Intelligent Manufacturing and Mechatronics: Proceedings of SympoSIMM 2021 (Lecture Notes in Mechanical Engineering) 9811689539, 9789811689536
This book presents the proceedings of SympoSIMM 2021, the 4th edition of the Symposium on Intelligent Manufacturing and
141 56 87MB
English Pages 586 [579] Year 2022
Preface
Organizing Committee
Patron
Advisors
Chairman
Vice Chairmen
Secretary
Treasurer
Technical
ICT and Website
IEEE Advisors
Registration, Souvenirs and Banquet
Sponsorship
Promotion and Publishing
Logistic
Floor Managers
Contents
List of Contributors
Intelligent Manufacturing and Artificial Intelligence
Development of a Posture Corrector Device with Data Analysis System
1 Introduction
2 Methodology
2.1 Microsensor Technique
2.2 Computer Vision Technique
3 Results
3.1 Verification and Test Results for Microsensor Technique
3.2 Verification and Test Results for Computer Vision Technique
3.3 Comparison of Microsensor Technique and Computer Vision Technique
4 Conclusion
4.1 Future Works
References
Automated Modulated Parameter Implementation Using Neural Network for Enhancement of Paint Spray
1 Introduction
2 Methodology
3 Results and Discussions
3.1 Number of Neurons for ANN Model
3.2 ANN Model
4 Conclusion
References
A Novel Voice-Activated Smart Irrigation System Implementation and Analysis Using Internet of Things
1 Introduction
2 Previous Work
3 Proposed Approach
3.1 Node MCU ESP8266
3.2 Temperature and Humidity Sensor
3.3 Soil Moisture Sensor
3.4 How It Works
4 Implementation and Results
5 Conclusion
6 Future Work
References
Conceptual Design of Cloud-Based Data Pipeline for Smart Factory
1 Introduction
2 Literature Review
3 System Development
4 System Functions
5 System Behaviour
6 Cloud-Based Data Pipeline
7 Application Example: Predictive Maintenance (PdM)
8 Conclusion
References
The Optimization of the Halophilic Cellulase Production: A 3-2-1 Multilayer Perceptron Artificial Neural Network Approach
1 Introduction
2 Research Methodology
3 Results and Discussion
4 Conclusion
References
Environmental Visual Features Based Place Recognition in Manufacturing Environment
1 Introduction
2 Environmental Visual Features
2.1 Color Features
2.2 Shape Features
2.3 Neural Network
3 Methodology
4 Result and Discussion
5 Conclusion
References
Comparison of Sampling Methods for Rao-Blackwellized Particle Filter with Neural Network
1 Introduction
2 Previous Works
3 Methodology
3.1 Map Update with Neural Network
3.2 Grid-Based SLAM Algorithm
4 Results and Analysis
4.1 Experiment Setup
4.2 Experiment Results
4.3 Analysis
5 Conclusion and Future Works
References
Instrumentation and Control
Performance Analysis of Conditional Integrator in NPID Controller as Cutting Force Compensator for Machine Tools Application
1 Introduction
2 Methodology
2.1 Simulation Setup
2.2 Cutting Force Analysis
2.3 Control Mechanism Design
3 Results and Discussions
4 Conclusion
References
Experimental Investigation on Acoustic Energy Conversion Using Single and Double PZT Film by Modified Resonator
1 Introduction
2 Methodology
2.1 Physics of Sound Energy Conversion
2.2 PZT Film Description
2.3 Geometry
2.4 Experimental Setup
3 Results and Discussion
3.1 Experimental Results
4 Conclusions
References
Low-Cost Air Quality Monitoring Platform Using Flying Wing Drone
1 Introduction
2 Literature Review
3 Methodology
4 Results and Discussions
4.1 Air Flow Test Without Smoke
4.2 Air Flow Test with Smoke
5 Conclusion
References
Design and Analysis of Modified Nonlinear PID Controller for Disturbance Suppression in Machine Tools
1 Introduction
2 System Modelling
3 Design of Controllers
3.1 PID Controller
3.2 NPID Controller
3.3 Modified NPID Controller
4 Results and Discussion
4.1 Tracking Error Reduction
4.2 Cutting Force Rejection
5 Conclusion and Future Recommendations
References
Enhance Ride Comfort and Road Handling on Active Suspension System by PSO-Based State-Feedback Controller
1 Introduction
2 System Description
2.1 Quarter-Car Suspension Model
2.2 Road Excitation Model
3 Controller Design
3.1 Performance Index as a Cost Function in Active Suspension System
3.2 State Feedback Control for Vibration Control
3.3 PSO Algorithm for Tuning State Feedback Gain Controller
4 Simulation Results and Discussion
4.1 Single Bump Road Profile Response
4.2 Sinusoidal Waveform Response
4.3 Chirp Response
5 Conclusions
References
The Development of Underwater Crawler Attachment for Remotely Operated Underwater Vehicle
1 Introduction
2 Literature Review
3 Methodology
4 Result and Discussion
4.1 Movement Test
4.2 Obstacle Test
5 Conclusion
References
Analysis and Development of a Self-dimming Traffic Light System
1 Introduction to Traffic Light System
2 Literature Review
2.1 Past Works/Previous Research
2.2 Fundamental Electronics Theory
3 Methodology
3.1 Dimming Circuit Implementation
3.2 Dimming Circuit with Sequence
3.3 Current Detection
3.4 Fault Detection Using Blink Circuit
3.5 Humidity and Temperature Detection
4 Results, Analysis and Discussion
4.1 The Current Reading for LED Traffic Light
References
Pressure Analysis in Water Hydraulics Machine: Continuous and Intermittent Extrusion Cycle in Dough Extrusion
1 Introduction
2 Methodology
3 Results and Discussion
3.1 Continuous Extrusion Cycle
3.2 Intermittent Extrusion Cycle
4 Conclusion
References
Investigation on Disturbance Force Compensation via State Observer Design and Cascade P/PI Controller Approach
1 Introduction
2 System Setup
3 Controller and State Observer Design
3.1 Cascade P/PI
3.2 State Observer
4 Results and Discussion
5 Conclusion
References
Review of Recent Grid Synchronization Techniques and Phase-Locked Loops for Power Converters
1 Introduction
2 PLL Description and Working
3 Review of Recent Trends in PLLs and Other Grid Synchronization Techniques
4 Conclusion
References
Fuzzy Logic Approach to Fire Monitoring and Warning System Design
1 Introduction
2 Implementing Fuzzification
2.1 Membership Function of Output Fire-Chances
2.2 Defining Fuzzy Rules
3 Result and Discussion
4 Conclusion
References
Pressure Analysis in Water Hydraulics Machine: Dough and Aluminum Beverage Can Compression Test
1 Introduction
2 Literature Review
3 Methodology
4 Results and Discussion
4.1 Pressure Analysis on Continuous Compression Test at 6 Bar System Pressure
4.2 Pressure Analysis on Continuous Compression Test at 9 Bar System Pressure
4.3 Pressure Analysis on Continuous Compression Test at 12 bar System Pressure
4.4 Pressure Analysis on Continuous Compression Test at 15 bar System Pressure
5 Conclusion
References
Performance Evaluation of Intelligent Fire Alarm System with Multi Data Fusion Sensor by Using IoT Platform
1 Introduction
2 Related Works
3 Materials and Methods
3.1 System Design Overview
3.2 Hardware System Design
3.3 Software Design
3.4 Experimental Setup
4 Results and Discussion
5 Conclusions and Future Tasks
References
Design and Development of Handheld Soil Assessment by Using Ion-Selective Electrode for Site-Specific Available Potassium in Oil Palm Plantation
1 Introduction
2 Related Works
3 Method and Materials
3.1 Data Acquisition System
3.2 Potassium Based Ion-Selective Electrode Sensor
3.3 Soil Electrical Conductivity Measurement
3.4 Initial Calibration Analysis
3.5 Soil Sensor Data Analysis
3.6 Soil Sampling Method
4 Result and Discussion
4.1 ISE Sensor Analysis
4.2 Soil Sampling Data Analysis
4.3 Soil Mapping Analysis
5 Conclusion
References
Performance Evaluation of Energy Harvesting Method for Wireless Charging System in Wearable Travel Aid Device for Visually Impaired Person
1 Introduction
2 Related Works
2.1 Developed Wearable Travel Aid Device
2.2 Energy Harvesting System
2.3 Energy Harvesting Device
3 Method and Materials
4 Experimental Result and Discussion
4.1 Comparison Between the Best Type of Energy Harvester
4.2 Comparison for Photovoltaic, PD, and RF in a Series Configuration
4.3 Comparison for Peltier, Photovoltaic, PD, And RF in a Parallel Configuration
4.4 Best Configuration for Energy Harvesting Method
5 Conclusions
References
Design and Application of ST-SMC Controller for Position Control of a Milling Table
1 Introduction
2 System Setup
3 Super Twisting Sliding Mode Controller
3.1 Switching Function
3.2 Control Law
4 Results and Discussion
5 Conclusion
References
Design, Modelling and Simulation
Lean Manufacturing Design of a Two-Sided Assembly Line Balancing Problem Work Cell
1 Introduction
2 Manufacturing: Lean and Just-In-Time
3 Assembly Line Balancing
4 Methods and Materials
4.1 Heuristic Method of Line Balancing
4.2 Experimental Techniques
5 Results and Discussion
5.1 Measures of Line Balancing
5.2 Experimentation Observation
6 Conclusions
References
Partial Transmit Sequence (PTS) Optimization Using Improved Harmony Search (IHS) Algorithm for PAPR Reduction in OFDM
1 Introduction
2 Related Works
2.1 Peak-To-Power Average Ratio (PAPR)
2.2 Partial Transmit Sequence (PTS)
2.3 Harmony Search (HS) Algorithm
2.4 Improved Harmony Search (HS) Algorithm
3 Methodology
3.1 Implementation of Parameters
3.2 Partial Transmit Sequence Implementation
3.3 Improved Harmony Search (IHS) Implementation
4 Results and Discussion
4.1 Simulation Result with Different Number of Subcarriers
4.2 Simulation Result with Different Number of Iterations
5 Conclusion
References
Ergonomics Study of Standing Work Postures in Assembly Process at Small Medium Industry Manufacturing Company
1 Introduction
2 Methodology
2.1 Participants
2.2 Body Symptoms Survey
2.3 RULA Analysis
2.4 Takt Time Calculation
3 Results
3.1 RULA Analysis at Current Workstation 1
3.2 RULA Analysis at Proposed Workstation 1
3.3 RULA Analysis for at Current Workstation 2
3.4 RULA Analysis at Proposed Workstation 2
3.5 RULA Analysis at Current Workstation 3
3.6 RULA Analysis at Proposed Workstation 3
3.7 Analysis of Takt Time and Productivity
4 Conclusion
5 Recommendation
References
Safety Helmet Head Impact Monitoring System Using Long Range (LoRa) Communication for Mining Industry
1 Introduction
2 Literature Review
2.1 Importance of Head Protection
2.2 Head Injuries in Industries
3 Methodology
3.1 Component Selection
3.2 Internet of Things Dashboard Design
3.3 Functionality Test
3.4 Alarm Based on Pushbutton
4 Result, Analysis and Discussion
4.1 Alarm Based on Impact Detection
4.2 Alarm Based on Pushbutton
5 Conclusion
References
Computational Analysis of Tool Geometry Effect on Cutting Process in Turning of AISI 1020 Steel
1 Introduction
2 Finite Element Analysis Input Parameters
3 Results and Discussion
3.1 Effect of Rake Angle
3.2 Effect of Relief Angle
3.3 Effect of Cutting Edge Radius
4 Conclusions
References
Structural Vibration Study of a New Concept Intelligent Rubber Tapping Machine
1 Introduction
2 Materials and Method
2.1 Conceptual Design of Intelligent RTM
2.2 Structural Vibration Analysis
3 Results and Discussion
3.1 Modal Analysis
3.2 Transient Analysis
4 Conclusion
References
Study of Long Range (Lora) Network Coverage for Multi Areas
1 Introduction
2 Research Background
2.1 IoT Communication Systems
2.2 Low-Power Wide-Area Network (LPWAN)
2.3 Different Between LoRa and LoRaWAN
3 Methodology
3.1 Experimental Setup
3.2 Topology Testing
3.3 Elevation Testing
3.4 Dynamic Motion Testing
4 Result and Analysis
4.1 Open Space
4.2 Residential Area
4.3 Elevation
4.4 Dynamic Motion
5 Conclusion
References
Simulation Experiments of Pipe Network and Pumps for Application of Fertigation System Using MATLAB Simscape
1 Introduction
2 System Model
2.1 Experiment’s Set up
3 Result and Discussions
4 Conclusion
References
Manual Material Handling Assessments Towards the Working Comfort in an Automotive Manufacturing Company
1 Introduction
2 Methodology
3 Results of Observation and RULA Analysis
4 Intervention Agenda on Reducing the MSDs Related Injuries During Manual Material Handling
5 Conclusion
References
Conceptual Architecture Development of Virtual Reality – Motion Capture System to Analyze Accessibility and Clearance in Front-End Engineering Design Process: An Exploratory Study
1 Introduction
2 Methodology
2.1 Overview
2.2 Problem Identification
2.3 Architecture Development
2.4 Preliminary Validation
3 Results
3.1 Problem Identification
3.2 Architecture Development
3.3 Preliminary Validation
4 Discussion
5 Conclusion
References
Nonlinear Control of Hexarotor System Using Proportional Derivative Sliding Mode Controller (PD-SMC)
1 Introduction
2 Mathematical Modelling
3 Sliding Mode Control (SMC)
4 Simulation Results
5 Conclusion
References
Aerial Based Traffic Tracking and Vehicle Count Detection Using Background Subtraction
1 Introduction
2 Literature Review
3 Methodology
3.1 Datasets for Traffic Analysis
3.2 Software Setup
3.3 Centroid Tracking Algorithm
4 Results and Discussion
4.1 Results from NTebal1 Dataset
4.2 Results from NTebal2 Dataset
4.3 Results from Jawi1 Dataset
5 Conclusion and Future Works
References
Simulating Solitary Foraging Behaviour of Chimpanzee in Hunting Red Colobus Monkeys Using Agent-Based Modelling Approach
1 Introduction
2 Methodology
3 Results and Discussions
3.1 Analyses of Task 1
3.2 Analyses of Task 2
3.3 Analyses of Task 3
3.4 Analyses of Task 4
4 Conclusion
References
Design of Drilling Mechanism for Aquilaria Tree Climbing Modular Robot
1 Introduction
2 Methodology
2.1 Conceptual Design
2.2 Circuit Diagram
3 Results and Discussion
3.1 Drilling Mechanism
3.2 Power Consumption
3.3 Discussion
4 Conclusion
References
Process and Machining Technology
Review on Experimental Design, Process Parameters and Responses of Compression Moulding Process
1 Introduction
2 Input Parameters and Responses
3 Design of Experiment (DOE)
4 Conclusion
References
Tool Life and Surface Roughness of Inconel 718 During End Milling Under Dry, Chilled Air and Chilled MQL
1 Introduction
2 Experimental Work
3 Result and Discussion
3.1 Tool Life and Wear Mechanism
3.2 Surface Finish
4 Conclusion
1. References
Application of Lean Manufacturing Tools: The Impact on Kaizen and Product Defection in Packaging Companies
1 Introduction
2 Quality Standard Kaizen
2.1 4M (Method, Man, Material, and Machine)
2.2 5S (Sort, Shine, Set in Order, Standardize, and Sustain)
2.3 PDCA (Plan, Do, Check, and Act)
2.4 Fishbone Diagram
3 Methodology
3.1 Conceptual Framework
3.2 Sample and Data
4 Results and Discussions
4.1 Descriptive Statistics
4.2 Multiple Regression Analysis
5 Conclusions
References
The Utilisation of Kansei Engineering in Designing Conceptual Design of Oil Spill Skimmer
1 Introduction
2 Review on Kansei Engineering
3 Framework of Kansei Engineering
3.1 Step 1: Collection of Kansei Words
3.2 Step 2: Setting of Semantic Differential (SD) Scale
3.3 Step 3: Collection of Product Sample
3.4 Step 4: A List of Item/category Classification
3.5 Step 5: Questionnaire Evaluation
3.6 Step 6: Analysis of Data Using Partial Least Squares (PLS)
4 Conclusion
References
A Study on Post Processor for 5-Axis CNC Milling via CAD/CAM System
1 Introduction
2 Methodology
3 Result and Discussion
4 Conclusion
References
Development of Image-Based Drill Bit Wear Detection System for Drilling Application
1 Introduction
2 Prototype Development
2.1 Experimental Setup
2.2 Image Analysis on the Visual Inspection System
2.3 Image Pre-processing
2.4 Image Processing
2.5 Edge Detection
3 Results and Discussions
3.1 Results of Non-wear and Wear Drill Bit
3.2 Experimental Results
3.3 Tool Life Cycle Results
4 Conclusions
References
Review on Turning Process Parameters, Responses and Experimental Method of AISI 1045 Carbon Steel
1 Introduction
2 Turning Process Parameters and Responses
3 Experimental Method
4 Conclusion
References
Dimensional Error Evaluation of Five-Axis Flank Strategies for an Angled Thin-Walled Pocketing: Aerospace Part
1 Introduction
2 Methodology
2.1 CAD Model Preparation
2.2 CAM Program Preparation and Cutting Strategy
2.3 Dimensional Error Evaluation
3 Result and Discussion
4 Conclusion
References
A Review on the Influence of Heat Treatment on the Fracture Strength and Microstructure of Zirconia Dental Restorations
1 Background
2 Heat Treatment
2.1 Temperature and Methods
2.2 Influence of Heat Treatment on Fracture Strength and Microstructure
3 Discussion
4 Conclusion
References
Influence of Humidity on the Tensile Strength of 3D Printed PLA Filament
1 Introduction
2 Materials and Method
3 Results and Discussion
4 Conclusion
References
Smart Material
Single Fibre Strength Test on Rattan as Natural Fibres
1 Introduction
1.1 Single Fibre Test Towards Natural Fibres
1.2 Sample Selection for Testing
2 Methodology
3 Results and Discussion
4 Conclusions
References
Structural, Morphological and Electrochemical Analyses of Graphene/Molybdenum Disulfide Supercapacitor
1 Introduction
2 Methodology
2.1 Electrode Preparation
2.2 Electrochemical Performance of Graphene/MoS2Electrodes
3 Results and Discussion
3.1 Structural and Morphological Properties of 75G:25MoS2
3.2 Electrochemical Performance of G/MoS2Electrodes
4 Conclusions
References
Brief Review on Potential Production of Plastic Waste Concrete Aggregates Using Water-Assisted Melt Compounding
1 Introduction
2 Materials
2.1 Types of Thermoplastic Waste
2.2 Plastic Aggregates for Concrete
3 Processing
3.1 Existing Processing Method of Plastic Concrete Aggregates
3.2 Potential of Water-Assisted Compounding
4 Conclusions
References
Physical Characteristics of Polyaniline-Graphene Nanoplatelets (PANI/GNPs) via Oxidation Polymerization of Aniline
1 Introduction
2 Experimental
2.1 Reagents and Apparatus
2.2 Synthesis of PANI/GNPs-DBSA
2.3 Material Characterization
3 Result and Discussion
3.1 FTIR Analysis
3.2 XRD Analysis
3.3 RAMAN Analysis
4 Conclusion
References
Tensile Strength, Fractograph and Microstructure of Aluminium Alloy (Al-Si) Reinforced by Nickel Oxide (NiO) at Various Temperature of Stir Casting Method
1 Introduction
2 Experimental Details
3 Result and Discussion
4 Conclusion
References
Synthesis and Characterization of Cobalt Oxide Powder with Sintering Duration Variation by Sol-Gel Method
1 Introduction
2 Materials and Method
3 Result and Discussion
4 Conclusion
References
Author Index
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Lecture Notes in Mechanical Engineering
Mohd Najib Ali Mokhtar Zamberi Jamaludin Mohd Sanusi Abdul Aziz Mohd Nazmin Maslan Jeeferie Abd Razak Editors
Intelligent Manufacturing and Mechatronics Proceedings of SympoSIMM 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
To submit a proposal or request further information, please contact the Springer Editor of your location: China: Ms. Ella Zhang at [email protected] India: Priya Vyas at [email protected] Rest of Asia, Australia, New Zealand: Swati Meherishi at [email protected] All other countries: Dr. Leontina Di Cecco at [email protected] To submit a proposal for a monograph, please check our Springer Tracts in Mechanical Engineering at https://link.springer.com/bookseries/11693 or contact [email protected] Indexed by SCOPUS. All books published in the series are submitted for consideration in Web of Science.
More information about this series at https://link.springer.com/bookseries/11236
Mohd Najib Ali Mokhtar Zamberi Jamaludin Mohd Sanusi Abdul Aziz Mohd Nazmin Maslan Jeeferie Abd Razak •
•
•
Editors
Intelligent Manufacturing and Mechatronics Proceedings of SympoSIMM 2021
123
•
Editors Mohd Najib Ali Mokhtar Department of Manufacturing Engineering Technical University of Malaysia Malacca Durian Tunggal, Melaka, Malaysia
Zamberi Jamaludin Department of Manufacturing Engineering Technical University of Malaysia Malacca Durian Tunggal, Melaka, Malaysia
Mohd Sanusi Abdul Aziz Department of Manufacturing Engineering Technical University of Malaysia Malacca Durian Tunggal, Melaka, Malaysia
Mohd Nazmin Maslan Department of Manufacturing Engineering Technical University of Malaysia Malacca Durian Tunggal, Melaka, Malaysia
Jeeferie Abd Razak Department of Manufacturing Engineering Technical University of Malaysia Malacca Durian Tunggal, Melaka, Malaysia
ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-981-16-8953-6 ISBN 978-981-16-8954-3 (eBook) https://doi.org/10.1007/978-981-16-8954-3 © 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
Preface
The Fourth Symposium on Intelligent Manufacturing and Mechatronics (SIMM2021) is organized by the Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka (UTeM), on 8 November 2021. This symposium was a manifestation of the commitment and strong belief of all who have contributed to its success in embracing Industrial Revolution 4.0 as the way forward for manufacturing entities, especially those in Malaysia. The relevance of this symposium was evidence from its successful previously hosted SIMM event in the year 2018 at the Faculty of Manufacturing Engineering, Universiti Malaysia Pahang (UMP), 2019 at the Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka (UTeM), and 2020 at Universiti Malaysia Perlis (UniMAP). This year’s event has attracted a substantial number of postgraduates and researchers in the field of manufacturing and mechatronics, providing opportunities for intellectual discourse on recent topics related to the subject matters. This symposium has enticed 74 submissions from authors nationwide and internationally. Each submission has undergone a rigorous peer review process according to the selected tracks. Reviews were based on the manuscript’s relevance to the tracks and the relevance of the topics to the theme of the symposium, that is Intelligent Manufacturing and Mechatronics. Following the review process, 53 submissions were accepted, while 4 submissions were withdrawn, and 17 submissions were rejected due to various technical reasons. The accepted papers were divided into five tracks covering various scopes of manufacturing engineering and mechatronics, namely intelligent manufacturing, and artificial intelligence (IMAI), instrumentation, and controls (IC), design, modelling and simulation (DMS), process and machining technology (PMT) and smart material (SM). This book is organized into five parts based on the tracks. We would like to express our gratitude to all members of the Organizing Committee, without whom the organization of this symposium would be impossible. Special appreciation to the management of Universiti Teknikal Malaysia Melaka, the management Faculty of Manufacturing Engineering, UTeM, the co-organizer from UMP, UniMAP, Institute of Electrical and Electronic v
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Preface
Engineering (IEEE) (Institute of Electrical and Electronics Engineering) (Institute of Electrical and Electronic Engineering), and all the sponsors who have been kind in their support towards the successful organization of this symposium. In addition, we would like to extend our thanks to all authors for their participation and contributions towards the success of this symposium, without whom this symposium will not be a reality. We hope that the contents of this book will benefit all readers in embracing the new era of Industrial Revolution 4.0 with a special focus on the field of manufacturing engineering and mechatronics. November 2021
Mohd Najib Ali Mokhtar Zamberi Jamaludin Mohd Sanusi Abdul Aziz Mohd Nazmin Maslan Jeeferie Abd Razak
Organizing Committee
Patron Ghazali Bin Omar Zulkifilie Bin Ibrahim
Advisors Zamberi Jamaludin Mohd Shukor bin Salleh
Chairman Mohd Najib b. Ali Mokhtar
Vice Chairmen Mohd Nazmin bin Maslan (UTeM) Nafrizuan bin Mat Yahya (UMP) Muhammad Syahril bin Bahari (UniMAP)
Secretary Fadzlin Binti Amzah
Treasurer Khairul Faiz Bin Zainal
Technical Mohd Sanusi bin Abdul Aziz Jeefferie bin Abdul Razak Mohd Najib bin Ali Mokhtar
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ICT and Website Mohd Nazrin bin Muhammad
IEEE Advisors Muhammad Fahmi bin Miskon Mariam Md. Ghazaly Chong Shin Horng
Registration, Souvenirs and Banquet Rahimah binti Abdul Hamid Zuhriah binti Ebrahim Siti Rahmah binti Shamsuri Adibah Haneem binti Mohamad Dom Mohd Hanafiah bin Mohd. Isa Mohd Ghazalan bin Mohd Ghazi Mohd Zahar bin Sariman @ Sarman
Sponsorship Mahasan bin Mat Ali
Promotion and Publishing Mohd Nazmin bin Maslan Aziza Binti Md Buang Azmi Harun (UniMAP) Anwar P. P. Abdul Majeed (UMP) Mohd Hasnun Arif Hassan (UMP)
Logistic Mohd Shahadan Bin Mohd Suan Lokman Bin Abdullah Mohd Remy Bin Ab Karim Muhamad Asari Bin Abdul Rahim
Floor Managers Hazman bin Hasib Mohd Shahrizan bin Othman
Organizing Committee
Contents
Intelligent Manufacturing and Artificial Intelligence Development of a Posture Corrector Device with Data Analysis System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhen Jie Tan and Boon Loo Seow Automated Modulated Parameter Implementation Using Neural Network for Enhancement of Paint Spray . . . . . . . . . . . . . . . . . . . . . . . W. Y. S. Hii, S. S. N. Alhady, A. A. A. Wahab, W. A. F. W. Othman, E. A. Bakar, and M. N. Akhtar A Novel Voice-Activated Smart Irrigation System Implementation and Analysis Using Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . Wong Mei Bei and A. T. I. Fayeez Conceptual Design of Cloud-Based Data Pipeline for Smart Factory . . . Peng Joo Goh, Zi Yang Hoe, Cheng Yee Low, Ching Theng Koh, Ubaidullah Mohammad, Kent Lee, and Chee Fai Tan The Optimization of the Halophilic Cellulase Production: A 3-2-1 Multilayer Perceptron Artificial Neural Network Approach . . . . . . . . . . Ahmad Afif Ahmarofi, Ahmad Anas Nagoor Gunny, Jastini Mohd Jamil, and Naimah Amlus Environmental Visual Features Based Place Recognition in Manufacturing Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fairul Azni Jafar, Nurul Azma Zakaria, Ahamad Zaki Mohamed Noor, and Kazutaka Yokota Comparison of Sampling Methods for Rao-Blackwellized Particle Filter with Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Norhidayah Mohamad Yatim, Amirul Jamaludin, Zarina Mohd Noh, and Norlida Buniyamin
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Instrumentation and Control Performance Analysis of Conditional Integrator in NPID Controller as Cutting Force Compensator for Machine Tools Application . . . . . . . I. A. Saidin, M. N. Maslan, L. Abdullah, F. Yakub, and A. S. Nur Chairat Experimental Investigation on Acoustic Energy Conversion Using Single and Double PZT Film by Modified Resonator . . . . . . . . . . . . . . . Md. Anayet U. Patwari, A. N. M. Nihaj Uddin Shan, Md. Zayed Mostafa, and Md. Iftekhar Uddin Shohan Low-Cost Air Quality Monitoring Platform Using Flying Wing Drone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Muhamad Firdaus Bin Mohd Razali and Ahmad Anas Bin Yusof
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Design and Analysis of Modified Nonlinear PID Controller for Disturbance Suppression in Machine Tools . . . . . . . . . . . . . . . . . . . 105 Tsung Heng Chiew, Weng Kang Chow, Zamberi Jamaludin, Ahmad Yusairi Bani Hashim, Lokman Abdullah, and Nur Aidawaty Rafan Enhance Ride Comfort and Road Handling on Active Suspension System by PSO-Based State-Feedback Controller . . . . . . . . . . . . . . . . . 116 Andika Aji Wijaya, Fitri Yakub, Mohd Nazmin Maslan, and Muhammad Zakiyullah Romdlony The Development of Underwater Crawler Attachment for Remotely Operated Underwater Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Nurul Ain Hassan and Ahmad Anas Yusof Analysis and Development of a Self-dimming Traffic Light System . . . . 135 Sugan Jessan Sreedran Pressure Analysis in Water Hydraulics Machine: Continuous and Intermittent Extrusion Cycle in Dough Extrusion . . . . . . . . . . . . . . 147 Ahmad Anas Yusof, Suhaimi Misha, Faizil Wasbari, Mohamed Hafiz bin Md Isa, Mohd Qadafie Ibrahim, Mohd Shahir Kasim, and Syarizal Bakri Investigation on Disturbance Force Compensation via State Observer Design and Cascade P/PI Controller Approach . . . . . . . . . . . . . . . . . . . 158 Z. Jamaludin, P. Y. Hau, C. T. Heng, L. Abdullah, and N. A. Rafan Review of Recent Grid Synchronization Techniques and PhaseLocked Loops for Power Converters . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Mohammad Faisal Akhtar, Mohammad Nishat Akhtar, Junita Mohamad-Saleh, Ahmad Faizul Hawary, and Elmi Abu Bakar Fuzzy Logic Approach to Fire Monitoring and Warning System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Ahmad Yusairi Bani Hashim, Ruziah Ali, and Fairul Azni Jafar
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Pressure Analysis in Water Hydraulics Machine: Dough and Aluminum Beverage Can Compression Test . . . . . . . . . . . . . . . . . . 187 Ahmad Anas Yusof, Suhaimi Misha, Faizil Wasbari, Mohamed Hafiz bin Md Isa, Mohd Qadafie Ibrahim, Mohd Shahir Kasim, and Syarizal Bakri Performance Evaluation of Intelligent Fire Alarm System with Multi Data Fusion Sensor by Using IoT Platform . . . . . . . . . . . . . 199 A. M. Kassim, M. M. Roslan, S. Sahak, T. W. Chian, M. A. S. A. Aziz, M. A. S. S. Izran, M. S. H. Basari, M. R. Yaacob, M. A. A. Abid, A. H. Azahar, M. M. Hashim, A. K. R. A. Jaya, T. Yasuno, and A. M. Mouazen Design and Development of Handheld Soil Assessment by Using Ion-Selective Electrode for Site-Specific Available Potassium in Oil Palm Plantation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 A. M. Kassim, S. Sahak, T. W. Chian, M. A. S. A. Aziz, M. A. S. S. Izran, M. S. H. Basari, M. M. Roslan, M. R. Yaacob, M. A. A. Abid, A. H. Azahar, M. M. Hashim, A. K. R. A. Jaya, T. Yasuno, and A. M. Mouazen Performance Evaluation of Energy Harvesting Method for Wireless Charging System in Wearable Travel Aid Device for Visually Impaired Person . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 A. M. Kassim, N. N. Ayub, A. Z. Shukor, M. R. Yaacob, W. M. Bukhari, M. A. A. Abid, A. H. Azahar, D. A. Prasetya, T. Yasuno, and A. K. R. A. Jaya Design and Application of ST-SMC Controller for Position Control of a Milling Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 Z. Jamaludin, P. Puveneswaran, C. T. Heng, and M. Maharof Design, Modelling and Simulation Lean Manufacturing Design of a Two-Sided Assembly Line Balancing Problem Work Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Kamarudin Abu Bakar, Mohd. Fazli Mohd. Sam, and Muhammad Imran Qureshi Partial Transmit Sequence (PTS) Optimization Using Improved Harmony Search (IHS) Algorithm for PAPR Reduction in OFDM . . . . 260 Nur Qamarina Muhammad Adnan, Aeizaal Azman Abdul Wahab, Sankari Muniandy, Syed Sahal Nazli Alhady, and Wan Amir Fuad Wajdi Othman
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Ergonomics Study of Standing Work Postures in Assembly Process at Small Medium Industry Manufacturing Company . . . . . . . . . . . . . . . 275 Seri Rahayu Kamat, Amirah Nasha Mohd Azli, and Mohammad Firdaus Ani Safety Helmet Head Impact Monitoring System Using Long Range (LoRa) Communication for Mining Industry . . . . . . . . . . . . . . . . . . . . . 285 Thinesh Vijayakumaran Computational Analysis of Tool Geometry Effect on Cutting Process in Turning of AISI 1020 Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Md. Anayet U. Patwari, Rahnum Tahia Meem, and A. N. M. Nihaj Uddin Shan Structural Vibration Study of a New Concept Intelligent Rubber Tapping Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Z. C. Chong, W. M. A. Ali, and A. Z. A. Mazlan Study of Long Range (Lora) Network Coverage for Multi Areas . . . . . 313 C. H. Ong, W. M. Bukhari, M. N. Sukhaimie, M. A. Norasikin, A. F. A. Rasid, A. T. Izzudin, and N. F. Bazilah Simulation Experiments of Pipe Network and Pumps for Application of Fertigation System Using MATLAB Simscape . . . . . 328 Ainain Nur Hanafi, Jason Tiong Ho Wei, and Mariam Md Ghazaly Manual Material Handling Assessments Towards the Working Comfort in an Automotive Manufacturing Company . . . . . . . . . . . . . . . 338 Al Amin Mohamed Sultan, Darrenveer Singh Gill, Muhammad Azmi, Ng Tan Ching, Mohd Rayme Anang Masuri, Mohd Shahrizan Othman, and Siti Nurfarahin Mohd Hayat Ahmad Conceptual Architecture Development of Virtual Reality – Motion Capture System to Analyze Accessibility and Clearance in Front-End Engineering Design Process: An Exploratory Study . . . . . . . . . . . . . . . . 347 Radin Zaid Radin Umar, Muhammad Naqiuddin Khafiz, Nazreen Abdullasim, Nadiah Ahmad, and Jalaluddin Dahalan Nonlinear Control of Hexarotor System Using Proportional Derivative Sliding Mode Controller (PD-SMC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Fadilah Binti Abdul Azis, Shankarao Rajasuriyan, Noor Hazrin Hany bt Mohamad Hanif, Mohd Shahrieel bin Mohd Aras, and Mariam Binti Md Ghazaly Aerial Based Traffic Tracking and Vehicle Count Detection Using Background Subtraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372 Muhamad Zulhilmi Bin Muhamad, Mohammad Nishat Akhtar, Elmi Abu Bakar, Zuliani Binti Zulkoffli, and Muhammad Faisal Mahmod
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Simulating Solitary Foraging Behaviour of Chimpanzee in Hunting Red Colobus Monkeys Using Agent-Based Modelling Approach . . . . . . 387 N. Idros, W. A. F. W. Othman, A. A. A. Wahab, N. R. M. Noor, and S. S. N. Alhady Design of Drilling Mechanism for Aquilaria Tree Climbing Modular Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Ahmad Mu’az Mohaspa, Muhammad Noor Sabri Md Yusoff, Wan Amir Fuad Wajdi Othman, Aeizaal Azman A. Wahab, Syed Sahal Nazli Alhady, and Elmi Abu Bakar Process and Machining Technology Review on Experimental Design, Process Parameters and Responses of Compression Moulding Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Noorfa Idayu Mohd Ali, Mohd Amran Md Ali, Shajahan Maidin, Mohd Amri Sulaiman, Mohd Shukor Salleh, and Mohd Hadzley Abu Bakar Tool Life and Surface Roughness of Inconel 718 During End Milling Under Dry, Chilled Air and Chilled MQL . . . . . . . . . . . . . . . . . 415 N. H. N. Husshini, M. S. Kasim, and W. N. F. Mohamad Application of Lean Manufacturing Tools: The Impact on Kaizen and Product Defection in Packaging Companies . . . . . . . . . . . . . . . . . . 424 Mohd Fazli Mohd Sam, Budi Suprapto, and Kamarudin Abu Bakar The Utilisation of Kansei Engineering in Designing Conceptual Design of Oil Spill Skimmer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 R. Ramanathan, L. Abdullah, and M. S. Syed Mohamed A Study on Post Processor for 5-Axis CNC Milling via CAD/CAM System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448 Siti Haslinda Mohamed Said and Mohd Salman Abu Mansor Development of Image-Based Drill Bit Wear Detection System for Drilling Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 M. F. Ibrahim, M. F. Mahmod, and E. A. Bakar Review on Turning Process Parameters, Responses and Experimental Method of AISI 1045 Carbon Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 Kesavan Kaniapan, Mohd Amran Md Ali, Mohd Amri Sulaiman, Mohamad Minhat, and Mohd Sanusi Abdul Aziz Dimensional Error Evaluation of Five-Axis Flank Strategies for an Angled Thin-Walled Pocketing: Aerospace Part . . . . . . . . . . . . . 477 S. A. Sundi, R. Izamshah, M. S. Kasim, I. S. Othman, H. Boejang, H. Hanizam, and M. S. Abd Rahman
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A Review on the Influence of Heat Treatment on the Fracture Strength and Microstructure of Zirconia Dental Restorations . . . . . . . . 486 Mira Mazlina Mahdzir, Rahimah Abdul Hamid, and Jeefferie Abd Razak Influence of Humidity on the Tensile Strength of 3D Printed PLA Filament . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Rahimah Abdul Hamid, Fatima Hanem Hamezah, and Jeefferie Abd Razak Smart Material Single Fibre Strength Test on Rattan as Natural Fibres . . . . . . . . . . . . . 505 M. S. Pazlin, M. Y. Yuhazri, and M. H. Amirhafizan Structural, Morphological and Electrochemical Analyses of Graphene/Molybdenum Disulfide Supercapacitor . . . . . . . . . . . . . . . 515 Tunku Aidil Ilham Tunku Adaham, Raja Noor Amalina Raja Seman, and Mohd Asyadi Azam Brief Review on Potential Production of Plastic Waste Concrete Aggregates Using Water-Assisted Melt Compounding . . . . . . . . . . . . . . 523 Noraiham Mohamad, Anis Aqilah Abd Ghani, Muhammad Arif Afif Amran, Jeefferie Abd Razak, Raja Izamshah Raja Abdullah, Mohd Amran Mohd Ali, Hairul Effendy Ab Maulod, and Se Sian Meng Physical Characteristics of Polyaniline-Graphene Nanoplatelets (PANI/GNPs) via Oxidation Polymerization of Aniline . . . . . . . . . . . . . . 533 Nor Aisah Khalid, Jeefferie Abd Razak, Hazman Hasib, Mohd Muzafar Ismail, Noraiham Mohamad, Moayad Husein Flaifel, and Poppy Puspitasari Tensile Strength, Fractograph and Microstructure of Aluminium Alloy (Al-Si) Reinforced by Nickel Oxide (NiO) at Various Temperature of Stir Casting Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 540 Poppy Puspitasari, Romy Romantiko, Sukarni Sukarni, Avita Ayu Permanasari, Jeefferie Abd Razak, and Muhammad Mirza Abdillah Pratama Synthesis and Characterization of Cobalt Oxide Powder with Sintering Duration Variation by Sol-Gel Method . . . . . . . . . . . . . . 549 Poppy Puspitasari, Dimas Ryan Qomarudin, Sukarni Sukarni, Avita Ayu Permanasari, Jeefferie Abd Razak, and Riana Nurmalasari Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557
List of Contributors
Hairul Effendy Ab Maulod Fakulti Teknologi Kejuruteraan Mekanikal dan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Anis Aqilah Abd Ghani Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Jeefferie Abd Razak Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia; Engineering Materials Department, Universiti Teknikal Malaysia, Melaka, Malaysia Lokman Abdullah Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Nazreen Abdullasim Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia M. A. A. Abid Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Melaka, Malaysia Mohd Salman Abu Mansor School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia Tunku Aidil Ilham Tunku Adaham Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Nur Qamarina Muhammad Adnan School of Electrical and Electronic, Universiti Sains Malaysia, Pulau, Pinang, Malaysia Nadiah Ahmad Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
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List of Contributors
Ahmad Afif Ahmarofi Department of Computer Science, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Merbok, Kedah, Malaysia M. N. Akhtar School of Aerospace Engineering, Universiti Sains Malaysia, George Town, Malaysia Mohammad Faisal Akhtar Higher Institution Centre of Excellence (HICoE), UM Power Energy Dedicated Advanced Centre (UMPEDAC), Level 4, Wisma R&D, University of Malaya, Kuala Lumpur, Malaysia Mohammad Nishat Akhtar School of Aerospace Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia Syed Sahal Nazli Alhady School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia Mohd Amran Md Ali Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malysia Melaka, Durian Tunggal, Melaka, Malaysia Ruziah Ali Deputy Vice Chancellor (Research and Innovation) Office, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia W. M. A. Ali School of Mechanical Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia Noorfa Idayu Mohd Ali Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malysia Melaka, Melaka, Malaysia M. H. Amirhafizan Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Naimah Amlus Universiti Utara Malaysia Information Technology, Universiti Utara Malaysia, Sintok, Kedah, Malaysia Muhammad Arif Afif Amran Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Mohammad Firdaus Ani Kolej Komuniti Taiping, Unit Teknologi Pembuatan, Kamunting, Perak, Malaysia Mohd Shahrieel bin Mohd Aras Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia; Center for Robotics and Industrial Automation (CeRIA), Universiti Teknikal Malaysia Melaka, Melaka, Malaysia N. N. Ayub Rehabilitation and Assistive Engineering Technology Research Group (REAT), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
List of Contributors
xvii
A. H. Azahar Faculty of Electrical and Electronic Engineering Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Mohd Asyadi Azam Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Fadilah Binti Abdul Azis Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia; Center for Robotics and Industrial Automation (CeRIA), Universiti Teknikal Malaysia Melaka, Melaka, Malaysia; Kuliyyah of Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia M. A. S. A. Aziz Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Mohd Sanusi Abdul Aziz Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malysia Melaka, Durian Tunggal, Melaka, Malaysia Amirah Nasha Mohd Azli VTech Communications (Malaysia) Sdn. Bhd. Industrial Estate, Muar, Malaysia Muhammad Azmi Facility Maintenance Engineering Section, Universiti Kuala Lumpur Malaysian Institute of Industrial Technology, Persiaran Sinaran Ilmu, Bandar Seri Alam, Masai, Johor, Malaysia Elmi Abu Bakar School of Aerospace Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia Kamarudin Abu Bakar Faculty of Technology Management and Technoprenuership, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia; Sustainable IT Economics Research Group (SuITE), Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Mohd Hadzley Abu Bakar Fakulti Teknologi Kejuruteraan Mekanikal dan Pembuatan, Universiti Teknikal Malysia Melaka, Melaka, Malaysia Syarizal Bakri Jabatan Matematik Sains dan Komputer, Politeknik Kuching Sarawak, Kuching, Sarawak, Malaysia Ahmad Yusairi Bani Hashim Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia M. S. H. Basari Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia N. F. Bazilah Fakulti Teknologi Kejuruteraan Mekanikal dan Pembuatan, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Wong Mei Bei Fakulti Kejuruteraan Elektronik and Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
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List of Contributors
H. Boejang Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia W. M. Bukhari Rehabilitation and Assistive Engineering Technology Research Group (REAT), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Norlida Buniyamin Faculty of Electrical Engineering (FKE), Universiti Teknologi MARA (UiTM) Shah Alam, Shah Alam, Selangor, Malaysia A. S. Nur Chairat Institut Teknologi PLN, Jakarta Bar, Indonesia T. W. Chian Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Tsung Heng Chiew Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Setapak, Kuala Lumpur, Malaysia Ng Tan Ching Department of Mechanical and Materials Engineering, Universiti Tunku Abdul Rahman, Selangor, Malaysia Z. C. Chong School of Mechanical Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia Weng Kang Chow Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Setapak, Kuala Lumpur, Malaysia Jalaluddin Dahalan Ergoworks Sdn. Bhd., Taman Kajang Sentral, Kajang, Selangor, Malaysia A. T. I. Fayeez Fakulti Kejuruteraan Elektronik and Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Moayad Husein Flaifel Department of Physics, Collage of Science, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia Mariam Md Ghazaly Centre for Robotics and Industrial Automation (CeRIA), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Mariam Binti Md Ghazaly Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia; Center for Robotics and Industrial Automation (CeRIA), Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Darrenveer Singh Gill Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Melaka, Malaysia Peng Joo Goh Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
List of Contributors
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Ahmad Anas Nagoor Gunny School of Bioprocess Engineering, Universiti Malaysia Perlis, Arau, Perlis, Malaysia Fatima Hanem Hamezah Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Rahimah Abdul Hamid Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Ainain Nur Hanafi Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Noor Hazrin Hany bt Mohamad Hanif Kuliyyah of Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia H. Hanizam Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia Ahmad Yusairi Bani Hashim Faculty of Manufacturing Engineering, Center for Smart System and Innovative Design, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia M. M. Hashim Faculty of Plantation and Agrotechnology, Universiti Teknologi MARA, Merlimau, Melaka, Malaysia Hazman Hasib Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Nurul Ain Hassan Department of Mechatronics, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Malaysia P. Y. Hau Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Ahmad Faizul Hawary School of Aerospace Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia Siti Nurfarahin Mohd Hayat Ahmad Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Melaka, Malaysia C. T. Heng Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Jalan Genting Klang, Setapak, Kuala Lumpur, Malaysia W. Y. S. Hii School of Electrical and Electronic Engineering, Universiti Sains Malaysia, George Town, Malaysia Zi Yang Hoe Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia N. H. N. Husshini Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
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List of Contributors
M. F. Ibrahim Faculty of Mechanical and Manufacturing Engineering, University Tun Hussein Onn Malaysia, Parit Raja, Malaysia Mohd Qadafie Ibrahim Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia N. Idros School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia Mohamed Hafiz bin Md Isa Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Mohd Muzafar Ismail Fakulti Teknologi Kejuruteraan Elektrik Dan Elektronik, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia R. Izamshah Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia M. A. S. S. Izran Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia A. T. Izzudin Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Fairul Azni Jafar Faculty of Manufacturing Engineering, Center for Smart System and Innovative Design, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Amirul Jamaludin Centre for Telecommunication Resesarch and Innovation (CeTRI), Fakulti Kejuruteraan Elektronik and Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Malaysia Zamberi Jamaludin Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Jastini Mohd Jamil School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Sintok, Kedah, Malaysia A. K. R. A. Jaya Auro Technologies PLT, Ayer Keroh, Melaka, Malaysia Seri Rahayu Kamat Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Kesavan Kaniapan Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malysia Melaka, Durian Tunggal, Melaka, Malaysia M. S. Kasim Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia; Advanced Manufacturing Center, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia; Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
List of Contributors
xxi
Mohd Shahir Kasim Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia A. M. Kassim Rehabilitation and Assistive Engineering Technology Research Group (REAT), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Muhammad Naqiuddin Khafiz Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Nor Aisah Khalid Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Ching Theng Koh Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia Kent Lee Keysight Technologies, Bayan Lepas, Pulau Pinang, Malaysia Cheng Yee Low Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia M. Maharof Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal Melaka, Malaysia Mira Mazlina Mahdzir Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Muhammad Faisal Mahmod Department of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia; Structural Integrity and Monitoring Research Group (SIMReG), Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia Shajahan Maidin Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malysia Melaka, Melaka, Malaysia Mohd Nazmin Maslan Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia, Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia Mohd Rayme Anang Masuri Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Melaka, Malaysia A. Z. A. Mazlan School of Mechanical Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia Rahnum Tahia Meem Department of Mechanical and Production Engineering, Islamic University of Technology (IUT), Dhaka, Bangladesh Se Sian Meng San Miguel Yamamura Plastic Films Sdn Bhd, Ayer Keroh, Melaka, Malaysia
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List of Contributors
Mohamad Minhat Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malysia Melaka, Durian Tunggal, Melaka, Malaysia Suhaimi Misha Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia W. N. F. Mohamad Advanced Manufacturing Center, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia; Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia; Fakulti Rekabentuk Inovatif dan Teknologi, Universiti Sultan Zainal Abidin, Kuala Terengganu, Terengganu, Malaysia Norhidayah Mohamad Yatim Centre for Telecommunication Resesarch and Innovation (CeTRI), Fakulti Kejuruteraan Elektronik and Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Malaysia Junita Mohamad-Saleh School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia Noraiham Mohamad Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Siti Haslinda Mohamed Said School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Seberang Perai Selatan, Pulau Pinang, Malaysia M. S. Syed Mohamed Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Ubaidullah Mohammad Advanced Technology Training Centre (ADTEC) Shah Alam, Shah Alam, Selangor, Malaysia Ahmad Mu’az Mohaspa School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia Mohd Amran Mohd Ali Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Zarina Mohd Noh Centre for Telecommunication Resesarch and Innovation (CeTRI), Fakulti Kejuruteraan Elektronik and Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Malaysia Mohd. Fazli Mohd. Sam Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Md. Zayed Mostafa Department of Mechanical and Production Engineering, Islamic University of Technology (IUT), Dhaka, Bangladesh
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A. M. Mouazen Department of Environment, Faculty of Bioscience Engineering, Ghent University, Gent, Belgium Muhamad Zulhilmi Bin Muhamad School of Aerospace Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia Sankari Muniandy School of Electrical and Electronic, Universiti Sains Malaysia, Pulau, Pinang, Malaysia A. N. M. Nihaj Uddin Shan Department of Mechanical and Production Engineering, Islamic University of Technology (IUT), Dhaka, Bangladesh Ahamad Zaki Mohamed Noor System Engineering and Energy Laboratory, Universiti Kuala Lumpur Malaysian Spanish Institute, Kulim, Kedah, Malaysia N. R. M. Noor School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia M. A. Norasikin Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Riana Nurmalasari Mechanical Engineering Department, State University of Malang, East Java, Indonesia C. H. Ong Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia I. S. Othman Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia Mohd Shahrizan Othman Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Melaka, Malaysia Wan Amir Fuad Wajdi Othman School of Electrical and Electronic, Universiti Sains Malaysia, Pulau, Pinang, Malaysia Md. Anayet U. Patwari Department of Mechanical and Production Engineering, Islamic University of Technology (IUT), Dhaka, Bangladesh M. S. Pazlin Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Bangladesh; Department of Mechanical Engineering, Politeknik Melaka, Melaka, Bangladesh D. A. Prasetya Jurusan Teknik Elektro, Universitas Merdeka Malang, Malang, Indonesia Avita Ayu Permanasari Mechanical Engineering Department, State University of Malang, East Java, Indonesia; Center of Advanced Materials for Renewable Energy, State University of Malang, East Java, Indonesia
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List of Contributors
Muhammad Mirza Abdillah Pratama Civil Engineering Department, State University of Malang, East Java, Indonesia Poppy Puspitasari Faculty of Engineering, Department of Mechanical Engineering, State University of Malang, Malang, Indonesia; Mechanical Engineering Department, State University of Malang, East Java, Indonesia; Center of Advanced Materials for Renewable Energy, State University of Malang, East Java, Indonesia P. Puveneswaran Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal Melaka, Malaysia Dimas Ryan Qomarudin Mechanical Engineering Department, State University of Malang, East Java, Indonesia Muhammad Imran Qureshi Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia; Sustainable IT Economics Research Group (SuITE), Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Radin Zaid Radin Umar Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Nur Aidawaty Rafan Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia M. S. Abd Rahman Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia Raja Izamshah Raja Abdullah Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Shankarao Rajasuriyan Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia R. Ramanathan Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia A. F. A. Rasid Fakulti Teknologi Kejuruteraan Mekanikal dan Pembuatan, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Jeefferie Abd Razak Engineering Materials Department, Universiti Teknikal Malaysia, Melaka, Malaysia Muhamad Firdaus Bin Mohd Razali Department of Mechatronics, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Malaysia
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Romy Romantiko Mechanical Engineering Department, State University of Malang, East Java, Indonesia Muhammad Zakiyullah Romdlony School of Electrical Engineering, Telkom University, Bandung, Jawa Barat, Indonesia M. M. Roslan Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia S. Sahak Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia I. A. Saidin Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Mohd Shukor Salleh Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malysia Melaka, Melaka, Malaysia Mohd Fazli Mohd Sam Faculty of Technology Management and Technoprenuership, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Raja Noor Amalina Raja Seman Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Boon Loo Seow Department of Mechanical Engineering, Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia A. N. M. Nihaj Uddin Shan Department of Mechanical and Production Engineering, Islamic University of Technology (IUT), Dhaka, Bangladesh Md. Iftekhar Uddin Shohan Department of Mechanical and Production Engineering, Islamic University of Technology (IUT), Dhaka, Bangladesh A. Z. Shukor Rehabilitation and Assistive Engineering Technology Research Group (REAT), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Sugan Jessan Sreedran Nexperia Malaysia Sdn. Bhd., Seremban, Malaysia Sukarni Sukarni Mechanical Engineering Department, State University of Malang, East Java, Indonesia; Center of Advanced Materials for Renewable Energy, State University of Malang, East Java, Indonesia M. N. Sukhaimie Melor Agricare PLT, Melaka, Malaysia Mohd Amri Sulaiman Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malysia Melaka, Durian Tunggal, Melaka, Malaysia Al Amin Mohamed Sultan Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Melaka, Malaysia
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S. A. Sundi Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, Malaysia Budi Suprapto Faculty of Economics, Atma Jaya Yogyakarla University, Yogyakarla, Yogyakarta, Indonesia Chee Fai Tan Department of Mechatronics and Biomedical Engineering, Universiti Tunku Abdul Raman, Petaling Jaya, Selangor, Malaysia Zhen Jie Tan Department of Mechanical Engineering, Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia Thinesh Vijayakumaran Plexus Riverside Sdn. Bhd., Bayan Lepas, Penang, Malaysia Aeizaal Azman Abdul Wahab School of Electrical and Electronic, Universiti Sains Malaysia, Pulau, Pinang, Malaysia Faizil Wasbari Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Jason Tiong Ho Wei Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Andika Aji Wijaya Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia; Department of Mechanical Engineering, University of Business and Technology, Jeddah, Saudi Arabia M. R. Yaacob Rehabilitation and Assistive Engineering Technology Research Group (REAT), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Fitri Yakub Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia T. Yasuno Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima, Japan Kazutaka Yokota Research Division of Design and Engineering for Sustainability, Graduate School of Engineering, Utsunomiya University, Utsunomiya-shi, Japan M. Y. Yuhazri Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia Ahmad Anas Yusof Department of Mechatronics, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Malaysia; Robotics and Industrial Automation Research Group, Faculty of Electrical
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Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Malaysia Ahmad Anas Bin Yusof Robotics and Industrial Automation Research Group, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Malaysia Muhammad Noor Sabri Md Yusoff School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia Nurul Azma Zakaria Center for Advanced Computing Technology, Faculty of Information and Communications Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia Zuliani Binti Zulkoffli Department of Mechanical Engineering, UCSI University, Kuala Lumpur, Malaysia
Intelligent Manufacturing and Artificial Intelligence
Development of a Posture Corrector Device with Data Analysis System Zhen Jie Tan and Boon Loo Seow(B) Department of Mechanical Engineering, Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Jalan Genting Kelang, Setapak, 53300 Kuala Lumpur, Malaysia [email protected]
Abstract. Prolonged sitting due to the current working environment had caused people to develop bad body posture which includes hunchback and shoulder imbalances. These issues may alter the passive stiffness of the lumbar spine. A posture corrector device is developed in this project to provide postural guidance to the user. Two techniques, namely microsensor technique and computer vision technique are adopted in this project to analyse the angle of two defined points on shoulder and on back. Gyro-Accelerometer Microsensor is used for microsensor technique to determine the shoulder and spine posture. For computer vision technique, the program is written in Python with Open Source Computer Vision Library to check the shoulder posture, through object tracking of two Hue-Saturation Value colour ranges. Analysed data are then displayed via computer with recommendation to aid the users to correct their posture. Both techniques are verified and tested physically. Results showed that the fabricated posture corrector equipment with data system analysis and feedback system able to detect the poor posture and provided feedback to the user. Keywords: Posture corrector device · Microsensor · Computer vision · OpenCV
1 Introduction Working culture had evolved towards sitting based career from the past 30 years due to the demand in professional occupation which required extensive hours of working in front of computers [1–3]. With the drastically increased of the covid-19 confirmed cases, more people are working from home which required even longer hour sitting in front of their computer [4]. Prolonged sitting may cause people to develop bad body posture which includes hunchback, shoulder imbalances, slouching and biased weight sitting. Most of the posture corrector products or devices available in the market may not be as effective due to the lack of feedback function [5]. In order to enhance the functionality of the posture corrector device, a posture corrector device with the integration of microsensor and computer vision techniques is developed, fabricated and tested in this project. The device developed provides collected data and feedback for the user to correct their body posture. The developed device able to improve the shortcoming of the products available in the market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 3–12, 2022. https://doi.org/10.1007/978-981-16-8954-3_1
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Throughout the literature researches, in order to achieve the correct path in designing the posture corrector device, there are a few adaptations needed to be considered. The main idea of posture corrector device is based on the therapeutic strapping method which provides tensional forces to correct the body posture [5]. Both authors in [6] and [7] suggested posture correcting back brace with spring(s) at the back would help to correct the slouching shoulder. On the other hand, a number of researches are focused on posture correction devices with alert system. An accelerometer sensor was used in [8] to detect the position of posture, mainly at the spine where a buzzer is triggered if the spine bend over a predefined angle while a flex sensor was applied in [9] to detect spine bending. The bending angle is displayed via a LCD display and a vibrator motor is used to alert the user’s poor posture. In addition, a MPU6050 gyroscope is used to detect the spine posture and poor posture can be alerted with both vibrator and voice command in [10]. From medical and postural research, in order to maintain good posture, there is a need to monitor shoulder imbalance. In this research, the posture corrector device is designed by adopting microsensor technique and computer vision technique. Microsensor technique can be applied to detect both shoulder imbalance and spine posture while computer vision technique can only be used to detect shoulder imbalance.
2 Methodology A posture corrector device is developed in this project to monitor both the shoulder and spine posture. The device enables the user to achieve self-monitoring of his own postural issue. A wearable posture corrector device is first assembled as shown in Fig. 1. A pair of mechanical tightening ratchet belts are added to the harness to achieve independent straps manual adjustment of the posture corrector device. With the metal ratchet belt attached to the waist belt of the harness, the straps that crossed over the shoulder can be further tighten or loosen according to the needs to control the shoulder imbalance. A wooden frame is assembled to the back of the harness to attach the microsensors.
Fig. 1. Assembly of the body corrector device
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2.1 Microsensor Technique Gyro-Accelerometer microsensor (MPU6050) is used in microsensor technique to determine the shoulder and spine posture. C programming language is used for the coding to process the data from microsensor through Arduino Uno. Mathematical algorithms are applied to compute the angles of the shoulder and spine relative to the initial neutral position [11–13]. The circuit system is illustrated in Fig. 2.
Fig. 2. Gyro-Accelerometer Microsensor is connected to Arduino Uno
The MPU6050 provides gyroscope and accelerometer values of x-, y- and z-axes, as the input data to the Arduino Uno microcontroller for angle calculation [12, 13]. When the program is executed, 1500 gyroscope values are read to calculate the mean initial or neutral position as a reference point. Thereafter, the microsensor data input are used to calculate the movement angle. The programming code is written with a loop timer of 4000 µs or 250 Hz and gyroscope is designed with 65.5º per second per count [13]. Considering the Gyro values, Eq. (1) is used to calculate the travelled pitch angle for x-axis and similarly for the travelled roll angle for y-axis. pitch angle = gyrox ∗
1 250Hz ∗ 65.5
(1)
Taking the gyroz value (yaw) and the roll angle into consideration, pitch angle is given by Eq. (2), π 1 pitch angle+ = roll angle ∗ sin gyroz ∗ (2) 250Hz ∗ 65.5 180 As for the accelerometer angle calculation, the total accelerometer vector is calculated with Eq. (3), ACC = ACC_x2 + ACC_y2 + ACC_z 2 (3)
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The pitch angle of accelerometer is then calculated using Eq. (4), ACCy 1 ∗ π pitch angleACC = asin ACC 180
(4)
The output pitch angle is calculated using Eq. (5), pitch angleoutput = pitch angle + pitch angleACC
(5)
This resultant angle calculated in Eq. (5) is the angle of movement. The block diagram shown in Fig. 3 summarised the methodology of this method.
Initialise
Obtain gyro-x, -
Calculate Pitch angle
Display
MPU6050 and
y, -z and
and Pitch angleACC
calculate mean
ACC_x, ACC_y and ACC_z
using Eq. 1, Eq. 2,
Pitch angleoutput
neutral position
Eq. 3 and Eq. 4
(Eq. 5) and message
Fig. 3. Pitch angle calculation
2.2 Computer Vision Technique In this technique, the program is written in Python with Open Source Computer Vision Library (OpenCV) for the angle tracking of the shoulder through object tracking of two Hue-Saturation Value (HSV) colour ranges. Computer vision technique is used to analyse the live video captured through camera. OpenCV provides functions to capture images from the video frame by frame and to detect coloured object within predefined HSV colour ranges for further analysis. Two easily identified colours, green and orange are used as the objects to be detected in the video. The Python program is written with two HSV colour ranges to detect or mask these two coloured objects. Once it is detected, the centres (x- and y- position) of these two objects will be used to calculate the angle between these two points using Eq. (6), −1 y1 − y2 (6) angle = tan x1 − x2 Two circles are drawn superimpose on the video with a right angle triangle and the acute angle calculated will be displayed. As the live video is captured continuously, the calculation and display is also continuous on the computer screen, as long as the program is executed. Verifications are first done on a few known angles before it is tested on the shoulder of the user.
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3 Results The verifications of the accuracy of both microsensor and computer vision technique are first carried out and then integrated into the posture corrector device as a solution to shoulder imbalance and spine posture. A male user equipped with the posture corrector device was selected as a test subject. Motion on shoulders (left) and back was executed by the user in standing position to test the functionality of both techniques. 3.1 Verification and Test Results for Microsensor Technique Verifications of the MPU6050 are done on three fixed positions which include 0° (horizontal), 45° and 90° (vertical). Figure 4 (a) shows the set up for 0° fixed position where the breadboard is set at horizontal position. When the program is executed, initiation will register this position as 0°, after the initiation, the relative angle of movement using Eq. (5) will be calculated. Without restart the program, the breadboard is raised to 45° and vertical position as shown in Fig. 4(b) and Fig. 4(c). Table 1 shows the verification results obtained from the considered positions with a relatively small variations (within 0.25°) due to the averaging reading during the calculation. All readings stabilised within several seconds after each position is set. Table 1. Angle detection with fixed position Angle
0°
45°
90°
Range of reading (°)
0.02°–0.16°
45.02°–45.25°
90.02°–90.07°
Fig. 4. Set up for (a) 0°, (b) 45° and (c) 90° verification for Gyro-Accelerometer Sensor
The microsensor is attached on a self-fabricated wooden frame and assembled to the back of the posture corrector device to detect the movement of shoulder (Fig. 5(a)) and spine posture (Fig. 5(b)). Note that the end of wooden frame is made to rest on both shoulder so that when the shoulder is raised, the horizontal wooden bar where the microsensor is fixed will also be raised for shoulder movement detection (Fig. 5(a)).
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Fig. 5. (a) Microsensor for shoulder angle tracking (b) Microsensor for spine angle tracking
For shoulder angle tracking, considering the left shoulder as shown in Fig. 5(a), at neutral or relax position, the program is executed to register the position of 0°. When the shoulder is raised up or lowered down, the MPU6050 will read the position and calculate the relative angles using Eq. (1) to (5). The algorithm is set with a tolerance of ± 10°. If more than 10° was detected, the user will be alerted to lower his shoulder while the user will be alerted to stand straight if more than −10° was detected (the user is leaning to the left). Figure 6 shows an example of results obtained.
Fig. 6. The program output the angle calculated and the message when the shoulder is raised
For spine angle tracking system, the maximum back extension is set at 9° (or 99° measured from horizontal) and maximum back flexion is set at 20° (or 70° measured from horizontal) [10]. This is illustrated in Fig. 7.
Fig. 7. Permissible bending zone
When the back extension is more than 9°, the user will be reminded not to lean backward. On the other hand, when the back flexion is more than 20°, the user will be reminded straighten his back. Two of the results for back extension and back flexion
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detection are shown in Figs. 8 and 9. Note that the angle calculation is with reference to the horizontal line.
Fig. 8. Angle measured less than 70° indicating back extension of over 20°
Fig. 9. Angle measured over 99° indicating back flexion of over 9°
3.2 Verification and Test Results for Computer Vision Technique For verification, five angle triangles are drawn on papers, with the acute angle between hypotenuse and the horizontal line set at 0° (horizontal line), 30°, 45°, 60° and 90° (vertical line). When the program is executed, the image of the video is analysed to identify the green and orange colour. The centres of the circles is then calculated as the input to determine the angle (using Eq. (6)) between the two circles. Figure 10 illustrates the drawn 30° right angle triangle and the result captured through life video. All five tests shown displayed angle exactly as drawn. The results are obtained under good lighting condition and the positioning of hand drawn triangle must be perpendicular to the camera. In other word, any tilting will affect the result. To apply the technique on the user, the clavicle or collarbone is chosen as reference because it is the only long bone in the body that lies horizontally. It is also a touchable bone and the location of the bone is clearly visible (Fig. 11).
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Fig. 10. Computer vision technique angle verification at 30°
Fig. 11. Clavicle
The tests are carried out on the user by attaching a green sticker to the clavicle pivot joint and a yellow sticker to the end of the shoulder. Figure 12(a) shows the detection of the two coloured stickers when the shoulder is at neutral. The 0° angle detection also proof that the clavicle is horizontal at neutral posture. Note that the range set for orange colour covered yellow colour and the object is not constraint to circular object and plain darker coloured shirt is preferred in this technique. Figure 12(b) shows that when the shoulder is raised, the angle between the shoulder and clavicle joint increased. Vice versa, as the shoulder is lowered to neutral position, the angle should return to 0°. Via this technique, the user may adjust his shoulder to neutral position with the guidance of the angle displayed.
Fig. 12. Angle detection (a) at neutral position (b) when shoulder is raised
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Through repeating tests, sometime the angle could not show 0° at neutral position. This may due to human error such as extensively lowering of shoulder. The other reason is during the live video, the planar of the clavicle is not perpendicular to the camera. This technique can be used together with the posture corrector device. The ratchet can be used to tighten the strap on corresponding side of shoulder which is always higher. The tension on the strap may serve the function to correct habit for bad posture. 3.3 Comparison of Microsensor Technique and Computer Vision Technique Both Microsensor Technique and Computer Vision Technique used in this project are able to detect the poor posture and provided feedback to the user. However, there are pros and cons for these two techniques. The comparison are shown in Table 2. Table 2. Pros and cons of microsensor technique and computer vision technique
Microsensor technique
Pros
Cons
• Can be applied to shoulder and spine to detect shoulder imbalance, back flexion and back extension • Measurement done in 3D space • Result not affected by the colour of shirt worn
• Posture corrective device needed • Posture corrective device and microsensor are relatively costly
Computer vision technique • Posture corrective device is optional • Setup is easier
• Cannot be applied to detect back posture with single camera • Measurement in 2D space • Result could be affected by the colour of the shirt worn
4 Conclusion The research work in this project has aimed to assess and alert the user with feedback messages based on their current posture. Two techniques, microsensor technique and computer vision technique are adopted and verified. It is also successfully implemented in the posture corrector device. The posture corrector device utilized independent strapping technique to correct each side of shoulder individually. The application of GyroAccelerometer Microsensor enabled the equipment to determine the current postural angle relative to the neutral position and provides users with instruction to correct their posture. The computer vision program is developed using Python with OpenCV to identify two coloured objects on the clavicle in live video, hence determine and display the angle of the clavicle. Based on the angle displayed, the user may adjust his shoulder to reduce the angle to 0°, which is the neutral posture.
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4.1 Future Works In this project, only one microsensor MPU6050 is applied to detect one shoulder or spine posture independently. In order to detect both shoulders and the spine postural angles, three same address of microsensor MPU6050 are needed. In order to make the expansion of I2C busses, multiplex can be introduced to track different part of the body posture. As for the computer vision technique, the limitation of 2D planar view can be improved by using two inter-cooperate camera vision which have overlaps vision. The angle judgement can be done in 3D and the output will be more accurate. More tests can be carried out for microsensor techniques such as wider range of angle inclination and time taken to stabilise the reading for fixed position verification. This may further clarify the deviation and reliability of the device. Tests on different shirt colours, different types of webcam for computer vision technique can be carried out to identify the robustness of the system. Survey can also be carried out through physiotherapists and end user to identify other requirements for the improvement of the functionality of this project.
References 1. Beach, T.A., Parkinson, R.J., Stothart, J.P., Callaghan, J.P.: Effects of prolonged sitting on the passive flexion stiffness of the in vivo lumbar spine. Spine J. 5(2), 145–154 (2005) 2. Pope, M.H., Goh, K.L., Magnusson, M.L.: Spine ergonomics. Annu. Rev. Biomed. Eng. 4(1), 49–68 (2002) 3. Malik, A.N., ur Rasul, H.N., Siddiqi, F.A.: Cross sectional survey of prevalence of low back pain in forward bend sitting posture. Rawal Med. J. 38(3), 253–255 (2013) 4. Dwivedi, Y.K.: Impact of COVID-19 pandemic on information management research. Int. J. Inf. Manage. 1, 2 (2020) 5. Griffin, A., Bernhardt, J.: Strapping the hemiplegic shoulder prevents development of pain during rehabilitation: a randomized controlled trial. Clin. Rehabil. 20(4), 287–295 (2006) 6. Vayntraub, V.: Posture correcting back brace. U.S. Patent 7,901,371 (2011) 7. Burke, S., Garth, G., Zimmer, E.: Aspen medical partners LLC: hyperextension brace. U.S. Patent Application 14/054,624 (2014) 8. Chopra, S., Kumar, M., Sood, S.: Wearable posture detection and alert system. In: 2016 International Conference System Modeling & Advancement in Research Trends (SMART), pp. 130–134. IEEE (2016) 9. Bramhapurikar, K., Prabhune, A., Chavan, S., Ghivela, G.C., Sengupta, J.: A wearable posture corrector device. In: 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–5. IEEE (2018) 10. Pratheep, V.G., Priyanka, E.B., Thangavel, S., Heenalisha, K., Ariya Manickam, M., Logaram, A.P.: Automatic body posture corrector for spinal cord patients. In: Mohan, S., Shankar, S., Rajeshkumar, G. (eds.) Materials, Design, and Manufacturing for Sustainable Environment. LNME, pp. 195–205. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-98098_16 11. Luinge, H.J., Veltink, P.H.: Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Med. Biol. Eng. Compu. 43(2), 273–282 (2005) 12. Alfian, R.I., Ma’arif, A., Sunardi, S.: Noise reduction in the accelerometer and gyroscope sensor with the kalman filter algorithm. J. Robot. Control (JRC) 2(3), 180–189 (2021) 13. Pappalardo, A.A.: Active self-balancing camera system design and implementation using Ar-duino (Specific) (Bachelor’s thesis) (2018)
Automated Modulated Parameter Implementation Using Neural Network for Enhancement of Paint Spray W. Y. S. Hii1 , S. S. N. Alhady1(B) , A. A. A. Wahab1 , W. A. F. W. Othman1 , E. A. Bakar2 , and M. N. Akhtar2 1 School of Electrical and Electronic Engineering, Universiti Sains Malaysia,
George Town, Malaysia [email protected] 2 School of Aerospace Engineering, Universiti Sains Malaysia, George Town, Malaysia
Abstract. Artificial neural networks (ANNs) were introduced to be implemented for the paint spray process. ANNs are biologically inspired computational networks. It is a technology based on studies of the brain and nervous system. ANN models simulate the electrical activities of the brain and nervous system. ANN models include three layers (an input layer, hidden layer and output layer). ANN can be simulated in Matlab to predict the optimum conditions for maximum production. Surrounding conditions are the inputs for simulation. Data are collected and tabulated as a database for the model to generate the result. The result will be evaluated, analysed in the end. The prediction was trained using 70% of the data, and in the validation process, 30% of data was used. The optimum number of neurons is determined by training the network using a different number of neurons, and the performance of each network is compared. The statistical performance measures consisting of root mean square error (RMSE) and the square root of the coefficient of determination (R). A high predictive accuracy ANN model will possess the value of R close to one and low RMSE. The results of the training set were not high, R = 68.466% validation set with R = 63.173%. Keywords: Temperature · Intensity
1 Introduction Spray painting is a painting technique in which a device sprays coating material (paint, ink, varnish) through the air onto a surface. The most common types of spray painting use compressed air to atomise and direct the paint particles or coating material onto the surface. This technique evolved from airbrush, which is more hand-held and usually used for detailed work. Spray painting is now frequently used in many industries, such as automotive and aeroplanes. Automation is now popular and significant to increase efficiency. Since the painting process is determined according to the surrounding conditions and chemical mixture, and they are currently determined manually, the operator © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 13–19, 2022. https://doi.org/10.1007/978-981-16-8954-3_2
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will have to select the appropriate setting for the spray gun. So, applying artificial intelligence can help reduce the operator’s load and increase efficiency and accuracy. It also ensures that this determination is always in line with the standards. Artificial Intelligence (AI) is the simulation of human intelligence in machines programmed to think and take action like humans. It is applied to the machine to exhibit traits of a human mind, such as learning and problem-solving. Artificial Neural Network (ANN) is the method used for this project. ANN is known as computational algorithms. It simulates the behaviour of a biological system of neurons. ANN is a computing system designed to simulate the way the human brain analyse and process information. It can solve problems that are impossible or difficult by human or statistical standards [1–4]. ANN has hundreds or thousands of artificial neurons called processing units, which are interconnected by nodes. These processing units are made up of input and output units. The input units receive various forms and structures of information, and the neural network attempts to learn about the information presented to produce one output. The network will compare the actual output with the output produced during the supervised phase. The difference between both outcomes is adjusted using backpropagation by modifying the weight of the connections between the units until the difference between the actual and desired outcome produce the lowest error.
2 Methodology To apply ANN, Matlab is used to apply this machine learning technique. An ANN model is built using Matlab. In this study, the reaction temperature is the inputs for the ANN model. The output for this model is the intensity of spray microspheres on a fixed area of the surface of a specified material surface. First, the data collected is imported into Matlab. The data is transformed into a histogram or graph to determine the relationship between the input parameters and the output. The graph plotted from the data to envisioned their relationship. Then, an artificial neural network can then be trained using different hidden layers with fixed training ratios, validation ratios, and test ratios. The model’s performance is determined by calculating the error percentage of the ANN model, and the root mean square error, RMSE of the training sets and validation sets. The root mean square error RMSE of training sets and the root mean square error RMSE of validation sets are plotted in the same graph to select the optimal number of neurons in the hidden layer. The number of neurons with the minimum root mean square error for both training sets and validations is chosen as the optimum number of neurons in the hidden layer. The prediction of the model can be visualised by plotting the raw data with the output data (Fig. 1).
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Fig. 1. Workflow of ANN
The next step is to analyse the performance of the selected model—the performance determined by plotting the four lines, Train, Validation and Test, in the same graph. The best line represents the model with the lowest MSE of the training, validation, and testing set. An example of the graph for the four lines is shown in the figure below (Fig. 2).
Fig. 2. Example of graph of mean square errors of training sets, validation sets, test sets and best conditions
The Levenberg-Marquardt algorithm is chosen as the algorithm to train the neural network. This algorithm is suitable for functions of the type sum-of-squared-error, and it is fast for training neural networks which work on this kind of error. Moreover, it has excellent performance, but it may require much memory. The Levenberg-Marquardt algorithm is implemented in the backpropagation of ANN as there are solutions available
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to its limitations by implementing the variable that employs Hessian matrix, a secondorder derivative of error vector in the weight updating process [5].
3 Results and Discussions For this research, the datasets from Dataset On The Effect Of The Reaction Temperature during spray pyrolysis to synthesise the hierarchical yolk-shell CNT-(NiCo)O/C microspheres [6] were used to simulate and analyse the performance of the proposed neural network model. 3.1 Number of Neurons for ANN Model An ANN model is trained using Matlab. The data from the dataset were inserted into Matlab. The number of neurons in the hidden layer needs to investigate as it provides the best network architectures with the lowest error. The network is trained with a different number of neurons. The optimum number of neurons will enhance the accuracy of the network in total. Increasing the number of neurons in the hidden layer will add to the complexity of the model. Although high model complexity can improve the model’s predictive accuracy, a neural network with too many neurons may overfit the data. Conversely, too few neurons can underfit the model, leading to high validation error and inaccurate predictions. Therefore, it is crucial to determine the optimum number of neurons in the hidden layer. The performance of each model was investigated by calculating the RMSE (root mean square error) of the training set and validation set of the network. The results are shown in Fig. 3.
Fig. 3. Graph of RMSE (root mean square error) of the training set (blue line) and validation set (red line) against the number of neurons
From Fig. 3, the RMSE (root mean square error) of the training set (blue line) decreases when the number of neurons increases. However, the validation set’s RMSE (root mean square error) (red line) increases when the number of neurons increases from 30 to 60. It shows that the neural network is overfitting. Therefore, the optimum number of neurons chosen is 11, with the lowest RMSE of both the training set and validation set.
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3.2 ANN Model
Fig. 4. MSE of the training set and validation set
Figure 4 shows the MSE values of the training set and validation set against the number of epochs. From the result, the network was trained for a total of 10 epochs. When the validation error stops decreasing after two iterations, the training stops. The epoch with the lowest validation error is indicated to have the best performance. Thus, epoch four is chosen as the best performance, and the model parameters were saved.
Fig. 5. Comparison of raw data (blue) with trained values (red)
The values from the dataset were compared with the trained values from the neural network, as shown in Fig. 5. It shows the trained values are similar and close to the actual data collected. The straight line indicates the perfect prediction.
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Fig. 6. The square root of the coefficient of determination for the ANN model
Figure 6 shows the square root of the coefficient of determination of training set, validation set, and the entire network are 0.68466, 0.63173 and 0.67061.
4 Conclusion In this study, the intensity of CNT-(NiCo) O/C microspheres is predicted and compared using the ANN method. The optimum number of neurons for the ANN method is determined by analysing the RMSE of the training set and validation sets. The square root of the coefficient of determination of the training set and validation set has also been calculated. Of the 267 collected data, 70% were used for training, and 15% were used for validation. The training stops at epoch 10. 11 neurons were used in the hidden layer for this ANN model, resulting in the lowest validation error epoch 4. The predicted values are compared and do not have a significant difference from the actual values. The model has the square root of the coefficient of determination of training set, validation set and the entire network with 0.68466, 0.63173 and 0.67061, respectively. The ANN model proposed is still not perfect as the regression is not satisfying.
References 1. Alhady, S.S.N., Kai, X.Y.: Butterfly species recognition using artificial neural network. In: Hassan, M.H.A. (ed.) Intelligent Manufacturing & Mechatronics. LNME, pp. 449–457. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8788-2_40 2. Ahmad, M.F., Alhady, S.S.N., Oon, O.Z., Othman, W.A.F.W., Wahab, A.A.A., Zahir, A.A.M.: Embedded artificial neural network FPGA controlled cart. Adv. Sci. Technol. Eng. Syst. J. 4(4), 509–516 (2019)
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3. Tat, L.Y., Alhady, S.S.N., Othman, W.A.F.W., Rahiman, W.: Investigation on MLP artificial neural network using FPGA for autonomous cart follower system. In: Ibrahim, H., Iqbal, S., Teoh, S.S., Mustaffa, M.T. (eds.) 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. LNEE, vol. 398, pp. 125–131. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-1721-6_14 4. Alhady, S.S.N., Arshad, M.R.: Design of an ECG signal peak recognition system using multiple HMLP network for diagnosis of heart disorder. In: WSEAS International Conference. Proceedings. Recent Advances in Computer Engineering (No. 9), WSEAS, September 2009 5. Senapati, N.P., Panda, D.K., Bhoi, R.K.: Prediction of multiple characteristics of friction-stir welded joints by Levenberg Marquardt algorithm based artificial neural network. Mater. Today Proc. 41, 391–396 (2021) 6. Oh, S.H., Cho, J.S.: Dataset on the effect of the reaction temperature during spray pyrolysis for the synthesis of the hierarchical yolk-shell CNT-(NiCo) O/C microspheres. Data Brief 25, 104302 (2019)
A Novel Voice-Activated Smart Irrigation System Implementation and Analysis Using Internet of Things Wong Mei Bei and A. T. I. Fayeez(B) Fakulti Kejuruteraan Elektronik and Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, 76100 Hang Tuah Jaya, Durian Tunggal, Melaka, Malaysia [email protected]
Abstract. In this day and age, wastage of water in the agriculture sector has aroused wide concern among human beings. The development of an irrigation system is necessary for agriculture to produce an irrigation system using modern technologies such as IoT and sensors which will improve the efficiency of water usage and further increase the crops yield that fulfil the food demand of humans and livestock. Hence, this project focuses on a smart irrigation system that is capable to overmaster the water pump through voice control and IoT applications with the aid of sensors. The soil moisture sensor will detect the water level of the soil to determine the automatic activation of the water pump. The temperature and humidity sensor which senses the ambient temperature and air moisture will assist farmers to make a proper decision when controlling the water pump manually. Moreover, the Internet of Things (IoT) platform is applied in this system that allows farmers to operate the irrigation process remotely with voice command through the android application and Google Assistant. ThingSpeak which is an analytic online platform aids farmers to visualize the real-time data stored in the cloud. Therefore, the improvement of the system with advanced tech will increase the production rate of crops and reduce the cost of water consumption and human errors. Keywords: Irrigation system · ThingSpeak · IoT · Arduino · Blynk
1 Introduction Nowadays, advanced development in the agriculture sector is necessary due to the continuously increasing of human population, food demand and economic development. Therefore, water consumption in agriculture also increases rapidly and lead to insufficient of the water resources. It is crucial to effectively manage the utilization of water without wasting it in order to minimize the scarcity of water and expensive water expenses. In the past, traditional irrigation system used to irrigate the plants cause the waste of large quantities of water and requires manual labour. Therefore, farmers are facing many problems which further leads to financial losses. In fact, farmers encounter difficulty to monitor the amount of water content in the soil and supply the least amount of water directly © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 20–28, 2022. https://doi.org/10.1007/978-981-16-8954-3_3
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to the soil in order to maintain the root of the plants moist. The traditional irrigation system such as flooding will cause temporary waterlogging which reduces the amount of oxygen that can be absorbed by plant roots. The leaves of the plant will become yellow and dying if the oxygen of roots is insufficient [4]. Thus, the smart irrigation system that is integrated with soil moisture sensor will improve the efficiency of water and reduce the human labour for manual watering process. The precise information of the sensors can prevent overirrigation and increase the productivity of crops [3]. In addition, the manual irrigation system is a time-consuming activity that requires farmers go to farms where is far away from home in order to control the water pump several times throughout a day and thus waste precious time. Hence, an automatic irrigation system based on IoT assists farmers to monitor the farmland’s condition and control water pump with fingertips only via mobile application without presenting at the farm. The IoT system is able to transmit data through Internet without interaction of human-to-computer or human-to-human. It allows farmers to take an appropriate action at once after observing and analysing the detailed real-time information of the sensors via a website such as ThingSpeak [1]. An Internet of Things (IoT) based smart irrigation system that proposes to automatically irrigate water to the farmland and keep track of the condition for soil moisture and temperature of the farmland. This system is capable to control the water pump with fingertips and all captured data is viewed through applications on mobile phone. The low-cost resistive soil moisture sensor operates based on the water content and electrical resistance to measure the moisture level in soil. The higher the water content in soil, the higher the electrical conductivity, the lower the resistance which shows the high soil moisture [1]. Next, the temperature and humidity sensor consists of a filtering capacitor and pull-up resistor. It uses a capacitive humidity sensor to measure the relative humidity of the environment and a thermistor to track changes in temperature [2]. In order to automatically activate the irrigation system, a high power L298N motor driver is needed to control and regulate the water pump based on the threshold moisture value. Hence, motor driver is an interface between the Node MCU and water pump that requires more power. Node MCU is easily programmed with the programming code using the Arduino IDE software. The sensors data are continuously programmed into the Node MCU and gives outputs to the motor driver. Node MCU which is connected to the internet always update the data from sensors and transfer it to the mobile. The irrigation system is controlled and monitored via mobile application called Blynk. The water pump can be easily controlled using the mobile phone with the voice control when encountering an emergency case.
2 Previous Work Harishankar et al. [3] proposed an automatic irrigation system designed to activate the water pumps using renewable resource that is solar power. The controller and soil moisture sensor are used to regulate the outlet valve of tank to control the amount of water flow from tank to agriculture field. On the other hand, Rawal and Srishti [4] aims to develop an automatic sprinkler irrigation system to monitor the soil moisture condition and moisture data is transmitted to GSM-GPRS SIM900A modem from Arduino board. Then, GSM modem plays a vital role to transmit the sensor values obtained from Arduino
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to the Internet platform which are ThingSpeak Cloud and online database. Meanwhile, the authors in [5] developed a smart agricultural monitoring and automatic irrigation system to identify the data of soil moisture, temperature, rain level, human detection and automatically turning on and off the water pump. GSM module is sending the sensors data to Amazon cloud services and data is stored in Amazon for future analysis. Besides, GSM module is also responsible to send the messages about the detailed information of the crops to farmers occasionally. According to the project Intelligent Farming Using IoT and Machine Learning [6], the researchers aim to combine the machine learning and IoT platform to assist farmers improve the shortcomings and development of the irrigation system. This system can provide some appropriate crop suggestions to farmers according to the types of soil, location of farm and season on which crop should be yield. The authors in [7] utilizes the distributed system for supporting smart irrigation using Internet of Things technology, Raspberry Pi is used to collect the real-time sensors values and send to the Azure cloud side. Thus, the collected sensors data will used to determine the duration of irrigation process according to threshold values [7]. Next, the researchers in [8] implements a project titled Design and Development of Irrigation System for Planting which utilizes a solenoid valve to control the state of valve and regulate the flowing of water. Besides, the diameter and distance of pipe are calculated to ensure that the pressure and the flow rate of water are suitable to irrigate the plants [8].
