Intelligent Manufacturing and Mechatronics: Proceedings of SympoSIMM 2020 9789811608650, 9811608652

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Lecture Notes in Mechanical Engineering

Muhammad Syahril Bahari · Azmi Harun · Zailani Zainal Abidin · Roshaliza Hamidon · Sakinah Zakaria Editors

Intelligent Manufacturing and Mechatronics Proceedings of SympoSIMM 2020

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 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 Machine 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 Francesca di Mare, Inst of Energy Tech, Building IC-2/63, Ruhr-Universität Bochum, Bochum, Nordrhein-Westfalen, Germany

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: • • • • • • • • • • • • • • • • •

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Muhammad Syahril Bahari Azmi Harun Zailani Zainal Abidin Roshaliza Hamidon Sakinah Zakaria •

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Editors

Intelligent Manufacturing and Mechatronics Proceedings of SympoSIMM 2020

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Editors Muhammad Syahril Bahari School of Manufacturing Engineering Universiti Malaysia Perlis Arau, Perlis, Malaysia

Azmi Harun School of Manufacturing Engineering Universiti Malaysia Perlis Arau, Perlis, Malaysia

Zailani Zainal Abidin School of Manufacturing Engineering Universiti Malaysia Perlis Arau, Perlis, Malaysia

Roshaliza Hamidon School of Manufacturing Engineering Universiti Malaysia Perlis Arau, Perlis, Malaysia

Sakinah Zakaria School of Manufacturing Engineering Universiti Malaysia Perlis Arau, Perlis, Malaysia

ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-981-16-0865-0 ISBN 978-981-16-0866-7 (eBook) https://doi.org/10.1007/978-981-16-0866-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 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

We have great pleasure in announcing the 3rd Symposium on Intelligent Manufacturing and Mechatronics (SympoSIMM 2020), hosted by the Universiti Malaysia Perlis, Arau, Malaysia. The symposium was held at the Faculty of Mechanical Engineering Technology, Universiti Malaysia Perlis, on 28 October 2020 in collaboration with Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, and Faculty of Manufacturing & Mechatronic Engineering Technology, Universiti Malaysia Pahang. This year’s edition of the symposium attracted more than 160 authors’ submission nationwide. The theme of the symposium, “Challenging the gap in I4.0”, is a timely theme in view of the current revolution and rapid developments in the field of artificial intelligent, manufacturing and robotics technology. The symposium was graced by a vibrant keynote speech entitled “Riding the Intelligent Manufacturing with I4.0” by Dr. Mohd Shahrul Azmi Mohamad Yusoff, the Director of Industrial Centre of Innovation in Smart Manufacturing, SIRIM Berhad. Based on the comments of reviewers and the scientific merits of the submitted manuscripts, 118 articles were accepted for publication in this year’s conference proceedings. We express our sincere appreciation to the authors for their contribution to this symposium. We would also like to express our sincere gratitude to all the experts and referees for their valuable comments and support extended during the review process. The primary focus of this symposium was to bring together academicians, researchers and scientists for knowledge sharing in various areas of intelligent manufacturing, mechatronics and other allied domains. This symposium covered topics encompassing 7 tracks, namely Intelligent Manufacturing and Artificial Intelligence, Instrumentation and Control System, Machining Technology, Materials and Processing, Mechanical and Design, Modelling and Simulation, and Ergonomics and Human Factor. We would like to express our sincere appreciation to everybody who has contributed to the symposium. Heartfelt thanks are due to all the team of organizers for their support and enthusiasm which granted success to the symposium. Finally,

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thanks to Springer team for their support in all stages of the production of the proceedings. Thank you. Arau, Perlis, Malaysia

Muhammad Syahril Bahari Azmi Harun Zailani Zainal Abidin Roshaliza Hamidon Sakinah Zakaria

Organization

Organizing Committee Patron R. Badlishah Ahmad Advisor Mohd Shukry Abdul Majid Chairman Muhammad Syahril Bahari Deputy Chairman Azmi Harun Zamberi Jamaludin Secretary Sakinah Zakaria Nuradilah Ahmad Treasurer Rozie Nani Ahmad Nurul Shatirah Mohd Rozi Promotion and Website Khairul Azwan Ismail Mohd Sazli Saad Mohamad Ezral Baharudin Nasir Murad Ahmad Nabil Mohd Khalil vii

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Technical and Proceedings Roshaliza Hamidon Zailani Zainal Abidin Mohd Nazrin Muhammad Mohd Najib Ali Mokhtar Mohd Hasnun Arif Hassan Certificate and Souvenir Narzrezal Abdul Razak Rashid Ramli Mohamad Zaki Kerya Mohd Izwan Ahmad Fikri Parallel Session Managers Azwan Iskandar Azmi Muhammad Saifuldin Abdul Manan Mazelan Abdul Hamid Mohamad Shaiful Ashrul Ishak Syamir Alihan Showkat Ali Jamali Md Sah Mokhtar Mat Salleh Nurul Ikhmar Ibrahim Ummi Noor Nazahiah Abdullah Muhammad Taufiq Mustaffa Mohd Zamzuri Mohammad Zain Mohd Azaman Md Deros Mohd Sabri Hussin Norshah Afizi Shuaib Azuwir Mohd Noor Muhammad Hasnulhadi Mohammad Jaafar Tan Chan Sin Nooraizedfiza Zainon Tan Chye Lih Norshah Aizat Shuaib Maliki Ibrahim Norashiken Othman Siti Aishah Adam Asmawi Sanuddin

Organization

Contents

Intelligent Manufacturing and Artificial Intelligence Roof Re-measurement in Building Works Using Un-manned Aerial Vehicle (UAV) Application System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Norsyakilah Romeli, Hazry Desa, Muhammad Azizi Azizan, and Faridah Muhamad Halil An Overview of Multi-Core Network-on-Chip System to Enable Task Parallelization Using Intelligent Adaptive Arbitration . . . . . . . . . . Mohammad Nishat Akhtar, Qummare Azam, Tarik Adnan Almohamad, Junita Mohamad-Saleh, Elmi Abu Bakar, and Ayub Ahmed Janvekar Prototype Design for Rubik’s Cube Solver . . . . . . . . . . . . . . . . . . . . . . . A. M. Andrew, W. Faridah, W. H. Tan, S. Ragunathan, A. S. N. Amirah, N. A. N. Zainab, and F. S. Lee Automatic People Counting System Using Aerial Image Captured by Drone for Event Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohd Saifizi Saidon, Wan Azani Mustafa, Vinnoth Raj Rajasalavam, and Wan Khairunizam Automatic Counting of Palm Oil Tree Using Satellite Aerial Imagery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohd Saifizi Saidon, Wan Azani Mustafa, and M. A. Izzat Diagnosis of Heart Disease Using Machine Learning Methods . . . . . . . . Azian Azamimi Abdullah, Nazirah Ahmad Alhadi, and Wan Khairunizam Investigation of Geomorphological Features of Kerian River Using Satellite Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emaad Ansari, Mohammad Nishat Akhtar, Elmi Abu Bakar, Naoki Uchiyama, Noorfazreena Mohammad Kamaruddin, and Siti Nur Hanisah Umar

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Review on the Potential of a Tidal Energy Harnessing System in Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 P. V. S. Hari Prashanth, Hadi Nabipour Afrouzi, Chin-Leong Wooi, Kamyar Mehranzamir, San Chuin Liew, and Jubaer Ahmed An Experimental Study of Deep Learning Approach for Indoor Positioning System Using WI-FI System . . . . . . . . . . . . . . . . . . . . . . . . . 113 A. H. A. Sa’ahiry, A. H. Ismail, L. M. Kamarudin, A. Zakaria, and H. Nishizaki Defect Factor Analysis Using Statistical Process Control Analysis: A Case Study in Spices Defected Packaging Production . . . . . . . . . . . . . 125 Nur Illa Idris, Tan Chan Sin, Safwati Ibrahim, Fadzli Ramli, and Rosmaini Ahmad Optimal Design of Step – Cone Pulley Problem Using the Bees Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Noor Jazilah Yusof and Shafie Kamaruddin Adapting Travelling Salesmen Problem for Real-Time UAS Path Planning Using Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 Dipraj Debnath and A. F. Hawary Predicting the Cycle Time at a Production Line Through the Development of the 3-3-1 Multilayer Perceptron Artificial Neural Networks with Formulated Momentum Rate . . . . . . . . . . . . . . . . . . . . . 165 Ahmad Afif Ahmarofi, Freselam Mulubrhan Kassa, and Mohamad Khairi Ishak Internet of Things Security: Modelling Smart Industrial Thermostat for Threat Vectors and Common Vulnerabilities . . . . . . . . . . . . . . . . . . 175 Omer Ali, Mohamad Khairi Ishak, and Muhammad Kamran Liaquat Bhatti Single Channel Magnetic Induction Measurement for Meningitis Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Aiman Abdulrahman Ahmed, Zulkarnay Zakaria, Marwah Hamood Ali, Jaysuman Pusppanathan, Ruzairi Abdul Rahim, Siti Zarina Mohd Muji, Anas Mohd Noor, Mohd Hafiz Fazalul Rahiman, Muhamad Khairul Ali Hassan, Muhammad Juhairi Aziz Safar, and Ahmad Faizal Salleh Reconstruction of Patient-Specific Cerebral Aneurysm Model Through Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Sheh Hong Lim, Mohd Azrul Hisham Mohd Adib, Mohd Shafie Abdullah, Nur Hartini Mohd Taib, Radhiana Hassan, and Azian Abd Aziz

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Obstacle Avoiding 4-Legged Mobile Robot Using 4-Bar Mechanism . . . 215 Han Shen Tee, Muhammad Akram Mohd-Idros, Kerpan Gunasegaran, Wan Amir Fuad Wajdi Othman, Syed Sahal Nazli Alhady, and Aeizaal Azman A. Wahab Development of a Simple Pole Climbing Robot . . . . . . . . . . . . . . . . . . . 227 Jun Xian Leong, Khairul Amin Abu-Johan, Nur Iffah Nasuha Kadir, Wan Amir Fuad Wajdi Othman, Aeizaal Azman A. Wahab, and Syed Sahal Nazli Alhady Improving the Infant-Wrap (InfaWrap) Device for Neonates Using MyI-Wrap Mobile Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Mohd Hanafi Abdul Rahim, Mohd Azrul Hisham Mohd Adib, Mohamad Zairi Baharom, and Nur Hazreen Mohd Hasni Research Objective in Assembly Line Balancing Problem: A Short Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Nurhanani Abu Bakar, Mohd Zakimi Zakaria, Mohammad Fadzli Ramli, Nashrul Fazli Mohd Nasir, Muhammad Mokhzaini Azizan, and Muzammil Jusoh Analysis on Weighted Average Between Features in Dictionary Learning and Sparse Representation Algorithms for Low-Resolution Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Suit Mun Ng and Haniza Yazid Bees Algorithm with Integration of Probabilistic Models for Global Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Muhammad Syahril Bahari, Nur Athirah Azmi, Zahayu Md Yusof, and Duc Truong Pham Machining Technology Tribological Performance of Palm Stearin in Cold Forging Test Using Aluminum Alloy 6061 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 Y. Aiman and S. Syahrullail Effects of Surfactant Concentration in the New Bio-based Nanolubricants for Machining of Inconel 718 . . . . . . . . . . . . . . . . . . . . 291 Mohamed Asyraf Mahboob Ali, Azwan Iskandar Azmi, Mohd Zahiruddin Mohd. Zain, Muhammad Nasir Murad, and Ahmad Nabil Mohd Khalil Evaluation of Coated Carbide Drills When Drilling Nickel-Titanium (NiTi) Alloys with Minimum Quantity Nano-lubricants . . . . . . . . . . . . . 299 Rosmahidayu Rosnan, Azwan Iskandar Azmi, Muhammad Nasir Murad, and Mohamed Ashraf Mahboob Ali

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Optimisation of Process Parameters in Plastic Injection Moulding Simulation for Blower Impeller’s Fan Using Response Surface Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 M. U. Rosli, S. N. A. Ahmad Termizi, C. Y. Khor, M. A. M. Nawi, Ahmad Akmal Omar, and Muhammad Ikman Ishak Simulation Based Optimization of Shrinkage in Injection Molding Process for Lamp Holder via Taguchi Method . . . . . . . . . . . . . . . . . . . . 319 C. Y. Khor, M. A. M. Nawi, Muhammad Ikman Ishak, Boon Aik Low, M. U. Rosli, and S. N. A. Ahmad Termizi The Effect of Parameters of Electrical Discharge Coatings on the Tool Electrode Erosion and Maximum Height Roughness on NiTi Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 A. F. Mansor, A. I. Azmi, M. Z. M. Zain, and R. Jamaluddin The Effect of Stacking Sequence Strategy in Drilling Hybrid Materials of Aluminum Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 Siti Rozakiyah Assurin Hassan Improvement on the Surface Quality in Machining of Aluminum Alloy Involving Boron Nitride Nanoparticles . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Zainal Abidin Zailani, Norrulfitri Mohamed Zaibi, Roshaliza Hamidon, Azmi Harun, Muhammad Syahril Bahari, and Sakinah Zakaria A Study on the Effect of Hybrid Nanolubricant on Cutting Energy During Turning of Inconel 718 Under Minimum Quantity Lubricant Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 Muhd Al-Hafiz Mohd Shariff, Yuzairi Abdul Rahim, Ahmad Nabil Mohd Khalil, Asyraf Mahboob Ali, Azwan Iskandar Azmi, and Hadisah M. Salleh Effect of Coco Amido Propyl Betaine (CAPB) on Thermal Conductivity of Bio-Based Hybrid Nanolubricant . . . . . . . . . . . . . . . . . 373 Muhd Al-Hafiz Mohd Shariff, Yuzairi Abdul Rahim, Asyraf Mahboob Ali, Ahmad Nabil Mohd Khalil, Azwan Iskandar Azmi, and Hadisah M. Salleh Study the Effect of Different Drilling Methods on Hole Dimensional Accuracy and Surface Roughness of AISI 1045 Steel . . . . . . . . . . . . . . . 381 R. N. Ahmad, N. F. Mohammad, A. Wahi, M. S. Abdul Manan, Muhamad Nasir. Murad, Z. Nooraizedfiza, and M. Marzuki Effect of Different Cutting Conditions on Tool Wear and Chip Formation in Drilling of Cobalt Chromium Molybdenum . . . . . . . . . . . 391 R. Hamidon, N. A. Zulkefli, R. Saravanan, Z. A. Zailani, M. S. Bahari, S. Zakaria, and H. Azmi

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Study the Effect of Cutting Parameter in Machining Kenaf Fiber Reinforced Plastic Composite Materials Using DOE . . . . . . . . . . . . . . . 401 H. Azmi, C. H. Che Haron, Z. A. Zailani, R. Hamidon, M. S. Bahari, S. Zakaria, and S. H. A. Hamid Machining of Cobalt Chromium Molybdenum (CoCrMo) Alloys: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 R. Saravanan, R. Hamidon, N. M. Murad, and Z. A. Zailani Machinability of Nickel Titanium Shape Memory Alloys: A Review . . . 425 Nurul Zahirah Mohd Noor, Zainal Abidin Zailani, Roshaliza Hamidon, and Norshah Afizi Shuaib Instrumentation and Control System Fault Detection Filter Design and State-Feedback Controller Design for Antenna Azimuth Position Control System . . . . . . . . . . . . . . . . . . . . 443 Masood Ahmad and Rosmiwati Mohd-Mokhtar Employing RFID with NUC140VE3CN Development Board for Automated Garage System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453 I. N. A. N. Azman, S. S. N. Alhady, N. I. Rizal, A. A. A. Wahab, and W. A. F. W. Othman Smart Kitchen Model Using Nuvotun Development Board . . . . . . . . . . 461 Y. P. Y. Aw, S. S. N. Alhady, S. E. Lee, A. A. A. Wahab, and W. A. F. W. Othman Sliding Mode Control with Tanh Function for Quadrotor UAV Altitude and Attitude Stabilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471 Aminurrashid Noordin, Mohd Ariffanan Mohd Basri, and Zaharuddin Mohamed Intraocular MEMS Capacitive Pressure Sensor . . . . . . . . . . . . . . . . . . . 493 Anas Mohd Noor, Zulkarnay Zakaria, and Norlaili Saad IC Engine Ignition Timing Controller Feature Extraction of Knocking Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503 Ajmir Mohd Saill, Elmi Abu Bakar, Mohammad Nazir Abdullah, and Mohammed Nishat Akhtar Flex Force Smart Glove for Therapy Treatment Using Arduino and Raspberry Pi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Chang Yi Neng, Mohamad Khairi Ishak, and Ahmad Afif Ahmarofi Cross-Platform Appliance Management and Remote-Control Mobile Application Using REST API Communication . . . . . . . . . . . . . . . . . . . . 525 Eizzat Ayman Zaikuan, Mohamad Khairi Bin Ishak, and Ahmad Afif Ahmarofi

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Hybrid Design of Model Reference Adaptive Controller and PID Controller for Lower Limb Exoskeleton Application . . . . . . . . . . . . . . . 539 Norazam Aliman, Rizauddin Ramli, and Sallehuddin Mohamed Haris Nature Driven IOT Based Automation of Aquaponic System . . . . . . . . 555 S. Zakaria, M. A. A. Ahmad Jafri, E. A. REngku Ariff, R. Hamidon, Z. A. Zailani, M. S. Bahari, and H. Azmi Implementation of PID Controller for Solar Tracking System . . . . . . . . 563 S. Zakaria, J. Q. Ong, E. A. R. Engku Ariff, R. Hamidon, Z. A. Zailani, M. S. Bahari, and H. Azmi Mechanical and Design Analysis of Vibration for Grass Trimmer . . . . . . . . . . . . . . . . . . . . . . . 575 W. H. Tan, A. S. N. Amirah, S. Ragunathan, N. A. N. Zainab, A. M. Andrew, and W. Faridah Acoustical Analysis and Optimization for Micro-Perforated Panel Sound Absorber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 W. H. Tan, A. S. N. Amirah, S. Ragunathan, N. A. N. Zainab, A. M. Andrew, W. Faridah, and E. A. Lim Rehabilitation Progress of Arm VR Game Based on Hand Trajectory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599 B. N. Cahyadi, Wan Khairunizam, S. Diny Syarifah, Wan Azani Mustafa, A. B. Shahriman, and M. R. Zuradzman Development and Design Humidity Controller for Hybrid Refrigerator System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 Mohd Saifizi Saidon, Wan Azani Mustafa, Mohd Hanif Ismail, and Muzammil Jusoh Fabrication of Parallel Ankle Rehabilitation Robot . . . . . . . . . . . . . . . . 623 Mohd Khairul Ashraf Bin Ismail, Muhammad Nazrin Shah, and Wan Azani Mustafa Development of Fragility Curve of Reinforced Concrete Buildings with Different Height Based on Dynamic Analysis . . . . . . . . . . . . . . . . . 639 N. A. N. Zainab, N. Amirah, W. H. Tan, W. Faridah, A. M. Andrew, and S. Ragunathan Evaluate the Performance of Regular and Irregular Shape of Building Based on Dynamic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 651 N. A. N. Zainab, W. H. Tan, W. Faridah, A. M. Andrew, S. Ragunathan, and A. S. N. Amirah

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Performance of Concrete Gravity Dam with Different Height of Dam and Water Level Under Seismic Loadings . . . . . . . . . . . . . . . . . . . . . . . 661 N. A. N. Zainab, A. M. Andrew, S. Ragunathan, A. S. N. Amirah, W. H. Tan, W. Faridah, and C. C. Mah Heat Level Mode in Vapour Phase Soldering Using Lead-Free Solder Paste for Surface Mount Technology: A Review . . . . . . . . . . . . . . . . . . 673 N. S. Syarfa and A. M. Najib Mechanical Design and Analysis of Safety Medical Syringe for Needlestick Injury Prevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691 Muhammad Ikman Ishak, M. U. Rosli, S. N. A. Ahmad Termizi, C. Y. Khor, Najdah Atirah Mohd, and M. A. M. Nawi Influence of Twisted Blades Distributor Towards Low Pressure Drop in Fluidization Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703 R. M. Zulkifli, M. A. M. Nawi, M. I. Ishak, M. U. Rosli, S. N. A. Ahmad Termizi, C. Y. Khor, and M. A. Faris A Short Review on Multi-stage Application in Fluidization Systems . . . 713 M. S. Muhamad Silmie, M. A. M. Nawi, M. I. Ishak, M. U. Rosli, S. N. A. Ahmad Termizi, C. Y. Khor, M. A. Faris, and M. A. B. Marzuki Computation Fluid Dynamics Simulation of Airflow Ventilation System in 3D Indoor Mushroom Cultivation House Model . . . . . . . . . . 721 S. N. A. Ahmad Termizi, Z. Zakaria, C. Y. Khor, M. A. M. Nawi, Chang Pei Thing, Muhammad Ikman Ishak, and M. U. Rosli The Effect of Surface Inclination to Knee Joint Contact Force: A Pilot Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729 Noor Arifah Azwani Abdul Yamin, Khairul Salleh Basaruddin, Ahmad Faizal Salleh, Ruslizam Daud, and Mohd Hanafi Mat Som Design Optimization of Formula Student Car Steering Knuckle . . . . . . 737 M. F. Hamid, M. Mazlan, N. Burhanuddin, U. A. Asli, A. H. Hilmi, A. B. M. Azhar, Pramod S. Kataraki, and M. N. Omar Dielectric and Colorimetric Analysis on Thermal Degradation of Cooking Oil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747 Cheng Ee Meng, Ammar Zakaria, Shahriman Abu Bakar, Eng Swee Kheng, Ahmad Nasrul Norali, Nashrul Fazli Mohd Nasir, Khor Shing Fhan, Mohd Shukry Abdul Majid, Lee Kim Yee, and Mohd Ridzuan Mohd Jamir Design and Mechanical Analysis on a Compact Bicycle Loader for a Small Cubic Centimeter Motorcycle . . . . . . . . . . . . . . . . . . . . . . . 761 M. S. Hussin, S. Hamat, and S. A. Showkat Ali

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Materials and Processing Fabrication and Mechanical Testing of Blended PVOH/Kenaf Reinforced Starch Composite for Future Packaging Application . . . . . . 777 S. Ragunathan, N. A. N. Zainab, A. M. Andrew, W. Faridah, W. H. Tan, A. S. N. Amirah, and N. S. Othman Characterization and Properties of PP/NBRv/Kenaf Fibre Composites with Silane Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 785 S. Ragunathan, N. A. N. Zainab, A. M. Andrew, W. Faridah, A. S. N. Amirah, W. H. Tan, and N. S. Othman Characterization and Properties of PP/NBRr/Kenaf Composites with PPMAH Compatibilizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 793 W. Faridah, W. H. Tan, A. S. N. Amirah, S. Ragunathan, N. A. N. Zainab, A. M. Andrew, and A. Farhana Characterization and Properties of Pp/Nbrr/Kenaf Composites with Epoxy Resin Compatibilizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 801 W. Faridah, A. M. Andrew, N. A. N. Zainab, S. Ragunathan, A. S. N. Amirah, W. H. Tan, and C. M. M. N. Iqbal Characterization of PP/NBRr/Kenaf Composites with and Without NaOH Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 811 W. Faridah, A. M. Andrew, N. A. N. Zainab, S. Ragunathan, A. S. N. Amirah, W. H. Tan, and A. W. N. Hidayah Development of B-Segment SUV Rear Door Interior Trim Fixture for 2500 Ton Injection Moulding Machine . . . . . . . . . . . . . . . . . . . . . . . 819 Mohammad Al Bukhari Marzuki, Rafidah Laili Jaswadi, Abdul Razak Naina Mohamed, Mohamad Amirul Afzam Musa, and Mohammad Firdaus Mohammed Azmi Effect of NaOH Treatment of Cellulosic Lipstick Palm Fiber on Tensile and Fiber-Matrix Interfacial Strength with Phenolic Resin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 831 Tamil Moli, Mohamed Thariq Hameed Sultan, Mohammad Jawaid, Abd. Rahim Abu Talib, Adi Azriff Basri, Ain Umaira Md Shah, and Syafiqah Nur Azrie Safri Drop Weight Impact Testing on Plant Fiber Reinforced Polymer Matrix: A Short Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 841 Muhammad Imran Najeeb, Mohamed Thariq Hameed Sultan, Ain Umaira Md Shah, and Syafiqah Nur Azrie Safri Application of Differential Evolution (DE) Optimization Method in CNC Turning Process for Surface Roughness . . . . . . . . . . . . . . . . . . 851 Azuwir Mohd Nor, Mohammed Faozi Ahmed Alsakkaf, Mohd Sazli Saad, Mohd Zakimi Zakaria, and M. E. Baharudin

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Multi Response Optimization of Injection Molding Parameters for Artificial Phalanx Bone Using Response Surface Methodology . . . . . 863 C. Y. Khor, M. A. M. Nawi, Muhammad Ikman Ishak, Salman Zainal, M. U. Rosli, and S. N. A. Ahmad Termizi Improvement of Corrosion Resistance of Rare-Earth Element (REE) – Based Anodic Coating on Biodegradable Magnesium Alloy . . . . . . . . . . 877 H. H. Nursyifa, M. R. N. Liyana, Z. Nooraizedfiza, and K. S. Khalijah Performance of Heavy Metal Potentiostat for Batik Industry . . . . . . . . 885 Siti Nur Hanisah Umar, Elmi Abu Bakar, Noorfazreena M. Kamaruddin, Naoki Uchiyama, and Mohammad Nishat Akhtar Synthesis of ZnO Nanorod Using Hydrothermal Technique for Dye-Sensitized Solar Cell Application . . . . . . . . . . . . . . . . . . . . . . . . 895 N. S. Noorasid, F. Arith, S. N. Alias, A. N. Mustafa, H. Roslan, S. H. Johari, H. R. A. Rahim, and M. M. Ismail Morphological Analysis and Phase Identification of Copper Oxide Doped Silicone Oxide (CuO/SiO) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 907 Alina Rahayu Mohamed Comparative Study Between TPU Flexible and Soft Epoxy Resin Materials on Development of Heart Model for CardioVASS Device . . . . 913 Nur Afikah Khairi Rosli, Mohd Azrul Hisham Mohd Adib, Idris Mat Sahat, Nurul Natasha Mohd Sukri, and Nur Hazreen Mohd Hasni Thermal Properties of the Graphene Oxide (GO) Reinforced Epoxy Composites (Thermal Adhesive Liquid Type): Application of Thermal Interface Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 933 M. Mohamed, M. N. B. Omar, A. I. M. Shaiful, R. Rahman, M. F. Hamid, P. S. Kataraki, and A. B. M. Azhar Effect of ZnO-B2O3-SiO2 (ZBS) Glass Additives to the Properties of CaCu3Ti4O12 Electroceramic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 941 Muhammad Qusyairie Saari, Nur Shafiza Afzan Mohd Shariff, Hasmaliza Mohamad, Mohd Fariz Ab Rahman, Zainal Arifin Ahmad, Mohamad Kamarol Mohd Jamil, and Julie Juliewatty Mohamed A Review on Polyaniline-Graphene Nanoplatelets (PANI/GNPs-DBSA) Based Nanocomposites Enhancing the Electrical Conductivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 951 Nor Aisah Khalid, Jeefferie Abd Razak, Hazman Hasib, and Mohd Muzafar Ismail

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Response Surface Methodology (RSM) Implementation in ZrO2 Particles Reinforced Aluminium Chips by Hot Equal Channel Pressing (ECAP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 959 Sami. Al-Alimi, M. A. Lajis, S. Shamsudin, B. L. Chan, Mohammed. H. Rady, Musleh Al-Zeqri, Ahmed Wahib, Abdalkarim Aladani, Abdulaziz Ali, and Nur Kamilah Yusuf Synthesizing and Optimization the Hydroxyapatite Based on Corbiculacea Seashells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 975 Mohd Riza Mohd Roslan, Nashrul Fazli Mohd Nasir, Nur Farahiyah Mohammad, Cheng Ee Meng, Nasrul Amri Mohd Amin, Mohd Farid Abdul Khalid, Mohd Zakimi Zakaria, Muhammad Mokhzaini Azizan, and Muzammil Jusoh The Performance of RBD Palm Oil Dielectric Fluid in Comparison with Kerosene in Electrical Discharge Machining (EDM) Process . . . . . 983 Aiman Supawi, Said Ahmad, Nurul Farahin Mohd Joharudin, and Nuraishah Nadhirah Ahmad Idhan Polylactic Acid (PLA) Bio-Composite Film Reinforced with Nanocrystalline Cellulose from Napier Fibers . . . . . . . . . . . . . . . . 997 E. F. Sucinda, M. S. Abdul Majid, M. J. M. Ridzuan, and E. M. Cheng Effect of Printing Temperature and Layer Thickness of Polymeric Scaffold on Bioactivity for Bone Tissue Engineering . . . . . . . . . . . . . . . 1005 Nooraizedfiza Zainon, Amirah Abdul Manaff, Nur Syahirah Binti Mohd Tamizi, Muhammad Helmi Bin Abdul Wahab, Marina Marzuki, and Rozienani Ahmad Investigation of the Physical Properties on the Fabricated Biopolymer Scaffold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 Nooraizedfiza Zainon, Muhammad Helmi Bin Abdul Wahab, Nur Fatnin Ismail, Nur Syahirah Binti Mohd Tamizi, Marina Marzuki, and Rozienani Ahmad Parameter Optimization of Sintering Ti-6Al-7Nb Powder for the Minimum Shrinkage and the Highest Surface Roughness Using Taguchi Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025 R. N. Ahmad, N. Mohamad, A. B. Sulong, M. R. A. Jaffar, M. S. Abdul Manan, A. Wahi, Z. Nooraizedfiza, and M. Marzuki Ergonomics, Logistic Management and Energy Management Assessment of a Self-sustaining Drainage Ditch: Water Quality Monitoring and Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1037 A. S. N. Amirah, W. H. Tan, W. Faridah, A. M. Andrew, N. A. N. Zainab, S. Ragunathan, and M. S. N. Shahniza

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Public Perception and Acceptance of Manual Saliran Mesra Alam (MSMA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045 A. S. N. Amirah, S. Ragunathan, N. A. N. Zainab, A. M. Andrew, W. Faridah, W. H. Tan, and S. N. Husna A Study to Asses Environmental Knowledge of Homeowner Behaviors Towards Their Lawn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1053 A. S. N. Amirah, W. H. Tan, W. Faridah, A. M. Andrew, N. A. N. Zainab, S. Ragunathan, and H. N. Helmira Preliminary Analysis of Human Behaviors Based on Buss-Perry Questionnaire Score for Designing Measures of Aggression . . . . . . . . . . 1061 Kai Xu Tung, Wan Khairunizam, Wan Azani Mustafa, A. B. Shahriman, M. R. Zuradzman, and Azian Azamimi Abdullah A Literature Review on Occupational Musculoskeletal Disorder (MSD) Among Industrial Workers in Malaysia . . . . . . . . . . . . . . . . . . . 1069 Munawwarah Solihah Muhammad Isa, Nurhidayah Omar, Ahmad Faizal Salleh, and Mohammad Shahril Salim Green Micro-grid Based on PV/WT Hybrid System for Remote and Rural Population in Iraq: A Case Study . . . . . . . . . . . . . . . . . . . . . 1081 Zaidoon W. J. Al-Shammari, Safaa Kother, Ihsan Ahmed Taha, H. Enawi Hayder, M. Almukhtar Hussam, Ali Hadi, M. M. Azizan, and A. S. F. Rahman Factors Affecting Blockchain in Fruit Retail Market: An Unveiling Myth of Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1095 Kamal Imran Mohd Sharif, Mohamad Ghozali Hassan, Mahadi Hasan Miraz, Effendy Zulkifly, Zulkifli Mohamed Udin, and Mazni Omar Blockchain-Based Smart Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1107 Mohamad Ghozali Hassan, Kamal Imran Mohd Sharif, Mahadi Hasan Miraz, Effendy Zulkifly, Zulkifli Mohamed Udin, and Mazni Omar The Influence of Grasping Technique and Arm Posture on Shooting Performance in Traditional Archery . . . . . . . . . . . . . . . . . . . . . . . . . . . 1119 Nurul Fitriyani, A. F. Salleh, M. S. Salim, M. F. Kasim, Nurhidayah Omar, M. J. Masnan, and Z. Ghazali Effect of Strength and Conditioning Trainings on Lower Limb Muscles Activity of High-Jumping Athletes . . . . . . . . . . . . . . . . . . . . . . 1127 Hamiza Mohamad Radzi, A. F. Salleh, N. A. Rahim, M. S. Salim, Nurhidayah Omar, Hamzah Sakeran, and Segaran K. Nair

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Ergonomics Study on Visual Contribution of Postural Stability Using Physio-Treadmill (PhyMill) for Kid with Cerebral Palsy . . . . . . . . . . . . 1137 Rabiatul Aisyah Ariffin, Mohd Azrul Hisham Mohd Adib, Nurul Shahida Mohd Shalahim, Narimah Daud, and Nur Hazreen Mohd Hasni Occupational Accident in Malaysian Manufacturing Sector . . . . . . . . . . 1151 Danish Ali Memon, Yusri Yasof, Anabia Adnan, Sheikh Kamran Abid, and Nur Raihan Mohamed Muscle Fatigue Assessment Using Multi-sensing Based on Electrical, Mechanical and Acoustic Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1159 Anas Mohd Noor, Zulkarnay Zakaria, and Ahmad Nasrul Norali The Effects of Temperature, pH and Moisture Exposure on Human Hair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1171 Nurul Adlina Nadhirah Zamani, Nur Shariena Md Heshamuddin, Ainon Atikah Jafri, Syarah Syahindah Abdullah, and Engku Azlin Rahayu Engku Ariff Modelling and Simulation Modelling and Simulation of DC-DC Buck Converter for Pedal Assisted Electric Bicycle Using Matlab/Simulink . . . . . . . . . . . . . . . . . . 1187 M. N. Abdullah, M. K. Mat Desa, E. A. Bakar, M. N. Mamat, and S. Kaharuddin Phyton-Based Smart Algorithm for 3  3 Rubik’s Cube Solver . . . . . . 1205 A. M. Andrew, N. A. N. Zainab, A. S. N. Amirah, S. Ragunathan, W. H. Tan, W. Faridah, and S. Rezal Development a Cost-Effective Impedance Tube for Sound Transmission Loss Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1217 W. H. Tan, A. S. N. Amirah, S. Ragunathan, N. A. N. Zainab, A. M. Andrew, and W. Faridah Mathematical Modelling Development of Sound Transmission Loss for Laminated Glass Using Response Surface Methodology . . . . . . . . . . 1227 W. H. Tan, W. Faridah, A. M. Andrew, N. A. N. Zainab, S. Ragunathan, A. S. N. Amirah, and E. A. Lim A Coupled Eulerian Lagrangian (CEL) Model in Prediction Tool Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1237 M. S. Zakaria, Turnad Lenggo Ginta, and A. I. Azmi Conceptual Design Selection of Motorcycle Handle Brake Lever Component by TRIZ and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1245 M. U. Rosli, S. N. A. Ahmad Termizi, C. Y. Khor, M. A. M. Nawi, C. S. Chong, and M. I. Ishak

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Simulating the Effect of the Raw Material Preparation on the Production Completion Time Through a System Dynamics Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1257 Ahmad Afif Ahmarofi, Norhaslinda Zainal Abidin, and Mohamad Khairi Ishak Simulation of Fluid Structure Interaction Air Duct System Using Finite Element Method Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1267 Haslina Abdullah, Reazul Haq Abdul Haq, Mohd Nasrull Abdol Rahman, Ho Fu Haw, Said Ahmad, Ahmad Mubarak Tajul Ariffin, and Mohd Fahrul Hassan Numerical Simulation of Transesterification Reaction in Y-Shaped Microreactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1281 S. N. A. Ahmad Termizi, C. Y. Khor, M. A. M. Nawi, I. S. Mohamad Saad, M. I. Ishak, and M. U. Rosli Variation of Stress Intensity Factor and Strain Energy Release Rate in Human Cortical Bone Using Finite Element Analysis . . . . . . . . . . . . 1287 Mohammad Shahril Salim, Anis Najwa Azahari, Ahmad Faizal Salleh, Ruslizam Daud, and Hamzah Sakeran A Case Study of Coffee Sachets Production Defect Analysis Using Pareto Analysis, P-Control Chart and Ishikawa Diagram . . . . . . . . . . . 1295 Nur Illa Idris, Tan Chan Sin, Safwati Ibrahim, Mohammad FadzliRamli, and Rosmaini Ahmad Improving Performance and Process in Food Manufacturing Industry Using Lean Engineering Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1307 A. H. Mazelan, S. A. Showkat Ali, and M. S. Saidatul Huda Effects on Ply Orientation of Kevlar/Epoxy for Ballistic Impact in Bulletproof Vest Using Non-linear Finite Element Analysis . . . . . . . . 1317 Sanusi Hamat, Mohd Sabri Hussin, Syamir Alihan Showkat Ali, and Maliki Ibrahim Analysis of Printing Parameters for Tensile Test Using Finite Element Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1325 Nor Aiman Sukindar, Wan Luqman Hakim Wan Abdul Hamid, and Shafie Kamaruddin

Intelligent Manufacturing and Artificial Intelligence

Roof Re-measurement in Building Works Using Un-manned Aerial Vehicle (UAV) Application System Norsyakilah Romeli, Hazry Desa, Muhammad Azizi Azizan, and Faridah Muhamad Halil

Abstract The diversities of contractual terms in construction projects leads to a diversified contractual interpretation in one project. In the long term, the contractual discrepancies would result the project suffered from the dispute. Given the circumstances of measurement contracts, which refers to re – measurement or measure and value contracts are normally used when the quantity of the project can’t be describe in the exact amount. In the construction projects that involving complicated design, the difficulty may rose from the initial stage of architectural features. Often, the re-measuring’s fatality occurred when the elements finishes in the building has been over claimed by the contractor. Even so, the uncertainties of client’s instruction have always contributing to the excessive utilization of the fancy finishes in the construction projects. As a result, a dispute ascended due to payments, claim and work done issue. Site re-measurement can be done. However, in regard to almost completed building construction, the site re-measurement may get difficult as human can’t reach the building that has been constructed. Thus, issues on safety and accuracy may followed. Advance technology has provided the ability to implement the building survey without risking of manpower which is using the application of Unmanned Aerial Vehicle (UAV). Therefore, the research is carried out to identify the attributes of UAV application in roof re – measurement in building works and investigate the implementation of the UAV application. Using the literature search, the attributes of UAV application has been identified and the prototype case study has been selected to investigate the process of UAV application for roof site re-measurement. The results indicated that the utilization of UAV application is feasible as the tools for re – measurement of building works as the issues on the safety and accuracy are safeguarded.

N. Romeli (B) · M. A. Azizan Department of Civil Engineering Technology, Universiti Malaysia Perlis, Arau, Malaysia N. Romeli · H. Desa · M. A. Azizan Centre of Excellence for Unmanned Aerial System, Universiti Malaysia Perlis, Arau, Malaysia F. M. Halil Department of Quantity Survey, Universiti Teknologi Mara Shah Alam, Shah Alam, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_1

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Keywords Roof re-measurement · Unmanned aerial vehicle application · Malaysian construction project

1 Introduction The complex building works has a tendency to re-measure which falls into the quantity subject to re-measure contact, or there is the existence of the variation order in the building works. Even so, the variation order may rise due to the changes of work scope, site condition, client’s instruction and changes of method statements. For an instance the quantity for an excavation works initially has some difficulties to quantify the exact amount until the construction works has begun, as the matter of fact consultant are not willing of taking a risk to put the exact quantity due to the uncertain underground condition. The soil investigation has been done by the land surveyor, yet the uncertainties may have occurred derived from many factors [4]. Therefore, the excavation works has been put under ‘provisional quantity’ which is subjected to re-measurement. In this research, the highlights were given on the roof construction and finishes that has been originally be putting under provisional quantity and subject to re-measure. The situation my gets way more tense due to the changes of material used for roof finishes. Uncertainties may be one of the factors why the consultant not taking a risk to put the exact quantity for each of the Bills of Quantities elements. Some of the exclusive material shall come in a customize shape, and in certain cases may in form of numbers as the dimension has been tailored by the supplier. The utilization of the material shall have affected the costing in roof construction [1]. Therefore, instead of giving the firm price, the consultant has highlighted the item as if provisional quantity. Besides, the rate stated in the contractor’s tender can be priced in the Bills of Quantity and annotated inside the Schedule of Rate. In the construction industry application, the quantity of the actual work done carried out on site will be measured and rates applied to the quantities. As a result, the quantities may vary from the original estimated and the actual work done amount has been paid to contractors. Even so, dispute occurred when the contractor has over claimed the price after the re-measurement process done [3]. The client stated that the work done of the contractor hasn’t similar to the percentage and total utilization of the money. The dispute has been overhauling to the point where the quantities on site has been argued brutally by both parties. The conventional method is used in current in topography inspection by using air-borne or aircraft surveillance. This implementation of this method causing several difficulties. The first one is in term of safety issues, the conventional method involved putting man on field. Based on The Star news, A Eurocopter EC 120 owned by a private company based in Sarawak is crashed inti the Sungai Lingga (Sarawak) during the topography inspection works. The incident happened due to the bad weather crashed into the Sungai Lingga estuary in Sri Aman due to bad weather [1]. Hence, with the acceleration of the new technology, it is possible to use the UAV Photogrammetry method as map producing, surveying, and some other engineering applications with the advantages of low-cost,

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Fig. 1 Conceptual appliances of unmanned aerial system application in roof re-measurement

time conservation, and minimum field work [5]. The roof re-measurement process has suffered hurdles in regards of two main areas which encapsulated of safe issues and accuracy issues. The safety issues often been neglected by the industry player, when regards to the small-scale development that consisted of low project cost. Some of the occurrence hasn’t been fairly treated according to the justice jurisdiction Meanwhile, some of the fatality on site in regards as built physical building survey always shall be deemed by the contractor and client and included in the risk management cost [3]. However, the value of human doesn’t rely on the solely on the monetary aspects. The hazardous activities can be minimized by replacing human workforce with the technological appliances on site. The human workforce that required only responsible on piloting the UAS and identifying the building exterior features with analyzing the accurate size, shape and measurement. The design properties of the building will be recorded as the UAS flight took off. In this case, the application of the Unmanned Aerial System is very much needed. The utilization of the UAS making the roof re-measurement safe as human needed to climb to the highest point for the sake of measuring as built building (Fig. 1). The accuracy aspects have been an overwhelming concern in roof remeasurement. The accuracy often leads to the dispute in between two respective parties that handle the construction industry. Often, doubt in between two parties making the measurement decision and accuracy to dispute. Initially, clients have agreed with the materials used which most of the cases using high – end roof finishes and structure [3]. The clients pay no worries at first due to wanting an extremely prestigious building that followed by the desire owning remarkable aesthetic assets. Nonetheless, the situation gets tense when the rate for the agreeable high end roof finishes materials cost more that client could pay. The dispute in term of quantities take off would resulted a prolong occurrence of as built building re-measurement. To add, the discrepancies of the information in between pictured in the construction drawing and as built physical building fired up the dispute and making both of the respective parties losing their trust in their construction project relationship. To be fair and justice, a non-bias tools that provided a high detection precision is highly obligatory to tackle the accuracy problem in roof re-measurement. The conventional physical building survey using human work force whom the workers are required to reach the Roof Covering Area, Overhang Ceiling, Shear Eaves, Gutter, Ridge Capping and Fascia Board manually.

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The occurrence can be seen in Figs. 2, 3 and 4 which are derived from a complicated Minangkabau roof design. The unreachable and risky area roof surveying can be reach by using UAV application and producing a 3-D photogrammetry images. The 3-D surveying in roofing is a process that requires geo-metrical information of a roof structure in different phases of the roof’s life cycle [5]. A study of the ecosystem of a roofing project illustrates the importance of this issue. In the bidding process of a roofing project, a contractor uses the construction drawing as the way to measure the quantities and building in the price. Having the original architectural drawings, a BIM file, or aerial photogrammetry services to estimate the roof area, can help the contractor to estimate the building cost [2]. Capturing the as built geometry or roof surveying is important which comprised of estimation level surveying for quantity take off and fabrication level surveying. The accuracy has been detected for (errors ≤ ±2 cm) digitally mapping the roof covering materials. This will have minimized the risk on accuracy as the techniques include edge extraction from the dense point clouds generated from the aerial images-es or videos. LiDAR data can be lower dense than the point cloud generated high definition image and the extraction process leads to detail (LoD) that can be achieved in the geometry. The concern given on the 3-D point where once the point cloud reaches the completeness in scanning. The Ground Sample Distance (GSD) between the two consecutive point has been detected as distance of the object in-creases from the camera sensor, the projected pixel gets bigger. Increasing the pixel and scaling for 3-D image completeness achieved using the exchangeable file format (EXIF) and GPS data for each image [5]. However, the parameter on the sensor quality, atmospheric condition and precision of the latitude, longitude and altitude must be under observatory level as the scaling accuracy is dependent on those constant. Later, the scaling reference is utilized to scale overall point cloud using the correct distance [3]. Theoretically, the precision of the measurement EXIF GPS files could provide the Fig. 2 Risky Site Roof Re-measurement

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Fig. 3 Risky area to reach for locating the measuring tape

Fig. 4 Inclination angle of the roof has changed from what that has been annotated in the Bills of Quantity and drawing

better scaling and measurement reference that could help the site pre-measurement dispute in determining the actual roof quantities (Figs. 5 and 6).

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Fig. 5 Unmanned Aerial System take off for roof remeasuring

Fig. 6 Conceptual drone surveying that produced ideal image acquisation for building

2 Methodology Figure 7 explained on the research methodology which initiated with Phase 1 that comprised of identifying the underlying objectives of the research. Equipped with definite aim, the data collection process has started with the literature review engine search on the Journal of Robotic, Networking and Artificial Life. The issues on the safety and accuracy of the UAV roof re-measurement has been obtained. Later, the preliminary investigation on the site has been identified. Factors as nearby the military jurisdiction and public parameters has been compromised and approval letter were issued towards the stipulated authority. Once the approval gains, a pre take off for UAV has been conducted to understand the existing circumstances. The wind speed, wind tail and existing flying fauna were detected. The research methodology

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Fig. 7 Research methodology

implementation has encapsulated Phase 2 which the fieldwork inspection has been conducted with aid of UAV drone (DJI Inspire 2), Terrestrial Laser Scanner and Tripod. Setting up procedure for UAV planning which including a thorough Standard Operation Procedure (SOP) as the 3D Laser Scanner has been placed properly. The photogrammetry data has been developed from the UAV take off using multiple overlap point to ensure the data interpolated to each other’s. Later on, Phase 3 took place where the imperative steps in this research is where the extraction of the data and processing of the data held using the software of PiX4D Mapper, ArcGIS and Autodesk BIM. The Pix4D Mapper gives aid in reconstruction the 3D photogrammetry data that derived from the point clouds and acquired from the drone imagery. The ArcGIS software provide aid in creating the 3D spatial data that are processing

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from the images and photogrammetry techniques. Finally, the Autodesk BIM software gives service in providing the cloud-based data to help researcher to import, interpreted, and transfigure the real time object into a conceptual BIM modelling that has derived from the point clouds. The as built drawing produced, then the absolute length were gain based on the UAV photogrammetry images. Different techniques, such as photogrammetry, laser scanning, drones, video and photographs were used for data acquisition of all features of the bungalow with the complicated roof design, which were then processed to create a 3D model and using Building Information Modelling (BIM). The data point cloud help the BIM to achieve the objective of this research. The data processing using UAV photogrammetry are extracted to produce the model in form of drawing sheets, plan and section as can be refer to Fig. 6. The drone flied with 45° camera angle. The second flights and third flight were directed around the building with increasement angle of flight height and declination angle of the DSLR camera angle with each round. The principle of 3-D unmanned surveying, the flights height shall not be increase twice between flight as the differentiation of high lead to different spatial resolution. The 3D model conversion was completed with high level detailing to ensure the plan, section and layout were fit for planning purpose. The setback in roof remeasurement is, due to the records of the sa built drawings were depletion, the UAV real time measuring can help and support the contractor and quantity surveyor to search for the information on the building for remeasurement, dispute and maintenance purpose.

3 Results and Discussion The case study is a construction of bungalow in central of Malaysia. The construction comprised of There are many types of roof coverings, the selection of the roof covering was asphalt shingles roof that has always a been preferable choice for architect, interior designer and client due to the properties of attractive appearance, sound insulation and durability feature against weather and wind impacts. Due to the competitive demand of asphalt shingle in Malaysia with undoubtedly high quality that require less cost for repair and maintenance, the cost per meter square for the asphalt shingles reach MYR 224.56 per meter square. The original cost of the roof construction and finishes is MYR 173, 361.00. The claim received from the contractor was over the budget at 20% due to material changes and area of covering that has been changed. The original material stated in Bills of Quantities was not specifying types of shingle. In the middle of the construction, client chose asphalt shingle which gave significant changes in rate of the roof covering. The dimension of the Roof Area, Overhang Ceiling, Shear Eaves, Gutter, Ridge Capping and Fascia Board. The comparative results on quantity gain using the conventional re- measurement and UAV re-measurement were tabulated in Table 1. Table 1 portrayed the differences in measurement aspect in between conventional which using manual roof premeasurement and to be plot back to the drawing and re-measurement process using UAV application. For Overhang Ceiling and Roof Area, the accuracy effected in

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Table 1. Comparison on roof re-measurement accuracy Elements of Roof

Re-measurement Quantity from Conventional Method

Re-measurement Quantity from Unmanned Aerial Vehicle (UAV) Application

Differences in Accuracy

+2m2

Roof Area

772 m2

774m2 -4m2

Overhang Ceiling

118 m2

114m2

Shear Eaves

none

37 m

37m (continued)

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Table 1. (continued)

Gutter

+1m

199m 198 m Ridge Capping

-0.7m

143 m

142.3m

– 4 and +2 m2 (respectively) which the actual measurement using UAV exceeded the original and has the highest impacts in cost among all of the roof elements, the lowest gives significant impacts are on the Ridge Capping which the ac-curacy runs around −0.7 m. The similar scaling has been detected for item Shear Eaves. The findings indicated that the UAV photogrammetry scaling accuracy plays imperative roles in deciding the roof re-measurement quantities. Based on the absolute length gain, the quantity surveyor in this project can used the information to develop the roof re-measurement document, calculate the quantity of the effected roof area, and solve the dispute. The accuracy on the UAV re measurement are proven due to the decimal places also can be identified. The 3D roofing surveying is helps to provide the geometrical in-formation in roof structure, according to roof life cycle. In this project, the final product of the roofing project was illustrating the importance of knowing the absolute length of the roof dimension using UAV application. The results also portrayed the feasible utilization UAV 3D roofing measurement in the phase of tendering where

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the quantity surveyor can be utilized the architectural drawing, BIM file and aerial photography provided to come up with the complicated roof area estimation. The UAV modelling that leads to estimation allows the quantity surveyor to engineer the material required, take off particular, labor cost to create the exact amount of the building. The complicated design of the roof in this project which using The Minangkabau Roof making the re-measurement difficult. Hence, the re-measurement based on the as built dimension of the roof structure is practically crucial. Adding the discrepancies on the designed and as built document, the UAV 3D surveying in this project helps to increase the roof layout accuracy and efficiency. The determination of the roof area in the as build building is imperative, to ensure the quantity of the finishes incurred to cover the roof area. This would be impossible without the aid of UAV application that enhance the accuracy in the measurement. The safety of the manpower was assured without risking the manpower to climb from on roof to another for the purpose of getting the accurate measurement. The dispute in between client and contractor can be solved using the instrument since it is bias free in providing the exact quantities for constructed building works Acknowledgements The researchers would like to acknowledge the boundless technical and academicals support received from Centre of Excellence for Unmanned Aerial System Universiti Malaysia Perlis, Department of Civil Engineering Technology Universiti Malaysia Perlis, and Department of Quantity Survey Universiti Teknologi Mara.

References 1. Romeli N, Halil FM, Ismail F, Hasim MS (2020) Cost control mechanism as procurement selection decision matrix for Malaysian infrastructure projects. In: IOP conference series: earth and environmental engineering, vol 476, p 012001 2. Evgenikou V, Georgopoulos A (2015) Investigating 3D reconstruction methods for small artifacts. Int Arch Photogrammetry Remote Sens Spat Inf Sci—ISPRS Archives 40(5W4):101–108 3. Samad AM, Kamarulzaman N, Hamdani MA, Mastor TA, Hashim KA (2013) The potential of Unmanned Aerial Vehicle (UAV) for civilian and mapping application. In: 2013 IEEE 3rd international conference on system engineering and technology 4. Watts AC, Ambrosia VG, Hinkley EA (2012) Unmanned aircraft systems in remote sensing scientific research: Classification and considerations of use. Remote Sens 4:1671–1692 5. Pojani D, Stead D (2015) Sustainable urban transport in the developing world: beyond megacities. Sustainability 2:7784–7805

An Overview of Multi-Core Network-on-Chip System to Enable Task Parallelization Using Intelligent Adaptive Arbitration Mohammad Nishat Akhtar, Qummare Azam, Tarik Adnan Almohamad, Junita Mohamad-Saleh, Elmi Abu Bakar, and Ayub Ahmed Janvekar Abstract Increasing the count of transistors packed within integrated circuits necessitates efficient communication architecture such as Network-on-Chip (NoCs) to deal with scalability, bandwidth, latency, and optimized CPU core utilization goals. To extend the applicability of Moore’s law, multiprocessor architectures have been introduced, which in turn requires a higher level of synchronization and concurrency to enable enhancement in system performance. This research deals with global communication for NoC-multi-core system using the Intelligent Adaptive Arbitration technique to enable task parallelization by showing a perspective for the deployment of Intelligent Adaptive Arbitration on different NoC architecture(s) to enable parallel computing with fair bandwidth distribution and low latency. This research also opens up room for the various network-on-chip topology to constitute a unification of the latest trends of intra-chip communication. Keywords System-on-Chip (SoC) · Network-on-Chip (NoC) · Arbitration · Multi-core · Intelligent adaptive arbitration · Fair bandwidth · Synchronization

M. N. Akhtar · Q. Azam · E. A. Bakar (B) School of Aerospace Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia e-mail: [email protected] T. A. Almohamad Department of Electrical-Electronics Engineering, Faculty of Engineering, Karabük University, 78050 Karabük, Turkey J. Mohamad-Saleh School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia A. A. Janvekar School of Mechanical Engineering, VIT University, Chennai 600127, TN, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_2

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1 Introduction Nowadays, to improve the system performance, the system architects and programmers depend on concurrency and synchronization in hardware-software co-design. Task parallelization in a multi-core environment system is of great need in this era. This is because doubling the core could benefit the users only if the designers come up with a program that has execution compatibility in multi-core environment by utilizing its resources to the maximum possible limit based on an advanced parallel programming technique. With respect to benchmark epitome, the multi-core-based schemes are considered as a benchmark platform over a broad range of computing field that comprises high-end schemes, for instance, giant servers, telecommunication infrastructure, and powerful workstations. Multi-core devices have been in use for different forms, such as in the way of uni or dual RISC cores inside QUICC machine communication utility [1, 2]. A multi-core-based device where all of its cores are bound to execute different tasks is called a heterogeneous multi-core device. On the other hand, if different cores of multi-core devices inherently execute identical tasks, then it is called a homogenous multi-core device [3]. In today’s situation, multi-core homogenous devices have become a central target in the industrial world. Furthermore, substantial benefits can be reaped by employing dedicated cores and accelerators in order to diminish the burden on the essential cores [2]. In the environment of billion transistors, the problem of deep sub-micron cannot be neglected. Therefore, to deal with scalability issues for the System-on-Chip (SoC) involved in such multi-core systems, it is essential for the system designers to adapt to Network-on-Chip architectures [4]. The objective of this review is to give an overview of multi-core based NoC topology, which can be modeled using Intelligent Adaptive Arbitration Technique to enable task parallelization and fair bandwidth utilization. Section 2 provides an overview of various NoC topologies followed by Sect. 3, which exemplifies some of the prominent NoC’s. Section 4 elaborates the design of functional modules (masters) associated with the arbiter to exhibit synchronization and task parallelism. Section 5 gives a detailed illustration of Intelligent Adaptive Arbitration which also comprises of the benchmark used to test Intelligent Adaptive Arbitration. In Sect. 6, we describe the implementation of Intelligent Adaptive Arbitration on an octa-core system for an optimized CPU core usage using task parallelization and also try to open up a room for the readers to deploy Intelligent Adaptive Arbitration on any of the NoC topology as per their requirements. Finally, in Sect. 7, we present the conclusion of our paper.

2 Network-on-Chip Topology Network-on-Chip has got various topologies from a communication point of view. Some of the famous topologies are ring, mesh, torus, binary-tree, butterfly, and other

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complex interconnections [5]. In a ring topology, all nodes are arranged using a ringlike structure as shown in Fig. 1. Each and every node has got 2 neighboring nodes which is independent on the size of the ring. Usually lower degree of ring topology in terms of diameter is preferable to avoid network disruptions [6]. Some of the advantages of ring-topology are that it makes troubleshooting easier by detecting faults in a very easy manner. In addition to this, ring-topology is easy to install if compared with other NoC topologies. It is disadvantageous in way that the whole network can crash if there is a single cut in any of the cable. In mesh topology, all nodes are arranged in the form of a grid which makes its expansion easy as shown in Fig. 2. Adding an extra node to the grid is not a tedious job. According the location of nodes within the grid, they have got different number of neighbors. It is advantageous in a way that it has got multiple paths between each Fig. 1 Ring topology

Fig. 2 Mesh topology

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pair of nodes; therefore it is tolerant to link failure [7]. It is disadvantageous in a way that the bandwidth is not fairly distributed to the corner nodes of the grid; therefore, large grids are not preferable for this NoC topology [8]. The torus topology is quite similar to single linked-list architecture. The torus topology looks complex but still the connectivity is bidirectional across all its nodes as like mesh, binary tree and butterfly topology. Here, nodes can be added at the two ends of the connection either in the same row or same column of the mesh architecture. If compared with mesh topology of NoC, the torus topology has got low diameter due to its wrapped-around links [9]. It is advantageous in a way that due to doubling of wire, the use of long wires could be avoided [10]. Figure 3 shows the torus topology. In binary-tree topology, the top node is known as the parent node or the root node and the bottom nodes are known as the child node or the leaf node. Each node has got two offspring except the parent node/root node. Child nodes are those that appear immediately beneath each node. The binary-tree topology is advantageous in a way that it is supported by various hardware vendors as well as network vendors [11]. However, it is disadvantageous in way that when the leaf node of the tree increases, then higher degree of complexity starts arising [12]. Figure 4 shows the binary tree topology. Butterfly topology is famous in a way that it maintains an accurate path between the source node and the destination node as shown in Fig. 5 [13]. This topology

Fig. 3 Torus topology

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Fig. 4 Binary tree topology

is suitable for small network ranges. However, this topology is disadvantageous due to its lack of diversity in maintaining different paths between source node and destination node. Secondly, its long wires hamper the data traffic. Figure 5 shows the butterfly topology. Apart from these topologies, there are other complex topologies too. For instance, there is a topology called hypercube topology which is also considered to be very useful. One major drawback of hypercube topology is the restriction of its network size due to its node degree limitation. Foremost possible solution proposed by Chen et al. [14] is the reduction of node degree. Apart from this the concept of folded hypercube is derived from hypercube topology where each node is connected to a unique node farthest from it [15]. The benefit of this topology is that its diameter reduces to half as compared to hypercube topology at the cost of more links. Hu and Zhang [16] proposed a novel NoC architecture known as Network Coding NoC (NC_NoC) architecture which is based on local bus to attain highly efficient multicast and data transfer through network coding. This architecture has got advantages of bus-based system as well as conventional NoC designs. If compared to traditional NoC architecture, NC_NoC design improves the data throughput using minimalistic hardware complexity [17]. Following Figs. 6 and 7 shows the NC_NoC architecture. According to the Fig., p1 has got an ability to transmit data in broadcasting mode to p2, p4, p5 and p13 using uni-directional local bus 1. Moreover, p2 can transfer data in broadcasting mode to p1, p3, p6 and p14 using uni-directional local bus 2. Thus, a 2-way connection is established between p1 and p2. In the same

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Fig. 5 Butterfly topology

Fig. 6 NC_NoC node linking

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Fig. 7 NC_NoC architecture

manner, connection for n p’s can be established using n local buses which finally formulate virtual torus architecture. According to Hu and Zhang, the gain (Gcode ) of network coding (NC) is formulated as shown in Eq. 1: Gcode = (N ∗ (N − 1) + X)/N2

(1)

Where X = N − M + 1, N stands for total number of source and destination nodes and M is the number of nodes which carries and executes the NC coding. In a broader perspective NoC problems can be categorized into three parts i.e., traffic behavior of data, communication architecture, and a routing algorithm with an efficient arbitration strategy. The traffic patterns can be dealt with an optimized scheduling algorithm. An NoC architecture may possess different mesh topology which in turn demonstrates the performance of routing algorithms. However, for the proposed review, our focus is on the NC_NoC bus-based architecture due to its hybrid form. In addition, for the proposed review, we also proposed to deploy an intelligent adaptive arbitration algorithm on Nc_NoC architecture for efficient multiprocessing. Now, the following Sect. 3 gives an overview for exemplification of some of the advanced NoC’s.

3 NoC Examples In this part we recapitulate specific NoC examples in a brief manner which describes the design choices of real-time implementation carried out by various research groups. The following are few categories of NoC at a macro level. However, this does not mean that it is a complete compilation of existing NoC’s architecture.

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3.1 ÆTHEREAL NoC ÆTHEREAL NoC developed at Philips guarantees services such as ordered, uncorrupted and lossless data delivery. It has got the feature of latency bound as it is essential for robustness of SoC’s. All routers on this network have a common time spectrum and the routers forward traffic data based on specified time slots [18]. As a result, sequenced slots implement a virtual circuit. The guaranteed service in this NoC is the base for its hardware/software codesign. Moreover, the router does not violate the protocol of providing guaranteed throughput to all the nodes attached [19]. Earlier in this type of NoC design, slot tables were used to determine the next hop of the data packet. However, in the recent version of this design of NoC, slot tables are not being used in order to save the area and the next-hop information is provided in the packet header. In addition to this, the slot allocation can be set statically during the initialization phase of the packet, or it can be set dynamically during the execution process. Dielissen et al. [20], synthesized an ETHEREAL router of 32-bits in 0.13 µm CMOS technology with 6 bidirectional ports. Moreover, Pestana et al. [21] and Goossens et al. [22] proposed an automated design flow for the initialization phase of the application specific ETHEREAL using XML input parameter.

3.2 Nostrum This NoC design evolved from system-level chip design approach was proposed at KTH, Stolkholm [23, 24]. Their emphasis was focused on architectural and platformbased design to enhance the application specific domains. Their proposed work highlighted the pros of router driven and grid-based communication media for IP communication as it involves high density VLSI technology. Moreover, their work did not neglect the issue of guaranteed throughput service. In Nostrum NoC design, guaranteed throughput is provided by looped containers which is implemented by virtual circuits using multiplexing mechanism through explicit time division [25, 26].

3.3 SPIN SPIN stands for Scalable Programmable Intelligent Network. This NoC design implements a fat-tree topology with two one-way-32 bit dispaths at link layer of the OSI model [27–29]. This topology is also considered as the best routing network in terms of network irregularity [30]. The fat-tree topology has got a feature of implementing an efficient network route using minimalistic hardware resources with only a polylogarithmic slowdown in latency.

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In SPIN NoC design, packets are transferred as sequential flits of 4 bytes each over the network. The first flit to be transferred over the network contains the header information which reserves one byte for addressing whereas the last byte of the packet contains payload checksum [31].

3.4 CHAIN The CHAIN (CHip Area INterconnect) NoC design was developed at University of Manchester. The unique feature of CHAIN NoC design is that its implementation is purely based on clock less or asynchronous circuit techniques. In addition, this NoC design also supports adaptivity along a path which consists links of different bit width. CHAIN NoC design emphasizes on low-power heterogeneous systems, therefore CHAIN is considered as system specific. It is widely used in smart-card applications as it is benefitted from the low idle power capabilities of asynchronous circuits. In terms of arbitration, Felicijan et al. [32], proposed an asynchronous low-latency arbiter.

3.5 MANGO The MANGO (Message-passing Asynchronous Network-on-chip providing Guaranteed services over OCP interfaces) was developed at Technical University of Denmark [33]. This is also a clock less NoC and it emphasizes on coarse-grained GALS type SoC. MANGO NoC design provides both wireless BE(Best Effort) routing and wired guaranteed services [18, 34]. Separate physical buffers are maintained for the routers using virtual channels to ensure design simplicity. Moreover, bundled-data circuits are dedicated for the router implementation whereas delay insensitive encoding is handled by the links. As a result, robustness in global timing is achieved as timing assumption is no more a necessity between routers. Scheduling algorithm called ALG(Asynchronous Latency Guarantees) [34] schedules access to various links which provides low latency and guarantees bandwidth. Moreover, this NoC design supports features like OCP based standard socket interfacing, interrupt handling and adapters synchronization.

3.6 XPIPES XPIPES NoC design was developed by University of Bologna and Standford University [35, 36]. XPIPES NoC design consists of soft macros of links and switches which can be modified to instance-specific network components at the initialization phase. This NoC design is based on pipelined links with multiple stages in order to

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ensure a high throughput. A unique strategy known as go-back-N retransmission is implemented as a part of link level error control to reduce switch complexity [18]. Therefore, this architecture is considered to be robust to interconnect errors. After having reviewed various NoC design and architecture, the following Sect. 4, shows the design of master modules to implement the different nodes of NoC in synchronization.

4 Design of Masters to Exhibit Synchronization and Task Parallelism Modelling of NoC enables a substantial approach to understand the required NoC architecture. Chen et al. [14] proposed a bandwidth guaranteed-based arbitration design for system-on-chip (SoC) bus transfer. In their work, they employed RT_Lottery algorithm in order to satisfy both hard real time and bandwidth necessities. In their proposed scheme, dual levels-based arbitration platform was demonstrated. The platform entailed both lottery and time division multiple access-based techniques. In their design, the traffic management criteria of the data flow were considered to implement different masters (functional modules) [14]. Nevertheless, their proposed masters suffered from lack of self-synchronization and also their masters design was not following a parallelization strategy. Three type of masters were designed by Akhtar and Sidek [37] to form the foundation of Intelligent Adaptive Arbitration (IAA) based on task parallelism and fair bandwidth requirements. It is also worth to be noted that Chen el al. [14] also implemented three masters designed as per the traffic behavior of the data using non-preemptive RT_Lottery technique in order to execute both real time-based and bandwidth-guaranteed arbitrations [14].

4.1 D_Type Master In D_Type master, D stands for dependent. This type of master has got no real-time requirements and the upcoming request is totally dependent on the finish time of the current request. Interval time between two continuous requests is the time, from the issued point of time of the previous request to the finish time of the latter [14, 37]. Figure 8 shows an example. A burst is generated of 4 beat at cycle 12, the request is not granted till cycle 16 and the execution starts immediately. If interval time is 14, then the next request is issued at-cycle 30 only, therefore no request can be issued between cycle 16 and cycle 30, however, at the completion time of first request, the second request of 4 beat burst is issued, whose permission is granted at cycle 34 and then its execution starts till cycle 44 which is represented by uni-directional line. The total execution time of first request was set to 14 cycles whereas the total execution

An Overview of Multi-Core Network-on-Chip System … 1st Request Issued

1st Request Grant

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2nd Request 2nd Request Issued Grant Exec. Of 2nd Request Starts

Exec. Of 1st Request Starts

12

Exec. Of 2nd Request Ends

Exec. Of 1st Request Ends

16

30

34

44

Cycle

Fig. 8 D_Type master

time for 2nd request was set to 10 cycles. Users can set their own configuration for execution time cycles.

4.2 DR_ Type Master This is same as D_Type master, the only difference is that they have extra real-time requirements (R) [14, 37]. Rcycle is the real-time requirement for master which is set to 10 cycles. It is shown in the Fig. 9 that the request issued at cycle 6 has to be finished before cycle 16 represented by dotted lines. A real-time violation occurs if the request is unable to finish before cycle 16. The bi-directional line represents first request which has to finish before cycle 16. The uni-directional line represents second request which is a 2 beat burst request and was continuously being issued from cycle 8, however, its permission is granted only after the completion of first request at cycle 14 and then the second request executes till cycle 20. It is to be noted that the total execution time of 2nd request was set to 6 cycles whereas the total execution time of first request was set to 4 cycles. 1st Request Issued

2nd Request 1st Request Issued Grant

2nd Request Issued

Exec. Of 1st Request Starts

6

8

Fig. 9 DR_Type master

10

2nd Request Grant Exec. Of 2nd Request Starts

Exec. Of 2nd Request Ends

Exec. Of 1st Request Ends

12

14

16

20

Cycle

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1st Request Issued

2nd Request Grant Exec. Of 2nd Request Starts

st 2nd Request 1 Request Grant Issued

Exec. Of 2nd Request Ends

1st

Exec. Of Request Starts

7

8

10

Exec. Of 1st Request Ends

13

14

17

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Cycle

Fig. 10 NDR_Type master

4.3 NDR_Type Master (Nd for not Dependent) The issued time of a request from a NDR_Type of master does not depend on the finish time of its previous request, and the interval time is the clock cycles between two successive requests [14, 37]. In Fig. 10, the first request which is a 3 beat burst was issued at cycle 7 and its permission was granted at cycle 10 and then the execution of first request started. Between the initiation and grant of the first request, a second request was also issued at cycle 8, however, the permission to 2nd request was not given because the grant permission of first request was on hold. The interval of the first request (execution time) was 4 cycles, therefore the execution of the first request ended at cycle 14. The second request was 1 beat burst request, therefore it was continuously being issued from cycle 8 and at cycle 13, the permission was granted to second request which is represented by unidirectional line. The execution time of 2nd request was 11 cycles. It is to be noted that, the second request directly depends on cycle 7 of the first request but not its completion time at cycle 14 which is represented by bi-directional line. In this case Rcycle is supposed to be smaller than minimum possible interval time because the current request must be finished before the issue of next request. It means that it is possible for the designer to include tight time constraints. Now so far, we have discussed NoC’s along with its modelling as per the traffic behavior of the data. It is worth to be noted that the design of these masters is the core part of the Intelligent Adaptive Arbitration (IAA) which was developed by Akhtar and Sidek in 2013 [37]. The following Sect. 5 illustrates the implementation of IAA.

