robots. Sec. Commun. Netw. (2020) 11. Du, E., Ren, Y.: Research on control algorithm for laser guided AGV based on proximal policy. In: 2020 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), pp. 1–7. IEEE (2020, April) 12. Rao, A., Vantagodi, N.V., Shanbhag, K.A., Mahesh, M.: Automated guided vehicles by permanent magnet synchronous motor: future of in-house logistics. Power Electron. Drives 4(39)), 151–159 (2019) 13. Wang, F., Lü, E., Wang, Y., Qiu, G., Lu, H.: Efficient stereo visual simultaneous localization and mapping for an autonomous unmanned forklift in an unstructured warehouse. Appl. Sci. 10(2), 698 (2020) 14. Reveliotis, S.A.: Conflict resolution in AGV systems. IIE Trans. 32(7), 647–659 (2000) 15. Fransen, K.J.C., van Eekelen, J.A.W.M., Pogromsky, A., Boon, M.A.A., Adan, I.J.B.F.: A dynamic path planning approach for dense, large, grid-based automated guided vehicle systems. Comput. Oper. Res. 123, 105046 (2020) 16. Moshayedi, A.J., Gheibollahi, M., Liao, L.: The quadrotor dynamic modeling and study of meta-heuristic algorithms performance on optimization of PID controller index to control angles and tracking the route. Int. J. Robot. Autom. (IJRA) 9(4), 256–270 (2020) 17. Rashidi, H., Matinfar, F., Parand, F.: Automated guided vehicles—a review on applications, problem modeling and solutions. Int. J. Transp. Eng. 8(3), 261–278 (2021) 18. Ang, J.L.F., Lee, W.K., Ooi, B.Y., Ooi, T.W.M.: Location sensing using QR codes via 2D camera for automated guided vehicles. In: 2020 IEEE Sensors Applications Symposium (SAS), pp. 1–6. IEEE (2020, March) 19. Prabhakar, M., Paulraj, V., Dhanraj, J.A., Nagarajan, S., Kannappan, D.A.K., Hariharan, A.: Design and simulation of an automated guided vehicle through webots for isolated COVID19 patients in hospitals. In: 2020 IEEE 4th Conference on Information and Communication Technology (CICT), pp. 1–5. IEEE (2020, December) 20. Benzidia, S., Ageron, B., Bentahar, O., Husson, J.: Investigating automation and AGV in healthcare logistics: a case study based approach. Int. J. Log. Res. Appl. 22(3), 273–293 (2019) 21. Pedan, M., Gregor, M., Plinta, D.: Implementation of automated guided vehicle system in healthcare facility. Procedia Eng. 192, 665–670 (2017) 22. Antony, M., Parameswaran, M., Mathew, N., Sajithkumar, V.S., Joseph, J., Jacob, C.M.: Design and implementation of automatic guided vehicle for hospital application. In: 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 1031–1036. IEEE (2020, June)
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23. Gokula Vishnu Kirti, D., Balaji, C.R., Joshuva, A.: A smart delimit scrutinization droid for defence border surveillance through LIDAR. In: Advances in Automation, Signal Processing, Instrumentation, and Control: Select Proceedings of i-CASIC 2020, pp. 795–800. Springer, Singapore (2021) 24. González, D., Romero, L., Espinosa, M.D.M., Domínguez, M.: An optimization design proposal of automated guided vehicles for mixed type transportation in hospital environments. PloS One 12(5), e0177944 (2017) 25. Kriegel, J., Rissbacher, C., Reckwitz, L., Tuttle-Weidinger, L.: The requirements and applications of autonomous mobile robotics (AMR) in hospitals from the perspective of nursing officers. Int. J. Healthcare Manag. 1–7 (2021)
Experimental Investigation of Performance and Emission Characteristics of Direct-Injection Compression-Ignition Engine Fuelled with Pond Water Algae Biodiesel K. Murali Krishna Prasad, P. Sravani, Upendra Rajak, Sk. Mohammad Shareef, Prem Kumar Chaurasiya, Nitin Malviya, and Pawan Yadav Abstract Algae are the fast-growing florae around the globe. The viability of biodiesel from pond water algae (PWA) as a 3rd generation biodiesel feedstock is examined in current investigation. First, oil was taken out from the algal biomass and then it is subjected to two stage transesterification technique. Ethanol is mixed up with the attained algal biooil in order to reduce its viscosity. The processed algal oil is blended with diesel in various proportions. Later, compression-ignition engine’s performance and emission characteristics are assessed at different engine loads using these prepared blends. Results have depicted that performance indices of engine viz., brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) are enhanced and emission parameters such as CO and HC emissions are reduced with the increase of algal biodiesel proportion in diesel fossil fuel. Keywords Diesel engine · PWA oil · Transesterification · Brake thermal efficiency · CO emission
K. Murali Krishna Prasad · P. Sravani Department of Mechanical Engineering, Narasaraopeta Engineering College, Narasaraopet, Guntur, Andhra Pradesh 522601, India U. Rajak (B) Department of Mechanical Engineering, RGM College of Engineering and Technology, Nandyal, Andhra Pradesh 518501, India Sk. M. Shareef Department of Mechanical Engineering, CVR College of Engineering, Ibrahimpatnam, Telangana 501510, India P. K. Chaurasiya · N. Malviya · P. Yadav Department of Mechanical Engineering, Bansal Institute of Science and Technology, Bhopal, MP 462003, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_85
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1 Introduction There exists an urgent need to reduce green-house gases and to enhance the span of fossil fuels. One method to do so is to improve the usage of alternate fuels. In transport sector, petroleum-based fuels are being replaced with biodiesels. During initial days most of the biodiesels are derived from vegetable oils [1]. The use of these oils as biodiesels not lasted for longer span, as it increased global food insecurity issues and less availability of agricultural lands. Later biodiesel from non-edible sources came into existence [2]. The main feedstocks for these biodiesels are non-edible oil seeds of jatropha, neem, karanja, mahua, etc., [3]. However, availability of land and large-scale production of these feedstocks are major problems of these biodiesels [4]. Hence researchers are started an extensive investigation over the feasibility of algae oil as biodiesel. The main reason for this can be attributed to the fact that algae are the fastgrowing flora around the world and are able to grow in anyplace. Also, less energy and processing treatments are required for algal oil. Because of these potentials, algae oil can replace 1st and 2nd generation biodiesels. Majority of studies conducted on algae as 3rd generation biodiesel employed spirulina, chlorella and PWA feedstocks [5]. In industries, algae are cultured in photo-bioreactors and closed systems. Reduction in NOx emissions is identified with enhancing ethanol proportion in the fuel [6]. Percentage release of exhaust gases from engine tail pipe are diminished with SMAB blends [7]. Improved engine performance and reduced percentage of emission release are noticed with ternary blend obtained from ethanol, biodiesel and fossil diesel fuel [8]. PEC of 2-stroke diesel engine is examined using exhaust gas recirculation technique at varying fuel injection timing [9]. From the above literature review, it is identified that investigation on fuel blended with open PWA oil, ethanol, and diesel fuel in CI engine are very scarce. Hence, in the present investigation, performance of the engine and emission characteristics are examined experimentally. The blends of PWA oil, ethanol, and diesel fuel are employed in the present work. Engine performance is assessed through brake thermal efficiency and brake specific fuel consumption, while engine emissions are studied by CO and HC emissions.
2 Material and Method 2.1 Material Blends of fossil fuel diesel, PWA oil and ethanol are employed in CI engine as fuels. Initially, PWA oil is extracted from the algae collected from local pond present in Krishna district of Andhra Pradesh state, India. The PWA oil is kept in conical flasks comprising 50 mL sterilized BG11 media [10]. The oil is properly agitated at 120 rpm for 30 min and then placed in broad sun light. Later, PWA oil is mixed with 20%
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Table 1 Properties of tested fuels Property/fuel
PWAB0 (Diesel)
PWAB10
PWAB20
PWAB30
Density (kg/m3 )
838
827
825
832
Kinematic viscosity at 40 °C (cSt)
3.4
3.05
2.4
2.6
Calorific value (MJ/kg)
42.5
39.3
38.9
37.6
Cetane number
47
40.02
40.7
39.8
volume portion of ethanol in order to lessen its viscosity. Finally, processed PWA oil is mixed with diesel in 3 volume quantities (10, 20 and 30%) to get three PWAbased biodiesel blends which are indicated by PWAB10, PWAB20 and PWAB30. The characteristics of diesel and PWAB blends are determined and presented in Table 1.
2.2 Engine Setup and Facilities The test engine rig used in the present study is a 4-stroke single cylinder water cooled compression-ignition engine. This engine is coupled to eddy type dynamometer as depicted in Fig. 1. The other technical details of engine are indicated in Table 2. AVL emission analyser is utilized to examine the engine tail pipe emissions such as CO and HC emissions. Prior to each test, engine is kept steadied for 30 minutes. To ensure the high confidence and reliability, each experiment is conducted thrice.
CO
Fig. 1 Engine setup
HC
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Table 2 Technical specifications of engine
Table 3 Error percentage
Specification
Details
Make
Kirloskar
Cylinder number
1
D*L
87.5 * 110 mm
Compression ratio
17.5
Rated output
4.5 kW at 1500 rpm
Rated speed
1500 rpm
Cooling system
Water-cooled
Fuel injection timing, CA bTDC
28°
S No
Parameter
Error (%)
1
BTE
± 0.7
2
BSFC
± 0.8
3
BP
± 0.5
4
CO emissions
± 0.7
5
HC emissions
± 0.5
2.3 Error Analysis Always some uncertainty will be existing in any parameter obtained in experiments. Error arises due to calibration, instrument selection and human error [ref]. Error associated in the present experimental analysis is calculated by using error density function (EDF) Eq. (1) [11]. Te =
E e2 + i e2
(1)
where, T e indicates total combined error, E e is repeatability EDF and ie is systematic EDF. Percentage of uncertainty is given in Table 3.
3 Results and Discussion 3.1 Performance Investigation 3.1.1
Brake Thermal Efficiency
BTE values of PWAB0, PWAB10, PWAB20 and PWAB30 blends with variation of brake power on engine are depicted through Fig. 2. From Fig. 2, it is noticed that
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Fig. 2 BTE at various BP
32 30
BTE (%)
28 26 PWAB0 PWAB10 PWAB20 PWAB30
24 22 20 1.5
2.0
2.5
3.0
3.5
4.0
4.5
BP, (kW)
BTE is obtained higher for engine fuelled with PWAB0 i.e., diesel. The main reason for this can be attributed to lesser calorific value of biodiesel blends compared to base fuel diesel. However, BTE values of PWAB blends is in reasonable range to that of diesel fuel. Also, from Fig. 2, it is seen that BTE is higher at high BP for all tested fuel blends. The reason behind this can be accredited to improved combustion and enhanced cylinder temperature.
3.1.2
Brake Specific Fuel Consumption
BSFC values of PWAB0, PWAB10, PWAB20 and PWAB30 blends with variation of brake power on engine are depicted through Fig. 3. From Fig. 3, it is noticed that Fig. 3 BSFC at various BP
0.44 PWAB0 PWAB10 PWAB20 PWAB30
BSFC, (kg/kWh)
0.40
0.36
0.32
0.28 1.5
2.0
2.5
3.0 BP, (kW)
3.5
4.0
4.5
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Fig. 4 CO emissions at varying BP
0.08
CO emission, (%)
0.07 0.06 0.05 0.04
PWAB0 PWAB10 PWAB20 PWAB30
0.03 0.02
1.5
2.0
2.5
3.0
3.5
4.0
4.5
BP, (kW)
BSFC is lower for engine fuelled with PWAB20 i.e., diesel and 20% PWA oil blend. Also, from Fig. 3, it is seen that BSFC is lower at high BP for all tested fuel blends. The reason behind this is at high BP, the fuel quantity utilized in the cylinder for combustion reduce which is in turn due to availability of more oxygen.
3.2 Emission Analysis 3.2.1
CO Emissions
Figure 4 depicts the change of CO exhaust for PWAB0, PWAB10, PWAB20 and PWAB30 at different BP. From Fig. 4, it is observed that CO emission is more from engine tail pipe at lower BP than that of at higher BP. This is due to the fact that lean mixture and low temperature are formed in the cylinder. While at higher BP, molecular O2 concentration enhances. Also, from Fig. 4 it is seen that PWAB20 has lower CO emissions at all test conditions.
3.2.2
HC Emissions
Figure 5 indicates the change of HC exhaust for PWAB0, PWAB10, PWAB20 and PWAB30 at different BP. From Fig. 5 trend, it is obvious that, HC gas release reduces linearly for PWAB blends. PWAB20 indicated a higher percentage drop of HC exhaust at maximum BP. The reason for this is PWAB contains more oxygen content in its fuel structure which leads to enhanced oxidation and complete fuel combustion.
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HC emissions (ppm)
50
40
PWAB0 PWAB10 PWAB20 PWAB30
30
20
10
1.5
2.0
2.5
3.0
3.5
4.0
4.5
BP, (kW)
4 Conclusions The subsequent conclusions are drawn from the current investigation. 1. 2. 3. 4. 5.
PWAB has a huge potential as an alternate fuel to fossil fuel diesel in directinjection compression-ignition engines. BTE of biodiesel blends shown slight lesser than that of conventional diesel fuel. At higher BP BSFC is lower than that of BSFC at lower BP. CO emissions is high at lower BP and it diminishes at higher BP. PWAB20 shown a higher percentage diminution of HC emissions of nearly 56% at higher BP.