3 Proposed Approach The soil moisture sensor is used to measure the water content in soil. The temperature and humidity sensor can monitor the state of climate through the temperature of certain time of the day and humidity of the environment. Water pump is automatically activated by the motor driver when the temperature is low and the soil is in dry condition. In this circuit, the soil moisture sensor gives an analog output which can be read through the A0 pin of ESP8266 Node MCU. Motor driver IC, L298N is an integrated circuit chip which is used to control the water pump in this irrigation system. DC water pump is used in this system to pump the water required for irrigation process from the water tank through pipes. This pump is selected as size is small so easy to install and enough efficiency to pump water for irrigation. 3.1 Node MCU ESP8266 NodeMCU version 3 which is based on ESP-12E is an open-source firmware and development kit that can be used to design project using a few LUA scripts lines. The interface of this module is primarily divided into two parts which are firmware and hardware. The firmware is based on LUA [9], an easy to learn scripting language that provides a simple programming environment layered with a fastest scripting language. An open source firmware allows users to edit, modify and rebuild the existing module until successfully optimize the module that fulfil user’s requirements. The Arduino IDE software is used to program this module. When using Node MCU board, needs to install the drivers on computer. LED will flash after connecting the board with computer using a cable supporting micro-USB port. In this IoT project, NodeMCU is used to interface with the sensors and motor driver.
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3.2 Temperature and Humidity Sensor The ambient humidity and temperature are measured using the temperature and humidity sensor (DHT11). It is integrated with a high-performance 8-bit microcontroller and providing excellent quality, fast response, anti-interference capability as well as costeffectiveness. This sensor has a resistive element humidity measuring component and a wet NTC temperature measuring component. Its sensing technology ensures high reliability and long-term stability. It is the best option for variety of applications as its size is small, low power usage and signal transmission up-to-20 m. The sensor contains 3 pins mainly divided to power, ground and data. The data pin must be connected to the digital pin of the NodeMCU [10]. 3.3 Soil Moisture Sensor The soil moisture sensor has a fork-shaped probe and an electronic module which are used to measure the volumetric water of the soil. The probe works as a potentiometer, resistance varies with soil moisture. Relationship between resistance value and moisture level are inversely proportional. The higher moisture level in soil, the conductivity increases and result in a lower resistance and vice versa. This sensor consists of electronic module that connects to the A0 pin of NodeMCU. This module generates an output voltage according to the resistance value of probe. Besides, the module contains a built-in variable resistor for adjusting the sensitivity of the digital output (D0). The analog output from sensor will vary from 0 to 1023 and mapping the values to the percentage range from 0 to 100 [11]. There are two methods to show the moisture value in terms of percentage which are the mapping method and the equation. 3.4 How It Works Figure 1 below shows the proposed approach where the system initializes its operation when the NodeMCU board, pre-programmed with necessary code, is powered on and the board will connect to the Internet. The blinking of on-board LED indicator proves that the code is uploaded. Once the Internet is connected to the board, the values of soil moisture, temperature and humidity will display on IoT application platform and serial monitor. When the percentage value of soil moisture is less than 41%, the water pump will be activated automatically by the motor driver to irrigate the plants. The Enable A pin of motor driver is high in this condition as the enable pin is used for enabling and controlling water pump. After the percentage of soil moisture exceeds 41%, motor driver deactivates the water pump to prevent over irrigation to crops that will lead to leaching and loss of soil nutrients. In some emergency cases, users require to stop the irrigation system manually and remotely. Therefore, this system is designed to allow the users control the water pump by pressing the button in Blynk or using voice command via Google Assistant. The main hardware components used in the proposed approach such as soil moisture sensor, temperature and humidity sensor and other peripherals can be seen in Fig. 2 below.
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Fig. 1. Flowchart of the proposed approach
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Fig. 2. The overall of the smart IoT irrigation system diagram.
The sensors are placed at the outside of the box to avoid the box from obstructing its sensing path so the sensors can operate efficiently. The drip irrigation system is designed by punching small holes in the tubing. When the water pump is turned on, the water will flow out from holes to drip slowly and directly to the soil of the plant. Hence, it can decrease evaporation and be more efficient contrast to sprinkler irrigation.
4 Implementation and Results Blynk is an IoT platform that allows users to control the Arduino such as Node MCU via the Internet using the Graphical User Interface (GUI) on Android or IOS applications. When starting to create a new project, Authentication Token will be given and sent to the email of the user. Authentication Token is vital because it is required to write into the coding and allows users to access the Blynk Cloud. Blynk app as shown in Fig. 3 enables the user to monitor the sensors data and control the button. Meanwhile ThingSpeak is a web server used to collect, store and monitor the realtime sensors data in the cloud. The essential element of the ThingSpeak platform is “ThingSpeak channel.” Each channel consists of eight fields to store and show the collected data in the charts form as well as three fields which are used to display the data of location fields. If the channel view is shared with everyone, other users can view the corresponding charts. The sensors data stored in the channel is useful for the user to increase the efficiency and reliability of the irrigation system by analyzing and determining the soil condition of crops. In ThingSpeak, there are three fields were created to observe the sensors readings throughout a day and the type of charts displayed are line charts. According to Fig. 4, the range of the y-axis which indicates the percentage values of soil moisture is from 0% to 100% whereas the x-axis shows the time and date. Furthermore, the timescale of the chart is adjusted to display only the first measurement within 60 min. The system is automatically turn on the water pump when the moisture value is lower than 41%. After the moisture value exceeds 41%, the water pump is turned off and the moisture value rises to 76%. Hence, could discover that the line between the first and second points show in the soil moisture chart is increases. Then, the chart shows
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that the moisture values gradually decrease as the water of soil is evaporates faster during the high temperature in the afternoon which is 36 °C.
Fig. 3. Blynk setup for smart irrigation system.
The moisture value falls to 40% on 24 of July so the water pump is automatically turn on again and the moisture value becomes 63%. From the soil moisture chart, could conclude that the plant is irrigated two times in a day. During the first irrigation, the water pump is automatically turn off when reaching the 67% yet the weather is too hot. Thus, the irrigation process is executed manually until the value is reaching 76%. It can be observed that the temperature sensor is used to trigger the water pump manually. Moreover, the highest value of temperature recorded is 55.8 °C because of the sun exposure directly to the sensor. From the 1 pm to 5 pm, the temperature and humidity sensor values are not constant due to the sensor is exposure to the sun occasionally. The automatic smart irrigation system via IoT was successfully developed based on the operation of the irrigation system and the results obtained.
Fig. 4. Line chart of the soil moisture, temperature and humidity
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5 Conclusion In this project, the development of a low-cost smart irrigation system with voice control via IoT is conducted successfully. The traditional irrigation method contains several drawbacks and its improper irrigation management lead to large amounts of water are wasted. Water is a vital natural resource and the usage of water is the largest in the agricultural territory. Hence, the enhancement of the irrigation system is introduced to reduce and solve the problem of water shortage that is usually faced by farmers. The detailed and accurate sensors data are important for future research and improvement of the system. The parameter of sensors can observe by farmers through the IoT platform that are ThingSpeak Channel and Blynk apps. This system is reliable, environmentfriendly solving method and easy to operate with the application of IoT and automation. Farmers are capable to irrigate the crops more effectively and remotely without overirrigation based on the moisture value. Thus, it maximizes the productivity and quality of crops in a short term to make sure that the crops yield are adequate to tackle the food shortage problem encountered by the country with the growing exponentially of the human population. In a nutshell, the overall benefits of this economical irrigation system are high and it eliminates human error in monitoring the soil moisture level.
6 Future Work After successfully completing this project, some suggestions are recommended here for future research works and development to further enhance the system. Firstly, the electric power cost of an irrigation system is high and it is occasionally unavailable, especially in remote regions. Thus, a water pump that is powered by the solar panel is an energy-saving system, more reliable and can reduce the electrical cost throughout the irrigation process. The solar water pump will convert the renewable energy which is solar energy into electrical energy to activate a motor driver. Secondly, the automatic irrigation system based on the threshold value is executed in this project with the assists of the Blynk application while the Thingspeak is just used to visualize, analyse and store the sensors data. The threshold value also can be set and store in ThingSpeak. The threshold value can be manipulated remotely at any time and the Node MCU board will read the latest threshold value to take immediate proper action. For instance, the irrigation system will stop the watering process at once when the moisture value has reaching the required threshold value. Meanwhile the notification can provide convenience and benefits to the farmers as it will send a notification to the mobile phone when the soil moisture is in dry condition. The farmers will be alerted and will check the water level in the water tank to ensure that the amount of water is sufficient to irrigate the crops. Besides, farmers do not need to monitor the moisture levels regularly as the notifications will remind them to maintain the moisture of the soil. Thus, it can curb the problem of shriveling of crops due to scarcity of water supply. Last but not least, a water meter is suggested to use in agriculture field to measure the amount of water that flows out of a pipe. It can determine and calculate the amount of water consumption during the irrigation process to enhance the operating system and plan for better water management. It plays a significant role in reducing the amount of water used and the water expenses that can be used for future system improvement.
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Acknowledgement. We would like to thank Fakulti Kejuruteraan Elektronik & Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka for their continuous support that enabled us to complete this project during this pandemic.
References 1. Ismal, N., Rajendran, S., et al.: Smart irrigation system based on internet of things (IOT). In: Journal of Physics: Conference Series, vol. 1339, no. 1. IOP Publishing (2019) 2. DHT11 Sensor Pinout, Features, Equivalents & Datasheet. Retrieved from Components101.com website. https://components101.com/sensors/dht11-temperature-sensor 5, June 2021 3. Harishankar, S., et al.: Solar powered smart irrigation system. Adv. Electron. Electr. Eng. 4(4), 341–346 (2014) 4. Rawal, S.: IOT based smart irrigation system. Int. J. Comput. Appl. 159(8), 7–11 (2017) 5. Revathi, G.P.: Development of smart agricultural monitoring and automatic irrigation system. Int. J. Innov. Res. Comput. Sci. Technol. (IJIRCST) 7 (2019) 6. Jadhav, B., et al.: Intelligent farming using IoT & machine learning. SSRN 3852982 (2021) 7. Abdelmoamen Ahmed, A., et al.: A distributed system for supporting smart irrigation using Internet of Things technology. Eng. Rep. e12352 (2021) 8. Rasyid, A.M., et al.: Design and development of irrigation system for planting part 1. In: 2nd Integrated Design Project Conference (IDPC) (2015) 9. Al Dahoud, A., Fezari, M.: NodeMCU V3 for fast IoT application Development. Notes 5 (2018) 10. Srivastava, D., Kesarwani, A., Dubey, S.: Measurement of temperature and humidity by using arduino tool and DHT11. Int. Res. J. Eng. Technol. (IRJET) 5(12), 876–878 (2018) 11. Last Minute Engineers: How soil moisture sensor works and interface it with arduino. Last minute engineers, 18, December 2020. https://lastminuteengineers.com/soil-moisture-sensorarduino-tutorial/
Conceptual Design of Cloud-Based Data Pipeline for Smart Factory Peng Joo Goh1 , Zi Yang Hoe1 , Cheng Yee Low1(B) , Ching Theng Koh1 , Ubaidullah Mohammad2 , Kent Lee3 , and Chee Fai Tan4 1 Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia,
86400 Parit Raja, Johor, Malaysia [email protected] 2 Advanced Technology Training Centre (ADTEC) Shah Alam, 40460 Shah Alam, Selangor, Malaysia 3 Keysight Technologies, Bayan Lepas Free Industrial Zone Phase 3, 11900 Bayan Lepas, Pulau Pinang, Malaysia 4 Department of Mechatronics and Biomedical Engineering, Universiti Tunku Abdul Raman, Sg. Long, Petaling Jaya, Selangor, Malaysia
Abstract. Through the digitalization of manufacturing, an abundance of data is available from machines, sensors and operations. This trend requires technical colleges and universities to enhance their syllabus. This paper describes a cloud-based data pipeline for a digital manufacturing lab that utilizes machine-to-machine communication and the internet of things (IoT). The factory model consists of four stations, i.e., a vacuum gripper robot, an automated high-bay warehouse, a sorting line with color detection, and a multi-processing station with an oven, and these stations can demonstrate a fully working digital production line prototype that is in line with Acatech Industrie 4.0 Maturity Index. The Programmable Logic Controller (PLC) on-site passes data via the internet to the Amazon Web Services (AWS) cloud computing platform. Data Analytics uses methods from statistics and machine learning to optimize processes, continuously monitor product quality and improve maintenance of equipment. The factory model and the data pipeline provide an intuitive hands-on learning experience for teaching Industry 4.0 and digital manufacturing at technical colleges and universities. Keywords: Smart factory · IoT · Amazon web services cloud · Maturity index
1 Introduction The fourth Industrial Revolution or Industrie 4.0 could be a collective term that includes advanced manufacturing, information technologies, cloud computing and supply chain innovations with connectivity through the Internet [1]. Industry 4.0 promotes the continuous digitization of conventional methods and advancing mechanization using cuttingedge innovation. Internet of Things (IoT) and machine-to-machine communication (M2M) are technologies that can be utilized to coordinates improved motorization, progressions in communication, and automated self-monitoring systems, that can acquire © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 29–39, 2022. https://doi.org/10.1007/978-981-16-8954-3_4
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data and get the circumstance without intervention for human impedances [2]. This permits multiple-way communication between everyone in a supply chain including the shop floor and a client, thus allowing for mass customization to be adopted as a manufacturing framework. The concept of mass customization is characterized as “producing merchandise and administrations to meet personal customer’s needs with close massproduction productivity” [3]. This benefits the customer needs for customization and provides the tools for industries to implement it. The concept of a smart factory that implements automated data acquisition and monitoring of machines’ health and productivity rate together with data analysis using Big Data and machine learning has gained interest within the manufacturing sector either it is done locally or through a cloud platform. The goal of adding ‘smart’ to a factory is to increase productivity, reduce cycle time, reduce waste and create a conducive work to life balance by automatically and continuously analyzing data collected and figuring out the hidden message behind the data. An Industry 4.0 environment requires speedy decisionmaking in real-time and not as it were. This can be done by monitoring the various manufacturing parameters and administering achievement control. Data-oriented analysis, real-time simulation, and risk-based consideration will provide valuable information which will aid proper real-time decision-making and improve production planning, control and management. This work describes the development of a smart factory model using an Industry 4.0 factory simulation from Fischertechnik (FT) and a conceptual design of a cloud-based data pipeline that will connect the smart factory model to a cloud platform. The smart factory model is controlled by a PLC and using an IoT gateway, it can transfer data to a cloud platform and subscribe to various services provided by this cloud platform. Here Amazon Web Service (AWS) is used as the cloud service provider.
2 Literature Review MyDIGITAL is a digital economy blueprint that represents the Malaysian government’s desire to successfully turn Malaysia into a digitally-driven, high-income country and a worldwide leader in the digital economy [4]. The manufacturing sector is currently facing significant challenges and a pandemic such as Covid-19 has left several factories to be closed down. Other problems include increased global competition, demands for better work-life balance, demands for customized things, and shorter product lifecycles. The anticipated industrial revolution may be a solution to deal with these issues [5]. Smart factories are one of the fundamental applications of Industry 4.0 that include various new advancements and technological disruption such as digitalization, advanced manufacturing innovation, connectivity, information and computer technology (ICT), automation innovation, and cloud computing.
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A smart factory can be ranked according to its maturity or the application that is implemented in it. According to the Acatech Industrie 4.0 Maturity Index [7], there are six stages in the maturity index. Stage 1 is computerization. The objective is to simplify the repetitive and manual tasks by introducing IT on the shop floor and anywhere else that is applicable. Stage 2 is the connectivity which is to streamline business and IT by connecting and integrating business processes. Stage 3 is visibility, which is to make data-driven decisions. This can be done by building real-time digital shadow or digital twin in a paperless factory or a digital factory. Next is stage 4 which is transparency. The objective is to grasp complex interaction by running data analysis and understanding its effects. Stage 5 is the predictive capacity which is to prepare for the upcoming situation. This can be done by simulating a possible future scenario to enable decision-making support. The last stage is adaptability to leave certain control for the system to make the decision and take over the control of a factory. This is done by letting the system adapt itself where possible in a self-configured process. These are the stages that need to be achieved by a system to become fully matured. The Smart Factory in Kaiserslautern, SmartFactoryKL [6], displays the world’s first manufacturer-agnostic Industrie 4.0 manufacturing plant, demonstrating how highquality, adaptable production may be deployed efficiently. The following IT systems are now in use at the SmartFactory KL pilot plant: order planning (ERP), order control (MES), plant engineering (PLM), data capture and data analytics, and remote monitoring/maintenance. As a higher-level cloud platform, the SmartFactory KL cloud has access to a variety of various suppliers’ IT systems. This enables the 4.0 plant to be monitored for irregularities [9]. Furthermore, there are the Swiss Smart Factory [8] and the Model Factory at ARTC-Singapore [10]. The adoption of advanced analytics processing, artificial intelligence, and industrial automation technology, as well as their interconnectivity, is the technology that uses interconnected machines and tools to improve manufacturing performance and improve energy and workforce requirements [11].
3 System Development The fischertechnik factory simulation model was used in this work. The model consists of a total of four stations, i.e., a Vacuum Gripper Robot, an Automated High-Bay Warehouse, a Sorting Line with Color Detection, and a Multi-Processing Station with an Oven. Figure 1 shows all four stations. The 3D vacuum suction gripper is an industrial robot that can perform certain tasks. The high-bay warehouse is a storage facility that saves space and allows for computer-assisted storage and retrieval of items. The colour recognition sorting process automatically separates distinct coloured workpieces. The vacuum suction gripper is the center of the factory and connects all stations. As in a real factory, the components are transferred fully automatically from the warehouse, processed and finally sorted by color in the sorting section. A workpiece is automatically fed through a series of stations in the multiprocessing station with an oven to replicate various operations [12]. The four stations are controlled by the Beckhoff C6015 IPC. The program is written in structured text. The PLC communicates via the standard OPC/UA protocol with the raspberry pi which acts as an IoT gateway to connect the factory to a cloud service
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Fig. 1. A smart factory simulation model on basis of fischertechnik modules
provider. The software used in the IoT gateway is Node-red. Node-red also provides a web-based dashboard that can be used to display and control the system. The built-in WLAN router sends real-time factory operation information to the cloud. The information is shown in a web-based dashboard. Training Factory 4.0’s dashboards allow for the display of modules from three different views. The customer view, supplier view, and production view are the three options. The images and data can be viewed and used remotely using internet-enabled devices. The USB camera can view the entire factory and is operated from the cloud [13]. The cloud service provider which in this case is AWS. The IoT gateway communicates with the txt controller via the standard MQTT protocol which in turn enables access to the Fischer Technique cloud platform. Mosquitto utilizes the MQ Telemetry Transport protocol, or MQTT, which uses an advertised message queuing mechanism to offer lightweight messaging. MQTT is the forthcoming machine-to-machine interface. Using MQTT also enables users to create a mobile application. The txt controller is programmed using C++ language. The txt controller manages the sensor image data and is monitored in the Fischer Technique cloud platform [13].
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To rotate To position workpiece
To pick up
To release
To transport
To simulate smart factory
To identify workpiece
To detect workpiece To recognize colour
To acquire sensor data To acquire data
To acquire actuator data To connect with cloud
To detect malfunction To analyze errors
To analyze malfunction To rectify fault
Fig. 2. Functions model for the factory simulation
4 System Functions An extract of the system functions is shown in Fig. 2. To Position Workpiece. The function of positioning workpiece is implemented at the High-Bay Warehouse and the Vacuum Suction Gripper. At the Human-Machine Interface, the current position is shown together with the respective target values of the encoder motors. To Identify Workpiece. At the sorting station, the change of the colour of the component provides the colour sensor with different analog values depending on the colour of the component. Using these analog values, the PLC can adjust the conveyor belt at the end of the sorting line to the correct shelf based on the colour value. All these values can be viewed and changed in the Human-Machine Interface. To Acquire Data. The factory model generates a lot of data, e.g., lead time and material flow. Observing the incoming and outgoing signals of PLC during series production, all activities in the production lines can be seen. Using OPC UA, for example, this machine data can be sent to the cloud or store in a database. The material flow is displayed in the panel. It is transparent to know at all times where a component is located and what color it is. Via the Human-Machine Interface, for instance, the user can determine the
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duration of the process or completely deactivate the burners. The PLC also determines the running times of the individual actuators. To Analyze Errors. Some errors are programmed to simulate the errors of a real industrial plant. The function “to analyze errors” protects the factory model from damage and reports irregularities in the process. If a malfunction occurs, the model stops and displays the malfunction in plain text on the panel. The fault can be rectified and the process can be restarted. The individual malfunctions are evaluated. In the analysis, it can be seen which malfunctions have occurred most frequently and which have affected the process the longest. With the help of a maintenance plan, maintenance orders are generated.
5 System Behaviour A Behaviour States diagram, as shown in Fig. 3. is used to design the operation of the simulation factory. In the beginning, the plant goes into an initialization state for sensor status check. Only if every sensor status is ok, the plant gets the basis position to start the plant. There are two modes of operation, i.e., “Make-to-Order” mode and “Series Production” mode. In the “Make-to-Order” mode, only one component is processed, while in the “Series Production” mode, a batch of components is processed, up to a maximum of nine workpieces.
E1 Make-to-Order Production
Initialization: sensor check
E4
E3
Legend State
E2
Series Production
Event Start Logic connection
Fig. 3. Behaviour-States model for the factory simulation
In the “Make-to Order” production mode, only one component will be retrieved from the High-Bay Warehouse. The transport carriage which carries a component goes off a specific location of the high-bay warehouse and transfers the component to the transfer area. The transfer station is the link between the High-Bay Warehouse and the VacuumSuction Gripper which simulates a three-axis robot arm. In the next step, the three-axis robot arm picks up the component using suction and places the component at the entrance of the oven of the Multi-Processing Station. At the Multi-Processing Station, the burning process is simulated by an indicator light. After the component is being burned, a small suction device transports the workpiece to the turntable. The turntable then positions the
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component for machining. There, another machining step takes place. Then, with the help of an exit conveyor, the workpiece continues in the direction of the Sorting Station. At the Sorting Station, an analog color sensor is located inside the red chamber. The PLC determines the shape of a component and sorts the components into the corresponding bins. Now the Vacuum Suction Gripper comes into play again. It picks up the workpiece and places it back in the transfer area of the High-Bay Warehouse. In the last step, the part is stored again and a new cycle can be started. In the “Series Production” mode of operation, all components are processed in sequence and then loaded again. A complete run takes about twenty minutes.
6 Cloud-Based Data Pipeline Figure 4 illustrates the connection between the IoT gateway to the AWS platform to transfer data from the PLC to the AWS services. The IoT gateway is a node that gathers all data from the system through the PLC, converts all the data to a readable data type and sends the data through the Internet to the AWS platform. In short, the IoT gateway is an entry and exit point between the local system and the AWS Cloud Platform. The IoT gateway communicates with the AWS cloud platform through the Internet, and the data from the IoT gateway is received by one of the AWS services which are the AWS IoT SiteWise. AWS IoT SiteWise is a data management entry point for AWS cloud to collect, process and monitor equipment data at scale. The next node is the AWS Core rules engine, which is a service to evaluate inbound messages published into AWS IoT Core and transforms thus delivers them to another device or another cloud service, based on the business rules that are defined. This is the node point to control the data flow to the other AWS modules or services. AWS Analytics is a data analysis cloud service that helps developers to run sophisticated analytics on massive volumes of streamed data. AWS Analytics can automate the data pipeline, by applying a mathematical algorithm for data processing and analyzing by running queries or using the built-in SQL query engine, and even machine learning by using Amazon SageMaker. The data is stored inside Amazon S3, which is a cloud storage service like Google drive. The output of AWS Analytics can be passed to AWS IoT Event, which a module is used to detect and respond to various events from the system on the shop floor for example from a sensor. This module will trigger alerts and actions will be taken based on a predefined event logic. The alert can also be sent to Amazon SNS, which is a module used to send push notifications to a person such as a factory operator [14].
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Internet
AWS Cloud Platform AWS IoT SiteWise
Core Rules engine
Amazon S3
Core Rules engine
AWS IoT Events
Amazon Athena
Amazon SageMaker
Amazon SNS
Data Scientist
Operators
Fig. 4. Cloud-based Data Pipeline
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7 Application Example: Predictive Maintenance (PdM) Predictive maintenance (PdM) is a preventative maintenance activity that monitors the behaviour, health, usage and status of certain components while it is in use and predicts when it will fail using a certain calculated algorithm and predefined condition to lower the risk of malfunction. PdM, also known as condition-based maintenance [15]. The objective of PdM is to optimize the utilization of assets while upkeeping them. By knowing when a certain part will fail, maintenance supervisors can plan maintenance work when it is necessary, at the same time dodging intemperate maintenance and preventing unforeseen breakdowns. If implemented effectively, PdM brings down operational costs, minimizes downtime issues, and maintains the overall asset health and operation. For example, vibration analysis of a machine permits the client to assess the condition of a machine because vibration causes wear and tear and is one of the indicators before a machine breakdown. If there is a significant change in vibration, the machine can be checked thus minimizing an impromptu downtime by planning required repairs during ordinary maintenance shutdowns [16]. Three fundamental aspects enable an online smart PdM system to track a component condition and caution someone approximately when that component will fail. Firstly, condition-monitoring sensors such as a vibration sensor needs to be installed to send real-time data of the machine’s wellbeing. Next is to establish a communication and data transfer network between the machine and a cloud platform by implementing IoT technology. Lastly, predictive maintenance models that analyze these data, based on a mathematical algorithm or predefined condition using the cloud services are nourished so that they can give failure forecasts [17]. Figure 5 shows the PdM workflow. The primary step in practicing PdM is establishing the condition baselines. After that, installing sensors to monitor a component condition. Then, start to compare the conditional data with the established baseline. That way, after starting to gather conditional information, there is a “control” to compare any variations from the norm [18]. The left shows the Multi-Processing Station with an oven station. The milling machine is a component that can be monitored as part of the smart PdM system. The vibration sensor is installed on the machine and is connected to AWS IoT Greengrass, which is a module to build, deploy and manage a device. Using the AWS IoT Greengrass, it is capable of programming the system to gather data from the sensor
Fig. 5. Predictive maintenance workflow
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on a local level, send it to a cloud platform and make predictions using machine learning modules the cloud platform provides [19].
8 Conclusion The conceptual design of a smart factory model that can be connected to a cloud platform specifically the AWS is described. Using cloud modules, the PLC on the site can pass data via the internet to be processed and analyzed by cloud computing. Eventually, a data-oriented analysis will provide a decision-making aid. With the increasing need to digitalize, connect and take advantage of cloud computing, this approach provides a smart factory concept that can be implemented for teaching purposes at technical colleges.
References 1. Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014). https://doi.org/10.1007/s12599-014-0334-4 2. Moore, M.: What is Industry 4.0? Retrieved from techradar. https://www.techradar.com/news/ what-is-industry-40-everything-you-need-to-know 22, May 2020 3. Banton, C.: Mass production. Retrieved from investopedia: https://www.investopedia.com/ terms/m/mass-production.asp 30, September 2020 4. Hussen, Y.D.: National fourth industrial revolution (4IR) policy. Perpustakaan Negara Malaysia cataloguing-in-publication data (2020) 5. Alexander, W.: A work process supporting the implementation of smart factory technologies developed in smart factory compliant laboratory environment (2019) 6. Zuehlke, D.: Smart factory towards a factory-of-things. Annu. Rev. Control. 34(1), 129–138 (2010) 7. Günther Schuh, R.A.: Using the industrie 4.0 maturity index in industry (2020) 8. Retrieved from Switzerland innovation (2021). https://www.sipbb.ch/wp-content/uploads/ 2021/08/Overview_2021_web.pdf 9. Gorecky, D., Weyer, S.: SmartFactoryKL systemarchitektur for industrie 4.0 production plants. whitepaper. technologie-initiative smartfactory kl e. v., Kaiserslautern. http://dfki3036.dfki.de/pdf/Whitepaper/SF_WhitePaper_EN.PDF (2016). Accessed on 17 Apr 2017 10. Seif, A., Toro, C., Akhtar, H.: Implementing industry 4.0 asset administrative shells in mini factories. Procedia Comput. Sci. 159, 495–504 (2019). https://doi.org/10.1016/j.procs.2019. 09.204 11. Sudip Phuyal, D.B.: Challenges, opportunities and future directions of smart manufacturing: a state of art review. ScienceDirect 2, 100023 (2020) 12. Fishertechnik Lernfabrik 4.0 (2020). file:///C:/Users/User/Downloads/fabrik_2019_englisch _neu%20.pdf 13. Plc Training Factory 24v.: Retrieved from github. https://github.com/fischertechnik/plc_tra ining_factory_24v 8, June 2021 14. AWS IoT Analytics - How it works (3:01): Amazon Web Services, Inc. https://aws.amazon. com/iot-analytics/. Accessed on 30, July 2021 15. Coleman, C.S.: Predictive maintenance and the smart factory. Retrieved from Deloitte. https://www2.deloitte.com/content/dam/Deloitte/us/Documents/process-and-operations/uscons-predictive-maintenance.pdf (2017)
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16. Yung, C.: Vibration analysis: what does it mean? Retrieved from Plant Services Articles. https://www.plantservices.com/articles/2006/154/ 9, June 2006 17. Christiansen, B.: A complete guide to predictive maintenance. Retrieved from LimbleCMMS. https://limblecmms.com/predictive-maintenance/ 15 Jan 2021 18. Ngamthonglor, J.: Onunkeep. https://www.onupkeep.com/learning/maintenance-types/pre dictive-maintenance 21 Mar 2021 19. What is AWS IoT Greengrass? (1:51): Amazon Web Services, Inc. https://aws.amazon.com/ greengrass/. Accessed on 25, July 2021
The Optimization of the Halophilic Cellulase Production: A 3-2-1 Multilayer Perceptron Artificial Neural Network Approach Ahmad Afif Ahmarofi1(B) , Ahmad Anas Nagoor Gunny2 , Jastini Mohd Jamil3 , and Naimah Amlus4 1 Department of Computer Science, Faculty of Computer and Mathematical Sciences, Universiti
Teknologi MARA, 08400 Merbok, Kedah, Malaysia 2 School of Bioprocess Engineering, Universiti Malaysia Perlis, Kompleks Pusat Pengajian
Jejawi 3, 02600 Arau, Perlis, Malaysia 3 School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia,
06010 Sintok, Kedah, Malaysia 4 Universiti Utara Malaysia Information Technology, Universiti Utara Malaysia, 06010 Sintok,
Kedah, Malaysia
Abstract. Lignocellulose is one of the bio-resources available on the earth. It could be hydrolyzed into simple sugar. The previous study found that carboxymethylcellulose (CMC), FeSO4·7H2O, and NaCl are the significant mediums that influence the production of halophilic cellulase. Despite that, an appropriate method is deemed crucial from an industrial perspective to optimize halophilic cellulose production for cost-effectiveness. In this regard, the optimum halophilic cellulose production is determined from the best-so-far parameter of the three significant mediums. A data mining process using Multilayer Perceptron (MLP) based on the Artificial Neural Networks (ANN) method is developed to optimize the parameter from a set of experimental data. A 3-2-1 MLP network was constructed to learn the experimental data. As a result, the root squared error from the MLP is 0.0118 during the validation process. Subsequently, the MLP network was considered to determine the parameter of the significant medium and the production of halophilic cellulose. Consequently, this finding provides beneficial guidance for the manufacturer in the chemical industry to achieve efficient halophilic cellulose production. Keywords: Halophilic cellulose · Data mining · Multilayer Perceptron · Artificial Neural Networks
1 Introduction The production of halophilic cellulases from the fungus namely Aspergillus terreus UniMAP AA-6 is vital for scarification of ionic liquids treated lignocelluloses [1]. Lignocellulose is one of the bio-resources available on the earth [2]. It could be hydrolyzed into simple sugar. Furthermore, lignocelluloses can be utilized for the production of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 40–46, 2022. https://doi.org/10.1007/978-981-16-8954-3_5
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fermented products such as enzymes [3]. For this reason, the hydrolytic enzyme is frequently utilized for the conversion of lignocellulose into simple sugars. From the lab experiment using Plackett-Burman’s design, it is found that carboxymethylcellulose (CMC), ferrous sulphate heptahydrate (FeSO4·7H2O), and sodium chloride (NaCl) are the most significant mediums for halophilic celluloses production [1]. However, halophilic cellulase production is vital to run in a cost-effective operation from an industrial perspective [1]. Thus, the optimum parameter for these three significant mediums is deemed crucial to be determined for the optimal halophilic celluloses production. From previous literature, the optimum parameter from a set of experimental data can be obtained through the data mining process [4]. Among all data mining methods, artificial neural networks (ANN) prove an excellent performance compared to decision tree method, and regression analysis [5]. Through the application of ANN, the number of experiments can be reduced while the output from the network provides a small percentage of error such as based on the value of root mean squared error (RMSE) [4]. Consequently, production efficiency and profits for the manufacturer in the chemical sector can be improved. An artificial neural network (ANN) is a network architecture that mimics a human brain structure [6]. A neuron is a cell in the biological human brain to process information while a neural is a computing term that refers to the development of computer networks [7]. Historically, ANN is established by McCulloch and Pitts through a vast of experiments of the biological human brain in 1943 [5]. A node is the main processing part for input and output in ANN. For that reason, ANN resembles the way of neuron function in a human brain to process information. From previous studies, ANN can determine the optimum parameter from experimental data [8]. Moreover, ANN has a better performance compared to the other methods such as regression analysis and decision tree [9]. In this regard, the ANN method is considered as the suitable data mining technique to determine the optimum parameter. Hence, the objective of this research is to determine the best-so-far parameters for these three significant mediums, i.e., carboxymethylcellulose (CMC), FeSO4·7H2O, and NaCl for the optimum production of the halophilic cellulase. Subsequently, the research methodology, results, and discussion are presented in the following section. This is followed by the conclusion of this research.
2 Research Methodology The flow of the development process in multilayer perceptron (MLP) in ANN to find the optimal solution is illustrated in Fig. 1 as follows.
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Development of ANN Network structure
Establishment of ANN learning algorithm
Allocaon of data for training and validaon process
Run ANN learning process by adjusng the connecon weight
Is the value of the error the smallest? No
Yes
Obtain opmal soluon
Fig. 1. The learning process of multilayer perceptron in artificial neural networks.
During the first stage of the learning process, a total of 55 readings from experimental data was considered as secondary data. In this research, carboxymethylcellulose (CMC), FeSO4·7H2O, NaCl, and their effect on halophilic cellulase production were recorded during the lab experiment as conducted by [1]. In the stage of network structure development, MLP was established since this to pology is often implemented to determine the optimum parameter from collected data. The basic topology of the MLP network is presented in Fig. 2.
The Optimization of the Halophilic Cellulase Production
Input layer
Input-hidden neuron
Hidden layer
Hidden-output neuron
43
Output layer
Fig. 2. The basic topology of multilayer perceptron.
The MLP network was constructed based on the number of input node-the number of hidden node-the number of output node, a-b-c. Furthermore, the backpropagation (BP) learning algorithm was chosen since the algorithm is suitable for MLP topology. The most important learning parameter for the BP learning algorithm is connection weight [10]. Connection weight is a relative strength within connections of nodes from layer to layer [11]. Moreover, the experimental data was allocated between the training and validation processes. For a better prediction result, more data was allocated for the training process. In this regard, 80% of data were allocated for the training, f , while 20% were allocated for the validation process, g. Consequently, the optimum parameter for each of the significant medium, i.e. carboxymethylcellulose (CMC), FeSO4·7H2O, NaCl, and their effect on halophilic cellulase production were obtained based on the smallest root square error. Finally, the output from this study in terms of halophilic cellulose production was compared with halophilic cellulose production profile as conducted by [1]. In their study, the maximal halophilic cellulose production is 0.029 u/ml.
3 Results and Discussion The MLP network is constructed based on the number of input node-the number of hidden node-the number of output node, a-b-c. The developed structure of the MLP with BP learning algorithm is presented in Fig. 3.
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A. A. Ahmarofi et al. Adjust weight No
Is the error the smallest value?
Obtain the opmum output Yes
Input node 1 CMC
Weight
Hidden node 1
Weight
Weight Weight
Output node
Input node 2 FeSO4.7H2O
Halophilic cellulose
Weight Weight Weight
Hidden node 2
Weight
Input node 3 NaCl
Flow of MLP learning process Flow of backpropagaon process
Fig. 3. The 3-2-1 MLP structure with a backpropagation learning algorithm
The input layer has three nodes, the hidden layer has two nodes while the output layer has one node. Hence, the established network is a 3-2-1 MLP network topology. Subsequently, a learning algorithm was formulated to learn the relationship between input and output data. The three significant mediums, i.e., carboxymethylcellulose (CMC), FeSO4·7H2O, and NaCl were treated as the input within the MLP while halophilic cellulase production was treated as the output. The iteration of the learning process, d, for the MLP network is accomplished once the result of the root mean squared error, Ed, gives the smallest value. The number of iteration during the learning process and final Ed of the 3-1-1 MLP network are presented in Table 1 as follows. Table 1. The Ed of the 3-2-1 MLP network MLP network
Separation of experimental data between training, f , and validation, g
Iteration
Root mean squared error
a-b-c
F
G
D
Ed
3-2-1 network
80%
20%
500
0.0118
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From Table 1, the smallest Ed for the 3-2-1 MLP network is 0.0118 during the 500th iteration of the learning process. Consequently, the 3-2-1 MLP network is implemented to determine the parameters of carboxymethylcellulose (CMC), FeSO4·7H2O, and NaCl. The parameters for these three significant mediums, i.e., carboxymethylcellulose (CMC), FeSO4·7H2O, and NaCl for the optimum production of the halophilic cellulose is presented in Table 2. Table 2. The best-so-far parameters for the significant mediums and the optimal halophilic cellulose production Parameters for related medium
Parameter
Unit
CMC
1.48
% w/v
FeSO4·7H2O
2.43
10–4 %, w/v
NaCl
7.70
% w/v
The optimal halophilic cellulose pro duction 0.0625 u/ml
Based on the learning process of the 3-2-1 MLP network, the best-so-far parameter for medium (CMC), FeSO4·7H2O, and NaCl is 1.48% (w/v), 2.43% x 10–4 (w/v), and 7.70% (w/v), respectively. As a result, the optimal halophilic cellulose production based on the parameters is 0.0625 u/ml. This finding indicates that the implementation of the 3-2-1 MLP model based on the ANN method can improve the production of halophilic cellulose production doubled from 0.029 u/ml to 0.0625 u/ml.
4 Conclusion The development of the 3-2-1 MLP network to determine the optimal halophilic cellulose production based on the experimental data showed a small root mean squared error, which is 0.0118 during the validation process. Thus, the finding from this study successfully determined the best-so-far parameter for the significant medium to produce halophilic cellulos, namely (CMC), FeSO4·7H2O, and NaCl. Furthermore, the developed 3-2-1 MLP model based on the ANN method improved the production of halophilic cellulose production from 0.029 u/ml to 0.0625 u/ml. Ultimately, the data mining based on the ANN method can reduce the number of experiments that physically deal with chemical substances. Consequently, the usage of excessive chemical substances and repetitive experiments can be reduced for better cost-effective production.
References 1. Gunny, A.A.N., Arbain, A., Jamal, P., Gumba, R.E.: Improvement of halophilic cellulose production from locally isolated fungal strain. Saudi J. Biol. Sci. 22(4), 476–483 (2014) 2. Wei, H., Xu, Q., Taylor, L.E., II., Baker, J.O., Tucker, M.P., Ding, S.Y.: Natural paradigm of plant cell wall degradation. Curr. Opin. Biotechnol. 20(3), 330–338 (2009)
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3. De Diego, T., Manjon, A., Iborra, J.L.: Biocompatibility of ionic liquids with enzymes for biofuel production. Prod. Biofuels Chem. Ionic Liq. 1, 275–301 (2014) 4. Ahmarofi, A.A., Ramli, R., Abidin, N.Z., Jamil, J.M., Shaharanee, I.N.: Variations on the number of hidden nodes through multilayer perceptron networks to predict the cycle time. J. Inf. Commun. Technol. 19(1), 1–19 (2020) 5. Turban, E., Sharda, R., Delen, D.: Decision Support and Business Intelligence System. Pearson Education Inc, New Jersey (2011) 6. Kumar, S.: Neural Networks: A Classroom Approach. McGraw Hill, New Delhi (2013) 7. Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, New Jersey (2009) 8. Hassan, M.G., Othman, S.N., Taib, C.A., Ahmarofi, A.A., Akanmu, M.D.: Predicting the occurrence of landside at Penang island, Malaysia, through artificial neural networks model. Int. J. Eng. Technol. 7(4.19), 217–222 (2018) 9. Ahmarofi, A.A.: An Integrated ANN and SD Models with Momentum Rate to Estimate Completion Time at a Semiautomatic Production Line (Unpublished thesis). Universiti Utara Malaysia, Sintok (2019) 10. Liu, Z., Yang, Y., Cai, Q.: Neural network as a function approximator and its application in solving differential equations. Appl. Math. Mech. 40(2), 237–248 (2019). https://doi.org/10. 1007/s10483-019-2429-8 11. Wang, C., Jiang, P.: Deep neural networks based order completion time prediction by using real-time job shop RFID data. J. Intell. Manuf. 30(3), 1303–1318 (2017). https://doi.org/10. 1007/s10845-017-1325-3
Environmental Visual Features Based Place Recognition in Manufacturing Environment Fairul Azni Jafar1(B)
, Nurul Azma Zakaria2 , Ahamad Zaki Mohamed Noor3 , and Kazutaka Yokota4
1 Center for Smart System and Innovative Design, Faculty of Manufacturing Engineering,
Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Melaka, Malaysia [email protected] 2 Center for Advanced Computing Technology, Faculty of Information and Communications Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Melaka, Malaysia 3 System Engineering and Energy Laboratory, Universiti Kuala Lumpur Malaysian Spanish Institute, Kulim Hi-Tech Park, 09000 Kulim, Kedah, Malaysia 4 Research Division of Design and Engineering for Sustainability, Graduate School of Engineering, Utsunomiya University, 7-1-2 Yoto, Utsunomiya-shi 321-8585, Japan
Abstract. This paper presents a visual features based place recognition method to be used in the computer vision or robotics research fields especially in the localization and navigation algorithm. Many research studies have been conducted on the navigation method for mobile robot (or AGV in the context of manufacturing environment), and introduced precise and accurate place recognition methods. However, we believe that in some situation, rather than precise and accurate recognition, identifying the place through an uncomplicated yet robust recognition method should be good enough for a mobile robot (or AGV) to move and perform its tasks in a manufacturing environment. In our proposed method, the recognition method depends only on the visual features which could be extracted from the environment, and evaluated by using a neural network. Experimental results demonstrate the effectiveness of our proposed method. Keywords: Place recognition · Visual features · Neural network
1 Introduction Human recognition system is very unique. Human recognize a previously visited place through a prominent landmark or through the whole panorama view of the place. For example, if we see Tokyo Skytree, definitely we know that the place is Sumida City, Tokyo. This is called a landmark based place recognition. Meanwhile, human can also recognize a place through the whole scenery view, and this is known as appearance based place recognition. For example, if we go to the Bandar Hilir of Malacca city, we can simply recognize that we are already in the Bandar Hilir through a panoramic view of the Bandar Hilir itself. In a panoramic view of certain places, objects or buildings in the entire view might be used to recognize the place. In other cases, instead of objects or © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 47–59, 2022. https://doi.org/10.1007/978-981-16-8954-3_6
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buildings in the view, some specific features could also be used as the distinction point of the panoramic view. Visual recognition of previous visited places is a fundamental part of human daily life [1]. Inspired by the uniqueness of human visual place recognition capabilities, place recognition has attracted a significant amount of attentions in computer vision and robotics research communities. There are many research works which make use of various features as landmarks such as lights, doors, windows, or even artificial landmarks, although artificial landmarks require for modifications [2]. Appearance-based place recognition was dominated by sophisticated local-invariant feature extractors such as SIFT [3] at the early stage, and recently many improved features generating methods have been introduced such as Convolutional Neural Networks (CNNs) [4], Convolutional Networks (ConvNets) [5] etc. Matsutomo et al. [6] stated that using global features for robot localization need memorizing the whole image and required large space of data stored in robot system. The issue of previous place recognition methods is that the computation method used is complicated that burden the recognition system to compute for the similarity marks. Although the methods produced remarkable results, we do believe that not every situations or tasks required for a complicated recognition system. For example, if an Automated Guided Vehicle (AGV) is used in a manufacturing environment to deliver parts or documents, precise localization is not always required. Most AGVs must be able to locate themselves in their working environment so that they are able to accomplish their tasks, for example to move from one place to another place. But as long as the AGV is able to arrive safely around the targeted point or place, then it should be good enough for the AGV navigation. The main target of the AGV is to arrive at the place even without any precise place recognition method. That has spark an idea to this research work to propose an approach of robust place recognition. From the point of view of place recognition, robustness to dynamic changes is a hard challenge for AGV that use vision robustly. The visual appearance of places varies in time because of illumination changes (day and night, artificial light on and off) and it could also happen when furniture moved around, or obstacle suddenly appear and so on. Considering this issue, and as this research work is proposing for a robust place recognition, later we found out that human also use color as the features to robustly recognize a previously visited place instead of using objects or buildings in a panoramic view. For example, in the case of Bandar Hilir in Malacca, the red color of most of the buildings there will help humans to identify that it is Bandar Hilir. Color is a meaningful constant for humans and helps humans not only to influences their purchases and sparks their emotions, but also to remember objects or places. In this research work, color features of a whole image has been identified to be used as the environmental visual features of the place recognition method. The proposed method is simple yet uncomplicated appearance-based place recognition method which makes use of environmental visual features to be the basis of the computation on the similarity of the captured image with the previously memorized place appearance. A part of color features, this research work is also proposing for shape features to be used as another environmental visual features in the proposed place recognition method.
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The paper is organized as follows. In Sect. 2, we do a brief review on the environmental visual features. Section 3 will explain in detail about the proposed place recognition method. Results of the experiment are presented in Sect. 4. Finally, with a summary, we conclude this paper.
2 Environmental Visual Features 2.1 Color Features Swain and Ballard [7] had pioneered the idea of using color histogram to match two images. They developed a clever yet simple scheme that identifies objects entirely on the basis of color. Since then, few researchers also make use of color where they employ color histogram in their method for robot localization [8, 9]. Zhou et al. developed a multidimensional-histogram that is used to describe the global appearance features of an image such as colors, edge density, gradient magnitude, textures and so on, where the matching of histograms determines the location of the robot [10]. These research works had previously successfully proved that color can be used as the features in mobile robot localization. However, our approach is different to those studies, in which we evaluate all the colors in an image, and use their dispositions as features, rather than histograms.
Fig. 1. CIE chromacity diagram.
Color information obtained by camera is affected by the photographing condition such as illumination etc. The data and computation cost is expected to become large if we are going to evaluate every single pixel of an image. Thus, we consider formulating a simple and easy way using the details of color information, by roughly separating the colors into 11 classifications through a separation of CIE chromaticity diagram (Fig. 1). In the chromaticity diagram where we regard those colors which are located in the same partition as the identical color, we separated the chromaticity diagram into 8 colors.
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The separation method is shown in Fig. 2. The non-coloring space at the center of the chromaticity diagram is separated into three colors which are white, black, and grey based on luminosity factor. We also used the luminosity to classify between black and the primary color of each partition in the separated chromaticity diagram. We examine all the colors which are exist in an image before translating them into numerical data. And those pixels whose colors fall into one color domain are considered to have the same color. Through this, a total of 33 data of visual features can be acquired from color features. 0.8
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Fig. 2. Separating chromacity diagram.
2.2 Shape Features In addition to color features, we also extracted edges form the images using Robert operator. We are able to obtain points which are connected through the edges through a further image processing done on the edges. And we used these points together with
Original image
1. Edge extraction & Noise elimination
2. Expansion
3. Thinning and Connecting point extraction
Fig. 3. Extracting edges and connecting points.
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the extracted edges as the visual features. The edges and connecting points are able to be extracted through the process shown in Fig. 3. From the extracted edges and connecting points of the shape features extraction process, 2 data of visual features can be acquired, which consist of; • Ratio of the edges. • Ratio of the connecting points. Through this, a robust system for place recognition that required just a simple algorithm and established fast processing capability is developed. 2.3 Neural Network Figure 4 shows the neural network architecture where neural network is generally organized in layer. Layer was made up from number of interconnected between nodes which have activation function. Patterns are represented as input layer and the actual processing is performing when input layers communicate with hidden layer, the hidden layer then connected to output layer in order to produce an output.
Fig. 4. The neural network framework.
In multilayer perceptron, the number of input layer may depend on number of combining data of both color features and shape features, for our approach we use only 4 layer of neural network. Meanwhile, the unit number is set to 30 and 15 for each middle layer and the number of this middle layer has been determined in previous investigation.
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3 Methodology The place recognition method in our approach is presented in Fig. 5. In this method, first we capture few images at those places where we want the propose system to recognize the places later in the recognition phase. In order to have a “domain of area” for the recognition, we capture 5 images at each designated place during the learning phase, as shown in Fig. 6. Image captured at point 2 to 5 is about 15 cm each from the center point. Data of visual features is extracted from all images and trained in Neural Network (NN) to obtain a NN Data which is to be used later for the recognition phase. For each NN Data, visual features data from all 5 images of that particular place, which will be recognized later in the recognition phase is trained to a value of 1.0. Meanwhile, visual data features from 1 image (image capture at the center point of the place) of other places is trained to a value of 0.0. This NN Data is developed for all the places designated for the experiment. In the recognition phase that is conducted right after that, we captured 2 sets of images for the performance test. The first set of images consist of images that were taken exactly in the same 5 points of images captured during the learning phase. Then, in the second set of images for the performance test in recognition phase, 25 images are captured randomly around the selected memorize places within 20 cm radius of area in order to observe the robustness performance of the proposed place recognition method.
Fig. 5. The recognition algorithm.
Through the extraction process, the visual data features of the test images (input data) is extracted and run through the NN against the NN data of each particular place. The visual data features of the test images, which consist of the color and shape features, are the input data for the recognition phase. Basically this input data is the same as instructor data since they are the color and shape features data of captured images. The difference between input data and instructor data is that, input data is extracted from the images
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taken during recognition phase (2 sets of images were taken), whereby instructor data is extracted from those images taken earlier during the learning phase. The image from the respective place should achieve an output of the NN higher than a certain set threshold where in our method we set the threshold to 0.7. Other than that is treated as fail result. The threshold 0.7 was determined in a preliminary investigation. Images taken from other places should not obtain an output of NN more than 0.7. In other word, recognition is done by matching the current visual features from images taken at the designated places against the sets of features of the places that are stored in a database.
Fig. 6. Positions of acquiring image during the learning phase.
4 Result and Discussion The experiments of this project were conducted in the environment of a manufacturing factory located at the Faculty of Manufacturing Engineering in our university. For the position of the place recognition, four positions have been considered and designed as Place 1–4, as shown in Fig. 7. The experimental platform of these experiments employ a CCD color video camera to capture the images. The images acquired in this experiment have a resolution of 320 × 240.
Fig. 7. The 4 places selected in the experiments.
Images captured at each place are tested on the NN Data of Place 1–4 in order to analyze the robustness of the proposed method. For the first set, 5 images were taken at each place during the recognition phase, in which a total of 20 images were taken from all the 4 places. The features data of all these 20 images were extracted and run through
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the NN Data of Place 1–4. Overall 80 tests were conducted for the first experiment. Results of the experiment are shown in Figs. 8, 9, 10 and 11. Images captured at the respective place are expected to obtain the set threshold 0.7 or more against the NN Data of the respective place, while those images taken at other places are not supposed to respond to the NN Data of the place. We classified an error as false negative when the image from certain particular place is not able to obtain 0.7 or more after run through the NN Data of the place itself. In other word, image that is captured from that place supposed to respond to the NN Data of the place but failed to do so. Meanwhile, we classified false positive for error that occurred when image taken from other nodes but unexpectedly respond (achieved 0.7 and above of the NN result) to NN Data of other places, other than the NN Data of the place where the image is taken.
NN Output
Localizaon Result against NN Data of Place 1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 PLACE 1
PLACE 2
PLACE 3
PLACE 4
Fig. 8. Recognition result tested against NN Data of Place 1.
With the classification of false negative and false positive, we found out that overall there are 4 false negative errors, mainly occur on images from Node 1, and 7 false positive errors occurred where majority are from images against the NN Data of Place 1 (3 errors) and Place 2 (4 errors). 3 images from Place 4 were mistakenly responded to the NN Data of Place 2. Although the success recognition rate in this experiment is 86.25% overall, which is considered good, but the issue on 4 images, out of 5 images, which were taken around Place 1 failed to respond against the NN Data of Place 1 need to be considered seriously. Further investigation need to be taken to find out the reason behind the failure. Percentage result of false positive error in this first experiment is about 8.75 and false negative error percentage is 5%. Next we conducted another experiment to observe the robustness performance of the proposed place recognition method. In this second set of images, 25 images were taken randomly at each memorize place within 20 cm radius of area, in which a total of 100 images were taken from all the 4 places. The features data of all these 100 images were extracted and run through the NN Data of Place 1–4. Overall 400 tests were conducted
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NN Output
Localizaon Result against NN Data of Place 2 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 PLACE 1
PLACE 2
PLACE 3
PLACE 4
Fig. 9. Recognition result tested against NN Data of Place 2.
NN Output
Recognion Result against NN Data of Place 3 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 PLACE 1
PLACE 2
PLACE 3
PLACE 4
Fig. 10. Recognition result tested against NN Data of Place 3.
for this second experiment. Results of the experiment are shown in Figs. 12, 13, 14 and 15. Surprisingly, in the result of the second experiment, there is no error in all 25 images taken around Place 1 against the NN Data of Place 1. Compared to the previous experiment’s result where only 1 images out of 5 images was able to achieve value more than the threshold 0.70, all the 25 images of Place 1 of the second set were able to respond correctly. However, some images taken around the other places were also responded to the NN Data of Place 1, especially those images from Place 3. Unfortunately, the same phenomenon can be seen in the result of tests against NN Data of Place 2. Although a lot of images taken around Place 2 managed to respond correctly against the NN Data of Place 2, but false positive error also occurred quite a number, especially from those images taken in Place 4. However, the tests against NN Data of Place 3 and Place 4 produced s
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NN Output
Recognion Result against NN Data of Place 4 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 PLACE 1
PLACE 2
PLACE 3
PLACE 4
Fig. 11. Recognition result tested against NN Data of Place 4.
promising result where almost 100% of the 25 images taken around the respective places of Place 3 and Place 4 were able to respond perfectly against the place’s own NN Data. And only a few images from other places were wrongly responded to NN Data of Place 3. Overall, this second experiment produced 80.75% of success recognition rate and most of the failure comes from the false positive error where 68 images from other places mistakenly responded to the NN Data of places where the images were not taken from. The false positive error percentage is 17% whereby the false negative error percentage is 2.25%. From the perspective of false negative error, we can concluded that the proposed place recognition system is able to recognize the memorize place correctly although there is a small problem in the recognition result of Place 1 in the first experiment that need to be analyzed after this.
Fig. 12. Recognition result against NN Data of Place 1 in the second experiment.
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Fig. 13. Recognition result against NN Data of Place 2 in the second experiment.
Fig. 14. Recognition result against NN Data of Place 3 in the second experiment.
Further analysis need to be done to identify the main reason of the false positive errors especially those that occurred against NN Data of Place 1 and 2. 24 images from Place 3 were responded to the NN Data of Place 1, and 19 images of Place 4 were responded to the NN Data of Place 2. Basically, a quick thoughtful reason of these errors is might due to the flickering condition of lighting condition in the environment, but further experiment or analysis need to be carried out to confirm this prediction. Another possibility that could be the reason of why false positive errors occurred in the test against NN Data of Place 1 and Place 2 is the angle of camera during capturing images. Perhaps the angle need to be raised a little bit, for example at the level of 90°, so that it will not focus more to the floor. A detail analysis on the visual features data value of images from Place 3 and Place 4 that mistakenly responded against NN Data of Place 1 and Place 2 respectively, need to be identified to understand why the images responded to NN Data
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Fig. 15. Recognition result against NN Data of Place 4 in the second experiment.
of different places. The color distribution, which contribute most of the visual features data use in the proposed recognition system, might give a high impact to the result of false positive errors. However, it needs to be clarified in our next progress. Furthermore, the reason why 4 images of Node 1 were not able to obtain 0.7 or more against the NN Data of the respective Place 1 also need to be clarified in order to carry out improvements during the next stage of this research work, to ensure for the robustness of the proposed recognition system. Anyway, the proposed place recognition system is still considered as successful, significant and possesses high potential to be applied on AGV in manufacturing system as the overall success rate of the localization performance is above 80%. However some improvements need to be done in order to reduce both false negative and false positive errors.
5 Conclusion This paper present the evaluation on the problem related to place recognition especially in manufacturing environment. The recognition system was developed upon the advantages of the visual features in the environment. The visual features are evaluated by back propagation neural network. The proposed system is able to achieve efficient place recognition with a good success rate. However, some issues arose that required to be tackled in a proper way in order to make sure that the proposed localization system is robust enough and ready to be used in any manufacturing environment. For that, the future work of this research study is to look into the visual features value, the method to extract the features especially for color features, as well as the method of capturing image such as the angle of camera etc. Perhaps, the method of providing domain area for recognition (5 images captured at the designed place for recognition) also need to be re-considered.
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References 1. Martinez-Miwa, C.A., Castelan, M., Torres Mendez, A., Maldonado-Ramirez, A.: Human and machine capabilities for place recognition: a comparison study. In: The Tenth International Conference on Advanced Cognitive Technologies and Applications, pp. 72–77 (2018) 2. Hong, Z., Petillot, Y., Lane, D., Miao, Y., Wang, S.: TextPlace: visual place recognition and topological localization through reading scene texts. In: International Conference on Computer Vision (2019) 3. P˘av˘aloia, I., Ignat, A.: Iris image classification using SIFT features. J. Procedia Comput. Sci. 159, 241–250 (2019) 4. Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. J. Insight Imaging 9, 611–629 (2018) 5. Abdelbaki, A.: ConvNet features for lifelong place recognition and pose estimation in visual SLAM. Master Thesis of Master of Science, University of Bonn (2019) 6. Matsumoto, Y., Inaba, M., Inoue, H.: View-based approach to robot navigation. In: Proceedings of IEEE International Conference on Intelligent Robots and Systems, pp. 1702–1708, (2000) 7. Swain, M., Ballard, D.: Color indexing. Int. J. Comput. Vision 7(1), 11–32 (1991) 8. Rizzi, A., Cassinis, R., Serana, N.: Neural networks for autonomous path following with an omnidirectional image sensor. J. Neural Comput. Appl. 11, 45–52 (2002) 9. Ulrich, I., Nourbakhsh, I.: Appearance-based place recognition for topological localization. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1023– 1029 (2000) 10. Zhou, C., Wei, Y.C., Tan, T.N.: Mobile robot self-localization based on global visual appearance features. In: Proceedings of the IEEE International Conference on Robotics and Automation (2003)
Comparison of Sampling Methods for Rao-Blackwellized Particle Filter with Neural Network Norhidayah Mohamad Yatim1(B) , Amirul Jamaludin1 , Zarina Mohd Noh1 , and Norlida Buniyamin2 1 Centre for Telecommunication Resesarch and Innovation (CeTRI), Fakulti Kejuruteraan
Elektronik and Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Malaysia [email protected] 2 Faculty of Electrical Engineering (FKE), Universiti Teknologi MARA (UiTM) Shah Alam, Shah Alam, Selangor, Malaysia
Abstract. Mobile robots can be used in domestic, industrial or humanitarian fields. Typically, low-cost mobile robot platforms are equipped with sparse and noisy sensors on board, such as array of infrared sensors. In robotics, the ability to map the surrounding area and determine self-location is essential for autonomous navigation. In this paper, the objective is to develop such capability known as Simultaneous Localization and Mapping (SLAM) algorithm for mobile robots with array of infrared sensors. To improve the robot’s observations from noisy sensor measurements, neural network was used to interpret adjacent sensor measurements into grid cells occupancy. In this grid-based SLAM algorithm, Raoblackwellized particle filter (RBPF) was integrated with neural network. Two different proposal distributions for RBPF; Gaussian approximation, and two-step sampling, were experimented with and without neural network integration in this paper. The results show that the two-step sampling method with neural network integration gives the lowest error of robot state estimate and the highest score of overall map estimate. This integration of grid-based SLAM algorithm, reduced the pose error by approximately 35% and increased accuracy of overall map estimate by 22%. From the experiments, it is concluded that the grid-based SLAM algorithm integrated with neural network and two-step sampling method is feasible for low-cost mobile robot with sparse and noisy sensor measurements. Keywords: SLAM · Occupancy grid map · Neural network · Infrared sensor · Particle filter
1 Introduction This paper aims to address one of autonomous robotic problem named Simultaneous Localization and Mapping (SLAM) for mobile robot platform with array of infrared sensors. SLAM algorithm is used by a robot to simultaneously estimate its current © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 60–75, 2022. https://doi.org/10.1007/978-981-16-8954-3_7
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position and build the map of the environment. For this purpose, a robot has to be aware of two questions; where am I? and what the environment looks like? To achieve this goal, the robot has to utilize its sensor measurements that inevitably comes with noise. Occupancy grid map is a common map representation in SLAM algorithm. Grid map shows the occupancy of spaces, whether there is any object in a particular area or it is a free space. Grid map is a dense map representation that is more suitable to map unstructured environment as there is no need for feature detection. In this paper, as a motivation of using low-cost robot platform for diverse applications, grid map is chosen as the map representation. SLAM algorithm with occupancy grid map representation is commonly known as grid-based SLAM. A consistent grid-based SLAM algorithm that is able to perform loop closure in unstructured environment would require sensor with dense measurements such as from range scanner or rangefinder [1–8]. However, in this research, array of infrared sensors was used due to the significantly lower cost of the sensors. Sensors in array configuration produce sparse measurements where only measurements at certain angle can be obtained as opposed to dense measurement. This increases the uncertainty in robot’s observation in addition to the noise that cause high variance in sensor measurements. Among the attempts on grid-based SLAM with sparse and noisy sensor had to apply orthogonolity restriction [11]. In this implementation, walls are assumed to be in parallel or perpendicular to each other. Another assumption was to use robot platform with rather accurate odometry data [17]. Both of these assumptions are not suitable to implement grid-based SLAM algorithm in diverse applications or low-cost mobile robot platform. Thus, in this paper, a grid-based SLAM algorithm integrated with neural network is developed without both limitations. Neural network is chosen because it has been used to interpret sensor measurements to grid cell occupancy value in [10, 11]. Grid-based SLAM algorithm uses Rao-Blackwellized Particle Filter also known as RBPF. RBPF is a technique that is based on particle filter algorithm. One of the main concerns when integrating occupancy grid map with RBPF algorithm is the high memory consumptions. This is because each particle maintains an estimate of grid map. Thus, the memory requirement of the algorithm increases with the number of particles used [12]. To reduce memory consumption is to reduce the number of particles used in the algorithm. To compensate for lower number of particles, a better proposal distribution is needed when sampling next generation particles [3, 9]. To address this problem, works on the sampling method in RBPF algorithm were reviewed. Overall in this paper, Sect. 2 describes the previous work in sampling method in RBPF algorithm. Section 3 describes the methodology used to carry out the experiments, which consists of the framework for RBPF algorithm. Consequently, Sect. 4 reports the results and analysis from the accuracy of robot’s state estimate and map estimate of the gridbased SLAM algorithm. Lastly, Sect. 5 concludes the finding of this paper and suggest future works.
2 Previous Works RBPF uses samples or particles to represent hypothesis of robot’s states and their associated maps. In RBPF, each particle assumed itself maintain the right trajectory and map
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according to that trajectory. This is also known as mapping with known poses assumption. Montemerlo et al. published FastSLAM 2.0 which integrated most recent sensor observation to restrict the space for sampling robot state [14]. Grisetti et al. transfer the idea of FastSLAM 2.0 which computing an improved proposal distribution to the situation in which dense grid maps are used instead of landmark-based representation [15]. This work combined scan-matching on a per particle basis. Then, the next generation of particles is sampled from Gaussian approximation that includes recent observation. Scan-matching maximizes the likelihood of current robot’s state and map, resulting a better proposal distribution can be obtained [3, 13]. Thus, instead of just using motion model with Gaussian distribution, a more accurate approximation can be sampled [13]. This allows for lower number of particles implementation [3, 13]. Magnenat et al. applied this method by using small Monte-Carlo localization to find the best orientation that maximized the scan matching. In certain situations, however, the proposal distribution can be non-Gaussian and multimodal. Thus, Gaussian distribution does not properly approximate the true distribution which in turn can lead to the divergence of the filter. Stachniss et al. presented in their paper an alternative sampling strategy that is able to handle multiple modes in the probability function used as the proposal distribution [16]. This method named two-step sampling, has the advantage of handling multiple modes in the distributions while at the same time keeping the efficiency of a Gaussian proposal distribution. The result of incorporating multimodal distribution improved the accuracy of their map estimate. The diagram in Fig. 1 illustrated how the works in sampling step of RBPF algorithm related to each other. The diagram shows that two-step sampling method is a superset method that includes Gaussian approximation, scan-matching and motion model in its algorithm. While sampling from motion model distribution is a simpler and more straight-forward approach without including any other sampling method.
Fig. 1. Superset and subset of methods for proposal distribution used in sampling step of RBPF algorithm.
Table 1 shows the comparison of sampling methods reviewed; motion model, Gaussian approximation, and two-step sampling. From this review, it is known that the first method, using motion model as proposal distribution has been tested with low-cost sensors, particularly array of sonar sensors [5, 6]. Two-step sampling with Gaussian approximation was implemented with infrared sensor as well, but with dense measurements [16]. In this research two methods; Gaussian approximation and two-step sampling were considered for implementation with the array of infrared sensors. The reason being, it is
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unclear which method is the most suitable with array of infrared sensor. This is because for this platform, uncertainties in both odometry data and sensor measurements are significant. Table 1. Comparison of sampling from proposal distribution method. Motion model
Gaussian approximation
Two-step sampling
Measurements applied
Motion model
Sensor model, kinematic model
Motion model, sensor model
Scan-matching
No scan-matching
Use scan-matching
Use scan-matching
Proposal distribution
Unimodal distribution
Unimodal distribution
Multi modal distribution
Sensor
Sonar sensors
Laser rangefinder
Infrared rangefinder
High
High
Computation cost Low
3 Methodology Particle filter approximates a distribution using samples or particles. This is done by using the most recent control, at and the most recent observation, zt . In particle filter a distribution is described using a weighted particles set. If N is the number of particles in the particles set, Xt . Thus, Xt is defined as in (1), where each particle denoted by xt[i] (with i ∈ {1, · · · , N }) is a hypothesis of robot’s state at time t. Xt := xt[1] , xt[2] , · · · , xt[N ]
(1)
Particle filter is a stochastic state estimation where a set of particle Xt is generated from previous state’s set of particle, Xt−1 to approximate xt . Algorithm 1 shows particle filter algorithm that generates particle set Xt from particle set Xt−1 . At line 4, each particle, xt[i] is sampled to form current generation particles, Xt by sampling from a proposal distribution. The basic proposal distribution is motion model proposal distribution, p(xt |xt−1 , at ). Then, at line 5, a weight is calculated for each particle to describe how much likely that this particle estimates the actual robot’s state. This is done by incorporating the recent measurement, zt . In other words, the weight describes the closer the particle to the actual state, the more important it is. Thus it is also called importance factor. In particle filter, importance factor is denoted as particle weight, wt[i] . To evaluate wt[i] , the common method is to use the probability of the measurement zt given particle hypothesis, xt[i] , p(zt |xt[i] ) as in (2). Here, xt[i] is from the predicted set of particles Xt . [i] wt[i] = wt−1 (2) × p zt |xt[i]
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The heart of particle filter is that a new set of particles are drawn from particle set, Xt , which has been associated with weight as the next generation of particles, Xt . This is done in the second loop at line 9 until 12. The way particle is drawn is by using the particles’ weight. The higher the weight, the more likely the associated particle to be drawn and included in the next generation particle, Xt . This step is known as resampling or importance sampling. When drawing the new particle set, particles with higher weight will be duplicated and particle with lower weight will be dropped from entering the new set of particles probabilistically. In the sampling step, a proposal distribution is needed to sample next generation of particles, Xt . In particle filter, motion model is frequently used as proposal distribution to predict robot’s state. Section 2 outlined two methods of proposal distributions to generate next set of particles. There are; Gaussian approximation and two-step sampling. Both methods were used as the proposal distribution for the Rao-Blackwellized particle filter with neural network algorithm in this paper. 3.1 Map Update with Neural Network The map update step is developed based on occupancy grid map algorithm with neural network described in [17]. In this research, neural network is used to evaluate the inverse sensor model, p(mi |xt , zt ) before it is converted into log odd notation. This model can be implemented by training a supervised learning algorithm to classify sensors data into a grid cell’s occupancy (i.e. 0 for free cell and 1 for occupied cell). However, instead of using the classification value, the probability of the output is used. The probability value is then fed into the occupancy grid map algorithm as p(mi |xt , zt ). In this step, only selected measurements are used to interpret cell occupancy with encoded cell’s position rather than the Cartesian coordinate [4, 15]. Hence, neural network, N with the following configuration was trained. The inputs of N are as follows: • Four sensors measurements, , k ∈ {1, 2, 3, 4} that are closest to cell, mi • Encoded cell’s position using the distance, dmi and angle, θmi .
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3.2 Grid-Based SLAM Algorithm In this section, the overall grid-based SLAM algorithm developed is described. Figure 2 shows overall grid-based SLAM algorithm which follows the RBPF framework.
Fig. 2. Overall algorithm of grid-based SLAM algorithm developed
The flow chart in Fig. 2 shows the steps from sampling of next generation particles to map update step of the particles’ density. A rectangle with double vertical edges indicates a process or sub-algorithm. A rectangle in this diagram indicates an entity or state (i.e. infrared sensor measurements and occupancy grid map). Essentially, the arrows show the flow of the algorithm. The arrows that go into a process block also indicate which elements needed to execute that process. The processes that are computed for each particle are grouped in the blue box, which involves sample a particle from proposal distribution, π , calculate its importance weight, wt[i] , and update its local and global map, p(m|z1:t , x1:t ). In the process of map update, a local map is build first before the global map is updated. The process of map update step is depicted in dashed arrow to indicate that it is done after the completion of importance weight calculation. This is because measurement likelihood in importance weight calculation, p(zt |xt , m), should only include map that has been build up to previous time step, t1. If map at current time step, t, is used (i.e. map that includes measurement zt ), zt will be compared against itself. This will cause the measurement likelihood to be bias. The thicker arrows indicate process that involves particles as a set. For example, the process to get the particles’ density, xest and to calculate the effective sample size, Neff . The green color shapes are processes for selective resampling. Finally, the density of robot’s state estimate, xest and its map, M are calculated as the final output on the far right of the flowchart. Robot’s state estimate, xest then updates the robot’s full trajectory, x1:t . These two outputs, M and x1:t are used to evaluate the performance of the grid-based SLAM algorithm.
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The integration of neural network, N requires modifications to the existing gridbased SLAM algorithms. The integrated grid-based SLAM algorithm is called as RaoBlackwellized particle filter with neural network or RBPF-NN in this paper. In the next subsection, the algorithm for RBPF-NN with Gaussian approximation is described. Gaussian Approximation Figure 3 shows the overall algorithm for RBPF-NN with Gaussian approximation proposal distribution. Essentially the color coding and shapes’ convention are the same as the overall RBPF framework in Fig. 2. Additionally, the process and entities in red color are for neural network, N integration.
Fig. 3. Overall algorithm for RBPF-NN with Gaussian approximation proposal distribution.
Sampling from Gaussian approximation proposal distribution, involves kinematic model of the robot, scan-match algorithm, computation of Gaussian parameter, (i.e. μt and t ) using K points, and sampling from the Gaussian distribution with mean μt and covariance t . The overall algorithm diagram shows that infrared sensor measurements are passed as input to preprocessing for neural network, N and into the scan-match algorithm. The scan-match algorithm is to obtain the corrected robot pose, xˆ t , by maximizing the measurement likelihood p(zt mt−1 , xt ). Here, xt , is the robot state that has been translated using robot’s kinematic model, g(xt−1 , at ). Thus, sensor measurement, zt , from infrared sensor is needed to maximize p(zt mt−1 , xt ). In this research, likelihood field model was developed to compute the probability of p(zt mt−1 , xt ).
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Then, K points around the robot’s corrected pose, xˆ t are selected and used in computation of μt and t and computation of the importance weight for each particle. These K points are termed as xj in the algorithm. In importance weight computation, both observation model probability (i.e. measurement likelihood in this algorithm), and motion model probability were used. The next generation particle, xt[i] is then sampled from the Gaussian distribution with mean, μt and covariance, t . xt[i] is used to compute the inputs to neural network, N . The output of N is used to build local grid map and eventually the global grid map using occupancy grid map algorithm. The rest of the processes involves selective resampling and computation of particles density xest , robot’s trajectory updates, x1:t and its global occupancy grid map, M . Two-Step Sampling The second model of RBPF-NN uses two-step sampling as a method to sample from proposal distribution. Figure 4 shows the flow chart of the overall algorithm.
Fig. 4. Overall algorithm for RBPF-NN with two-step sampling proposal distribution.
RBPF-NN with two-step sampling is similar to the previous RBPF-NN with Gaussian approximation. The only difference, in this model, xt is sampled from motion model distribution, p(xt |xt−1 , at ) rather than translated using kinematic model. The rest of the process follows RBPF-NN with Gaussian approximation model.
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4 Results and Analysis 4.1 Experiment Setup To investigate the performance of the grid-based SLAM algorithm with neural network two sets of experiments were conducted. The first one was to evaluate the performance of grid-based SLAM algorithms without neural network. These models are coined as RBPF-XNN later on in paper. The performance of the RBPF-XNN models were recorded in terms of robot’s state accuracy and map accuracy. The accuracy will then be compared with the accuracy of grid-based SLAM algorithm integrated with neural network (i.e. RBPF-NN algorithm). Experiment one and two were executed with both sampling methods Gaussian approximation and two-step sampling. To organize the results, sampling from Gaussian approximation is termed as Model 1 and the two-step sampling is termed as Model 2. Primarily, Khepera III mobile robot was set to navigate in the test environment. In this experiment, trajectory of the robot was set to be near the walls so that it can obtain observation from sensors at all time. Figure 5 shows the ground truth occupancy grid map build for the test environment along with the path that the robot took in the experiment, showed in green line. In the test arena, waypoints 1 until 11 were set up to help the robot navigate near to the wall. During the experiment, measurements from wheel encoder and array of infrared sensor were recorded. These measurements were then executed with Model 1 and Model 2 of RBPF-XNN algorithm. In this experiment, the number of particles, N used was only 20 to reduce the memory consumption and computation cost. Furthermore, both sampling methods has scan matching algorithm which took significantly higher time of execution.
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Fig. 5. Ground truth map and ground truth trajectory of mobile robot
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Other parameters such as error parameter in motion model algorithm (i.e. α1 , α2 , α3 , and α4 ) and grid size, were set as in Table 2. To evaluate the performance of the algorithm, the accuracy of the particles density, xest state estimate and its map estimate were compared with the ground truth data of robot’s state and the ground truth occupancy grid map respectively. Table 2. Summary of parameters’ setting for experiment. Parameters
Values
Number of particles, N
N = 20
Angular error Translational error
α1 = 1◦ and α4 = 1◦ α2 = 0.005 m and α3 = 0.005 m
Grid size of cell, mi
2 cm2
The ground truth of robot’s trajectory in the simulator was obtained by installing a GPS module with zero error on the Khepera III robot. In the second experiment, the dataset of infrared sensor measurements and wheel encoders’ captured during the previous experiment were used to execute RBPF-NN models algorithm as well. The consistency of data set used allows for both RBPF-XNN and RBPF-NN models to be compared for validation purpose. 4.2 Experiment Results Analysis on map estimate and robot’s pose estimate accuracy using fitness score and RMSE are reported in Table 3 to evaluate the performance of RBPF algorithms. The fitness score functions, focc and fmap as well as the RMSE equation are described in [17]. From Table 3, it can be observed that RBPF-NN Model 2 which is the algorithm with two-step sampling shows the highest fitness score on occupied cells, focc , at 60%. As for robot’s state estimate, RBPF-NN Model 2 also has the lowest error followed by RBPF-XNN Model 1. Table 3. Map accuracy and robot’s state estimate error of each model of RBPF algorithm Algorithm
focc score (occupied cells)
fmap score (all cells)
RMSE pose error (cm)
RBPF-XNN Model 1
0.4923
0.6908
12.67
RBPF-XNN Model 2
0.4056
0.6398
19.14
RBPF-NN Model 1
0.4353
0.7223
28.68
RBPF-NN Model 2
0.6002
0.7813
12.34
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4.3 Analysis To observe the effect of neural network integration in grid-based SLAM algorithm, the bar charts in Fig. 6 and Fig. 7 are plotted for all models. It is observed that by integration of neural network with two-step sampling (i.e. Model 2) shows increase in performance. The pose error is reduced by 35.5% while the fitness score of occupied cells and overall map increased by 47.8% and 22% respectively. The improve in accuracy shows that integration with neural network is compatible with implementation of grid-based SLAM algorithm with two-step sampling method in this experiment. 40 RBPF-XNN RBPF-NN
35
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Mean Pose Error (cm)
30 25
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Model 2 (Two-Step Sampling)
Fig. 6. Robot state estimate error for RBPF-NN and RBPF-XNN models.
However, adverse impact is seen for integration with Model 1 which is Gaussian approximation method. The difference of Model 1 compared to Model 2, is that motion model proposal distribution is included in the sampling step for Model 2. Another observation from Fig. 6 and 7, is that occupied cells fitness score, focc and pose error of robot’s state estimate is mostly in agreement, where a higher value of fitness score, focc will result a lower pose error. This observation proves that as the accuracy of occupied cells
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Fig. 7. Fitness scores of RBPF-NN and RBPF-XNN models
increases, the accuracy of robot’s state estimate is increased as well. This is because, in grid-based SLAM algorithm, the occupied cells are used in computation of measurement likelihood and particles’ weight. Consequently, this computation effects the state estimate of robot’s position. Conversely, the result of fitness score for RBPF-NN Model 1 algorithm shows a contradiction where the focc is better than RBPF-XNN Model 2, but the RMSE is the highest at 28.68 cm. Figure 8 show the map from RBPF-NN algorithm for Model 1 and Model 2, to be compared qualitatively. In each map, dashed green line is robot’s true trajectory, red dashed line is trajectory from robot’s odometry and lastly the solid blue line is the robot’s state estimate using respective RBPF-NN model. The visual observations of the resulting maps are described as follows: Observations at Corners. At the edge labelled number 1, it is observed that Model 1 has started to have significant different in map estimate where robot’s state was estimated backward. This has caused accumulative error in the robot’s state and map estimate later on. Sensor measurements at corners are greatly reduced due to the sparseness of sensors’ configuration. This has affected the Gaussian approximation method (i.e. Model 1) that depends on sensor observation for both proposal distribution and measurement likelihood. Compared to
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Fig. 8. The map and robot’s trajectory of (top) RBPF-NN with Gaussian approximation and (bottom) RBPF-NN with two-step sampling.
Model 2 that included motion model as proposal distribution, the robot’s state estimate didn’t gain visible error. Miss Observations. Both models managed to capture most features in the map. However, it can be observed that walls on the middle and upper left corner (i.e. between label 3 and 4) were not mapped clearly. This is due to neural network could not interpret the sensor measurements correctly as both models showed the same effect. In the ground truth map (see Fig. 4), the robot’s trajectory between corner 3 and 4 was a little further away from the walls which has caused the miss observations. Due to this, map estimates were affected as robot’s state estimate began to deteriorate at that time. Both Model 1 and Model 2 use Gaussian approximation, but the performance of Model 2 was better. This is because, Model 2 still make use of motion model as its
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proposal distribution. Thus, particles can still maintain hypothesis at larger area. Furthermore, these results further support the fact that short range sensor can cause multi modal distribution for proposal distribution in particle filter algorithm as reported in [19]. As two-step sampling method is used to cater for multimodal proposal distribution [16], this makes Model 2 algorithm is more suitable and hence has better robot’s state estimate and map accuracy than Model 1.
5 Conclusion and Future Works The objective of this paper is to compare the performance of RBPF-NN algorithms with different sampling method. The performance was measured based on the accuracy of robot state estimate and map estimate of RBPF-NN models through simulation. The accuracy was validated by comparing them with the accuracy of existing grid-based SLAM algorithm, termed as RBPF-XNN in this paper. The results show that RBPF-NN Model 2 has the lowest error of robot state estimate and the highest score for overall map estimate. As for Model 1, the adverse effect is observed where integration of neural network has increased the error of robot’s state estimate and reduced the score of occupied cells’ estimate. The reason of two-step sampling has better performance is due to the implementation of infrared sensor as robot’s observation. Array of infrared sensor is chosen as robot’s sensor because it has significantly lower cost. In two-step sampling, an additional sampling step that uses motion model to sample particles was applied. This caused the two-step sampling to be less dependent on sensor measurements. The results also supports the fact that short range sensor implementation such as the infrared sensor can cause multi modal proposal distribution in particle filter algorithm [12, 17]. There are two findings identified in this experiments. The first one, at the end of the robot’s trajectory the robot’s state estimate began to diverge. This indicates that the algorithm was unable to recognize the visited area (i.e. loop closure). To overcome this, vision sensor can be applied as means to recognize visited place [20]. Secondly, integration of neural network interprets value of each cell. This has improved the robot’s observation from such noisy sensor, but still requires a lot of computation despite the lower number of particles. Thus, improvement is needed for real-time implementation. From this experiment, it is feasible to use array of infrared sensors as sensors measurements for SLAM algorithm. This can reduce the total cost of a mobile robot. However, computation cost is high. Alternatively, fusion with other low-cost sensors such as monocular camera can be done. Furthermore, vision sensor has become increasingly dominant in the SLAM domain [21, 22]. Other approach is by using low-cost light detection and ranging (LiDAR) and inertial measurement unit (IMU) [18, 20]. Lowcost LiDAR has significantly lower cost compared to laser rangefinder but gives dense measurements compared to sparse measurements from array of range sensors. Acknowledgement. The authors would like to thank the Centre for Telecommunication Research & Innovation (CeTRI), Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM) and the Ministry of Higher Education Malaysia for the grant RACER/2019/FKEKK-CETRI/F00404 that has made this research work possible.
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References 1. Sileshi, B.G., Oliver, J., Toledo, R., Gonçalves, J., Costa, P.: Particle filter SLAM on FPGA: a case study on Robot@Factory competition. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds.) Robot 2015: Second Iberian Robotics Conference, pp. 411–423. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-27146-0_32 2. Gifford, C.M., et al.: Low-cost multi-robot exploration and mapping. In: IEEE International Conference on Technologies for Practical Robot Applications, TePRA 2008, pp. 74–79 (2008) 3. Magnenat, S., et al.: Affordable slam through the co-design of hardware and methodology. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 5395–5401 (2010) 4. Thrun, S.: Learning metric-topological maps for indoor mobile robot navigation. Artif. Intell. 99(1), 21–71 (1998) 5. Schröter, C., Böhme, H.-J., Gross, H.-M.: Memory-efficient gridmaps in rao-blackwellized particle filters for SLAM using sonar range sensors. In: EMCR (2007) 6. Yap, T.N., Shelton, C.R.: SLAM in large indoor environments with low-cost, noisy, and sparse sonars. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1395– 1401 (2009) 7. Waniek, N., Biedermann, J., Conradt, J.: Cooperative SLAM on small mobile robots. In: 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1810–1815 (2015) 8. Beevers, K.R., Huang, W.H.: SLAM with sparse sensing. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, pp. 2285–2290 (2006) 9. Abrate, F., Bona, B., Indri, M.: Experimental EKF-based SLAM for mini-rovers with IR sensors only. In: European Conference on Mobile Robots (2007) 10. Patel, J.: Implementation of Real-Time Simultaneous Localization and Mapping with Particle Filter. University of Maryland (2012) 11. Einhorn, E., Schröter, C., Gross, H.-M.: Finding the adequate resolution for grid mapping-cell sizes locally adapting on-the-fly. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1843–1848 (2011) 12. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005) 13. Grisetti, G., Tipaldi, G.D., Stachniss, C., Burgard, W., Nardi, D.: Fast and accurate SLAM with Rao-Blackwellized particle filters. Rob. Auton. Syst. 55(1), 30–38 (2007) 14. Roller, D., Montemerlo, M., Thrun, S., Wegbreit, B.: Fastslam 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In: Proceedings of the International Joint Conference on Artificial Intelligence (2003) 15. Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with raoblackwellized particle filters. IEEE Trans. Robot. 23(1), 34–46 (2007) 16. Stachniss, C., Grisetti, G., Burgard, W., Roy, N.: Analyzing Gaussian proposal distributions for mapping with Rao-Blackwellized particle filters. In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3485–3490 (2007) 17. Yatim, N.A., Buniyamin, N., Noh, Z.M., Othman, N.A.: Occupancy grid map algorithm with neural network using array of infrared sensors. J. Phys: Conf. Ser. 1502(1), 12053 (2020) 18. Arleo, A., Millán, J.R., Floreano, D.: Efficient learning of variable-resolution cognitive maps for autonomous indoor navigation. IEEE Trans. Robot. Autom. 15(6), 990–1000 (1999) 19. Pfingsthorn, M., Birk, A.: Simultaneous localization and mapping with multimodal probability distributions. Int. J. Rob. Res. 32(2), 143–171 (2013) 20. Jiang, G., Yin, L., Jin, S., Tian, C., Ma, X., Yongsheng, O.: A simultaneous localization and mapping (SLAM) framework for 2.5D map building based on low-cost LiDAR and vision fusion. Appl. Sci. 9(10), 2105 (2019). https://doi.org/10.3390/app9102105
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21. Lee, T., Kim, C., Cho, D.D.: A monocular vision sensor-based efficient SLAM method for indoor service robots. IEEE Trans. Ind. Electron. 66(1), 318–328 (2018) 22. López, E., et al.: A multi-sensorial simultaneous localization and mapping (SLAM) system for low-cost micro aerial vehicles in GPS-denied environments. Sensors 17(4), 802 (2017)
Instrumentation and Control
Performance Analysis of Conditional Integrator in NPID Controller as Cutting Force Compensator for Machine Tools Application I. A. Saidin1
, M. N. Maslan1(B) , L. Abdullah1 and A. S. Nur Chairat3
, F. Yakub2
,
1 Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah
Jaya, Durian Tunggal, 76100 Melaka, Malaysia [email protected] 2 Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia, Jalan Semarak, 54100 Kuala Lumpur, Malaysia 3 Institut Teknologi PLN, Menara PLN, Jl. Lingkar Luar Barat, Duri Kosambi, Cengkareng, Jakarta Barat 11750, Indonesia
Abstract. In machine tools application, a control mechanism is needed to compensate the presence of disturbance force. Disturbance force can be in many forms depends on the machine tool used or the application. There are a few adaptations that can be made to a controller to achieve a robust control mechanism. The aim of this research is to evaluate the effect of adapting a conditional integrator in a Nonlinear Proportional-Integral-Derivative (NPID) controller to compensate cutting force disturbance in XY ball screw drive table. The control mechanism is evaluated based on the Fast Fourier Transform (FFT) error generated by when simulating the machine operation. The comparison between the normal NPID and NPID with conditional integrator are made in terms of fundamental peak frequency of FFT error to conclude the effect of conditional integrator for cutting force compensation. The outcomes concluded that the addition of conditional integrator further improves the control mechanism as a disturbance compensator. Keywords: Machine tools · Nonlinear PID (NPID) · Conditional integrator
1 Introduction In machine tools application, a lot of controller or control mechanism have been developed to achieve a robust performance. One of the controller functions is to compensate the presence of disturbance force in the machine tools system. The disturbance presented in the system can be in the form of cutting force and friction force [1]. A few control mechanisms that have been developed or utilized by the previous researchers are Nonlinear Cascade Feedforward (NCasFF) [2] and Sliding Mode Control [3]. These controllers can be modified or combined with each other to achieve a desired output. The example of a simple, yet effective modification implemented by previous researcher is © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 79–85, 2022. https://doi.org/10.1007/978-981-16-8954-3_8
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the combination of nonlinear function to a Proportional-Integral-Derivative (PID) controller [4]. This controller can cope with the nonlinearity behavior of some machine tools. The Nonlinear PID (NPID) controller also can offer a good positioning as well as a good transient response [5]. Despite that, the basic PID controller is still relevant to use with a few alteration or combination on the controller based on the required output. This research proposed the usage of conditional integrator in the NPID controller as a cutting force compensator module. In general, integrator is used to remove the steadystate error of system response in a control system. However, it contains 90° phase lag at all frequency which may lead the controller to be unstable. Therefore, conditional integrator was designed to reduce phase lag while maintaining the steady-state error at minimum [6] and to prevent integration wind-up [7]. In this paper, the simulation result for the NPID controller with conditional integrator are presented. The result includes Fast Fourier Transform (FFT) error and the comparison to the previously designed controller which is the conventional NPID controller.