5 Intelligent Adaptive Arbitration Algorithm Existing research work deduced that there is still an effective deficiency in latency and CPU exploitation. However, there are some arbitration techniques that showed better

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performance for bandwidth allotment. Regardless of the satisfied bandwidth allotment, the parallel programming paradigm has not been considered while designing many existing arbitration schemes. In other words, previous arbitration techniques were not able to exploit the multiple compute cores. In contrast, this substantial property of parallel processing has to be effectively exploited by an arbiter which in turn will lead to improve the system performance through the arbitration policy. In order to overcome the complications of previous arbiters in terms of maximum CPU cores usage and fair bandwidth allocation, IAA was proposed [37]. The implementation of Intelligent Adaptive Arbitration was tested with STREAM (sustainable memory bandwidth in high performance computers) synthetic benchmark which is designed to measure the sustainable memory bandwidth and their corresponding computation rate [38]. Figure 11 shows the location of an arbiter in a shared memory system. Figure 12 shows the location of an arbiter in NoC architecture at a macro level whereby the Node A, Node B, Node C and Node D are considered as the different processing units. The Arbiter ensures that there are no cache misses, as the data is fetched parallelly by each master from the cache memory while implementing the stream operations. Therefore, the kernel or the processor does not remain idle to satisfy the cache misses. Pseudocode in Fig. 13 shows the priority set by the arbiter. The flowchart for IAA with respect to implementation of STREAM benchmark is shown in Fig. 14 [37]. As per the topology, switching mode and routing algorithm on the basis of designed masters, the network could be parametrized as shown in Fig. 15. Considering a 2D structure, topology could be dimensionalized along the number of nodes in X and Y axis. The mesh structure could be chosen as discussed in Sect. 2.

Fig. 11 Arbiter in shared memory system

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Node A

Node B

Arbiter

Node C

Node D

Fig. 12 Arbiter connection with different NoC based nodes

Arbiter(){ set priority: 1st priority: D_Type master 2nd priority: DR_Type master 3rd priority: NDR_Type master if request = = D_Type master {arbiter grnt permission with high priority} else if request = = DR_Type master {arbiter grnt permission with 2nd priority && DR_Type master synchronizes with D_Type master} else if request = = NDR_Type master {arbiter grnt permission with 3rd priority && NDR_Type master synchronizes with DR_Type master} }

Fig. 13 Arbiter priority

Simplex or duplex link connection could be chosen depending upon the data width. The mode of switching can be chosen with respect to deflection routing or wormhole routing. Deflection and wormhole are considered as two prominent routing policy in NoC architecture. For immediate packet transmission, deflection routing is the best policy. Wormhole routing is considered for the cases where parallel packet transmission is required.

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Fig. 14 Flowchart for IAA

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Fig. 15 Network configuration tree

6 Discussion After having reviewed various NoC architectures, we intend to propose that in order to have a smooth implementation of multiple compute cores in a NoC, an arbiter is deemed necessary to be placed at the most strategic location in the NoC topology. Different arbiters could either be dedicated to a group of NoC compute cores or a single arbiter could be dedicated for the compute cores in the NoC topology. In order to facilitate the D_Type master, DR_Type master and NDR_Type master, we propose the users to use auto-parallelization tool, preferably OpenMP with all its necessary features. With reference to OpenMP, the D_Type master could be placed in the OpenMP non-blocking thread region, so that it can utilize the maximum cores. For D_Type master, the next request is issued at the time depending on the finish time of the current request. This master is compatible with the design of typical OpenMP non-blocking thread process, as any method is invoked, it executes completely until it returns a value. It is worth to be noted that using OpenMP, the CPU cores usage could be harnessed to its maximum value. Since D_Type is the parent master, therefore, the arbiter can analyze the overall input data to be executed through D_Type master using suitable profilers. The data sharing attribute of this D_Type master thread could be kept private using OpenMP in built function, which means that each thread possesses a local copy and uses it as a temporary variable. The code lying within the OpenMP thread region could be designed according to the clocked thread process to get a better synthesis result. The task of D_Type master is to copy data from primary memory to the cache memory without being interrupted by other masters. Moreover, other masters are not allowed to synchronize with D_Type master whilst the copying of data from primary memory to the cache memory. The DR_Type master is also considered as the blocking master and it is similar to the D_Type master. In order to function this module in the most appropriate manner, the data within the parallel region is supposed to be kept shared, as the code needs to be executed in parallel with NDR_Type master. In order to satisfy the extra real-time

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requirement, this master has two kinds of real-time parameter. One is the independent real-time parameter and the other is dependent real-time parameter. The independent real-time parameter has to finish its execution within a specific count value set by the system whereas the dependent real-time value has to synchronize with NDR_Type master and has to finish its execution until the next request is issued by NDR_Type master. The synchronization for the code (dependent real-time parameter) could be placed in the OpenMP region which could be done using the parallel mode function of the OpenMP, which means that the code lying within the OpenMP region will be executed in parallel with other OpenMP threads. The task of DR_Type master is to divide the code block and implement it in parallel with NDR_Type master therefore, DR_Type master synchronizes with NDR_Type master to implement its dependent real-time parameter. The initiation of the NDR_Type master does not depend upon the finish time of the DR_Type master. However, request to initiate NDR_Type master is issued by DR_Type master to implement its dependent real-time parameter. The function of NDR_Type master is to implement the divided code block tasks in parallel with DR_Type master by synchronizing with each other. This implementation could be done in the OpenMP shared area region and high degree of auto parallelization could be maintained using omp_parallel function. Initial experiment with respect to the performance of IAA was performed on an octacore system with 3.2 GHz processor and 8 GB RAM. For the proposed implementation of IAA, high core context switching was enabled for optimized usage of CPU cores with best possible task parallelization. Since D_Type master is the non-blocking master, therefore in this regard, we made a setup combination of D_Type, DR_Type and NDR_Type master. The performance metrics were based on degree of task parallelization by observing the CPU cores utilization, total execution time of the STREAM tasks and the bandwidth fluctuation. Since we have a combination of 3 masters, therefore in this regard minimum number of chosen cores was 3. For the proposed experiment, IAA has been implemented on 3, 4, 5, 6, 7 and 8 cores as shown in Figs. 16, 17, 18, 19, 20 and 21. For IAA the average cores usage was 48% on single core with a total execution time of 161580.3 s and 74% on double core with an execution time of 123291.8 s [37]. It is deemed necessary to point out the fact that for a NoC architecture task parallelization with an optimization in cores usage is essential. The execution time is an important parameter of performance metrics, however, just to compensate the execution time enhancement, it is not a viable option to neglect cores usage optimization and bus bandwidth optimization. This approach is in accordance with energy aware task scheduling for NoC architecture. It is evident from Figs. 16, 17, 18, 19, 20 and 21, that the combination of D_Type, DR_Type master and NDR_Type master does not work well with increment of cores. As observed for 3 cores in Fig. 16, the enhanced performance of three masters when executed in synchronization enables task parallelization which in turn reduces the execution time by utilizing CPU cores (21% usage) in an optimized manner. If we observe the execution on 4 cores (25% usage) in Fig. 17, then it could be observed that there is mere 1000 ms reduction in execution time which clearly signifies the overhead of core context switching. Moreover,

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Fig. 16 Average CPU utilization using 3 cores

Fig. 17 Average CPU utilization using 4 cores

as the number of cores goes high, then the communication overhead also increases substantially which is visible due to the high execution time as shown in Figs. 18, 19, 20 and 21. It is also important to note that the execution time does not decrease directly by increasing the number of cores due to the fact that there are communication overheads which arises when multiple threads communicate with each other simultaneously. This clearly signifies that all tasks cannot be parallelized 100%. Therefore, it is observed that, for an octacore system the optimized performance is achieved with three cores if compared to the single core and double implementing made by Akhtar and Sidek [37]. In addition to this, four cores implementation could also be opted due to the fact that with only 4% increment in CPU cores average usage, a decrement in execution time up to 1000 ms is obtained. Depending upon the overall

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Fig. 18 Average CPU utilization using 5 cores

Fig. 19 Average CPU utilization using 6 cores

specs of the system, the user can explore the best possible optimized performance i.e., for 16 cores system, 5–6 cores can give an optimized performance using the IAA. In terms of bus-bandwidth consumption, IAA works well as it utilized optimum bus bandwidth utilization as per the implementation by Akhtar and Sidek [37], whereby 4 masters were used i.e., D_Type, DR_Type, NDR_Type and NDR_Type to implement the STREAM modules with ten iterations of each function for a fine accuracy. In their proposed experiment, uniform 25% bus bandwidth were allotted to each master and post IAA implementation, the fluctuation in the bandwidth were within the range of ±5% for each master. If compared to other arbitration techniques i.e., Static Fixed

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Fig. 20 Average CPU utilization using 7 cores

Fig. 21 Average CPU utilization using 8 cores

Priority (SFP), Round Robin (RR), Adaptive Arbitration (AA), the fluctuations in bus bandwidth optimization were minimum for IAA as shown in Fig. 22.

7 Conclusion It can be concluded on the basis of various NoC topology, task parallelism, modeling of functional modules (masters) and Intelligent Adaptive Arbitration, that there is a possible room of research to be carried out in this segment, whereby Intelligent Adaptive Arbitration could be used for modeling multi-core based NoC devices.

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100.00% 80.00%

SFP

60.00%

RR

40.00%

AA

20.00% IAA

0.00% D Type

D_R Type ND_R Type ND_R Type

Fig. 22 Average bandwidth fluctuation for various arbitration techniques

Moreover, we would like to open up a room for the researchers that in terms of optimized CPU cores usage, fair bandwidth allocation and low latency, the Intelligent Adaptive Arbitration combined with NC_NoC architecture could be a viable option and superior if compared with other conventional NoC arbiters. In the new generation of computing, when the speed of the processor is getting increased by including higher number of cores, it becomes essential to exploit the multiple cores of the processor, using the concept of multiprocessing programming. Researchers are free to explore the possibilities to utilize intelligent adaptive arbitration algorithm to exploit multiple cores of the processor using thread level parallelism and map-reduce computing [39, 40]. The proposed review intends to help the researchers for optimized task scheduling in NoC architectures. Acknowledgements This research is supported by the School of Aerospace Engineering, Universiti Sains Malaysia using the RUI Grant 1001/PAERO/8014035; RU Top Down Grant 1001/PAERO/870052 and Institute of Post Graduate Studies (IPS) under USM Fellowship and School of Electrical and Electronics Engineering (EEE).

References 1. Dumitrescu C, Ciocoi V, Pop M (2006) Power QUICC™ II pro family of communications processors: a broad range of advanced functionality in IP convergence. WSEAS Trans Electron 3(6):330 2. Kyriakakis E, Ngo K, Öberg J (2017) Implementation of a fault-tolerant, globallyasynchronous-locally-synchronous, inter-chip NoC communication bridge on FPGAs. In: 2017 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and international symposium of System-on-Chip (SoC) 3. Tutsch D, Hommel G (2008) MLMIN: a multi-core processor and parallel computer network topology for multicast. Comput Oper Res 35(12):3807–3821 4. Ebrahimi M, et al (2012) HARAQ: congestion-aware learning model for highly adaptive routing algorithm in on-chip networks. In: 2012 IEEE/ACM sixth international symposium on networks-on-chip 5. Gray J (2016) Grvi phalanx: a massively parallel risc-v fpga accelerator. In: 2016 IEEE 24th annual international symposium on Field-Programmable Custom Computing Machines (FCCM)

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6. Shieh W-Y, Pong C-C (2013) Energy and transition-aware runtime task scheduling for multicore processors. J Parallel Distrib Comput 73(9):1225–1238 7. Márquez AL et al (2011) Parallelism on multi-core processors using Parallel, FX. Adv Eng Softw 42(5):259–265 8. Abadi M et al (2018) A scalable and adaptable hardware NoC-based self organizing map. Microprocess Microsyst 57:1–14 9. Agarwal A, Iskander C, Shankar R (2009) Survey of network on chip (noc) architectures & contributions. J Eng Comput Architect 3(1):21–27 10. Zitouni A, Tourki R (2008) Arbiter synthesis approach for SoC multiprocessor systems. Comput Electr Eng 34(1):63–77 11. Abid N et al (2013) A modular and generic router TLM model for speedup network-on-chip topology generation. In: 10th international multi-conferences on Systems, Signals & Devices (SSD13) 12. Babu YA, Prasad G, Solomon JB (2018) Design of low-power and high-performance network interface for 2 × 2 SDM-based NoC and implementation on spartan 6 FPGA. In: Progress in advanced computing and intelligent engineering. Springer, pp 545–551 13. Andión JM et al (2013) A novel compiler support for automatic parallelization on multi-core systems. Parallel Comput 39(9):442–460 14. Chen C-H, Lee G-W, Huang J-D, Jou J-Y (2006) A real-time and bandwidth guaranteed arbitration algorithm for SoC bus communication. In: Asia and South Pacific conference on design automation: IEEE, pp 600–605 15. Loucif S (2013) Performance evaluation of hierarchical-torus NoC. In: 2013 27th international conference on advanced information networking and applications workshops: IEEE 16. Hu J-h, Zhang S-w (2011) NoC architecture with local bus design for network coding. In: 2011 6th international ICST conference on communications and networking in China (CHINACOM): IEEE 17. El-Moursy MA, Ismail M (2008) High throughput high performance NoC switch. In: 2008 NORCHIP: IEEE 18. Kunthara RG, James RK (2019) Performance comparison of asynchronous NoC router architectures. In: International conference on computer networks and communication technologies, Springer 19. Attia S et al (2018) Optimizing FPGA-based hard networks-on-chip by minimizing and sharing resources. Integration 63:138–147 20. Dielissen J, Radulescu A, Goossens K, Rijpkema E (2003) Concepts and implementation of the phillips network-on-chip. In: Proceedings of IP based SOC (IPSOC): IFIP 21. Pestana SG et al (2004) Cost-performance trade-offs in networks on chip: a simulationbased approach. In: Design, automation and test in Europe conference and exhibition, 2004: Proceedings 22. Goossens K (2005) Formal methods for networks on chips. In: Fifth international conference on application of concurrency to system design, 2005. ACSD 2005 23. Kumar S et al (2002) A network on chip architecture and design methodology. In: Proceedings of IEEE computer society annual symposium on VLSI, 2002 24. Mahadevan TBS (2006) A survey of research and practices of network-on-chip. ACM Comput Surv (CSUR) 38(1):50–51 25. Jantsch A, Lauter R, Vitkowski A (2005) Power analysis of link level and end-to-end data protection in networks on chip. In: IEEE international symposium on circuits and systems, ISCAS 2005 26. Babu YA, Prasad G (2018) Performance analysis and implementation of highly reconfigurable modified SDM-Based NoC for MPSoC platform on Spartan6 FPGA. In: Progress in intelligent computing techniques: theory, practice, and applications, Springer, pp 441–449 27. Guerrier P, Greiner A (2000) A generic architecture for on-chip packet-switched interconnections. In: Proceedings on design, automation and test in Europe conference and exhibition 2000

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28. Andriahantenaina A, Greiner A (2003) Micro-network for SoC: implementation of a 32-port SPIN network. In: Design, automation and test in Europe conference and exhibition 29. Pandey K, Gaikwad MA (2018) Review of different topologies for Noc architecture using NS2 30. Leiserson CE (1985) Fat-trees: universal networks for hardware-efficient supercomputing. IEEE Trans Comput C-34(10):892–901 31. Yao Y, Lu Z (2018) iNPG: accelerating critical section access with in-network packet generation for NoC based many-cores. In: 2018 IEEE international symposium on high performance computer architecture (HPCA). IEEE 32. Felicijan T, Bainbridge J, Furber S (2003) An asynchronous low latency arbiter for Quality of Service (QoS) applications. In: Microelectronics, 2003. ICM 2003. Proceedings of the 15th international conference 33. Bjerregaard T (2005) The MANGO clockless network-on-chip:Concepts and implementation. In: Informatics and mathematical modeling. 2005, Technical University of Denmark: Lyngby 34. Bjerregaard T, Mahadevan S, Olsen RG, Sparsø J (2004) A channel library for asynchronous circuit design supporting mixed-mode modeling. In: Proceedings of the 14th international workshop on power and timing modeling, optimization and simulation (PATMOS) 35. Dall’Osso M, et al (2003) Xpipes: a latency insensitive parameterized network-on-chip architecture for multiprocessor SoCs. In: Computer design, 2003. Proceedings. 21st international conference (2003) 36. Bertozzi D, et al (2005) NoC synthesis flow for customized domain specific multiprocessor systems-on-chip. Parallel Distrib Syst IEEE Trans 16(2):113–129 37. Akhtar MN, Sidek O (2013) An intelligent adaptive arbiter for maximum CPU utilization, fair bandwidth allocation and low latency. IETE J Res 59(1):48–54

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Prototype Design for Rubik’s Cube Solver A. M. Andrew, W. Faridah, W. H. Tan, S. Ragunathan, A. S. N. Amirah, N. A. N. Zainab, and F. S. Lee

Abstract Rubik’s cube is a modern day plastic material puzzle, where the one need to twist and turn to solve the multicolour squares. At the end, the puzzle need to be solved to make all the same colour cubes on the same surfaces. High intelligence is needed to solve it at a shorter time. Therefore, this research is to create a robot solver to solve the Rubik’s cube in short time smartly. Initially, the algorithm will scan the six surfaces of unsolved cube through a webcam and register the colours in the memory. Once it is completed, the solving algorithm will analyse and instruct the servo motors to twist and turn based on the computed solution. It is then pass it to the solving algorithm to identify the solving process and send the moving instruction to the motors by Raspberry Pi. The robot body was constructed using laser cutter. It is to make sure that the measurements are accurate and correct. To solve the cube, four servo motors with high efficiency were used to twist the cube in certain legit with the rules of the game. Another four servo motors are connected to the rack and pinion gears by clamping to hold the Rubik’s cube from falling when it is twisted. An improvised “Kociemba Algorithm”, also called as “The Two-Phase-Algorithm” is used. It reduced the required moves to solve the cube into a maximum of 25 moves and a minimum of 19. Conclusively, the final prototype discussed in this paper is tested with the solving algorithm. The results are presented. Keywords Rubik’s cube · Robot solver · Image processing · Raspberry · Pi · Servo motor

A. M. Andrew (B) · F. S. Lee Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia e-mail: [email protected] W. Faridah · S. Ragunathan · A. S. N. Amirah · N. A. N. Zainab Faculty of Civil Engineering Technology, Universiti Malaysia Perlis (UniMAP), Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia W. H. Tan Faculty of Mechanical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_3

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1 Introduction The Rubik’s cube has been one of the most interesting and challenging puzzles ever built. It continues to remain in the top among the puzzle games because of the design of the puzzle [1]. For instance, one correct solution is out of the 43 quintillion other possibilities [2]. If a solution needs 1 byte, the total memory needed will be 43 million terabytes hard disks size. In construction, the Rubik’s cube is a cube structure with 3 × 3 × 3 smaller cubes arranged in it. It has a total of six colours on its six faces. All the smaller cubes arranged around it can be moved vertically or horizontally. A professional solver can solve the cube as fast as less than one minute, and an amateur solver can take even hours to solve it [3]. There are other varieties and sizes of Rubik’s cube available, with 33 × 33 × 33 size is the solvable cube with Guinness World Record [2]. However, only those who know the trick can solve the shuffled cube. McManus, E. (2018) mentioned that the mathematical formations behind Rubik’s cube make it solvable [2]. It might take minutes, hours or days to solve it. It starts from a step-by-step solving of the scrambled cube’s layer until each surface have same colour. Therefore, having intelligent algorithm with image processing capability implemented and Internet-of-Things (IoT) in designed mechanical structure, a robot could be made to solve it [4]. A good mechanical design must be supported by the right algorithm [5, 6]. Thus, in this research, a cage-like prototype is constructed to hold and solve the Rubik’s cube. The developed robotic system can solve any randomised 3 × 3 Rubik’s cube with less than 25 steps of rotations. The main objective of this research is to build a prototype to support the Rubik’s cube solver algorithm that is able to read the initial state of the Rubik’s cube reliably, find a solution and then solve the cube using the solver.

2 Literature Review There are several methods and devices used to solve the Rubik’s cube [7]. Several researches had performed by many institutions throughout the world to offer the best Rubik’s cube Solver [2]. Therefore, this section gives a brief survey on various Rubik’s Cube solvers in the world.

2.1 Mind Cuber [2] MindCuber is a single armed solver by David Gilday. The single arm is responsible for holding the cube in place whilst the lower platform rotates the D face of the cube. The single arm is also responsible for performing cube rotations. This robot can be built using an EV3 LEGO Mindstorms set which makes it cheaper to be built. The

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limitations lie with its design. Since only a single side can be turned at a time, the cube needs to be rotated every time a face need to be turned. This is extremely time consuming. The MindCuber uses a single Red, Green and Blue (RGB) sensor to read each square individually [8].

2.2 JPBrown’s CubeSolver [2] JPBrown’s CubeSolver is one of the first attempt in building a cube solving robot. The robot uses 3 arms built from LEGO. This allows it to move three independent faces without cube rotations. JPBrown’s clamping mechanism uses a complex gearing system which makes the face move slowly but accurately [9, 10]. The vision system is webcam based. The cube must be presented in a very specific area of the camera frame. The robot itself is powered by 2 Retrocommissioning (RCX) Intelligent Bricks and a Personal Computer (PC). The PC is responsible for finding a solution and parsing the camera frames for the vision. The Bricks are responsible for robot movement. Since the CubeSolver has a PC at its disposal, Kociemba’s algorithm is used as the algorithm of choice. This is because it will find a solution within a relatively short time. Since the execution of moves is slow, the solution needs to be as short as possible.

2.3 GoCube [2] GoCube is a new and improved puzzle that is fitted with smart sensors and Bluetooth connectivity to make puzzle-solving more engaging. It is basically designed to track and measure the movements in order to help players to solve the classic puzzle, sharpen the skills and beat other players too. Moreover, it is built as a speed cube. It is designed with special mechanisms to make it turn smoother, faster and less clunky than the classic Rubik’s cube, making it more enjoyable to play with. This smart connected toy can also help players to learn how to solve the puzzle in just one hour. From there, the player can keep on tracking and improving their solving skills to solve the cube in shorter time.

2.4 MIT Rubik’s Cube Solver [2] The Rubik’s cube solver built by MIT can solve the 3 × 3 cube within a second, with staggering 20 moves/seconds. The project was targeted as the fastest Rubik’s Cube Solver in MIT. The project used six stepper motors as actuators. However, this project uses no sensor. The middle Rubik’s cube could not be taken out as is it fixed to the main body since they only focused on fast solving.

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3 Methodology In this section, the methodology used in this developing the prototype design is carefully explained. Figure 1 shows the flowchart of the prototype design. Before building the 3 × 3 Rubik’s cube solver prototype, the required components are identified. When the fabrication of the solver robot prototype has been completed, a few test runs are performed to make sure that the prototype is functioning properly. The frame of the robot is carefully designed in Fusion 360 designing software. The Computer-Aided Design (CAD) was developed around the size of the cube. The design is meant to be easy to be manufactured, simple to construct and repair, and most importantly, providing a relatively unobstructed view of the cube during the entire solving process [9, 10]. Once it is perfected in the software, the design is transferred to laser cutting machine for physical prototype preparation. 5 and 10 mm acrylic are used for the manufacturing process. Figure 2 shows the base design using Fusion 360. Figure 3 shows the base prototype done by laser cutting process. The prototype is to be rigid and strong, so that, the movements of the servo motors can be translated into precise rotations of the Rubik’s cube faces. The robot is designed to consist of only two unique parts, consists of only two identical plates and four identical side plates. Having identical parts enables to create more spare parts in case of replacement needed. The main idea of having minimalist and transparent materials in the prototype design is to provide unobstructed view during the cube solving process.

3.1 Rack and Pinion Rack and pinions are the type of mechanical gears which enables the rotary to translational movement conversion or vice versa. In this prototype, the rack is attached to servo motor MG92B. The height has been set to ensure the Rubik’s cube can be twisted fully 360°. On the other hand, the pinion is attached to servo motor SG92R. The pinion will be static. When the servo motor SG92R turns, the rack will follow it. The rack and pinions are fixed in rigid condition. Figures 4 and 5 shows the rack and pinion, designed in Fusion 360 and printed in 3D printer, respectively. The 3D printed parts are connected to the base and frames using a small sized screws and nuts.

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Fig. 1 Prototype design flowchart

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Fig. 2 Base design using fusion 360

Fig. 3 Base prototype using laser cutting

3.2 Logitech C270 Webcam In this research, Logitech C270 webcam is used to capture the six faces of the scrambled cubes. The captured images are used as the inputs for the image processing algorithm. The webcam is controlled by Raspberry Pi module. Webcam provides more accurate and fast input compared to other vision cameras. Webcams also are easily available and cost effective compared to other vision sensors. Webcam will convert colour signal into binary format and arrange it in matrix order. This will allow computer to process image quickly using image processing. MATLAB and Python is used to convert the images to colour input. Figure 6 shows the webcam used in this research.

Prototype Design for Rubik’s Cube Solver Fig. 4 Rack and Pinion using fusion 360

Fig. 5 Rack and Pinion by 3D printer

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Fig. 6 Logitech C270 webcam used in this prototype

Fig. 7 Thistlethwaite’s algorithm used in cube explorer software

3.3 Kociemba Algorithm Kociemba’s algorithm is a two-phase algorithm that is also known as God’s algorithm. God’s algorithm is a fancy term of most efficient algorithm in the sense that the Rubik’s Cube can be turned in the fewest number of times to be solved in any given state. Kociemba’s algorithm allows for the Rubik’s cube to be solved in only less than 25 turns. Kociemba’s algorithms is considered a twophase algorithm because after an initial solution is reached, additional optimal solutions are searched for solving. It was developed from the former most efficient algorithm known as Thistlethwaite’s algorithm. It solves the cube by looking at it in groups and phases like in Fig. 7. The

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Fig. 8 Graphical user interface developed using phyton

first group is the G0 group which is the randomly mixed cube group. At this stage there are no restrictions on what moves can be made when utilizing this algorithm. To complete this state, the cube needs at most 12 moves. The next group is the G1 group which is when the cube gets in a specific state of U, D, L2, R2, F2, B2. When the cube is in this state, there are specific moves that cannot be made. To complete this state, the cube needs at most 25 moves. This method is used in this research. Figure 8 shows the Graphical User Interface developed for this research using Phyton language.

3.4 Raspberry Pi 3B+ Raspberry Pi 3B+ is used in this research to store and process data. The motor is controlled by Raspberry Pi through General-Purpose Input/Output (GPIO) pin. This Raspberry Pi module uses 1.4 GHz microprocessor and 1 GB RAM which allows the image processing to run smoothly. This project uses Virtual Network Computing (VNC) which allows the project to run from a distance without the need of any physical connection. Figure 9 shows the complete prototype design of the Rubik’s cube solver developed using Fusion 360 design software in this research. Figure 10 shows the physical prototype made from acrylic and 3D printing materials.

4 Experimental Result and Discussions The final prototype is tested with the solving algorithm developed using Phyton language. The cube managed to be solved successfully in 19 moves. Figure 11 shows the Phyton programming and the developed GUI used for solving the cube. The program has 3 functions, Start, Solve and Calibrate.

48 Fig. 9 Complete prototype design using fusion 360 software

Fig. 10 Complete prototype using acrylic and 3D printer material

Fig. 11 Testing with Kociemba algorithm for Rubik’s cube solving

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Fig. 12 Colour space redundancy between red and orange colour

START – to capture six images. SOLVE – to calculate solution and to actuate servo for Rubik’s cube solving. CALIBRATE – to set boundaries of the hue, saturation and value for colour contours in system. The program works with 97% average accuracy, tested in various lighting conditions. The faces of Rubik’s cube can be capture easily when a proper lighting condition present. However, for some cases, there is misclassification between red and orange colour due to almost similar colour shades, which makes orange colour to be classified as red. Figure 12 shows the colour space redundancy between red and orange colours. The range of orange colour is overlapping with red. In order to overcome this misclassification, the algorithm is improvised with the correction algorithm. Firstly, the program detects the red pixels (Step 1). There is a chance of few orange pixels to be misclassified as red. Then, the orange pixels are detected separately (Step 2). Step 1 is compared to Step 2, and the overlapping pixels are identified. The overlapping pixels are re-registered as orange pixels.

5 Conclusion In conclusion, the paper focuses on the method involved in building the Rubik’s cube solver prototype, and on how this research combines the prototype and the algorithm to solve the Rubik’s cube successfully. The prototype uses 8 servo motors that are controlled by one pin I2C from raspberry PI. The base of the prototype is made of acrylic and 3D printing materials. The prototype is designed in such a way that the view of solving the cube will not be obstructed by components. The images captured using webcam and the images are processed by Raspberry Pi to create solution for the Rubik’s cube. Phyton programming language is used for programming of the

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Kociemba algorithm used in solving the Rubik’s cube. This research comprises the methods and experimental results in development of software for solving Rubik’s cube, no matter how hard the combination is. The main task of the program to create a solution for any scrambled Rubik’s cube. The prototype and the solving program can be used and improved further for education purpose. In the future research, the unfilled research gaps can be filled. The usage of better materials and motors can increase the efficiency of the robot immensely. Acknowledgements The authors would like to express sincere appreciation to Vice Chancellor of Universiti Malaysia Perlis for giving permission to use the facilities in the university for the system development and testing. The authors also thanking Universiti Malaysia Perlis for the financial support given.

References 1. Jozef V, Frantisek D, Juraj K (2014) Design of pneumatical Rubik’s cube solver. AMM 613:265–272 2. Fristrom B, Ludvig B (2016) Autonomous Rubik’s Cube Solver. Thesis 3. Higo R, Yamakawa Y, Senoo T, Ishikawa M (2018) Rubik’s cube handling using a high speed multi-fingered hand and a high-speed vision system. In: IROS, Madrid, pp 6609–6614. IEEE 4. Ilge A, Marcin A, Maciek C (2019) Solving Rubik’s cube with a robot hand 5. Zhao G, Tui H, Liu Y (2019) A design of magic cube robot based on STM32. In: MSE, vol 428, CACRE. IOP Conference Series 6. Zhou YL, Wang ZL, Zhu SQ, Gao HT, Li Y (2017) Design of a multi-servo control system based on PCA9685. JNIT 4(5) 7. Timothy S, Zheng C (2015) Computational design of twisty joints and puzzles. In: ACM Columbia education 8. Liu S, Jiang D, Feng L, Wang F, Feng Z, Liu X, Guo S, Li B, Cong Y (2019) Color recognition for Rubik’s cube robot. In: Computer vision and pattern recognition 9. Shaw JS, Debey V (2016) Design of servo actuated robotic gripper using forcecontrol for range of objects. In: ARIS, pp 1–6. IEEE 10. Hajduk M, Varga J, Durovsky F (2014) Optimization and design of four gripper pneumatical Rubik’s Cube solver. ACS 10(3):57–67

Automatic People Counting System Using Aerial Image Captured by Drone for Event Management Mohd Saifizi Saidon, Wan Azani Mustafa, Vinnoth Raj Rajasalavam, and Wan Khairunizam

Abstract Event management refers to the ability to apply project management skills in order to initiate large scale social or business events. Hence, it requires the use of organizational as well as business management skills to envision, plan, and finally execute any such event. Therefore, to count or estimate the number of people who attend such events is one of important tool in event management. In common, counting number of people in events can be done by counting manually traditional headcount system. Nevertheless, this process or technique consumes much time and is also a difficult task to execute for a considerable number of people or a bigger crowd. Therefore, a modern counting system like automatic people counting system is developed to enhance the process of counting people. Thus, various method of counting has been proposed in the past decades. Consequently, automatic counting people using digital image processing technique is introduced to overcome this problem. Thus, to monitor or to count the number of people can be done by using Unmanned Aerial Vehicle (UAV) or drones. The use of drones can take a broader picture, saving time and becoming more efficient. For this research, the DJI Mavic Pro Drone is used to scout the areas. This paper is focusing on counting the number of people images. Thus, the images are firstly compared between RGB and HSV colour model. Then, the HSV colour model has been chosen for the thresholding process. Here, the images are compared between Otsu thresholding and manual thresholding. Both thresholding method gives a good segmentation result, but Otsu’s method is chosen because of its higher accuracy. Moreover, noise removal technique is employed in order to get good smoothing performance and produce better counting results. This paper is fully developed with MATLAB R2013a software. This technique has proven

M. S. Saidon (B) · W. A. Mustafa · V. R. Rajasalavam Department of Electrical Engineering Technology, Faculty of Electrical Engineering, Universiti Malaysia Perlis, Arau, Malaysia e-mail: [email protected] W. A. Mustafa Sport Engineering Research Centre (SERC), Universiti Malaysia Perlis, Arau, Malaysia W. Khairunizam School of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Arau, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_4

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to be a good image processing technique with total accuracy of 91%. The hardware system is also developed to transmit the counting results. Keywords Unmanned Aerial Vehicle (UAV) · Matrix Laboratories (MATLAB) · Graphical User Interface (GUI) · Red, Green, Blue (RGB) · Hue, Saturation, Value (HSV)

1 Introduction Generally, event management is the coordination and design of an event. Firstly, the event has expanded with the development of mankind. Hence, events may be part of the cultural, business environment, social and also part of the tertiary sector. Events also can be described as non-standard services in which the behaviour, knowledge and commitment to such services providers are crucial. There are few criteria for excellent event management. Hence, knowing the number of attendance or participation for an event is also one of the criteria. Thus, the main objectives of the research are to study the estimation of people for event management. Nevertheless, to estimate the number of people for a large and undefined area is difficult. Therefore, a counting system is needed to develop for estimation of the total number of people come to an event, especially in outdoor activity. Hence, a simple estimation method which combines a count and measure of people involved in the activity can be used to generate an estimate of the total number of people attends the event [1, 2]. For instance, a group of people can be recorded or counted from taking part in a particular activity from the event. A vision system is a method used to provide an imaging-based automated view for many implementations [3, 4]. A camera is used to take image or pictures of any object or a location. Hence, it has been widely used for various purpose in daily applications. The people counting system is a system which able to estimate the number of people in outdoor or indoor [5–8]. Hence it is widely used nowadays mainly because it saves time and cost. In 2015, Cetinkaya et al. [9] proposed on a counting method on face detection, which automatically detects the face of a human from a video. Thus, a camera was placed at several places to capture 5-min video footage. Hence, an automatic faced detection counting system has developed. Thus, when a face is detected a new address will be given and if the formerly detected faced is detected the address of the formerly detected will be given to the recently detected faced, this will avoid the overcount of the same person again. To avoid noises being counted, a threshold method is then used to avoid or minimize the noise [10]. Besides that, computer vision is a discipline that deals with how a computer can acquire a higher level of understanding from digital videos or images. There is an article proposed on a passenger counting system based on computer vision [11, 12]. In addition, passenger counting has been a difficult task because it has a different background and the number of people or passenger getting in and out randomly. Thus, a solution to overcome is the problem is by hanging the camera in higher positions or can use a camera with a wide-angle lens. Moreover, people counting in inside building scenes is a demanding task that needs to be executed because there

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will be a coexistence of both stationary and moving crowd. Hence, few methods need to be done for this problem like detection of the head and shoulder and temporary refinement. Firstly, need to propose the detection of head and shoulder based on the counting of the crowd mainly for an indoor site, followed by segmentation of the foreground and noise removal. Then, need to temporal filter to further clarify the counting results. Firstly, UAV or drone has been widely used for various purpose nowadays. This is because it is easy to collect data in video or image [13, 14]. Besides that, it has been widely used in many applications such as scientific, commercial, agricultural, and as well as in military applications [15–18]. UAV or drones has it been widely used nowadays in various purpose to collect data as digital images or videos. It can travel with a predefined speed and direction which can remotely control. In addition, it also can cover a broad area and can be focused on a particular area. Hence, [19] has proposed a study on the usage of the unmanned aerial vehicle as an alternate technique on monitoring pedestrian traffic. Hence, UAV has many advantages over common data collection techniques. For instance, it can collect data in wide and compress area coverage. In addition, it also labour-intensive and can overcome space limitation. Firstly, digital image processing (DIP) is about the assign of various digital images by a computer [20, 21]. It is the area of signal and system which focus on images. Then, it also has a centre of interest in enhancing a computer system to execute a task on image processing. Colour plays two important roles in image processing. First, the process of object identification and object extraction from the scene would be utilizing colours that are easier to simplify. As such, a human is more lightly recognize thousands of colour shades and intensities. The colour model of HSV (hue, saturation, value) is the type of colour image segmentation. There are colour models of which is based on three aspects like technical, physical and physiological. Besides that, the HSV colour of models can also be denoted as HSB (Hue, saturation, Brightness). Hue can be categories as few types like magenta, blue, yellow and green. As for Hue, it has a range of 0–360°. For the range between 0–255, the value and saturation colour model will take place. Image segmentation is the most important technique that needs to follow as a part of image processing. One of the main reason of image segmentation is to segment or to separate the entire original image into a few parts that will be easier to carry out the further process [22, 23]. Besides, segmentation also depends on assorted features that comprise in the image like texture or colour. It is essential to segment any image by recovering the original image before removing any noise. Thus, it is vital that the image needs to be segmented first before any process is carried out later, on which is also to reduce the information from the original image for easier and smoother analysis. There are few image segmentation techniques like feature-based clustering, thresholding, edged-based, region-based and model-based. From here, thresholding is the simpler, easier and computationally faster image segmentation technique [24–26]. Image processing plays an important for the manipulation and analysis of the collected data. Thus, image processing will be mostly dealing with image acquisition or retrieval, image segmentation, image enhancement, image classification and features extraction. Thus, before any imaging process is carried out, there should be a sample or data to analysis. Hence, image acquisition or retrieval process need to carry out first. It is a process to acquire a digital

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image. There are many methods to collect digital images such as digital cameras, scanner or aerial photography like usage of drones. The images that collected usually would be in RGB format.

2 Methodology Image acquisition is the starting of methodology and followed by with image processing as shown in Fig. 1. Fig. 1 Image processing step

Start

Capturing image by using drone.

Transferring data from drone.

Image processing

ClassificaƟon of image processing

Process of counƟng number of people

No EsƟmaƟon the number of people

Verify Yes End

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For image processing, the steps for segmentation techniques that will be applied to people counting images will be discussed. Meanwhile, for the explanation about the techniques uses will also clarify in the image segmentation process.

2.1 Unmanned Aerial Vehicle (UAV) or Drones DJI Mavic Pro Drone is the type of drone used in this paper as shown in Fig. 2. This drone is already built-in with the high-quality camera with resolution 12 megapixels. DJI Mavic Pro Drone is easy to handle and able to capture the excellent image in any height applied. The drone and controller need to be calibrated first before starting to capture the data. Camera conditions and battery rate is also the critical criteria that need to monitor before the drone start flying. The good quality image cannot be captured if the camera covered with the dust or ash. Meanwhile, the battery of the drone must be fully charged. Then, with a full battery, the drone will be flying approximate 30 min. The place for drone flying is also needed to ensure it is safe. The place with obstacles needs to avoid because the drone cannot fly or land properly on such conditions.

2.2 Image Acquisition Image acquisition is the first step of the paper to get the data. Image acquisition can be defined as the activity of retrieving an image from some source, so it can go through any procedures or process need to happen subsequently. Several factors need to be considered before doing the research, such as weather and wind flow before collecting the data. The position of the drone must be at an appropriate height to get a clear and smooth image. The images have been saved in jpeg (*.jpg) format. After the image has been obtained, various image processing techniques have been applied in order to segment the image. Fig. 2 DJI mavic pro drone

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2.3 Image Processing The image processing is the next step after image data is being transferred completely to undergo the people counting. There are few image processing techniques has been proposed to segment the people counting images. The image segmentation approach is conversion the images into Hue and Value components based on the HSV colour model. Then, conversion of R, G, B components based on the RGB colour model.

2.4 Otsu Thresholding Method Otsu’s method is used to automatically perform histogram shape-based image thresholding, or, the reduction of a grey level image to a binary image. The algorithm assumes that the image to be thresholded contains two classes of pixels or bi-modal histograms like foreground and background then calculates the optimum threshold separating those two classes so that their combined spread (intra-class variance) is minimal.

2.5 Manual Thresholding Method Thresholding is used to segment an image by setting all pixels whose intensity values are above a threshold to a foreground value and all the remaining pixels to a background value. There are two types of thresholding which are manual thresholding and automatic thresholding. Manual thresholding is the selection of thresholding values for image segmentation. The values of hue and value are set manually to determine the human pixels. After the colour conversion of HSV colour space is obtained, the images are then segmented by applying manual thresholding method, and this manual threshold is also applied at RGB colour space. In the simplest implementation, the output is a binary image representing the segmentation. Black pixels corresponds to the background and white pixels corresponds to the foreground (or vice versa).

2.6 Gaussian Filter After the process of segmentation by using thresholding techniques, there will be small spots that still need to be encountered in the image. These small spots are noise, and this has to be removed. It is because later on, it will affect the accuracy of people counting. Hence, the image has been filtered by removing the noise. The Gaussian filter has been chosen to filter the image by removing the pixels that do not belong to the object in question. In addition, the Gaussian filter is best known for

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remove noises by smoothing. Gaussian provides gentler smoothing and preserves edges better than a similarly sized mean filter [27].

2.7 Classification of Accuracy (Automated People Counting Method) This is the process of identifying the number of people. The counting is based on the number of the bounding box on the images. The eventual result of this detection and counting process is the generation of the number of people in the message. Then the accuracy of the classification can be measured using Eq. 1. Accuracy =

Manual count − [Manual count − System Count] × 100% (1) Manual count

2.8 System Development of GUI Graphical User Interface (GUI) is developed to make it the system friendly for user to use. GUI will conduct the segmentation process and estimation number of people as shown in Fig. 3. There has one button for all processes, which means with one uploaded image button will directly segment the image and classified their class right after the one image uploaded. Moreover, GUI also used as software applications which eliminate the need to type commands into MATLAB in order to run the system. After the completion of software development, system development is proceeding

Fig. 3 The development of GUI

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with the development of the hardware module. This is used as an indicator for the people to know the number of people.

2.9 System Configuration for Real-Time Development The real-time development of the automated passenger counting system is crucial for real-time application. Hence, this section will describe the hardware development of this paper. Thus, the detailed information of this paper development is important for understanding and real-time development.

3 Results 3.1 Segmentation of Image Based on the HSV Colour Model Figure 4(a) is presented with the original image. The researchers will then need to resize the image to make the data is much accurate. Then, the colour segmentation based on the HSV components method has been applied. Hence, both the H and V

Fig. 4 Segmentation of images based on HSV color model; a: Original image, b: Hue (H), c: Saturation(S) and d: Value (V)

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component were chosen, whereas the S component is excluded. The decision has been made because it is more accurate for further analysis. As such, 10 images have been analysed on its accuracy for both HSV and RGB colour model, and the percentage is presented below as details.

3.2 Segmentation of Image Based on the RGB Colour Model Figure 5(a) is presented with the original image. The researchers will then need to resize the image to make the data much more accurate. Next, the colour segmentation based on the RGB components method has been applied. Hence, all the R, G, B has been chosen. The decision has been made because it is more accurate for further analysis. Hence, from the percentage of accuracy, it shows that the HSV model gives more accurate counting then RGB model in Eqs. 2 and 3. Hence, the HSV colour model has chosen to next image processing technique. In terms of digital image processing, the RGB model is commonly be used for colour monitors and a broad class of colour video cameras. However, the RGB model does not correspond closely to the way human describe and interpret colour. The HSV colour model corresponds closely to the way human

Fig. 5 Segmentation of images based on RGB color model; a: Original image, b: Red(R), c: Green (G) and d: Blue (B)

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interprets colours. Besides, the HSV colour model has an advantage in combining the colour and grayscale information in an image, thus making it suitable for many applications of grey level processing techniques. For display or printing, can choose a primary colour; thus that more colour can be produced. Hence RGB can be used for displaying and CMY for printing purposes. For analytical analysis of colour differences, HSV is more suitable. Percentage of accuracy for HSV :

128 − [128 − 135] × 100% = 94.5% 128

(2)

Percentage of accuracy for RGB :

128 − [128 − 146] × 100% = 85.9% 128

(3)

3.3 Comparison Between Otsu Thresholding and Manual Thresholding Thresholding is used to segment an image by setting all pixels whose intensity values are above a threshold to a foreground value and all the remaining pixels to a background value. Hence, thresholding is a simple segmentation whereby the conversion of a grayscale image to a binary image where the image is partitioned into two sets. Otsu thresholding is an automatically perform histogram shape-based image thresholding, or, the reduction of a grey level image to a binary image, whereas manual thresholding needs to be manually threshold the image. Hence, both manual threshold and Otsu threshold can be used to convert the grayscale image into a binary image, but Otsu method is chosen because it is much accurate and takes a shorter time compared to manual thresholding method. As such, 10 images have been analysed on its accuracy for both Manual and Otsu thresholding as shown in Fig. 6, and the percentage is presented below as detailed in Eqs. 4 and 5. Percentage of accuracy for Manual Threshold :

129 − [129 − 136] × 100% = 94.5% (4) 129

Percentage of accuracy for Otsu Threshold :

129 − [129 − 124] × 100% = 96.1% 129 (5)

3.4 Gaussian Filter After the process of thresholding, the image will undergo a filtering process. There are still few large unwanted objects appears with similar people colour pixel value that is

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(a): Otsu Thresholding

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(b): Manual Thresholding

Fig. 6 Thresholding result for otsu and manual thresholding

(a)

(b)

Fig. 7 Image undergo a few processes; a Gaussian filter and b Region growing technique

still inside the segmented image. Thus, the filtering method used in this segmentation is a Gaussian filter as shown in Fig. 7. This process will to make the image more apparent and remove some noises. Thus, it would be easier for the counting process. Then, the region growing techniques are also applied to remove the unwanted objects with an area less than 2500 pixels from the image.

3.5 Classification of Accuracy After performing all the image processing techniques, the counting of people has been carried out. The final process is counting the number of people in the image as shown in Fig. 8. Hence, for this part, 30 samples have been taken to carry out the process and also for the accuracy as in Eqs. 6 and 7.

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Fig. 8 Image with a bounding box for the counting process

Percentage of accuracy for 30 Images :

427 − [427 − 388] × 100% = 91% 427

(6)

[427 − 388] × 100% = 9% 427

(7)

Percentage of error for 30 Images :

3.6 The System of GUI for People Counting System The Estimation People Counting System was purposed by using the Graphical User Interface (GUI) in Matrix Laboratory (MATLAB). This system builds for easier the user to conduct the segmentation process without the need to install the MATLAB software in their computer to segment the image. Based on Fig. 9, there are two buttons can be used to load the images. The first button can load only single images and then the images will directly be segmented after the image was selected and upload. Then, another button is to load multiple images and then will segment all the images and will perform the overall counting. The result of classification the number of people will be produced after the finish segment the image.

3.7 The Results of Proposed Automated People Counting This section will describe the real-time development of the Automated People Counting system. The hardware of this system was developed to implement the

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Fig. 9 GUI development for automated people counting system

Fig. 10 Notification message at mobile phones

proposed system. Overall, this section will focus on the results for this proposed system on real-time application. The MATLAB software is merged with the Arduino Uno and GSM module to display the results of counting at mobile phones. Based on the real-time application, the counting of people’s image has been carried out by using the images taken by drone. Here, the results of the counting will be displayed as a notification text on a mobile phone. Hence, these results of counting are able to receive or send the information to other channels as well (Fig. 10).

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4 Conclusions In this research, an automated people counting system for event management which focus on image processing technique to count people has been developed. During the counting process, the images of people were taken as samples. Thus, to perform the counting process, various methods and applications of image processing techniques have been applied on images. Firstly, the paper is started with the segmentation of images based on the HSV and RGB colour model. Hence, a comparison was made between both the colour model and HSV colour model has better accuracy. Thus, it has been chosen to proceed to the next process. For the next process, the segmentation has been compared between manual thresholding and Otsu thresholding. For manual threshold, the best values of Hue (H) and Value (V) has chosen and been analysed to provide better segmentation results. For Otsu’s threshold, the Hue (H) has chosen to segment the image automatically. Here both the method gives almost a similar accuracy, but Otsu’s method is chosen because it is more accurate and also faster. Then, after the segmentation process, the removal of noise technique has been applied to remove the object that has a small pixel than the desired object pixel. Thus, the Gaussian filter has been chosen as the image filtering method to make the image smoother and achieved a better accuracy in the calculation of results. Then, several samples of the image of people were counted manually and automatically. In contrast, the automated counting of people images was compared with manual counting. Based on the counting comparison, there is a slight difference between both the manual and automatic counting. Later on, a graphical user interface (GUI) has been developed to enhance the system faster and easier. In overall, the automated people counting system is able to count the number of people with an accuracy of 91%. In addition, a real-time application also has been developed for a better application of this system. In short, the main objective of the paper is to establish a system to estimate the number of people for event management has been achieved successfully based on the information and data collected through the research. Acknowledgements The authors gratefully acknowledge the financial support from UniMAP.

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6. Reddy GB, Aruna M, Arthi B, Padmapriya G (2020) An information system for counting people. Test Eng Manag 82:2034–2036 7. Harasse S, Bonnaud L, Desvignes M (2005) People counting in transport vehicles. Proc World Acad Sci Eng Technol 4(4):221–224 8. Wahyuni ES, Alinra RR, Setiawan H (2018) People counting for indoor monitoring. In: 3rd international conference on computing, engineering, and design, ICCED 2017, pp 1–5 9. Cetinkaya HH, Akcay M (2015) People counting at campuses. Procedia Soc Behav Sci 182:732–736 10. Mustafa WA, Yazid H (2016) Background correction using average filtering and gradient based thresholding. J Telecommun Electron Comput Eng 8:81–88 11. Lengvenis P, Simutis R, Vaitkus V, Maskeliunas R (2013) Application of computer vision systems for passenger counting in public transport. Elektron. ir Elektrotechnika. 19:69–72 12. Khan SH, Yousaf MH, Murtaza F, Velastin S (2020) Passenger detection and counting for public transport system. NED Univ J Res XVII:35–46 13. Nguyen HD, Na IS, Kim SH, Lee GS, Yang HJ, Choi JH (2019) Multiple human tracking in drone image. Multimed Tools Appl 78:4563–4577 14. Rubinstein D, Tuck S (2019) Drone alliances. In: Fragmentation of the photographic image in the digital age, pp 73–79 15. EIJ Earth Image Journal: A Robotic Water Sampling Drone to Improve Aquatic Understanding « Earth Imaging Journal: Remote Sensing, Satellite Images, Satellite Imagery 16. Silver A (2019) Drone takes to the skies to image offshore reefs 17. Tanaka KI, Oyama K (2017) Drone and security. J Inst Image Electron Eng Jpn 46:595–598 18. Jong-Hwan B, Jin-Seong J, Myeong-Suk P, Sang-Hoon K (2016) Design of walking drone using image processing for logistics. Adv Sci Lett 22:2288–2291 19. Sutheerakul C, Kronprasert N, Kaewmoracharoen M, Pichayapan P (2017) Application of unmanned aerial vehicles to pedestrian traffic monitoring and management for shopping streets. In: Transportation research procedia, pp 1717–1734 20. Proakis G, Ingle VK (2010) Digital Signal Processing using Matlab 21. Blanchet G, Charbit M (2013) Digital Signal and Image Processing using MATLAB® 22. Mustafa WA, Khairunizam W, Ibrahim Z, Ab S, Razlan MZ (2018) A review of different segmentation approach on non uniform images. In: IEEE international conference on computational approach in smart systems design and applications (ICASSDA), pp 1–6. IEEE 23. Mustafa WA, Abdul-nasir AS, Yazid H, Jaafar M (2017) A comprehensive study of different approaches for malaria parasites segmentation. In: International conference on computer science and computational mathematics, pp 347–353 24. Yogamangalam R, Karthikeyan B (2013) Segmentation techniques comparison in image processing. Int J Eng Technol 5:307–313 25. Mustafa WA (2017) A proposed optimum threshold level for document image binarization. J Adv Res Comput Appl 7:8–14 26. Mustafa WA, Kader MMMA (2018) Binarization of document image using optimum threshold modification. J Phys Conf Ser 1019:1–8 27. Mustafa WA, Yazid H, Yaacob S (2015) Illumination correction of retinal images using superimpose low pass and gaussian filtering. In: International conference on biomedical engineering (ICoBE), pp 1–4

Automatic Counting of Palm Oil Tree Using Satellite Aerial Imagery Mohd Saifizi Saidon, Wan Azani Mustafa, and M. A. Izzat

Abstract Palm oil is an important economic crop in Malaysia and other tropical areas. The amount of palm oil in a plantation area is critical to forecast the yield of palm oil, to track and improve the productivity of palm-trees. The conventional manual counting approach is an unreliable result. So many processes are needed to pass and to document the outcome take a lot of time. In specific agricultural areas, palm tree counting is a crucial problem. A satellite (Google Earth Pro) has been used to gather data. The technique for the processing of images is identified and will detect and segment palm tree counts. Palm oil can be properly detected by counting palm oil based on this method in this study area. The image is processed in various techniques such as Gaussian filtering, sectional division and the unwanted object is removed. This enables a realistic, viable and effective image processing technique to calculate the average palm tree number with 94%. Keywords Counting system · Palm oil · Satellite · Image

1 Introduction The process for this application has become high-resolution images for remote sensing [1, 2] with an automatic palm tree detection. Along with automated processing devices, these images are a faster, time-consuming and costly alternative rather than an in-situ computation [3, 4]. An automated detection technique of the single palm tree was suggested in this paper using clouds of photogrammetric points. Visual SFM is the use of a movement-toolchain system to process images recorded using a single camera. The pictures were divided into three categories: palm, bust/trees and soil. The classification in which the classification is trained for M. S. Saidon (B) · W. A. Mustafa · M. A. Izzat Department of Electrical Engineering Technology, Faculty of Electrical Engineering, Universiti Malaysia Perlis, Arau, Malaysia e-mail: [email protected] W. A. Mustafa Sport Engineering Research Centre (SERC), Universiti Malaysia Perlis, Arau, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_5

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the training and test data are set at different scale factors has been subject to a multiscale dimensionality criterion [5, 6]. The local dimensional characteristics of point clouds are used to distinguish palm and soil plants. For these algorithms, the creation of a dataset classifier is constrained. Since more computing resources and the time consumption are required in the training classifier, the classificator must be trained before detection of trees for each type of tree [7, 8]. The Scale Invariant Feature Transform SIFT [9, 10], used to extract key points and to identify the key issues extracted using an Extreme Learning Machine (ELM) [11, 12] pretrained classifier. Each palm tree detected as key points as production was identified by the ELM classification. The key points extracted are fused using the active contour-based method based on the level-set (LS) [13, 14] in order to capture shape. As some grain crops can be mistaken for palm, texture analyses are performed in the classification of areas of palm and non-palm. The high cost of training including time and computer power are limited by kernel based learning tools. The detection/classification method is also restricted to individual species if ELM needs to be trained before use if trees of different species must be detected. According to literature [15–17], a threshold algorithm was applied to cover the pixels associated with tree palms, soil and other trees. In the meantime, they used their technique to achieve consistent results. In this paper, MATLAB has been chosen to finish. Instead, the use of the water-shifting algorithm was used for row removal where it was difficult to select thresholds because of the distinctions between palm oil which combined. The watershed transformation is dependent on the scale of the gradient, with pixels with the highest strength of gradients corresponding to the area.

2 Methodology Next, the Google Earth Pro captured image. Check the number of palm oil trees by using the imaging processing technique. The first step in getting the data is the acquisition of the file. Image Acquisition can be described as a source retrieval process. All processes that will eventually take place may also be transferred. This move gave the picture of the palm oil tree. Google Earth Pro software is used to capture and collect a picture of the palm tree farm, saving time and the workforce needed. For counting proposed 50 photographs were collected of the palm oil tree with different conditions. Various image processing methods have been used to segment palm oil pictures after the picture has been collected.

2.1 Image Processing The next step after the images are completely transferred to the palm oil tree count is image processing. Several techniques have been suggested for image processing to segment palm oil tree pictures. The method of image segmentation is to transform

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images into green components on the basis of the RGB colour. Then a gaussian filter filters the image of the G-component. Instead, by using the watershed method, the filtered image is transformed into a binary image. This paper seeks to segment and count palm oil tree for the purpose of image segmentation. For segmenting the palm oil tree from soil and water, multiple image segmentation techniques are employed. The nucleus color segmentation is done based on the RGB ( Red, Green, Blue) and HSV (Hue, Saturation, Value) color patterns in an image to exploit the color content of an image. So only the G component of the transformation of the image RGB (Red, Green, Blue) is used for those three color models. The Gaussian filter [18, 19] is the most common order statistical filter replacing the pixel value in the neighborhood of the same pixel by the gaussian of the grey point. Medium computation is included in the original pixel value The Gaussian Smoothing Operator is a 2D convolution operator that uses images blurred and details and noises are removed. The noise characteristics of other random noise types make it fairly well-known by Gaussian filters. Moreover, it is blurred rather than a linear smoothing filter of similar scale, so a fragment of the tree can be reassembled into a tree for accurate counting by means of that filter. G(x) = √

1 2π σ

−x2

e 2σ 2

(1)

where x is the distance from the origin of the horizontal axis, the distance from the origin of the vertical axis is y, and the standard deviation of the Gaussian distribution is ÿ. When applied in two dimensions, this method produces a surface with concentrated circles contours from the center with a Gaussian distribution. Segmentation on the watercourse focuses mainly on finding the catchment basins and watercourse lines of any gray image. In other words, the separation of the tree fragment is helpful. This technique of segmentation is based on the concept of topographic image intensity representations. This method is typically used to isolate the overlapping objects, which results in a decent counting result.

2.2 Counting Process The counting process is performed after full segmentation of the images. The accuracy of each picture is different, since different types of pictures may not be using the same process. Compared with the manual count recorded during the images, the counting process is first recorded. The bounding box helps to identify all the palm oil trees that count successfully in comparison with the original picture.

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3 Result and Discussion The result below illustrated the technologies obtained to obtain a good sample for the counting process. The first objective of this paper was to find the best height and condition for palm oil plantation in the environment. Google Earth Pro is a free program to get in this document a image of palm tree oil. Pictures from various eye levels are taken for the best results for processing images. The benefits of this application are that it is possible to download Google Earth Pro’s save mode for 1116 × 632-pixel resolution without any map downloader software. For this paper the captured picture started on the palm oil tree with an altitude of 125 m in height. The view of the palm olive tree at 125 m of altitude shows in Fig. 1. First a 160 m eye alt image shot. Figure 2 depicts the 160 m high eye alt image of the palm oil tree and shows the effects of a more and more distorted palm oil tree Fig. 1 125 m eye alt of palm oil tree image

Fig. 2 160 m eye alt image of palm oil tree image

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Fig. 3 280 m eye alt of palm oil tree image

Fig. 4 Original image of palm oil tree

snapshot. Figure 3 shows that at a height of 280 m, the alt is more blurred than Fig. 2 and covers more than the area of a palm oil plantation. This research is focused on the following figure. The picture was not appropriate for this paper studies from 160 m, up to 280 m. The explanation is that the photo had been deleted. If not, the next image processing approach should have made a wrong decision. The 125 m high eye image was therefore the best suited for this paper as a picture test.

3.1 Segmentation of Image The original image of a palm oil plantation was shown in Fig. 4. To make data accurate, the researcher must resize the image. The RGB and HSV method color

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segmentation was used. Apply. The best visibility choices were R, G and V. Gcomponents have been chosen from other component colours, after comparing these three components. That is because the picture in palm oil is more precise than the R and V elements. This is the result of this decision. This image was shown in Fig. 5 between the six components.

Fig. 5 The different of image color models for; a R component, b G component and c B component d H component e S component f V component

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Figure 5 shows the picture created between these six color components. After the part RGB and HSV were chosen, the image selected was G, where it was improved. The method of updating the image was filtered. The selected picture was G and the enhancement method after comparing the components of R, G, and V. The picture was then converted to a binary picture and continues to be improved at the next stage. An image filter was used for the improvement process. Figure 6 shows that filtering rendered the image clearer by fusing the tree fragment back to one tree to obtain accurate data. Figure 6 shows A Gaussian filter is the filtering tool in this segmentation. The next step would be to remove any unwanted noise after filtering the image. Removes a small object which has less pixels than palm oil (object), then creates another binary image that can only be read from the binary image for the accuracy calculation process. Figure 7 showed the image made. For two dimensions, the connectivity default is 4, 8 and 6 and 26. Fig. 6 The gaussian filter of palm oil tree image

Fig. 7 Remove unwanted object of pal oil tree image

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3.2 Counting Performance The palm oil tree was counted on the basis of the precision of the image references and the uploaded image. The benchmark or reference pictures used a perfect image. In order to promote the comparison, the reference images already stop all segmentation. This illustration of the palm oil tree with binding box at the end is shown in the reference figure. Prior to this, the picture in Fig. 7 showed the fusion of trees and the process of separation of the fused trees into a single tree. The image with watershed for this paper is shown in Fig. 8 to make tree pictures clearer. After removing another non-count pixel which is not considered a tree because the pixel size (object) is considered to be too small and large, then the number of palm oil trees is taken by counting orders. Figure 9 illustrates the region props image in Fig. 10, which considers the tree image and the number of palm oil tree with a bounding box.

Fig. 8 Watershed image of palm oil tree

Fig. 9 Region props size

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Fig. 10 Counting the palm oil

Table 1 Accuracy for the automatic count palm oil tree system

Images

Total average accuracy (%)

Palm oil tree

94%

The details of the accuracy of the palm oil method are presented in Table 1. The results are evaluated on the basis of the image. The average precision for 33 palm oil images was 94%. This successfully achieved precision performance. Overall, the classifier is well performed on previously unseen data sets and is able to identify oil palm precisely from the background. The remote sensing dataset and machines are useful in detecting palm oil plantations in order to improve productivity.

4 Conclusion This paper primarily seeks to evaluate the methodology obtained by the use of a satellite image and the data transfer methodology. With the capture of images with the difference between 125, 160 and 280 m, this objective has been achieved successfully. At an eye altitude of 125 m, photographs capture the amount of palm oil tree more reliably and quickly. The counting process for the palm oil tree was successful. Acknowledgements The authors gratefully acknowledge the financial support from UniMAP.

References 1. Wang C, Liu J, Xu A, Wang Y, Sui X (2018) High resolution remote sensing image. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics Inf Sci Wuhan Univ 43:922–929 2. Chen XM, Ni GQ (2009) Multifractional image segmentation in high resolution remote sensing image. Guangxue Jishu/Optical Tech 35:178–180

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3. Srestasathiern P, Rakwatin P (2014) Oil palm tree detection with high resolution multi-spectral satellite imagery. Remote Sens 6:9749–9774. https://doi.org/10.3390/rs6109749 4. Santoso H, Tani H, Wang X (2016) A simple method for detection and counting of oil palm trees using high-resolution multispectral satellite imagery. Int J Remote Sens 37:5122–5134 5. Li Y, Duin RPW, Loog M (2012) Combining multi-scale dissimilarities for image classification. In: Proceedings - international conference on pattern recognition, pp 1639–1642 6. Wu P, Manjunath BS, Shin HD (2000) Dimensionality reduction for image retrieval. In: IEEE international conference on image processing 7. Kattenborn T, Sperlich M, Bataua K, Koch B (2014) Automatic single tree detection in plantations using UAV-based photogrammetric point clouds. ISPRS - Int Arch Photogram Remote Sens Spat Inf Sci XL–3:139–144 8. Sperlich M, Kattenborn T, Koch B, Kattenborn G (2014) Potential of unmanned aerial vehicle based photogrammetric point clouds for automatic single tree detection. In: Gemeinsame Tagung 2014 der DGfK, der DGPF, der GfGI und des GIN, pp 1–6 9. Lindeberg T (2012) Scale invariant feature transform. Scholarpedia 7:10491 10. Burger W, Burge MJ (2016) Scale-invariant feature transform (SIFT) 11. Zhu QY, Qin AK, Suganthan PN, Huang G (2005) Bin: evolutionary extreme learning machine. Pattern Recognit 38:1759–1763 12. Deng W, Zheng Q, Chen L (2009) Regularized extreme learning machine. In: 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 – Proceedings, pp 389–395 13. Ananth KR, Pannirselvam S (2012) A geodesic active contour level set method for image segmentation. Int J Image Graph Signal Process 4:31–37 14. Lv T, Yang G, Zhang Y, Yang J, Chen Y, Shu H, Luo L (2019) Vessel segmentation using centerline constrained level set method. Multimed Tools Appl 78:17051–17075 15. Rahmat RF, Azzakirot Y, Lini TZ (2019) Tree identification to calculate the amount of palm trees using haar-cascade classifier algorithm. In: 2019 3rd International Conference on Electrical, Telecommunication and Computer Engineering, ELTICOM 2019 - Proceedings. pp 36–39 16. Guo SY, Zhang XF (2005) Model based adaptive thresholding algorithm. Zhejiang Daxue Xuebao (Gongxue Ban)/J Zhejiang Univ (Eng Sci) 39 17. Oliva D, Abd Elaziz M, Hinojosa S (2019) Otsu’s between class variance and the tree seed algorithm. In: Studies in computational intelligence, pp 71–83 18. Matthews JA (2014) Gaussian filter. In: Encyclopedia of environmental change 19. Gaussian Filter (2008). In: Computational surface and roundness metrology, pp 33–38

Diagnosis of Heart Disease Using Machine Learning Methods Azian Azamimi Abdullah, Nazirah Ahmad Alhadi, and Wan Khairunizam

Abstract The World Health Organization (WHO) estimated 12 million deaths around the world appear each year from heart disease. Heart disease includes coronary artery disease, heart rhythm problem and heart defects. Each disease has similar symptoms but cause different effects and severity on patient. The common factors of heart disease include high blood pressure, diabetes, cholesterol and age. These factors are independent of each other; thus, the use of artificial intelligence and machine learning will be a suitable choice to model them. Correct diagnosis of heart disease is difficult due to the complicated processes and different system and it is vital because heart disease can lead to a heart attack, chest pain, stroke and sudden death. Hence, an accurate and early detection of heart disease with proper and adequate treatment is needed. The main aim of this research is to identify suitable feature selection method and machine learning algorithms for the diagnosis of heart disease. Chi Square Feature Selection (CSFS), Random Forest Feature Selection (RFFS), Forward Feature Selection (FFS), Backward Feature Selection (BFS) and Exhaustive Feature Selection (EFS) are the feature selection methods applied in this research. These feature selection methods are then implemented in the machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and k-Nearest Neighbour (KNN). The performance of these machine learning algorithms is evaluated through accuracy, sensitivity and specificity based on Confusion Matrix, ROC Curve and area under ROC (AUC). Based on the results, combination of RF with RFFS produced the highest accuracy value with 85.25% accuracy. Keywords Heart disease · Machine-learning · Random forest

A. A. Abdullah (B) · N. A. Alhadi · W. Khairunizam School of Mechatronic Engineering, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_6

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1 Introduction Heart is an organ in circulatory system that functioning by pumping blood throughout the blood vessel in our body. Heart disease is varying which include heart failure, heart rhythm problems and coronary artery disease. These diseases can lead to heart attack, chest pain, stroke and sudden death. Heart disease is also one of the vital causes of death in today’s world. The World Health Organization (WHO) estimated 12 million deaths around the world appear each year from heart disease [1]. Patients are diagnosed by a doctor based on their heart failure symptoms and functional limitations. Each disease has similar symptoms but cause different effects and severity on patient. Some diseases only take a short amount of time before lead to death. Heart disease has various kind of risk factors that can be used effectively for diagnosis purposes including blood pressure, diabetes, smoking, cholesterol and age [2]. Artificial intelligence (AI) and machine learning systems will be a great choice to model them since these factors are independent to each other. Many researches related to diagnosis of heart disease were done using intelligent information technology-based system. This system could give positive impact in improving the accuracy of diagnosis [3]. Many experts proposed their own method and used various classifiers such as Classification Multiple Association Rules (CMAR), Support Vector System, Bayesian Classifiers and C4.5 [4]. Chhikara et al. compiled the latest method rate of data mining and proved that Naïve Bayes algorithm is one of the famous machine learning used for classification of coronary heart disease [5]. Also, Gladence et al. proposed a method, which is an automatic classifier for risk assessment in patients suffering from congestive heart failure [6]. The research was conducted for the prediction of heart disease by using modified differential evolution algorithm and its effects on accuracy of the prediction [7]. The researchers compared the accuracy and precision of the integrated model of fuzzy AHP and feed-forward neural network (FFNN) when using selected features and all features. The accuracy results when using selected features was 83%, which is lower than when using all features which gained 85%. Ensemble classifier, which combined KNN, ANN and SVM algorithms using voting technique was proposed to classify heart disease [8]. The evaluation result shows that this proposed technique scored the highest accuracy with 94.12% compared to other algorithms used in the study. The ROC area of the proposed method is 0.981 compared to SVM, KNN and ANN which are 0.923, 0.979 and 0.837 respectively. The fusion technique combining three algorithms including rough set, neural network and naïve bayes was proposed to classify heart disease and the accuracy, sensitivity, precision and F-measure of the proposed method are 89, 88.3, 88.7 and 89.1% respectively [9]. Many researchers has presented a comprehensive review on heart disease classification problem, along with future research directions [10–12]. A comparative analytical approach was performed to determine how the ensemble technique can be applied for improving prediction accuracy in heart disease [13].

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In this study, several machine learning algorithms are implemented and evaluated when using all features and selected features. The performance of algorithms will be analyzed to study on which method give better accuracy when diagnosing heart disease.

2 Proposed Method Process flow of this study is shown in Fig. 1. The proposed method consists of data extraction, data exploration/visualization, data pre-processing, feature selection, machine learning implementation and the evaluation of machine learning performance.

2.1 Data Extraction The dataset is obtained from Cleveland data and the database is from UCI repository [14]. The total number of patients participate in this database is 303 people with 46% of them is diagnosed with heart disease while the other 54% is healthy. In this study, the number of features used is 14 out of 76. The features include age, sex, chest pain type, resting systolic blood pressure on admission to the hospital (mmHg), serum cholesterol (mg/dL), fasting blood sugar over 120 mg/dL, resting electrocardiographic results, maximum heart rate achieved, exercise induced angina, ST depression induced by exercise relative to rest, the slope of the peak exercise ST segment, number of major vessels colored by fluoroscopy, exercise thallium scintigraphic defects and diagnosis of heart disease. The output of this study is determined by the existence of coronary artery disease in the patient. It also includes an integer constant that can take any value from 0 to 4. These values indicate the number of blocked vessels. The 0 value indicates the disease nonexistence while the value from 1 to 4 indicates the existence of disease.

Fig. 1 Process flow of the proposed method

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2.2 Data Visualization In this study, the data is visualized using correlation heat map and bar charts. Correlation heat map is a good visualization data platform that enable user to detect any correlation between features. This can show which features are reliable to each other. The level of correlation between features is judged by a range of color.