References 1. Senthil Kumar, T., Senthil Kumar, P., Annamalai, K.: Experimental study on the performance and emission measures of direct injection diesel engine with Kapok methyl ester and its blends. Renew. Energy 74, 903–909 (2015) 2. Muthiya, S.J., Pachamuthu, S.: Electrochemical NOx reduction and oxidation of HC and PM emissions from biodiesel fuelled diesel engines using electrochemically activated cell. Int. J. Green Energy 15(5), 314–324 (2018) 3. Yaakob, Z., Mohammad, M., Alherbawi, M., Alam, Z., Sopian, K.: Overview of the production of biodiesel from Waste cooking oil. Renew. Sustain. Energy Rev. 18, 184–193 (2013) 4. Mofijur, M., Hazrat, M.A., Rasul, M.G., Mahmudul, H.M.: Comparative evaluation of edible and non-edible oil methyl ester performance in a vehicular engine. Energy Procedia 75, 37–43 (2015) 5. Metta, V.R., Ramakrishna, K., Abhishek, D.: Thermal design of spiral plate heat exchanger through numerical modelling. Int. J. Mech. Eng. Technol. 9(7), 736–745 (2018)
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6. Narendra, K., Upendra, R., Prem, K.C., Thokchom, S.S., Anil, K.B., Tikendra, N.V.: Investigations of spirulina, waste cooking and animal fats blended biodiesel fuel on auto-ignition diesel engine performance emission characteristics. Fuel 276, 118123 (2020) 7. Vedharaj, S., Vallinayagam, R., Yang, W.M., Chou, S.K., Chua, K.J.E., Lee, P.S.: Experimental investigation of kapok (Ceiba pentandra) oil biodiesel as an alternate fuel for diesel engine. Energy Convers. Manage. 75, 773–779 (2013) 8. Talebian-Kiakalaieh, A., Amin, N.A.S., Mazaheri, H.: A review on novel processes of biodiesel production from waste cooking oil. Appl. Energy 104, 683–710 (2013) 9. Upendra, R., Prerana, N., Prem, K.C., Tikendra, N.V., Devendra, K.P., Gaurav, D.: Experimental and predicative analysis of engine characteristics of various biodiesels. Fuel 285, 119097 (2021) 10. Singh, T.S., Upendra, R., Abhishek, D., Muthukumar, M., Verma, T.N.: Performance and ecological parameters of a diesel engine fueled with diesel and plastic pyrolyzed oil (PPO) at variable working parameters. Environ. Technol. Innov. 22, 101491 (2021) 11. Abhishek, D., Ramakrishna, K., Kiran Naik, B.: Evaluation of heat and mass transfer coefficients at beetroot-air interface during convective drying. Interfacial Phenom. Heat Transfer 8(4), 303–319 (2020)
ACO-Based Resource Allocation Hybrid Algorithm for Cloud Environment Bhavana Gupta and Nishchol Mishra
Abstract Distributed computing is the result of the advancement of data enumeration. The more the exploration and applications on cloud computing more will be the innovation of processing gets more extensive. The cloud processing has a huge gathering of user. A huge number of errands are managed it. The principle issue in distributed computing emerges while allotting equipment assets to the assignments and planning the undertakings to the resource pool. This paper delivers to the current circumstance of asset portion strategy and occupation planning calculations under cloud condition [1, 2]. Matrix figuring, so, as to help complex issues uses the conveyed heterogeneous assets. Lattice can be categorized into two sorts: figuring matrix and information network. Employment planning for registering lattice is a significant issue. For effective use of networks, we need a productive employment planning calculation for the task of occupations to assets in matrices. Characteristic environment put center around the ants that they have a gigantic capacity to shape a group and to locate an ideal pathway to food resources. The conduct of ants is reproduced by an Algorithm. In this paper, the prime commitments of our undertaking are load balancing for the whole framework while attempting to limit the make span of a given arrangement of occupations. ACO can beat other employment booking calculations as per the test results when analyzed. Keywords ACO · ACS · Cloud computing · Dynamic scheduling · SPI
1 Introduction Late consistent issues are flighty and require also figuring power and additional room. Past advances, for instance, spread or equivalent enlisting are not suitable for these continuous legitimate issues close by a ton of data. Taking care of and dealing with B. Gupta (B) · N. Mishra School of Information Technology, Bhopal, India N. Mishra e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_86
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tremendous data opus may result in great deal of time. Grid preparing is another framework for handling complex issues. In cross sections, we need to think about conditions, for instance, network status and resource status. If the association or resources are unreliable, positions would crash and burn or the outright computation time would be colossal. So need a compelling work booking count for these very issues in the system condition. The rule purpose behind work arranging is load balancing (i.e., to modify the entire system load) while completing all the current errand at the soonest open door as demonstrated ordinarily status. Since the earth may change a significant part of the time, show all work booking counts as “first come first serve” (FCFS), “shortest job first” (SJF), etc., may not be reasonable for the dynamic condition in networks. In a structure area, customers can have hundreds even a considerable number of PCs to utilize. It is not useful for anyone to truly give out situations to enroll resources in lattices. Thus, grid work arranging is a huge issue and part of casing work enlisting. Since it is noteworthy, numerous occupation arranging figurings for grids have been proposed. In the event that it is not all that much difficulty imply a survey, which furthermore speaks to some open issues. ACO [3] is a heuristic estimation having effective close by journey for combinatorial issues. ACO mirrors the direct of veritable bug states. Various kinds of assessment use ACO to deal with NP-troublesome issues, for instance, portable salesman issues, graph concealing issues, vehicle coordinating issues, and so on. The NP-complete leads of this issue can be hence affirmed.
1.1 About Cloud Computing The significance of disseminated registering relies upon five credits: multiinhabitance, (basic resources), huge flexibility, adaptability, pay all the more just as expenses emerge, and self-provisioning of benefits [4]. • Multi-inhabitance (shared resources): Unlike past preparing models, which expected gave resources (i.e., figuring workplaces focused on a singular customer or owner), conveyed processing depends on a strategy where resources are shared (i.e., different customers use a similar resource) at the association level, have level, and application level. • Massive versatility: Although affiliations may have hundreds or thousands of structures, dispersed comp chime enables to scale to countless systems, similarly as the capacity to extraordinarily scope move speed and extra room. • Elasticity: Users can rapidly increment and decrease their preparing assets fluctuating, similarly as conveyance resources for various uses when they are not, now needed. • Pay as you go: Users pay for simply the advantages they truly use and for simply the time they require them.
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Fig. 1 Cloud computing pattern
• Self-provisioning of advantages: Users self-plan resources, for instance, extra systems (getting ready capacity, favorable to gramming, storing) and association resources. Distributed computing has an example like Fig. 1.
1.2 The SPI Framework for Cloud Computing A usually settled upon structure for portraying distributed computing administrations passes by the abbreviation “SPI.” This abbreviation represents the three significant administrations given by Service-oriented architecture in this context (Figs. 2 and 3). Notwithstanding, the way that cloud has been supportive of aggressively seen as the phase that can maintain flexible applications, it faces certain requirements identifying with focus issues, for instance, ownership, scale, and area. For instance, a cloud can simply offer a foreordained number of encouraging capacities (virtual machines and enlisting laborers) to application organizations at a given event of time, in this manner, scaling application’s capacity past a particular degree gets tangled. As needs be in those circumstances, where the amount of solicitations overshoots the cloud’s capacity, an application encouraged in a cloud can choose all around QoS passed on to its customers. One response for this issue is to between network different
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Fig. 2 SPI service model
fogs as an element of an association and make front line dynamic provisioning strategies that can get benefits by the building. Such union of topographically flowed fogs can be molded reliant on past plans among them, to profitably adjust to variety in organizations demands. This approach licenses provisioning of uses over various fogs that are people from a/the association. This further aides in viably fulfilling customer SLAs through direct development of utilization organization case to the cloud in the partnership, which is closer to the origination of requesting.
1.3 Motivation of the Present Work One consequences of cloud stages are the ability to logically change (scale-up or cut back) the amount of advantages provisioned to an application in order to attend assortments searched after that are either un-astonishing, and happen due to get to patterns saw during the day and during the night, or unexpected, and happens due to an unpretentious augmentation in the prominence of the application organization. This limit of fogs is especially significant for adaptable (thusly scaling of) uses, for instance, Web encouraging, content movement, and casual associations that are frail to such direct. These applications normally show transient direct (use plan) and have contrast entQoS necessities depending upon a period criticality and customers’ participation plans (on the Web/separated). Therefore, the headway of dynamic provisioning techniques to ensure that these applications achieve QoS under transient conditions is required.
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Fig. 3 Architecture for the relevant technology
1.3.1
ANT’S Behavior
Extraordinary bugs like ants, bumble bees, etc., that live in a settlement is prepared for handling their step by step complex life issues. These practices which are found in a one of a kind social event of bugs is called atom swarm knowledge. Huge number information methodology base on the social affair’s direct and study the reactions of the get-together administrators with each other and with the earth. The huge number information structure fuses a mix of fundamental close by rehearses for making general lead and there is no central control in it [5–9]. Various kinds of explicit ants can store pheromone on the ground and to follow, in probability, pheromone as of late kept by various ants. By storing this compound substance, the ants leave a follow on their ways. By perceiving this follow, various ants of the region can follow the path found by various ants to find food. For finding the briefest way to deal with
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Fig.4 The ant’s behavior
get food, these ants can for the most part follow the pheromone trails as showed up (Fig. 4).
1.4 Historical Development of Ant Colony Optimization Subterranean insect calculations are a populace-based methodology that has been effectively applied to a few NP-hard combinatorial enhancement issues. As the name recommends, subterranean insect calculations have been reused by the conduct of genuine subterranean insect provinces. One of the primary thoughts of subterranean insect calculations is the roundabout correspondence of a settlement of specialists, called fake ants, in view of pheromone trails. The counterfeit pheromone trails are a sort of dispersed numeric in-development which is changed by the ants to mirror their experience while tackling a specific issue. The first ACO calculation called the subterranean insect framework (AS) has been applied to the mobile sales rep issue (TSP) by Dorigo. Despite cheerful outcomes, the calculation results were not similar to the next cutting-edge calculations which were at that point applied to take care of this issue. Notwithstanding this reality, this calculation constructed significant standards in making further developed calculations. Right now, numerous calculations have been recommended dependent on the improvement of the ACS calculation and utilized for tackling different issues.
1.4.1
Ant Colony Optimization
Subterranean insect state streamlining is created by “full scale DORIGO” in 2006. It depends on subterranean insect’s investigating conduct of pheromone for discovering food from their home to source [10, 11]. In Fig. 5, the insect’s conduct: (a) the ants arrive at the purpose of settling on a choice. (b) The ants pick one of the two ways arbitrarily. (c) If the ants move at a similar speed, the ants which have picked the shorter way to arrive at sooner to the point of settling on the following choice. (d) The measure of pheromone in the shorter branch increments at a higher rate.
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Fig. 5 Ant moves from nest to food in different iteration
1.4.2
Ant Colony System (ACS)
The reformed form of the AS algorithm and functions is as follows. Each ant generates a complete solution by choosing the nodes according to a probabilistic state transition rule as in Fig. 6. The state transition rule is given in Eq. (1.1) and (1.2) are called a pseudorandom proportional rule:
Fig. 6 Ants with probabilistic state transition (1.1)
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S=
β arg Max j∈Nik τi j ηi j i f q ≤ q0
S β τi j ηi j Pikj = β , l∈Nik τi j ηi j
f q > q0
where q is a random number uniformly distributed in [0 ,…, 1], q0 is a parameter between 0 and 1, S is a random variable selected according to the probability distribution given in (1.2), τ ij is the amount of pheromone in edge i j, ηi j = 1/δ i j where δ i j is the cost of edge i j, β is a parameter that determines the relative importance of η versus τ.
2 Literature Survey Here [12], it utilizes various sorts of subterranean insect to locate different ideal ways for network steering. The thought can be applied to discover different benefit capable assets to adjust asset utilization in work planning. The way in to the thought is each extraordinary sort of subterranean insect can just detect their own sort of pheromone so it can discover various ways including the most limited way by various types of an insect. There are still a few issues that if a wide range of insect locate a similar way, it will be equivalent to utilizing one sort of subterranean insect. Instructions to think about the presentation for every sort of insect make another issue. Besides, one arrangement from this calculation may work effectively in a situation, however, it might work wastefully in another. It applies the cross breed ACO calculation with various perceivability to work shop planning issues. The cross breed ACO calculation comprises of two thoughts. One thought is the fundamental ACO calculation, and the other thought utilizes the posthandling calculation in the aspect of the nearby inquiry in the ACO calculation. At that point, the post-preparing calculation utilizes the trade procedure on the squares. In the event that the trade refines the makespan, the new way is acknowledged; something else, the trade is invalid and the traded block returns to the past status. The ACO calculation has likewise been applied to hard combinatorial advancement issues, for example, the mobile sales rep issue (TSP) [13], stream shop issue [14], venture introduction planning [15], diagram shading issue, vehicle directing issue, and attendant booking. The quantity of utilizations of ACO algorithms is enormous, however, it is the most youthful meta-heuristic cycle. In principle, ACO can be applied to any combinatorial enhancement issue for which some iterative arrangement development mechanism can be imagined. Most applications of ACO manage NP-hard combinatorial streamlining issues. ACO algorithms have likewise been stretched out to deal with issues with various targets, stochastic information, and powerfully changing issue in-arrangement. There are extensions of the ACO meta-heuristic for bargaining with
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issues with constant decision factors also. A portion of the applications of ACO is given underneath [16, 17].
2.1 Job Scheduling in Grids Employment planning is concentrated inside the computer working frameworks [18]. A large portion of them can be applied to the matrix environment with reasonable adjustments. In the accompanying, I present a few techniques for matrices. The fastest processor to largest task first (FPLTF) [19] calculation schedules errands to assets as per the remaining burden of assignments in the framework. The calculation needs two fundamental boundaries, for example, the CPU speed of assets and remaining burden of assignments at that point relegates the biggest assignment to the quickest accessible asset. In the event that there are numerous assignments with a hefty outstanding task at hand, its exhibition might be exceptionally terrible. Dynamic FPLTF (DPLTF) [20] depends on the detail icFPLTF, it gives the most elevated need to the biggest errand. PDF needs expectation data on processor speeds and undertaking remaining burden. The work queue with replication (WQR) depends on the work line (WQ) calculation [20]. The WQR sets a quicker processor with a bigger number of errands than a more slow processor and it applies FCFS and arbitrary exchange to allot assets. WQR reproduces undertakings so as to move to accessible assets. The measure of replications is characterized by the client. At the point when one of the replication undertakings is done, the scheduler will drop the rest of the replication errands. The WQR’s inadequacy is that it requires some investment to execute and move replication undertakings to assets for execution. Min-min [21] sets the errands which can be finished most punctual with the most noteworthy need. The fundamental thought of min-min is that it doles out undertakings to assets that can execute errands the quickest. Max-min [21] sets the errands which have the most extreme earliest culmination time with the most elevated need. The primary thought of max-min is that it over-laps the assignments with long running time with the errands with a short running time. For example, if there is just one long undertaking, min-min will execute short assignments in resemble and afterward execute a long errand. max-min will execute short assignments and long errands in equal. The round robin (RR) calculation centers around the reasonableness issue. RR utilizes the ring as its line to store occupations. Be that as it may, if the heap is weighty, RR will set aside a long effort to complete all positions. Need planning algorithm gives each activity a need worth and utilizations it to dispatch occupations. The need estimation of each activity relies upon the activity status, for example, the prerequisite of memory sizes, CPU time, etc. The fundamental issue of this calculation is that it might cause inconclusive impeding or starvation if the prerequisite of a vocation is failing to be fulfilled. The first come first serve (FCFS) calculation is a straightforward activity planning calculation. A vocation that makes the principal necessity will be
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executed first. The primary issue of FCFS is its escort impact [18]. In the event that all positions are trusting that a challenging task will complete, the caravan impact happens. The guard impact may prompt a more drawn out normal holding up time and lower asset use. Distributed computing disseminated bunch utilizes a master/slave structure. There is a master hub liable for controlling and managing all the slave hubs. Since the particular state of the asset is obscure under cloud situation, and the organizations do not have a fixed geography, the structure and the asset designation of the entire cloud condition are capricious. For this situation, the area and nature of processing assets for information hubs is obscure. Ant (insect) colony optimization (ACO) is a refreshed bionic streamlining calculation that is in the reproduction of insect rummaging conduct. It is started by M. Dorigo et al. who were propelled by the exploration consequence of the gathering conduct of genuine ants. ACO calculation shows characteristics of rate, dissemination, and worldwide enhancement when taking care of complex optimization issues. Furthermore, the speed of finding the ideal arrangement is because of the regenerative input component of pheromone. While its component of circulated calculation maintains a strategic distance from untimely convergence of the calculation. In the interim, the insect framework, with the component of voracious heuristic pursuit, it can locate a satisfactory arrangement right off the bat in the inquiry cycle. The pseudocode of the model arrangement of the subterranean insect state calculation can be appeared in Fig. 7 and ex-squeezed as follows: Fig. 7 ACO algorithmic diagram
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While (termination condition not met) do Place each ant on initial node For i = 1to n do (# ants) For k = 1 to m do (# locations) Apply State Transition Rule (pseudorandom proportional) Apply Local Update pheromone End for (build one route) End for (run one set) Apply Global Update End while End Ant colony system
2.2 Job Scheduling Algorithm in Cloud Environment A. Background Employment booking of distributed computing alludes to the way toward altering assets between diverse asset clients as per certain guidelines of asset use under a given cloud condition. Asset management and occupation planning are the key innovations of distributed computing. At present, there is definitely not a uniform norm for work planning for the cloud. Most calculations center around work dispatcher, which is practically liable for all the undertaking assignments, reactions, and retransmissions. Over-dependence on the scheduler may prompt some virtual machines over-burden while others are inert after the dispatcher apportioning assignments as indicated by a heap of virtual machines. At the point when this happens, the main arrangement is to appoint assignments for the following time frame as per what the criticism planning gadget gets. The cycle in various virtual machines is autonomous. A virtual machine does not approach other. B. Dynamic Scheduling Algorithm Based on Threshold To get constant criticism on the condition continuous criticism on the heap of the errand on the virtual machine, and afterward make a constant change on work designation upon the reality of virtual machines. The other one is to utilize the dynamic planning among virtual machines themselves to get the ongoing condition of a heap of
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virtual mama chines. In the event that over-burden or inaction happened, undertakings could be rearranged and redistributed among virtual machines. Dynamic work modification is led to abbreviate the absolute cost time, consequently enhancing proficiency [22, 23]. Notwithstanding, task allocation between virtual machines alludes to synchronization issues, which are likewise the most serious issue of the dynamic scheduling calculation dependent on an edge. Since each virtual machine is free of one another, as such, they are non-meddling. They can perform errands in equal mode. In the event that virtual machines are synchronized, they unavoidably carry impacts to their presentation. Hence, the synchronization activity ought to be kept to a base range. So as to lessen the effect of this synchronization, two estimations are taken. Undertaking task includes setting task grouping as indicated by PRI. In the event that the two numbers were bigger than the threshold esteem simultaneously, these virtual machines would be synchronized. Furthermore, their undertakings will be adjusted and will continue working. Undertaking balance implies that if there is at any rate one inactive virtual mama chine and in any event one over-burden virtual machine, other virtual machines will execute assignments freely. C. Improved Ant Colony Algorithm The embodiment of employment booking is to choose a method of a powerful blend of resources with moderately great execution among all the asset assignment methods. From the viewpoint of critical thinking, a streamlined insect province algorithm is entirely appropriate for asset allocation in a cloud domain. As the randomness of the insect province calculation is huge, it is effectively caught in neighborhood ideal arrangement and moderate assembly. Subsequently, research laborers present GA, which has an ability of quick and arbitrary worldwide inquiry, to every cycle of the insect state calculation [24, 25]. This can significantly quicken the speed of assembly and guarantee the exactness of the first calculation. For every asset requester, the distributed computing administration bunch should give a genuinely decent blend of undertakings and assets. An improved subterranean insect settlement calculation, simultaneously, the variables influencing the asset state can be depicted by pheromone, and the booking cycle can get unsurprising outcomes just and rapidly. In cloud condition, take the ant colony system (ACS) calculation model dependent on the ACO calculation, for instance, the progression of the activity planning measure dependent on ACO calculation can be portrayed as Wang’s paper depicts the simple simulation of the im-demonstrated ACO calculation dependent on an extended distributed computing reproduction stage [26]. It was contrasted and the round robin (RR) calculation and the first ACO calculation. For the most part, an improved ACO calculation takes less time and has a higher proficiency than the other two calculations. M. Dorigo and L. M. Gambardella gave a helpful learning way to deal with the traveling sales rep issue by applying an insect state streamlining procedure. The regular allegory on which insect calculations is based is that of insect settlements. Genuine ants are equipped for finding the briefest way from a food source to their home
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without utilizing the obvious prompts by abusing pheromone in-arrangement. While strolling, ants store pheromone on the ground and follow, in likelihood, pheromone recently saved by different ants. The primary clever thought introduced by an insect calculation is the synergisspasm utilisation of collaboration among numerous relatively simple operators that impart by circulated memory actualized as pheromone stored on diagram edges.