2 Methodology 2.1 Simulation Setup The simulation works for this research utilized the function of MATLAB and Simulink software. The control mechanism for the XY ball screw drive table was de-signed in the software. Then, a system identification method was used to obtain a transfer function, as a substitute for the real plant which is the XY ball screw drive table, when doing simulation works. A step by step process to obtain the transfer was described in [8]. The transfer function obtained is Eq. 1 as follows: G(s) =
s2
78020 + 163s + 193.3
(1)
2.2 Cutting Force Analysis Research done by [9] explored that the cutting force characteristics can be observed through the harmonic frequency from the generated FFT error graph, as shown in Fig. 1. The cutting force was obtained from the cutting process by two different spindle speeds which is 1500 rpm and 2500 rpm. The peak value of the cutting force in the FFT error graph are encircled in red. The peak value from the graph are tabulated in Table 1. The difference between the peak values of the FFT error for each control mechanism will determine its ability to reduce cutting force disturbance.
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Fig. 1. Cutting force using Fast Fourier Transform (FFT) at (a) 1500 rpm and (b) 2500 rpm. Table 1. Peak value of force for spindle speeds of 1500 rpm and 2500 rpm Cutting force
1500 rpm
2500 rpm
Peak frequency (Hz)
26.07
39
Highest peak of force, (N)
4.66
5.42
2.3 Control Mechanism Design The proposed control mechanism is nonlinear PID with conditional integrator. The parameter for the PID and the nonlinear function need to be determined. This research used parameters from [10] as the researcher also used the same controller and system which is PID controller on an XY ball screw drive table. The conditional integrator with the following equations in (1) and (2) was then adapted to the existing NPID controller: ⎧ ⎛ > us ⎞ u + u + u p i d ⎪ ⎪ ⎨ ⎠ 0, ⎝ and (2) ui = ⎪ e.ui ≥ 0 ⎪ ⎩ e, otherwise Where up , ui , and ud represents each signal of PID gain, proportional signal, integral signal and derivative signal respectively. The value of us , which is the maximum desired position of XY ball screw drive table is set at 15 mm. The value of ui represents the difference between ui and us . ⎧ ⎨ ui,sat , ui + ui > ui,sat ui = −ui,sat , ui + ui < −ui,sat (3) ⎩ ui + ui , otherwise
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The integral signal saturation limit, ui,sat is determined by the maximum value of cutting force injected. The control scheme for the proposed control mechanism are shown in Fig. 2. The nonlinear function is connected before the PID gains of PID controller, with the integral part of PID rearranged to construct the conditional integrator.
Fig. 2. Nonlinear PID with conditional integrator (NPID Icond ) control scheme.
3 Results and Discussions The FFT error graph was generated to compare the control mechanism in terms of fundamental peak frequency. The control mechanism that produce a lower peak value with no spike afterward is considered effective. Therefore, the performances of NPID with conditional integrator are compared with the other benchmark controller namely NPID. FFT error is the error in terms of frequency domain after being translated from time domain. In the time domain system, a sine waveform of 15 mm amplitude and 0.2 Hz frequency was used as an input signal. The system was set by injecting two different spindle speed of 1500 rpm and 2500 rpm simultaneously. The outcomes for 1500 rpm and 2500 rpm are shown in Figs. 3 and 4, respectively. For the 1500 rpm, the NPID controller recorded a highest peak error of 1.22 mm while the addition of conditional integrator further reduces the highest peak error to 1.21 mm. The highest peak error recorded for both controllers are located on the 26 Hz of fundamental peak frequency plot. Thus, the outcome shows that the nonlinear PID controller with conditional integrator came out as the best performer out of the two controllers. Meanwhile, at 2500 rpm, the NPID controller recorded a highest peak error of 1.24 mm while the addition of conditional integrator further reduces the highest peak
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error to 1.19 mm. The highest peak error recorded for both controllers are located on the 39 Hz of fundamental peak frequency plot. NPID with the addition of conditional integrator module still manage to produce the lower peak fundamental frequency compared to the normal NPID controllers. The conditional integrator proven to improve the output signal tracking in comparison to the reference signal. When the absolute error between the reference signal and the input signal are less than the threshold, the conditional integrator injects an extra signal to aid the existing signal to track the reference signal better [6]. The errors at the lower spike other than the peak were not discussed as it does not necessarily reduce since the decrease of error at one frequency will resulted in the increase of error at the other frequency. So the overall system performance can be improved by only reducing the highest spike. The peak value of the FFT error for both control mechanisms are tabulated in Table 2.
Fig. 3. Peak frequency at 1500 rpm: (a) NPID (b) NPID with conditional integrator.
NPID
NPID with conditional integrator
10-3
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10 -3
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1.19
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Error (mm)
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0.5
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0.5
0
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150
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200
0
50
100
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Frequency (Hz) (b)
Fig. 4. Peak frequency at 2500 rpm: (a) NPID (b) NPID with conditional integrator.
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1500 2500
Peak frequency (Hz)
26
Highest peak of error (mm) NPID
39
1.22 1.24
NPID Icond 1.21 1.19
4 Conclusion As a conclusion, the NPID controller with the adaptation of conditional integrator performs better in in terms of fundamental peak frequency in the FFT error plot for both spindle speeds. The conditional integrator responsible for preventing integration wind-up as well as reducing the phase lag thus, improve the performance of the control mechanism. As a recommendation, the conditional integrator can be evaluated in terms of compensating friction force to further proven the effectiveness of the module. It is also suggested that more plant can be used for the experiment to assess the control mechanism in different situation. Acknowledgments. Authors are grateful to Universiti Teknikal Malaysia Melaka and Ministry of Higher Education Malaysia for the financial support through RACER grant with reference number RACER/2019/FKP-COSSID/F00410.
References 1. Abdullah, L., et al.: Assessment on tracking performance of Cascade P/PI, NPID and NCasFF controller for precise positioning of XY table ballscrew drive system. Procedia CIRP 26, 212–216 (2015) 2. Abdullah, L., et al.: Evaluation on tracking performance of PID, gain scheduling and classical cascade P/PI controller on XY table ballscrew drive system. World Appl. Sci. J. 21(1), 1–10 (2013) 3. Anang, N.A., et al.: Tracking performance of NPID controller for cutting force disturbance of ball screw drive. J. Mech. Eng. Sci. 11(4), 3227–3239 (2017) 4. Armstrong, B., Neevel, D., Kusik, T.: New results in NPID control: tracking, integral control, friction compensation and experimental results. IEEE Trans. Control Syst. Technol. 9(2), 399–406 (2001) 5. Junoh, S.C.K., Salim, S.N.S., Abdullah, L., Anang, N.A., Chiew, T.H., Retas, Z.: Nonlinear PID triple hyperbolic controller design for XY table ball-screw drive system. Int. J. Mech. Mechatron. Eng. 17(3), 1–10 (2017) 6. Maharof, M., et al.: Suppression of cutting forces using combined inverse model based disturbance observer and disturbance force observer. J. Adv. Manufact. Technol. (JAMT) 12(1(3)), 73–86 (2018) 7. Maslan, M.N., Sato, K., Shinshi, T.: Position measurement and control of a thin and compact linear switched reluctance motor with a disposable-film mover. Sens. Actuators A 285, 80–88 (2019)
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8. Saxena, A., Kumar, J., Agrawal, R.: Dynamics and control structure for NPID controller. IOP Conf. Ser.: Mater. Sci. Eng. 1116(1), 012141 (2021) 9. Tong, W.S., Chiew, T.H., Jamaludin, Z., Bani Hashim, A.Y., Abdullah, L., Rafan, N.A.: Design of sliding mode controller using smoothening method for chattering suppression in machine tools. In: Jamaludin, Z., Ali Mokhtar, M.N. (eds.) SympoSIMM 2019. LNME, pp. 102–111. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-9539-0_11 10. Yuan, J., Ates, A., Dehghan, S., Zhao, Y., Fei, S., Chen, Y.Q.: PID2018 Benchmark challenge: model-based feedforward compensator with a conditional integrator. IFAC-PapersOnLine 51(4), 888–893 (2018)
Experimental Investigation on Acoustic Energy Conversion Using Single and Double PZT Film by Modified Resonator Md. Anayet U. Patwari(B) , A. N. M. Nihaj Uddin Shan, Md. Zayed Mostafa, and Md. Iftekhar Uddin Shohan Department of Mechanical and Production Engineering, Islamic University of Technology (IUT), Dhaka, Bangladesh [email protected]
Abstract. Sound and vibration-based energy harvesting techniques have been drawing the attention of many researchers because of energy scarcity. Lowfrequency energy harvesting is a highly complicated process. However, the physical characteristics of piezoelectric materials can be utilized to extract energy from low-frequency acoustic or vibration-based waves. In this study, experiments have been conducted on acoustic energy harvesting from low-frequency sound sources using modified resonators with single and double PZT film configurations. Based on the results obtained from the experiment, it is clear that double film configuration is better for the purpose. Under the condition of 100 Hz constant frequency, the single unit double film configuration showed a significant improvement in voltage over single film units. The overall power output for both single and double film configuration in the noise barrier was recorded. The results show that double film configuration increases the power output. A modified resonator of double film configuration can be used to power up low power-consuming devices and is extensively applicable for noise reduction in big cities and industrial areas. Keywords: Sound energy extraction · High wavelength sound energy · Resonator · PZT film · Sound energy extraction barrier
1 Introduction Acoustic and vibrational waves are forms of mechanical energy created by a wide range of sound and vibrational sources. When the acoustic wave is unwanted, it is referred to as noise. Aircraft, trains, factories, and expressways are familiar sources of noise. Now many researchers [1, 2] have focused on energy harvesting and they have developed many ways of converting ambient vibration energy in the environment and transforming it into valuable energy in the form of electricity. The expression “energy harvesting” refers to small-scale energy production on the order of micro-to-milli-watts. Theoretically and experimentally, harvesting energy from solar [3], wind [4], biochemical [5], thermal [6], and mechanical vibration [7] has been investigated for driving low-power appliances. However, not enough attention has been paid to developing methods of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 86–95, 2022. https://doi.org/10.1007/978-981-16-8954-3_9
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harvesting acoustic energy compared to other energy sources. Acoustic energy is an available, ample, viable, and non-pollution source extracted and converted into electrical energy. Recently, there have been substantial resources to enhance mechanisms to harvest acoustic energy from industries, road traffic, work plants, construction sites, airports, and expressways. Also, mechanical vibration (sound) energy to electrical energy can be performed using three types of generator, namely piezoelectric generators, electromagnetic and electrostatic. Amongst these three mechanisms, most consideration has been devoted to piezoelectric transduction. Piezoelectric (PZT) is a sophisticated replacement for batteries that can generate energy on the go. A lot of research has been done on this topic. Horowitz et al. [2] was introduced to the first micromachined acoustic energy harvester using a piezoelectric ring attached to one of the walls of the Helmholtz resonator. When the sound pressure of the impedance wave is at a level of 149 dB, he was able to reach a maximum power of 250 µWcm−2 . Then, based on the same principle, an electromechanical Helmholtz resonator (HR) was developed by Fei Liu et al. [8], which bends the piezoelectric plate using the uniform pressure of the sound incident wave in the resonator compartment. When the sound pressure of the impedance wave is at a level of 160 dB, this study was able to harvest 30 mW power which can run small powered electronic appliances. Kim et al. [6] used the HR to extract wind and airbase sound energy using the magnet driven by the acoustic pressure principle. The results showed a peak voltage output of 4 mV at 1.4 kHz, where the incident pressure was 0.2 kPa. Yuan et al. [9] suggested focusing on increasing the output power of acoustic energy harvesting and introduced a unique design for harvesting acoustic energy. Zhao et al. [10] proposed a double-tube HR where the optimum output frequency of 70 Hz and SPL of 85.3 dB, the open-circuit voltage and short-circuit current can reach 132 V and 32 µA, respectively. Xincun et al. [11] introduced a quarter spherical Acoustic energy harvester and the proposed AEH was found to be efficient in testing, where the output voltage was found to be about 28 mV at 470 Hz frequency band. However, all previous studies produced power using a single piezoelectric beam, and the use of dual piezoelectric beams has not been investigated. As a result, the primary aim of this study is to figure out a new technique for extracting energy from low-frequency sound waves that employ dual piezoelectric beams with a honeycomb structure Helmholtz resonator. Due to the use of dual PZTs, this method can double the amount of energy harvested. A honeycomb structure Helmholtz resonator will be fabricated after designing an acceptable PZT setup to amplify the voltage produced by the PZT setup (Fig. 1).
Fig. 1. The architecture of the sound energy extracting barrier (SEEB).
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2 Methodology 2.1 Physics of Sound Energy Conversion At the outset of the study, before going into the conversion of energy let’s look into the physics behind propagation of sound energy in surroundings. Sound energy travels in atmosphere as a longitudinal wave, creating contraction and expansion throughout the medium of propagation. Sound can be heared when the wave hits the diaphragm of human ears, creating electric signals in nerve cells. Sound energy conversion methods involve similar mechanisms where the sound wave hits a diaphragm, film or plate at a certain sound pressure level. This mechanical energy then can be converted into electrical energy. In this study, focused has been on the application of piezoelectric materials as a conversion method for sound energy. Piezoelectricity is the property in solids due to which electric charge is generated when the body is deformed under the effect of mechanical stress. The fundamental reason behind the generation of electricity of electric potential is due to shapeshifting of polarization domains in the solid body [12]. 2.2 PZT Film Description The principal property of these film products meets the substance constraints of Directive 2011/65/EU restricting hazardous substances (RoHS2). The RoHS2 Limited for the absence of mercury, cadmium, hexavalent chromium, PBB, PBDE, lead, DEHP, BBP, DBP, and DIBP above-defined thresholds in these electric equipment products. The PZT film part generates more than ten millivolts per microwave; approximately 60 expanded its product than voltage output of the slate strain gauge. Capacity is proportional to the area and roughly equal to the size of the element. PZT film was used for the experiment, which is shown in Fig. 2.
Fig. 2. Photograph of PZT film used in this study.
The PZT series testers are the simplest type of piezo film sensors, mainly used as flexible dial gauges and touch microphones for vibration or impact detection. These are usable without a lead for such systems that the user needs to make their lead connection. The sensor can be conveniently connected to the dual tape or epoxy board. Lead binding can be accomplished by compressive clamping, crimping, eyelets, conductive epoxy, or low-temperature clamping. 2.3 Geometry For creating this prototype, 3D design and fabrication have been made to the proper scale using commercial software and a 3D printer. The main challenge was a fabrication of the
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continuous ear-like structure due to the complex shape of the resonator. The distribution of the weight of the PZT film was one of the main problems. This problem was solved by handcrafting a extended part on the inner side of the resonator. A fully specified and effective model has been created according to the requirement of the study. Hexagonal shapes are the most optimized shape in terms of effective utilization of space since a honeycomb-like structure is formed without any gap among the resonators. The scale was achieved in a trial-and-error process to achieve the most optimized model. The single film concept was already developed in earlier studies on the topic [13]. The resonator is modeled in such a way that more than one film can be used for achieving better results having the option of two films configuration. The dimension of the designed modified prototype was given in Fig. 3 (a) and Fig. 3 (b).
(a)
(b)
Fig. 3. Schematic diagram of the sound energy extracting unit; (a) Front V iew and (b) Bottom V iew SEE unit.
For creating the physical model, PLA (Polylactic acid) was used because of its easy availability. Figure 4 shows the prototype’s bottom and top views.
Fig. 4. Photograph of the fabricated SEE Unit
Low frequency sound will enter through the top part of the prototype and the sound will also create pressure in the piezoelectric film.
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2.4 Experimental Setup The experimental setup and acquiring data from the experiment components are shown in Fig. 5. In this arrangement, the power amplifier is connected to the function generator to provide the desired signal response in a definite sine wave produces by the function generator. Then the function generator connects to the oscilloscope to observed the signal, which another action was to observe the change in a wave function because of the displacement of the piezoelectric film. Speaker will generate the sound signal that will be set on the function generator that the power amplifier can control sound from the speaker. For a start, the power amplifiers are set to a 1.1 amplify rate.
Fig. 5. Experimental setup for sound energy extracting unit.
This sound signal will enter the piezoelectric film through a prototype, and the sound pressure from the speaker makes the impedance on the film that action will make the film displaced from its position, and that displacement will generate electricity. After that, the voltage of the film can be measured by the multimeter and store the data accordingly.
3 Results and Discussion 3.1 Experimental Results The acoustic energy travels from two subwoofers, and the combined wave travels through the inlet of the resonator and impinges on the piezoelectric films creating excitation. The voltage output from the piezoelectric film terminals is recorded in the oscilloscope and multimeter to analyze the energy harvesting unit. However, the place where experimentation was done was not soundproof, and thus background noise cancellation was not an option. Therefore, the experimental setup is placed in the vicinity of the subwoofers to minimize noise and error. The RMS voltage and frequency of the sound wave generated by the function generator are recorded to analyze the input energy. The function generator generated a sine wave with an amplitude of 2Vpp, and the frequency is varied
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between 100–200 Hz. The incident wave sound pressure level (SPL) has been calculated using the following equation. Vrms ) (1) SPL(dB) = (20 log10 S × Pref Here, the sensitivity of the microphone, S = 12.6 mV/Pa. The reference pressure, Pref is taken 20 µPa, which is the lowest threshold for the human hearing with an SPL of 0 dB. The SPL ranges from 125.95 dB to 135.49 dB. The testing of the piezoelectric energy harvester was started without the Helmholtz resonator at first to understand the importance of the single degree of freedom (SDOF) system. The output voltage from the terminals of a single film was recorded under varying conditions of input voltage and frequency in the function generator. Figure 6. depicts the variation of output voltage with increasing input voltage in the function generator from 500 mV to 1500 mV with a step size of 100 mV. Without the resonator, the acoustic energy harvesting pattern is unpredictable and irregular. The output voltage increases up to around 900 mV input voltage. The peak output here is 107.7 mV. Then for increasing input voltage, the output significantly drops and again rises at 1300 mV input. For the further rise in input voltage, the output falls sharply. This kind of irregular behavior at high input voltage might happen due to the intervention of background noise with subwoofer sound waves. Irregular behavior in output voltage is also seen in the output voltage vs. frequency at 500 mV constant input voltage. The frequency is set from 100 Hz to 200 Hz with a step size of 10 Hz. The overall plot shows increasing output voltage with increasing frequency which should be the average trend but is not free of irregularities. Figure 7 shows the relation between the output voltage and input frequency for a single film without any resonator at a constant 500 mV input voltage set in the function generator. The highest output voltage is 120 mV for 200 Hz input in the function generator.
Fig. 6. Input voltage vs Output voltage for a single film without the resonator.
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Fig. 7. Frequency vs voltage for a single film without the resonator.
Then the experiment is done with a resonator, and significant improvements in the output voltage regularity are seen. Figure 8 shows the plot of output voltage with increasing input voltage at a constant frequency of 100 Hz for both single and double film configuration, and as expected, double film configuration brings out far better voltage output at the terminals. The highest output voltage achieved by single film configuration of acoustic energy harvester is 201.3 mV at 1500 mV input voltage. The highest output of the double film configuration is 208.2 at only 1100 mV input voltage. The graph truly understands the advantage of double film in the figure. Double film configuration gives the output as high as of single film configuration at any certain input level.
Fig. 8. Input vs Output Voltage at 100 Hz for both single and double film configuration with a resonator.
One point of concern is the decline in the output voltage of double film units after 1100 mV input. This experiment again might have happened due to background noise in the experimentation space. Figure 9 depicts the difference between single and double film units in energy harvesting capacity more clearly. The figure shows the plot of output voltage with frequency and compares single and double film configurations at a constant 500 mV input voltage set in the function generator. At 170 Hz input, the double film configuration gives 237.6 mV output, whereas the single film unit gives 80.2 mV only.
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Fig. 9. Output voltage vs frequency at 500 mV input voltage for both single and double film configuration with a resonator.
Figure 10 depicts a similar plot of output voltage vs frequency but at a constant 1000 mV input voltage set in the function generator. The visual depiction of the output voltage shows a similar trend as in the previous figure. For instance, at an input frequency of 130 Hz, the double film unit gives 166.4 mV output voltage, whereas the single film unit gives only 106.7 mV output only. The analysis of output voltage with increasing input voltage or frequency clearly shows step by step that Helmholtz resonator can give better acoustic impedance on the piezoelectric film, and a double film configuration is better in capacity than a single film configuration, and further addition of films might also be possible for further improvements in energy harvesting technologies.
Fig. 10. Output voltage vs frequency at 1000 mV input voltage for both single and double film configuration with a resonator.
Figure 11 depicts the change in output voltage with increasing input SPL. The single film configuration shows a promising trend in the graph, and the highest output is obtained at the highest input sound pressure level (SPL). The trend of output voltage change with increasing SPL depicts the increment in output compared to the graph of single film configuration. The output voltage increases up to around 133 dB, and then a sharp fall is observed. This irregularity is observed several times due to unfavorable experimentation space with lots of noise and unregulated sound sources.
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Fig. 11. Output voltage vs sound pressure level for single and double film configuration.
Based on the above information, it was found that considerable output voltage could be obtained with the modified resonator. With a similar sound pressure level, the output voltage of this modified resonator was almost 4 times higher than the other acoustic energy [11, 13] harvesters.
4 Conclusions In this study, a sustainable high-wavelength sound and vibration-based energy extracting prototype are developed for industrial areas and metro rail, especially in residential areas where noise pollution is high. The proposed system provides a practical solution for lowfrequency sound absorption by using vast arrays of barriers and generation of electricity to power up low power consuming devices like automobile lights, factory lights, and low power electronic devices. The developed system achieves sound pollution reduction and usable power generation by enhancing sound energy conversion systems using double PZT (Lead Zirconate Titanate) film in a modified resonator structure with better acoustic effect on the PZT film. A designed structure with better acoustic impedance on PZT film may yield better results in the future.
References 1. Glynne-Jones, P., Tudor, M.J., Beeby, S.P., White, N.M.: An electromagnetic, vibrationpowered generator for intelligent sensor systems. Sens. Actuators A Phys. 110(1–3), 344–349 (2004) 2. Horowitz, S.B., Sheplak, M., Cattafesta, L.N., Nishida, T.: A MEMS acoustic energy harvester. J. Micromech. Microeng. 16(9), S174 (2006) 3. Abrams, Z.R., Niv, A., Zhang, X.: Solar energy enhancement using down-converting particles: a rigorous approach. J. Appl. Phys. 109(11), 114905 (2011) 4. Ovejas, V.J., Cuadras, A.: Multimodal piezoelectric wind energy harvesters. Smart Mater. Struct. 20(8), 085030 (2011) 5. Hansen, B.J., Liu, Y., Yang, R., Wang, Z.L.: Hybrid nanogenerator for concurrently harvesting biomechanical and biochemical energy. ACS Nano 4(7), 3647–3652 (2010) 6. Kim, S.H., et al.: An electromagnetic energy scavenger from direct airflow. J. Micromech. Microeng. 19(9), 094010 (2009)
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7. Jo, S.E., Kim, M.S., Kim, Y.J.: A resonant frequency switching scheme of a cantilever based on polyvinylidene fluoride for vibration energy harvesting. Smart Mater. Struct. 21(1), 015007 (2012) 8. Liu, F., et al.: Acoustic energy harvesting using an electromechanical Helmholtz resonator. J. Acoust. Soc. Am. 123(4), 1983–1990 (2008) 9. Yuan, M., Cao, Z., Luo, J., Zhang, J., Chang, C.: An efficient low-frequency acoustic energy harvester. Sens. Actuators A: Phys. 264, 84–89 (2017) 10. Zhao, H., et al.: Dual-tube helmholtz resonator-based triboelectric nanogenerator for highly efficient harvesting of acoustic energy. Adv. Energy Mater. 9(46), 1902824 (2019) 11. Ji, X., Yang, L., Xue, Z., Deng, L., Wang, D.: Enhanced quarter spherical acoustic energy harvester based on dual helmholtz resonators. Sensors 20(24), 7275 (2020) 12. Shao, Z., Cao, X., Luo, H., et al.: Recent progress in the phase-transition mechanism and modulation of vanadium dioxide materials. NPG Asia Mater. 10, 581–605 (2018) 13. Wang, Y., et al.: A renewable low-frequency acoustic energy harvesting noise barrier for high-speed railways using a Helmholtz resonator and a PVDF film. Appl. Energy 230(April), 52–61 (2018)
Low-Cost Air Quality Monitoring Platform Using Flying Wing Drone Muhamad Firdaus Bin Mohd Razali1 and Ahmad Anas Bin Yusof2(B) 1 Department of Mechatronics, Faculty of Electrical Engineering, Universiti Teknikal Malaysia
Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Malaysia 2 Robotics and Industrial Automation Research Group, Faculty of Electrical Engineering,
Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Malaysia [email protected]
Abstract. Air pollution has been one of the biggest problems in South-East Asia for decades, affecting primarily Malaysia, Singapore and Indonesia due to haze, open burning, and the rapid growth of urbanization. In Malaysia, air quality information is usually collected by ground stations that’s been located nationwide by the Department of Environment (DOE). Although ground station provides accurate and reliable data, it cannot track the changes of air quality in areas that are not covered by the stations especially in remote areas and in emergency. This research focuses on the development of air quality monitoring sensors using flying wing drone technologies. The development of such system can provide a low cost solution for future air quality monitoring. There are two objectives of this study, which are to design and develop the air quality monitoring module and to monitor the effect of sensors’ positioning onboard the drone with regards to the reading values. Keywords: Air quality · Internet of Things · Unmanned aerial vehicle · Drone monitoring · Multisensory
1 Introduction Air pollution has been one of the most serious concerns in Southeast Asia for decades, particularly in Malaysia, Singapore and Indonesia, due to the process of urbanization and rapid growth of the industrial sector, the increase in traffic volumes, and the expansion of the petroleum and forestry industries [1]. Haze has emerged as a major source of concern for air pollution in Southeast Asia in recent years, with a wide range of consequences ranging from public health to national economics [2]. To monitor air quality in remote areas, remote air quality monitoring systems can be built by combining drones or unmanned aerial vehicle (UAV) technologies with appropriate sensors. In recent years, drones have become a popular experimental platform for high-spatial, near-surface vertical profiling of air pollution. Although ground stations provide accurate and reliable data, they are insufficient to cover large areas or in the event of an emergency, necessitating the use of remote sensing. Consumers all over the world have advocated for © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 96–104, 2022. https://doi.org/10.1007/978-981-16-8954-3_10
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the Internet of Things (IoT), defined as a network of connected air pollution sensors, to collect data on air quality [3]. The most recent IoT systems require the installation of large sensors in urban infrastructure. Because static sensors can only measure data with low spatial resolution, they cannot easily track changes in the spatial dimension of air pollution.
2 Literature Review This paper proposes a low-cost drone platform for remote air quality sensing. The development of such a system can provide appropriate analysis for future research. The sections of this paper that follow discuss related works, hardware configuration, experiment setup, experiment results, conclusion, and future tasks. Many UAV-based air quality monitoring systems have been proposed and developed in recent years [4–7]. One of the study involves the use of a fixed-wing solar powered UAV, with potential to perform perpetual flight using solar power and generate air quality data in real-time [8]. A researcher also creates an IOT-based air quality monitoring system using the Raspberry Pi. It is based on real-time autonomous monitoring system for air quality that includes PM 2.5, carbon monoxide, carbon dioxide, temperature, humidity, and air pressure. The cloud-based Internet of Things (IoT) offers a novel technique for better data management from various sensors, which is collected and transmitted by the low-power, low-cost ARM-based Raspberry Pi [9]. An aerial-ground air quality sensing system is also being developed. The project focuses on fine-grained air quality monitoring and forecasting by combining haze images captured by an unmanned aerial vehicle (UAV) with AQI data collected by a ground-based three-dimensional (3D) wireless sensor network (WSN). The system uses the advantages of computer vision to determine the AQI scale from haze images, where haze-relevant features and a deep convolutional neural network (CNN) are designed for direct learning between haze images and the corresponding AQI scale [10]. Others have created a drone-based multi-gas sensor for measuring the air pollution. A drone is used to detect carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), and nitrogen monoxide (NO) (NO). For particle detection, a particle counter for aerosols (10–500 nm) was used. The system has been tested by detecting diesel fumes and particles. A mathematical model was also proposed to estimate the effects of the sensors at various distances from the drone’s center. The data gathered has been transmitted to a ground station and is being displayed in real time [11]. Simultaneously, another researcher published the findings of a study conducted in Saudi Arabia on the concentration and composition of dust at various altitudes of 0.6 m, 400 m, and 800 m using a fixed wing UAV. After the UAV landed, samples at 0.6 m are collected. The air samples are collected by the UAV using 0.8 m (pore size) polycrystalline filters and analyzed for a total of 16 substances, mostly metal elements. The goal is to create a three-dimensional map of potential hazards in the region’s airspace [12]. In the meantime, an unmanned helicopter has been used to monitor chemical pollutant gases. The platform for measuring gas concentrations at various heights is an unmanned helicopter, the HD400. The pollutant gas concentration is determined using the UAV’s composite gas detector. A pump-suction multi-gas detection equipment is used to remove inaccurate data caused by the airflow apparatus and fuselage vibration. The results are obtained by flying the
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UAV in two different directions while the onboard composite gas detector collects polluted air samples at heights of 150, 200, 250, and 300 m [13]. Some researchers also proposed a system for collecting air sampling data in 3D space for a specific location. Three MQ sensors (MQ7, MQ2, and MQ135) mounted on a drone are used to monitor carbon monoxide (CO), carbon dioxide (CO2), and methane (CH4), which are processed by an Arduino UNO microcontroller with Wi-Fi connectivity. The data is uploaded to the Blynk server (the cloud) and displayed on the mobile app. A base station unit with GPS, vision positioning system, 3-axis stabilized camera, long range live view, and 4K camera controls and monitors the drone [14] (Figs. 1 and 2).
Fig. 1. Air quality monitoring onboard a quadcopter drone [15]
Fig. 2. Air quality monitoring onboard a fixed wing drone [16]
3 Methodology The air quality monitoring module is developed by integrating Arduino-based sensors with the microcontroller. The data will be passed to the NodeMCU board through serial communication and uploaded to the internet cloud (Blynk). The data will then be displayed in the users’ smartphone or laptops by using Blynk application. The components and the process flow of the system is as shown in Fig. 3 and Table 1 (Fig. 4).
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Fig. 3. Air quality monitoring system process flow
Table 1. Hardware used for the air quality sensor module Components
Weight Notes
Cost
ESP8266
2g
Wi-Fi module, that i.e., can measure signal strength
MQ-135
4g
For air quality: NH3, NOx, alcohol, benzene, smoke RM 8.90 and CO2
DHT11
2g
Humidity and temperature
Arduino Nano
7g
Zohd Orbit Wing 455 g
RM 7.90
RM 9.90
Open source microcontroller
RM 99.00
Flying wing drone with radio controller
RM 900.00
Total cost
RM 1025.70
Based on Fig. 5, the proposed setup is carried out by placing the air quality sensor module on the flying wing drone platform that is placed on a stand. The responding variable of this experiment is the reading of the air quality versus the natural air flow and smoke-induced air flow. This experiment is carried out during clear weather where the presence of wind is minimum and there is no haze in the area. Figure 6 shows the Blynk application, with readings of the air quality, humidity and temperature.
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Sensor 3 (Above Fuselage) Sensor 1 (Above Right Wing)
Sensor 2 (Below Fuselage) Fig. 4. Air quality monitoring module sensors (above) and the location of the sensors on board the flying wing (below)
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Air Source
100 meter Fig. 5. Schematic diagram for the test
Fig. 6. The air quality reading on the Blynk application
4 Results and Discussions 4.1 Air Flow Test Without Smoke The first experiment shows that all three sensors are able to sense the flow of air without too much differences. The wind velocity is measured at an average of 1.9 m/s. The range of the measured air quality are around 120 ppm to 190 ppm, as shown in Fig. 7. The reading at sensor 1, which is located above the wind are mostly higher than other sensors. This is followed by readings at sensor 2, which is located below the fuselage and sensor 3, located at the above fuselage. Some fluctuation can be seen, especially in the 6th , 10th and 24th min of the 32 min measurement durations.
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Sensor 2 Sensor 1
Sensor 3
Fig. 7. Air quality reading against time for air flow without smoke
4.2 Air Flow Test with Smoke When smoke is introduced in the air flow, it is noted that there is a steep increase in the reading of all the sensors, as shown in Fig. 8. A steady increase is clearly noted within the 32 min of measurement duration. Although not much changes can be seen in the increase, some spikes can be seen in the data taken between the sensors, from the readings at the 18th , 20th , 26th and 28th min. At the 18th min, the reading of air quality spikes to the maximum reading of 789 ppm, according to sensor 3’s reading. Another spike is seen at the 20th min, which is at 854 ppm, and 898 ppm at 26th min and 976 ppm at the 28th min respectively. At the same time, sensor 1 also reports a reading spike at 26th min and 28th min, with readings of 923 and 940 ppm respectively. It is noted that the spikes mostly occur on the air flow above the drone.
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Sensor 2
Sensor 1
Sensor 3
Fig. 8. Air quality reading against time for air flow with smoke
5 Conclusion This project demonstrates the concept of developing a remote sensing application for air quality monitoring using flying wing drone. The air quality monitoring system is made up of air quality sensors that can detect a variety of dangerous gases, including methane, NH3, NOx, alcohol, benzene, smoke, and carbon dioxide. Using the Internet of Things (IoT) wireless system, the data transmission system between the Arduino microcontroller and the internet cloud for real-time monitoring is successfully tested. Experiments to measure and analyze the influence of airspeed on air quality values are also carried out. The first experiment demonstrates that all three sensors can detect air flow with little variation. It should be noted that the air movement around the sensor has an impact on the air quality readings. When smoke is injected into the air flow, all of the sensors show a significant rise in readings. Within the 32-min measurement duration, a continuous increase may be seen. Although there aren’t many variations in the rise, there are some spikes in the data collected between the sensors. Acknowledgement. The authors wish to thank Universiti Teknikal Malaysia Melaka for the support in the research.
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References 1. Gan, C.M., Gross, B., Wu, Y.H., Moshary, F.: Application of remote sensing instrument in air quality monitoring. In: Air Quality Monitoring Assessment and Management, vol. 3, pp. 93–112 (2011) 2. Manan, N.A., Abdul Manaf, M.R., Hod, R.: The Malaysia haze and its health economic impact: a literature. Malays. J. Public Health Med. 18(1), 38–45 (2018) 3. Gu, Q., Michanowicz, D.R., Jia, C.: Developing a modular unmanned aerial vehicle (UAV) platform for air pollution profiling. Sensors 18(12), 1–14 (2018) 4. Molina, M.A.: Design and development of a methodology to monitor PM10 dust particles produced by industrial activities using UAVs. Ph.D. thesis, University of Queensland (2018) 5. Anderson, K., Gaston, K.J.: Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front. Ecol. Environ. 11(3), 138–146 (2013) 6. Yadav, P., Porwal, T., Jha, V., Indu, S.: Emerging low-cost air quality monitoring techniques for smart cities with UAV. In: 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp. 1–6 (2020). https://doi.org/10.1109/CON ECCT50063.2020.9198487 7. Boon, M.A., Drijfhout, A.P., Tesfamichael, S.: Comparison of a fixed-wing and multi-rotor UAV for environmental mapping applications: a case study. In: International Conference on Unmanned Aerial Vehicles in Geomatics, Bonn, Germany, 4–7 September 2017 (2017) 8. Zou, Y., Yin, Y., Song, J.: Flight control of a flying-wing UAV based on active disturbance rejection control. In: 3rd IEEE International Conference on Control Science and Systems Engineering, ICCSSE 2017, pp. 50–55 (2017) 9. Kumar, S., Jasuja, A.: Air quality monitoring system based on IOT using raspberry PI. In: Proceeding of IEEE International Conference on Computing, Communication and Automation, ICCCA, pp. 1341–1346 (2017) 10. Yang, Y., Hu, Z., Bian, K., Song, L.: ImgSensingNet: UAV vision guided aerial-ground air quality sensing system. In: Proceeding of IEEE International Conference on Computer Communications INFOCOM, no. 3, pp. 1207–1215 (2019) 11. Villa, T.F., Salimi, F., Morton, K., Morawska, L., Gonzalez, F.: Development and validation of a UAV based system for air pollution measurements. Sensors 16(12), 2202 (2016) 12. Omar, S.H.: 3D mapping of hazardous airborne dusts adversely affecting air/ground transportation, health and environment using unmanned aerial vehicle northern Saudi Arabia. Int. J. Appl. Innov. Eng. Manag. 4(8), 051–068 (2015) 13. Yungaicela-Naula, N.M., Garza-Castanon, L.E., Mendoza-dom, A., Minchala-avila, L.I.: Design and Implementation of an UAV-based platform for air pollution monitoring and source identification. In: Congreso Nacional de Control Automático Mexico, pp. 288–293 (2017) 14. Wivou, J., Udawatta, L., Alshehhi, A., Alzaabi, E., Albeloshi, A., Alfalasi, S.: Air quality monitoring for sustainable systems via drone based technology. In: IEEE International Conference on Information and Automation for Sustainability, ICIAFS, pp. 1–5 (2016) 15. Smith, B.J., John, G., Christensen, L.E., Chen, Y.: Fugitive methane leak detection using SUAS and miniature laser spectrometer payload: system, application and groundtruthing tests. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 369–374 (2017) 16. Nathan, B.J., et al.: Near-field characterization of methane emission variability from a compressor station using a model aircraft. Environ. Sci. Technol. 49, 7896–7903 (2015)
Design and Analysis of Modified Nonlinear PID Controller for Disturbance Suppression in Machine Tools Tsung Heng Chiew1(B) , Weng Kang Chow1 , Zamberi Jamaludin2 , Ahmad Yusairi Bani Hashim2 , Lokman Abdullah2 , and Nur Aidawaty Rafan2 1 Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Jalan
Genting Kelang, 53300 Setapak, Kuala Lumpur, Malaysia [email protected] 2 Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
Abstract. Ball-screw drive system is one of the popular systems applied in machine tools such as milling machine due to its capability in producing high speed and accurate positioning. However, the typical linear PID controller in such system was unable to cope with the unavoidable nonlinear disturbances such as the cutting forces during the milling operation. Such cutting forces are varied with respective to different setting of the milling parameters. This paper proposes a modified nonlinear PID that is able to adjust its gains automatically to suit various ranges of tracking errors caused by the cutting forces. The parameters of the nonlinear function applied for proportional, integral, and derivative gains in the proposed controller were tuned independently, which differ to typical nonlinear PID that used a common parameters of nonlinear function for all three gains. Three controllers, namely; PID, nonlinear PID, and modified nonlinear PID were designed and numerically validated using a ball-screw drive positioning system. Cutting forces generated from milling process with the spindle speed of 1000 rpm, 1500 rpm, and 2000 rpm were injected into the system as the disturbance forces. The tracking performances of the designed controllers were compared and analyzed in terms of tracking error reduction, and cutting force rejection through Fast Fourier Transform analysis. The simulation results showed that the proposed controller outperformed the PID controller in both tracking error reduction and cutting force rejection with an average improvement of 28.05% and 45.39% respectively, and produced at least 2 folds better results than NPID controller. Keywords: Ball-screw drive · Cutting force compensation · Nonlinear PID
1 Introduction In the era of industrial revolution 4.0, the machine tools such as milling machines or computerized numerical control milling machines are the part and parcel in industries due to their capability in producing desired pattern or shape on metal-based products © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 105–115, 2022. https://doi.org/10.1007/978-981-16-8954-3_11
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with high accuracy and precision. Ball-screw drive system is one of the typical drive system applied in milling machine as it is able to provide high speed and accurate positioning that enables the good quality of production [1]. However, the presence of disturbance forces such as cutting forces during the milling operation greatly affects the performances of the machine tools. Cutting forces is one of the unavoidable disturbance forces occurred due to the collision of work piece and cutting tools, causing the vibration of machine tools and the deflection of either tools or work piece [1]. Thus, a sophisticated control algorithm is desired to improve the tracking performances of the machine tools by compensating these cutting forces. The classical proportional-integral-derivate (PID) controller is one of the widely used controllers in the machine tools. However, the linear PID controller is no longer able to cope with the nonlinear harmonics of cutting forces. The performance of PID controller is limited by its fixed gain parameters that preventing it from adapting to the ever-changing cutting forces with respect to various setting of milling parameters [2]. Many advanced controllers has been proposed, validated and implemented in the literature throughout the decades to compensate the cutting forces. The classical sliding mode controller (SMC) [3, 4] and super twisting SMC [5, 6] was applied in machine tools drive system for disturbance forces compensation due to its high robustness and disturbance rejection properties. Yet, the undesirable chattering effect limited the practical application of SMC and higher order SMC [7]. In addition, application of repetitive controller [8] and H-infinity controller [9] in compensating cutting forces of end milling also could be found in literature. However, the complexity in the design process limited their practical applications. Another famous method is the model-based force observer approach. An inverse-model-based disturbance observer and a state disturbance observer were designed and implemented in [10] for cutting force compensation occurred in ball-screw drive system. Although an almost total elimination of the cutting forces was observed in [10], the performances of the model-based approaches were limited by the bandwidth [10]. Alternatively, many research works were performed with the efforts of improving the existing PID controller due to simpler design process. The nonlinear PID (NPID) controller was introduced in [11] by cascading a nonlinear function to the gains of the existing PID controller. The cascaded nonlinear function in the NPID controller enable the adaptive feature as the self-adjustment of proportional, integral, and derivative gains became possible under pre-defined boundaries [11]. The performance of the NPID controller were proven to be adaptive through the works of [2, 12, 13]. Variation of NPID called NPID double hyperbolic and triple hyperbolic controllers were proposed and implemented in [14] for the cutting force compensation in milling machine. Positive results were observed compared to conventional NPID controller [14]. This paper focuses on the enhancement of NPID controller in [12, 13] and proposes a modified NPID controller in which a common nonlinear function was cascaded to respective proportional, integral, and derivative gains independently. A unique set of parameters was identified for the cascaded nonlinear function in each gains. It is differ to the original NPID controller that used a same set of parameters on the cascaded nonlinear function for all three gains or variation of NPID that used different nonlinear functions on each gains. The performances of the proposed controller were analyzed
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and compared to the PID and typical NPID controllers numerically in terms of tracking error reduction, and cutting force rejection, through spectral analysis using the Fast Fourier Transform (FFT) method. This paper is organized as follows. Section 2 presents the system modelling of the considered ball-screw drive positioning system. Section 3 presents the design of proposed controllers including the control schemes involved while the simulation results and discussion would be presented in Sect. 4. Lastly, Sect. 5 concludes the results and recommends related future research works.
2 System Modelling The considered system is the x-axis of the Googol Tech XY ball-screw driven positioning system equipped with an incremental encoder of 0.0005 mm per pulse resolution as shown in Fig. 1.
Fig. 1. The Googol Technology XY ball-screw driven positioning system [17].
The considered system dynamics was described as a single-input-single-output (SISO) model using frequency domain identification method. The H1 estimator [15] was used to approximate the SISO frequency response function (FRF) based on measured input voltage, u(t) and output position, y(t) signals with a sampling frequency of 5000 Hz. The band-limited white noise excitation signal filtered at a frequency of 15 Hz was used with a total measurement duration of 300 s. A second order model with a time delay of 0.0012 s was obtained as shown in Eq. (1) by fitting a parametric model into the measured FRF using nonlinear least square frequency domain identification method [15]: G(s) =
A Y (s) = 2 . U (s) s + Bs + C
(1)
where A = 78 020 mm/Vs2 , B = 163 V/s, and C = 193.3 V/s2 .
3 Design of Controllers Three controllers, namely; (i) PID controller, (ii) NPID controller, and (iii) Modified NPID controller were designed and simulated based on Eq. (1).
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3.1 PID Controller The classical PID controller was used as the benchmark controller and it was designed using traditional loop shaping method, compliance to universal rules of gain and phase margins as documented in [16]. The three fixed gain parameters, namely; proportional gain K p , integral gain K i , and derivative gain K d are identified as 0.5853 V/mm, 13.94 V/mm·s, and 0.003566 V·s/mm respectively using heuristic tuning method and the stability was ensured based on Nyquist diagram. 3.2 NPID Controller The NPID controller is the extension version of PID controller with a cascaded nonlinear gain function, K(e) before the gains of PID controller. Figure 2 portrays the general control scheme of a NPID controller. The r(t), y(t), and e(t) represent the reference input position, actual output position, and tracking error signals respectively. The motor constant, k f = 2500.19 N/V was applied based on previous work [13].
Fig. 2. The general control scheme of a NPID controller.
Equation (2) shows the considered nonlinear gain function [13] while Eq. (3) and Eq. (4) describe the scaled tracking error f(e), and the transfer function of NPID controller, GNPID (s) respectively: exp(ko e) + exp(−ko e) |e| ≤ emax e ; e= K(e) = , (2) emax sign (e) |e| ≥ emax 2 f (e) = K(e) × e(t),
(3)
K GNPID (s) = K(e) × Kp + i + Kd s , s
(4)
where k o is a positive constant that represented the rate of variation while emax is the range of variation of error in the unit of millimeter. Based on the Fig. 2 and Eq. (2), the K(e) is acted as the gain adjustment function for all fixed gain parameters of GPID (s), bounded in the range of 0 ≤ K(e) ≤ K(emax ). Figure 3 shows the Popov plot of the designed NPID controller using the methodology recorded in [14, 15]. The K(emax ) was calculated as 4.7461 based on the point (−Re, j0) in Fig. 3 using Eq. (5), complied to the
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Fig. 3. Popov plot of the designed NPID controller.
Popov stability criterion [11–13]. The parameters k o and emax in Eq. (3) were selected as 28 and 0.05 mm respectively, fulfilled the bounded range of 0 ≤ K(e) ≤ 4.7461. K(emax ) = −
1 , |GRe (jw)|
(5)
where GRe (jw) represents the real components of the open-loop transfer function of the considered system. 3.3 Modified NPID Controller The modified NPID controller was designed based on the same design methodology of the NPID controller presented in Sect. 3.2. Figure 4 presents the control scheme of the proposed modified NPID controller. The uniqueness of the proposed modified NPID controller is on the design of K(e) in which the k o and emax of the respective proportional, integral, and derivative gains were selected independently. A Popov plot was plotted for each fixed gain of the designed modified NPID controller. Table 1 tabulates the calculated K(emax ), Popov point (−Re, j0), selected k o , emax , and bounded range for the respective proportional, integral, and derivative gains. Although the modified NPID controller used the same nonlinear function (Eq. (2)), it produced unique scaled errors: f P (e), f I (e), and f D (e) for respective gains independently, which was differ to the NPID controller that produced only a single f(e) for all gains. This provided a more flexible range in forming the boundaries for respective gains and thus further enhanced the adaptive ability of the controller.
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Fig. 4. The control scheme of the proposed modified NPID controller.
Table 1. The calculated K(emax ) based on respective (−Re, j0), selected k o , emax , and bounded range for respective proportional, integral, and derivative gains based on Eq. (5). Gains
(−Re, j0)
K(emax )
Proportional
(−0.3260, 0)
3.0675
Integral
(−4.1e−6, 0)
2.439 × 105
Derivative
(−0.1949, 0)
5.1308
ko
emax
Bounded range
35
0.05
0 ≤ K(eP ) ≤ 3.0675
110
0.05
0 ≤ K(eI ) ≤ 2.439 × 105
45
0.05
0 ≤ K(eD ) ≤ 5.1308
4 Results and Discussion The control performances of all designed controllers were simulated based on the control schemes shown in Fig. 2 and Fig. 4, and compared in terms of tracking error reduction, and cutting force compensation ability. A sinusoidal waveform with an amplitude of 10 mm and frequency of 0.4 Hz was used as the reference input position r(t). The cutting force generated from down milling with a fixed 1 mm depth of cut and three different spindle speeds: (i) 1000 rpm, (ii) 1500 rpm, and (iii) 2000 rpm were used as the disturbance forces d(t) [17]. 4.1 Tracking Error Reduction The tracking error reduction was analyzed using the root mean square tracking error (RMSE) based on the collected e(t) through the simulation. Table 2 tabulates the RMSE of respective designed controllers with the presence of cutting forces generated from different spindle speed as disturbance forces. According to Table 2, both NPID and the modified NPID controllers performed better than classical PID controller in terms of RMSE reduction regardless of the presence of cutting forces. The NPID controller produced slightly lower RMSE with an average improvement of 4.07% while a significant improvement of 28.05% has been observed in the modified NPID controller, compared to PID controller. The modified NPID outperformed NPID at least 5 folds, showed that it able to adapt well with various cutting forces compared to NPID controller. This also proved that the independent design of K(e) in the modified NPID was more effective compared to the typical design that using common K(e) for all gains. It was also observed that the RMSE was larger with the
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Table 2. Respective RMSE for all designed controllers with the presence of cutting force generated from different spindle speed. Spindle speed (rpm)
PID
NPID
Modified NPID
RMSE (x10–3 mm)
RMSE (x10–3 mm)
Improvement (%)
RMSE (x10–3 mm)
Improvement (%)
None
7.366
7.124
2.89
5.702
22.27
1000
9.112
8.837
3.02
5.552
28.09
1500
8.511
8.101
4.82
5.969
29.87
2000
8.509
8.038
5.54
5.790
31.95
presence of greater cutting forces (cutting force generated from 1000 rpm is largest, followed by 1500 rpm and 2000 rpm), agreed with the findings in [13]. 4.2 Cutting Force Rejection The spectral analysis was used to analyze the collected tracking error signals through the Fast Fourier Transform (FFT) method. Figure 5 presents one of the comparison results for the performances of all designed controllers in rejecting cutting force generated from 1000 rpm. The peak amplitude of tracking error at different harmonics was caused by the presence of cutting forces. Thus, the greater reduction of peak amplitudes would indicate the better cutting force rejection ability. Table 3, Table 4 and Table 5 summarize the overall results of spectral analysis on tracking errors for all the designed controllers with the presence of cutting forces generated from 1000 rpm, 1500 rpm and 2000 rpm. Table 3. Amplitude of tracking errors for all designed controllers on respective harmonic frequencies of cutting force generated from 1000 rpm. Frequency (Hz)
PID
NPID
Amplitude (x10–3 mm)
Amplitude (x10–3 mm)
Modified NPID
16
6.089
5.586
32
2.931
2.569
12.35
0.683
76.71
48
1.544
1.819
−17.82
0.405
73.78
64
2.257
2.444
−8.25
1.707
24.36
80
0.573
0.582
−1.59
0.379
33.85
Improvement (%) 8.26
Amplitude (x10–3 mm)
Improvement (%)
4.977
18.26
Based on the results of Fig. 5 and Table 3, it was observed that both NPID and modified NPID controllers were able to reduce the peak amplitude of tracking errors at specific harmonic frequencies (dotted circle in Fig. 5). These indicated that both controllers were effective in compensating the cutting forces. The NPID controller able
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Table 4. Amplitude of tracking errors for all designed controllers on respective harmonic frequencies of cutting force generated from 1500 rpm. Frequency (Hz)
PID
NPID
Amplitude (x10–3 mm)
Amplitude (x10–3 mm)
24
4.262
3.944
Modified NPID Improvement (%) 7.44
Amplitude (x10–3 mm)
Improvement (%)
3.007
29.43
48
1.041
1.116
−7.21
0.269
74.17
95.6
0.680
0.693
−1.91
0.659
3.16
Table 5. Amplitude of tracking errors for all designed controllers on respective harmonic frequencies of cutting force generated from 2000 rpm. Frequency (Hz)
PID
NPID
Amplitude (x10–3 mm)
Amplitude (x10–3 mm)
Modified NPID Improvement (%)
Amplitude (x10–3 mm)
Improvement (%)
30
5.026
4.623
8.01
2.373
52.79
59.6
0.735
0.812
−10.44
0.212
71.13
89.6
0.135
0.145
−7.65
0.132
1.93
to reduce the amplitude at harmonic frequencies of 16 Hz and 32 Hz with maximum improvement of 12.35%. However, waterbed effect has been observed for the peak amplitudes started from harmonic frequency of 48 Hz where the chattering from lower frequencies has been propagated to higher frequencies, worsen the chattering at high frequencies. On the other hand, the modified NPID controller able to compensate the cutting force up to the harmonic frequencies of 60 Hz with an average improvement of 45.39%, which is about four folds of the NPID controller. Furthermore, no waterbed effect was observed for the range of considered harmonics (up to 100 Hz). These results indicated that the performance of the modified NPID was pronounced in cutting force compensation compared to NPID controller. This again proved that the independent design of K(e) was desired. Results from Table 4 and Table 5 also suggested the similar conclusion as Table 3. The performance of NPID controller was limited by the waterbed effect, aligned with the findings in [2, 13]. In contrast, the modified NPID still managed to achieve an average improvement of 35.59% and 41.95% for the considered harmonic frequencies produced by cutting forces at spindle speed of 1500 rpm and 2000 rpm.
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Fig. 5. The spectral analysis on tracking errors for (a) PID controller, (b) NPID controller, and (c) modified NPID controller, with the presence of cutting force generated from 1000 rpm.
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5 Conclusion and Future Recommendations In conclusion, this paper proposes a modified NPID controller by introducing independent bounded range for respective proportional, integral, and derivative gains. The unique design allows greater flexibility that further enhance the adaptive ability of the original NPID controller. The proposed controller achieved an average improvement of 28.05% in RMSE reduction, compared to PID controller. For the considered harmonic frequencies of cutting force generated from 1000 rpm, an average improvement of 45.39% was achieved, four folds of the improvement by the original NPID controller. Similar achievement were attained for the considered harmonics in 1500 rpm and 2000 rpm. These results proved the effectiveness of the proposed controller in adapting to the changing cutting forces due to different spindle speed. For future recommendations, the proposed controller could be validated on real system and further analyzed using more sets of cutting forces. Acknowledgement. The authors would like to acknowledge the Centre for Autonomous Systems and Robotics Research (CASRR), Faculty of Engineering and Technology, Tunku Abdul Rahman University College, for the financial and facilities support. The authors also would like to thank Universiti Teknikal Malaysia Melaka for the equipment provided.
References 1. Altintas, Y.: Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design, 2nd edn. Cambridge University Press, New York (2012) 2. Abdullah, L., et al.: Evaluation of tracking performance of NPID triple hyperbolic and NPID double hyperbolic controller based on fast fourier transform (FFT) for machine tools. J. Adv. Manuf. Technol. 12(1), 25–38 (2018) 3. Altintas, Y., Erkorkmaz, K., Zhu, Z.-H.: Sliding mode controller design for high-speed feed drives. CIRP Ann. 49(1), 265–270 (2000) 4. Rubio, L., Ibeas, A., Kollar, L.E.: On the sliding mode control for precision machining. Eng. Inform. Solutions 1(2), 32–41 (2020) 5. Rubio, L., Ibeas, A., Luo, X.: P-PI and super twisting sliding mode control schemes comparison for high-precision CNC machining. In: 24th Iranian Conference on Electrical Engineering (ICEE), Shiraz, pp. 1825–1830. IEEE (2016) 6. Heng, C.T., Jamaludin, Z., Hashim, A.Y.B., Abdullah, L., Rafan, N.A.: Design of super twisting algorithm for chattering suppression in machine tools. Int. J. Control Autom. Syst. 15(3), 1259–1266 (2017). https://doi.org/10.1007/s12555-016-0106-7 7. Utkin, V.: Discussion aspects of high-order sliding mode control. IEEE Trans. Autom. Control 61(3), 829–833 (2016) 8. Steinbuch, M.: Repetitive control for systems with uncertain period-time. Automatica 38(12), 2103–2109 (2002) 9. Choi, C., Tsao, T.-C.: Control of linear motor machine tool feed drives for end milling: robust MIMO approach. Mechatronics 15(10), 1207–1224 (2005) 10. Maharof, M., et al.: Suppression of cutting forces using combined inverse model based disturbance observer and disturbance force observer. J. Adv. Manufact. Technol. 12(1), 73–86 (2018)
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11. Su, Y.X., Sun, D., Duan, B.Y.: Design of an enhanced nonlinear PID controller. Mechatronics 15(8), 1005–1024 (2005) 12. Rahmat, M.F., Salim, S.N.S., Sunar, N.H., Faudzi, A.A.M., Hilmi, Z., Huda, I.K.: Identification and non-linear control strategy for industrial pneumatic actuator. Int. J. Phys. Sci. 7(17), 2565–2579 (2012) 13. Anang, N.A., et al.: Tracking performance of NPID controller for cutting force disturbance of ball screw drive. J. Mech. Eng. Sci. 11(4), 3227–3239 (2017) 14. Junoh, S.C.K., Salim, S.N.S., Abdullah, L., Anang, N.A., Chiew, T.H., Retas, Z.: Nonlinear PID triple hyperbolic controller design for XY table ball-screw drive system. Int. J. Mech. Mechatron. Eng. 17(3), 1–10 (2017) 15. Pintelon, R., Schoukens, J.: System Identification – A Frequency Domain Approach, 2nd edn. Wiley, New York (2012) 16. Skogestad, S., Postlethwaite, I.: Multivariable Feedback Control Analysis and Design, 2nd edn. Wiley, Chichester (2005) 17. Chiew, T.H., Jamaludin, Z., Hashim, A.Y.B., Leo, K.J., Abdullah, L., Rafan, N.A.: Analysis of tracking performance in machine tools for disturbance forces compensation using sliding mode control and PID controller. Int. J. Mech. Mechatron. Eng. 12(6), 34–40 (2012)
Enhance Ride Comfort and Road Handling on Active Suspension System by PSO-Based State-Feedback Controller Andika Aji Wijaya1,2(B) , Fitri Yakub1(B) , Mohd Nazmin Maslan3 and Muhammad Zakiyullah Romdlony4
,
1 Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia,
Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia [email protected], [email protected] 2 Department of Mechanical Engineering, University of Business and Technology, Dahban, Jeddah 21361, Saudi Arabia 3 Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia, Hang Tuah Jaya, Durian Tunggal, 76100 Melaka, Malaysia 4 School of Electrical Engineering, Telkom University, Jalan Telekomunikasi, 40257 Bandung, Jawa Barat, Indonesia
Abstract. Determine the state feedback controller gain in the LQR controller is quite challenging as it requires multiple attempts of trial and error process. To eliminate the trial and error method when selecting the optimal controller parameter, we propose a PSO-based state feedback controller for the active suspension system. It is an intelligent-based method to determine state feedback gain controller by employing optimization technique using Particle Swarm Optimization (PSO). Unlike optimization-based LQR controller which seek the optimum Q and R matrices and then calculate the LQR feedback gain, in this study, the PSO algorithm is used to determine feedback gain controller parameters directly. In addition to the simple and straightforward controller design approach, the proposed controller is designed to obtain the optimum state feedback gain for improving both ride comfort and road handling aspects simultaneously by employing a multi-objective optimization technique. The proposed controller is applied on a quarter-car active suspension model. The controller performance is evaluated using Performance Index value based on the response of the suspension system under different road excitation, i.e. bump road profile and sinusoidal road profile at the frequency range from 1 to 10 Hz. The simulation results showed that the proposed controller improves both ride comfort and road handling successfully. Keywords: Active suspension · Vibration control · Particle swarm optimization · Full-state feedback controller
1 Introduction A Suspension system is an integral part of a vehicle. It is responsible for providing comfortability of the passenger as well as maintain the stability of a vehicle as it transmits © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 116–126, 2022. https://doi.org/10.1007/978-981-16-8954-3_12
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all disturbances caused by engine vibration, road irregularity, braking, turning, and any external source to the car body. When designing the suspension system, it should consider two main aspects: ride comfort of the passengers and road handling performance of a vehicle. Unfortunately, these two property is conflicting with each other. Savaresi et al. [1] show that having a soft suspension system generally will resulting good ride comfort but on the other hand, decrease the road handling ability and vice versa. Therefore, there will always be a compromise between ride comfort and handling when choosing the spring and damper in the passive suspension system. Els et al. [2] investigate the design criteria to determine the suspension requirement for both ride comfort and road handling. He proposed a three-dimensional graph based on his experimental results to choose a compromise solution. A classical passive suspension system has been used for decades. Due to its design simplicity and low price, most of the automotive industry is still prefers the passive suspension system in their cars. To overcome the limitation of passive suspension, many researchers proposed a next-generation suspension system called the smart suspension system. A smart suspension system is considered a controlled suspension system as it required additional components consist of sensors, controllers, and actuators apart from the existing spring and damper as in a passive suspension system. The smart suspension system can be divided into two categories namely semi-active suspension and active suspension. To improve both road comfort and ride handling over a wider range of operating conditions, many researchers are working on the active suspension system. One of the unique features is that an active suspension system utilizes a controllable force actuator to reject the disturbances. By having an active actuator, an active suspension system is considered the most effective way to improve ride comfort and road handling [3]. It will be the future trend of suspension design [4] and therefore, this research area has remained attractive for many years [5]. One of the challenges in active suspension systems is to design the control strategy because it has a significant impact on the trade-off between passenger comfort and road handling. Since early research development, various control strategies have been proposed in the literature. To mention some of them, Bharali [6] perform comparative analysis using PID, LQR, and Fuzzy logic controller on quarter-car model suspension system. The fact that the suspension system is inherently nonlinear, many nonlinear control methods have been investigated. These studies include adaptive control, sliding mode control, feedback linearization, etc. Some of them can be found in [7–11]. Few other researchers also work on improving the PID and LQR controller performance using optimization algorithms. Zhao et al. [12] used Particle Swarm Optimization (PSO) to optimize the PID parameters. In his result, PSO optimized PID controller can effectively improve the ride comfort and road handling based on random road profile with grade B and C. The hybrid fuzzy PID controller was investigated by Chao et al. [13]. In his study, Gravitational Search Algorithm (GSA) was used to adjust the optimum PID gains. They claimed the advantage of GSA having a quick convergence rate although it required intensive computational effort. While Nagarkar et al. showed that the optimal PID and LQR gain controller can be tuned using Genetic Algorithm (GA). In the LQR controller, he applied GA to search the optimum values of Q and R matrices that will minimize the cost function. On the other hand, Pedro et al. [14] also utilized the PSO
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algorithm when designing Model Predictive Control (MPC) for a half-car suspension system. This article focuses on the intelligent-based method for optimizing full-state feedback control gain to eliminate the trial and error method when selecting the optimal controller parameter as in conventional methods. Unlike optimization-based LQR controller which seek the optimum Q and R matrices and then calculate the LQR feedback gain, in this study, an optimization algorithm i.e. Particle Swarm Optimization is used to determine feedback gain controller parameters directly without requiring to determine Q and R parameters. In addition to the simple and straightforward controller design approach, the controller is designed to obtain the optimum state feedback gain for improving both ride comfort and road handling aspects simultaneously by employing a multi-objective optimization technique. This is done by assigning a weighting factor that represents the importance of each objective.
2 System Description 2.1 Quarter-Car Suspension Model The active suspension system model can be simplified using the quarter-car suspension model as shown in Fig. 1 below. The parameters m, k, and b indicate the mass, stiffness, and damping coefficient of the model, respectively. The subscript s, u, r, and a denote the sprung objects, unsprung objects, road, and actuator respectively. The objective is to suppress the vibration of the vehicle due to road disturbance, x r using controlled force, F a generated by the actuator. Suppressing the vibration means improving the ride comfort while at the same time maintain the stability of the vehicle.
Fig. 1. Quarter-car model with active suspension system
Using Newton’s law, the active suspension system can be described as the following set of second-order differential equations, shown in Eq. 1 and 2. ms x¨ s = −bs (˙xs − x˙ u ) − ks (xs − xu ) + Fa
(1)
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mu x¨ u = −bu (˙xu − x˙ r ) − ku (xu − xr ) + bs (˙xs − x˙ u ) + ks (xs − xu ) − Fa
(2)
Let define the state variables as: x1 = xs − xu ; x2 = x˙ s ; x3 = xu − xr ; x4 = x˙ u therefore, we can define the state-space model of the system as in Eq. 3: x˙ = Ax + Bu; y = Cx where
⎡
0
1
⎢ −ks −bs ⎢ A = ⎢ ms ms ⎣0 0 ks mu
bs mu
0 0 0
−1 bs ms
1
−ku −bs −bu mu mu
⎤
⎡
0 0 ⎢0 1 ⎥ ⎢ ⎥ ms ⎥, B = ⎢ ⎣ −1 0 ⎦
bu −1 mu mu
(3) ⎤
⎡
⎤ 1000 ⎥ ⎢0 1 0 0⎥ ⎥ ⎥ ⎥, C = ⎢ ⎣0 0 1 0⎦ ⎦
(4)
0001
The detailed value of each parameter can be found in Table 1. Table 1. Suspension parameter description [4] Symbol
Description
Value
Unit
ms
Sprung mass or car body mass
320
kg
mu
Unsprung mass
40
kg
ks
Sprung stiffness
18,000
N/m
ku
Unsprung stiffness
200,000
N/m
bs
Sprung damping coefficient
1,000
N.s/m
bu
Unsprung damping coefficient
10
N.s/m
2.2 Road Excitation Model To analyze the response of the suspension system, the input disturbance can be generated based on a single bump road profile that represents the shock events and a sinusoidal waveform that represents the vibration caused by the road surface. Single Bump Road Profile The single bump road profile model is expressed by the following equation:
H 1 − cos 2πL v t 0 ≤ t ≤ Lv 2 xr = 0 t > Lv
(5)
Where H, L, and v represent the amplitude of bump height, bump length, and vehicle velocity respectively. In this study, the single bump road profile model is generated by assigning H = 5 cm, L = 5 m, v = 45 km/h. Substitute all the parameters to Eq. 5, the bump road profile is then plotted in time domain and distance as shown in Fig. 2 below.
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0.05
0
0
0.5
1
Bump Road Profile in Distance
0.1
Height [m]
Height [m]
0.1
1.5
2
0.05
0
0
1
Time [sec]
2
3
4
5
Distance [m]
Fig. 2. Single bump road profile model
Sinusoidal Waveform The mathematical model of the sinusoidal waveform is given by Eq. 6 xr =
H sin(2π ft) 2
(6)
Where H denotes the amplitude of road height, and f is linear frequency. Two different frequencies sample at 4 Hz and 8 Hz are chosen. The reason is that the region of greatest human sensitivity to vertical vibration lies between 4 and 8 Hz, which roughly includes the various resonances of human internal organs [15]. In addition, the robustness of the controller is also evaluated for all frequency bands from 1–10 Hz, using chirp signal input, a signal in which the frequency increases with time.
3 Controller Design 3.1 Performance Index as a Cost Function in Active Suspension System In an active suspension system, the control objective is increasing passenger comfort while maintaining road handling. In addition, it is also important to maintain the suspension space (rattle space) below an acceptable range to prevent excessive displacement that might lead to the deterioration of ride comfort. To quantify the controller performance, it can be expressed by the following performance index, J which represent the ratio of active and passive measured variables:
Obj active RMS (7) Jn = Obj passive RMS
Where Obj refers to the entity measured, i.e. acceleration, dynamic tire load, or suspension space. Subscript n refers to assigned number for each objective. Finally, the cost function that is optimized by using the PSO algorithm can be stated in Eq. 8: J = w1 J1 + w2 J2 + w3 J3
(8)
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Where w1 , w2, and w3 is the weighting factor assigned for ride comfort, road handling, and suspension space respectively. The bigger value of the weighting factor indicates the higher degree of importance of the objective. 3.2 State Feedback Control for Vibration Control Linear Quadratic Regulator (LQR) is one of the popular control methods that enable good performance and stability in the closed-loop system. The feedback gain in the LQR controller is determined by solving by the Riccati equation or pole placement method. However, the designer needs to select appropriate Q and R matrices by trial and error until it satisfies the desired performance. To eliminate the hassle of selecting the optimum controller parameter by trial and error, a PSO-based optimization algorithm is used in this study to tune the feedback gain, K until it satisfies the optimum performance, i.e. minimum car body acceleration. The control architecture of the PSO-based feedback gain controller is similar to the LQR controller as shown in Fig. 3 below.
Fig. 3. Block diagram of closed-loop active suspension system during (a) tuning controller gain using PSO algorithm and (b) implementing in the plant
3.3 PSO Algorithm for Tuning State Feedback Gain Controller Particle Swarm Optimization (PSO) is an optimization algorithm that is inspired by the swarming behavior of a group of birds, fish, or bees that randomly moved in a certain area to search for foods or any living resources. It was first introduced in 1995 [16] and since then a number of modified PSO was proposed such as introducing a new parameter, called “inertia weight”, which had a significant impact to improve the performance of the original PSO algorithm [17]. In PSO, individuals known as particles represent a potential solution to a problem. By cooperation and competition among themselves, the particles will evolve through generations. Initially, the swarm of particles initializes their position and velocity randomly in search space. Then each particle calculates the current objective value or known as the cost function value for the initial population. The initial objective values and positions
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are automatically assigned as the personal best value and best position. Then, all particles are moved to their new positions and all cost function value are evaluated again. At this phase, the personal best positions are updated for particles that have a better cost function value than the previous one. The global best position is updated only if there is any particle with a cost function value that is better than the old global best value. These steps keep continuing until it satisfies the stopping criteria, i.e. reaching the number of maximum iterations. More detailed steps and to understand of the impact of each PSO parameter can be found in [18]. The PSO algorithm calculates the cost function to search for the optimum particle parameters. To accommodate multi-objective optimization, the weighting factor is assigned for each objective according to its priority. In a passenger car, the main priority of the active suspension system is to provide comfortability to the passengers. At the same time, the suspension system should also maintain vehicle stability and prevent excessive suspension displacement. For this reason, the weighting factor for ride comfort, w1 in Eq. 8, should be the biggest among other weighting factors of w2 and w3 . By understanding the degree of importance between the objectives, the weighting factor is selected as w1 = 0.7, w2 = 0.2, and w3 = 0.1. Before running the algorithm, some of the tuning parameters need to be set. Table 2 shows all the values for each parameter. After that, the PSO algorithm can be started. The evolution of the cost function for each iteration is presented in Fig. 4. At the end of the iteration process, the global best particles, which represent the optimum state feedback gain, K is extracted. MATLAB software linked to Simulink model of a quarter-car model is used to perform the PSO optimization. Finally, the PSO-based optimum feedback gain is found with K = [0.001, 5000, −5000, 935.8], and the minimum Performance Index of J = 0.33404. Table 2. PSO tuning parameters Symbol
Description
Value
NoP
Particles size
50
maxIter
Maximum iteration number
20
wMax
Max inertia weighting factor
0.9
wMin
Min inertia weighting factor
0.2
c1, c2
Learning factors
1.49
vMax
Max velocity of the particle
1000
vMin
Max velocity of the particle
−1000
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Fig. 4. Evolution of performance index for each iteration
4 Simulation Results and Discussion 4.1 Single Bump Road Profile Response The closed-loop response under bump road profile is shown in Fig. 5. It shows that the controller not only enhances the ride comfort by decreasing the magnitude of acceleration but also improving the road handling and maintains suspension space. The performance
Fig. 5. Single bump road profile response
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index is given by J 1 = 0.2829; J 2 = 0.3643, and J 3 = 0.7059. Note that, the lower value is better and J n < 1 means better performance than passive response. 4.2 Sinusoidal Waveform Response
5
Acceleration [m/s 2 ]
Acceleration [m/s 2 ]
From Fig. 6, it is clearly shown that the controller decreases the magnitude acceleration in both 4 and 8 Hz. However, almost no or small reduction on both tire load and suspension space. This is acceptable as the aim of the controller is mainly to improve passenger comfort. The performance index at frequency of f = 4 Hz is given by J 1 = 0.4803; J 2 = 0.9417, and J 3 = 1.0077 while at higher frequency f = 8 Hz is given by J 1 = 0.4006; J 2 = 1.039, and J 3 = 1.1153.
Uncontrolled Controlled
0
-5 0
0.5
1
1.5
20 Uncontrolled Controlled
10 0 -10 -20
2
0
0.5
Time [sec]
Tire Load [N]
Uncontrolled Controlled
Tire Load [N]
1.5
2
104
2
5000
0
-5000
Uncontrolled Controlled
1 0 -1 -2
0
0.5
1
1.5
2
0
0.5
0.1 Uncontrolled Controlled
0.05 0 -0.05 -0.1 0
0.5
1
Time [sec]
(a)
1
1.5
2
Time [sec]
1.5
2
Suspension Space [m]
Time [sec] Suspension Space [m]
1
Time [sec]
0.1
Uncontrolled Controlled
0.05 0 -0.05 -0.1 0
0.5
1
1.5
2
Time [sec]
(b)
Fig. 6. Sinusoidal Response for (a) f = 4 Hz and (b) f = 8 Hz
4.3 Chirp Response To get a better view of road comfort performance in a wider band of frequency, the controller performance is evaluated using a chirp signal. The result is then plotted in the frequency domain as in Fig. 7 below. It is clearly shown that the controller can attenuate the magnitude of car bode acceleration in all frequencies.
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Fig. 7. PSD vertical acceleration
5 Conclusions In this article, the PSO-based full-state feedback controller for enhancing ride comfort and road handling in an active suspension system has been designed and evaluated. In comparison to the LQR controller that requires the designer to select appropriate controller parameters of Q and R by trial and error, our approach is to optimize state feedback gain controller directly, by applying the PSO algorithm which reflecting the advantage of this approach over the classical method. Based on the simulation results on a quarter-car suspension model, the proposed controller able to meet the control objective under different road excitation. Under single bump road profile, all of the performance index values that represent the ride comfort (J 1 = 0.2829), road handling (J 2 = 0.3643), and suspension space (J 3 = 0.7059) are less than 1 which indicate better performance over passive suspension system. In addition, a sinusoidal road profile with different frequencies is also used to evaluate the robustness of the controller. It showed that the proposed controller is able to enhance the ride comfort, which is the most important feature for passenger cars, at the frequency range from 1 to 10 Hz, while less significant improvement in road handling and suspension space. Acknowledgements. This research is supported by Autonomous vehicle for traffic flow at signalized and roundabout scenario project scheme (Project number: Q.K130000.3643.02M92).
References 1. Savaresi, S.M., Poussot-Vassal, C., Spelta, C., Sename, O., Dugard, L.: Semi-Active Suspension Control Design for Vehicles. Elsevier, Amsterdam (2011) 2. Els, P.S., Theron, N.J., Uys, P.E., Thoresson, M.J.: The ride comfort vs. handling compromise for off-road vehicles. J. Terramechanics 44, 303–317 (2007) 3. Advanced Seat Suspension Control System Design for Heavy Duty Vehicles. Elsevier (2020) 4. Sun, W., Gao, H., Shi, P.: Advanced Control for Vehicle Active Suspension Systems. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-15785-2
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5. Tseng, H.E., Hrovat, D.: State of the art survey: active and semi-active suspension control. Veh. Syst. Dyn. 53, 1034–1062 (2015) 6. Bharali, J., Buragohain, M.: A comparative analysis of PID, LQR and Fuzzy logic controller for active suspension system using 3 degree of freedom quarter car model. In: 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pp. 1–5 (2016) 7. Huang, Y., Na, J., Wu, X., Liu, X., Guo, Y.: Adaptive control of nonlinear uncertain active suspension systems with prescribed performance. ISA Trans. 54, 145–155 (2015) 8. Sun, W., Zhao, Z., Gao, H.: Saturated adaptive robust control for active suspension systems. IEEE Trans. Industr. Electron. 60, 3889–3896 (2013) 9. Kim, C., Ro, P.I.: A sliding mode controller for vehicle active suspension systems with non-linearities. Proc. Instit. Mech. Eng. Part D: J. Automob. Eng. 212, 79–92 (1998) 10. Mustafa, G.I.Y., Wang, H., Tian, Y.: Optimized fast terminal sliding mode control for a halfcar active suspension systems. Int. J. Automot. Technol. 21(4), 805–812 (2020). https://doi. org/10.1007/s12239-020-0078-8 11. Alleyne, A., Hedrick, J.K.: Nonlinear adaptive control of active suspensions. IEEE Trans. Control Syst. Technol. 3, 94–101 (1995) 12. Zhao, L., Zeng, Z., Wang, Z., Ji, C.: PID control of vehicle active suspension based on particle swarm optimization. J. Phys.: Conf. Ser. 1748, 032028 (2021) 13. Chao, C.-T., Liu, M.-T., Chiou, J.-S., Huang, Y.-J., Wang, C.-J.: A GSA-based adaptive fuzzy PID-controller for an active suspension system. Eng. Comput. 33, 1659–1667 (2016) 14. Pedro, J.O., Nhlapo, S.M.S., Mpanza, L.J.: Model predictive control of half-car active suspension systems using particle swarm optimisation. IFAC-PapersOnLine 53, 14438–14443 (2020) 15. Mastinu, G., Ploechl, M. (eds.): Road and Off-Road Vehicle System Dynamics Handbook. CRC Press, Boca Raton (2014) 16. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995) 17. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360), pp. 69–73 (1998) 18. Wang, D., Tan, D., Liu, L.: Particle swarm optimization algorithm: an overview. Soft. Comput. 22(2), 387–408 (2017). https://doi.org/10.1007/s00500-016-2474-6
The Development of Underwater Crawler Attachment for Remotely Operated Underwater Vehicle Nurul Ain Hassan1 and Ahmad Anas Yusof1,2(B) 1 Department of Mechatronics, Faculty of Electrical Engineering, Universiti Teknikal Malaysia
Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Malaysia [email protected] 2 Robotics and Industrial Automation Research Group, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Malaysia
Abstract. This project develops and evaluates the functionality of a modified payload skid that can be used as a remotely controlled underwater crawler. A crawler moves on the seafloor and can be remotely controlled by a human operator on the surface. An advanced mobility function must be developed in order to expand research and development on the underwater seafloor excursion for surveying, sampling, or working on irregular terrain. The goal of this project is to create a continuous track attachment for a remotely controlled underwater vehicle that would be used for seafloor crawling. To crawl on the seafloor, the vehicle must be set to completely negative buoyancy. SolidWorks is used to create a 3D model of the crawler. The crawler is later created by combining the robot’s gear wheel, rod, and side frame. An Arduino UNO and an H-bridge L298N are also used, and the project is tested with a Microzone MC6C remote controller. The preliminary ground field test demonstrates the crawler’s ability to crawl on a variety of surfaces, including pavement, road, and plain field, and move around an obstacle of 8 cm in height. A maximum velocity of 0.2 m/s on cement surface and minimum velocity of 0.17 m/s on the grass surface have been recorded in the test. Keywords: Underwater crawler · Remotely operated underwater vehicle
1 Introduction Remotely operated underwater crawlers are specialized vehicles that maintain constant contact with the seafloor, allowing for underwater intervention. The crawler provides a very stable platform for moving objects or taking measurements. Crawlers are also ideal for long-term projects. Crawlers are well-known platforms that can be used for a wide range of applications. Planetary rovers, for example, have demonstrated their worth on missions to the moon and Mars. A remotely operated underwater crawler has been created at Universiti Teknikal Malaysia Melaka for undersea and scientific activities in coastal parts of the ocean [1–3]. For corrosion resistance, the vehicle is © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 127–134, 2022. https://doi.org/10.1007/978-981-16-8954-3_13
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composed of stainless steel and measures 10 cm high, 30 cm broad, and 45 cm long. The elements that restrict the mobility of an underwater vehicle have been discovered to include drag and turbulence. The unsteady fluid flow could cause the crawler to wheelie, lowering its velocity and making it uncontrollable. Underwater crawler is also used for inspection and intervention. A prototype underwater crawler for waterworks has been tested for kinematics and hydrodynamics in Poland, and has been utilized to identify surface cracks [4]. This project also includes the development of image processing and analysis algorithms to determine the robot’s position and attitude in relation to the investigated surface and detect wall surface faults. In Greece, a subsea robotic crawler used in the oil and gas industry is tasked with deploying radiographic inspection equipment. The robot uses the riser to reach every point on its surface [5]. The principle applies not only to flexible risers, but to almost any cylindrical shaped structure that the end effectors can grip, under or above water. The novel design of the end-effectors allows the robot to secure itself while crawling along and around the riser while causing no damage to the external riser surface. Meanwhile, in United State of America, a third generation of modifications to an undersea recovery crawler/vehicle have been developed at the Florida Institute of Technology [6]. Known as the RGIII, it is a high-powered remotely-operated vehicle with the ability to take on a wide range of underwater archeological projects. The vehicle’s underwater capabilities include the recovery of lost valuables with sensitive structures, visually examining underwater scenarios, high maneuverability at depth, and the ability to translate its position while neutrally buoyant in the mid-level depths. In Japan, the response to the accident at Fukushima Daiichi Nuclear Power Plant following the Great East Japan Earthquake has resulted to the development of the Tri-diver underwater vehicle fitted with crawlers, four thrusters for vertical movement, and two for lateral movement [7]. The ROV’s sensors include ultrasound sonar as well as its underwater camera, and it is designed to detect currents, something that is difficult to achieve by optical means. In an underwater experiment, the Tri-diver successfully identified the shape of holes from a distance of 3 m, using the onboard equipment. Thus, this paper presents a development of a modified payload skid that can be used as a remotely underwater crawler, which basically acts as a hybrid continuous track on the available remotely operated underwater vehicle (Fig. 1).
Fig. 1. A remotely operated underwater vehicle with a payload skid (left) and the detached payload skid (right)
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2 Literature Review Continuous tracks, tires and omni-wheels are the typical driving mechanism for a crawler. Continuous track is a system that runs on a continuous band of treads or track plates driven by two or more wheels. While the large surface area of the tracks distributes the weight of the vehicle and reduce the chances of becoming stuck in soft surfaces, the omniwheels on the other hand allow a crawler to quickly slide laterally, thus multiplying the possibilities of movement. Nevertheless, in this project, continuous track is chosen due to the fact that because it can move on slippery and uneven underwater surfaces that have less ground pressure. The continuous track can also support heavy load, when emerging from underwater. Table 1 shows some of advantages and disadvantages between the continuous track, tire, and Omni-wheel [8, 9]. Table 1. Comparison between continuous track, tires and omni-wheel
Type of wheel crawler Continuous track
Advantages
Disadvantages
• Have a great ability to • Complicated system climb. • Turning ability is slow • Have good ability in turning at tight place
Tire
• Have good efficiency in • Poor ability in turning at tight place. speed performances • High power and speed to manoeuvre
Omni-wheel
• Have ability in turning • Less power and speed in all direction • High cost implementation
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3 Methodology The complete design of the crawler can be seen in Fig. 2. The main component of the system includes the payload skid, sprockets, chains, Arduino Uno microcontroller, HBridge, brushed motor and Microzone MC6C. Table 2 shows the specification of the crawler.
Fig. 2. A CAD design of a payload skid attached to a continuous track Table 2. Specification of the underwater crawler Component
Specification
Length
46 cm
Width
33.5 cm
Weight
3.565 kg
Type of wheels
Track or chain type wheels
Gear ratio
1:1 (use sprocket and chain)
Motor type
Brushed motor
Material
Stainless steel
Controller
Arduino Uno
Remote control
Microzone MC6C
Motor speed
2 km/h
Maximum load
20 kg
The crawler is divided into three sections: the input unit, the control unit, and the output unit. This system’s input is a Microzone MC6C. It must adhere to the instructions given to it in accordance with its needs. The process unit consists of an Arduino Uno and an H-Bridge. The Arduino Uno is used to connect the receiver to the Microzone MC6C. The H-Bridge is used in the output unit to control the direction of the brushed motor. Figure 3 depicts the process’s block diagram, while Fig. 4 shows the ground field test of the crawler.
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Fig. 3. The input, process and output units of the crawler
Fig. 4. The ground field test of the crawler
4 Result and Discussion The crawler’s test consists of forward and reverse movement tests on various surfaces. The crawler is also tested for its ability to overcome various obstacles.