2.3 Data Pre-processing Data pre-processing is a method that is used to convert raw data into a clean dataset. Most of raw data is composed of missing data, noisy data and inconsistent data. Hence, this step is necessary for this project to achieve a better result, as the data itself must be in a proper manner. There are some steps involves in data pre-processing including data cleaning, data integration, data transformation and data reduction. Data cleaning process works on handling noisy data, missing data and removal of outliers within the data. Data integration is used when the data is assembled from various data source and combined to form a consistent data. In data transformation, the raw data is converts into desired format depending to the need of the model. There are three options for data transformation such as normalization, aggregation and generalization of data. For normalization of data, the nominal data will be converted into specified range. In data aggregation, the data will be combined to form a new data. Meanwhile, lower level features are converted to a higher standard feature in data generalization. During data reduction, the redundant data will be removed, and a new clean data is formed. However, before applying any pre-processing method, the data must be checked first. A clean data with no missing and null value is not obligated for pre-processing step.

2.4 Feature Selection Five feature selection methods are applied in this study including Chi Square Feature Selection (CSFS), Random Forest Feature Selection (RFFS), Forward Feature Selection (FFS), Backward Feature Selection (BFS) and Exhaustive Feature Selection (EFS). Each method is explained below. Chi Square Feature Selection (CSFS). This feature selection method measures the lack of independence between features and group. The feature is selected based on the χ2 value. The feature with larger the χ2 value will be selected.

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2  c  l  Ni j − E i j χ = ( ) Ei j i=1 j=1 2

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(1)

Random Forest Feature Selection (RFFS). Random Forest Feature Selection is one of the examples of embedded method, which is able to pick out intrinsic properties of the data and detect possible interaction between features. It is a method that can perform both feature selection and machine learning algorithm simultaneously. Forward Feature Selection (FFS). Forward Feature Selection method is a method that using repetitive system where a feature will be added one by one until the addition of new feature would not improve the performance of model. Backward Feature Selection (BFS). In Backward Feature Selection method, a least important feature will be removed at each cycle to improve the performance of model. The removal of feature will be stopped when there is no improvement in the system. Exhaustive Feature Selection (EFS). Exhaustive Feature Selection method detects the possible interaction between features. It will choose important features by evaluating the best combination of feature subsets.

2.5 Machine Learning Algorithms Machine learning is divided into two types, which are supervised machine learning and unsupervised machine learning. Supervised machine learning can be implemented into the system when the system has input and output while unsupervised machine learning can be applied when the system only has an input but no corresponding output variables. In this study, supervised machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and K-Nearest Neighbour (KNN) will be implemented into the system where it has categorized data input and targeted output. Random Forest (RF). Random Forest algorithm is known as one of the most accurate learning algorithms. It can be used for classification, regression and feature selection analysis. Also, it can be used for balancing error in class population. This algorithm is created by using bootstrap samples of the training dataset and random feature selection in tree induction. Large datasets can be process efficiently when using this algorithm. Support Vector Machine (SVM). Support vector machine is used to analyse data for classification and regression analysis. This algorithm works as the data is plotted as a point in n-dimensional space with the features being the value of a particular coordinate. The classification is performed by finding the hyper-plane that can differentiate the two classes. There are two types of SVM; Linear SVM (L-SVM)

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and Radial SVM(R-SVM). Linear SVM is a parametric model while radial SVM is not a parametric model. In Linear SVM, the boundary is a straight line between two dimensions and a hyperplane in higher dimensions. Radial SVM can be implement into the system when the data is not linearly separable. Logistic Regression (LR). Logistic Regression is one of the popular techniques used to predict binomial outcomes. It performs better when unrelated feature is already removed from the dataset. This algorithm measures the relationship by estimating probabilities between the categorical dependent variable and one or more independent variables. K- Nearest Neighbour (KNN). K-Nearest Neighbour is a simple algorithm which can be used for both classification and regression. All the training dataset must be stored in memory, which make KNN is more suitable to be implement on a smaller dataset which consists of few numbers of features. The n in this machine learning is a positive integer which is specified along with new sample. N is selected based on which are the closest to the new sample. In this study, KNN model is evaluated when using the value of n from 1 to 10.

2.6 Performance Evaluation All five machine learning algorithms will be evaluated based on Confusion Matrix, accuracy, sensitivity, specificity, Receiver Operating Characteristics (ROC) Curve and area under ROC Curve (AUC). Confusion Matrix, which is also known as error matrix, is a table that is used to visualize the performance of classification model. These matrices will give matrix as output and describes the complete performance of the model. There are four important terms, which are true positives (TP), true negatives (TN), false negatives (FN) and false positives (FP). In this study, the term for TP is when a patient with heart disease is predicted correctly while TN is when a healthy patient is predicted correctly. FP is when a patient with heart disease is predicted wrongly while FN is when a healthy patient is incorrectly diagnosed with heart disease. Figure 2 shows a table of confusion matrix. The accuracy of a test is its ability to differentiate the positivity and negativity of the disease. It can be estimated by calculating the proportion of true positive (TP) and true (TN) negative in all evaluated case. Sensitivity test is used to determine the presence of disease (TP) correctly. The proportion of true positive in the presence of disease must be calculated to estimate the sensitivity. Specificity test is conducted to determine the absence of disease (TN) cases. The proportion of true negative in absence of disease cases is calculated to gain the specificity value. The equation for accuracy, sensitivity and specificity are shown below:

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Fig. 2 Example of confusion matrix

Fig. 3 Example of ROC graph

TP + TN TP + TN + FP + FN

(2)

Sensitivity =

TP TP + FN

(3)

Specificity =

TN TN + FP

(4)

Accuracy =

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ROC Curve is a graph that shows the performance of classification system at various thresholds settings. It is formed by plotting the true positive (sensitivity) rate against the true negative (specificity) rate. Area under curve (AUC) represents the degree or measure of separability as shown in Fig. 3. When the AUC is higher, the better the model in distinguish between patients with disease and without disease.

3 Results and Discussion The dataset, which contained 303 patients is extracted from UCI repository. This dataset has the total of 14 input features and one output. For data visualization, correlation heat map is the method used to determine the correlation between features. The color ranges from red to green indicates the correlation between features. The features correlation is better when the color is nearer to green. From the results based on Fig. 4, the map shows that any correlation between features is relatively low. Thus, require the feature selection method to improve the quality of the inputs.

Fig. 4 Correlation heat map

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Fig. 5 Bar chart showing patient’s age group

Fig. 6 Bar chart showing chest pain type

Other than correlation heat map, bar charts are used to visualize the details for each feature. In Fig. 5, the result shows that people in age fifties to sixties hold the highest number of heart disease patients whom participated in this study. Figure 6 shows four types of chest pain suffered by patients in this data. The value 0, 1 2 and 3 in this graph indicates typical angina, atypical angina, nonanginal pain and asymptomatic respectively. From the graph, it shows that almost 150 patients out of 303 patients are suffered from typical angina chest pain while approximately 25 patients is suffered from asymptomatic. Before applying pre-processing method, the data is checked either it contains any missing or null feature’s value. The results in Fig. 7 shows 0 value for each feature which indicates that there is no missing and null value in the data. Thus, this data can be concluded as a clean dataset and does not required to apply any pre-processing technique.

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Fig. 7 Result after checking null data

For feature selection, five methods including Random Forest (RFFS), Chi Square (CSFS), Forward Feature Selection (FSS), Backward Feature Selection (BFS) and Exhaustive Feature Selection (EFS) were implemented in this study to pick the most crucial features. The results of selected feature after implementing these methods into system are as in Table 1. Table 2 shows the highest results of each machine learning when using selected features chosen by different feature selection method. The final result shows that none of the machine learning is able to score high accuracy level without using any feature selection method. Among of machine learning and feature selection method, Random Forest achieves the highest accuracy level with 85.25% when using features chosen by Random Forest Feature Selection method. It also achieves the highest sensitivity, specificity and area under ROC Curve (AUC) with 96.97%, 71.43% and 0.842 respectively.

Diagnosis of Heart Disease Using Machine Learning Methods Table 1. Selected features by each of feature selection methods.

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Table 2 Performance of machine learning algorithms Machine learning Feature selection Accuracy (%) Sensitivity (%) Specificity (%) AUC method RF

RFFS

85.25

96.97

71.43

L-SVM

EFS

81.96

93.94

67.86

0.842 0.809

R-SVM

EFS

83.60

84.85

82.14

0.835

LR

EFS

81.96

93.94

67.86

0.809

KNN(n = 5)

EFS

83.61

96.97

67.86

0.824

4 Conclusions This study is aimed to identify suitable feature selection method among CSFS, RFFS, FFS, BFS and EFS. These feature selection methods are then implemented in the machine learning algorithms including RF, L-SVM, R-SVM, LR and KNN. The performance of these machine learning algorithms is evaluated through accuracy, sensitivity, specificity, Confusion Matrix, ROC Curve and area under ROC. After implementing all the related machine learning algorithm and applying feature selection method and without applying any feature selection method, the results shows that Random Forest with features selected by Random Forest Feature Selection scores the highest accuracy with 85.25%. Based on the results, it can be concluded that the accuracy of machine learning algorithms is higher when using feature selection method compared to when using all features. The most suitable feature selection method for majority algorithms is Exhaustive Feature Selection method. Also, Random Forest Feature Selection method and Random Forest algorithm is the most suitable feature selection method and machine learning algorithm to diagnose heart disease as the combination of this model produce the highest accuracy value. Future work on the current research may be led to obtain more data related to heart disease by collaborating with the local hospital. The data used in current project is from existing database, thus by using data from local community may help in broadening the knowledge on which symptoms that attacks heart diseased patient in Malaysia. Then, a good machine learning system may be implemented for early diagnosis of heart disease in Malaysia. Other than that, a system for early diagnosis of heart disease in mobile app and Graphic User Interface (GUI) can be developed in the future. This can relatively ease the diagnosis process as the test can be conducted by the patients themselves and not necessarily in the hospital. Patients can perform self-monitoring on their heart conditions by using mobile apps. Acknowledgements The authors gratefully acknowledge the financial support from UniMAP.

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References 1. Ravish DK, Shenoy NR (2014) Heart function monitoring, prediction and prevention of heart attacks: using artificial neural networks. In: 2014 int. conf. contemp. comput. informatics inst. electr. electron eng. 2. Murthy HSN, Meenakshi M (2014) Dimensionality reduction using neuro-genetic approach for early prediction of coronary heart disease. In: International conference on circuits, communication, control and computing, pp 329–332 3. Mustaqeem A, Anwar SM, Khan AR, Majid M (2017) A statistical analysis based recommender model for heart disease patients. Int J Med Inform 108:134–145 4. Purushottam KS, Sharma R (2016) Efficient heart disease prediction system. Procedia Comput Sci 85: 962–969 5. Chhikara S, Sharma P (2014) Data Mining Techniques on Medical Data for Finding Locally Frequent Diseases, vol 2. www.ijraset.com 6. Mary Gladence L, Ravi T, Karthi M (2014) An enhanced method for detecting congestive heart failure - Automatic Classifier. In: 2014 IEEE international conference on advanced communications, control and computing technologies, 2014, pp 586–590 7. Vivekanandan T, Sriman Narayana Iyengar NC (2017) Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease. Comput Biol Med 90:125–136 8. Mahboob T, Irfan R, Ghaffar B (2017) Evaluating ensemble prediction of coronary heart disease using receiver operating characteristics. In: 2017 internet technologies and applications (ITA), 2017, pp 110–115 9. Esfahani HA, Ghazanfari M (2017) Cardiovascular disease detection using a new ensemble classifier. In: 2017 IEEE 4th international conference on knowledge-based engineering and innovation (KBEI), 2017, pp 1011–1014 10. Chatterjee P, Leandro JC, Ricardo LA (2019) Nonlinear systems in healthcare towards intelligent disease prediction. Nonlinear Systems-Theoretical Aspects and Recent Applications. IntechOpen 11. Dube H, et al (2020) Review on heart disease classification. In: 2020 5th international conference on communication and electronics systems (ICCES). IEEE 12. Martin-Isla C, et al (2020) Image-Based Cardiac Diagnosis With Machine Learning: A Review. Frontiers in cardiovascular medicine vol 7 1. 24 13. Latha CB, Carolin Jeeva S (2019) Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Informatics in Medicine Unlocked, vol 16 14. Blake CL, Merz CJ (1998) UCI repository of machine learning databases

Investigation of Geomorphological Features of Kerian River Using Satellite Images Emaad Ansari, Mohammad Nishat Akhtar, Elmi Abu Bakar, Naoki Uchiyama, Noorfazreena Mohammad Kamaruddin, and Siti Nur Hanisah Umar Abstract Satellite images have nowadays become an important tool for studying geographical features ranging from small areas to large areas, easily accessible locations to complex field, small streams to lengthy rivers. Kerian River which is situated in northern Malaysia and runs through the state of Perak, Kedah and Penang is investigated in this study using satellite images. The river originates in Bintang range in Perak flows in western direction and discharges in the Straits of Melaka. The geomorphological features such as length of river, river basin size, width of the mouth of river, flood prone areas, agricultural land in proximity of the river, etc. are studied using satellite images such as LANDSAT-8 OLI images, Google Earth Pro images and Google Map images. The ease of using satellite images in analysing lengthy rivers such as Kerian River which cannot be analysed by field work in short span of time is elaborated in the study. Keywords Satellite images · Geomorphological features · Kerian river

1 Introduction Investigation of various geomorphological features and hydrological features of river basin is vital for understanding the behavior of river. Although hydrological features need field visits and is widely carried out manually, geomorphological features can be thoroughly studied using satellite images. Unlike hydrological feature study which requires data collection at some points due to similar features along the stretch, the geomorphological features can vary a lot along the river basin. Thus, for a long river, studying the geomorphological features by conducting field visits is a tedious task.

E. Ansari · M. N. Akhtar · E. A. Bakar (B) · N. M. Kamaruddin · S. N. H. Umar School of Aerospace Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia e-mail: [email protected] N. Uchiyama Toyohashi University of Technology, Tenpaku Cho, Hibarigaoka, Toyohashi-shi, Aichi, Japan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_7

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Hence, satellite images become an important tool for investigating the geomorphological features of the river under such circumstances. Numerous researchers have previously used satellite data for river study, some of them are reported below. Gupta et al. [1] made use of satellite images for evaluating a part of Mekong River in Lao PDR which is the 12th largest river in the world. In terms of water discharge, it ranks 8th largest in the world. Gupta et al. [1] used SPOT satellite images from Centre of Remote Imaging, Sensing and Processing at National University of Singapore in the year 1996, 1998 and 1999 to compare the changes in geomorphic features with time. Bhadra et al. [2] integrated satellite images of river Saraswati in Haryana, India from the year 2009 and images of same river taken on ground during Vedic period to observe the changes in the river behaviour. The difference in human work conducted along the river basin and its effect on river structure is reported. Colin and Laurence [3] made a complete use of satellite images to predict the absolute river discharge. LANDSAT Thematic Mapper images were used on different rivers in United States, Canada and China. The results fetched using satellite images were 20–30% of site measurements which is of good accuracy, thus paving way for a complete discharge study using satellite images. Kussul et al. [4] made use of Synthetic-Aperture Radar (SAR) images to monitor the extent of flood which is impractical using field study. Grids were formed using artificial neural networks from river Huaihe, China and applied on river Tisza, Hungary and Ukraine for floor extent monitoring. This method proved out to be useful for studying the extent of river floods. Mengyi and Baoquan [5] analysed the diffusion of suspended sediment discharged from Changjiang River using satellite images. Using satellite images the diffusion of sediments in four stages namely muddy stretch, turbid flow, plumes and freshening stream was studied. Hossain et al. [6] used satellite images from eight dry seasons between the years 1973 to 2009 to investigate the changes in Ganges River in Bangladesh which is an alluvial river. It was concluded that the erosion rate of Ganges is very high because of its high discharge during monsoon and basin made of highly erodible materials. Luoto et al. [7] made use of LANDSAT TM images and topographic data to predict the plant species density in an agricultural river area of Finland. The hotspots for different plant were found out and carefully interpreted. The results from GIS and satellite imaging were reported for data needed for land planning. Yepez et al. [8] used LANDSAT-8 OLI images for calculating the suspended sediment concentration (SSC) in Orinoco River, Venezuela. The tedious job of calculating SSC in a lengthy river by field visits was simply carried out by using satellite images thus elaborating the reliability and feasibility of remote sensing technology. Soille and Jacopo [9] used satellite images to predict the river networks which can be formed by combining mathematical morphology and hydrology. The valley configuration were analysed using satellite images and future formation of river networks were predicted. This method can also be used for understanding the blood vessels in human body. Henri and Minciroli [10] studied the characteristics of river basin using SAR satellite images. Various characteristics of hydrological basins in Maroni basin in Amazon forest were reported.

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Liu et al. [11] studied the coastline change in Yellow River delta due to tides. Beach slopes were estimated using LANDSAT images for understanding the coastline position and beach volume which help in fetching the difference in coastline. Villar et al. [12] combined the field observation results and satellite images to study the characteristics of low monitored regions in Amazon River catchment. Different cases are reported for evaluating the surface suspended sediment (SSS). Thus, various characteristics of river are studied using satellite images as well as some future characteristics can be predicted. The following Sections of the manuscript, gives an overview of Kerian river by highlighting its geomorphological features comprising of agricultural compatibility, river mouth feature, flood prone areas and soil erosion effect on sedimentation.

2 Features of Kerian River The Kerian River originates in the Bintang range in the northern Malaysian state of Perak. The length of Kerian River is 90 km and its basin size is 1420 square kilometres. It flows in the western direction towards the Straits of Melaka where it discharges its water. Along its flow, it also passes through the Malaysian states of Kedah and Penang as well. It also serves as a border of Perak and Kedah state up to Penang state and at a point it serves as a border of all the three states. Its tributaries are Mahang River, Selama River, Serdang River, Ijok River and Bojak River as seen in Fig. 1. Along the upstream, it has a small width and it gets wide downstream in the final 35 kms. Therefore, more focus is done on the downstream of Kerian River in this study.

2.1 Agricultural Plains Along Kerian River Although, agricultural activities are carried out at different locations along the Kerian River, the largest agricultural field lies in the Perak state near Kampung Raja and Kampung Tali Air as shown in Fig. 2. The ease of water drawing for agricultural purpose and less urbanization in the locality has made this place a haven for agriculture. As the agricultural plains are alluvial, huge amount of sediments are added in the flow from these locations.

2.2 The Mouth of Kerian River The Kerian River meets the Strait of Melaka on the northern coast of Malaysia in the Penang state. The width of the mouth of Kerian River is 74.693 m (Fig. 3). The amount of sediments flowing along the Kerian River gets diffused in the Strait of

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Fig. 1 The morphology of Kerian River and its tributaries (Image Courtesy: Google Images)

Fig. 2 Agricultural field alongside Kerian River in Perak State (Image courtesy: Google earth Pro)

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Fig. 3 Width of the mouth of Kerian River (Image Courtesy: Google Map)

Melaka (Fig. 4). The diffusion of sediments is visible till 2 km from the mouth of river and eventually it disperses in the Strait of Melaka (Fig. 4).

2.3 Flood Prone Areas The downstream side of rivers are more prone to floods as the flow velocity, depth of water, river width gets maximized in this zone. In case of Kerian River, the most flood prone area is Kampung Sungai Udang which is just 350 m before the mouth of the Kerian River (Fig. 5). This locality is widely occupied by fishermen and it is the most flood prone area in the proximity of Kerian River. The width of Kerian River at the beginning of the Kampung Sungai Udang is 19.952 m (Fig. 6) and at its end it stretches to 33.175 m (Fig. 7). As the Kampung is situated on the river bank and next to river mouth, it is considered to be the flood prone region.

3 Discussion on Soil Erosion Information about the creation and development of residue on the land surface before it reaches the river system is essential to likely quantify the load of sediment and

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Fig. 4 Diffusion of suspended sediments of Kerian River in Straits of Melaka (Image Courtesy: USGS Earth Explorer)

Fig. 5 The Flood Prone Region near the Mouth of Kerian River (Image Courtesy: USGS Earth Explorer)

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Fig. 6 Width of Kerian River at the beginning of Flood Prone Region (Image Courtesy: Google Map)

thus, comprehend silt elements, which are of basic significance for a wide assortment of Earth system process and worldwide biogeochemical cycles. Locations near agricultural field such as location in Fig. 8 have greater amount of soil erosion as compared to urbanized place as seen in Fig. 9. This is due to the fact that the soil of agricultural fields become alluvial with time due to ploughing and other cultural activities. On the other hand, the location where industries and houses are built have erected permanent walls due to which the boundaries at these locations remain intact. Over large scales with constrained information accessibility, potential soil erosion rate gauges are frequently founded on simplistic models such as the revised universal soil loss equation (RUSLE). The RUSLE method is able to anticipate the amount of soil being eroded by water at the pixel scale, and recent efforts have generated results at greater spatial resolution [13]. However, analysing the flow and sediment relation between pixels and the amount of sediment actually reaching out to the river system is still a challenge. Change in soil erosion can be seen using Normalized difference water index (NDWI) defined by Eq. (1). N DW I =

Band 2 − Band 5 Band 2 + Band 5

(1)

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Fig. 7 Width of Kerian River at the End Point of Flood Prone Region (Image Courtesy: Google Map)

Fig. 8 River banks near agricultural fields

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A NDWI image can be seen in Fig. 10. Using LANDSAT 8 images and ArcGIS 10.8, the NDWI plot is generated. Band 2 is green band and band 5 is near infrared band. NDWI can also be used for early alarm in case of flooding.

Fig. 9 Intact boundaries at urbanized locations

Fig. 10 Normalized difference water index (NDWI)

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Fig. 11 Normalized difference water index (NDVI)

4 Normalized Difference Vegetation Index (NDVI) Another benefit of using LANDSAT 8 images and ArcGIS software is detection of normalized difference water index (NDVI). NDVI can be calculated using Eq. (2). NDVI =

Band 5 − Band 4 Band 5 + Band 4

(2)

Band 4 is Red, while band 5 is near infrared. Figure 11 shows the NDVI plot wherein the vegetation can be classified from unhealthy to healthy on a scale of −1 to 1.

5 Conclusion The investigation of some of the geomorphological features of the Kerian River is conducted using different satellite images. The measurement of length of river, width at different locations, suspended sediment diffusion, flood prone areas which

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are time consuming task are easily measured using satellite images. Thus, saving time as well as manpower. The satellite images of Kerian river basin can also be used for planning of agricultural activities, industry setup and setting up of living localities. The simplicity of satellite images can be used for better planning as well as precaution for any flood prone areas in the future. In addition to the sedimentation results, the image analysis of the river hydro morphology can also be carried out on parallel computing node and correlate the results [14]. Acknowledgements The experiments with respect to the proposed review is being carried out in School of Aerospace Engineering of Universiti Sains Malaysia. The authors would like to acknowledge the RUI grant 1001.PAERO.8014035 and RU-Top-Down grant 1001. PAERO.870052.

References 1. Gupta A, Hock L, Xiaojing H, Ping C (2002) Evaluation of part of the Mekong River using satellite imagery. Geomorphology 44(3–4):221–239 2. Bhadra BK, Gupta AK, Sharma JR (2009) Saraswati Nadi in Haryana and its linkage with the Vedic Saraswati River—integrated study based on satellite images and ground-based information. J Geological Soc India 73(2), 273–288 3. Gleason CJ, Smith LC (2014) Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry. Proc Natl Acad Sci 111(13):4788–4791 4. Kussul N, Shelestov A, Skakun S (2008) Grid system for flood extent extraction from satellite images. Earth Sci Inform 1(3–4):105 5. Mengyi YCC, Baoquan W (1981) An analysis of the diffusion of suspended sediment discharged from the changjiang river based on the satellite images. Oceanologia Et Limnologia Sinica 5 (1981) 6. Hossain, MdA, Thian YG, Baki ABM (2013) Assessing morphological changes of the Ganges River using satellite images. Quaternary int 304:142–155 7. Luoto M, Toivonen T, Heikkinen RK (2002) Prediction of total and rare plant species richness in agricultural landscapes from satellite images and topographic data. Landscape Ecol 17(3):195– 217 8. Yepez S, Laraque A, Martinez J-M, De Sa J, Carrera JM, Castellanos B, Gallay M, Lopez JL (2018) Retrieval of suspended sediment concentrations using Landsat-8 OLI satellite images in the Orinoco River (Venezuela). Comptes Rendus Geosci 350(1–2):20–30 9. Soille P, Grazzini J (2007) Extraction of river networks from satellite images by combining mathematical morphology and hydrology. In: International conference on computer analysis of images and patterns. Springer, Heidelberg, pp. 636–644 (2007) 10. Maître H, Pinciroli M (1999) Fractal characterization of a hydrological basin using SAR satellite images. IEEE Trans Geosci Remote Sensi 37(1):175–181 11. Liu Y, Huang H, Qiu Z, Fan J (2013) Detecting coastline change from satellite images based on beach slope estimation in a tidal flat. Int J Appl Earth Obs Geoinf 23:165–176 12. Villar RE, Martinez J-M, Guyot J-L, Fraizy P, Armijos E, Crave A, Bazán H, Vauchel P, Lavado W (2012) The integration of field measurements and satellite observations to determine river solid loads in poorly monitored basins. J Hydrology 444: 221–228 13. Borrelli P, Robinson DA, Fleischer LR, Lugato E, Ballabio C, Alewell C, Meusburger K, Modugno S, Schütt B, Ferro V, Bagarello V (2017) An assessment of the global impact of 21st century land use change on soil erosion. Nature Commun 8(1):1–13 14. Akhtar MN, Saleh JM, Awais H, Bakar EA (2020) Map-Reduce based tipping point scheduler for parallel image processing. Expert Syst Appl 139:112848, 1–15

Review on the Potential of a Tidal Energy Harnessing System in Malaysia P. V. S. Hari Prashanth, Hadi Nabipour Afrouzi, Chin-Leong Wooi, Kamyar Mehranzamir, San Chuin Liew, and Jubaer Ahmed

Abstract Tidal Energy is one the most prominent and promising renewable energy sources due to its consistency in electricity generation. Malaysia, being a country with its vast resources is persistently affecting its research towards the transition of using conventional petroleum-based power to renewable energy production. This review paper will aim to study the potential of implementing tidal energy harvesting system in Malaysia by analyzing the gathered raw data of Malaysian region and comparing it with existing tidal harnessing system that has already been implemented in Norway, a country that has a similar geographical, climatic and weather conditions as Malaysia. This review paper will give a broad knowledge on the tidal energy harnessing system and the means to implement it in Malaysia. Keywords Horizontal axis tidal turbine · Water flow velocity · Water depth · Energy generation

1 Introduction One of the most promising renewable energy researched around the world is tidal energy as the nature of this energy source can be studied accurately. The study will then allow the researcher to predict the property of the source over a long period of time. As Malaysia is leaning towards renewable energy in recent years, tidal energy’s potential to be implemented in Malaysia should be studied. P. V. S. H. Prashanth · H. Nabipour Afrouzi (B) · S. C. Liew · J. Ahmed Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak, 93350 Kuching, Sarawak, Malaysia e-mail: [email protected] C.-L. Wooi Centre of Excellence for Renewable Energy, Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia K. Mehranzamir Department of Electrical and Electronic Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_8

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Horizontal Axis Tidal Turbine or HATT is one of the most common design that were used around the world. All the research materials used for this study had HATT as its tidal turbine and they were used for several of its benefits. One of the benefits that HATT design brought is its simple design allow it to be implemented at shallow sea level while producing sustainable power. There are three main factors that were always considered when tidal energy was implemented or studied. These factors are the local tidal property, sustainability of the system as well as potential defects of the system. Tidal stream and tidal range are the two important tidal property that were studied to see the performance of the tidal energy harnessing system. Tidal stream would be ocean current at which it flows the fastest at the highest or lowest peak tides. Tidal range refers to the difference in height between the highest tide peak and the lowest tide peak [1]. Sustainability is another important factor that is considered when tidal energy is modelled. The system is to be studied through life cycle assessment to ensure that the system is sustainable as the use of tidal energy is to promote clean energy usage. Reducing environmental risk during energy production is a clearly stated target of the environmental control programmes of many industrialised countries. This is especially true for the manufacturing and implementation of renewable energy harnessing system. Material defect is taken into consideration as this would determine how long would the system last when it was pitched against aggressive and corrosive marine environment. In addition to the harsh environment, the system must sustain high operational force as the density of water is higher than that of air. To sustain these two conditions, metal is often used. Metal may be able to sustain the operational force; it would still suffer from the harsh environment. Thus, the consideration of materials used for the structure to sustain the system [2].

2 Existing Tidal Energy Harnessing System To properly review tidal energy harnessing system in Malaysia, the study will first review a country that has a high potential to implemented tidal energy harnessing system based on several factors and research studies. European countries are generally known for their usage of tidal energy harnessing system as their coast produce tidal stream and tidal range that is suitable for producing power. Norway, for an instance, has high potential for tidal energy harnessing system as it has many sites around the country that has favorable tidal properties. Through theoretical evaluation, Norway is expected to produce electricity from an estimated range of 3.4 to 6.8 TWh per year if tidal energy harnessing system was implemented in all of Norway potential site [1, 2]. As there are roughly 58 sites around Norway that has tidal stream and tidal range characteristics that favors tidal energy harnessing system, it was expected that Norway as a whole, will be able to produce sustainable renewable energy [3]. All the while preserving the marine life and reduce carbon emission. There are 2

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main types of tidal turbine used or researched in Norway, floating tidal turbine and horizontal axis tidal turbine. Across the country, the number of horizontal axis tidal turbine implemented is more dominant than that of the floating tidal turbine. Rather, floating tidal turbine is relatively new in Norway [4]. Therefore, this review will be conducted around horizontal axis tidal turbine (HATT). The relationship between the tidal current, sea depth and the power produced from HATT can be studied from this review. Additionally, the potential factors that affect the efficiency of the system can be studied as well, to further understand how the system can be improved [5]. The main reason for Norway to study tidal energy harnessing system through HATT is that this system can be used in shallow water. Most of the potential sites in Norway was reported to have depth of range from 5 to 40 m deep [3]. HATT’s design is rather simplistic compared to other systems, it can be manufactured according to these depth and requirement, while maintaining its efficiency. Another reason why it is beneficial to use HATT for study is because of the community extensive research on the system, as HATT design is similar to that of the wind turbine and it has been innovated over several decades ago, researchers have full understanding on the limitations of HATT. A research in Norway studied several factors that would affect the overall performance of the tidal energy harnessing system. These factors are system efficiency, marine current efficiency and the operation conditions used for energy harnessing. Actual runtime of the tidal turbine was estimated as well [6]. The percentage efficiency taken for HATT system design is around 40% efficient [3]. This efficiency value had taken the system’s mechanical and electrical limitation into considerations and not the environmental effect on the system performance. Some of these limitations are from the system gearbox, rotor and generator where it tends to lose energy due to heat produced. Different environmental seasons and geological factors affect the tidal stream at the local potential site [7]. The value used for this factor cannot be estimated unless in-depth study and testing was done at the local site for tidal energy harnessing system. The last factor that had taken into consideration when reviewing tidal energy harnessing system is the system operation conditions [6]. The tidal energy system is estimated to operate at a much lower hour even though the system were meant to operate 365 days a year. After these efficiency values were taken into considerations, the estimated operation hour of horizontal axis tidal turbine is expected to be around 3500 h [3].

3 Potential Tidal System Location in Malaysia and Discussion There are multiple potential sites Norway that can utilize tidal energy harnessing system, sites that have similar tidal properties as Malaysia will be shortlisted and reviewed instead of reviewing every site in Norway. Through background research regarding the tidal properties in Malaysia, average tidal speed and the depth of the

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coast can be obtained and used as the criteria to shortlist the states in Norway to be reviewed [2]. Malaysia would have an average tidal speed of 1 m/s along its coasts. Although Norway have potential sites that utilize 1 m/s of tidal speed, the data available is much lesser. Therefore, states with tidal speed of 2 m/s will be used for review instead as there are more data available while the velocity difference is only 1 m/s. Malaysia also have an average depth of 40 m around certain coast that have potential to utilize tidal energy harnessing system. As a result, depth of 40 m will be used as the criteria to shortlist the sites in Norway for review. By definition, tidal can be considered as a change on the ocean envelope that is caused by periodic variation of gravitational forces from the Earth, Moon, and Sun. The three main types of tides, namely diurnal, semidiurnal and mixed tides. In Peninsular Malaysia, on the west side of the coastal area, it is dominantly semidiurnal tides. Meanwhile, at the east coastal area, mixed tide with dominant semidiurnal [8]. Since the study focused on the region that has the semidiurnal type of tides, it is expected that the type of turbine used is horizontal axis tidal turbine (HATT) as discussed earlier in Norway that is using the same type of turbine for semidiurnal tides.

3.1 Peninsular Malaysia The well-known Straits of Malacca is one of the longest straits in the world, as it is one of the strategic locations for shipping that is connecting between the Indian Ocean, Pacific Oceans as well as the South China Sea. Within the Strait of Malacca, Malaysia alone covered 325, 550 km2 of the straits and 4675 km along the coastline. Simulations study shows the Malacca Strait has current flow between 0.5 m/s and up to 4 m/s at a certain region of the strait. Also, the average depth of the straits is approximately 40 m, with the average current flow of 2 m/s (equivalent to 4 knots). These conditions are said to be sufficient to run a horizontal axis tidal turbine (HATT) [9, 10]. Any installations of HATT in Malaysia’s region of the Straits of Malacca able to supply electricity to the capital city, Kuala Lumpur as the distance is relatively short from the coastal area [9, 11]. This provides the ease of maintenance, operation monitoring process from the coast to the metropolitan area of Kuala Lumpur. However, installations of connection of HATT to East Malaysia may be difficult as it required long connections (underwater cables).

3.2 Sarawak These eight locations in Sarawak were selected as the potential sites for studying the tidal stream speed. The locations were Off Tanjung Po, Off Kuala Paloh, Off Kuala

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Rajang, Off Tanjung Sirik, Off Kuala Igan, Bintulu Port, Off Kuala Miri and Pulau. Most of the locations were located near the coastline except for Pulau Triso which located at the end of Batang Lupar river and Off Kuala Igan is located further away than the coastline. The tidal stream speed was recorded throughout for approximately one month. When recorded the tidal stream speed, the tidal stream was assumed to be constant across the width of the channel [12]. The potential of this selected sites to harness tidal energy will be considering these factors: • The flow velocity of the water • The water depth The tidal speed was recorded by putting a measuring device 5 m deep into the water. The tidal current flow was assumed to be steady throughout the year. The tidal speed for Off Tanjung Po was 0.36 m/s while Off Kuala Paloh had a tidal speed of 0.41 m/s. Both Off Kuala Rajang and Off Tanjung Sirik had a tidal speed of 0.36 m/s. At Off Kuala Paloh, the tidal current speed had 0.41 m/s. It was shown that in Off Tanjung Sirik also had a speed of 0.36 m/s. In Bintulu Port and Off Kuala Miri had a tidal speed of 0.15 m/s and 0.21 m/s respectively. Finally, the last locations which is Pulau Triso, its had velocity of 2.06 m/s [13]. The highest tidal stream velocity was in Pulau Triso which is 2.06 m/s and the lowest tidal stream velocity were 0.15 m/s located in Bintulu Port. It is shown that none of the locations had exceeded the requirements speed which is 1.5 m/s except for Pulau Triso [12]. Pulau Triso had the requirements tidal stream velocity in order for a basic tidal turbine to work. However, the tidal speed is expected to be inconsistent, drop lower or rise higher than the required speed to turn the tidal speed at times, affecting the power production. Based on the study, Sarawak required a low speed and a smaller type of HATT, which requires less water flow speed. Hence, the basic design of HATT was required to be modify in order to work properly. Some other designs can be used as reference like the enclosed tips (venture) turbine designs. However, since the speeds were recorded under 5 m deep, it is hard to predict the speed below the depth of 5 m. The design for the this HATT mostly only able to supply sufficient electrical energy for the light at the port on that location. The swept area of the tidal turbine depends on few factors, one of them was the depth of the ocean. It is important that the turbine blades to avoid large forces to prevent the blades from getting damaged. It is assumed that the requirements depth of 7 m from the surface water was for a tidal turbine to be installed and operated under suitable water flow condition [8]. The tidal turbine only can be installed with a depth of 20 m or greater [8]. The depth for Off Tanjung Po was 9 m while both Off Kuala Paloh and Off Kuala Rajang had a depth of 10 m. Meanwhile, Off Tanjung Sirik had an ocean depth of 13 m. At Off Kuala Igan, the ocean depth was 48 m deep. Next, it is analyzed that the ocean depth for Bintulu Port 15 m and Off Kuala Miri had a depth of 10 m. Finally, at Pulau Triso, it had a depth of 10 m too [14]. The deepest depth between all of the potential sites was Off Kuala Igan while the shallowest locations were at Off Tanjung Po. However, since none of the locations had reached a depth of 20 m or greater except Off Kuala Igan. Since, Off Kuala Igan

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located in the middle of the ocean. Hence, it had the greatest water depth. All of these locations were unable to build a normal design of HATT. Hence some changes needed to be done in order to make sure that the HATT able to work in those locations. Off Kuala Igan required a slow speed type of HATT while others required a small size of HATT.

3.3 Sabah For case study of Sabah, four locations were chosen as a potential site to harness tidal energy. The locations were Pulau Jambongan, Semporna, Balambangan, Kota Belud. Kota Belud located at the northwest of Sabah and surrounded by the South China Sea. Balambangan is located the most north island of Sabah and surrounded by South China Sea and Sulu Sea. To understand the flow velocity of the area, some research had been done. The flow of water mostly around the range of 1.0 to 1.1 m/s for Pulau Jambongan for every 550 h per day [8]. For Semporna, Barangbongan, and Kota Belud, the tidal speed was about 1.1 to 1.2 m/s which happened every 750 h per day. However, the current of the tidal stream were not consistent because there a fluctuation in the speed. As the result, the tidal speed was unable to operate the turbine at a full capacity. If compared the tidal energy in Norway, the tidal stream was lower in Sabah. Hence, producing a high amount of power is impossible. However, Sabah able to implement a type of ‘low speed’ turbine. The turbine was required to be redesigned. In order to build a HATT, the requirements depth is 20 m or greater. It is noticed that Pulau jambongan, Semporna, Balambangan, Kota Belud had a depth grather than 20 m. Therefore, these locations were suitable to build a HATT [8]. Sandakan had a high tidal range. This data was collected from the National Hydrographic Centre [15]. If assume the tidal barrage was 1 km long and accommodate by 20 turbines with a length of 5 m, the average power availability for Tawau will be 63.68% by using the computation software called Fluent. From the same software, the monthly yield power was able to be generated. Tawau was able to provide an average power of 202.68kWh which sufficient to provide the city in Tawau. It is identified that Tawau able to capture and utilize the tidal energy for development purposes. Any tidal power plants in Tawau will bring benefit to the development of the city.

4 Conclusion Summarized tidal properties of potential sites to build tidal harnessing system in Norway was tabulated in Table 1 where the potential sites have similar tidal properties as potential sites in Malaysia. The data from Table 1 will be used as a benchmark to

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Table 1 Tidal properties from norwegian sites Site

Tidal speed (m/s)

Buholmsflaget

2.06

Dyna-Folfoten

2.06

Store vagsoysundet

2.06

Width (m)

Mean depth (m)

Resource (MWh/year)

800

45

284

800

32

202

1000

40

316

Mageroysundet

2.06

1100

40

348

Grotoysundet troms

2.06

1100

35

304

Table 2 Tidal properties in Malaysia in term of tidal stream

Peninsula Locations

Tidal speed (m/s)

Water depth (m)

Pangkor Island near shore

0.05 to 0.5

2.1 to 8.7

Pangkor Island deeper area

1 to 2

~50

Tidal speed (m/s)

Water depth (m)

Sarawak Locations Off Tanjung Po

0.36

9

Off Kuala Paloh

0.41

10

Off Kuala Rajang

0.36

10

Off Tanjung Sirik

0.36

13

Off Kuala Igan

0.51

48

Bintulu port

0.15

15

Off Kuala Miri

0.21

10

Pulau Triso

2.06

10

Locations

Tidal speed (m/s)

Water depth (m)

Pulau Jambongan

1.0–1.1

~20

Seporna

1.1–1.2

~20

Balambangan

1.1–1.2

~20

Kota Belud

1.1–1.2

~20

Sabah

see if HATT can be implemented in Malaysia. Power generation in Malaysia can be estimated with Table 4 as a benchmark. Table 2 summarise the potential sites in Peninsula, Sarawak and Sabah, Malaysia where the tidal stream properties were exhibited. According to Table 4, only deeper area of Pangkor Island in the Peninsula has very similar tidal properties as potential sites in Norway. Other potential sites in Malaysia have much slower tidal stream or shallower depth. This means that only deeper area of Pangkor Island can produce

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Table 3 Lake sihwa, south korea as benchmark for tidal barrage Site

Tidal speed (m/s)

Tidal range (m)

Construction cost (USD)

Resource (MWh/year)

Lake Sihwa, South Korea

3

0 to 5

355 million

552700

Table 4 Tidal properties in malaysia in term of tidal range

Sabah Locations

Tidal range (m)

Kota Kinabalu

1.7

Kudut

1.6

Sandakan

1.5

Lahad Datu

1.75

Tawau

3

power similar to that of Norway while other potential sites in Malaysia would produce power several times smaller compared to that of Norway. Another tidal property that were considered was tidal range where tidal barrage can make use of it to generate power. It can be observed from Table 3 that the use of barrage in Lake Sihwa, South Korea is quite successful as it produces 552700 MWh of electricity per year. The tidal range at this site goes as high as 5 m and this generate sustainable power at this station as given in Table 3. Table 4 tabulates the data regarding potential sites in Sabah in term of tidal range. It was discovered through study of research papers that only Sabah, Malaysia has sustainable tidal range to be used to generate power through tidal barrage. These potential sites locate at Sabah and from Sabah, Tawau have the highest tidal range of 3 m. As discussed from previous section, Tawau will be able to produce sustainable power as it has similar tidal range as Lake Sihwa from South Korea. From these evaluations, it can be observed that tidal energy harnessing system can indeed be implemented in Malaysia. The scale of power production at each potential site depend on the local tidal properties and it is worth mentioning that this power generated is useful regardless of its size. Small power generation can be used by local communities while larger power generation could benefit town. On the long run, the use of tidal energy harnessing system in Malaysia would still be beneficial. The only drawback is the high initial cost of the system. Assuming the HATT project uses all equipment and tools that were imported from the United States, the total cost for all those components are USD 1683 per kW which is equivalent to RM 5722.20 per kW. It is assumed that the maintenance and operation cost is assumed to be RM300 per kW [16]. From Table 3, the manufacturing cost for tidal barrage was observed to be around USD 335 million and if tidal barrage were to be implemented in Malaysia, the manufacturing cost would be similar.

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However, if the economical drawbacks could be supported and overcame, the implementation of tidal harnessing system in Malaysia would be green and sustainable due to their consistent power generation.

References 1. Energy in Norway (2007) Norwegian Water Resources and Energy Directorate. http://www. nve.no/ 2. Bahaj AS, Myers L (2004) Analytical estimates of the energy yield potential from the alderney race (Channel Islands) using marine current energy converters. Renew Energ 29:1931–45 3. Grabbe M, Lalander E, Lundin S, Leijon M (2009) A review of the tidal current energy resource in Norway. Renew Sustain Energ Rev 13(8):1898–1909 4. Andre H (1978) Ten years of experience at the La Rance tidal power plant. Ocean Manag 4:165–78 5. The development and market potential for tidal current power in Scotland (2003) Scottish Enterprise. Centre for Environmental Engineering and Sustainable Development. Robert Gordon University 6. Douglas CA, Harrison GP, Chick JP (2008) Life cycle assessment of the Seagen marine current turbine. Proc IMechE Part M J Eng Marit Environ 222:1–12 7. Røkke A, Nilssen R (2017) Marine current turbines and generator preference. A technology review. Renew Energ Power Q J 1(11):336–341 8. Lim YS, Koh SL (2010) Analytical assessments on the potential of harnessing tidal currents for electricity generation in Malaysia. Renew Energ 35(5):1024–1032 9. Chong HY, Lam WH (2013) Ocean renewable energy in Malaysia: the potential of the straits of malacca. Renew Sustain Energ Rev 23:169–178 10. Sakmani AS, Lam WH, Hashim R, Chong HY (2013) Site selection for tidal turbine installation in the strait of Malacca. Renew Sustain Energ Rev 21:590–602 (In press) 11. Tide Tables—Malaysia (2012) National Hydro- graphic Centre, Royal Malaysian Navy, vol 1 12. Hagerman G, Polagye B, Bedard R, Previsic M (2006) Methodology for estimating tidal current energy resources and power production by tidal in-stream energy conversion (TISEC) devices. EPRI North American Tidal in Stream Power Feasibility Demonstration Project 13. Jakhrani AQ, Ragai A, Rigit H, Samo SR (2013) Estimation of tidal stream energy resources at sarawak coastline and their potential impact on environment. Aust J Basic Appl Sci 7(6):503– 514 14. Rigit ARH, Jakhrani AQ, Kamboh SA, Kong WH, Samo KA (2013) Mapping of tidal stream energy resources in the coastline of sarawak. World Appl Sci J 22(9):1252–1261 15. National Hydrographic Centre (2007) Royal Malaysian Navy, Tide Tables Malaysia 2007 16. O’Doherty T, Mason-Jones A, O’Doherty DM, Evans PS, Wooldridge C, Fryett I (2010) Considerations of a horizontal axis tidal turbine. Proc Inst Civil Eng- Energ 163(3):119–130

An Experimental Study of Deep Learning Approach for Indoor Positioning System Using WI-FI System A. H. A. Sa’ahiry, A. H. Ismail, L. M. Kamarudin, A. Zakaria, and H. Nishizaki

Abstract Global navigation satellite system (GNNS) is known for its capability to detect the whereabouts of any desired target such as vehicles and places. However, there is some disadvantage of these technologies as it can only get a precise location outside of the building because as the signal goes to indoor building, the signal becomes weaker due to attenuation. The Wi-Fi systems are the best alternative to GNNS in an indoor environment since the architecture is massively deployed in many recent buildings. However, Wi-Fi also has its disadvantage where its signal is non-linear due to various factors such as multipath and signal blockage indoors thus limiting the system accuracy. In this paper, a deep learning approach with standalone Wi-Fi technologies will be used to have a precise indoor positioning by using the fingerprinting method. The overall result shows that the average distance error between actual and estimated location is 20-cm and the highest error is 62-cm in an experimental area of 180-cm and 120-cm in x and y axis. This shows that deep learning is a possible method to have accurate and precise indoor positioning. Keywords Indoor positioning · Fingerprinting · Deep learning · Deep neural network

1 Introduction In past years, awareness for indoor positioning has been a popular topic among researchers [1, 2], and [3]. Many applications are related to this like asset tracking, detection of a personal item, or finding a person location inside a huge unfamiliar A. H. A. Sa’ahiry · A. H. Ismail (B) School of Mechatronic Engineering, Universiti Malaysia Perlis, 01000 Arau, Perlis, Malaysia e-mail: [email protected] L. M. Kamarudin · A. Zakaria Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia H. Nishizaki University of Yamanashi, 4-3-11 Takeda, Kofu 400-8511, Yamanashi, Japan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_9

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Fig. 1 Illustration of human indoor positioning with multiple router

building. It shows that there is a great need for an indoor positioning system (IPS). Different from outdoor situation where the global navigation satellite system (GNSS) has taken a big role in positioning. This satellite system can reach up to 1-m accuracy which is corresponding to the accuracy of the outdoor positioning level demand [4]. Implementing the satellite system to the IPS could not get the same result as it will hit an obstacle such as roof and wall resulting to signal attenuation and scatter, this shows that the existing infrastructure could not provide sufficient accuracy. Wi-Fi technologies have rapidly developed in these recent years. At the same time, houses and big buildings mostly will have Wi-Fi signals for communication purposes, making it is very reliable to be used in IPS as depicted in Fig. 1. However, this technology has its drawback where the signal fluctuates over time. The Fingerprinting method was introduced to solve this problem to get better accuracy rather than using raw signal data from Wi-Fi. Fingerprinting is a method where it takes the raw signal data from Wi-Fi and make a unique identification on each grid for offline phase and save it in the database to be used later on for predicting new location on the online phase [5]. On online phases, there are several algorithms, or the mathematical model used for predicting the new location. Conventional and most popular methods are the knearest neighbors (KNN) where it can get accuracy around 3–5 m which pretty bad. In this paper, a deep learning approach will be used to get a better result to compare to the previous method.

1.1 Related Work Over the years, there a lot of studies to tackle the IPS issue to get better accuracy and the highest precision in terms of positioning error. A lot of technologies have been used like thermal infrared sensors used by [6] where the main objective to reduce the

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complexity. Some use the ultrasonic sensor, [1] and [7] try to get a better accuracy because of the high sensitivity. Other than that, RFID also one of the technologies that have been used by the researcher [8] where he gets an average of the absolute position errors about 0.1 m. Works described in [2, 9], and [10] used Bluetooth technologies to get good accuracy and optimize the Bluetooth location. However, there is a drawback in terms of the complexity and initial setup. Then Wi-Fi technologies were adopted by [11] on WI-FI based fingerprinting method by using K-nearest neighbor(KNN) and Viterbi-like algorithm which getting accuracy around to 3–5 m while getting precision almost 90% among 5.9 m. While [12] gets a 3.05-m average by using Nearest K Nearest Neighbor (NK-NN) which improvement of the previous work algorithm. Researcher in paper [12] also compare the method with another algorithm like kernel and histogram and shows that their method is the best among others. These recent years, [3] has got 0.6 m to 2.9 m accuracy by using a weighted centroid algorithm with the help of a Gaussian fitting and Kalman filter before the signal feed to the algorithm. From the previous works, it can be seen many technologies were deployed to get a good IPS accuracy. IPS has made so much improvement across the years, however, there is still a lack precision and accuracy which targeted to get below 1-m accuracy with 90% above precision. To improve the system deep learning approach has been introduced in this paper.

2 Methodology This chapter discussed the experimental works in this paper. The methodology includes data collection, pre-process data, and designing a deep learning model. Data collection mainly to get the data on the grid systems for future processing in a deep learning model. Pre-process data is required to get the data ready for a deep learning model to learn the data and predict the location later on. Lastly, is to design the optimize deep learning model to get the most optimize hyperparameters for the model. Deep learning learns new data from the previous data which the user feeds into it. There are two stages which are training the model by using Eq. 1 where x is the input, w is the weight and u is the output. The activation function is using Eq. 2 which is a linear activation function. After getting the best model new data will feed into it to get the prediction based on that model. In this paper as shown in Fig. 2, before proceed to train the data, the data will have to pre-process first. After pre-process the data, then it can be train. Data training will take a while to get the best model. After getting the best model. Then, the model can be used for prediction stages. New random location of pre-process data will fit into the model and then predict the location based on the algorithm of the model.

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Fig. 2 Deep learning workflows

u=

n 

wi xi u =

n 

i=0

wi xi

(1)

i=0

y = f (u)

(2)

The Fingerprinting method has two phases online and offline. In offline phases, the database is collected by using a module. For this experiment, ESP12 is used to get the signal and save it to the database. The database for this experiment was saved in MYSQL database which will be arranged according to the time of each signal transmit and received. The time is neglected as the deep neural network only used the data from the access point (ESP12) which acts as a router in this experiment. While in the online phases, the user or robot will stand on the random position and the deep neural network will try to predict the location of the user or robot based on the database collected before by using the ESP12 module. The overall architecture of the deep neural network system offline and online is shown in Fig. 3 below.

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Fig. 3 Architecture of the overall system

2.1 Data Collection Esp12 module is used as the main Wi-Fi module which contains ESP8266 Wi-Fi microchip. One of the reasons, due to its function to received signal and transmit relatively same compared to the router for Wi-Fi. As the chip is very low cost and was used widely for pre-research purposes, due to this the module was chosen. In this experiment, four of the modules will be used as the router to collect the fingerprinting database. The experiment was conducted in a grid system length of 120- centimeters and wide of 180-cm. One module will be placed inside the grid that has six locations which will be recorded for the database to feed on the deep neural network model. The module will be placed on each of the six locations and recorded four-access point module signal strength and will be saved in the MYSQL database. Each location was recorded in 30 min. The setup was shown in Fig. 4 below where all the setup is conducted on the same floor. The database is compulsory for the fingerprinting method as it will use the database to make the prediction. After collected the database, four of location is randomly selected to validate the deep neural network method or to test the model in the real situation whether it can predict the actual location. Four of the random locations are located inside the grid system. Figure 4 below shows four random locations to predict the estimated and compared with the actual location measured and recorded.

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Fig. 4 Grid system for database and evaluation

2.2 Pre-processing Data Collecting data is crucial but before proceeding to predict the location, pre-processing of the raw rata is needed. As signal data from the ESP12 is not stable when it wants to save into the database. There are missing data where it required to interpolate. Deep learning requires non-null data. For this experiment, the data were interpolated to the nearest data which taking data before the missing data. One more problem to overcome is the data do not balance even time taken for the RSSI signal is same on each of the locations for the database. As a result, the database was balanced to get the same number of data in the database for each location. The database must be balanced so it will not bias to the major number of data. This will prevent an inaccurate development of the model of the deep neural network.

2.3 Deep Learning The deep neural network is a technique or method where it needs features as input and the deep neural network will train to get the desired model and the model can be used to predict the location of the new access point (AP). The important item in a deep neural network is developing or designing the model of the neural network. To build the model, the architecture and the hyperparameters of the model are needed to be comprehended. One of the examples of hyperparameters is the hidden layer. In this paper, the hidden layer is heuristically chosen and is shown in Fig. 5. In this experiment four access point (AP) were used as the input. Input is flattened to get the shape in one dimensional. There are two hidden layers on this designed model with 32 and 8 nodes in the hidden layers. To get a stable and less fluctuate result, a dropout function of 0.2 is used. The activation function for each of the layers is using linear activation function to get the accurate result as the linear will give us

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Fig. 5 Flowchart of the model

all the range of the database in decimal value. Finally, two output was designed to get the location of x and y by using linear activation function also. The optimizer of the designed model is by using ADAM [13] which can get the optimal learning rate for backpropagation. The size of the batch of the input is 100 so the computational time would be faster and to get the better result 200 epochs are used. Validation of the model is used by using mean absolute error is used as the signal are fluctuated. After getting the result of the estimated and actual location the error is calculated by using Euclidean distance equation as in Eq. 3. d=

 (x1 − x2 )2 + (y1 − y2 )2

(3)

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3 Result and Discussion In this section, three results will be presented and discussed. Three of the results are the raw database get from the ESP 12, the model loss gets from the model, and the average location error from the actual location. All of these results will be discussed in-depth in this section.

3.1 Raw Data Raw data that has been collected via the devices to get the RSS signal produce the fluctuated graph as expected because the signal will not be linear or stable as time increase. Figure 6 shows one of the locations of the database. The RSSI signal data has been interpolated due to its missing data. These data in Fig. 6 taken in one of the locations from the grid database. As shown in Fig. 6, the RSSI fluctuates as time increase. This is due to the radio frequency for Wi-Fi is not stable because of many factors such as multipath propagation. Some may be drop too much like in Access point (AP4) which drops too critical. But this is expected and deep learning will try to minimize this problem and give a better result.

Fig. 6 RSS signal data

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Fig. 7 Model loss of the train data

3.2 Propagation Model The raw data has been pre-processed and afterward, it will be trained to get the best function for the model and the model will be used for prediction or validation process. The train for the input into the model shows a gradual increase in accuracy. Hence, reducing the loss of the model to predict the actual location. In the experiment, 200 epochs are used to get the best loss reduction while maintaining high accuracy. Each epoch will be trained in a batch size of 100 to fasten the computational process. The graph of the trained model is shown in Fig. 7. The loss or error is calculated by using mean absolute error as the signal fluctuates over time. As it can be seen that the location of X and Y will be different in term of loss. This is due to the grid for the database is different in terms of the data which for the X-axis it has 3 locations collected while the Y-axis only has 2 locations collected. The difference will make the loss bigger in the X-axis as regards to X-axis has more range value up to 180-cm while the Y-axis has only a 120-cm range. The average percentage loss for the X-axis is 24% and for Y-axis is 11%.

3.3 Distance Error Four tests have been made to know the error of the distance difference between the actual and estimated location. The test location placement is randomly placed on the grid of the same as the database grid. The average reading of the estimated location for the X and Y axis has been calculated as the reading will have some resolution. The error was calculated using the Euclidean distance formula to get the distance difference between actual and estimated location. The unit of all the value is in centimeters. Before proceed to predict the location, the actual reading was recorded to compare with the estimated location. Figure 8 shows the difference of the location error and it indicates that the error is manageable. In the X-axis for test 2, the error distance is 62-cm and it’s the highest

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error calculated between other test locations. For the X-axis, as mentioned before this the loss is quite high compare to the Y-axis due to the range. However, the error still can be considered better if we want to compare it to another method. The minimum distance error was 7-cm in the Y-axis for test 4. On average, the distance error around 2-cm which is better if want to compare it to other methods. Finally, the result of the X and Y location of the validation process on four of the location is shown in Fig. 9. The graph is shown, that the distance error is not very bad and the result shows that deep neural networks may overcome other methods as in Table 1 we can see the maximum error is 62-cm and the minimum is 18-cm. This show the result is significantly good compare to other method result in comparing the distance error.

Fig. 8 Distance error of the X and Y axis

Fig. 9 Overall distance error validation

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Table 1 Distance error between estimated and actual location distance error between estimated and actual location Location 1

Location 2

Location 3

Location 4

X

Y

X

Y

X

Y

X

Y

Estimated

75

72

152

89

172

28

106

53

Actual

60

60

90

90

165

45

90

60

Error

19

62

18

18

4 Conclusion A deep learning approach is a good method to get a better result compare to the conventional method like KNN. A deep neural network that has been used in this paper shows a better result which yields approximately 20-cm average distance error. The maximum of the error is 62-cm which is a good result compared to KNN which is around 0.6 to 2.9 m to predict the positioning location. However, this approach needs a lot of data due to the system of deep learning. It requires more data to get a better result. It can be seen in this paper as the X and Y axis has different data collection which X has 3 positions while Y has only 2 positions. This contributes to a higher loss in the Y position. Despite that, the approach still is better compared to other approaches due to its accuracy. In the future, the experiment will be tested on real surroundings with a larger area and reduce the number of AP and see the effect of the positioning accuracy. Acknowledgements The authors would like to acknowledge the support from the Fundamental Research Grant Scheme (FRGS) under a grant number of FRGS/1/2018/ICT05/UNIMAP/02/4 from the Ministry of Education Malaysia.

References 1. Kim HS, Choi JS (2008) Advanced indoor localization using ultrasonic sensor and digital compass. In: 2008 International Conference on Control, Automation and Systems ICCAS 2008, pp 223–226 2. Zuo Z, Liu L, Zhang L, Fang Y (2018) Indoor positioning based on bluetooth low-energy beacons adopting graph optimization 18(11):1–20 3. Wang P, Luo Y (2017) Research on WiFi indoor location algorithm based on RSSI Ranging. In: Proceedings - 2017 4th International Conference on Information Science and Control Engineering ICISCE 2017, no 2, pp 1694–1698 4. Kim YK, Choi SH, Lee JM (2013) Enhanced outdoor localization of multi-GPS/INS fusion system using mahalanobis distance. In: 2013 10th International Conference Ubiquitous Robots and Ambient Intelligence, URAI 2013, pp 488–492 5. Ismail AH, Kitagawa H, Tasaki R, Terashima K (2017) WiFi RSS fingerprint database construction for mobile robot indoor positioning system. In: 2016 IEEE International Conference on Systems Man, Cybernetics, SMC 2016, pp 1561–1566

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6. Kemper J, Linde H (2008) Challenges of passive infrared indoor localization. In: 5th Work Positioning, Navigation and Communication 2008, WPNC 2008, vol 2008, pp 63–70 7. Lazik P, Rajagopal N, Shih O, Sinopoli B, Rowe A (2015) ALPS: A bluetooth and ultrasound platform for mapping and localization. In: SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, pp 73–84 8. Liu M, Wang H, Yang YY, Zhang Y, Ma L, Wang N (2019) RFID 3-D indoor localization for tag and tag-free target based on interference. IEEE Trans Instrum Meas 68(10):3718–3732 9. Gwon Y, Jain R, Kawahara T (2004) Robust indoor location estimation of stationary and mobile users. In: IEEE INFOCOM 2004, vol 2, pp 1032–1043 10. Kotanen A, Hännikäinen M, Leppäkoski H, Hämäläinen TD (2003) experiments on local positioning with Bluetooth. In: Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing, pp. 297–303 11. Bahl P, Padmanabhan VN (2000) RADAR: an in-building RF-based user location and tracking system. In: Proceedings of - IEEE INFOCOM, vol 2, pp 775–784 12. Alfakih M, Keche M (2019) An enhanced indoor positioning method based on Wi-fi RSS fingerprinting. J Commun Softw Syst 15(1):18–25 13. Buznˇa E, Cernea D (1991) Atitudine terapeuticˇa în neuropatia opticˇa prin alcool metilic. Oftalmologia 35(1):39–42

Defect Factor Analysis Using Statistical Process Control Analysis: A Case Study in Spices Defected Packaging Production Nur Illa Idris, Tan Chan Sin, Safwati Ibrahim, Fadzli Ramli, and Rosmaini Ahmad

Abstract The problems faced by food and beverages in Malaysia are still the emergence of defect products in every production and it escaped from quality control checked thus it reaches the consumer. This study aims to analyze the variability of defects in the production line in XX Food Industries Sdn. Bhd., using p-chart control and fishbone diagram. This company produce a food spices products whether it is well within the control or not and looking for the factors that causing to the defect products. Data collection and information obtained from interviews and documentation for the number of products and production defects for three months in XX Food Industries. The methods used to analyze products quality control by using p-chart, one of the statistical process control and fishbone diagram to determine the cause of defects products. The defects that usually occur in the production line are types of packaging defects such as leaking sachets, eyemark on the sachet sealing, and less product fill in the sachet that cause lightweight sachet. Results showed that products quality control in XX Food Industries Sdn. Bhd. is still controlled by the largest type of defects that are leaking with defects percentage 46.3 and with 36.28% percentage of defects caused by eyemark. Then, followed by defect caused by lightweight is 17.5%. Factors causing defects products are human, machine, work methods, and materials. In this case unskilled worker and the material of the plastic wrapping for the spices packet are primary cause of defects products. This study is useful in providing useful information in identifying causes for rejection or defect analysis. This research also helpful in proposing optimal solution to be implemented for productivity and quality improvement. Keywords Quality control · P-chart · SPC · Fishbone diagram

N. I. Idris · T. C. Sin (B) · R. Ahmad School of Manufacturing Engineering, Universiti Malaysia Perlis, Kampus Tetap Pauh Putra, 02600 Arau, Perlis, Malaysia e-mail: [email protected] S. Ibrahim · F. Ramli Institute of Engineering Mathematics, Universiti Malaysia Perlis, Kampus Tetap Pauh Putra, 02600 Arau, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_10

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1 Introduction Manufacturing industry in automated flow line focuses on productivity assessment and analysis since it presents an important indicator of the company’s profit and performance [1]. Along the advances information and technology today, the company is required to face consumers who are increasingly sensitive to the quality of goods or services that they wish to consume. To face this condition the company must pay attention to the quality of its products and intensified to produce quality of goods or services, so that the product can be accepted by consumers and competence in the market with the other products. Quality has gone through an evolution process, from an operational level to a strategic level, and some scholars have given strong support for the view that quality must be adopted as a strategic goal in organizations [2]. Many methods can be used to control the quality with each characteristic. Using Statistical Process Control (SPC) in quality control means quality is controlled from the beginning of production process, during the production process until the finished products [3]. Before products will be sell, the products have been examined then the good products will be separated with the defect products. SPC charts have been widely used since Stewhart first introduced them in the early 1930s. SPC is a powerful tool to reduce variability in practice. A major objective of SPC is to quickly detect the occurrence of any assignable causes of variations or called out-of- control (OC) so that investigation of the process and corrective actions can be taken before more nonconforming units are produced [4]. The primary application domain for SPC charts has been in process control and process improvement manufacturing business. One of SPC tools is control chart and depend on one research say that it is not easy to successfully implement effective and sustainable control methods. The control chart is one commonly used tool in the measure and control phase. Control charts can also act as a means of organizational learning [5]. In other hand, the review about paradigm shift in types of SPC control chart does show clearly that the application boundaries extend considerably beyond manufacturing and that the range of problems to which SPC control chart techniques can be applied are much wider than commonly assumed. The paper has highlighted the critical fundamental and technical issues which need to be addressed with nonstandard SPC chart applications [6]. SPC has the potential to improve the quality of maintenance, delivery process and ultimately the safety of customers [7]. The application of statistical tools such as control charts, histogram, caused and effect diagram along with process capability analysis is presented in the study to eliminate quality problems arising out of various assignable causes during spice packaging process. It is observed from previous study that when the special or assignable causes of variation are present in manufacturing, the process is turns out to easily out-of-control, so here comes the use of SPC [8]. Depend on this view in this research SPC will use for knowing the condition of quality control. Large and small companies realized the importance of quality, but each company has a different view on the implementation of quality control.

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XX Food Industries Sdn. Bhd. is a company of producing food spices in the northern region in Malaysia, where market covering the area of all over the country including east and west Malaysia. As one of big company in the northern region and has large market coverage, they need a system of quality control for increase consumer loyalty to the products. The problems that will be discussed in this research are: • Does the implementation of quality control in XX Food Industries Sdn. Bhd. are under control limits. • What factors are causing in the implementation of quality control in XX Food Industries Sdn. Bhd. The purposes of this study are: • To analyze the variability of defects in the production line in XX Food Industries Sdn. Bhd., by using p-chart control and fishbone diagram. • To identify defects factors that cause in the implementation of quality control on products manufactured by XX Food Industries Sdn. Bhd.

2 Literature Review Quality control (QC) is the management function which aims to measure, understand and improve the production process and the materials flow in order to produce products according to specification [9]. The main objective the implementation of quality control is to get a guarantee that the quality of the products or services produced in accordance with the quality standards have been established and in accordance with the desire consumers by the most optimal cost. Accurate and valid data can be obtain as well as for the analysis by using seven tools. The main statistical tools can be used as tools for quality control, which is; check sheet, histogram, control chart, Pareto diagram, causal diagram, scatter diagram, and process diagram. SPC widely used to ensure that the process match with standards so that the products also have a good quality [10]. The framework used in this study illustrates how quality control using SPC can analyze the degree of defects product which produced by XX Food Industries Sdn. Bhd. that over the tolerance limit. After that, the root cause of defect can be identify. Thus, we can make suggestions or recommendations for improvement of quality in the future.

3 Methodology The variable of this research is quality control, in which the quality control problem is a problem that can’t be measured directly and need detailed indicators to be measure clearly. Thus the problem of quality control is a latent variable. Latent variable is a

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formation variable or hidden variables that must be declared by using an indicator. Another name for the latent variable is a factor, construct, or unobserved variable [8]. The indicators regarding quality control in this study is the number of defective products, the number of samples. The population and the sample in this study is the number of packet of spices produced by XX Food Industries Sdn. Bhd. during the three months from January to March 2019. Data used in this study consisted of secondary data about the number of production, product defect and the number of samples. There are also primary data based on interviews to employees and customers of XX Food Industries Sdn. Bhd. Data obtained is quantitative and most of the data obtained from the company that became a place of research. Quantitative data in few articles mentioned such as numerical data in spreadsheets, databases or log files, and only a handful discuss quantitative data above and beyond aggregate usage statistics [9]. Quantitative data in this research obtained from the interviews and the document/records of the production and the quality control department.

4 Result and Discussion Quality control of finished product is done through the inspection. In general, the characteristics of good quality standard for packet of spices are clean, accurate packaging cutting size, no sharp edges, no packaging leaking, and correct amount of weight per packet. So the types of defects that usually occurred in the spice packaging line are lightweight of packaging, eyemark on the packaging seal (which will lead to sharp edges or tear) and packaging leaking. The first step taken to analyze statistical quality control is to create a table (check sheet) production quantities and product defect/incompatible with quality standards. The data as been stated in the Table 1. In January, the most dominant types of defects caused by leaking. The defect caused by leaking was 485 packets or 52.6% from average total defects. The defect caused by eyemark was 265 packet or 28.74% from average total defects. The lightweight defects was 172 packets or 18.65%, less than the average number and percentage of defect caused by leaking and eyemark. In addition the percentage of defects at this machine is 0.39% from total production or products are checked for the whole three months. Table 1 Total production and types of defects for spices packets in January – March 2019 Total production

Lightweight

Eyemark

Leaking

January

236280

172

265

485

Total defect

February

123912

94

235

241

570

March

101936

185

411

482

1078

922

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In February, the most dominant types of defects caused by leaking. The defect caused by leaking was 241 packets or 42.28% from average total defects. The defect caused by eyemark was 235 packet or 41.23% from average total defects. The average for the lightweight defects was 94 packets or 16.49%, less than the average number and percentage of defect caused by leaking and eyemark. In addition, the percentage of defects at this machine is 0.47% from total production or products are checked for the whole three months. Meanwhile in March, the type of most dominant defect surprisingly was same which is caused by leaking. The defects caused by leaking was 482 packets or 44.71%. The defect caused by eyemark was increasing from last month which was 411 packets or 38.13% from average total defects. The average for lightweight defects was 185 packets or 17.16%. The percentage of defects at this machine is 1.06%, which are the highest defect rate from total production or products are checked for the whole three months.

4.1 Calculating Percentage of Defects Next step is to create a p-chart or control chart, which the function to see the company quality control position. The steps in create a map of the control is as follows. Table 2 shows the total production, products defects and the percentage for 3 months. Table 2 Total production, product defects and defects product percentage for 3 months

Month

Total production

Jan-19

236280

Feb-19 Mar-19

Total defects

Defect (%)

922

0.39

123912

570

0.46

101936

1078

1.06

4.2 Calculating Central Line (CL) Central Line is the middle line between the upper control limit (UCL) and lower control limit (LCL). The center line is a line that represents the average defect rate in a production process. To calculate the center lines use the formula:  _ np CL = p =  n Annotation: 

np = T otal De f ects

(1)

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n = T otal Pr oduction

By using the formula given, the details has been sort in the Table 3 as follow: Table 3 Total no of defects, total production, central line (CL) of product from January – March 2019



Month

np



n

Central line, p

Jan-19

922

236280

0.004

Feb-19

570

123912

0.005

Mar-19

1078

101936

0.011

4.3 Calculating Upper Control Limit (UCL) To calculate upper control limit performed by the formula: 

_

U C L = p +3

p(1 − p) n

 (2)

Annotation: p = pr oduct de f ects average/central line n = si ze o f each sample 1)

January 2019 Subgroup 1 :  UCL = p + 3

p(1 − p) n



 = 0.004 + 3

U C L = 0.011 And so on.