3 Proposed Job Scheduling Algorithm in Cloud Computing 3.1 System Architecture The above model makes it more likely to schedule the job with the physical servers available or in a waiting or suspended state (Figs. 8 and 9).
Mj Tj + P Ii j = bandwidthi CPU_ speedi × (1 − loadi )
The system employs the pheromone indicators with different processes and their submission to the requests made.
Fig. 8 System architecture
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Fig. 9 Mapping between the ant system and the grid system
m×
n i=1
i=
mn(n + 1) 2
Therefore, ACO has good scalability even if n or m grows very large.
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4 Implementation and Experimental Results 4.1 Implementation The API implements over 25 × 25, 50 × 50, or 100 × 100 are physical servers. An agent driven policy which is the ant is adopted. The home screen looks like (Fig. 10). The interface contains a panel with its following attribute: Auto-Adjust Automatically, it adjusts parameters over time. Deltas How fast each pheromones should adjust. Max pheromone The maximum pheromone allowed in the environment. Evaporation How fast pheromones dissipate. Drop-off Pheromones get weaker further from nest and food.
Fig. 10 API for job scheduling
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Trail strength How strictly ants follow strongest pheromones. Adapt time How fast all parameters should adjust. Food needed (One) Ants must find one food before returning. Food needed (All) Ants must find all food before returning (TSP). An ant travels to its neighborhood to get allocated with at least one or all food (in case of TSP) which physically are the resources.
4.2 Modules and Its Implementation The ACS is best dealt on distributed system so is the case in cloud computing. The following modules are applied to achieve the job-resource allocation: • • • • • • •
AdvancedControlPanel.java Ant.java Ants.java AntsApplet.java AntsApplication.java AntsControlPanel.java Cell.java Java code for all the modules can be seen in appendix A.
4.3 Simulation and Testing: In unit testing, the simulation [27] is carried on. It has given the desired result. A control panel is simulated which when compiled has given the following as result. The API is run and following are the results (Figs. 11, 12, 13, 14, 15, 16 and 17): This examination centers on the makespan and framework load balance. Occupations in our investigations are to process the result of frameworks with various sizes. At the point when a customer conveys a solicitation, the framework functions as follows: A.
A customer utilizes the customer interface to send a solicitation that contains the all-out number of occupations, the size of the lattice and the activity planning calculation to the portal.
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Fig. 11 Compilation of all the modules
Fig. 12 Three distributed foods for the yellow nest
B.
C.
The jobs scheduler gets the message from the portal and utilizations it as boundaries for the ACO calculation. The ACO calculation begins to ascertain the significant boundaries. Simultaneously, the information server would likewise give the resource data to the jobs scheduler. The calculation chooses an asset for presenting the solicitation (work) by finding the biggest passage in the PI network among the accessible positions to be executed. At that point a neighborhood pheromone update is performed.
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Fig. 13 Ants searching for their food
Fig. 14 Ants returning home (blue color indicator)
D.
E.
When an asset completes an occupation, a worldwide pheromone update is performed and the asset will send the conclusive outcomes back to the portal. On accepting the execution results, the portal would send it back to the customer to be shown on the customer UI. Repeat step A to step D until all positions are finished.
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Fig. 15 Ants liberating pheromones on their go to food
Fig. 16 Ants merging on the optimistic trail
5 Conclusion In this paper, we propose a benefit designated through the ACO computation to pick suitable resources for execute occupations according to resource status and the size of a given movement in the grid condition. The overall pheromone update capacities do alter the structure load. The close by pheromone update work revives the status of the picked resource after positions task. The overall pheromone update work invigorates
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Fig. 17 Blue and black color indicator for ants returning nest and non-returning nest
the status of each advantage for all situations after the completion of an occupation. It reaches out to the employment opportunity scheduler the most forward-thinking information of all benefits for the accompanying position task. The exploratory result shows its gainful ability to modify the entire system load. Later on, we will consider whether there are some different conditions in which we do not consider our implications of the pheromone pointer or the pheromone update limits. We will moreover endeavor to apply the ACO computation to various grid figuring applications. For example, instead of independent positions, acknowledge now we are arranging work measures. That is, there are need relations among occupations. By then the figuring must be acclimated to fuse a synchronization plan among resources. Right when work is to be dispensed to an advantage for execution, we ought to be certain that all its perspective occupations running on various resources have been done. Finally, this paper fixates on the enrolling system. We may rename the pheromone marker and pheromone update plans for the data system to consider the impersonation technique to pick or predict which resources have all the more accumulating or are fitting for record replications by their freshest status later on. Most current status later on.
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22. Rahmeh, O.A., Johnson, P., Taleb-Bendiab, A.: A dynamic biased random sampling scheme for scalable and reliable grid networks. INFOCOMP J. Comput. Sci. 7(4), 1–10 (2008) 23. Randles, M., Lamb, D., Taleb-Bendiab, A.: A comparative study into distributed load balancing algorithms for cloud computing. Proceedings of the IEEE 24th International Conference on Advanced Information Networking and Applications Workshops, pp. 551–556 (2010) 24. Li, K., Xu, G., Zhao, G., Dong, Y., Wang, D.: Cloud task scheduling based on load balancing ant colony optimization. In: Proceeding of the Annual China grid Conference, pp. 3–9 (2011) 25. Liu, J., Luo, X.-G., Zhang, X.-M., Zhang, F., Li, B.-N.: Job scheduling model for cloud computing based on multiobjective genetic algorithm. Int. J. Comput. Sci. Issues 10(3), 134–139 (2013) 26. Wang, X., Wang, Y.: An energy and data locality aware bi-level multiobjective task scheduling model based on mapreduce for cloud computing. In: Proceedings of the IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 648–655 (2012) 27. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Experience 41(1), 23–50 (2011)
Operational Control Decisions Through Random Rule in Flexible Manufacturing System K. Sai Sandeep, M. Nageswara Rao, K. Rakesh, D. Phanindra Kshatra, and K. M. V. Ravi Teja
Abstract Recital of Flexible Manufacturing System (FMS) with the help of computer model like JAVA programming. Concert of machines and AGVs are assessed by using priority rules. The experiments are conducted by considering various factors of dispatching rules, AGVs and machine scheduling rules. Concert measures consist of operational completion time with the help of random priority rule by considering 40 problems with t/p ratio > 0.25. Keywords Priority rules · AGVs scheduling · Machine scheduling · Travel time
1 Introduction A FMS consists of CNC machines, an AGVs, and other supporting peripherals such as, a storage rack, loading and unloading stations, associated and proscribed by a central computer. Benefits like both high flexibilities usually allied with job shops and high efficiency generally connected with transfer lines to produce medium-volume, medium variety products, e.g., high-machine utilization, low work-in-process inventory, and short production lead time, etc. An enormous number of sequencing rules are utilized in research and practically speaking to arrangement the positions hanging tight for preparing at a work place [1–5]. Later, heuristic enhancement calculations (heuristics) look for great plausible answers for streamlining issues in conditions where the intricacy of the issue or the restricted time accessible for its answer doesn’t permit definite arrangement [6–10]. Further to build the opportunity of getting global ideal arrangement with considering populace size through meta-heuristic calculation likes Genetic Algorithm. The work can likewise be reached out by expanding the size of the populace through meta-heuristic Algorithms. Booking of an FMS is a perplexing issue to address and consequently, it has made interest among the K. Sai Sandeep Department of Mechanical Engineering, NRIIT, Vijayawada 521212, India M. Nageswara Rao (B) · K. Rakesh · D. Phanindra Kshatra · K. M. V. Ravi Teja Department of Mechanical Engineering, K L E F, Guntur, AP 522502, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_87
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specialists [11–18]. Despite the fact that FMS planning issue are viewed as before, booking of material dealing with framework was not considered [19–21]. Among the individuals who decided on material taking care of framework, just scarcely any thought to be concurrent booking of machines and AGVs [22–24].
2 FMS Configuration The input data (i.e., traveling time matrix and machine sequence with processing time) from Bilge & Ulusoy (1995) (Fig. 1).
2.1 Purpose of the Study Operation j and job i completion time = Oi j = Ti j + Pi j Completion time of all jobs = (Ci ) =
n
Oi j
(1)
(2)
i=1
Makespan = max (C1, C2, C3 . . . .. Cn) Tij = traveling time, Pij = operation preparing time.
Fig. 1 Layout example
(3)
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3 Simultaneous Scheduling Initiate the layout and job set number as per order of dispatching rule (Random), the detailed simultaneous scheduling is given below in Fig. 2.
Fig. 2 Machine and AGVs scheduling
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3.1 Random Sequence The means engaged with Random are given beneath: Stage 1: Job set and Layout Consider. Stage 2: Adding jobi in the queue through random line.
3.2 Routing of AGVs Stage 1: Vehicles are at Load and unload. Stage 2: Use AGV1 and AGV2 for 1st and 2nd operations. Stage 3: Check vehicle suitability for next operation. Stage 4: With travel time make out the location of 1st and 2nd vehicle. Stage 5: Job/operation location of machine number for next operations. Stage 6: Vehicle empty and loaded travel time calculation like. Travel time of {AGV (Vehicle and Machine number) + (current location-job piece) + (job piece-next operation machine number). Stage 7: For next operation, select least loaded travel time vehicle.
4 Random Rule Implementation Four layouts each layout consists of 4 machines and load unload stations with necessary tools to conduct experiments to investigate impact of random rule on an FMS.
4.1 Simultaneous Scheduling—Random Rule See Table 1.
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Table 1 Random sequence measurement O.No
M.No
V.No
VPL
POMN
POCT
VLT
MRT
Process time
Comp time
13
1
1
0
0
0
4
0
10
14
10
1
2
0
0
0
4
14
10
24
1
1
1
1
0
0
26
24
10
36
3
2
2
1
0
0
30
0
10
40
7
2
1
1
0
0
52
40
10
62
5
1
2
2
0
0
54
36
10
64
8
3
1
2
2
62
70
0
15
85
2
4
2
1
1
36
64
0
18
82
14
2
1
3
1
14
82
62
15
97
6
3
2
4
1
64
84
85
20
105
4
4
1
2
2
40
88
82
18
106
11
2
2
3
1
24
96
97
15
112
9
4
1
4
3
85
100
106
12
118
15
3
2
2
2
97
105
105
12
117
12
4
2
3
2
112
118
118
12
130
Job order 2 and layout 4 as an example consider for execution of random rule in FMS Step 1: job set 4 taking into consideration Step 2: Produce in random order operations sequence like ------------------------------------------------------Layout-4 ------------------------------------------------------JobSet-2 Start Process Shuffled Op nums : [5, 10, 3, 7, 13, 1, 2, 11, 4, 6, 14, 8, 12, 15, 9]-144 Shuffled Op nums : [13, 5, 1, 3, 7, 10, 2, 8, 11, 14, 6, 4, 12, 9, 15]-138 Shuffled Op nums : [13, 10, 1, 3, 7, 5, 8, 2, 14, 6, 4, 11, 9, 15, 12]-130 Shuffled Op nums : [7, 1, 3, 5, 10, 13, 2, 6, 8, 14, 11, 4, 15, 9, 12]-130 Shuffled Op nums : [3, 10, 13, 7, 1, 5, 4, 2, 14, 11, 8, 6, 12, 15, 9]-136 Shuffled Op nums : [3, 7, 5, 10, 13, 1, 14, 11, 8, 4, 6, 2, 12, 9, 15]-140 Shuffled Op nums : [3, 13, 7, 1, 10, 5, 14, 4, 11, 2, 6, 8, 12, 9, 15]-141 Shuffled Op nums : [3, 10, 13, 7, 5, 1, 8, 14, 6, 4, 2, 11, 9, 15, 12]-134 Shuffled Op nums : [5, 10, 3, 7, 13, 1, 14, 8, 2, 6, 4, 11, 12, 9, 15]-147 Shuffled Op nums : [5, 1, 13, 7, 3, 10, 6, 14, 4, 2, 11, 8, 9, 12, 15]-144 Shuffled Op nums : [7, 10, 5, 3, 1, 13, 8, 14, 4, 6, 11, 2, 9, 15, 12]-152 Shuffled Op nums : [1, 7, 10, 13, 3, 5, 2, 14, 6, 4, 8, 11, 15, 9, 12]-149 Shuffled Op nums : [13, 7, 3, 10, 1, 5, 4, 2, 6, 14, 11, 8, 12, 9, 15]-157 Shuffled Op nums : [3, 7, 5, 1, 10, 13, 8, 6, 11, 2, 14, 4, 9, 12, 15]-144 Shuffled Op nums : [10, 7, 1, 13, 5, 3, 8, 6, 4, 14, 2, 11, 9, 12, 15]-142 ------------------------------------------------------Step 3: Calculation of makespan for the above sequences is shown below
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Fig. 3 Iterations values for layout 4 job order 2
The above table shows operation scheduling through random order for job set 4 layout 2. Variation completion time for 15 sequences are shown in below (Fig. 3).