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4.1 Movement Test This experiment is carried out over a distance of 1.0 m on various surfaces. Tables 3 and 4 show the time required for forward and reverse movement, respectively. Table 3. The result for forward movement Surfaces Test 1
2
3
Mean time Velocity taken (s) (m/s)
Cement
5.05 5.02 5.10 5.06
0.20
Sand
5.10 5.15 6.20 5.18
0.19
Rocky
5.20 5.50 5.32 5.34
0.19
Dirt
5.18 5.52 5.70 5.47
0.18
Grass
6.01 5.58 5.74 5.78
0.17
Table 4. The result for reverse movement Surfaces Test 1
2
3
Mean time Velocity taken (s) (m/s)
Cement
5.05 5.02 5.10 5.06
0.20
Sand
5.10 5.15 6.20 5.18
0.19
Rocky
5.20 5.50 5.32 5.34
0.19
Dirt
5.18 5.52 5.70 5.47
0.18
Grass
6.01 5.58 5.74 5.78
0.17
The crawler moves slowly on the grassy track, which is followed by dirt on the second slowest surface, which is slower than other surfaces such as cement, sand, and rocky track. This indicates that the crawler’s base created more friction with the grass. The velocity of surface grass is the slowest. The grassy path also has an uneven and bumpy surface. The crawler’s fastest moving surface is cement. This is because the cement on the crawler’s base has a low coefficient of friction. When forward and reverse movement are compared, forward movement is faster than reverse movement. Despite the fact that the tests were carried out on the same terrain, there is a time difference between them. This is because other external factors, such as the battery’s power supply, are deteriorating and causing uneven velocity movement on the surface. As a result of the water resistance and terrain surfaces, if this test is performed underwater, the time required will be much longer. The test can be performed underwater if the battery and receiver are sealed in a compartment to prevent water from entering.
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4.2 Obstacle Test The purpose of this experiment is to put the crawler’s ability to crawl over any obstacle to the test. The crawler is put through its paces on the ground, as shown in Fig. 5. The crawler’s time spent climbing the obstacle for any height of wooden plank is recorded. Table 5 displays the time required for the crawler to climb an obstacle of varying heights. Table 5. The time taken for the crawler to climb obstacle at different height Test
Height (cm)
Time taken (s)
Description
1
1.0
1.83
Able to climb
2
1.6
1.94
Able to climb
3
2.0
2.03
Able to climb
4
2.6
2.09
Able to climb
5
4.0
2.17
Able to climb
6
4.6
2.26
Able to climb
7
8.0
3.51
Slightly stuck
8
9.0
–
Not able to climb
Fig. 5. The obstacle test of the crawler
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The result indicates that the crawler’s maximum ground clearance height is 8 cm. This is due to the base being stuck to the edge of the wooden plank. This problem can be solved by using a larger drive wheel or sprocket and a longer chain track.
5 Conclusion This project demonstrates the development of a continuous track for a remotely operated underwater vehicle’s payload skid attachment. The payload skid, sprockets, chains, Arduino Uno microcontroller, H-Bridge, brushed motor, and Microzone MC6C are used to construct the track. Experiments are also carried out to measure and analyze the impact of the ground surface on the crawler’s forward and reverse movement, as well as its obstacle breaching capability. The crawler moves slowly on the grassy track, followed by dirt on the second slowest surface, which is slower than other surfaces such as cement, sand, and rocky surfaces. The result also indicates that the crawler’s maximum ground clearance height is 8 cm. A maximum velocity of 0.2 m/s on cement surface and minimum velocity of 0.17 m/s on the grass surface have been recorded respectively. Acknowledgement. The authors wish to thank Universiti Teknikal Malaysia Melaka for the support in the research.
References 1. Aras, M.S.M., Zainal, I., Abdullah, S.S., Kassim, A.M., Jaafar, H.I.: Development of an unmanned underwater remotely operated crawler (ROC) for monitoring application. J. Mech. Eng. Technol. 7(2), 41–55 (2015) 2. Aras, M.S.M., Kamarudin, M.N., Rusli, M.B.C., Zainal, M.M.: Small scale unmanned underwater remotely operated crawler (ROC). Indonesian J. Electr. Eng. Comput. Sci. 3(3), 481–488 (2016). https://doi.org/10.11591/ijeecs.v3.i2.pp481-488 3. Zainal, I., et al.: Design process and hydrodynamic analysis of underwater remotely operated crawler. J. Telecommun. Electron. Comput. Eng. 8(7), 35–39 (2016) 4. Kohut, P., Giergiel, M., Cieslak, P., Ciszewski, M., Buratowski, T.: Underwater robotic system for reservoir maintenance. J. Vibroeng. 18(6), 1392–8716 (2016) 5. Chatzakos, P., Papadmitriou, V., Psarros, D., Nicholson, I., Gan, T.-H.: On the development of an unmanned underwater robotic crawler for operation on subsea flexible risers. In: IEEE Conference on Robotics, Automation and Mechatronics (2010) 6. Wood, S., et al.: Hybrid robot crawler/flyer for use in underwater archaeology. In: OCEANS 2013 MTS/IEEE Conference, pp. 1–11 (2013) 7. Okada, S., Hirano, K., Kobayashi, R., Kometani, Y.: Development and application of robotics for decommissioning of Fukushima Daiichi nuclear power plant. Hitachi Rev. 69(4), 556–557 (2020) 8. SeaBotix Remotely Operated Vehicles. Little Benthic Vehicles. Teledyne SeaBotix Product Overview Brochure. Rev 3 040116 (2016) 9. Nexus Omni Wheel Mobile Robot with Arduino. https://www.roboticgizmos.com/nexus-omniwheel-mobile-robot/. Accessed 1 Aug 2021
Analysis and Development of a Self-dimming Traffic Light System Sugan Jessan Sreedran(B) Nexperia Malaysia Sdn. Bhd., No. 1, Taman Tuanku Jaafar, Negeri Sembilan, 71450 Seremban, Malaysia Abstract. Low power consumption in maintaining public infrastructure is essential in saving taxpayers’ money. In this project, a self-dimming system is developed to reduce the power consumption in the conventional traffic light. This model was designed using the voltage divider concept with Light Dependent Resistor (LDR). The power output analysed for each display board’s current data is updated into the phpMyAdmin server by using the IoT controller and the data is monitored 24/7. When the current value is low compared to the usual value the microcontroller will send a signal to the blinker circuit and the indicator LED will blink as the indication of low current in the circuit. Next, when the humidity is high to a certain level, it is considered that there is rain and a relay module will trigger its output from NC to NO [1]. This is done to ensure the dimming circuit won’t work during the time of high humidity. The energy consumption is reduced by 30% [2–4]. Keywords: Dimming · Light dependent resistor · Power · Voltage · Current · Normally open · Normally closed
1 Introduction to Traffic Light System A traffic light, also known as a traffic signal or stoplight, is a signalling device that uses a global colour code to indicate when it is safe to drive, ride, or walk at a road intersection, pedestrian crossing, or other sites [5, 6]. Power consumption becomes a huge deal because the traffic light is operated at 100% brightness, whether it is on day or night. JKR maintenance team must monitor and fix the breakdown of a traffic light when there is a power failure. Currently, the breakdown information is sent manually which is not efficient and result in a delay in the repair work [7]. Therefore, this project will use the LDR sensor for a self-dimming traffic light model to save power and the current sensor for fault detection and will be conveyed through an IoT platform for real-time monitoring. There is also humidity and temperature detection to bypass the dimming system during rainy days [8].
2 Literature Review 2.1 Past Works/Previous Research The Concept of Dimming System Dimming is the process of lowering the brightness of the light to conserve energy, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 135–146, 2022. https://doi.org/10.1007/978-981-16-8954-3_14
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ensuring optimum sight, and reducing blinding by road users [9]. Dimming systems, which use various techniques and technologies to lower excessive amounts of light to a minimum, are widely used and rapidly being included in lighting. Fault Detection Using GSM Technology A beta prototype of a traffic light electrical and mechanical problem detection system has been developed based on an innovative idea. When incorrect electrical faults or mechanical damages are identified, this system sends a wireless alarm to the appropriate employees so that they can be repaired as soon as possible. This idea would be the greatest quick solution for ensuring that all traffic lights are repaired as soon as possible, resulting in a significant reduction in road accidents at intersections. A detailed SMS will be provided to the appropriate personnel, including the traffic light pole ID #, street, town, and fault incidence time so that urgent corrective action can be taken [3, 7, 10, 11]. Automated Traffic Light System The road construction safety traffic light system is intended to hold the role of manual traffic control on job sites. When there are two-lane closures due to road work, this gadget can replace one or both flaggers. One of the best devices in work zone traffic flow control systems is the road construction safety traffic light system. The device is a portable traffic light unit that can be used to control traffic flow at a road construction site for long or short-term lane closures, as well as to control two-way traffic in a single lane. By implementing modern technology in traffic flow automation at road building sites, it is possible to eliminate traffic congestion [12]. The use of a traditional flagman could be eliminated if new technologies for automating traffic flow in road building sites are implemented. The traffic light system is a realistic solution to difficulties that contribute to the safety of construction workers, dangers at road building sites, as well as to comply with the road safety regulations that are assumed by the service providers. The automated portable traffic light system can save money in the long run by reducing manpower costs. By eliminating the need for a human flagman, road users and construction workers will be safer [13]. 2.2 Fundamental Electronics Theory Voltage Divider Application The voltage divider is also known as a potential divider is a simple passive linear circuit that produces an output voltage that is a fraction of its input voltage. The result of disturbing the input voltage among the components of the divider is voltage division. The components of the divider could be only resistors or only capacitors [6, 14]. A voltage supply is connected to a series of resistors, and the output voltage is taken from the connection between the two resistors. The Darlington Pair As you have seen, Bac is the main consideration in deciding the input resistance of an amplifier. The Bac of as far as possible the greatest feasible input resistance you can get from a given emitter-follower circuit (Fig. 1).
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Fig. 1. Darlington pair circuit [14]
A Darlington pair multiplies Bac , thus increasing the input resistance. Application of Current Sensor A Current Sensor is an important device in power calculation and management applications. It measures the current through a device or a circuit and generates an appropriate signal that is proportional to the current measured. Usually, the output signal is an analogue voltage. The ACS712 Current Sensor is a product of Allegro MicroSystems that can be used for precise measurement of both AC and DC currents [15, 16, 17]. This sensor is based on Hall Effect, and the IC has an integrated Hall Effect device. This is the sensor used for current sensing throughout the project. Fault Detection System The fault detection system mainly includes a current sensor where it senses low current value and will cause the LED to blink. The following circuit design demonstrates the development with the 555 timer IC of the blinking LED (Light Emitting Diode). In this configuration 555 timer IC has connected in Astable mode [18, 19]. Temperature and Humidity Detection The DHT11 is a normally used Temperature and humidity sensor. The sensor comes with a dedicated NTC to measure temperature and an 8-bit microcontroller to output the values of temperature and humidity as serial data. The sensor is also manufacturing facility calibrated and hence easy to interface with other microcontrollers. The sensor can sense temperature from 0 °C to 50 °C and humidity from 20% to 90% with an accuracy of ±1 °C and ±1% [20].
3 Methodology 3.1 Dimming Circuit Implementation The figure shows the schematic diagram of the dimming circuit for the display. The BJT voltage divider bias was used to design the circuit. The LDR was connected in parallel
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with R1 and a 10 k resistor was used as R2 [14]. The observed current value was high when the LDR was exposed to light, and the current value drops to the very minimum as the LDR was placed in a dark environment. This is because the resistance value of LDR increases up to 1 M in darkness. The current value decrease when the value of R2 increases (Fig. 2).
Fig. 2. Testing of the dimming circuit with 1 LED
3.2 Dimming Circuit with Sequence A Darlington transistor is also called a Darlington pair. It contains bipolar junction transistors connected in such a way that the second transistor further amplifies the current amplified by the first transistor. This configuration of transistors can provide a much higher current gain in comparison to that obtained by each transistor taken separately. The current gain of a typical transistor is 1000 or more. Therefore, a small base current is enough to make the pair switch on higher switching currents (Figs. 3 and 4).
Fig. 3. Three high power transistors used for sequence dimming
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Fig. 4. Testing of Darlington transistors to control the sequence
3.3 Current Detection For current detection, the current sensor ACS 712 is used to measure ‘the current value in the circuit, and it is interfaced with the microcontroller NodeMCU ESP 8266 (Figs. 5, 6 and 7).
Fig. 5. Current detection using ACS 712
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Fig. 6. Implementation of the current sensor into the circuit
Fig. 7. The flowchart of current detection
3.4 Fault Detection Using Blink Circuit When the measured current value is lower than 1 A, the three LEDs in the middle will blink to indicate that the current is low (Figs. 8 and 9).
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Fig. 8. LEDs will blink to indicate current is lower than the threshold
Fig. 9. Blink circuit in the schematic
3.5 Humidity and Temperature Detection For temperature and humidity detection, the DHT 11 sensor is used. The main reason this sensor is used in this project is to identify whether there is rain. High relative humidity will indicate rain and will trigger the relay output from NC to NO. The relay will not be connected to the dimming circuit when the relative humidity is higher than 90. This is done to avoid the dimming system from working during rainy days as it might cause a problem to the visibility of the road users (Figs. 10 and 11).
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Fig. 10. DHT 11 sensor and relay connected in the circuit
Fig. 11. Flowchart of the humidity detection system
4 Results, Analysis and Discussion 4.1 The Current Reading for LED Traffic Light The current is measured for both the 5 V and 12 V power supplies. The data collected is compiled in a table and graph.
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Table 1. The table for current and power for each colour of LED Voltage
Different colour for LED Traffic Light Red
5V
12 V
Yellow
Green
Current (A)
0.5000
0.5000
0.6000
Power (W)
2.5000
2.5000
3.0000
Power (kwH)
0.0025
0.0025
0.0030
Current (A)
1.7000
1.7000
2.4000
Power (W)
20.4000
20.4000
28.8000
0.0204
0.0204
0.0288
Power (kwH)
The current reading is measured using a clamp meter for 12 V and 5 V. Then, the power is calculated by using the formula below (Figs. 12 and 13): To compute power: P = IV To compute power in kWh: P(kWh) =
P(W ) 1000
Current Reading between 12V and 5V
Current Reading
2.5 2 1.5 1 0.5 0 5V
12V
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Yellow
Green
Fig. 12. The graph for current measurement between 12 V and 5 V
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Power Consumpon between 12V and 5V 35
Power consumpon
30 25 20 15 10 5 0 5V
12V
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Yellow
Green
Fig. 13. The graph for power consumption between 12 V and 5 V.
Table 1 shows the difference between current usage and power consumption by 12 V and 5 V when the LED is testing on the testing board, and the data indicates by using a bar chart. The current applied by 12 V is almost half from if compared to the current supplied by 5 V. However, the power consumed by 12 V is quite high than 5 V which shows more power savings can be made at night. Besides, the significant difference in the result is seen in the figure due to the voltage value as the value of current is multiplied by the voltage used to get the power usage for this system. Table 2. Comparison between power consumption with dimming and without dimming system Power in 1 day without dimming (kWh)
Power in 1 day with dimming (kWh)
Difference of both
Percentage of power-saving
RED
0.681
0.184
0.497
27%
YELLOW
0.167
0.045
0.122
27%
GREEN
0.316
0.095
0.221
30%
Table 2 is created to find the difference between power consumption for a day with and without a dimming system. The comparison states the percentage of power-saving obtained for Red, Yellow and Green LED as this is the most commonly used component for display. Based on the findings, the red display can save up to 27% with the dimming method/technique. The savings obtained for the yellow display is the same as the red
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display which is 27%. While the Green LED has higher specifications as compared to Red and Yellow, the power saving obtained is at 30% with the dimming technique. Figure 14 shows the graphical presentation of the data comparison in Table 1.
Power (kWh)
Difference between with and without dimming system 0.8 0.6 0.4 0.2 0 RED
YELLOW
GREEN
power in 1 day without dimming(kWh )
power in 1 day with dimming(kWh )
difference of both
percentage of power-saving
Fig. 14. Difference between traffic light system with dimming and without dimming.
References 1. Handson Technology: User Guide - 4 Channel 5V Optical Isolated Relay Module. Occup. Heal. Saf. 74(2), 24 (2015). http://search.ebscohost.com/login.aspx?direct=true&db=bth& AN=16274161&site=ehost-live 2. Bozorg, S., et al.: Department of statistics Malaysia press release social statistics bulletin, Malaysia, 2016. Transp. Res. Part F Psychol. Behav. 565, 1–14 (2016). https://doi.org/10. 1109/EECEA.2016.7470780 3. Kulasooriyage, C.S., Namasivayam, S.S., Udawatta, L.: Analysis on energy efficiency and optimality of LED and photovoltaic based street lighting system. Eng. J. Inst. Eng. Sri Lanka 48(1), 11 (2015). https://doi.org/10.4038/engineer.v48i1.6844 4. Ehsan, M.: Smart Traffic light controller based on Microcontroller, no. October (2016) 5. Subramaniam, S.K., Esro, M., Aw, F.L.: Self-algorithm traffic light controllers for heavily congested urban route. WSEAS Trans. Circuits Syst. 11(4), 115–124 (2012) 6. Subramaniam, S.K., Ganapathy, V.R., Subramonian, S., Hamidon, A.H.: Automated traffic light system for road user’s safety in two lane road construction sites. WSEAS Trans. Circuits Syst. 9(2), 71–80 (2010) 7. Sivarao, S.K.S., Esro, M., Anand, T.J.: Electrical & mechanical fault alert traffic light system using wireless technology. Int. J. Mech. Mechatron. Eng. 10(4), 19–22 (2010) 8. Naser, M., Abuamara, F.: Fault Light Detection and Identification System using RFID, no. Peccs, pp. 125–128 (2018). https://doi.org/10.5220/0006835801250128 9. Mishra, S., Gupta, S., Singh, S., Tiwari, T., Mohan, A.: Arduino based led street light auto intensity control system. Int. J. Adv. Res. Eng. 3(4), 2394–2819 (2016). www.ijarets.org 10. Deng, H., Xie, X., Ma, W., Han, Y.: A LED street lamp monitoring system based on Bluetooth wireless network and LabVIEW. In: 2016 2nd IEEE International Conference on Computer and Communications, ICCC 2016 - Proceedings, pp. 2286–2291 (2017). https://doi.org/10. 1109/CompComm.2016.7925107
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11. Ahmad, A., Buyamin, S., Marzuki, M.Z.A., Hj Mat Said, S., Abas, K.H.: Fault monitoring system for traffic light. J. Teknol. 73(6) (2015). https://doi.org/10.11113/jt.v73.4407 12. Mumtaz, Z., et al.: An automation system for controlling streetlights and monitoring objects using arduino. Sensors (Switzerland) 18(10), 1–14 (2018). https://doi.org/10.3390/s18103178 13. Al Subramaniam, S.K., Binti Husin, S.H., Binti Yusop, Y., Bin Hamidon, A.H.: Real time mailbox alert system via SMS or email. In: 2007 Asia-Pacific Conference on Applied Electromagnetics Proceedings, APACE 2007, no. January (2007). https://doi.org/10.1109/APACE. 2007.4603963 14. 7 Voltage and Current Dividers (2014) 15. Allegro: Datasheet ACS712, pp. 1–14 (2017). www.allegromicro.com 16. Attia, H.A., Omar, A., Takruri, M.: System (2016) 17. Wang, B., Liang, R., Liu, L., Wang, J., Xu, J.: Temperature dependence of the hole mobility in indacenodithiophene-benzothiadiazole OTFTs. In: 2017 IEEE 24th International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA), Chengdu, pp. 1–3 (2017) 18. Harshitha, K.M., Taranum, L., Mamatha, G., Divya, K.V.: Automatic street light control, fault detection and traffic density control. Int. J. Innov. Res. Comput. Commun. Eng. 5(5), 45–50 (2017) 19. Jaafar, A., Soin, N., Hatta, S.W.M.: An educational FPGA design process flow using Xilinx ISE 13.3 project navigator for students. In: 2017 IEEE 13th International Colloquium on Signal Processing and its Application, no. March, pp. 7–12 (2017) 20. ESP8266 DHT11/DHT22 Temperature and Humidity Web Server with Arduino IDE (2019). https://randomnerdtutorials.com/esp8266-dht11dht22-temperature-and-hum idity-web-server-with-arduino-ide/
Pressure Analysis in Water Hydraulics Machine: Continuous and Intermittent Extrusion Cycle in Dough Extrusion Ahmad Anas Yusof1,2(B) , Suhaimi Misha2 , Faizil Wasbari2 , Mohamed Hafiz bin Md Isa2 , Mohd Qadafie Ibrahim2 , Mohd Shahir Kasim2 , and Syarizal Bakri3 1 Robotics and Industrial Automation Research Group, Faculty of Electrical Engineering,
Malacca, Malaysia [email protected] 2 Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya Durian Tunggal, 76100 Melaka, Malaysia 3 Jabatan Matematik Sains Dan Komputer, Politeknik Kuching Sarawak, KM 22, Jalan Matang, 93050 Kuching, Sarawak, Malaysia
Abstract. This research focuses on the impact of pressure transient on the use of a water hydraulics system in a food processing machine. Water is used as a pressure medium to control the movement of double acting cylinders within a custom-made food processor machine. The system uses Cartesian robotics movement, with one cylinder controlling the batches of extruded dough and the other handling the dough extrusion system. In this experiment, the pressure during the dough extrusion is measured and analyzed. Thus, this paper presents an analysis of pressure transient during the continuous and intermittent cyclic of the extrusion process of the dough, which is introduced as the malleable substance for the test. It is noted that during both tests, the extrusion process are faster when the system pressure is increased, and the time for the system to reach maximum pressure is also increased, with respect to the increase in system pressure. Keywords: Water hydraulics · Food processing · Pressure transient
1 Introduction The goal of this project is to create a water hydraulics system for a sustainable food processor system. This is an attempt to make a water hydraulic system available for local industrial and domestic use. Tap water is used to transfer energy and pressure from the pump to the cylinder. Water is inexpensive, environmentally friendly, and simple to incorporate into food industries [1, 9]. Simultaneously, pressurized fluid can be integrated into the system and used to control the machine’s operation with the help of a suitable controller. As a result, a study of pressure changes in water, or pressure transients, is critical. This will almost certainly have an impact on the quality of the food produced [10]. A test rig for an automatic food processing machine was built. The controller can © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 147–157, 2022. https://doi.org/10.1007/978-981-16-8954-3_15
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be used interchangeably with a relay system a programmable logic controller or even an embedded system such as Arduino and Raspberry Pi. The triplex piston pump used in this study is a spray pump with maximum pressure up to 40 bars which is usually used for car wash. This pump has a built-in pressure regulator with an electric motor as its prime mover. The size of the pump is 64 cm × 50 cm × 50 cm. A custom-build water hydraulic cylinder has been also developed as the actuator for the system. The cylinder has double acting configuration, with bore size and stroke of 40 mm and 125 mm respectively. All components are assembled into a robotic manipulator that is driven by the low-cost water hydraulic system [11, 12]. In this paper, the test focuses on the analysis of pressure transient during the compressive test of the viscoelastic wheat flour dough, as shown in Fig. 1.
Fig. 1. Automatic Traditional Cookies machine, showing batch and extrusion cylinders (above) and the transparent water tank and pump (below)
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2 Methodology Figure 2 shows a set of data logger Hydrotechnik Multisystem 5060 plus used to record data of pressure flow over time in the system. It has 24 channels and 2 GB of memory, and can be used to measure pressure up to 100 bar. In Fig. 3, the setup and configuration of a test rig for an automatic food processing machine, which includes a pump, inverter, controller, valve, sensor, and cylinder used in the experiment is illustrated. There will be two types of experiments: continuous and intermittent extrusion cycles. The malleable substance is a simple flour/water dough. The flour dough was mixed using a mixer. The flour was prepared with a blend composition of: 13.25% ± 0.75% moisture, 10.5% ± 0.35% protein and 0.5% ± 0.03% sugar contents. Sugar and protein are both quoted on the 14% moisture basis standard. The dough was made by mixing 350 g of wheat, custard and corn flour with 120 g distilled water and 5 g sugar, giving a total of 475 g of dough from each mix. All samples were mixed for three minutes at a constant speed in ambient conditions. The term “continuous cycle” refers to the fact that the stroke is released without stopping until it is fully extended. The valve is fully open to allow water to flow and closes immediately after the stroke is fully extended. The time it takes for a water-based hydraulic cylinder to fully extend is determined by the pressure supplied by the pump. The intermittent cycle will be used in the second test. This is a condition in which the stroke is extended in 3 s of intervals. The valve is opened and closed repeatedly with the same time interval. In both tests, the flour dough will be used as the malleable substance that deforms under compressive pressure, in which the water pressure is measured by a sensor placed at the end cap inlet of the cylinder.
Fig. 2. Data logger hydrotechnik multisystem 5060 plus
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Fig. 3. Test rig setup and configuration
3 Results and Discussion 3.1 Continuous Extrusion Cycle The results of the pressure distribution in the continuous extrusion cycle are shown in Fig. 4 to Fig. 7. Figure 4 shows the test at a system pressure of 6 bar. The pressure
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rises from 3 bar to an average of 5 bar after 5 s, with a bit of pressure fluctuation. The dough is extruded with an average of 5 bar of pressure for about 4.4 s, commencing from the point of contact. It is noted that the dough is completely extruded at time equals to 9.4 s. The pressure rises to the maximum 6 bar system pressure at time equal to 10.3 s, as it reaches maximum system pressure before dropping to the initial 3 bar pressure during the retraction of the cylinder.
t = 0s Cylinder extends
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Pressure (bar)
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t = 10.3s Maximum pressure reaches. Cylinder retracts
5 4 3 t = 9.4s Extrusion ends. Dough completely extruded
t = 5s Extrusion starts
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Fig. 4. Pressure distribution at 6 bar system pressure (Continuous Extrusion Cycle)
The test continues with a 3 bar increase in pressure, for a system pressure of 9 bar, as shown in Fig. 5. It is found that after 5 s of cylinder extension, the pressure fluctuates, before begins to increase, during the beginning of the extrusion process. There is a significant increase in pressure, with an average pressure of 7 bar. This happens for 2 s, with time intervals ranging from 5.6 s to 7.6 s. The hydraulic transient is observed during the average 9 bar of pressure, because the pressure relief valve is rapidly opening and closing in order to reduce the system pressure, which was previously set at 9 bar. Sudden relief valve actions cause a transient that lasts approximately 0.8 s. Figure 6 shows the following test, which has been performed at a system pressure of 12 bar. From time intervals of 5.3 s to 6.9 s, the pressure climbs from an average of 4.7 bar to a range of pressures ranging from 9.4 bar to 9.7 bar. At time equals to 6.9 s, whereby all the dough has been completely extruded from the dough chamber, the pressure continues to rise to a maximum pressure of 12 bar, before decreasing to a 6 bar constant average pressure during the retraction process. Figure 7 shows the final continuous extrusion cycle test. The cylinder extends till time equal to 5 s, when it contacts the dough, at the start of the experiment. During extrusion, the dough resistance generates an increase in average pressure of 10 bar for 1.5 s. When the dough is completely extruded, a pressure increase to a maximum average
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t = 8.8s Maximum pressure reaches. Cylinder retracts
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Fig. 5. Pressure distribution at 9 bar system pressure (Continuous Extrusion Cycle)
t = 0s Cylinder extends
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Fig. 6. Pressure distribution at 12 bar system pressure (Continuous Extrusion Cycle)
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Fig. 7. Pressure distribution at 15 bar system pressure (Continuous Extrusion Cycle)
pressure of 15 bar at time equals to 7.2 s. This is followed by a reduction to a constant average pressure of 7.5 bar during the retraction process. The continuous extrusion cycle shows two distinctive characteristics: • The extrusion process are faster when the system pressure is increased, and • The time for the system to reach maximum pressure is decreasing, with respect to the increase in system pressure. 3.2 Intermittent Extrusion Cycle The results of the pressure distribution in the intermittent extrusion cycle are shown in Fig. 8 to Fig. 11. The test indicated in Fig. 8 is preset with a system pressure of 6 bar. The experiment records a 5 bar average pressure with 10 complete extrusions. The lowest pressure measured is 4.5 bar at time equals to 7.9 s, while the highest pressure measured is at 5.7 bar, at time equal to 32.2 s, before the cylinder is finally retracted. It takes about 24.3 s for the cylinder to fully extrude the dough, with 10 complete extrusion. At a system pressure of 9 bar, the outcome of the pressure analysis is shown in Fig. 9. In the experiment, an average pressure of 8.5 bar has been obtained with 9 complete extrusions. Before the cylinder is ultimately retracted, the lowest pressure measured is 7 bar at time equal to 8 s, and the highest pressure measured is 9 bar at time equals to 10.4 s. With 9 complete extrusions, the cylinder takes roughly 21.5 s to fully extrude the dough. The analysis shown in Fig. 10 is of system pressure at 12 bar. For eight complete extrusions, an average pressure of 10.8 bar is obtained in the experiment. Before the cylinder is finally retracted, the lowest pressure measured for the entire extrusion is 10.2 bar at 8 s, and the highest pressure measured is 11.9 bar at 27.1 s. The cylinder takes approximately 21.1 s to fully extrude the dough after 8 complete
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8 5.1 bar, 10.1 s 4.5 bar, 7.9 s
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5 bar, 5 bar, 24 s 18.5 s 5.2 bar, 5.3 bar, 5.1 bar, 5.1 bar, 26.8 s 29.4 s 16 s 21.4 s
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Fig. 8. Pressure distribution at 6 bar system pressure (Intermittent Extrusion Cycle)
extrusions. The data of the first pressure rise recorded at 3.3 bar and 5.3 s is not counted as the dough is not fully extruded at the time of experiment.
9 bar, 10.4 s
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10.6 bar, 11.8 bar, 10.6 bar, 10.7 bar, 10.7 bar, 24.3 s 13.3 s 10.3 bar, 13.3 s 18.7 s 18.7 s 10.5 s
14 12 Pressure (bar)
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10 8
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6 4 2
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0 0
5
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20 25 Time (s)
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Fig. 10. Pressure distribution at 12 bar system pressure (Intermittent Extrusion Cycle)
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16 11.3 bar, 8s
14 Pressure (bar)
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12 6.5 bar, 5s
10 8 6 4 2 0 0
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Figure 11 shows the final intermittent extrusion cycle test at 15 bar of system pressure. As usual, the data of the first pressure rise recorded at 6.5 bar at time equals to 5 s is not counted as the dough is not fully extruded at the beginning of experiment. An average of 12.8 bar with 5 complete extrusions are recorded in the experiment. The lowest pressure measured for the complete dough extrusion is 11.3 bar at time equals to 8 s, while the highest pressure measured is at 14.6 bar, at time equals to 15.8 s, before the cylinder is finally retracted. The cylinder takes approximately 10.5 s to fully extrude the dough after 5 complete extrusions. The intermittent extrusion cycle shows two distinctive characteristics: • The duration in between the intermittent extrusion cycle is about + - 3 s, which is equal to the preset timing of the experiment. • The time for the complete intermittent extrusion cycle or to fully extrude the dough is decreasing, with respect to the increase in system pressure.
4 Conclusion This project demonstrates the development of a water hydraulic system for a sustainable food processor system. In this paper, pressure fluctuation and transient in the water powered food processing machine using continuous and intermittent extrusion cycle for malleable substance has been presented. The relationship between stroke movement and pressure transient has been analyzed. It is noted that during the compression of the dough, the pressure increases to a certain amount of value. The continuous extrusion cycle shows two distinctive characteristics, where the extrusion process are noted to be faster when the system pressure is increased, and the time for the system to reach maximum pressure is noted to be decreasing, with respect to the increase in system pressure. For the intermittent extrusion cycle, the duration in between the intermittent extrusion cycle is noted to be at an average of 3 s, which is equal to the original preset timing of the experiment. The recorded time for the complete intermittent extrusion cycle or to fully extrude the dough is also decreasing, with respect to the increase in system pressure. Acknowledgement. This work is funded by Ministry of Higher Education (MOHE) of Malaysia, under the Fundamental Research Grant Scheme (FRGS). FRGS/1/2016/TK03/FKM- CAREF00317. The authors wish to thank Ministry of Higher Education and Universiti Teknikal Malaysia Melaka for their support.
References 1. Higgins, M.: Water hydraulics – the real world. Ind. Rob. Int. J. 23(4), 13–18 (1996) 2. Trostmann, E.: Water Hydraulics Control Technology. Marcel Dekker, New York (1996) 3. Backe, W.: Water or oil hydraulic in the future. In: The Sixth Scandinavian International Conference on Fluid Power, SICFP 1999, Tampere, Finland, pp. 51–65 (1999) 4. Trostmann, E., Bo, F., Bo, H.O., Bjarne, H.: Tap water as a Hydraulic Pressure Medium. Marcel Dekker, New York (2001)
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5. Rydberg, K.: New materials and component design – key factors for water hydraulic systems. SAE Trans., 154–161 (2002). 14 Aug 2021. http://www.jstor.org/stable/44718531 6. Lim, G.H., Chua, P.S.K., He, Y.B.: Modern water hydraulics - the new energy transmission technology in fluid power. Appl. Energy 76, 239–246 (2003) 7. Krutz, G.W., Patrick, S.K.C.: Water hydraulics – theory and applications 2004. In: Workshop on Water Hydraulics, Agricultural Equipment Technology Conference, Louisville, Kentucky, United States, pp. 1–33 (2004) 8. Finn, C.: Trends in design of water hydraulics – motion control and open-ended solutions. In: Proceedings of the 6th JFPS International Symposium on Fluid Power. Tsukuba, Japan, pp. 420–430 (2005) 9. Pham, P.N., Ito, K., Ikeo, S.: Energy saving for water hydraulic pushing cylinder in meat slicer. JFPS Int. J. Fluid Power Syst. 10, 24–29 (2017) 10. Yusof, A.A., Bakri, S., Suhaimi, M.: TDS and pH analysis for water quality monitoring in water hydraulics food processor. Int. J. Integr. Eng. 11(4), 218–224 (2019) 11. Yusof, A.A., Misha, S., Isa, M.H.M., Wasbari, F., Ibrahim, M.Q., Kasim, M.S.: Low cost water hydraulics technology for malaysian traditional cookies production. In: The 10th JFPS International Symposium on Fluid Power, Fukuoka, Japan (2017) 12. Yusof, A.A., Misha, S., Isa, M.H.M., Wasbari, F., Ibrahim, M.Q., Kasim, M.S.: Simulation and experimentation of water hydraulics technology for automatic traditional cookies production. J. Adv. Res. Fluid Mech. Therm. Sci. 47(1), 136–150 (2018)
Investigation on Disturbance Force Compensation via State Observer Design and Cascade P/PI Controller Approach Z. Jamaludin1(B) , P. Y. Hau1 , C. T. Heng2 , L. Abdullah1 , and N. A. Rafan1 1 Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,
76100 Durian Tunggal, Melaka, Malaysia [email protected] 2 Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Jalan Genting Klang, Setapak, 53300 Kuala Lumpur, Malaysia
Abstract. This paper presents numerical results on dampening of cutting force acting on a servo drive positioning system in milling application. In any machining process, accuracy of the servo drive system that governs positioning of the work piece material relatives to the cutting tool are of utmost important. Aside from machine structural vibration and friction, cutting force from the cutting process exerts undesired influence on the servo drive system. Therefore, the servo drive position controller must compensate and damp any acting disturbance force. Disturbance forces are difficult to estimate and physical sensors have various disadvantages such as high cost and reduce reliability. A state observer approach is an attractive alternative to physical sensors. The aim of this work is to estimate and compensate acting disturbance force in milling cutting process simulation via state observer and controller design. The observer was designed based on the principle of conservation of forces utilizing input measurement of relative acceleration of the positioning system. The position cascade P/PI controller and the state observer were designed in MATLAB/SIMULINK environment. The control system performances were measured based on maximum tracking errors for sinusoidal input disturbance signals of single frequency and multi frequency. Numerical results showed reduction of 25.00% and 38.18% in maximum tracking errors signifying the advantages of this control strategy. Keywords: Cutting force · Force compensation · Disturbance rejection
1 Introduction According to [1, 2] manufacturing industry is among the main engines of growth for the global economy. Aside from demands for complex parts and products and as the manufacturing industry continues to grow, the industry is also facing additional challenge as demands for improvement in quality of parts and products increases. With stiff competition, manufacturers must strategies to meet requirements for higher productivity, reduction in cost of manufacturing, and high quality of parts and products. Over the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 158–170, 2022. https://doi.org/10.1007/978-981-16-8954-3_16
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years, one of the most significant developments in manufacturing technology that meets these requirements is the advancement in CNC machining technologies. According to [3], CNC machining was first introduced in the early 1970s. CNC is the automation of machine tools using pre-programmed sequences of machine control command and advanced computing processing technology. CNC machining has the advantages in terms of reduce setup time and lead time, ability to perform complex shapes and contours, high degree of accuracy, and increase in productivity. There exist many different types of CNC machines. This paper focuses on further development of CNC milling machine alone. In milling machine, the cutting force generated from the interaction between the cutting tool and the work piece exerts additional force to the servo drive system mechanism that control the positioning of the milling table [4]. The cutting force has special characteristic; the force contains frequency components that are harmonics [5]. These frequency harmonics are characterized and determine by the specifics of the cutting process; for example, the spindle speed rotation, depth of cut, tool diameter and etc. The cutting force, even though desired for material removal process, must be damped especially ones related to significant harmonics in order to preserve precision of the servo motor mechanism of the positioning table. Disturbance force is defined as undesired inputs to a system and its effect must be compensated. Various methods for disturbance force compensation exist in literature. The methods can be divided into direct and indirect approaches. In indirect approach, position controllers were designed with characteristics such as robustness against system variations and external input disturbances. These controllers have other characteristics such as high dynamic stiffness and compliance. Such controllers have been designed and validated through the work presented in [6–11]. In all these examples, no direct attempts were made in estimating the actual disturbance forces; that is knowledge of the disturbance force characteristics are not desired. Unlike indirect control approach, the direct approach considers the nature and characteristics of the disturbance forces to be compensated. The control system explicitly estimates the disturbance force whereby the estimated force was then applied in the controller scheme for direct compensation. Such examples of these techniques were presented in [12–15]. This direct approach eliminates the complexity of the controller design approach that often requires strategies and intense mathematics unsuitable for practical application in industry. This paper presents an indirect control design approach utilizing a state observer that explicitly estimates the input disturbance force from real-time measurement of the control command input signal and the system acceleration. This approach applies a simple cascade P/PI controller most often found in CNC machine’s position control structure. Cascade P/PI [16–18] is a position controller that belongs to the family of the classical PID controller. This approach therefore has the advantage over the more complex control strategies as the controller scheme is much simpler and easily understood by practical users. The objective of this study is to investigate the effectiveness of the designed state observer with cascade P/PI position controller for input disturbance of varying amplitudes and tracking frequencies. The outcomes of this study will be beneficial in understanding the impact that the multiple parameters of the state observer have on dampening effect of the cutting force. The controller performances were analysed using maximum tracking errors measurements and estimation of the input disturbance
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signals. This paper is organized as follows; the following section introduces the system setup followed by details of the cascade P/PI and the state observer designs. The next section illustrates the numerical results obtained using SIMULINK software followed by a section on discussion and conclusion with recommendations for further extension of this current work.
2 System Setup The experimental setup at which the work was based on consists of a direct drive single axis positioning system. Figure 1 shows the system setup that was developed by Googol Technology. The positioning system is driven by a U-shape slot linear motor equipped with a 4 µm resolution linear encoder. The movable slider total mass is 0.65 kg.
Fig. 1. Schematic diagram of the system setup.
The linear time-invariant model of the system dynamics is described as a single-inputsingle-output (SISO) model and this behavior was obtained using frequency domain identification method. A frequency response function (FRF) of the SISO system was first generated. The SISO FRF was estimated using an H1 estimator based on measured input voltage, u(t) and output position, y(t) signals corresponding to input signal, r(t) in the form of band-limited white noise filtered at a frequency of 15 Hz. The system identification measurements were recorded for a total duration of 5 min or 300 s at sampling frequency of 5000 Hz. A Hanning window was applied to reduce the leakage errors and the number of samples per window was 4096; yielding an approximate sampling resolution of 1 Hz. The system transfer function relating the input voltage u(t) to the drive system and the output position, y(t) is given by the following relationship: Gm (s) =
A Y (s) = 2 · e−sTd , U (s) s + Bs + C
where the parameters are given in Table 1: Table 1. Parameters of system model. Parameter A (mm/V·s2 ) B (s−1 ) C (s−1 ) T d (s) Value
7.5 × 108
3622
0
0.00045
(1)
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3 Controller and State Observer Design 3.1 Cascade P/PI Cascade P/PI is a common position controller for machine tool. The controller is based on the classical PID controller commonly known to many machine users. Cascade P/PI controller design steps include tuning of the PI speed loop controller (Eq. 2) and the P controller (Eq. 3) for the position loop. GPI (s) = kp +
ki s
(2)
GP (s) = kv
(3)
Both PI and P control parameters were selected based on analyses of the open loop and closed loop characteristics with consideration on the gain margin, phase margin and Nyquist plot that ensures system stability. The control parameters are listed in Table 2. Table 2. Controller parameters of cascade P/PI Parameters
Velocity loop
Position loop
Gain margin
4.79 dB
5.93 dB
Phase margin 56.1°
55.4°
kp
3.809 × 10–6 V·s/µm –
ki
0.007618 V/µm
–
kv
–
1050.8 s−1
3.2 State Observer A state observer that estimates the input disturbance force is designed using inputs of control command signal, u(t) and acceleration of the system, a(t). The observer is designed in state space environment and for that reason the system introduced in Eq. (1) is converted to state space. ⎤ ⎡ ⎡ ⎤ ⎡ ⎤⎡ ⎤ 0 0 x˙ 01 0 x u kf 1 ⎥ + ⎣ v˙ ⎦ = ⎣ 0 0 1/M ⎦ ⎣ v ⎦ + ⎢ ⎣ M −M ⎦ F(v) d˙ 00 0 d 0 0 (4) ⎡x ⎤
01 0 u x ⎣ v ⎦ + k0 0 y= = f 1 1 F(v) a − 00 M M M d
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where, x = position v = velocity d = input disturbance x˙ = derivative of position v˙ = derivative of velocity d˙ = derivative of input disturbance y = output vector M = mass of the system k f = motor constant = 487.5 N/V F(v) = friction force The observer is designed based on the work in [19, 20]. From Eq. (4), the estimate of the input disturbance d(t) that acts on the single axis slider is given as follows (friction is ignored in this case): dˆ = M · a − kf · u
(5)
The estimate of the input disturbance is a function of system acceleration and control command input signal of the controller. The overall Simulink diagram of the control scheme with the state observer is given in Fig. 2. Figure 3 shows the block diagram of the state observer.
Fig. 2. Overall control scheme including the cascade P/PI position controller and the state space observer.
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Fig. 3. Block diagram of a state observer.
4 Results and Discussion First, the tracking performance of the cascade P/PI position controller without any input disturbance and the state observer was analysed. An input reference of amplitude 10 mm and tracking frequency of 0.5 Hz was inserted into the control loop and numerically analysed using SIMULINK. Figure 4 shows the input reference and the resulting tracking error. It shows that the tracking error was only 0.3% of the amplitude of the reference signal; highlighting the superior tracking performance of the controller. Next, in order to quantify the disturbance rejection property of the controller, a sinusoidal input disturbance, d(t) of amplitude 0.5 V and frequency 3 Hz was concurrently inserted during reference tracking. Results shows that cascade P/PI controller has high dynamic stiffness as only 1.34% to 1.44% of RMSE variations were recorded in the presence of input disturbance. Table 3 summarizes the overall results. Table 3. Variations in root means square of errors (RMSE) with and without input disturbance. Reference input
RMSE (µm)
Amplitude Frequency Without With (mm) (Hz) d(t) d(t)
% variation
10
0.5
21.14
21.42 1.34
10
1.0
42.28
42.89 1.44
Secondly, the performances of the state observer were analysed for its ability to estimate input disturbance signal using varying control strategies. The observer was analysed for (i) constant input disturbance frequency, (ii) constant input disturbance amplitude, (iii) varying state observer gain, (iv) varying time delay. Table 4 shows results of constant input disturbance frequency whereby the frequency was defined at 3 Hz while the amplitude was varied between 0.3 V to 0.5 V at observer gain value of 0.55. Results shows increasing error for increases in disturbance input amplitude.
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Fig. 4. Reference position and tracking error. Table 4. Performances of the observer for constant frequency input disturbance.
Also, Fig. 5 shows that time delay exists between the input disturbance and the estimated disturbance. This was due to the sampling frequency and delay of the system. Table 5 then shows results of constant input disturbance amplitude with varying gain of the observer. Errors refer to the maximum difference in amplitude between actual and estimated disturbance signals.
delay
Fig. 5. Time delay between input disturbance and the estimated signal.
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Table 5. Performances of the observer for constant disturbance amplitude.
Results listed in Table 5 indicate consistent estimation of the observer where minimal influence of the disturbance input frequency on quality of estimation were observed. However, these results were obtained as the observer gain parameter was varied indicating the influence of this parameter for better quality estimation. Finally, in order to address the issue of time delay in estimation, the earlier control scheme was updated as in Fig. 6 to include adjustable time delay block (identified in red). Table 6 then summarizes the results obtained using this scheme. A delay on the estimated signal is expected to improve the overall performance of the control system for disturbance rejection. Results shown in Table 6 illustrate significant improvement in estimation error as compared to results listed in Table 5. For example, the error in estimation (in unit of volt) was reduced by a factor of more than 100 times in the case of 2.5 Hz tracking frequency. Figure 7 then compares the estimated disturbance force and the input disturbance force for the case of multi sine disturbance input of amplitude 0.1 V, frequencies of 4 Hz and 4.5 Hz with observer gain value of 0.19 at delay of 0.15 s. Table 6. Performances of the observer with adjustable time delay block.
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Fig. 6. Control scheme with additional time delay block for the observer.
Next, the estimated disturbance signal, was inserted as feedback into the velocity loop to actively compensate for the input disturbance. The control scheme is shown in Fig. 8. The control performances were then analysed for the cases listed in Table 7.
Fig. 7. Comparison between estimated and input disturbance signals.
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Fig. 8. Control scheme with feedback of disturbance estimate Table 7. System configuration for direct compensation of input disturbance signal Amplitude Frequency Time delay
Gain Constant
Single 0.3 V sine
2 Hz
0.375 s 1.85
Multi sine
3 Hz & 3.5 Hz
0.2 s
0.1 V
0.43
Figure 9 shows the numerical results obtained. These results are summarized in Table 8. Results obtained showed that the state observer was able to produce 25% and 38% reduction in position error for the case of single sine wave and multi sine wave disturbance input signals. Table 8. Summary of results for system with and without observer feedback Types of input disturbance
Error (volt) Without observer
With observer
Percentage of error reduction (%)
Single frequency sine wave
0.6279
0.4709
25.00
Multi frequencies sine wave
0.7436
0.4597
38.18
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(a)
(b) Fig. 9. Position errors for system with single sine input disturbance (a) without observer and (b) with observer
5 Conclusion In this study, a cascade P/PI position controller was developed and analysed for its tracking performance. The control scheme was embedded with a state observer module that estimates input disturbance force using the principle of force equivalent. The proposed method provides a much simpler alternative to disturbance rejection strategy in machine tools application thus ensuring high tracking and position performance of the positioning system. Numerical analyses performed using MATLAB/SIMULINK software have validated the strength of this approach. The observer was able to estimate input disturbance force in the form of single sine wave and multiple sine wave through manipulations of the state observer parameters that include adjustable time delay and gain factor. Next, the control structure is to be experimentally validated on the system setup. The results obtained in this work indicate the necessity to improve further the robustness of this control approach using adaptive control strategy. Acknowledgements. The authors would like to extend our appreciation to Fakulti Kejuruteraan Pembuatan and Universiti Teknikal Malaysia Melaka for the facilities and the research grant provided PJP/2020/FKP/TD/S01724.
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References 1. Groover, M.P.: Fundamentals of Modern Manufacturing: Materials, Processes, and Systems. 6th edn. Wiley Binder (2015) 2. Kalpakjian, S.: Manufacturing Engineering and Technology. 6th edn. Pearson (2010) 3. Valentino, J.V., Goldenberg, J.: Introduction to Computer Numerical Control (CNC), 5th edn. Pearson/Prentice Hall, Upper Saddle River (2013) 4. Nijiri, J.G., Ikua, B.W., Nyakoe, G.N.: Cutting force control for ball end milling of sculptured surfaces using fuzzy logic controller (2012) 5. Chiew, T.H., Jamaludin, Z., Bani Hashim, A.Y., Leo, K.J., Abdullah, L., Rafan, N.A.: Analysis of tracking performance in machine tools for disturbance forces compensation using sliding mode control and PID controller. Int. J. Mech. Mechatron. Eng. 12, 34–40 (2012) 6. Altintas, Y., Erkorkmaz, K., Zhu, W.H.: Sliding mode controller design for high speed feed drives. CIRP Ann. Manuf. Technol. 49(1), 265–270 (2000) 7. Heydarzadeh, M.S., Rezaei, S.M., Mardi, N.A., Kamali, E.A.: Motion control of a two-axis linear motor-driven stage in the micro-milling process. Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng. 232(1), 65–76 (2018) 8. Jia, Z.Y., Ma, J.W., Song, D.N., Wang, F.J., Liu, W.: A review of contouring-error reduction method in multi-axis CNC machining. Int. J. Mach. Tools Manuf. 125, 34–54 (2018) 9. Bao, D.F., Tang, W.C., Dong, L.: Integral sliding mode control for flexible ball screw drives with matched and mismatched uncertainties and disturbances. J. Central South Univ. 24(9), 1992–2000 (2017) 10. Ozcan, A., Rivière-Lorphèvre, E., Huynh, H.N., Verlinden, O., Filippi, E.: Modelling of pocket milling operation considering cutting forces and CNC control inputs. Procesia CIRP 58, 239–244 (2017) 11. Rubio, L., Ibeas, A., Luo, X.: P-PI and super twisting sliding mode control schemes comparison for high-precision CNC machining. In: 24th Iranian Conference on Electrical Engineering, ICEE 2016, pp. 1825–1830 (2016) 12. Hao, W., Zhu, X., Li, X., Turyagyenda, G.: Prediction of cutting force for self-propelled rotary tool using artificial neural networks. J. Mater. Process. Technol. 180(1–3), 23–29 (2006) 13. Heydarzadeh, M.S., Rezaei, S.M., Azizi, N., Kamali, E.A.: Compensation of friction and force ripples in the estimation of cutting forces by neural networks. Meas. J. Int. Meas. Confederation 114, 354–364 (2018) 14. Kumar, G., Pezhinkattil, R.: A Six Sigma approach for precision machining in milling. In: 12th Global Congress on Manufacturing and Management. Procedia Engineering, vol. 97, pp. 1474–1488 (2014) 15. Lu, L., Zhang, L., Ji, S., Han, Y., Zhao, J.: An offline predictive feedrate scheduling method for parametric interpolation considering the constraints in trajectory and drive systems. Int. J. Adv. Manuf. Technol. 83(9–12), 2143–2157 (2015). https://doi.org/10.1007/s00170-0158112-0 16. Jamaludin, Z., Brussel, H.V., Swevers, J.: Classical cascade and sliding mode control tracking performances for a XY feed table of a high-speed machine tool. Int. J. Precis. Tech. 1 (1), 65–74 (2007) 17. Chiew, T.H., et al.: Design and analysis of super twisting sliding mode control for machine tools. Jurnal Teknologi 78(10–3), 25–29 (2016) 18. ˙Ibrahim, K., Nusret, T., Derek, P.A.: Improved cascade control structure for enhanced performance. J. Process Control 17(1), 3–16 (2007)
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19. Pritschow, G., Eppler, C., Lehner, W.D.: Ferraris sensor – the key for advanced dynamic drives. CIRP Ann. Manuf. Technol. 52(1), 289–292 (2003) 20. Pritschow, G., Fritz, S., Pruschek, P.: Reconstruction of process forces of direct drives using the Ferraris sensor. In: Proceedings of the VIIth International Conference on Monitoring and Automatic Supervision in Manufacturing AC 2004, Zakopane, pp. 19–21 (2004)
Review of Recent Grid Synchronization Techniques and Phase-Locked Loops for Power Converters Mohammad Faisal Akhtar1 , Mohammad Nishat Akhtar2 , Junita Mohamad-Saleh3 , Ahmad Faizul Hawary2 , and Elmi Abu Bakar2(B) 1 Higher Institution Centre of Excellence (HICoE), UM Power Energy Dedicated Advanced
Centre (UMPEDAC), Level 4, Wisma R&D, University of Malaya, Jalan Pantai Baharu, 59990 Kuala Lumpur, Malaysia 2 School of Aerospace Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia [email protected] 3 School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia
Abstract. In this paper, emerging grid synchronization techniques for power electronic converters are reviewed. In recent years, there has been a significant increase in the penetration of Distributed Generation (DG) in modern grid architecture. DGs such as solar photovoltaics, wind turbines are interfaced with the grid by way of power electronic converters. For ideal operation, these power electronic converters should be properly synchronized with the grid, i.e. there should be minimal difference between the grid and converter output voltage parameters such as frequency, phase and magnitude, in order to avoid abnormalities such as current transients. Frequency and phase should be tracked especially for driving internal control systems of the power electronic converter. Recent research has geared towards maintaining this synchronization – even during abnormal grid conditions – and exploring techniques which either advance or move away from conventional phase locked loop (PLL) approach. Through this review, modern advances in grid synchronization methods for power electronic converters will be highlighted, and the trajectory for subsequent research shall also be predicted. Keywords: Grid synchronization · Phase-locked loop (PLL) · Power electronic converter · Phase angle detection
1 Introduction In an effort to minimize the impact of fossil fuel-based power generation on the environment, the world is gradually transitioning towards renewable energy sources. As a result, the penetration of distributed generation (DG) such as solar photovoltaics, wind turbines, etc. has increased in the current power grid infrastructure. These DGs are interfaced with
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 171–178, 2022. https://doi.org/10.1007/978-981-16-8954-3_17
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the grid though power electronic converters. Furthermore, uses of power electronic converters have also further increased due to diversification of loads and energy storage [1]. However, with this rise in power converters, there come issues affecting the overall grid relating to power quality, grid stability, etc. These issues, in particular grid stability, are exacerbated due to inherent low inertia nature of converter-distributed generation setup. Hence, proper control becomes crucial in order to maintain a stable grid and ensure improved power quality. To fulfill this purpose, it is necessary to ensure a measure of grid frequency and phase. This is where synchronization strategies such as phase locked loops (PLLs) are utilized, which play a key role of maintaining the phase angle and frequency of the grid-connected power electronic converter. Grid synchronization techniques such as PLLs ensure that the grid-connected power converter works in perfect harmony with the grid. The output data obtained through grid synchronization algorithms (frequency, phase) are used at different control levels of grid-connected power electronic converters, which may be used to efficiently trip the disconnection procedure in case grid conditions go out of bounds of the specified grid codes [2]. As an example, the phase data obtained through PLLs may be used to carry out the transformation of captured three-phase grid variables (current, voltage) from the stationary three-phase (abc) reference frame to synchronous reference frame (dq0 reference frame) as shown below. Here, the dq0 transformation is demonstrated for the three phase grid current vector. ⎤⎡ ⎤ ⎡ ⎤ ⎡ 2π cosθ cos(θ − 2π ) cos(θ + ) ia id 3 3 2π 2π ⎥ ⎣ iq ⎦ = ⎢ (1) ⎣ −sinθ −sin(θ − 3 ) −sin(θ + 3 ) ⎦⎣ ib ⎦ √1 √1 √1 i0 i c 2 2 2 This transformation is widely used in converter control schemes such as for reactive power compensation, harmonic elimination, etc. Furthermore, the frequency captured through grid synchronization schemes can be used as part of over/under frequency passive islanding detection. Hence, it can be observed why grid synchronization schemes are advantageous from a control system point of view, In recent years, synchronization strategies such as PLLs have become a topic of interest among researchers [6–12]. Many different configurations for PLLs have been described over the years, with an aim to make phase angle and frequency detection more accurate even in the presence of imbalances in the supply. Furthermore, designs have been proposed to minimize computational burden. In this regard, new grid synchronization strategies have also been proposed which eliminate the need for a PLL altogether, thus simplifying design by a great extent [2]. In this paper, we shall carry out a review of grid synchronization techniques – primarily PLLs. We shall observe the advancements and current research trajectory with regard to grid synchronization techniques, while at the same time taking an in-depth look at the working of a basic PLL. The main contribution of this paper is to review important concepts relating to PLLs and summarize the current trends in the grid synchronization research. The rest of the paper is organized as follows: Sect. 2 describes the working of a basic PLL and its goals, Sect. 3 looks at emerging trends relating to PLL design and other grid synchronization techniques. Lastly Sect. 4 concludes the paper with a summary, and a suggestion of the way forward in terms of grid synchronization research.
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2 PLL Description and Working The phase-locked loop (PLL) is a closed-loop control system responsible for controlling the phase and frequency of the system [1]. It generates an output signal in-phase with the input signal. The main components of the PLL are as shown in Fig. 1, which are the phase detector, loop filter, and voltage controlled oscillator. The phase detector outputs the error signal (phase error) , which is calculated using the phase of the PLL and the grid. This error is fed to the loop filter, which filters out miscellaneous frequency components, thus leaving only the estimation of the frequency deviation from nominal frequency (ω) Δω. Finally, this frequency deviation is then used by the voltage controlled oscillator (VCO) to generate a sinusoidal signal at an adjusted frequency ω0 which is given as shown. ω0 = ω + ω
(2)
In the locked condition, the PLL is in phase in the grid voltage and provides a frequency almost identical to that of the grid.
θ*
Phase detector
Loop filter ϵ
Δω
Voltage controlled oscillator
θ
Fig. 1. PLL Block diagram
PLL performance metrics (response time, overshoot, and steady-state error) remain dependent on the characteristics of the functional blocks used, and also external disturbances and grid voltage waveform condition. First order loop filters are widely used for the purpose of grid synchronization due to the fact that they can monitor grid phase during slow-frequency variation events [2]. Higher order loop filters are useful in providing harmonic attenuation and tracking gridfrequency variations. Adaptive loop filters are also utilized for eliminating harmonic distortion, but the main disadvantage associated with them is the increased computational burden. Voltage controlled oscillators (VCOs) used in the PLL control system may be implemented either in analog or in digital. In the analog implementation, the VCO involves a dedicated circuit controlled by the loop filter output voltage; its digital implementation may involve the digitally controlled oscillator (DCO) as mentioned in [3]. Based on the type of phase detector used, PLLs can be classified into (a) Enhanced PLLs (EPLLs) (b) Power-based PLLs (pPLLS, used widely in three-phase electrical systems) and (c) Quadrature signal generation PLLs, which is based on Park transformation, and has been described in [5]. Lastly, a concise summary of the types of functional blocks used in PLLs is also described in [5].
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3 Review of Recent Trends in PLLs and Other Grid Synchronization Techniques A great deal of research work has been carried out in recent years on how to optimize PLL performance and coming up with simpler grid synchronization strategies. PLL design tends to involve a trade-off between metrics such as computational burden, minimization of steady-state error, and dynamic response. This trade-off governs the choice of PLL to be used for a specific application [3]. A simple PLL strategy for 3-phase systems that has been described in literature is the synchronous reference frame PLL (SRF-PLL) as shown in Fig. 2. This involves the use of three phase stationary abc reference frame to rotating dq0 reference frame transformation. As shown, the output of the feedback loop determines the phase and the direct component determines the amplitude of fundamental of positive sequence input voltage. However, in the event of unbalanced/distorted three-phase voltages, SRF-PLL shows unreliable operation [8]. To deal with this, inclusion of pre-filtering stages in PLL implementations has been described in [7]. SRF-PLL has further been improved upon though schemes such as double SRF (DSRF) PLL [9] and decoupled double SRF (DDSRF) [10], which are meant to ensure accurate phase-angle detection, even in the presence of grid voltage asymmetry or harmonics, which would otherwise cause stability issues in grid connected converters. Implementation of DDSRF-PLL cancels out any oscillations in the d and q components of the input signal, thus eliminating the need of filtering stages. For accurate phase angle detection under distorted grid conditions, the second order generalized integrator (SOGI) based PLL has been described in [6]. However, this techniques increases hardware burden as one SOGI PLL is required for each phase. A recent advancement in 3-phase PLL is mentioned in [16], wherein an adaptive Clark transform matrix is implemented in the PLL structure, which utilizes estimations of phase deviations in the individual three-phase voltages. Experimental results described in this paper prove the accuracy in the estimation of phase in the presence of amplitude and/or phase imbalances. However, due to the presence of the PI controller, there may be an increase in the computational burden owing to the tedious tuning process
3-phase voltages
dq0 abc
vq
vd
ω* +
Loop filter (PI controller)
+ ω
Fig. 2. Basic SRF-PLL control scheme, for a 3-phase system
1/s
θ
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Adaptive filters have also been incorporated in PLL structures in order to enhance its filtering and disturbance rejection capability, as has been described in [12]. However, the scheme may falter in the event of inter-harmonics in the grid voltage. Moving average filters (MAFs) have also been incorporated into the PLL structure as in [11], wherein a delayed signal cancellation (DSC) block is also used to form DSC-PLL and it ensures accurate and fast grid synchronization. A further key advancement in phase detection and grid synchronization strategies is also detailed in [4], wherein the complexity of the phase angle detection structure is greatly reduced compared to a conventional SRF-PLL. The direct phase angle detection (DPD) technique uses basic trigonometry and eliminates the need for a PI controller to reduce complexity and delay in the phase angle detection system. A signal reconstruction (SR) technique is also integrated along with the DPD structure to bolster performance even under unbalanced voltage conditions. Through simulation studies and experimental analysis, it was determined that this DPD-SR technique showed superior performance compared to state-of-the-art PLLs such as SRF-PLL and DDSRF-PLL, especially under system voltage unbalances. Furthermore, an open-loop control scheme for grid synchronization has been reported in [13], wherein a two consecutive samples based frequency estimator has been developed for frequency estimation, and for eliminating DC offset and harmonics ban-pass filters and delayed signal cancellation block is also used. Furthermore, a hybrid pre-filtering approach for grid voltage is also utilized which aims to improve transient response. This open loop control scheme avoids the complex structures and rigorous tuning processes normally seen in a conventional PLL. All of the PLL and grid synchronization structures discussed here have been summarized in Table 1. Table 1. Comparison of PLL and other grid synchronization structures discussed in this paper Scheme
Reference Advantage
Disadvantage
Second order generalized Integrator (SOGI) based PLL
[6]
Provides accurate phase angle detection under Distorted grid conditions
Implementation is complex as 1 such PLL may be required per phase
Synchronous reference frame based PLL (SRF-PLL)
[8]
Provides reliable operation for balanced 3-phase system, and is easy to implement
Shows abnormal operation in asymmetrical conditions (grid voltage imbalance, phase/frequency deviation) Also rigorous tuning required due to presence of PI controller (continued)
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Scheme
Reference Advantage
Disadvantage
Double SRF PLL (DSRF) [9, 10] PLL and Decoupled Double SRF PLL (DDSRF-PLL)
Double synchronous reference frame is used which provides accurate phase detection in unbalanced 3-phase voltage conditions
Possess complex control structures and rigorous tuning required for PI controller
Delayed signal cancellation PLL (DSC-PLL) with moving average filter
[11]
Provides advanced filtering and harmonic rejection
Comparatively high computational burden
PLL with additional adaptive filters
[12]
Enhanced filtering ability and harmonic rejection
May lead to increased time delay due to presence of additional filters
Direct Phase Angle Detection with Signal Reformation (DPD-SR)
[4]
Tuning not required as PI controller is eliminated. Provides superior phase-detection even in the presence of unbalanced grid voltage conditions. Comparatively simple design
There is scope for further optimization for further computational burden and may be explored, especially for signal reformation
Hybrid pre-filtered open loop phase detection
[13]
Open loop structure greatly simplifies design and no tuning required due to no PI controller
There is scope for further optimization for further computational burden and may be explored
3-phase PLL with adaptive Clark transform
[16]
Provides accurate estimation of phase in the presence of amplitude and/or phase imbalance
Presence of PI controller may increase computational burden, owing to tedious tuning process
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4 Conclusion Through this paper, a review of grid synchronizing structures – especially PLL – has been carried out, wherein we have examined the functional blocks of a PLL, and their impact on it’s performance. Ultimately, there is a tradeoff between computational burden, dynamic performance and time response, which can vary according to the application of the PLL. Furthermore, recent trends in PLL research have been elaborated, wherein it is found that in the grid synchronization techniques that have come up recently, the complexity related to PLL structures has been eliminate and are found to be superior performance-wise. Hence, it can be determined that in the near future, conventional PLLs may be slowly phased out in favor of such emerging grid synchronization techniques. Furthermore, in future PLL implementations, in order to reduce computational burden, the scope of eliminating PI controllers may be explored as the process of tuning them is extremely tedious. The use of adaptive controllers may help in this regard, or optimization techniques may be explored which may efficiently set PI coefficients. Grid synchronization techniques have found immense applications in recent years, mainly due to high penetration of distributed generation and diversification of loads and energy storages, thus leading to an increase in the use of grid-connected power converters. A prominent example of grid synchronization application is highlighted in [14], wherein a steady state linear Kalman Filter PLL (SSLKF-PLL) is used for applications in more electric aircraft (MEA) power grid, which synchronizes different power sources and loads within the aircraft electrical grid. Further applications of grid synchronization are highlighted in [15] wherein PLLs are used in smart inverter control. Hence, it can be concluded that with the ever-increasing use of grid-connected power converters, gridsynchronization has become indispensable in order to maintain stability of the power electronic converter and the grid. Acknowledgement. The authors would like to acknowledge the RUI grant 1001.PAERO.8014035 by RCMO, Universiti Sains Malaysia.
References 1. Gardner, F.M.: Phaselock Techniques. Wiley, Hoboken (2005) 2. Teodorescu, R., Liserre, M., Rodriguez, P.: Grid Converters for Photovoltaic and Wind Power Systems, vol. 29. Wiley, Hoboken (2011) 3. Lamo, P., Pigazo, A., Ruiz, G.A., Azcondo, F.J., López, F.: An optimized implementation of a two-sample phase locked loop with frequency feedback for single-phase sensorless bridgeless PFC. In: 2018 IEEE 19th Workshop on Control and Modeling for Power Electronics (COMPEL), pp. 1–6 (2018). https://doi.org/10.1109/COMPEL.2018.8459965 4. Sadeque, F., Benzaquen, J., Adib, A., Mirafzal, B.: Direct phase-angle detection for threephase inverters in asymmetrical power grids. IEEE J. Emerg. Sel. Top. Power Electron. 9(1), 520–528 (2021) 5. Lamo, P., Pigazo, A., Azcondo, F.J.: Evaluation of quadrature signal generation methods with reduced computational resources for grid synchronization of single-phase power converters through phase-locked loops. Electronics 2020, 9 (2026)
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6. Xie, M., Wen, H., Zhu, C., Yang, Y.: DC offset rejection improvement in single-phase SOGIPLL algorithms: methods review and experimental evaluation. IEEE Access 5, 12810–12819 (2017) 7. Golestan, S., Guerrero, J.M., Vasquez, J.C.: Three-phase PLLs: a review of recent advances. IEEE Trans. Power Electron. 32(3), 1894–1907 (2017) 8. Kanjiya, P., Khadkikar, V., Moursi, M.S.E.: Obtaining performance of type-3 phase-locked loop without compromising the benefits of type-2 control system. IEEE Trans. Power Electron. 33(2), 1788–1796 (2018) 9. Hadjidemetriou, L., Kyriakides, E., Blaabjerg, F.: A new hybrid PLL for interconnecting renewable energy systems to the grid. IEEE Trans. Ind. Appl. 49(6), 2709–2719 (2013) 10. Rodriguez, P., Pou, J., Bergas, J., Candela, J.I., Burgos, R.P., Boroyevich, D.: Decoupled double synchronous reference frame PLL for power converters control. IEEE Trans. Power Electron. 22(2), 584–592 (2007) 11. Huang, Q., Rajashekara, K.: An improved delayed signal cancellation PLL for fast grid synchronization under distorted and asymmetrical grid condition. IEEE Trans. Ind. Appl. 53(5), 4985–4997 (2017) 12. Golestan, S., Guerrero, J.M., Vasquez, J.C.: A robust and fast synchronization technique for adverse grid conditions. IEEE Trans. Ind. Electron. 64(4), 3188–3194 (2017) 13. Verma, A.K., Jarial, R.K., Roncero-Sánchez, P., Ungarala, M.R., Guerrero, J.M.: An improved hybrid prefiltered open-loop algorithm for three-phase grid synchronization. IEEE Trans. Industr. Electron. 68(3), 2480–2490 (2021) 14. Tang, M., Bifaretti, S., Pipolo, S., Formentini, A., Odhano, S., Zanchetta, P.: A novel low computational burden dual-observer phase-locked loop with strong disturbance rejection capability for more electric aircraft. IEEE Trans. Ind. Appl. 57(4), 3832–3841 (2021) 15. Mirafzal, B., Adib, A.: On grid-interactive smart inverters: features and advancements. IEEE Access 8, 160526–160536 (2020). https://doi.org/10.1109/ACCESS.2020.3020965 16. Islam, M.Z., Reza, M.S., Hossain, M.M., Ciobotaru, M.: Three-phase PLL based on adaptive Clarke transform under unbalanced condition. IEEE J. Emerg. Sel. Top. Ind. Electron. https:// doi.org/10.1109/JESTIE.2021.3065205
Fuzzy Logic Approach to Fire Monitoring and Warning System Design Ahmad Yusairi Bani Hashim1(B) , Ruziah Ali2 , and Fairul Azni Jafar1 1 Faculty of Manufacturing Engineering, Center for Smart System and Innovative Design,
Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Melaka, Malaysia [email protected] 2 Deputy Vice Chancellor (Research and Innovation) Office, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Melaka, Malaysia
Abstract. Some household activities required fire to perform the task, like heating and cooking but such fire-based activities result in severe damage to the victim when it is out of control. The existing option of home-based Fire Monitoring and Warning System (FMWS) can be classified as two types; smoke alarm and visionbased camera system. Smoke alarm is not quite reliable since it is often failed to provide alert and warning insufficient time due to slow response rate. The later solution requires a high cost to apply in household area. Therefore, this paper aims to propose and design of fuzzy logic-based fire monitoring and warning system. Fuzzy logic is an intelligent approach to uncertainties computation. Keywords: Fuzzy logic · Fire monitoring · Alarm
1 Introduction The basic structure of the fuzzy logic-based fire monitoring and warning system is shown in Fig. 1 [1]. In the proposed design, the fuzzy logic control system is used to determining the fire chances with 2 inputs, which are the change rate of temperature (C-R-Temperature) and the change rate of humidity (C-R-Humidity). The output is the percentage of fire chances occurs [2]. Method in Table 1, the first column represents the function of every memberships; and the Universe of Discourse for input variables is represented by the second column. For instance, if the C-R-Temperature is 4, it lies between the range of 2–5, and the change rate is moderate. The Universe of Discourse for output Fire-chances is shown in the third column, in which it is the probability of fire as output. The advantage of fuzzy logic system is that it is very easy and understandable. Furthermore, fuzzy logic system is capable of providing the most effective solution to complex issues. The system can be modified easily to improve or alter the performance, and helps in dealing engineering uncertainties. Fuzzy logic is widely used for commercial and practical purposes.
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 179–186, 2022. https://doi.org/10.1007/978-981-16-8954-3_18
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Fig. 1. Fuzzy logic structure. Table 1. The universe of discourse for every membership function. Variable
Input
Output 2
Variable
Low
Universe of Discourse for C-R-Temperature (°C)
The universe of Discourse for C-R-Humidity (%)
Low
Moderate
Less than 2
Less than 7
Moderate
High
From 2 to 5
From 7 to 12
High
Variable
From 5 to 10
From 12 to 20
Variable
2 Implementing Fuzzification The membership functions [2] are used to convert a crisp value into a fuzzy linguistic variable in fuzzification. Two inputs, the change rate of temperature and the change rate of humidity, are employed in this research, and one output, fire-chances, is evaluated. The input and output variables are fuzzified in the sub-sections that follow. The C-R-Temperature High, Moderate, and Low linguistic variables are employed. The x-axis depicts the temperature change rate from 0 °C to 10 °C, while the y-axis depicts the membership degree from 0 to 1. The trapezoidal membership function was chosen for the linguistic variables Low and High since the basic feature of membership function has a set of values.
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For example, if the change rate of temperature is 1 or 9.8, both values are near to the extreme of Low or High categories, which give the degree of membership as one. While the triangular membership function is chosen for the linguistic variable of Moderate due to the peak value is narrow, which is the value of 3.5 that gives the highest degree of membership (1) of the Moderate category. Figure 2 shows the Membership Function of Change Rate of Temperature (C-R-Temperature). The mathematical expression of the membership function of C-R-Temperature are shown as follow;
(1)
(2)
The C-R-Humidity High, Moderate, and Low linguistic variables are employed. The x-axis shows the percentage change in humidity from 0% to 20%, while the y-axis shows the membership degree from 0 to 1. The trapezoidal membership function was chosen for the linguistic variables Low and High because the primary feature of the membership function has a set of values. For example, if the change rate of humidity is 1% or 19.8%, both values are near to the extreme of Low or High categories, which give the degree of membership as one. While the triangular membership function is chosen for the linguistic variable of Moderate due to the peak value is narrow, which is the value of 9.5 that gives the highest degree of membership (1) of Moderate category. Figure 3 shows the Membership Function of Change Rate of Humidity (C-R-Humidity). The mathematical expression of membership function of C-R-Humidity are shown as follow;
(3)
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Fig. 2. Membership function of change-of-temperature.
Fig. 3. Membership function of change-of-humidity.
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2.1 Membership Function of Output Fire-Chances Three language output variables are used: High, Moderate, and Low Fire-chances. The y-axis shows the membership degree from 0 to 1, while the x-axis represents the value of changing rate of humidity from 0% to 100%. Triangular membership function is chosen for the linguistic variable of Moderate due to the peak value is narrow, which is the value of 45 that gives the highest degree of membership (1) of the Moderate category. The mathematical expression of the membership function of Fire chances are shown as follow;
(4)
Fig. 4. The membership function of fire-chances.
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2.2 Defining Fuzzy Rules Following the architecture of the membership functions, the fuzzy rules are illustrated using simple IF-Then rules [3]. Two inputs with three linguistic variables are combined in this study, yielding 3 × 3 = 9 rules. The rule set in Table 2 is based on two inputs, CR-Temperature and C-R-Humidity, with variables High, Moderate, and Low. The rules are written as follows. i. ii.
If C-R-Temperature is Low AND C-R-Humidity is Low, then Fire-chances is Low. If C-R-Temperature is Low AND C-R-Humidity is Moderate, then Fire-chances is Low. iii. If C-R-Temperature is Low AND C-R-Humidity is High, then Fire-chances is Moderate. iv. If C-R-Temperature is Moderate AND C-R-Humidity is Low, then Fire-chances is Moderate. v. If C-R-Temperature is Moderate AND C-R-Humidity is Moderate, then Firechances is Moderate. vi. If C-R-Temperature is Moderate AND C-R-Humidity is High, then Fire-chances is Moderate. vii. If C-R-Temperature is High AND C-R-Humidity is Low, then Fire-chances is Moderate. viii. If C-R-Temperature is High AND C-R-Humidity is Moderate, then Fire-chances is High. ix. If C-R-Temperature is High AND C-R-Humidity is High, then Fire-chances is High.
Table 2. The universe of Discourse for each membership function. C-R-humidity
C-R-temperature Low
Moderate
Low
Low
Low
Low
Low
Moderate
Low
Moderate
Low
High
Moderate
High
Moderate
3 Result and Discussion The value of change rate of temperature (C-R-Temperature) is 4 and the value of change rate of humidity is 8.5 was used to determine the result of experiment. The degree of membership for each of the membership function is computed using mathematical expression and it is tabulated as shown in Table 3. The output of this experiment is
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Table 3. Degree of membership for each membership function with input [4]. Variable
µx
µy
Low
0
0
Moderate
0.75
00.667
High
0.25
0
58.33, which means according to input values, there are 58.33% chances of fire occur. The working step is shown in subsection manual calculation. A program is build using Octave to determine the crisp output. The program is run with the inputs value from manual calculation, which are 4 for C-R-Temperature an 8.5 for C-R-Humidity. The program’s output is 57.316%. Figure 4 shows the results of the fire monitoring and warning system graphically in a surface viewer, with the x-axis representing C-R-Temperature input, the y-axis representing C-R-Humidity input, and the z-axis representing the output value of fire possibilities. Figure 5 clearly shows that the value of both inputs increases in the fire region and decreases in the non-fire region. Based on the result of Octave program, the percentage of fire chances is slightly lower than the calculation that made manually, which the difference is about 1%.
Fig. 5. Octave Surface Viewer for the suggested fire monitoring and warning system.
4 Conclusion An operation of fuzzy logic-based fire monitoring and warning system is proposed in this research. The rate of temperature change and the rate of humidity change are both used as inputs. The crisp output ‘fire chances’ is achieved after applying rules and defuzzification. There are total of nine rules to be applied in the proposed design system.
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Based on the results obtained, the crisp output of Octave program is slightly lower than the manual computation. The triangular and trapezoidal membership function are chosen and used to determine the degree of membership of the inputs entered [4]. Acknowledgement. The authors would like to thank Universiti Teknikal Malaysia Melaka for all the support given.
References 1. Yadav, S., Patil, S.S.: Intelligent learning of fuzzy logic controllers via neural network and genetic algorithm. IOSR J. Electron. Commun. Eng. 1–11 (2013) 2. Siddique, N., Adeli, H.: Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing. John Wiley & Sons, Oxford (2013) 3. Bani Hashim, A.Y.: Development of Artificial Intelligent Techniques for Manipulator Position Control. Universiti Putra Malaysia, Seri Kembangan (2002) 4. Negnevitsky, M.: Artificial Intelligence: A Guide to Intelligent Systems. Pearson Education, London (2005)
Pressure Analysis in Water Hydraulics Machine: Dough and Aluminum Beverage Can Compression Test Ahmad Anas Yusof1,2(B) , Suhaimi Misha2 , Faizil Wasbari2 , Mohamed Hafiz bin Md Isa2 , Mohd Qadafie Ibrahim2 , Mohd Shahir Kasim2 , and Syarizal Bakri3 1 Faculty of Electrical Engineering, Robotics and Industrial Automation Research Group,
76100 Durian Tunggal, Melaka, Malaysia 2 Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal,
Melaka, Malaysia [email protected] 3 Jabatan Matematik Sains dan Komputer, Politeknik Kuching Sarawak, KM 22, Jalan Matang, 93050 Kuching, Sarawak, Malaysia
Abstract. This research focuses on the impact of pressure transient on the use of a water hydraulics machine in a food processor. Water is used as a pressure medium to control the movement of the double acting cylinders within a custom-made food processor machine. The system uses Cartesian robotics movement, with one cylinder works horizontally, while the other works vertically, which provides the compression process. In this experiment, the machine is modified into a simple compression machine, so that the pressure transient during the process can be measured and analyzed. Thus, this paper presents an analysis of pressure transient during the continuous compression test of flour dough and aluminum beverage cans. It is noted that during the tests, several pressure transient is noted during the compression process, and exactly after the fully stroke of the cylinder, which prompt the pressure relief valve to rapidly open and close the line to release the build-up pressure. Keywords: Water hydraulics · Food processing · Pressure transient
1 Introduction A hydraulic transient, also known as water hammer or hydraulic shock, is a sharp pressure surge or a wave produced when flow is forced to stop suddenly or change direction abruptly. Sudden valve actions and the full extension and retraction stroke of the hydraulic cylinder can cause a high pressure wave to propagate in the circuit. If the necessary precautions are not taken, the transient conditions instigating high pressures can cause failures of pipes, valves and fittings and thus collapse of the circuit. Hence, the main objective of this research is to see the effect of pressure transient or fluctuation in water hydraulics compression process, which is subjected to various type of material, to induce © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 187–198, 2022. https://doi.org/10.1007/978-981-16-8954-3_19
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the pressure effect. In general, compressive testing demonstrates how a material will react when compressed. Compression testing can be used to identify a material’s behavior or response under crushing loads, as well as to determine its plastic flow behavior and ductile fracture limits [1]. They are essential for determining the elastic and compressive fracture properties of brittle and low-ductility materials. Nevertheless, in this research, the focus is on the pressure of the water medium used in the compression machine, and not the material itself. In general, the long term goal of this project is to create a sustainable, water hydraulics system for a food processor. This is an attempt to make water hydraulics application available for local industrial and domestic use [2–4]. A test rig based an automatic food processing machine has been built for the test. The controller can be used interchangeably with a relay system a programmable logic controller or even an embedded system such as Arduino and Raspberry Pi. The triplex piston pump used in this study is a spray pump with maximum pressure up to 40 bars which is usually used for car wash. This pump has a built-in pressure regulator with an electric motor as its prime mover. The size of the pump is 64 cm × 50 cm × 50 cm. A custombuild water hydraulic cylinder has also been developed as the actuator for the system. The cylinder has double acting configuration, with bore size and stroke of 40 mm and 125 mm respectively. Tap water is used to transfer energy and pressure from the pump to the cylinder. All components are assembled into a robotic manipulator that is driven by the low-cost water hydraulic system, as shown in Fig. 1. In this paper, the test focuses on the analysis of pressure transient during the compressive test of two malleable materials,
Fig. 1. Automatic traditional cookies machine
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which are the viscoelastic wheat flour dough and the metallic tin can. The materials are subjected to pressure ranging from 6, 9, 12 to 15 bar of system pressure.
2 Literature Review The use of water hydraulics opens up the possibility of designing fluid power systems that are both sustainable and environmentally friendly [5]. Water hydraulics can be defined in this context as a technology that uses tap water or water-based fluids to transmit energy and power [6]. The goal of using water as a hydraulic medium is to transfer energy, power, and resources in a sustainable manner that does not harm the natural environment. In order to promote green technology for a better future, the use of nonrenewable resources such as fossil fuels, natural gas, and coal must be reduced. This includes the use of hydraulic oil derived from petroleum-based mineral oil. The use of water is critical in developing a long-term fluid power system. Simultaneously, water characteristics such as hygiene, safety, and low maintenance cost offer intriguing perspectives for design engineers due to concerns about hydraulic fluid disposal, contamination, costly maintenance, and flammability. The remarkable uniqueness of energy and power transmission has also expanded the technology’s range of applications. Water’s low lubricity and highly corrosive properties have been overcome by the recent development of new materials, particularly ceramics, and the machining of parts to tighter tolerances. All of these factors have contributed to an increase in water-related hardware costs [7]. Water, on the other hand, differs significantly from oil, which, in many ways, can provide advantages in one area while producing disadvantages in others. Fortunately, these technical challenges have been effectively addressed by the use of specialized materials, coatings, and designs [8]. Water hydraulics can provide a design solution for hygiene in a variety of industries, as evidenced by the development of a water hydraulics-driven burger machine, beef cutter, and ice-filled machine in the food processing industry, and the use of an environmentally friendly waste packer lorry [9, 10]. Water hydraulics can also be used in cheese production, industrial water cleaning, die-castings, robotics, humidification equipment, and fire protection and fighting systems [11–15].
3 Methodology Figure 2 illustrates the configuration of a test rig for an automatic food processing machine, which includes a pump, inverter, controller, valve, sensor, and cylinder used in the experiment. The machine is modified to compress various type of materials, in an effort to induce water pressure in the system, for transient study. There will be two types of malleable materials used in the experiments: viscoelastic flour dough and aluminum beverage can. The flour dough was prepared with a blend composition of: 13.25% ± 0.75% moisture, 10.5% ± 0.35% protein and 0.5% ± 0.03% sugar contents. Sugar and protein are both quoted on the 14% moisture basis standard. The dough was made by mixing 350 g of wheat, custard and corn flour with 120 g distilled water and 5 g sugar, giving a total of 475 g of dough from each mix. The 240 ml aluminum beverage can is also used in the test. Both materials will deform under compressive pressure, whereby the water pressure due to the compression will be measured by a sensor placed at the
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end cap inlet of the cylinder. A set of data logger Hydrotechnik Multisystem 5060 plus is used to record data of pressure flow over time in the system. It has 24 channels and 2 GB of memory, and can be used to measure pressure up to 100 bar (Fig. 3).
(a)
(b)
Fig. 2. Test rig configuration (a) for dough (b) for aluminum beverage can
Fig. 3. Data logger hydrotechnik multisystem 5060 plus
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4 Results and Discussion 4.1 Pressure Analysis on Continuous Compression Test at 6 Bar System Pressure The results of the pressure distribution on continuous compression test at 6 bar system pressure are shown in Fig. 4 and Fig. 5. A compression test using flour dough at system pressure of 6 bar is shown in Fig. 4. The pressure rises from 3 bar to an average of 5 bar after 5 s, with a bit of pressure fluctuation. The dough is extruded with an average of 5 bar of pressure for about 4.4 s, commencing from the point of contact. It is noted that the dough is completely extruded at time equals to 9.4 s. The pressure rises to the maximum 6 bar system pressure at time equal to 10.3 s, as it reaches maximum system pressure before dropping to the initial 3 bar pressure during the retraction of the cylinder. The dough undergoes deformation in every phase of the compression process. During the compression, the dough is allowed to extrude through a small hole, which resulted in a constant average pressure of 5 bar. As a result, applying pressure to the dough creates a viscoelastic characteristics, a rheological principles where the dough behavior appears in the properties that display both viscous and elastic characteristics when deformed. When a stress is applied, viscous materials, such as water, resist shear flow and strain linearly with time. When elastic materials are stretched, they stretch and then return to their former state once the force is removed.
t = 0s Cylinder extends
7
Pressure (bar)
6
t = 10.3s Maximum pressure reaches. Cylinder retracts
5 4 3 t = 9.4s Extrusion ends. Dough completely extruded
t = 5s Extrusion starts
2 1 0 0
5
10
15 Time (s)
20
25
30
Fig. 4. Pressure distribution at 6 bar system pressure (Dough)
The test continues at the same system pressure but with different setup for different material. The dough is replaced with an aluminum beverage can in the machine. Pressure distribution for a compression test on the aluminum beverage can is observed in Fig. 5. It is found that after 5 s of cylinder extension, the pressure begin to fluctuate, at point where the aluminum beverage can begins to rupture, and starts the plastic deformation
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process, with pressure fluctuations starting at 3.1 bar and increases to a maximum spike of 5.2 bar at time equals to 5.4 s, and continue to fluctuate. This happens for 2 s, with time intervals ranging from 5.4 s to 7.4 s. At this point, the plastic deformation stops. After that, the pressure rises to the maximum pressure of 6 bar, at time equals to 9.3 s before decreasing and settling to a pressure average of 3.8 bar during the retraction process. It is noted that it takes about 1.3 s for the pressure to rise to the maximum after the plastic deformation of the aluminum beverage can ends. Here, the term plastic deformation is the permanent distortion caused by compressive stresses that exceed the yield strength of the tin can, causing it to buckle and finally be completely crushed.
t = 5.4 s The can ruptures
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Fig. 5. Pressure distribution at 6 bar system pressure (Aluminum Can)
4.2 Pressure Analysis on Continuous Compression Test at 9 Bar System Pressure The pressure distribution due to the extrusion of the viscoelastic dough is repeated with an increase of system pressure setting. Figure 6 shows the dough compression test at system pressure of 9 bar. It is found that after 5 s of cylinder extension, the pressure fluctuates to a minimum pressure of 3.4 bar, before starting to increase, during the beginning of the extrusion process for the dough. There is a significant increase in pressure, with an average pressure of 7 bar. Small fluctuations can be seen for 2 s, with time intervals ranging from 5.6 s to 7.6 s. After that, the pressure rises to a maximum average of 9 bar at time equals to 8.8 s before decreasing and settling to a pressure average of 5 bar during the retraction process. The hydraulic transient is noted during the average 9 bar of pressure, since the pressure relief valve is opening and closing rapidly, in order to reduce the system pressure preset earlier at 9 bar. Sudden relief valve actions causes a transient that propagates for about 0.8 s.