0.004(1 − 0.004) 821



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2)

131

February 2019 Subgroup 1:  UCL = p + 3

p(1 − p) n



 = 0.005 + 3

0.005(1 − 0.005) 3949



U C L = 0.008 And so on. 3)

March 2019 Subgroup 1:  UCL = p + 3

p(1 − p) n



 = 0.011 + 3

0.011(1 − 0.011) 8804



U C L = 0.014 And so on.

4.4 Calculating Lower Control Limit (UCL) To calculate upper control limit performed by the formula:  LC L = p − 3

p(1 − p) n

 (3)

Annotation: p = pr oduct de f ects average/central line n = si ze o f each sample 1)

January 2019 Subgroup 1 :  LC L = p − 3

p(1 − p) n



 = 0.004 − 3

LC L = −0.003 And so on.

0.004(1 − 0.004) 821



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2)

N. I. Idris et al.

February 2019 Subgroup 1 :  LC L = p − 3

p(1 − p) n



 = 0.005 − 3

0.005(1 − 0.005) 3949



LC L = 0.002 And so on. 3)

March 2019 Subgroup 1 :  LC L = p − 3

p(1 − p) n



 = 0.011 − 3

0.011(1 − 0.011) 8804



LC L = 0.008 And so on. From the calculation above, a p-chart using Microsoft Excel 2013 is made which can be seen in Fig. 1. As seen in Fig. 1, on January , there are 3 points above the line of upper control limit (UCL) and no point under the line of lower control limit (LCL). From 31 points, there are 3 points above UCL its means 9.67% defects products is over from tolerable

P-Chart for Defect Proportion in January 2019

Defect Proportions

0.040 0.030 0.020 0.010 0.000 1 -0.010

3

5

7

9

11 13 15 17 19 21 23 25 27 29 31

Subgroup (day) Defect proportions

UCL (0.011)

CL (0.004)

LCL (-0.003)

Fig. 1 P-chart of proportion defect spices packets in January 2019

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P-Chart for Defect Proportion in February 2019 0.03 Defect Proprtions

0.025 0.02 0.015 0.01 0.005 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Subgroup (day) Defect proportions

UCL (0.008)

CL (0.005)

LCL (0.002)

Fig. 2 P-chart of proportion defect spices packets in February 2019

limits. Zero percent of defects under LCL indicates there are no proportion of small defects. While the rest of 28 points or 90.3% still in line between LCL and UCL, indicates the defects are still in tolerance. Figure 2 is P-chart in February, there are 4 from 28 point are above the line of upper control limit (UCL). Its means 14.3% defects are above the control limits. Meanwhile, 28.6% defect under LCL line indicates the proportional of small defects. 57.1% still in line between LCL and UCL control limits. The percentage of defect that over the control limit are increase 4.63% from January. This indicates the control of supervision of quality declined from the previous month. As referring Fig. 3, the point that over the control limit is 6 points from 31 points. Its mean 19.4% defects over the control limit. 35.5% or 11 points below the LCL and 14 points defects still in control of the boundary area or still between UCL and LCL line. This condition shows declining condition from January until March. In January defects are still in line of UCL and LCL is 90.3%, then decrease to 57.1% in February, then finally become 45.1% in March. From three control maps above, we can see that the defect of spices packets over the control limit still fluctuate each month. The fluctuating number of defect products above UCL show inconsistency quality control of product in XX Food Industries Sdn. Bhd. Due to that reason, the company need a better quality control system. In January, 9.67% defects is over the control limit and then increase to 14.3% in February. However, in for the month of March, the defects above the UCL line increase to 19.4% but still the highest percentage of January and February. In addition, in February there are 2 points outliers then increase to 3 points in March. Outliers point is higher point than the others because the defects number and the

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P-Chart for Defect Proportion in March 2019

Defect Proportions

0.04 0.03 0.02 0.01 0 1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031

Subgroup (day) Defect proportions

UCL (0.014)

CL (0.011)

LCL (0.008)

Fig. 3 P-chart of proportion defect spices packets in March 2019

percentage of defects are high on that day. This p-chart result was seen the number of points that are above the upper limit are still fluctuating, rising in February and declined in March. Moreover, with increasing outlier points that are outside the UCL line indicates declining activities in quality control and this condition need more attention. Causal diagram or Fishbone diagram was made after the p-chart to analyze the factors that become the cause of product defects. The factors that influence and cause defects products can generally be classified as: i. ii. iii. iv. v.

Man: workers in the production process Material: the components in producing the products into finished goods Machine: equipment used during the production process Method: instructions or work order to be followed in the production process Environment: the circumstances around the production site, which directly or indirectly affect the production process

From the data in Table 1 and 2, we can see that there are three types of defects product in the production process are lightweight, eyemark and leaking. As a tool to find the cause of the defect, fishbone diagram is used to explore each type of defects. Here is the causal diagram use for lightweight, eyemark and leaking. The spices packet were lightweight caused by few factors as appear in Fig. 4 below. One of the factor is inaccurate plastic wrapping position. So the spices powder cannot be filled accordingly to the accurate weight. Some are overfill (also been recognized as lightweight defect) and some powder are spilled out of the packets. In addition, other factors that caused the lightweight packets is the changing of plastic wrapping.

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Fig. 4 Fishbone diagram of defect lightweight

When the workers are less focus or lack of skill during the plastic wrapping changing, the defects continue to be happened. Unscheduled corrective maintenance of the machine itself also be part of defect factor. The spices packet with eyemark defect caused by the wrong plastic sealing pressure that has been set by workers with lack of skill. Even though there are constant pressure should be set on the machine, but during first morning shift the machine need to run, it requires a few manual adjustment by skillful worker. Also, if they need to change the plastic wrapping roll if the roll is finished, the defects continue to be happened until the plastic is well set. Improper adjustment of the machine itself also be part of defect factor. All of the factor can clearly be seen as in Fig. 5 below: The spices packet with leaking defect caused by the wrong plastic sealing pressure and when the sealing is unheated properly. So when it is unheated, the plastic is leaked and can be fill by air or water. Workers with lack of skill or get less focus during the machine adjustment or maintenance, will be the main caused to this types of defects. Again, changing of plastic wrapping roll if the roll is finished, can affected the leaking defects. The factors contribute to leaking defects are stated in Fig. 6 below:

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Fig. 5 Fishbone diagram of defect eyemark

Fig. 6 Fishbone diagram of defect leaking

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5 Conclusion Based on the check sheet, the highest defect for 3 months is caused by leaking with average value of percentage 47% and defect cause by eyemark is 35.44%. Then, followed by defect caused by lightweight is 17.5%. Based on the result of control map (p-chart) can be seen that the quality control is over the control limit. This can be seen where there are many points that are over the control limit and the points is fluctuating. In January, 9.67% defects is over the control limit and then increase to 14.3% in February. However, in for the month of March, the defects above the UCL line increase to 19.4% but still higher than the percentage of January and February. Based on the fishbone diagram, the factors that cause the quality control are man, machine, work methods and materials. Where the main cause of defect factor are by unskilled worker and material of the plastic wrapping for the spices packet. This study is useful in providing useful information in identifying causes for rejection or defect analysis. This research also helpful in proposing optimal solution to be implemented for productivity and quality improvement. Acknowledgements The author would like to acknowledge the support from Universiti Malaysia Perlis (UniMAP) and the fundamental research Grant Scheme (FRGS) under a grant number FRGS/1/2019/TK03/UNIMAP/02/7 from the Ministry of Higher Education Malaysia.

References 1. Sin TC, Usubamatov R, Fairuz MA, Hamzas MFMA (2014) Mathematical model of productivity with reliability and losses parameters for serial structure linear production automated flow line: a simulation analysis. Int Rev Mech Eng 8(4):772–778 2. Illa IN, Sin TC, Fathullah GM, Rosmaini A (2018) Mathematical modeling of quality and productivity in industries: a review. American Institute of Physics 3. Putri SI, Septyandi CB, Rohandani DP (2016) Quality control of product: statistical process control. In: Proceedings of the 2016 global conference on business, management and entrepreneurship, vol 15, pp 259–267 4. Xiao X, Jiang W, Luo J (2017) Combining process and product information for quality improvement. Int J Prod Econ 130:1–14 5. Rantama J, Tiainen E, Ka T (2013) A case of implementing SPC in a pulp mill. Int J Lean Six Sigma 4(3):321–337 6. MacCarthy T, Wasusri BL (2002) A review of non-standard applications of statistical process control ( SPC ) charts. Int J Qual Reliab Manage 19(3):295–320 7. Able CM, Hampton CJ, Baydush AH, Munley MT (2011) Initial investigation using statistical process control for quality control of accelerator beam steering. Radiat Oncol 6:1–9 8. Sharma R, Kharub M (2014) Attaining competitive positioning through SPC – an experimental investigation from SME. Measur Bus Excellence 18(4):86–103 9. Cesar F, Fernandes F, Filho MG, Bonney M (2009) A proposal for integrating production control and quality control. Ind Manage Data Syst 109(5):683–707 10. Woodall WH, Montgomery DC, Woodall WH, Montgomery DC (2018) Some current directions in the theory and application of statistical process monitoring. J Qual Technol 46:78–94

Optimal Design of Step – Cone Pulley Problem Using the Bees Algorithm Noor Jazilah Yusof and Shafie Kamaruddin

Abstract Nowadays, there is a lot of optimization algorithms available to find an optimal solution in engineering problems. Most of these algorithms were developed based on the collective behavior of social swarms of ants, bees, a flock of birds, and schools of fish. It is commonly known as Swarm Intelligence (SI). Examples of algorithm categorized under SI are Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and the Bees Algorithm (BA). The Bees Algorithm is considered one of the recent optimization algorithms and it has been successfully solved various types of problems. It is inspired by the food foraging behavior of honeybees in nature. This study applies the Bees Algorithm to minimize the weight of the stepped-cone pulley in its design and satisfy the constraints. The Bees Algorithm is used in this study to find the optimum solution for stepped-cone pulley design and found better results compared to other optimization algorithms. Keywords Bees algorithm · Optimization algorithm · Mechanical design · Step-cone pulley

1 Introduction Optimization methods have attracted the attention of researchers in engineering and technology applications. One of the alternative methods to solve the optimization problem is using an optimization algorithm [1]. The most common optimization algorithm used is inspired by natural phenomena such as ants, fishes, bees etc. Currently, there is a lot of optimization algorithms available, such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Fish Swarm Optimization (FSO), Artificial Bee Colony (ABC), the Bees Algorithm (BA), the Genetic Algorithm (GA), and Teaching- Learning Based Optimization (TLBO) [2]. This study focuses on applying the Bees Algorithm for the constrained mechanical design problem. N. J. Yusof · S. Kamaruddin (B) Department of Materials and Manufacturing Engineering, Faculty of Engineering, International Islamic University Malaysia, IIUM, Kuala Lumpur, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_11

139

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The Bees Algorithm has been successfully applied to different types of optimization problems [3]. It also has been used to solve unconstrained benchmark functions problems. The Bees Algorithm is inspired by the food foraging behavior of honey bees in finding the optimal solution. The unique behavior of bees in finding food sources has inspired a group of researchers from Cardiff University to establish this algorithm [4]. A colony of bees has their scout to explore and to allocate where the foods acquire in the greatest abundance. It also managed to arrange the best strategy to localize searches using the best efficient possible food-recovery process.

2 The Bees Algorithm 2.1 Bees in Nature During the harvesting season, a bee colony sends scout bees to the fields surrounding their hive. The scout bees move randomly to find the food sources. Most of the time the scout bees looking for flower patches that are high-quality (sugar level). When the scout bees return to the hive, it fills up the pollen that they have been collected from their searching. Then, if the scout bees found a food source with high-quality, they will share the position of their finding to other members through a dance ritual known as ‘waggle dance’ [5]. The waggle dance gives three important information about the food sources; the distance between the food source and hive, the direction of the food source, and the quality of the food source. The dance route in eight shapes. Firstly, the scouts produce a loud buzz by vibrating their wing muscles and runs in the straight line the direction vertically to hive and it shows the direction of the food source relative to the sun. Next, the scout bees circles back to alternate return path. The speed and duration of the dance show distance to the food source, while the frequency of the buzzing indicates the quality of the food source [6].

2.2 The Bees Algorithm The Bees Algorithm uses exploitative neighborhood search and explorative random search. It also has several parameters to be set to use the Bees Algorithm (refer to Table 1). The flowchart of the Bees Algorithm is shown in Fig. 1 Both Table 1 and Fig. 1 are taken from [5]. The Bees Algorithm starts with sending scout bees (ns) randomly across the search space. Then, the fitness value for each scout bee is evaluated via fitness function. The scout bees with higher fitness function are promising solutions which will attract more recruit bees compared to other bees. The sites found by this group of scout bees are known as the best sites (nb). These best sites are divided into two types which are the elite sites (ne) and best sites (nb). The recruited bees for elite sites (nre) are

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Table 1 Bees algorithm parameters

Fig. 1 Flowchart of Bees Algorithm

more compared to recruited bees for remaining best sites (nrb). Furthermore, the remaining unselected scout bees are sent randomly again to form a new population of bees with the best recruit bees from each patch [7].

3 Stepped Cone Pulley Problem Step-cone pulley is a series of pulleys forming a stepped cone. It is used in pairs for varying the velocity ratios of shafts. Pulleys are used to transmit power from one shaft to another at a considerate distance away through a belts or ropes running over it. The mechanical design problem used in this study is taken from [2]. The main objective is to find a design of a 4 step-cone pulley with minimum weight using 5 design variables, consisting of four design variables (d 1 , d 2 , d 3 , d 4 ) for the diameters

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Fig. 2 Step-cone pulley 2D [2]

of each step, with the fifth being the width (w) of the pulley. The details objective function and constraints are given in the Appendix [2]. The figure above shows step - cone pulley in 2-Dimensional design (Fig. 2).

4 Methods The Bees Algorithm was run using an open-source software known as the “R software”[8]. The stopping criterion for this experiment is > 10000 function evaluations. Then, the Bees Algorithm was run with different sets of parameters that have been selected as shown in Table 2. The Bees Algorithm was run 100th times for each set of parameters to obtain statistical results (mean, best fitness value, and worst fitness value). The results were compared with the results of other algorithms available in the literature. The results were compared in terms of the best fitness value found and mean fitness value. Lower fitness value indicates better results as the objective of this problem is to minimize weight.

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Table 2 Set of parameters for the bees algorithm No. of set of parameters

n

e

nep

ngh

1

10

2

20

0.01

m (m > e) 5

nsp 10

2

10

3

15

0.04

5

10

3

10

2

10

0.06

5

10

4

10

2

15

0.10

5

10

5

10

2

20

0.10

4

15

6

10

3

15

0.15

5

17

7

10

2

5

0.20

6

10

8

15

4

15

0.50

10

10

9

15

2

15

0.10

5

10

10

15

2

15

0.10

5

20

Table 3 Result obtained at different set of parameters No. of set of parameters Best

Worst

Mean

Median

Standard deviation

1

15.46922 16.14674 15.7646195 15.76345

2

15.37425 20.0305

0.06403316

3

15.37919 20.87329 15.812490

4

15.48781 16.76818 15.7932915 15.736565 0.22093459

5

15.44954 16.29711 15.782476

15.754285 0.1779077

6

15.55054 17.29778 16.038573

15.99321

7

15.66025 17.75656 16.381425

16.296545 0.3833378

8

16.19534 19.81072 17.641056

17.49569

0.8092773

9

15.46642 16.54786 15.814982

15.78669

0.2007992

10

15.44399 17.48639 15.96265

15.899595 0.312266

15.7775311 15.598045 0.711320907 15.71659

0.60446005

0.3079141

5 Results and Discussion 5.1 Results The results of the Bees Algorithm for solving step-cone pulley problem using a different set of parameters are shown in Table 3. Based on this result, the Bees Algorithm found the best mean fitness value over 100 runs using the second set of parameters. This set of parameters are; number of scout bees (n) = 10, number of best sites out of selected (e) = 3, number of bees recruited for best sites (nep) = 15, patch size (ngh) = 0.04, number of sites selected out of visited (m) = 5, and number of bees recruited for the other selected sites (nsp) = 10. The best mean fitness value result found is 15.37425 kg, with 0.711320907 of standard deviation value. Meanwhile, the worst result was found using 8th set of parameters, where the parameters are; the

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Fig. 3 The comparison of fitness value found by the Bees Algorithm at different set of parameters over 100 runs

number of scout bees (n) = 15, number of best sites out of selected (e) = 4, number of bees recruited for best sites (nep) = 15, size of patches (ngh) = 0.50, number of sites selected out of visited (m) = 10, and number of bees recruited for the other selected sites (nsp) = 10. The worst mean fitness value recorded is 16.19534 kg with 0.8092773 as the standard deviation value. The function evaluations for each set of parameters were set as 10000 function evaluations. Figure 3 shows the comparison of fitness value found by the Bees Algorithm at different sets of parameters over 100 runs. It is observed that using the second (2nd) set of parameters achieved low fitness values at most of the run numbers compared to other sets of parameters. Meanwhile, the 8th set of parameters shows the worst result among ten set of parameters. Previously, the step-cone pulley design problem also has been solved by several other algorithms such as Teaching-Learning based Optimization (TLBO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Harmony Elements Algorithm (HEA), and Hybrid Biogeography-based Artificial Bee Colony (HBABC) [2]. To verify the results found by the Bees Algorithm, it is compared with the result of other algorithms. This results comparison is shown in Table 4. This table shows that the Bees Algorithm found the best weight value for step-cone pulley which is 15.37425 kg. Meanwhile, the HEA gives the worst weight value for step-cone pulley which is 10135.74 kg. The result of other algorithms is taken from [2]. Next, Table 5 shows the value of the objective function, design variables, and constraint values for the best fitness value of the Bees Algorithm and TLBO algorithm. It is observed from Table 5, the Bees Algorithm found better results compared to other optimization algorithms and the design variables correspond to the best fitness satisfy all constraints. Then, Figs. 4 and 5 show the comparison of convergence rate for the Bees Algorithm, Artificial Bees Algorithm, and Teaching-Learning based

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Table 4 Results comparison with other optimization algorithms Type of optimization

Best

Worst

Mean

Standard deviation

TLBO

16.63451

74.022951

24.0113577

N/A

PSO

64.576689

N/A

4138.975827

N/A

ABC

16.634655

145.4705

36.0995

N/A

HEA

10135.74

N/A

17478.76

N/A

HBABC

16.642827

N/A

56.3661545

N/A

Bees Algorithm

15.37425

20.0305

15.7775311

0.711320907

Table 5 Objective functions, design variables and constraints comparison Variables

TLBO

PSO

ABC

HEA

HBABC

Bees algorithm

x1

40

–

–

–

–

0.015447

x2

54.7643

–

–

–

–

0.049711

x3

73.01318

–

–

–

–

0.06629

x4

88.42842

–

–

–

–

0.077154

x5

85.98624

–

–

–

–

0.098981

f(x)

16.63451

64.576689

16.634655

10135.74

16.642827

15.37425

g1

5.14E−09

–

–

–

–

−1.83E−04

g2

1E−09

–

–

–

–

−2.06E−04

g3

1E−10

–

–

–

–

−1.52E−02

g4

0.986864

–

–

–

–

3.002729

g5

0.99736

–

–

–

–

3.00275

g6

1.010154

–

–

–

–

3.002953

g7

1.020592

–

–

–

–

3.003106

g8

698.5773

–

–

–

–

560.6476

g9

475.8272

–

–

–

–

1082.558

g10

209.0369

–

–

–

–

802.0265

g11

1.05E−06

–

–

–

–

560.0947

*(−) = Not Available

Optimization. It is observed that the convergence rate of other algorithms (TLBO and ABC) is almost similar to the Bees Algorithm but the Bees Algorithm found better best fitness value. This shows the capability of the Bees Algorithm in handling constrained mechanical design problem.

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Fig. 4 Convergence plot for stepped-cone pulley using the Bees Algorithm

Fig. 5 Convergence plot for stepped-cone pulley using TLBO and ABC [2]

5.2 Discussion Uses of Different Set of Parameters. Several sets of parameters have been used to test this algorithm; it is because most other algorithms require parameters tuning to find a good result. Similarly, the Bees Algorithm also need parameters tuning because a set parameter may produce a good result for a certain problem only. Thus, several sets of parameters have been selected to test the performance of the Bees Algorithm. Each set parameter performs differently as shown in Table 4. Comparison with Other Optimization Algorithms. The step-cone pulley has been tested by Teaching - Learning based Optimization (TLBO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Harmony Element Algorithm (HEA), and Hybrid Biogeography-based Artificial Bee Colony (HBABC). Artificial Bee Colony (ABC) algorithm was chosen because it has a similar work mechanism as the Bees Algorithm (food foraging behavior), whereas Particle Swarm Optimization (PSO) was selected due to it swarm behavior, which almost similar like the Bees

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Algorithm. Meanwhile, the remaining algorithms were chosen because of the availability of the results in the literature. Therefore, the Bees Algorithm results have been compared with the other optimization algorithms and the result proved that the Bees Algorithm found the best solution. Based on results obtained in Table 4, the 2nd set of parameters produce better result compared to others set of parameters. It could be due to the size of neighbourhood (ngh) factor. The smaller size of neighbourhood (ngh) leads to better exploitation and more accurate results. This explains the superiority of the Bees Algorithm over Particle Swarm Optimization (PSO) as Particle Swarm Optimization (PSO) is based on exploration strategy. As for the comparison with Artificial Bee Colony (ABC) algorithm, the reason why the Bees Algorithm found better results could due to the long stagnation limit of Artificial Bee Colony (ABC) algorithm. Despite has good exploitation capability, a long stagnation limit causes it to be slow and requires more time to find the best solution.

6 Conclusion This study aimed to apply the Bees Algorithm to one of the constrained mechanical design problems. The objective is to find the best combination of five design variables that contribute to the minimum weight of step – cone pulley design problem. There are 11 constraints in this problem and 3 out of 11 are equality constraints. The constraints are to make sure the same belt length for all the steps, tension ratios, and power transmitted by the belt. This study has demonstrated the superiority of the Bees Algorithm finding better results in terms of mean and standard deviation values. It is found that the Bees Algorithm is better compared to other algorithms such as Teaching-Learning based Optimization (TLBO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Harmony Elements Algorithm (HEA), and Hybrid Biogeography-based Artificial Bee Colony (HBABC). Further work should extend the study to more benchmark mechanical design problems. Further work also is necessary to determine the best tuning procedure for this problem.

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Appendix

References 1. Tsai JF, Carlsson, JG, Ge D, Hu YC, Shi J (2014) Optimization theory, methods, and applications in engineering 2013. Mathematical problems in engineering 2. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Compu.-Aid Des 43(3):303–315 3. Pham DT, Castellani M (2013) Benchmarking and comparison of nature-inspired populationbased continuous optimisation algorithms. Soft Comput 18:871–903

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Adapting Travelling Salesmen Problem for Real-Time UAS Path Planning Using Genetic Algorithm Dipraj Debnath and A. F. Hawary

Abstract Route preparation for unmanned aerial vehicle (UAV) or unmanned aerial system (UAS) is one of the biggest challenges when it comes to carrying out any mission. Analysis of UAS path planning has gained significant attention from researchers as it is an essential aspect of the UAS collaboration project mission. With the objective of solving the path planning issue, we adapt the travelling salesmen problem (TSP) and propose to solve it by operating an improved Genetic Algorithm (GA) that can find out the path within the shortest distance and period. In this paper, we first develop an algorithm using the concept of GA to solve TSP as per the requirements of UAS path planning. The UAS is considered the travelling salesman in the TSP concept and the mission objective is regarded as the visiting city, since TSP is a complex NP-hard problem, this paper performs optimization algorithm as GA to solve TSP, which can be both successfully and expertly planned. Generally, the algorithm used to find the lowest possible distance to visit all of the TSP’s access points. The optimal solution to various challenges tends to require the use of GA. Some conditions and operators that depend on this algorithm are population size, selection method, mutation and crossover. We will present an improved way of the GA operator in this paper. In contrast with several other techniques, the suggested approach for TSP was more successful in the application of UAS path planning. This paper deals with GA as a part of the simulation to represent the TSP model for UAS route planning. The outcomes show that the approach of the GA is relatively effective in finding the minimum path direction. GA is efficient to solve TSP for UAS path planning problem within a reasonable period of less than 1 min for simple problem and less that 5 min for complex problem. Keywords UAS path planning · Travelling Salesmen Problem · Genetic algorithm

D. Debnath · A. F. Hawary (B) School of Aerospace Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_12

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1 Introduction Unmanned Aerial Vehicle (UAV) symbolizes aircrafts which are centrally controlled or independently piloted. They have many significant benefits, such as minimal cost, decent maneuverability, and a high rate of survival. It is initially used for defense purposes, as unmanned combat aerial vehicles (UCAVs), for transportation of missiles and airstrikes [1]. UAVs have now been operated as a system and commonly called Unmanned Aircraft System (UAS) for various purposes, including scientific commercial, monitoring, and industrial applications, due to these features. In recent decades, the issue of how the UAS operates has been an important area as the operator needs to improve its efficiency and reduce the operating cost at the same time. Thus, the application of UAS has evolved in various fields including path planning, trajectory planning, task assignment, cooperation, detecting, and communications. As the mission priorities of UAS have become increasingly complicated and crucial, planning the mission becomes a significant challenge to ensure the mission to reach or cover a specific location on the map [2]. The issue of path planning focuses on identifying the valid route sequence for trips to accomplish those strategic goals while minimizing costs, such as the overall trip length. Such solution has long been related to a solving a Travelling Salesmen Problem (TSP) where travel expenses measure the distance between the two path points from one point to the next [3]. Such an approach encourages general optimization that shares similar problem with UAS routing though UAS routes are far more complex. TSP is a standard NP-hard issue, the concept of which can summarize as a quest for the shortest route for a salesman in N locations [4]. TSP requires the trader to schedule a return trip to all locations to make the most of it without visiting every location twice [5] that can be represented using a graph theory where, TSP = (G, f, t) Where G = (V, E) is a graph, f is a function V × V →Z and t e Z, G is a graph containing a trip of travelling sellers that do not exceed t if t is limited. Figure 1 demonstrates how TSP is used to obtain the shortest return path from point A. Routing Path 1 form A, B, C, D, E and A and Path2 from A, B, C, F, D, and A. Path 1 covers a distance of 24 units whereas Path 2 covers 31 units. Path 2 is, of course, advantageous as its distance is shorter. Resolving this type of issue over hundreds of points involves a precise algorithm that can be mathematically represents based on Hamiltonian circuit as shown in Eq. 1. f = min

n−1 i−1

distance(X i , X i+1 ) + distance(X n−1 , X 1 )

(1)

Where f is a total route distance function to be minimized, x is the region and i = 1, 2, 3 and… n is the number of cities.

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Fig. 1 A simple TSP routing problem

Several researchers solved the TSP of different approximation approaches, such as genetic algorithm (GA) [6], particle swarm optimization (PSO) [7], ant colony optimization [8] and [9]. In this article, we use an improved GA to adapt UAS route planning. GA as it stands is an adaptive bio-inspired algorithm, focused on John Holland’s concepts of “Normative Selection and Genetic Inheritance” [10]. GA is useful for problems with NP-hard features that cannot determine the corresponding target area [11]. This technique is ultimately used in several implementations, where a large number of NP-hard issues occur [12]. The operation of GA is predicated on genetic regulators, such as range, crossover, and mutation [13]. It is designed as a wellestablished scalable form of programming. It can be used as an optimization algorithm that solves both the minimization and maximization problems. GA considers the chromosome community where each of the chromosomes is a unique type of candidate solution [14]. The first group of chromosomes starts early with genetic algorithms. The problem is managed to solve by a chromosome. Chromosomes are randomly produced when the problem is genetically expressed. Then a selection operator will be used to select a group of chromosomes from the initial population [15]. This group of chromosomes is involved in the reproduction of offspring. In the initial population, these newly generated children are introduced, and then the mutation is carried out to produce the next group. Such procedures, i.e., collection, repetition, and crossover, are performed over and over for a certain amount of iteration numbers or until no optimal solution is found. The next segment includes an analysis of different TSP solution approaches utilizing genetic algorithms [16]. This problem was discussed by many scholars in different parts of the world, but most of them used the Euclidean distance as a cost function. In the first example, Obermeyer used a genetic algorithm methodology to tackle the Dubins vehicle dynamics of the Travelling Salesmen Problem with Neighborhoods (TSPN), then a samplebased roadmap process which we call RCM, and which has been shown to be accurate. The approach to the Generalized Travelling Salesmen Problem (GTSP) has been suggested by Oliviu Matei [17]. The GTSP involves many domains of science and engineering. Through selecting node sets (clusters) and by having precisely one node from every bunch, the suggested algorithm is used to find the lowest distance.

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The hybrid Hopfield Neural Network (HNN) and Genetic Algorithm (GA) was introduced by Gohar Vahdati et al [18]. The algorithm proposed has both HNN and GA benefits that enable the search field to be analyzed and the best approach to be used. The travelling salesman problem (TSP) approaches also suggested by Yang Yi and Qian-sheng Fang and They used to Handle-C to define a new, hybrid genetic algorithm to overcome TSP [19]. Besides Yuxin Liu, Chao Gao, Zili Zhang, Yuxiao Lu, Shi Chen, Mingxin Liang, and Li Tao have suggested NP-Hard Problem Solution with the Physarum Based Ant Colony Model [20]. In an attempt to optimize the travelling salesmen problem, Rishita Kalyani is interacting with an Ant Colony and the Genetic Programming Algorithm [21]. In TSP, a sales representative also requires visiting a specified number of towns. The sales representative needs to start the voyage from one of the cities and then visit all the distinct cities exactly once and then return to the starting town. The restriction in the travelling salesman problem is that sales associates must find the shortest path to visit all the cities. In this article, we aim to find out the solution for UAS path planning in the TSP model and in doing so, a new GA was developed. The GA finds the least linear way based on locations predefined from the TSP library. We found the length of all placed points by using the Pythagoras Distance Formula in the algorithm. Simulation outcomes are measured for comparison issues in the TSP library.

2 Methodology A genetic algorithm is a type of randomization search methodology which emerges from the biosphere’s evolutionary laws. As the analysis data, the genetic algorithm uses the value of the specific fitness function in the population, and the search area is all the population individuals. For the algorithm, Pythagoras Distance Formula has been used to find every location point to another location point distance. The central concept is to measure the distance between all points for further calculation of path planning. The Pythagoras Distance Formula to find the distance between two points (X1 , Y1 ) and (X2 , Y2 ), as shown in Eq. 2 below. Distance, d, d=

 (X 2 − X 1 )2 + (Y2 − Y1 )2

(2)

Genetic algorithm is a process of selection mechanism. It strives for a conclusion that is equivalent to or similar to a problem’s solution. Existing solution layout is constructed from former model solutions. The fundamental strategy in GA for developing and improving solutions/offspring is the collaboration of parent chromosomes. The crossover operator configuration has a more significant influence on GA outcomes. In our improved GA first, we make population from the cites of TSP library as an example If TSP library has 10 cities like [1 2 3 4 5 6 7 8 9 10] we make our population from that by using random permutation like population1 (2 3

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4 1 6 5 9 7 10 8). After that, we calculated the total distance between all population and in this algorithm, we also declared our iteration time for solving the problem. The population those have a minimum distance from that we select four parent and make it as our next offspring. After that mutation process start in algorithm by using sliding, swapping and flipping between cities of parent chromosomes until the iteration process complete. The algorithm also measured the total path distance when the mutation process starts and keep the lowest minimum distance path. The distance obtained from GA is compared with the TSP library and the percentage is calculated using Eq. 3 and the error are recorded in Table 1. Err or (%) =

G A r esult − T S P librar y r esult × 100 T S P librar y r esult

(3)

Here all location points are taken in two dimensional the algorithm was based on the following steps. Step 1: Declare the waypoints as location points. Example [(x, y) = (2, 4; 1, 5;…)] Step 2: Create a distance matrix based on Pythagoras Formula for waypoints. As describe in Eq. 1 Step 3: Fill the distance matrix for each point to point Cartesian distance using Theorem Pythagoras. Step 4: Declare the Chromosome number called Population consists of waypoints consider as a route. Suppose the Population consists of two numbers. Population 1 [2, 4; 1, 5; … until the number of waypoints] Step 5: Define the maximum number of the Iteration process for the algorithm. The iteration process means how long the algorithm will run to find the solution. Step 6: Verify all the waypoints size of the matrix if not go to step 1. Step 7: Check the population size and number of iterations. Step 8: Using random permutation to make Parents from Population. Step 9: Calculate the total distance for every population. Step 10: Find the minimum distance from the total length of the population. Step 11: Random permutation to cross over between each population, use. Step 12: Go to step 9 and step 10 to find the minimum distance population. Step 13: The mutation process starts between the minimum distance population. Step 14: Find the minimum distance population until the iteration process end. Step 15: Calculate the distance from the minimum distance population. Step 16: Plot the graph contains minimum distance Route. The above steps have been coded into Matlab. We set the population size as 12 and the number of iterations to 1000000 and for consistency, the same parameters remain for all problems. Finally, we compare the best solution within five solutions due to the random nature of GA.

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3 Results The performance of GA was tested using 10 selected problems of different levels of complexity as described in TSP library [22]. The list of the problem is shown in Table 1. TSP library provides a collection of TSP problems with the near-optimal solution to be compared and analyzed. The naming of the problems is defined based on the route name followed by the number of cities/points e.g. BERLIN52 represent route in BERLIN consisting of 52 cities. Upon running the GA, the algorithm will optimize and plot the optimized route. For every problem, the solution from GA is plotted in figure (b) in all figures from Fig. 2 to Fig. 11. The first problem name ATT48 in the TSP library produces a total distance of 33587 units against 33522 units as shown in Fig. 2. The second problem is the BAYG29 give an equal final distance of 9073 units as shown in Fig. 3. The third one is the BERLIN52 town problem and the total distance is 7542 and GA also calculates the same length as the given result shown in Fig. 4. The next one named EIL101 city problem, and the given result is 629. GA measure total distance 643 as shown on the right side of Fig. 5. Figure 6 result shown for a problem called ST70 and given distance 675 and GA result is 675 also. This problem contains 150 cities named ch150 and the final result is 6528. GA calculates the total distance of 6726, shown in Fig. 7. The problem called GR202, and its final result is 547. GA measures 488 for this problem shown in Fig. 8. The next challenge consists of 666 towns and final distance for this is 3936. GA finds the most efficient result than the given one as the final length is 3255, shown in Fig. 9. The second last problem is contained in 561 cities and a given result for this 19311. GA calculates the final distance of 15995 as much efficient than the given one shown in Fig. 10. The last problem called PR1002 and the total distance for this problem given as 259045. GA calculates the final length for this 300637 shown in Fig. 11.

Fig. 2 ATT48: a TSP Library solution, b GA solution

Adapting Travelling Salesmen Problem for Real-time...

Fig. 3 BAYG29: a TSP Library solution, b GA solution

Fig. 4 BERLIN52: a TSP Library solution, b GA solution

Fig. 5 EIL101: a TSP Library solution, b GA solution

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Fig. 6 ST70: a TSP Library solution, b GA solution

Fig. 7 CH150: a TSP Library solution, b GA solution

Fig. 8 GR202: a TSP Library solution, b GA solution

D. Debnath and A. F. Hawary

Adapting Travelling Salesmen Problem for Real-time...

Fig. 9 GR666: (a) TSP Library solution, (b) GA solution

Fig. 10 PA561: (a) TSP Library solution, (b) GA solution

Fig. 11 PR1002: (a) TSP Library solution, (b) GA solution

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All the results obtained are compared with the TSP library result as shown in Table 1. The error indicates how close the GA to the TSP library solution. Positive error means the GA performs less efficient and negative errors means the GA solution surpasses the TSP Library solutions. Table 1 Comparison between TSP Library and GA results TSP problem Att48

TSPLib result

GA result

Distance

Best distance

Error(%) Iterations (s)

33522

33587

Bayg29

9073

9073

44.51

0.0

Berlin52

7542

7542

49.02

0.0

Eil101

629

643

60.89

2.3

St70

675

675

53.83

0.0

6528

6726

73.11

3.1

Ch150

48.61

0.2

Gr202

547

488

89.34

−10.7

Gr666

3936

3255

231.37

−17.3

Pa561

19311

15995

211.11

−17.1

Pr1002

259045

300637

321.07

16.0

For problem BAYG29, BERLIN52, ST70, we get the same result and their percentage level is the same as seen in Table 1. The problem named ATT48, EIL101, CH150 we get almost near results compares to TSP library result with almost closed to 0% error as shown in Table 1. For other problems like GR202, GR666 and PA561 GA results surpass TSP library as much as 10.7%, 17.3 and 17.1 respectively. But GA performs worse in PR1002 as much as 16% less than that of TSP Library. In addition to Table 1, for every problem, we analyze 5 results obtained from GA to determine the repeatability and consistency as shown in Fig. 12. It is observed that the GA results for BAYG29, BERLIN52 and ST70 are consistent with less than 5% 120 110

(%)

100 90 80 70 60

ATT48

BAYG29 BERLIN52 EIL101

TSP library value

Result#1

ST70

Result#2

CH150

GR202

Result#3

Fig. 12 Comparison Between TSP library value and GA result

GR666

Result#4

PA561

PR1002

Result#5

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110

(%)

100 90 80 70 60

PR1002

TSP library value

Result#1

PR1002(a)

Result#2

Result#3

Result#4

Result#5

Fig. 13 GA result for PP1002 when the iteration limit increases to 10000000

error between the solutions of which BAYG29 shows 0% error. Other percentage values also very closely correspond to the TSP library result for ST70. ATT48, EIL101 and CH150 percentage levels were very much close to the reference value. All these three problems got approximately 4% error for every run. Interestingly GA perform well for GR202, GR666, and PA561 in which the results surpass TSP lib approximately 10%. However, as mentioned earlier, GA did not perform well for PA1002 and further investigation found out that the GA has prematurely converge due to iteration limit of 1000000. By lifting iteration limit to 10000000 demonstrates a better result which brought up approximately 9% closer to the TSP Library solution as shown in Fig. 13. It is simply because GA needs more time to calculate the best outcome due to the complexity of the route. In many cases, GA performs well if the iteration limit increase, but the processing time would be longer for an onboard UAS system to make decision. Therefore, the same limit of 1000000 remained.

4 Discussions The practical design of the trajectory is always a complicated job. The algorithm creates to make it possible to identify the least route separately for this research. The results indicate that the Genetic algorithm method is comparatively efficient to find the minimal path. Though the Genetic Algorithm sometimes finds the same result as the given one, it takes different time for different waypoints. The most effective technique in GA for finding the minimum path is to use the different number of population size and extend the iteration time. Though it is not suitable for taking a long time to find the minimum route. For considering many waypoints, we can use the Genetic Algorithm as its accuracy level is high. However, the execution time will be different every time to change the waypoints, population size, and the number of iteration process time. The fastest solution always gets from GA by increasing the iteration time and by changing the population size, GA gives the different optimal path for TSP.

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5 Conclusion The results show that the Genetic algorithm is able to optimizes the UAS route within a reasonable period. Though for complex problem that has more points, the algorithm needs more iteration time to find the near optima route. This method gives the best practical solution for all TSP library problems as compared to exhaustive method that take much longer time for a mere improvement. Therefore, this solution can be used to optimize the UAS pat planning particularly for real-time on-board decision.

References 1. Storm B et al (2018) Unmanned combat aerial vehicle path planning 27:15–24 2. Isaacs JT, Klein DJ, Hespanha JP (2011) Algorithms for the traveling salesman problem with neighborhoods involving a Dubins vehicle. In: Proceedings of the 2011 American control conference, pp 1704–1709 3. Klein DJ, Schweikl J, Isaacs JT, Hespanha JP (2010) On UAV routing protocols for sparse sensor data exfiltration. In: Proceedings of 2010 American control conference ACC 2010, pp 6494–6500 4. Chen J, Ye F, Li Y (2017) Travelling salesman problem for UAV path planning with two parallel optimization algorithms, pp 19–22 5. Hawary AF, Razak NA (2018) Real-time collision avoidance and path optimizer for semiautonomous UAVs. In: IOP conference series: materials science and engineering, vol 370, no 1 6. Juneja SS, Saraswat P, Singh K, Sharma J, Majumdar R, Chowdhary S (2019) Travelling salesman problem optimization using genetic algorithm. In: Proceedings- 2019 Amity International Conference Artificial Intelligence AICAI 2019, pp 264–268 7. Wang Q, Li Y, Diao M, Gao W, Qi Z (2015) Performance enhancement of INS/CNS integration navigation system based on particle swarm optimization back propagation neural network. Ocean Eng 108:33–45 8. Ma Y, Zhang H, Zhang Y, Gao R, Xu Z, Yang J (2019) Coordinated optimization algorithm combining GA with cluster for multi-UAVs to multi-tasks task assignment and path planning. In: 2019 IEEE 15th international conference control automation, pp 1026–1031 9. Patle BK, Pandey GBLA, Parhi DRK, Jagadeesh A (2019) A review : on path planning strategies for navigation of mobile robot Def. Technol 15(4):582–606 10. Holland J (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge 11. Zakaria MZ, Jamaluddin H, Ahmad R, Muhaimin AH (2011) Effects of genetic algorithm parameters on multiobjective optimization algorithm applied to system identification problem school of manufacturing engineering, universiti malaysia perlis, department of applied mechanics, faculty of mechanical engineering system 12. Andersson K (2019) A new crossover technique to improve genetic algorithm and its application to TSP, pp 7–9 13. Khan FH, Khan N, Inayatullah S (2009) Solving TSP problem by using genetic algorithm, pp 79–88 (2009) 14. Zakaria MZ, Jamaluddin H, Ahmad R, Loghmanian SM (2012) Comparison between multiobjective and single-objective optimization for the modeling of dynamic systems. In: Proceedings of the institution of mechanical engineers, part i: journal of systems and control engineering, vol 226, no 7, pp 994–1005

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15. Zakaria MZ, Mansor Z, Nor AM, Saad MS, Mohamad ME, Ahmad R (2018) NARMAX model identification using multi-objective optimization differential evolution. Int J Integr Eng 10(7):188–203 16. Vaishnav P, Choudhary N, Jain K (2017) Traveling salesman problem using genetic algorithm : a survey, vol 2, no 3, pp 105–108 17. Matei O, Pop P (2010) An efficient genetic algorithm for solving the generalized traveling salesman problem. In: Proceedings of- 2010 IEEE 6th International conference intelligent computer communication process. ICCP10, pp 87–92 18. Vahdati G, Ghouchani SY, Yaghoobi M (2010) A hybrid search algorithm with hopfield neural network and genetic algorithm for solving traveling salesman problem. In: 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE) 2010, vol 1, pp 435–439 19. Yi Y, Fang QS (2010) The improved hybrid genetic algorithm for solving TSP based on Handel-C, ICACTE 2010 - In: 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), vol 3, pp V3–330-V3-333 20. Liu Y et al (2017) Solving NP-hard problems with physarum-based ant colony system. IEEE/ACM Trans Comput Biol Bioinform 14(1):108–120 21. Kalyani R (2015) Application of multi-core parallel programming to a combination of ant colony optimization and genetic algorithm. Indian J Sci Technol 8(S2):138 22. “MP-TESTDATA - The TSPLIB Symmetric Traveling Salesman Problem Instances. http:// elib.zib.de/pub/mp-testdata/tsp/tsplib/tsp/index.html. Accessed 28 Jan 2020

Predicting the Cycle Time at a Production Line Through the Development of the 3-3-1 Multilayer Perceptron Artificial Neural Networks with Formulated Momentum Rate Ahmad Afif Ahmarofi, Freselam Mulubrhan Kassa, and Mohamad Khairi Ishak Abstract The uncertainty of cycle time due to manpower performance, material availability and machine constraint could affect the efficiency of completion time. Hence, the cycle time of a specific task must coordinate efficiently to ensure the smoothness of production operation. Thus, predicting cycle time is an essential issue in production operation and is deemed crucial to be foreseen. From previous studies, various techniques have been utilised to predict cycle time. It is found several works show that the smallest measurement error has been achieved through their proposed Artificial Neural Networks (ANN) models compared to the other predictive techniques. In this regard, the objective of this research is to develop an ANN model to predict the cycle time of a product based on several factors. A feed-forward multilayer perceptron (MLP) network was established and subsequently trained by the developed Backpropagation (BP) learning algorithm to predict cycle time. As a result, the predicted cycle time of the new audio products is 5 s based on the collected data at a selected case company in manufacturing audio speaker products. Consequently, the ANN model could assist production planner in predicting cycle time from historical data for producing new audio speaker products. Keywords Cycle time · Production line · Artificial neural networks · Multilayer perceptron networks · Momentum rate

1 Introduction Cycle time is the time required by manpower to complete a specific task [1]. Cycle time affects the performance of completion time since the cycle time is measured A. A. Ahmarofi (B) · F. M. Kassa Faculty of Industrial Management, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang, Malaysia e-mail: [email protected] M. K. Ishak School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_13

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during pre-production by considering the number of manpower [2], material preparation [3], and machine breakdown [4]. Pre-production is a test run of several pieces of a product before accepted to run as an actual production run [5]. Thus, the cycle time is ultimately contributing to the determination of completion time in processing a product. However, the expected cycle time is uncertain at semiautomatic production line due to unforeseen performance of manpower as human being experiences fatigue and stress during production run [6]. Furthermore, several studies also found out that the completion time of a product always deviated which caused by the uncertain cycle time, thus contribute to tardiness. Hence, the cycle time appears as a frequent problem and considered as a crucial factor in completion time. In determining completion time with on-time delivery, the uncertain cycle time becomes a big problem for the management [7]. Thus, predicting cycle time is an essential issue in production operation and is deemed crucial to be foreseen. In previous studies, researchers implemented various prediction techniques for production operation such as regression analysis [8], decision tree [3], and artificial neural networks (ANN) [9]. It is found that the regression analysis generally has low performance for data mining processes [10]. On the other hand, the decision tree is only best utilized for classification purposes [3]. However, in many cases, ANN has demonstrated its competence in solving prediction problems [11] since the method can integrate the relationship between input and output effectively [12]. Moreover, several works show that the smallest measurement error had been achieved in their proposed ANN models compared to the other predictive techniques [13]. Therefore, the ANN technique is the best predictive method to predict the cycle time at a production line. The ANN technique is defined as a mathematical estimation tool inspired by the human biological neuron with learning parameters and network architecture for information processing [14]. The function of ANN is to forecast or predict the new occurrence based on a learning process of data [15]. In this regard, the objective of this research is to develop an ANN model to predict the cycle time of a product based on the manpower, material preparation time and machine breakdowns factors. The research methodology of the study is discussed in the subsequent section. This is followed by related results and the conclusion.

2 Research Methodology To develop the ANN model, a problem based on a real company situation was considered. The company is a global business manufacturer for audio products in the automotive sector for both the local and international markets. The company produced a new audio product to replace end-of-life (EOL) products in meeting customer requirements with the latest technology and upgraded features. However, the company was facing an issue with an uncertain cycle time of the new audio products at its semiautomatic production line based on several factors, which were manpower, material

Predicting the Cycle Time at a Production Line... Table 1 Source of secondary data

167

Secondary data

Source

The number of manpower (in person)

Production control document

Waiting time for material (in hours)

Production daily report

Machine breakdown rate (in 1 Production daily report per pieces) Cycle time of a specific task (in seconds)

Production daily report

Completion time of existing audio product (in hours)

Production daily report

preparation time and machine breakdowns. This situation has persisted frequently and the management was facing difficulties to overcome the problem. Data on manpower, material, machine, and cycle time were collected. Daily reports from the Production Control Department and Production Department were used as secondary data. Table 1 shows the sources of secondary data collected from the department. The number of manpower refers to the number of workers who performed a specific task at the semiautomatic production line. Waiting time for materials refers to the periods taken by the material warehouse to prepare materials after being unloaded by suppliers for the production line to produce audio products at the semiautomatic production line. Machine breakdown rate refers to the occurrence of machine malfunction at the production line. Cycle time refers to the time needed to complete a single specific task by manpower at the production line. Subsequently, a feed-forward multilayer perceptron (MLP) network was established for the learning process to predict cycle time. The learning process within the established MLP network was guided through the development of a learning algorithm to obtain the desired cycle time. The MLP network was trained by the developed Backpropagation (BP) learning algorithm to circulate back at a different value between the output network and desired output. Figure 1 demonstrates the steps of developing the BP learning algorithm for the MLP network to predict cycle time. The learning process of a BP learning algorithm was generated by the initialization of connection weight. The connection weight value was initialized at this stage in representing the importance of each input parameters, i.e., manpower n , material n , and machinen towards desired output, i.e., cyclen during the learning process. The value of the connection weight was initially set to a random value and no restriction of formulating the connection weight in a learning process. Thus, the value of the connection weight between the ith input node and jth hidden node in the MLP network to predict cycle time was formulated as the following Eq. (1): wi j = W I J

(1)

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A. A. Ahmarofi et al. Start formulating BP learning algorithm for learning process

Initialize weight

Formulate summation function

Formulate sigmoid transfer function

Formulate square error function

Formulate learning rate parameter

Formulate momentum rate parameter

A new approach in calculating momentum rate based on equalization learning speed technique to improve convergence

Allocate percentage of data for training and validation set

No

Is the square error gives the smallest value? Yes Deploy the network for predicting cycle time

Fig. 1 The flowchart of the backpropagation learning algorithm

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where, i = the number of input node at the input layer where i = 1, 2, . . . , I j = the number of hidden node at the hidden layer where j = 1, 2, . . . , J On the other hand, the value of the connection weight between jth hidden node and kth output node was formulated as follows: w jk = W J K

(2)

where, j = the number of hidden node at the hidden layer where j = 1, 2, . . . , J k =the number of output node at the output layer where k = 1, 2, . . . , K The MLP network is developed based on the number of input node-the number of hidden node-the number of output node, i-j-k. In this research, the input layer has three nodes, the hidden layer has three nodes while the output layer has one node. Hence, the established network is a 3-3-1 MLP network. Figure 2 illustrates the flow of each step during the learning process within the feed-forward MLP network to adjust the connection weight through the 3-3-1 MLP network. The flow for the learning process in the MLP network was generated in two directions which are feed-forward and backpropagate. Both of the learning processes were iterated until the desired output of cycle time was achieved. After that, several learning parameters, i.e., summation function, sigmoid transfer function, square error function, learning rate parameter, and momentum rate parameter were formulated to adjust the connection weight. Finally, the smallest value of square error, E, between the value of network output from the sigmoid function of cth output node and desired

Yes

No Increment of weight, Δw

Momentum, μ

Learning rate, ε

Is the square error gives the smallest value?

Gradient, dO

1 manpower n

Square error function, ErO

1

W11

W12

summation function sigmoid function

W13

2 Start

material n

summation function

W22

2 summation function

3

1

cycle time n

W21

W23

machine n

W11

W31

sigmoid function

W21

sigmoid function

W32 W33

3

W31

summation function sigmoid function

Input layer

Input-hidden neuron

Hidden layer

Hidden-output neuron

Output layer

Flow of back propagate learning process Flow of feedforward learning process

Fig. 2 The flow of the learning process within the multilayer perceptron network

End (the network is chosen to predict cycle time)

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output of cycle time was selected for predicting cycle time. Thus, the connection weights were adjusted gradually to obtain the desired output in predicting cycle time through summation and sigmoid functions. The adjustment of connection weight is then adjusted by the momentum rate once the speed of the learning process is controlled by the learning rate. The value of the momentum rate is normally within the range of 0 ≤ | μ | < 1 as highlighted by Turban et al. (2011). In this regard, a new approach in determining momentum value is implemented in this research. The approach taken in calculating the momentum rate is motivated from the equalization learning speeds concept as mentioned by Ahmarofi (2019) such that for a given neuron, the process of learning should be inversely proportional to the square root of connections to the neuron. However, generally, this concept is only suggested for a learning process. As such, a similar formulation is introduced in this research for the momentum rate in the learning process. Hence, the formulation of a proposed equation for determining momentum rate value for this research is formulated as follows: 1 μ=  fi j

(3)

where, μ= momentum rate f i j = the number of neuron between the ith input node and jth hidden node Subsequently, the increment of the connection weight for the next iteration is adjusted. Then, the feed-forward learning process is repeated as shown in Fig. 3. Therefore, the learning process is iterated until the network has a minimum value of square error, Er. A training process is established to adjust the connection weight from a set of data through a learning algorithm. Subsequently, the connection weight value from the training process for predicting cycle time is validated through a different set of data during the validation process. By assigning a higher percentage of data to the training set, the MLP network gives a better prediction performance result in the data learning process since the more the data are trained, the stronger the predictive relationship it has. Therefore, 80% of data is allocated for the training process while 20% is allocated for the validation process. The learning process of the MLP network is run iteratively according to the allocated data until the network has a minimum value of square error, Er. As a result, the predicted cycle time with the final connection weight w i j and w j k on the oth iteration of the learning process is determined. J   ctn = wi j ]o x Bn + wi j ]o xCn + [wi j ]o x Dn +

j=1

where, ctn = predicted cycle time of nth production lot

[w jk ]o sig j

(4)

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[wi j ]o = final connection weight for ith input node and jth hidden node of oth iteration x Bn = transformed value of Bn , i.e., number of manpower for an nth lot xCn = transformed value of C n , i.e., waiting time of material for an nth lot x Dn = transformed value of Dn , i.e., machine breakdown rate for an nth lot [w jk ]o = final connection weight for the jth hidden node and the kth output node of oth iteration sig j = the sigmoid value of the jth hidden node

3 Results and Discussion In the 3-3-1 MLP network, the number of neuron between the ith input node and jth hidden node, f i j , is 9 neuron. Hence, the formulated momentum rate based on Eq. (3) for the type 3-3-1 MLP network is calculated as follows: 1 μ=  fi j 1 =√ 9 = 0.333 Therefore, the value of the formulated momentum rate, μ is 0.333. The connection weights for the ith input node to the jth hidden node, wij , and the jth hidden node to the kth output node, wjk , were initialized with random values (0.1, 0.3, 0.5, 0.7, 0.9, 1 and 1.5) since the determination of weight had no restriction in an ANN learning process. Besides, the value of learning rate, ε, was set randomly to 0.2 while the value of momentum rate, μ = 0.333. Furthermore, the data from production lots were randomly separated into 80% (80 production lot numbers) and 20% (20 production lot numbers) between the train1 set and valid 1 set for each of the MLP networks since the more data were allocated for training, the stronger the predictive relationship it has as. Finally, the iteration of the learning process, o, for each of the MLP network is accomplished once the result of the square error, Er o , gives the smallest value. Subsequently, the final Er o for the experiment of the 3-3-1 MLP network is presented in Table 2. From Table 2, the smallest Er o for the 3-3-1 MLP network with formulated momentum rate is 0.0271 during the 34th iteration of the learning process which is obtained from wij = 0.1 and wjk = 0.1. Consequently, the 3-3-1 MLP network with the formulated momentum rate is selected for predicting cycle time. If the company would like to predict the cycle time of the new audio products for the next production lot, n = 121 with the available number of manpower, manpower 121 = 30 persons, waiting time of material,

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Table 2 The Er o of the 3-3-1 for the experiment with formulated μ MLP network

Connection weight

Learning rate

Momentum rate

Separation of data from production lot number

Iteration

Final square error

i-j-k

wij

wjk

ε

μ

train1

valid 1

o

Er o

3-3-1 network

0.1

0.1

0.2

0.333

80%

20%

34

0.0271

0.3

0.3

45

0.0462

0.5

0.5

56

0.0371

0.7

0.7

50

0.0482

0.9

0.9

56

0.0945

1

1

63

0.0627

1.5

1.5

68

0.0418

Table 3 The best-predicted cycle time based on Eq. (4) for the new audio speaker products during 121st production lot

Input parameters

Corresponding input value

The predicted cycle time

manpower 121

30 persons

5s

material 121

1h

machine121

0.0013

material 121 = 1 h and machine breakdown rate, machine121 = 0.0013, the predicted cycle time of the new audio products in transformed output value during the 121st production lot, ct 121 is expressed based on Eq. (4) as follows:     ct 121 = 0.161]56 x B 121 + 0.654]56 x C 121 + 0.246]56 x D121 + 0.201]56 si g 1 As a result, the best-predicted cycle time, E 121 , for the new audio products during 121st production lot based on the transformed values of the number of manpower, waiting time of material, machine breakdown rate, i.e., x B121 , x C 121 , x D121 and ct 121 , respectively, is presented in Table 3. It is predicted that for a production line with 30 manpower, 1 h for waiting time of material preparation at materials warehouse, and 0.0013 machine breakdown rate requires 5 s of cycle time as presented in Table 3. Hence, the assembly process of audio speaker product at the case company with the semiautomatic production line is smooth to avoid any tardiness issue in future.

4 Conclusion Throughout this study, the prediction of the cycle time was successfully determined by implementing the ANN technique through the development of the 3-3-1 MLP

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network. The ANN technique has proved its ability to solve the uncertain cycle time in production operation and becomes a superior predictive model. Furthermore, it is also identified that the ANN has good capability to capture the relationships among various variables through a learning process on the dataset to provide a better prediction result. The 3-3-1 MLP network could reduce frequent pre-production process at the semiautomatic production line. Hence, it can reduce the occurrence of tardiness efficiently and fulfil customer delivery on-time.

References 1. Schäfer R, Chankov S, Bendul J (2016) What is really “on-time”? a comparison of due date performance indicators in production. Procedia CIRP 52:124–129 2. Lembang LA (2015) Factors related to intention to stay among gen Y in Malaysian manufacturing companies (Unpublished dissertation). Universiti Utara Malaysia, Sintok 3. Ahmarofi AA, Ramli R, Abidin NZ, Jamil JM, Shaharanee IN (2020) Variations on the number of hidden nodes through multilayer perceptron networks to predict the cycle time. J Inf Commun Technol 19(1):1–19 4. Khan SAMN, Ahmarofi AA (2014) Determining the optimal product-mix using integer programming: an application in audio speaker production. In: AIP conference proceedings, American institute of physics, vol 1635, no 1, pp 601–608 5. Russell RS, Taylor BW (2011) Operations Management. John Wiley & Sons, Alaska 6. Ahmarofi AA, Abidin NZ, Ramli R (2017) Effect of manpower factor on semiautomatic production line completion time: a system dynamics approach. J Mech Eng Sci 11(2):2567–2580 7. Bülbül K, Sen ¸ H (2017) An exact extended formulation for the unrelated parallel machine total weighted completion time problem. J Sched 20(4):373–389 8. Ismail Y, Mir SA, Nazir N (2018) Utilization of parametric and nonparametric regression models for production, productivity and area trends of apple (malus domestica) in Jammu and Kashmir, India. Int J. Curr Microbiol App Sci 7(4):267–276 9. Ahmarofi AA, Ramli R, Zainal Abidin N (2017) Predicting completion time for production line in a supply chain system through artificial neural networks. Int J Supp Chain Manag 6(3):82–90 10. Turban E, Sharda R, Delen D (2011) Decision support and business intelligence system. Pearson Education Inc, New Jersey 11. Liu Z, Yang Y, Cai Q (2019) Neural network as a function approximator and its application in solving differential equations. Appl Math Mech 40(2):237–248 12. Raja MAZ, Samar R, Haroon T, Shah SM (2015) Unsupervised neural network model optimized with evolutionary computations for solving variants of nonlinear MHD Jeffery-Hamel problem. Appl Math Mech 36(12):1611–1638 13. Ahmarofi AA (2019) An integrated ANN and SD models with momentum rate to estimate completion time at a semiautomatic production line (Unpublished thesis). Universiti Utara Malaysia, Sintok 14. Wang C, Jiang P (2017) Deep neural networks based order completion time prediction by using real-time job shop RFID data. J Intell Manuf 1–16 (2017) 15. Hassan MG, Othman SN, Taib CA, Ahmarofi AA, Akanmu MD.: Predicting the occurrence of landside at penang island, malaysia, through artificial neural networks model. Int J Eng Technol 7(4.19):217–222

Internet of Things Security: Modelling Smart Industrial Thermostat for Threat Vectors and Common Vulnerabilities Omer Ali, Mohamad Khairi Ishak, and Muhammad Kamran Liaquat Bhatti

Abstract Internet of Things (IoT) made it possible to realize the vision of connected world, where devices ranging from wearables to industrial IoT solutions provide near real-time data insights. These IoT systems generates staggering amounts of data every single day which is prone to security risks. The threat surface for these IoT devices includes the entire hardware stack, processes, and associated applications, thus requires a systematic threat modeling approach to mitigate system vulnerabilities. In this paper, an industrial Smart Thermostat is threat modelled using the industry leading STRIDE framework to report system vulnerabilities, threat surface and its associated threat vectors. An attack tree is designed to investigate the threats on physical resources of the smart industrial thermostat under study identifying system wide vulnerabilities based on Common Vulnerability Scoring System (CVSS). Finally, the CVSS scores are calculated on the entire threat surface for an improved system design. Keywords CVSS · Internet of Things · Security modeling · Threat modeling

1 Introduction Internet of Things (IoT) devices, technology platforms and enterprise solutions have witnessed an unprecedented growth in last decade. At the core of this technological revolution, low-power small form-factor devices are responsible to gather data over which actionable insights are garnered in near real-time fashion which is steering towards the future of connected world. The proliferation of IoT in industries such as health care, home and office environment led to the coordinated information sharing between humans and machines. These coordinated responses are achieved by mapping human behavior and predicting machine actions in response [1]. With rapid O. Ali · M. K. Ishak (B) Universiti Sains Malaysia (USM), 14300 Nibong Tebal, Pinang, Malaysia e-mail: [email protected] O. Ali · M. K. L. Bhatti NFC Institute of Engineering and Technology, Multan 60000, Pakistan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_14

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adoption of IoT based solutions, the boundary between physical processes, machine interaction and human private information is boxed and raises grave concerns for trust and identification, which cannot be realized without a trust-identity relationship model [2]. Therefore, the security aspect for IoT systems starts from the data itself which is then linked to every layer of the technology stack. Contrary to conventional internet enabled devices where security is mostly aimed at the core network, IoT systems require robust security framework that not only addresses the core network security issues but at the same time mitigates the threat vectors associated at every layer of data flow in order to maintain data integrity for these systems [3]. As the core security principles in a conventional network mainly focused on the network and transmission layers, IoT networks on the other hand need fine-grained security deployments at every layer. IoT systems can be thought of as a major sub-paradigm of cyber physical systems comprising of devices, networks and softwares with data at its core. Therefore, cybersecurity stems as a critical part of IoT systems to ensure that the components, processes, and the associated data is less prone to cybersecurity risks. Threat modeling helps to understand the system wide vulnerabilities in both development and system evaluation; however, it still lacks a common security footprint to understand and estimate the host of security issues and vulnerability at each stage. Threat modeling helps to model this broader threat surface area by implying threat vectors and vulnerabilities at every system layer and process, therefore, providing a unique perspective into system failure points that are more likely vulnerability to security attacks. However, it is critical to understand that various device capabilities, application scenarios and physical environments accounts for application specific threat modeling to completely investigate the security risks. In this research, a smart thermostat is studied for vulnerabilities and security risks. The trust boundary for processes, inter-process communication and the overall data is modelled using Microsoft Threat Modelling tool, which is fundamentally important for application developers to know the data leakage points. The smart thermostat model was then investigated using STRIDE model to quantify threat vector attacks at every component and process level. The generated model unfolds various vulnerable threat surfaces of device under observation, that are investigated by a comprehensive attack tree to generate CVSS scores at every point of failure. In Sect. 2, a brief background of recent cyber security attacks is discussed. Section 3 discusses IoT security model frameworks and schemes are discussed. Section 4 introduces “security by design” approach, where a typical IoT system is decoupled to investigate the vulnerable components. Industry standard framework for threat identification and qualification are presented. STRIDE framework is used to model an industrial IoT based thermostat for its vulnerabilities to security threats. Section 5 discusses the attack tree scenario for the industrial thermostat scenario under investigation and the associated threat vectors and their implications are presented in industry-standard CVSS matrix. Section 6 concludes the article by comparing the traditional cybersecurity practices with the desired IoT practices that are needed to secure future IoT systems.

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2 Background Despite having a modern secure enterprise network to provide reliable network communication, the limited on-board compute capabilities and lack of standards creates a typical IoT deployment scenario with a heterogenous mix of components, technologies, protocols and associated applications that are prone to security threats at every layer. This presents the IoT network as an open playground to threat actors to exploit weaknesses at every layer of deployment. Such was the scale of Distributed Denial of Service (DDoS) attacks carried out in 2016. KasperSky in its SecureList series DDoS intelligence reports highlighted the extent, volume and monetary damages for these attacks carried out in 2016 alone. Malicious actors targeted the Domain Name System (DNS) infrastructure on a French hosting provider OVH with almost close to 1 Tbps, making it one of the biggest attack internet has witnessed [4]. Another such attack was carried out on former Washington Post employee and cyber security correspondent Brian Kerbs’s website knocking it offline with a payload of almost 620 Gbps. The biggest point of concern here was the use of 152,464 internet-enabled devices to carry out this attack [5]. The internet connected devices with limited safeguard against security threats are vulnerable to hijacked and used in a co-ordinated DDoS attack that can hamper or completely halt enterprise networks and its resources. Another alarming incident was reported by IBM Security intelligence where a group of internal researchers carried out an attack on a SUV’s CAN bus by exploiting a firmware vulnerability. The researchers were able to control the car speed and could even steer the vehicle off the road. This attack, though only a single of its nature, could be considered as a proof of concept on exploiting the vulnerabilities of IoT enabled systems [6]. Therefore, it is fundamentally important to build the security into every layer of IoT deployments by carefully modelling the threat surface and associated threat vectors per application basis. In this article, the traditional cybersecurity threats on IoT devices are investigated. Later, an industrial thermostat is chosen as an attack surface, for which the associated attack vectors and their implications are modelled.

3 Related Work In a traditional networking environment, Information Technology (IT) and Operational Technology (OT) work in parallel and with a certain contrast to their nature, where the former is more dynamic and focuses on technology stacks, the latter is based on content. OT being deterministic in nature, is more inclined towards pushing the processes and the control over the content, which mostly remains static, therefore, providing a lesser threat surface. Whereas, OT is always data oriented, where data storage, retrieval, transmission and manipulation is always the prime objective [7]. Rizvi et al. [8] presented a typical IoT deployment scenarios where commerce, health-care and personal IoT deployments at home were studied to identify the threat

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surfaces and the associated threat vectors. In their research these application scenarios were decoupled and associated vulnerabilities, system complexities and vulnerability scores were reported. Casola et al. [9] presented a unique perspective to simplify the complexity of IoT technologically landscape by automating the know system-wide vulnerabilities based on a ranking system. The proposed model accounts for device configuration, deployment scenarios and can automatically integrate threat reporting per application basis. Ngo et al. [10] focused on static analysis on IoT malware detection. The research focused on static feature detection such as op-code to detect IoT malware which is then validated against a vulnerability scoring system for model accuracy. However, this approach may only seem to address the security challenged of the physical layer where physical device tampering such as firmware tampering can be identified. Safaei et al. [11] presented a macroscopic approach in their research where ISP and other internet traffic patterns were analyzed specifically for IoT based application traffic. In their research, they proposed a clustering algorithm that focus on reducing the complexity of traffic patterns and can outline the orchestrated IoT attacks over a certain region, sector or domain. An Artificial Neural Network (ANN) based deeplearning attack handling technique is devised that analyzed the traffic patterns of various IoT devices [12]. In this research the packet headers emerging from these resource-constrained IoT devices are investigated and a supervised machine learning model is trained for known attack vulnerabilities. This scheme promises a scalable attack handling model that works at the network level, however, does not covers the broader IoT landscape. Jung et al. [13] presented a unique approach which deploys a convolutional neural network (CNN) to observe the power consumption of various IoT devices. The normal operation and the power consumption values are then stored and trains the CNN model. This model is then deployed on all network traffic emerging from these devices and reports any spikes or variations to identify outliers, which helps to differentiate between a healthy versus botnet traffic. Though this is a unique scheme that can be adopted to mitigated DoS and DDoS attacks, but it only focuses on the network layer alone. Arias et al. [14] showed that a non-secure hardware platform will eventually result in a multitude of vulnerabilities that are mostly exploited in the software stack to gain control over the device which could further be used to weaponize against the security schemes. Most of the schemes discussed in this section focus on a certain layer of technology stack leaving behind the remaining threat landscape exposed. Therefore, a comprehensive threat modeling is required to identify the associated threat vectors at every layer.

4 Identifying IoT Security Risks: IoT Threat Surface The first stage of threat assessment starts from threat modeling [9]. Ideally, the best system design practices involve “security by design” where threat surface and threat

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vectors are modelled during the design phase. The IoT landscape, with its limited on-board computational and network resources, however, poses greater challenges and it is most suited to build security during the product development, which requires a concrete understanding of the system wide vulnerabilities and their modeling. The threat modeling can be generally classified as a three-step process, including: i. ii. iii.

System/components decomposition Threat Identification Threat Qualification

In a typical IoT based system, several systems, modules and interconnected technologies builds up the entire system. Therefore, in order to model IoT systems for threat, it is very important to investigate these components individually by deconstructing them based on their roles and nature [15]. The threat model framework enumerates, logs and categorizes the potential security threats on all the input points of these components that helps to model and predict the real-time device behavior to fight the security vulnerabilities. In some scenarios, most of the components may be overlooked but a well-balanced threat surface analysis is required before a certain modelling framework is applied. Too little component identification may result in system vulnerabilities that might appear once the solution is deployed. On the other hand, unnecessary modelling may advocate for securing certain components that are not part of the attack surface. Once the key system components are identified, the next step involves the identification of these threat vectors, mostly, by utilizing threat modeling frameworks and tools that have now served the traditional cyber-security domain well [16]. Some of the most common attacks on IoT devices emerge from the underlying WSNs including. i. ii. iii. iv.

Denial of Service (DoS) Sybil Sinkhole Hello Flood

These attacks mostly target the communication layer alone and can be modelled using the cyber-security threat modeling frameworks. However, as IoT systems have other interfaces and layers to it, therefore, it is very important to identify the asset as well as the threats on each level. In this article, two cyber-security threat modeling tools are considered because of their popularity and ability to incorporate security by design [17]. Peter Aufner in his research identified some of the industry leading tools and frameworks for cybersecurity threat modelling that can be extended to IoT networks [18] as listed in Table 1.