5 Result and Discussion The FMS work shop situation introduced here with digits that follow 10.1 exhibits the job set 10 and layout 1. Calculations for completing time for different blends of occupation sets and formats for random rules with t/p > 0.25 are shown below (Table 2).
6 Final Remarks High priority tasks are regularly released to the shop floor for practice, for example result of random priority rules, machine and AGVs selection rules can be way out this problem. When performance measures are completion time, it is found for completion time random rule is the best dispatching rule for completion time (Figs. 4 and 5).
Operational Control Decisions Through Random Rule in Flexible …
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Table 2 Measured values in FMS through Random with t/p > 0.25 Job/Iter
1
2
3
4
5
6
7
8
9
10
11
12
13
1.1
112
114
108
110
121
128
116
107
108
116
117
116
125
2.1
96
98
94
92
102
94
88
98
92
106
100
98
106
3.1
102
98
107
114
100
92
112
114
100
106
96
98
96
4.1
120
126
124
131
122
126
118
117
122
128
112
118
116
1.2
126
128
139
140
133
138
134
128
134
118
128
145
128
2.2
106
102
123
90
118
100
123
128
106
104
112
118
118
3.2
124
116
138
114
117
114
110
119
124
114
114
124
110
4.2
144
138
130
130
136
140
141
134
147
144
152
149
157
1.3
153
132
143
138
139
138
146
151
131
139
145
140
141
2.3
116
118
116
110
114
122
104
116
106
110
122
108
121
3.3
134
117
118
117
131
118
124
123
119
116
132
134
116
4.3
164
169
140
160
171
142
156
172
151
146
165
160
148
1.4
155
145
145
160
153
142
133
142
142
157
147
156
141
2.4
119
114
128
123
116
124
110
115
114
109
129
108
132
3.4
125
122
127
125
125
125
121
112
122
122
107
131
120
4.4
168
164
162
179
167
180
158
170
145
158
171
173
169
1.5
109
111
116
106
116
117
100
100
106
104
113
100
97
2.5
94
89
87
87
83
86
100
89
79
78
92
81
90
3.5
98
98
90
95
90
88
103
90
88
96
85
85
90
4.5
114
123
116
107
120
128
118
102
123
111
135
113
135
1.6
160
162
166
170
155
184
173
170
182
183
171
178
178
2.6
150
145
164
158
168
145
162
143
139
163
171
150
138
3.6
157
140
129
151
161
167
157
170
147
169
176
158
168
4.6
151
197
174
166
183
162
190
167
190
190
190
174
148
1.7
155
155
152
155
154
169
153
138
155
144
156
149
157
2.7
117
119
117
115
123
123
109
115
107
115
120
125
110
3.7
121
127
138
118
121
124
121
135
132
118
119
139
134
4.7
181
176
175
187
169
168
173
171
180
162
192
176
178
1.8
202
202
224
200
194
218
206
214
217
195
212
223
202
2.8
194
193
172
169
204
166
190
184
175
163
204
181
196
3.8
188
178
193
175
210
196
186
176
208
191
193
210
198
4.8
219
228
201
234
216
210
224
213
210
210
234
221
218
1.9
148
147
153
141
159
167
153
136
138
143
168
150
145
2.9
120
133
134
124
123
125
118
114
119
115
123
124
116
3.9
125
120
129
125
118
141
129
120
129
126
145
121
133
4.9
157
143
145
150
147
153
143
150
151
160
162
134
139
(continued)
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Table 2 (continued) Job/Iter
1
2
3
4
5
6
7
8
9
10
11
12
13
1.10
212
214
206
201
200
208
189
215
180
204
182
200
200
2.10
198
204
192
199
181
170
184
180
184
190
169
194
213
3.10
187
204
210
200
190
182
194
204
192
194
185
187
182
4.10
214
207
197
234
238
216
197
220
228
192
231
212
224
Fig. 4 Job set 10 layout 4—Completion time fluctuation
Operational Control Decisions Through Random Rule in Flexible …
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Fig. 5 Job Set 6 layout-3—Completion time after 18 iterations
Acknowledgements Shore up from DST-SERB, GOI, (Sanction No: SB/EMEQ-501/2014).
References 1. Mönch, L., Zimmermann, J.: Simulation based assessment of machine criticality measures for a shifting bottleneck scheduling approach in complex manufacturing systems. Comput. Ind. 58, 644–655 (2007) 2. Sachin, G., Narasimha, K.K., Ramakrishna, K., Abhishek, D.: Thermodynamic analysis and effects of replacing HFC by fourth generation refrigerants in VCR systems. Int. J. Air-cond. Refrig. 26(1), 1850013–1–1850013–12 (2018) 3. Geyik, F., Cedimoglu, I.H.: A review of the production scheduling approaches based-on artificial intelligence and the integration of process planning and scheduling. In: Proceedings on swiss conference of CAD/CAM’99, (pp. 167–174). Neuchatel University, Switzerland (1999) 4. Pinedo, M.: Scheduling theory, algorithms and systems. Prentice-Hall, pp. 378. New Jersey (1995) 5. Al-Turki, U., Andijani, A., Arifulsalam, S.: A new dispatching rule for the stochastic singlemachine scheduling problem. Simulation 80–3, 165–170 (2004) 6. Dasore, A., Ramakrishna, K., Kiran Naik, B.: Evaluation of heat and mass transfer coefficients at beetroot-air interface during convective drying. Interfacial Phenom. Heat Transf. 8(4), 303–319 (2020)
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7. Olafsson, S., Li, X.: Learning effective new single machine dispatching rules from optimal scheduling data. Int. J. Prod. Econ. 128, 118–126 (2010) 8. Dasore, A., Ramakrishna, K., Naveen, P.: A paranoma of ideas in the adoption of an effective and efficient drying technique for an agricultural produce. Int. J. Mech. Prod. Eng. Res. Develop. 8(6), 955–962 (2018) 9. Nageswara Rao, M., Sai Bharath, C., Venkatesh, Y., Lokesh, K., Harish, V., Vara Kumari, S.: Simultaneous scheduling in FMS through priority rules. J Adv Res Dyn Control Syst 9, 1995–2008 2017 10. Nageswararao, M., et al.: Scheduling of machines and automated guided vehicles in FMS using shuffled frog leap algorithm. Int. J. Mech. Eng. Technol. 8(5), 496–503 (2017) 11. Kumar, P.A., Nageswararao, M., Kumar, T.Y., Kumar, M.M., Bhanu, V.: Scheduling of machines and automated guided vehicles in FMS using scatter search algorithm. Int. J. Mech. Eng. Technol. 8(5), 471–481 2017 12. Nageswara Rao, M., et al.: Application of BPSO in flexible manufacturing system scheduling. Int. J. Mech. Eng. Technol. 8(5), 186–195 (2017) 13. Dasore, A., Ramakrishna, K., Naveen, P.: Experimental investigation and mathematical modeling of convective drying kinetics of white radish. Frontiers Heat Mass Transf 13(21), 1–7 (2019) 14. Kanakavalli, P.B., Vommi, V.B., Nageswara Rao, M.: Fuzzy heuristic algorithm for simultaneous scheduling problems in flexible manufacturing system. Manag. Sci. Lett. 8(12), 1319–1330 (2018) 15. Konda, J.R., N.P., M.R., Konijeti, R., Dasore, A.: Effect of non-uniform heat source/sink on MHD boundary layer flow and melting heat transfer of Williamson nanofluid in porous medium. Multidiscip. Model. Mater. Struct. 15(2), 452–472 (2019) 16. Returi, M.C., Konijeti, R., Dasore, A.: Heat transfer enhancement using hybrid nanofluids in spiral plate heat exchangers. Heat Transf—Asian Res. 48(7), 3128–43 (Nov 2019) 17. Singh, T.S., Upendra, R., Abhishek, D., Muthukumar, M., Verma, T.N.: Performance and ecological parameters of a diesel engine fueled with diesel and plastic pyrolyzed oil (PPO) at variable working parameters. Environ. Technol. Innov. 22, 101491 (2021) 18. Metta, V.R., Ramakrishna, K., Abhishek, D.: Thermal design of spiral plate heat exchanger through numerical modelling. Int. J. Mech. Eng. Technol. 9(7), 736–745 (2018) 19. Nageswara Rao, M., Lokesh, K., Harish, V., Sai Bharath, CH., Venkatesh, Y., Vara Kumari, S.: Simultaneous scheduling through heuristic algorithm. Int. J. Eng. Technol. 7(2.32), 125–130 (2018) 20. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Integrated scheduling of machines and AGVs in FMS by using dispatching rules. J. Prod. Eng. 20(1), 75–84 (2017) 21. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Machines and AGVs scheduling in flexible manufacturing system with mean tardiness criterion. Int. J. Adv. Mater. Manuf. Charact. 4, 100–105 (2014) 22. Subbaiah, K.V., Nageswara Rao, M., Narayanarao, K.: Scheduling of AGVs and machines in FMS with make span criteria using sheep flock heredity algorithm. Int. J. Phys. Sci. 4(2), 139–148 (2010) 23. Nageswararao, M., Narayanarao K., Ranagajanardhana, G.: Simultaneous scheduling of machines and AGVs in flexible manufacturing system with minimization of tardiness criterion. In: International conference on advances in manufacturing and materials engineering (ICAMME-2014) (2014) 24. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Scheduling of machines and automated guided vehicle in FMS using gravitational search algorithm. Appl. Mech. Mater. 867, 307–313 (2017)
Experimental Investigation on FMS Environment with Operational Completion Time G. Durga Prasad, K. M. V. Ravi Teja, M. Nageswara Rao, D. Phanindra Kshatra, and K. Rakesh
Abstract By taking into account travel time and process time with ration of t/p < 0.25 in view of travel time half and process time double in recital of flexible manufacturing system (FMS) with the help of computer model like JAVA programming. Using priority, random rule machines and AGVs are assessed simultaneously to minimize makespan value. The experiments are conducted by considering various factors of FMS environment for 40 problems. Keywords Travel time · Process time · T/p ratio · Scheduling
1 Introduction For instance, with the help of FMS to get low work-in-process inventory, high machine utilization and short production lead time. Benefits like both high efficiency and high flexibility usually allied with job shops and generally connected with transfer lines to produce medium-volume, medium variety products. An FMS consists of CNC machines, a AGVs, and other supporting peripherals, such as a storage rack, loading and unloading stations, associated and proscribed by a central computer. An enormous number of sequencing rules are utilized in research and practically speaking to arrangement the positions hanging tight for preparing at a work place [1–5]. Later, heuristic enhancement calculations (heuristics) look for great plausible answers for streamlining issues in conditions where the intricacy of the issue or the restricted time accessible for its answer does not permit definite arrangement [6–10]. Further to increase the chance of getting Global optimal solution with considering population size through meta- heuristic algorithm likes Genetic Algorithm. The work can likewise be reached out by expanding the size of the populace through meta-heuristic algorithms. Booking of a FMS is a perplexing issue to G. Durga Prasad Department of Mechanical Engineering, NRIIT, Vijayawada 521212, India K. M. V. Ravi Teja · M. Nageswara Rao (B) · D. Phanindra Kshatra · K. Rakesh Department of Mechanical Engineering, KLEF, Guntur, AP 522502, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_88
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Fig. 1 Layout example
address, and consequently, it has made interest among the specialists [11–20]. Despite the fact that FMS planning issue is viewed as before, booking of material dealing with framework was not considered [21–24]. Among the individuals who decided on material taking care of framework, just scarcely any thought to be concurrent booking of machines and AGVs [25, 26].
2 FMS Configuration From Bilge and Ulusoy (1995), the input system configuration collected along with travel time and process time (Fig. 1).
2.1 Purpose of the Study Operation j and job i completion time = Oi j = Ti j + Pi j Completion time of all jobs = (Ci ) =
n
Oi j
(1)
(2)
i=1
Makespan = max (C1, C2, C3 . . . Cn)
(3)
T ij = traveling time, Pij = operation preparing time.
3 Simultaneous Scheduling Initiate the layout and job set number as per order of dispatching rule (random), the detailed simultaneous scheduling is given below in Fig. 2.
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Fig. 2 Machine and AGVs scheduling
3.1 Random Sequence The means engaged with random are given beneath: Stage 1: Job set and layout consider. Stage 2: Adding jobi in the queue through random line.
3.2 Routing of AGVs Stage 1: Vehicles are at load and unload. Stage 2: Use AGV1 and AGV2 for 1st and 2nd operations. Stage 3: Check vehicle suitability for next operation. Stage 4: With travel time make out the location of 1st and 2nd vehicle. Stage 5: Job/operation location of machine number for next operations. Stage 6: vehicle empty and loaded travel time calculation like. Travel time of {AGV (vehicle and machine number) + (current location—job piece) + (job piece—next operation machine number). Stage 7: For next operation, select least loaded travel time vehicle.
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4 Implementation of Random Rule Necessary tools like pallets and trucks in FMS with four layouts, each layout consists of 4 machines and load unload stations to conduct experiments to investigate impact of random rule.
4.1 Random Rule-Scheduling In FMS environment, job order 5 and layout 1 as an example consider for execution of random rule with travel time half and process time double (Table 1). Table 1 Makespan measurement through random sequence O.No
M.No
V.No
VPL
VRT
POCT
VETT
VLTT
Proces time
Comp time
1
1
1
0
0
0
0
3
6
15
4
1
2
0
0
0
0
3
18
51
10
4
1
1
3
0
9
15
6
27
12
3
2
1
3
0
9
14
3
20
7
3
1
4
15
0
18
23
9
41
2
2
2
3
14
15
18
21
12
45
8
4
1
3
23
41
23
44
3
50
13
1
2
2
21
20
24
28
9
69
5
3
2
1
28
51
28
55
6
67
11
2
1
4
44
27
44
48
15
78
9
1
1
2
48
50
52
57
12
93
3
4
2
3
55
45
58
62
9
80
6
2
1
1
57
67
61
70
15
108
Experimental Investigation on FMS Environment with Operational …
955
Step 1: job set 5 taking into consideration Step 2: Produce in random order operations sequence like ------------------------------------------------------Layout-1 ------------------------------------------------------JobSet-5 Start Process Shuffled Op nums : [1, 4, 10, 7, 12, 5, 11, 8, 13, 2, 6, 9, 3]-115 Shuffled Op nums : [4, 1, 10, 12, 7, 2, 13, 5, 8, 11, 6, 9, 3]-138 Shuffled Op nums : [10, 1, 7, 12, 4, 8, 13, 2, 11, 5, 6, 9, 3]-129 Shuffled Op nums : [4, 12, 1, 7, 10, 5, 13, 8, 2, 11, 6, 3, 9]-138 Shuffled Op nums : [7, 12, 4, 1, 10, 13, 8, 2, 5, 11, 3, 9, 6]-147 Shuffled Op nums : [4, 1, 12, 7, 10, 8, 5, 2, 13, 11, 3, 6, 9]-138 Shuffled Op nums : [10, 12, 1, 7, 4, 5, 8, 2, 11, 13, 6, 9, 3]-127 Shuffled Op nums : [7, 10, 1, 12, 4, 8, 5, 13, 2, 11, 6, 9, 3]-120 Shuffled Op nums : [7, 10, 1, 4, 12, 2, 5, 13, 8, 11, 3, 6, 9]-111 Shuffled Op nums : [1, 12, 4, 10, 7, 8, 11, 5, 2, 13, 3, 6, 9]-115 Shuffled Op nums : [1, 4, 10, 12, 7, 2, 8, 13, 5, 11, 9, 3, 6]-108 Shuffled Op nums : [1, 4, 12, 10, 7, 5, 13, 2, 11, 8, 6, 9, 3]-114 Shuffled Op nums : [7, 1, 10, 4, 12, 13, 5, 2, 8, 11, 9, 6, 3]-120 -------------------------------------------------------
Step 3: Calculation of makespan for the above sequences is shown below The above table shows operation scheduling through random order for job set 4 layout 2. Variation completion time for 15 sequences is shown in below (Fig. 3).