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Fig. 7. Pressure distribution at 9 bar system pressure (Aluminum Can)
The compression test of the aluminum beverage can is shown in Fig. 7 at the same system pressure of 9 bar. The compression from the water powered cylinder causes
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plastic deformation for the tin can, which also causes an increase in water pressure for the system. The pressure fluctuates to a maximum pressure of 5 bar for about 1.7 s after 5 s of cylinder extension, before starting to increase, during the beginning of the plastic deformation of the tin can. The fluctuations that is caused by the tin can’s plastic deformation can be seen in time intervals ranging from 5 to 6.7 s. It is noted that the fluctuations introduce a transient for about 1.7 s due to the abrupt contact of the hydraulic cylinder with the aluminum can. It propagates during the compression process, before further increases when the can is completely crushed. There is also a significant increase in pressure, with a maximum pressure of 9 bar at time equals to 7.5 s, which represents the end of the deformation, before the pressure starts to decrease and settle down to a pressure average of 5 bar during the retraction process. The hydraulic transient is also noted during the pressure relief valve’s rapid opening and closing action, for about 1.2 s. 4.3 Pressure Analysis on Continuous Compression Test at 12 bar System Pressure
t = 0s Cylinder extends
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Fig. 8. Pressure distribution at 12 bar system pressure (Dough)
Figure 8 shows the following test, which has been performed at a system pressure of 12 bar. A similar graph pattern can be seen through the 30 s process, with differences seen during the extrusion and the maximum pressure. From time intervals of 5.3 s to 6.9 s, the pressure climbs from an average of 4.7 bar to a range of pressures ranging from 9.4 bar to 9.7 bar. At time equals to 6.9 s, whereby all the dough has been completely extruded from the dough chamber, the pressure continues to rise to a maximum pressure of 12 bar, before decreasing to a 6 bar constant average pressure during the retraction process.
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Fig. 9. Pressure distribution at 12 bar system pressure (Aluminum Can)
At the same system pressure of 12 bar, the compression test of the aluminum beverage can is presented in Fig. 9. A similar pattern is also noted, with respect to the compression of the same aluminum beverage can at 9 bar of pressure. The tin can deforms due to the compression from the water-powered cylinder, which also generates an increase in water pressure in the system. During the beginning of the plastic deformation of the tin can, the pressure fluctuates to a maximum of 6.2 bar for about 1.3 s after 5 s of cylinder expansion, before continuing to increase. In time intervals ranging from 5 to 6.3 s, the fluctuations generated by the tin can’s plastic deformation may be noticed. It should be noted that the fluctuations cause a 1.1 s of pressure transient due to the hydraulic cylinder’s abrupt contact with the aluminum can. It propagates during the compression process before increasing further when the can is completely crushed. There is also a significant increase in pressure, with a maximum pressure of 12 bar at time equals to 7 s, which represents the end of the deformation, before the pressure begins to decrease at time equal to 7.8 s and settles down to an average pressure of 5 bar during the retraction process. The hydraulic transient is also observed during the rapid opening and closing action of the pressure relief valve, which lasts approximately 0.8 s.
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4.4 Pressure Analysis on Continuous Compression Test at 15 bar System Pressure
t = 0s Cylinder extends
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t = 5s Compression starts. Pressure fluctuations detected between t = 5s to t = 6.1 s
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Fig. 11. Pressure distribution at 15 bar system pressure (Aluminum Can)
Figure 10 presents the outcomes of the pressure distribution on wheat dough extrusion at 15 bar. After 5 s, the pressure increases from 4 bar to an average of 11 bar, with
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some pressure fluctuation. Starting at the point of contact, the dough is extruded with an average of 11 bar of pressure for roughly 1.5 s. It should be noted that the dough is completely extruded after time equal to 6.5 s. The pressure later increases to the maximum 15 bar system pressure after 7.2 s, before decreasing to 8 bar pressure during the cylinder’s retraction. Every step of the compression process causes the dough to deform. During the compression, the dough is allowed to extrude through a small hole, which resulted in a constant average pressure of 11 bar. Small fluctuations are recorded at both 11 bar and 15 bar of pressure. This two level of constant average pressure are noted in all graph, from Fig. 4 bar till Fig. 11. The compression test of the aluminum beverage can is shown in Fig. 11 at the same system pressure of 15 bar. The compression from the water powered cylinder causes plastic deformation for the tin can, which also causes an increase in water pressure for the system. The pressure fluctuates to a maximum pressure of 5 bar for about 1.1 s after 5 s of cylinder extension, before starting to increase, during the beginning of the plastic deformation of the tin can. The fluctuations that is caused by the tin can’s plastic deformation can be seen in time intervals ranging from 5 to 6.1 s. It is noted that the fluctuations introduce a pressure transient for about 1.1 s due to the abrupt contact of the hydraulic cylinder with the aluminum can. It propagates during the compression process, before further increases when the can is completely crushed. There is also a significant increase in pressure, with a maximum pressure of 15 bar at time equals to 6.2 s, which represents the end of the deformation, before the pressure starts to decrease and settle down to a pressure average of 8 bar during the retraction process. The hydraulic transient is also noted during the pressure relief valve’s rapid opening and closing action, for about 0.2 s.
5 Conclusion This project demonstrates the development of a water hydraulics machine in food processing application. In this paper, pressure analysis in the water powered food processing machine has been presented. The relationship between stroke movement in compressing different materials and the induced pressures due to the process has been analyzed. It is noted that during the compression of the dough and the aluminum can, the pressure transient time laps decreases with the increase of system pressure. At the same time, the pressure fluctuations are noted to be increasing with substantial amount, during the constant compression process of the cylinder, but in smaller amount when the cylinder reaches the maximum strokes, which activates the rapid opening and closing of the pressure relief valve. In comparison to both materials, substantial amount of pressure transient are recorded in the compression of the aluminum beverage can, relative to the dough. Acknowledgement. This work is funded by Ministry of Higher Education (MOHE) of Malaysia, under the Fundamental Research Grant Scheme (FRGS). FRGS/1/2016/TK03/FKM- CAREF00317. The authors wish to thank Ministry of Higher Education and Universiti Teknikal Malaysia Melaka for their support.
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References 1. Chua, C.K., Wong, C.H., Yeong, W.Y.: Benchmarking for additive manufacturing. In: Standards, Quality Control, and Measurement Sciences in 3D Printing and Additive Manufacturing, pp. 181–212. Academic Press, New York (2017) 2. Yusof, A.A., Misha, S., Isa, M.H.M., Wasbari, F., Ibrahim, M.Q., Kasim, M.S.: Low cost water hydraulics technology for Malaysian traditional cookies production. In: Proceedings of the 10th JFPS International Symposium on Fluid Power, Fukuoka (2017) 3. Yusof, A.A., Misha, S., Isa, M.H.M., Wasbari, F., Ibrahim, M.Q., Kasim, M.S.: Simulation and experimentation of water hydraulics technology for automatic traditional cookies production. J. Adv. Res. Fluid Mech. Therm. Sci. 47(1), 136–150 (2018) 4. Yusof, A.A., Bakri, S., Suhaimi, M.: TDS and pH analysis for water quality monitoring in water hydraulics food processor. Int. J. Integr. Eng. 11(4), 218–224 (2019) 5. Koskinen, K.T., Leino, T., Riipinen, H.: Sustainable development with water hydraulics possibilities and challenges. In: Proceeding of the 7th JFPS International Symposium on Fluid Power, Toyama, pp. 11–18 (2008) 6. Trostmann, E., Bo, F., Bo, H.O., Bjarne, H.: Tap Water as a Hydraulic Pressure Medium. Marcel Dekker, New York (2001) 7. Backe, W.: Water or oil hydraulic in the future. In: The Sixth Scandinavian International Conference on Fluid Power, SICFP 1999, Tampere, pp. 51–65 (1999) 8. Hilbrecht, B.: Water as a pressure medium in water hydraulics. In: Proceedings of the 48th National Conference on Fluid Power, Chicago, pp. 555–559 (2000) 9. Pham, P.N., Ito, K., Ikeo, S.: Energy saving for water hydraulic pushing cylinder in meat slicer. JFPS Int. J. Fluid Power Syst. 10, 24–29 (2017) 10. Finn, C.: Trends in design of water hydraulics – motion control and open-ended solutions. In: Proceedings of the 6th JFPS International Symposium on Fluid Power, Tsukuba, pp. 420–430 (2005) 11. Higgins, M.: Water hydraulics – the real world. Indust. Robot Int. J. 23(4), 13–18 (1996) 12. Ikeo, S., Nakashima, H., Ito, K.: Water hydraulics system for high speed cylinder drive. In: Proceeding of the 7th JFPS International Symposium on Fluid Power, Toyama, pp. 95–100 (2008) 13. Lim, G.H., Chua, P.S.K., He, Y.B.: Modern water hydraulics - the new energy transmission technology in fluid power. Appl. Energy 76, 239–246 (2003) 14. Rydberg, K.: New materials and component design – key factors for water hydraulic systems. In: SAE Transactions, pp. 154–161 (2002) 15. Krutz, G.W., Patrick, S.K.C.: Water hydraulics – theory and applications 2004. In: Workshop on Water Hydraulics, Agricultural Equipment Technology Conference, Louisville, Kentucky, pp. 1–33 (2004)
Performance Evaluation of Intelligent Fire Alarm System with Multi Data Fusion Sensor by Using IoT Platform A. M. Kassim1(B) , M. M. Roslan1 , S. Sahak1 , T. W. Chian1 , M. A. S. A. Aziz1 , M. A. S. S. Izran1 , M. S. H. Basari1 , M. R. Yaacob1 , M. A. A. Abid2 , A. H. Azahar3 , M. M. Hashim4 , A. K. R. A. Jaya5 , T. Yasuno6 , and A. M. Mouazen7 1 Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,
76100 Durian Tunggal, Melaka, Malaysia [email protected] 2 Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 3 Faculty of Electrical and Electronic Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Melaka, Malaysia 4 Faculty of Plantation and Agrotechnology, Universiti Teknologi MARA, Jasin Kampus, 77300 Merlimau, Melaka, Malaysia 5 Auro Technologies PLT, No. 76, Jalan TU 42, 75450 Ayer Keroh, Melaka, Malaysia 6 Graduate School of Technology, Industrial and Social Sciences, Tokushima University, 2-1 Minami-Josanjima, Tokushima 770-8506, Japan 7 Department of Environment, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, Belgium
Abstract. Conventionally, the detection and notification of the fire accident are based on the siren triggered by the fire alarm system. However, this system needs human interaction to call the fire department to inform the fire location. Therefore, an intelligent fire alarm system is highly in demand. The intelligent fire alarm system is a solution that can notify the area and the time that occurred to the fire station and the owner automatically without any human intervention. This product is generated by using an intelligent sensor system, GPS, and the choice algorithm to identify and notify effectively. The IoT system is integrated with the intelligent fire alarm system to ensure the owner and the fire station could monitor the air quality of the premises. This product can be used by commercial and domestic buildings to ensure the safety and smart cities can be realized. Keywords: Fire alarm system · Multi-sensor · Internet of Things · Assistive technology · Smart system
1 Introduction Fire structs rural areas frequently causing the large value of losses, it is recorded that solely in Malaysia, After the movement protection order was issued on March 18, 2020, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 199–209, 2022. https://doi.org/10.1007/978-981-16-8954-3_20
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there have been 15,393 fires. Open fires accounted for nearly 46% (7,032) of the total, while structural fires accounted for 1,955 of the total, causing damage to buildings and other smaller structures [1]. Most domestic fires happen to a late alert from the victims. By that, the fire department will face severe fires as it was not handled early. By that, an intelligent fire alarm system is in demand to notify the locals for prevention and fast response as fire has occurred, ideally, by reducing human intervention in detection and alerting the fire brigade. Parallelly, the fire detection system with a multi-criteria alarm algorithm and multi-component sensor system must be implemented to reduce the number of false alarms caused by individual fire detectors, as well as the time it takes for a fire to be detected [2].
2 Related Works Since the early 1990s, indoor fire detection through gas chemical sensing has been an area of debate. This method takes advantage of the fact that chemical volatiles appears before smoke particles in some fires. As a result, chemical sensing-based devices can deliver faster fire alarm responses than traditional smoke-based fire detectors. Furthermore, because most fire deaths are caused by toxic emissions rather than physical burns, gasbased fire detection might provide an extra layer of protection for building occupants: ionization detectors and photoelectric detectors (light scattering). However, researchers are actively looking for other detecting approaches to increase occupant safety and minimize the frequency of false alerts. Currently, the application of gas sensor-based fire detection systems is confined to specialized settings, such as fire detection in coal mines [3] or coal power plants [4]. Furthermore, smoke detectors are unable to distinguish between fire smoke and smoke from other sources, resulting in a high proportion of false positives. Due to the high related expenses and frequency, false alarms are always a worry for fire detection. According to the annual statements of the Malaysian Fire and Rescue Department, 0.33% (363 instances) of 110,150 cases are false reports [5]. To improve the reliability of fire detectors, multi-sensor systems, including heat, CO electrochemical cells, and smoke detectors, have been explored over the years [6]. To cater false readings in the sensor data collection, multiple sensors approach using array sensing [7], algorithms like neural networks [8], fuzzy logic rules [9, 10], etc.
3 Materials and Methods 3.1 System Design Overview The intelligent fire alarm system consists of both hardware and software architecture to fulfill its function, as shown in Fig. 1. As a fire event or dangerous condition occurs, the sensor will pick up abnormal readings utilizing the selection algorithm. Then, the device will select the suitable action to prevent or stop the current event: air quality, flammable gaseous heat, and flame sensors. The device will notify either the closest fire station dashboard or the owner informing the inconvenience condition. The device will notify locally for less severe conditions by utilizing the LED lights and its internal Piezo
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Fig. 1. Overview of an intelligent fire alarm system
buzzer. The communication method used for the internet of things device is through both Bluetooth 5.0 BLE and 2.4 GHz Wi-Fi communication [11–13]. By having access to the internet, the device is programmed to function over a single web application, which is the Main dashboard hub. The owner of the device will also be notified through a Telegram API bot. For each fire station, a designated dashboard with the ma of its covered range will be displayed using the Google Map API. Each device will update its location to the dashboard. Geolocation reading is obtained using a Gy-Neo6m GPS module, giving the Longitude and Latitude of the device [14–16]. 3.2 Hardware System Design In this part, the project hardware and system configuration will be explained. Figure 2 shows the schematic diagram of the developed intelligent fire alarm system. To be able to build a standalone device, the device is operated by an ESP 32 DEV module shown in Fig. 3. The device’s embedded controller was an Espressif low-power device on a chip microcontroller with integrated 802.11 Wi-Fi and Bluetooth module. The chip uses a
Fig. 2. Schematic diagram
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32-bit Xtensa LX6 microprocessor that runs at 160 MHz or 240 MHz and can handle up to 600 DMIPS. It has 520 KiB SRAM memories and a 2-bit SAR ADC with up to 18 channels. It also has four SPI ports, two I2C ports, digital GPIOs, and three UART ports communication interfaces. For remote monitoring, the embedded controller may collect, store, process, and transmit data wirelessly to a host device [17].
Fig. 3. ESP32 development module
To be able to read its location, a GY-NEO6Mv2 module is put in place. On these modules, the U-Blox NEO-6M GPS engine is very decent, and it also has a high sensitivity for indoor applications. There’s also a rechargeable MS621FE battery for backup and an EEPROM for saving configuration settings. A DC input in the 3.3 to 5 V range works well with the module (thanks to its built-in voltage regulator) [18]. The multiple sensors are built with four different sensors. Sensors are pick based on the Fire Triangle and the combustion equation, the general equation for a complete combustion reaction is fuel + O2 → CO2 + H2O (Fig. 4).
Fig. 4. Fire triangle.
The triangle depicts the three components that must be present for a fire to start: Heat, Fuel, and an oxidizing agent (usually oxygen). If all the elements are present and in the proper proportions, a fire will inevitably occur. The first sensor is, flame sensor brake outboard consisting of YG1006 NPN phototransistor and an LM393 voltage comparator as in Fig. 1, which senses the infrared radiations in wavelength range 70 nm to 1100 nm to electrical conductivity causing voltage difference. The flame sensor uses a 3DCV direct current supply to be able to operate [18]. It has analog reading for the intensity of the flame detected in wideband infrared (1.1 µm and higher) flames, as shown in Fig. 5. The gas sensor used is a Metal Oxide Semiconductor. Figure 2 shows the sensor that can be used to detect flammable gases: liquified petroleum gas (LPG), alcohol, propane, hydrogen, methane, and carbon monoxide, ranging from 200 to 10000 ppm [19]. To implement gas detection, a smoke sensor made of tin oxide, SnO2, is used. Tin oxide is less conductive than clean air. By that, this sensor will suit best quantify the smoke
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Fig. 5. The flame sensor module and fire infrared wavelength emission rate [15].
presence in the monitored area [20]. Heat is known energy that may be the cause or the product of fire. The significant value of heat change may show possible fire. An integrated circuit thermistor is used to detect such a manner [21]. 3.3 Software Design The architecture of the system is implemented by both internal and external of the device shown in Fig. 6. The internal program has the routine to check the status of each sensor value. The threshold will determine the abnormal readings from the sensors, and there are two possibilities which are either sensor problems or abnormal status. These abnormal statuses will be counted as data to be processed through the selection algorithm. This algorithm will form the readings of the sensors and buttons in an array form, sorted
Fig. 6. The software architecture of the intelligent fire alarm system
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based on its reading. The array sequence will be identified, and the action will be taken based on the array sequencing code [2, 22]. The device uses an API designed to update the GPS, multi-sensors, and button status to the dashboard. The dashboard is designed for local fire stations’ coverage radius. This dashboard uses the Google map API to locate and mark up the location of the alerting device. For early preventions, Sensor reading with insufficient proof of fire will only notify the owner and the locals through telegram and local alarm, and LED flashes. 3.4 Experimental Setup For this experiment, the aim is to observe the responsiveness of the sensor used by using the IoT platform. The experiment is set up with fixed environments and suitable materials, hence, the substances are stained or have memory gas upon them. The sensor value is plotted in a graph and mapped to monitor the difference (Table 1). Table 1. Experiments were taken for the multi-sensors in an intelligent fire alarm system. Experiments Sensor
Material tested
1
MQ-2 (Flammable gas)
Butane
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MQ-135 (Air quality: Paper smoke Smoke)
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Flame sensor
Candle lit
4 Results and Discussion From the experiment conducted, various types of sensors are tested and the responsiveness and threshold to alert the user are determined. The first experiment was conducted for the MQ-2 flammable gas sensor that will be tested with the butane gas. Based on Fig. 7, the sensor value is measured in ppm and as an analog value. Through Fig. 7(a), the initial condition without any detection of the sensor is in steady-state at 750 ppm. When the experiment started by using the butane gas detected by the MQ-2 gas sensor, the detection time taken is within 1 s, which is can detect an immediate response. However, Fig. 7(c) shows the result for the regeneration of the sensor will take around 60–100 s in order to return to its steady-states. After the first experiment was done, the second experiment has been done by using the MQ-135 which can be used to detect the air quality especially when smoke can be detected. In this experiment, the burned paper that produces smoke is used to identify the smoke condition and detection response. Based on Fig. 8, the sensor value is measured in ppm and as an analog value. Through Fig. 8 (a), the initial condition without any detection of the sensor are in steady-state at 500 ppm. When the experiment started by using the smoked paper detected by the MQ-135 sensor, the detection time taken is
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Fig. 7. Experiment for MQ-2 flammable gas at three conditions a) steady-state, b) detection state, and c) regeneration state
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Fig. 8. Experiment for MQ-135 flammable gas at three conditions a) steady-state, b) detection state, and c) regeneration state
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within 1 s, which is can detect immediate response shown in Fig. 8 (b). However, Fig. 8 (c) shows the result for the regeneration of the sensor will take around 100–120 s in order to return to its steady-states. The third experiment has been done to detect the flame by using the infrared sensor. By using this sensor, the existence of the flame can be identified. This experiment has been conducted by using the candlelit that will be used to detect the flame. Based on Fig. 9, the detection time taken is within 2 s, which has an instant response. However, the regeneration of the sensor to become its steady states take around 60–100 s. In addition, there is a lot of noise which is interfered with in the sensor value. To cancel the noise, the action taken is by using a noise cancellation algorithm to clean out the steady-state value. This solution is using continuous interval reading in short milliseconds. Meanwhile, the regeneration time could be catered by pull down the voltage for 1 s, as reset voltage to the sensor [23, 24].
Fig. 9. Experiment for the infrared sensor for flame detection at three conditions a) steady-state (no fire), b) detection state (fire)
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5 Conclusions and Future Tasks In conclusion, the multi-sensor built in this system was designed and integrate with IoT in this project. The developed system can be used as a basis for a fire alarm system when more sensor devices are connected to the system. Besides, the developed system also can be used to monitor the condition of the monitored area, and a suitable approach can be created for the controls in the future if necessary. It was also important to be able to store and set sensor values related to warning messages. The parameter monitoring of the user interface and mapping interface design is to ease the firefighters to understand the condition of fire and the rate of severeness. Some analysis frameworks proposed also help firefighters to decide on future prevention steps. Acknowledgment. This project is fully funded by collaboration research between Universiti Teknikal Malaysia Melaka and AURO Technologies PLT under MTUN Research Matching Grant 2020 which no. INDUSTRI(MTUN)/AURO/2020/FKE-CERIA/I00049. This project also has been funded by Yayasan Inovasi Malaysia under ministry of Science, technology and Innovation (MOSTI) under Malaysia Social Innovation Accelerator Program (MYSIAP) 2021 for pre-commercialization purposes.
References 1. Timbuong, J.: Firefighters dealt with more fires in first MCO | The Star. https://www.thestar. com.my/news/nation/2020/12/12/firefighters-deal-with-more-fires-in-first-mco. Accessed 10 May 2021 2. Chen, S.J., Hovde, D.C., Peterson, K.A., Marshall, A.W.: Fire detection using smoke and gas sensors. Fire Saf. J. 42(8), 507–515 (2007) 3. Reimann, P., Schütze, A.: Fire detection in coal mines based on semiconductor gas sensors. Sens. Rev. 32(1), 47–58 (2012) 4. Kohl, D., Kelleter, J., Petig, H.: Detection of fires by gas sensors. Sensors Updat. 9(1), 161–223 (2001) 5. JBPM. Jabatan Bomba dan Penyelamat Malaysia (Malaysian Fire and Rescue Department), Laporan Tahunan 2018 (Annual Report 2018), pp. 1–224 (2018) 6. Kanoun, O., Tränkler, H.R.: Sensor technology advances and future trends. IEEE Trans. Instrum. Meas. 53(6), 1497–1501 (2004) 7. Fonollosa, J., Solórzano, A., Jiménez-Soto, J.M., Oller-Moreno, S., Marco, S.: Gas sensor array for reliable fire detection. Procedia Eng. 168, 444–447 (2016) 8. Okayama, Y.: Approach to detection of fires in their very early stage by odor sensors and neural net. Fire Saf. Sci. 3, 955–964 (1991) 9. Oyabu, T.: An algorithm for evaluating disasters by fuzzy reasoning. Sens. Actuators B. Chem. 10(2), 143–148 (1993) 10. Yoshida, S., Suzuki, H., Kitajima, T., Kassim, A.M., Yasuno, T.: Correction method of wind speed prediction system using predicted wind speed fluctuation. In: 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), vol. 254, pp. 1054–1059, Tsukuba, Japan, 20–23 September 2016 11. Pravalika, V., Rajendra Prasad, C.: Internet of things based home monitoring and device control using Esp32. Int. J. Recent Technol. Eng. 8(1), Special Issue 4, 58–62 (2019) 12. Thirupathi, V., Sagar, K.: Implementation of home automation system using mqtt protocol and esp32. Int. J. Eng. Adv. Technol. 8(2C2), 111–113 (2018)
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13. Babiuch, M., Foltynek, P., Smutny, P.: Using the ESP32 microcontroller for data processing. In: Proceedings of 2019 20th International Carpathian Control Conference ICCC 2019 (2019) 14. NEO-6M GPS Module — An Introduction. https://www.electroschematics.com/neo-6m-gpsmodule/. Accessed 10 May 2021 15. Kassim, A.M., et al.: Performance analysis of acceleration sensor for movement detection in vehicle security system. Int. J. Adv. Comput. Sci. Appl. 10(10), 396–401 (2019) 16. Kassim, A.M., Shukor, A.Z., Zhi, C.X., Yasuno, T.: Exploratory study on navigation system for visually impaired person. Aust. J. Basic Appl. Sci. 7(14), 211–217 (2013) 17. Getting Started with the ESP32 Development Board | Random Nerd Tutorials. https://random nerdtutorials.com/getting-started-with-esp32/. Accessed 10 May 2021 18. Arduino Flame Sensor Interface - Working, Circuit Diagram, Code. https://www.electroni cshub.org/arduino-flame-sensor-interface/. Accessed 10 May 2021 19. Mluyati, S., Sadi, S.: Internet of Things (IoT) to prototype gas leak detector was made using MQ-2 and SIM800L. J. Tek. 7(2) (2019) 20. Interfacing of MQ-135 Gas Sensor with Arduino. https://microcontrollerslab.com/interfacingmq-135-gas-sensor-arduino/. Accessed 10 May 2021 21. Oyebola, B.O., Odueso, V.T.: LM35 based digital room temperature meter : a simple demonstration. Equatorial J. Comput. Theor. Sci. 2(1), 6–15 (2017) 22. Milke, J.A., Hulcher, M.E., Worrell, C.L., Gottuk, D.T., Williams, F.W.: Investigation of multi-sensor algorithms for fire detection. Fire Technol. 39(4), 363–382 (2003) 23. Abdulsahib, G.M., Khalaf, O.I.: An improved algorithm to fire detection in forest by using wireless sensor networks. Int. J. Civ. Eng. Technol. 9(11), 369–377 (2018) 24. Yang, J., et al.: Botanical internet of things: toward smart indoor farming by connecting people, plant, data and clouds. Mobile Netw. Appl. 23(2), 188–202 (2017). https://doi.org/ 10.1007/s11036-017-0930-x
Design and Development of Handheld Soil Assessment by Using Ion-Selective Electrode for Site-Specific Available Potassium in Oil Palm Plantation A. M. Kassim1(B) , S. Sahak1 , T. W. Chian1 , M. A. S. A. Aziz1 , M. A. S. S. Izran1 , M. S. H. Basari1 , M. M. Roslan1 , M. R. Yaacob1 , M. A. A. Abid2 , A. H. Azahar3 , M. M. Hashim4 , A. K. R. A. Jaya5 , T. Yasuno6 , and A. M. Mouazen7 1 Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,
76100 Durian Tunggal, Melaka, Malaysia [email protected] 2 Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 3 Faculty of Electrical and Electronic Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 4 Faculty of Plantation and Agrotechnology, Universiti Teknologi MARA, Jasin Kampus, 77300 Merlimau, Melaka, Malaysia 5 Auro Technologies PLT, No. 76, Jalan TU 42, 75450 Ayer Keroh, Melaka, Malaysia 6 Graduate School of Technology, Industrial and Social Sciences, Tokushima University, 2-1 Minami-Josanjima, Tokushima 770-8506, Japan 7 Department of Environment, Faculty of Bioscience Engineering, Ghent University, 9000 Gent, Belgium
Abstract. In agriculture, maintaining or monitoring soil nutrients is one of the most important factors that must be done to make sure crops grow healthy and get enough nutrients from the soil. The conventional method to measure the soil nutrient is by laboratory test which takes time to get the result. To overcome this problem, a measurement system that can give a result in a short time should be produced. In this paper, the design and development of a hand-held soil assessment device to measure the real-time and on-the-go soil potassium by using the ion-selective electrode sensor is described. The objective of this paper is to validate the accuracy of the ISE sensor compared to the soil electrical conductivity in measuring soil nutrients. This study has been done at the UiTM palm oil plantation in Jasin, Melaka. A total of 42 locations were studied. The experimental works are conducted to validate the accuracy of the sensor. By choosing a suitable sensor that can give an accurate reading in a short time must be equipped in the system. An ion-selective electrode (ISE) sensor can be used to measure the soil nutrient. Consequently, the precision agriculture system is needed to develop this measurement system which can help the farmer to monitor their farm easily. Keywords: Ion-selective electrode · Potassium · Soil nutrient · Precision agriculture · Smart system
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 210–221, 2022. https://doi.org/10.1007/978-981-16-8954-3_21
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1 Introduction The world population is expected to reach 9.5 billion people by 2050, according to the statistics collected and assessed by researchers from the UN Food and Agricultural Organization, resulting in a global food crisis [1]. More crop farming and animal production is seen as an unsustainable option, owing to the scarcity of land caused by the growing global population, as well as the scarcity of resources required for plant nutrition. Agriculture is a critical component in maintaining food security, maintaining economic stability, and strengthening countries’ economies. It must adhere to an increasing range of global and quality requirements [2]. One common factor that must be measured in agriculture is the level of soil nutrients. This is to ensure that the soil is fertile, and the plant will get enough nutrients to grow. Based on [3], environmental degradation is a global issue caused by inefficient cropping methods, land-use, and soil management techniques, and it has sparked interest in sustainable agricultural production systems. A smart farming system and precision agriculture are needed to maintain or monitor the level of the nutrient in the soil. Precision agriculture is made up of three primary components: information, technology, and management. Precision farming necessitates a lot of data. Precision agriculture is a management method that employs information technology to gather valuable data from a variety of sources and incorporate it into decision-making [4]. In this paper, precision agriculture is referred to the usage of the sensor system in agriculture which is used to monitor or to measure the level of soil nutrients. The most popular method for analyzing the variability of soil samples is laboratory soil variability analysis. However, this procedure usually takes about 2 to 3 months to complete. Normally, researchers gather soil samples manually and submit them to a lab for analysis. Because soil variability changes over time, this may lead the soil variability to become inaccurate. Real-time soil nutrient analyses are necessary to overcome this. A real-time soil nutrient measurement system with the aid of a sensor will minimize the time taken to get the result and save cost compared to the laboratory method. The type of sensor that can be used to measure the soil nutrient is the Ion-selective electrode (ISE) and electrical conductivity (EC) sensor. An ion-selective electrode (ISE) is an electrochemical sensor that works on the principle of potentiometry, which involves measuring the cell at near-zero current [5]. The current flow in the soil is defined as electrical conductivity, which is proportional to the total dissolved solids (TDS) in the soil [6]. Soil salinity, clay content, and water content have all been effectively measured using EC measurements [7]. Experiments will be carried out to assess the sensor’s performance and efficiency. Furthermore, one of the goals of this study is to measure the nutrient level in the soil at the site. As a result, the time it takes to achieve a result utilizing the laboratory method is reduced.
2 Related Works According to Nangonoa et al. [8], stated that ISE sensor is used in-field soil measurement to measure the level of the nutrient in the soil. According to paper [9], the suitability of the nitrate ion-selective electrode for use in an automated soil nitrate monitoring system
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was tested in the lab. The following soil properties were tested: extractant ratio, extract clarity, and electrode response time. The electrode is suited for in situ measurement of soil nitrate content with correct calibration, according to the results, and reliable readings may be achieved in less than four seconds. According to paper [10], ion-selective electrodes are used to measure the soil nutrient especially the potassium by using the ion activity in naturally moist soil. The study focuses on the stability, repeatability, and dependability of polymer membrane, combination, flat-surface, ion-selective electrodes. In the paper [11–13], various type of sensor is used in the measurement of soil nutrients such as electrical and electromagnetic sensors, optical and radiometric sensors, mechanical sensors, acoustic and pneumatic sensors, and electrochemical sensors. Only electrical and electromagnetic sensors have been widely used in precision agriculture. Meanwhile, the paper [14] stated that with correct calibration techniques, nitratebased ISE is ideal for quick in-field soil NO3 − N monitoring. The criteria developed to speed up the measurement process reveal that in silty clay loam soil, the real or ultimate nitrate concentration may be predicted accurately within 6 s of the extraction starts.
3 Method and Materials The study area of this paper is at Universiti Teknologi Mara (UiTM) Jasin Campus, Melaka. This section will present the hardware and material that is used to set up the process of the experiment to conduct the real-time measurement of soil nutrients for a different type of key soil parameter. Two types of parameters that are studied in this paper are ISE sensors for detecting available potassium (K) in the soil site specifically. The potassium-based ISE sensor is potential to be used for determining the nutrient level measurement in the soil in real-time. Besides, it also can be used on the go during the field measurement. In addition, the other type of sensor that will be used is electrical conductivity (EC). The hardware that is needed to conduct this experiment such as Raspberry Pi, ISE sensor, EC sensor, mixing cup, soil, and water to ensure the soil that needs to be assessed is in slurry condition. The ISE sensor was chosen in this project since the objective of this project is to determine the correlation between the concentration of nutrients in the soil especially for the potassium and the electrical conductivity. The technique involves mixing soil samples with water to create an aqueous solution. The ISE sensor will evaluate the concentration reading in milligrams per liter (mg/L). The concentration data will be automatically saved as a CSV file on the SD card of the Raspberry Pi. The project’s system configuration is presented in Fig. 1. In the methodology, two experiments will be conducted to measure the nutrients contained in the soil by using each sensor. The key soil parameters that will be measured are potassium and electrical conductivity content in the soil. This comparison for each experiment is conducted to observe the accuracy of sensor reading for soil nutrient content to evaluate the developed site specifically the soil measurement system. Figure 2 shows the developed handheld device to be the experimental setup for measuring the soil potassium.
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Fig. 1. System configuration block diagram
Fig. 2. Real-time and hand-held soil potassium analyses with ISE sensor and LCD.
3.1 Data Acquisition System In this project, the Raspberry Pi 4 Model B was chosen as the main data acquisition system. Software such as Python and Node-Red were used to program the Raspberry Pi to recollect and record data automatically. Python was used to control the sensor’s
Fig. 3. Node-Red system flow
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data collection, and Node-Red was used to analyze and merge data from the ISE sensor. The collected data will be shown on the LCD and saved as a CSV file. The developed Node-Red system flow is shown in Fig. 3. 3.2 Potassium Based Ion-Selective Electrode Sensor In the experiment, two experiments are conducted using an ISE sensor but with a different type of detection. The detection that will be tested is the concentration of soil potassium and nitrate content. The model of the sensor that will be used is Vernier ion-selective electrode. This experiment is conducted to validate the accuracy of the measurement from the sensor reading to develop a site specification soil measurement system. The sensor measured the concentration of both mixtures at the same time and sent the data to the Raspberry Pi via the serial port. The Vernier ISE sensor used in this project is shown in Fig. 4. The specification of the Potassium based ion-selective electrode sensor is shown in Table 1.
Fig. 4. Potassium based ion-selective electrode (ISE)
Table 1. Potassium based ion-selective electrode sensor specification Parameters
Specification
Range (mV)
±1000 mV
Range (concentration)
1 to 39,000 mg/L (or ppm)
Accuracy
±10% of full scale (calibrated 10 to 1000 mg/L)
Interfering ions
Cs+, NH4+, H+, Ag+, Li+, Na+
pH range
2–12 (no pH compensation)
Temperature range
0–40 °C (no temperature compensation)
Electrode slope
56 ± 3 mV/decade at 25 °C
Electrode resistance
100 to 200 k
Minimum sample size
Must be submerged 2.8 cm (1.1 in)
3.3 Soil Electrical Conductivity Measurement As the comparison matrix, an electrical conductivity (EC) sensor will be used to measure the general nutrient content in the soil. The sensor that will be used is a developed
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multi-soil environment sensor. Figure 5 shows the photo of the developed multi-soil environment sensor (MSES). This sensor is used to validate the accuracy of the sensor and to determine the correlation between the potassium-based ion-selective electrode sensor (ISE) and the EC sensor. In this sensor, the light intensity, temperature, and soil moisture also can be measured. This sensor is suitable to be used as in situ measurement method.
Fig. 5. Developed multi soil environment sensor
3.4 Initial Calibration Analysis Following the completion of the system’s experimental work, the system’s analysis also will be validated to verify that the data collected is valid. A simple calibration procedure will be done to conduct the initial sensor calibration. In this methodology, the ISE sensor’s and GPS’s performance were evaluated. i. Evaluation of ISE sensor To determine the percentage of error of the ISE sensor, it was immersed in a known concentration aqueous solution, the low concentrated potassium(K) chloride, KCl (1.00 mg/L), and highly concentrated potassium(K) chloride, KCl (50.00 mg/L) were used to make the aqueous solution. The sensor will be soaked in each solution, and the readings will be recorded, as well as the sensor’s percentage of error. The experiment is done three times to determine that the results are valid. The formula for the percentage error is: Percentage of Error: =
|Actual value − Observed value| × 100% Actual value
(1)
ii. Evaluation of GPS For the GPS, the NMAE sentence reading in (Gpgll) format was exported to the NMAE decoder online, and the sensor’s geographical location reading was shown on a Google map. Then QGIS software will be used to generate the map based on the data that will be measured. 3.5 Soil Sensor Data Analysis The recorded data will be analyzed by using the partial least squares regression (PLSR) approach. Partial least squares regression (PLSR) is a statistical analysis approach for
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predicting group membership given a set of data. The relationship and structure between the input and output variables are also expressed using linear regression. In comparison to linear regression, multiple linear regression predicts more than one category of data. Since there are many input variables, multiple linear regression is utilized, and it can be defined as Eq. 2. Y = β0 + β1 XI + β2 XI2 + ∈I
(2)
where Y is the output value, X is the observed value, 0 is the data curve, and I is the error The data from the ISE sensor and the EC measurement were compared using linear regression. However, because the units are different, some calculations must be performed before the comparison. mg/L = ppm = (μS/cm) ∗ 0.64
(3)
3.6 Soil Sampling Method Soil samples were taken in the UiTM palm oil plantation in Jasin, Melaka. A total of 42 soil samples were taken at a depth of 20 cm. The sampling area was 1 acre, and the sampling strategy was grid point sampling with a 10-m separation between samples as shown in Fig. 5. 42 soil samples were gathered for lab testing to determine the sensor’s validity. ISE sensor-based real-time and on-the-go soil nutrient analyses were also performed during sampling, and a soil nutrient table was developed (Fig. 6).
Fig. 6. Soil sampling grid
4 Result and Discussion 4.1 ISE Sensor Analysis The initial calibration of the ISE sensor has been done by applying the percentage of error for the sensor that has been analyzed to assess the ISE sensor’s performance. The ISE sensor analysis results were shown in Tables 2 and 3.
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Table 2. Percentage of error of ISE soak in LOW (1 mg/L) concentration Potassium(K) Chloride solution. Potassium(K) Chloride (K+)
LOW 1
2
3
Concentration (mg/L)
0.949
1.0951
1.0852
Percentage of error (%)
5.14%
9.51%
8.52%
Table 3. Percentage of Error of ISE soak in HIGH (50 mg/L) concentration Potassium(K) Chloride solution. Potassium(K) Chloride (K+)
HIGH 1
2
3
Concentration (mg/L)
50.407
50.103
50.179
Percentage of error (%) 0.81%
0.21%
0.09%
Based on Tables 2 and 3, a low percentage of error is recorded based on the test that has been carried out. This will be resulting in the ISE sensor is suitable to be used in measuring soil nutrients. The accuracy of a sensor is the most important element in choosing a parameter for measuring data to get an accurate result. Because the economic and/or environmental risk of applying the incorrect treatment to the crop might be significant, accurate measurements are critical in determining the appropriate management treatment [15]. 4.2 Soil Sampling Data Analysis The linear regression graph was formed by comparing the concentration of potassium(K) with the EC value through data recorded using MSES. The plotted graph shows the value of potassium-based ISE and the recorded EC value shown with the samples locations. Meanwhile, the linear regression of potassium(K)in Fig. 7 is slightly lower than the linear regression of the EC value obtained at the field, which is R2 (K) = 0.3168 versus EC R2 = 0.3325. Figure 7 shows that the relationship between potassium and EC value is similar in a pattern throughout the different locations. Based on the graph the EC more to have a similar result to the ISE sensor for potassium(K). Figure 8 shows the correlation study between ISE potassium to MSES EC value. From this figure, the correlation of potassium to EC value is R2 = 0.9138. Correlation for potassium is very high and near to the value of 1. The relationship between both parameters is also linear and the linear prediction modeling can be made for farmer recommendation for fertilization. Hence, the ISE potassium sensor has reliable data that can be used in measuring soil nutrients in real-time and directly to the field.
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Concentraon (mg/L)
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80 70 60 50 40 30 20 10 0
R² = 0.3325
R² = 0.3168 1
3
5
7
K(ppm)
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41
ec(ppm)
Linear ( K(ppm))
Linear (ec(ppm))
Fig. 7. Relationship of K and EC at a different location
Relationship of K (mg/L) to EC (ppm) 35 30
K (mg/L)
25 20 15 10 5 0 0
2
4
6
8
10
12
14
16
18
EC (ppm) K (mg/L)
Linear (K (mg/L))
Fig. 8. Correlation study between ISE potassium to MSES EC value
4.3 Soil Mapping Analysis On the other hand, the fertility mapping of the measured palm oil field was constructed by using the data collected. The QGIS software was used to map the potassium ISE value with the EC value from the MSES. Figure 9 illustrates the field’s fertility mapping in terms of potassium (K). To identify the nutrient level of the soil, a color legend was added to the fertility mapping of soil nutrients. Figure 9 depicts 42 samples with various color indicators. Five different legend colors are labeled in Fig. 10 with a range of 6 to
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70 mg/L. The optimum Potassium (K) for plant growth is between 70 and 150 mg/L. This plotted indicator is based on the soil sampling that was done at the location. Besides, the system can also keep track of the information gathered through the user interface. The user interface may track the sensor’s real-time GPS location, soil nutrient levels, and battery life. Using the VNC viewer application, the user interface can be viewed on the system’s LCD monitor, a personal computer, or a mobile phone. The system’s user interface is depicted in Fig. 10.
Fig. 9. Fertility mapping of Potassium(K)
Fig. 10. User dashboard interface of the system.
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5 Conclusion In this paper, the design and development of the hand-held device to measure the soil nutrient site specifically was done. The performance efficiency of the ion-selective electrode also was evaluated in this study. The potassium-based ISE sensor is used to measure the potassium level in the soil. The correlation of potassium to EC value is R2 = 0.9138. Correlation for potassium is very high and near to the value of 1. The relationship between both parameters is also linear and the linear prediction modeling can be made for farmer recommendation for fertilization. Hence, the ISE potassium sensor has reliable data that can be used in measuring soil nutrients in real-time and directly to the field. In the future, experimental work for various plantations and crops also will be conduct to verify the accuracy for different crops. On the other hand, the correlation between the lab soil analysis also needs to be done and a soil library could be developed for prediction analysis and modeling. Acknowledgment. This project is fully funded by collaboration research between Universiti Teknikal Malaysia Melaka from High Impact Short Term Grant Scheme no. PJP/2020/FKE/HI22/S01720.
References 1. Njoroge, B.M., Fei, T.K., Thiruchelvam, V.: A research review of precision farming techniques and technology. J. Appl. Technol. Innov. 2(1), 22–30 (2018) 2. Bersani, C., Ouammi, A., Sacile, R., Zero, E.: Model predictive control of smart greenhouses as the path towards near zero energy consumption. Energies, 13(14), 3647 (2020) 3. Nanganoa, L.T., Ngome, F.A., Suh, C., Basga, S.D.: Assessing soil nutrients variability and adequacy for the cultivation of Maize, Cassava, and Sorghum in Selected Agroecological Zones of Cameroon. Int. J. Agronomy 2020, 1–20 (2020) 4. Mehta, A., Masdekar, M.: Precision agriculture – a modern approach to smart farming. Int. J. Sci. Eng. Res. 9(2), 23–26 (2018) 5. Sohail, M., Downs, S.: ELECTRODES | Ion-Selective Electrodes. Elsevier Inc. (2013) 6. Othaman, N.N.C., Isa, M.N., Ismail, R.C., Ahmad, M.I.: Factors that affect soil electrical conductivity (EC) based system for smart farming application, vol. 020055 (2020) 7. Heiniger, R.W., Clay, D.E.: using soil electrical conductivity to improve nutrient management, November 2015 8. Sibley, K.J., Brewster, G.R., Astatkie, T., Adsett, J.F.: In-field measurement of soil nitrate using an ion-selective electrode, vol. 3 (2018) 9. Thottan, J., et al.: communications in soil science and plant analysis laboratory evaluation of the ion-selective electrode for use in an automated soil nitrate monitoring system, pp. 37–41, February 2015 10. Adamchuk, V.I., Hall, K., Morgan, M.T.: Feasibility of on-the-go mapping of soil nitrate and potassium using ion-selective electrodes, vol. 0300(2) (2002) 11. Adamchuk, V.I., Hummel, J.W., Morgan, M.T., Upadhyaya, S.K.: On-the-go soil sensors for precision agriculture. Comput. Electron. Agric. 44, 71–91 (2004) 12. Kassim, A.M., Nawar, S., Mouazen, A.M.: Potential of on-the-go gamma-ray spectrometry for estimation and management of soil potassium site specifically. Sustainability 13, 661 (2021)
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13. Kassim, A.M.: Design and development of mechanical based proximal soil sensing system for precision agriculture application. In: Proceedings of Mechanical Engineering Research Day (2020) 14. Adsett, J.F., Thottan, J.A., Sibley, K.J.: Development of an automated on-the-go soil nitrate monitoring system. Appl. Eng. Agric. 15(902), 351–356 (1999) 15. Viacheslav, I., Viscarra Rossel, R.A., Kenneth, A., Schulze, P.: Sensor fusion for precision agriculture. In: Sensor Fusion for Precision Agriculture (2011)
Performance Evaluation of Energy Harvesting Method for Wireless Charging System in Wearable Travel Aid Device for Visually Impaired Person A. M. Kassim1(B) , N. N. Ayub1 , A. Z. Shukor1 , M. R. Yaacob1 , W. M. Bukhari1 , M. A. A. Abid2 , A. H. Azahar3 , D. A. Prasetya4 , T. Yasuno5 , and A. K. R. A. Jaya6 1 Rehabilitation and Assistive Engineering Technology Research Group (REAT), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, 76100 Melaka, Malaysia [email protected] 2 Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka Hang Tuah Jaya, Durian Tunggal, 76100 Melaka, Malaysia 3 Faculty of Electrical and Electronic Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, 76100 Melaka, Malaysia 4 Jurusan Teknik Elektro, Universitas Merdeka Malang, Jl. Taman Agung, No. 1, Malang, Indonesia 5 Graduate School of Technology, Industrial and Social Sciences, Tokushima University, 2-1 Minami-Josanjima, Tokushima 770-8506, Japan 6 Auro Technologies PLT, No 76, Jalan TU 42, 75450 Ayer Keroh, Melaka, Malaysia
Abstract. Energy harvesting is a process that harvests ambient energy from surrounding to useful electrical energy such as from light, thermal, wind, kinetic, radio-frequency, and biochemical. The wireless energy harvesting and charging system allow electronic devices to operate without a conventional power source by eliminating the physical connection to the wearable device. This paper describes the evaluation of the wireless charging system by using the energy harvesting method to be used in the wearable device for the visually impaired person. This paper presents a comparison between the best energy harvester such as the photovoltaic, photodiode, and radio-frequency. The piezoelectric, wind, and biochemical methods were not done because it was not appropriate to be used in the wearable device for the visually impaired person. The experiments for comparing the best configuration for the energy harvesting method and evaluation of energy harvesting performance by various configurations such as single, series, and parallel connection are conducted. This wireless charging system by using a wireless docking system desired to eliminate the usage of the bulky battery that has been installed in the wearable device to reduce weight and easy to charge especially for the visually impaired person. Consequently, the wireless charging system by using the energy harvesting method could give the comfortability and increase the quality of life of the visually impaired person. Keywords: Energy harvesting · Wireless charging · Wearable device · Travel aid · Visually impaired person · Assistive technology · Smart system © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 222–235, 2022. https://doi.org/10.1007/978-981-16-8954-3_22
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1 Introduction Nowadays, World Health Organization (WHO) has released the statistics of disabled persons worldwide are 15% or more than 1 billion people of the total population. From the statistic, there is 285 million visually impaired persons which 39 million are fully visually impaired and 246 million are the low vision [1]. Therefore, an intelligent wearable device that is useful and the comfort to the user is very important. In the previous study, there are some travel aid devices have been developed. The wearable device can be used to detect obstructions at the top of the human body such as front, left, and right. The downside will be covered by the white cane which is usually used by the visually impaired person [2]. The ultrasonic sensors will be used to sense the obstructions in these directions. Besides, the smaller and lightweight rechargeable battery is an important aspect that needs to be optimized to be fit inside the mainframe of the wearable device. The wireless charging system is proposed by using the wireless docking system to eliminate a conventional battery, which needs to be changed every time it depletes. As a consequence, the user felt uncomfortable and difficult to use the wearable device [3, 4]. Recent technical developments have increased the efficiency of energy harvesting modules in converting trace amounts of energy from the environment into electricity. The ability to harvest RF energy, from ambient and/or dedicated sources, enables continuous charging of low-power devices and will possibly eliminate the need for a battery [5]. In both cases, these devices can be free of connectors, cables, and battery access panels with significant mobile freedom while charging and in use. The ability of electronic circuits to obtain their source of power from the surrounding environment is a feature that has gained increased attention, either for sensor networks, or embedded systems. Sensor networks that only rely on power grid connections are limited to a relatively small range of applications, as network nodes can never be too far from a power outlet [5]. Energy harvesting is a process that harvests ambient energy from surrounding to useful electrical energy. In the past few years, the amount of research devoted to energy harvesting has been rapidly increasing due to significant growth in producing wireless electronics without chemical batteries as the power source in long-life wireless applications, such as wireless sensors nodes (WSN). The most common ambient energy harvesting technologies are sunlight, thermal gradient, vibration, electromagnetic radio frequency (RF) energy, body motion, and human heat. This study concludes that, for WSN devices that are supposed to last for about 1 year, battery technology alone is sufficient to provide enough energy [5]. However, if a device requires a longer service life, an energy harvester can provide a better solution than battery technologies, although the form factor and cost of the device should be carefully analyzed concerning the operation life. In producing energy for low power applications, the researcher has focused on methods that have high efficiency in producing output voltage and are suitable for wearable wireless sensors.
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2 Related Works 2.1 Developed Wearable Travel Aid Device In the previous study, some wearable devices have been developed to be used by the visually impaired person. The design was similar to the spectacle which usually being used by visually impaired people to cover their eyes. The current developed wearable devices are bulky and heavy which is weighing about 590 g not including the conventional battery as shown in Fig. 1(a). It cannot be fit as spectacle and is easy to fall when the user wearing the electronic spectacle. The visually impaired person feels uncomfortable when using it. Besides, the design of the spectacles also needs to be studied ergonomically to give comfortability to the user [2]. Thus, some solutions to improve the bulky spectacle have been made to reduce the weight of the wearable device such as the usage of the type of ultrasonic sensor, type of battery, and electronic circuit. Figure 1(b) shows the improved version of a wearable travel aid device by changing the different types of sensors and a power bank that functioned as a rechargeable battery. The current solution is by replacing the type of battery with a power bank but it is still not suitable for the visually impaired person to use. Even though the power bank is used in the improved design, the power bank cannot be easily connected by the visually impaired person since it uses the USB connector to charge it with the power adapter. It is difficult for the visually impaired person to connect to the power bank and the battery needs to change every time it depleted. For the solution to the current problem, I would like to propose a wireless charging system using energy harvesting from surrounding to design a wireless docking system that desired to eliminate the usage of the conventional battery. The proposed energy harvesting system will help to ease the visually impaired person to charge the spectacle easily using energy sources from the surrounding.
(a) First version wearable travel aid device
(b) Second version wearable travel aid device
Fig. 1. Developed wearable device version 1 in the previous study
2.2 Energy Harvesting System In the past few years, the amount of research devoted to energy harvesting has been rapidly increasing due to significant growth in producing wireless electronics without
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chemical batteries as the power source in long-life wireless applications, such as wireless sensors node (WSN) [5]. The most common ambient energy harvesting technologies are sunlight, thermal gradient, vibration, electromagnetic radio frequency (RF) energy, body motion, and human heat. This study concludes the WSN devices that are supposed to last for about 1 year, battery technology alone is sufficient to provide enough energy. However, if a device requires a longer service life, an energy harvester can provide a better solution than battery technologies, although the form factor and cost of the device should be carefully analyzed concerning the operation life [6]. In producing energy for low power applications, the researcher has focused on methods that have high efficiency in producing output voltage and are suitable for wearable wireless sensors. The ability of electronic circuits to obtain their source of power from the surrounding environment was a feature that has gained increased attention, either for sensor networks or embedded systems [2]. The use of batteries allows for freedom about sensor distribution, but as their stored charge gets depleted, they need to be replaced. Thus, the energy harvesting system was developed to avoid battery replacement and can eliminate the usage of long cables. Figure 2 shows the applications of energy harvesting circuits with wearable devices.
Fig. 2. Applications of energy harvesting circuit with wearable devices
2.3 Energy Harvesting Device Photovoltaic energy harvesting had been focused on by many researchers due to its capability in powering low power applications. It was an interesting and effective system in outdoor applications because of its high power density [7]. Even though photovoltaic power had inefficiency in producing output voltage on cloudy days and nighttime due to lack of sunlight, it still was one of the most commonly used energy sources. Embedded system for low power application was capable to be powered without the use of batteries [8]. System integration cost, parasitic effects, and overall size can be reduced by the integration of photovoltaic cells on the same chip with a circuitry sensor [9]. When comparing with other ambient energy sources, light energy has the highest energy density compared to other sources [10]. To achieve a perpetual operation for low power applications, a small size of photovoltaic (PV) system had been invented to increase the autonomy of embedded devices [11]. Low power consumption can be achieved using photovoltaic energy harvesting with low-cost components and high efficiency of output voltage. A photovoltaic cell (PV) was mainly focused by most researchers to use as an energy resource to power up low power applications and autonomous devices [12]. By using photovoltaic conversion,
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photovoltaic energy harvesting produces the highest output power density that makes it a good choice to power an embedded system that consumes few watts using a reasonable small harvesting module [13]. A new generation for wireless sensor networks and low power applications can be implemented by using the combination of integrated photovoltaic energy scavenging that enables a low cost, longer lifetime span, and small volume systems [14]. On the other hand, photodiode was used in many photonics applications such as compact disc players, smoke detectors, medical devices, and applications that used sensing devices. The photodiode was also known as semiconductor diode or PN junction that consumes light energy and converts it into electrical energy [15]. This device is also known as a photodetector and photosensor that was specifically designed to operate in reversed bias mode. Photodiode consists of a p-n junction or PIN structure made from the light-sensitive semiconductor. Photodiodes operate in different ways based on their structure and function but the basic operation remained the same. On the other hand, thermal energy harvesting is a renewable energy that generates electrical energy from the temperature difference. This energy harvesting is another alternative used to power up electrical devices by using Thermoelectric Energy Generator (TEG). The devices use heat energy and convert it into electrical energy known as the Seebeck effect. Heat waste in a thermoelectric energy harvester was extracted from the device to produce electrical energy. Few researchers found that heat from the human body can also be used as a source of thermoelectric energy through the thermoregulation process from the human body [16]. Thermoelectric can also use heat from sunlight to produce electrical energy as long as it satisfies the condition of the thermoelectric power harvester in the difference of temperature measurement. The sources are considered as one of the permanently available sources. Even though thermoelectric energy harvester can produce electrical energy, some disadvantages were found in the system such as energy conversion efficiency is low compared to other renewable energy resources [8]. Wireless sensors and wearable devices can be powered by using TEG on the human body. The conversion of heat transfer from the human body can be used to power up wearable devices and wireless sensors for health monitoring. The most suitable place for wearable devices was at the wrist, where it can be resembling a watch or a thin device in clothing. Besides, radio frequency (RF) is a type of energy harvester that harvest energy from an ambient or dedicated source that provides continues electric charging for low power electronic devices and possible to eliminate the usage of battery. RFID tracking tags, wearable devices, and low-power electronic devices can be charged and operate by harvesting RF energy [17]. Even though RF can produce output voltage for low-power electronic devices, the conversion efficiency of power can drop to 50% depends on the frequency of the operation. The efficiency of RF decreases at a longer range as the transmitted radiation to the surrounding [18]. RF energy harvesting is useful in charging a capacitor or battery especially in the area that difficult to reach such as chemical industrial and aircraft. Table 1 shows the specification of each energy harvester device which will be tested by using the developed portable and outdoor data acquisition for energy harvester.
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Table 1. The specification of energy harvester device
Photo of energy harvester Manufacturer/ Supplier Device name Source of energy Size (L x W x H)(mm) Weight (g) Cost (RM)
Cytron Technologies Photovoltaic panel Light 60 x 60 x 2 8 9.80
Vishay Semiconductor Photodiode Light 5.4 x 4.3 x 3.2 0.1 5.30
Cytron Technologies Peltier Heat 40 x 40 x 3.8 17 8.90
Cytron Technologies Radio Frequency RF waves 15 x 10 x 1 9 25.00
3 Method and Materials Figure 3 shows the developed portable and outdoor data acquisition for energy harvester. The experiment was conducted using a photodiode, Peltier, photovoltaic panel, and RF. In the experimental setup, the photodiode was connected to channel A, the Peltier was connected to channel B, the photovoltaic panel, and RF was connected to channel C. Then, the equipment was placed at the center where maximum coverage of sunray can reach. For photodiode, photovoltaic panel, and Peltier for outdoor experiment while RF was experimented indoor. The output voltage from each resource was recorded into the SD card. The equipment was placed for 10 h. The data taken started from 7 am to 7 pm. These steps were continued in multiple components but different arrangements. The arrangement of sources used in this project is series and parallel configuration. The experiment for photovoltaic and photodiode for the indoor experiment was taken at a place with dim lights. This to not affect the value for the output voltage taken using the equipment of the energy harvester by using the ruler to measure the distance between sources. Since most of the energy harvesting circuits produced low output voltage that cannot be used to power up low power applications. The harvested energy was unable to generate a sufficient amount of energy needed specifically for low power applications. Thus, various types of the method were introduced to amplify the voltage to the maximum level. As an alternative to boost the output voltage to the desired value, a boost converter was used to amplify the output voltage. Different energy harvester produces different output voltage, thus different boost converter needed for each of the harvester technology. Boost converter, maximum power point tracking (MPPT), charge pump, and additional components were the possible method used to amplify output power. The MPPT technique was commonly used in the photovoltaic system and had advantages in cost efficiency even though they had low accuracy [5]. MPPT optimized the efficiency of output voltage in the system [12]. Besides the integration of the MPPT circuit, there was also a boost converter that was commonly used in the low output power circuit.
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Fig. 3. Developed portable and outdoor data acquisition for the energy harvester
A more efficient approach to boost the low output voltage-applying the DC-DC boost converter instead of stacking multiple components can be done [14].
4 Experimental Result and Discussion 4.1 Comparison Between the Best Type of Energy Harvester Figure 4 shows the output voltage and current comparison between Photovoltaic, PD, and RF in a single configuration. The graph shows the highest output voltage produced by a photovoltaic panel at 3.71 V for a single configuration. RF produced at 0.47 V and photodiode with 0.46 V. Based on the graph, the photovoltaic panel produced the highest output voltage due to its large size and integration of photovoltaic cells in the panel. By using photovoltaic conversion, photovoltaic energy harvesting produces the highest output power density that makes it a good choice to power an embedded system that consumes few watts using a reasonable small harvesting module [11]. Photodiode using the same system as a photovoltaic panel by using the energy conversion by harvesting the light energy to electrical energy. Due to its sensitivity to light, the light energy is converted into electrical current when a sufficient amount of light strikes the surface of the photodiode. The photovoltaic cell is a large area of photodiode where it converts light energy to electrical energy and only works in bright light. Based on Fig. 4, the graph shows the output voltage and current for photovoltaic, photodiode, Peltier, and radio-frequency in a single configuration. According to Ohm’s law, current (I) is proportional to voltage (V) but inversely proportional to resistance(R) shown in Eq. 1. V = IR
(1)
Thus, the value of current increases if the value of voltage increase too while resistance stays the same. Electrical power is the multiplication of voltage with current, P =
4
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0
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Fig. 4. Relation between Peltier, photovoltaic, PD, and RF in a single configuration
IV, units in Watt (W). By using Ohm’s law, the formula for power can be found in Eq. 2 and Eq. 3. P = I 2R
(2)
V2 R
(3)
P=
The relationship between voltage, current can influence the amount of electrical power as the greater the value of current, the greater the value of power. The greater the value of voltage, the greater the value of voltage. The smaller the value of voltage and current, the smaller the value of power. Based on the graph, the output voltage produced by photovoltaic increases linearly with current from 7 am until 7 pm. The output voltage and current may be varied due to change in light intensity from the surrounding. Photovoltaic produced the highest voltage at 3.71 V with a current at 0.15 A by using the electrical power formula shown in Eq. 4. P = IV P = (0.15)(3.17)
(4)
P = 0.56W Thus, electrical power for the photovoltaic is equal to 0.56 W. This is followed by a photodiode at 0.46 V with a current at 0.2 mA and power at 0.1 mW. Radio-frequency with 0.47 V, 0.22 A and 0.103 W (Fig. 5).
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Power,W
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0
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Fig. 5. Relation between power with time in a single configuration
4.2 Comparison for Photovoltaic, PD, and RF in a Series Configuration Figure 6 shows a comparison between photovoltaic, photodiode, and radio-frequency in series configuration starting from 7 am to 7 pm. The graph shows the highest output voltage produced by photovoltaic at 4.33 V followed by PD at 1.49 V and RF at 3.28 V. Based on Fig. 6, the graph shows the output voltage and current for the photovoltaic, photodiode, and radio-frequency in a series configuration. Based on the graph, the output voltage produced by the photovoltaic, photodiode, and RF increase linearly with current from 7 am until 7 pm. Photovoltaic produced the highest voltage at 4.33 V with current at 0.34 A indicated in Eq. 5.
1 0.8 0.6 0.4 0.2 0
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P = IV P = (4.33)(0.34) P = 1.47W
Time, sec Voltage-PD Current-PD
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Voltage-RF Current-RF
Fig. 6. Relation between voltage and current with time for series configuration
Thus, electrical power for the photovoltaic is equal to 1.49 W. This is followed by a photodiode at 1.47 V with a current at 3.47 mA and power at 5.17 mW. Radio frequency with 3.286 V, 0.16 A, and 0.52 W. In addition, electrical power for the photovoltaic is
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2 1.5 1 0.5 0
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equal to 1.49 W. This is followed by a photodiode at 1.47 V with a current at 3.47 mA and power at 5.17 mW. Radiofrequency with 3.286 V, 0.16 A, and 0.52 W is shown n Fig. 7.
Time, sec Power-PD
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Fig. 7. Relation between power with time in a series configuration
4.3 Comparison for Peltier, Photovoltaic, PD, And RF in a Parallel Configuration Figure 8 shows a comparison between photovoltaic, PD, and RF in parallel configuration starting from 7 am to 7 pm. The graph shows the highest output voltage produced by photovoltaic at 3.68 V followed by PD at 0.44 V and RF at 0.59 V. Based on the result given above, the photovoltaic panel shows the highest output voltage compared to others such as a photodiode, and RF. The highest output voltage produced by a photovoltaic panel is at 4.33 V followed by 3.71 V in and the least is at 3.68 V. Even though photovoltaic panels produced the highest output voltage, desired energy harvesting system needs to be small in size to be fit into the wearable device for a visually impaired person to ensure comfortability and efficiency to the user to be used. Compared with RF, RF is harmful to use as source energy especially when it was located near the human body. Even though RF radiation has smaller energy, it can lead to burning sensation and tissue damage when absorbed into a human body. Even though it can produce an output voltage, it is not suitable to be used for spectacle for the visually impaired person. Thus, the best type of energy harvester is photodiode as it was chosen due to its smaller size and the ability to produce sufficient output voltage for the wearable device. Based on Fig. 8, the graph shows the output voltage and current for photovoltaic, photodiode, Peltier, and radio frequency in a parallel configuration. Based on the graph, the output voltage produced by the photovoltaic, photodiode, and RF increase linearly with current from 7 am until 7 pm. Photovoltaic produced the highest voltage at 3.68 V with current at 0.14 A shown in Eq. 6. P = IV P = (3.68)(0.14) P = 0.52W
(6)
1 0.8 0.6 0.4 0.2 0 7:00:00 7:34:30 8:09:00 8:43:30 9:18:00 9:52:30 10:27:00 11:01:30 11:36:00 12:10:30 12:45:00 13:19:30 13:54:00 14:28:30 15:03:00 15:37:30 16:12:00 16:46:30 17:21:00 17:55:30 18:30:00
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Time, sec Voltage-PD Current-PD
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2 1.5 1 0.5 0 7:00:00 7:30:15 8:00:30 8:30:45 9:01:00 9:31:15 10:01:30 10:31:45 11:02:00 11:32:15 12:02:30 12:32:45 13:03:00 13:33:15 14:03:30 14:33:45 15:04:00 15:34:15 16:04:30 16:34:45 17:05:00 17:35:15 18:05:30 18:35:45
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Thus, electrical power for the photovoltaic is equal to 0.52 W. This is followed by a photodiode at 0.44 V with a current at 0.82 mA and power at 0.35 mW. Radio frequency with 0.59 V, 0.45 A and 0.27 W. Figure 9 shows the graph for power in a parallel configuration.
Power-PD
Time, sec
Power-Solar
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Fig. 9. Relation between power with time in a parallel configuration
4.4 Best Configuration for Energy Harvesting Method Even though photovoltaic produced the highest output voltage, photodiode was chosen for energy harvester for blind spectacle due to its smaller size and ability to produce enough output voltage. Based on Fig. 10, the highest output voltage produced by the photodiode is 1.49 V in series configuration followed by a single configuration at 0.46 V and the least at 0.44 V in a parallel configuration. Thus, the series configuration was chosen as the best configuration for the energy harvesting method. In terms of voltage, the series configuration produced the highest voltage compared to the single and parallel configuration according to Kirchhoff’s law. In a series configuration, the amount of current flow through each component has the same value while the amount of voltage is the sum of each voltage drops on each component. In a parallel configuration, the
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2 1.5 1 0.5 0 7:00:00 7:32:45 8:05:30 8:38:15 9:11:00 9:43:45 10:16:30 10:49:15 11:22:00 11:54:45 12:27:30 13:00:15 13:33:00 14:05:45 14:38:30 15:11:15 15:44:00 16:16:45 16:49:30 17:22:15 17:55:00 18:27:45
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amount of voltage through each component has the same value while the amount of current is the total current from each component.
Time, sec Single-PD Current-Single PD
Series-PD Current-Series PD
Parallel-PD Current-Parallel PD
Fig. 10. Comparison between photodiode configuration
5 Conclusions In conclusion, all types of energy harvesters can produce an output voltage. The photovoltaic panel produced the highest output voltage compared to another energy harvester at 4.33 V while the lowest is photodiode at 0.44 V. This is because photodiode has smaller conversion efficiency than photovoltaic due to its smaller size only a small amount of voltage can be produced. RF was not chosen as the best energy harvester as it is harmful to the human body. Even though the photodiode produced the lowest output voltage and power, it was chosen due to its smaller size to fit into the spectacle and the low voltage can be amplified using a voltage booster to supply voltage to the battery by increase the quantity of the photodiode will increase the voltage that can be supplied to the intelligent spectacle. Thus, the photodiode was chosen as the best energy harvester as it was able to produce output voltage at 1.49 V with a current of 3.47 mA. It can be fit into the spectacle and can be used by visually impaired persons comfortably. For the best configuration for the energy harvesting method, the series configuration was chosen as it can produce high output voltage compared to other configurations according to Kirchhoff’s law. The energy harvesting performance by distance shows that the shorter distance, the higher the component can produce an output voltage. It shows that photovoltaic produced the highest output voltage in a shorter distance compared to photodiode but photodiode was chosen due to its suitability to be used for wearable devices. To able providing a sufficient amount of voltage for the wearable device, the photodiode needs to have a voltage amplifier such as a boost converter. The converted electrical energy needs to be stored in a storage element such as a supercapacitor or rechargeable batteries. The photodiode was chosen to be used as an energy harvester for a wearable device to be fit into the spectacle and ease the usage of a visually impaired person.
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Acknowledgment. This project is fully funded by collaboration research between Universiti Teknikal Malaysia Melaka and the Ministry of Higher Education Malaysia under Prototype Research Grant Scheme no. PRGS/2019/FKE-CERIA/T00022.
References 1. World Health Organization and the World Bank Group: Disability- a global picture, World Report on Disability 2011, Chap. 2, 29 (2011). http://www.who.int/disabilities/world-report/ 2011/report.pdf. Accessed on 5 Oct. 2010 2. Kassim, A.M., Jamri, M.S., Aras, M.S.M., Rashid, M.Z.A., Yaacob, M.R.: Design and development of obstacle detection and warning device for above abdomen level. In: 2012 12th International Conference on Control, Automation and Systems (ICCAS), pp. 410–413, Jeju, Korea (2012) 3. Kassim, A.M., Shukor, A.Z., Zhi, C.X., Yasuno, T.: Performance study of developed SMART EYE for visually impaired person. Aust. J. Basic Appl. Sci. 7(14), 633–639 (2013) 4. Kassim, M., Yasuno, T., Suzuki, H., Jaafar, H.I., Aras, M.S.M.: Improvement and evaluation of electronic spectacle device based on obstacle detection system for visually impaired person. In: Proceedings of Electronic, Information and System Conference, Electronic, Information and System Society, I. E. E Japan, No. GS10–5, pp. 1246–1251, Kobe (2016) 5. Hakobyan, L., Lumsden, J., O’Sullivan, D., Bartlett, H.: Major review: Mobile assistive technologies for the visually impaired. Survey Ophthalmol. 58, 513–528 (2013) 6. Bin Mohamed Kassim, A., Yasuno, T., Jaafar, H.I., Aras, M.S.M., Abas, N.: Performance analysis of wireless warning device for upper body level of deaf-blind person. In: SICE Annual Conference 2015, No.196, pp.341–346, Hangzhou, China (2015) 7. Carvalho, C., Paulino, N.: On the feasibility of indoor light energy harvesting for wireless sensor networks. Procedia Technol. 17, 343–350 (2014) 8. Sil, I., Mukherjee, S., Biswas, K.: A review of energy harvesting technology and its potential applications. Environ. Earth Sci. Res. J. 4(2), 33–38 (2017) 9. Yoshida, S., Suzuki, H., Kitajima, T., Kassim, A.M., Yasuno, T.: Correction method of wind speed prediction system using predicted wind speed fluctuation. In: 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), No.254, pp.1054–1059, Tsukuba, Japan (2016) 10. Brunelli, D., Moser, C., Thiele, L., Benini, L.: Design of a photovoltaic-harvesting circuit for batteryless embedded systems. IEEE Trans. Circuits Syst. I Regul. Pap. 56(11), 2519–2528 (2009) 11. Ghosh, S., Wang, H.T., Leon-Salas, W.D.: A circuit for energy harvesting using on-chip solar cells. IEEE Trans. Power Electron 29(9), 4658–4671 (2014) 12. Nasiri, A., Zabalawi, S.A., Mandic, G.: Indoor power harvesting using photovoltaic cells for low-power applications. IEEE Trans. Indu. Electron. 56(11), 4502–4509 (2009) 13. Raghunathan, V., Chou, P.H.: Design and power management of energy harvesting embedded systems. In: ISLPED 2006, October 4–6, 2006, Tegernsee, Germany (2006) 14. Carvalho, C.: A step-up µ -Power Converter for Photovoltaic Energy Harvesting Applications, using Hill Climbing Maximum Power Point Tracking. 1924–1927 (2011) 15. Guilar, N.J., Kleeburg, T.J., Chen, A., Yankelevich, D.R., Amirtharajah, R.: Integrated solar energy harvesting and storage. IEEE Trans. Very Large Scale Integ. (VLSI) Syst. 17(5), 627–637 (2009) 16. Alippi, C., Galperti, C.: An adaptive system for optimal photovoltaic energy harvesting in wireless sensor network nodes. IEEE Trans. Circuits Syst. I Reg. Pap. 55(6), 1742–1750 (2008)
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Design and Application of ST-SMC Controller for Position Control of a Milling Table Z. Jamaludin1(B) , P. Puveneswaran1 , C. T. Heng2 , and M. Maharof1 1 Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,
76100 Durian Tunggal Melaka, Malaysia [email protected] 2 Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Jalan Genting Klang, 53300 Setapak, Kuala Lumpur, Malaysia
Abstract. In milling machining process of parts, various factors could affect the precision and accuracy of the machined parts. These factors if left unattended would degenerate the machining performance resulting in part with poor dimensional accuracy thus rejected by the customers. Machine performance and accuracy are greatly influenced by the effectiveness of the milling table positioning control system in rejecting elements that reduce the tracking quality. Various modern control algorithms have been developed and recently Super Twisting Sliding Mode Controller (ST-SMC) has attracted great interest from many researchers due to its excellent attributes in rejecting input disturbance thus preserving positioning accuracy of the system to be controlled. The controller consists of the switching function and control law with tunable parameters L, W and λ. This paper presents the application of ST-SMC controller in positioning control of an xy milling table. The performances of this controller were analysed numerically using MATLAB and Simulink in terms of its tracking accuracy, and disturbance rejection in the form of input measured cutting forces. Data analysis was focused on evaluating the effectiveness of ST-SMC controller in compensating input disturbance and was measured based on maximum tracking errors and magnitudes of the Fast Fourier Transform of the tracking errors. Results showed consistent tracking performance even in the present of measured cutting force as input disturbance where 0% and 2.23% different in tracking errors were recorded for x and y axes respectively. Keywords: Sliding mode control · Motion control · Disturbance rejection
1 Introduction In manufacturing industries, machine tool is defined as a power-driven machine that is used to cut and remove materials through typical machining processes such as milling, turning, shearing and grinding [1] Machine tools allow different types of materials to be transformed into desired form through shape deformation from common to the most complex products with applications ranging from automotive parts to aerospace components [2]. Computer numerical controlled (CNC) milling machine is one example of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 236–246, 2022. https://doi.org/10.1007/978-981-16-8954-3_23
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a machine tool commonly used in manufacturing process engineering involving metal based parts and products. CNC machine technologies must be able to meet the ever-demanding request of consumers for high-end parts and products of high degree of accuracy and precision. Cutting performances of machine tools including that of CNC machines are typically influenced among others by the presence of disturbance forces during the machining process itself. Two most common types of disturbance forces presence during the manufacturing process are the cutting forces and friction forces whereby both of these forces act on the workpiece either directly or indirectly. These disturbance forces if left uncompensated could reduce the accuracy and precision of the final parts and components. Inaccuracy of CNC machine tool is normally the results of errors originating from the servo drives and the vibration as result of the cutting force ([3–5]). Therefore, effective motion control strategy applied is highly desired to produce high quality finished products. As cutting forces cannot be eliminated, an effective compensation approach is desired to compensate the negative impacts of the cutting force thus enhancing the overall performances of the motion system. For precise motion, the accuracy of the servo drive positioning system is highly critical. In order to meet accuracy requirement, the best and right selection of the motion controller is needed. The accuracy of the relative motion between the cutting tool and the workpiece mounted on the positioning table of the machine tool will dictate the level of accuracy achieved. Effective motion controller would improve the tracking performance, minimizing tracking errors and disturbance attenuation. Various compensation techniques existed in literature divided into direct and indirect compensation approaches. Indirect compensation of the disturbance force includes the applications of classical controller approach such as PID-based controller. PID-based controller has simple configuration and is a basic controller commonly used in various applications [6, 7]. Cascade P/PI controller and many of its variations is a PID-based controller that has been shown in literature to perform better in both position and velocity control. Many others advanced position controllers have been presented as further initiatives to improve tracking performances in more advanced and complicated systems. Such controllers are the sliding-mode control (SMC) and many of its variations. Among them are the work in [8] on high accuracy control in machine tools utilizing a model reference sliding mode control (MRSMC) method to compensate of unknown disturbances. These methods have some disadvantages as the algorithms are mostly complex and time costly. As in SMC applications, chattering affects the performances of drive system. A further improvement was recorded through the derivation of higher order sliding mode control (HOSMC), an extension of SMC, introduced in 1980s to address the issue of chattering effect. The method addresses the chattering issue as well as enhancing further the robustness and disturbance rejection properties of the controller [9, 10] Subsequently, a second order sliding mode control (SOSMC), a variation to HOSMC was made popular by [11, 12] as another solution to compensating the chattering effect. Among the SOSMC variants, super twisting sliding mode control (ST-SMC) has the advantage for its simple concept in application and its ability to suppress chattering more effectively while maintaining the performance level of the systems [13, 14]. The
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continuous control action enforced in ST-SMC is able to suppress chattering due to the discontinuous input. Literature reviews have shown that various applications of ST-SMC [15–17] and its variants had been designed and implemented for application ranging from aerospace, marine engineering, power system, servo drives and robotics systems. ST-SMC has been proven to perform effectively in tracking control and disturbance rejection. However, there are minimal application and analysis of ST-SMC in machine tools where cutting force from cutting process acts as the input disturbance. This paper focuses on design and application of an ST-SMC controller on an XY positioning table of a milling machine and is organized as follows. Section 2 presents the system setup including the system transfer function. Section 3 details the design steps for the ST-SMC while Sect. 4 presents the results obtained before a conclusion is made with regards to the overall outcomes of this work.
2 System Setup The system setup consists of an XY positioning milling table ball screw driven system made by Googol Tech as shown in Fig. 1. The XY milling positioning table is driven each by Panasonic MSMD 022GIU A.C. servo motors. Figure 2 shows the the x-axis which produces horizontal motion while the y-axis produces the vertical motion on the XY plane. Cutting tool is located on the spindle attached to the z-axis. The XY positioning table has a dimension of 630 mm (length) × 470 mm (width) × 815 mm (height). The respective mass of the x and y axes are 36.8 kg and 23.4 kg with a maximum effective travel distance of 300 mm for each axis. Both x and y axes are equipped with incremental encoders for positioning measurement with resolution of 0.0005 mm/pulse. Each servo motor is coupled with the ball screw drive mechanism using a bracket and guided by sliding rod mechanism. For safety purposes, three limit switches are attached at the near end of all the axes. The linear time-invariant model of the system dynamics is described as a singleinput-single-output (SISO) model and the dynamics characterization of the system was obtained using frequency domain identification method. A frequency response function (FRF) of the SISO system was first generated. The SISO FRF was estimated using an H1 estimator based on measured input voltage, u(t) and output position, y(t) signals corresponding to input signal, r(t) in the form of band-limited white noise filtered at a frequency of 15 Hz. Relevant data for the system identification process were recorded for a total duration of 300 s at sampling frequency of 2000 Hz. A Hanning window was applied to reduce the leakage errors [18]. The number of sample per window was 4096, yielding a sampling resolution frequency of 0.5 Hz. The sampling frequency is derived from division of the sample frequency with the number of samples per window.
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3 Super Twisting Sliding Mode Controller There exist two main components in the design of super twisting algorithm, namely; the switching function and the control laws [19]. 3.1 Switching Function The traditional sliding surface, s(t) was used as the switching function as shown in Eq. (2) and (3). It relates the tracking error, e(t) and the first derivative of tracking error, e˙ (t) with positive constant λ. s(t) = (λ +
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e(t) = y(t) − r(t)
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3.2 Control Law The control laws of ST-SMC consist of three parts, namely; the equivalent control, a continuous state function, and an integrated discontinuous input, u1 (t) as shown in Eqs. (6) and (7). The continuous state function and the integration of discontinuous input mark the significant differences between ST-SMC with the traditional SMC. These functions contribute in suppressing chattering; a factor that is lacking in SMC. u(t) = ueq (t) − L|s(t)|0.5 sign(s(t)) + u1 (t),
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Fig. 2. Overall control scheme of an ST-SMC controller.
4 Results and Discussion The tracking performance of ST-SMC controller was evaluated on both axes of the XY positioning table. The performance criteria were measured in the form of maximum tracking error, root mean square of the errors and Fast Fourier Transfer (FFT) of the position error. Figure 3 shows the Simulink scheme for the numerical analyses performed.
Fig. 3. Simulink diagram of the control scheme.
First, the tracking performances of ST-SMC controller were analysed without any input disturbance. The controller parameters applied were L = 0.5, W = 3000 and λ = 2000. Input reference of amplitude 100 mm and 300 mm with tracking frequency of 0.5Hz and 2 Hz were inserted into the control loop. Figure 4 and Fig. 5 showed the input references and corresponding tracking errors recorded. At 100 mm input reference amplitude, results showed that the tracking error was only 0.002% and 0.01% for the two distinct frequencies. These confirmed the superior performance of ST-SMC. Similar observations and trends were recorded in case of reference amplitude of 400 mm. Table 3 summarizes the results obtained. The drop in performance at higher tracking frequency is expected as the controller bandwidth drop with increasing frequency. In order to quantify the disturbance rejection property of the controller, a disturbance signal, d(t) that consists of a measured cutting force recorded from a milling cutting process was inserted before the plant to simulate actual cutting process. The measured cutting force was recorded from a milling cutting process using Al workpiece at spindle
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5 Conclusion This paper presents design and application of ST-SMC for control of both the x and y axes of a milling machine positioning table. The controller performances were numerically analysed first for its tracking accuracy followed by its robustness against input disturbance in the form of actual measured cutting forces. For reference tracking of sinusoidal signals of 100 mm and 300 mm amplitudes at frequencies of 0.5 and 2 Hz, results obtained showed the superior performance of the controller as tacking errors recorded were less than 0.01% of the reference amplitude. The second analysis captured the controller ability to compensate for input disturbance. Measured cutting force of milling process was inserted as input disturbance to simulate actual cutting process. Results obtained showed little deviation from the case of without input disturbance. This clearly shows the superior ability of ST-SMC in tracking and disturbance rejection. In the future, the controller is to be applied in the actual milling machine for experimental validation and further compared with a standard cascade P/PI controller as a mean of comparison and benchmarking. Acknowledgements. The authors would like to extend our appreciation to Fakulti Kejuruteraan Pembuatan and Universiti Teknikal Malaysia Melaka for the facilities and the research grant provided PJP/2020/FKP/TD/S01724.
References 1. Kalpakjian, S., Schmid, S.R.: Manufacturing Engineering and Technology, 7th edn. PrenticeHall, New Jersey (2013)
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2. Yamada, Y., Kakinuma, Y.: Damping force-based cutting force monitoring in ball-screwdriven stage. In: Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology (2016) 3. Ramesh, R., Mannan, M.A., Poo, A.N.: Tracking and contour error control in CNC servo systems. Int. J. Mach. Tools Manuf. 45(3), 301–326 (2005) 4. Heng, C.T., Jamaludin, Z., Hashim, A.Y.B., Abdullah, L., Rafan, N.A.: Design of super twisting algorithm for chattering suppression in machine tools. Int. J. Control Autom. Syst. 15(3), 1259–1266 (2017) 5. Kasim, M.S., et al.: Investigation of tangential force on ball nose rake face during high-speed milling of inconel 718. Adv. Mater. Proc.Technol. 698, 1–7 (2018) 6. Dulger, L.C., Das, M.T., Haliciogl, U.R., Kapucu, S., Topalbekiroglu, M.: Robotics and servo press control applications: experimental implementations. In: Proc. Int. Conf. Control Decision. Inf. Technology (CoDIT), pp. 102–107 (2016) 7. Dewantoro, G.: Robust fine-tuned PID controller using taguchi method for regulating DC motor speed. In: International Conference on Information Technology and Electrical Engineering (ICITEE), Chiang Mai, Thailand, pp. 173–178 (2015) 8. Li, Y.F., Wikander, J.: Model reference discrete-time sliding mode control of linear motor precision servo systems. Mechatronics 14, 835–851 (2004) 9. Fridman, L., Moreno, J., Iriarte, R.: Sliding Modes After the First Decade of the 21st Century: State of the Art, Springer Science & Business Media (2011). https://doi.org/10.1007/978-3642-22164-4 10. Furat, M., Eker, ˙I: Second-order integral sliding-mode control with experimental application. ISA Trans. 53(5), 1661–1669 (2014) 11. Levant, A.: Sliding order and sliding accuracy in sliding mode control. Int. J. Cont. 58(6), 1247–1263 (1993) 12. Bartolini, G., Ferrara, A., Usai, E.: Chattering avoidance by second-order sliding mode control. IEEE Trans. Autom. Control 43(2), 241–246 (1998) 13. Levant, A.: Principles of 2-sliding mode design. Automatica 43(4), 576–586 (2007) 14. Utkin, V.: On convergence time and disturbance rejection of super-twisting control. IEEE Trans. Autom. Control 58(8), 2013–2017 (2013) 15. Levant, A.: Finite-time stability and high relative degrees in sliding-mode control. Lecture Notes Control Inform. Sci. 412, 59–92 (2011) 16. Ismail, Z.H., Putranti, V.W.E.: Second order sliding mode control scheme for an autonomous underwater vehicle with dynamic region concept. Math. Prob. Eng. 13 (2015) 17. Merheb, A., Bateman, F., Noura, H.: Passive and active fault tolerant control of octorotor UAV using second order sliding mode control. In: IEEE Conference on Control Applications (CCA), Sydney, NSW, Australia, pp. 1907–1912 (2015) 18. Pintelon, R., Schoukens, J.: System Identification - A Frequency Domain Approach, 2nd edn. John Wiley & Sons Inc, New York (2012) 19. Utkin, V., Guldner, J., Shi, J.: Sliding Mode Control in Electro-Mechanical Systems. 2nd ed., CRC Press (2009)
Design, Modelling and Simulation
Lean Manufacturing Design of a Two-Sided Assembly Line Balancing Problem Work Cell Kamarudin Abu Bakar1,2(B) , Mohd. Fazli Mohd. Sam1 , and Muhammad Imran Qureshi1,2 1 Faculty of Technology Management and Technopreneurship, Universiti Teknikal Malaysia
Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia [email protected] 2 Sustainable IT Economics Research Group (SuITE), Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
Abstract. Line balancing is a very important concept in Lean manufacturing. It ensures that operators are not idling but instead work effectively for continuous productivity particularly in two-sided assembly lines. In the study conducted at a glove making facility, cuffing task (of 210 mm length) at three workstations/side has an average lower speed of 23 pieces/min. Effective cycle time of 0.4 min calculated using heuristic method was well applied for line balancing. However, the idea of additional workstation was not sustainable to meet new customers’ demand. Alternatively, a practical integrated approach was introduced as the solution. Load balancing of higher than 68 packs/min that streamed down the work cell could be enhanced by stopper jigs installation and operators’ positions rotation (and repositioning). As a result, the value stream levelling (Mura) has shown improvement in the output by 27–30 pieces/min and 70–72 packs/min at maintainable fixed costs. The findings were evidence that the techniques used could reduce operational wastes (Muda) namely transport, stocks, movement, and waiting. Just-In-Time (JIT) concept as depicted by smooth production flow also helped to accommodate the cells production being carried out at constant and predictable rate. Hence, the process-based practice could effectively encourage a flexible Lean practice. Keywords: Lean manufacturing · Just-in-Time manufacturing · Two-sided assembly line balancing problem
1 Introduction Cuffing is a critical and an important process in the highly massed glove manufacturing facility portrayed by this study. Cuff is a part of glove encircling the wrist. It is essential to determine effective cycle time such as for the repeated gloves entries into u-shaped lines, parallel workstations or alternative processes [1]. In defining the underlying concept, an assembly line here involved similar work done manually at individual workstations in 7 work cells. The cuffed gloves were streaming down from one operator to another until they reached the end of the cell line for packing. Lean manufacturing would enhance © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 249–259, 2022. https://doi.org/10.1007/978-981-16-8954-3_24
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manufacturing capacity by controlling relevant work levelling and waste [2]. To present the study, there were 3-mated (or 6 in total) workstations in the parallel production line or an assembly line that flow from P1 to P2 and to P3 (by precedence constraint). However, all the workstations produced different number of units per time outputs. The history baseline recorded 23 pieces/min cuffing speed, which translated into a load balancing of 68 packs/min. The respective pieces per unit time difference from a captured 30 pieces/min at maximum rate (on the first pair workstation) possibly netted an 86% and an 82% of workstation efficiency for the second and third mated workstations. In other words, the P2-mated workstations would remain idle for 14% of its time and P3 for 18% of its time.