4.1 Modeling IoT Security Based on STRIDE Framework One of the rapidly growing IoT markets is smart homes that features IoT enabled smart devices from smart TVs, assistants, washing machines, refrigerators, heating

180 Table 1 Threat modeling frameworks [18, 19]

O. Ali et al. Framework

Mode

Automated tool

STRIDE

Data-flow diagram

No

CORAS

UML diagram

Diagram editor

LINDDUN

Data-flow diagram

No

STRIDE average model

Data-flow diagram

No

Attack trees

Attack trees

Secure tree

Fuzzy logic

Data-flow diagram

MATLAB fuzzy logic toolset

T-MAP

UML diagram

Tiramisu

and thermostats to interactive security systems. According to Statista, the overall smart home market size is projected to be 53.5 billion USD by year 2022. The smart home security market alone is expected to reach a turnover of 22 billion USD by year 2021 [20]. These devices are always connected to internet and mostly linked to our personal or corporate accounts to provide us with real-time like information on unified dashboards. Smart home device security mostly features the communication layer where they are either made secure by home network firewalls or gateways or in some cases with encrypted links to enterprise clouds. However, this leaves the threat surface wide open and requires proactive architectural decisions to reduce system wide threats [21]. A recent report from Forbes suggested that the overall cyber-crime damage costs may reach a staggering amount of 6 trillion USD by 2021 [22].One such segment with rapid adaptation of these IoT based technologies in both residential and commercial sector is smart thermostats [23–25]. The adaptation of smart thermostats in both residential and industrial environments rings alarm bells as a simple security breach may not only expose private information to the internet but on the other hand with malicious activities by a threat actor a malfunctioning device can also pose severe life threatening risks as well. Therefore, a smart thermostat model is put under test to investigate the potential security threats associated with various layers and technologies. Microsoft Threat Modeling tool is utilized to implement STRIDE model to monitor the system level threats on every data entry and exit points as given in Fig. 1. Not only the devices are prone to physical attacks involving the hardware, electrical and storage components, but at the same time the system software as well as the firmware presents a wide attack surface. In most of the cases, the communication to the cloud are encrypted but is still prone to DoS attacks. The next critical stage is to identify the risks associated with these attack surfaces and are presented in Fig. 2. The system implementation which seems straightforward has a lot of vulnerabilities and leak points that can open the entire enterprise resources as a threat surface. Most of the enterprises put a strong emphasis on securing their backbone infrastructure with dedicated resources such as system firewalls, proxies, intrusion detection (ID) as well as intrusion prevention (IP) devices. However, such measures only appeal to the network layer, whereas modern IoT and Cyber-physical systems

Internet of Things Security: Modelling Smart Industrial Thermostat...

Fig. 1 Smart industrial thermostat under test with attack surfaces at every layer

Fig. 2 Threat identification in smart thermostat assets

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Fig. 3 Smart thermostat attack tree scenario

(CPS)’s attack surface goes beyond conventional measures. As evident from Fig. 3, more than 50% of attack vectors can target the actual physical resources of these devices which can hamper, spoof or completely deny the system resources.

5 Results and Discussion Modeling an attack tree gives valuable insight into the potential threat vectors that the threat actor can exploit to disrupt the normative system behavior. Figure 3 presents an attack surface tree that focuses on the physical vulnerabilities of the device that can escalate the complete system wide malfunctions and information leaks. The attack surfaces and attack tree scenario were modelled using Microsoft Threat Modelling tool as given in Fig. 4. The next step in line, once the risks are identified is to consolidate and highlight the risk severity, thus the system designers can deal with them on priority. One of the most utilized industrial standards is the Common Vulnerability Scoring System (CVSS) that asses the system security vulnerabilities. The CVSS score as given in Table 2 outlines the severity of potential vulnerabilities at every resource entry point in the STRIDE model. These risks can be eliminated significantly reduced by certain levels by incorporating security be design concept which follows improving security from physical layers all the way to resources in the cloud. In this example, the threat surface can be reduced by incorporating secure identity, secure boot and regular firmware updates which will cover almost all high severity physical device level threats.

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Fig. 4 Smart thermostat modeling using microsoft threat modeling tool

6 Conclusion IoT and CPS security landscape is completely different from traditional cybersecurity. The traditional IP based networks that connects the entire world mostly focus on the backbone gateway network, enterprise level security policies that are applied directly to network interfaces attached to enterprise resources. A common trend is to use on-board hardware or software-based services to combat the security attacks and improve the vulnerability metrics. In contrast to traditional cybersecurity practices, IoT systems cannot adopt such policies due to limited on-board computing and networking resources, which therefore, requires a ground-up security model, that industry refers to as “security by design”. This model should add security from device components to processes, intermediary middlewares to the cloud. In fact, security by design would require a secure horizontal technology fabric connected to a secure and trust-worthy vertical application model on the edge or on the cloud. IoT systems have an enormous threat surface where more than 50% threat vectors are targeted directly at the physical resources. Therefore, incorporating secure systems designs with modern secure IP networks is the first step towards re-engineering the IoT security landscape. Threat modeling helps to identify the system wide vulnerabilities at every layer by directly looking at the weak links in the information exchange points. In this research, an industrial smart thermostat is studied for security vulnerabilities and the associated threat vectors at various layers of the technology stack. Microsoft Threat Modeling tool is utilized to study the trust boundary and the exposed threat landscapes. These threat surfaces per layer are deeply studied and the industry standard STRIDE model is used to identify the vulnerabilities and severity at every layer. CVSS scoring system then helped to evaluate the threat vectors and a corresponding threat score matrix is generated that deeply investigates the severity of threats and its implications at every layer. The IoT security modeling, threat vectors and frameworks also require standardization so that it can be adopted across the board from small research units to large enterprise solutions. Due to limited resources, these devices are incapable of

C

C

Denial of service

Escalation of privilege

C

H

H

N/A

C

N/A

Certificates

C M

H

M

M

H

M

Communication

N/A

H

H

H

H

Credentials

Where: N/A – Not Applicable C – Critical H – High M – Medium

N/A

H

Information disclosure

C

Tamper

Repudiation

N/A

Spoofing

Firmware

Table 2 CVSS Threat score for smart thermostat STRIDE model

H

C

H

N/A

C

H

Configurations

M

N/A

M

H

H

M

Device records

M

N/A

M

N/A

M

M

Logs

H

H

M

N/A

H

N/A

Resources

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defending themselves against cybersecurity attacks, but at the same time it threatens the overall enterprise security by opening a vulnerable interface to malicious actors. Therefore, when it comes to IoT and CPS security, not only the design needs to be defensive (by building security in the processes) but the enterprises networks must also provide offensive security to be able to mitigate security risks and fight against real-time attacks. Acknowledgements The authors would like to thank Universiti Sains Malaysia (USM) for providing the research grant (RUI: 8014049) that helped to carry out this research.

References 1. Byun J, Kim SH, Kim D (2014) Lilliput: ontology-based platform for IoT social networks. In: 2014 IEEE international conference on services computing, pp 139–146 2. Xiao H, Sidhu N, Christianson B (2015) Guarantor and reputation based trust model for social internet of things. In: 2015 International wireless communications and mobile computing conference (IWCMC), pp 600–605 3. Lin J, Yu W, Zhang N, Yang X, Zhang H, Zhao W (2017) A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J 4(5):1125–1142 4. Oleg Kupreev JS, Khalimonenko A, (2016) Kaspersky DDOS intelligence report for Q3 2016. In: SecureList, KasperSky Labs, KasperSky website, quarterly report 31 October 2016, vol 1 https://securelist.com/kaspersky-ddos-intelligence-report-for-q3-2016/76464/,. Accessed 8 Feb 2019 5. Varghese S (2016) French hosting provider hit by DDoS close to 1TBps. https://www.itw ire.com/security/74970-french-hosting-provider-hit-by-ddos-close-to-1tbps.html. Accessed 8 Feb 2016 6. Bonderud D (2015) Eight crazy hacks: the worst and weirdest data breaches of 2015. https://securityintelligence.com/eight-crazy-hacks-the-worst-and-weirdest-data-bre aches-of-2015/. Accessed 8 Feb 2015 7. Pettey C (2017) When IT and operational technology converge. https://www.gartner.com/sma rterwithgartner/when-it-and-operational-technology-converge/. Accessed 10 Feb 2017 8. Rizvi S, Pipetti R, McIntyre N, Todd J (2020) Threat model for securing Internet of Things (IoT) network at device-level. Internet Things 100240 9. Casola V, De Benedictis A, Rak M, Villano U (2019) Toward the automation of threat modeling and risk assessment in IoT systems. Internet of Things, 7:100056 10. Ngo QD, Nguyen HT, Le VH, Nguyen DH (2020) A survey of IoT malware and detection methods based on static features. ICT Express 11. Safaei Pour M, Bou-Harb E, Varma K, Neshenko N, Pados DA, Choo KKR (2019) Comprehending the IoT cyber threat landscape: a data dimensionality reduction technique to infer and characterize Internet-scale IoT probing campaigns. Digit Invest 28:S40-S49 12. Yoon J (2020) Deep-learning approach to attack handling of IoT devices using IoT-enabled network services. Internet Things 100241 13. Jung W, Zhao H, Sun M, Zhou G (2020) IoT botnet detection via power consumption modeling. Smart Health 15:100103 14. Arias O, Wurm J, Hoang K, Jin Y (2015) Privacy and security in internet of things and wearable devices. IEEE Trans Multi-Scale Comput Syst 1(2):99–109 15. Akatyev N, James JI (2019) Evidence identification in IoT networks based on threat assessment Future Gen Comput Syst 93:814–821

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16. Puron D (2017) IoT security audits: IoT threat modelling, IoT security audits, p 5, https://bar baraiot.com/articles/iot-security-audits-24-iot-threat-modelling/. Accessed 10 Mar 2020 17. Shevchenko N (2018) Threat modeling: 12 available methods, threat modeling best practices in network security, p. 8, Security Report. https://insights.sei.cmu.edu/sei_blog/2018/12/threatmodeling-12-available-methods.html. Accessed 7 Mar 2020 18. Aufner P (2020) The IoT security gap: a look down into the valley between threat models and their implementation. Int J Inf Secur 19(1):3–14 19. Hussain KAS, Ahmad D, Rasool G, Iqba S (2014) Threat modeling methodologies: a Survey Sci Int 26 20. Department SR (2020) Smart home - statistics & facts, consumer electronics, p 36. https:// www.statista.com/study/27165/smart-homes-statista-dossier/. Accessed 3 Mar 2020 21. Shevchuk N, Oinas-Kukkonen H, Benson V (2020) Risk and social influence in sustainable smart home technologies: a persuasive systems design model. In: Cyber influence and cognitive threats, Benson V, McAlaney J, Eds.: Academic Press, pp 185–216 22. Brooks C (2018) A scoville heat scale for measuring cybersecurity, cognitive world, p 8. https://www.forbes.com/sites/cognitiveworld/2018/09/05/a-scoville-heat-scale-for-measur ing-cybersecurity/#6a0f6fe33327. Accessed 16 Mar 2020 23. Png E, Srinivasan S, Bekiroglu K, Chaoyang J, Su R, Poolla K (2019) An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings. Appl Energy 239:408–424 24. Fabrizio E, Ferrara M, Monetti V (2017) Smart heating systems for cost-effective retrofitting. In: Cost-effective energy efficient building retrofitting, Pacheco-Torgal F, Granqvist CG, Jelle BP, Vanoli GP, Bianco N, Kurnitski J (eds).: Woodhead Publishing, pp 279–304 25. Huang Q, Lu C, Chen K (2017) Smart building applications and information system hardware co-design. In: Hsu HH, Chang CY, Hsu CH (eds) Big data analytics for sensor-network collected intelligence. Academic Press, pp 225–240

Single Channel Magnetic Induction Measurement for Meningitis Detection Aiman Abdulrahman Ahmed, Zulkarnay Zakaria, Marwah Hamood Ali, Jaysuman Pusppanathan, Ruzairi Abdul Rahim, Siti Zarina Mohd Muji, Anas Mohd Noor, Mohd Hafiz Fazalul Rahiman, Muhamad Khairul Ali Hassan, Muhammad Juhairi Aziz Safar, and Ahmad Faizal Salleh Abstract Bacterial meningitis is one of the most common and prominent infections which infects the central nervous system through the tissue layers and membranes that cover our brain and spinal cord. It is a staggering and fatal illness that kills patients within hours. The number of meningitis cases that has been recorded annually around the world are one million cases and 135,000 deaths. Early detection and start of sufficient treatment are considered as the main determinants for better result. MIT mechanism is noncontact electrodes of impedance measurement. This mechanism uses induction principle instead of contact electrodes to get the required information. This paper presents an overview on the potential of Magnetic induction tomography (MIT) in detecting meningitis disease. In MIT principle, single channel measurement process which consist of transmitter (Tx) and receiver (Rx) coil has been studied. In this field is disclosed about passive electrical field (PEP) which focuses on the three parameters which are dielectric permittivity, electrical conductivity, and magnetic permeability. In addition, this research project involves experimental setup. The applied frequency is between 1–10 MHz. Finally, in this project, the performance of the square coil with 12 number of turns (5Tx–12Rx) with 10 MHz frequency has been identified as the suitable transmitter-receiver pair

A. A. Ahmed · Z. Zakaria (B) · A. M. Noor · M. H. F. Rahiman · M. K. A. Hassan · M. J. A. Safar · A. F. Salleh School of Mechatronic Engineering, Universiti Malaysia Perlis, Arau, Perlis 02600, Malaysia e-mail: [email protected] M. H. Ali Faculty of Dentistry, University of Science and Technology, Sana’a, Yemen J. Pusppanathan Sport Innovation and Technology Centre (SiTC), Institute of Human Centered Engineering (iHumen), Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia R. A. Rahim Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia S. Z. M. Muji Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Johor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_15

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and the optimum frequency for detecting the conductivity property distribution of brain tissues. Keywords Magnetic induction tomography · Phase shift measurement · Non-invasive · Soft field · Meningitis

1 Introduction Meningitis consider as a life-threatening disease and a significant infection to the central nervous system (CNS). Bacteria enter the brain area through the CNS either from a site of infection or by crossing the blood barrier [1]. It can progress rapidly and characterized by inflammation and infection of the meninges with high morbidity and mortality in young children [2]. The danger in meningitis disease is that it can be fatal in many cases which may reach 50% of cases if no immediate treatments or actions taken to the patient. Besides that, 8–15% of the cases die usually within 24 and 48 h of the moment that symptom appears on the patient. Furthermore, Survivors from meningitis death 10–20% of them pone to have a permanent debilitation [2]. Yearly, more than one million cases and 135,000 deaths accounts worldwide because of bacterial meningitis. Also, there are neurological complications in 9–25% of cases [3]. Many current modalities and techniques used for diagnosing meningitis such as lumber puncture [4] blood cultures [5, 6], and tomography modalities: Magnetic Resonance Imaging (MRI) [7–10], Computed Tomography (CT) Scan [3, 11, 12], X-ray [13, 14], Nuclear imaging [15], Ultrasonography [16, 17], Single Photon Emission Computed Tomography (SPECT) [18, 19], Positron Emission Tomography (PET) [20, 21] and several others. These techniques used for high resolution imaging procedures with high details images because they apply high radiation and high magnitude of magnetic field method in their principles. Despite the advantages of these modalities, there are drawback as well, like ionizing radiation and very high magnetic field which is not suitable for certain patients especially those with pacemaker [22]. There are also some other non-ionizing techniques proposed as an imaging technologies but these technologies still cannot be applied in clinics as it is still at research stage. Among the techniques are Electrical Impedance Tomography (EIT) [23, 24], Electrical Capacitance Tomography (ECT) [25, 26], Magnetostatic Tomography [27], Magnetic Induction Tomography (MIT) [28, 29] and Magnetic Induction Spectroscopy (MIS) [30]. Tomography has been associated with meningitis detection and monitoring as alternatives to lumbar puncture. MIT is an electromagnetic tomography technique that has been using for some processes in industries as well as in medical imaging applications. A non-contact excitation coil and detecting coil act as the main components in MIT where excitation coil generate time-varying sinusoidal magnetic field in order to generate eddy current in brain model, and as a result the receiver coil detects the magnetic field induced from the eddy current. In this project, phase shift

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measurement is used in order to differentiate the normal brain and the tumor tissue [31, 32].

2 Physiology of Meningitis 2.1 Brain Structure Brain has three parts which are the main parts: forebrain, midbrain and hindbrain and surrounded by complex blood capillary. Also, there is a barrier in brain called bloodbrain barrier and it is a complex barrier in terms of physiological form. The function of this barrier is maintaining the brain homeostasis and separating the brain tissue from the bloodstream [33]. The brain consists of multiple layers called meninges. Meninges are made of three tissue membranes wrapping the brain and protecting the brain and spinal cord [34]. Meninges layers are Dura mater, pia mater and arachnoid mater [35]. Brain located in the cranial cavity and the cranial bones function to stabilize the position of the brain, nerves, lymphatic vessels and blood vessel [36].

2.2 Meningitis Affection Meningitis continued to be a serious infection of the layers around the brain and spinal cord caused by bacteria, parasites, fungi or viruses [37]. Most of meningitis types caused by bacteria or viruses. Viral type usually does not leave effects on the patient such as lingering affect but bacterial meningitis does. It is swelling of the layers and membranes around the brain and spinal cord. It attacks the central nervous system (CNS) and considered as a life-threatening which may lead to death or permanent complications [38]. The bacteria enter the circulatory system and progress rapidly and can cause eternal brain damage [39]. Figure 1 shows the pathways and the progress of the meningitis bacteria in the brain area.

2.3 Electrical Properties of Biological Tissues Electrical properties of biological tissues are vary based on the structure of that tissue. They consist of permittivity, conductivity and permeability which can be presented in the Eq. (1):  B B ∝ ω (ω0 r − jσ )

(1)

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Fig. 1 Bacterial meningitis progress [40]

Where, ω is frequency, B is magnetic field, σ is conductivity ε0 is permittivity at free space and εr is relative permittivity. These properties are important in electromagnetic fields and electrical simulations that study the development of diagnostic techniques of the human tissues. In Magnetic induction tomography (MIT) applications only conductivity will be considered as it is the dominant property in biological tissue [40–42].

3 Magnetic Induction Tomography (MIT) Magnetic induction tomography (MIT) is one of the imaging techniques used to detect the properties of the electromagnetic by using the effect of eddy current. It is known also by some different names such as electromagnetic tomography (EMT), electromagnetic induction tomography, eddy current testing and eddy current tomography [29]. Moreover, MIT is a mechanism with no contact electrodes of impedance measurement. This mechanism uses induction instead of contact electrodes to get the required information. This mechanism applied as stated that: A magnetic field will be excited and used to produce eddy currents during the test. After that a secondary magnetic field will be generated by these eddy currents, which can be measured by a receiver coil. The volume and phase of the resulting secondary will be calculated by comparing the excitation and secondary fields. MIT is common challenging mechanism, than direct electrical impedance. In MIT, The desired sensitivity is higher. Thus, the equipment design specification is more exacting. However, the mechanism is fully non-contact, eliminating the necessity [31, 32].

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Fig. 2 Fundamental principles of MIT [31]

3.1 Fundamental of Magnetic Induction Tomography (MIT) Magnetic Induction Tomography utilizes inductive coils to map the electromagnetic properties of an object. The fundamental principles of MIT can be explained by using basic mutual inductance and eddy current theories. As shown in Fig. 2, by passing an alternating current into an exciting coil, a primary magnetic field can be generated, which induces an electric field that can be detected by a measuring coil. From this field the induced voltage can be measured [31].

3.2 Principle Operation of Magnetic Induction Tomography (MIT) MIT is completely contactless and electrode-less method utilize the concept where transmitter coils generate a primary field, together with excitation of eddy current during the propagation in the medium. It then generate its own secondary field, Bs or magnetic perturbation field which will be sensed by receiver coils [29]. The principle of MIT is shown in Fig. 3. MIT is the basic operating principle ordinally used to generate the magnetic field, to generate an image reconstruction, the equation and the analysis. MIT operating principle is based on electromagnetic theory, which combines law and Maxwell Faraday’s law. Signal time varies with the angular frequency ω, and phase differential equations of Maxwell in the magnet [43]. Ampere’s Law stated that the electric currents and changes in electric fields are directly proportional to the magnetic field circulating about the area they pierce. The equation expressed as: ∇ × H = (σ + jω)E

(2)

Farady’s Law is the voltage accumulated around a closed circuit is proportional to the time rate of change of the magnetic flux it encloses. The equation is written as

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Fig. 3 Magnetic induction tomography principle [29]

Fig. 4 Phasor diagram of magnetic induction tomography principle [44]

∇ × E = − jωB

(3)

Gauss for electric fields also known as gauss law is the electric fields leaving a volume is proportional to the charge inside. The equation expressed as: ∇ ·= ρ

(4)

Gauss for magnetic fields also known as gauss law magnetism is there are no magnetic monopoles; the total magnetic flux piercing a close surface is zero. ∇·B= 0

(5)

The principle of magnetic induction tomography, generates the magnetic field. In terms of that, there are two fields which are primary field (B1) and secondary field (B2). When the primary field (B1) excites current it will produce a secondary field (B2). The magnitude and phase of total magnetic field (Btot) depend on the object magnetic and electrical properties [44]. Figure 4 shows the relationship between the primary and secondary field.

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4 Hardware and Measurement Single channel magnetic induction tomography system has been used in the measurement. Figure 5 shows the block diagram for MIT system. From the block diagram of MIT, It is shown that the system developed from two main parts which are the measurement part which consist of transmitter unit, region of interest (Tested solution), receiver unit and signal conditioning unit. The other main part is the reference signal part. The two parts are connected to the phase detector which is connected to the multimeter that shows the reading of the measurement. Figure 6 shows the experimental setup of the MIT circuit.

Fig. 5 Block diagram for MIT system

Fig. 6 Experimental setup of the MIT circuit

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Table 1 The conductivity range for the samples prepared No. of the sample

The conductivity value (ms/cm)

1

10.92

2

15.95

3

20.99

4

25.94

5

30.93

6

35.97

Fig. 7 Process of preparing the samples

4.1 Sample Preparation In preparation of the sample solutions, sodium chloride was chosen to represent blood of the brain. The number of the samples prepared is six samples with the conductivity range shown in Table 1. The conductivity meter used to measure the conductivity during the process of adding the NaCl to the distil water in order to prepare the samples. Figure 7 shows the process of preparing the sample.

4.2 Data Collection with MIT Hardware The process of collecting the data starts with taking the reading for Vcoil which is the voltage of the MIT system without the sample. After that, taking the reading of V1

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Fig. 8 MIT hardware system

which is the voltage with the samples, this step starts by placing the sample solution based on the range of the conductivity in the area of interest between the transmitter and the receiver unit. Then set up the frequency in the function generator each time which the range of the frequency is between 1–10 MHz. After that the reading taken by using the multimeter. Figure 8 shows the process of collecting the data from the MIT hardware system. After getting the values of the voltage (V1) from the multimeter, the phase shift calculated by Eq. 6. G = 0.01(V1) + 90 Where, G is the phase shift V1 is the voltage value 90 is our reference when V = 0 0.01 is the resolution for the phase detector.

(6)

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4.3 Data Accuracy In this part, The data accuracy used to test the validation of the accuracy of the equation to determine the phase shift with varying conductivity values. The data of 10 MHz frequency and square coil with 12 number of turns (5Tx–12Rx) has been considered as the best frequency and coil based on the plotted graphs which will be shown in the result part. The conductivity values are taken and inserted into the mathematical equation which has been created along with the linear graph by using Matlab to get the calculation value. The percentage error is calculated between the actual value and the estimate value by using Eq. 7 shown below. Err or Per centage(%) =

|Estimate value − Actual value| × 100% |Actual value|

(7)

5 Result The result is based on the experiment using single channel MIT system. It also discussed on identifying the suitable frequency transceiver pair for optimum signal detection. The analysis had been done to verify the performance of the developed system.

5.1 Hardware and Measurement Result Hardware and Measurement experiment is very important step which included the circuit design, hardware testing and phase shift measurement for the prepared samples solution with varies frequency values 1–10 MHz using single channel MIT system. Phase shift with varying frequency for normal brain tissue with different coils and number of turns. Figure 9 shows a plot of the calculated phase shift with varying frequency and coil types. It can be noticed that the distribution of the phase shift in the normal brain tissues is increasing with increasing the frequencies when approaching the receiver coil. Phase shift with varying frequency for normal brain tissue with different conductivity of meningitis using different coils and number of turns (Fig. 11 and Fig. 12) i). ii). iii).

Circular coil with 12 Number of turns (5Tx–12Rx) Circular coil with 8 Number of turns (5Tx–8Rx) (Fig. 15 and Fig. 16) Square coil with 12 Number of turns (5Tx–12Rx)

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Circular coil with 12 number of turns (5Tx-12Rx)

Circular coil with 8 number of turns (5Tx-12Rx)

Square coil with 12 number of turns (5Tx-12Rx)

Square coil with 8 number of turns (5Tx-12Rx)

Fig. 9 Phase shift for different circular and square types of coils

Fig. 10 Phase shift of different conductivity for circular coil 5Tx–12Rx

iv).

Square coil with 8 Number of turns (5Tx–8Rx)

Figure 10 until Fig. 13 show plots of the phase shift with varying frequency for the four coils used based on the conductivity of the samples presented the normal brain tissue and varying conductivity of meningitis. It can be seen that the phase

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Fig. 11 Phase shift of different conductivity for circular coil 5Tx–8Rx

Fig. 12 Phase shift of different conductivity for square coil 5Tx–12Rx

Fig. 13 Phase shift of different conductivity for square coil 5Tx–8Rx

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Fig. 14 Phase shift of different conductivity for circular coil 5Tx–12Rx

Fig. 15 Phase shift of different conductivity for circular coil 5Tx–8Rx

shift increases with increasing the frequency. Therefore, from the graph it could be noticed that the highest value of the phase shift is at 35.97 ms/cm which is the highest selected value of the meningitis conductivity. Phase shift with normal brain tissue conductivity (10.92 ms/cm) and varying conductivity of meningitis based on the frequency range for different coils and number of turns. i).

Circular coil with 12 Number of turns (5Tx–12Rx)

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Fig. 16 Phase shift of different conductivity for square coil 5Tx–12Rx

Fig. 17 Phase shift of different conductivity for square coil 5Tx–8Rx

ii). iii). iv).

Circular coil with 8 Number of turns (5Tx–8Rx) Square coil with 12 Number of turns (5Tx–12Rx) Square coil with 8 Number of turns (5Tx–8Rx)

Figure 14 until Fig. 17 show plots of the phase shift with normal brain tissue conductivity (10.92 ms/cm) and varying conductivity of meningitis for the four coils used based on the frequency range 1–10 MHz. From the graphs, it could be seen that the phase shift increases with increasing conductivity. Therefore, based on the

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Table 2 Calculated value of the phase shift with varies frequency and conductivity values Conductivity (ms/cm)

Frequency (MHz) 2

4

6

8

10

10.92

90.0053

90.0061

90.0073

90.0082

90.0092

15.95

90.0054

90.0061

90.0075

90.0083

90.0092

20.99

90.0055

90.0062

90.0076

90.0084

90.0093

25.94

90.0056

90.0063

90.0076

90.0085

90.0094

30.93

90.0058

90.0065

90.0078

90.0086

90.0095

35.97

90.0059

90.0066

90.0079

90.0088

90.0096

Table 3 Phase shift for the 10 MHz frequency with varying conductivity of the normal and abnormal

Conductivity (ms/cm)

Phase shift

10.92

90.0092

15.95

90.0092

20.99

90.0093

25.94

90.0094

30.93

90.0095

35.97

90.0096

frequency range, the value 10 MHz has the highest performance and have been selected among the five values of the frequency to be used in data accuracy analysis. The values of the phase shift that has been calculated based on the values of the V1 gotten from the hardware experiment with varies frequency for the normal brain tissue conductivity (10.92 ms/cm) and the meningitis conductivity range (15.92– 35.97 ms/cm) for the square coil with 12 number of turns (5Tx–12Rx) are shown in Table 2.

5.2 Data Accuracy In this part, the data accuracy used to test the validity of the accuracy of the equation to determine the phase shift with varying conductivity values. The result of 10 MHz frequency and square coil with 12 number of turns (5Tx–12Rx) has been considered as the best frequency and coil based on the graphs shown above. The frequency value 10 MHz was having the highest performance. As well as, the square coil with 12 Number of turns (5Tx–12Rx) was having the highest performance compare to the other coils. Thus, Table 3 shows the phase shift for the 10 MHz frequency with the normal brain tissue conductivity (10.92 ms/cm) and the range of the meningitis conductivity (15.95–35.97 ms/cm). By using matlab, the linear regression has been plotted and shown in Fig. 18 and the mathematical Eq (8) from the linear regression was created and tested.

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Fig. 18 The linear regression graph of different phase shift and conductivity

y = 90.009 + 1.7124E − 05X

(8)

Where X is the variable where the conductivity values could be substituted in order to calculate the estimate phase shift. Table 4 shows the percentage error between the actual and the estimate value to test the validity of the accuracy of the driven mathematical equation. Sample of calculation: Err or Per centage(%) =

|Estimate value − Actual value| × 100% |Actual value|

Err or Per centage =

Table 4 The percentage error between the actual and the estimate value

90.0093 − 90.0092 × 100 = 0.0001% 90.0092

Conductivity (ms/cm)

Actual value

Estimate value

Error %

10.92

90.0092

90.0093

0.0001%

15.95

90.0092

90.0093

0.0001%

20.99

90.0093

90.0094

0.0001%

25.94

90.0094

90.0096

0.0002%

30.93

90.0095

90.0097

0.0002%

35.97

90.0096

90.0098

0.0002%

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This method used to test the validity of the accuracy of the equation to determine the phase shift with varying conductivity values, conductivity value is substituted into the mathematical equation of each conductivity to get the value of calculation. Moreover, the percentage error is calculated between the measurement and the calculation value. The percentage error which has been calculated shows less than 1% for each one of the values. It proves that the derived mathematical equation is valid and reliable to be used to predict phase shift when the conductivity value is substituted into the equation. This also proves the utility of our MIT system to detect conductivity changes in blood and detect meningitis tumor in the brain tissues indirectly.

6 Conclusion Bacterial meningitis is one of the most common and prominent infections which infects the central nervous system through the tissue layers and membranes that cover our brain and spinal cord. More than one million cases and 135,000 deaths has been recorded yearly worldwide because of bacterial meningitis. In Malaysia, significant, enduring neurological complications occurs in 9–25% of cases, which affirms the most serious risk from bacterial meningitis is in early life. Meningitis is a staggering and fatal illness that kills patients within hours. Despite several new antibacterial specialists, bacterial meningitis casualty rates still high. It is the reason for mortality in around 1,000 individuals around the world daily; large portions of them children and young adults. Survivors can be left with serious disabilities. In this paper, we discussed the methodology of identifying the suitable frequency transceiver pair for optimum signal detections. The system of Magnetic Induction Tomography (MIT) had built experimentally as a hardware design and the dielectric properties of both tissues is used as parameters. By using four circular and square coils act as transceivers, the data distribution and reading is collected for each coil with frequency range 1–10 MHz. Every sensor was induced with 0.3 A. After all the data collected, the phase shift has calculated and the graphs have plotted to compare the performance of the different coils. Then, the data accuracy analysis has been done to validate the accuracy of the equation in order to determine the phase shift when the conductivity value is varied. Finally, the capability of this MIT application in meningitis detection is achieved. Acknowledgment This project has been supported by the grant UTMSHINE 09G18, TDR 06G17, CRG 05G04 and financial support by University Malaysia Perlis.

References 1. Koelman D, Brouwer M, van de Beek D (2019) Targeting the complement system in bacterial meningitis. Brain 142(11):3325–3337

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Reconstruction of Patient-Specific Cerebral Aneurysm Model Through Image Segmentation Sheh Hong Lim, Mohd Azrul Hisham Mohd Adib, Mohd Shafie Abdullah, Nur Hartini Mohd Taib, Radhiana Hassan, and Azian Abd Aziz

Abstract The diagnostic assessment of cerebrovascular disease makes use of computational simulation as a predicting tool to determine hemodynamics factor contributing to the disease from patient-specific models which imitate the actual shape of the object of interest. However, the patient-specific models are generally reconstructed from the medical images subjectively. Image segmentation is commonly performed to produce object of interest with high visualization. In order to produce patient-specific anatomical model, a systematic adjustment on image intensity was performed in this study. This paper tends to present the reconstruction of three-dimensional (3D) patient-specific cerebral aneurysm model through systematic image segmentation by using threshold coefficients, Cthr es of 0.2, 0.3, 0.4, 0.5, and 0.6. 25 models were extracted from digital subtraction angiography (DSA) images. The results show that there is an obvious physical change of geometry on the models reconstructed with Cthr es of 0.5 and 0.6, especially on the artery branch. The models reconstructed with Cthr es of 0.2 to 0.4 are considered sufficient in term of arterial geometry configuration and they would be opted for further computational study. Keywords Cerebral aneurysm · Model reconstruction · Segmentation · Threshold coefficient

S. H. Lim (B) · M. A. H. Mohd Adib Medical Engineering and Health Intervention Team (MedEHiT), Department of Mechanical Engineering, College of Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Kuantan, Pahang, Malaysia M. S. Abdullah · N. H. Mohd Taib Department of Radiology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia Hospital Universiti Sains Malaysia, Health Campus, 16150 Kubang Kerian, Kelantan, Malaysia R. Hassan · A. Abd Aziz Department of Radiology, Kulliyyah of Medicine, International Islamic University Malaysia, 25200 Kuantan, Pahang, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_16

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1 Introduction The application of medical imaging in the diagnosis of cerebrovascular diseases has provided an abundance of information in terms of physiological and pathological conditions on particular cerebrovascular system [1]. Besides, the analysis of hemodynamics in the cerebrovascular system can be acquired more accurately and specifically through computational study on the anatomical model. The information acquired from the hemodynamics analysis can be used as a reference in medical studies, treatment planning, and surgical intervention. There has been research reported that the circumferential enhancement along the aneurysm wall (CEAW) which deals with the model configuration is highly associated to the analysis of aneurysm hemodynamics factor [2]. In other words, the analysis of hemodynamics is strongly depending on the model configuration, therefore it is crucial to obtain anatomically realistic model which does not compromise the actual structure or shape of the object [3–7]. In order to obtain the anatomically realistic model, image segmentation is performed by extracting the object of interest from digital and medical images. The image segmentation is a common image processing technique and has been used widely in medical [8–10], agricultural [11, 12], archaeological [13], and construction [14, 15] sectors for research, demonstration as well as device intervention purposes. However, there is lack of information in the research community concerning on the model reconstructed with image segmentation based on threshold image intensity other than the boundary condition set-up for conducting simulation [16–18]. The commercial software packages that are available nowadays provide automatic adjustment on image intensity [9, 19–24], aiding users in model extraction and reconstruction at fast pace, and also eliminating the complex process to remove image noise. With the automatic feature, the model might not be segmented consistently since it is extracted by trial and error [22–24] and this technically has an impact on the hemodynamics analysis [25]. The medical image resolution is an uncontrollable factor affecting the wall shear stress (WSS) distribution especially on the complex flow region [26]. Therefore, the extracted model configuration has to be maintained as precise as possible to minimize the error arisen from the model configuration. In this paper, the image segmentation was performed systematically on the digital subtraction angiography (DSA) images by adjusting the threshold image intensity calculated through threshold determination method [21]. The models extracted with five different threshold coefficients, Cthr es of 0.2, 0.3, 0.4, 0.5, and 0.6 were reconstructed and compared. Some differences in term of geometry configurations have been identified.

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2 Methodology This study uses 768 slices of DSA images from a patient diagnosed with internal carotid artery (ICA) aneurysms. The images are stored as a DICOM file. The image segmentation was performed by using AMIRATM 2019.3. Before image segmentation was performed, the DSA images were exported to ImageJ and presented in 8-bit depth with 256 × 256 acquisition matrices. Then, a line probe was constructed at the representative cross-section of the proximal artery as shown in Fig. 1 to measure the image intensity. A profile curve of the image intensity was generated according to the line probe across the artery as shown in Fig. 2. In the present data, the image intensity ranged between 7.56 and 7997.27. The minimum and maximum values of the image intensity along the line probe, Imin and Imax were obtained. They were then used to determine the threshold image intensity, Ithr es with different threshold coefficient, Cthr es such as 0.2, 0.3, 0.4, 0.5, and 0.6 by using the formula defined in the threshold determination method [21] as shown in Eq. (1). The calculated values of Ithr es as listed in Table 1 were then used for image segmentation. Ithr es = Cthr es (Imax − Imin ) + Imin

(1)

The three-dimensional (3D) patient-specific cerebral aneurysm models were extracted according to the calculated values of threshold image intensity, Ithr es . Moreover, the arteries or branches which were out of the region of interest would be removed manually to protect the actual geometry configuration of the object of interest. There were 25 segmented vascular models created in total from five different cases of ICA aneurysm. A smoothing procedure with standardized smoothing factor of 0.5 was performed to reconstruct the models with good surface finishing and

Fig. 1 Line probe at representative cross-section of proximal artery

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Fig. 2 Profile curve along the line probe

Table 1 Values of threshold image intensity with respective threshold coefficient

Threshold coefficient, Cthres Threshold image intensity, Ithres 0.2

1605.501

0.3

2404.473

0.4

3203.444

0.5

4002.415

0.6

4801.386

without compromising the local or global geometry configuration. The overall process flow for the present study is shown in Fig. 3.

3 Results and Discussion Figures 4, 5, 6, 7, and 8 show the 3D patient-specific cerebral aneurysm models reconstructed with respective Cthr es from the DSA images. By comparing among the models, there is no significant change in term of geometry except for the model reconstructed with Cthr es of 0.5 (Case 4) and 0.6 (Case 1, 2, 3, 5, and 6). There are dislocation and disappearance of the artery or branch where bifurcation exists as indicated in the red box for every case. Furthermore, it can be noticed that the arteries after bifurcation become narrower and the smaller arteries disappear as Cthr es increases. This might be due to the vasculature adherence to the object of interest, the patient-specific cerebral aneurysm model with arteries reduces as Cthr es increases and thus, causing some important parts to be marked out unconsciously.

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Fig. 3 Methodology flowchart for the present study

Fig. 4 Reconstruction of case 1 using threshold coefficient, Cthres of 0.2 to 0.6 (left to right). The red box indicates the dislocation and disappearance of artery or branch

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Fig. 5 Reconstruction of case 2 using threshold coefficient, Cthres of 0.2 to 0.6 (left to right). The red box indicates the dislocation and disappearance of artery or branch

Fig. 6 Reconstruction of case 3 using threshold coefficient, Cthres of 0.2 to 0.6 (left to right). The red box indicates the dislocation and disappearance of artery or branch

Fig. 7 Reconstruction of case 4 using threshold coefficient, Cthres of 0.2 to 0.6 (left to right). The red box indicates the dislocation and disappearance of artery or branch

Fig. 8 Reconstruction of case 5 using threshold coefficient, Cthres of 0.2 to 0.6 (left to right). The red box indicates the dislocation and disappearance of artery or branch

According to the previous research, it was reported that the geometry configuration of reconstructed model has high impact on the aneurysmal hemodynamics especially in computational simulations [2, 3, 8]. Some researchers also claimed that small arteries can be neglected for physiological analysis as compared to the large arteries which have high visualization [23]. However, the reconstructed models would not be neglected as long as they contain the actual arterial geometry configuration.

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Among all the models which have been created in the current first phase investigation, the models reconstructed with Cthr es of 0.2 to 0.4 are considered sufficient for further use since the actual arterial configurations are contained. From the current obtained results, it is confirmed that the model reconstructed based on threshold image intensity has noticeable impact on model configuration. However, further investigation has to be conducted to explore the effect of image segmentation with different threshold image intensity on the reconstructed models in more details such as blood flow behavior, WSS distribution, and velocity flow field through computational study.

4 Conclusion From the present data, the patient-specific cerebral aneurysm models reconstructed with Cthr es of 0.2 to 0.4 would be used for further computational study and analysis due to the preserved geometry configuration with minimal difference. The noticeable difference on the models reconstructed with different threshold image intensity is the primary evidence from the current study proving that the systematic image segmentation based on image threshold intensity without post processing editing has revealing impact on the model configuration. However, more data would be obtained to maintain the consistency of justification. Besides, the effect of geometry configuration on aneurysmal hemodynamics is yet to be investigated. Acknowledgement The support from Universiti Malaysia Pahang under grant RDU190153, Ministry of Higher Education (MOHE) under FRGS grant (FRGS/1/2018/TK03/UMP/02/23) and MedEHiT are gratefully acknowledged.

References 1. MacDonald M, Frayne R (2015) Cerebrovascular MRI: a review of state-of-the-art approaches, methods and techniques NMR Biomed 28 2. Omodaka S, Endo H, Niizuma K, Fujimura M, Endo T, Sato K, Sugiyama S, Inoue T, Tominaga T (2018) Circumferential wall enhancement on magnetic resonance imaging is useful to identify rupture site in patients with multiple cerebral a neurysms. Neurosurgery 82:638–644 3. Lim SH, Mohd Adib MAH, Abdullah M, Hassan R (2020) Qualitative and quantitative comparison of hemodynamics between mri measurement and CFD simulation on patient-specific cerebral aneurysm – a review J Adv Res Fluid Mech Therm Sci 68:112–123 4. Tadjfar M (2006) Flow into an arterial branch model. J Eng Math 54:359–374 5. Nerem RM, Cornhill JF (1980) The role of fluid mechanics in atherogenesis. J Biomech Eng 102:181–189 6. Friedman MH, Deters OJ, Mark FF, Brent BC, Hutchins GM (1983) Arterial geometry affects hemodynamics: a potential risk factor for atherosclerosis. Atherosclerosis 46:225–231 7. Cebral JR, Löhner R (2001) From medical images to anatomically accurate finite element grids Int. J Numer Meth Eng 51:985–1008

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8. Adib MAHM, Hasni NHM (2015) Effect on the reconstruction of blood vessel geometry to the thresholds image intensity level for patient aneurysm. J Biomimetics Biomater Biomed Eng 22:89–95 9. Antiga L, Piccinelli M, Botti L, Ene-Iordache B, Remuzzi A, Steinman DA (2008) An imagebased modeling framework for patient-specific computational hemodynamics Med. Biol Eng Comput 46:1097–1112 10. Mahmud AS, Mustafa WA, Jamlos MA, Syed Idrus SZ, Khairunizam W, Mohd Nawi MAH (2020) Blood vessel detection monitoring system and mobile notification for diabetic retinopathy diagnosis BT. In: Intelligent manufacturing and mechatronics presented at the (2020) 11. Nasir AAF, Abdul Ghani AS, Rahman AMN (2018) Parallel guided image processing model for ficus deltoidea (Jack) moraceae varietal recognition BT - Intelligent manufacturing & mechatronics presented at the (2018) 12. Abd Rahman M, Mamat A (2012) A study of image processing in agriculture application under high performance computing environment. Int J Comput Sci Telecommun 3:16–24 13. Mustafa WA, Khairunizam W, Mat Yusoff AS, Syed Idrus SZ, Rohani MNKH (2020) Niblack algorithm modification using maximum-minimum (max-min) intensity approaches on low contrast document images BT - Intelligent manufacturing and mechatronics presented at the (2020) 14. Wu Y, Kim H (2017) Digital imaging in assessment of construction project progress. In: Proceedings of 21st international symposium automatic robotic construction (2017) 15. Izadi M, Saeedi P (2010) Automatic building detection in aerial images using a hierarchical feature based image segmentation. In: 2010 20th international conference on pattern recognition, pp 472–475 16. Gao H, Zhu X, Wang J-X (2020) A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations. Comput Meth Appl Mech Eng 366: 17. Schneiders JJ (2014) Hemodynamics in intracranial aneurysms, Universiteit van Amsterdam [Host] 18. Tessitore P, Ravanelli M, Gavazzi E, Cuomo R, Maroldi R (2013) Computational Fluid Dynamic: a new perspective on aortic diseases? Presented at the (2013) 19. Venugopal P, Valentino D, Schmitt H, Villablanca JP, Viñuela F, Duckwiler G (2007) Sensitivity of patient-specific numerical simulation of cerebal aneurysm hemodynamics to inflow boundary conditions. J. Neurosurg JNS 106:1051–1060 20. Hassan T, Timofeev EV, Saito T, Shimizu H, Ezura M, Matsumoto Y, Takayama K, Tominaga T, Takahashi A (2005) A proposed parent vessel geometry-based categorization of saccular intracranial aneurysms: Computational flow dynamics analysis of the risk factors for lesion rupture. J Neurosurg 103:662–680 21. Omodaka S, Inoue T, Funamoto K, Sugiyama SI, Shimizu H, Hayase T, Takahashi A, Tominaga T (2012) Influence of surface model extraction parameter on computational fluid dynamics modeling of cerebral aneurysms. J Biomech 45:2355–2361 22. Rayz VL, Boussel L, Acevedo-Bolton G, Martin AJ, Young WL, Lawton MT, Higashida R, Saloner D (2008) Numerical simulations of flow in cerebral aneurysms: comparison of CFD results and in vivo MRI measurements J Biomech Eng 130 23. Cebral JR, Castro MA, Appanaboyina S, Putman CM, Millan D, Frangi AF (2005) Efficient pipeline for image-based patient-specific analysis of cerebral aneurysm hemodynamics: technique and sensitivity IEEE Trans. Med Imaging 24:457–467 24. Chang HH, Duckwiler GR, Valentino DJ, Chu WC (2009) Computer-assisted extraction of intracranial aneurysms on 3D rotational angiograms for computational fluid dynamics modeling. Med Phys 36:5612–5621 25. Miller GM (2003) The promise of computational fluid dynamics as a tool for delineating therapeutic options in the treatment of aneurysms. Am J Neuroradiol 24:556 26. Potters WV, Van Ooij P, Marquering H, VanBavel E, Nederveen AJ (2015) Volumetric arterial wall shear stress calculation based on cine phase contrast MRI. J Magn Reson Imaging 41:505– 516

Obstacle Avoiding 4-Legged Mobile Robot Using 4-Bar Mechanism Han Shen Tee, Muhammad Akram Mohd-Idros, Kerpan Gunasegaran, Wan Amir Fuad Wajdi Othman, Syed Sahal Nazli Alhady, and Aeizaal Azman A. Wahab

Abstract This paper describes the design and creation of an automated walking robot with a closed-loop feedback system that can avoid an obstacle that is blocking the way of the robot. The mobility system of the robot is by utilizing 4 bar mechanism to build the two legs for each side which is powered by 2 DC motors and controlled by Arduino Mega. IR sensors are attached to the robot to detect any obstacle that is blocking the way. The information obtained will then be fed into the Arduino and make the robot stops, moves backward, then turns right. Besides that, the robot is also equipped with limit switches to overcome the inability to achieve synchronization of both motors’ speed by detecting the current pace of robot and adjusting the equal pace of legs back to unequal pace as the same phase of legs will cause the robot to be unable to walk. The overall performance of the robot is estimated as 65% effective after considering its performance in terms of speed (51% effective), repeatability of IR sensor (80% effective), and the smoothness of the flow of operation. Keywords Walking robot · Obstacle avoidance · Arduino

1 Introduction 1.1 Overview In this project, an automated walking robot with four legs and has a closed-loop feedback system is designed and built. The automated robot is a robot that can complete a specific task based on the programming of a microcontroller with the help of sensors, transducer, and actuator. Arduino Mega is used in this work. Arduino is open-source electronics prototyping platform based on flexible, easy-to-use hardware and software. So, our task in this work is to design the mechanism of the walking H. S. Tee · M. A. Mohd-Idros · K. Gunasegaran · W. A. F. W. Othman (B) · S. S. N. Alhady · A. A. A. Wahab School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_17

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robot, fabricate the robot’s parts, write the program for the microcontroller and then combine all the hardware and electronic parts, sensor, actuators such as motors and microcontroller to build a prototype of the walking robot. There are many walking methods for robots, and many of them are designed by mimicking the locomotion methods of humans [1–4] or animals/insects [5–10]. For instance, Singh et al. developed a passive mechanism mimicking human locomotion by using a four-bar mechanism which can control the two joints at knee and ankle with only the actuation of the hip joint [1]. Liu et al. designed the musculoskeletal architecture of human lower limbs, consisting of hip joint, knee joint, ankle joint, and several muscles [2]. Karakurt et al. designed a walking robot by mimicking the locomotion of spiders called TKSPIDER1. TKSPIDER1 has several walking behaviors and the effectiveness of the robot measured by the performance of walking on rough terrain [6]. The walking techniques can be categorized into a few categories, which are mammal type, reptile type, arthropods type, human type, and other special groups. Commonly, the walking robot moves by using servo motors or dc motor with the help of linkage [3, 5, 6, 8–10]. There are many types of linkage which can be implemented in a walking robot such as Jansen’s linkage [11], Klann’s linkage [12], etc. However, the usage of linkage will restrict the degree of freedom of the walking robot. Implementation of servo motors will allow the robot to make more complex motions. In this work, an IR sensor is added to detect any obstacle that is blocking the way. If an obstacle is detected, the robot will stop and then move backward, then turn right by controlling the speed of each side’s motors. The speed of the motor can be controlled with the aid of a motor driver. This closed-loop feedback system allows the robot to avoid obstacles. In the motor’s implementation into a walking robot, the synchronization of the motor’s speed for both sides is crucial. The inability to achieve synchronization of the motor’s speed at both sides might cause an imbalance of the robot, and the robot might be unable to walk in a specific leg position based on the mechanism.

2 Methodology 2.1 Hardware Selection Table 1 shows the tabulation of the components used in the design of a walking robot. In this work, the controller used is the Arduino Mega. One unit IR sensor to detect obstacles. One unit motor driver L298N to drive the two SPG30–60 K DC geared motor on both sides. One unit 11.1 V LiPO battery to power the motors and a unit power bank supplies power to the controller. Two limit switches are to give stimulation to the Arduino in adjusting the legs.

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Table 1 Parts and components used in the proposed 4-legged mobile robot No.

Item

Quantity

1

Arduino Mega microcontroller

1

2

IR sensor module

1

3

L298N Dual H Bridge DC motor driver module

1

4

SPG30−60 K DC geared motor with coupling

2

5

11.1 V LiPo battery

1

6

Mobile phone power bank (5000 mAh)

1

Fig. 1 Model drawing of the walking robot with its significant dimensions (in millimeter) in Solidworks

2.2 Conceptual Design Figure 1 shows the model drawing of the 4-legged walking robot with its significant dimensions (in millimeter) in Solidworks. After the mechanism to use in the robot is decided, the dimensions of every bar are then calculated manually, and every part is drawn out using Solidworks. In the Solidworks, all the parts and the body are assembled to build a model to simulate the motion of the legs to get desired elliptical

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motion. Adjustment on the dimensions of the linkage is made to obtain a smoother simulated motion. From Fig. 1, it can be seen that the size of the body of the robot is 220 × 147 × 38 mm, excluding the leg. After including the legs, the width of the robot will become 215.60 mm. All the links that connect the legs and body have a width of 5 mm, whereas the width of the 4-leg is 7 mm to prevent them from breaking. All the holes have a diameter of 3.30 mm, such that M3 screw can pass through them fitly. The base of the body is made of bar skeleton form, and cardboard is placed on the bar skeleton to reduce the weight of the robot so that less torque is needed for the motor to move the robot. The combination of the crank, connecting bar, and the rear leg forms a simple 4 bar mechanism that is implemented as the walking mechanism of the proposed robot. The operation of the mechanism is that the motor at the front that connects to one end of the crank will rotate the crank, which then causes the rear leg to swing forward and backward via the connecting bar link that is connected to the other end of the crank. When the crank rotates, the front leg will move in rotational motion while its motion is also restricted by the restricting bar, which will produce an elliptical motion that will move the robot forward.

2.3 Electronic Circuit Development Figure 2 illustrates the block diagram of the control system of the walking robot. The autonomous 4-legged walking robot locomotion is controlled by—the linkages of the moving legs for both sides, which are controlled by two DC geared motors independently. IR sensors & limit switches are acting as eyes and touch sensors for the robot in avoiding obstacles. The rotational motion of the DC geared motor is transformed by links and converted into the elliptical motion of the front leg and the rear leg’s swinging motion. Hence, the walking robot can move forward.

Fig. 2 Block diagram of the control system for the walking robot proposed in this work

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The direction of the robot can be controlled by changing the speed of one of the motors. Furthermore, by reversing the direction of both motors will make the robot to move in the reverse direction. IR sensor module is used as an eye for the robot to detect obstacles ahead. It consists of a pair of IR transmitter and receiver. If an obstacle is detected, the IR ray will be reflected and received by the receiver, and the signal will be sent to the Arduino microcontroller. Limit switches are used to detect the same phase of the leg on both sides. The synchronization of pace on both sides’ legs will cause the robot to shuffle and unable to walk. Limit switches are mounted on each side of the leg. When the limit switches at both sides are pressed at the same time, it means that the legs are in the same phase, and the microcontroller will send a signal to the motor driver to stop one motor while another motor remains rotating for 0.7 s. With this feedback, the legs on both sides will not be in the same phase, and the robot can continue walking. The microcontroller to control the robot is Arduino Mega 2560, a Microcontroller board based on Atmega 2560. It comes with more memory space and I/O pins as compared to other boards available in the market. SPG30−60 K DC geared motor is used in this system as the actuator to move the robot. DC geared motors are DC motor with gear to transmit higher torque. Thus DC geared motor is more suitable than DC motor as the mechanism to drive the links needs high torque. The rated speed and rated torque of this motor are 75 rpm and 294 mN.m. Since the voltage rating of this motor is 12 V, an external power source of 11.1 V Lipo Battery is enough to provide the motor with a voltage supply. Besides, L298N Dual H Bridge Motor Driver is needed to control the speed and direction of the motor by processing and executing the command signal given from the Arduino. Figure 3 below illustrates the circuit diagram of the connections of electronic components, and Table 2 shows the pin connection of every electronic component to the Arduino. Figure 3 shows the 5 V pin and 12 V pin of motor drivers are connected to the 5 V pin on Arduino and positive terminal of LiPo battery, respectively. The negative terminal of the LiPo battery is common grounded with the Arduino at the motor driver’s ground pin. The ENB and ENA pins on the motor driver are connected to PWM pins on Arduino, which correspond to the left motor and right motor, respectively.

2.4 Software Development Figure 4 depicts the flowchart of the program. When the program starts, there will be a one-time 3 s delay each time the Arduino starts up. Then, Arduino will read the digital value from IR sensor and limit switches and assign the values as variables, which are initially set as 1. Then, the program will compare the inputs of both limit switches.

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Fig. 3 Electronic circuit diagram of the walking robot used in this work

Table 2 Tabulation of the time taken for the robot to walk 1 m and its full load speed in 5 trials

Trial

Recorded time (s)

Speed (m/s)

1

12.10

0.083

2

13.57

0.074

3

11.62

0.086

4

12.50

0.080

5

12.45

0.080

Average

12.45

0.081

If they are the same and equal to 0 at the moment, the Arduino will send the motor drive signal to stop the right motor while the left motor maintains its rotation for 0.7 s. It is to balance the legs back to out of phase with each other. Then the program ends, and the flow is repeating. However, if the values of both limit switches are not equal to 0 at the same time, the program will come to the process of making the robot moves forward. At the moment, if the value from IR sensor is 0, Arduino will send the motor drive signal

Obstacle Avoiding 4-Legged Mobile Robot Using 4-Bar Mechanism...

Fig. 4 Flowchart of the 4-legged robot behavior use in this work

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to stop the robot’s movement. Then the robot will move backward with both the dc motor rotating in the opposite direction for 5 s then stop again. In the 5 s of moving backward, if both the legs are in the same phase, the Arduino will command the motor driver to adjust back the legs with the same operation as in moving forward. After 5 s, the robot will stop and turn right direction by reversing the rotation of the right motor while the left motor rotates positively for 7 s. Then, the whole process is repeating.

3 Results 3.1 Prototype Figure 5 shows the prototype of the walking robot. The parts of the prototype were printed using ABS material so that it is sturdy and can withstand the weight of the robot and the high torque of the motor. The total mass of the robot is 0.804 kg, including the power sources and the motors. Figure 6 shows the robot walking straight when there is no obstacle in front of it. The unsynchronized pace of legs at both sides makes the robot to walk forward with one step at front for each side once a time. When the legs at both sides walk at the same pace, the robot adjusts back its pace for legs at both sides from synchronized pace back to unsynchronized pace by stopping the motor on the right, while the motor on the left maintains its rotation for 0.7 s.

Fig. 5 The prototype of the walking robot

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Fig. 6 Robot walking straight when no obstacle was detected

The occurrence of synchronized pace is due to the unequal motor speed on both sides, and the unequal distribution of weight load on the robot. This situation will cause the robot to shuffle at the same place. The occurrence of this condition can be detected by using limited switches mounted on both sides. When both the limit switches are touched by part of the legs at the same time, it means synchronization of pace occurs and adjusting the pace is made.

3.2 Speed of the Robot Table 2 shows the tabulation of the time for the robot to walk 1 m and its speed in 5 trials. The speed of the robot to walk 1 m is calculated by dividing the displacement of the robot over time taken. Although the rated speed of the DC geared motor is 75 rpm, which is 7.85 ms-1 , the walking speed of the robot is not exactly same as the rated speed of motor as the heavyweight of the robot and the transfer of force from the motor to other links applies higher torque to the motor, causing the motor to rotate at a slower speed. In addition, the slipping of the legs also contributes to the reduction of the robot’s speed as sometimes the robot may shuffle at the same place. The full load speeds calculated are based on the performance of the robot walking on a ceramic tiles floor, which has a friction coefficient of 0.77. The coefficient of friction between the ceramic tiles floor with the leg of the robot is measured manually. The speed of the robot may be higher when walking on a rough surface with a higher friction

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30.82 (a)

50.13 (b)

Fig. 7 The reach of the front leg’s farthest extension (a); and farthest retraction (b) respectively from the center point of the motor’s coupling

coefficient and lower when walking on a smooth surface, which has a lower friction coefficient as the slip differs on different types of surface. Figure 7(a) shows the reach of the front leg’s farthest extension from the center point of the motor’s coupling, whereas Fig. 7(b) shows the length of the front leg’s farthest retraction from the center point of the motor’s coupling. The robot’s no-load speed can be calculated by placing the robot non-contact with the ground, and the motor started to measure the time the robot completes the number of steps that total up has a distance of 1 m. The number of steps equivalent to the total length of 1 m can be calculated by dividing the displacement in one step with the total required displacement.

3.3 Power Consumption The estimated power consumption in 10 min is calculated using the parameters of voltage and current consumed during a testing duration of 10 min. The current consumed is measured as 1.04 A as the weight of the robot is quite heavy to move, and the motor requires high torque, thus consuming more current than its rated current 0.6 A. The initial voltage was measured, then the robot is let operated for 10 min, and the final voltage is measured again to obtain the voltage drop. The voltage drop will be the voltage consumed in 10 min. By using the estimation of the power consumption equation (P = IV), the power consumption of the motor at full load is 0.832 W, which is 1.73 times higher than the estimated rated power consumption of 0.480 W. This result shows that the walking robot consumes more power during its walk due to the heavyweight of the robot and slipping problem.

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4 Conclusion In conclusion, the 4-legged mobile robot is functioning well as it can execute the commands accordingly. The robot also can walk, detect obstacles, and avoid the obstacle in a smooth sequence. The robot also can readjust back its pace when the robot is unable to walk due to the same phase of legs. Currently, the robot suffers from slipping due to having a smooth surface of feet. However, this problem can be eliminated or reduced by applying shoes to reduce slipping. Besides, the asynchronous speed of both motors that cause the robot unable to walk when both legs are in the same phase, resulting in the robot becomes less efficient. In terms of power consumption, the robot at full load is consuming 0.832 W during walking, which is about 1.7 times higher than its rated power consumption of 0.480 W. Acknowledgment All authors have disclosed no conflicts of interest, and authors would like to thank the Ministry of Education Malaysia and Universiti Sains Malaysia for supported the work by RU Grant Scheme (Grant number: USM/PELECT/8014113).

References 1. Singh G, Singla A, Virk GS (2016) Modeling and simulation of a passive lower-body mechanism for rehabilitation. In: Conference on mechanical engineering and technology (COMET-2016), IIT (BHU), Varanasi, India 2. Liu Y, Zang X, Liu X, Wang L (2015) Design of a biped robot actuated by pneumatic artificial muscles. Bio-Med Mater Eng 26(s1):S757–S766 3. Mikolajczyk T, Borboni A, Kong XW, Malinowski T, Olaru A (2015) 3D printed biped walking robot. In: Applied mechanics and materials, vol 772. Trans Tech Publications, pp 477–481 4. Vanderborght B, Verrelst B, Van Ham R, Lefeber D (2006) Controlling a bipedal walking robot actuated by pleated pneumatic artificial muscles. Robotica 24(4):401–410 5. Tang Z, Qi P, Dai J (2017) Mechanism design of a biomimetic quadruped robot. Ind Robot Int J 44(4):512–520 6. Karakurt T, Durdu A, Yilmaz N (2015) Design of six legged spider robot and evolving walking algorithms. Int J Mach Learn Comput 5(2):96 7. Antonescu O, Robu C, Antonescu P (2016) Structural synthesis of linkages for quadruped bio-robot legs. In: IOP Conference series: materials science and engineering, vol 147, no. 1. IOP Publishing, p 012081 8. Bennani M, Giri F (1996) Dynamic modelling of a four-legged robot. J Intell Robot Syst 17(4):419–428 9. Sang L, Wang H, Yu H, Vladareanu L (2017) A novel human-carrying quadruped walking robot. Int J Adv Robot Syst 14(4):1729881417716592 10. Stelzer A, Hirschmüller H, Görner M (2012) Stereo-vision-based navigation of a six-legged walking robot in unknown rough terrain. Int J Robot Res 31(4):381–402 11. Vujoševi´c V, Mumovi´c M, Tomovi´c, A, Tomovi´c, R (2018) Robot based on walking Jansen mechanism. In: IOP Conference series: materials science and engineering, vol 393, no 1. IOP Publishing, p 012109 12. Kulandaidaasan Sheba J, Elara M, Martínez-García E, Tan-Phuc L (2016) Trajectory generation and stability analysis for reconfigurable klann mechanism based walking robot. Robotics 5(3):13

Development of a Simple Pole Climbing Robot Jun Xian Leong, Khairul Amin Abu-Johan, Nur Iffah Nasuha Kadir, Wan Amir Fuad Wajdi Othman, Aeizaal Azman A. Wahab, and Syed Sahal Nazli Alhady

Abstract A Pole climbing robot is a robot that can climb a pole without using external control to control the robot’s movement. Pole climbing robots can accomplish many tasks such as trimming a tree, wiring and repairing distribution lines. In this project, a pole climbing robot is built to climb a pole, which is 1 m in height. The robot needs to use grippers to climb to the highest end of the pole and then climb back to the ground. A microcontroller such as Arduino Mega 2560 is used to analyze and control the movement of the robot. Electrical components such as 12 V high torque motors, 12 V linear actuator, and L298N motor drivers are used to provide sufficient force for the robot to climb up or climb down the pole. Software such as Arduino IDE is used to program the microcontroller so that the programmer can adjust the movement of the robot. Finally, the robot is tested by climbing a P.V.C. pipe, which is 1 m in height. The average speed of the robot is 1.437 cm/s, which is moderate, but it completes its task. Keywords Pole climbing robot · Gripper · Arduino

1 Introduction In recent years, climbing robots are increasingly sought after to carry out high-risk tasks for humans. These tasks include aerial work such as climbing poles, trees, or trusses, which can result in injuries to humans. The robot is also a kind of robot that can help the person to pass an instrument or gear at development or dangerous place. Pole climbing robots are used to solve tasks such as wiring and repairing the power distribution lines. Besides, some of the harvesters are using the pole climbing robot to climb up the coconut tree and harvest the coconut. This method is time-saving, and it also reduces the minimum workforce needed in solving the tasks. J. X. Leong · K. A. Abu-Johan · N. I. N. Kadir · W. A. F. W. Othman (B) · A. A. A. Wahab · S. S. N. Alhady School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_18

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The climbing robot can be divided into two categories based on the locomotion of the robot, i.e., grasping-based and wheeled-based. The grasping-based climbing robot is where the robot uses a claw or a gripper type mechanism to hold onto the surface; these include the works of [1–7]. Meanwhile, the wheeled-based climbing robot uses wheels to have contact with the climbing surface; the robot is moving when the wheels are rotating [8–20]. In this work, an autonomous pole climbing robot is built to climb a P.V.C. pipe. The pole is 1 m in height and 90 cm in diameter. The robot needs to climb the pole and then touch the upper plate on the top end of the pole. Once the robot reached the plate, the robot will climb down the pole and back to the ground itself.

2 Robot Design 2.1 Selected Materials Arduino IDE and SolidWorks software were used to design and develop a pole climbing robot. The hardware selected includes Arduino Mega 2560 microcontroller, 2-unit Ping Ultrasonic Range Finder, 2-unit 12 V 75 RPM 3 kgfcm Brushed DC Geared Motor (Cytron SPG-30), 12 V Linear Actuator, 12 V LiPo Battery, Latch Pushbutton (Red), 3 pins Toggle Switch, KW11 Micro Switch, and 2-unit L298N Dual H-Bridge Motor Driver Module.

2.2 Conceptual Design Figure 1 shows the 2D drawing of the gripper used in this work. The top view is shown in Fig. 1 shows the dimension of the keyhole, which has an opening radius of 4.25, 4 mm at the side with 5.5 mm at the upper key shape. At each vertex, it keeps the same measurement, which is at a 10 mm radius. The shape that completes the gripper like shape has a radius of 50 mm along with 12 mm and 12.44 mm of the part that helps the gripper to hook at the pole. The right view gives off the width of the gripper, which is 125 mm with a height of 13 mm for the gripper. Figure 2 shows its full length, which is 256.38 mm, which is not including the gripper. As for the width, it still the same as the bottom plate that is 220 mm. There also four holes that connect with the actuator at the bottom; it is similar to a square that has a length of 70 mm each. As for the above, it is identical to a rectangular shape, with a length of 32 mm and a width of 44 mm. Figure 3 shows the total height of the robot is 366.75 mm. It also demonstrated that the width of the assembly robot with gripper included are 303.05 mm. As for the actuator, it has a height of 212 and 72.7 mm of some parts attached to the actuator.

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Fig. 1 2D drawing dimension (in mm) of the gripper claw used in this work; (left) the top view, (right) the right view

Fig. 2 2D drawing dimension (in mm) fully assembled climbing robot used in this work

2 motors have a height of 41.80 mm and a diameter of 37 mm seen attached to the body of the robot. Figure 4 (left) shows the 3D drawing of the climbing robot in SolidWorks. The wood plate that covers the gripper both has a length of 55 mm, a width of 113 and 5 mm. The screws and nuts at the gripper have a diameter of 6 mm while at the

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Fig. 3 2D drawing dimension (in mm) fully assembled climbing robot used in this work

Fig. 4 (Left) 3D drawing of the assembly of the robot in isometric view; (Right) exploded view of the climbing robot used in this work

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bottom that holds the plate, and the linear actuator is 5 mm in diameter. The distance between the hole that helps connect the gear is 52 mm. The wood plate that holds the actuator at the bottom has a length of 208 mm, a width of 220 mm, and a height of 5 mm. The top wood has a range of 185, 190 mm of width, and a height of 5 mm. Figure 4 (right) shows the exploded view, which helps to see how the connection is made to make the robot complete. In Fig. 6, it is also shown that where the motor driver and the Arduino mega are placed on the robot which is above the bottom wood that support the robot.

2.3 Control Strategy of the Robot Figure 5 shows the control strategy of the climbing robot. The robot uses two batteries, 9 and 12 V. 9 V battery uses as the power supply for the microcontroller, whereas the 12 V LiPo battery uses to power up motors and actuators. Once the robot is on, Arduino Mega 2560 microcontroller will start to read and analyze the instructions which are already programmed and stored into the microcontroller. The microcontroller also reads the input signals from the control switches and analyze them. The microcontroller will send signals to the motor drivers so that the motor will operate based on the instructions given by the microcontroller. Motor drivers act as an interface between the motors and the control circuits so that the motor can be operated efficiently. The ultrasonic sensor is used to detect and measure the distance between the robot and the highest end of the pole. The ultrasonic sensors will send the result through a feedback signal to the microcontroller.

Fig. 5 Block diagram of the control strategy proposed in this paper

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Furthermore, the limit switch will send a feedback signal to the microcontroller if the top plate presses the limit switch at the highest end of the pole. The microcontroller will analyze the feedback signal and give instructions to the motor drivers based on the feedback signal.

2.4 Climbing Robot Behavior Figure 6 illustrates the robot behavior in this work. Before the robot starts climbing, switch on the toggle switch to “GRIP MODE” first so that both grippers will grasp the pole. Then, press the push button to start the climbing operation of the robot. First, the upper gripper releases the pole, and the actuator extends upward. Once the actuator is fully extended, then the upper gripper will grasp the pole. The lower gripper will then release the pole, the actuator retracts, and the lower gripper grasps the pole again. The robot will continue climbing until the ultrasonic sensor at the top detects the top plate within 15 cm. The robot will still climb until the limit switch touches the top plate. Then, the lower gripper will release the pole. The actuator retracts, and the lower gripper grasps the pole. Then the system delay for 5 s before the robot starts climbing down. After 5 s, the robot will start climbing down. The lower gripper releases the pole first, followed by the actuator extends downwards, then the lower gripper grasps the pole. Next, the upper gripper will release the pole. The actuator retracts, and the upper gripper grasps the pole. The robot continues to climb down the pole until the lower ultrasonic sensor detects the ground within 15 cm. Then, the whole climbing process of the robot is stopped and ended.

2.5 Experiment Setup The climbing robot was then fitted on a P.V.C. pipe to test its functionality and climbing speed. The task of the robot is the robot needs to climb and reaches the plate and climb back down. The time for the robot to climb up and down is measured, and the speed is then calculated. The process is repeated 8 times and the average speed of the robot is calculated.

Development of a Simple Pole Climbing Robot...

233 Upper gripper grasps the pole. Lower gripper releases the pole. The actuator retracts. Lower gripper grasps the pole.

Start

Switch on toggle switch to “GRIP MODE”

Wait for 5s.

Both upper and lower gripper will grip

Lower gripper releases the pole. The actuator extends downward. Lower gripper grasps the pole.