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Fig. 3 Makespan variation for layout 1 job order 5 after 13 iterations
5 Result and Discussions The FMS work shop situation introduced here with digits that follow 5.1 exhibits the job set 5 and layout 1. Calculations for completing time for different blends of occupation sets and formats for random rules with t/p > 0.25 are shown below (Table 2) (Figs. 4 and 5).
6 Final Remarks High priority tasks are regularly released to the shop floor for practice, for example, result of random priority rules, machine, and AGVs selection rules can be way out this problem. When performance measures are completion time, it is found for completion time random rule is the best dispatching rule for completion time.
Experimental Investigation on FMS Environment with Operational … Table 2 Makespan measurement with travel time half and process time double (t/p 0.25 are appeared in Table 2. From Table 2 it is pragmatic that SPT is to reduce the lateness of due date. In the ideal grouping of AGVs and machines are forbidden by utilizing priority rules for t/p ratios < 0.25 are appeared in Table 3. From Table 3 it is pragmatic that LPT is to reduce the lateness of due date. In the ideal grouping of AGVs and machines are forbidden by utilizing Priority rules for t/p ratios < 0.25 are appeared in Table 4. From table 4 it is pragmatic that LPT is to reduce the lateness of due date. Mean tardiness values are reported in Table 5 for various t/p ratios. Table 2 Calculation of tardiness with task scheduling rules with given process time and travel time Job
Lay out
First Come First Serve
Shortest Processing Time
Longest Processing Time
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
1
1
221
−48
0
230
−37
0
224
−47
0
2
1
221
−63
0
230
−72
0
224
−47
0
3
1
221
−19
0
230
−6
0
224
−26
0
4
1
221
42
42
230
37
37
224
40
40
5
1
221
−73
0
230
−66
0
224
−76
0
6
1
221
10
10
230
10
10
224
3
3
7
1
221
−26
0
230
−20
0
224
−23
0
8
1
221
40
40
230
31
31
224
42
42
9
1
221
49
49
230
47
47
224
44
44
10
1
221
87
87
230
78
78
224
86
86
1
2
181
−38
0
190
−17
0
185
−20
0
2
2
181
−57
0
190
−66
0
185
−55
0
3
2
181
−19
0
190
−2
0
185
−25
0
4
2
181
36
36
190
33
33
185
39
39
5
2
181
−63
0
190
−46
0
185
−54
0
6
2
181
−1
0
190
−21
0
185
−20
0
7
2
181
−32
0
190
−30
0
185
−36
0 (continued)
984
M. Nageswara Rao et al.
Table 2 (continued) Job
Lay out
First Come First Serve
Shortest Processing Time
Longest Processing Time
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
8
2
181
0
0
190
−9
0
185
13
13
9
2
181
69
69
190
59
59
185
59
59
10
2
181
109
109
190
98
98
185
102
102
1
3
185
−40
0
194
−19
0
189
−22
0
2
3
185
−55
0
194
−64
0
189
−53
0
3
3
185
−25
0
194
−4
0
189
−27
0
4
3
185
48
48
194
43
43
189
41
41
5
3
185
−65
0
194
−48
0
189
−56
0
6
3
185
−3
0
194
−23
0
189
−22
0
7
3
185
−30
0
194
−28
0
189
−38
0
8
3
185
−2
0
194
−11
0
189
11
11
9
3
185
67
67
194
57
57
189
57
57
10
3
185
108
108
194
100
100
189
104
104
1
4
245
−56
0
256
−49
0
227
−38
0
2
4
245
−71
0
256
−82
0
227
−53
0
3
4
245
−25
0
256
−6
0
227
−15
0
4
4
245
56
56
256
45
45
227
71
71
5
4
245
−74
0
256
−67
0
227
−56
0
6
4
245
4
4
256
−4
0
227
10
10
7
4
245
−28
0
256
−14
0
227
−76
0
8
4
245
40
40
256
29
29
227
−27
0
9
4
245
47
47
256
55
55
227
63
63
10
4
245
105
105
256
94
94
227
118
118
7 Final Remarks Intelligent Manufacturing framework is accepted as improved choice to confront the undertakings of worldwide challenge. Yet, for successful order compelling booking is significant. Scheduling of a FMS is an exceptionally convoluted issue on account of extra necessities like material dealing with. In this paper, an exertion has been influenced to take care of the NP-difficult issues by priority rules. Achievements of Priority rules are evaluated by examining 120 benchmark issues involving of various occupation sets and designs.
Implementation of Priority Rules in Flexible Manufacturing …
985
Table 3 Calculation of tardiness with task scheduling methods with double the process time and halves the travel time Job
Lay out
First come First serve
Shortest processing time Longest processing time
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
1
10
285
−78
0
297
−49
0
297
−45
0
2
10
285
−68
0
297
−80
0
297
−72
0
3
10
285
−28
0
297
30
30
297
−15
0
4
10
285
18
18
297
31
31
297
20
20
5
10
285
−133
0
297
−107
0
297
−110
0
6
10
285
19
19
297
−16
0
297
0
0
7
10
285
−54
0
297
−57
0
297
−33
0
8
10
285
53
53
297
41
41
297
50
50
9
10
285
105
105
297
70
70
297
62
62
10
10
285
167
167
297
132
132
297
147
147
1
20
269
−75
0
281
−43
0
286
−40
0
2
20
269
−75
0
281
−87
0
286
−80
0
3
20
269
−28
0
281
30
30
286
−16
0
4
20
269
16
16
281
31
31
286
12
12
5
20
269
−127
0
281
−101
0
286
−102
0
6
20
269
23
23
281
−21
0
286
−2
0
7
20
269
−57
0
281
−63
0
286
−37
0
8
20
269
37
37
281
38
38
286
48
48
9
20
269
111
111
281
74
74
286
61
61
10
20
269
176
176
281
142
142
286
153
153
1
30
271
−76
0
283
−44
0
287
−40
0
2
30
271
−74
0
283
−86
0
287
−78
0
3
30
271
−31
0
283
29
29
287
−16
0
4
30
271
21
21
283
34
34
287
14
14
5
30
271
−130
0
283
−102
0
287
−104
0
6
30
271
25
25
283
−22
0
287
−2
0
7
30
271
−56
0
283
−62
0
287
−37
0
8
30
271
36
36
283
37
37
287
48
48
9
30
271
110
110
283
73
73
287
61
61
10
30
271
177
177
283
143
143
287
155
155
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Table 4 Calculation of tardiness with task scheduling methods with triple the process time and halves the travel time Job
Lay out
First Come First Serve
Shortest Processing Time
Longest Processing Time
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
1
11
406
−116
0
421
−72
0
428
−67
0
2
11
406
−107
0
421
−122
0
428
−112
0
3
11
406
−40
0
421
52
52
428
−17
0
4
11
406
20
20
421
46
46
428
20
20
5
11
406
−191
0
421
−159
0
428
−157
0
6
11
406
37
37
421
−23
0
428
5
5
7
11
406
−81
0
421
−87
0
428
−49
0
8
11
406
82
82
421
67
67
428
80
80
9
11
406
154
154
421
100
100
428
81
81
10
11
406
246
246
421
196
196
428
213
213
1
22
391
−111
0
406
−67
0
417
−59
0
2
22
391
−115
0
406
−130
0
417
−120
0
3
22
391
−41
0
406
51
51
417
−18
0
4
22
391
16
16
406
44
44
417
12
12
5
22
391
−186
0
406
−154
0
417
−149
0
6
22
391
41
41
406
−29
0
417
3
3
7
22
391
−92
0
406
−91
0
417
−53
0
8
22
391
78
78
406
63
63
417
78
78
9
22
391
159
159
406
103
103
417
80
80
10
22
391
254
254
406
206
206
417
221
221
1
33
393
−114
0
407
−67
0
418
−61
0
2
33
393
−114
0
407
−128
0
418
−118
0
3
33
393
−44
0
407
51
51
418
−18
0
4
33
393
19
19
407
46
46
418
12
12
5
33
393
−189
0
407
−154
0
418
−151
0
6
33
393
40
40
407
−29
0
418
3
3
7
33
393
−91
0
407
−89
0
418
−53
0
8
33
393
77
77
407
63
63
418
78
78
9
33
393
158
158
407
103
103
418
80
80
10
33
393
255
255
407
208
208
418
223
223
1
44
412
−116
0
429
−73
0
431
−68
0
2
44
412
−105
0
429
−122
0
431
−112
0
3
44
412
−42
0
429
47
47
431
−20
0
4
44
412
22
22
429
42
42
431
20
20 (continued)
Implementation of Priority Rules in Flexible Manufacturing …
987
Table 4 (continued) Job
Lay out
First Come First Serve (Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
5
44
412
−194
0
429
−160
0
431
−161
0
6
44
412
33
33
429
−24
0
431
2
2
7
44
412
−83
0
429
−85
0
431
−46
0
8
44
412
81
81
429
64
64
431
77
77
9
44
412
148
148
429
104
104
431
89
89
10
44
412
254
254
429
204
204
431
221
221
Table 5 Mean Tardiness comparison
Shortest Processing Time
Longest Processing Time
Mean Tardiness (t/p < 0.25) Layout
FCFS
SPT
LPT
1
22.8
20.3
21.5
2
21.4
19
21.3
3
22.3
20
21.3
4
25.2
22.3
26.2
Mean Tardiness (t/p > 0.25) 1
36.2
30.4
27.9
2
36.3
31.5
27.4
3
36.9
31.6
27.8
4
37
30.8
28.9
Mean Tardiness (t/p > 0.25) 1
54
46.1
39.9
2
54.8
46.7
39.4
3
54.9
47.1
39.6
4
53.8
46.1
40.9
Acknowledgements Shore up from DST-SERB, GOI (SB/EMEQ-501/2014)
References 1. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Machines and AGVs scheduling in flexible manufacturing system with mean tardiness criterion. Int. J. Adv. Mater. Manuf. Charact. 4, 100–105 (2014) 2. Subbaiah, K, V., Nageswara Rao, M., & Narayanarao, K., Scheduling of AGVs and Machines in FMS with Make Span Criteria Using Sheep Flock Heredity Algorithm. International Journal of Physical Sciences, 4(2), 139–148, 2010.
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3. Medikondu Nageswararao, Dr.K.Narayanarao, Dr.G.Ranagajanardhana “Simultaneous Scheduling of Machines and AGVs in Flexible Manufacturing System with Minimization of Tardiness Criterion” International Conference on Advances in Manufacturing and Materials Engineering (ICAMME-2014),2014. 4. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Scheduling of Machines and Automated Guided Vehicle in FMS using Gravitational search algorithm. Appl. Mech. Mater. 867, 307–313 (2017) 5. M. Nageswara Rao, K. Lokesh, V. Harish, Ch. Sai Bharath, Y. Venkatesh, Vara Kumari. S, Simultaneous Scheduling through Heuristic Algorithm, International Journal of Engineering & Technology, 7 (2.32), 125–130, 2018. 6. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Integrated Scheduling of Machines and AGVs in FMS by Using Dispatching Rules. Journal of Production Engineering 20(1), 75–84 (2017) 7. M. Nageswara Rao, S.Vara Kumari,P. Manohar, B. Madesh, P. Naveen Krishna, R. Suraj Krishna, Simultaneous Scheduling of Machines and AGVs in FMS Through Ant Colony Optimization Algorithm. International Journal of Engineering and Advanced Technology (IJEAT), 9(3), 1392–1397, 202 8. M. Nageswara Rao, Vara Kumari S., Praneeth I., Gaya Prasad K., D.Venkata Reddy E.Vineeth, D. Maheshwar Reddy. Simultaneous Scheduling of Machines and AGVs in FMS through Simulated Annealing Algorithm. International Journal of Innovative Technology and Exploring Engineering, 9(4), 2235–2240, 2020. 9. Kanakavalli Prakash Babu, Vommi Vijaya Babu, Medikondu Nageswara Rao . Scheduling of Machines and AGVs Simultaneously in FMS through Hybrid Teaching Learning Based Optimization Algorithm. International Journal of Engineering and Advanced Technology, 9(2), 2048–2055, 2019. 10. Durga Rajesh, K.V., Chalapathi, P.V., Nageswara Rao, M., Krishna, C.E., Anoop, K., Neeraj, Y.: An efficient sheep flock heredity algorithm for the cell formation problem. ARPN J. Eng. Appl. Sci. 12(21), 6074–6079 (2017) 11. Prakash Babu, K., Vijaya Babu, V., Nageswara Rao, M.: Implementation of heuristic algorithms to synchronized planning of machines and AGVs in FMS. Manag. Sci. Lett. 8(6), 543–554 (2018) 12. Dasore, A., Ramakrishna, K., Kiran, N.B.: Evaluation of heat and mass transfer coefficients at beetroot-air interface during convective drying. Interfacial Phenomena and Heat Transfer 8, 303–319 (2020) 13. Reddy, B.S.P., Rao, C.S.P.: A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS. Int. J. Adv. Manuf. Technol. 31, 602–613 (2006) 14. Metta, V.R., Ramakrishna, K., Dasore, A.: Thermal design of spiral plate heat exchanger through numerical modeling, Int. J. Mech. Eng. Tech. 9(7), 736–745 (2018) 15. Nageswara Rao, M. et al.: Application of BPSO in flexible manufacturing system scheduling. Int. J. Mech. Eng. Technol. 8(5), 186–195 (2017) 16. Sachin, G., Narasimha, K.K., Ramakrishna, K., Dasore, A.: Thermodynamic analysis and effects of replacing HFC by fourth generation refrigerants in VCR systems. Int. J. Air-cond. Ref. 26(1), 1850013–1–1850013–12 (2018) 17. Nageswara Rao, M., Dileep, K., Khadar, S.B., Vara Kumari, S.: Modrak algorithm to minimize completion time for n-jobs m-machines problem in flexible manufacturing system. Int. J. Emerg. Trends Eng. Res. 8(8), 4560–4566 (2020) 18. Sreekara Reddy, M.B.S., Ratnam, C., Rajyalakshmi, G., Manupati, V.K.: An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem. Measurement 114, 78–90 (2018) 19. Kanakavalli, P.B., Vommi, V.B., Nageswara Rao, M.: Fuzzy heuristic algorithm for simultaneous scheduling problems in flexible manufacturing system. Manage. Sci. Lett. 8(12), 1319–1330 (2018)
Mean Tardiness with Heuristic in Intelligent Manufacturing System M. Nageswara Rao , T. Vijaya Kumar, K. Prakash Babu, G. R. Sanjay Krishna, Prem Kumar Chaurasiya, and Anshul Raj
Abstract The Adaptable Manufacturing System (FMS) is an amalgam framework containing of basics similar to work environments, mechanized putting away and recuperation frameworks, and material control gadgets like machines and AGVs. An endeavor is made to concentrate simultaneously the machine and AGVs arranging highlights in a FMS for decrease of the tardiness D. S. Palmer is used to solve FMS simultaneous scheduling problems. One hundred and twenty issues and their current arrangements with various methodologies are analyzed. Keywords FMS · Slope indices · Tardiness · AGVs
1 Introduction Need deals are utilized to choose which occupation will be prepared next grinding away focus, where a few positions are standing by to be handled. The positions hanging tight for preparing are sequenced utilizing one of numerous need sequencing rules. It is expected that the work place can deal with just each work in turn. An enormous number of sequencing rules are utilized in research and practically speaking to arrangement the positions hanging tight for preparing at a work place [1–3]. Later heuristic enhancement calculations (heuristics) look for great plausible answers for streamlining issues in conditions where the intricacy of the issue or the restricted time accessible for its answer does not permit definite arrangement [4–6]. Further to build the opportunity of getting global ideal arrangement with considering populace size through meta-heuristic calculation likes genetic algorithm. The work can likewise M. Nageswara Rao (B) · T. Vijaya Kumar · G. R. Sanjay Krishna Department of Mechanical Engineering, K.L.E.F University, Guntur, AP, India K. Prakash Babu Department of Mechanical Engineering, VRSEC, Kanuru, Vijayawada, AP, India P. K. Chaurasiya · A. Raj Department of Mechanical Engineering, Sagar Institute of Science and Technology, Gandhi Nagar, Bhopal, MP, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_92
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be reached out by expanding the size of the populace through meta-heuristic algorithms. Booking of a FMS is a perplexing issue to address and consequently it has made interest among the specialists [7–9]. Despite the fact that FMS planning issue is viewed as before, booking of material dealing with framework was not considered [10, 11]. Among the individuals who decided on material taking care of framework, just scarcely any thought to be concurrent booking of machines and AGVs [12–14]. A painstakingly planned and oversaw material taking care of framework is imperative to accomplish the necessary coordination of FMS [15–17]. Henceforth there is a requirement for booking both the machines and material taking care of framework all the while for the fruitful execution of a FMS.