2 Manufacturing: Lean and Just-In-Time Lean manufacturing and Just-In-Time manufacturing (JIT) might be of similar nature although both have quite different concepts [3]. While Just-In-Time manufacturing focuses on efficiency of inventory strategy to eliminate waste and enhance productivity; Lean manufacturing uses efficiency in its system setups to reduce cycle, flow, and throughput times being the added values to customers [4, 5]. However, many times JIT behave as a stand-alone system. It streamlines manufacturing process and slash inventory. For a robust Lean manufacturing, JIT emerges as one of the Lean system activities. Similarly, the productivity enhancement pushes for faster products delivery resulting in minimal costs and improved customer satisfaction. And here comes the Lean manufacturing role to determine the actual customer demand (output). It lowers the need to have stock sitting around in warehouses. So, such inventory (supply and demand) and control system availability definitely encouraged operational improvements at many levels. Taiichi Ohno started ordering parts in small quantities for the Toyota Production System (TPS). The short inventory cycle was to ensure that only needed numbers of parts would arrive on time for production use. Inventory costs money. Each process reacted on time to ‘Kanban’ information, synchronizing each inventory request [4]. This helped to eliminate quality issues pertaining to various wastes causing inefficiency in inventory control. JIT can also work well with other popular productivity approaches or systems that included Total Quality Management (TQM), Continuous Improvement (PDCA), Kaizen, Housekeeping (5S), Total Preventive Maintenance (TPM), etc. However, a JIT implementation depends on a fully integrated system like the Enterprise Resources Planning (ERP) for optimizing timely information flow to measure, analyze, interpret and deliver key metrics [6]. Consequently, Toyota claimed to reduce the ordering lead-time by one-third and save the production costs by 50%. The production system therefore shifted from one-by-one production to flow production in which when one task is finished, the next task must start immediately. Hence, the cycle time of each task should play a very significant role [7]. Lean manufacturing replicates the JIT concept and reflects the real values of the product to the customers. Likewise, Lean system holds the following principles to ensure its success [8]: First principle: Every step in the production process must have added value(s) of what the customers’ wants.
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Second principle: Examine closely the product manufacturing activities and differentiate activities that add value from those that do not. Remove activities that do not add value (where necessary) and replace them with new process that add value (where possible) so that the manufacturing process will flow more efficiently. Third principle: Manufacture what customers request only. The ‘pull’ system practice to control inventory is equal to the Just-In-Time short-term request of parts. Nevertheless, the driving force behind Lean is the efficiency of the process to fulfil product added value(s) to the customers.
3 Assembly Line Balancing Design of an assembly line concept intends to reduce mass production costs of standardized products and to enhance workers’ specialization [9–11]. Basic assembly line consists of a set of workstations arranged in a linear fashion, with each station connected by material handling device (transfer lines, roller conveyors, cranes, etc.) [5]. The products assembly completion period or flow time (process time) depends on a set of tasks performed at each workstation [3]. The common time taken to accomplish a task at each workstation is called cycle time. The configuration of an assembly line is critical due to the costly investment decisions to develop the production line system (cycle time, number of workstations, task assignment, and operations sequence). There are two types of assembly line balancing problems (ALBP) namely ALBPI and ALBP-II [3]. The two-sided assembly line balancing problems (ALBP-II) has become a more important study today. The ALBP-II differs from one-sided assembly line balancing problems (ALBP-I) because all assembly tasks must follow specific directions of either ‘left’ (L), ‘right’ (R), or ‘both’ (LR) [5, 12, 13]. In this sense, products flow along a conveyor system with workstations on both sides of the production line. The objective of ALBP-I is to minimize the number of workstations for a given cycle time while the objective of ALBP-II is to minimize the cycle time for a given number of workstations [14]. Hence, the study undertaken the latter concept in improving the manufacturing efficiency.
4 Methods and Materials The study presents an integrated approach to resolve the assembly lines problem from the manufacturer (practitioners and problem solvers) point of view [29]. It was built on two approaches: the heuristic method and the experimental techniques. The practical integrated approach presented a more complete view of the manufacturer and the systems in use compared to conventional approach, offering a more meaningful guidance for supply management activities [4, 16]. As mentioned earlier, the use of heuristic approach was to determine the cycle time, the number of workstations, the tasks assignment, and the operations sequence for the ALBP-II [10]. Having management constraints on further investment of the project at this point of time, therefore, it was important to support the numerical findings with the real-life practice by the industry to ensure that the objectives of the study was achievable.
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4.1 Heuristic Method of Line Balancing Heuristic method is popular because it uses logic and common sense, but still able to produce reliable solutions [3, 12]. To define the heuristic model of the two-sided assembly line balancing problem (ALBP-II), it must be clear that the objective of the study is to arrange the number of workstations for an estimated cycle time. Heuristic method was preferred to gain better insight of the problem especially due to the production target set of greater than 68,000 packs per day was vital with regard to process quality and efficiency. Additionally, there was intermittent flow system (with opportunity of process interception for quality screening and control) and simple, which should necessitate less time and money [7]. For a continuous flow involving mass production, linear or dynamic programming method might be appropriate to yield optimum solutions to the costly and time-consuming process. Conversely, there involved some rules to comply when applying the heuristic concept [17]. These rules were: Rule-1: Assigning tasks to a specific side (left or right) due to process nature. Rule-2: Tasks carried out were in accordance with their precedence. Rule-3: Two tasks were allowable to start at the same time. Rule-4: All precedence tasks must be completed prior to starting of a new task. A dummy task may be involved in this situation (if drawn on activity-on-arrow). Rule-5: The total time for all tasks assigned to one workstation must not be greater than its cycle time (or maximum time allowable). Rule-6: Every workstation of each side must have at least one task assigned. Apart from that, heuristic method required a precedence diagram to indicate possibility of the tasks transfer laterally and flexible from one column to another. Thus, the following procedural steps were involved in the heuristic approach [18]: Step-1: Identifying the works or jobs (tasks). Step-2: Breaking down each task into smaller tasks or steps (sub-tasks) where possible. Step-3: Listing the tasks or sub-tasks and their durations in a table. Step-4: Drawing the relevant precedence diagram showing all the tasks or sub-tasks. Step-5: Calculating the number of workstations required (it may be equivalent to the number of tasks or sub-tasks). It was done by dividing the total duration of all the tasks (maximum time available at any workstation) by the cycle time. Step-6: Arranging (permutation) tasks to workstations. Any task or step of a column could be combined to make up their total time as long as their total time did not exceed the cycle time (time available at a workstation). Tasks of different columns could be combined provided that the precedence constraint was maintain. The analysis should determine whether a task (workstation) can be moved to another column. 4.2 Experimental Techniques The objective of the current study was to achieve a cuffing speed target of 27 pieces/min for each operator and the load balancing capacity of higher than 68 packs/min by both
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sides of workstations at a consistent glove cuffing length of about 210 mm (Fig. 1). The baseline data (based on history) of recent cuffing speed was average at 23 pieces/min per operator. Therefore, two solutions were proposed including the installation of a stopper jig at every workstation and positions rotation of the operators. An extra solution was carrying out to check the repositioning effects on the operators’ view during cuffing process. The before (original) and after (new) cuffing outputs for both sides were recorded and taken average during the 2 weeks monitoring period.
Fig. 1. Cuff length of gloves.
Fig. 2. Add-on stopper jig on cuffing tool.
Humans were prone to making mistakes when carrying out their tasks [19]. Hence, ‘Poke Yoke’ could be the simplest total quality management (TQM) or Kaizen tool that could be practically integrated [15, 16, 20]. ‘Poka Yoke’ or mistake proofing was developed by Shigeo Shingo and widely used in TPS to function as ‘jidoka’ (builtin quality) and ‘autonomation’ (automation with a human touch) [19–21]. It involved the application of non-expensive devices to prevent defects and trigger them before any product could pass through the next operations. Typical device examples included various guide pins, error detectors, sensors, and alarms. So, for the study, stopper jig was found to be the most suitable ‘Poka Yoke’ device to install. It could easily alert the operators whenever the gloves standard cuffing length of 210 mm was reach (Fig. 2). Modification of the two-sided assembly lines was done by adding stopper jigs on the left (L) and right (R) workstations to carry out similar cuffing process. Accordingly, it was wiser for management to decide on positions rotation that shifted operators between the workstations at regular time intervals [22]. Apart from exposure to different situation of the tasks, it really helped the management to discover the employees’ talent. The employees (operators) were tested their skills and competencies before they could be placed to perform their work at the right workstations [22, 23]. Figure 3 shows the three operators (P) arrangement on left (L) side and right (R) side, who would each rotate position at P1L, P2L, P3L, P1R, P2R, and P3R. Generally, P1 needed to work faster than the other two. P2 has to cuff the gloves to back up P1 while P3 must perform cuffing (and check the gloves quality) to back up P1 and P2. The purpose of positions rotation was to assure that the best operators able to produce more outputs when customers’ demands were higher [24].
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Fig. 3. Positions rotation.
Fig. 4. Repositioning.
Next, repositioning was implemented in compliance with the ergonomic principle [25]. However, it only affected the left side, in particular P1L and P2L. As operators were right-handed, the change of views helped P1L and P2L to work comfortably. This is exhibited in Fig. 4. The repositioning idea came from robotic system that could easily detect things on its ‘sightline’ [26]. The method also demonstrated better capability for on-line quality control down the assembly lines. Therefore, both operators should be able to improve on their speed and achieve new target after they were repositioned.
5 Results and Discussion 5.1 Measures of Line Balancing The 6-tasks numerical data was collected. A precedence graph was presented in Fig. 5. On the other hand, Table 1 shows their duration times and directions of operations. In this case, it should be noted that cuffing of the left gloves and the right gloves were done on the respective left side and right-side workstations. There were a total number of 7 work cells. Since gloves were made in pairs, it explained that all left-right workstations in the work cell were mated (paired) and aligned either in zone I, II or III with the possibility for ‘permutation’ (transfer of zone for line balancing) [12, 27–29]. Subsequently, all calculations were done based on average values.
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Fig. 5. Precedence graph of cuffing process. Table 1. Duration time, operation direction and efficiency Task Direction Cuffing speed (pieces/min)
Average Efficiency (%)
Output Idling 1L
L
30 (max)
0
1R
R
26
−4
2L
L
26
−4
2R
R
22
−8
3L
L
25
−5
3R
R
21
−9
28
100
24
86
23
82
Based on the above table data, the line balancing restriction was determined as follow [30, 31]: From the history and baseline data, Available time = 1,340 min (for 24 h operation) Number of mated workstations = 3 Number of work cells = 7 Desired output = 68,000 packs (load balancing) As such, Cycle time = Production time/Desired output = 0.4 min per pack. Number of mated workstations = Summation of tasks time/Desired cycle time = 1.2/0.4 = 3 mated workstations
(1)
(2)
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Efficiency of work cell = All tasks completion time/(Number of workstations ∗ Cycle Time) = 1.07/1.2 = 89.2%
(3)
Hence, to summarize the heuristic data outcomes, each mated workstation would roughly require about 24 s (minimum) cycle time to cuff a pair of left-right hand gloves to produce an impact. The number of 6 workstations per work cell remain as existing and needed no change. Finally, the assembly line efficiency of almost 90% was adequately very high. In fact, this could be better by relevance practical solutions. 5.2 Experimentation Observation After two weeks of practical monitoring, comparison between before and after stopper jigs installation was analysed. Results from Fig. 6 clearly demonstrated that the stopper jigs have improved the cuffing speed. The left-side operators were able to cuff more gloves than the right-side operators. Productivity exceeded the expected target by 30 pieces/min for the left workstations.
Fig. 6. Improved output rate with stopper jigs.
Stopper jigs had successfully fixed the standardization issue of the cuffing gloves length. The load balancing was raised from 68 packs/mins to 72 packs/mins with six operators. However, load balancing remains the same for left and right sides of P3 workstations, while P1 and P2 could achieve 70 and 72 packs/mins respectively. Although the results were positive, operators P3 must maintain its critical role to speed-up due to the buffers built-up because P1 and P2 must work fast and needed to anticipate more gloves from checking and screening process. The operators repositioning as in Fig. 4
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facilitated the stopper jigs installation and eased to off-set the cuffing process setback due to rejection and other quality issues. Further analysis showed by Fig. 7 indicated another possible solution played by P1L and P1R operators. For this case, position 1 when attached or placed nearer to the respective inspection stations could save on the gloves loading time. As such, the output would also increase on both sides of the workstations. Therefore, it should help position 2 to increase buffers for downstream packing process. Consequently, the load balancing between all left and right sides workstations was indeed equally effective.
Fig. 7. Attachment of P1 with inspection station.
6 Conclusions This study explored about a two-sided assembly line balancing problem (ALBP-II) and Lean (Just-In-Time) strategies. Assembly line needs to be balanced regularly to meet the ever-changing customers’ demand [3, 4]. A balanced line of 3 mated workstations allows slightest interruptions of material and product flow after appropriate cycle time of 0.4 min was adapted with a speed of 27 pieces/min and new products target set to 75,000 packs/day. The study also presented an effective model to improve ALBP-II implementation. In this sense, for the integrated systems, it was essential to introduce add-on modification in the forms of stopper jigs and positions rotation (and repositioning) as alternative solutions to Lean manufacturing. Left side workstations showed dominant results compared to the right-hand side. At no additional cost, the Lean value stream or ‘Mura’ as a process-based practice enable production to eliminate ‘Muda’ factors mainly transportation, inventory, motion, and waiting as wastes that were bottleneck to manufacturing process (gloves cuffing) [8, 32]. These were observed by the higher speed achieved of 30 pieces/min and 72 packs/min. Hence, it is possible to undertake and maintain flexible cell production using two-sided assembly line with the right methodology [4, 12]. While this model can be useful for other cell workstations of such nature,
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various products samples and operators’ profiling (and numbers) should be considered for better understanding of the future research on two-sided assembly line. Acknowledgements. The authors would like to thank the Sustainable IT Economics Research Group (SuITE) and the Universiti Teknikal Malaysia Melaka (UTeM) for all the support given for the research work.
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Partial Transmit Sequence (PTS) Optimization Using Improved Harmony Search (IHS) Algorithm for PAPR Reduction in OFDM Nur Qamarina Muhammad Adnan, Aeizaal Azman Abdul Wahab(B) , Sankari Muniandy, Syed Sahal Nazli Alhady, and Wan Amir Fuad Wajdi Othman School of Electrical and Electronic, Universiti Sains Malaysia, Nibong Tebal, 14300 Pulau, Pinang, Malaysia [email protected]
Abstract. The most widely used technique for high-data-rate transmission is orthogonal frequency division multiplexing (OFDM). However, one disadvantage of OFDM systems is that the transmitted signals have a high peak-to-average power ratio (PAPR). High PAPR causes more interference and lower resolution in analogue to digital (A/D) and digital to analogue (D/A) converters. In this paper, Partial Transmit Sequence (PTS) technique using Improved Harmony Search (IHS) algorithm is proposed to solve the high PAPR problem. The PTS technique has a high search complexity because it involves an extensive random search over all possible phase vector combinations, which grows exponentially with the number of phase vectors. IHS improves the accuracy and convergence rate of conventional algorithms while requiring only a few parameters to be adjusted. The modulation method used is Quadrature Phase Shift Keying (QPSK). The performance of PTS and IHS-PTS are analyzed using CCDF graphs with different values of subcarriers and iterations. The PAPR reduction percentage increases more with using IHS-PTS compared to PTS only. To conclude, implementing IHS together with PTS can improve computational complexity in PTS and helps to reduce PAPR values even better in OFDM system. Keywords: Improved Harmony Search (IHS) · Orthogonal frequency division multiplexing (OFDM) · Peak average power ratio (PAPR) · Partial Transmit Sequence (PTS)
1 Introduction Orthogonal frequency division multiplexing (OFDM) has become a reliable technique that has been used in many wireless standards and wired communication systems for example Digital Audio Broadcasting (DAB), Asynchronous Digital Subscriber Line (ADSL). Wireless Local Area Network (WLAN), Broadcast Radio Access Network (BRAN), WI-MAX and LTE. OFDM system has a lot of advantages such as high-speed data transmission over multipath fading channels, impulse interference and tolerance to multipath delay spread. In addition, Fast Fourier Transform (FFT) process helps © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 260–274, 2022. https://doi.org/10.1007/978-981-16-8954-3_25
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the OFDM system to display high spectral power efficiency, smooth equalization, and flexible hardware implementation [1–3]. In OFDM systems, a high-rate data stream block is split into N parallel low-rate streams where each of these streams is sent using one subcarrier for each symbol. N is the number of subcarriers that is modulated independently using phase-shift keying (PSK) or quadrature amplitude modulation (QAM) at low temperature. The OFDM signal which is needed to be sent is generated by the Inverse Discrete Fourier Transform (IDFT) [4–6]. The fundamental concept of the OFDM system is to divide high data rate transmission into lower data rates which are distributed over the number of subcarriers at the same time [5–7]. OFDM system has many distinctive characteristics however the high peak-to- average power ratio (PAPR) is considered as the main disadvantage that causes the OFDM system to suffer from in-band distortion (IB) and out-of-band radiation (OOB). These also can cause spectral spreading, inter-modulation and changes in the signal constellation which reduces the power efficiency of the system. High PAPR value complicates the process when using certain devices such as analog to digital converter (ADC) and digital to analog converter (DAC). Thus, High Power Amplifier (HPA) with high input back-off power (IBO) and long word length are required by the OFDM system to comply with the high PAPR value. In addition, reducing PAPR also plays an important role in removing non-linear effects and improving the power performance of power amplifiers [1, 4]. As mentioned before, a high PAPR value is the main downfall for the OFDM system. The corresponding solution to the high PAPR issue is to find an effective method that can tackle the PAPR value before sending the signal rather than the common method that uses a costly amplifier with a large linear area. There are several PAPR value reduction techniques that have been proposed which can be categorized into two parts, the first category comprises signal distortion methods such as clipping, peak windowing, clipping and filtering, active constellation extension (ACE), non-linear companding transformation and trellis assisted constellation subset selection (TACSS). The second category comprises signal scrambling methods such as selective mapping (SLM), partial transmit sequence (PTS), block coding, interleaving technique, tone reservation (TR) and tone injection. Among all other methods, PTS has been the most effective technique which has a great PAPR reduction capability without limiting the number of subcarriers. The data block is split into disjoint sets called subblocks in PTS. The subblocks are merged followed by phase vector multiplication. The most challenging problem in PTS is the design of the optimal phase vector from a set of known solutions because the computational complexity for a large number of subcarriers is very high. In Traditional PTS (T-PTS), the exhaustive search space for an optimal phase factor rises exponentially with the number of subblocks. The rise in the search space results in increased computational complexity [1, 2, 8, 9] There are many optimization methods that have been proposed to reduce PAPR for phase weight searches in the PTS technique to achieve desirable PAPR reduction with low computational complexity [4]. Thus, an improved harmony search (IHS) algorithm based on harmony search (HS) is proposed to reduce the PAPR value. Harmony Search (HS) algorithm is the metaheuristic algorithm inspired by the musicians developed by Geem
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et al. in 2001 and has found applications in various fields due to its efficiency and benefits. For instance, municipal water distribution networks, traffic routing, multi- objective optimization, rostering problems, and structural design. HS is good at identifying the solution space’s high-performance regions within a reasonable time, but there are some disadvantages of the HS algorithm. The major disadvantage of this algorithm is the number of iterations required for the algorithm to get an optimal solution. Small Pitch Adjustment Rate (PAR) values combined with large distance bandwidth (bw) values may result in poor algorithm performance and a significant increase in the number of iterations required to identify the best solution. There are many variants that have been proposed to solve this problem such as improved harmony search (IHS), adaptive harmony search-particle swarm optimization (AHSPSO), self-adaptive harmony search (SAHS) and improved global-best harmony search (IGHS) algorithm. In this paper, we have chosen IHS to improve the performance of HS algorithm. Two parameters from HS algorithm are set and initialized which are PAR and bw. It exhibits low performance and to find the optimal solution, a higher number of iterations are needed. Moreover, better optimal solutions can be seen by adjusting PAR and bw in each improvisation step. However, these parameters have low values in the final stages for the exploitation of an optimal solution. This also provides a suboptimal solution for improving the traditional HS algorithm’s efficiency [2]. This paper is organized as follows, related works and mathematical equation related to OFDM, PAPR, PTS and HIS are shared in Sec. 2. The proposed method is explained in Sec. 2.4 and the methodology of the process is explained Sec. 3. The simulation results are discussed in Sec. 4. Lastly, the conclusion will be stated in Sec. 5 and followed by references.
2 Related Works 2.1 Peak-To-Power Average Ratio (PAPR) The OFDM signals usually have amplitude variation in the time domain and a relatively wide dynamic range due to their multicarrier nature which is referred to as PAPR. The OFDM signal will be clipped when signal passes through a non-linear HPA with a high PAPR. This results in performance degradation and in-band distortion and outof-band radiation. Therefore, OFDM transmitters require a costly linear HPA with a large dynamic range. Moreover, a high-resolution digital-to-analog converter (DAC) and analog-to-digital converter (ADC) are needed for high PAPR. Low PAPR increases the efficiency of the power amplifier while also reducing the complexity of the DAC and ADC. PAPR =
Ppeak Paverage
Ppeak means peak output power and Paverage means average output power max |xn |2 PAPR = Et |xn |2
(1)
(2)
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Where discrete time signal xn is max |xn |2 PAPR = En |xn |2
(3)
In an OFDM device, the input signal to the amplifier is analog and the timedomain samples of the output from the Inverse Fast Fourier Transform (IFFT) are xn which defines the transmitted OFDM signals obtained by performing IFFT operation on modulated input symbols. xn is expressed as N−1 1 xn = √ Xk Wnk N N k=0
(4)
When phase values are the same in an OFDM system with subcarriers, the peak power of received signals is N times the average power. One of the most widely used methods for assessing PAPR output is the Complementary Cumulative Distribution Function (CCDF). The probability of the PAPR value exceeding a certain threshold is the CCDF of PAPR performance [1–3]. According to the central limit theorem, the real and imaginary parts of the OFDM signal in the time domain for a large number of subcarriers N obeys Gaussian distribution random variable with mean and variance equal to 0 and 0.5 respectively. Furthermore, the power distribution of the signals becomes a central chi-square with two degrees of freedom, while the amplitude of the signal |x(n)| uses the Rayleigh distribution. The time-domain signal’s CCDF with the Nyquist sampling rate is calculated as [1] N Pr(PAPR ≥ PAPR0 ) = 1 − 1 − e−PAPR0
(5)
PAPR0 is the threshold value. Moreover, when oversampling L is used the OFDM signal’s CCDF can be written as [15] NL Pr(PAPR ≥ PAPR0 ) = 1 − 1 − e−PAPR0
(6)
2.2 Partial Transmit Sequence (PTS) The key idea behind PTS technique is that data blocks are separated into non-overlapping subblocks each with its rotation factor. This rotation factor produces time domain data with the smallest amplitude [1]. Furthermore, PTS technique has better PAPR reduction performance compared to other probabilistic techniques. On the other hand, the PTS technique has a high computational complexity when determining the optimum rotation factor and requires sending side information (SI) as index information to recover the original data at the receiver side [2]. The PTS technique divides an input data block of N symbols into M disjoint sub-blocks, the IFFT for each subblock is performed separately and then multiplied by a phase vector [4]. The phase optimization techniques are used to determine the optimal phase vector resulting in the lowest PAPR at this
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stage. The data input block X = [X1 , X2 , . . . XN ] is divided into M disjoint subblocks, Xm = Xm, 1 , Xm, 2 , . . . Xm, N where m = 1, 2, . . . M . These subblocks are orthogonal to one another, and X represents the sum of all M subblocks [7–9]. X=
M i=1
Xm
(7)
The PTS approach aims to find the best phase weighted combination to reduce the PAPR. In time domain, the combined transmitted signal can be formulated as
x (b) =
M
bj xi
(8)
i=1
(b) . The selection of the phase factors is Where x (b) = x1 (b), x2 (b), . . . , xNL limited to a finite set of elements to minimize the search complexity. The set of phase factors that are allowed can be displayed as
j2π l (9) P = e W |l = 0, 1, . . . , W − 1 W denotes the number of possible phase factors. As a result, the optimal set of phase factors is found by searching through WˆM sets of phase factors. The PTS technique’s main drawback is its complexity. For an optimal exhaustive search, this technique uses all possible combinations of phase factors resulting in an exponential increase in the number of subblocks. Besides, another factor that influences the PAPR is subblock partitioning which is a method of separating sub bands into multiple disjoint subblocks. There are three types of subblock partitioning methods which are adjacent, interleaved and pseudorandom. The best option has been determined to be pseudorandom partitioning. Therefore, the PTS technique works with random number of subcarriers and all modulation schemes [2]. The process of PTS can be seen from Fig. 1.
Fig. 1. Block diagram of PTS technique
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2.3 Harmony Search (HS) Algorithm In 2001, Harmony Search (HS) algorithm which stimulates the principle of generating harmony in musical performance is a novel intelligent optimization algorithm proposed by Geem et al. The HS algorithm stands out because of its simplicity and search efficiency. This algorithm has been successfully applied in fields such as function optimization, mechanical structure design, pipe network optimization and data classification system optimization in recent years. Musicians usually experiment with different combinations of music pitches stored in their memory when they compose the harmony. This effective search for a perfect harmony is similar to the technique for discovering the optimal solutions to engineering problems. The HS method was influenced by the principles of harmony improvisations. The HS algorithm has the following steps below Step 1: Parameter Initialization The dimensions of the variable D, the scope of each variable, Harmony Memory Size (HMS), Harmony Memory Consideration Rate (HMCR), Pitch Adjusting Rate (PAR), maximum number of iterations. Step 2: Harmony Memory (HM) Initialization The initial HM composed of a set of randomly generated solutions to the optimization challenges under consideration. The HM stimulates short term memory of musicians therefore the number of solutions shown by Harmony Memory Size (HMS) is low. Assuming an optimization problem with N decision variables with lower limits LBi and upper limits UBi respectively for each decision variable. The HM is then filled as follows xii = LBi + r × (UBi − LBi ), 1 ≤ i ≤ N, j = 1, 2, . . . , HMS, where r ∼ U(0, 1) (10) Step 3: Improvisation Following the initialization process, HS generates a new solution based on its three rules: Random Selection (RS), Harmony Memory Considering (HMC), Pitch Adjustment (PA). The HMC rule with a probability of Harmony Memory Considering Rate (HMCR) or the RS rule with a probability of HMCR can be used to select the value of each decision variable (1-HMCR). Then, the values of those decision variables chosen using the HMC rule were altered using the probability of (HMCR x PAR) PA rule. Step 4: Harmony Memory (HM) update If the new solution’s objective function value is higher than the HM’s worst solution, the HM’s worst solution is substituted by the new one. The new solution will be rejected if it does not meet the above criteria. Step 5: Termination Steps 3 and 4 were repeated until the termination criterion is reached and then produce the best solution.
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2.4 Improved Harmony Search (HS) Algorithm Mahdavi et al. proposed an improved version of the Harmony Search (HS) algorithm in 2007 known as Improved Harmony Search (IHS) algorithm after the fundamental presentation of the HS in 2001. The main changes in this improvement are in PAR and bw, which are dynamically updated as PARmac − PARmin k (11) PAR(k) = + k PARmin
bwmin ln bw max bw(k) = exp − k (12) K bwmin where k is the current improvisations and K is the maximum improvisations. The key feature of IHS is its method for generating new solution vectors which improves the accuracy and convergence rate of harmony search. The main distinction between IHS and traditional HS methods is how PAR and bw are adjusted. The IHS algorithm uses variables PAR and bw in the improvisation step to improve the performance of the HS algorithm and eliminate the drawbacks associated with fixed values of PAR and bw. The HS algorithm uses a standard value of PAR and bw for specific reasons. The PAR and bw values in the HS method are adjusted during the initialization step and remain constant throughout the algorithm. These parameters are crucial in optimizing the adjustment of optimized solution vectors and can be useful in adjusting the algorithm’s convergence rate to the optimal solution. Thus, fine tuning of these parameters is of great interest. When small PAR and large bw values are combined, the algorithm performs poorly and requires more iterations to find the best solution. The small values of bw in the final generations can be used to adjust solution vector tuning. However, in the early generations, bw must be set to a high value in order to see an increase in the diversity of solution vectors. On the other hand, large PAR values combined with small bw values usually result in the best solutions in the final generations where the algorithm converged to the optimal solution vector. The proposed IHS-PTS algorithm has the following steps below: 1. Parameter Initialization The specified parameters are the harmony memory considering rate (HMCR), the harmony memory size (HMS), the pitch adjustment rate (PAR), the number of musical instruments, the pitch range of each instrument, and the stopping criterion k. 2. Harmony Memory Initialization A possible set of phase vectors is used to initialize the harmony memory (HM). Each row in the matrix represents a set of solutions derived from evaluating the objective function between lower and upper bound values. As a result, the solutions for each structure (i = 1, 2, …, HMS) are generated at random. The objective function f(b) is evaluated, and its value is determined by the collection of phase vectors {+1, −1}. 3. New Solution Construction M The HS algorithm creates a new harmony vector, b ∈ ejϕm , from scratch, using three operators:
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(i) Memory consideration (ii) Random consideration (iii) Pitch adjustment. 4. Update Harmony Memory By comparing the new harmony vector to the worst-fit solution, updates to the harmony vector can be produced. If the new vector is better than the worst solution, the new harmony vector takes its place. 5. Stopping Criterion The above algorithm is repeated until K (total number of function evaluations) is reached. Step 2 will be repeated if this is not the case. The IHS-PTS algorithm has a search complexity equal to MKW. The IHS attempts to improve HS accuracy and convergence rate by adjusting the PAR and bw values.
3 Methodology This section explains about reduction of PAPR method in OFDM system using PTS technique with IHS algorithm. This research consists of two sections which are research and simulation. The research part contains literature review while simulation part contains the use of MATLAB to obtain the simulation result. 3.1 Implementation of Parameters The parameters are manipulated to evaluate and analyze the simulation results. The input parameter such as number of subcarriers are applied to analyze the PAPR and CCDF values. All the parameters used can be seen in Table 1. Table 1. HIS-PTS simulation parameters Simulation parameters
Values
Number of subcarriers (N)
64, 128, 256, 512
OFDM blocks
1000
Phase factors (B)
{+1 −1 +j −j}
Harmony memory considering rate (HMCR)
0.9
Harmony memory size (HMS)
5
Minimum pitch adjustment rate (PARmax )
0.35
Maximum pitch adjustment rate (PARmax )
0.35 (continued)
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Simulation parameters
Values
Pitch adjustment rate (PAR)
0.4
Maximum bandwidth of adjustment (bwmax )
0.002
Minimum bandwidth of adjustment (bwmin )
0.00001
Bandwidth of adjustment (bw)
0.02
Generation (gn)
1
Oversampling factor (L)
4
Number of iterations (K)
1000, 2000, 3000, 4000, 5000
Modulation technique
QPSK
3.2 Partial Transmit Sequence Implementation PTS was chosen as the reduction technique because it has better reduction performance compared to other techniques. Phase factors with the combination of {±1, ±j} are generated with all possible combinations. When the OFDM signals are modulated with QPSK, the input signal will be converted from serial to parallel signal and partitioned into subblocks. The subblocks are then multiplied with the phase factors. After this process, IFFT will be applied to transform the phase factors into the time domain
N N N bN X N = bN IFFT{XN } = bN XN (13) x = IFFT N=1
N=1
N=1
Then optimum phase factor value with minimum PAPR will be chosen to be transmitted. 3.3 Improved Harmony Search (IHS) Implementation The main function of IHS algorithm is to generate new solution vectors which improves the accuracy and convergence rate of harmony search to solve the optimization problem of PTS in order to obtain OFDM signals with the lowest PAPR. Once the signals are converted to time domain by IFFT, harmony memory (HM) will be initialized where the HM matrix contains the same number of randomly generated solution vectors as the HMS matrix. Then, a new harmony vector is formed based on improvisation which consist of three parts memory consideration, pitch adjustment and random selection. As for the memory consideration part, the value of the first decision variable for the new harmony vector and other decision variables is chosen from any of the values in the stated HM range generated from the initialization. The values obtained through memory consideration is evaluated to see whether it should be pitched adjusted with the probability
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Next, random selection with a probability of (1 –HMCR) assigns decision variables that are not allocated with values according to memory consideration to their possible range. After that, if the HM’s the new harmony vector outperforms worst harmony in terms of the objective function value, the new harmony is added to the HM while the old worst harmony is removed. The above process is repeated until the maximum iterations reached. In this research, maximum iterations as 1000, 2000, 3000, 4000 and 5000 are used to study the effect of number of iterations on the CCDF performance. Figure 2 will show the flowchart of the implementation process.
Fig. 2. Implementation process
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4 Results and Discussion 4.1 Simulation Result with Different Number of Subcarriers In this section, the number of subcarriers used are 64, 128, 256 and 512 to evaluate the PAPR performance. QPSK is used as the modulation technique. Table 2 shows the PAPR values for OFDM, PTS and IHS-PTS obtained using various subcarriers. The improvement of the values of PAPR is calculated for PTS and IHS-PTS. As IHS is combined with PTS in OFDM, the PAPR values decrease more compared to when PTS is used alone. According to the table, when the number of subcarriers increases, the values of PAPR also increases but have decrement for IHS-PTS, N = 512. From Fig. 3, For the subcarrier N = 64, the PAPR value for PTS decreases by 58.44% but by implementing IHS-PTS, the value decreases by 87.87%. For the subcarrier N = 128, the PAPR value for PTS decreases by 59.10% and 89.85% with IHS-PTS. Then, for subcarrier N = 256, PAPR reduces by 59.49% for PTS and 89.01% for IHS-PTS. Lastly, PAPR value reduces 59.57% with PTS only while 91.73% for IHS-PTS when subcarrier N = 512. In conclusion, the PAPR reduction percentage increases when the number of subcarriers increases but by implementing IHS-PTS the reduction percentage is more compared to the PTS method only. The value of PAPR increases as the number of subcarriers increase due to high workload requirement and decrease percentage can be seen increasing with the value of subcarriers. This shows that high number of subcarriers will increase the effectiveness in transmitting data. Figure 2 shows the CCDF performance comparison between OFDM, PTS and IHS-PTS when N = 64. The most essential aspect in determining the PAPR is the CCDF. The amount of CCDF reduction obtained is used to analyze PAPR reduction capabilities. The x-axis represents the PAPR value while y-axis is the CCDF. We can observe that the IHS-PTS method enhances CCDF performance compared to the original OFDM and PTS algorithms. Table 2. PAPR Values for N = 64, N = 128, N = 256, N = 512 Number of subcarriers
OFDM
PTS
IHS-PTS
Improvement PTS
IHS
64
6.9471
2.8871
0.8425
4.0600
6.1046
128
7.0611
2.8880
0.7166
4.1731
6.3445
256
7.1314
2.8889
0.7836
4.2425
6.3478
512
7.1475
2.8900
0.5908
4.2575
6.5567
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Fig. 3. CCDF performances comparison between OFDM, PTS and IHS-PTS
4.2 Simulation Result with Different Number of Iterations In this section, the performance of IHS-PTS is compared in the terms of number of iterations. The modulation technique used in this is QPSK. The number of iterations used to compare the PAPR values are 1000, 2000, 3000, 4000 and 5000. The subcarrier value N = 64 is used for the simulation above. Figures 4, 5, 6, 7 and 8 shows that PAPR performance improves as number of iterations K increases. This is because computational complexity decreases as the number of iterations increases. Furthermore, significant increase in the number of iterations required to identify the best solution in IHS algorithm. The PAPR values of OFDM and PTS are almost similar for all the iterations, but the IHS values shows slight difference in all the iterations. The processing time increases as the value of K increases resulting in an improvement in the quality of computational complexity due to increased function evaluation.
Fig. 4. CCDF performances for OFDM, PTS and IHS-PTS for K = 1000
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Fig. 5. CCDF performances for OFDM, PTS and IHS-PTS for K = 2000
Fig. 6. CCDF performances for OFDM, PTS and IHS-PTS for K = 3000
Fig. 7. CCDF performances for OFDM, PTS and IHS-PTS for K = 4000
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Fig. 8. CCDF performances for OFDM, PTS and IHS-PTS for K = 5000
5 Conclusion OFDM, PTS and IHS implementations have been discussed in this paper. OFDM system has many advantages however the high PAPR is considered as the main disadvantage that causes the OFDM system to suffer from low efficiency due to its high amplifier. In this study, a combination of PTS and IHS was effectively created to reduce PAPR in OFDM. The objectives stated have been achieved. To complete the research, CCDF graphs were studied with different number of subcarriers and number of iterations. The primary objective is to combine PTS with IHS algorithm. QPSK is the modulation method utilized in this project. The simulation results show that by applying this technique, PAPR value can be successfully lowered. The second objective is to compare the CCDF performance in the OFDM system before and after applying the IHS-PTS algorithm which was proven in results and discussion. Based on the simulation results, the proposed technique has a significant impact on PAPR reduction performance. The value of PAPR after implementing IHS-PTS was much lowered compared to the original signal and PTS signal. The number of subcarriers and number of iterations used in this research were also examined. The PAPR reduction percentage increases when the number of subcarriers increases but by implementing IHSPTS the reduction percentage increases more compared to implementing PTS method only. In conclusion, both PTS and IHS-PTS can help lower PAPR values, but IHS-PTS can help create better output than PTS alone. The limitation faced in this research would be combating the computational complexity as the number of subblocks increase that can results in longer processing time and decreasing the reducing percentage. Acknowledgment. The authors would like to thank the referees and editors for providing very helpful comments and suggestions. This project was supported by FRGS grant from Ministry of Higher Education MOHE (203/PELECT/6071476).
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References 1. Gayathri, R., Sangeetha, V., Prabha, S., Meenakshi, D., Raajan, N.R.: PAPR reduction in OFDM using partial transmit sequence (PTS). Int. J. Eng. Technol. 5(2), 1428–1431 (2013) 2. Jawhar, Y.A., et al.: A review of partial transmit sequence for PAPR reduction in the OFDM systems. IEEE Access 7, 18021–18041 (2019). https://doi.org/10.1109/ACCESS.2019.289 4527 3. Shawqi, F.S., Hammoodia, A.T., Audaha, L., Falih, A.A.: PAPR Reduction Of A Universal Filtered Multicarrier Using A Selective Mapping Scheme. J. Southwest Jiaotong Univ. 54(5) (2019) 4. Aghdam, M.H., Sharifi, A.A.: PAPR reduction in OFDM systems: an efficient PTS approach based on particle swarm optimization. ICT Express 5(3), 178–181 (2019). https://doi.org/10. 1016/j.icte.2018.10.003 5. Jaradat, A.M., et al.: Modulation options for OFDM-based waveforms: classification, comparison, and future directions. IEEE Access 7, 17263–17278 (2019) 6. Pathuri, L., et al.: Suitability of OFDM in 5G waveform – a review. Orient. J. Comput. Sci. Technol. 12, 66–75 (2019) 7. Singh, M., Patra, S. K.: On the PTS optimization using the firefly algorithm for PAPR reduction in OFDM systems. IETE Tech. Rev. (Institution Electron. Telecommun. Eng. India) 35(5), 441–455 (2018). https://doi.org/10.1080/02564602.2018.1505563 8. Joo, H., Kim, K., No, J., Shin, D.: New PTS schemes for PAPR reduction of OFDM signals without side information, pp. 1–9 (2017) 9. Ann, P.P., Jose, R.: Comparison of PAPR reduction techniques in OFDM systems. In: Proceedings International Conference on Communication and Electronics Systems ICCES 2016, no. 1 (2016). https://doi.org/10.1109/CESYS.2016.7889995
Ergonomics Study of Standing Work Postures in Assembly Process at Small Medium Industry Manufacturing Company Seri Rahayu Kamat1(B) , Amirah Nasha Mohd Azli2 , and Mohammad Firdaus Ani3 1 Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka,
Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia [email protected] 2 VTech Communications (Malaysia) Sdn. Bhd. Industrial Estate, No. 5, Kawasan Perindustrian Tanjung Agas, 84000 Muar, Malaysia 3 Kolej Komuniti Taiping, Unit Teknologi Pembuatan, No.25, Laluan Kamunting 3, 34600 Kamunting, Perak, Malaysia
Abstract. This project was conducted in one manufacturing company is the worldwide pioneer in electronic learning products and the world’s biggest maker of cordless telephones. Now, in manufacturing, competition can be progressively extreme. In the same time, the welfare and health of the workers ought to be considered. Factors of workstation design and workers’ posture should be taken into consideration to raise the productivity and in the meantime prevent the workers from any musculoskeletal disorders (MSD). The main purpose of this study is to analyse the working posture and re-design the assembly workstations in assembly process. Three workstations have been chosen for the postural analysis. Rapid Upper Limb Assessment (RULA) Analysis in DELMIA software was used to analyse the current workstation design and the operator posture. Productivity calculation have been used to analyse and justify the current and productivity improvement. Hence, Takt time been used for validation of operators’ posture and improvement of workstation design. RULA scores of working posture improved for all three workstations (Workstation 1 = from 6 to 3, Workstation 2 = from 7 to 4 and Workstation 3 = from 5&6 to 3&4 respectively) and the productivity increased after the redesigning of workstation (Workstation 1 = 7.1% increase, Workstation 2 15.3% increase and Workstation 3 19.3% increase). The findings of this study suggest that by considering ergonomics at work, productivity will rise, hence improving the industry’s overall performance. Aside from that, more emphasis should be placed on the importance of ergonomics in the industrial business, since it affects the operators’ safety and health. Keywords: Musculoskeletal Disorders (MSD) · Rapid Upper Limb Assessment (RULA) · Ergonomic · Workstation and Productivity
1 Introduction Musculoskeletal disorders or MSDs are injuries and disorders that affect the worker’s movement or musculoskeletal system mainly muscle, tendon, nerve and joint according © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 275–284, 2022. https://doi.org/10.1007/978-981-16-8954-3_26
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to Occupational Health Clinics for Ontario Workers Inc. [1]. These manifest as swelling, as human tissues irritated, feeling pain, stiffness and range of motion loss and having no ability to perform the work. The cause of MSDs is the exposure to the risk factors. Risk factors can be distinguished into two categories which are ergonomic risk factors and individual risk factors. High task repetition, forceful exertions and repetitive or sustained awkward postures are the examples of tasks that lead to ergonomic risk factors while poor work practices, poor health habits, poor fitness, poor diet and inadequate rest time are the examples of individual risk factors. Workplace conditions, layout of the workstation, the speed of the work, and the weight of the objects being handled by operators are the risk factors of MSDs [2]. Prolonged exposure to a risk factor eventually causes the occurrence of MSD [3] and [4]. In essence, reducing exposure to these risk factors will reduce the MSD cases in any industry, especially manufacturing industry. From the study of accident cases (3,345 cases) that reported to Department of Occupational Safety and Health Malaysia (DOSH) in 2015, 61% of the cases are from manufacturing sector showing that manufacturing sector is the most vulnerable sector with workplace accidents. In addition, 46 cases (21%) of fatal accident in manufacturing sector in the report are caused by three agents in the workplace which are workplace condition, transport or lifting equipment and machine itself. This is consistent with the reports by Social Security Organization (SOCSO) as the manufacturing sector scored 82% in 2014 and 88% in 2015 indicating an approximated increase of 7% which equals to 1,846 cases of workers that had occupational diseases and reported to SOCSO. The high percentage of MSDs in manufacturing industry is alarming because this problem negatively affects their workers and their productivity as they face risk factors of MSD and starts feeling fatigue and taking more time to complete their job task which in turn slowing down overall production assembly line. The productivity is the predominant sign of economic widening and social health [5]. A decline in productivity caused by MSDs signifies poor workers’ well-being and is detrimental to company’s growth. Hence, a manufacturing company must maintain a good productivity level to run a successful business. Standing as a global leader that produces electronic learning products from childhood through toddler and preschool as well as the world’s largest manufacturer of cordless phones. This issue also adversely affects this company in study, VTech Communication Malaysia based on a prior pilot study. Observations revealed the manufacturing company has a large number of female operators compared to male which more chances to have work-related musculoskeletal disorders (WMSDs). The study by [6] showed that the susceptibility for MSDs in women is greater than in men. In addition, all operators experienced fatigue and pain during their job task. This means all operators were exposed to the MSDs. For fatigue level, 36.84% of operators score 25 out of 50 points and for pain level, 21.05% of operators showed similar score.
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The possible causes of this problem are the posture of operators and the workstation design. Good working posture and workstation design considering the anthropometry data of human factor can support the improvement of productivity, thus an increase in productivity can be used as a good indicator to the success of these ergonomic changes. Essentially, workstation improvement can increase productivity [7]. Ergonomic tool such as RULA has been proven to reduce MSDs through work posture analysis [5, 8] and [9]. Hence, this study aims to investigate and improve the working posture in assembly process by workstation redesign. This study also analysed the human’s productivity to validate the effectiveness of posture in improving productivity. Aside from that, all data collected from this study can also serve as reference for future research in manufacturing industry.
2 Methodology 2.1 Participants The sample for this project was the operators who works at an assembly line P6 in final assembly department of electronic company, situated in Muar, Johor. Operators were chosen as the project respondents due to these processes involved repetitive task higher than other processes and their working posture were in bad condition. Additionally, these workstation and postures encountered a considerable measure of ergonomic issues due to the awkward posture that can contribute to MSDs. Three workstations (Workstation 1, 2 and 3) were selected. A total of seventeen (n = 17) operators in these three workstations, in which the worst postures adopted in these three workstations were analysed. 2.2 Body Symptoms Survey The questionnaire form was prepared and distributed to the participants. Before distribution of final questionnaire form, it was validated to pass the benchmarking alpha Cronbach value of 0.7. This study focuses on two sections of the questionnaire which are (i) the level of fatigue and pain at body parts and (ii) the postural factors respectively. There are ten body parts that has been focused on operators while performing their job tasks. The ten body parts are neck, shoulder, upper back, upper arms, mid back, lower arms, lower back, buttocks, thigh and leg. For fatigue and pain examination, operators need to do scoring by using scale one (1) to five (5). In the first section, operators justified their time span for pain experienced during past 12 months. While in second section, there are seven (7) criteria, namely neck position, trunk position, shoulder position, wrist position, upper and lower arm position, and leg position. Besides that, the weight of load is included. The questionnaire form more focused on collecting the data regarding operators’ posture and concentrated on gathering the information about. However, the essential objective of questionnaire is to collect the data to analyse the current workstation and operators’ posture.
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2.3 RULA Analysis Digital Enterprise Lean Manufacturing Interactive Application (DELMIA) V5R20 software has been used in this project to analyse and validate the operators’ posture while performing their job task in assembly process. In DELMIA simulation, RULA analysis was applied for assessing all working postures of operator while handling their task based on recorded observation. The data related to the workers’ physical and their working conditions has been deliberate and incorporates the workers’ anthropometry data, information of the workspace and the postures’ modes for workers if static, intermittent and alternately repeater. On determining the postural loads of evaluated workers, these constantly on data were observed, measured, recorded and keyed into the software. The calculated RULA scores from one (1) to seven (7) with their correspond- ing action levels from one (1) to four (4) determine the necessity of posture change [10]. The higher the action level, the more urgent the postural change. After a new posture was implemented, a new RULA score was generated in order to determine the effectiveness of the change. 2.4 Takt Time Calculation The overall productivity of the manufacturing assembly line was calculated based on company record. On the other hand, the productivity at each workstation was determined using takt time before and after the postural change. Takt time is the rate at which you required to finish an item or product so as to meet client demand. To define the takt time, the production time are divided by the customer demand. In manufacturing industry, takt time was assist to maintain an eternal flow of labour and scale back unevenness in work flow because it is necessary for reducing the waste of the processes. Additionally, takt time can facilitate trade over production. Hence, it is efficacious for optimizing the storage costs. In this project, takt time was used to support the productivity improvement after the redesigning the workstation.
3 Results Observation revealed three workstations with awkward postures, designated as Workstation 1, 2 and 3 for simplicity. From each workstation, the most significant posture was identified via RULA analysis with separate arrangement and posture. The posture and RULA score for each workstation before and after workstation redesign is tabulated in Table 1. The next sections will discuss the details of current workstations, the redesign choices made, and RULA analysis at each workstation before and after redesign.
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Table 1. The summary of the RULA analysis before and after redesign Workstation 1
Before redesign
After redesign
RULA score = 6
RULA score = 3
RULA score = 7
RULA score = 4
RULA score (Operator 1) = 5 RULA score (Operator 2) = 6
RULA score (Operator 1) = 3 RULA score (Operator 2) = 4
2
3
3.1 RULA Analysis at Current Workstation 1 Workstation 1 is called Assembly 3 station where the process of inserting FFC to the top part of the product is carried out. Besides, it is considering the marking and checking process of the screwing part and FFC areas. In this workstation, a marker and a control panel support jig has been used as the tools that facilitate operator to perform their job task easier and faster. The final score of six (6) indicates that further investigation is required and need to be change the posture soon because operator is facing the medium risk of MSD and easy to feel pain and fatigue. The cause of the score is that operator had to extra stooping and lateral bending their body to see clearly that the FFC is inserted and align correctly.
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Operator needs to bend the right hand while holding the marker though for the left hand, operator needs to bend and compress to hold their body. The designer of the workstation should have considered the height of the jig and the product to ensure that the operator only need less effort to bend their body while performing daily job tasks. 3.2 RULA Analysis at Proposed Workstation 1 The main issue of Workstation 1 design is that the height of the jig is higher when the button is pressed. Based on the guidelines listed by International Labour Organization [12], the surface of working area should be adjustable for workers of various heights and for various job tasks. The design change of the entire workstation is not possible due to the several factors which did not suit the flow of jig transportation on the roller’s conveyer. Instead, the improvement focuses on the design of the control panel support jig by adding the extending height of the jig with the use of a hydraulic lifting system. The height that proposed for the extending of jig is 150 mm. This value can suit with the height of the workstation so there will no jig violation issues with the top of pallet on the workstation. Besides, the maintenance of jig should be conducted once a month in order to ensure the height of extension jig is sustained. One checklist for jig maintenance scheduling is constructed for ensuring the maintenance is taken every month to sustain the jig. A switch is also added to extend the height of jig suitable for the operator. Hence, the operator bends their body less than 45˚ when the process of checking is carried out. The RULA score is drastically reduced from six to three. Although that is significant improvement, this final score still indicates that the operator is working in a posture that could present some risk of injury from their working posture, and this score most likely is the result of one part of the body being in a strayed and awkward position, so this should be examined and corrected. However, the compression toward operator’s arm also is reduced. According to the guiding principle when carrying out standing work provided by International Labour Organization [12], the operators must be able to work with their upper arms at their sides and without extreme bending or twisting of the back. From the figures below, it can be seen there the operator body’s posture is in green colours which indicates the operator are in a good standing posture. Hence, the risk factors of MSD can be reduced if all the postures are having a small RULA scores. 3.3 RULA Analysis for at Current Workstation 2 The second workstation 2 is also known as 100% Checking which runs the process of assembling the top and base part of product occurred. In this workstation, operator joins the FFC from the top part with the base part of product. After the FFC joining process, operator checks and marks the screwing area. Then, the assemble product is transferred to Workstation 3 by involving two operators (Operator 1 and Operator 2). Some tools are used on this workstation to ease operator’s activities. The tools that been used are marker and automatic screwdriver. The bad posture in workstation 2 is where operator need to bend their body to reach the workstation that placed vertically with a degree of 70 for a certain time period. In this posture, operator needs to align the FFC and marking the screwing area on the top of the product. The final score of RULA for the left side of posture is seven (7), indicating the
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need for instant study and changes to improve the working posture. Alteration is needed to prevent the operator from suffering the back pain and MSD. Hence, the score proves that operator in this workstation were suffering fatigue and pain. The op- erator should avoid bending forward as it will affect the muscles and ligaments of their backs. 3.4 RULA Analysis at Proposed Workstation 2 The design of the workstation is adjusted in order to suit the operator’s anthropometry, especially the height and can provide more comfort to operator while handling their tasks. The width of the workstation is reduced to 470 mm from 545 mm. This width enables Operator 1 to bend their back at minimum so the risk factor of MSD is reduced and less pain will be faced by the operator. The design of the workstation that being altered is the roots for Operator 1 in Posture B less bending when handling the job tasks. This new design of workstation enables the operator to work in comfortable standing position. The final RULA score is reduced from seven (7) to four (4). This final score indicates the posture is better, yet some risk of injury could be present. The red-coloured area is at operator’s forearm only. This shows that operator is suffering pain at the forearm and operator might feel a little pain at their wrist. Nevertheless, the other body parts expresses that the operator is working in the good postures. 3.5 RULA Analysis at Current Workstation 3 Workstation 3 is called Chassis 2 station and is the last workstation that been investigated in this research. Two operators are included in this workstation to perform the daily task. However, there will be a time that only one operator is required. A control panel support jig and three automatic screwdrivers are used by the operators to ease their daily job task and make the processing time faster compared to the absence of these tools. The most problematic posture is where two operators bend their bodies and rotate their wrists to rotate the assembly set of products for placing the product on the control panel support jig. A 9 kg of load has been taken into consideration in RULA analysis. The final score of RULA analysis for both operators was recorded. Operator 1 has a score of five (5) while Operator 2 has a score of six (6). The orange-coloured area for Operator 1 are at wrist and arm. This happened because operator’s wrist and arm are twisted and flexion more than 15°. In the meantime, there are some red areas at body part of Operator 2 which is wrist is at operator’s forearm and wrist. The scores indicate that the operator is facing a high-risk level which is improvement actions are needed in the short time. 3.6 RULA Analysis at Proposed Workstation 3 The redesign of workstation entails the width adjustment of the workstation so that the operators feel comfortable when performing their daily job task on the new design of Chassis 2 station and increase their productivity. The width of the workstation is reduced from 605 mm to 500 mm. In results, Operator 1 and Operator 2 has to slightly bend their
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body forward at minimum to reach and grab the automatic screwdrivers that are located in front of them. From the analysis of RULA, the final scores of both operators are reduced to the better level. Final RULA scores for Operator 1 is reduced from five to three. In addition, the changes of the RULA score is changes from six to four for both sides of Operator 2 postures. This three and four of RULA final scores expresses that both Operator 1 and Operator 2 are working in a posture that could present some risk of injury from their working posture. This analysis shows that both operators do not have to excessively bend their body forward to place the product hence the MSD problems is reduced. 3.7 Analysis of Takt Time and Productivity This section is deliberated and examined the takt time and productivity of the operator at all three workstations. Based on the production schedule in February 2019, the production run per day is 149 units per day. The working time encompasses a 10-h shift per day with an hour allowance for 22 days in a month. From the data, the standard available time for every operator is 540 min or 32,400 s. Hence, the takt time for every operator to produce one product is 217.45 s. The overall productivity of the actual company record was calculated using the rate of product manufactured per hour. Based on company report, a total of 149 units is produced within a 9-h shift per day. This results in a produce of 16 units/h. However, productivity for each workstation was calculated by using the cycle time before and after improvement in DELMIA simulation with a normal moving pace of human. The productivity before and after improvement was then computed in percentage to evaluate whether there is a rise or drop in productivity after redesign. In general, the productivity increased for all three workstations. This is due to the reduction in cycle time after workstation redesign which in turn increases the quantity of product produced. To elaborate, the productivity of humans in Workstation 1 increases as much as 7.1% due to the increase in the quantity of product from 14 units to 15 units. Meanwhile, Workstation 2 has an increase of 15.3% in productivity as the quantity of product increases from 13 units to 15 units. Lastly, the productivity of humans in Workstation 3 shows an increase of 19.3% as the quantity of product rises from 31 units to 37 units. Even though the percentage value is relatively modest, workstation redesign is considered successful in the productivity improvement simultaneously lowering the risk of MSDs among operators. Refinement and further study with other considered factors are advised in the future for better outcome.
4 Conclusion The postural analysis, workstation improvement and productivity calculation were successfully carried out in this project. Three existing workstations was designed and examined using DELMIA software and RULA Analysis for recognising the probable parts of postures improvement. These enhancements have been made in order to diminish some bad postures and at the same time to condense the risk factor of MSD among operators.
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The achievement should act as an example of better ergonomics leading to better productivity. Ergonomic issues will affect the operator either slow or speedy. By way of considering the ergonomic at working place, the productivity will increase similarly to enhance the overall performance of the industry. Besides that, more attention should be drawn to the significance of ergonomic in manufacturing industry as the safety and health of the operators also depends on it.
5 Recommendation The recommendation for the further study has been made based on the conclusions above in order to reduce and minimized the ergonomic issues or work-related musculoskeletal disorders occurred at the working places. Some recommendation that can be proposed for further study are as follow: (a) Company should give some training on proper postures for the operators while handling their daily job tasks. (b) Company should print the guiding principle of ergonomic postures and pasted at the suitable space of workstations in order to alert the operators regarding the appropriate posture for handling the job tasks. (c) Analysis of Biomechanics Single Action should be conduct in order to analyse body load and spine compression of the operators. (d) Force required, repetition tasks and the duration of working period for the operators should be analysed and the load of work must be balanced. (e) This analysis should be continuing to the other workstations at the production line to improve the human productivity and reduce the MSD problems.
Acknowledgements. This study was supported by Universiti Teknikal Malaysia Melaka (UTeM). We would like to express our gratitude to the VTech Communication (M) Sdn. Bhd. for give permission for publish the paper and Center for Research Management and Innovation (CRIM) UTeM for the support in this study. Not to forget, our sincerest gratitude to the company and participants who took part in this research.
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5. Wanave, S.B., Bhadke, M.k, Jibhakate, M.: Study and validation of workers posture in transformer manufacturing industry through RULA. In: International Journal of Analytical, Experimental and Finite Element Analysis, 1(July), pp. 62–66 (2014) 6. Fororesh, E., et al.: Ergonomic evaluation of body postures and effective risk factors contributing musculoskeletal disorder in barbers in Sardasht. J. Health Safety Work 1(2), 45–50 (2012) 7. Karwowski, W., Salvendy, G.: Ergonomics in manufacturing: raising productivity through workplace improvement. Soc. Manuf. Eng. (1998). 13: 9780872634855 8. Husdin, A., Shabanon, M., Nor, M.: Guidelines on Ergonomics Risk Assessment at Workplace. Ergonomics risk factor, pp. 4–32. http://mjphm.org/in-dex.php/mjphm/article/dow nload/688/126/ (2017) 9. McAtamney, L., Nigel, C.E.: RULA: a survey method for the investigation of work-related upper limb disorders. J. Appl. Ergon. 24(2), 91–99 (1993) 10. Dockrell, S., et al.: An investigation of the reliability of Rapid Upper Limb Assessment (RULA) as a method of assessment of children’s computing posture. Appl. Ergon. 43(3), 632–636 (2012). https://doi.org/10.1016/j.apergo.2011.09.009 11. Halim, I., Omar, A., Saman, A., Othman, I., Ali, M.: Ergonomics Risk Factors at Manufacturing Industry: A Prelude Study. http://103.86.130.60/handle/123456789/37418 (2009) 12. International Labour Organization.: Your health and safety at work ergonomics. https://www. ilo.org/global/topics/safety-and-health-at-work/resources-library/training/WCMS_113080/ lang--en/index.htm (2012)
Safety Helmet Head Impact Monitoring System Using Long Range (LoRa) Communication for Mining Industry Thinesh Vijayakumaran(B) Plexus Riverside Sdn. Bhd., Plot 87, 3114, Kawasan Perindustrian Bayan Lepas, 11900 Bayan Lepas, Penang, Malaysia
Abstract. The underground mining industry is significant to the world’s economy. However, it isn’t perhaps the most secure industry to work in. The safety precautions of miners are consistently in question. This task endeavors to help the miners in trouble with the goal that they get quick assistance. The head protector utilized by the miners can be improved by adding important sensors, which will help in checking the ailment of the miner remotely utilizing the LoRa (Long Range) correspondence organization. At whatever point the heap fall on the protective cap or the head protector hits hard on any harsh surface and the edge esteem surpassed, a trouble alert will be shipped off the Control Room through the remote organization. At the point when a pain caution got at the Control Room, authorities from the room can send in a clinical group. The miner’s chief and co-workers in adjoining rooms can be educated with the goal that they can help intentionally. Notwithstanding, a reset catch can be utilized by the miner in case there are no significant wounds and the user is in the protected condition. Then again, a signal for an emergency response is additionally positioned in the protective cap so the miner can caution the Control Room in case there are any genuine wounds to them, adjoining miners, or any unusual condition in the workspace. Subsequently, the proposed item guarantees the wellbeing and dependable remote correspondence inside the underground mines. Keywords: Helmet · Impact · LoRa · Pressure · Safety · Smart
1 Introduction In 2018, two construction workers were dead due to a couple of concrete slabs crushed on them. This accident occurred at Jalan Dewan Sultan Sulaiman, KL, around 2 am. The workers were working on the formwork structure with four other workers on the 15th floor [1, 2]. A similar case, in July 2019 was happed in a quarry at Perak. The victim, Loi Thiam Fatt, 56, was buried under debris at a quarry in Sungai Raia mukim for 45 min before the firefighters locate him [2, 3]. The main reason for death is the lack of proper communication with the rescue team and the time taken to pass the alert message to the responsible team. If a worker working at an open place could face this kind of accident, how about the miners who © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 285–294, 2022. https://doi.org/10.1007/978-981-16-8954-3_27
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work underground? They could face some worst cases to pass the alert message to the responsible team. It makes it more difficult for the miners who work at radiation hazard places such as petroleum mining where they cannot communicate using mobile phones [4]. Alternatively, people used walkie-talkie in those areas as the main communication device. The problem with a walkie-talkie is a half-duplex communication device where only one person can use the network at a time as shown in Fig. 1. If there is an emergency at two different places, then only one of the incidents can be informed at a time while the others need to wait for its turn.
Fig. 1. Half-duplex communication system
Figure 2 shows the widely used safety helmet and its function of each part. People are still involved in accidents at the workplace that leads to head injuries even though wearing a safety helmet. These injuries lead to paralyzing or death sometimes. The big problem nowadays is that people are working hidden places such as underground where only a few people will be in the surrounding. Moreover, the base of the workplace will be far away from the working area. This will be difficult for the other workers to pass the emergency message during an accident. The time is taken for the medical team to arrive at the working area also will be late once they received the emergency call. Sometimes the delayed medical attention to the victim may cause some serious health issues in the future.
Fig. 2. Safety Helmet parts and its functions [5].
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The Occupational Safety and Health Administration (OSHA) commands explicit prerequisites for head security in the work environment and trains that, paying little heed to industry, it’s the employer’s job to guarantee their laborers wear head assurance when presented with chances/risks [6]. Safety Helmet Standards: OSHA has two standards that regulate safety helmet requirements: i. 29 CFR 1910.135: Governs safety helmet requirements for general industry workers [7] ii. 29 CFR 1926.100: Refers to head protection requirements for construction, demolition, and renovation workers [7]
2 Literature Review 2.1 Importance of Head Protection The head is the main organ of the human body that is completely encased in bone. This by the pronouncement of nature expresses the significance of securing an indispensable working part of our body, the brain [8]. Head is one of the sensitive parts of the human body. Head injuries can lead to lifetime injuries such as paralyze and death. The human brain is fragile and the skull around the brain is not good enough to protect it from all shocks or impacts [9]. Head protection in Personal Protective Equipment (PPE) is the protection against impact injury and some burn injuries. It commonly shields the scalp area but not the face area. This is because eye and face protection, noise protection, and respiratory protection are different types of PPE [10]. The general objective of head protection is to diminish the amount of force or acceleration on the head to an acceptable level. The head protection is designed to absorb some of the energy from an impact which reduces the amount of force or acceleration assisted by the head [9]. Besides, head protection also prevents objects from penetrating the head using the impulse force principle [9]. The maximum force or acceleration on the head can be reduced by increasing the duration of the impact. 2.2 Head Injuries in Industries Table 1 shows 851 fatal occupational injuries that occurred in all sectors in Malaysia from 2013 to 2016. All the data were collected from The Department of Occupational Safety and Health (DOSH) reports. According to A. Ayob, the majority of fatalities are due to falling from heights [11]. Falling from height contributes 46.28% of the total fatalities. According to Hui-Nee’s survey in 2014, 37% of the fatal injuries in Malaysia were accounted for by Construction and mining industries [11]. The statistic also shows that more than half of the fatal injuries are caused by an accident that leads to head injuries initially. The reason behind this situation is the late medical attention to the injured labors due to the delay in receiving emergency calls.
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Occupational sectors Manufacturing Mining and quarrying
2013
2014
2015
2016
58
45
46
72
5
15
4
4
Construction
69
72
88
99
Agriculture, forestry, logging, and fishery
33
42
31
25
Utility
7
1
6
2
Transport, storage and communication
8
15
22
13
Wholesale and retail trade
5
6
3
0
Hotel and restaurant
2
1
0
3
Financial, insurance, real estate, and business services
2
4
14
16
Public service and statutory bodies Total
2
5
0
6
191
206
214
240
In another survey, Heap Yih Chong and Thuan Siang Low state that the working environment and the complexity of working practices was the main reason for the safety and health issues remain critical in the construction industries [12]. They also discussed those head injuries had the highest possibility of death or permanent disabilities. Figure 3 shows the number of death due to falls and collapses in the last 10 years in Malaysia [13]. From the information, it can be proven that the construction industry contributes to the high number of fatal injuries in Malaysia due to falls and collapses. Another statistic from the Health and Safety Authority shows that the mining industry is a top 3 position of contributing fatal injuries in the last 10 years.
Fig. 3. Chart of death due to falls and collapses in Construction Industries from 2009 to 2018 [13].
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3 Methodology 3.1 Component Selection There are many components in the market that meets the project’s specifications. However, the component selection must be done by comparing the datasheet and the performance of each sensor that will be best fit for this project. Microcontroller The main functions of a microcontroller are to communicate with other component or sensors, automate the desired work and collect the necessary data [13, 14]. All these functions are depending on the instruction given through programming. The main concern of choosing the microprocessor in this project is the size, where the microprocessor needs to be fitted into the safety helmet. Thus, the Arduino NANO was selected, as it is small and is effective as other microcontroller. RFM-95 (LoRa Transceiver Module) Communication system to transmit and receive the data between the helmet and Internet of things dashboard is very important in this project. Various type of Wireless communication system can be used in this project. However, full-duplex non-radiation hazardous communication system was preferred in this project as it will be suitable to use in the oil and gas industries too [4, 16]. Thus, wireless communication system such as Bluetooth, Zigbee, Wi-Fi and Long Range (LoRA) communication system were considered to be used [15, 16]. After a deep background study, LoRa communication system was preferred for this project. According to the Malaysian Communication and Multimedia Commission (MCMC), 915 Mz is the standard frequency that can be utilized in Malaysia for IoT applications [4, 17]. LoRa wireless modules and devices can achieve much better coverage by using lower frequencies than 2.4 GHz or 5.8 GHz of ISM bands specifically when the nodes are within the same buildings [21]. LoRa uses the spread spectrum modulation technique to transmit data [18, 22]. The fluctuation level of frequency over time is used to encode the data to be transmitted [20]. The major advantage of this spread spectrum communication technique is that it can avoid interference. The signals with this modulation technique are hard to interfere with and jammed [19]. Thus, LoRa can overcome the security problem in IoT applications. Besides, LoRa technology consumes less power compared to other communication system, which increase the durability of the battery used. Although LoRa communication system have a huge advantage over other wireless communication system, the size LoRa shield is very big to be fit inside the helmet. Alternatively, the RFM-95 module was integrated with Arduino NANO. 3.2 Internet of Things Dashboard Design The Internet of Things (IoT) platform was used to view the sensor’s readings. A ThingsBoard webpage dashboard was created and programmed to send the sensor data from microcontroller via the LoRa module [23]. Figure 4 shows the dashboard design to view the pressure sensor reading.
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Fig. 4. Pressure sensor data view dashboard
3.3 Functionality Test Alarm Based on Impact Detection A bottle partially filled with water was dropped from a height of 0.5 m to indicate the “LOW” impact as shown in Fig. 5. The weight of the bottle is 0.07 kg. The helmet assumed to be moved 0.1 m downward as the bottle hits the helmet. The average impact act on the helmet is calculated as 3.43 N [24].
The bottle hits the helmet.
Fig. 5. Set up to trigger low impact
Figure 6 shows a 0.5 kg hammer dropped from a height of 0.5 m to indicate the “HIGH” impact. The average impact act on the helmet is calculated as 24.5 N by assuming the helmet moved 0.1 m downward during impact.
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Hammer hitting the helmet
Fig. 6. Set up to trigger high impact
3.4 Alarm Based on Pushbutton A Red LED is placed on the helmet as shown in Fig. 7, to indicate the alarm created at the dashboard. The RESET pushbutton can be used to clear the alarm manually by user when there is no danger.
Fig. 7. Red LED on the helmet to indicate the alarm created at the dashboard
4 Result, Analysis and Discussion 4.1 Alarm Based on Impact Detection Figure 8 shows the impact sensor details dashboard for low impact as picturized in Fig. 5. The sensor’s state is high at 1:26:08 and there is no alarm created.
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Same time as FSR reading
Fig. 8. Impact sensor dashboard indicating low impact
However, the FSR sensor value exceeds the threshold value at 1:26:08, and an alarm is created as shown in Fig. 9. The alarm created from the FSR sensor can be cleared by the user using the “RESET” pushbutton when there is no danger.
Same time as impact sensor reading
Alarm
Fig. 9. Pressure sensor dashboard indicating low impact with high pressure
4.2 Alarm Based on Pushbutton Panic pushbutton is another alternative way to create an emergency alarm on dashboard manually by the helmet user. The “PANIC” pushbutton needs to be pressed and hold for 2 s to create an alarm at dashboard. Figure 10 shows the panic pushbutton’s details dashboard. As soon as the panic pushbutton is pressed and hold for 2 s an alarm is created at the time of 1:47:03.
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Panic button triggered Alarm
Fig. 10. Panic pushbutton details dashboard
5 Conclusion The design and development of a safety helmet to monitor the impact on the safety helmet wirelessly using Long Range, LoRa communication technology was completed. A circuit connecting all the sensors with Arduino Nano was initially designed and developed. The circuit then extended by connecting an RFM 95 module to transmit data to the LoRa gateway wirelessly. A dashboard was designed using the ThingsBoard application to display the received data. The dashboard is also useful to retrieve data about the amount of pressure act on the helmet for last 7 days. The dashboard is effective in displaying the pressure value according to the impact act on the helmet and show an alert message when the pressure value exceeds the threshold value.
References 1. Two Workers Crushed to Death in KL in Another Construction Casualty - WORLD OF BUZZ. https://www.worldofbuzz.com/ 2. Official Website Department of Occupational Safety and Health - Official Website Department of Occupational Safety and Health. http://www.dosh.gov.my/index.php/component/content/ article/352-osh-info/accident-case/955-accident-case. Accessed 1 Dec. 2019 3. Falling rocks at Simpang Pulai quarry crush worker to death | Malaysia | Malay Mail. https://www.malaymail.com/news/malaysia/2019/07/01/falling-rocks-at-sim pang-pulai-quarry-kills-worker/1767091. Accessed 1 Dec. 2019 4. Lee, M.Y., Azman, A.S., Subramaniam, S.K., Feroz, F.S.: Wireless sensor networks in midstream and downstream in oil and gas industry. In: Jamaludin, Z., Ali Mokhtar, M.N. (eds.) SympoSIMM 2019. LNME, pp. 466–474. Springer, Singapore (2020). https://doi.org/10. 1007/978-981-13-9539-0_45 5. Safetyware Group Sdn. Bhd. https://safetyware.com/wp-content/uploads/2016/06/STWSGCatalogue.pdf 6. Federation, O.: A brief history of the. Gastrointest. Endosc. 83(1), 13850 (1986). https://doi. org/10.1016/j.cell.2008.07.033 7. Understanding Safety Helmet Standards - HexArmor. https://www.hexarmor.com/posts/und erstanding-safety-helmet-standards. Accessed 31 Jul. 2021 8. Importance of Wearing Safety Helmets at Work. https://www.industrybuying.com/articles/ importance-of-wearing-safety-helmets-at-work/. Accessed 26 Sep. 2019 9. Importance of Head Protection - Health and Safety International. https://www.hsimagazine. com/article/protect-your-head-496/. Accessed 15 Aug. 2020
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10. Bartkowiak, G., Kuhl, K., Jachowicz, M.: Protective helmets – requirements and selection – OSHWiki. Personal Protective Equipment (2013). https://oshwiki.eu/wiki/Protective_helm ets_requirements_and_selection. Accessed 26 Sep. 2019 11. Hui-Nee, A.: Safety culture in malaysian workplace: an analysis of occupational accidents. Heal. Environ. J. 5(3), 32–43 (2014) 12. Chong, H.Y., Low, T.S.: Accidents in Malaysian construction industry: statistical data and court cases. Int. J. Occup. Saf. Ergon. 20(3), 503–513 (2014). https://doi.org/10.1080/108 03548.2014.11077064 13. Health and Safety Authority: Fatal Accidents - Health and Safety Authority (2018). https:// www.hsa.ie/eng/Your_Industry/Agriculture_Forestry/Further_Information/Fatal_Accide nts/. Accessed 26 Sep. 2019 14. Microprocessor - Overview - Tutorialspoint. https://www.tutorialspoint.com/microprocessor/ microprocessor_overview.htm. Accessed 15 Aug. 2020 15. Sivarao, S., Subramanian, K., Esro, M., Anand, T.J.S.: Electrical mechanical fault alert traffic light system using wireless technology. Int. J. Mech. Mech. Eng. 10(4), 19–22 (2010) 16. Subramaniam, S.K., Husin, S.H., Anas, S.A., Hamidon, A.H.: Multiple method switching system for electrical appliances using programmable logic controller. WSEAS Trans. Syst. Control 4(6), 243–252 (2009) 17. Omar, H.A., Abboud, K., Cheng, N., Malekshan, K.R., Gamage, A.T., Zhuang, W.: A survey on high efficiency wireless local area networks: next generation WiFi. IEEE Commun. Surv. Tutor. 18(4), 2315–2344 (2016). https://doi.org/10.1109/COMST.2016.2554098 18. Mikhaylov, K., Petäjäjärvi, J., Hänninen, T.: Analysis of capacity and scalability of the LoRa low power wide area network technology. Eur. Wirel. Conf. 2016, EW 2016, 119–124 (2016) 19. Communications, M., Commission, M.: Public Inquiry Allocation of spectrum bands for mobile broadband service in Malaysia. no. (2019) 20. Rajamanickam, S.: Spread spectrum modulation investigation using matlab developed tool on automotive dc-dc converter. Int. J. Adv. Trends Comput. Sci. Eng. 9(4), 6631–6639 (2020). https://doi.org/10.30534/ijatcse/2020/355942020 21. Edward, P., Elzeiny, S., Ashour, M., Elshabrawy, T.: On the coexistence of LoRa- and interleaved chirp spreading LoRa-Based modulations. In: 2019 Int. Conf. Wirel. Mob. Comput. Netw. Commun., pp. 1–6 (2019) 22. Azman, A.S., Subramaniam, S.K., Lee, M.Y., Feroz, F.S.: Dual designated path routing algorithm for congestion control in high-density network. J. Theor. Appl. Inf. Technol. 99(7), 1608–1620 (2021) 23. Subramaniam, S.K., Husin, S.H., Yusop, Y., Hamidon, A.H.: SMS or E-mail alert system for centralize mail compartment, pp. 52–56 (2007) 24. Energy of falling object. http://hyperphysics.phy-astr.gsu.edu/hbase/flobi.html. Accessed 16 Aug. 2020
Computational Analysis of Tool Geometry Effect on Cutting Process in Turning of AISI 1020 Steel Md. Anayet U. Patwari(B) , Rahnum Tahia Meem, and A. N. M. Nihaj Uddin Shan Department of Mechanical and Production Engineering, Islamic University of Technology (IUT), Dhaka, Bangladesh [email protected]
Abstract. In this study, the effect of tool geometry on cutting forces, temperatures, pressure, heat rate and burr formation in the turning process are investigated by computational analysis. As a workpiece material, AISI 1020 steel was used, Cemented carbide was used as a cutting tool and the simulations were performed under wet cutting conditions with different tool geometry and process variables. The geometric variables (rake angle, relief angle and cutting edge radius) of the tool were changed using selected cutting parameters; thus the cutting forces, pressure, temperature and heat rate variations on the tool face were determined. The selected cutting parameters and the tools in different geometries were tested with finite element method simulation. During the tests, the cutting speed, feed, and depth of cut were kept constant and each test was conducted with a sharp uncoated tool insert. According to the results obtained, it has been observed that cutting forces and temperature gets decreased when both rake and relief angle are increased. Increased cutting edge radius causes the temperature of the tool-tip to decrease. Also, increasing the cutting edge radius causes cutting forces to increase. Cutting force components and temperature readings offered extensive data for exploring the orthogonal cutting process. Keywords: Finite element modeling simulations · Tool geometry · Rake angle · Relief angle · Cutting edge radius
1 Introduction In industry, cutting is the most familiar process of manufacturing. For simulating the metal cutting process, FEM has introduced one of the leading models. The ability to simulate mechanical and thermal activity instead of using experimentation of both the tool and material is a major benefit of using numerical simulation [1, 2]. FEM-based simulations are very essential in the case of predicting how the chip formation forms. Machining covers a huge collection of designing manufacturing processes. Removing material from workpiece is one of the main concerns. Some machining process named Turning, where using a cutting tool, cylindrical forms can be generated [3, 4]. In advanced manufacturing technology, high-speed machining has become the most essential aspect. Once, there have been difficulties in machining difficult metals [5]. Athulan and Joel [6] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 295–304, 2022. https://doi.org/10.1007/978-981-16-8954-3_28
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have shown three different simulations named AdvantEdge, Deform-3D and ABAQUS where they have described the robustness for the performance of machining simulations. The complexity of the workpiece’s flow stress can be ignored with the use of models called Lagrangian while there is a fundamental template for work metal that is experimentally obtained from temperature deformation and high strain rate testing. The FEM simulation system used in the orthogonal cutting phase can be an absolute and workable review as long as the work material’s flow stress behavior is well established which is stated by Ozel [7]. A method is described by Bouzid [8] where models are used for cutting forces, roughness and tool life. Using FEM models, Altan et al. [9] analyzed the role of cutting tool edge planning on chip shape, cutting forces and process variables. The prediction made in this study was for the tool rake face, while the material flow around the edge was defined by the location of the stagnation point. P. Bakul Barua [10] experimented three different cutting parameters of AISI 1020 during turning which have been tested and optimized to achieve minimal roughness of the surface using Taguchi L27 orthogonal array to investigate the interaction effect. Results showed that there has a significant effect on both average and variability. T. Panneerselvam [11] experimented using L9 orthogonal array on cutting tool performance and came to a conclusion that powder metallurgy (P/M) exhibited great performance than HSS cutting tool. An experiment had done by Bedada [12] where the benefit of dry machining over wet machining was explored by utilizing a cemented carbide tool on CNC machine in during turning of AISI 1020 and results showed that dry machining was cheaper than the other one. To know about the basic mechanics of machining processes, the orthogonal model is very helpful. Albert [13] analyzed the effects of rake angle in orthogonal metal cutting. In this analysis, a plane strain finite element approach is applied to model orthogonal low carbon steel metal cutting with chip forming. Another researcher Tugrul Ozel [14] analyzed FE modeling studies on turning in 3D with basic and varying edge layout PcBN additives. On rigid and variable fringe micro geometry tools, 3D FEM is used to determine chip formation, loads, pressures, strains, and tool wear. In this research, 2D simulation has been made for turning operation of AISI 1020 steel using uncoated cemented carbide cutting tool. The prime goal is to analyze the effects of tool geometry on cutting processes to anticipate tool life and the quality of machining of various cutting edge radius, rake angles and relief angles using FEM simulations.
2 Finite Element Analysis Input Parameters Finite element analysis has been used to simulate the machining processes on turning processes in 2D configurations and process parameters effects were analyzed using virtual engineering. It allows users to evaluate machining parameters and tool and workpiece settings that can minimize cutting temperature and pressure and components distortions. As workpiece, AISI 1020 steel has been used because of its properties, high machinability, high ductility and high weldability. As cutting tool, uncoated Cemented Carbide tool has been used. The reason behind choosing this tool is high hardness, high strength and high rigidity. It offers a stronger finish and makes it possible to work faster. For cemented carbide cutting tool, several numbers of FEM simulations has been performed by changing the Rake angle, Relief angle and cutting edge radius.
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2D model geomteries with cutting parameters and tool geometries used in this simulation are listed in Table 1. Table 1. FEM of machining of Steel (AISI 1020) input parameters Workpiece Workpiece length, L
5 mm
Workpiece height, h
2 mm
Workpiece material
AISI 1020
Tool Rake angle, a
3°,6°,9°,12°,15°
Rake face length, q
2 mm
Relief angle, b
7°,10°,13°,16°,19°
Relief face length, p
2 mm
Cutting edge radius, r
(0.02,0.04,0.06,0.08,0.10) mm
Material
Uncoated cemented carbide
Process Depth of cut, d
2 mm
Length of cut, l
5 mm
Feed, f
0.15 mm/rev
Cutting speed, V
150 m/min
Initial Temperature, To
20 °C
Coolant Heat transfer co-efficient
9933 W/m2. k
Density
981 kg/m3
Temperature
20 °C
Area option
Immersed
Simulation Maximum number of nodes
72,000
Maximum element size
0.1 mm
Minimum element size
0.02 mm
FEM Model description and details for the 2D turning process are shown in Fig. 1. Figure 1(a) shows a better understanding of the interaction between these inputs parameters and calculated distortion of the element mesh are shown in Fig. 1(b) for a particular conditions where the cutting speed is 1000 m/min, feed rate is 0.14 mm/rev and cutting depth is 2 mm.
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(a)
(b) Fig. 1. (a) 2D model with process paramters; (b) Calculated distortion of the element mesh (72,000 nodes) used in simulations. The cutting speed was 1000 m/min, feed was 0.14 mm/rev and depth of cut was 2 mm.
3 Results and Discussion 3.1 Effect of Rake Angle Rake angle is one of the most important parameters, which determines the tool and chip contact area. From the calculated distortion of the elment mesh it has been observed that rake angle variation has significant effects on cutting force, feed force and cutting temperature. Figure 2 shows the effect of rake angle variations on the stated parameters. The variation of cutting forces and feed forces as the rake angle changes is shown in Fig. 2(a).
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(b)
Fig. 2. Distribution of (a) cutting and feed forces; (b) temperature with the variation of rake angle for Cemented carbide tool.
(a)
(b)
(c) Fig. 3. Distribution of (a) temperature; (b) pressure; (c) heat rate in the workpiece, tool (cemented carbide) and chip at 150 m/min simulation.
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It has been observed that increase of rake angle has the largest effect on reducing cutting force due to the decrease of chip thickness and reduction of friction force consequently. By increasing positive rake angle, cutting force and feed force were decreased and also less power/force was required. The reason is that simply the tool can plunge into the workpiece. A Positive rake angle yields a larger shear angle; consequently, it helps to minimize cutting forces. It also releases a better surface finish since it helps the chip to flow away from the workpiece. But, excessive rake angle weakens the tool. During the metal cutting process, a high rate of mechanical energy from cutting forces is converted into heat. The influence of rake angle on cutting temperature is displayed in Fig. 2(b), where it can be seen that the maximum temperature occurs at the tool rake face. It was attained that it was contrasted to increased the rake angle, the tool-tip temperature values were decreased. Saglam et al. [15] observed that whenever the feed rates were increased, cutting forces were also increased. According to the result, if the cutting forces and temperature were considered together, the optimum rake angle could be assumed as 12°. While increasing the value of rake angle, lowers the cutting edge strength. Also, by increasing the rake angle the pressure and heat rate were decreased. Moreover, the tool is subjected to stronger tension forces and lesser compressive forces at higher rake angles. When the rake angle is increased, the tool becomes sharper and keener, reducing the tool’s effectiveness and cutting forces. In Fig. 3, a more detailed overview of the distribution of temperature, pressure and heat rate in the tool, workpiece, chip and burr is showed. 3.2 Effect of Relief Angle The development of the cutting forces and feed forces as the relief angle changes is shown in Fig. 4. It can be seen that, as the relief angle increased, the cutting force decreased as well as feed force but the variation is not significant.