Press the push button Upper gripper releases the pole. The actuator retracts. Upper gripper grasps the pole. Upper gripper releases the pole. The actuator extends upward. Upper gripper grasps the pole.

Distance between the robot and ground is less than 15cm?

Lower gripper releases the pole. The actuator retracts. Lower gripper grasps the pole.

Yes Distance between the robot and the top plate is less than 15cm?

No End

Yes Upper gripper releases the pole. The actuator extends upward.

No

Limit switch is pressed?

Yes

Fig. 6 Flowchart of the climbing robot behavior

No

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3 Results and Discussion 3.1 Results Figure 7 shows the actual system of the pole climbing robot, which is built in this project. The figure at the left shows the grippers’ release mode, whereas the figure at the right shows the grippers’ grip mode in the robot. Table 1 and Table 2 show the recorded time-taken and the calculated speed of the robot during climbing up and climbing down, respectively. From Table 1, the average speed of the pole climbing robot during climbing up is 1.401 cm/s, whereas, from Table 2, the average speed of the pole climbing robot during climbing down is 1.494 cm/s. Lastly, the average speed of the pole climbing robot for the whole climbing process is 0.01437 m/s. The linear actuator’s theoretical speed is 0.070 m/s (for 100% P.W.M. duty cycle). In this work, the duty cycle of P.W.M., which controls the actuator’s speed, is set to 58.8%; this is to reduce jerking on the robot body. Thus, if the time taken for the grippers to grasp and release the pole is assumed to be negligible, then the linear actuator’s theoretical speed is 4.117 cm/s.

Fig. 7 The prototype of the proposed pole climbing robot, (left) gripper in release mode; (right) gripper in gripping mode

Development of a Simple Pole Climbing Robot... Table 1 Recorded time and calculated speed of the robot during the climb up process

Table 2 Recorded time and calculated speed of the robot during the climb down process

235

Experiment #

Displacement (cm)

Time taken (s)

Speed (cm/s)

1

90

64

1.406

2

90

63

1.429

3

90

64

1.406

4

90

66

1.364

5

90

66

1.364

6

90

64

1.406

7

90

63

1.429

8

90

64

1.406

Average

90

64.250

1.401

Experiment #

Displacement (cm)

Time taken (s)

Speed (cm/s)

1

90

55

1.636

2

90

65

1.385

3

90

58

1.552

4

90

60

1.500

5

90

62

1.452

6

90

60

1.500

7

90

62

1.452

8

90

61

1.475

Average

90

60.375

1.494

3.2 Discussion Before the robot starts climbing, the grippers can successfully grasp the pole when the robot is switched to grip mode. It means that the friction between the grippers and the surface of the pole is enough to withstand the weight of the robot. Then, when the robot starts to climb, the robot may fall to the back because the center of gravity of the robot is at its back. Besides, the friction contributed by one gripper may not be enough to withstand the weight of the robot; thus, the robot may slip down during the robot is climbing up. The robot’s speed is moderate because the robot’s faster performance may damage the motors, the grippers, and the robot body, but the time for the robot to climb up and climb down is not too long. It takes about 2 min to climb a pole, which is 1 m in height. Therefore, the performance of the speed of the robot is excellent. Then, by comparing the theoretical speed with the actual speed of the robot, the actual speed is lower than the theoretical speed because the time taken for the grippers to grasp and release the pole is significant.

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Thus the time taken to grasp and release the pole must be considered during the theoretical calculation. For the sensor system, the ultrasonic sensors and the limit switch can detect the environment’s changes well. They send feedback signals to the microcontroller immediately when they detect changes in the surroundings, such as the distance between the top and the robot. Hence, we conclude that the sensor system works effectively during the process of climbing.

4 Conclusion Pole climbing robot is a robot that can climb up a pole and is used in industry, agriculture, etc. In this work, a pole climbing robot is built to climb a pole that is made of P.V.C. pipe. The pole is 1 m in height and having a diameter of 9 cm. The robot tasked to climb up the pole and then touch the upper plate on the top end of the pole. Next, the robot needs to climb down the pole and back to the ground itself. The circuit design of the robot is designed and drawn first, then the prototype of the circuit is built and tested with the program code, which is written by using Arduino IDE. After all of that, the pole climbing robot is started to build by assembling all of the body parts of the robot, grippers, and circuits, including the motors, actuators, and sensors together. Acknowledgement All authors have disclosed no conflicts of interest, and authors would like to thank Ministry of Education Malaysia and Universiti Sains Malaysia for supported the work by RU Grant Scheme (Grant number: USM/PELECT/8014113).

References 1. Khan S, Prabhu S (2018) Design and fabrication of wheeled pole climbing robot with high payload capacity. In: IOP. Conference series: materials science and engineering, vol 402, no 1, p 012021 2. Zhang Y, Dodd T, Atallah K, Lyne I (2010) Design and optimization of magnetic wheel for wall and ceiling climbing robot. In: 2010 IEEE international conference on mechatronics and automation. IEEE pp 1393–1398 3. Mustapa MA, Othman WAFW, Bakar EA, Othman AR (2018) Development of pole-like tree spiral climbing robot. In: Intelligent manufacturing & mechatronics. Springer, Singapore, pp 285–293 4. Ahmadabadi MN, Moradi H, Sadeghi A, Madani A, Farahnak M (2010) The evolution of U.T. pole climbing robots. In: 2010 1st international conference on applied robotics for the power industry. IEEE, pp 1–6 5. Megalingam RK, Reddy SV, Sriharsha G, Teja PS, Kumar KS, Gopal P (2015) Study and development of android controlled wireless pole climbing robot. In: 2015 IEEE international W.I.E. Conference on electrical and computer engineering (WIECON-ECE). IEEE, pp 439–442

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6. Eich M, Vögele T (2011) Design and control of a lightweight magnetic climbing robot for vessel inspection. In: 2011 19th mediterranean conference on control & automation (MED). IEEE, pp 1200–1205 7. Muslim MA, Nusantoro GD, Hasanah RN, Asy’ari MH (2018) Control of pole-climbing robot orientation using self-tuning method. Int J Power Electron Drive Syst 9(3):1029 8. Kim JH, Lee JC, Choi, YR, Lee S (2016) Automatic grasping of a pole climbing robot using a visual camera with laser line beams. In: MATEC web of conferences, vol 42. EDP Sciences, p 03005 9. Qiaoling D, Yan L, Sinan L (2019) Design of a micro pole-climbing robot. Int J Adv Robot Syst 16(3):1729881419852813 10. Nor Faizal MI, Othman WAFW, Syed Hassan SSNA (2015) Development of pole-like tree climbing robot. In: 2015 IEEE international conference on control system, computing and engineering. Malaysia, pp 224–229 11. Kalra LP, Gu J, Meng M (2006) A wall climbing robot for oil tank inspection. In: 2006 IEEE international conference on robotics and biomimetics. IEEE, pp 1523–1528 12. Koo YC, Elmi AB, Wajdi WAF (2012) Piston mechanism based rope climbing robot. Procedia Eng 41:547–553 13. Peidro A, Gil A, Marin JM, Reinoso O (2015) Inverse kinematic analysis of a redundant hybrid climbing robot. Int J Adv Robot Syst 12(11):163 14. Zhu H, Guan Y, Wu W, Chen X, Zhou X, Zhang H (2014) A binary approximating method for graspable region determination of biped climbing robots. Adv Robot 28(21):1405–1418 15. Lau SC, Othman WAFW, Bakar EA (2013) Development of slider-crank based pole climbing robot. In: 2013 IEEE International conference on control system, computing and engineering. IEEE, pp 471–476 16. Xiao Z, Wu W, Wu J, Zhu H, Su M, Li H, Guan Y (2012) Gripper self-alignment for autonomous pole-grasping with a biped climbing robot In: 2012 IEEE international conference on robotics and biomimetics (R.O.B.I.O.). IEEE, pp 181–186 17. Stylos AC, Pounds DC, Brown ES, Strickland ML Pounds SE (2015) Project squirrel 2.0: a tree climbing robot 18. Chen R, Liu R, Chen J, Zhang J (2013) A gecko inspired wall-climbing robot based on electrostatic adhesion mechanism. In: 2013 IEEE international conference on robotics and biomimetics (R.O.B.I.O). IEEE, pp 396–401 19. Pope MT, Kimes CW, Jiang H, Hawkes EW, Estrada MA, Kerst CF, Cutkosky MR (2016) A multimodal robot for perching and climbing on vertical outdoor surfaces. IEEE Trans Robot 33(1):38–48 20. Das M, Agrawal A, Sonone A, Gupta R, Upadhyay D, Rao YVD, Javed A (2016) Developing a bioinspired pole climbing robot. In: 2016 international conference on robotics: current trends and future challenges (R.C.T.F.C.). IEEE, pp. 1–6

Improving the Infant-Wrap (InfaWrap) Device for Neonates Using MyI-Wrap Mobile Application Mohd Hanafi Abdul Rahim, Mohd Azrul Hisham Mohd Adib, Mohamad Zairi Baharom, and Nur Hazreen Mohd Hasni

Abstract Nowadays, a biomedical instrument holds a prominent position in medicine. The increased processing and integration capacity of electronic devices and the progress of wireless communications have enabled medical devices to be developed. InfaWrap device is a non-invasive method developed to measure the oxygen saturation, body temperature, and heart rate of a person. InfaWrap is designed to assist doctors and parents in tracking the baby’s heart rate and oxygen level by using advanced wireless network sensors. The inability to detect any discomforts that the babies doing the initial stage of life may lead to permanent disabilities and even death due to Critical Congenital Heart Disease (CCHD). In this paper, we focused on improving the Infant-Wrap (InfaWrap) for neonates using MyI-Wrap Mobile Application. A Bluetooth virtual serial port protocol is used to send test results to the smartphone from the oximeter sensor and from the temperature sensor. Two sensors were used; the MAX30100 heart rate sensor with the pulse oximeter and the LM35 with a synchronized Arduino platform with a mobile application. As a result, the device’s sensitivity reaches 96% for oxygen, 81.03 bpm for heart rate, and 35 °C for body temperature. The performance value for 2 h begins to shift in minutes 100 but still below the maximum limit. Keywords Neonates · Medical device · Mobile application · Infant-Wrap · Ankle · Pediatrics · Oximeter

M. H. A. Rahim · M. A. H. M. Adib (B) Medical Engineering and Health Intervention Team (MedEHiT), Department of Mechanical Engineering, College of Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Abdul Razak, 26300 Kuantan, Pahang, Malaysia e-mail: [email protected] M. Z. Baharom Human Engineering Group (HEG), Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia N. H. M. Hasni Family Health Unit, Pahang State Health Department, Jalan IM 4, 25582 Bandar Indera Mahkota, Kuantan, Pahang, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_19

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1 Introduction Recently, the increase in the processing and integration capacity of electronic devices, as well as the development of modern technology to the medical device especially in wireless technology is expected to be enormous. Among the advantages of incorporeity, mobile technology and medical devices help improve patient safety, reduce the risk of medical errors and increase the effectiveness and efficiency of physicians to track patients [1]. Therefore, InfaWrap has been developed to monitor the health of the baby. This system has been designed to help most parents monitor children’s health, especially when the baby sleeps [2]. Currently, the connection cable used in the monitoring system is messy and not mobile and hospital. By implementing wireless healthcare technologies, one of them, doctors at the pediatric center, can monitor the healthcare of babies with longterm and close distance to provide real-time hospital recovery care advice. Wireless sensors allow the baby to move freely (to prevent cables from hindering them) and facilitate new ways of monitoring patients [2, 3]. With mobile devices, doctors or parents can access health information from anywhere quickly, effectively, and safely. Using mobile application handheld devices, physicians are able to view patient medical history. One of the critical goals of patient telemonitoring is to build medical monitors that replace conventional wired instruments with intelligent wireless sensors that can detect and transmit medical alarms [4]. Wireless sensors must meet several critical requirements, such as small, lightweight, ergonomic, and low power consumption (or long battery life) [5]. Other than that, the current InfaWrap device has difficult to wrap on the ankle [6]. In this paper, we present the detail improvement for InfaWrap device using MyIWrap mobile application for recording oxygen level along with heart-rate and body temperature using mobile apps platform.

2 Methodology 2.1 Design and Development of the InfaWrap Device The 3D modelling of the InfaWrap device developed using the SolidWorks 3D CAD software. InfaWrap designed with a split of the sensor device and ankle wrap for easy storing. The device casing is 3D printed using PLA filament material to ensure the electronic sensor can easily attach to the device (see Fig. 1). The cotton fabric is used to provide comfort to the neonate’s skin. InfaWrap equipped with an electronic box where the electronic parts consist of 3.7 V battery and other electronic components. This concept can perform three modes of operations which are, heart rate, oxygen level (SpO2), and temperature body. This device has an external controller and screen to prevent too many electronic parts inside it. Table 1 shows the bill material (BOM) for this InfaWrap. The device also provides to view real-time wellness data and

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Filament is led to the extruder

InfaWrap model

The print head and/or bed is moved to the correct X/Y/Z position for placing the Fig. 1 InfaWrap parts printed using 3D printer

Fig. 2 Final assembly of the InfaWrap device

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Table 1 Specification of 3D Printer Specifics

HEG printer

Dimensions

7.8 (w) × 59.1 (h) × 57.8 (d) [cm]

Maximum build size

20.04 (w) × 23 (h) × 27.04 (d) [cm]

Z Axis resolution

0.100 [mm]

Layer thickness

70, 200 and 300 [µm]

Print tolerance

X and Y axis + 1 [%] dimension or + 0.2 [mm] whichever is greater. Z axis + half the processed z resolution

Maximum operating temperature at extruder tip.

280(°C)

Support material

PLA/ABS/dissolvable natural PLA

Purchase price

RM 1,100

receive direct notification by phone. Besides, it is designed to rechargeable the battery inside the device body using the android cable.

2.2 Design and Development of MyI-Wrap Mobile Application A platform to measure the heart rate was designed and integrated with communication solutions. The infant’s heart rate, body temperature, and oxygen saturation were all measured using the specified sensors. All the measured values were monitored and analyzed using an Arduino board and displayed on the LCD screen. Then, the results were sent to an android application which was pre-installed on the doctor’s or parent’s mobile phone via Bluetooth module. InfaWrap is capable to analyze the health status and the warning indicators notifications can be sent to the physician in case of an emergency [6]. (see Fig. 3) shows the solution diagram.

3 Results 3.1 InfaWrap Device Components This device has MAX30100 and LM35 sensors. MAX30100 is a mixture of two LEDs, a photodetector, advanced optics, and low-noise analog signal processing to monitor heart rate and SpO2 level parameters, while LM35 controls the infant’s body temperature [7]. This sensor is healthy for children since it does not release the child any dangerous electromagnetic radiation. Bluetooth HC05 is used to show

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Fig. 3 InfaWrap’s device system diagram

device parameter data. Besides, the buzzer and monitor are used in this method to warn physicians and parents if the device parameter value shows a negative value as shown (see Fig. 4).

3.2 MyI-Wrap Mobile Application MyI-Wrap Mobile app developed to monitor and view the infant’s heart rate, blood pressure, and temperature pattern results as shown in Fig. 5. The program was configured to work in two-parameter guidance modes and display the measurement state [8]. These details can be used to monitor or re-search emergency treads.

3.3 Implementation of MyI-Wrap Mobile Application The purpose of MyI-Wrap Mobile Application implementation is to provide advanced signal processing using the wireless transmitter system with transmits long distance the results to the mobile phone or server, which allows the health care providers to know their patient’s health promptly. Figure 6 shows how the InfaWrap system works. First, is attach to the ankle. To keep the baby’s foot comfortable to safe while wearing it, a layer of the sponge was applied to shield the neonate’s skin from direct contact with the material. Patient heart rate, body temperature, and oxygen saturation were all measured using the specified sensors. Secondly, all the calculated values were recorded and evaluated using the Arduino board and displayed on the monitor screen. The results were then

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Fig. 4 Schematic diagram for the control system of InfaWrap device

sent to the android application that has been installed on the patient’s mobile phone through the Bluetooth module.

3.4 Advantage Using MyI-Wrap Mobile Application The MyI-Wrap mobile application offers a wide range of advantages. Firstly, the babies with InfaWrap devices can move easily from complex connecting cables without a barrier. Secondly, the physicians in the remote server can monitor the health of a patient and thus provide medical care in real-time and long-term care services [9].

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Fig. 5 a mobile apps login b parameter guidelines c supported d parameter display

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Fig. 6 The working principles of InfaWrap device

4 Discussions 4.1 Accuracy Test Output Data for 2 Hours The device parameter data is taken every 10 min for two hours to measure the accuracy of the unit as shown in Table 2. This test is essential to ensure the performance value of the device is good at the start of reading following voltage decreases in the battery. Based on the analysis, output read for three parameters could be acceptable.

4.2 MIT Apps Inventor The MyI-Wrap mobile application developed using the MIT App Inventor (see Fig. 7). The MIT App Inventor is a model for programming android mobile applications, particularly for those with little to no experience in programming [10]. This software

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Table 2 Output result in 2 h Time taken (s)

Parameter output Heart rate (Bpm)

SpO2 (%)

Temperature (Co )

600

66.28

99

35

1200

68.92

99

35

1800

70.23

98

36

2400

64.56

98

37

3000

72.66

97

36

3600

71.68

98

34

4200

74.23

98

36

4800

79.64

98

35

5400

78.66

96

36

6000

80.22

96

37

6600

76.23

96

35

7200

81.03

96

35

Fig. 7 Mobile apps process

is commonly used to create modern mobile apps. MIT App Inventor is an online platform designed to teach computational thinking concepts through developing mobile apps. MIT App Inventor is designed to build Android applications on your browsers.

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5 Conclusion This paper proposes an affordable heart rate monitoring device based on an Arduino platform synchronized with a mobile application. It can monitor SpO2, heart rate and body temperature of infants. Based on the measured value, it can analyze the health status for warning indicators. Nevertheless, results are sent through mobile applications via Bluetooth to be stored and shared with the doctor and parents or any designated device. Acknowledgement A big thank you dedicated to University Malaysia Pahang (UMP) under grant PDU203205, post-graduate grant PGRS2003200 and MedEHiT are gratefully acknowledged for providing us with a good environment and facilities in order to complete these research activities. By this opportunity, we would like to thank Mr. Idris Mat Sahat from Human Engineering Group, Universiti Malaysia Pahang for sharing valuable information in accordance with our research interest. We would face many difficulties without his assistance.

References 1. Moron MJ, Casilari E, Luque R, Gázquez JA (2005) A wireless monitoring system for pulseoximetry sensors. In: Proceedings of - 2005 system communication ICW 2005, wireless ICHSN 2005, high speed networks - ICMCS 2005, multimedia communication systemsSENET 2005, sensor networks, pp 79–84 2. Rahim MHA, Adib MAHM, Baharom MZ, Hasni NHM (2021) Non-invasive study: monitoring the heart rate and SpO2 of the new born using infaWrap device. In: 2020 IEEE-EMBS conference on biomedical engineering and sciences (IECBES), pp 212–217 3. Rotariu C, Manta V (2012) Wireless system for remote monitoring of oxygen saturation and heart rate. In: 2012 Federated conference on computer science and information systems (FedCSIS), pp 193–196 4. Kadarina TM, Priambodo R (2018) Monitoring heart rate and SpO2 using Thingsboard IoT platform for mother and child preventive healthcare. In: IOP conference series: materials science and engineering, vol 453, no 1 5. Ajith S, Praveen KSII, Nagaraj M (2018) Wireless monitoring of patients by iot through pulseOx & heart rate sensor 119(15):973–979 6. Rahim MHA, Adib MAHM, Hasni NHM (2020) The comprehensive study of product criteria on infant-wrap (InfaWrap) device: engineering perspective. J Phys Conf Ser 1529(5): 052082 7. Zaltum MA, Ahmad MS, Joret A, Abdul MM (2010) Design and development of a portable pulse oximetry system. Pulse 05(03):37–44 8. Baktha K (2017) Mobile application development: all the steps and guidelines for successful creation of mobile app : case study, 6(9):5–20 9. Hashmi SWA, Alvi BA, Rehan M, Kamal MS, Zaheen MY (2014) Energy efficient vital signs monitoring system (VSMS) using wireless sensor networks. Wirel Pers Commun 76(3):489– 501 10. Schiller J et al (2014) Live programming of mobile apps in app inventor. In: Promotion 2014 Proceedings of the 2nd Workshop on Programming for Mobile & Touch, Part SPLASH 2014, pp 1–8

Research Objective in Assembly Line Balancing Problem: A Short Review Nurhanani Abu Bakar, Mohd Zakimi Zakaria, Mohammad Fadzli Ramli, Nashrul Fazli Mohd Nasir, Muhammad Mokhzaini Azizan, and Muzammil Jusoh

Abstract Presently, the arising of industrial revolution has encouraged the advance of technology especially in manufacturing engineering industry. Assembly line balancing (ALB) plays very importance role in manufacturing due to improve the process of efficiency and increase the production rate. In this case, authors have a tendency to study and explore in detail about assembly line balancing problem (ALBP). This paper aims to review the research objectives on ALBP based on 60 articles that try to solve this problem using various approach. In general, this paper performs a survey of ALBP research from 2017 to 2018 in order to see the trend of current study. The publication that listed in this survey have create some division section of ALBP research that describe in short the classification of ALB and the objectives of research. The literature review in research objective may highlight the significant objectives in ALB and provide guidance for future work to look into the research gap. Keywords Assembly line balancing problem · Workstation · Cycle time

N. A. Bakar · M. F. Ramli Institute of Engineering Mathematics, Universiti Malaysia Perlis, Main Campus Pauh Putra, Perlis, Malaysia M. Z. Zakaria (B) School of Manufacturing Engineering, Universiti Malaysia Perlis, Main Campus Pauh Putra, Perlis, Malaysia e-mail: [email protected] N. F. M. Nasir School of Mechatronic Engineering, Universiti Malaysia Perlis, Main Campus Pauh Putra, Perlis, Malaysia M. M. Azizan School of Electrical Engineering, Universiti Malaysia Perlis, Main Campus Pauh Putra, Perlis, Malaysia M. Jusoh BioElectromagnetics Research Group, School of Computer and Communication Engineering, Universiti Malaysia Perlis, Main Campus Pauh Putra, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_20

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1 Introduction An assembly line (AL) is a production flow that involves with component and series of workstations that carry out some tasks to assemble a product. In mass production, assembly line is important part for manufacturing process and regularly applied. The act of task allocating to the workstations for improving the production line is known as assembly line balancing (ALB). While the assembly line balancing problem that denoted as ALBP is an optimization problem [1]. The problem comes from the difficulty in determining process for assigning task to achieve objective function and constraints. In addition, this task has to be implemented to the workstations based on precedence diagram. For this reason, the ALBP which called as NP-hard problem has been explored by most researchers up to 16 years due to the optimize the assembly line [2]. The remainder of this paper is organized into four sections. Firstly, the following section is explained about the classification of ALB. Section 3 answered the open question on what are the objectives study in assembly line balancing by listing the research goals of publications. In Sect. 4, some of the research potential is highlighted. The significant objective in ALB identified in this survey are presented in Sect. 4. Finally, Sect. 5 concludes the paper and presents a summary of the whole research.

2 Assembly Line Balancing Basic Generally, assembly line can be classified into three types of model depending to the production strategy and number of products assembled in the line system [3]. The model of AL is: i. ii. iii.

Single Model AL: Produce one model of a product Mixed Model AL: Produce different model of same product Multi Model AL: Produce different model of same product in group with setting process

On the other hands, the line configuration in AL can be categorized into some characteristics, which are the arrangement of workstations and layout design. In traditionally, the workstations in assembly line are arranged in straight line. But then, some studies shows that the arrangement in U-shaped line has gained more attention over straight line [4]. Moreover, there also exist some cases where the assembly line are arranged in two sided and in parallel line. Study by [5] has conducted a research regarding to these cases.

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3 Research Objective Studied in ALBP At this time, mostly authors are performed a research based on the problems that raised in assembly line balancing environment. The main open questions that concerned to the ALBP is how to optimize the production line that might led to the improvement in manufacturing industry. Some common problems that has become problematic to ALB are in perspective of workstation, cycle time, zoning constraint, worker assignment and cost reduction. Therefore, this paper aims to review the research objective and goals in ALBP based on the 60 papers that has been published in 2018 and 2017. Research objective is very essential for a research in achieving an impact result. In ALB, it is commonly used an objective function due to handle with optimization problem. This will provide a guidance to the researchers in finding the promising solution either in minimizing or in maximizing. In this research, there are 10 objectives have identified based on studies performed by 60 papers. The objectives are listed in Table 1 with some supporting explanation in short. While Table 2a and 2b lists the survey of the research objective due to highlight most popular objective in solving ALBP.

4 Discussions This section summarizes the finding of survey from 60 papers regarding to research objective in ALBP due to optimize the production line. Referring to Table 2a and 2b, all the references comes from the papers since 2017 and 2018. Therefore, this survey able to reveals the list of significant and well-known objectives among researchers up to the present time. Together, the present findings confirm that the study on ALBP still becomes a hot topic to be investigated and still relevant to conduct a future research in this area. Figure 1 illustrates the number of papers versus research objective. From this graph, it is clear that numerous researchers have performed studies in ALB with aims to minimize number of workstation and followed by minimizing cycle time, maximizing line efficiency, minimizing cost and others. The used of research objective referring to the situation of assembly line and the production plans. Some authors are believed that the minimization number of workstation able to optimize the assembly line compare to research objective 6 and 10. In this case, most papers tend to used research objective 1 in their studies. Besides, most of the authors present a research by establishing multi objectives over single objective due to the problem complexity in ALB. For this reason, they used to have one main objective along with second objective. Thus, the solution for ALBP could be more significant to the production line. However, in two-sided ALB, it seems to be more challenging and quite complicated due to the many constraints to be followed. Therefore, future research in two-sided ALB should be conducted in more realistic aims, especially in using multiple objective. According to Fig. 1,

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Table 1 Research objective and description Research objective

Description

1. Minimize number of workstation

It can be denoted as problem type 1. Most studies has concerned to this objective that might give huge positive changes in line efficiency

2. Minimize number of mated station This objective is related to two-sided AL, as there exist two line of workstation that place in opposite side. The mated station only used in two-sided AL, which represented by a pair of two facing stations that performed an E-type task 3. Minimize cycle time

Cycle time is a maximum time spend at any workstation to complete a task. The reducing of cycle time could be affected to minimize idle time and enhancing the workstation efficiency

4. Maximize workload smoothness

There are some cases where the assigned workload to workstation are not balance enough. Some workstation might suffer to the situation that has too low and high workload. Hence, the line balancing can be improved by smoothing the workload evenly

5. Minimize line length

In assembly line, the larger space has become a problem due to long line of workstation. Thus, numerous researchers conducted a study on two-sided prefer to use this objective as the second objective in order to have a shorter assembly line

6. Minimize linear area

Refer to the area that provide a physical space to place the tools and equipment that will be used by workers to perform tasks

7. Minimize cost

Some researchers are concerned with cost reduction, which can improve the production rate in assembly line for example the equipment cost, labor cost and inventory holding cost

8. Maximize line efficiency

It is commonly to use this objective as a second objective

9. Minimize idle time

Idle time is an unproductive time undergoes by a work piece when it needs to wait to move to the next workstation

10. Minimize number of positions

Refer to the study of position of station in two-sided AL

it shows that some of the objectives has gain less attention which are can said as a research gap for upcoming study such as minimize area, idle time and number of positions.

Research Objective in Assembly Line Balancing Problem … Table 2a Research objective reviewed in ALBP

253

Author/s Research objectives from Table 1 1

2

[6] [7]

3

4

5

6

7

8

X

10

X

[8]

X

X

[9]

X

[10]

X

X

[11]

X

[12]

X

[13]

X X

[14]

X

[15]

X

[16]

X

[4]

X

[3]

X

[2]

X

[17]

X

X

[18] [19]

9

X

X X

[20]

X

[21]

X

[22]

X

[23]

X

[24]

X

X X

X

X

[25]

X

[26]

X

[27]

X

[28]

X X

[29]

X

X

[30]

X

X

[31]

X

[32]

X

[33]

X

[34]

X

X X X X

X X

[35]

X

X

[5]

X

[36]

X

X

X (continued)

254 Table 2a (continued)

N. A. Bakar et al. Author/s Research objectives from Table 1 1

2

3

4

5

6

7

[37] [38]

X

X X X X

X

[42]

X X

[43]

X

[44]

X X X

X

X X

[46]

X

[47] [48]

X

X

X

[49]

X

[50] [51]

X X

Table 2b Research objective reviewed in ALBP (continue)

X X

X

[54]

X

[55]

X

[56]

Author/s

X

X

[52] [53]

X

X

Research Objectives from Table 1 1

2 3

4

[37]

5 6 7

8

9 10

X

[57]

X

[58]

X

[59] [60]

10

X

[40]

[45]

9

X

[39] [41]

8

X

X

X X

Total papers 27

5 22 8

4 2 11 12 3 2

Number of papers

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Number of papers vs. Objective

30 25 20 15 10 5 0 1

2

3

4

5

6

7

8

9

10

Research objective

Fig. 1 Number of researches for consideration of research objective in ALBP

5 Conclusion At this instant, the studies in ALB has become a familiar area among authors to be explored. This is because, the problems in ALB are getting tougher and complicated day by day. Moreover, most researchers have performed a case study to solve ALBP in order to optimize the productivity in manufacturing industry. Also, most of them are attracted to solve this problem and intentions to achieve some objectives in their studies. Therefore, this paper presents a survey of research objectives in ALB that cover up to 60 papers from 2017 to 2018. The finding displays that the objective in minimizing number of workstations has gained a lot of attention among authors. Thus, this survey might highlight the main objectives in ALB and give a guidance for future work to look into the research gap. Acknowledgements The author would like to acknowledge the support from the Fundamental Research Grant Scheme (FRGS) under a grant number of FRGS/1/2019/TK03/UNIMAP/02/7 from the Ministry of Education Malaysia and financial support from University Malaysia Perlis.

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Analysis on Weighted Average Between Features in Dictionary Learning and Sparse Representation Algorithms for Low-Resolution Images Suit Mun Ng and Haniza Yazid

Abstract Recently, the problem of Low-Resolution (LR) is happened to be the key challenge in the field of image processing. To tackle this problem, Super-Resolution (SR) techniques have been developed. Generally, SR is used to acquire more information about an image by recovering an HR image from the LR image without losing high frequency details [1]. This paper is focused on analysing the effectiveness of using combined features in dictionary learning and sparse representation algorithms for producing images with better resolution. The method used to combine the properties of features: energy (F1 ) and entropy (F2 ) extracted in this paper is known as the weighted average techniques. In this case, different combination of weightage which was written as [W1 , W2 ] will be assigned to F1 and F2 respectively. As a result, the weightage combination of [2, 8] achieved the highest improvement in PSNR values of 6.0863 dB and the second highest improvement in SSIM values of 0.2559. In conclusion, the SR system constructed based on the dictionary learning and sparse representation algorithms with the use of weighted average between features is able to solve the image LR problems. This work can be improved by testing on more input images obtained from databases. Keywords Image processing · Dictionary learning and sparse representation

1 Introduction In recent years, a key challenge in image processing field lies in dealing with lowresolution (LR) images especially in face recognition, military or surveillance applications. To tackle this problem, various Super-Resolution (SR) techniques have been developed. The SR image are basically known as techniques which are able to obtain more information about an image by reconstructing an High-Resolution (HR) image from the relative LR image without reducing the image quality [1]. In this case, the S. M. Ng · H. Yazid (B) School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Kampus Pauh Putra, 02600 Arau, Perlis, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_21

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hardware solutions have been replaced by algorithm based solutions due to the physical limitations of the image devices [2]. Then, SR approaches can be classified into three categories: reconstruction-based techniques, interpolation-based techniques and also learning-based techniques [3]. In this paper, a learning-based SR scheme is proposed based on the dictionary learning and sparse representation algorithms in solving the LR problems. Sparse representation-based algorithms can be considered as the most successful learning approaches learning-based SR methods [4]. Many researchers also have done works regarding dictionary learning and sparse representations. For example, Elad and Aharon proposed that sparse representation shows strong robustness to noise [5]. In [6], Rubinstein et al. found that the structure with k-SVD algorithm provides a better generalization results than the non-constrained structure. Aharon et al. also suggested that the k-SVD algorithm is able to work with any pursuit methods [7]. Hence, the k-SVD algorithm is proposed in our paper to create the trained dictionary for sparse coding. After that, Li and Qi in [8] proposed a nonlocal Douglas-Rachford (NLDR) algorithm that is able to solve the compressed sensing (CS) image recovery problem. Therefore, the Douglas-Rachford algorithm is suggested to be used as the sparse coding method in obtaining the sparse representation coefficients for denoising purpose in our paper. The details of this algorithm was discussed by Zhang et al. in [9]. The motivation of this study arises from the current studies regarding the sparse representation-based algorithm are lack of implementing the use of features representation extracted from the LR patches. The current SR algorithms typically applied the Gaussian filters, high pass filter or the first and second derivatives as in [4] to extract the edge and contour information from the LR patches. However, the blurring, aliasing and ringing effects in the LR image are always ignored by the researches and this situation will directly affect the quality of the final image produced at the end of the SR process. Thus, the contribution of this paper is to analyse the effectiveness of the combined features in dictionary learning and sparse representation algorithms to solve the LR image problems. The method proposed for combining the properties of the features extracted in this paper is known as the weighted average techniques.

2 Methodology This paper focuses on the analysis of weighted average between features which extracted from the LR patches in dictionary learning and sparse representation algorithms for producing a final HR image. Figure 1 shows the process of the system proposed in this paper.

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261

Start (HR Images)

Construction of LR Image Patches

Feature Extraction, F2

Feature Extraction, F1 Weighted Average

Dictionary Learning and Sparse Representation Algorithms Final Image

Evaluation

End

Fig. 1 System architecture

2.1 Construction of LR Image Patches Firstly, the image of Lena in grey scale which obtained from the standard database [10] was used as the input image for the system proposed in this paper. The image with size the of 512 × 512 pixels will be used to construct the LR image by going through two processes: down-sampling process and adding of Gaussian noise. In the down-sampling process, the image was down-sampled and cropped to obtain an image with size of 256 × 256 pixels. After that, the final LR image was produced by randomly adding the additive white Gaussian noise into the cropped image. Since random Gaussian noise was added in this case, hence, the LR image produced will be different in each time of the stimulation. Then, a large number of patches, yj were randomly extracted from the whole LR image produced and stored in the matrix Y. Basically, the size of the image patches is considered as a factor which can affect the image quality of the output image, therefore, the size of the image patches was fixed as 10 × 10 pixels.

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2.2 Feature Extraction Before conducting the feature extraction process, the mean was first subtracted from the image patches. Then, as shown in Fig. 1, there are two different features were extracted from the image patches, yj . The features extracted in this case are known as the energy (F 1 ) and entropy (F 2 ). Generally, energy is used to describe the distribution of the grey level in the images, while entropy is defined as the equivalent states of intensity level which individual pixels can adapt. The equations of these two features are shown in Eq. 1 and 2 respectively. Since grey scale images with LR problem is focused in this paper, hence, these two features are suitable and efficient to be applied in the proposed system.  2 y j 

(1)

  2     y j  · − log y j

(2)

Energy = Entr opy =

The feature values calculated was then sorted in descending order to ensure that only the largest values were kept from the image patches for the further process.

2.3 Weighted Average In this step, the properties of both features were combined by using a method called weighted average as explained by the Eq. 3 where W 1 and W 2 are known as the weightage for the features: energy (F 1 ) and entropy (F 2 ) respectively. Then, the range for the weightage were also set to be [1, 9] which refers the importance of features in terms of percentage from 10 to 90%. Therefore, different combinations of the weightages between F 1 and F 2 were used in each stimulation in order to choose the best combination for producing the HR image at the end of the system. SOW =

W1 F1 + W2 F2 W1 + W2

(3)

At the end of this step, an initial dictionary, D0 with zero mean and unit norm properties was computed.

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2.4 Dictionary Learning and Sparse Representation Algorithms Next, the learned dictionary, D for this system was computed by using the k-SVD algorithm. The general formula for k-SVD algorithms is defined by Eq. 4, where X is the matrix of sparse coefficients, x j and k is the limit of sparsity. The limit of sparsity was set to be a constant value of 4 in this case. arg min  ||Y − D X ||2F s.t xi 0 ≤ k, i = 1, 2, . . . , N D, X

(4)

After the trained dictionary, D was successfully produced by using the k-SVD algorithm, the denoising steps were started by applying the sparse representation algorithm called Douglas-Rachford. Noted that a different image patches will be used in the denoising process to avoid the problem of overfitting in conducting the final results. The mean was also removed from the image patches since the final dictionary produced with k-SVD algorithm was having zero mean and unit norm properties. Finally, with the use of Douglas-Rachford algorithm, the sparse representation vectors, x j were obtained.

2.5 Final Image The sparse representation coefficients, x j obtained from the previous step is an important factor in computing the HR image patches, y˜ j . The formula used to calculate the denoised image patches, y˜ j is shown in Eq. 5. y˜ j = Dx j

(5)

Then, the mean which subtracted from the image patches at the first place was inserted back to it. Lastly, the final HR image was formed by averaging the image patches, y˜ j obtained.

2.6 Evaluation The evaluation of this paper was done by comparing the improvement in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) values obtained by feature sets with various weightage combination. The reference image used to measure these values is the original input HR image. Thus, the best features set which is able to create an image with the best resolution will be assessed in this case.

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3 Results and Discussion In this section, the results obtained will be presented in both quantitative and qualitative ways. Firstly, Fig. 2 shows the image produced in the process of constructing the LR image using the standard image of Lena in grey scale with pixel size of 512 × 512 as shown in Fig. 2(A). Figure 2(B) shows the cropped image with size of 256 × 256 pixels after the down-sampling process and Fig. 2(C) shows the final LR image produced after adding of random Gaussian noise onto the Fig. 2(B). It was clearly seen that the resolution of the original image was reduced after these processes. Hence, this LR image will be used to extract a total number of 12,000 image patches, yj with pixel size of 10 × 10. As mentioned in the previous section, the mean was subtracted from the image patches, yj before going through the feature extraction process. After that, both of the feature vectors: energy (F 1 ) and entropy (F 2 ) were extracted from the image patches, yj . Since this paper is mainly focused on the analysis of weighted average between features in dictionary learning and sparse representation algorithms, different combination of weightage will be assigned to these two features. The range of the weightage was set between 1 and 9, thus, there will be 9 possible weightage combination between features to be tested in this paper. The combination with the best result on image resolution will be selected and used in the proposed system. Then, the averaged values calculated by each of the combination will be sorted in descending order and the first 4000 image patches were kept for the dictionary learning process using k-SVD algorithm. The final dictionary, D produced by using different weightage combination between features will also be different. After that, a total number of 15,876 images patches were extracted again from the LR image in Fig. 2(C) and these image patches will be used for the denoising process by applying the sparse representation algorithm called Douglas-Rachford. Lastly, the denoised

Fig. 2 a Original HR image, b down-sampled image and c LR image with Gaussian noise

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image patches, y˜ j which calculated by using the sparse representation coefficients, x j obtained from the previous step were combined to form the final HR image. Figure 3 shows the output images produced with all the possible combination of weightage between features where the weightage of F 1 and F 2 were written as [W1 , W2 ] respectively. By analysing the results qualitatively, all the output HR images produced better image quality and resolution as compared with the LR image as shown in Fig. 2(C). On the other hand, Table 1 shows the PSNR, SSIM and the improvement in both SSIM and PSNR values for all the images produced in Fig. 3. The highlighted values indicate the best results obtained in terms of the improvement in PSNR and SSIM values. As shown in Table 1, the weightage combination of [2, 8] achieved the highest improvement in PSNR values of 6.0863 dB and the second highest improvement in SSIM values of 0.2559. The difference between the improvement in SSIM obtained

Fig. 3 Denoised image produced by weightage combination of: a [1, 9], b [2, 8], c [3, 7], d [4, 6], e [5, 5], f [4, 6], g [3, 7], h [2, 8] and i [1, 9]

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Table 1 PSNR and SSIM values for all the denoised images produced Weightage of features

PSNR (dB)

Energy (F 1 )

Entropy (F 2 )

Before

After

(+)

SSIM Before

After

9

1

24.4790

30.5155

6.0365

0.6062

0.8604

0.2542

8

2

24.4197

30.4566

6.0419

0.6028

0.8593

0.2565

7

3

24.4025

30.4310

6.0284

0.6011

0.8576

0.2565

6

4

24.4360

30.3339

5.8979

0.6044

0.8575

0.2531

5

5

24.4195

30.3206

5.9011

0.6026

0.8567

0.2541

4

6

24.4536

30.3684

5.9148

0.6046

0.8576

0.2531

3

7

24.4424

30.4339

5.9916

0.6050

0.8586

0.2536

2

8

24.4493

30.5356

6.0863

0.6056

0.8614

0.2559

1

9

24.4328

30.4534

6.0205

0.6031

0.8577

0.2546

(+)

by weightage combination of [2, 8] is only 0.0006 as compared with the highest values reached by weightage combination of [2, 8] and [3, 7]. Therefore, the weightage combination of [2, 8] between F 1 and F 2 were selected as the final combination for the proposed system.

4 Conclusion and Future Development As a conclusion, the results obtained in this paper show that the weighted average between features will affect the output HR image produced in SR scheme implemented based on the theory dictionary learning and sparse representation. By referring to Fig. 3, the output HR images produced by using the averaged values obtained from features: energy (F 1 ) and entropy (F 2 ) with different weightage are able to improve the image quality of the LR image. As shown in Table 1 also, the denoised images with different PSNR and SSIM values were produced at the end of the process if different weightage combination was assigned to F 1 and F2 . Thus, the weightage combination of [2, 8] between F 1 and F 2 were selected as the final combination for the proposed system. This is because the weightage combination of [2, 8] is able to achieve the highest improvement in PSNR values of 6.0863 dB and also the second highest improvement in SSIM values of 0.2559. In other words, the works done in this paper is able to solve the image LR problem by proposing a SR system constructed based on the dictionary learning and sparse representation algorithms with the use of weighted average between features. Lastly, this work can be improved by testing on more input images obtained from databases. Acknowledgements The authors would like to acknowledge the support from the Fundamental Research Grant Scheme (FRGS) under a grant number of FRGS/1/2019/TK04/UNIMAP/02/23 from the Ministry of Education Malaysia and Universiti Malaysia Perlis for financial support.

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References 1. Ayas S, Ekinci M (2020) Single image super resolution using dictionary learning and sparse coding with multi-scale and multi-directional Gabor feature representation. Inf Sci (Ny) 512:1264–1278 2. Nasrollahi K, Moeslund TB (2014) Super-resolution: a comprehensive survey 3. Tian J, Ma KK (2011) A survey on super-resolution imaging. SIViP 5(3):329–342 4. Jiang C, Zhang Q, Fan R, Hu Z (2018) Super-resolution CT image reconstruction based on dictionary learning and sparse representation. Sci Rep 8(1):1–10 5. Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15(12):3736–3745 6. Rubinstein R, Zibulevsky M, Elad M (2010) Double sparsity: learning sparse dictionaries for sparse signal approximation. IEEE Trans Sig Process 58(3 PART 2):1553–1564 7. Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing over complete dictionaries for sparse representation. IEEE Trans Sig Process 54(11):4311–4322 8. Li S, Qi H (2015) A Douglas-Rachford splitting approach to compressed sensing image recovery using low-rank regularization. IEEE Trans Image Process 24(11):4240–4249 9. Zhang Z, Xu Y, Yang J, Li X, Zhang D (2015) A survey of sparse representation: algorithms and applications. IEEE Access 3:490–530 10. Image Databases. https://www.imageprocessingplace.com/root_files_V3/image_databases. htm. Accessed 03 Jan 2020

Bees Algorithm with Integration of Probabilistic Models for Global Optimization Muhammad Syahril Bahari, Nur Athirah Azmi, Zahayu Md Yusof, and Duc Truong Pham

Abstract The standard Bees Algorithms (SBA) is a population-based search algorithm inspired by the nature that revolves around mimicking the food foraging behavior of honey bees in order to solve optimization problems. This study had implemented the probabilistic method in Estimated Distribution Algorithm (EDA) into the SBA to improve the performance of the algorithm in terms of speed and accuracy. The newly proposed algorithm is tested on ten benchmark test functions. Then, the accuracy and speed are compared to SBA. The performance of the algorithm had also been validated on two engineering design optimization problems with specific constraints condition. The results of the benchmark test functions showed that the proposed algorithm provides very competitive results in terms of improved speed and convergence rate. The results of the design engineering optimization problems prove that the proposed algorithm can perform well in solving challenging problems with constrained and unknown search spaces. Keywords Optimization algorithm · Bees algorithm · Probabilistic model

1 Introduction The classical optimization method is having great difficulty when facing the challenge of solving hard problems which involves time and precision. Much interest is generated in approximation algorithms that find near-optimal solutions within reasonable running times. In the present time, there are many algorithms develop by M. S. Bahari (B) · N. A. Azmi School of Manufacturing Engineering, Universiti Malaysia Perlis, Arau, Perlis, Malaysia e-mail: [email protected] Z. M. Yusof School of Quantitative Sciences, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia D. T. Pham School of Mechanical Engineering, College of Physical Sciences, University of Birmingham, Birmingham, UK © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_22

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researchers that are increase in popularity and easy to implement such as the standard Bees Algorithm (SBA). SBA is a population-based search algorithm which is inspired by the nature. It revolves around mimicking the food foraging behavior of honey bees in order to solve unconstrained functions in the domain of continuous space. This algorithm is modelled based on the studies of the allocation of bees to different flower patches to maximize the nectar intake. Self-organization plays an important role in the colony as in short-term harvesting season, achievement is a hard task for that small-sized animals. Karl Von Frish had discovered the special domains behavior named as the waggle dance for positive feedback bees. The dance is important for finding good source of flower patches. Randomness is the key in the searching process of the bee colony. They use the basic random search ideas which is essential for the allocation of food sources [1]. Figure 1 shows the flowchart of SBA. One of the problems faced by the SBA is that the speed of the algorithm is not fast enough and not accurate in order to solve the optimization problems. Based on review by previous researchers, SBA is having difficulties in finding optimum values when tested with benchmark test functions and optimization problems [2, 3]. New method which is the probabilistic method is introduced to solve the problems of the algorithms with efficient speed and accuracy. Probabilistic methods also play

Fig. 1 Flowchart of SBA [2]

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271

an important role in algorithm development. The most efficient known algorithms rely primarily on randomization for many optimization problems [4].

2 Methodology 2.1 The Proposed Algorithm At the initialization stage, ns scout bees are generated randomly across the search space with uniform random distribution. The probabilistic method in the EDA is implemented after this stage. The ns scout bees are scored using a fitness function. The fitness function gives a numerical ranking for each solution, with the higher the fitness value, the better the solutions. From this ranked population, a subset of the most promising solutions is selected by the selection operator. For example, a selection operator will select 50% best solutions. A probabilistic model is then performed by the algorithm which tries to estimate the mean and standard deviation of the chosen solutions. Once the model is built, new solutions is generated by sampling the distributions encoded by this model. These new solutions are inserted back into the old population or is entirely be replaced [5]. This will ease the finding of the position with the highest profitability of food source. The new population fitness is evaluated and proceed to the next stage which is the local search. The remaining phases of proposed Estimation Distribution Bees Algorithm (EDBA) are similar to the SBA. The steps are repeated until the stopping criteria is met [6]. The new population constructed will consist of the scout bees with the better fitness position from the neighborhoods. This is because the sampling will produce best point from the top ranking. The flowchart of the proposed EDBA is shown in the Fig. 2.

2.2 Testing with Benchmark Test Function For each algorithm, each benchmark test function will run 50 times individually. The termination conditions for each run are based on the maximum fitness function evaluation achieved or the predefined fitness precision value found. It will set as 500,000 fitness evaluations or (f(x) − f(x*)) less than 0.001 as accuracy in this experiment [7], where f(x) denotes the optimum global value and f(x*) denotes the minimal fitness value. The performance comparison of two different algorithms is based on their mean (i.e. median) accuracy and speed (fitness evaluations) over 50 runs. The reported accuracy will set to zero if the algorithm achieved adequate accuracy (f(x) − f(x*) < 0.001) [2].

272

M. S. Bahari et al. Random initialization Selection Probabilistic method

Fitness evaluation

Elite Sites (ne)

Best Sites (nb-ne)

nre bees per patch

nrb bees per patch

Fitness evaluation

Fitness evaluation

Select patch fittest

Select patch fittest

Local search

Global search

New population Random ns-ne

Stop?

Fitness evaluation

Solution

Fig. 2 Flowchart of EDBA

Moreover, for the various benchmarks, the two algorithms are statistically to be compared at the meaning level of α = 0.05 [2]. The significant difference of speed and accuracy vector of both algorithms will be tested using the Mann-Whitney test.

2.3 Compare with the Standard Bees Algorithm A number of parameters were used in the proposed algorithm as shown in Table 1. The EDBA was built with a common set of parameters for a fair comparison with the SBA. In addition, the population size for this experiment was set for both the SBA and the proposed algorithm for 0.01 populations. The R programming language has been used to run both algorithms [2].

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Table 1 Parameters setting for testing benchmark test function [2] Parameter

Symbol

Value

Number of scout bees

ns

26

Number of elite sites

ne

2

Number of best sites

nb

6

Recruited bees for elite sites

nre

20

Recruited bees for remaining best sites

nrb

10

Initial size of neighbourhood

ngh

0.01

Limit of stagnation cycle for site abandonment

stlim

10

Table 2 Parameters setting for testing engineering design optimization problem Parameter

Symbol

Value

Number of scout bees

ns

10

Number of elite sites

ne

2

Number of best sites

nb

5

Recruited bees for elite sites

nre

8

Recruited bees for remaining best sites

nrb

5

Initial size of neighbourhood

ngh

1

Limit of stagnation cycle for site abandonment

stlim

5

2.4 Application in Engineering Design Optimization Problems Below are some of the examples of the optimization problems that have been solved by previous researchers using different type of metaheuristics algorithm. The general parameter that were used to solve the optimization problems using EDBA and SBA are as shown in Table 2. There were two discrete and two continuous design variables to minimize a pressure vessel’s overall cost including material, forming and welding costs, which were shell thickness (T s ), head thickness (T h ), inner radius (R) and length of the vessel’s cylindrical segment (L), excluding the head, which depends on three linear and one nonlinear inequality constraint. The variables’ thicknesses were multiples of 0.0625 inches in integer [8]. This problem was formulated as follows: f(x) = 0.6224x1 x3 x4 + 1.7781x2 x32 + 3.1661x12 x4 + 19.84x12 x3

(1)

subject to: g1 (x) = −x1 + 0.0193x3 ≤ 0

(2)

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g2 (x) = −x2 + 0.00954x3 ≤ 0

(3)

4 g3 (x) = −πx32 x4 − πx33 + 1296.000 ≤ 0 3

(4)

g4 (x) = x4 − 240 ≤ 0

(5)

where, 0 ≤ xi ≤ 100, i = 1, 2 10 ≤ xi ≤ 200, i = 3, 4

3 Results and Discussion 3.1 Benchmark Test Functions The benchmark test functions used in the study are shown in Table 3. Next, the statistical results of median value of speed and accuracy were presented in Tables 4 for 10D respectively. In this table, “Median”, stand for the median of optimal value. Moreover, the best results were highlighted in bold. The results in Table 4 show that EDBA succeeded in finding optimum values for 4 benchmark test functions except for Rosenbrock, Dixon-price, Griewank, Schwefel, Rastigin and Ackley functions. The SBA also succeeded in finding optimum values for 3 benchmark test functions, except Table 3 Benchmark test functions Function

Type

Domain

Optimum value

Dimension

Rosenbrock

Unimodal

[−50, 50]

0

10

Sphere

Unimodal

[−5.12, 5.12]

0

10

Sumsquare

Unimodal

[−5.12, 5.12]

0

10

Zakharov

Unimodal

[−5, 10]

0

10

Dixon-price

Unimodal

[−10, 10]

0

10

Griewank

Multimodal

[−600, 600]

0

10

Rastigin

Multimodal

[−5.12, 5.12]

0

10

Schwefel

Multimodal

[−500, 500]

0

10

Ackley

Multimodal

[−32, 32]

0

10

Levy

Multimodal

[−10, 10]

0

10

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275

Table 4 The statistical results of accuracy and speed of the applied algorithms on 10 benchmark functions with 10D Function

Accuracy SBA

Rosenbrock

Speed EDBA

SBA

EDBA

Median

Median

Median

Median

4.636187

5.275506

500,000

500,000

Sphere

0.00076

0.00079

12,726

2,294

Sumsquare

0.000847

0.000857

18,526

3,428

Zakharov

0.000837

0.000894

20,876.5

7,838

Dixon-price

0.027912

0.666667

500,000

500,000

Griewank

0.101415

0.014525

500,000

500,000

Rastigin

10.97186

1.989926

500,000

500,000

Schwefel

414.6479

237.0125

500,000

500,000

Ackley

2.120931

0.125363

500,000

500,000

Levy

0.998414

0.000759

500,000

27,123

for Rosenbrock, Dixon-price, Griewank, Schwefel, Rastigin, Levy and Ackley functions. For the other benchmark test function which did not reach the optimum values, EDBA outperforms SBA on 4 functions which is Griewank, Rastigin, Schwefel, and Ackley. In terms of number of function evaluations, the EDBA has reached the limit of the maximum fitness function evaluation which is set at 500,000 for most of the function except for Sphere, Sumsquare, Zakharov and Levy functions. As for the SBA, the limit of the maximum fitness function evaluation has been reached for 7 functions except for Sphere, Sumsquare and Zakharov. This is mainly because for the proposed EDBA, the initialization stage will produce best bees which carry good points. From the probabilistic method, a mean and standard deviation is produced from the points in order to generate a better point from the best bees. So, a better point has already been generated before proceeding to the local stage. This will ease the finding of the optimum value of the problems for the EDBA. Based on the above analysis, it was clear that the proposed method can offer better solutions than the compared algorithms in solving most test functions. Note that EDBA was superior to SBA on at least 8 functions with 10D. The proposed EDBA was superior to SBA on function Sphere, Sumsquare, Zakharov, Griewank, Rastigin, Schwefel, Ackley and Levy, respectively as the Mann Whitney test showed that these results were statistically significant. In fact, SBA only surpass EDBA on function 5 which was the Dixon-price function. Furthermore, EDBA had the same performance with SBA on Rosenbrock only which was the Rosenbrock as the Mann Whitney test showed that these results were not statistically significant. These results were likely to be related to characteristic of these functions. For the Rosenbrock, function, it was often difficult to find the global optimum, due its location of global optimum inside a long, narrow, parabolic shaped flat valley.

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Table 5 A comparison of results for the pressure vessel design problems Algorithm

Optimum variable

Optimum Cost

Ts

Th

R

L

SBA

0.8125

0.4375

42.0926

177.388

6084.04

EDBA

0.8125

0.4375

42.0794

177.085

6074.74

Meanwhile the Dixon-price was a function with characteristics valleys and ridges. For this reason, the probabilistic operators were unable to give the algorithm any information to direct the search process toward the minima.

3.2 Pressure Vessel Design Optimization Problem In this segment, the performance of the proposed EDBA was tested by solving two complex design optimization problems and comparing the optimization results with SBA. Furthermore, the population sizes and the independent run were also set as constant which is 1 and 30 respectively. The results are shown in Table 5. The best result was written in bold. The comparison of results for this problem were shown in Table 5. From the table, EDBA achieve the best solution of f(x) = 6074.74 after 30000 function evaluations. The results show that EDBA outperforms SBA for the pressure vessel design problems.

4 Conclusion The main idea of the proposed algorithm was to implement the probabilistic method used in EDA. More specifically, as the initialization stage has sorted out the best bees which carry good points, the probabilistic method generated the mean and standard deviation from the best bee points to generate more better points before proceeding to the local search. This step will ease the algorithms to find the optimum value. In order to examine the performance of the proposed algorithm, EDBA was firstly used to solve 10 well-known unconstrained benchmark function which consist of unimodal and multimodal function of various characteristics. In 8 of the 10 benchmark cases considered, the EDBA performed better than the SBA. Such comparison suggests that EDBA was more successful than SBA. This was because SBA has low rate of convergence than the proposed algorithm. There were two challenging engineering design optimization problems solved by the proposed EDBA and SBA which showed that EDBA was superior to SBA on the pressure vessel design problems.

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Acknowledgements The authors gratefully acknowledge the financial support from UniMAP and Ministry of Higher Education Malaysia under Fundamental Research Grant Scheme (FRGS) with grant No: FRGS 9003-00736.

References 1. Koc E, et al (2005) Bee algorithm a novel approach to function optimization. Manuf Eng Cent 0501 2. Kamaruddin S, Bahari MS, Pham DT, Hamzas MFMA, Zakaria S (2019) Bees algorithm enhanced with Nelder and Mead method for numerical function optimization. Appl Phys Condens Matter 2131:020166 Apcom 2019 3. Pham DT, Darwish AH (2008) Fuzzy selection of local search sites in the Bees algorithm. In: 4th international virtual conference on intelligent production machines and systems, IPROMS 2008, pp 1–14 4. Dietzfelbinger M, Teng S, Upfal E (2007) Probabilistic Methods in the Design and Analysis of Algorithms - 07391 Abstracts Collection Dagstuhl Seminar, Analysis, vol 7, no 04, pp 1–22 5. Zhang Y, Jin Z, Chen Y (2020) Hybrid teaching–learning-based optimization and neural network algorithm for engineering design optimization problems. Knowl Based Syst 187:104836 6. Hauschild M, Pelikan M (2011) An introduction and survey of estimation of distribution algorithms. Swarm Evol Comput 1(3):111–128 7. Pham DT (2014) Castellani M (2014) Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms. Soft Comput 18(5):871–903 8. Li G, Shuang F, Zhao P, Le C (2019) An improved butterfly optimization algorithm for engineering design problems using the cross-entropy method. Symmetry (Basel) 11(8):1049

Machining Technology

Tribological Performance of Palm Stearin in Cold Forging Test Using Aluminum Alloy 6061 Y. Aiman and S. Syahrullail

Abstract Nowadays, the use of lubricant in metal forming processes using vegetable oil is highly desired because it is a renewable resource and has a high biodegradability relative to mineral oil. Non-renewable resources, such as mineral oil, have been widely used from the outset because of its ability to act as a reservoir to the wearing contact that functions as a film material or supports chemical transformation into a film material. This article is highlighting the use of refine bleached and deodorized (RBD) palm stearin as a bio lubricant in ring compression test by benchmarking with unlubricated sample and Daphne oil using annealed aluminum AA6061 as a workpiece material. The test undergoes four different formation that is 10, 20, 30 and 40% at room temperature, that will be compare to finite element method, to predict the friction of each test. From the result obtain, palm stearin has improved the Tresca shear friction (m) from 0.4 to 0.3, but when compare to Daphne (0.25), RBD Palm stearin has higher shear friction. From the wear observation, it shows that RBD palm stearin has a very high in surface roughness value (about 7.4%) when compare to the Daphne oil. Keywords Tribology · Palm oil · Cold forging · Friction · Bio-lubricant

1 Introduction The increasing concern in using a renewable material has triggered the researcher on the development of more environmentally lubricants [1], yet vegetable oil products are among the most promising green lubricant options of this century [2]. Vegetable oils demonstrate superior properties in terms of biodegradability compared with the mineral oil [3]. Several parties have focused their efforts on producing vegetable oils as an industrial lubricant and also biodiesel. Palm oil is one of the popular vegetable oil which has the potential to replace the lubricant of mineral oil. Several researchers have developed a bio-lubricant with different type of classes, such as study of pure Y. Aiman (B) · S. Syahrullail School of Mechanical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_23

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palm oil [4], palm oil with additive [5], palm oil as additives [6] and uses palm oil as emulsion [7]. According to Hafis et al., [8], most the researcher has proven and shown that palm oil is providing satisfactory results and has a promising future that can be commonly used in engineering applications has the potential to reduce the dependency on mineral based oil lubricants. In many metal forming operations, Metal forming lubricants are added to the work surface of the tool to reduce friction and wear which generally affect the life of the tool, metal flow, energy consumption, heat evolution and surface finish [9]. Friction has a considerable influence on processes of metal formation. The friction that occurs at the die-workpiece interface is often represented with Tresca (shear friction) friction model Eq. (1) [10]. τ = mK

(1)

Where τ the frictional is shear stress; m is the Tresca friction factor K is the yield stress in shear. The ring compression test is an established technique for analysing the shear friction (m) for the bulk forming process. It’s a simple, indirect, inexpensive and fast approach using standard rings and flat dies [10, 11]. The ring compression method offers a methodology to study the frictional behavior of metals and their alloys over a range of test conditions, such as temperature deformation, degree of deformation, deformation rate lubricants, etc. [12]. According to Groche, P. et al. [13], numerical simulations are essential for an efficient design of modern process chains, where the quality of the gained results is influenced by the input parameters of the numerical model. As stated by Tan [14], a friction model is one of the primary input boundary conditions for finite element simulations. It is said that the friction model plays a significant role in monitoring the precision of the planned performance effects. Therefore, by comparing the experimental method with the finite element method, this paper had aimed to assess the tribological of palm stearin and mineral oils (Daphne) through an analysis of formation of ring, force, surface roughness and viscosity levels of the sample. From the result obtain it shows that Palm stearin improve the friction performance about 14.46% compare to the unlubricated ring test, but its shows 7.4% high shear friction performance when compare to the Daphne oil.

2 Experimental and Method 2.1 Test Materials Figure 1 shows the experimental set-up for the ring compression test. The main components are top punch, workpiece (ring) and bottom die. The workpiece of the experiment was commercial aluminium alloy A6061. The workpiece is been heated

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Fig. 1 Schematic geometry of the ring compression test

Table 1 Experimental material and condition

Properties

Value

Workpiece

Aluminuum alloy (A6061)

Tooling material

SKD-11

Workpiece size (mm)

30;15;10 (OD:ID:H)

Reduction in height (%)

10, 20, 30 and 40

Compression speed (mm/min)

1

Temp

(◦ C)

24–26

Table 2 Lubricant properties Sample

Kinematic viscosity (mm2 /s) 25

RBD Palm Stearin Daphne

◦C

40

◦C

100

Viscosity index

Density (g/cm3 )

◦C

48.29

38.01

8.56

171

870

107.71

42.05

11.20

273

900

at 330 °C for 4 h, and let its slow cool to annihilate rolling texture and establish a recrystallized structure with isotropic mechanical properties (see Table 1). This experiment was performed at room temperature at laboratory presses machine. At each test, this ring compression test was mounted and placed on the load cell to calculate the extruder load (Y-axis). The ram stroke displacement (X-axis) was also reported using the displacement sensor which is attached to the system holder. The hydraulic press machine is press downwards for 10, 20, 30 and 40% deformation. The load cell is recorded the force for every 0.01 s press movement of the punch. Refine bleached and deodorize (RBD) palm stearin that is undergoes a purifying process to dissipate the unnecessary fatty acid and odour is used as a type of palm oil-based. Industrial oil (Daphne) will used as a benchmark lubricant in order to find out the ability of the RBD Palm stearin to be used as a commercial lubricant in cold forging industry. Table 2 shows the physicochemical properties of all sample lubricant.

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2.2 Experimental Method As illustrate in Fig. 1, an A6061 aluminum ring with a dimensional ratio of 6:3:2 is compressed at room temperature up to 40% deformation. The compression rig has been designed to determine the load-strain behaviour of cold forging tests according to the required parameters. The instruments die for each test, and the workpiece is washed with ethanol and abrasive paper to ensure that all pieces are clean and in good condition during the testing, so that no contamination will affect the compression test result. The lubricant quantity for each test is estimated to 0.1 mg, and has been spread across the both die surface. The die was weighted before and after being lubricated using micro-weight scale. The hydraulic pressing machine is pressed down 10, 20, 30 and 40% deformation. For each punch pressing of 0.01 s, the load cell will record the force.

2.3 Finite Element Method Table 3 shows the necessary parameters for elastic plastics analysis such as elastic modulus, E; mass density ρ; and poisons ratio, ν. In FEM, material is a significant component to the model. The A6061 aluminum alloy was then tested using a unidirectional testing machine based on the standard ASTME8-91 test process. The condition analysis is adopted based on Table 3 condition. The workpiece is known as a rigid-plastic body during the compression test, and the dies were as rigid bodies. The material properties used in the study were extracted from the results of the experiment. The friction factor (m) was used at constant compression rate of 1 mm/min for the interfacial friction between the die and workpiece. Calibration curves for the ring compression tests are established using either analytical methods or the FEM. The curves formed by the FEM are more precise, as the material properties and conditions of formation can be found. For this analysis, the FEM was used for the elaboration of friction calibration curves. During the compression process the height (h) and inner diameter (d) of the metal ring could then be predicted with numerical simulations. The accompanying calculations could then be used to determine a reduction percentage for both the height (%H) in Eq. (2) and the inner diameter (%Di) in Eq. (3). Table 3 Material properties of A6061 aluminum

Properties

Value

Density, ρ(kg/m3 )

2700

Youngs modulus, E(GPa)

68.9

Shear modulus (GPa)

26.0

Poisons ratio, ν

0.33

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%H =

H0 − H × 100% H0

(2)

%Di =

Di 0 − D × 100% Di 0

(3)

3 Result and Discussion 3.1 Calibration Prediction Curve Friction Figure 2 shows the determination of Tresca friction factor by comparing the experimental results on the calibration curves. However, according to Zhang et al., [10] There is no clear standard for how experimental results can be matched to the most appropriate calibration curve, which is the key to assessing friction conditions. For low friction coefficient values, the change in bore diameter, increases with deformation percent [9]. From the calibration curves we can see that, Palm sterin has improve its lubrication performance in terms of shear friction from m = 0.4 to m = 0.30 (about 14.46%

Fig. 2 Friction calibration curves and experimental data (curves are generated with FEA while PS and Daphne is obtained from experimental)

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improvement). However, when compare to the Daphne oil (m = 0.25), it shows that palm sterin has poor performance, where the shear friction is about 7.4% higher. To get better prediction in shear friction coefficient of the lubricant sample in cold work compression test using the FEM, the results of force according to the displacement in time behaviour during steady-state condition were recorded at different stroke positions in the experimental analysis (See Fig. 3) and be compared to the simulation in FEM under different shear friction factor. For a good understanding of the physical system being modelled, the best polynomial fit between 0.9953 and 0.9976 was chosen for the data. The FEM is utilised according to ALE mesh formulation to overcome the excessive distortion of mesh. From 0.1 mg of palm sterin (Fig. 3(a)), it shows that the shear friction of 0.3 is fitted to the experimental results at the steady-state condition when compare to the compression load distribution. The same trend is observed for the 0.1 mg of Daphne and unlubricated workpiece (Figs. 3(b) and 3(c)) at shear friction is equal to 0.25

Fig. 3 FEM and experimental compression load as a function of stroke with different type of lubricant (a) PS (b) Daphne (c) Unlubricated (NA-O)

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and 0.4 respectively. All of the force distributions were in a good agreement from the prediction of calibration curve that based on deformation of the ring. Palm stearin has a composition of oleic acid that would help in reducing sliding friction [15] and also an absorption of free fatty acid (FFA) from palm oil helps to maintain a thin lubrication layer between the taper die and workpiece [6]. The poor performance of Palm stearin in shear friction compare to Daphne is may due to the presence of double bonds in Palm stearin, especially those that has multiple double bond (linoleic acid), that increase the rate of oxidation acceleration on the workpiece surface. According to Farhanah and Syahrullail, [16], polyunsaturated fatty acids also resulted in poor anti-wear performance due to the chemical reaction during the process, whereby the protective film on the surface was destroyed.

3.2 Surface Roughness and Observation on the Ring The arithmetic mean surface roughness distributions, Ra along the experimental surface workpiece (compressed part) is calculated using a surface profiler device. Figure 4 shows the distribution of Ra in product area. From the measurement obtained from both lubricated samples show higher in surface roughness compare to the non-lubricated sample. Palm stearin however shows a very high surface roughness value compare to the Daphne oil with a different within range 0.30–0.50 μm at 10–20% deformation, and increase drastically at 30–40% deformation with 1.00–1.13 μm range different. According to Nurul and Syahrullail [17], Palm stearin are least viscous lubricant that has high possibilities to supply the

Fig. 4 Arithmetic mean surface roughness distribution for non-lubricated, palm stearin and Daphne sample

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Fig. 5 Surface observation at 40% deformation

lubricant until 0 mm compare to Daphne that tend to stay at the surface of contact area due to their high concentrated physical attribute. Figure 5 reveals a 40% deformation of the ring sample surface, it can be shown that the unlubricated sample has different structure relative to the lubricated sample where the line groove structure is already formed after the compression. This line grooves shape is thought to increase the friction during compression and the surface of the grooves is filled until lubricated with the oil that acts as a friction reducer. The widening of the ring caused by cavity oil filling leads to the neutral point moving towards the inner side. Surface measurement is associated with surface ruggedness performance.

4 Conclusion With a focus on to find the tribological ability of palm stearin in a cold forging test, a study was successfully done with ring compression test using aluminium alloy AA6061 as a workpiece. The result shows that palm stearin has successfully reduce the load of the compression but when compare to Daphne oil, it shows that it has high friction that lead to high compression load. From surface roughness observation, palm stearin has a very high surface roughness compare to the Daphne oil. Acknowledgements The authors would like to acknowledge to the RMC of UTM for the Research Grant, UP (17H96,15J28,20H29), TDR Grant (05G23), FRGS Grant (5F074), School of Mechanical Engineering, UTM and Ministry of Higher Education for their support.