2 FMS Narrative In FMS, narration having four CNC machines with tool and pallet changer is shown in Fig. 1. The input data (i.e., traveling time matrix and machine sequence with processing time) [18].
2.1 Purpose of the Study The objective of FMS study is to minimize the mean tardiness.
Fig. 1 Design plans in model issues
Mean Tardiness with Heuristic in Intelligent Manufacturing System
991
Finish time of operation j and job i = Oi j = Ti j + Pi j Job consummation time (maximum) = (Ci ) =
n
Oi j
(1)
(2)
i=1
Makespan = max (C1, C2, C3 . . . .. Cn)
(3)
T ij = traveling time, Pij = operation preparing time. Di = (C1 + C2 + C3 + . . . Cn)/n
(4)
Lateness value (Li) = Tardiness−Due date
(5)
Tardiness (T i) = Max [Li, 0]
(6)
n 1 Mean Tardiness Value T = Ti n i=1
(7)
3 Scheduling with D. S. Palmer Jobs are planned dependent on the bustle grouping conditional by the calculations. The issue considered necessities booking of material taking care of framework alongside that of machines. In this work D.S. Palmer is altered to tackle concurrent planning issues.
4 D. S. Palmer Heuristic Algorithm In this work Palmer Heuristic Algorithm was created by Palmer (1965). The means associated with Palmer heuristic calculation are given underneath: Stage 1: To consider a task set Stage 2: To discover the Slope record for the work set utilizing Si = (M–1) ti, M + (M–3) t i, M–1 + (M–5) t i, M–2 + … - (M–3) t i, 2−(M–1) t i, 1 Stage 3: according to the slant record to mastermind the positions in plunging request as
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si1 ≥ si2 ≥ … ≥ sin, Stage 4: Based on the slant list esteems the activity grouping is to be acquired
5 Route Plan of Vehicle Stage 1: At the start vehicles are positioned at load and unload. Stage 2: For 1st and 2nd operations use AGV1 and AGV2. Stage 3: From 3rd operation onwards check which vehicle is suitable for next operation. Stage 4: Make out the location of both vehicles with travel time. Stage 5: Recognize the next operation machine number and job piece location. Stage 6: Calculate vehicle travel time like. Travel time of {AGV (vehicle and machine number) + (current location−job piece) + (job piece−next operation machine number). Stage 7: Select least travel time vehicle for next operation.
6 Execution of D. S. Palmer Layout 4 and set 4 are considered for execution of D S Palmer. D. S. Palmer heuristic is clarified in the accompanying strides for the work set 4: Stage 1: Work set 4 taking into consideration. Set no
Layout
Jobs
operations
Machines order
4
4
5
19
1: 4-1-2 2: 3-2-4 3: 2-3-1-3 4: 2-4-1-2 5: 1-2-4-2-3
Stage 2: For each work slant file is found. For job-1 the slope index is. S1 = (4–1)*11 + (4–3)*0 + (4–5)*7 + (4–7)*10 = 33 + 0–7-30 = -4. For job-2 the slope index is.
Mean Tardiness with Heuristic in Intelligent Manufacturing System
993
S2 = (4–1)*8 + (4–3)*12 + (4–5)*10 + (4–7)*0 = = 24 + 12–10 + 0 = 26. For job-3 the slope index is. S3 = (4–1)*0 + (4–3)*18 + (4–5)*7 + (4–7)*9 = 0 + 18–7-27 = -16. For job-4 the slope index is. S4 = (4–1)*8 + (4–3)*0 + (4–5)*13 + (4–7)*12 = 24 + 0–13-36 = -25. For job-5 the slope index is. S5 = (4–1)*8 + (4–3)*8 + (4–5)*17 + (4–7)*9 = 24 + 8–17-27 = -12. Slope index for all the jobs are. S1 = −4, S2 = 26, S3 = −16, S4 = −25 and S5 = −12. Stage 3: Arranging the slant file esteems into dropping request. S2 (26)—S1 (−4)—S5 (−12)—S3 (−16)—S4 (−25). Stage 4: According to slope index values the sequence is obtained as. 4–5–6–1–2–3–15–16–17–18–19–7–8–9–10–11–12–13–14. Stage 5: According to the work request grouping tasks in the line are performed. Stage 6: For recognizing the greatest operational consummation season of the above arrangement are determined estimations of different boundaries for all activities are appeared in Table 1. Table 1 shows activity planning of through D. S. Palmers rule for work set 4 design 4 is appeared. The operational culmination time (makespan) is 299. In the same way calculate makespan for left over 9 jobs and makespan values are. 209, 195, 225, 299, 182, 235, 223, 285, 295 and 353. Due date (Di) = 209 + 195 + 225 + 299 + 182 + 235 + 223 + 285 + 295 + 353/10 = 250. Lateness (Li) = 299–250 = 49. Tardiness (Ti) = Max (Li, 0) = Max(49, 0) = 0.
7 Result and Discussion The FMS work shop situation introduced here with digits that follow 8.4 exhibits the job set 8 and layout 4. Here, having a 0 or 1 as the last digit suggests that the run times are 2 times and 3 times, where in the two cases travel times are half. In the ideal grouping of AGVs and machines are forbidden by utilizing D. S. Palmer for t/p ratios > 0.25 are appeared in Table 2. From Table 2, it is pragmatic that D. S. Palmer is to reduce the lateness of due date.
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Table 1 Completion time with the help of D. S. Palmer Number operation
M.No
V.No
Empty travel
Ready time
Reach time
Make span
4
3
1
0
5
2
2
22
10
10
22
28
28
6
4
1
38
38
44
44
52
1
4
2
1
2
48
62
62
73
1
73
87
87
3
97
2
2
97
101
101
108
15
1
1
105
109
109
118
16
2
1
118
122
122
129
17
4
2
129
135
135
143
18
2
1
143
155
155
165
19
3
2
165
173
173
181
7
2
1
175
183
183
190
8
3
2
190
198
198
208
9
1
1
208
216
216
225
10
3
2
225
231
231
239
11
2
1
234
242
242
249
12
4
2
249
255
255
263
13
1
1
263
277
277
289
14
2
2
289
293
293
299
In the ideal grouping of AGVs and machines are forbidden by utilizing D. S. Palmer for t/p ratios < 0.25 are appeared in Table 3. From Table 3, it is pragmatic that D. S. Palmer is to reduce the lateness of due date. In the ideal grouping of AGVs and machines are forbidden by utilizing D. S. Palmer for t/p ratios < 0.25 are appeared in Table 4. From Table 4, it is pragmatic that D. S. Palmer is to reduce the lateness of due date. Mean tardiness values are reported in Table 5 for various t/p ratios.
8 Final Remarks Intelligent manufacturing framework is accepted as improved choice to confront the undertakings of worldwide challenge. Yet, for successful order compelling booking is significant. Scheduling of a FMS is an exceptionally convoluted issue on account of extra necessities like material dealing with. In this paper an exertion has been influenced to take care of the NP difficult issues by D. S. Palmer. Achievements of
Mean Tardiness with Heuristic in Intelligent Manufacturing System
995
Table 2 Execution examination of tardiness with original process and travel time (t/p>0.25) Job
Lay out
First come First serve
Shortest processing time
Longest processing time
D. S. Palmer
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
(Di)
(Li)
(Ti)
1
1
221
−48
0
230
−37
0
224
−47
0
228
−30
0
2
1
221
−63
0
230
−72
0
224
−47
0
228
−53
0
3
1
221
−19
0
230
−6
0
224
−26
0
228
−17
0
4
1
221
42
42
230
37
37
224
40
40
228
37
37
5
1
221
−73
0
230
−66
0
224
−76
0
228
−68
0
6
1
221
10
10
230
10
10
224
3
3
228
−7
0
7
1
221
−26
0
230
−20
0
224
−23
0
228
−29
0
8
1
221
40
40
230
31
31
224
42
42
228
33
33
9
1
221
49
49
230
47
47
224
44
44
228
45
45
10
1
221
87
87
230
78
78
224
86
86
228
87
87
1
3
185
−40
0
194
−19
0
189
−22
0
194
−2
0
2
3
185
−55
0
194
−64
0
189
−53
0
194
−55
0
3
3
185
−25
0
194
−4
0
189
−27
0
194
−18
0
4
3
185
48
48
194
43
43
189
41
41
194
37
37
5
3
185
−65
0
194
−48
0
189
−56
0
194
−43
0
6
3
185
−3
0
194
−23
0
189
−22
0
194
−13
0
7
3
185
−30
0
194
−28
0
189
−38
0
194
−51
0
8
3
185
−2
0
194
−11
0
189
11
11
194
−11
0
9
3
185
67
67
194
57
57
189
57
57
194
57
57
10
3
185
108
108
194
100
100
189
104
104
194
96
96
1
4
245
−56
0
256
−49
0
227
−38
0
250
−41
0
2
4
245
−71
0
256
−82
0
227
−53
0
250
−55
0
3
4
245
−25
0
256
−6
0
227
−15
0
250
−25
0
4
4
245
56
56
256
45
45
227
71
71
250
49
49
5
4
245
−74
0
256
−67
0
227
−56
0
250
−68
0
6
4
245
4
4
256
−4
0
227
10
10
250
−15
0
7
4
245
−28
0
256
−14
0
227
−76
0
250
−27
0
8
4
245
40
40
256
29
29
227
−27
0
250
35
35
9
4
245
47
47
256
55
55
227
63
63
250
45
45
10
4
245
105
105
256
94
94
227
118
118
250
103
103
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Table 3 Execution examination of tardiness with process time double and travel time half (t/p 0.25) 1
54
46.1
39.9
33.8
2
54.8
46.7
39.4
35.1
3
54.9
47.1
39.6
34.8
4
53.8
46.1
40.9
34.1
D. S. Palmer algorithm are evaluated by examining 120 benchmark issues involving of various occupation sets and designs.
References 1. Nageswara Rao, M., Lokesh, K., Harish, V., Sai Bharath, Ch., Venkatesh, Y., Vara Kumari, S.: Simultaneous scheduling through heuristic algorithm. Int. J. Eng. Technol. 7(2.32), 125–130 (2018) 2. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Integrated scheduling of machines and AGVs in FMS by using dispatching rules. J. Prod. Eng. 20(1), 75–84 (2017) 3. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Machines and AGVs scheduling in flexible manufacturing system with mean tardiness criterion. Int. J. Adv. Mater. Manuf. Charact. 4, 100–105 (2014) 4. Subbaiah, K.V., Nageswara Rao, M., Narayanarao, K.: Scheduling of AGVs and machines in FMS with make span criteria using sheep flock heredity algorithm. Int. J. Phys. Sci. 4(2), 139–148 (2010) 5. Nageswararao, M., Narayanarao, K., Ranagajanardhana, G.: Simultaneous scheduling of machines and AGVs in flexible manufacturing system with minimization of tardiness criterion. In: International Conference on Advances in Manufacturing and Materials Engineering (ICAMME-2014) (2014) 6. Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Scheduling of machines and automated guided vehicle in FMS using gravitational search algorithm. Appl. Mech. Mater. 867, 307–313 (2017) 7. Sachin, G., Narasimha, K.K., Ramakrishna, K., Dasore, A.: Thermodynamic analysis and effects of replacing HFC by fourth generation refrigerants in VCR systems. Int. J. Air-cond. and Ref. 26(1), 1850013–1–1850013–12 (2018)
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8. Chen, C.L., Vempati, V.S., Aljaber, N.: Theory and methodology an application of genetic algorithms for flow shop problems. European J. Operations Res. 80, 389–396 (1995) 9. Metta, V.R., Ramakrishna, K., Dasore, A: Thermal design of spiral plate heat exchanger through numerical modeling, Int. J. Mech. Engg. Tech. 9(7), 736–745 (2018) 10. Al-Hakim, L.: An analogue genetic algorithm for solving job shop scheduling problems. Int. J. Prod. Res. 39(7), 1537–1548 (2001) 11. Abdelmaguid, T.F., Nasef, A.O., Kamal, B.A., Hassan, M.F.: A hybrid GA/heuristic approach to the simultaneous scheduling of machines and automated guided vehicles. Int. J. Prod. Res. 42(2), 267–281 (2004) 12. Reddy, B.S.P., Rao, C.S.P.: A hybrid multi-objective GA for simultaneous scheduling of machines and AGVs in FMS. Int. J. Adv. Manuf. Technol. 31, 602–613 (2006) 13. Dasore, A., Ramakrishna, K., Kiran, N.B.: Evaluation of heat and mass transfer coefficients at beetroot-air interface during convective drying. Interfacial Phenomena and Heat Transfer 8, 303–319 (2020) 14. Nageswara Rao, M., Dileep, K., Khadar, B.S., Vara Kumari, S.: Modrak algorithm to minimize completion time for n-jobs m-machines problem in flexible manufacturing system. Int. J. Emerg. Trends Eng. Res. 8(8), 4560–4566 (2020) 15. Nageswara Rao, M., Vara Kumari, S., Manohar, P., Madesh, B., Naveen Krishna, P., Suraj Krishna, R.: Simultaneous scheduling of machines and AGVs in FMS through ant colony optimization algorithm. Int. J. Eng. Adv. Technol. (IJEAT) 9(3), 1392–1397 (2020) 16. Nageswara Rao, M., Vara Kumari, S., Praneeth, I., Gaya Prasad, K., Venkata Reddy, D., Vineeth, E., Maheshwar Reddy, D.: Simultaneous scheduling of machines and AGVs in FMS through simulated annealing algorithm. Int. J. Innov. Technol. Exploring Eng. 9(4), 2235–2240 (2020) 17. Kanakavalli, P.B., Vommi, V.B., Nageswara Rao, M.: Scheduling of machines and AGVs simultaneously in FMS through hybrid teaching learning based optimization algorithm. Int. J. Eng. Adv. Technol. 9(2), 2048–2055 (2019) 18. Pappula, L., Ghosh, D.: Cat swarm optimization with normal mutation for fast convergence of multimodal functions. Appl. Soft Comput. 66, 473–491 (2018)
Simultaneous Scheduling with J N D Gupta Heuristic Algorithm with Mean Tardiness M. Nageswara Rao , K. Prakash Babu, T. Vijaya Kumar, Santosh Kumar Malyala, and Sanjay Kumar Singh
Abstract The Flexible Manufacturing System (FMS) is an amalgam framework containing of basics similar to work environments, mechanized putting away and recuperation frameworks, and material control gadgets like machines and AGVs. An endeavor is made to concentrate simultaneously the machine and AGVs arranging highlights in a FMS for decrease of the tardiness. J N D Gupta is used to solve FMS simultaneous scheduling problems. 120 issues and their current arrangements with various methodologies are analyzed. Keywords FMS · Quasi equivalent sorting problem · Tardiness · AGVs
1 Introduction Need deals are utilized to choose which occupation will be prepared next grinding away focus, where a few positions are standing by to be handled. The positions hanging tight for preparing are sequenced utilizing one of numerous need sequencing rules. It is expected that the work place can deal with just each work in turn. An enormous number of sequencing rules are utilized in research and practically speaking to arrangement the positions hanging tight for preparing at a work place [1–4]. Later, Heuristic enhancement calculations (heuristics) look for great plausible answers for streamlining issues in conditions where the intricacy of the issue or the restricted time accessible for its answer doesn’t permit definite arrangement [5–8]. Further to build M. Nageswara Rao (B) · T. Vijaya Kumar Department of Mechanical Engineering, K.L.E.F University, Guntur, AP, India K. Prakash Babu Department of Mechanical Engineering, VRSEC, Kanuru, Vijayawada, AP, India S. Kumar Malyala Department of Mechanical Engineering, Acharya Institute of Technology, Banglore, India S. Kumar Singh Department of Mechanical Engineering, Sagar Institute of Science and Technology, Bhopal, MP, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_93
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the opportunity of getting Global ideal arrangement with considering populace size through meta-heuristic calculation likes Genetic Algorithm. The work can likewise be reached out by expanding the size of the populace through Meta-heuristic Algorithms. Booking of a FMS is a perplexing issue to address and consequently it has made interest among the specialists [9–11]. Despite the fact that FMS planning issue are viewed as before, booking of material dealing with framework was not considered [12–15]. Among the individuals who decided on material taking care of framework, just scarcely any thought to be concurrent booking of machines and AGVs [16–18] a painstakingly planned and oversaw material taking care of framework is imperative to accomplish the necessary coordination of FMS [19–23]. Henceforth, there is a requirement for booking both the machines and material taking care of framework all the while for the fruitful execution of a FMS.