(a)
(b)
Fig. 4. Distribution of (a) cutting and feed forces; (b) temperature with the variation of relief angle for Cemented carbide tool.
Increasing the relief angle decreases the temperature and increases the pressure and heat rate. Smaller relief angles resulted in higher contact area and roughness between the
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removal face as well as the workpiece, as well as an elevation in cutting edge temperature. According to the result, if the cutting forces and temperature were considered together, the optimum relief angle could be assumed as 7°. In Fig. 5, a more detailed overview of the distribution of temperature, pressure and heat rate in the tool, workpiece, chip and burr is showed.
(a)
(b)
(c) Fig. 5. Distribution of (a) temperature; (b) pressure;(c) heat rate in the workpiece, tool (cemented carbide) and chip at 150 m/min simulation.
3.3 Effect of Cutting Edge Radius The variations of the cutting and feed forces with the cutting edge radius are linearly approximated as displayed in Fig. 6(a). It can be seen that, as the cutting edge radius increased, the cutting force increased as well with a slight increase in the feed force. Cutting force raises when the cutting depth and feed rate raise and reduces as the cutting speed rises. The influences of edge radius on cutting temperature are displayed in Fig. 6(b). The cutting temperatures of the tool-tip decrease with the increase of edge radius which is good for a tool. But it gives us higher pressure.
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(a)
(b)
Fig. 6. Distribution of (a) cutting and feed forces; (b) temperature with the variation of cutting edge radii for Cemented carbide tool.
(a)
(b)
(c) Fig. 7. Distribution of (a) temperature (b) pressure (c) heat rate in the workpiece, tool (cemented carbide) and chip at 150 m/min simulation.
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The heat rate also decreases along with the increase of cutting edge radius. It is beneficial for tool life if the cutting edge radius is higher. It gives a tool better surface finish. So, when the nose is overly sharp, the tool wear is increased and tool life is reduced. Altan et al. [9] observed that an engineering analysis is also possible of tool wear. In this study, a minimum seems to exist at a moderate edge radius (r = 0.04 mm) while the cutting temperature is monotonically decreasing with the cutting edge radius. In Fig. 7, a more detailed overview of the distribution of temperature, pressure and heat rate in the tool, workpiece, chip and burr is showed.
4 Conclusions In this study, the impact of various tool geometries (rake angle, relief angle and cutting edge radius) on cutting forces, cutting temperature, pressure, heat rate were examined in the orthogonal straight turning of AISI 1020 steel using finite element method. Precise measurements of machining temperature as well as heat rate and forces are crucial for choosing the optimal machining parameters to enhance machining efficiency and component surface integrity, which is the machining industry’s end goal. Based on the results of the simulation, it can be concluded that: • Increasing the rake angle caused a decrease in cutting force and feed force. The increasing temperature may damage a tool very shortly. Smaller temperatures give us better tool surfaces and higher tool life. According to the result, if the cutting forces and temperature were considered together, the optimum rake angle could be assumed as 12°. • Relief angles are used to assist prevent tool fracture and extend the life of the tool. The cutting tool may chip or shatter if the relief angle becomes too large. So, a smaller relief angle will give a better life to a tool as like as 7°. • Increasing in cutting edge radius will decrease the temperature as well as heat rate which is good for any type of tool. If the radius is too sharp, the surface roughness will be greater and life of the tool will reduce which means cutting edge radius gives an impact on surface finish and tool life as well. These findings offer the industry some visions into the design of tool geometry to enhance the machining characteristics. Also, the FEM simulations can be used as a predictive dependable tool for industrial appliances to avoid performing too many costly and time-consuming experimental investigations, which will be more desired and affordable in industrial contexts.
References 1. Bil, H., Kiliç, S.E., Tekkaya, A.E.: A comparison of orthogonal cutting data from experiments with three different finite element models. Int. J. Mach. Tools Manuf. 44(9), 933–944 (2004) 2. Davim, J.P., Reis, P., Maranhão, C., Jackson, M.J., Cabral, G., Grácio, J.: Finite element simulation and experimental analysis of orthogonal cutting of an aluminium alloy using polycrystalline diamond tools. Int. J. Mater. Prod. Technol. 37(1–2), 46–59 (2010)
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3. Mackerle, J.: Finite-element analysis and simulation of machining: a bibliography (1976– 1996). J. Mater. Process. Technol. 86(1–3), 17–44 (1998) 4. Mackerle, J.: Finite element analysis and simulation of polymers - an addendum: a bibliography (1996–2002). Model. Simul. Mater. Sci. Eng. 11(2), 195–231 (2003) 5. Li, L., He, N., Wang, M., Wang, Z.G.: High speed cutting of Inconel 718 with coated carbide and ceramic inserts. J. Mater. Process. Technol. 129(1–3), 127–130 (2002) 6. Joel, D., David, A., Gardner, J.D.: Consortium on Deburring and Edge Finishing (2005) 7. Özel, T.: The influence of friction models on finite element simulations of machining. Int. J. Mach. Tools Manuf. 46(5), 518–530 (2006) 8. Bouzid, W.: Cutting parameter optimization to minimize production time in high speed turning. J. Mater. Process. Technol. 161(3), 388–395 (2005) 9. Yen, Y., Jain, A., Altan, T.: A finite element analysis of orthogonal machining using different tool edge geometries. J. Mater. Process. Technol. 146, 72–81 (2004) 10. Sonowal, D., Sarma, D., Barua, P.B., Nath, T.: Taguchi optimization of cutting parameters in turning AISI 1020 MS with M2 HSS tool. IOP Conf. Ser. Mater. Sci. Eng. 225, 1 (2017) https://doi.org/10.1088/1757-899X/225/1/012186 11. Panneerselvam, T., Kandavel, T.K., Kishore, P.: Experimental investigation on cutting tool performance of newly synthesized P/M alloy steel under turning operation. Arab. J. Sci. Eng. 44(6), 5801–5809 (2019). https://doi.org/10.1007/s13369-019-03763-4 12. Bedada, B.D., Woyesssa, G.K., Jiru, M.G., Fetene, B.N., Gemechu, T.: Experimental investigation on the advantages of dry machining over wet machining during turning of AISI 1020 steel. J. Mod. Mech. Eng. Technol. 8, 12–25 (2021) 13. Mech, I.J., Vol, S., Shih, A.J.: Pergamon Finite Element Analysis of the Rake Angle Effects in Orthogonal Metal Cutting F ¢ F ~ The Plane-Strain Orthogonal Metal Cutting Process, with the Direction of Relative Movement of the Wedge-Shaped Cutting Tool Perpendicular to its Straight Cutti, vol. 38, no. 1 (1996) 14. Özel, T.: Computational modelling of 3D turning: Influence of edge micro-geometry on forces, stresses, friction and tool wear in PcBN tooling. J. Mater. Process. Technol. 209(11), 5167–5177 (2009) 15. Saglam, H., Unsacar, F., Yaldiz, S.: Investigation of the effect of rake angle and approaching angle on main cutting force and tool tip temperature. Int. J. Mach. Tools Manuf. 46(2), 132–141 (2006). https://doi.org/10.1016/j.ijmachtools.2005.05.002
Structural Vibration Study of a New Concept Intelligent Rubber Tapping Machine Z. C. Chong, W. M. A. Ali, and A. Z. A. Mazlan(B) School of Mechanical Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia [email protected]
Abstract. Rubber products are important and took up about 20% of Malaysia’s total exports since year 2000. Due to this, it is necessary to improve the quantity of natural rubber that been produced. However, the number of skilled rubber tappers are getting lower nowadays, and the available manual tools are unable to cope with the market demand. In this study, a new concept of intelligent rubber tapping machine (RTM) is introduced as a solution to this problem. The designed RTM using rotational cutting tool can complete the tapping process in faster time with minimum labor contact. By using ANSYS v20 software, the RTM structural vibration in terms of modal, frequency response and transient analyses are investigated. From the study, the first sixth modes of the structural have been determined in the range of 0–100 Hz, and the most dominant mode resulted at 54.88 Hz. Even though the running speed of motor is set at 3000 RPM (50 Hz), the resonance does not occur since the disturbances during operation has shifted the transient vibrational peak to about 100 Hz with considerable low amplitude of 1.506 m/s2 . Hence, the proposed RTM is applicable for the real tapping process. Keywords: Rubber tapping machine · Vibration · Intelligent system
1 Introduction Natural rubber is produced by exudations of certain tropical plants such as Hevea brasiliensis or rubber tree. It is an essential raw material which can be used to manufacture the products such as tires, shoes, gloves etc. [1]. In modern tapping process, when the bark of the rubber tree is tapped, a milky liquid will flow out from the tree and the bark is usually cut about 3 to 5 mm deep into the trunk without damaging the cambium with a thickness of 1.5 to 2 mm [2]. Currently, most of Asean counties faced the problem of insufficient labor for rubber tapping, as it is a skill-oriented job which need to be done repetitively and quite exhausting. Due to the aging of skilled rubber tapper and less interest from the young generation, the number of skilled rubber tapper has been decreased over the years [3]. A solution such as the development of automatic or semi-automatic rubber tapping machine (RTM) is necessary as it will decrease the required workload and allows the unskilled labor to perform the tapping process very well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 305–312, 2022. https://doi.org/10.1007/978-981-16-8954-3_29
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Some detail comparisons have been made from the available RTMs in the market as shown in Table 1. From Table 1, there are four different designs of RTM. The first RTM by [4] is a fully automated which able to turn the rubber tapping process into fully automated and required minimum labor force. The proposed RTM uses camera and multiple sensors such as ultrasonic, moisture, rain, and infrared. The cutting head can move in three axis directions and the carriage can move the RTM from tree to tree with a single controller used. The second RTM design by [5] required to be manually clamped on the tree. A circular rack and pinion system are used to determine the cutting trajectory. On the rack, a permanent magnet DC motor is mounted, and a knife is fixed on the DC motor. The motor can be controlled to move forward or backward, and the depth of cutting can be adjusted by changing the bolt and nut arrangement on the knife. The third RTM design by [6] is a semi-automatic and battery powered. It uses an oscillating power tool with a mechanical guiding structure and sensor-based user assistive system. The cutting blade is specially designed for the tapping process which based on the Jabong knife. It also has an Inertia Measurement Unit (IMU) installed to monitor the orientation of the device with 5 LED indicator lights. The final RTM design by [7] consists of two linear guides, two stepper motors, three infrared sensors which controlled by Arduino system. The design aimed to mimic manual hand tapping motion and increasing the latex production. The first linear guide A performed the upward, and downward movements and the second linear guide B performed the angular movements The traditional tapping knife is wielded to the linear guide B and infrared sensors used to control its movement. Table 1. Available RTMs in the market. Type of RTM
Design
Type of RTM
a) Yatawara et al., 2019 [4]
c) Soumya et al., 2016 [6]
b) Prasad, 2020 [5]
d) Kamil et al., 2020 [7]
Design
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Based on the advantages and disadvantages of RTMs in Table 1, a new concept intelligent RTM is proposed in this study. The RTM is firstly design using a SolidWorks v20 software with the unique functions such as an integrated cutting tool with two different motors, faster tapping completion time and minimum labor contact. Then, the structural vibration performances are investigated using ANSYS v20 software for modal, frequency response and transient analyses. This study is important to avoid any structural disturbances during the operation of RTM.
2 Materials and Method 2.1 Conceptual Design of Intelligent RTM The available RTMs shown in Table 1 previously can be categorized into two general types which is portable and fixed RTMs. In this study, the design alternatives are compared using a decision matrix as shown in Table 2. The criteria are based on the price, battery, ease of use and the needs of labor. The rating r is set between 0 (Worst) to 10 (Best) with the formula of weight rating W r as follows: Wr = (W /100) × r
(1)
Table 2. Decision matrix of RTM. Criteria
Weightage (W ) %
Design alternatives 1 (Portable)
2 (Fixed, Semi-auto)
2 (Fixed, Auto)
Rating
Weight rating
Rating
Weight rating
Rating
Weight rating
Price (Low-high)
30
8
2.4
6
1.8
4
1.2
Battery (Low-high)
20
8
1.6
8
1.6
4
0.8
Ease of use (Easy-hard)
30
5
1.5
8
2.4
9
2.7
Needs of labour (None-necessary)
20
2
0.4
8
1.6
9
1.8
Total
100
5.9
7.4
6.5
From Table 2, the rating for price and battery will be towards 10 marks if both price and battery power consumed are considered low. As a result, Design 2 shows the highest rating which is 7.4. Hence, the design of RTM will be based on these features. Figure 1 shows the complete 3D design of the new concept intelligent RTM.
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Fig. 1. Complete 3D design of the new concept intelligent RTM.
From Fig. 1, the RTM will be mounted on the tree using 2 stainless steel plates and nuts. It has an upper and lower rail for the movement around the tree. Two railing blocks are assembled on each rail and the RTM movement is controlled using a gear which connected to the rack at the lower rail. Between the two rail blocks, there is a platform to mount the motor and rotating cutting tool, which can move up, down and around the tree. The RTM is expected to complete the tapping in 16 s for each of tree. 2.2 Structural Vibration Analysis In this study, the RTM structural vibration is investigated in terms of modal, frequency response and transient analyses using ANSYS v20 software. Modal analysis (MA) is performed to obtain the natural frequencies and mode shapes of the RTM in order to avoid the structural disturbance during operation such as resonance phenomenon [8]. The frequencies range are set from 0 to 100 Hz to determine the first six modes of the RTM. Transient analysis is then be carried out to observe the resonance effect when the DC motor is running at 3000 rpm (50 Hz). All the analyses used the linear mesh [9] because of the computational limitation to run the simulation with higher degree meshes. The materials of the RTM in simulation are selected from the engineering data library. The plastic parts are assigned as acetal resin (Young’s Modulus = 2.5 × 109 Pa), while the fasteners and the vertical guide are set as stainless steel (Young’s Modulus = 1.9 × 1011 Pa) and aluminium (Young’s Modulus = 6.8 × 1010 Pa), respectively. Figure 2 shows the boundary condition set in the MA simulation. From Fig. 2, there are total of four fixed support faces (blue color) that been assigned to fix the RTM on the rubber tree. For transient analysis, the sinusoidal forces with amplitude of 1 N, 3 N and 5 N are set at the DC motor to represent the input vibration of the DC motor towards the tree surface when it is running as shown in the following equation: F = A sin(2π ft), where A = 1, 3, 5 N and f = 50 Hz
(2)
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Fig. 2. Boundary conditions of fixed supports (a) Upper fixtures (b) Lower fixtures
The sinusoidal force function is plotted using MATLAB software and 100 sets of data are obtained from the function. The 100 sets data are used as the input vibration force in the ANSYS software for the transient analysis. The simulation step end time is set at 0.1 s and the time step is set at 0.001 s. From this analysis, the total vibration acceleration of the RTM during operation can be obtained.
3 Results and Discussion 3.1 Modal Analysis Table 3 shows the list of natural frequencies for the RTM. From Table 3, it can be observed that the frequency range of the first six modes lay between 33.96 Hz to 90.40 Hz. Each of the natural frequency has produced different mode shapes and the most dominant is shown by the 3rd mode (Fig. 3), whereby there is a significant deflection at the upper rail block, cutter mount and lower rail block. This mode is considered critical as it closed to the operating frequency of the RTM motor at 50 Hz, and this may induce the resonance phenomenon. Figure 4 shows the acceleration frequency response of the RTM between 0–100 Hz. Each peak of the frequency response represents the natural frequency values listed in Table 3 and as what can be observed, the 3rd peak of 54.88 Hz has produced the highest amplitude among all. Table 3. List of the first six natural frequencies of the RTM. Mode
Natural frequency (Hz)
1
33.96
2
43.90
3
54.88
4
69.58
5
80.73
6
90.40
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Fig. 3. Dominant mode shapes at 3rd mode
Fig. 4. Acceleration frequency response of the RTM
3.2 Transient Analysis Figure 5 shows the graph of transient vibration acceleration of the RTM in time domain for all the three excitation forces (1 N, 3 N and 5 N). From Fig. 5, it can be observed that, as the amplitude of excitation increases, the response acceleration also increases. The highest peak of time acceleration response is determined at 7.8 m/s2 during the highest excitation force of 5 N. However, this magnitude is considerably small and acceptable as the RTM is operated without the help of operator, so there is no direct vibration transmitted to the operator hand-arm. Figure 6 highlights the graph of transient vibration acceleration of the RTM in frequency domain. The Fast Fourier Transform (FFT) conversion of the time domain graph in Fig. 5 is carried using MATLAB software to obtain the acceleration vibration response at the specific frequencies. From the figure, the RTM operation peaks are occurred at 20 Hz (1st peak) and 100 Hz (2nd peak), while the peak at 0 Hz represents the static deflection. The resulting of two operational peaks is due to the interactions between the motor operating frequency and varies RTM structural modes, which resulting the operating frequency of 50 Hz been shifted around 20 Hz and 100 Hz. In the graph of transient vibration acceleration against frequency, when the sinusoidal forces of 1 N, 3N and 5 N are applied, the magnitudes of the total acceleration occurred at 0.3013 m/s2 , 0.9038 m/s2 and 1.506 m/s2 , respectively at 100 Hz.
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In these analyses, the resulting vibrational amplitudes caused by the original rotational speed of cutting motor (50 Hz) does not resonate with the RTM structural modes, thus, the RTM can be applied for the tapping process.
Fig. 5. Time domain vibration acceleration with forces of 1 N, 3 N and 5 N
Fig. 6. Frequency domain vibration acceleration with forces of 1 N, 3 N and 5 N
4 Conclusion In this study, the structural vibration performance of the new concept RTM has been investigated. The proposed RTM is a fixed and semi-automatic type since it has the highest rating point of 7.4/10 from the decision matrix that been carried out. In term of structural vibration performances, the 3rd mode of MA at 54.88 Hz shows the most dominant mode shapes with the highest vibration deflection amplitude. However, this mode does not coincide with the original operating speed of the RTM (50 Hz), as it has been shifted to 20 Hz and 100 Hz due to the interactions between the motor operating speed and varies RTM structural modes during operation. Based on the low resulting
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transient vibration acceleration of 1.506 m/s2 , it can be concluded that the proposed RTM is applicable for the real tapping process. Acknowledgement. The authors would like to thank Universiti Sains Malaysia for providing the research funding through RUI grant 1001.PMEKANIK.8014129.
References 1. Gent, A.N.: Rubber | Tropical Plants, Petroleum, & Natural Gas | Britannica, Encyclopedia Britannica. https://www.britannica.com/science/rubber-chemical-compound. Accessed 2 Dec 2020 2. Susanto, H., Ali, S.: The design of flexible rubber tapping tool with settings the depth and thickness control. IOP Conf. Ser. Mater. Sci. Eng. 506, 012002 (2019) 3. Malaysia Rubber Board. Natural Rubber Statistic: Malaysia Rubber Board. http://www.lgm. gov.my/nrstat/Statistics Website 2020 (Jan-Mar).pdf. Accessed 2 Dec 2020 4. Yatawara, Y.A.I., Brito, W.H.C., Perera, M.S.S., Balasuriya, D.N.: “Appuhamy”-the fully automatic rubber tapping machine. Engineer 27, 1 (2019) 5. Prasad, R.: Rubber tapping machine. Int. Res. J. Eng. Technol. 7, 1235–1238 (2020) 6. Soumya, S.J., Vishnu, R.S., Arjun, R.N., Bhavani, R.R.: Design and testing of a semi-automatic rubber tree tapping machine (SART). In: 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 1–4. IEEE (2016) 7. Kamil, M.F.M., Zakaria, W.N.W., Tomari, M.R.M., Sek, T.K., Zainal, N.: Design of automated rubber tapping mechanism. IOP Conf. Ser. Mater. Sci. Eng. 917, 012016 (2020) 8. Wu, Y., Li, S., Liu, S., Dou, H.S., Qian, Z.: Vibration of Hydraulic Machinery. Springer, Dordrecht (2013) 9. Arbain, A., Mazlan, A.Z.A., Zawawi, M.H., Mohd Radzi, M.R.: Vibration analysis of Kenyir dam power station structure using a real scale 3D model. Civ. Environ. Eng. Rep. 29, 48–59 (2019)
Study of Long Range (Lora) Network Coverage for Multi Areas C. H. Ong1 , W. M. Bukhari1(B) , M. N. Sukhaimie2 , M. A. Norasikin3 , A. F. A. Rasid4 , A. T. Izzudin1 , and N. F. Bazilah4 1 Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
[email protected]
2 Melor Agricare PLT, MK18, Kg. Padang Panjang, Alor Gajah, Melaka, Malaysia 3 Fakulti Teknologi Maklumat dan Komunikasi,
Universiti Teknikal Malaysia Melaka, Melaka, Malaysia 4 Fakulti Teknologi Kejuruteraan Mekanikal dan Pembuatan, Universiti Teknikal Malaysia
Melaka, Melaka, Malaysia
Abstract. Understanding the limitations of the long-range (LoRa) network to similar communication technologies providing greater insight into how these constraints might affect implementations and strategies that use a LoRa network. This paper is supported by several testing for the analysis in two different areas that verifies the signal strength of a LoRa network. LoRa network will get affected by physical and environmental attributes such as height, distance, and crowded level of surrounding by measuring the Received Signal Strength Index (RSSI), Signal to Noise Ratio (SNR), and Packet Reception Rate (PRR). The signal strength of LoRa in the straight road from 0m until the signal is breakdown also discussed. The experiments are conducted by using two RFM95W LoRa transceivers as the gateway and receiver, at UTeM Kampus Induk environments. The findings are that the LoRa network is suitable for small-scale projects with low gateway elevation and a large distance communication range. This is because the obstruction of the signal makes the message is broken and unusable levels at around 150m in a residential area and 300m in an open space. In conclusion, to make the line of sight free by elevation the LoRa hardware has to increase the PRR and reduce the noise. Keywords: Long Range Network (LoRa) · Internet of Things (IoT) · Arduino
1 Introduction The importance of IoT (Internet of Things) usage has grown substantially in current times; it is projected that more than 50 billions of devices will be connected in 2025 [1]. Most of these connected devices will be used in WAN (Wide Area Network) solutions. This created a desire for new communication protocols that focus on important aspects required for societal deployment of technology, such as low-power consumption and extended range coverage. This resulted in the development of LPWANs (Low Power Wide Area Networks), which are intended towards smaller bit rate of long-distance © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 313–327, 2022. https://doi.org/10.1007/978-981-16-8954-3_30
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communication. The objective is to substitute electronic equipment that currently relies on cellular communications such as GSM or 4G. This results in greater cost and high power consumption compared to devices adapted to this small bit rate communication style to provide battery lifetimes of up to 10 years while maintaining a communication range of up to 30 km under ideal conditions. Because the technology is fresh and not fully fleshed out, as well as reachable and wholly open to the public. Hence, this paper will concentrate on assessing and evaluating one of these LPWAN technologies, in particular, namely, LoRa and its communication protocol LoRaWAN. Currently, LoRa research is very limited and primarily focused on how certain factors such as temperature, humidity, and precipitation affect the maximum range and battery life, while research on how a LoRa network actually performs and what its limitations are primarily limited to simulations and theories. It will give more insight into how these limits affect prospective use cases in this work by demonstrating RSSI, SNR, and PRR in diverse environmental topologies or locations. The RSSI is a metric that quantifies the amount of power in a radio broadcast. It is a rough estimate of the signal intensity received by an antenna. One method for determining the quality of a communication link is to measure the signal strength at the receiving antenna. SNR is the ratio of the desired information or signals power to the undesirable signal or background noise power. The PRR is a rate to measure the number of packet messages that are able to receive.
2 Research Background 2.1 IoT Communication Systems Currently, a wide range of IoT technologies are in use across the world, each tailored to a certain specialty. When it is possible, we utilize WiFi for our “smart home” and when it is not, we utilize Bluetooth. The range covers for different technologies in the network are illustrated as shown in Fig. 1. Short-range networking technologies usually come with a low data transfer rate, as compared to cellular technologies which range
Fig. 1. Comparison of communication technologies in terms of data rate and range [1]
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is acceptable and higher transfer rate. However, a relatively recent contender that is utilized for home applications using ZigBee, whose power is relatively low and range is quite limited, is best for tools such as lighting, temperature, security, and sensors. Due to distancing problems, these kinds of technologies become unsuitable because WiFi and ZigBee can only cover a range up to 100 m, hence the cellular networks devices that used 5G or GSM are required for the long-range networks. The trade-off relevant to these technologies is high at a cost in terms of licensing costs and battery lifetime [2]. 2.2 Low-Power Wide-Area Network (LPWAN) LPWAN is a main acronym for systems that utilized extremely high power economy while retaining a large transmission range; to achieve this aim, a compromise in data rate is required. The purpose of these methods is to fulfill the core of applications or devices that demand extended battery lifespan, covers from medium to long-range, and relatively low duty cycles [3]. Agriculture and industrial sensors are examples of such products. The smart city is known as the extended version of smart home, which the application’s control like traffic lights or even ticketing systems for parking utilized using lifetime sensors that communicate with the user to arrange for the best flow to avid congestion or justify which parking spots are available. These LPWAN modulations come in a variety of flavors, including SS Chirp, OFDMA, and NB. Table 1 tabulated comparison of different modulations, with the majority of them having distinct characteristics that make them suitable for a certain use case. 2.3 Different Between LoRa and LoRaWAN Lora is a wireless modulation technique that serves as the physical layer for connecting devices to a gateway. Similar to the connection layer between the laptop and the router, WiFi may connect a laptop to an access point. LoRa has exceptionally low bandwidth and energy usage, with a typical link budget of roughly 155 decibels (dB). Similar to WiFi, LoRa works on the ISM bands with free licensing. Although at a lower frequency, such as 433 MHz in Asia, almost double for European countries (868 MHz), and for North America regions around 915 MHz. It is also governed by transmission standards, which vary for certain regions such as Europe, with a transmission power of 14 dB, duty-cycle less than 1%, and bandwidth of 125 kHz [5]. LoRa communicates using the LoRaWAN protocol. For example, if LoRa is the WiFi connection, LoRaWAN is the IP protocol. It is primarily aimed towards wireless long-life battery span devices and addresses essential IoT concepts such as mobility, localization, and bi-directional communication. A LoRaWAN network is often designed in a star-ofstars topology, with their gateways acting like channels, transmitting messages between endpoint devices and a central network server [6]. Devices in a LoRaWAN network are distant items that can range from a thermometer to a geolocation-based tracking system, as seen in Fig. 2.
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As stated above, the primary focus of this paper will be on the performance of a LoRa network. The experiments will provide information on how different types of factors affect performance and how it varies from one environment to the next. This paper does not consider changing weather conditions while conducting tests and assessing the data. Although this might have a favorable or negative impact on the outcome. However, the measurements will be carried out under similar weather and temperature circumstances across the test suite to ensure that the findings are as consistent as possible throughout the numerous test scenarios.
Fig. 2. Example structure of LoRa network [4]
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The range for the testing in the residential area is not expected to be extremely wide for this study. This is due to the fact that the signal intensity will vary substantially between distances since the signal will have to pass through objects that impair signal quality. In the experiments done at various elevations, this would anticipate that the signal quality would improve when the gateway is lifted higher. This study also assumes that the rise would decrease when the gateway reaches its “optimal” height, implying that any additional elevation serves no function for that specific gadget. Furthermore, the expectation of this study is that the location of a gateway and endnode devices affect performance in the deployment of a LoRa network, which is critical in order to guarantee that all data provided from the devices is received. Especially when a single device may only provide data a few times a day. Mekki [1] noted in his study that several of these LPWAN technologies will have a future sharing in the IoT market since they are better suited to diverse use cases, with some focusing on longer battery life and others on the maximum range and throughput. A comparison is given for several conceivable LPWAN implementations where use cases such as smart farming such as humidity, temperature, and alkalinity sensors which greatly benefit the LoRa and Sigfox technologies since they do not rely on cellular coverage whereas applications like a retailer’s sale terminal requires low latency so that the number of transactions are not limited; this makes a Narrow-Band solution more appealing because it may trade power consumption for throughput. Using these technologies results in a wide range of performance since radio transmitted signals at such a low frequency might suffer considerably from a variety of environmental conditions. Cattani et al. demonstrates this in their investigations of how weather conditions, temperature, humidity, and precipitation influence the dependability and performance of an LPWAN system [7]. They concluded that the environment must be heavily examined when deciding what technology to utilize and if such an implementation is even conceivable in the first place, as a temperature shift as little as 20 °C to 30 °C can result in 50% packet loss, as seen in Fig. 3. Cattani in [7] also shows that a 10 °C rise in temperature reduces RSSI by 1 dBm. In principle, a drop in signal strength induced by temperature change may render a perfectly excellent LoRa connection inoperable. A temperature shift from 15 °C to 60 °C would result in a 100% PPR being lowered to 0%. Since LoRa networks are accessible to anybody, several networks may be established in close proximity to one another. Interference is introduced by neighbouring networks. Thiemo Voigh [8] analyses these interferences and how to prevent them by employing directional antennas or several base stations. His studies demonstrate through simulations that adjacent networks have a detrimental influence on performance owing to interference, especially when a large number of end-node devices are deployed. He employed five separate networks, each with 200 end-node devices. Only one of the networks is of interest, while the others exist only to cause interference. He concluded that utilizing directional antennas and directing the radiated energy towards the target gateway enhances RSSI while reducing interference on surrounding gateways. As a result, the performance of all individual networks improves. Although directional antennas boost performance, they believe that employing several gateways produces a superior outcome. More gateways are a less expensive option, but they may be more practical.
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Fig. 3. Increase of packet corruption and loss at higher temperatures (°C) on a LoRa link at the edge of the communication range [7].
3 Methodology This paper is primarily concerned with the performance and limits of a LoRa network, and our major purpose in looking for references is to discover research papers and publications that examine its limits and the reasons behind them. This paper focused mostly on articles that conducted live tests, as models of a LoRa network are still quite incorrect in comparison to actual results. Then, depending on these constraints and reasoning, modify the test. This paper also looked for research on the performance of LoRa and IoT technologies in general, with the purpose of comparing them. 3.1 Experimental Setup The both receiving node and gateway are equipped with HopeRF RFM95W as shown in Fig. 4. All experiments were conducted under specification as below: Supply voltage: 3.3 V Temperature setup: 25 °C Channel: 915 MHz Bandwidth: 125 kHz Spreading Factor, fixed: 12 Transmission power: 13 dBm
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Fig. 4. HopeRF RFM95W LoRa Transceiver
3.2 Topology Testing The gateway is set up using an ESP32 and RFM95W LoRa shield with a 10.9 cm of antenna. The gateway is powered by an outdoor AC plug as shown in Fig. 5(a). While the receiving node is set up using an Arduino Mega 2560 and RFM95W LoRa shield with a 10.9 cm of antenna. The receiving node is attached to a laptop to collect the message packet and specifications such as RSSI and SNR.
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Fig. 5. a) LoRa gateway setup in outdoor experiment, and b) LoRa receiving node attached to laptop in outdoor environment at UTeM Kampus Induk.
In each experiment, the gateway sends a packet with a 5-byte payload every one second at a transmitting strength of 13 dBm. The spreading factor is fixed at the SF12.
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All experiments are carried out between the hours of 11 a.m. and 5 p.m. in the month of May. They must also be conducted in bright sunlight and at temperatures ranging from 28 °C to 33 °C, as shown in Fig. 6. This is done to reduce the impact these characteristics may have on the outcome because they are not otherwise accounted for. To improve the accuracy of the experimental data, this method has been repeated for each experiment 30 samples of data for each distance. The mean of these values is then utilized to generate conclusions. The result table displays the average, lowest value, maximum value, and standard deviation for each measurement. For the open space, the receiving node should be far away with the gateway of at least 200 m. There are some trees and plants that may be the obstacle in this experiment but the discussion of this paper will be ignored. In this experiment, receiving node is trying to get the message packet from the gateway in five different locations. The longest distance in this experiment is 360 m as shown in Fig. 6(a).
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Fig. 6. a) Measurement of five locations from gateway for open space, located in UTeM Kampus Induk and b) measurement of five locations from gateway for residential area of Taman Desa Idaman, Durian Tunggal Melaka, illustrated using Google Map.
While the compact space was conducted in the residential area which includes the single and double story houses. The highest height of the obstacle in this experiment is approximately 15 m. The receiving node was set up in five different locations to conduct the experiment. The distance range between gateway and receiving node is from 80 m to 200 m as shown in Fig. 6(b). 3.3 Elevation Testing The receiving node is set at the position horizontal distance apart from the gateway with 260 m. Then the receiving node will conduct an experiment from the vertical height of
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the gateway in 0 m to 30 m. The receiving node will conduct on the ground floor of the building first then the first floor and repeat until the third floor. The environment between the location of the gateway and the receiving node building is considered open space as shown in Fig. 7. 3.4 Dynamic Motion Testing Dynamic motion testing is conducted on two different straight roads using a moving car. The LoRa gateway is set up at the starting point of the straight road, while the receiving node is moved from starting point by using a car with a speed of around 8 m/s. During the experiment, receiving node will receive the message packet until the connection with the gateway is broken, then it is ended. The effective connectivity distance and signal strength parameter of the experiment will be recorded.
Fig. 7. Measurement of vertical height of receiving node for elevation experiment.
4 Result and Analysis 4.1 Open Space In the first experiment, this paper measured a range of distances where the line of sight between the device and the gateway was totally clear and they were as near to the same height as feasible. The experiment’s goal was to learn about the limitations of sea and cropland-based use cases, which typically include transmissions with no noise or signal interference in the open space. Based on the result in Fig. 8 shown that the RSSI, SNR, and PRR values will drop when the receiving node getting far away from the gateway.
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From the PRR graph in Fig. 8(c), the LoRa network able to receive 100% of packet message within 200 m in open space environment but the PRR will drop when over 200 m and approach to zero in around 360 m. From the experiment, the receiving node was not able to get a single packet message when over 360 m in open space. From the SNR graph, the value was reaching approximately 0 in around 200 m and become a negative value when a distance greater than 200 m. This means that the signal power is lower than noise power, and signal power continues to reduce until the signal break. In short, the LoRa network is able to transmit 100% of messages in open space within 200 m and partially receive packet messages between 360 m and 200 m. The results also prove that LoRa network is able to transmit messages in a circular range and the signal strength may reduce if the receiving node enough far away from the gateway.
Fig. 8. Open space graphical results for, a) Fig. 9. Residential area graphical results for, RSSI, b) SNR, and c) PRR, with respect to the a) RSSI, b) SNR, and c) PRR, with respect to distances. the distances.
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4.2 Residential Area In this experiment, this paper measured a range of distances where the line of sight between the device and the gateway was totally clear and they were as near to the same height as feasible. The experiment’s goal was to learn about the limitations of sea and cropland-based use cases, which typically include transmissions with no noise or signal interference in the residential area. Based on the result in Fig. 9, the a) RSSI and b) SNR is similar to the open space experiment where the value will reduce when receiving node is far away. The c) PRR value is lower than open space experiment, this is because there was a lot obstacle like house and car in the residential area. From this experiment, the packet transmission success rate not only depends on distance but also the number of obstacles. For example, in this experiment, the LoRa network is able to transmit messages up to a distance of 210 m but in some locations below 200 m not able to receive the message packet. This is because the location of 210 m of this experiment has a pathway and no obstacle. By comparing to those locations have more obstacles but distance below 200 m cannot receive message packet. In short, LoRa network is able to transmit data in the urban area but depend on the distance and number of obstacles between the gateway and receiving node. The effective transmission range from 0 m to around 150 m. 4.3 Elevation In this experiment, this paper measured the different vertical height of receiving node can affect the quality of transmission message. The experiment’s goal was to learn about the higher vertical height of receiving node can increase signal strength. Based on the result in Fig. 10 the SNR and PRR will be increased when the receiving node is put at the higher vertical height while the RSSI value was maintained at around −100 dB per m. From the PRR chart, the receiving node only can receive 70% of the packet message but can achieve 100% when the receiving node setup at 5 m vertical height and higher. The SNR value also has a dramatic improvement from −8.00 dB at the ground until −1.50 dB at 15 m vertical height. In short, the receiving node has to be set at a higher vertical height in order to improve the signal strength. According to Franksson and Liljegren [4], the elevation experiment is set up as the sender node at higher vertical height while receiving node at ground level, the experiment result also proves that the idea can improve the quality of transmission message. Hence, the idea can be applied to sender or receiving node setup at a higher vertical height to improve the quality of the signal.
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Fig. 10. Elevation graphical results for, a) RSSI, b) SNR, and c) PRR, with respect to the vertical height.
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4.4 Dynamic Motion In this experiment, this paper measured the signal strength and limitation of receiving node in a constant speed motion. Based on the result in Fig. 11, the experiment is conducted in two different locations and compare the result each other. The unmarked value in the graph represents the receiving node that fails to receive the packet message. From the RSSI graph, the result is similar to other experiments where the receiving node far away from the gateway will be reduced RSSI value and maintain at around − 100 dBm.
Fig. 11. Dynamic motion graphical results for, a) RSSI, b) SNR, and c) received points, with respect to distance.
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In the SNR graph, the result has shown that the message packet starting fail to receive message packet or received broken message packet in a negative value of SNR. This is because the noise power was higher than signal power when SNR was in negative value. From Fig. 11, the LoRa network is able to achieve 320 m in location B but only 210 m in location A. This has shown that different environments or locations may affect the quality of transmission messages. In short, the LoRa network set at this experiment with the constant moving speed of receiving node in 8 m/s able to have a good connection with the gateway in 200 m and able achieve to 300 m but when over 200 m the connection become weak.
5 Conclusion In conclusion, LoRa network is able to work outdoor with different environments or topologies like open spaces and residential areas. The LoRa network proves that signal strength may reduce due to the distance between gateway and receiving node increase and also the number of obstacles between the gateway and receiving node. This study also proves that the quality of LoRa signal strength can be better when the receiving node is set at a higher vertical elevation height. Based on the results presented here, it is concluded that the LoRa RFM95W module can be applied to open space chili farms with an elevation of gateway to reduce the noise while data is being transmitted and will increase the network coverage. The achievement of this study can be used for future development as a benchmark in integrating the networks systems for IoT implementation in the agriculture industries such as chili fertigation, and of course with a low cost of development. The cover range of the LoRa can be improved by using a better performance of the gateway. It can be improved also by reducing the bandwidth and increase the Spreading Factor (SF) to reduce the loss of packet message and greater cover range. This can lead the LoRa network able to handle greater units of the sensor at the same time. Acknowledgement. The authors would like to thanks for the financial supports from the Universiti Teknikal Malaysia Melaka (UTeM) under the Center of Research and Innovation Management (CRIM). This project is also linked with the chili fertigation industry based in Alor Gajah Melaka. The short-term grant number for the project is PJP/2020/FKE/PP/S01747.
References 1. Mekki, K., et al.: A comparative study of LPWAN technologies for large-scale IoT deployment. In: ICT Express (2018) 2. Lethaby, N.: Wireless connectivity for the Internet of Things: one size does not fit all (2017) 3. Adelantado, F., et al.: Understanding the limits of LoRaWAN. In: IEEE Communications Magazine (2017) 4. Franksson, R., Liljegren, A.: Measuring a LoRa Network (2018) 5. Sanchez-Iborra, R., et al.: Performance evaluation of LoRa considering scenario conditions. In: Sensors (2018)
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6. Ray, B.: What Is LoRaWAN? (2015). https://www.link-labs.com/blog/whatis-lorawan. Accessed 10 June 2021 7. Cattani, M., Boano, C., Römer, K.: Experimental evaluation of the reliability of LoRa longrange low-power wireless communication. J. Sens. Actuat. Netw. 6(2), 7 (2017) 8. Wennerström, H., et al.: A long-term study of correlations between meteorological conditions and 802.15.4 link performance. In: 2013 IEEE International Conference on Sensing, Communications and Networking (SECON) (2013)
Simulation Experiments of Pipe Network and Pumps for Application of Fertigation System Using MATLAB Simscape Ainain Nur Hanafi1(B) , Jason Tiong Ho Wei1 , and Mariam Md Ghazaly2 1 Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,
76100 Durian Tunggal, Melaka, Malaysia [email protected] 2 Centre for Robotics and Industrial Automation (CeRIA), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
Abstract. Fertigation helps farmers to manage the plant’s growth, quality and yields by controlling the amount of nutrients and water supply to the plants. The traditional method of fertigation is risky over a long period for plant growth and return in productivity due to ineffective use of nutrients and water. The aim of this paper is to analyse the relationship between the pipe length and pipe diameter with water flow rate and pressure. The MATLAB Simscape library is used to design the hydraulic network for the fertigation application. Three types of pumps, namely centrifugal pump, fixed-displacement pump and variable displacement pump are investigated. The flow rate and pressure at the end of the pipe system are recorded and compared. The designed fertigation system in this project is working and may help the pre-design of the fertigation system to save cost and improve the water usage efficiency. From the simulation data, it can be concluded that the centrifugal pump has better performance than other pumps as it gave the highest flow rate and lowest pressure. These indicate faster and uniform distribution of the fertiliser and water. Keywords: Fertigation system · Variable speed drive (VSD) · Liquid flow rate and pressure · Simscape model
1 Introduction Traditional farming is a method of farming that has been applied since the beginning of time. The traditional farming method in the agriculture sector has a high risk in longer periods needed for plant growth and low return in productivity [1]. This is because farmers provided insufficient and excessive amounts of water and fertiliser solution injected into the plants may cause the plant to wilt or grow unhealthy. Fertigation also can be defined as a process of supplying fertilisers to the crops by using irrigation water. This system helps farmers better control the plant’s growth, quality and yields by managing the amount of water and fertiliser solution injected into the plants. Fertigation © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 328–337, 2022. https://doi.org/10.1007/978-981-16-8954-3_31
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system is an option for plants largely planted. Fertigation system has better performance and more effective than traditional farming to control the plant’s growth, quality, and yield. The goal of the fertigation system is to increase the yield production by optimising water and fertiliser used efficiency with a reduction in the quantity of fertiliser, water, cost, and environmental pollution. Since farming in every country might differ and the plantation area is not similar, a fertigation system needs to be custom-designed. This paper contributes in the preliminary studies of fertigation system design with the objectives i) to explore MATLAB Simscape Fluids in designing a hydraulic system for fertigation application and ii) to analyse different types of pumps and the effect of pipe length and diameter on system flow rate and pressure. The selection of a pump is an essential step in the design of a fertigation system since an incorrect pump selection may cause system damage and poor system performance. Selecting a pump in a fertigation system is based on the performance and characteristics of the liquid to be handled. Thus, the positive displacement pumps and centrifugal pumps are the most recommended pump applied in the fertigation system. On the other hand, there are two types of methods that have been applied in the industrial area to control the factors which are rotational speed of the pump which are variable frequency drive (VFD) and variable speed drive (VSD). The pump which has the ability in controlling the rotational speed is considered as an effective technique of responding to changes in flow and pressure demand in the system (Table 1). Table 1. Comparison between VFD and VSD Specification
VFD
VSD
Definition
An effective instrument for improving the performance of a motor pump system in lower voltage and it has a better performance compared to the traditional system [2]
An electrical or mechanical device that controls the speed of the motor or generator to match the requirements as well as save energy and improve efficiency [3]
System input Electrical power components (voltage)
Frequency (angular velocity)
Application
Irrigation system, pumping plant power Hot gas bypass, electronic expansion and pump drive system [4] valve in evaporator temperature control, clearance volume control, multiple compressors, and cylinder unloading [5]
Advantages
It has can handle high power levels, It can automatically save energy by simple programming of desired control, reducing capacity and slow down the steadily response, increases reliability, response based on the requirement [3] ruggedness and reduces unit size
2 System Model In this section, Simscape library is used to design the fertigation system in MATLAB Simulink [6]. The input and output block for MATLAB Simulink Simscape library
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should be physical signal due to input and output ports need a scalar or vector value. Since the speed of the pump is controlled by a variable speed drive, it is adjusted into unit frequency. The angular velocity is applied as an input to control the speed of the pump. First, the hydraulic system for fertigation application is designed as shown in Fig. 1. There are two nutrients tanks which are tanks A and B. The acid and alkaline solutions are kept separately inside tank A and tank B, respectively, through the return line (port R) [7]. This is because the desired pH value of fertiliser can be obtained by controlling the amount of acid and alkaline at the mixing process. Next, the pumps A and B are used to transfer the nutrients from the pump line (port P) of tank A and tank B through suction port (port T) of the centrifugal pump. The solver configuration block is required in the Simulink diagram to troubleshoot any connection error in this simulation.
Fig. 1. Fertigation system in MATLAB Simulink.
The nutrients flow through the centrifugal pump in the positive direction from port T to port P. Then, both pumps transfer the nutrients from the outlet port (port P) on the centrifugal pump to the mixing tank. The mixing tank is filled with water before the nutrients are transferred into the mixing tank. Pumps A and B turn off when the desired pH value of the fertiliser has been obtained. After that, PS-Simulink and Simulink-PS converters are needed to convert a physical signal to a Simulink signal and vice versa. The constant block represents a Simulink input signal and is converted to a physical signal by Simulink-PS. Then, the ideal angular velocity source receives the signal to control the speed of the pump. The mechanical rotational reference is required for all input of the mechanical rotational port to run the simulation [8, 9]. The fertiliser is pumped from the mixing tank into irrigation line through the hydraulic pipeline by using a centrifugal pump. The hydraulic flow rate and pressure sensors measure the flow rate and pressure at the output after the hydraulic pipeline. The PS-Simulink converter is needed to convert
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the physical signal from the output of the Simscape blocks into a time domain signal and then plot by the scope block. 2.1 Experiment’s Set up Three experiments are set up to achieve the paper’s objectives. In the first experiment, three types of pumps; centrifugal pump, fixed-displacement pump, and variable displacement pump, are placed in the hydraulic system for the fertigation application without the pipeline. The flow rate and pressure values for an angular velocity of 126 rad/s, 147 rad/s, 168 rad/s, 189 rad/s, and 210 rad/s in each pump are observed and compared. In another two experiments, the relationship between the pipe length and the pipe diameter with water flow rate and pressure controlled by the angular velocity is observed. The pipe diameter is changed to four different lengths, which are 12 mm, 20 mm, 25 mm, and 40 mm while the pipe length is constant. Then, the pipe length is changed for ten different values; range from 1 m to 50 m. Results of the experiments are presented in the next section.
3 Result and Discussions This section covers the experiment results of this project based on the framework mentioned in previous section. The results of the experiment are shown in a graphical presentation. Then, the data is analysed and discussed. The results of the first experiment is presented in Figs. 2 and 3. Figure 2 shows the flow rate of pumps has increasing trends when the angular velocity increases. The higher the angular velocity of the pump, the higher the output parameters flow rate of the pump. For a different value of the angular velocity, the centrifugal pump produces the highest flow rate than the fixed displacement pump and the variable displacement pump. Figure 3 shows the pressure of pumps has increasing trends when the angular velocity increases. The higher the angular velocity of the pump, the higher the output parameters
Fig. 2. Flow rate against angular velocity for three different pumps
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Fig. 3. Pressure against angular velocity for three different pumps
pressure of the pump. For a different value of the angular velocity, the centrifugal pump produces the lowest flow rate compared to the fixed displacement pump and variable displacement pump. The results of the second experiment that is, the relationship between the pipe length and diameter with the water flow rate, are presented in Figs. 4, 5, and 6. Figure 4 shows the flow rate versus the pipe’s length and diameter, controlled by the angular velocity of 210 rad/s in the centrifugal pump. The flow rate in the pump has decreasing trends when the value of pipe length increasing. For the flow rate of 210 rad/s, the pipe length is fixed at 1 m in the beginning, then having the water flow rate of 1111 cm3 /s for 12 mm of pipe diameter and 4309 cm3 /s for 40 mm of pipe diameter. When the pipe length is 50 m, the water flow rate is 182.391 cm3 /s for 12 mm of pipe diameter and 3095 cm3 /s for 40 mm
Fig. 4. Flow rate against pipe’s length and diameter for a centrifugal pump with 210 rad/s angular velocity
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of pipe diameter. It can be seen that the water flow rate is inversely proportional to the pipe length and proportional to the pipe diameter. Figure 5 shows the flow rate versus the pipe’s length and diameter, controlled by the angular velocity with 210 rad/s in a fixed displacement pump. The flow rate of the pump has decreasing trends. The flow rate between the pipe diameter 20 mm, 25 mm, and 40 mm has small differences. However, for 12 mm pipe diameter, the water flow rate is inversely proportional to the pipe length. For the flow rate of 210 rad/s, the pipe length is fixed at 1 m in the beginning, where the flow rate is 1001 cm3 /s for 12 mm of pipe diameter and 1003 cm3 /s for 40 mm of pipe diameter. When the pipe length fixed at 50 m, the water flow rate is 965.164 cm3 /s for 12 mm of pipe diameter and 1002 cm3 /s for 40 mm of pipe diameter. It can be seen that the flow rate decreases when the pipe length increases. On the other hand, the flow rate has insignificant difference when the pipe diameter increases above 20 mm.
Fig. 5. Flow rate against pipe’s length and diameter for a fixed displacement pump with 210 rad/s angular velocity
Figure 6 shows the flow rate versus pipe’s length and diameter, controlled by the angular velocity with 210 rad/s in a variable displacement pump. The flow rate of pumps has decreasing trends and just slight difference between the pipe diameter 20 mm, 25 mm, and 40 mm. However, for 12 mm pipe diameter, the flow is inversely proportional to the pipe length. For the flow rate in 210 rad/s, the pipe length is fixed at 1 m in the beginning, where the flow rate is 16.704 cm3 /s for 12 mm of pipe diameter and 16.711 cm3 /s for 40 mm of pipe diameter. When the pipe length is fixed at 50 m, the water flow rate is 16.519 cm3 /s for 12 mm of pipe diameter and 16.710 cm3 /s for 40 mm of pipe diameter. It can be seen that the water flow rate decreases when the pipe length increase, and the flow rate has insignificant difference when the pipe diameter increases above 20 mm similar to a fixed displacement pump. Figures 7, 8, and 9 display the results of the third experiment, that is the relationship of pipe/s length and diameter with pressure for three different types of pumps. Figure 7 shows the pressure response for a centrifugal pump. When the angular velocity
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Fig. 6. Flow rate against pipe’s length and diameter for a variable displacement pump with 210 rad/s angular velocity
is 210 rad/s, the maximum pressure of the centrifugal pump is 284.156 kPa. The pressure is proportional to the angular velocity of the centrifugal pump. Moreover, the pipe’s length and diameter only change the time response and do not affect the steady-state value of the pressure.
Fig. 7. Pressure of different pipe’s length and diameter for a centrifugal displacement pump with 210 rad/s angular velocity
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Figure 8 shows the pressure due to changes in pipe’s length and diameter, controlled by the angular velocity with 210 rad/s for a fixed displacement pump. When the angular velocity is 210 rad/s, the maximum pressure of the fixed displacement pump reached 131.871 MPa. When the angular velocity of the fixed displacement pump is increased, the pressure of the pump increased. However, the pipe length and diameter do not affect the pressure at steady state. There is a small deviation when the pipe length and pipe diameter are adjusted.
Fig. 8. Pressure of different pipe’s length and diameter for a fixed displacement pump with 210 rad/s angular velocity
Figure 9 shows the pressure exhibits by the angular velocity with 210 rad/s in a variable displacement pump. When the angular velocity is 210 rad/s, the maximum pressure of the variable displacement pump is 2.198 MPa. When the angular velocity of the variable displacement pump is increasing, the pressure of the pump increases. However, the pipe length and diameter do not affect the steady-state value of the pressure there is a small deviation in between the results when the values of pipe length and pipe diameter are adjusted.
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Fig. 9. Pressure of different pipe’s length and diameter for a variable displacement pump with 210 rad/s angular velocity
4 Conclusion In conclusion, the hydraulic system for fertigation application was successfully designed using the MATLAB Simulink Simscape library. There are several experiments conducted to test the performance of three types of pumps. The fertigation system’s flow rate and pressure for each experiment are recorded and compared. After that, the pipe length and pipe diameter are adjusted in the experiments to simulate the different sizes of farms. This is to ensure that water or nutrients are uniformly distribution to each plant in the farming area. The designed fertigation system in this project is working and may help in the pre-design of the agriculture field to save costs and improve water usage. The result shown that the centrifugal pump has a better output, that is the highest flow rate and lowest pressure under variation of input velocity, pipe’s length and pipe’s diameter. Acknowledgement. The authors wish to express their gratitude to Motion Control Research Laboratory (MCon Lab), Center for Robotics and Industrial Automation (CeRIA) and Universiti Teknikal Malaysia Melaka (UTeM) for supporting the research and publication. This research is supported by Ministry of Education Malaysia (MOE) under the Fundamental Research Grant Scheme (FRGS) grant no. FRGS/2018/FKE-CERIA/F00353.
References 1. Salih, J.E.M., Adom, A.H., Shaakaf, A.Y.M.: Solar powered automated fertigation control system for Cucumis Melo L. cultivation in green house. APCBEE Proc. 4, 79–87 (2012) 2. Rahman, M.I., Salim, K.M.: Comparison of conventional induction motor-pump system with one containing a variable frequency drive: a quantitative performance analysis in low-voltage conditions. Int. J. Electr. Energy 3(2), 68–73 (2015)
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3. Saidur, R., Mekhilef, S., Ali, M.B., Safari, A., Mohammed, H.A.: Applications of variable speed drive (VSD) in electrical motors energy savings. Renew. Sustain. Energy Rev. 16(1), 543–550 (2012) 4. Salih, H.R., Abdulrazzaq, A.A., Guzun, B.D.: Dynamic Modeling of Pump Drive System Utilizing Simulink/MATLAB Program, pp. 21–24 (2016) 5. Xue, Z., Shi, L.: Modeling and experimental investigation of a variable speed drive water source heat pump. Tsinghua Sci. Technol. 15(4), 434–440 (2010) 6. MatlhWorks, Simscape. https://www.mathworks.com/products/simscape.html. Accessed 01 Oct 2020 7. Aisham, B., Rahim, A.: Design of reservoir tanks modelling to mix several types of fertilizer for fertigation planting system: part A. J. Phys. Conf. Ser. 1150, 1 (2019) 8. Gevorkov, L., Rassolkin, A., Kallaste, A., Vaimann, T.: Simulink based model for flow control of a centrifugal pumping system. In: Proceedings of the 25th International Workshop of Electrical Drives Optimal Control Electronic Drives, IWED 2018, January 2018, pp. 1–4 (2018) 9. Gevorkov, L., Vodovozov, V., Raud, Z.: Simulation study of the pressure control system for a centrifugal pump. In: Proceeding of the 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University RTUCON 2016 (2016)
Manual Material Handling Assessments Towards the Working Comfort in an Automotive Manufacturing Company Al Amin Mohamed Sultan1(B) , Darrenveer Singh Gill1 , Muhammad Azmi2 , Ng Tan Ching3 , Mohd Rayme Anang Masuri1 , Mohd Shahrizan Othman1 , and Siti Nurfarahin Mohd Hayat Ahmad1 1 Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,
76100 Melaka, Malaysia [email protected] 2 Facility Maintenance Engineering Section, Universiti Kuala Lumpur Malaysian Institute of Industrial Technology, Persiaran Sinaran Ilmu, Bandar Seri Alam, 81750 Masai, Johor, Malaysia 3 Department of Mechanical and Materials Engineering, Universiti Tunku Abdul Rahman, Sungai Long Campus, Selangor, Malaysia
Abstract. Musculoskeletal disorders remain one of the most prevalent occupational injuries in the manufacturing sector. This study was performed to determine the types of working postures that cause discomfort to workers at a selected tooling plant in an automotive manufacturing company. The working postures of the coworkers were assessed while performing manual material handling activities such as stamping die, grinding, operating machines, assembling jigs, and polishing to reduce the possibility of developing musculoskeletal disorders. Data collections were done through direct observation while the Rapid Upper Limb Assessment was utilized to assess the ergonomic risk state of the working positions. The assessment showed that the assembling process and try-out chores are contributed to the most pain and anguish with the RULA score of seven which indicated in red. It was captured that the body areas such as muscles, neck, torso, legs, and arms are more prone to experience pain and discomfort as they are part of the musculoskeletal system. The critical processes could be intervened by implementing some improvements such as adjusting the angular limitation, preferred angles and reduce the duration of work. By doing this, the risk of MSDs could be reduced. Keywords: Working posture · RULA analysis · Musculoskeletal disorders
1 Introduction The manufacturing sector is part of the super-sector of goods-producing industries that heavily contributed to the economic growth either domestically or globally. Despite the positive contribution in increasing the income for the nation, creating more job opportunities, and fulfil customer demands, it could not run away from the work-related illness © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 338–346, 2022. https://doi.org/10.1007/978-981-16-8954-3_32
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issues. Amongst the most frequent manufacturing, work-related illnesses are musculoskeletal diseases (MSDs). MSDs are injuries and diseases that impact the human body’s mobility or musculoskeletal system. Musculoskeletal conditions have impacted about 1.71 billion people on the planet who work in manufacturing fields [1]. Carpal Tunnel Syndrome, Tendonitis, Ligament Sprain, Tension Neck Syndrome, Thoracic Outlet Compression, and Rotator Cuff Tendonitis are all common musculoskeletal diseases in the manufacturing industry. Low back discomfort, Mechanical Back Syndrome, Degenerative Disc Disease, Ruptured or Herniated Disc, and a variety of other conditions are all possible. Low back pain, is the most common musculoskeletal disease, affecting 568 million individuals that work in the manufacturing field globally [2]. MSDs are widespread, with prevalence rates rising from 28% to 96% during a one-year time [3]. MSDs are the most common type of occupational injury, accounting for 30% of total worker compensation expenditures. Malaysia as a developing country also reported having cases linked with MSDs in manufacturing sectors. According to the Social Security Organisation (SOCSO), MSDs are one of the most frequently diagnosed disorders in Malaysia especially in manufacturing sectors, accounting for more than 15% of all cases compared to other diseases or industrial accidents [4]. Working on jobs that necessitate repetitive manual handling can result in tiredness, pain, and MSDs. Workers’ declining health will have an impact on their productivity, affecting the industrial operation’s efficiency [5, 7]. One localleading automotive manufacturer with main responsibilities in stamping dies, grinding, running machines, constructing jigs, and polishing was selected to investigate the manual material handling and working postures concerning reduce the MSDs impact. Currently, there is a heavy concern about the health risks associated with poor working postures but with a lack of critical assessment as evidence in place. In addition, the workers’ declining health quality was captured as possible to influence productivity and lowering the efficiency of the industrial process. Thus, this study performed critical analysis to comprehend the impacts of workers’ working postures when doing manual handling activities and how it connects to the cause of MSDs.
2 Methodology The working postures practiced by the workers are captured and classified to the types of tasks with the relevant processes during the direct observation at the company. The working postures were later simulated using the Rapid Upper Limb Assessment (RULA) analysis provided in the Human Activity Analysis workbench in CATIA software. The dedicated rating system of RULA was used to classify the risk posture [6]. The scoring system was divided into four action levels, each of which indicates the urgency of the inquiry. Apart from the score, the levels are also indicated by the colours (i.e., green, yellow, orange, and red). This was explained in Table 1.
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Action level
Score
Colour
Action to take
1
1–2
Green
Acceptable
2
3–4
Yellow
Investigate further
3
5–6
Orange
Investigate further and change soon
4
7
Red
Investigate further and change immediately
RULA is intended to measure the force, posture, and movement-related to sedentary jobs such as manufacturing, retail, computer tasks, laboratory work, or situations in which the subject is sat or standing without moving [6]. The final score for each type of working posture is identified along with the comfort level for the upper limbs. The final score is then classified into Action Level which will determine the action that needs to be taken to the performed working posture.
3 Results of Observation and RULA Analysis Table 2 shows the images of the six manual handling activities with the diverse types of postures that were captured during the direct observation at the company. The postures later were modelled into the CATIA. During this stage, the RULA analysis was performed with the right and left side views of the postures were modelled. The assorted colors highlighted to the body areas in both right and left parts are used to classify the levels of comfort. The red signifies that the worker is in severe discomfort and those postures must be altered immediately. Orange signifies that the worker is in pain and that posture must be corrected right away. Yellow implies that the worker is not in pain, the position can uphold but not for an extended amount of time. Green implies that the individual is exceptionally comfortable with the working posture. Working posture 1 scores six and seven for the right side and left side, respectively. While working posture 2 score seven for both sides. This means that the postures are classified in Action Level 4 where the investigations need to be done and require immediate changes to the posture. Working posture 3 scores six for both sides of the body which makes it is classified in Action Level 3; indicates investigations and changes are required soon. The muscle, neck, trunk, and legs are the most strained body parts which make the worker feel discomfort. Working posture 4 scores five for both sides of the body which classified it in the Action Level 3; indicates investigations and changes are required soon. Working posture 5 has a right-side score of seven and a left side score of six. Both the right and left sides are classed as Action Level 4; investigations are needed and immediate adjustments to the posture are necessary, and Action Level 3; investigations and changes are needed shortly, respectively. Working posture 6 scores five for both sides of the body, which classify this posture in Action Level 3; indicates investigations and changes are required soon. Since this working posture is not considered critical, changes to the posture can be made later.
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Table 2. The captured original of six working postures and modelled RULA assessment
No
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4 Intervention Agenda on Reducing the MSDs Related Injuries During Manual Material Handling The suggested improvement of working postures and the RULA analysis for each issue highlighted in Sect. 3 were shown in Table 3. For working posture 1 (refer to Table 2), reducing the angle of the worker’s trunk is not possible as the action will disable the worker from reaching the die for the assembly process. The use of the extended platform as in Table 3 could increase the height of the tooling die thus makes it easier to be reached by the worker without the need to bend the trunk. This has decreased the simulated RULA final score from seven to six. Improving this posture from a standing position to kneeling lead the worker not to over bend the body and the object of the task will be within reach. This posture made the final score be reduced from seven to five. The preferred angle for the worker’s lumbar in working posture 2 was reduced from 45° to 40° as one of the intervention strategies. The work is also was transformed from static to intermittent. After those changes have been made, the final score of simulated RULA of this posture was reduced to five. For working posture 3, the muscle, neck, trunk, and legs are the most strained body parts which make the worker feel discomfort. However, by changing the angle of the trunk bending, the worker cannot complete the task because the die is not reachable. So, the worker should change the work from static to intermittent. The task can still be completed but the time taken will be longer. As shown in Table 3, posture 3 is changed from static to intermittent. The changes made reduce the final score to five which reduced the strain on the worker’s muscle, neck, trunk, and leg. The preferred angles of the body cannot be adjusted in working posture 4 since the level of the die that needs to be ground is fixed. So, the current height is already specified. Another proposal that can be implemented is by lowering the level of the crane that holds the die. If the level of the crane holding the tooling die is lowered. The head of the worker will not have to be elevated and will be less strained. The changes made to this posture has reduced the final score to four. The right arm position in the working posture 5 could be adjusted by slightly reducing the angle. The worker in this new position is still able to reach the parts on the die even after the angle is reduced. There are no strained muscles noticed in the working posture 6 but the analysis from the RULA assessment suggested the immediate changes to be
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Table 3. Improvement of working posture
Posture
Improvement RULA Analysis
Posture
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3
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4
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made on the neck, trunk, and legs as they were highlighted in orange. Thus, the workers could be suggested to take a simple break or rest if they feel any discomfort in those body parts while doing the task. Table 4 shows the before and after comparison of the RULA score for all the working postures. Table 4. The Summary of RULA score before and after the improvement Working Before improvement Posture Score Colour
After improvement Score
Colour
1
7
Red
6
Orange
2
7
Red
5
Orange
3
6
Orange
5
Orange
4
5
Orange
4
Yellow
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7
Red
5
Orange
6
6
Orange
5
Orange
The final score is determined to identify the actions that need to be taken on the working postures, either they need to be rectified immediately or could be saved for later. Based on the score before improvement from working postures 1, 2 and 5, the score is 7 which could be considered as high and need an immediate countermeasure. For working posture 3, the score is 6 which is indicated as ‘needs to be changed’. Working postures 4 and 6 have a score of 5, which is indicated as ‘needs to be investigated further and change soon’ and this posture is considered intermittent. Thus, after the post analyses considering the suggested improvement through simulation, the scores eventually decreased and shows a good comfort level within the workers. The score dropped from 7 to 6 for working posture 1. For working postures 2 and 5 have dropped from 7 to 5. While scores for working postures 3 and 6 have reduced from 6 to 5. Lastly, the score for working posture 5 has decreased to 5 from the initial score of 6.
5 Conclusion It can be concluded that it is critical to examine the human body’s posture to avoid injuries and illnesses, particularly musculoskeletal problems. This involves the worker at the workplace. Based on the results using RULA analysis for comfort level, it has been determined that the workers are primarily suffering pain and discomfort from the assembly and try out tasks. The body parts used for the work are strained as is highlighted in red after the simulation is done. Muscles, neck, torso, legs, and arms are the bodily areas most prone to pain and suffering. All the body parts are identified to be a part of the musculoskeletal system. It proves that the working postures performed by the workers are affecting their musculoskeletal system. It was demonstrated that the proposed solution produces a better outcome by lowering the final score while changing the colour from orange to green. Suggested improvements in the workplace and posture will minimise the likelihood of developing MSDs.
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Acknowledgement. The authors are grateful to Universiti Teknikal Malaysia Melaka (UTeM) and the Ministry of Education Malaysia for the financial support through RACER/2019/FKPCOSSID/F00411.
References 1. WHO. Musculoskeletal Conditions, February 2021. https://www.who.int/news-room/fact-she ets/detail/musculoskeletal-conditions. Accessed 02 Jul 2021 2. Dong, Y., et al.: Study on the associations of individual and work-related factors with low back pain among manufacturing workers based on logistic regression and structural equation model. Int. J. Environ. Res. Publ. Heal. 18(4), 1525 (2021). https://doi.org/10.3390/IJERPH18041525 3. Luan, H.D., et al.: Musculoskeletal disorders: prevalence and associated factors among district hospital nurses in Haiphong, Vietnam. BioMed Res. Int. 2018, 1–9 (2018). https://doi.org/10. 1155/2018/3162564 4. Abidin, N.Z., Rohani, J.M., Nordin, A.N., Zein, R.M., Anak Ayak, A.S.: Financial impact and causes of chronic musculoskeletal disease cases in Malaysia based on social security organization of Malaysia claims record. Int. J. Eng. Technol. 7(3), 23–27 (2018). https://doi. org/10.14419/ijet.v7i3.24.17295 5. Cesar da Silva, P., Cardoso de Oliveira Neto, G., Ferreira Correia, J.M., Pujol Tucci, H.N.: Evaluation of economic, environmental and operational performance of the adoption of cleaner production: survey in large textile industries. J. Clean. Prod. 278, 123855 (2021). https://doi. org/10.1016/j.jclepro.2020.123855 6. Bowden, S.: Rapid Upper Limb Assessment (RULA) - A Step by Step Guide, London, April 2018. https://www.morganmaxwell.co.uk/rapid-upper-limb-assessment-rula-worksheettool-free-pdf-download/. Accessed 31 July 2021 7. Al Amin, M.S., Nuradilah, Z., Isa, H., Nor Akramin, M., Febrian, I.: A review on ergonomics risk factors and health effects associated with manual materials handling. Adv. Eng. Forum 10, 251–256 (2013). https://doi.org/10.4028/www.scientific.net/aef.10.251
Conceptual Architecture Development of Virtual Reality – Motion Capture System to Analyze Accessibility and Clearance in Front-End Engineering Design Process: An Exploratory Study Radin Zaid Radin Umar1(B) , Muhammad Naqiuddin Khafiz1 , Nazreen Abdullasim2 , Nadiah Ahmad1 , and Jalaluddin Dahalan3 1 Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka,
76100 Melaka, Malaysia [email protected] 2 Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, 76100 Melaka, Malaysia 3 Ergoworks Sdn. Bhd., Taman Kajang Sentral, 43000 Kajang, Selangor, Malaysia
Abstract. Human Factors Engineering (HFE) study is an established process used by ergonomists and HFE practitioners to analyze human-system interactions during engineering design stages of new and remodeled work facilities. Human accessibility and clearance evaluation is one of the basic components in the task analysis process conducted during HFE study. This study systematically investigates the underlying hypothesis that accessibility and clearance evaluation can potentially be improved through utilization of virtual reality (VR) and motion capture (MoCaP) technologies. The technologies are envisioned to allow a platform for a dynamic and immersive based analysis. This three-stage study looks into 1) existing issues related to current HFE accessibility and clearance evaluation methods, 2) development of a new method architecture to evaluate accessibility and clearance during HFE study, and 3) preliminary validation on the proposed architecture. The first stage utilizes semi-structured interview with experienced HFE practitioners. Generative approach was used to generate the architecture for the second stage. The third stage employs 5-scale rating instrument measure to pre-validate the newly proposed architecture. Several interaction and visualization issues were identified in current HFE accessibility and clearance evaluation methods. A conceptual architecture of a dynamic and immersive based VR and MoCaP system to assist accessibility and clearance analysis is proposed to allow for realtime motion capture human-system interactions within virtual space. The preliminary validation process showed overall agreement on the usability, usefulness, and desirability of the proposed architecture. Keywords: Human Factors Engineering · Virtual Reality · Motion capture · Human-system interactions
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 347–360, 2022. https://doi.org/10.1007/978-981-16-8954-3_33
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1 Introduction Virtual Reality (VR) and Motion Capture (MoCap) technologies have been widely used in entertainment industries, especially gaming, marketing, and advertising [1–5]. VR technology provides an immersive platform for a user to enter a virtual world, in which the user can dynamically view and in some degrees, engage with virtual representatives of items from the real world [6–8]. On the other hand, the MoCap technology focuses on real-time capture of postural and movement data [9]. These motion data from the real world can be then translated, represented, and visualized in the virtual world. Despite widespread usage of these technologies in entertainment industries especially in gaming and filmmaking sectors, the technologies have seen limited applications in the engineering design area. Human Factors Engineering (HFE) is an established discipline within the front-end engineering design process. HFE study looks into components of human-system interactions during the design stage and provides a common platform for both design engineers and end users to communicate and optimize designs before fabrication, construction, prototyping activities take place [10, 11]. Early identification of interaction mismatches between the worker and work system provides opportunity for design revisions and corrections with lower resources consumption [12]. The design revisions generally cost significantly higher once the design has been finalized and in production [13]. Workers in occupational settings often complain safety and health issues due to late ergonomics incorporation in design process. Incorporation of ergonomics in early design stage can help identification of human interaction issues early on and allows for design changes before facility commissioning [14, 15]. The early considerations can potentially reduce occupational safety and health issues affecting workers at a later time, when the facility is in operation. Human Factors engineers that focuses on optimizing interactions between workers and works system may directly benefit from the integration of VR and MoCap technologies [13, 16–21]. This is because the technologies afford an additional layer of interaction and immersive experience in virtual space. Design engineers, especially those involved in the front-end design stages already utilize 3D model technology extensively to visualize, analyze, and evaluate design. Thus, integration of VR and MoCap technologies can be viewed as an extension process, which may provide additional values to existing design methods and processes. The main goal of this study is to explore the conceptual idea on application of VR and MoCap technologies to analyze human accessibility and clearance requirements in front-end engineering design process. This proposed conceptual system architecture may assist designers, engineers, and management in identifying, analyzing and making changes of workplace design while it is still in the early stage of engineering design.
2 Methodology 2.1 Overview The study consisted of three stages, each with its own objective, methods, and findings. The first stage of study focuses on investigating problems, in which existing issues related to current human accessibility and clearance evaluation methods were obtained
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from HFE practitioners. Second stage focuses on development of a new architecture that can assist evaluation of accessibility and clearance during HFE study. The last stage focuses on preliminary validation of the proposed architecture with HFE practitioners. 2.2 Problem Identification Preliminary investigation on clearance and accessibility challenges was initiated through semi-structured interview sessions with 3 industrial practitioners familiar with HFE studies in engineering design stage. The respondents include HFE specialists with 54 years of experience (mean = 18 years, standard deviation (SD) = 10.6 years). All participants had extensive experiences in the integration of HFE components in the engineering design process of new and remodeled facilities. All respondents have been involved in at least 18 facility design projects throughout their professional careers. Upon recruitment, participants were asked to provide consent and demographic information. Specific questions were asked on 1) current method of conducting accessibility and clearance evaluation in HFE study, 2) challenges on evaluating HFE accessibility and clearance, and 3) suggestions on improving accessibility and clearance analysis. The data were transcribed, processed, and analyzed for recurring themes using approach described by Saldaña [22]. 2.3 Architecture Development An architecture to evaluate accessibility & clearance in HFE study was developed through generative method, as described by Sanders and Stappers [23]. Cognitive mind map and collaborative approaches in which research team design an initial architecture was based upon extracted data from HFE practitioners’ interview. The architecture draft underwent several iterations which include considerations of theoretical and practical components. The developed architecture draft was finalized through consensus from research team members. 2.4 Preliminary Validation Semi-structured interview sessions were conducted among 5 HFE practitioners to obtain preliminary feedback on the newly developed architecture (from previous activity). The respondents consisted of HFE practitioners with a cumulative of 35 years of experience (mean = 7 years, SD = 5 years). All have been involved in at least 15 facility design projects. After providing consent, the practitioners were presented with the proposed architecture, and qualitative feedbacks were obtained through an instrument measure. An instrument measure consisted of several questions on the architecture’s potential usability, usefulness, and desirability were asked to each practitioner, and their subjective agreements/disagreements were captured through 5-scale ratings measure (where 1 = strongly disagree, 5 = strongly agree). These preliminary data were processed and analyzed through a descriptive statistic approach. The approach to obtain usability, usefulness, and desirability (UUD) feedback from potential end users was similar to studies found in other publications [24–26]. In addition, qualitative data were analyzed through qualitative thematic analysis, as per approach described by Saldaña [22].
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3 Results 3.1 Problem Identification Interview sessions with the HFE practitioners revealed several common themes. All practitioners interviewed described similar challenge to accurately estimate space requirements in current 3D model design review workshop. This can largely be attributed to the 3D design model process by the design engineers. As common practice, design engineers would usually choose to place 3D manikin only in specific location within the 3D model to avoid visual crowdedness (see Fig. 1). All respondents highlighted the importance of 3D manikin as it provides a relative scale of reference to visualize and evaluate human space requirements. The practitioners pointed out that while the 3D manikin can be inserted during the design review workshop, it was found to be a cumbersome task and an inefficient process. Importing the 3D manikin and placing it would take time that would otherwise be dedicated for discussion. The practitioners commented that the 3D manikin is also not easily positioned to the specific location where they wanted. One of the practitioners mentioned about a challenge where the design team may not be using proportionately sized manikins to represent sample population they are designing for (e.g. using 3D manikin with anthropometrical dimensions of Southeast Asia population in a facility designed for Middle East operators). As a result, the reference scale can mislead and confuse the HFE workshop participants. Even when properly sized manikins are used and placed strategically, the respondents pointed out that these default 3D manikins used in the design review are fixed and cannot be easily manipulated at will (see Fig. 2). The respondents also highlighted an issue in which the default postures from the 3D manikin are static and has very limited capability to represent motion and movement. As a result, the fixed 3D manikin may not represent realistic work posture that would allow for visual information needed to evaluate the adequacy of space requirements. Since the static manikin doesn’t allow visual assessment of dynamic movements of task activities in reality, HFE workshop participants are required to rely on their imagination and experience to assess clearance and accessibility requirements in the 3D model of proposed workplace design. One of the practitioners mentioned that although there is simulation software in the market that can model motion and movements of 3D manikin in 3D space, the design team usually skips the process as the simulation process would add an additional step to the design process, which is both time consuming and costly. This represents a challenge to HFE practitioners as they would prefer to capture the dynamic motions and movement of 3D manikin when assessing space requirements. Another common thematic challenge raised by the respondents was related to the limitation of reviewing the 3D model only from 3rd person view angle (see Fig. 3). Perspective error tends to occur when evaluating spaces in 3rd person view angle, as the viewers can misjudge space (e.g. equipment height and depth) from certain viewing angles. All respondents pointed out that perspective error can be minimized through double checking of space from multiple viewing angles, although this step would require extra time during HFE workshop session (see Fig. 4). In addition, two respondents shared their need of having the ability to review the design from the 1st person view angle. They
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reported that this would help them to convince HFE workshop participants on specific clearance issues as everyone can view the design from the 3D manikin’s point of sight.
Fig. 1. Examples of only limited number of 3D manikins placed (highlighted) within 3D model of proposed workplace design.
Fig. 2. Examples of static 3D manikins with fixed unrealistic work posture placed in certain locations within 3D model of proposed workplace design.
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Fig. 3. Example of proposed workplace design from 3rd angle point view.
(a)
(b)
(c)
(d)
Fig. 4. Example of perspective error that may happen during 3D model design review process: (a) The valve positioning may look low from a specific 3rd point of view. (b) The same valve may look more accessible from different 3rd point of view angle. (c) Inserting a 3D manikin or measure provide relative references, but time consuming to do this for each accessible asset. (d) A 1st point of view perspective option may improve time as it provides the location of asset to be evaluated relative to the eye level.
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3.2 Architecture Development Based upon feedbacks from HFE practitioners, the research team extracted the challenges and translated them into architecture development requirements. Among requirements were: 1) easy to position, navigate the 3D manikin within the 3D model. 2) Allow capturing and visualizing dynamic motion of 3D manikin, 3) allow viewing 3D manikin and 3D work unit design from multiple perspectives. These requirements became the base for generation of an architecture. A preliminary conceptual architecture was developed by the research team, and underwent several design iterations. In the proposed architecture (see Fig. 5 and Fig. 6), the system is made up of a few technologies and is envisioned to be used by few end users with specific roles. The technologies include hardware such as VR, MoCap, and computer system. In addition, the system also consisted of a software system to interface real-time data from Computer Aided Design (CAD) software, VR, and MoCap.
Fig. 5. Conceptual architecture of virtual reality – motion capture system to analyze accessibility and clearance in front-end engineering design process.
In terms of operation, the architecture proposes additional value-added activities to be imbedded in the traditional HFE workshop. In traditional HFE workshop, the 3D model design is visualized and evaluated through discussions facilitated with HFE practitioner and joined by engineers and operators. This architecture proposed integration of data for virtual interactions in the virtual environment. As per traditional approach, design engineers will upload the 3D model of proposed workplace design into a system that can be visualized by all HFE workshop participants. However, there is an additional step proposed in this architecture, in which the 3D model would first be imported into a processing PC prior to be uploaded for visualization of all HFE workshop participants. In this processing PC, the existing polygons in 3D model will be redefined to include
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Fig. 6. Prototype system of the proposed conceptual architecture.
its collision parameter. This redefinition will enable the polygons to be treated as an interacted objects or items in virtual space, while retaining its main purpose as a visualization tool. The redefined 3D model data will then be imported to the main PC, where it will be projected to the participants in HFE workshop, as per traditional approach. Simultaneously, HFE specialist and other participants can monitor manikin’s movement on projected screen and evaluate workspace design in terms of required clearance and accessibility. Another additional step proposed in this architecture is the platform for the targeted end user in the workshop (operators and worker representatives) to interact real-time, and on one-to-one scale with the uploaded 3D model data of workplace in the virtual space. The end user will be wearing a VR head mounted device (HMD), in front of a MoCap system. The HMD that is connected to the main PC will project the 3D model of proposed workplace design from a first point of view angle. At the same time, the MoCap system positioned in front of the end user will capture movements data, convert into character animation format and will feed the motion data back into the main PC. The character animation data is treated as controller input data, which will allow for
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virtual interaction between 3D manikin and 3D model of proposed workplace design in virtual space. In addition, the end user can also navigate through the 3D model as a controller. The main PC acts like a centralized computing platform where it will process and integrate data input from 1) 3D model of proposed workplace design, and 2) character animation data. Concurrently, the main PC will also provide data output to project and visualize those 3D data of the virtual system into 1) HMD, and 2) screen display. The main PC allows toggling between 1st person and 3rd person views during HFE workshop. First person view is dedicated to the end user through the HMD, while 3rd person view can be viewed by the ergonomists and other workshop participants through shared screen display. Overall, the proposed architecture suggests a system that enables real-time human interaction analysis in the design stage, which provides engineers with human performance and usability data prior to fabrication/prototyping stage. The concept behind the proposed architecture is envisioned to provide HFE practitioners a specialized design tool to assist analysis of human-system interactions during the design stage. End users will be able to immerse, interact, and navigate through the proposed 3D design of the workplace. The concept also provides the opportunity for dynamic simulation of tasks, as the end user can interact in some degree with the proposed 3D design in the virtual world. Users can interact with the design in virtual space and provide design feedbacks to engineers. This is expected to allow a more representative and accurate space requirements analysis to be conducted during the HFE workshop. Lastly, the architecture also suggests enabling multiple viewpoints during the HFE workshop. The ability to view from 1st person and 3rd person angle is expected to provide alternative views that can minimize perspective errors during design reviews, as well as better visualization of accessibility and clearance requirements. 3.3 Preliminary Validation Practitioners’ feedbacks were generally promising, as they were in overall agreement on the contents and process flow of the developed architecture. They agreed that the concept behind the architecture can add value to their evaluation of clearance and accessibility during the 3D model reviews of facility designs. The ability to insert and manipulate manikin at will is an interesting feature as all practitioners recounted their challenges when relying on static, inflexible, and unrealistic 3D manikin during their HFE workshops. The architecture suggests the ability to conduct a more comprehensive and accurate task analysis as the system would allow visualization of a more natural and realistic human movement inside the proposed 3D model. They expressed interest on the possibility to have technologies that can provide real-time and representative visualizations of tasks, as compared to the current approach where the worker-work system interactions and movements have to be imagined. In addition, a couple of practitioners reported that the proposed architecture will encourage dynamic hand on sessions in the workshop, which will make the sessions more lively, active and engaging. Practitioners also commented that the concept proposed in the architecture may reduce perspective errors, as it allows visualization from multiple view angles. Two practitioners reported that having to double check the potential perspective error is time consuming, and the ability to
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view critical dimensions from the first view angle can likely improve efficiency, as time needed to prepare and conduct HFE workshop can be reduced. Several questions related to usability, usefulness, and desirability of the architecture were posed to the practitioners. In general, the subjective ratings show promising results. Practitioners generally agreed that the concept proposed in the architecture is feasible to be implemented in actual work setting (mean rating = 4.4/5, SD = 0.5, where 1 = strongly disagree, 5 = strongly agree). They also agreed that most people would learn to adopt the concept behind the architecture quickly (mean rating = 4.8/5, SD = 0.4). Practitioners rated highly on its potential usefulness. Majority rated agreement on the likelihood of utilizing the concept in this framework in their work (mean rating = 4.0/5, SD = 0.7). In addition, they also believe this concept would add quality to their work (mean rating = 4.6/5, SD = 0.5). In terms of desirability, the practitioners believe they would really benefit from the use of the concept in this architecture (mean rating = 4.4/5, SD = 0.5). They also believe that their clients would really benefit from the use of the concept in this architecture (mean rating = 4.8/5, SD = 0.4). Despite the potential advantages discussed by the practitioners, there were also some concerns raised by them. One practitioner raised the issue that some users may not be comfortable wearing the HMD, as it is well established that VR can result in dizziness and disorientation effects. In terms of usefulness, majority of respondents reported that the concept may not be applicable to evaluate clearance and accessibility requirements in all areas of the facility, as it will likely be time consuming. One respondent suggests developing specific criteria to determine when and where to utilize the system. Among the parameters suggested were complexity, novelty, and integrated level of the tasks in the 3D design evaluation process. He estimated that the system will be applicable in 20% of the facility areas, where worker-work system design interactions need to be further investigated. One practitioner also mentioned the concern on the accuracy of the task analysis, as the interaction evaluation would depend on the accuracy, readiness, and maturity of the 3D model at the time. However, these issues were deemed to be manageable if proper guidance on preparation and procedures were provided in advance to the relevant stakeholders.
4 Discussion The architecture proposed in this study enable HFE design review participants to mimic dynamic postures on representative 3D manikin (human model) in the reviewed 3D design model with MoCap technology. In order to improve user’s experience when simulating task activities, user is equipped with VR head unit so that the 3D model can be viewed in 1:1 scale, allowing immersive experience in performing task activities as if the user is in the actual workplace. VR and MoCap technologies are not entirely new, considering their wide application in entertainment industry. The development of VR and MoCap technologies embedded in consumer-grade hardware in the past few years has enable more developers and researchers to explore their application beyond entertainment purposes, also known as Gamification or Serious Gaming [27, 28]. In a study conducted to evaluate the potential of VR in engineering design review, Wolfartsberger [13] concluded that the use of VR can accelerate and improve design review quality as
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it provides safe, immersive, and realistic experience for users that are hard to recreate in real world setting. In addition, another study that utilizes VR and finger tracking technologies to evaluate complex product design conducted by Rentzos et al. [29] also highlights its flexibility and cost efficiency to simulate real world scenarios during early phase of design. Despite the urge to integrate technologies into actual practice, there are still few practical challenges to be resolved. In general, the 3D assets that are used in engineering application require complex shape and high polygon density level. In the hardware front, 3D renders require higher end processing power to handle complex and high polygon density assets [30]. Compared to 3D asset modeling in application of other sectors such as gaming and filmmaking, higher 3D polygon modeling mesh requirement is optional and programmers can usually get by with approximation when creating 3D assets [31]. In contrast, application in engineering modelling requires higher degree of precision measure, consequently higher degrees of rendering details [13]. Higher end hardware requirement is a practical challenge for the new architecture to fit in the current workflow, as investments in resources are needed. Similar studies on VR in engineering application conducted by other researchers also pointed out this gap as one of the practical challenges to start implementing game technology in industrial application despite its potential usefulness, especially during design review [32, 33]. In the software front, one of the main challenges would revolve around developing algorithm and programming codes with specific considerations of task complexities and current HFE analysis requirements. Rentzos et al. [29] in a study to propose VR method to evaluate complex tasks pointed the need to consider practitioners’ specific analysis techniques in the programming structure. As the proposed architecture is designed for specialized activity, integrating specific analysis techniques into the new system would likely to improve the adoption of the new system. Furthermore, current VR and MoCap technologies exist in various architectures and operating systems. Cross-platforms compatibility allow real-time interaction in shared digital domain, regardless of user’s type of devices [34]. Development of a system that can communicate cross platforms provide wider potential reach of end users. However, enabling compatibility between different platforms may pose a practical challenge from programming point of view. In addition, remote or internet based HFE design review is expected to be the new norm in the digitalized world [32]. Collaborative design review is expected to be conducted in remote setup (online), as opposed to the traditional co-located face-to-face workshops, as companies become increasingly globalized and more stakeholders to become involved [35]. Coordinated design review efforts require appropriate and welldesigned tool to stay effective and competitive in respective industries [16, 36, 37]. As such, software design needs to be intuitive as it can influence adoption or rejection of the new technology [38]. Readiness to accept or adopt a new technology is partly influenced by the design [39], thus usability concerns from interfaces are also among the critical issues that need to be addressed prior to deployment in practice. Future development of the prototype system, based upon the proposed architecture should focus on these limitations and considerations as it further progresses into the next technological readiness levels.