References 1. Chan CH, Tang SW, Mohd NK, Lim WH, Yeong SK, Idris Z (2018) Tribological behavior of biolubricant base stocks and additives. Renew Sustain Energy Rev 93:145–157

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2. Razak DM, Syahrullail S, Sapawe N, Azli Y, Nuraliza N (2015) A new approach using palm olein, palm kernel oil, and palm fatty acid distillate as alternative biolubricants: improving tribology in metal-on-metal contact. Tribol Trans 58(3):511–517 3. Golshokouh I, Syahrullail S, Ani FN, Masjuki HH (2014) Investigation of palm fatty acid distillate oil as an alternative to petrochemical based lubricant. J Oil Palm Res 26(1):25–36 4. Jabal MH, Ani FN, Syahrullail S (2014) The tribological characteristic of the blends of Rbd palm olein with mineral oil using four-ball tribotester. J Teknologi 69:6 5. Sapawe N, Samion S, Zulhanafi P, Nor Azwadi CS, Hanafi MF (2016) Effect of addition of tertiary-butyl hydroquinone into palm oil to reduce wear and friction using four-ball tribotester. Tribol Trans 59(5):883–888 6. Maleque MA, Masjuki HH, Haseeb ASMA (2000) Effect of mechanical factors on tribological properties of palm oil methyl ester blended lubricant. Wear 239(1):117–125 7. Husnawan M, Masjuki HH, Mahlia TMI, Saifullah MG (2009) Thermal analysis of cylinder head carbon deposits from single cylinder diesel engine fueled by palm oil–diesel fuel emulsions. Appl Energy 86(10):2107–2113 8. Hafis SM, Ridzuan MJM, Farahana RN, Ayob A, Syahrullail S (2013) Paraffinic mineral oil lubrication for cold forward extrusion: effect of lubricant quantity and friction. Tribol Int 60:111–115 9. Abdulquadir BL, Adeyemi MB (2008) Evaluations of vegetable oil-based as lubricants for metal-forming processes. Ind Lubr Tribol 60(5):242–248 10. Zhang D, Liu B, Li J, Cui M, Zhao S (2019) Variation of the friction conditions in cold ring compression tests of medium carbon steel. Friction 8(2):311–322 11. Tatematsu Y, Morimoto M, Kitamura K (2018) Experiment and FE analysis of compression of thick ring filled with oil. Key Eng Mater 767:141–148 12. Harikrishna C, Davidson MJ, Srinivasa Raju P, Srinivasa Rao G (2018) Utilization of ring compression test to investigate the mushroom effect and adhesive nature of AA2014 billets. Adv Mater Res 1148:96–102 13. Groche P, Fritsche D, Tekkaya EA, Allwood JM, Hirt G, Neugebauer R (2007) Incremental bulk metal forming. CIRP Ann 56(2):635–656 14. Tan X (2002) Comparisons of friction models in bulk metal forming. Tribol Int 35(6):385–393 15. Ing TC, Mohammed Rafiq AK, Azli Y, Syahrullail S (2012) The effect of temperature on the tribological behavior of RBD palm stearin. Tribol Trans 55(5):539–548 16. Farhanah AN, Syahrullail S (2016) Evaluation of lubrication performance of RBD palm stearin and its formulation under different applied loads. J Tribol 10:1–15 17. Nurul MA, Syahrullail S (2015) Lubricant viscosity: evaluation between existing and alternative lubricant in metal forming process. Proc Manuf 2:470–475

Effects of Surfactant Concentration in the New Bio-based Nanolubricants for Machining of Inconel 718 Mohamed Asyraf Mahboob Ali, Azwan Iskandar Azmi, Mohd Zahiruddin Mohd. Zain, Muhammad Nasir Murad, and Ahmad Nabil Mohd Khalil Abstract Machining Inconel 718 with bio-based nanolubricants can be advantageous in reducing the impact on the overall operating as well as machining expenditure. Added to that, the effective supply of Minimum Quantity Lubricants (MQL) can strengthen and intensify the cooling and lubrication effects of the bio-based nanolubricants. With a proper fluid formulation, the contamination risk of bio-based nanolubricants can be alleviated and their cooling and lubrication ability can be enriched. Up to date, there are limited research works and findings regarding the effectiveness of bio-based nanolubrications for machining of Inconel 718. Hence, in this study, the contrariety of bio-based nanolubricants with different concentration of surfactant content (Coco amido propyl betaine + Sodium dodecyl benzene sulfonate) while machining Inconel 718 with uncoated carbide tool has been explored. The turning parameters were kept constant at 80 m/min cutting speed, 0.1 mm/rev feed rate and 0.1 mm depth of cut, and the effects on tool wear, surface roughness, cutting and spindle power were quantified. Overall, the bio-based nanolubricants with a higher content of surfactant promotes a lower tool wear of 0.509 mm, surface roughness of 1 µm, cutting power of 240 W and spindle power of 5119 W as compared to that of the bio-based nanolubricants with low surfactant content. Keywords Inconel 718 · Machining · MQL · Nanolubricants

1 Introduction Machining hard-to-cut alloy such as Inconel 718 has been a challenging task for manufacturing industries as the demand for this material is increasing from dayto-day. Inconel 718 is well known for its unique characteristic such as high thermal resistance, high creep and corrosion resistance as well as ability to maintains strength and toughness at higher temperatures [1]. Such a feature makes this alloy preferable in different fields of the manufacturing sector such as nuclear power plant, aerospace M. A. M. Ali · A. I. Azmi (B) · M. Z. Mohd. Zain · M. N. Murad · A. N. M. Khalil Faculty of Mechanical Engineering Technology, Universiti Malaysia, Perlis (UniMAP), 02000 Pauh, Perlis, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_24

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industry, petrochemical plants and ship engines. Yet, Inconel 718 induces severe issue as a work material during machining process due to their unique combined properties such as hardness and chemical wear, high-temperature strength and toughness, and creep resistance [2, 3]. Although these properties are pleasant for design necessities, unfortunately, they triggered a substantial challenge to manufacturing engineers due to generation of high stresses and temperatures during machining, which contributes to low tool life and high surface damaged [4]. Due to the aforementioned characteristics of Inconel 718, the use of cutting fluids during machining of this alloy has been vastly practiced. Application of cutting fluids will lower the induced cutting temperature in the shear zone and also lessen the friction in the tool-workpiece interface [5]. Furthermore, various research has proved that cutting fluids supplied by minimum quantity lubricants (MQL) is more efficient compared to that of the flood lubrication [6]. It is worth mentioning that MQL delivers a fusion of air and oil on the tool-workpiece interfaces, promotes convective and evaporative heat transfer phenomenon, and produces adequate cooling/lubricating conditions during machining [8, 9]. Consequently, this approach minimises the cutting fluids consumption, cost and also improves the machining efficiency and environmental sustainability. Recently, the development of bio-degradable oils from vegetable substances have initiate a fresh and wide interest as metal cutting fluids (MCFs) [10, 11]. Extending to these existing properties, alterations on the bio-based fluid with the addition of nanoparticles have been hypothesised to surge their machining efficiencies. Addition of nanoparticles improved the tribiological properties of based lubricants, which is the main factor towards superior machinability [12]. The combined effects of MQL and bio-based nanolubricants are expected to enhance the machining of Inconel 718. However, most of the existing bio-based oils were limited in term of their chemical enhancement. Thus, in this study, the effects of new formulated bio-based nanolubricants with different content of surfactant (Coco amido propylbetaine, CAPB and Sodium dodecyl benzene sulfonate, SDBS) on tool wear, surface roughness, cutting and spindle power were explored during turning of Inconel 718.

2 Experimental Details 2.1 Preparation of Bio-based Nanolubricants A new approach of bio-based nanolubricants made up of 50% of water, and 50% of oil-emulsion was carefully prepared based on certain formulation and methodology. The 50% of oil-emulsion contained three main ingredients which are the virgin coconut oil (based lubricants), Coco-amido propylbetaine (CAPB) as an emulsifying and bacterial control agent, and sodium dodecyl benzene sulfonate (SDBS) as a thickener agent. The volume of the component and the mixing method are decided on the basis of series of preliminary experiments that are conducted until a stable cutting

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fluid (when there is no isolation of oil and water and nanoparticles sedimentation) is attained. To identify the effect of surfactant content on machining performance, two types of oil-emulsion was prepared. The first batch contains a high amount of coconut oil (130.5 g) and a low concentration of surfactant mixture (SDBS: 7.25 g, CAPB: 7.25 g). While, the second batch consists of a moderate amount of coconut oil (98.6 g) and high content of surfactant mixture (SDBS: 22.5 g, CAPB: 22.5 g). The production of the bio-based nanolubricants started with the mixing of SDBS and CAPB using a magnetic stirrer at 350 rpm. The coconut oil was then slowly added to the solution, and the mixing process was continued for 10 min. Subsequently, distilled water was added into the oil-emulsion, and the mixing process was continued for the last 10 min at speed of 500 rpm. The pH value of lubricants was maintained at 9.2 with the addition of EDTA (pH buffer and oxidation agent). Lastly, 0.5% of aluminium oxide (Al2 O3 ) nanoparticles were added prior to sonication process, which was carried out for 3 h at 15% amplitude.

2.2 CNC Machines, Turning Inserts and Workpiece Material The machining process is conducted on the two axes (x and z directions) CNC turning machine model Chevalier FCL-608. This is a three-phase (415 V) CNC machine with a 6000 RPM maximum spindle. A Sandvik uncoated carbide inserts, namely; CNMG 120,408, grade H13A with nose radius of 0.794 mm are used as the cutting tool. The tool holder was supplied by CHAIN with part code of ECLNR-2020K12 and a rake angle of (−7). The turning process was performed on the 50-mm-diameter and 100-mm-length Inconel 718 workpieces.

2.3 Lubricating Condition and Cutting Parameters For present experiment, the Minimum quantity lubricant (MQL) is used as a medium for lubricant application. An external type of MQL supplied by UNIST is used in this experiment. This kind of MQL needs to be fixed separately from the Computer Numerical Control (CNC) machine. Settings used for the MQL; are as followed: air pressure of 6 bar, a flow rate of 80 ml/hr. Meanwhile, the machining conditions were set at 80 m/min of cutting speed, 0.1 mm depth of cut and 0.1 mm/rev of feed rate. Each cutting process is carried out by removing a 0.2 mm-depth of cut of material for a 40-mm length. The process is be continued until 1 mm of the diameter of the workpiece is removed.

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2.4 Experimental Measurement Procedure Surface roughness, Ra , tool wear, cutting power and spindle power are the machining outputs that have been selected for machinability evaluations. Cutting and spindle power data were obtained through calculation based on the measured cutting force and current using Eqs. 1 and 2. Cutting force data were acquired using a Kistler 9129A dynamometer with Kister 9070 charge amplifier and Kistler Dynoware. Electrical current was measured using PicoLog CM3 current data logger. Meanwhile, the tool wear was measured at a predetermined interval using Xoptron XST60 stereomicroscope at 35× optical magnifications. A portable surface roughness tester, Accretech E-35b was used for the surface roughness measurements. Cutting power = Fc × Vc Spindle power = V × I ×

√ 3

(1) (2)

Where; V = 415 V, I: Spindle current, Fc: Cutting force, Vc: Cutting speed.

3 Result and Discussion 3.1 The Effects of Surfactant Content on the Tool Wear and Surface Roughness Figures 1(a) and (b) show the effects of surfactant content on the tool wear and surface roughness, respectively under the abovementioned MQL settings. As apparent, the result of tool wear and surface roughness were nearly similar in both bio-based

Fig. 1 a Tool wear result and b Surface roughness result for bio-based oil with different surfactant content

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nanolubricants with high and low surfactant content. However, the higher content of surfactant in the bio-based nanolubricants managed to minimise the tool wear of 0.509 mm, which promoted a smoother surface roughness of 1 µm. This is in comparison to the lower surfactant content, which recorded a slightly higher tool wear of 0.529 mm and surface roughness of 1.22 µm. According to M. Asyraf et al. [13], this could be attributed to the influence of surfactant viscosity on the lubricants. N. Talib et al. [14] and M. Asyraf et al. [15] also asserted that the high viscosity lubricants tend to develop a thicker lubrication film, which promotes a spacer function between cutting region. The presence of nanoparticles will further strengthen the lubricant film and function as ball-bearing in the film. Thus, it separated the sliding surfaces at the intermolecular contacts resulting in no-direct contact between the metal to metal surfaces. The high viscosity of the lubricants also eases the flow of cutting fluids at minimum condition to efficiently reduce friction and transfer any heat accumulation at the cutting region [16]. The calculated coefficient of friction on Table 1 supports this claim. Meanwhile, microscopic images in Fig. 2 of the wear land on the tool flank face under both conditions showed that the tool wear mechanisms were generally abrasion. This concurs with most of results related to machining Inconel alloys [4, 17]. Table 1 Coefficient of friction and cutting force data for bio-based nanolubricants Spindle current (A)

Coefficient of friction, µ

Cutting force (N)

Low

7.15

1.40

198

High

7.12

1.32

180

Surfactant content

Fig. 2 Tool wear mechanisms for bio-based nanolubricants, a Low surfactant content, b High surfactant content

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Fig. 3 a Cutting power and b Spindle power for different surfactant content.

3.2 The Effects of Surfactant Content on Cutting and Spindle Power The effects of surfactant content in the bio-based nanolubricants was also tested on the cutting and spindle power. It should be noted that both of this data were obtained through calculation, in which the acquired cutting force data was used for the calculation of cutting power. Likewise, the measured spindle current was used to calculate of spindle power. As depicted in Fig. 3, the power of spindle is significantly higher than the cutting power. This is because the torque required to accelerate the spindle system is substantially more significant than the torque needed to remove material [18]. Nevertheless, the effect was considered marginal in terms of the surfactant content. The bio-based nanolubricants with a higher content of surfactant consumed a minor cutting and spindle power of 240 and 5119 W as compared to the 264 and 5139 W in bio-based nanolubricants with low content of surfactant. Previous studies conducted by researchers in Ref. [1, 3, 6, 7] have found that a higher content of surfactant not only improved the viscosity of based lubricants but also its thermal conductivity. Enhanced viscosity and thermal conductivity of the oil will be beneficial in reducing the friction and heat transfer during the machining process. Sayuti et al. [21] claimed that a lower coefficient of friction would develop a minor frictional component that able to overcome the sticky friction. This occurrence causes the chip flow to occur on the tool-workpiece interface rather than within the workpiece [20].

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4 Conclusion Comparisons of bio-based nanolubrications with two different contents of surfactant under turning of Inconel 718 have been presented in this study. The results obtained can be summarised as follows: a) b)

c)

The bio-based nanolubricants were found suitable for machining Inconel 718 at different surfactant contents. The higher content of surfactant managed to improve all of the four-machining output (Ra, tool wear, cutting and spindle power) as compared to bio-based nanolubricants with lower content of surfactant, although the different was marginal. Tool wear mechanisms were generally due to the abrasion wear as expected.

Acknowledgements The authors gratefully acknowledge the financial support they received from the Ministry of Education Malaysia through the Fundamental Research Grant Scheme (FRGS) no: FRGS/1/2016/TK03/UNIMAP/03/2.

Reference 1. Xavior MA, Patil M, Raj M (2016) Machinability studies on INCONEL 718. Conf J Mater Sci Eng 149:012019 2. Thrinadh J, Mohapatra A, Datta S, Masanta M (2019) Machining behavior of Inconel 718 superalloy: Effects of cutting speed and depth of cut. Mater Today Proc 26:200–208 3. Díaz-Álvarez J, Cantero JL, Miguélez H, Soldani X (2014) Numerical analysis of thermomechanical phenomena influencing tool wear in finishing turning of Inconel 718. Int J Mech Sci 82:161–169 4. Marques A, Paipa M, Falco W, Rocha Á (2019) Turning of Inconel 718 with whisker-reinforced ceramic tools applying vegetable-based cutting fluid mixed with solid lubricants by MQL. J Mate Proc Tech 266:530–543 5. Khanna N, Shah P, Chetan (2020) Comparative analysis of dry, flood, MQL and cryogenic CO2 techniques during the machining of 15-5-PH SS alloy. Tribol Int 146:106196 6. Uysal A, Demiren F, Altan E (2015) Applying minimum quantity lubrication (MQL) method on milling of martensitic stainless steel by using nano Mos2 reinforced vegetable cutting fluid. Proc Soc Behav Sci 195:2742–2747 7. Deiab I, Raza SW, Pervaiz S (2014) Analysis of lubrication strategies for sustainable machining during turning of titanium ti-6al-4v alloy. Proc CIRP 17:766–771 8. Debnath S, Reddy MM, Yi QS (2014) Environmental friendly cutting fluids and cooling techniques in machining: a review. J Clean Prod 83:33–47 9. Padmini R, Vamsi Krishna P, Krishna Mohana Rao G (2016) Effectiveness of vegetable oil based nanofluids as potential cutting fluids in turning AISI 1040 steel. Tribol Int 94:490–501 10. Srikant RR, Ramana VSNV (2015) Performance evaluation of vegetable emulsifier based green cutting fluid in turning of American Iron and Steel Institute (AISI) 1040 steel an initiative towards sustainable manufacturing. J Clean Prod 108:104–109 11. Burton G, Goo CS, Zhang Y, Jun MBG (2014) Use of vegetable oil in water emulsion achieved through ultrasonic atomization as cutting fluids in micro-milling. J Manuf Process 16(3):405– 413

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12. Alves SM, Mello VS, Faria EA, Camargo APP (2015) Nanolubricants developed from tiny CuO nanoparticles. Tribol Int 100:1–9 13. Ali MAM, Azmi AI, Zain MZM, Khalil ANM, Mansor AF, Salleh HM (2018) The effect of concentration of coco amido propyl betaine (CAPB) as green additive in bio-based coconut oil lubricant on the machining performance of Inconel 718. AIP Conf Proc 2030:1–6 14. Talib N, Rahim EA (2018) Performance of modified jatropha oil in combination with hexagonal boron nitride particles as a bio-based lubricant for green machining. Tribol Int 118:89–104 15. Ali MAM, Azmi AI, Murad MN, Zain MZM, Khalil ANM, Shuaib NA (2020) Roles of new bio-based nanolubricants towards eco-friendly and improved machinability of Inconel 718 alloys. Tribol Int 144:106106 16. Ju L, Zhang W, Wang X, Hu J, Zhang Y (2012) Aggregation kinetics of SDBS-dispersed carbon nanotubes in different aqueous suspensions. Colloids Surf A Physicochem Eng Asp 409:159–166 17. Rahmati B, Sarhan AD, Sayuti M (2014) Morphology of surface generated by end milling AL6061-T6 using molybdenum disulfide (Mos2 ) nanolubrication in end milling machining. J Clean Prod 66:685–691 18. Lv J, Tang R, Tang W, Liu Y, Zhang Y, Jia S (2017) An investigation into reducing the spindle acceleration energy consumption of machine tools. J Clean Prod 143:794–803 19. Wusiman K, Jeong H, Tulugan K, Afrianto H, Chung H (2013) Thermal performance of multiwalled carbon nanotubes (MWCNTs) in aqueous suspensions with surfactants SDBS and SDS. Int Commun Heat Mass Transf 41:28–33 20. Kedzierski M (2013) Viscosity and density of aluminum oxide nanolubricant. Int J Refrig 36(4):1333–1340 21. Sayuti M, Sarhan AD, Tanaka T, Hamdi M, Saitoy Y (2012) Cutting force reduction and surface quality improvement in machining of aerospace duralumin AL-2017-T4 using carbon onion nanolubrication system. Int J Adv Manuf Technol 65(9–12):1493–1500

Evaluation of Coated Carbide Drills When Drilling Nickel-Titanium (NiTi) Alloys with Minimum Quantity Nano-lubricants Rosmahidayu Rosnan, Azwan Iskandar Azmi, Muhammad Nasir Murad, and Mohamed Ashraf Mahboob Ali

Abstract Nickel-titanium (NiTi) alloys are considered as hard metal alloys which are extremely difficult to machine materials. This is attributed to the exquisite features of the NiTi alloys that includes high strength, high ductility, and excellent work hardening. As a result, these properties are likely to contribute towards rapid tool wear and high cutting forces during machining processes. In this study, the performance of TiAlN carbide drills in the drilling of NiTi alloys was evaluated in terms of wear growth on the cutting edge of the carbide drills. The thrust forces and the surface finish of drilled holes were also considered as other performance criteria. The drilling experiments were performed under minimum quantity nano-lubrication (MQNL). The results showed that there was a rapid growth in the tool wear on the coated carbide drills when drilling at a high cutting speed. The effect of MQNL towards tool wear growth was only pronounced at a lower range cutting speed. Additionally, application of MQL nanolubricants assisted in reducing the friction between the cutting tool and workpiece, which lowered the thrust force. Improvement in the surface roughness was also evidenced at higher cutting speed due to the complex formation of built-up-edge with the cutting speed. Keywords Drilling · NiTi alloys · Tool wear · Thrust force · Tool life · Surface roughness

1 Introduction Nickel-titanium (NiTi) alloys are high mechanical strength metal alloys of the wellknown nickel and titanium elements. The alloys are mixed in a roughly equivalent atomic ratio or percentage to achieve excellent mechanical properties when compared to other metallic material counterparts. The demands for these alloys are rapidly growing among a number of industries and academic researchers in recent years. Owing to the extraordinary mechanical properties that depict them as excellent R. Rosnan · A. I. Azmi (B) · M. N. Murad · M. A. M. Ali Faculty of Mechanical Engineering Technology, Universiti Malaysia Perlis (UniMAP), 02000 Pauh, Perlis, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_25

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materials, NiTi alloys has attained necessary spotlight in several key applications. This includes thermal and electrical actuators for the automotive and aerospace parts and components. A number of literatures have also highlighted that the foremost application, (which is rigorously evolving) is in the biomedical areas. These applications are particularly for implants, bracket, and wires for orthodontics as well as for stent materials [1]. The unique properties of NiTi alloys are known as follows: high strength-to-weight ratio, remarkable toughness, super-elasticity, excellent corrosion and wear resistance [2]. These alloys are also capable of retaining their mechanical and chemical properties under a high temperature and excessive pressure [3]. Nevertheless, NiTi alloys are well known and acknowledged as extremely difficult to cut or machined materials. This leads to several machining challenges and complexities. Predominantly, the heat dissipation is poor at the cutting tool, chip, and workpiece interfaces due to the low heat conductivity of the alloys [2]. Whereas, the work hardening behavior inflicts rapid wear on the cutting tool and generate high cutting forces to induce poor surface finish or quality of the machined alloys [4]. Weinert and Petzoldt [5] reported that tool wear was still existed even when the optimised cutting parameters were employed during the drilling of NiTi alloys. Other reported problem with machining of these alloys is the poor chip breaking and the formation of burrs. This is mainly caused by the remarkable high ductility of these materials. A proper selection of cutting tools is inevitable since it influences the machinability of hard alloys such as titanium, nickel, and even the NiTi alloys [2]. It is well understood that tungsten carbide (WC-Co) drills are highly recommended for machining any hard alloys. In addition to that, coatings on the cutting tool can significantly influence the machinability of NiTi alloys through significant wear resistance behavior between the tool and workpiece surfaces. Apart from that, cutting fluids also play an important role in improving the machining performance of the tool and work material. A significant development of cutting fluids has been reported in the literature, which has been impressively effective toward reducing friction and flushing away the chip from the cutting zone. This corresponds toward cutting tool effectiveness and efficient surface finish [6]. One of the recent developments in the cutting fluids is the minimum quantity lubrication (MQL), in which the combinations of air and cutting fluids are applied close to the cutting zone at a small quantity [7]. It provides the cooling and lubrication effects near the contact point of the tool (cutting edge). This alleviates the formation of built-up-edge (BUE) and contributes to an enhance surface finish [8]. In recent years, the development of cutting fluids with nanometer (nm) sized particles known as nanofluids, has been very aggressive among research communities. Nanofluids are state-of-the-art generation of fluids with improved thermophysical features and enhanced heat transfer characteristics that exhibit anti-wear and antifriction behavior as compared to the base fluids. In addition, the competency of nanoparticles is due to its size that prevent clogging in the flow passage during machining [9]. Nanolubricants become a keen interest in recent investigations of the machining and machinability of difficult-to-cut materials [10, 11]. Unfortunately, experimental studies on the machinability performance in drilling NiTi alloys using coated carbide drills under MQL nanolubricants have been very limited [12, 13]. Hereby, this paper

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aims to fill the gap in the literature by evaluating the TiAlN coated drills when drilling NiTi alloys under a nano-enhanced minimal quantity lubrication. The performance of the coated drills has been evaluated with respect to tool wear growth, thrust force, tool life, and surface roughness on the NiTi alloys.

2 Experimental Procedure 2.1 Preparation of Nanolubricants Aluminum oxides (Al2 O3 ) nanoparticles with the particles size of 50 nm) with SDBS

Method

Mist lubrications (mix of air and cutting fluids)

Lubricant supply

≈50 ml/h (through external nozzles)

3 Result and Discussion 3.1 Tool Wear Growth The average of flank wear growth against drilling time for the three cutting speeds are depicted in Fig. 2. It is to note that the tool wear growth on both of the main

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Table 2 Machining details Item

Descriptions

Machine Tool: CNC milling machine

Tongtai Machine EZ-5A

Equipment: Microscope

Xoptron Stereo Microscope XST60

Dynamometer

Kistler Type 9129A

Portable surface roughness

HandySurf E-35B

Work Specimen: Materials

Nickel-Titanium (NiTi)

Dimensions

94 mm × 70 mm × 10 mm

Cutting Tools: Material

Tungsten carbide (WC-Co)

Types of drills

Twist drill

Diameter

6 mm

Point and helix angle

30°, 135°

Coatings

TiAlN

No. of flutes

2

Cutting Speed

10, 20, 30 m/min

Feed rate

0.02 mm/rev

Fig. 2 Average of flank wear on the coated drills

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cutting edges of the drills were measured to obtain the average flank wear length. As apparent, the drills reached a given tool life criteria (maximum flank wear VBmax ≤ 0.2 mm) after a certain number of drilled holes. From Fig. 2, the tool wear gradually and consistently grow until the wear criteria of 0.2 mm flank wear has reached. The drilling time to fulfill this tool wear criteria was measured to be 2015 and 814 s, respectively for 10 and 20 m/min. On the other hand, it is evident that the development of flank wear accelerated rapidly from the 1st hole until the tool failed after the 6th hole, specifically for the highest cutting speed of 30 m/min. The drilling time of 127 s was recorded to reach the aforementioned number of holes. Agree with some of previous reported studies [5, 11, 14], it can be disclosed that the progression of flank wear at the highest cutting speed of 30 m/min experienced a rapid growth of tool wear as compared to the lower cutting speeds of 10 and 20 m/min. From these current results, MQL nanolubricants exhibited the suitability towards a lower range of cutting speed in drilling the NiTi alloys. Table 3 differentiates the tool wear growth on one of the drill cutting edges for the first drilled hole under the specified cutting speeds. For the 30 m/min speed, a formation of built-up edge (BUE) was visible, which could be attributed to the accelerated increase in the cutting temperature near the cutting zone. The presence of this BUE would influence the value of surface roughness. As explained by previous researcher in [15, 16], surface quality deteriorates significantly due to scratching effect of the BUE. Table 3. Comparison of tool wear land for the first drilled holes at each of the cutting speeds employed.

Cutting Speeds, (m/min) 10

20

30

Wear Growth

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3.2 Thrust Force Development Effect cutting speed on the development or growth of the thrust force is denoted in Fig. 3. It is apparent that at the early stage of drilling experiment, the generated thrust force was in the range of 150 to 200 N. A consistent increment of the thrust force was evident throughout the drilling experiments and in-line with the tool wear growth shown earlier. At the lowest cutting speed of 10 m/min, the TiAlN coated carbide drills induced a highest drilling thrust force of 327 N after 2015s of drilling time. While, the lowest drilling thrust force of 255 N was generated at the highest cutting speed of 30 m/min with the drilling time of 127 s. This shows that the generated thrust force displays a declining trend when the cutting speed increases from 10 to 30 m/min. This concurs with the previous research, which was reported in [15] when machining nickel-based alloys. In that research, the authors claimed that the induced thrust force was lowered as the drilling speed increases. This was attributed to the low heat dissipation in the cutting zone that indirectly reduced the hardness of material to enhance material removal process. In our case, the application of MQL nanolubricants contributed towards a reduced coefficient of friction between the toolworkpiece interface. This is proved in Fig. 3, as it displays with a significant reduction of the thrust force under the MQL nanolubricants for the cutting speed of 30 m/min, particularly when the tool has reached its wear criteria.

Fig. 3 The development of thrust force for coated carbide drills

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Fig. 4 Tool life versus cutting speeds for the coated carbide drills

3.3 Thrust Force Development A tool life is often described by the number of holes produced prior to the tool failure. Tool life can also be described as the time a tool reached a predefined wear criterion. Results of the tool life for each of the cutting speeds are depicted in Fig. 4. For the lowest cutting speed of 10 m/min, the highest tool life of 2015 s was recorded with a total of 31 holes produced or drilled. Whereas, the shortest tool life was recorded at 814 s with a total of only 6 holes for highest cutting speed of 30 m/min. It was obvious that the application of MQL nanolubricants for the lower range of cutting speeds facilitated the tool wear resistance, which contributed to an increase in the tool life for the coated carbide drills. This shows that MQL nanolubricants attributed towards effective cooling and lubricating, which eliminate the effect of the extreme heat generation and indirectly reduced the friction during drilling operations. Similar results were obtained when machining titanium alloys and aluminium alloys as reported in [11, 17].

3.4 Surface Roughness Figure 5 represents the results of average surface roughness on the drilled hole surfaces against the cutting speeds. It is clear that the surface roughness values of the first drilled holes displayed a decreasing trend of 0.853, 0.795 and 0.773, respectively for 10, 20 and 30 m/min. In fact, a similar trend is evidence on the last drilled holes of the NiTi alloys. It is important to highlight specifically that a complex formation of BUE with an increase in cutting speed could affect or improve the generated surface roughness as claimed by Oliaei and Karpat [14]. Due to the loss of sharpness in the

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Fig. 5 Average of surface roughness against cutting speed for the coated carbide drills

main cutting edge of the drills, it was observed that the surface roughness deteriorated significantly, regardless of the cutting speed employed.

4 Conclusion The performance of TiAlN drills when drilling the NiTi alloys under the MQL nanolubricants was reported with respect to the tool wear, thrust force, tool life, and surface roughness. The following concluding remarks can be made: a)

b)

c) d)

The progression of tool wear was increased with respect to the increment in the cutting speed. It was showed that the MQL nanolubricants were only deemed suitable at a lower cutting speed range. The formation of built-up-edge (BUE) was evidenced at the highest cutting speed of 30 m/min. However, as for thrust force, it displayed a decreasing trend as the drilling speed increases. It could be attributed to the application of MQL nanolubricants that assisted in reducing the friction between the cutting tool and the workpiece. As expected, the performance of coated carbide drills with respect to the tool life was pronounced at a lower cutting speed. The surface roughness improved when using a higher cutting speed due to the complex formation and relationship of BUE with the cutting speed.

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Acknowledgements The authors acknowledged the financial support provided by the Ministry of Higher Education through Fundamental Research Grant Scheme (FRGS) no: FRGS/1/2015/TK03/UNIMAP/02/6. Special thanks to School of Manufacturing Engineering and Faculty of Engineering Technology, Universiti Malaysia Perlis for the overall lab facilities equipped.

References 1. Markopoulos AP, Pressas IS, Manolakos DE (2015) A review on the machining of nickeltitanium shape memory alloys. J Adv Mater Sci 42:28–35 2. Perviaz S, Rashid A, Deiab I, Nicolescu M (2014) Influence of tool materials on machinability of titanium and nickel-based alloys. J Mater Manuf Process 29:219–252 3. Ulutan D, Ozel T (2011) Machining induced surface integrity in titanium and nickel alloys. Int J Mach Tools Manuf 51:250–280 4. Hassan MR, Mehrpouya M, Dawood S (2014) Review of the machining difficulties of nickeltitanium based shape memory alloys. J Appl Mech Mater 564:533–537 5. Weinert K, Petzoldtz V (2004) Machining of NiTi based shape memory alloys. J Mater Sci Eng 378:180–184 6. Boubekri N, Shaikh V (2015) Minimum quantity lubrication (MQL) in machining: benefits and drawbacks. J Ind Intell Inf 3:205–209 7. Sharma VS, Singh G, Sorby K (2015) A review on minimum quantity lubrication for machining processes. J Mater Manuf Process 30:935–953 8. Kundrak J, Mamalis AG, Gyáni K, Markopoulos A (2006) Environmentally friendly precision machining. J Mater Manuf Process 21:29–37 9. Shen B, Shih AJ, Tung SC (2008) Application of nanofluids in minimum quantity lubrication grinding. J Tribol Trans 51:730–737 10. Khalil ANM, Ali MAM, Azmi AI (2015) Effect of Al2 O3 nanolubricant with SDBS on tool wear during turning process of AISI 1050 with minimal quantity lubricant. J Procedia Manuf 2:130–134 11. Ali MAM, Azmi AI, Khalil ANM, Leong KW (2017) Experimental study on minimal nanolubrication with surfactant in the turning of titanium alloys. Int J Adv Manuf Technol 92:117–127 12. Ezugwu EO, Wang ZM (1997) Titanium alloys and their machinability. J Mater Process Technol 68:262–274 13. Kaya E, Kaya I (2019) A review on machining of NiTi shape memory alloys: the process and the post process perspective. Int J Adv Manuf Technol 100:2045–2087 14. Thakur DG, Ramamoorthy B, Vijayaraghavan L (2009) Machinability investigation of Inconel 718 in high speed turning. Int J Adv Manuf Technol 45:421–429 15. Rival. Machinability study of coated and uncoated carbide tools in drilling INCONEL 718. Ph.D. Dissertation, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia Skudai (2010) 16. Oliaei SNB, Karpat Y (2016) Investigating the influence of built-up-edge on forces and surface roughness in micro scale orthogonal machining of titanium alloy Ti6Al4. J Mater Process Technol 235:28–40 17. Najiha MS, Rahman MM (2016) Experimental investigation of flank wear in end milling of aluminum alloy with water-based TiO2 nanofluid lubricant in minimum quantity lubrication technique. Int J Adv Manuf Technol 86:2527–2537

Optimisation of Process Parameters in Plastic Injection Moulding Simulation for Blower Impeller’s Fan Using Response Surface Methodology M. U. Rosli, S. N. A. Ahmad Termizi, C. Y. Khor, M. A. M. Nawi, Ahmad Akmal Omar, and Muhammad Ikman Ishak Abstract Injection moulding process parameters such as moulding temperature, melt temperature, injection pressure, and injection time have a direct impact on the quality and cost of polymer products especially in a thin and highly complex product. In any case, the improvement of these parameters is a difficult and challenging task. Thus, a good parameter setting combination through simulation and optimisation is highly required. Response Surface Methodology (RSM) is utilized as optimisation method in finding the significant factor and recommended settings in minimizing the defects of volumetric shrinkage and warpage on plastic blower impeller’s fan. In simulations, three levels of melting temperature, moulding temperature, injection time and injection pressure processing parameters are selected. The material used is Polypropylene (PP). The result showed the optimum values suggested by the software are melt temperature of 210 °C, moulding temperature of 110 °C, cooling time of 0.8 s and injection pressure of 212.81 MPa. With below 15% differences error value between recommended setting and simulation, the result was in acceptable and shows RSM ability in finding the optimal setting in producing the part. Keywords Simulation · Injection moulding · Response Surface Methodology

1 Introduction Blowers are mechanical or electro-mechanical devices used to induce gas flow through ducting, electronic chassis and process stacks wherever flow is required to exhaust, aspire, cool, ventilate, and convey. Blower cool electronic enclosures, induce boiler drafts, increase engine airflow. Typical applications include climate control and personal thermal comfort, engine cooling systems located in front of a radiator [1], machinery cooling systems [2, 3], ventilation, fume extraction, winnowing, removing dust, drying and to provide draft for a fire. Blowers are configured in a variety of M. U. Rosli (B) · S. N. A. A. Termizi · C. Y. Khor · M. A. M. Nawi · A. A. Omar · M. I. Ishak Simulation and Modelling Research Group (SimMReG), Faculty of Engineering Technology, Universiti Malaysia Perlis (UniMAP), Kampus UniCITI Alam, Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_26

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designs such as centrifugal flow or rotating lobe styles. Motors usually drive blowers, although other means such as engines can be used to power them. Blowers are lowpressure gas movers with typical 5–20 psi (approximately 0.3–1.3 bar) differential pressures [4]. A fan impeller is made up of a number of blades mounted at a pitch angle and mounted on a hub mounted on a drive shaft, or integrated with it. Fans are used to transfer large volumes of air at very minimum pressures and are to be found in ventilation systems, conveying, vacuum cleaning and supercharging, engine and compressor cooling [5]. It can be cheaper to cast an impeller and its spindle as one piece, rather than separately. Thus, it is usually produced by the injection moulding process, one of the most important methods for mass-production of products from thermoplastics, generally without extra finishing required. One of the lightest and most versatile polymers is polypropylene [6–9]. Plastic injection moulding process is known for its ability to produce parts with small thickness and highly complex at a low cost. In the process of the injection moulding part, the defects always encounter the produced plastic part shape and affected the product functionality. Thus, decision making in determining the optimal processing parameter is highly needed and should be routinely performed as it influences product quality, functionality and cost [10– 13]. Plastic parts without defects such as shrinkage and warpage is beneficial to the industrial because it will produce qualitative injection moulding plastic products without wasting time and resources. Simulation and optimisation method is the most effective way to obtain appropriate processing parameters before the actual fabrication processes begin without consuming a lot of time and money [14–16]. No matter how simple or complex the plastic shapes, inappropriate mould design or parameter setting for processing could have a bad impact on moulded part appearance.

2 Methodology 2.1 Experimental Setup: 3-Dimensional Model Development CAD software was chosen to design the blower impeller’s fan for analysis. Figure 1 shows the isometric and top view of a complete CAD drawing of blower impeller’s fan. This component is mostly made of Polypropylene (PP) thermoplastic material and the thickness is less than 2 mm and suitable with this study. Figure 2 shows the recommended gate location by CAE software. For part meshing, the global edge length which is triangle size is set to 1.80 mm length. This had been decided based on the time taken for the mesh to complete and the match percentage of the triangle.

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Fig. 1 CAD drawing of blower impeller’s fan model

Fig. 2 Gate location

2.2 Design of Experiment Response Surface Methodology (RSM) has been utilised in this study to find out the most significant parameter and recommended the most optimised settings in minimizing the defect. Design Expert software was used to analyse the data. The selected numerical factors are moulding and melt temperature, injection time and injection pressure. Cavity layout design is selected as categorical factor for this study. A total of 60 runs were determined for the simulation based on parameter and level stated in Table 1. Two quality characteristics are selected in this study are volumetric shrinkage and warpage. Higher volumetric shrinkage indicates that the material of the plastic part has a greater transition of specific volume. In other words, extensive crystallization happens in the course of solidification of molten material during the cooling process. It means that the volumetric shrinkage is influenced by the compressibility properties of the polymer material [17, 18]. However, the warpage depends on the phenomenon of the polymer shrinkage. The warpage happens when non-uniform shrinkage occurs in the plastic part [19].

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Table 1 Levels of designing parameters Column Factor

Unit

Level Low (−1) Medium (0) High (+1)

A

Moulding temperature °C

110

115

120

B

Melting temperature

°C

210

220

230

C

Injection time

s

0.70

0.75

0.80

D

Injection pressure

Mpa

210

215

220

E

Cavity layout design

Circular, Herringbone

3 Results and Discussion From the result of 60 simulation runs in Table 2, the maximum value of shrinkage observed during simulation run 33 and run 54 at 13.38% while the minimum value of 9.722% recorded at simulation run 15 and run 60. The maximum values of the warpage were recorded at simulation run 18 and run 49 at 0.5351% while the minimum value of warpage observed during simulation run 15 and run 60 at 0.4464%. Analysis of Variance (ANOVA) provides mathematical model equations for the predicted response of shrinkage and warpage. The model predicted the purpose of the accurate value to determine different results between simulations. Each of the factors investigated is subjected to F-model analysis of variance test to study the interaction relationship between factors prior to shrinkage and warpage responses. The main consideration in the regression model relies on minimizing the difference of the values between “Adjusted R-Squared” and “Predicted R-Squared”. The value of “R-Squared” represents the percentage of response fitting for the space model. The complete analytical comparison between simulation and model predicted for both responses are displayed in Table 2.

3.1 3D Surface Model Graph The effect of the factors on response is further investigated by observing and analyzing 3D surface model graphs with the help of the contour plot. The contour plot is a two-dimensional (2D) visualization of the response and the combinations of moulding temperature and melts temperature. Figure 3 illustrates the 3D surface model graph for shrinkage in Circular and Herringbone layout design. Meanwhile, Fig. 4 illustrates 3D surface model graph for warpage in Circular and Herringbone layout design. In Circular layout design, the minimum value of shrinkage indicates by blue color area while in Herringbone layout design the minimum value of shrinkage indicates by green color area.

Optimisation of Process Parameters …

313

Table 2 Analytical comparison between simulation and model predicted Run

Factors

Responses (%) Simulation

Predicted

A

B

C

D

E

Y1

Y2

Y1

Y2

1

115

220

0.75

215

Circular

10.53

0.471

10.5438

0.472493

2

110

230

0.70

220

Circular

10.88

0.4488

10.8621

0.450317

3

110

210

0.70

210

Circular

4

120

230

0.80

220

Herringbone

13.36

0.5177

13.3495

0.518755

5

115

230

0.75

215

Circular

11.12

0.4705

11.0745

0.467809

6

110

220

0.75

215

Herringbone

12.5

0.5047

12.4859

0.502992

7

110

230

0.70

210

Circular

10.88

0.4488

10.8521

0.449995

8

120

210

0.70

210

Herringbone

12.54

0.5305

12.5263

0.532769

9.754

0.462

9.74433

0.459661

9

115

230

0.75

215

Herringbone

13.13

0.5065

13.1343

0.505942

10

115

220

0.75

215

Herringbone

12.71

0.5143

12.7159

0.513836

11

115

220

0.80

215

Herringbone

12.7

0.5123

12.7087

0.514114

12

120

220

0.75

215

Herringbone

12.96

0.5227

12.9459

0.524681

13

115

220

0.75

220

Herringbone

12.71

0.5143

12.7159

0.514159

14

120

210

0.70

220

Herringbone

12.54

0.5305

12.5263

0.532363

15

110

210

0.80

210

Circular

16

120

230

0.80

220

Circular

11.3

0.486

11.3283

0.488062

17

110

230

0.70

210

Herringbone

12.91

0.4927

12.9505

0.495566

18

120

210

0.80

210

Herringbone

12.52

0.5351

12.5594

0.534

19

120

210

0.70

220

Circular

10.22

0.5028

10.2364

0.501725

20

115

220

0.75

215

Herringbone

12.71

0.5143

12.7159

0.513836

21

115

220

0.75

215

Herringbone

12.71

0.5143

12.7159

0.513836

22

115

220

0.75

215

Circular

10.53

0.471

10.5438

0.472493

23

120

220

0.75

215

Circular

10.75

0.4919

10.7854

0.494376

24

120

230

0.70

210

Circular

11.26

0.4911

11.2732

0.490111

25

115

220

0.80

215

Circular

10.58

0.4672

10.5586

0.469532

26

120

230

0.80

210

Circular

11.3

0.486

11.3183

0.487814

27

115

220

0.75

215

Circular

10.53

0.471

10.5438

0.472493

28

110

230

0.70

220

Herringbone

12.91

0.493

12.9505

0.49661

29

115

220

0.75

215

Circular

10.53

0.471

10.5438

0.472493

30

120

210

0.70

210

Circular

10.22

0.5028

10.2264

0.502853

31

110

210

0.70

220

Herringbone

12.08

0.5138

12.0673

0.511974

32

110

220

0.75

215

Circular

10.33

0.4554

10.3023

0.450609

33

120

230

0.70

220

Herringbone

13.38

0.5168

13.3485

0.513924

34

120

230

0.80

210

Herringbone

13.36

0.5177

13.3495

9.722

0.4464

9.75839

0.449464

0.517786 (continued)

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Table 2 (continued) Run

Factors

Responses (%) Simulation

Predicted

A

B

C

D

E

Y1

Y2

35

115

220

0.75

215

Circular

10.53

36

115

210

0.75

215

Circular

37

115

220

0.70

215

Circular

38

110

210

0.70

220

Circular

39

120

210

0.80

220

Circular

10.37

0.503

10.3135

0.497128

40

115

220

0.75

215

Herringbone

12.71

0.5143

12.7159

0.513836

41

110

210

0.70

210

Herringbone

12.08

0.5138

12.0673

0.511655

42

115

220

0.75

210

Circular

10.53

0.471

10.5388

0.472532

43

110

230

0.80

210

Herringbone

12.9

0.4927

12.8885

0.494797

44

110

230

0.80

210

Circular

10.83

0.447

10.8342

0.442748

45

115

220

0.75

215

Circular

10.53

0.471

10.5438

0.472493

46

115

220

0.70

215

Herringbone

12.71

0.514

12.7232

0.513558

47

110

230

0.80

220

Herringbone

12.9

0.4982

12.8885

0.496491

48

110

210

0.80

210

Herringbone

12.07

0.5094

12.0374

0.507936

49

120

210

0.80

220

Herringbone

12.52

0.5351

12.5594

0.534244

50

110

210

0.80

220

Herringbone

12.07

0.5094

12.0374

0.508905

51

115

220

0.75

210

Herringbone

12.71

0.5143

12.7159

0.513514

52

115

210

0.75

215

Herringbone

12.28

0.5166

12.2976

0.521731

53

115

220

0.75

215

Herringbone

12.71

0.5143

12.7159

0.513836

54

120

230

0.70

210

Herringbone

13.38

0.5168

13.3485

0.513605

55

115

220

0.75

220

Circular

10.62

0.4703

10.5488

0.472454

56

120

210

0.80

210

Circular

10.37

0.503

10.3035

0.497606

57

115

220

0.75

215

Herringbone

12.71

0.5143

12.7159

0.513836

58

120

230

0.70

220

Circular

11.26

0.4911

11.2832

0.489709

59

110

230

0.80

220

Circular

10.83

0.447

10.8442

0.44372

60

110

210

0.80

220

Circular

9.977 10.53 9.754

9.722

Y1

Y2

0.471

10.5438

0.472493

0.4722

10.0132

0.477176

0.4759

10.529

0.475454

0.462

0.4464

9.75433

9.76839

0.459259

0.449712

3.2 Optimisation of Process Parameters In Design Expert software, after the optimization goal for each parameter and responses have been determined, the results of suggested optimum parameter values for optimal response were obtained as displayed in Table 3. The optimal parameters are then simulated again for validation.

Optimisation of Process Parameters …

315

Fig. 3 Shrinkage 3D surface graphs for Circular and Herringbone layout design

Fig. 4 Warpage 3D surface graphs for Circular and Herringbone layout design

Table 3 Optimal value of each parameter

Design of parameter

Unit

Optimal values

Moulding temperature

(°C)

110

Melt temperature

(°C)

210

Injection time

s

0.8

Injection pressure

MPa

212.81

Design layout

-

Circular

3.3 Validation of Results In this validation process, a simulation with optimum moulding temperature levels, melt temperature, injection time and injection pressure was performed as shown in Fig. 5 and Fig. 6. The percentage difference of shrinkage and warpage has been calculated by using Eq. 1.

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M. U. Rosli et al.

Fig. 5 Optimized shrinkage results

Fig. 6 Optimized warpage results

Table 4 Validation result of optimisation

Response

Simulation Model predicted Differences (%)

Shrinkage (%) 9.997

9.76041

2.39491

Warpage

0.449528

5.06750

Per centage di f f er ent =

0.4729

|Simulation − Pr edicted| × 100% |Average|

(1)

The validation results are shown in Table 4. This optimisation is considered succeed and valid since the percentage difference for both investigated responses were in acceptable range of below 15%.

Optimisation of Process Parameters …

317

4 Conclusion In this study, a simulation and optimisation method were carried out to identify the most optimized value of parameter setting for plastic blower impeller’s fan model. The optimum values suggested by the RSM are mould temperature of 110 °C, melt temperature of 210 °C, 0.8 s injection time and injection pressure of 212.81 MPa. Using these values, the responses of volumetric shrinkage and warpage are 9.997% and 0.4729% respectively. The optimization result is valid since the percentage difference is below 15%. As a conclusion, the simulation based optimisation by utilising RSM method can identify the optimized parameter setting in injection moulding as to produce less defect product. The validation results prove that RSM method is an effective method which industry can use to produce complex product without wasting cost and resource. Acknowledgements The authors gratefully acknowledge the financial support from UniMAP.

References 1. Rosli MU, Ariffin MK, Sapuan SM, Sulaiman S (2014) Survey of Malaysian Car owner needs of a car interior. Int J Mech Mechatro Eng 14:62 2. Khan B, Rosli MU, Jahidi H, Ishak MI, Zakaria MS, Jamalludin MR, Khor CY, Faizal WM, Rahim WM (2017) Nawi MAM (2017) Effect of zinc addition on the performance of aluminium alloy sacrificial anode for marine application. AIP Conf Proc 1885:020074 3. Ishak MI, Khor CY, Jamalludin MR, Rosli MU, Shahrin S, Wasir NY, Zakaria MS, Yamin AFM, Dahlan ND, Draman WNAW (2017) Conceptual design of automotive compressor for integrated portable air conditioning system. MATEC Web Conf 97:1–5 4. Bloch HP (2017) Subject Category 5 – Blowers, Book of Petrochemical Machinery Insights 5. Nair AB, Joseph R (2014) Eco-friendly bio-composites using natural rubber (NR) matrices and natural fiber reinforcements. Chem Manuf Appl Nat Rubber 2014:249–283 6. Crawford CB, Quinn B (2017) Physiochemical properties and degradation. In: MicroplasticPollutants, pp. 57–100 (2017) 7. Ibrahim MIF, Rosli MU, Ishak MI, Zakaria MS, Jamalludin MR, Khor CY, Rahim WMFWA, Nawi MAM, Shahrin S (2018) Simulation based optimization of injection molding parameter for meso-scale product of dental component fabrication using response surface methodology (RSM). AIP Conf Proc 2030:1–6 8. Luqman M, Rosli MU, Khor CY, Zambree S, Jahidi H (2018) Manufacturing process selection of composite bicycle’s crank arm using analytical hierarchy process (AHP). IOP Conf Ser Mater Sci Eng 318:012058 9. Rosli MU, Jamalludin MR, Khor CY, Ishak MI, Jahidi H, Wasir NY, Faizal WM, Draman WNAW, Lailina NM, Ismail RI (2017) Analytical hierarchy process for natural fiber composites automotive armrest thermoset matrix selection. MATEC Web Conf 97:2–5 10. Rosli MU, Ariffin MKA, Sapuan SM, Sulaiman S (2013) Integrating TRIZ and AHP: A MPV’s utility compartment improvement design concepts. Int J Mater Mech Manuf 1:32–35 11. Che Hassan MF, Mohd Rosli MU, Mohd Redzuan MA (2018) Material selection in a sustainable manufacturing practice of a badminton racket frame using elimination and choice expressing reality (ELECTRE) method. J. Phys. Conf. Ser. 1020:012012

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12. Rosli MU, Ariffin MKA, Sapuan SM, Sulaiman S (2013) Integrated AHP-TRIZ innovation method for automotive door panel design. Int J Eng Technol 5:3158–3167 13. Rosli MU, Ariffin MKA, Sapuan SM, Sulaiman S (2014) Integrated TRIZ-AHP support system for conceptual design. Appl Mech Mater 548–549:1998–2002 14. Tan JS, Khor CY, Rahim WMFWA, Ishak MI, Rosli MU, Jamalludin MR, Zakaria MS, Nawi MAM, Aziz MSA, Ani FC (2017) Influence of solder joint length to the mechanical aspect during the thermal stress analysis. AIP Conf Proc 1885:20063 15. Ishak MI, Abdul Kadir MR (2013) Biomechanics in dentistry: evaluation of different surgical approaches to treat atrophic maxilla patients. SpringerBriefs in applied sciences and technology. ISBN 9783642326028 16. Ishak MI, Shafi AA, Rosli MU, Khor CY, Zakaria MS, Rahim WMFWA, Jamalludin MR (2017) Biomechanical evaluation of different abutment-implant connections – a nonlinear finite element analysis. AIP Conf Proc 1885:20064 17. Khor CY, Abdullah MZ (2013) Analysis of fluid/structure interaction: Influence of silicon chip thickness in moulded packaging. Microelectron Reliab 53(2):334–347 18. Ong EES, Abdullah MZ, Khor CY, Loh WK, Ooi CK, Chan R (2012) Analysis of encapsulation process in 3D stacked chips with different microbump array. Int Commun Heat Mass Transf 39(10):1616–1623 19. Abdul Aziz MS, Abdullah MZ, Khor CY, Fairuz ZM, Iqbal AM, Mazlan M, Rasat MSM (2014) Thermal fluid-structure interaction in the effects of pin-through-hole diameter during wave soldering. Adv Mech Eng 2014. Art. no. 275735

Simulation Based Optimization of Shrinkage in Injection Molding Process for Lamp Holder via Taguchi Method C. Y. Khor, M. A. M. Nawi, Muhammad Ikman Ishak, Boon Aik Low, M. U. Rosli, and S. N. A. Ahmad Termizi Abstract Defect minimization is important in any injection molding process in order to ensure the quality of the injected part or product. The aims of the present study are to determine the optimum processing parameters and to identify the most significant factor during the injection molding process for the lamp holder via Taguchi method. The numerical simulation analysis of injection molding process was carried out by software. The set of numerical simulation for different parameters was designed by L18 Orthogonal array. Six process parameters were considered, which are melting temperature, injection time, cooling time, filling time, injection pressure and filling pressure. The main response of the study is volumetric shrinkage. Main effect and ANOVA analysis were also highlighted in this study. Filling pressure and melting temperature are the most significant factor in the injection molding process of the lamp holder. The present study revealed the setting of process parameters (i.e., 334 °C melting temperature, 0.16 s of injection time, cooling time with 50, 20 s of filling time, 18.65 MPa of injection pressure and 90 MPa of filling pressure) yield the optimum quality of the lamp holder. Keywords Simulation and modeling · Injection molding · Taguchi method

1 Introduction Fabrication of plastic parts is one of the most important manufacturing process due to high demand of supply, since plastic parts are very useful and almost can be spotted on many product goods. Plastic parts that formed by injection molding in no strange to all of us since it can be found and spotted easily at surrounding areas. In order to produce the high demand of plastic parts, injection molding is often used in the manufacturing sector to fulfil this high demand of supply because the general C. Y. Khor (B) · M. A. M. Nawi · M. I. Ishak · B. A. Low · M. U. Rosli · S. N. A. A. Termizi Simulation and Modelling Research Group (SimMReG), Faculty of Engineering Technology, Universiti Malaysia Perlis (UniMAP), Kampus UniCITI Alam, Sungai Chuchuh, 02100 Padang Besar, Perlis Indera Kayangan, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_27

319

320

C. Y. Khor et al.

process cycle time for injection molding is just varies from a couple seconds to couple minutes based on the product parts and parameters [1]. In the study of injection molding process, various approaches such as Taguchi method [2] and response surface method [3] have been attempted to achieve the optimum process and high quality of the injected part. The most popular method used in the study of injection molding is the Taguchi method [4–6]. Taguchi method has been employed to optimize the processing parameters for various products such as sisal/glass fiber hybrid biocomposites [7], brake booster valve body [8], spoon part [9] and thin shell plastic part [10]. The process parameters of the injection molding were typically considered as the variables such as mold temperature, melt temperature, injection pressure, holding pressure, holding time and cooling time. Besides, curing time, gate number and gate size were also considered in the previous studies. The optimal process parameters could yield the minimum or zero defects and improve the quality of the plastic products. Moreover, the most significant factors toward the responses can be also identified via the Taguchi method. For example, the responses that typically considered in the injection molding process are shrinkage, warpage [11], voiding, and burn-mark, jetting of the injected part and other serious defects or failures [9, 10]. Among these responses, shrinkage and warpage are the most commonly found defects in the injection molding process and it should be minimized for any injection process [12]. With the aid of the simulation software, the process of the injection molding can be visualized clearly in the build-in post-processing tools. The predictions of the molten plastics can be done in a very short cycle time. The simulation method is not only limited to the injection molding, but it has been widely applied to solve various engineering problems, such as IC encapsulation process [13], molded undefill process [14], plastic encapsulation [15, 16], reflow oven [17], reflow soldering process [18] and wave soldering process [19]. In the simulation analysis, finite element (FE) [20] and finite volume (FV) [21] are the popular solvers that use to discretize the governing equation of the airflow, liquid flow and molten plastic flow in the commercial software. Besides, the other decision methods [22] can be integrated with the simulation technique and also applicable for the various parametric studies [23]. Aforementioned, inappropriate parameter controls in the injection molding will lead to unintended defects and failures. These undesired features of the injected part will lead to the deterioration of mechanical strength of the injected part [24]. Therefore, it is important to determine which parameters that affect the injected part or product. In this study, the injection molding of lamp holder is considered due to the thin design of the plastic holder. The shrinkage of the lamp holder may lead to assembly problems for the brass threaded part, hence lead to lamp installation problem. In this research work, the Taguchi method was applied to design the numerical experiments, to determine the significant factor and to optimize the processing parameters.

Simulation Based Optimization of Shrinkage …

321

Fig. 1 a Three-dimensional model and b meshed model of lamp holder

2 Methodology 2.1 3D Model and Meshed Model Lamp holder is an electrical equipment device used for firming a lamp to its lighting fixture and supply electrical connections to the lamps or bulbs by contacting electrical connection of lamp holder with the metal end of the bulb or lamp. Another function of lamp holder is to enable the bulb or lamp can be changed or replaced easily and quickly. The lamp holders are often called as bulb holders, light bulb sockets or lamp sockets. Figure 1 shows the three-dimensional model and meshed model of the lamp holder used in this study. The model of the lamp holder was created by using Solidwork. The dimensions of the lamp holder are, 75 mm for the base diameter, 7 mm of base’s height and the holder’s height is 21 mm. The 3D model was then exported to the injection molding software to further the meshing step and setting for the analysis. The mesh quality was controlled with low aspect ratio (1.16), and more than 90% of match percentage and reciprocal percentage.

2.2 Material Selection Panlite L-1225LM is used as a material for the application of the lamp holder. This material is selected because it has excellent electrical resistance properties and high thermal resistance. Panlite L-1225LM only shows a little bit of change throughout a wide range of temperature. Besides, it is a good insulating material, it means that it is suitable for electrical and electronic application. Table 1 summarizes the description of material and recommended processing parameters in the injection molding process.

322 Table 1 Description of Panlite L-1225LM and recommended processing

C. Y. Khor et al. Material description Family name

:

Polycarbonates (PC)

Family abbreviation

:

PC

Material structure

:

Amorphous

:

95 °C

Recommended processing Mold surface temperature Melt temperature

:

300 °C

Mold temperature range

:

70–120 °C

Melt temperature range

:

260–340 °C

Absolute maximum melt temperature

:

380 °C

Ejection temperature

:

127 °C

Maximum shear stress

:

0.5 MPa

Maximum shear rate

:

40,000 1/s

2.3 Design of Numerical Experiment Taguchi method is used widely applied in the engineering process to optimize and enhance the performance of the process. Besides, it also enhances the quality of the product and processing parameters of injection molding. Orthogonal array is considered in this study to determine the optimal process parameters. In Taguchi method, signal to noise ratio and analysis of variance (ANOVA) were carried out to determine the quality characteristics and to identify the impact or effect of process parameters towards response. The processing parameters used in this study are (A) melting temperature, (B) injection time, (C) cooling time, (D) filling time, (E) injection pressure and (F) filling pressure. These parameters were considered based on the literature review. The effect of parameters that demonstrates the most significant influences towards shrinkage was studied. The volumetric shrinkage was considered as the response in the study. The results of the shrinkage were obtained after running the simulation analysis in the software. In Taguchi method, six processing parameters and three levels for each of the parameters was designed using the orthogonal array. The degree of freedom (DOF) was determined by three levels minus with one means that there are two degrees of freedom for each parameter. The sum of each DOF parameter is twelve degrees of freedom. Therefore, L12 or higher orthogonal array is recommended for better performance. Therefore, L18 orthogonal array with three levels and six processing parameters is selected as numerical experimental layout. Table 2 summarizes the orthogonal array layout for the design of numerical experiment.

Simulation Based Optimization of Shrinkage …

323

Table 2 L18 orthogonal array layout for six processing parameters Number of run

Factor A: Melting temperature (°C)

B: Injection time (s)

C: Cooling time (s)

D: Filling time (s)

E: Injection pressure (MPa)

F: Filling pressure (MPa)

1

334

0.16

20

10

14.65

70

2

334

0.16

35

15

16.65

80

3

334

0.16

50

20

18.65

90

4

334

0.18

20

10

16.65

80

5

334

0.18

35

15

18.65

90

6

334

0.18

50

20

14.65

70

7

338

0.20

20

15

14.65

90

8

338

0.20

35

20

16.65

70

9

338

0.20

50

10

18.65

80

10

338

0.16

20

20

18.65

80

11

338

0.16

35

10

14.65

90

12

338

0.16

50

15

16.65

70

13

342

0.18

20

15

18.65

70

14

342

0.18

35

20

14.65

80

15

342

0.18

50

10

16.65

90

16

342

0.20

20

20

16.65

90

17

342

0.20

35

10

18.65

70

18

342

0.20

50

15

14.65

80

3 Results and Discussion 3.1 Simulation Analysis The simulations of the injection molding for the lamp holder were carried out according to the design of numerical experiment (Table 2). In the current study, the main response, volumetric shrinkage was focused. The volumetric shrinkage of each run was obtained after the ‘cool + fill + pack + warp’ analysis. There are a total of 18 runs were completed for the analysis. Figure 2 illustrates the volumetric shrinkage of the lamp holder for trial run 1. In trial run 1, the processing parameters used are A1, B1, C1, D1, E1 and F1. The highest volumetric shrinkage are 8.919% as clearly shown around the circular holder that connected to the holder base. The shrinkage occurred around this region may attribute by the thickness of wall surface to the lamp holder design and the intersection area of the base and the circular holder. However, the blue contour indicates the lowest volumetric shrinkage. The shrinkage of amorphous materials may also cause by the

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Fig. 2 Result of volumetric shrinkage in trial run 1

Table 3 Result of volumetric shrinkage in L18 OA Run

Factor

Volumetric shrinkage (%)

A

B

C

D

E

F

1

1

1

1

1

1

1

8.968

2

1

1

2

2

2

2

8.677

3

1

1

3

3

3

3

8.459

4

1

2

1

1

2

2

8.829

5

1

2

2

2

3

3

8.578

6

1

2

3

3

1

1

8.729

7

2

3

1

2

1

3

8.888

8

2

3

2

3

2

1

8.831

9

2

3

3

1

3

2

8.730

10

2

1

1

3

3

2

8.722

11

2

1

2

1

1

3

8.746

12

2

1

3

2

2

1

8.810

13

3

2

1

2

3

1

9.047

14

3

2

2

3

1

2

8.841

15

3

2

3

1

2

3

8.680

16

3

3

1

3

2

3

8.809

17

3

3

2

1

3

1

8.948

18

3

3

3

2

1

2

8.782

Simulation Based Optimization of Shrinkage …

325

molecular forces when the injection flow stops. The molecules of material tend to relax and return to the random orientation when injection stop. At the same time, the intermolecular forces acting on each molecule pull them closer together until the molten temperature decreases to freeze. The presence of these forces resulted in shrinkage of the injected part. Shrinkage is a reduction in volume of the injected part and it will not cause the deformation to the part. However, shrinkage leads to a smaller size of the injected part after the molding process. The results of the numerical experiment are based on a combination set of processing parameters, which designed in Taguchi L18 orthogonal array table. The results and the combination set of parameters are shown in Table 3. The minimum value for volumetric shrinkage is 8.459%. The maximum value is 9.047%. The results obtained by using an optimal combination set of processing parameters after main effect analysis will be compared with the minimum value for the verification test.

3.2 Main Effect Analysis The main effect analysis was carried out to determine the optimal processing parameter combination by plotting a graph based on response values of parameters with each level. The graph of main effect analysis is plotted based on data in Table 3. The main effect plot for volumetric shrinkage is shown in Fig. 3. In Fig. 3, it is obvious that the optimal combination of processing parameters is A1, B1, C3, D3, E3 and F3, which are 334 °C of melting temperature, 0.16 s of injection time, cooling time with 50, 20 s of filling time, injection pressure with 18.65 and 90 MPa of filling pressure. Thus, the setting of this optimum combination set (A1, B1, C3, D3, E3 and F3) of processing parameters for shrinkage was applied in the injection molding process of the lamp holder. The results obtained from this optimal combination were

Fig. 3 Main effect plot for volumetric shrinkage

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Table 4 Summary of ANOVA result for volumetric shrinkage Column

Parameter

SS

Variance

F-Ratio

Percentage (%)

A

Melting temperature

DOF 2

0.0630

0.0315

10.3874

19.02

B

Injection time

2

0.0307

0.0153

C

Cooling time

2

0.0616

0.0308

10.1694

18.62

D

Filling time

2

0.0237

0.0119

3.9152

7.17

E

Injection pressure

2

0.0192

0.0096

3.1634

5.80

F

Filling pressure

2

0.1177

0.0589

19.4198

35.56

Error

5

0.0152

0.0030

Total

17

0.3310

5.05575

9.26

4.58 100

then compared with the lowest value that obtained from Taguchi L18 orthogonal array table. This comparison is purposely to prove that the set of combination from the main effect analysis yields the optimum parameters to minimize the volumetric shrinkage.

3.3 Analysis of Variance (ANOVA) Analysis of variance (ANOVA) was carried out to determine the most significant factors towards response and significant of each parameter. ANOVA analysis is widely applied in the other optimization method such as Response Surface Methodology [25, 26] Table 4 summarizes the results of ANOVA for volumetric shrinkage. The results revealed that the most significant factor for shrinkage response is filling pressure as its percentage is as high as 35.56% and followed by melting temperature with 19.02%, cooling time by 18.62%, injection time by 9.25%, filling time with 7.17% and injection pressure by 5.79%. Based on F Table F.05 with 95% confidence level, there are three significant parameters for volumetric shrinkage which are melting temperature, cooling time and filling pressure because they have an F-ratio that exceeds 5.7861. The F-ratio for melting temperature, cooling time and filling pressure are 10.3874, 10.1694 and 19.4198, respectively.

3.4 Verification Test Verification test was carried out and the results were compared with minimum output obtained in L18 orthogonal array. This test is to ensure the optimal result obtained from the optimal processing parameters and to identify if any reduction of shrinkage in the injection molding process. For shrinkage, the optimum combination set of processing parameters is A1, B1, C3, D3, E3 and F3, which is similar to the combination of trial

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327

Table 5 Optimum setting of processing parameters Factor

Response

A: Melting temperature (°C)

B: Injection time (s)

C: Cooling time (s)

D: Filling time (s)

E: Injection pressure (MPa)

F: Filling pressure (MPa)

Volumetric shrinkage (%)

334

0.16

50

20

18.65

90

8.459

run 3 as shown in Taguchi L18 orthogonal array. Therefore, the result of shrinkage is similar (8.459%). Thus, this indicated that the trial run 3 is the best among 18 runs to yield the minimum volumetric shrinkage in the injection molding of the lamp holder. The optimum setting of the processing parameters that yield the minimum volumetric shrinkage is summarized in Table 5.

4 Conclusion Simulation based optimization analysis via Taguchi method has been carried out to determine the optimum processing parameters and the most significant factors towards the volumetric shrinkage. Six processing parameters were selected and three levels were considered for each parameter. L18 Taguchi orthogonal array was used to in the design the numerical experiment. The processing parameters used are melting temperature, injection time, cooling time, filling time, injection pressure and filling pressure. The findings of this study revealed the set of optimal processing parameters is A1 (334 °C), B1 (0.16 s), C3 (50 s), D3 (20 s), E3 (18.65 MPa) and F3 (90 MPa) (trial run 3). The setting of optimum condition is 334 °C of melting temperature, 0.16 s of injection time, cooling time with 50, 20 s of filling time, injection pressure with 18.65 and 90 MPa of filling pressure. Moreover, melting temperature, cooling time and filling pressure were found to be the most significant factors that influence the volumetric shrinkage of the lamp holder. This study is expected to be extended to investigate other responses such as sink mark and warpage in the future work. Acknowledgements The authors gratefully acknowledge the financial support from UniMAP.