2 FMS Narration In FMS narration having four CNC machines with tool and pallet changer is shown in Fig. 1. The input data (i.e., traveling time matrix and machine sequence with processing time) [24].
Fig. 1 Design plans in model issues
Simultaneous Scheduling with J N D Gupta Heuristic Algorithm …
1003
2.1 Purpose of the Study The Objective of FMS study is to minimize the mean tardiness. Finish time of operation j and job i = Oi j = Ti j + Pi j Job consummation time (maximum) = (Ci ) =
n
Oi j
(1)
(2)
i=1
Makespan = max (C1, C2, C3 . . . Cn)
(3)
T ij = traveling time, Pij = operation preparing time. Di = (C1 + C2 + C3 + . . . Cn)/n
(4)
Lateness value (Li) = Tardiness−Due date
(5)
Tardiness (Ti ) = Max [L i , 0]
(6)
n 1 Mean Tardiness Value T = Ti n i=1
(7)
3 Simultaneous Scheduling—J. N. D. Gupta. Jobs are planned dependent on the activity assemblage contingent by the calculations. The issue considered necessities booking of material taking care of framework alongside that of machines. In this work, J. N. D. GUPTA is altered to tackle concurrent planning issues.
3.1 J. N. D. Gupta Algorithm. In this work, J. N. D. Gupta calculation is accustomed to take care of planning issues. Gupta Algorithm was created by Gupta (1986). The means engaged with Gupta calculation are given. Stage 1: To consider a task set.
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Stage 2: To diminish ‘m’ machines and ‘n’ occupations issue to two machines and ‘n’ Jobs. Stage 3: After lessening to two machines and ‘n’ occupations issues the base interaction time is to establish for each work. Stage 4: To know ei esteem. (in the event that pi1 < pim , ei = 1; in any case ei = −1). Stage 5: To ascertain the slant record (S i ) an incentive for each work as per. S i = ei /min (pi1 …. pim ). Stage 6: According to the slant record to mastermind the positions in diving request as. si1 ≥ si2 ≥ … ≥ sin , Stage 7: Based on the slant record esteems the activity arrangement is to be acquired.
4 Route Plan of Vehicle Stage 1: At the start vehicles are positioned at Load and unload. Stage 2: For 1st and 2nd operations use AGV1 and AGV2. Stage 3: From 3rd operation onwards check which vehicle is suitable for next operation. Stage 4: Make out the location of both vehicles with travel time. Stage 5: Recognize the next operation machine number and job piece location. Stage 6: Calculate vehicle travel time like. Travel time of {AGV (Vehicle and Machine number) + (current location—job piece) + (job piece—next operation machine number). Stage 7: Select least travel time vehicle for next operation.
5 Execution of J. N. D. Gupta Layout 5 and work set 1 are considered for execution of J. N. D. Gupta. It registers the slant records for various positions and the successions are gotten dependent on the file esteems organized in the slipping request. The Gupta heuristic is clarified in the accompanying strides for the work set 5: Stage 1: work set 5 taking into considerations.
Simultaneous Scheduling with J N D Gupta Heuristic Algorithm …
1005
Set number
Layout
Jobs
Operations
Machine order job wise
5
1
5
13
1: 1-2-4 2: 1-3-2 3: 3-4-1 4: 4-2 5: 3-1
Stage 2: In view of the interaction time esteems for each work as: Job
1
2
3
4
5
M(1)
6
18
12
0
9
M(2)
12
15
0
15
0
M(3)
0
6
9
0
3
M(4)
9
0
3
6
0
Stage 3: Reducing the ‘m’ machines and ‘n’ occupations issue to two machines and ‘n’ jobs. Job
1
2
3
4
5
M(1) + M(2)
18
33
12
15
9
M(3) + M(4)
9
6
12
6
3
Stage 4: For each work least interaction times is found as: Job
1
2
3
4
5
Min M(1) + M(2),M(3) + M(4)
9
6
12
6
3
Stage 5: For each work ei esteem is found (in the event that pi1 < pim , ei = 1; in any case ei = −1). Job
1
2
3
4
5
pi1 < pim
6 0.25 out of 40 issues 9 issues Table 2 Execution examination t/p > 0.25
t/p < 0.25
t/p < 0.25
Job. No
Layout
Palmer
Job. no
Layout
Palmer
Job. no
Layout
Palmer
1
1
198
1
10
318
1
11
464
2
1
175
2
10
314
2
11
314
3
1
211
3
10
300
3
11
431
4
1
265
4
10
329
4
11
465
5
1
160
5
10
236
5
11
341
6
1
221
6
10
302
6
11
435
7
1
199
7
10
329
7
11
317
8
1
261
8
10
338
8
11
488
9
1
273
9
10
369
9
11
524
10
1
315
10
10
403
10
11
577
1
2
190
1
20
314
1
21
460
2
2
137
2
20
204
2
21
291
3
2
178
3
20
285
3
21
419
4
2
225
4
20
315
4
21
451
5
2
149
5
20
232
5
21
337
6
2
179
6
20
285
6
21
418
7
2
139
7
20
208
7
21
297
8
2
181
8
20
319
8
21
469
9
2
249
9
20
357
9
21
512
10
2
284
10
20
393
10
21
569
1
3
192
1
30
315
1
31
461
2
3
139
2
30
207
2
31
294
3
3
176
3
30
284
3
31
418
4
3
231
4
30
316
4
31
452
5
3
151
5
30
233
5
31
338
6
3
181
6
30
286
6
31
419
7
3
143
7
30
209
7
31
298 (continued)
Flexible Manufacturing System Simultaneous …
1019
Table 2 (continued) t/p > 0.25
t/p < 0.25
t/p < 0.25
Job. No
Layout
Palmer
Job. no
Layout
Palmer
Job. no
Layout
Palmer
8
3
183
8
30
320
8
31
470
9
3
251
9
30
358
9
31
513
10
3
290
10
30
396
10
31
572
1
4
209
1
40
321
1
41
467
2
4
195
2
40
235
2
41
322
3
4
225
3
40
269
3
41
435
4
4
299
4
40
334
4
41
470
5
4
182
5
40
239
5
41
344
6
4
235
6
40
270
6
41
439
7
4
223
7
40
223
7
41
329
8
4
285
8
40
343
8
41
493
9
4
295
9
40
388
9
41
535
10
4
353
10
40
430
10
41
591
give improved outcomes utilizing Palmer in correlation with LPT, SPT, and FCFS (Nageswara rao et al. 2020). With t/p < 0.25 out of 40 problems 10 issues gives improved outcomes utilizing Palmer in correlation with FCFS, LPT, and SPT. And with t/p < 0.25 out of 40 issues 12 issues gives improved outcomes utilizing Palmer in correlation with FCFS, SPT, and LPT.
7 Conclusion Intelligent manufacturing framework is accepted as superior choice to confront the undertakings of worldwide challenge. Yet, for unbeaten order compelling booking is significant. Scheduling of a FMS is an exceptionally convoluted issue on account of extra necessities like material dealing with. In this paper, an exertion has been influenced to take care of the NP difficult issues by Palmer Heuristic. Achievements of dispatching rules and Palmer heuristic are evaluated by examining 120 benchmark issues involving of various occupation sets and designs. Acknowledgements Shore up from DST-SERB, GOI (SB/EMEQ-501/2014).
References 1. Nageswara Rao, M., Lokesh, K., Harish, V., Sai Bharath, CH., Venkatesh, Y., Vara Kumari, S.:
1020
2. 3.
4.
5.
6.
7. 8.
9.
10.
11.
12.
13.
14. 15. 16.
17. 18. 19.
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Simultaneous scheduling through heuristic algorithm. Int. J. Eng. Technol. 7(2.32), 125–130 (2018) Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Integrated scheduling of machines and AGVs in FMS by using dispatching rules. J. Prod. Eng. 20(1), 75–84 (2017) Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Machines and AGVs scheduling in flexible manufacturing system with mean tardiness criterion. Int. J. Adv. Mater. Manuf. Charact. 4, 100–105 (2014) Subbaiah, K.V., Nageswara Rao, M., Narayanarao, K.: Scheduling of AGVs and machines in FMS with make span criteria using sheep flock heredity algorithm. Int. J. Phys. Sci. 4(2), 139–148 (2010) Nageswararao, M, Narayanarao, K., Ranagajanardhana, G.: Simultaneous scheduling of machines and AGVs in flexible manufacturing system with minimization of tardiness criterion. In: International Conference on Advances in Manufacturing and Materials Engineering (ICAMME-2014) (2014) Nageswara Rao, M., Narayana Rao, K., Ranga Janardhana, G.: Scheduling of machines and automated guided vehicle in FMS using gravitational search algorithm. Appl. Mech. Mater. 867, 307–313 (2017) Metta, V.R., Ramakrishna, K., Dasore, A.: Thermal design of spiral plate heat exchanger through numerical modeling. Int. J. Mech. Engg. Tech. 9(7), 736–745 (2018) Dasore, A., Ramakrishna, K., Kiran, N.B.: Evaluation of heat and mass transfer coefficients at beetroot-air interface during convective drying. Interfacial Phenom. Heat Transfer 8, 303–319 (2020) Nageswara Rao, M., Dileep, K., Khadar, B.S., Vara Kumari, S., Modrak, G.: Algorithm to minimize completion time for n-jobs m-machines problem in flexible manufacturing system. Int. J. Emerg. Trends Eng. Res. 8(8), 4560–4566 (2020) Nageswara Rao, M., Vara Kumari, S., Manohar, P., Madesh, B., Naveen Krishna, P., Suraj Krishna, R.: Simultaneous scheduling of machines and AGVs in FMS through ant colony optimization algorithm. Int. J. Eng. Adv. Technol. (IJEAT) 9(3), 1392–1397, 202 Nageswara Rao, M., Vara Kumari, S., Praneeth, I., Gaya Prasad, K., Venkata Reddy, D., Vineeth, E., Maheshwar Reddy, D.: Simultaneous scheduling of machines and AGVs in FMS through simulated annealing algorithm. Int. J. Innov. Technol. Exploring Eng. 9(4), 2235–2240 (2020) Kanakavalli, P.B., Vommi, V.B., Nageswara Rao, M.: Scheduling of machines and AGVs Simultaneously in FMS through hybrid teaching learning based optimization algorithm. Int. J. Eng. Adv. Technol. 9(2), 2048–2055 (2019) Sachin, G., Narasimha, K.K., Ramakrishna, K., Dasore, A.: Thermodynamic analysis and effects of replacing HFC by fourth generation refrigerants in VCR systems. Int. J. Air-cond. Ref. 26(1), 1850013–1–1850013–12 (2018) Aytug, H., Khouja, M., Vergara, F.E.: Use of genetic algorithms to solve production and operations management problems: a review. Int. J. Prod. Res. 41(17), 3955–4009 (2003) Nearchou, A.C., Omirou, S.L.: Differential evolution for sequencing and scheduling optimization. J Heuristics 12, 395–411 (2006) Sreekara Reddy, M.B.S., Ratnam, C., Rajyalakshmi, G., Manupati, V.K.: An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem. Measurement 114, 78–90 (2018) Patle, B.K., Parhi, D.R.K., Jagadeesh, A., Kashyap, S.K: Matrix-binary codes based genetic algorithm for path planning of mobile robot. Comput. Electr. Eng 67, 708–728 (2017) Pappula, L., Ghosh, D.: Cat swarm optimization with normal mutation for fast convergence of multimodal functions. Appl. Soft Comput. 66, 473–491 (2018) Durga Rajesh, K.V., Chalapathi, P.V., Nageswara Rao, M., Krishna, C.E., Anoop, K., Neeraj, Y.: An efficient sheep flock heredity algorithm for the cell formation problem. ARPN J. Eng. Appl. Sci. 12(21), 6074–6079 (2017) Nageswara Rao, M., Sai Bharath, C., Venkatesh, Y., Lokesh, K., Harish, V., Vara Kumari, S.: Simultaneous scheduling in FMS through priority rules. J. Adv. Res. Dyn. Control Syst. 9, 1995–2008 (2017)
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21. Nageswararao, M., et al.: Scheduling of machines and automated guided vehicles in FMS using shuffled frog leap algorithm. Int. J. Mech. Eng. Technol. 8(5), 496–503 (2017) 22. Kumar, P.A., Nageswararao, M., Kumar, T.Y., Kumar, M.M., Bhanu, V.: Scheduling of machines and automated guided vehicles in FMS using scatter search algorithm. Int. J. Mech. Eng. Technol. 8(5), 471–481 (2017) 23. Nageswara Rao, M et al.: Application of BPSO in flexible manufacturing system scheduling. Int. J. Mech. Eng. Technol. 8(5), 186–195 (2017) 24. Prakash Babu, K, Vijaya Babu, V., Nageswara Rao, M.: Implementation of heuristic algorithms to synchronized planning of machines and AGVs in FMS. Manage. Sci. Lett. 8(6), 543–554 (2018)
Design and Optimization of Engine Block Using Gravity Analysis B. Indrakanth, S. Udaya Bhaskar, CH. Ashok Kumar, and N. Srinivasa Rajneesh
Abstract The engine blocks used in automotive are subjected to dynamic forces, which creates an imbalance force on it. To avoid the impact of unbalanced forces, dynamic analysis helps in reducing the stresses induced. Component mode synthesis method is used for gravity analysis using the finite element method. After meshing of the engine block, refinement of the element size is studied for optimum results to reduce the induced stresses. Dynamic analysis of the engine block is carried out to study the frequency response. Aluminum alloy 2024 and stainless steel materials have been used for the engine block to analyze, and the results are compared for an optimum design and shape of the engine block. By using the CMS method, optimum size of the engine block is obtained. The frequency model analysis has been done on the optimized size of engine blocks. Keywords Aircraft engine block · Gravity analysis · CMS · Optimization
1 Introduction CMS method is used for analyzing large problems for discretization into finalizing structure. By this method, optimization of the size of a given component can be done by different modifications. Many researchers have been using this technique for design considerations of various components. For the dynamic analysis, CMS technique is used for determining the eigen properties. The finite element model is reduced by CMS technique to reduce the static deformation. An assembly model for the engine aircraft has been considered. The attention has been paid to the outer profile of the engine block. Further an assembly for the engine of a model aircraft has been considered to find out the stress which should not exceed the permissible stress limits for the material. Here the component used is examined and compared according to their performance, stress, displacement, weight B. Indrakanth (B) · S. Udaya Bhaskar · CH. Ashok Kumar · N. Srinivasa Rajneesh Department of Mechanical Engineering, Malla Reddy Engineering College (Autonomous), Hyderabad, Telangana 500100, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_95
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Fig. 1 Aircraft engine block
of stainless steel and aluminum alloy 2024.The task involves a rigid-body analysis. The goal is to start with the given shape and check if changes in shape can reduce stresses, while the second assignment makes use of the capability of RADIOSS to mix both forms of analysis that is motion body dynamics (MBD) analysis and the gravity analysis using components mode synthesis (CMS) (Fig. 1).