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5 Conclusion In summary, the new conceptual architecture proposed in this study is expected to contribute to a new way of assessing accessibility and clearance requirements during HFE workshops conducted at the early stage of engineering design. The content and flow of the developed architecture are based upon several case scenarios and problems faced directly by experienced HFE practitioners when evaluating accessibility and clearance requirements. The proposed framework suggests integration of emerging technologies such as VR and MoCap into the traditional HFE evaluation method. These technologies, to be used in the proposed architecture, are envisioned to provide opportunities for better 3D manikin navigation, manipulation of postures to represent actual tasks, dynamic task simulations to capture movement parameters, and options for multiple viewpoints to enhance visualization. It is envisioned that the concept in this proposed architecture would provide a more sensitive and accurate method to assess accessibility and clearance requirements during the design stage. It provides a platform with better visualization to enhance communication and become an added value activities to improve communication between stakeholders during the design stage. This is expected to bridge gaps between theory and application and improve the overall quality of design that can potentially improve both workers and work performances in the workplace. Acknowledgement. The authors are grateful to the Malaysian Government, Universiti Teknikal Malaysia Melaka (UTeM) and Ergoworks Sdn. Bhd. (EWSB) for supporting this study. The study is funded by research grant INDUSTRI (IRMG)/ERGOWORKS/2019/FKP-COSSID/I00037.
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Nonlinear Control of Hexarotor System Using Proportional Derivative Sliding Mode Controller (PD-SMC) Fadilah Binti Abdul Azis1,2,3(B) , Shankarao Rajasuriyan1 , Noor Hazrin Hany bt Mohamad Hanif3 , Mohd Shahrieel bin Mohd Aras1,2 , and Mariam Binti Md Ghazaly1,2 1 Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,
76100 Durian Tunggal, Melaka, Malaysia [email protected] 2 Center for Robotics and Industrial Automation (CeRIA), Universiti Teknikal Malaysia Melaka, Melaka, Malaysia 3 Kuliyyah of Engineering, International Islamic University Malaysia, P.O. BOX 10, 50728 Kuala Lumpur, Malaysia
Abstract. An Unmanned Aerial Vehicle (UAV) or Uncrewed Aerial Vehicle is a multirotor type of vehicle and is commonly known as a drone. Hexarotor type of UAV has six rotors and has several characteristics that give more operational advantages over lower rotors of UAV. This paper presents the mathematical modeling of the hexarotor system with the Proportional Derivative Sliding Mode Controller (PD-SMC) approach as the nonlinear controller. The mathematical model of the UAV’s body dynamics was modeled using the Newtonian method. This research implemented the SMC controller to the hexarotor system and coupled it with PD as the sliding surface for the attitudes controller. For comparison, Proportional Integral Derivative (PID), PD, and Linear Quadratic Regulator (LQR) controllers were also applied to the hexarotor system. Hence, better attitudes controller performances were achieved using the coupled controller, which is the PD-SMC controller. The performances were analyzed in percentage overshoot, settling time, rise time, and steady-state error. Matlab Simulink simulation was used throughout the research to measure the performances of hexarotor. As a result, for roll angle, rise time was 0.06 s, settling time was 0.50 s, percentage of overshoot was 0.0002%, and the steady-state error was 0.0001. In conclusion, PD-SMC shows the best stabilization controller for the hexarotor system with almost zero overshoot, zero steady-state errors, and faster settling times, and faster rising time. Keywords: Hexarotor · PID · PD-SMC · UAV
1 Introduction Unmanned Aerial Vehicles (UAV), sometimes known as drones, are aircraft that do not have a human pilot on board. UAVs can be remotely controlled or autonomous, flying © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 361–371, 2022. https://doi.org/10.1007/978-981-16-8954-3_34
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according to pre-programmed flight plans or advanced dynamic automation systems [1]. Unmanned aerial vehicles (UAV) are part of an Unmanned Aircraft System (UAS), which contains a UAV, a ground-based controller, and a communications system. UAVs can fly with varying degrees of autonomy, depending on whether they are controlled remotely by a human operator or onboard computers. In many countries, UAVs are widely used in the army or military unit. It has been used for target and decoy applications that provide a target that simulates the enemy aircraft. The military UAV also can be upgraded by installing small-scale weapons. Nowadays, UAV has arisen as a famous testbed for aerial inspection and surveying because of its simple design, easy to construct, and low maintenance requirement. It also has vertical take-off, landing, and hovering capabilities. Moreover, UAVs are widely used by polices, reporters, and photographers for surveillance, search and rescue, photographing, and video recording. Recently, due to pandemic covid19, Malaysia country has been put under lockdown situation by the government to reduce the spreading of the virus. Thus, polices use drones to facilitate their jobs in crowded areas. These drones are used for surveillance purposes and help the police monitor and control people’s movement by just looking at the screen without personally going to the site. UAVs are characterized by underactuated, highly nonlinear, and intrinsically coupled dynamics, making designing an autonomous controller challenging and leading to fascinating research problems. Hence, proper mathematical modeling and a suitable controller of the UAV are needed as they can minimize the failure errors, therefore, can help in the better stability of the UAV system. In this paper, we construct a mathematical model of hexarotor system using the Newtonian approach. Then, we propose a nonlinear control strategy which is PD-SMC controller based on hexarotor dynamics. The control strategy is using the attitude controller with three angles (θ, ψ, φ), roll, pitch and yaw respectively and altitude control using z-position. The performances of the controller is evaluated in the simulations and compared to the existing nonlinear controller in the literature.
2 Mathematical Modelling The mathematical model of mechanical and electronic systems can formulate using the Newtonian, Lagrangian, and Hamiltonian approaches. Both Newtonian and Lagrangian approaches are widely used since it was introduced. The same UAV system may be represented as several distinct mathematical models by changing the state variables. For example, the Newtonian approach used the rotation matrix as state variables, and the Lagrangian approach adopts Euler angles and angular velocity as state variables [2]. For this hexarotor system, the mathematical model is derived using the Newtonian method. The dynamic model of hexarotor includes positional and orientation coordinates. The positional or translational of the hexarotor is given, T (1) X = x, y, z where for orientation or rotational for hexarotor given as, = [θ, ψ, φ]T
(2)
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The three Euler angles, names as roll, pitch, and yaw angles, are bounded by (− π2 < φ < π π π 2 ) for roll angle, while the pitch angle is bounded by (− 2 < θ < 2 ) and the yaw angle is bounded by (−π < ψ < π). Let assume the linear velocity vector, V = [u, v, w]T and the angular velocity vector as Ω = [p, q, r]T which can be expressed in the form of a bodyfixed frame. The relationship between the velocity vectors can be given by X = R(Ω)V and θ˙ = M −1 (θ )Ω where (θ) and M(θ) can be representing the transformation rotation with the rotation velocity matrices, which are between the B frame and the I frame. Given the rotational matrix for x-, y-, and z-axis are shown as: ⎡
⎤ 1 0 0 Rx = ⎣ 0 cosφ −sinφ ⎦ 0 sinφ cosφ ⎡ ⎤ cosθ 0 sinθ Ry = ⎣ 0 1 0 ⎦ −sinθ 0 cosθ ⎡ ⎤ cosψ −sinψ 0 Rz = ⎣ sinψ cosψ 0 ⎦ 0
0
(3)
(4)
(5)
1
Now, the transformation vector can be given by the rotational matrix below, ⎡
⎤ cosθ cosψ cosψsinθ sinφ − sinψcosφ cosψsinθ cosφ + sinψsinφ ⎢ ⎥ R(θ ) = Rx Ry Rz = ⎣ sinψcosθ sinθ sinψsinφ + cosψcosθ cossinψsinθ − cosψsinφ ⎦ −sinθ sinφcosθ cosθ cosφ
(6)
The M () can be given by, ⎡
⎤ cosψ cosφsinψ 0 M (θ ) = ⎣ −sinψ cosφcosψ 0 ⎦ 0 −sinφ 1
(7)
Assume that the structure is symmetrical, ⎛
⎞ Ix 0 0 I = ⎝ 0 Iy 0 ⎠ 0 0 Iz
(8)
Now, the controlling of the hexarotor system is depending on varying the six rotors’ speed. So, the six inputs such as roll movement (u2 ), pitch movement (u3 ) and yaw movement (u4 ) can be written as, (9) u1 = b ω12 + ω22 + ω32 + ω42 u2 = b ω42 − ω22
(10)
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u3 = b ω32 − ω12
(11)
u4 = d ω22 + ω42 − ω12 − ω32
(12)
u5 = d ω22 + ω42 + ω12 − ω52
(13)
u6 = d ω22 + ω42 + ω12 + ω62
(14)
Lastly, the final mathematical modeling includes the gyroscopic effects can be written as, I − Iz IR L ¨φ = θ˙ ψ˙ y − θ˙ .g(u) + u2 (15) Ix Ix Ix IR ˙ L Iz − I x ˙ ¨ ˙ − φ.g(u) (16) + u3 θ = φψ Iy Iy Iy Ix − I y L ¨ ˙ ˙ ψ = θψ + u4 (17) Iz Iz u5 x¨ = −(cosψsinθcosφ + sinψsinφ) (18) m u6 y¨ = −(sinψsinθ cosφ + cosψsinφ) (19) m u1 z¨ = g − (cosθcosφ) (20) m
3 Sliding Mode Control (SMC) Sliding Mode Control is a simple and reliable method for designing controllers for both linear and nonlinear systems. SMC consists of two parts: firstly, a discontinuous control law is used to push the error vector toward a decision rule known as the sliding surface during the reaching phase. The control in this section is switched between the two sides of the sliding surface equation. Secondly, after confining the error vector to the sliding surface, an equivalent part of the controller acts to follow the dynamics imposed by the equations describing the sliding surface [3]. Four control equations are used to keep the hexarotor on the reference value. The signal U 1 will be used to control the altitude follow reference value, while the signal U 2 , U 3, and U 4 are used for controlling the roll, pitch, and yaw angles of the hexarotor system. The main idea of this SMC is to control the altitude of the hexarotor. The sliding surface equation can be written as [5, 6]: s = e˙ + λe
(21)
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where λ is the turning parameter. The error equation is given by, e = Zd −Z
(22)
where Z d is the desired state and Z is the measured state. s = Z˙ d − Z˙ + λ(Zd −Z)
(23)
Thus, the complete SMC controller equation for hexarotor are, U1 = Z¨ d + g + λ Z˙ d − Z˙
s m + kD |s| + δ cosφcosψ
(24)
The same method is used to get the controllers for roll, pitch and yaw angles. Ix Iy − Iz Jr ˙ ˙ ˙ ˙ ¨ ˙ + λ φd − φ Ueqφ = φd + θ ω − θ ψ Ix Ix l Iy I − Ix Jr ˙ − φ˙ ψ˙ z Ueqθ = θ¨d + φω + λ θ˙d − θ˙ Iy Iy l Ix − Iy Ueqψ = ψ¨ d − φ˙ θ˙ + λ ψ˙ d − ψ˙ Iz Iz
(25) (26) (27)
4 Simulation Results The physical measurements from experimental and calculation of the hexarotor system are shown in Table 1. The F550 hexarotor model is used in the experiments. Table 1. Physical measurements of the hexarotor system Parameters name
Symbol
Value
Unit
Gravity
g
9.81
m/s2
Mass of the hexarotor
m
0.288
kg
Lever length
L
0.233
m
Mass of the single rotor
mr
0.055
kg
Radius
R
0.062
m
Moment of inertia
I x , Iy
4.167 × 10−3
kgm2
Moment of inertia
Iz
7.891 × 10−3
kgm2
Moment of inertia
IR
0.003
kgm2
Two types of controllers were designed and used in this research. They were used to control both the altitude or z-position of the hexarotor and the attitudes angle or roll, pitch, and yaw angles of the hexarotor orientation. Firstly, the PID controller is used to
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control both the altitude and attitudes of the hexarotor. For the second controller, PD and SMC controllers were coupled to become PD-SMC controller. This combination has effectively produced the best result for controlling the altitude position with PD controller and maintaining good attitudes control with SMC controller. All the controllers were designed using the Matlab Simulink block diagram and are shown in Fig. 1 and Fig. 2.
Fig. 1. Simulink block diagram of PID controller
Fig. 2. Simulink block diagram of PD-SMC controller
The desired input was feed to each controller, and the performances of the controllers have been analyzed in terms of rise time, settling time, percentage of overshoot, and the steady-state error. The desired input value for the altitude or z desired was set at 30 m, while for the roll desired, pitch desired and yaw desired were set at 18° in radian respectively. Later, all the z, roll, pitch and yaw desired were connected to the controller which controls the four input u1 , u2 , u3 and u4 . The complete block diagram is simulated and tuned until it reaches the desired output response. The value of the gains Kp , Ki and
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K d for PID controller, Kp and K d for PD-SMC controller and Kp and K d PD controller was tuned using the MATLAB software’s tuning method, and the value of tuning is shown in Table 2. Table 2. Tuning parameters Controllers
Motions
Kp
Ki
Kd
PD
Z position
−25.05
–
−12.05
PID
Roll angle Pitch angle Yaw angle Z position
0.65 0.85 0.65 −5.64
0.024 0.024 0.024 −2.01
0.36 0.36 0.36 −4.05
PD-SMC
Roll angle Pitch angle Yaw angle
1.2 2.5 −6.51
– – –
0.25 0.25 −5.28
Finally, the output graph of the altitude and attitudes of the hexarotor were being compared to the initial desired input value. The step responses of the PID controller are shown in Fig. 5, 6, 7 and Fig. 8 for roll angle, pitch angle, yaw angle, and z position. From the PID responses, high % overshoot and longer settling time can clearly be seen at the altitude response while high overshot values are depicted for attitudes responses (Fig. 3).
Fig. 3. Altitude with PID controller
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Fig. 4. Attitudes (Roll Angle) of PID and PD-SMC controller
Fig. 5. Attitudes (Pitch Angle) of PID and PD-SMC controller
Fig. 6. Attitudes (Yaw Angle) of PID controller
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Fig. 7. Attitudes (Roll Angle) of SMC controller
Figure 7 shows the output responses of roll angle for the SMC controller. This result shows the unstable attitudes response with a lot of noise detected. Thus, a PD-SMC controller is introduced to overcome these problems. Figure 4, 5 and Fig. 6 show the attitudes response of the PD-SMC controller. These results show significant improvement with almost zero overshoot, zero steady-state errors, and faster settling times and rise time. The PID and PD-SMC controller performances were analyzed and compared in terms of peak time, overshoot, settling time, rise time, and the steady-state error. Hence, for the comparison to be more competitive, two more controllers were added. The extra two controllers taken from a paper [7] are Proportional Derivative (PD) controller for the attitudes and Linear Quadratic Regulator (LQR) controller for all the motions. Thus, these four controller performances will be analyzed in percentage overshoot, settling time, rise time, and the steady-state error for all altitude and attitudes motions. After analyzing the Table 3, PD-SMC controller shows the best stabilization of the hexarotor system. It is because for the roll angle, the rise time is 0.06 s, settling time is 0.50 s, percentage of overshoot is 0.0002% and the steady-state error is 0.0001. For the pitch angle, the rise time is 0.08 s, settling time is 0.61 s, percentage of overshoot is 0.0002% and the steady-state error is 0.0001. And goes to the yaw angle, the rise time is 0.10 s, settling time is 0.89 s, percentage of overshoot is 0.0002% and the steady-state error is 0.0001. Table 3. Comparison performances of all controllers Motions
Controllers
Rise time, Tr (s)
Settling time, Ts (s)
Percentage overshoot, OS (%)
Steady state error, ess
Roll angle
PID PD-SMC PD [7] LQR [7]
0.07 0.06 0.81 2.10
0.91 0.50 1.47 3.49
6.9 0.0002 0.0003 0.54
0.0000 0.0001 0.0000 0.0054 (continued)
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Motions
Controllers
Rise time, Tr (s)
Settling time, Ts (s)
Percentage overshoot, OS (%)
Steady state error, ess
Pitch angle
PID PD-SMC PD [7] LQR [7]
0.07 0.08 0.81 2.10
0.81 0.61 1.47 3.49
8.58 0.0002 0.0003 0.54
0.0000 0.0002 0.0000 0.0054
Yaw angle
PID PD-SMC PD [7] LQR [7]
0.13 0.10 0.82 2.09
1.29 0.89 1.46 3.45
10.9 0.0002 0.05 0.57
0.0000 0.0001 0.0000 0.0057
Z position
PID PD PD [7] LQR [7]
0.32 0.12 0.79 2.71
2.27 1.13 1.42 4.41
21.1 11.2 13.4 0.34
0.0000 0.0001 0.0003 0.0021
5 Conclusion The presented works show mathematical modelling of the hexarotor dynamics system using the Newtonian method and control for a hexarotor using the PD-SMC approach. For comparison, PID controller was designed and to make the comparison more competitive, two more controllers are taken into account, which are PD and LQR controllers. The performances of all four controllers are analyzed in terms of overshoot, settling time, rise time and the steady-state error. The simulation results for PID controller and PD-SMC controller was being illustrated. As a conclusion, PD-SMC controller was resulting the best stabilization controller for the hexarotor system with almost zero overshoot, zero steady-state errors, and faster settling times and faster rise time. Acknowledgement. The authors wish to express their gratitude to Motion Control Research Laboratory (MCon Lab), Center for Robotics and Industrial Automation (CeRIA) and Universiti Teknikal Malaysia Melaka (UTeM) for supporting the research and publication. This research is supported by Ministry of Education Malaysia (MOE) under the Fundamental Research Grant Scheme (FRGS) grant no. FRGS/2018/FKE-CERIA/F00353.
References 1. Wu, Y., Hu, K., Sun, X.M.: Modeling and control design for quadrotors: a controlled hamiltonian systems approach. IEEE Trans. Veh. Technol. 67(12), 11365–11376 (2018) 2. Vazquez-Nicolas, J.M., Zamora, E., González-Hernández, I., Lozano, R., Sossa, H.: PD+SMC quadrotor control for altitude and crack recognition using deep learning. Int. J. Control Autom. Syst. 18(4), 834–844 (2020). https://doi.org/10.1007/s12555-018-0852-9
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3. Herrera, M., Chamorro, W., Gómez, A.P., Camacho, O.: Sliding mode control: an approach to control a Quadrotor. In: 2015 Asia-Pacific Conference on Computer Aided System Engineering (2015) 4. Moussid, M., Sayouti, A., Medromi, H.: Dynamic modeling and control of a HexaRotor using linear and nonlinear methods. Int. J. Appl. Inf. Syst. 29, 2–5 (2015) 5. Chaudhary, A., Bhushan, B.: Design of a Sliding Mode Controller (SMC) based on reaching law. Int. J. Comput. Appl. 182(10) (2018). ISSN 0975-8887 6. Baldeon, J., Escorza, J., Chávez, D., Camacho, O.: Control for hexacopters: a sliding mode control and PID comparison. Revista Tecnica De La Facultad De Ingenieria Universidad Del Zulia 39 (2016) 7. Shauqee, M.N., Rajendran, P., Suhadis, N.M.: Proportional double derivative linear quadratic regulator controller using improvised grey wolf optimization technique to control quadcopter. Appl. Sci. 10, 1–10 (2021)
Aerial Based Traffic Tracking and Vehicle Count Detection Using Background Subtraction Muhamad Zulhilmi Bin Muhamad1 , Mohammad Nishat Akhtar1 , Elmi Abu Bakar1(B) , Zuliani Binti Zulkoffli2 , and Muhammad Faisal Mahmod3 1 School of Aerospace Engineering, Universiti Sains Malaysia,
14300 Nibong Tebal, Penang, Malaysia [email protected] 2 Department of Mechanical Engineering, UCSI University, 1, Jalan Puncak Menara Gading, Taman Connaught, 56000 Kuala Lumpur, Malaysia 3 Department of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia
Abstract. With the advent of increasing population, the traffic density is also increasing. Thus, it becomes deemed necessary for the urban planners to analyse the traffic condition via video surveillance for a successful improvisation to the existing town planning. The objective of this study was to develop a high altitude video surveillance setup with centroid tracker and compare different variants of background subtraction comprising of K-Nearest Neighbour, Mixture of Gaussian and Geometric Multi-Grid on a vision-based system for road vehicle counting and tracking. This project uses Python as its programming language and Open Computer Vision (OpenCV) as an open-source library for developing a high altitude video surveillance system for vehicle counting and directional motion detection. The designed system was able to achieve high count precision even in difficult scenarios related to occlusions or the presence of shadows. The principle of the system was to install a camera on the pedestrian bridges and track the vehicular traffic congestion by incorporating a unique ID. Moving objects were tracked using different background subtraction algorithm and object tracking was conducted using the centroid tracker. The video processing model was combined with a motion detection procedure, which correctly classified the positioning of moving vehicles depending on the space and time when the experiment was conducted on the site location. From the results it was revealed that both K-Nearest Neighbour and Mixture of Gaussian showed better accuracy with 93% and 100% depending upon the traffic density modalities. Using the proposed setup design, the identification of severe shadows based on solidity can be computed through the nature of the shape and this classification allows its accuracy to be estimated. Keywords: Traffic tracking · Background subtraction · K-Nearest Neighbour · Mixture of Gaussian · Geometric Multi-Grid
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 372–386, 2022. https://doi.org/10.1007/978-981-16-8954-3_35
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1 Introduction Traffic congestion has long been a big issue. In this regard, it is recognised recognized that improvements to preliminary transportation infrastructure, such as additional pavements and expanded roads, have failed to alleviate city congestion. As a result, numerous investigators focused on intelligent transportation systems (ITS), which forecast traffic flow by tracking activity at traffic intersections for the purpose of detecting congestions. Traffic tracking with the fixed camera is getting increasingly inefficient as they cannot identify issues beyond their immediate location [1]. Urban planners need to analyse traffic density, road capacity and traffic flow to draw strategies to reduce urban congestion. This will optimize traffic flow, cut fuel use, and may help to tackle urban environmental issues. Traffic tracking via high altitude surveillance camera can overcome the limitations of traditional methods of tracking due to its simplicity, mobility and ability to cover large areas. High-resolution real-time videos from aerial can be relayed to the command and control centre to assist on-ground personnel in road tracking, traffic guidance, traffic activity analysis, identify and track individual vehicles, read the license plate and many more activities. Aerial analysis can be equipped with different type of payloads like HD camera and thermal camera for day and night surveillance [2]. It is evident that in the current situation, the traffic pressure has increased in the metropolitan cities across the globe, whereby the role of ITS incorporated with dedicated image processing features has become significant [3, 4]. In this regard, under ITS, high altitude surveillance camera analysis can provide on-ground situational awareness in emergencies like road accidents and other catastrophic mishaps. The data collected by high altitude cameras can be analysed to improve traffic flow and road safety. This paper presents an intelligent vehicle counting system using different variants of background subtraction. In this regard, Sect. 2 gives a brief literature review of the existing system. Section 3 provides the methodology which illustrates the setup and the OpenCV functionalities used with respect to background subtraction. Section 4 gives the results and discussion followed by Sect. 5 which presents the conclusion.
2 Literature Review Related studies have come up with embedded systems capable of instantly analysing video frames and counting the number of vehicles on the road using a Raspberry Pi controller. The new YOLO and Tensorflow frameworks are the most often used, as both include in-built object identification models [4, 5]. Another proposed solution makes use of a technique called background subtraction to locate foreground objects in a video clip. To improve the accuracy of the detection of moving vehicles, many computer vision techniques are then performed, including thresholding, hole filling, and adaptive morphological procedures. To count vehicles, a virtual detection zone was used, and in this regard the experimental results indicate that the proposed vehicle counting technique is approximately 96% accurate [6]. Additionally, it has been proposed to use real-time counting in that. One study discussed the design and development of a novel, intelligent vehicle counting and classification system (iVCCS) that conducts real-time traffic tracking via a wireless magnetometer sensor. The other study proposed an automatic
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real-time background updating strategy for vehicle identification as well as an adaptive pattern for vehicle counting, both based on virtual loop and detection line methodologies [7, 8]. For nonparametric classification methods, it gets separated into two phases of development: training and detection. When the training period is sufficiently long, nonparametric approaches are efficient. In this regard, the KNN method is frequently used for classification in pattern recognition and data mining [9]. The algorithm’s underlying idea is that if the majority of the k most comparable samples to a query point in the feature space belong to a particular category, then the query point must also belong to that category. The KNN method is easy to implement and can handle high-dimensional data sets. However, if the test set, train set, and data dimension are all bigger than predicted, the computational complexity and operation time will be enormous [9]. Majority of moving object extraction algorithms rely on the temporal development of each pixel in the image. The detection technique entails categorising each pixel in the object or background classes individually. Based on this, a Mixture of Gaussian (MOG), was first suggested by KaewTraKulPong and Bowden [10]. Each backdrop pixel was modelled by a blend of k Gaussian distributions, with k values between 3 and 5. The authors presumed that various distributions indicate various colour in the background and foreground. On the model, the weight of each of the utilised distributions is related to the amount of time each colour remains on that pixel. As a result, when the weight of a pixel distribution is low, that pixel is labelled as foreground. MOG has a low rate of compatibility, complexity, and memory consumption and the ability to detect objects in an outside area. In the background subtraction method, this technique is much more adaptable and resilient, and it can handle multi- modal distributions as evident by Mohamed et al. [11]. As an improvisation to this, Mixture of Gaussian 2 (MOG2) was built by Zivkovic [12] on the works to overcome one of MOG’s limitations which was the fixed number of usable distributions. MOG2 achieves a better depiction of the complexity of colours in each frame by employing a configurable number of Gaussian distributions that are mapped pixel by pixel. Tracking algorithms are prone to accumulating errors. To solve these problems with tracking algorithms, a detection algorithm is run regularly to deal with the problems with the tracking algorithm. As an alternative, Geometric Multi-Grid (GMG) was also introduced as background subtraction variant. GMG method models the background using a mixture of Bayesian Inference and Kalman Filters which was evident from the work of Godbehere and Goldberg [13]. The first stage of the approach gathers weighted values for each pixel based on how long a colour remains in that place. New observations are added to the model for each frame by changing weight values. Background colours are those that remain steady for an extended period of time. To minimise noise from the first stage, the second step filters pixels in the foreground. The tracking data can be used to predict the position of the object in the next frame. It can also lose track of an object if it is obstructed for an extended period of time or moves so quickly that the tracking algorithms cannot keep up with pace. A good tracking algorithm will handle some level of occlusion [14]. Vehicle occlusion, changes in vehicle perception, and abrupt vehicle motion are all major problems. Some approaches need the vehicle to be detected first and then tracked, whilst others use vehicle tracks as detection signals [14]. Other difficulties make a vehicle detection and tracking difficult.
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The study identified three key challenges: vehicle-vehicle, infrastructure-carrier, and shadow identification and removal.
3 Methodology Vehicles can be classified according to their size, colour or form. The selection of an appropriate and robust vehicle feature representation about the application is a crucial question during start-up. In general, the tracking technology is defined by vehicle representation. The capability to keep vehicle tracks alive in the scene for as long as feasible is the most essential aim. Vehicle tracking must be accurate to perform posterior trajectory analysis and behaviour identification. In this study, the device used to record video throughout the analysis of traffic conditions was a mobile smartphone camera. The images were taken from a mobile smartphone camera, Mi Note 10 that is mounted on an octopus mini tripod stand mount for mobile smartphone. The camera has an overall resolution of 1920 × 1080 pixels with a focal length of 44 mm, and a resolution of 30 fps. The resolution and frame rate were chosen to provide enough detail in the image to identify individual vehicles and to collect sequential images quickly enough that individual vehicles can be tracked precisely. The camera was wrapped around a bridge pole with a viewing angle facing straight into the road. The smartphone was fixed in a horizontal position for the CVF (Camera’s View Field) angle. The standard height of a pedestrian bridge was 5.5 m and the height of the camera from the road approximately was 7.0 m. Figure 1 shows how the camera setup at the bridge.
Fig. 1. Octopus mini tripod stand mount for mobile smartphone
3.1 Datasets for Traffic Analysis Over the last decade, there has been an increase of interest in traffic tracking at pedestrian bridges, with an emphasis on environment modelling and vehicle behaviour analysis. The co-creative effort benefits in the exchange of data and the advancement of research. However, this has resulted in the creation of difficult datasets for evaluation and benchmarking [1]. The recording was taken from around 1.00 pm to 4.00 pm on the same
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day. All videos were recorded for 2 min at 30 fps. Figure 2 shows the location points in South Penang. Figure 3(a) represents the traffic dataset of NTebal1 from front view of pedestrian bridge for research on activity analysis of low traffic scenes.
Fig. 2. Location points of the sites used for study
Fig. 3. Frame taken from a video (a) (NTebal1) - Low Traffic, (b) (NTebal2) - Low/Medium Traffic, (c) (Jawi1) - High Traffic
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NTebal2 as shown in Fig. 3(b) represents the traffic dataset from back view of pedestrian bridge of NTebal1. This was used for research on activity analysis of lowmedium traffic scenes. Jawi1 as shown in Fig. 3(c) represents the traffic dataset from front view of pedestrian bridge. This is specifically intended for activity analysis and behaviour understanding of high traffic flow. It contains a big shadow coming from the tree in the left frame of the video which gives a noise for detection and tracking activities. 3.2 Software Setup Linux-Ubuntu 20.04 LTS was installed using Oracle VM VirtualBox. Oracle VM VirtualBox is cross-platform virtualization software that enables users to expand their existing machine to run several operating systems concurrently. To jumpstart the development process, OpenCV-Python was installed whereby the version of Python used was 3.8. To capture a video, cv2.VideoCapture function was used. For the proposed project, background subtraction function used from OpenCV were KNN, MOG, MOG2 and GMG which are highlighted as:
Figure 4 shows the flowchart of the proposed research. Once the background subtraction method was assigned using OpenCV function then the contours were defined whereby its subsequent edges were enhanced using dilation. Post contour defilement, centroid tracker was applied to identify and track the directional motion of vehicle. A virtual line (yellow colour in Fig. 6(a), 8(a), 10(a)) was defined in the frame which detects the count of the vehicle if it passes by that line in either direction. 3.3 Centroid Tracking Algorithm In the centroid tracking algorithm, we assume that some sets of the bounding box are passed having (x, y) coordinates for each detected object in the frame. These bounding boxes can be produced by any type of object detector like colour thresholding with contour extraction, Haar cascades, etc. It is necessary that bounding boxes are computed for each frame in the video once the background subtraction function is chosen. After bounding boxes were assigned in the frame with their (x, y) coordinates, their centroid was calculated and each bounding box was assigned a unique ID. For every subsequent frame bounding box concept which we have discussed earlier was applied for computing object’s centroid. But assigning new unique ID for each detection of the object may hinder the purpose of object tracking. So to overcome this, we can correlate the centroid of the new object with the existing object. To obtain this, we calculate Euclidean distance between the two objects, using the following equation: (1)
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Fig. 4. Flowchart of the system
The following pseudocode shows the registration of new objects with reference to Fig. 5 shown below.
Pseudocode: 1. Accept bounding box coordinates and compute centroids 2. Distance between new bounding boxes and existing objects 3. Update (x, y)-coordinates of existing objects 4. Register new objects (#ID1, #ID2)
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To be efficient, an object tracking algorithm must be able to overcome the circumstance when an object travels out of the frame or the region of interest, and to do so, the procedure of deregistering the object was utilized. Deregistering the object involves removing the unique ID and other information about that single vehicle. This is determined by comparing that particular object to other existing objects in the frame. When an object does not match another existing object for the given number of frames, it was assumed that the object has been lost or moved out of the field of view.
Fig. 5. Exemplification of bounding box with centroid and ID
4 Results and Discussion The experimental result consists of comparing between the background subtraction methods which is KNN, MOG, MOG2 and GMG. The traffic flow in the video showed a different density pattern. Some displayed comparatively high and low traffic, while some showed average medium traffic flow. There were several instances whereby the vehicle counting system counted somewhat less than the actual number of vehicle due to congestion and heavy traffic flow situation. Such cases were recorded with respect to false positive error factor. The following subssection shows the experimental results with respect to vehicle count on different site locations obtained by the proposed setup with different background subtraction method. 4.1 Results from NTebal1 Dataset Figure 6 (a) shows there are two vehicles been detected and tracked with the unique ID that appears to the vehicles. The count started after the vehicles pass through the yellow line and the colour of the unique ID also changed. With the implementation of KNN method, there was a small amount of shadow formation around the vehicles. In comparison to KNN, MOG performed better as there was subsequently less shadow detection and gave better quality of object detection as shown in Fig. 6(b). On the other
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hand MOG2 detoriated the performance in comparison to MOG, as there was mild shadow detection with the object. Moreover, when GMG was implemented, then there came out to be a morphological changes in the object shape. Table 1 indicate the results obtained from video NTebal1. The table consists of the number of vehicles calculated by system using different background subtraction method.
Fig. 6. (a) Tracking and counting vehicles in low traffic for Ntebal1, (b) Background subtraction using MOG for NTebal1
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The number of vehicles calculated by using KNN, MOG, MOG2 and GMG were 27, 29, 22 and 25 respectively. It can be observed from Fig. 7 that the MOG method has detected and counted the vehicles with high accuracy of the object. The MOG method succeeded to count the same as the actual number of vehicles in the video frame. The success rate for KNN, MOG, MOG2 and GMG were 93.00%, 100.00%, 75.86% and 86.21% respectively. Table 1. Results from video of site NTebal1 Video
Background subtraction methods
Exact no. of vehicle in video
No. of vehicles detected
NTebal1
KNN
29
27
MOG
29
MOG2
22
GMG
25
Fig. 7. Success rate in percentage from video NTebal1
4.2 Results from NTebal2 Dataset Figure 8(a) shows the traffic scenario for NTebal2. The unique ID for the vehicles that have been counted disappears when far away from the yellow line. For NTebal2, when the background subtraction was implemented using KNN (as shown in Fig. 8(b)), then the classification was not done with complete accuracy because there was camouflage problem due to the same colour intensity of tar and the windscreen because of high intensity of sun light. Moreover, in such modality, MOG and MOG2 detoriated the
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classification results further. However, GMG gave better results compared to MOG and MOG2. Table 2 indicate the results obtained from video NTebal2. The table consists of the number of vehicles calculated by the system using different variants of background subtraction method. The number of vehicles calculated by using KNN, MOG, MOG2 and GMG were 46, 35, 28 and 37 respectively.
Fig. 8. (a) Tracking and counting vehicles in low traffic for Ntebal2, (b) Background subtraction using KNN for NTebal2
From Fig. 9, it can be observed that the KNN method performed well compared to others. The KNN, MOG and GMG method counted the vehicles somewhat less than the actual number due to congestion in the frame. Meanwhile, with respect to MOG2, the difference between the actual value and the computed value was more. This was due to
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Table 2. Results from video of site NTebal2 Video
Background subtraction methods
Exact no. of vehicle in video
No. of vehicles detected
NTebal2
KNN
52
46
MOG
35
MOG2
28
GMG
37
the high false positive error factor because of location modalities. The success rate for KNN, MOG, MOG2 and GMG were 88.46%, 67.31%, 53.85% and 71.15% respectively.
Fig. 9. Success rate in percentage from video NTebal2
4.3 Results from Jawi1 Dataset Figure 10 (a) shows the traffic scenario for Jawi1. It is worth to be noted that, the car has been detected while the bike is not because of the two contours merging into one. If the contour splits into two, the algorithm will create another ID. At Jawi1 site, the sunlight intensity was less compared to NTebal1 and NTebal2. When the background subtraction was done using KNN, vehicle classification was well visible as a solid shape that can be more easily tracked. With the implementation of MOG, the shadows from the vehicles were reduced slightly and it resulted in better accuracy compared to KNN (shown in Fig. 10(b)). Moreover, when MOG2 was implemented, then there was a slight noise factor which was not filtered out. Nonetheless, there was only an approximately 2% reduction in the accuracy of MOG2 compared to MOG. With the implementation of GMG, a lot of noise factor persisted and the vehicle morphology also got distorted. As a result, the accuracy of classification was lowest among all methods.
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Fig. 10. (a) Tracking and counting vehicles in low traffic for Jawi1, (b) Background subtraction using MOG for Jawi1
Table 3 indicate the results obtained from video Jawi1. The table consists the similar parameters as shown in Table 1 and Table 2. Table 3. Results from video of site Jawi1 Video
Background subtraction methods
Exact no. of vehicle in video
No. of vehicles detected
Jawi1
KNN
70
62
MOG
69
MOG2
63
GMG
31
The number of vehicles calculated using KNN, MOG, MOG2 and GMG were 62, 69, 63 and 31 respectively. From Fig. 11, it can be observed that the MOG method has
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detected and counted the vehicles with a difference of only less than one from the actual number of vehicles in the video. That gives MOG the success rate of 98.57% while KNN, MOG2 and GMG only obtained 88.57%, 90.00% and 44.29% respectively. It is worth to be noted that for all three different location dataset, MOG2 underperformed with respect to MOG. This was due to the fact that MOG2 was unable to adapt to the varying scenes due to the change in illumination.
Fig. 11. Success rate in percentage from video Jawi1
5 Conclusion and Future Works Vehicle detection under a mixed traffic situation of the low, medium and high traffic works precisely as expected, and the counting approach is accurate. In this regard, KNN and MOG worked well in terms of accuracy with respect to different traffic density modalities. In high sunlight intensity and camouflage conditions, KNN showed high stability in vehicle count whereas MOG gave better accuracy in overcast conditions involving substantial noise. The developed system has a disadvantage due to the fact that in high-traffic circumstances, it necessitates a little extra processing time. Due to heavy wind, camera might get affected due to vibrations. This leads to partial detection of vehicles. Future work will be focused on a more detailed evaluation of the results of the system. The current work has been done only for normal sunny day light conditions and mild overcast conditions. Further room of algorithm optimizations needs to be explored by coming up with hybrid approach of a single background subtraction technique which can give better results for both sunny and overcast conditions involving substantial noise. In this regard, we intend to propose a dedicated reconfigurable RPi board for hybrid background subtraction which can be installed at high altitude for vehicle tracking and counting purpose.
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Acknowledgement. The authors would like to acknowledge the RUI grant 1001.PAERO.8014035 by RCMO, Universiti Sains Malaysia.
References 1. Kamble, S.J., Kounte, M.R.: Machine learning approach on traffic congestion tracking system in internet of vehicles. Procedia Comput. Sci. 171, 2235–2241 (2020) 2. Gleason, J., Nefian, A.V., Bouyssounousse, X., Fong, T., Bebis, G.: Vehicle detection from aerial imagery. In: 2011 IEEE International Conference on Robotics and Automation, Shanghai, pp. 2065–2070. IEEE (2011) 3. Hao, Y., Yin, Y., Lan, J.: Vehicle tracking algorithm based on observation feedback and block symmetry particle filter. J. Electr. Comput. Eng. 2014 (2014) 4. Xu, M., Zhang, X., Liu, Y., Huang, G., Liu, X., Lin, F.X.: Approximate query service on autonomous IoT cameras. In: Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services, Toronto, pp. 191–205. ACM (2020) 5. Ge, Y.: A spatial-temporal-map-based traffic video analytic model for large-scale cloud-based deployment. Rutgers The State University of New Jersey, School of Graduate Studies (2020) 6. Seenouvong, N., Watchareeruetai, U., Nuthong, C., Khongsomboon, K., Ohnishi, N.: A computer vision based vehicle detection and counting system. In: 8th International Conference on Knowledge and Smart Technology (KST), Chiangmai, pp. 224–227. IEEE (2016) 7. Anandhalli, M., Baligar, V.P.: Improvised approach using background subtraction for vehicle detection. In: 2015 IEEE International Advance Computing Conference (IACC), Bangalore, pp. 303–308. IEEE (2015) 8. Lu, X., Izumi, T., Takahashi, T., Wang, L.: Moving vehicle detection based on fuzzy background subtraction. In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, pp. 529–532. IEEE (2014) 9. Seenouvong, N., Watchareeruetai, U., Nuthong, C., Khongsomboon, K., Ohnishi, N.: Vehicle detection and classification system based on virtual detection zone. In: 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), Khon Kaen, pp. 1–5. IEEE (2016) 10. Kaewtrakulpong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Proceedings of the 2nd European Workshop on Advanced Video Based Surveillance Systems, Kingston, pp. 1–5. Kluwer Academic Publishers (2001) 11. Mohamed, S.S., Tahir, N.M., Adnan, R.: Background modelling and background subtraction performance for object detection. In: 6th International Colloquium on Signal Processing & Its Applications, Malacca, pp. 1–6. IEEE (2010) 12. Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, vol. 2, pp. 28–31. IEEE (2004) 13. Godbehere, A.B., Goldberg, K.: Algorithms for visual tracking of visitors under variablelighting conditions for a responsive audio art installation. In: LaViers, A., Egerstedt, M. (eds.) Controls and art, pp. 181–204. Springer, Cham (2014). https://doi.org/10.1007/978-3319-03904-6_8 14. Cheong, Y.Z., Chew, W.J.: The application of image processing to solve occlusion issue in object tracking. In: MATEC Web of Conferences, vol. 152, p. 3001 (2018)
Simulating Solitary Foraging Behaviour of Chimpanzee in Hunting Red Colobus Monkeys Using Agent-Based Modelling Approach N. Idros, W. A. F. W. Othman(B) , A. A. A. Wahab, N. R. M. Noor, and S. S. N. Alhady School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia [email protected]
Abstract. This paper reports the solitary hunting behavior of chimpanzees. Four tasks have been set up in Netlogo to simulate the hunting behavior of chimpanzees. Simulation result in task one displayed that chimpanzee prefers the lightest monkeys, i.e., monkey A with a mass of 4 kg in the simulation for 50 ticks, monkey B with a mass of 16 kg was in the simulation world for 450 ticks. Meanwhile, in task two, which is to determine the frequency of the sound produced by a monkey, simulation result showed the monkey that has a mass of 17 kg and weight Active was 5, 15, 24, 30, and 45, the chimpanzee requires the longest time to hunt as compared to when the weightage was at 15, 24, 30, and 45. Nevertheless, chimpanzees needed the longest time, i.e., 740 ticks, to hunt when the weight of Active was at 30. Task three, which is to discover the tendency of chimpanzees to hunt their prey, revealed that when the weight of Chances-towards-immature-monkey was 0.3, the monkey had a mass of 17 kg existed in the simulation world was longer than that of when the weight of Chances-towards-immature-monkey was 0.9. Simulation result in task four exhibited that chimpanzees only hunt one monkey under hungry conditions as the number of monkeys remains unchanged until 50 ticks. Keywords: ABM · Chimpanzee · Netlogo · Red colobus monkey · Solitary hunting · Swarm intelligence
1 Introduction Agent-Based Modelling (ABM) is used to understand the complex system’s behavior [1, 2]. A complex system is a system that involves several agents interacting with each other [3], where the results are hard to foresee [4]. In ABMs, the agent is the main item in the simulation world [1] since it is a “living” object that has its own rules to follow and interact with each other in the environment [5]. An agent can be anything, such as households, formal organizations, or even entire governments [2]. ABM is also © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 387–396, 2022. https://doi.org/10.1007/978-981-16-8954-3_36
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known as Individual-Based Modelling (IBM), and it does not demand solid mathematical knowledge [4]. Thus, it is favorable among researchers. Swarm intelligence is a field that utilizes ABM, also known as a swarm-based algorithm [6]. Most of the animals that live in groups have their intelligence. The action that mimics their intelligence has been defined as swarm intelligence [7]. Swarm based algorithms are such as Rat Swarm Optimizer (RSO) [8], Red Deer Algorithm (RDA) [9], Racoon Optimization Algorithm (ROA) [10], Cheetah Chase Algorithm (CCA) [11], Jaguar Algorithm (JA) [12], Octopus Algorithm (OA) [13], Emperor Penguin Optimiser [14], Bald Eagle Search Optimisation [15], Bear Smell Search Algorithm (BSSA) [16], Squirrel Search Algorithm (SSA) [17], Meerkat Clan Algorithm [18], Grasshopper Optimization Algorithm [19], Butterfly Optimization Algorithm (BOA) [20], Crow Search Algorithm [21] and Lion Optimization Algorithm (LOA) [22]. This work aims at chimpanzees’ behavior. Pan Troglodytes is the scientific name of chimpanzee [23] that lives in the group, which is called communities [23] or unit groups [24]. Chimpanzee lives in various habitat such as beside forest, dry woodland and savanna zone [23, 25]. Apart from that, chimpanzees’ types of motion include swing, branchiaton, vertical leap, ground leap, bipedal walk, steep climb, gallop, rapid run, quadruple run, and quadruple walk [25]. This benefits the chimpanzee to move either on the ground or on trees. The main approach to notice the presence of prey is due to limited forest eyesight. The maximum distance that chimpanzees can see is only 20 m [26]. The chimpanzee is an omnivore. Nevertheless, wild chimpanzee eats meat while non-wild chimpanzee eats vegetable foods [25, 27]. Two hunting mechanisms of wild chimpanzees are single and group-dependent on the habitat [26]. Red colobus monkey (Piliocolobus spp.) is the chimpanzee’s primary prey [26, 28–30]. Chimpanzee favors young red colobus monkeys compared to the old ones [26, 28, 30–32]. Apart from that, a chimpanzee is only very alert to the foliage’s rustle sound in which the monkey jumps [26]. Chimpanzee is attracted to any branchy movement and will stop looking whenever motion is detected [31]. Generally, chimpanzee does not kill more than one red colobus monkey per kill [26]. The red colobus monkey is almost going to extinct not just due to habitat destruction and disease [33], but because it is the main prey of the chimpanzee [31, 34]. Red colobus monkeys live in groups [29], with 25 to 127 members [35]. The age group splits into the infant, juvenile, adolescent, and adult and is indicated by mass. The mass of less than 3 kg is categorized as an infant, while the mass ranging from 3 kg to less than 6 kg is juvenile. Adolescents and adults have a mass ranging from 6 kg to less than 10 kg and ranging from 10 kg to less than 18 kg, respectively [28, 36]. New sub-groups will be formed when the group is enormous [35].
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Conventionally, red colobus monkeys will avoid being aware of a chimpanzee, but the chimpanzee has yet to be aware of them. The red colobus will keep quiet instantly and move to a more secure place, i.e., higher than the current place [26, 37, 38]. There is a case where the red colobus monkey does not run from chimpanzees when they encounter each other; they attack the chimpanzee. This is because the chimpanzee is alone, or the group of red colobus monkeys is big [26]. On contrarily, the red colobus monkey will run [35]. This paper shows the simulation results of chimpanzees’ solitary hunting behavior. The article is organized as follows; Sect. 2 explains the methodology used to simulate chimpanzees’ solitary hunting behavior. In Sect. 3, simulation results are presented in detail and followed by the conclusion in Sect. 4.
2 Methodology In this work, simulations of the solitary hunting behavior of chimpanzees have been done using Netlogo 6.0.4. Netlogo is one of the programming platforms by Uri Wilenski [39], which enables the user to simulate any model of different complexity [39, 40]. For the simulation world setup, 16 by 16 patches are used. Six sliders are luminance, number-of-chimpanzee, number-of-monkey, chances-towards-immature-monkey, chances-towards-lower-age, and active, are utilized at the interface. Four of the sliders, i.e., luminance (ranging from 0 to 10), chances-towards-immature-monkey (ranging from 0 to 1), chances-towards-lower-age (ranging from 0 to 1), and active (ranging from 0 to 100), as shown in Fig. 1(a), act as weightage in the study.
Fig. 1(a). Four sliders were used in the simulations that act as weightage.
The luminance slider is created to visualize the sound produced by monkeys. In order to visualize the sound, the color of the background will appear yellow—the louder the sound, the brighter the color is. Figure 1(b) shows the scenario when sound is present. Normally, the closer patch from the sound source displays a higher value than the farther patch. For example, if the source of sound is the circled monkey, and has a value of 8, then the value at box one will be less than eight but greater than the value at box two.
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2 Fig. 1(b). The visualization of the sound in the simulation world
Another two sliders, number-of-chimpanzee, and number-of-monkey are applied to control the two animals’ populations. The number of chimpanzees is fixed at 1 in the simulations since this work is to model chimpanzees’ solitary hunting behavior. The visibility range of the chimpanzee in the simulation is set to 10 m and 120°. The number of monkeys is set at 25 due to the minimum number of monkeys to form a group [35]. Monkey is assumed to be blind and unaware of the presence of chimpanzees to hunt them. While sliders, chances-towards-immature-monkey, chances-towards-lower-age, and active are probabilities. The weight of chances-towards-immature-monkey and chancestowards-lower-age are used to determine the tendency of chimpanzees to hunt their prey. As for active, it determines the monkey’s activities in the simulation, i.e., active. In an active mode, a monkey’s frequency of sound is higher than in an inactive mode. The weight of the sliders will be set depending on the task requirement. Four tasks have been divided in this work: 1) Chimpanzee prefers the lightest monkey. In this task, simulation takes place five times for accuracy. All the weights are fixed for all the simulations. 2) Simulation takes place five times, with a weightage of active varies at 5, 15, 24, 30, and 45 to determine the frequency of the monkey’s sound. 3) Simulation occurs three times, with a weight of chances-towards-immature-monkey, manipulated at 0.3, 0.5, and 0.9 to determine the chimpanzee’s tendency to hunt its prey. 4) A new parameter, which is hungry-condition, is added in the simulation. The time needed for a chimpanzee to hunt monkeys in all tasks will be recorded to interpret the data.
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3 Results and Discussions 3.1 Analyses of Task 1 Figure 2 shows five simulations of chimpanzee hunting behavior where the weight of chances-towards-immature-monkey, the weight of chances-towards-lower-age, and weight of active have been fixed to 0.3, 0.99, and 5, respectively. From Fig. 2, chimpanzees hunted the lightest monkey. This has been proven by the shortest time interval of monkeys in the simulation world compared to that of the heavier monkeys. For example, monkey A with a mass of 4 kg was in the simulation for 50 ticks, yet monkey B with a mass of 16 kg in the simulation world for 450 ticks.
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3.2 Analyses of Task 2 Figure 3 depicted the simulation results of hunting behavior when the weight of chancestowards-immature-monkey and weight of chances-towards-lower-age was fixed at 0.3 and 0.99, respectively. The manipulated variables are the weight of active, which was set to 5, 15, 24, 30, and 45. As the weight of active is increased, monkeys’ sound production is more frequent, thus notifying the chimpanzee. This is because hearing is the chimpanzee’s main approach to notice monkeys. Based on Fig. 3, the monkey with a mass of 17 kg and weight of active was 5, 15, 24, 30, and 45; they died at ticks 550, 700, 600, 450, and 740. Logically, when the active weight is at 5, the chimpanzee requires the longest time to hunt compared to when the weight was at 15, 24, 30, and 45. Nevertheless, chimpanzees needed the longest time, i.e., 740 ticks, to hunt when the weight of active was at 30. When the weight of active was at 5, many younger monkeys were present in the simulation world. Therefore, the sound produced by monkeys encourages chimpanzees to alert the presence of monkeys, not to hunt the monkey that produced the sound. The chimpanzee will still hunt the lightest monkey in the simulation world. Active Active Active Active Active
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3.3 Analyses of Task 3 Figure 4 displays the simulation results of hunting behavior when the weight of chancestowards-lower-age and weight of active have been fixed at 0.9 and 5, respectively. Meanwhile, the weight of chances-towards-immature-monkey was manipulated to 0.3, 0.5, and 0.9. It can be clearly seen that when the weight of chances-towards-immaturemonkey was 0.3, the monkey that had a mass of 17 kg existed in the simulation world was longer than that of when the weight of chances-towards-immature-monkey was 0.9. This happens because when the weight of chances-towards-immature-monkey was 0.3, the probability of chimpanzees hunting mature monkeys was low. Chances-towards-immature-monkey = 0.3 Chances-towards-immature-monkey = 0.5 Chances-towards-immature-monkey = 0.9
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3.4 Analyses of Task 4 Figure 5 illustrates the simulation results of hunting behavior when the chimpanzee is hungry. The simulation has been repeated five times. Initially, the number of monkeys was fixed at 25. Based on the simulations, chimpanzees hunted only one monkey at that time. The number of monkeys remains unchanged until 50 ticks. This is because the chimpanzee is full. Under the non-hungry condition, the chimpanzee ignores the monkeys and patrols. Thus, no occurrence of hunting activity.
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4 Conclusion This paper discloses the solitary hunting behavior of chimpanzees. Four tasks have been generated in Netlogo to verify the hunting action of the chimpanzee. Simulation result in task one displayed chimpanzee preferred the lightest monkeys. Meanwhile, in task two, which determines the frequency of the sound produced by a monkey, the simulation result showed that the sound produced by monkeys encourages chimpanzees to alert the monkey’s presence, not to hunt the monkey that produced the sound. While simulation results in task 3, which is to discover chimpanzees’ tendency to hunt their prey, revealed that low weightage resulted in a low probability of chimpanzees hunting mature monkeys. Simulation result in task four exhibited that chimpanzees only hunt one monkey at a time under hungry-condition. Acknowledgments. This work was supported by Universiti Sains Malaysia – RU Grant Scheme (Grant number: USM/PELECT/8014113).
References 1. Han, Z., Zhang, K., Yin, H., Zhu, Y.: An urban traffic simulation system based on multi-agent modeling. In: The 27th Chinese Control and Decision Conference (2015 CCDC), pp. 6378– 6383. IEEE (2015)
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2. Rai, V., Henry, A.D.: Agent-based modelling of consumer energy choices. Nat. Clim. Change 6(6), 556–562 (2016) 3. Chimeh, M.K., Richmond, P.: Simulating heterogeneous behaviours in complex systems on GPUs. Simul. Model. Pract. Theory 83, 3–17 (2018) 4. Hakim, G., Braun, R.: Agent based modeling of a flange climb derailment. In: 2018 26th International Conference on Systems Engineering (ICSEng), pp. 1–5. IEEE (2018) 5. Sanz, V., Bergero, F., Urquia, A.: An approach to agent-based modeling with Modelica. Simul. Model. Pract. Theory 83, 65–74 (2018) 6. Yong, W., Tao, W., Cheng-Zhi, Z., Hua-Juan, H.: A new stochastic optimization approach — Dolphin swarm optimization algorithm. Int. J. Comput. Intell. Appl. 15(02), 1650011 (2016) 7. Jain, M., Maurya, S., Rani, A., Singh, V.: Owl search algorithm: a novel nature-inspired heuristic paradigm for global optimization. J. Intell. Fuzzy Syst. 34(3), 1573–1582 (2018) 8. Dhiman, G., Garg, M., Nagar, A., Kumar, V., Dehghani, M.: A novel algorithm for global optimization: Rat Swarm Optimizer. J. Ambient. Intell. Humaniz. Comput. 12(8), 8457–8482 (2020). https://doi.org/10.1007/s12652-020-02580-0 9. Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M., Tavakkoli-Moghaddam, R.: Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft. Comput. 24(19), 14637–14665 (2020). https://doi.org/10.1007/s00500-020-04812-z 10. Koohi, S.Z., Hamid, N.A.W.A., Othman, M., Ibragimov, G.: Raccoon optimization algorithm. IEEE Access 7, 5383–5399 (2019) 11. Goudhaman, M.: Cheetah chase algorithm (CCA): a nature-inspired metaheuristic algorithm. Int. J. Eng. Technol. 7(3), 1804–1811 (2018) 12. Chou, Y.H., Kuo, S.Y., Yang, C.Y.: Next generation metaheuristic: jaguar algorithm. IEEE Access 6, 9975–9990 (2018) 13. Mukherjee, A., De, D.: Octopus algorithm for wireless personal communications. Wirel. Pers. Commun. 101(1), 531–565 (2018). https://doi.org/10.1007/s11277-018-5703-8 14. Dhiman, G., Kumar, V.: Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl.-Based Syst. 159, 20–50 (2018) 15. Alsattar, H.A., Zaidan, A.A., Zaidan, B.B.: Novel meta-heuristic bald eagle search optimisation algorithm. Artif. Intell. Rev. 53(3), 2237–2264 (2019). https://doi.org/10.1007/s10462019-09732-5 16. Ghasemi-Marzbali, A.: A novel nature-inspired meta-heuristic algorithm for optimization: bear smell search algorithm. Soft. Comput. 24(17), 13003–13035 (2020). https://doi.org/10. 1007/s00500-020-04721-1 17. Jain, M., Singh, V., Rani, A.: A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol. Comput. 44, 148–175 (2019) 18. Al-Obaidi, A.T.S., Abdullah, H.S., Ahmed, Z.O.: Meerkat clan algorithm: a new swarm intelligence algorithm. Indones. J. Electr. Eng. Comput. Sci. 10(1), 354–360 (2018) 19. Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017) 20. Arora, S., Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft. Comput. 23(3), 715–734 (2018). https://doi.org/10.1007/s00500-018-3102-4 21. Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016) 22. Yazdani, M., Jolai, F.: Lion Optimization Algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Des. Eng. 3(1), 24–36 (2016) 23. Tokuyama, N., Clay, J. Z.: Chimpanzee and Bonobo. In: Encyclopedia of Animal Behavior, pp. 324–334 (2019) 24. McCown, E.R., Hamburg, D.A.: The Great Apes. Benjamin/Cummings (1979)
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25. Reynolds, V.: The Apes: The Gorrilla, Chimpanzee, Oranguatan, and Gibbon; Their History and Their World. Harper and Row, New York (1971) 26. Boesch, C., Boesch, H.: Hunting behavior of wild chimpanzees in the Tai National Park. Am. J. Phys. Anthropol. 78, 547–573 (1989) 27. Hobaiter, C., Samuni, L., Mullins, C., Akankwasa, W.J., Zuberbühler, K.: Variation in hunting behaviour in neighbouring chimpanzee communities in the Budongo forest, Uganda. PLoS ONE 12(6), e0178065 (2017) 28. Watts, D.P., Mitani, J.C.: Hunting behavior of chimpanzees at Ngongo, Kibale National Park, Uganda. Int. J. Primatol. 23(1), 1–28 (2002) 29. McGrow, W.S., Zuberbühler, K., Noë, R.: Monkeys of the Tai Forest: An African Primate Community, vol. 51( 2007) 30. Stanford, C.B., Wallis, J., Matama, H., Goodall, J.: Patterns of predation by chimpanzees on red colobus monkeys in Gombe National Park, 1982–1991. Am. J. Phys. Anthropol. 94(2), 213–228 (1994) 31. Mitani, J.C., Watts, D.P.: Demographic influences on the hunting behavior of chimpanzees. Am. J. Phys. Anthropol. 109, 439–454 (1999) 32. Uehera, S.: Characteristics of predation by the chimpanzees in the Mahale Mountains National Park, Tanzania (1992) 33. Bonnell, T.R., Sengupta, R.R., Chapman, C.A., Goldberg, T.L.: An agent-based model of red colobus resources and disease dynamics implicates key resource sites as hot spots of disease transmission. Ecol. Model. 221(20), 2491–2500 (2010) 34. Watts, D.P., Amsler, S.J.: Chimpanzee-red colobus encounter rates show a red colobus population decline associated with predation by chimpanzees at Ngogo: impact of chimpanzee predation on red colobus. Am. J. Primatol. 75(9), 927–937 (2013) 35. Snaith, T.V., Chapman, C.A.: Red colobus monkeys display alternative behavioral responses to the costs of scramble competition. Behav. Ecol. 19(6), 1289–1296 (2008) 36. Boesch, C.: Cooperative hunting in wild chimpanzees. Anim. Behav. 48(3), 653–667 (1994) 37. Noë, R., Bshary, R.: The formation of red colobus–diana monkey associations under predation pressure from chimpanzees. Proc. R. Soc. Lond. 264(1379), 253–259 (1997) 38. Teelen, S.: Influence of chimpanzee predation on the red colobus population at Ngogo, Kibale National Park, Uganda. Primates 49(1), 41–49 (2008) 39. Wilensky, U., Rand, W.: An Introduction to Agent-Based Modeling: Modeling Natural Social, and Engineered Complex Systems with Netlogo. MIT Press, Cambridge (2015) 40. Bodine, E.N., Panoff, R.M., Voit, E.O., Weisstein, A.E.: Agent-based modeling and simulation in mathematics and biology education. Bull. Math. Biol. 82(8), 1–19 (2020). https://doi.org/ 10.1007/s11538-020-00778-z
Design of Drilling Mechanism for Aquilaria Tree Climbing Modular Robot Ahmad Mu’az Mohaspa1 , Muhammad Noor Sabri Md Yusoff1 , Wan Amir Fuad Wajdi Othman1(B) , Aeizaal Azman A. Wahab1 , Syed Sahal Nazli Alhady1 , and Elmi Abu Bakar2 1 School of Electrical and Electronic Engineering, Universiti Sains Malaysia,
14300 Nibong Tebal, Pulau Pinang, Malaysia [email protected] 2 School of Aerospace Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia
Abstract. A specific tree produces agarwood in response to a fungal infection. The creation of agarwood had been modernized by inducing the disease in the tree. This is done by drilling into the tree and injecting a concoction to invoke the condition. A person usually does the act of drilling on the tree. This process, however, can be automated using a climbing robot, a drilling mechanism, and a sensor. Many climbing robots have already been proposed; a drilling mechanism that is fully equipped with sensors has to be mounted on these said robots. This paper aims to offer such a mechanism that will allow it to be mounted on a robot. The idea of the mechanism is to have a drill that sits on top of a sliding platform and is then powered by a linear actuator to push and pull it back. This can be accomplished by 3D Printing a chassis for the linear actuator and mounting a drawer slider to act as the sliding platform. The whole mechanism is controlled by an Arduino Mega, which features a DualShock controller for interfacing with the mechanism as well as an LCD that features a simple text-based interface (TUI). This work will allow the automation of the drilling process in the agarwood industry. Keywords: Aquilaria tree · ps2 Bluetooth controller · PWM
1 Introduction Aquilaria Sinensis tree is a tree that is exceptionally high in value. The woods and leaves themselves are not anything special and not that useful for the general public. However, the product that this tree made is something that is considered wooden gold. Pound-perpound, the product is worth more than gold. This miracle product is called agarwood. Agarwood is a key component in making fragrances. Ever since the early golden ages of the Muslim empire, this type of wood had already been traded worldwide for people to burn and use to make their homes smell heavenly. The people who use these are of royalties and riches and is often referred to as the smell of heaven. In modern-day © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 397–403, 2022. https://doi.org/10.1007/978-981-16-8954-3_37
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culture, top perfume companies such as Versace, DKNY, and many more use agarwood as their product’s key ingredient [1]. But how did the agarwood come to be? It turns out, the production of agarwood is caused by an infection of fungi within the tree. This calls out the tree to flood the infected region with its sap and harden it. The compound created from this interaction of hardened sap mixed with fungi and all the enzymes released by the fungi creates the iconic agarwood. Further studies show that this infection starts because of beetles and ants that burry into the tree, making a hole within the tree. This hole exposes both the xylem and phloem of the tree, which kick starts the infection. This process takes an extremely long time in the wild, with the tree hardening small parts of itself and new exposure coming into place. This back and forward interaction cause the production of agarwood to take several years to reach a good amount. Due to the deforestation that happens in the natural habitat of these trees, the production of naturally occurring agarwood plummets to the ground. But people started to grow their trees and synthetically induced infection in the tree [2, 3]. They do this by drilling into the tree just deep enough to expose the secondary xylem but not the tree’s pith. This is crucial because if the center of a tree is exposed, its chances to die increase exponentially at a young age. A chemical compound is then funneled through these holes into the xylem of the tree. Since xylems pull up water through transpiration, the chemical is then spread thoroughly throughout the tree and creates a systemic wound spanning the entire tree length. For this process to be efficient, farmers grow trees in a large area and hire people to drill holes and install the chemical bag that will funnel the chemical compound into the holes. This action is highly repetitive and simple enough that innovation in terms of automation could be done easily. Since the tree itself does not have a lot of branches at the stem and tends to grow up straight, a simple pole climbing robot that utilizes wheels can be used to scale up and down the tree. A drill mounted on the robot should be fairly simple to automate the whole task using the robot. The control mechanism for this robot can also be fully automated by using sensors such as the ultrasonic sensor. There are many previous studies related to pole-like tree climbing robots; such studies use several types of mechanisms for climbing, such as using grippers [4–8], wheels for locomotion of the robot [9–12], and many more. This paper focuses on the drilling machine which can be mounted on the said pole climbing robot.
2 Methodology 2.1 Conceptual Design The conceptual design for the system is separated into two main parts. The first part is the mechanical drilling system, and the second part is the sensor placements and usage. Figure 1 shows the full assembly for the drilling mechanism. The housing for the linear actuator is designed to be mounted onto the aluminum profile of the main robot. The drill sits on a sliding platform that is made up of a drawing slider. The slider is then driven by a linear actuator that sits below the slider and acts as the mainframe of the contraption.
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Fig. 1. Full assembly of the drilling mechanism
The sensors are placed in various locations on the robot. Figure 2 shows the sensor placement throughout the robot. The first sensor is located at the bottom of the robot and is used to detect the height at which the robot had climbed. The second sensor is located at the back of the drilling mechanism to see the depth of the hole drilled. The third sensor is located at the top of the robot inside the control box. It is used to detect any obstacle above the robot like a branch and stop it in its track. The last sensor is located between the tire and the robot’s frame to detect the circumference of the tree to prevent it from climbing a tree that is too small.
Fig. 2. Sensor placement on the pole-climbing robot
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2.2 Circuit Diagram Figure 3 shows the circuit connection of the system. The power supply is a 12 V LiPoBattery. The circuit has five motors, five motor drivers, one LCD, and one DualShock controller. The PWM and Direction pin for the driving motor is shared across the motor driver since they move synchronously. The PWM pin for the linear actuator is always kept at 255 by shorting the ENA pin to the 5 V using a mini jumper. A total of six pins on the Arduino is dedicated for the PWM and direction for all motors. The L298N motor driver uses two pins for direction in order to stop the motor without using PWM values (since the PWM value is always 255). A total of six pins are used for the LCD and another four pins used by the DualShock controller. The grounds are made common via the ground pins on the motor drivers.
Fig. 3. Complete circuit diagram of the proposed pole-climbing robot
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3 Results and Discussion 3.1 Drilling Mechanism The final drilling mechanism is shown in Fig. 4. This final design is able to withstand the force of the drilling process. The drawer slider is mounted on top of the linear actuator holder. The tail of the slider is then tied to the linear actuator using a geared rod. This allowed the actuator to move the slider back and forth. The excess rod is kept in order to be used by an ultrasonic sensor. The drilling motor is then mounted on top of the slider, and the whole mechanism is completed.
Fig. 4. Full drilling mechanism on the proposed pole-climbing robot
3.2 Power Consumption The power consumption of the mechanism is calculated by using the power-law formula that defines the relationship between the current and voltage to be inversely proportional to one another. P = IV
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PDrill mechanism = PDrilling motor + PLinear actuator = 42W
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This brings the total power consumption of the drilling machine to be 42 W. The total current supplied to the Arduino mega, the LCD, DualShock Bluetooth slave, and four ultrasonic sensors are determined to be 0.15 A at 12 V, which gives us 1.8 W of power. 3.3 Discussion Drilling The drilling speed of the drilling machine is the same as the speed of the linear actuator. At 10 mms−1 , the drilling process did just fine when drilling into the tree. However, the chuck might detach from the spindle since the mating is purely frictional when drilling out from the tree. Hence it is wiser to pull the actuator out slowly to keep the friction between the spindle and the chuck. The linear actuator can supply a linear pushing or pulling force of up to 1500 N. This is more than enough to supply the need for the drilling mechanism. However, the torque of the drilling motor is sufficient to start drilling the tree up to a certain point. During the drilling, the friction between the drill bit and the surrounding wood is too much, putting the drilling motor into a stall condition. Battery Life The battery life for the drilling mechanism is surprisingly long. Operating at 42 W at 12 V gives us approximately 3.5 A by using the relationship of amp-hour and ampere we can get the time taken for a specific capacity of battery to finish.
4 Conclusion To conclude this paper, the objectives are referred back to evaluate whether they had been completed. The objective of this work is to first and foremost build a drilling mechanism that is able to drill an Aquilaria tree. This was done, and the design is heavily based on a drill that is sitting on top of a sliding surface. The sliding surface was built using a drawer slider and is powered by a linear actuator. The next objective is to design this mechanism in such a way that it can be mounted on top of a climbing robot. This was done by designing and 3d Printing a chassis that can be mounted using bolts and nuts. The last objective of this study is to utilize sensors to automate the drilling task. This was also successfully implemented by using ultrasonic sensors that is strategically placed to provide various information needed for the process. In conclusion, all of the objectives of the study were fulfilled, and the full mechanism was built. With this mechanism, an automated drilling machine could be made to hopefully help in the agarwood industry. Acknowledgments. This work was supported by Universiti Sains Malaysia – RU Grant Scheme (Grant number: USM/PELECT/8014113).
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References 1. Zhang, X.L., et al.: Production of high-quality agarwood in Aquilaria sinensis trees via wholetree agarwood-induction technology. Chin. Chem. Lett. 23(6), 727–730 (2012) 2. Abas, M.A.H., Ismail, N., Ali, N.A., Tajuddin, S., Tahir, N.M.: Agarwood oil quality classification using support vector classifier and grid search cross validation hyperparameter tuning. Int. J. 8 (2020) 3. Xie, Y., et al.: Rapid authentication of agarwood by using liquid extraction surface analysis mass spectrometry (LESA-MS). Phytochem. Anal. 31(6), 801–808 (2020) 4. Lau, S.C., Othman, W.A.F.W., Bakar, E.A.: Development of slider-crank based pole climbing robot. In: 2013 IEEE International Conference on Control System, Computing and Engineering, pp. 471–476. IEEE, November 2013 5. Faizal, M.N., Othman, W.A.F.W., Hassan, S.S.: Development of pole-like tree climbing robot. In: 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), pp. 224–229. IEEE, November 2015 6. Leong, J.X., Abu-Johan, K.A., Kadir, N.I.N., Othman, W.A.F.W., Wahab, A.A.A., Alhady, S.S.N.: Development of a simple pole climbing robot. In: Bahari, M.S., Harun, A., Zainal Abidin, Z., Hamidon, R., Zakaria, S. (eds.) Intelligent Manufacturing and Mechatronics. LNME, pp. 227–237. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-08667_18 7. Jairan, M.H., et al.: Development of a mini pole-climbing robot. In: Isa, K., et al. (eds.) Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020. LNEE, vol. 770, pp. 651–661. Springer, Singapore (2022). https://doi.org/10.1007/978-98116-2406-3_50 8. Koo, Y.C., Elmi, A.B., Wajdi, W.A.F.: Piston mechanism based rope climbing robot. Procedia Eng. 41, 547–553 (2012) 9. Mustapa, M.A., Othman, W.A.F.W., Abu Bakar, E., Othman, A.R.: Development of pole-like tree spiral climbing robot. In: Hassan, M.H.A. (ed.) Intelligent Manufacturing & Mechatronics. LNME, pp. 285–293. Springer, Singapore (2018). https://doi.org/10.1007/978-981-108788-2_26 10. Khairam, H., Choong, Y.M., Ismadi, N.S.N., Othman, W.A.F.W., Wahab, A.A.A., Alhady, S.S.N.: Design and development of a low-cost pole climbing robot using Arduino Mega. In: Journal of Physics: Conference Series, vol. 1969, no. 1, p. 012008. IOP Publishing, July 2021 11. Mohaspa, A.M., Idris, N.F., Siao, E.Y.E., Othman, W.A.F.W., Din, A.S.: Inchworm-inspired semi-autonomous pole-climbing robot. In: 11th International Conference on Robotics, Vision, Signal Processing and Power. Springer, Singapore (2021) 12. Md Yusoff, M.N.S., Cheah, K.B., Mohamad Wazir, A.R., Othman, W.A.F.W., Alhady, S.S.N., Wahab, A.A.A.: Steel pipe climbing robot development. In: 11th International Conference on Robotics, Vision, Signal Processing and Power. Springer, Singapore (2021)
Process and Machining Technology
Review on Experimental Design, Process Parameters and Responses of Compression Moulding Process Noorfa Idayu Mohd Ali1 , Mohd Amran Md Ali1(B) , Shajahan Maidin1 , Mohd Amri Sulaiman1 , Mohd Shukor Salleh1 , and Mohd Hadzley Abu Bakar2 1 Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malysia Melaka, Hang Tuah Jaya,
Durian Tunggal, 76450 Melaka, Malaysia [email protected] 2 Fakulti Teknologi Kejuruteraan Mekanikal dan Pembuatan, Universiti Teknikal Malysia Melaka, Hang Tuah Jaya, Durian Tunggal, 76450 Melaka, Malaysia
Abstract. Compression moulding is a process in which a mixture of material is placed into a mould cavity and compressed especially in composite productions. The aim of this review paper is to find the influence of various process parameters towards various output responses and to determine the most suitable experimental design. It is found that mould temperature was the most selected parameters however preheat time and weight of material are rarely mentioned in the compression moulding process. Meanwhile, tensile strength, young’s modulus and flexural strength are the most output responses investigated. Further, experimental matrix design using the design of experiment (DOE) approached is rarely used in the selected studies where many previous researchers applied one-way method in their research. Thus, we can conclude that the implementation of DOE in experimental study in the compression moulding processes is still lacking and need to be studied deeper. Keywords: Compression moulding · Polymer composite · Material characteristics
1 Introduction Many authors have studied the influence of compression moulding parameters on part quality. Compression moulding parameters influence the part quality where many researchers interested to explore which is the best optimum parameters can be applied in a production. Nowadays, many studies have applied the design of experiment (DOE) as a tool to study the influence of parameters on the responses. Figure 1 shows the operation of compression moulding including input parameters, experimental method and output responses. Compression moulding is a moulding technique that involves placing a feeding material into an open, heated mould cavity. The mould is compacted with hydraulic presses to ensure that the material reaches every part of the mould. In the heated mould, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 407–414, 2022. https://doi.org/10.1007/978-981-16-8954-3_38
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the charge cures and the material is formed into the desired shape as illustrated in Fig. 2. In addition, sheet moulding compound (SMC) and bulk moulding compound (BMC) are two types of compression moulding that differ in the feedstock material form. It’s commonly in the form of 5 mm thick sheets (material feedstock sandwiched between polyethylene film carriers) for SMCs, and 20–50 mm thick pelletized sheets for BMCs [1].
Experimental method (Full Factorial, RSM, Taguchi)
Input parameters
Compression moulding process
Output /Responses
Fig. 1. Operation of compression moulding including input parameters, experimental method and output responses.
Fig. 2. Compression moulding process [1].
Fig. 3. The cycle time in seconds for each operation that makes up a compression moulding cycle [1].
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In general, depending on the thickness of the part, the compression moulding process cycle duration ranges from 1 to 3 min as shown in Fig. 3. The advantage of compression moulding is no waste materials as compared with other processing such as injection moulding process [2].
2 Input Parameters and Responses In compression moulding, temperature, pressure, and time are important parameters in compression moulding techniques that are almost similar to the injection moulding process [3]. Table 1 shows the selected review papers which the parameters selected in each paper are summarized where mould temperature (MoT), compression pressure (CP), compression time (CT), weight of material (W) and preheat time (PT). The responses that have been investigated also are summarized in the table where tensile strength (TS), young’s modulus (YM), percentage of elongation (%EL), hardness (H), flexural strength (FS), compressive strength (CS), density (D), porosity (P), shrinkage (S) and other responses (O). In addition, a bar chart of the compression moulding process parameters and the responses are presented in Fig. 4 and Fig. 5 respectively. Table 1. Related review of compression moulding process parameters with responses. Author Process parameters
[4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]
Material matrix
MoT CP CT W PT √ √ √ √
√
√
√
√
√
√
Carbon/PEEK
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
PLA/hemp
PLA/jute, cotton and flax fibre PE Biocomposite PLA/hemp/Lyocell biocomposite
√
√
PP/Kevlar and Basalt composite (TPU) composite Chopped carbon/thermoplastics Carbon fibres/epoxy Flax/TPO Fillers/thermoset MWCNT/NG/composite
Output responses
DOE
TS YM E% FS CS S √ √ √ √ √
P
D
H
O –
√
√
–
√
√
√
√
√
√
√
√
√
√
√
√
√ √
√
√ √ √ √ √
√
RSM
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Number of researchers
14 12 10 8 6 4 2 0 Mould temperature
Compression pressure
Compression time
Preheat time
Weight of material
Process Parameters
Fig. 4. Number of researchers versus process parameters in compression moulding.
6
Number of researchers
5 4 3 2 1 0
Output Responses
Fig. 5. Number of researchers versus output responses in compression moulding.
From the bar chart in Fig. 4, it can be seen that mould temperature is the most parameter to be selected in the compression moulding process followed by compression pressure as shown in Fig. 5, compression time, preheat time and weight of the material. For the output responses, tensile strength and young’s modulus are the most responses to be investigated followed by flexural strength, percentage of elongation, compressive strength, shrinkage, density, porosity, hardness and others.
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According to Alam et al. [16], ultimate tensile strength was related to the highest stress that the material can sustain, and the toughness was determined by the area under each curve, which represents the amount of work done up until fracture. In 2013, Memon and Nakai [17] have fabricated unidirectional jute spun yarn/PLA composites. They found that the mechanical properties of the unidirectional composite were affected by the moulding temperature. It was obvious that increasing the moulding temperature improved the impregnation quality and dispersion of the fibre bundle, resulting in a higher elastic modulus achievement ratio. Rubio-López et al. [6] optimised the manufacturing process for high-strength biocomposites and found that lower pressure indicated a lack of biocomposite cohesiveness, whereas higher pressure results in fibre breaking. They also stated that proper preheating time and the heating under pressure time are required to ensure PLA melting and uniform distribution in the laminate. In addition, the heating temperature must be high enough to melt the matrix but not too high to harm the fibres. The flowability of polymer melts is also very important during the compression moulding process [18]. According to Porras et al. [19] when higher processing temperatures and moulding pressures were applied, as well as increased moulding time, the tensile strength and Young’s modulus were enhanced for PLA composite.
3 Design of Experiment (DOE) According to Chang et al. [20], DOE is a control experimental design technique and it enables the statistical study of the effect of parameters on responses of a certain process. Kandar and Akil [21] have optimized the compression moulding process parameters to improve the mechanical properties of the woven flax/PLA composites using Response Surface Methodology (RSM). According to San and Okur [14], RSM is a useful method to determine if the multiple regression equations that correlate the factors and experimental results. They have implemented RSM to optimize compression moulding parameters such as moulding temperature, pressure and time on electrical and physical properties of polymer composite bipolar plates. Other than RSM, the Taguchi method is a simpler and useful tool for the systematic study of the effect of process parameters on the investigated responses. In the Taguchi method, signal to noise ratio (S/N ratio) is employed to measure the quality of output [22]. Porras et al. [19] have conducted an experiment to optimize mechanical properties of bio-composite and compression moulding parameters such as temperature, pressure, and time were analyzed. They stated that the Taguchi method used orthogonal arrays to determine the minimum number of experiments needed to provide enough information to determine factor effects and optimal values. Investigation on the effect of compression moulding process such as temperature, pressure and time on tensile properties of a hybrid composite material has been conducted and it was found that pressure and temperature were the most influential factors [23]. From the reviewed papers, it was found that a few researchers have implemented DOE in the compression moulding parameters to study their effect on the response output. This is because most of the researchers used one-way method to examine the effect of changing one parameter on another while holding the other parameters constant and without DOE. Nowadays, new trend research
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was conducted using simulation software where process optimization can be conducted and estimated in advance [24]. Many commercial software had been embedded DOE in their input application to ensure the process optimization can be done directly from simulation to optimization [25–27].
4 Conclusion In this paper, a review of experimental design, process parameters and responses of the compression moulding process has been conducted. It is found that mould temperature is the most selected parameters in the compression moulding process followed by compression pressure, compression time, preheat time and weight of material. For the output responses, tensile strength is the most investigated response followed by young’s modulus, flexural strength, percentage of elongation, hardness, flexural strength, compressive strength, density, porosity, shrinkage and other. Hence, compression moulding process parameters are critical in controlling the process and should be precisely measured and monitored throughout the process. In addition, the implementation of DOE in an experiment is also important to determine the relationship between parameters affecting the compression moulding process and the output of the process. Acknowledgement. The authors would like to thank Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka for providing facilities for this research to be conducted successfully. This research is funded by the Ministry of Higher Education through Fundamental Research Grant Scheme (FRGS) FRGS/2018/FKP-AMC/F00377.
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Tool Life and Surface Roughness of Inconel 718 During End Milling Under Dry, Chilled Air and Chilled MQL N. H. N. Husshini2 , M. S. Kasim1,2(B) , and W. N. F. Mohamad1,2,3 1 Advanced Manufacturing Center, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya,
Durian Tunggal, 76100 Melaka, Malaysia [email protected] 2 Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, 76100 Melaka, Malaysia 3 Fakulti Rekabentuk Inovatif dan Teknologi, Universiti Sultan Zainal Abidin, 21300 Kuala Terengganu, Terengganu, Malaysia
Abstract. High speed machining is an inevitable challenge in milling of hardto-machine materials. These materials have poor thermal conductivity which precludes efficient heat dissipation, resulting in extremely high temperatures in the cutting zone. These high temperatures cause reduction of tool life and deterioration of surface finish. As a result, machining Inconel is often associated with low productivity and high machining costs. In this paper, the impact of various cooling techniques on machinability of Inconel alloy in high speed milling is investigated. The investigations clearly revealed that machining Inconel 718 at high speeds with a novel combination cooling system consisting of chilled air cooling and minimum quantity lubrication offers significant advantages. The analysis indicated that tool life was extended by threefold using chilled MQL when compared to dry and chilled air whilst generating an average surface roughness Ra of 0.28 µm. Keywords: Inconel 718 · End-milling · High speed machining · Cooling lubrication condition
1 Introduction Inconel 718, also known as heat resistance superalloy (HRSA) is widely employed in aerospace industry due to its unique characteristics, which include the ability to withstand its mechanical properties at escalated temperatures, as well as high strength and hardness and high resistance to wear and corrosion [1, 2]. However, due to its peculiar characteristics such as low heat conductivity, strong chemical reactivity and presence of hard abrasive carbides in their microstructure makes it difficult to machine. As a result, nickel alloys are referred as hard-to-cut materials [3]. Machining is a crucial component in the manufacturing process. It entails cutting metals to achieve the desired geometry while ensuring precise dimensional tolerances and good surface finish. However, this process causes heat to generate at the cutting © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 M. N. Ali Mokhtar et al. (Eds.): SympoSIMM 2021, LNME, pp. 415–423, 2022. https://doi.org/10.1007/978-981-16-8954-3_39
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zone. The generation of heat results in the temperature rising, thus making the cutting speed increment impossible as the effect is parallel with the temperature. The new challenge in machining is to use high cutting speed in order to attain high productivity and have better surface quality. The challenge is due to the nature of Inconel alloy having low thermal conductivity which prone to elevated temperature at vicinity cutting tool region. This is the main reason for rapid tool wear. The strategy is by implementing high speed machining (HSM). The HSM for nickel based alloy would be in the range of 60–100 m/min [4]. Some literature reported the HSM can be maximum of 500 m/min [5, 6]. Besides controlling the cutting speed, the cutting fluids were conventionally used to reduce tool wear with the main function of lubricating and cooling at the cutting zone. The application of cutting fluid enable to increase the cutting speed by up to 30% without affecting the tool life [7]. Cutting fluids, on the other hand, impose high disposal management costs, accounting 20–30% of machining expenses. This is because it may pollute the environment and pose a risk to operators’ health, which requires proper handling [8]. Alternatively, minimum quantity lubrication (MQL) and cryogenic technologies are positioned as major options for improving both technical and environmental pollution caused by machining processes. MQL lubrication consists of spraying a small quantity of lubricant (10–100 mL/hr) into the cutting zone by compresses air flow. This technique lowers the environmental and economic costs of machining operations. The small droplet of mist capable to reduce cutting temperature effectively than conventional flooded [9]. A study by Kasim et al. [10] reported that the tool life during end milling can reach up to 100 min of cutting time. The surface finish also ranged from 0.173 µm to 0.3 µm [11]. Another technique is by applying liquefied gases like carbon dioxide (CO2 ) or liquid nitrogen (LN2 ) that have been done by Haron et al. [12] and Musfirah et al. [13], respectively to reduce cutting temperature at the cutting region. They discovered that the tool life was extended due to an efficient cooling effect that slowed the wear progression and the use of cryogenic coolant resulted in significantly better machined surface roughness, approximately equivalent to manual grinding (≈0.50 µm). The mixture of cutting fluid and cryogenic machining had been done by [14–17]. The results were very promising where the improvement can be clearly seen in both responses of tool life and surface roughness when compared to MQL and cryogenic due to effective cooling and lubrication which helps to reduce friction coefficient thus lowering the force [15]. This strategy is preferable because the gases do not produce any waste and are absolutely safe for workers’ health. However, there were some drawbacks of cryogenic technique reported. Lack of tools specially designed for cryogenic cooling and cost of the tools is another issue to be considered [18]. In this research work, different cooling and lubrication conditions are investigated for milling Inconel 718. The aim of this paper is to evaluate the performance of a novel strategy based on a combination of chilled MQL. MQL is delivered together with chilled air to the cutting zone during machining process. In this study, finishing end-milling of Inconel 718 is performed under three different cooling and lubrication conditions: (a) dry; (b) chilled air and (c) chilled MQL. After the milling process, the tool life, wear mechanism and surface roughness were measured and discussed.
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2 Experimental Work Inconel 718 (AMS5663) with a block size of 171.6 × 41.6 × 95.8 mm had been agehardened and solution-treated. Its hardness ranged within 42 ± 2 HRC and the chemical composition is provided in Table 1. Cutting tool grade ACK 300 Sumitomo carbidecoated (PVD TiAlN/AlCrN) ball nose milling with 10 mm diameter was used in this experiments. The insert is mounted on a WRCX-E tool holder with 20 mm diameter. The tool overhang constantly at 50 mm, axial and radial runout were 10 µm and 30 µm, respectively. The detail of experimental set-up is presented in Fig. 1. Table 1. Chemical composition of Inconel 718 [19] Element
Ni
Cr
Fe
Nb
Mo
Ti
Al
Co
Mn
Si
Wt%
53.00
18.30
18.70
5.05
3.05
1.05
0.49
0.30
0.23
0.08
C
Cu
B
P
S
0.08
0.051
0.004