2 Literature Review (Henderson et al. [1]) Aircraft design with environmental consideration has been done, and with the help of generic algorithm technique, aircraft framework was analyzed. (Chaietal [2]) Center of gravity position of aircraft was estimated. Position of center of gravity is achieved by two different methods. These two methods will give center of gravity position with considerable range. (Daniel Bohnke et al. [3]) Conceptual design method is used to design aircraft with environmental aspects, i.e., airframe and central body. (Tao et al. [4]) Marine engine design optimization is done with vibration isolation. This optimization is achieved by sequential programming technique. (Giordano Camicia et al. [5]) Grain refinement of cylinder head is achieved by gravity die casting method. For analyzing the microstructure changes, image analysis technique is used. An error has been identified for structural model by using CMS method when compared to exact solution [6]. The results obtained from the CMS technique were very much near to the analysis Rayleigh–Ritz method and subspace iterations [7]. In order to decrease the error using subspace iteration method, AMLS technique was considered which obtained reliable results [8]. For enhanced measurement of error which were near to exact error were defected for upper bonds on errors using CMS and subspace iteration. For improved techniques
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to obtain frequencies and mode shapes, Lanczos transformation was employed to obtain solutions with error bonds in comparison with subspace.
3 Methodology Craig–Bampton method uses CMS technique for determining approximate eigen values and eigen vectors for large continuum FEM problems. The governing equations for the engine block are developed by using FEM. The dynamic equilibrium equations for the engine block are written in terms of mass matrix M, stiffness matrix K, force matrix F and displacement vector x (Table 1) (Fig. 2). Dynamic response of the spacecraft is given by M X¨ + K X = F
MN N MN I M NT I M I I
Table 1 Comparison between material properties of aluminum alloy 2024 and stainless steel
X¨ N X¨ I
KNN KNI + K NT I K I I
X¨ N X¨ I
=
Physical Properties Aluminum alloy 2024 Density
3.1e−9 ton/mm3
FN F1
Stainless steel 9.01e−9 ton/mm3
Mechanical properties Hardness, Brinell
120
80
Hardness, Rockwell A
46.8
58
Hardness, Rockwell B
75
130
Hardness, Vickers
137
82
Ultimate tensile strength
586 MPa
3000 MPa
Elongation at break 100%
88%
Modulus of elasticity
73100 MPa
210000 MPa
Bearing yield strength
441 MPa
262 MPa
Poisson’s ratio
0.33
0.3
Fatigue strength
138 MPa
1070 MPa
Machinability
70%
65%
Shear modulus
28,000 MPa
86,500 MPa
Shear strength
283 MPa
597 MPa
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Fig. 2 Imported aircraft engine model in hypermesh through IGES
X=
XN XI
The generalized eigenvalue problem is
K − 2n M φn = 0
Transform the physical coordinate x as X=
XN XI
φ N N φC N = 0 1
qN X1
The eigenvectors are mass normalized. Thus, M˜ N N = φ NT N M N N φ N N = I φ NT N K N N φ N N = 2n where 2n is the eigenvalue matrix for the fixed interface modal analysis.
4 Dynamic Analysis of Engine Block Aircraft engines are subject to multiple types of dynamic loads from rotor harmonic vibrations, pressure fluctuations from turbulence, unbalance loads and frequency shock due to landing and payload handling. Many of the loads are periodic and occur at harmonics of the rotor system. Other loads are random in nature and still others are truly frequency or shock pulses. Often these loads occur simultaneously and thus analyze the need to evaluate their combined effect. This complex interaction
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of different dynamic loads is well supported by the Altair OptiStruct FEA response simulation capabilities which were used for all forced response analysis and function processing. Unit varying load is applied about 20 N at a particular node number 47817; at this node location, forced vibration is acting when engine gets started. Frequency response analysis is carried to solve the dynamic behavior of engine block.
4.1 Analysis Results for Engine Block Made of Stainless Steel Material Stainless steel material is also applied for the same engine block and calculated the results for comparing the stress and displacement. Using Radioss software, gravity analysis is carried out for stainless steel engine block. Acceleration results are shown below in all directions. Aluminum 2024 alloy generally has better corrosion resistance than steel and weighs roughly 1/3 of stainless steel. The two different analyses of the engine block are displayed. Based on the results, the shape optimized stainless steel engine block is having less percentage variation compared to aluminum alloy 2024, but stainless steel engine block is little better in displacement comparison.
4.2 Optimization Results and Redesign The modified shape of engine block is given by shape optimization. The same model should be redesigned and should rerun the analysis of optimization and check the comparison of new shape of engine block and previous shape of engine block (Figs. 3, 4, 5 and 6). For two materials, the same shape of engine block is given by Altair OptiStruct software. Create a load collector in which card image should be GRAV. In that mention the gravity value 1 along which axis the rotation is required. In all three directions are the requirement, so separately created for x-direction, y-direction and z-direction. This is shown below how to apply in hypermesh. Results for engine block after shape optimization displacement and stresses are shown below for new design model of aluminum alloy 2024 material (Figs. 7 and 8) (Table 2). Displacement and stresses that are compared for stainless steel engine block and aluminum 2024 engine block are shown in results. The shape optimized model of the engine block will have more strength in running condition, because when compared, the displacement and stresses results of second (shape optimized) model with the old or reference model of the engine block give stiffer outcome. With the engine block shape optimized results, there is no requirement of doing any kinematic simulation. When compared to stainless steel shape optimized model, the stress and displacement variation is little, whereas in aluminum alloy 2024 shape optimized model, variation
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Fig. 3 Displacement magnitude
Fig. 4 Stress contour plot of engine block made of aluminum alloy 2024 material Fig. 5 Modified shape of engine block in Altair HyperView
Design and Optimization of Engine Block Using Gravity Analysis Fig. 6 Engine block is meshed using tetra mesh with 30 mm element size
Fig. 7 Displacement of new design in magnitude
Fig. 8 Engine blocks new shape optimized analysis results SS
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Table 2 Comparison of displacement and stress for stainless steel and aluminum alloy 2024 Displacement Minimum
Stress Maximum
Minimum
Maximum
Stainless steel
0
2.924
1.1.36
7.11
Aluminum alloy 2024
0
3.265
4.473
2.688
in stress and displacement is somewhat countable that may help in providing stiffer engine block model.
4.3 Cost Analysis The results conclude that the weight of stainless steel engine block is more (1,285,000 g), when compared to aluminum alloy 2024 engine block that weighs very less (498,400 g) which may result in weight and cost reduction at the same time. The purpose of the project is partially achieved here with a good percentage of saving on material cost and efficiency of the engine due to the less weight. The cost of aluminum alloy 2024 in market is about Rs. 113/-, whereas the stainless steel costs about Rs. 200/-. The weight of the aluminum alloy 2024 engine block is 498.400 Kgs, and the stainless steel engine block weighs 1285Kgs. The engine block material cost for aluminum alloy 2024 498.400 × 113 = Rs. 56,319/- and stainless steel is 1285 × 200 = Rs. 257,000/-, thus the saving is about 257,000−56,319 = Rs. 200,681. The result outcome is a net savings of Rs. 200,681/-.
5 Results and Discussion 5.1 Comparison of Results of Old and New Designs of Engine Block The results obtained from the comparison of displacement and stresses of the reference engine block and the new shape engine block are displayed.
5.1.1
Dynamic Analysis Results Are Shown Below
Displacement curve for both the designs are shown in above graph, which represents that red curve is for aluminum 2024 optimized design, blue color indicates base design, and green indicates stainless steel material. Stainless steel material curve if we observe in above graph is having large displacement at 190 to 300 Hz range. Green
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curve is failing at 20–110 Hz range. Blue curve is for aluminum base design which is having frequency about 80–118 Hz. Red curve indicates optimized aluminum model graph. If we observe the curve above, it is not having any vibration from 0 to 118 Hz. Remaining two cases are failed at the vibration level itself (Graph 1). Finally, by seeing the graph, we can conclude that optimized model is perfect which suits the engine in running condition. Stainless steel weight is 1285000 g, and aluminum 2024 engine block weight is 498400. By observing the weight of optimized engine block design of two materials that is stainless steel and aluminum 2024, aluminum material is perfect suitable for engine block. (a) (b)
Aluminum alloy 2024 engine block weight = 498400 g Stainless steel engine block weight = 1285000 g
Graph 1 Displacement curve between magnitude and frequency
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Engine block weight is more if stainless steel is considered, whereas weight of aluminum alloy 2024 is very less compared to stainless steel material. For stress and displacement graphs for new shape optimized models, the 2D plot is matching which indicates that same graph is replaced for two materials: stainless steel and aluminum alloy 2024. There is no huge change in results of optimized models. Complete analysis is done for base design of engine block using two different materials like stainless steel and aluminum alloy 2024 which are regularly used in aircraft engines.
6 Conclusions Gravity analysis has been done by using CMS technique. The experimental data provided by the engine manufacturer was compared with the computed results. The results quite match with reference to model virtual analysis. To give better design for engine manufacturer, the shape optimization is used and solved using Altair OptiStruct. The frequency response analysis for the engine block using CMS technique has drawn the displacement difference between the materials used in the analysis when running at different frequencies. The aluminum alloy 2024 has shown 11.24% more deformation when compared with stainless steel material. From the cost analysis due to the weight of materials, by using aluminum alloy 2024, the cost decreased by 78.08%. Vibrations were not observed until 118 Hz for the optimized engine block. The optimized model with aluminum alloy 2024 which was 61.21% lesser weight has shown better performance when compared to the stainless steel material. Finally concluding that second shape optimized aluminum alloy 2024 engine block design can be suggested to original equipment manufacturer for the latest design of the engine block for the market of model aircraft.
References 1. Henderson, R.P., Martins, J.R.R.A., Perez, R.E.: Aircraft conceptual design for optimal environmental performance. Aeronaut. J. 116(1175), 1–22 (2012) 2. Chai, S., Crisafulli, P., Mason, W.: Aircraft center of gravity estimation in conceptual/preliminary design. In: Aircraft Engineering, Technology, and Operations Congress (1995) 3. Böhnke, D., Nagel, B., Gollnick, V.: An approach to multi-fidelity in conceptual aircraft design in distributed design environments. In: 2011 Aerospace Conference. IEEE (2011) 4. Tao, J.S., Liu, G.R., Lam, K.Y.: Design optimization of marine engine-mount system. J. Sound Vib. 235(3), 477–494 (2000) 5. Camicia, G., Timelli, G.: Grain refinement of gravity die cast secondary AlSi7Cu3Mg alloys for automotive cylinder heads. Trans. Nonferrous Met. Soc. China 26(5), 1211–1221 (2016) 6. Filippone, A.: Theroretical framework for the simulation of transport aircraft flight. J. Aircr. 47(5), 1679–1696 (2010) 7. Jakobsson, H., Larson, M.G.: A posteriori error analysis of component mode synthesis for the elliptic eigenvalue problem. Comput. Meth. Appl. Mech. Eng. 200, 2840–2847 (2011)
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8. Zingoni, A.: Symmetry recognition in group-theoretic computational schemes for complex structural systems. Comput. Struct. 94–95, 34–44 (2012) 9. Bathe, K.J.: The subspace iteration method—revisited. Comput. Struct. 126, 177–183 (2013) 10. Yin, J., Voss, H., Chen, P.: Improving eigenpairs of automated multilevel sub structuring with subspace iterations. Comput. Struct. 119, 115–124 (2013) 11. Jenkinson, L., Simkin, P., Rhodes, D.: Civil jet aircraft design. Butterworth Heinemann (1999) 12. Koch, A., Nagel, B., Gollnick, V., Dahlmann, K., Grewe, V., Karcher, B., Schumann, U.: Integrated analysis and design environment for a climate compatible air transport system. AIM Aviation Technology, Integration and Operations Conference (2009) 13. Kroo, I., Takai, M.: A Quasi-procedural, knowledge-based system for aircraft design. AIM 6502 (1988)
Performance of an Evaporative Condenser: A Review Vivek M. Korde, Shivam N. Dekate, Yash A. Bais, and Chirag P. Raut
Abstract Involving the proper design of an evaporative condenser to improve, system efficiency is a complex and difficult undertaking. Careful study is required in the construction of an evaporative condenser, since splitting of water, reduced effective cooling, lower power capacity and tubes identification are difficulties to deal with. The study is meant to offer a methodology for engineers to follow whilst working on evaporative-cooled heat exchangers. A method for increasing performance is discovered, and the research with several kinds of evaporative condensers is investigated. As it was discovered, it was possible to boost the system’s performance by incorporating features like water beds, forced method, exhaust procedure above nozzles, tapering walls to the sump, dome-shaped fans and elliptical (oval) tube designs. Finally, conclusions on designing improved evaporative condensers are given. Keywords Evaporative · Condenser · Water pad · Forced · Cooling · Efficiency · Industry
Nomenclature (Taken as references from papers) hw hd Gw Ga T wm T wm
Film water heat transfer Coefficient, (W/m2 K) Air water mass transfer coefficient, (W/m2 K) Water mass velocity, (Kg/m2 s) Air mass velocity, (Kg/m2 s) Mean deluge water temperature, (°C) Water Temperature, (°C)
V. M. Korde · S. N. Dekate (B) · Y. A. Bais · C. P. Raut Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 P. K. Chaurasiya et al. (eds.), Technology Innovation in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-7909-4_96
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Mass transfer coefficient for water vapour, (W/m2 K) Heat transfer coefficient between tube surface and water film, (W/m2 K) Mass flow rate, (Kg/s) Maximum mass flow rates, (Kg/s) Efficiency of evaporative condenser. Flow rate of liquid, (Kg/s) Flow rate of air, (Kg/s) Temperature of heating steam, (°C) Wet bulb temperature, (°C) Efficiency of air condenser. Outer heat transfer coefficient, (W/m2 °C) Percentage of evaporated water
1 Introduction Vital factors for the design of cooling systems include water shortage, energy conservation and effluent control. Because of this, the realisation of water uniformity from a single source with fine dispersion of water and minimal use is called for. An industrial unit that is run in accordance with all of these guidelines is known as an evaporative condenser. A machine that takes heat from operating refrigerant and transmits it to the environment using cooling tubes with water sprinklers is known as an evaporative condenser. Acquiring the circulation of air over the surface using axially fitting fans may be done, however these condensers are found in air conditioning plants, water cooling systems and industry facilities. Air velocity range of 1.5–4 m/s is generally accepted for economic design considerations. As the temperature of the air increased from 10 to 20 °C, finally, the air became warm. With a forced flow concept, most evaporative condensers are utilised for cooling purposes since they are needed to have excellent efficiency. However, improvements to the condenser unit’s efficiency are currently being explored. These passive and active type performance-improving factors are analysed first in this study. By contrast, ‘active methods’ include any alteration to the character design, such as tube modification, expanded surface area or even the addition of elements. To get particular attention, experimental studies are conducted along with condenser losses.
2 Passive Technique This terminology may describe methods that are concerned mainly with solving design issues for evaporative condensers using numerical simulation, and where software simulations are used without any external changes.
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2.1 Numerical Simulation In the development of mathematical simulations [1–3], several design models with various modifications to size were used. They also included the properties of heat and mass transport in the calculations, using empirical formulas. According to these studies [4], in contrast to the research that concentrated on the heat transfer coefficient for water-only systems, the research on evaporative effectiveness and mass transfer coefficient, as well as air flow, for wet-only systems was performed. When the air flow rises and the coolant and product flow rates increase, heat transfer coefficient for the water side decreases. The calculation was performed with an accuracy of 20% in range, ranging from 13 to