References 1. Altan M (2010) Reducing shrinkage in injection moldings via the Taguchi, ANOVA and neural network methods. Mater Des 31(1):599–604 2. Huang C-T, Hsu Y-H, Chen B-S (2019) Investigation on the internal mechanism of the deviation between numerical simulation and experiments in injection molding product development. Polym Testing 75:327–33622

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3. Rosli MU, Ikman Ishak M, Riduan Jamalludin M, Khor CY, Nawi MAM, Mohamad Syafiq AK (2019) Simulation-based optimization of plastic injection molding parameter for aircraft part fabrication using response surface methodology (RSM). IOP Conf Ser Mater Sci Eng 551(1):012108 4. Li K, Yan S, Zhong Y, Pan W, Zhao G (2019) Multi-objective optimization of the fiberreinforced composite injection molding process using Taguchi method, RSM, and NSGA-II. Simul Model Pract Theory 91:69–8255 5. Sreedharan J, Jeevanantham AK (2018) Optimization of injection molding process to minimize weld-line and sink-mark defects using Taguchi based grey relational analysis. Mater Today Proc 5(5-Part 2):12615–12622 6. Prasad Kumar B, Venkataramaiah P, Siddi Ganesh J (2019) Optimization of process parameters in injection moulding of a polymer composite product by using GRA. Mater Today Proc 18(7):4637–4647 7. KC B, Faruk O, Agnelli J, Leao A, Tjong J, Sain M (2016) Sisal-glass fiber hybrid biocomposite: optimization of injection molding parameters using Taguchi method for reducing shrinkage. Compos Part A Appl Sci Manuf 83:152–159 8. Wang Y, Kim J, Song J (2014) Optimization of plastic injection molding process parameters for manufacturing a brake booster valve body. Mater Des 56:313–317 9. Oliaei E, Heidari BS, Davachi SM, Bahrami M, Davoodi S, Hejazi I, Seyfi J (2016) Warpage and shrinkage optimization of injection-molded plastic spoon parts for biodegradable polymers using Taguchi, ANOVA and artificial neural network methods. J Mater Sci Technol 32(8):710– 720 10. Oktem H, Erzurumlu T, Uzman I (2007) Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part. Mater Des 28(4):1271–1278 11. Othman M, Hasan S, Khamis S, Ibrahim M, Amin S (2017) Optimisation of injection moulding parameter towards shrinkage and warpage for polypropylene-nanoclay-gigantochloa scortechinii nanocomposites. Proc Eng 184:673–680 12. Singh G, Verma A (2017) A Brief Review on injection moulding manufacturing process. Mater Today Proc 4(2):1423–1433 13. Khor CY, Abdullah MK, Abdullah MZ, Abdul Mujeebu M, Ramdan D, Majid MFMA, Ariff ZM (2010) Effect of vertical stacking dies on flow behavior of epoxy molding compound during encapsulation of stacked-chip scale packages. Heat Mass Transf 46(11–12):1315–1325 14. Khor CY, Abdullah MZ, Lau C-S, Leong WC, Abdul Aziz MS (2014) Influence of solder bump arrangements on molded IC encapsulation. Microelectron Reliab 54(4):796–807 15. Ong EES, Abdullah MZ, Khor CY, Loh WK, Ooi CK, Chan R (2014) Fluid-structure interaction analysis on the effect of chip stacking in a 3D integrated circuit package with through-silicon vias during plastic encapsulation. Microelectron Eng 113:40–49 16. Ramdan D, Abdullah MZ, Khor CY, Leong WC, Loh WK, Ooi CK, Ooi RC (2012) Fluid/Structure interaction investigation in PBGA packaging. IEEE Trans Compon Packag Manuf Technol 2(11):1786–1795 17. Najib AM, Abdullah MZ, Khor CY, Saad AA (2015) Experimental and numerical investigation of 3D gas flow temperature field in infrared heating reflow oven with circulating fan. Int J Heat Mass Transf 87:49–58 18. Lau CS, Khor CY, Soares D, Teixeira JC, Abdullah MZ (2016) Thermo-mechanical challenges of reflowed lead-free solder joints in surface mount components: a review. Soldering Surf Mount Technol 28(2):41–62 19. Aziz MSA, Abdullah MZ, Khor CY, Fairuz ZM, Iqbal AM, Mazlan M, Rasat MSM (2015) Thermal fluid-structure interaction in the effects of pin-through-hole diameter during wave soldering. Adv Mech Eng 6:275735 20. Khor CY, Abdullah MZ (2013) Analysis of fluid/structure interaction: influence of silicon chip thickness in moulded packaging. Microelectron Reliab 53(2):334–347 21. Ong EES, Abdullah MZ, Khor CY, Loh WK, Ooi CK, Chan R (2012) Analysis of encapsulation process in 3D stacked chips with different microbump array. Int Commun Heat Mass Transf 39(10):1616–1623

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22. Rosli MU, Ariffin MKA, Sapuan SM, Sulaiman S (2013) Integrating TRIZ and AHP: a MPV’s utility compartment improvement design concepts. Int J Mater Mech Manuf 1:32–35 23. Khan B, Rosli MU, Jahidi H, Ishak MI, Zakaria MS, Jamalludin MR, Khor CY, Faizal WM, Rahim WM, Nawi MAM (2017) Effect of zinc addition on the performance of aluminium alloy sacrificial anode for marine application. AIP Conf Proc 1885:020074 24. Chien RD, Chen S-C, Lee P-H, Huang J-S (2004) Study on the molding characteristics and mechanical properties of injection-molded foaming polypropylene parts. J Reinf Plast Compos 23(4):429–444 25. Khor CY, Abdullah MZ (2012) Optimization of IC encapsulation considering fluid/structure interaction using response surface methodology. Simul Model Pract Theor 29:109–122 26. Leong WC, Abdullah MZ, Khor CY (2013) Optimization of flexible printed circuit board electronics in the flow environment using response surface methodology. Microelectron Reliab 53(12):1996–2004

The Effect of Parameters of Electrical Discharge Coatings on the Tool Electrode Erosion and Maximum Height Roughness on NiTi Alloy A. F. Mansor, A. I. Azmi, M. Z. M. Zain, and R. Jamaluddin

Abstract This paper presents the influence of electrical discharge coatings parameters on the material loss due to the tool electrode erosion (MLTE) and the maximum height roughness, Rz on the NiTi alloy substrate. Five parameters were investigated; namely polarity, discharge duration, peak current, pulse interval and gap voltage. The experimental study was carried out using 2-level factorial design and analyzed using analysis of variance (ANOVA). The analysis results showed that the discharge duration dominates the effect on MLTE and Rz up to 39.39 and 72.41%, respectively. Then, this followed by the peak current at 15.52 and 4.63%, respectively. Furthermore, several interactions between discharge duration with other parameters were also significant on the model for both responses. Higher MLTE and Rz were recorded during high discharge duration and peak current due to the impact of increasing the discharge energy. Keywords Electrical discharge coatings · NiTi alloy · Material loss of tool electrode · Maximum height of roughness

1 Introduction Nickel-titanium alloy is an intermetallic alloy composed of nearly equal atomic element of nickel and titanium. This alloy is categorized as a smart alloy due to its unique shape memory effect and super-elasticity property that capable of reverting back to its initial shape after external deformation. Thus, this advantage enhances the use of the alloy in several key industries such as in civil, aerospace and biomedical fields. Furthermore, this alloy also exhibits low elastic modulus and good biocompatibility that is imperatively use in interventional, dental and orthopedic implants [1]. Unfortunately, the high content of nickel element in this alloy can be harmful to human body if released due to corrosion or abrasion. The nickel ions can be dangerous that attributed to inflammatory reactions and carcinogens [2]. Thus, it is A. F. Mansor · A. I. Azmi (B) · M. Z. M. Zain · R. Jamaluddin Faculty of Mechanical Engineering Technology, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_28

331

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a great concern to effectively reduce the ion release during the implantation period and increase the feasibility of using this alloy. There are several novel techniques that were employed to develop a coating barrier between the implant material and human bodies such as through chemical diamond deposition (CVD), physical vapor deposition (PVD), and ion implantation. However, the techniques consume high operation cost with complicated procedures and machineries [3]. Fortunately, the application of electrical discharge coatings (EDC) is seen to overcome this issue. The EDC process is the adaption operation from the electrical discharge machining (EDM) that implements repetitive electrical pulses to generate heat energy on the workpiece. Conceptually, the workpiece material is eroded and the debris is flushed away from the machining gap. However, for EDC application, the eroded material is kept or maintained by avoiding the flushing function in order to intensify the re-solidification of the molten material on the substrate surface. Figures 1 and 2 depict the schematic of electrical discharge pulse and EDC mechanism, respectively. At phase I, an open gap voltage generates an electrical field between Ti electrode and NiTi alloy. Initial electrons start to accelerate, collide and ionize neutral molecules of dielectric fluids (deionized water). The collisions generate a large number of electrons in the gap that resulted in the electron avalanche. Then, in phase II, a discharge spark generates a plasma channel during the discharge duration at high temperature in the range of 8000 to 12,000 °C [4]. Consequently, localized

Fig. 1 Schematic of electrical discharge pulse of EDM

The Effect of Parameters of Electrical Discharge Coatings …

333

Fig. 2 Schematic of EDC mechanisms

melt pools are developed on the Ti electrode and NiTi alloy. The molten debris from the Ti electrode migrate to the melt pool of NiTi alloy, combine with oxygen from the deionized water breakdown, cool rapidly and develop a new coating layer on the surface. At the end of the discharge as shown in phase III, the electrical strength of the deionized water is recovered and ready for the next pulse. The influence of the EDC parameters on material loss of tool electrode and surface roughness to metal matrix composite has been reported by P. Prasanna, et al. [5]. They found that current has significant effect on both responses. A. Tiwary, et al. [6] and M. Kolli, et al. [7] also found similar results after performed EDM process on Ti6Al-4V alloy. Meanwhile, R. Tyagi, et al. [8] investigated the parameter effects of EDC process to the tool wear rate by using green compact tool. They concluded that the tool wear rate increases with the increase of current and duty cycle [pulse duration/(pulse duration + pulse interval)] due to the discharge energy generation. Moreover, H. Sabouni, et al. [9] stated that the tool wear rate increased with the expansion of plasma channel during the process. The plasma channel expansion has direct correlation with the increasing of the discharge duration. In this study, the effect of the EDC parameters on the material loss of tool electrode and maximum height of roughness, Rz are reported based on statistical analysis using analysis of variance (ANOVA).

2 Material, Equipment and Procedure The experimental was carried out using the EA8 Mitsubishi die sinker machine. A fabricated reservoir was utilized to hold 200 ml of deionized water as the dielectric fluid and also functional as a fixture to the substrate material. The deionized water was circulated using two stirrers to increase the dielectric strength between electrode

334

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gap during the process. The material substrate was a medical grade of nickel-titanium shape memory alloy accordance to ASTM F2063-12 with dimension of 70 × 70 × 5 mm. Grade II of pure titanium rod (dia. 10 × 8 mm) was used as the tool electrode, that was attached on a titanium rod as a holder using copper adhesive film. The maximum height roughness, Rz of the deposited surface was measured using Handysurf E-35B tester. The Rz data of eight locations on the substrate surface were measured and the average value was taken for each trial. Bruker D2 Phaser and Xoptron X80 series optical microscope were utilized to identify the XRD profile of the substrate and capture single crater images, respectively. The experimental work was designed using a 2-level of full factorial with replication of center points. The parameters were polarity, discharge duration, pulse interval, peak current and gap voltage. The effect of the parameters was investigated from the variance analysis (ANOVA) on the material loss weight of tool electrode and the maximum height of roughness, Rz. The data of material loss weight was collected from the different weight measurement of the tool electrode before and after of each trial. Meanwhile, the maximum height roughness, Rz was measured under 0.8 mm of cut-off length with sampling length of 4.0 mm. The Rz roughness was taken in order to have better understanding of the surface texture by monitoring the distance between the peaks and valley of the roughness profile.

3 Results and Discussion 3.1 Recast Layer Formation The EDC process enhances the substrate surface from the deposition of hard alloying material. Figure 3 depicts the SEM image of recast layer and the material phase under the XRD profile. The XRD profile demonstrates that the formation of oxide coating (TiO) on the substrate under low operating condition, as shown in Fig. 3(b). TiO (ICCD: 01-072-0020) peaks are appeared on the profile between the NiTi peaks. This composition comprises oxygen from water breakdown and titanium that was transferred from the erosion of electrode or substrate. The high temperature of the plasma channel insists the absorption of the oxygen gas to form the oxide coating [10]. In Fig. 3(a), a recast layer can be observed on the substrate surface after the EDC process. The irregularity of the recast layer formation deteriorates the surface roughness of the substrate which depend on the operating condition of the process. Table 1 presents the experimental layout with measurement data of the material loss of tool electrode (MLTE) and the maximum height roughness, Rz. Analysis on the percentage contribution of each parameter to the responses is depicted in Fig. 4. The graph shows that discharge duration dominates the contribution up to 39.39 and 72.41% on the MLTE and Rz, respectively, and this followed by peak current at 15.52 and 4.63%. Likewise, the MLTE and Rz are marginally influenced by pulse interval and gap voltage at 6.00 and 2.97%, respectively. All other parameters are

The Effect of Parameters of Electrical Discharge Coatings …

335

Fig. 3 Recast layer formation on NiTi alloy under low operating condition, a SEM image of cross-sectioned substrate, b XRD profile of the substrate

336

A. F. Mansor et al.

Table 1 Experimental layout and measurement data Std. no.

A-Polarity

B-Discharge duration, µs

C-Pulse interval, msec

D-Peak current, Amp

E-Gap voltage, Volt

Material loss Max. of tool height electrode, g roughness (Rz), µm

1

Straight

50

6

3

80

0.05

8.12

2

Reverse

50

6

3

80

0.01

11.52

3

Straight

540

6

3

80

0.02

23.49

4

Reverse

540

6

3

80

0.03

20.86

5

Straight

50

8

3

80

0.00

10.73

6

Reverse

50

8

3

80

0.01

13.76

7

Straight

540

8

3

80

0.04

20.98

8

Reverse

540

8

3

80

0.04

19.35

9

Straight

50

6

9

80

0.02

12.78

10

Reverse

50

6

9

80

0.01

13.54

11

Straight

540

6

9

80

0.14

21.38

12

Reverse

540

6

9

80

0.23

22.84

13

Straight

50

8

9

80

0.01

13.12

14

Reverse

50

8

9

80

0.01

15.46

15

Straight

540

8

9

80

0.16

22.12

16

Reverse

540

8

9

80

0.23

24.90

17

Straight

50

6

3

260

0.01

6.37

18

Reverse

50

6

3

260

0.16

9.50

19

Straight

540

6

3

260

0.03

19.05

20

Reverse

540

6

3

260

0.06

19.91

21

Straight

50

8

3

260

0.00

5.73

22

Reverse

50

8

3

260

0.00

8.07

23

Straight

540

8

3

260

0.05

22.06

24

Reverse

540

8

3

260

0.06

16.54

25

Straight

50

6

9

260

0.02

26

Reverse

50

6

9

260

0.25

13.8

27

Straight

540

6

9

260

0.22

26.7

28

Reverse

540

6

9

260

0.32

21.84

29

Straight

50

8

9

260

0.01

7.62

30

Reverse

50

8

9

260

0.01

8.91

31

Straight

540

8

9

260

0.23

28.24

32

Reverse

540

8

9

260

0.25

23.84

33

Straight

295

7

6

170

0.05

12.00

34

Reverse

295

7

6

170

0.01

11.45

7.47

(continued)

The Effect of Parameters of Electrical Discharge Coatings …

337

Table 1 (continued) A-Polarity

B-Discharge duration, µs

C-Pulse interval, msec

D-Peak current, Amp

E-Gap voltage, Volt

Material loss Max. of tool height electrode, g roughness (Rz), µm

35

Straight

295

7

6

170

0.03

11.20

36

Reverse

295

7

6

170

0.01

11.05

37

Straight

295

7

6

170

0.07

11.05

38

Reverse

295

7

6

170

0.03

9.82

Percentage of contribution (%)

Std. no.

100 90 80 70 60 50 40 30 20 10 0

72.41

39.39 15.52 6

2.34 0.57 A-Polarity

0.002

4.63

1.49 2.97

B-Discharge C-Pulse interval D-Peak current E-Gap voltage duration Parameters

Material loss of tool electrode

Max. Height of Roughness, Rz

Fig. 4 Percentage contribution of parameters on the responses

Table 2 Effect tendency of the EDC parameters Response

A-Polarity

B-Discharge duration

Material loss of tool electrode

Slight higher on reverse

Positive effect Negative (significant) effect (significant)

Max. height of Slight higher roughness, Rz on reverse

C-Pulse interval

D-Peak current

E-Gap voltage

Positive effect Positive effect (significant)

Positive effect Positive effect Positive effect Negative (significant) (significant) effect (significant)

deemed insignificant to the both response based on the ANOVA analysis due to low percentage of contribution. The tendency of effects of each parameter is shown in Table 2. Most of the parameters have positive effect on the both responses. Only pulse interval and gap voltage have negative effect on MLTE and Rz, respectively. Despite the insignificant effect of polarity on both responses, this parameter showed a slightly higher tendency on the reverse polarity. In general, the results indicated that the erosion on the tool electrode and the surface irregularity are highly correlated with the EDC parameters

338

A. F. Mansor et al.

that influence the intensity of discharge energy. The discharge duration and peak current have direct contribution to the discharge energy. Although the increase in the both parameters have enhanced the material erosion on the tool electrode but it deteriorated the substrate surface quality. A lower MLTE is recorded when increase the pulse interval. A higher setting of pulse interval can lead to the decrease of duty cycle in a pulse, thus restricted the amount of material erosion during an operating period. On other hand, the gap voltage affected the Rz through manipulation of the gap distance between the tool electrode and the substrate surface. By increase the gap voltage, the gap distance is enlarged, thus it reduced the impact of the sparking discharge on the substrate surface. Hence, less excessive of material erosion occurs and superior surface roughness can be obtained.

3.2 Material Loss Weight of Tool Electrode Table 3 presents the ANOVA output of the reduced model for MLTE. The significant level of the analysis is below 0.05. The model is significantly acceptable for further prediction. The lack of fit is insignificant that indicates the data is adequately fitted. Moreover, a transformation to natural log (lambda = 0, k = 0.0032) is required to improve the model plots for normal shape curve. Figure 5 illustrates the effect of significant interactions of parameters on the MLTE. Figure 5(a) shows a different effect of the responses when the pulse interval was increased during at low and high discharge durations. Almost a stagnant trend is observed when the discharge duration at high level. The influence of the pulse Table 3 ANOVA output of reduced model for material loss weight on the tool electrode Source

Sum of square

df

Model

46.17

5

B-Discharge duration

24.61

C-Pulse interval

Mean square

F-value

p-value Prob > F

9.23

19.41

50,000 In the condition I, if all the criteria met, LED light up and the motor is off. Figure 4 shows Condition II. • Leakage threshold > 1000

466

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Fig. 3 Condition I

Fig. 4 Condition II

• Temperature < 30 °C • Gyroscope X > 50,000 Condition II make red LED light up and motor turning on. Figure 5 shows Condition III. Leakage threshold > 1000 Temperature > 30 °C Gyroscope X > 50,000 Figure 5 shows Condition III. If all the criteria met, the red LED on the board light up and motor turning on. Figure 6 shows Condition IV. • Leakage threshold < 1000

Smart Kitchen Model Using Nuvotun Development Board…

467

Fig. 5 Condition III

Fig. 6 Condition IV

• Temperature < 30 °C • Gyroscope X < 50,000 Condition IV occurs as all the criteria in Fig. 6 met. In this condition, LCD backlight is turning off. The designed project Smart Kitchen can detect three different conditions that could seriously endanger the safety of the users. The threshold value of detected gas and the temperature level lowered to 1000 and 30 °C. But in fact, these threshold values should be far higher in the real gas leakage and fire scene. Therefore, the Smart Kitchen actually can also be a home automation system which can help to regulate

Temperature, °C

0, to satisfy the Hurwitz condition. Then tracking error and its derivative value is defined as: e = xd − x e˙ = x˙d − x˙ e¨ = x¨d − x¨

(19)

s˙ = e¨ + λe˙

(20)

Derivation of (18) gives

Replacing (19c) and (8) into (20) gives ˙ − s˙ ) U = g1 (x¨d − f + λ(x˙d − x)

(21)

478

A. Noordin et al.

For the reduction of high frequency chattering, s˙ can be replaced by continuous tanh function as follows: s˙ = −ηs − k.tanh

s , η > 0, k > 0,  > 0 

(22)

where −ηs is an exponential term, and its solution is s = s(0)e−ηt . Clearly, by adding the proportional rate term −ηs, the state is forced to approach the switching manifolds faster when s is larger. The steepness of the tanh function determines how the tanh can approximate the sign function. The hyperbolic tangent function is defined as: tanh

x 

=

x

x

e  −e−  x x e  +e− 

,>0

(23)

where, the steepness of the tanh function is determined by  value [28]. From (21), the control law for altitude and attitude using tanh function can be described as follows:

m sz z¨ d − g + λz (˙z d − z˙ ) + ηz sz + k z tanh U1 = uz z

 1 sφ φ¨ d − a1 ψ˙ θ˙ − a2 θ˙ + λφ φ˙ d − φ˙ + ηφ sφ + kφ tanh U2 = b1 φ

 1 sθ θ¨d − a3 ψ˙ φ˙ − a4 d φ˙ + λθ θ˙d − θ˙ + ηθ sθ + kθ tanh U3 = b2 θ

 1 sψ ψ¨ d − a5 θ˙ φ˙ + λψ ψ˙ d − ψ˙ + ηψ sψ + kψ tanh (24) U4 = b3 ψ For SMC using reaching law, (21) can be written as: m (¨z d − g + λz (˙z d − z˙ ) + k z sgn(sz )) uz   1 φ¨d − a1 ψ˙ θ˙ − a2 θ˙ + λφ φ˙ d − φ˙ + kφ sgn sφ U2 = b1  1 U3 = θ¨d − a3 ψ˙ φ˙ − a4 d φ˙ + λθ θ˙d − θ˙ + kθ sgn(sθ ) b2   1 ψ¨ d − a5 θ˙ φ˙ + λψ ψ˙ d − ψ˙ + kψ sgn sψ U4 = b3 U1 =

For SMC using exponential reaching law (21) can be written as: m (¨z d − g + λz (˙z d − z˙ ) + k z sgn(sz ) + ηz sz ) uz   1 φ¨ d − a1 ψ˙ θ˙ − a2 θ˙ + λφ φ˙ d − φ˙ + kφ sgn sφ + ηφ sφ U2 = b1

U1 =

(25)

Sliding Mode Control with Tanh Function for Quadrotor UAV …

 1 θ¨d − a3 ψ˙ φ˙ − a4 d φ˙ + λθ θ˙d − θ˙ + kθ sgn(sθ ) + ηθ sθ b2   1 U4 = ψ¨ d − a5 θ˙ φ˙ + λψ ψ˙ d − ψ˙ + kψ sgn sψ + ηψ sψ b3

479

U3 =

(26)

For SMC using saturation function (21) can be written as:

m s z¨ d − g + λz (˙z d − z˙ ) + k z sat uz φb

 1 sφ φ¨ d − a1 ψ˙ θ˙ − a2 θ˙ + λφ φ˙ d − φ˙ + kφ sat U2 = b1 φb

 1 sθ ˙ ¨ ˙ ˙ ˙ ˙ U3 = θd − a3 ψ φ − a4 d φ + λθ θd − θ + kθ sat b2 φb

 1 sψ ψ¨ d − a5 θ˙ φ˙ + λψ ψ˙ d − ψ˙ + kψ sat U4 = b3 φb

U1 =

(27)

3.1 Stability Analysis For stability, Lyapunov function V = 21 s 2 is chosen. Thus, we get V˙ = s s˙

(28)

Replace (22) into (28) we get   s  V˙ = s −ηs − k.tanh s   = −k.s.tanh − ηs 2 

(29)

For stability V˙ < 0, therefore (28) can be split into two terms and can be described as V˙ = V˙1 + V˙2

(30)

For the first term of (30) V˙1 = −k.s.tanh

s 

(31)

According to Lemma 1.1, we have    |s|tanh s  ≥ 0

(32)

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For the second term of (30) V˙2 = −ηs 2

(33)

Replace s 2 = 2V into (33) gives V˙2 = −η ∗ 2V2 = −2ηV2

(34)

Use Lemma 1.2, and choose α = 2η and f = 0; we obtained V2 (t) ≤ e−2η(t−t0 ) V2 (t0 )

(35)

Therefore, if η is a positive constant value,V2 (t) tends to be zero exponentially with the value of η. Therefore, when V˙1 ≤ 0 and V˙2 ≤ 0, it proves that V˙ ≤ 0 guarantees the stability of the system.

4 Simulation Results Table 1 shows the quadrotor parameters [27] used in this simulation. During the simulation, for SMC with tanh function, the parameters k and η are choosen due to the larger parameter values than λ and  as shown in Table 2. Then, the quadrotor model is simulated in MATLAB Simulink using the parameters in Tables 1 and 2. In this simulation, the altitude z is set at 10 m with initial condition zero (ground level). The initial roll, pitch and yaw are set as η = [0.2, 0.2, 0.2] (radian), respectively for attitude stabilization observation. This roll, pitch and yaw should converge to zero during stabilization. Two simulations were conducted to investigate the quadrotor performances without and with the presence of parameter uncertainties and external disturbances. Table 1 Parameters’ for ×-mode configuration quadrotor

Specification

Parameter

Unit

Value

Quadrotor mass

m

kg

1.033

Lateral moment arm

l

m

0.225

b

N s2

2.8625 × 10–7

Drag coefficient

d

N ms 2

4.4212 × 10–10

Rolling moment of inertia

Ix x

kgm 2

0.0183

Pitching moment of inertia

I yy

kgm 2

0.0183

Yawing moment of inertia

Izz

kgm 2

0.0385

Thrust coefficient

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Table 2 SMC tanh parameters Parameter

Roll (φ)

Pitch (θ)

Yaw (ψ)

Altitude (z)

k

10

10

10

20

λ

2

2

2

2

η

8

8

8

15



0.5

0.5

0.5

0.15

Fig. 4 Output for altitude z without the presence of parameter uncertainties and external disturbance

The simulation results show the attitude stabilization for the roll, pitch, yaw and the altitude z for the height. In these results, four (4) methods of SMC law were compared, and the system performance based on the integral square error (ISE) is tabulated in Table 3. Method 1 represents SMC tanh control law, Method 2 represents SMC using general reaching law, Method 3, represents SMC using exponential reaching law, and Method 4 represents SMC using saturation function. All control inputs U1 , U2 , U3 , and U4 are also provided without and with the presence of parameter uncertainties and external disturbances. Simulation results of altitude z are shown in Fig. 4, where the height of 10 m can be reached within 4 to 5 s by Method 1 to 4. However, referring to Table 3, Method 1 shows the best

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Table 3 Integral square error comparison between SMC law φ

θ

ψ

33.93

0.00934

0.01596

0.01152

86.29

0.05031

0.06144

0.12140

151.70

0.05025

0.00710

0.03025

0.01601

0.02497

0.01473

z Method 1

Tanh function

Method 2

Reaching law [16–19]

Method 3

Exponential reaching law [20–23]

Method 4

Saturation function [15]

203.40

Fig. 5 Output for attitude roll, pitch and yaw without the presence of parameter uncertainties and external disturbance

transient responses with less ISE (33.93). Figure 5 shows attitude output without the presence of parameter uncertainties and external disturbance. Method 1 shows consistent transient responses for φ, θ , and ψ with settling time about 2 s. In addition, as refer to Table 3, performance index of φ, θ , and ψ Method 1 produce small ISE compared to other Method 2 – 4.

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Fig. 6 Control Input for SMC using tanh function (Method 1)

Figures 6, 7, 8 and 9 show the control inputs for Method 1 to 4 where SMC control inputs with tanh function (Method 1) provided better signal without being affected by chattering phenomenon compare to other Method 2 to 4. The inputs control signal, as shown in Fig. 6, converges faster within 1 s. Since Method 2 and Method 3, both bases on reaching law technique, the input control signal display the same shapes as shown in Figs. 7 and 8, respectively. However, Method 3 provides small improvement due to exponential terms where the state is forced to approach the switching manifolds faster. The differences can be seen in Table 3, as ISE for φ, θ , and ψ for Method 3 is smaller compare to Method 2. The inputs control signal in Fig. 9 used SMC with saturation function (Method 4) where the control signal is bounded in a certain range of boundary layer, which can reduce chattering. In order to test the robustness of the system with SMC tanh control law, the simulation was continued with the presence of parameter uncertainties and external disturbance. As per described in (17), w is lumped of uncertainties and defined as w = f + gu + d. Hence, for f , and g, the system is tested with 10, 20, 30 and

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Fig. 7 Control Input for SMC using reaching law (Method 2)

40% adjustments in parameter uncertainties and the model external disturbances, d is assumed to be normal Gaussian noise [16] described as follows: d = [N (0, 0.5)N (0, 0.5)N (0, 0.5)N (0, 0.5)]

(36)

Figure 10 shows the altitude z response tested with 10, 20, and 40% changes in parameter uncertainties and Gaussian noise as external disturbances. Clearly shows, by using SMC with tanh function (Method 1), the altitude can converge within 3 s with 0.1 steady-state error when 40% changes of parameter uncertainties and external disturbances are applied to the system. Figure 11 shows attitude roll, pitch and yaw responses throughout 10, 20, and 40% changes of parameter uncertainties and Gaussian noise as external disturbances, respectively. Clearly shows with Method 1, even though with parameter uncertainties and external disturbance, φ, θ , and ψ can converge within 2 s. Hence, the response shows that control law using SMC with tanh function (Method 1) has

Sliding Mode Control with Tanh Function for Quadrotor UAV …

485

Fig. 8 Control Input for SMC using exponential reaching law (Method 3)

greater robustness compared to up to 40% changes of parameter uncertainties and external disturbances.

486

Fig. 9 Control Input for SMC using saturation function (Method 4)

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Fig. 10 Altitude Z with the presence of parameter uncertainties and external disturbance by Method 1

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Fig. 11 Attitude roll, pitch and yaw angle with the presence of parameter uncertainties and external disturbance by Method 1

5 Conclusion In this paper, a control law using the tanh function of the SMC is proposed to improve the control performance of quadrotor UAV for altitude and attitude stabilization. In the simulation, the SMC using tanh function performances is compared to others SMC control laws such as, reaching law, exponential reaching law and saturation function. All SMC control techniques show the ability to stabilize quadrotor at the desired altitude (z), and the desired attitude (φ, θ , ψ) control without or with the presence of parameter uncertainties and external disturbances. However, the proposed SMC using the tanh function shows greatest robustness without being affected by the chattering phenomenon. Acknowledgements The authors would like to thank Universiti Teknologi Malaysia (UTM) under the Research University Grant (R.J130000.2651.17J42), Universiti Teknikal Malaysia Melaka (UTeM), and Ministry of Education Malaysia for supporting this research.

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Appendix Lemma 1.1: [28, 29] For every given scalar x and positive scalar , the following inequality holds: x.tanh

x 

      = x.tanh x  = |x|tanh x  ≥ 0

Lemma 1.1 can be proved as follows: according to the definition of tanh function, we have x. tanh

x 

x

−x

= s e x −e− x =

1 x x e2  +1

e +e



x e2  − 1

Since x e2  − 1 ≥ 0 i f x ≥ 0 2 x e −1 < 0 ifx < 0 Then   x x e2  − 1 ≥ 0 Therefore x. tanh

x  

=

 x  1 x e2  − 1 ≥ 0 e +1 2 x

And x. tanh

x  

   x   x      = x.tanh  = |x|tanh ≥0  

Lemma 1.2, [28, 30] Let f , V : [0, ∞] ∈ R, then V˙ ≤ −αV + f , ∀t ≥ t0 ≥ 0 implies that t

V (t) ≤ e−α(t−t0 ) V (t0 ) + ∫ e−α(t−τ ) f (τ )dτ t0

For any finite constant α. According to [28, 30], we have the proof as follows: Let ω(t)  V˙ + αV − f , we have ω(t) ≤ 0, and V˙ = −αV + f + ω Implies that t

t

t0

t0

V (t) = e−α(t−t0 ) V (t0 ) + ∫ e−α(t−τ ) f (τ )dτ + ∫ e−α(t−τ ) ω(τ )dτ

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Because ω(t) < 0 and ∀t ≥ to ≥ 0, we have t

V (t) = e−α(t−t0 ) V (t0 ) + ∫ e−α(t−τ ) f (τ )dτ t0

Moreover, if we choose f = 0, then we have V˙ ≤ −αV , implies that V (t) = e−α(t−t0 ) V (t0 ) If α is a positive constant value, V (t) will tend to zero exponentially with α value.

References 1. Ajmera J, Sankaranarayanan V (2016) Point-to-point control of a quadrotor: theory and experiment. In: IFAC-international federation of automatic control, pp 401–406 2. Bazin J, Fields T, Smith AJ (2016) Feasibility of in-flight quadrotor individual motor thrust measurements. In: AIAA atmospheric flight mechanics conference, pp 1–12 3. Deepak BBVL, Singh P (2016) A survey on design and development of an unmanned aerial vehicle (quadcopter). Int J Intell Unmanned Syst 4:70–106 4. Števek J, Fikar M (2016) Teaching aids for laboratory experiments with AR.Drone2 quadrotor. IFAC-PapersOnLine 49:236–241 5. Gaitan AT, Bolea Y (2013) Modeling and robust attitude control of a quadrotor system. In: 10th international conference on electrical engineering, computing science and automatic control, pp 7–12 6. Bai Y, Liu H, Shi Z, Zhong Y (2012) Robust control of quadrotor unmanned air vehicles. In: Proceedings - 2012 31st Chinese control conference, pp 4462–4467 7. Liu H, Yu T, Yu Y (2016) Robust trajectory tracking control for quadrotors with uncertainties and delays. In: Proceedings 35th Chinese control conference, pp 2998–3001 8. Bouabdallah S, Murrieri P, Siegwart R et al (2004) Design and control of an indoor micro quadrotor. In: Proceedings 2004 IEEE international conference on robotics and automation, New Orleans, LA, April 2004, pp 4393–4398 9. Garc RA (2012) Robust PID control of the quadrotor helicopter. IFAC Conference on Advances in PID Control, PID 12 Brescia (Italy) 2:10–15 10. Zeng Y, Jiang Q, Liu Q, Jing H (2012) PID vs. MRAC control techniques applied to a Quadrotor’s attitude. In: Proceedings of the 2012 2nd international conference on instrumentation, measurement, computer, communication and control, IMCCC 2012 11. Ahmed N, Chen M (2018) Sliding mode control for quadrotor with disturbance observer. Adv Mech Eng 10:1–16 12. Babaie R, Ehyaei AF (2017) Robust control design of a quadrotor UAV based on incremental hierarchical sliding mode approach. In: Iranian conference on electrical engineering, pp 835– 840 13. Dey P (2016) Robust attitude control of quadrotor using sliding mode. In: International conference on automatic control and dynamic optimization techniques, pp 268–272 14. Jayakrishnan HJ (2016) Position and attitude control of a quadrotor UAV using super twisting sliding mode. IFAC-PapersOnLine 49:284–289 15. Runcharoon K, Srichatrapimuk V (2013) Sliding mode control of quadrotor. In: 2013 the international conference on technological advances in electrical, electronics and computer engineering, pp 552–557

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16. Shaik MK, Whidborne JF (2016) Robust sliding mode control of a quadrotor. In: UKACC 11th international conference on control, pp 1–6 17. Xiong J-J, Zhang G (2016) Discrete-time sliding mode control for a quadrotor UAV. Opt - Int J Light Electron Opt 127:3718–3722 18. Mercado D, Castillo P, Castro R, Lozano R (2014) 2-sliding mode trajectory tracking control and EKF estimation for quadrotors. IFAC Proc Vol (IFAC-PapersOnline) 47:8849–8854 19. Ganzalez I, Salazar S, Lozano R, Escareno J (2013) Real-time altitude robust controller for a quad-rotor aircraft using sliding-mode control technique. In: International conference on unmanned aircraft systems, pp 650–659 20. Sudhir SA (2016) Second order sliding mode control for quadrotor. In: 2016 IEEE first international conference on control, measurement and instrumentation, pp 92–96 21. Sumantri B, Uchiyama N, Sano S (2016) Least square based sliding mode control for a quadrotor helicopter and energy saving by chattering reduction. Mech Syst Signal Process 66– 67:769–784 22. Xiong J, Zhang G (2016) Sliding mode control for a quadrotor UAV with parameter uncertainties. In: 2nd international conference on control, automation and robotics, pp 207–212 23. Zheng E-H, Xiong J-J, Luo J-L (2014) Second order sliding mode control for a quadrotor UAV. ISA Trans 53:1–7 24. Domingos D, Camargo G, Gomide F (2016) Autonomous fuzzy control and navigation of quadcopters. IFAC-PapersOnLine 49:73–78 25. Ghommam J, Luque-Vega LF, Castillo-Toledo B, Saad M (2016) Three-dimensional distributed tracking control for multiple quadrotor helicopters. J Franklin Inst 353:2344–2372 26. Basri MAM, Husain AR, Danapalasingam KA (2014) Enhanced backstepping controller design with application to autonomous quadrotor unmanned aerial vehicle. J Intell Robot Syst Theory Appl 79:295–321 27. Noordin A, Basri MAM, Mohamed Z, Abidin AFZ (2017) Modelling and PSO fine-tuned PID control of quadrotor UAV. Int J Adv Sci Eng Inf Technol 7(4):1367–1373 28. Jinkun L (2017) Sliding mode control using MATLAB. Academic Press, Elsevier 29. Aghababa MP, Akbari ME (2012) A chattering-free robust adaptive sliding mode controller for synchronization of two different chaotic systems with unknown uncertainties and external disturbances. Appl Math Comput 218:5757–5768 30. Ioannou PA, Sun J (1996) Robust Adaptive Control. Prentice-Hall, Hoboken

Intraocular MEMS Capacitive Pressure Sensor Anas Mohd Noor, Zulkarnay Zakaria, and Norlaili Saad

Abstract Microelectromechanical system (MEMS) sensors are suitable for measuring intraocular pressure (IOP). IOP measurement is useful for monitoring diseases such as glaucoma. The average pressure range for healthy persons is within 10–20 mmHg. A pressure beyond this range could damage the eye nerves and causes of blindness. Thus, a sensor for measuring the pressure should provide excellent accuracy and sensitivity. Intraocular capacitive pressure sensors are widely used in measurement of IOP. They offer high sensitivity and low noise, including invariance to temperature. Thus, the capacitive pressure sensor is performed better than other types of sensing methods. In this work, capacitive pressure sensors are designed and analyzed using FEM. The sensitivity and performance of a corrugated diaphragm, slot-type, square, and circular types of sensors designed are analyzed. Different shape of the sensor provides a different characteristic such as sensors pressure sensitivity, mechanical stress, and maximum deflection. As a result, corrugated diaphragm and slot-type sensors designed performs better than the flat diaphragm and non-slotted sensors designed. We show that four slotted non-corrugated square and circular designs have a high sensitivity, which is 0.157 mF higher than the eight slotted design. However, for corrugated design, eight slotted shows sensitivity is 0.147 mF higher and linearity analysis than four slotted sensor design. Circular shape design for eight slotted design, on the other hand, have 0.631 mF higher than the four slotted design. Corrugated design is more sensitive when a load is applied, while slotted design reduces the effect of residual stress and stiffness of the diaphragm. Thus, it is an advantage of using the FEM method for further analysis of sensor performance optimization. Keywords MEMS capacitive pressure sensor · Intraocular pressure

A. Mohd Noor (B) · Z. Zakaria · N. Saad School of Mechatronic Engineering, Universiti Malaysia Perlis, Arau, Perlis, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_42

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1 Introduction Glaucoma is a condition that damages your eye’s optic nerve. The glaucoma disease was first described by Englishman Richard Bannister in 1622. Glaucoma gets worse over time and often linked to a build-up of pressure inside your eye and defined as intraocular pressure (IOP). The increase in pressure is attributed to the buildup of ocular fluid, the aqueous humor, in the anterior chamber of the eye due to increased resistance to the fluid flow in the drainage pathway. This pressure could cause significant damage to the optic nerve tissue. The damage is often caused by abnormally high pressure in the eyes [1, 2]. The typical IOP ranges from 10–20 mmHg for a normal healthy person. Therefore, an early diagnosis, detection, and proper treatment of this disease could prevent the loss of vision. Currently, IOP is measured based on the force required to flatten a fixed area of the cornea gently. The measurement approach could be classified as invasive or non-invasive (non-contact measurement). This conventional technique is called tonometry, where changes in eye pressure are detected or measured. Several tonometry use for the detection is applanation, non-contact, and indentation tonometry [3]. Moreover, most of these methods rely on sensing element such as capacitive, inductive, piezoelectric and resonant type sensor. However, most of the current tonometry devices are using a capacitor as a sensing element [4]. Others sensing methods such as inductive and piezoelectric are having some limitations due to sensors require a sensing circuitry to read the pressure and they are bulky [5]. Furthermore, sensing circuitry such as amplifier can be placed on the external circuit, but suffers from high temperature drift, requiring temperature compensation. The capacitive sensors use a diaphragm and pressure cavity to create a variable capacitor to detect strain due to applied pressure, and capacitance decreases as pressure deform the diaphragm. A microelectromechanical system (MEMS) was introduced in the IOP measurement due to its very low power consumption, reducing temperature effect or sensor drift, and having a high sensitivity, which is suitable for the IOP application. A MEMS IOP sensor, such as capacitive sensor characteristics are depending on the sensor design, including the diaphragm size, sensor thickness, the gap size as well as the material. To develop a suitable MEMS IOP sensor, optimization on the sensor design including shape and suitable material are required. In the current works from many researchers, the designs of MEMS IOP sensor are relied on certain popular shape such as conventional square type sensor. Thus, this will limit the sensor performance of sensor analysis which influenced by many parameters such as sensor shape, sensing types and materials used. This paper is proposed to study the MEMS capacitive sensor for the IOP application. Few designs, such as corrugated diaphragm and shape, are presented. The thickness of the diaphragm is equal to all design to provide a performance analysis. The eight slotted corrugated shape shows a good capacitance sensitivity, among others design. Thus, this study could provide valuable findings for the further design optimization of MEMS IOP sensor.

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2 Methodology 2.1 MEMS IOP Sensor The designs having a three (3) different diaphragm shape which is a corrugated square, non-corrugated (flat) square and non-corrugated circular type. All designs are based on four slotted and eight slotted type. Figure 1 shows the MEMS IOP sensors design. As an Initial design of MEMS IOP, we used the basic design from previous researchers [5]. In the paper, the design was based on square four slotted polysilicon diaphragm with gold plate. The sensor dimensions (length, width and thickness) is set at 550 × 550 × 4.2 µm for upper plate (diaphragm) and 550 × 550 × 2 µm for bottom gold plate and a 50 µm air gap in between the plates (Fig. 2). The sensor is designed for a maximum pressure range of 60 mmHg where this is the typical range of intraocular pressure. This sensor design is used as the initial design whereas, other designs dimensions shown in the Table 1.

(a)

(b)

(c)

(e)

(f)

(d)

Fig. 1 The top view of sensor designs. a Corrugated square with four slotted, b Corrugated square with eight slotted, c Non corrugated square with four slotted, d Non corrugated square with eight slotted, e Non corrugated circular with four slotted, f Non corrugated with eight slotted

Diaphragm

Pressure applied

Poly-silicon

Air gap

Air Bottom plate (gold)

Gold

Fig. 2 Corrugated square shape capacitive pressure sensor [6]. Poly-silicon is used as a material for upper plate (diaphragm) and gold for bottom fixed plate

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Table 1 Sensors dimension and design. All value in µm unit Square * 4 slotted

Square * 8 slotted

Square 4 slotted

Square 8 slotted

Circular 4 slotted

Circular 8 slotted

Length

100

100

100

100

–

–

Width

25

25

25

25

–

–

Diaphragm thickness

4.2

4.2

4.2

4.2

–

–

Sensor area

205 × 205 × 4.2

205 × 205 × 4.3

275 × 275 × 4.2

275 × 275 × 4.2

–

–

Corrugated channel

49

49

–

–

–

–

Width of corrugated

45.8

45.8

–

–

–

–

Radius of slot

–

–

–

–

50

50

Radius of sensor

–

–

–

–

275

275

Dimension

3 Analysis of Capacitive Sensor and Finite Element Method (FEM) Electrical Analysis of Capacitive Sensor. Capacitive pressure sensors measure pressure by detecting changes in electrical capacitance caused by the movement of a diaphragm. A model represents the capacitive pressure sensor (Fig. 2) as shown in Fig. 3. The basic working principle for capacitive pressure sensor is similar to a parallel plate capacitor as  C = εr εo

A D

 (1)

where C is capacitance, Eo is the permittivity of free space (8.854 × 10−14 F/cm), Er is the relative dielectric constant of the material between the plates (which is unity for air), A is effective electrode area, and D is a gap between plates. Capacitance value will vary when pressure is applied and distances between the electrodes also changes by the deflected diaphragm (Fig. 3). Thus, capacitance could be determined by, ¨ C=

ε0 d xd y d − w(x, y)

(2)

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Fig. 3 Cross-sectional view of pressure sensor diaphragm [7]

where w(x,y) is the deflection of the diaphragm. If an effective plate deflection, s is defined as ¨ 1 S= wd xd y (3) A the change in capacitance with pressure could be determined as  C = C0

S d−S



  S ∼ = C0 d

(4)

where C 0 is the zero-pressure capacitance. For deflections which are small compared with h, the effective deflection, s is about one-fourth of the deflection w, and the capacitive sensitivity, Sc can be determined as Sc =

C C0 P

(5)

Mechanical Analysis of Capacitive Sensor. Pressure is determined by the deflection of the diaphragm due to applied pressure. The diaphragm deflects when exposed to an external uniform pressure P, causing a decrease in the air gap that result in an increase in capacitance between the diaphragm and the backplate, d is the distance of air gap, h is the thickness of the diaphragm, The diaphragm side length is 2a (a is the half-side length), Wc is the deflection diaphragm distance at center of the sensor and w at the near edge of the diaphragm. The pressure, P could be determined by,   Wc h4 Wc 2 E  4.2 + 1.58 3 P= h h 1 − v2 a4

(6)

where E is the Youngs’s Modulus and ν is the Poisson’s ratio. In case of large value of initial tension, the pressure can be determined by,

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 P=

4δWc h a2

 (7)

where δ is the initial stress of the diaphragm. The mechanical sensitivity of a diaphragm is defined as: Sm =

dWc dP

(8)

Finite Element Analysis. For the simulations of the actual proposed device, the finite-element method (FEM) becomes necessary since there is a difficulty to apply direct analytical solution, due to the complexity of the structures. The MEMS IOP sensors is analyzed using electrostatic module in the COMSOL Multiphysics® (COMSOL, USA). The simulation study of static and models analysis was conducted by applying pressure in the range of 0–60 mmHg (0–8000 Pa) on the diaphragms which is the typical range of intraocular pressure for normal and abnormal condition [7, 8]. For poly-silicon parameters, we set the density, ρ and the Young’s Modulus of 2330 kg/m3 and 185 GPa respectively.

4 Result and Discussion All the designed sensors are analyzed for the maximum intraocular pressure, 60 mmHg (8 kPa). Figure 5 shows the sensor’s capacitance values at maximum pressure, 8 kPa. The capacitance values are influenced by area and the distance of the two terminals, as shown in Eq. (1). However, the capacitance values change due to the gap between the terminals plate by the diaphragm’s deflection, as shown in Fig. 3. Four slotted non-corrugated capacitance value shows only small changes lower than eight slotted corrugated sensors. For instance, the differences are only about 0.21 fF for non-corrugated square designs (Fig. 5). This is due to the deformation of sensor diaphragm for four slotted non-corrugated sensors is higher than eight slotted, which is 1.79 µm compared to 1.05 µm for eight slotted sensors (Fig. 6). Therefore, all the designs with eight slotted sensors show that the capacitance is slightly higher than four slotted sensors (Fig. 6). The deformation of diaphragm shows four slotted designs with less stiffness than eight slotted designs, including corrugated and non-corrugated designs (Fig. 6). Figure 7 shows the variation of capacitive sensitivity values for different design models. Sensitivity decreases as the separation gap between the parallel plates increases, as shown in Eq. (5). Four slotted non-corrugated design shows higher sensitivity than eight slotted design. The difference is about 0.157 mF for noncorrugated models. However, for corrugated design, eight slotted designs have a higher sensitivity than four slotted designs where the difference is 0.147 mF. On the other hand, the circular designs sensor shows that four slotted design is higher than eight slotted design by 0.631 mF. These capacitances sensitivity varies due to

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the slotted number, which influenced the deformation of the area of the diaphragm. Corrugated sensor design with a higher number of slotted will have higher sensitivity than the lower slotted number because the corrugations technique reduces the residual stress (Fig. 4) and increases the linear diaphragm range and mechanical sensitivity [9]. The calculated linear regression for corrugated models shows that eight slotted corrugated designs provide high linearity, in which the coefficient of determination (R2 ) is equal to 100% (R2 = 1.00). In contrast, four slotted corrugated design shows 99% (R2 = 0.99) (data not shown). Therefore, eight slotted corrugated designs have a higher capacitance sensitivity than the four slotted corrugated design (Fig. 7). For circular sensors design, four slotted shows higher sensitivity than the eight slotted design. The result analyzed is similar to the non-corrugated design.

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5 Conclusion We have shown that capacitive sensors for the IOP application could be optimized by altering the design, including sensors material and shape. In the model designed, the diaphragm is based on polysilicon material and design, such as corrugated and noncorrugated (flat) with square and circular shapes. In conclusion, eight slotted with corrugated diaphragm performed better than four slotted with corrugated for sensors capacitance and sensitivity. However, for non-corrugated sensors design, there was not much difference in capacitance value. The non-corrugated sensor with four slotted

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designs has higher sensitivity than eight slotted models where diaphragm deformation influenced the result. Circular shape design, on the other hand, performed alike to the flat sensor design. Thus, regardless of any design shape and materials used of a sensor, a non-corrugated diaphragm is less suitable for high sensitivity performance. However, this kind of design performance could still be improved by using the slot-type diaphragm because of rigidness on the diaphragm and reducing the complex process of sensor fabrication. Further consideration of the essential parameters such as size, shape, and material selection of the pressure sensor design for the IOP application, including sensor fabrication process and sensor packaging, need be considered. Acknowledgements The authors gratefully acknowledge the financial support from UniMAP.

References 1. Selvarajah S (1998) An analysis of glaucoma patients seen at the General Hospital Kuala Lumpur over a five year period: 1986 to 1990. Med J Malaysia 53(1):42–45 2. Razeghinejad MR, Lee D (2019) Managing normal tension glaucoma by lowering the intraocular pressure. Surv Ophthalmol 64(1):111–116 3. Katuri KC, Asrani S, Ramasubramanian MK (2008) Intraocular pressure monitoring sensors. IEEE Sens J 8(1):12–19 4. Sanchez I, Martin R (2019) Advances in diagnostic applications for monitoring intraocular pressure in Glaucoma: a review. J Optom 12(4):211–221 5. Yu L, Kim BJ, Meng E (2014) Chronically implanted pressure sensors: challenges and state of the field. Sensors 14(11):20620–20644 6. Ali M, Noorakma AC, Yusof N, Mohamad WNF, Soin N, Hatta SWM (2016) Optimization of MEMS intraocular capacitive pressure sensor. In: 2016 IEEE international conference on semiconductor electronics (ICSE), pp 173–176. IEEE 7. Saad N, Soin N (2018) Optimization of intraocular capacitive pressure sensor. Int J Nanoelectron Mater Meas 4(5):71–78 8. Vaajanen A, Tuulonen A (2016) Abnormal increase of intraocular pressure in fellow eye after severe ocular trauma: a case report. Medicine 95(31):1–4 9. Zou Q, Li Z, Liu L (1996) Design and fabrication of silicon condenser microphone using corrugated diaphragm technique. J Microelectromech Syst 5(3):197–204

IC Engine Ignition Timing Controller Feature Extraction of Knocking Condition Ajmir Mohd Saill, Elmi Abu Bakar, Mohammad Nazir Abdullah, and Mohammed Nishat Akhtar

Abstract A study is presented of the influence ignition timing controller to initiate spark ignition by using single cylinder engine. Crank position sensor (CPS) is used to actuate spark ignition from the engine. Arduino controller is connected to a circuit receiving CPS signal to actuate spark ignition timing. The circuit is used to filter the CPS signal for the controller. An accelerometer is mounted to the nearest location of the engine piston. While ignition pulse is connected from the controller to identified engine speed. The engine rotational speed is between 1000 to 4000 rpm. Instrustar DAQ is used in recording the signals from the controller and accelerometer to be analyze and compare with previous research. The analyzed signal identified knocks by different ignition timing. It shows different types of knocking and pre spark timing intensively. The location of pre spark timing is shown each knocking occurs. Keywords Spark plug · Timing · Accelerometer · Signal

1 Introduction The growing number of vehicles on the road around the world arose the possibility of high pollution environment. This issue is a major concern with respect to future generation. Mansha et al. [1] mentioned about the pollution from exhaust emission is dangerous to human health and can produce more hazardous pollution. Engine maintenance made by user will affect the condition toward the knocking condition Reits et al. [2]. Gas emission is the major concern. The engine will combust uncertain mixed air fuel, will caused pollution. Ignition timing will play the major role for the engine running smoothly. Merkisz et al. [2] and Zaim et al. [3] stressed out components lead to ignition timing such as ignition coil, intake sensors, spark plugs A. M. Saill (B) · E. A. Bakar · M. N. Akhtar School of Aerospace Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia M. N. Abdullah School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_43

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and engine leaks should be restore in good condition to avoid knocking. engines speed increases from low to high speed without combusted knock at surface running condition Luo et al. [4]. Spark ignition combustion engine is well known in automobile industry and widely be implemented for every vehicle. Spark plus is used to ignite the flame inside the combustion chamber. Libin et al. [5], Mansha et al. [1] and Jenson et al. [6] suggested spark plugs can be manipulated to overcome the imbalance combustion. With the advent combination of air fuel mixture, octane number of fuel and ion current supplied are considered as significant constituents towards contribution to pollutants. However, the aforementioned problem can be solve by coming up multiple variable controller to actuate the right timing to the spark plugs by Laurain et al. [7]. Over decades knocking condition had been studied by Harle [8], Galtier [9], and Yutaka [10]. Knocking condition will produces high pressure, that leads to damaging engine components, Azher et al. [11]. Super knock was produced by pre spark condition. It is the major possible of creating super knock by Reitz et al. [12]. In different technic of producing knocking, Lin et al. [13] modify in cylinder swirls ratios could make a critical knocking conditions. Wang et al. [14] creating knocking conditions by early varying pre ignition timing and increasing pre injection fuel mass ratio. The presented paper is to design and study of spark plug timing controller by identify the CPS signal to generate spark timing to the engine. It will monitor the signals by knocking sensor of uncertainty behavior for further analysis.

2 Methodology The Crank position sensor (CPS) from the engine is used to actuate spark ignition timing. The frequency is used to design spark plug controller for the engine. Ajmir et al. [15] is manipulating spark ignition timing for the controller to produce uncertainty of the engine. The accelerometer is mounted to the nearest position of the engine piston. Figure 1 illustrates the flowchart of the proposed work. With respect to the proposed work a controller was designed to initiate timing of spark plug. It is deemed necessary to identify the ideal angel of spark plug timing based from the CPS signals. In other hand, the controller is used to actuate the spark ignition for further analysis.

2.1 Experiment Setup A four-stroke engine, Yamaha 135 cc single engine at ISI Laboratory, School of Aerospace Engineering, Universiti Sains Malaysia cylinder is mounted on an engine bench. A test bench is design withstand the vibration of the engine during the experiment. Signals of CPS are recorded to identify frequency to spark plug timing. The

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data are used to design the engine controller. Figure 2 shows the computer used for the data collection, DAQ connected to the computer and the engine bench setup for the experiment. A circuit receiving CPS is to filter the signal. The signal is used to activate spark ignition. Arduino controller will be controlling the spark timing on each CPS signal received. A knock sensor mounted on the engine is connected to a DAQ (refer to Fig. 3), it will record the signal of uncertainty condition. Figure 3 shows the connection from the engine to the controller, then actuating spark ignition. The accelerometer is mounted to the engine block, red is attached to positive terminal and black wire is attached to negative terminal of DAQ to record signals. There are few wire wrapped with a black tape. It contains charging circuit and CPS wire circuit. CPS wire circuit is attached to oscilloscope to identify the signals. The signals are identified to design the Arduino controller for the engine.

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3 Result and Discussion Figure 4 demonstrates the signals from accelerometer at channel 1 and spark actuator at channel 2 are recorded. The signals indicate an acceleration speed of 1000 to 4000 rpm. The spark actuator signal show it produced drop voltage, constantly within 5 V. The voltage drop is used to calculate engine speed. Signals from the accelerometer identifies knocking. The signal is extracted to be analyze. Signals from is analyze for the knocking condition. At position 22880 ms, for 10 ms length is used to plot a detail condition of the engine. Figure 5 show the condition of the knocking. It shows the largest knocking occurs about 0.531 V during the engine running at 2000 rpm. Based on a calculation, a pre spark ignition occur 3.968 ms, 398°. Normal spark angle is 10° BTDC at 1400 rpm, Yamaha Co. [14].

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At position 30220 ms, Fig. 6 show the condition of the knocking. It shows the largest knocking occurs about 5.78 V during the engine running at 4000 rpm. Based on a calculation, a pre spark ignition occur 3.969 ms, 398°. Thus, the condition of knocking occurs earlier than the normal spark ignition timing. The pre spark ignition actuated during the exhaust stroke till intake stroke begins. This condition

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will perform back fire to the carburetor, while intake valve still open. Furthermore, it starts to burn till the power stroke begins. Furthermore, Fig. 7 show the compilation of engine speed and knocking condition. It shows during low RPM, within 1000 to 2000 rpm knocking occurs. The knock size within 1 V size. The knock size increases when the engine at high RPM. The knock size 2.49 V at 3000 rpm and 5.78 V at 4000 rpm.

4 Conclusion The work presented are based on the use of single cylinder four stroke engine. CPS signals from 4 to 0.234 V, voltage drop is detected to trigger the spark ignition. By constantly tested, accelerometer is used to identify uncertainty behaviour to be analyse. Misfire and knocking is detected. In this study, knocking condition is analyse for the pre spark timing and the size of knocking. Super knock is identified by the size produced by the accelerometer accede 5.78 V. It is identified the spark ignition actuated during the compression stroke. Thus, the condition may affect badly to the engine components. The experiment setup can identify uncertainty behaviour of an engine to be analyse and design for better function of the controller. Acknowledgements The authors would like to thank the Ministry of Higher Education, Malaysia for the financial support of this research through Hadiah Latihan Persekutuan (HLP) and Innovative System and Instrumentation (ISI) Research Team, School of Aerospace Engineering, Engineering Campus, Universiti Sains Malaysia for support of this work.

References 1. Mansha M, Shahid EM, Qureshi AH (2012) Control of combustion generated emissions from spark ignition engines: a review. Pak J Eng Appl Sci 11(x):114–128 2. Merkisz J, Bogus P, Grzeszczyk R (2001) Overview of engine misfire detection methods used in on board diagnostics. J Kones Combust Engines 8(1–2):326–341 3. Pauzi MZM, Bakar EA, Ismail MF (2016) Feature Identification and filtering for engine misfire detection (EMD) using zirconia oxygen sensor. IOP Conf Ser Mater Sci Eng 114:012140 4. Luo Q, Sun B (2016) Inducing factors and frequency of combustion knock in hydrogen internal combustion engines. Int J Hydrogen Energy 41(36):16296–16305 5. Jia L, Naber JD, Blough JR (2016) Review of sensing methodologies for estimation of combustion metrics. J Combust 2016, Article ID 8593523 6. Abraham J, Bhende AR (2015) IC engine fault diagnosis using vibration & acoustic signals – a review. Int J Recent Innov Trends Comput Commun 3(2):129–132 7. Laurain T, Lauber J, Palhares RM (2015) Advance for idle speed management of a SI engine. In: 2015 IEEE 10th conference on industrial electronics and applications 8. Harle JFBN (1987) Detection of knocking for spark ignition engines based on structural vibration, pp 1744–1747 9. Galtier F (2005) (12) United States Patent

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10. both of Chiryu MN, Kariya YT, Taga Y, Nakamura S, both of Toyota, Okazaki KN, Inagaki T, Ito H (1998) United States Patent (19) 11. Witwit AR, Yasin A, Abas MA, Gitano H (2013) Modern methods in engine knock signal detection. Procedia Technol 11(ICEEI):40–50 12. Wang Z, Liu H, Reitz RD (2017) Knocking combustion in spark-ignition engines. Prog Energy Combust Sci 61:78–112 13. Chen L, Zhang R, Wei H, Pan J (2020) Effect of flame speed on knocking characteristics for SI engine under critical knocking conditions. Fuel 282:118846 14. Wang H, Gan H, Theotokatos G (2020) Parametric investigation of pre-injection on the combustion, knocking and emissions behaviour of a large marine four-stroke dual-fuel engine. Fuel 281:118744 15. Saill ABM, Abdullah MN, Bakar EA, Bin Jafri MNA (2019) Engine misfire and knocking detection on vehicle using closed loop system. Int J Recent Technol Eng 8(2 Special Issue 11):3478–3481

Flex Force Smart Glove for Therapy Treatment Using Arduino and Raspberry Pi Chang Yi Neng, Mohamad Khairi Ishak, and Ahmad Afif Ahmarofi

Abstract The process of rehabilitation of a stroke patient is long and complex, yet it is necessary to recover the flexibility of muscle movement and reduce the risk of a recurrent stroke. Thus, this paper uses flex force smart gloves to assist physicians to remotely monitor the rehabilitation process of stroke patients via data collection gloves using flex sensors. In this work, Raspberry Pi is used to connect to the Internet and, in conjunction with the Arduino platform, to interface with the electronic components of the system. Performance analysis on Arduino and Raspberry Pi has shown that Arduino is capable of output data at an average interval of 1,785 ms at a baud rate of 115,200 baud. However, communication between Arduino and Raspberry Pi reduces performance to an average of 5,421 ms. In addition, this paper also demonstrated the ability of Raspberry Pi to perform complex processes without significant performance degradation in this application. The final system has shown its ability to read, process, record and relay data from the data gloves. Keywords Microcontroller · Microcomputer · Flex sensor · Smart Glove

1 Introduction Stroke was deemed Malaysia’s number 3 killer [1], especially due to the high proportion of obese in Malaysia which increases the risk of suffering from stroke. Most stroke patients are unable to experience a full recovery, and the rehabilitation process is lengthy and complex, while the lack of rehabilitation may increase the likelihood of a recurrent stroke [2]. Therefore, a tool to aid doctors and physicians in the rehabilitation of their patient remotely through Internet of Things is to be investigated. C. Y. Neng · M. K. Ishak (B) School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia e-mail: [email protected] A. A. Ahmarofi Faculty of Industrial Management, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_44

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Data collecting gloves have been researched multiple times, using various sensors to detect the flexion of fingers. An article by Intel documented the usage of Cyclone® V SoC FPGA in a Flex Force Smart Glove (FFSG), which can integrate data from 5 sets of pressure and flex sensors, and 5 inertial measurement units to compute the position and orientation of each finger, as well as generate a 3D representation of the hand for stroke rehabilitation [3]. Another uses solely IMU sensors, one for each joint for a total of 16 units. In this case, the system is used to determine the loss of Range of Motion (ROM) in patients with Rheumatoid Arthritis [4]. One article uses flexible resistive sensors and resistive force sensors to monitor four fingers, which is used in finger injury rehabilitation [5]. In addition, triboelectric nanogenerator (TENG) were also frequently used as sensors on data collecting gloves, such as a research done on data collecting gloves for sign languages [6], and another research using TENG combines it with piezoelectric haptic mechanical stimulators for tactile feedback, which is used as an HMI for augmented reality (AR) and virtual reality (VR) systems [7]. There is also a patent for a smart glove which uses a wide variety of micro-sensors such as capacitors and thin film resistors to monitor motion and temperature of a hand, and includes an embedded application-specific integrated circuit (ASIC) [8]. Microcontrollers originated as a key part in automated products. Recent developments greatly reduced the size and price available, hence the application of microcontrollers emerged within electronic enthusiasts and students alike. While most projects use microcontrollers from the Arduino family due to its open source nature which leads to its considerably cheaper price, it provides an adequate capability in the electronics domain but lacking in additional built-in functions which requires additional components to implement them. On the other hand, Raspberry Pi, as a microcomputer, includes more functions, notably its internet connectivity and desktop interface, but lacks the analogue-to-digital converter (ADC) included in Arduino boards. Multiple researches have been done involving both Raspberry Pi and Arduino. One of the methods to establish communication between Arduino and Raspberry Pi is through a nRF24L01 + Wireless Transmitter, which allows wireless data transmission between two or more of the devices as used in a Smart Home Control System [9]. Another irrigation system incorporating the Arduino and Raspberry Pi realizes the communication through ZigBee Modules, which are based on the IEEE 802.15.4 standard for low-rate wireless personal area networks. [10]. Similarly, an article on industrial process monitoring uses ATmega to interface with sensors, which then relays data to a Raspberry Pi using the Zigbee Module [11].

2 Hardware Description The 2 GB RAM Raspberry Pi 4 Model B is used as the microcontroller unit (MCU) of the system, while the sensors used to measure the bends of the fingers are flexible bend sensors. An Arduino Mega 2560 is used as the combination of ADC and multiplexer. In addition, an android mobile phone installed with a dedicated application is used

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as a remote terminal to access the data processed by the Raspberry Pi. Furthermore, a display device with a High-Definition Multimedia Interface (HDMI) input port or a handheld device works as the display for the Raspbian operating system on the Raspberry Pi, which allows access to the graphical user interface (GUI). The handheld device requires the installation of a Virtual Network Computing (VNC) application to access the desktop of the Raspberry Pi. Figure 1 shows the system.

2.1 Raspberry Pi 4 Model B 2 GB RAM The Raspberry Pi 4 Model B is the latest product in the Raspberry Pi range of computers. It has 40 general-purpose input/output (GPIO), 2 micro HDMI ports, 2.4 and 5.0 GHz Wireless local area network (LAN) and a Gigabit Ethernet, 2 universal serial bus (USB) 2.0 ports and 2 USB 3.0 Ports. It also has a micro SD card slot for the operating system [12]. It is available in 2, 4 or 8 GB RAM version, while the version used in this system has 2 GB. The Broadcom BCM2711 onboard is a 64-bit system on a chip (SoC) running at 1.5 GHz. The Raspberry Pi runs on Raspbian OS.

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Fig. 2 Flexible bend sensor

2.2 Arduino Mega 2560 The Arduino Mega 2560 is a microcontroller board based on the ATmega2560. It has 54 digital input/output ports including 15 pulse width modulation (PWM) output ports, and 16 analogue input ports. The 8-bit ATmega2560 chip is clocked by a 16 MHz crystal oscillator [13].

2.3 Flexible Bend Sensors The sensors used in this project are 2.2 Flexible Bend Sensors (see Fig. 2) which produces a change in resistance when it is bent. This change in resistance will be converted to a voltage reading using a potential divider.

3 System Design The overall interaction between devices in the system is shown in Fig. 3. The analogue output of the flex sensors is sent into the Arduino, which passes the data to the Raspberry Pi through the USB cable connecting the type B port on the Arduino to the type A port of the Raspberry Pi. The Raspberry Pi outputs its desktop through the micro HDMI port, which allows the GUI of the program to be displayed on a display device. Alternatively, the desktop can be displayed and controlled through VNC, where a VNC server running on the Raspberry Pi allows remote display and control with a device installed with any VNC client through WiFi. The Raspberry Pi processes the data and packs it as a JavaScript Object Notation (JSON) which is

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broadcasted on the LAN, allowing devices installed with the Android application to access and display. There are 5 flex sensors attached on the glove, situated on a location that allows the observation of the flex of metacarpophalangeal joint. The sensors work as variable resistors, where the output is converted to a voltage through potential dividers, where the sensors acts as pulldown resistors, thus varying the voltage in accordance with the flex as in Fig. 4. The data obtained by the Arduino consists of 5 readings as voltages, ranging from 0 to 1023 as per the resolution of the ADC available on the Arduino. All 5 readings are sent as a single line, ordered and separated with commas, through the USB cable at a baud rate of 115,200. A simple handshake protocol is introduced between the Arduino and the Raspberry Pi, where after the first sent data, the Arduino awaits the reply from the Raspberry Pi

Fig. 4 Circuit diagram of the glove

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Fig. 5 Graphical user interface in the Raspberry Pi

of a single character ‘1’. This is due to the slower serial communication read speed at the Raspberry Pi compared to the Arduino, which causes the buffer of the Raspberry Pi to fill up and causes substantial delays between the displayed data and the actual data. The Raspberry Pi processes the data by calibrating the readings with the minimum and maximum flex offsets and displays it on the GUI (see Fig. 5) through Eq. 1. calibrated value =

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The GUI contains the buttons to set the offset values for all 5 sensors. Calibrated readings are displayed as the indicators on the sliders as well as the corresponding values above the indicators. In addition, a recording button is present below the GUI, which allows recording of values as a comma-separated values (CSV) file, including the time at which each reading is taken since the recording began to ease analysis. The program also broadcasts the readings through User Datagram Protocol (UDP) on the local area network, which allows access to all devices installed with the Android application. The readings are encoded as a JSON, which is then decoded on the application shown in Fig. 6. The received JSON can be viewed at the bottom of the app.

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Fig. 6 Android application

4 Results and Discussion In order to obtain the runtime of the Arduino program, multiple loops are added to the program to run the program for a variable number of readings. Readings are obtained at defined number of loops, within a range of 400 to 645 in intervals of 5, for a total of 50 readings. Loop time is then calculated by dividing the total time taken by the amount of readings taken for the loop. Timers are added to the program to time the loops. In addition, loop time at different baud rate is observed. Figure 7 shows the histogram of loop time at 115,200 baud rate in milliseconds. However, it can be noticed that an outlier exists at the lower end, which is also present at the histogram of different baud rates. Incidentally, the outliers are the first data of each

Fig. 7 Histogram of 115,200 before rectification

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Fig. 8 Histograms of loop time at different baud rates in milliseconds

set, that is the data taken for the 400 loops. This signifies that the first data taken is inaccurate, possibly due to a delay to which the timer for millis() function actually activates. Removing the first entry of each baud rate produces histograms that are normal (see Fig. 8). The variations can be attributed to the quantized readings given by the timer, which has the smallest unit of 1 ms. This can be proven by finding the cumulative deviation, which is calculated by Eq. 2. cumulative deviation = |time per loop − mean| × number o f loops

(2)

The resulting value is the cumulative deviation for the specific data from the mean in milliseconds and is calculated for each data entry in a specific baud rate. Figure 9 shows that most deviations are less than 1 ms. While uncommon, there is also the effect of noises within the flex sensor output which considerably affects the analogue reading, altering the size of data sent through serial communication, thus affecting the time taken for the loop. Since the obtained histogram is normal, the mean can be taken in subsequent calculations. Assuming constant data size, the function for time per loop can be expressed in formula 3. Loop T ime =

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Fig. 9 Cumulative deviations from the mean for 38,400 baud rate, arranged according to number of loops with first entry removed. The data for 38,400 was displayed as it has the highest standard deviation

However, fitting the data obtained to the function produces the following result in Fig. 10, expressed as milliseconds, with the baud rate adjusted accordingly. Notice that the calculation time for the fitted data returns a negative value, albeit small. This suggests that the calculation time is miniscule compared to the time taken for data transmission, accounting to 0.0258 ms, which is easily affected by variations in loop time. In addition, the fitted plot returns the data size, 210.165 in bits. Accounting for start and stop bits appended which gives 10 bits per byte for data, this results in 21 bytes. Comparatively, a typical data sent, which is a string of 5 sets of 3 characters separated by 4 commas in total, and an EOL character that takes up 2 bytes sums up to 21 bytes. The above data were obtained with the Arduino running at full speed while connected to a PC, without the handshake protocol implemented. However, this setup produces an unsatisfactory result when connected to the Raspberry Pi, as the Raspberry Pi was unable to read the data received as fast as the Arduino, which was a problem not present when connected a PC. This causes the data received to be placed within the buffer of the Raspberry Pi, therefore introducing significant delay between readings and possible data loss. Therefore, a simple handshake protocol was implemented, where the Arduino awaits a reply from the Raspberry Pi before sending subsequent data. Performance analysis on the Arduino shows that it is capable of sending data at an interval of 1.785 ms. However, performance analysis on the Raspberry Pi shows that the bare bones program with only the read and reply function, has

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Fig. 10 Fitted plot for mean loop time and baud rate

an average loop time of 5.421 ms (see Table 1). Since the reply from the Raspberry Pi consists of a single character ‘1’ without an EOL character, which constitutes to 1 byte, the loop time is mostly taken up by the Raspberry Pi to read the data from the USB port. On the other hand, the full program which includes the calibration calculations, GUI display and UDP packets, takes 6.303 ms, which is a 16.27% or 0.882 ms increase from the bare bones program. It is also worth noting that the full program only polls the serial input every 1 ms due to the usage of GUI, therefore a delay up to 1 ms may exist. This shows that while the performance of Raspberry Pi with peripherals are somewhat lacking, it makes up for with its performance in terms of software. A record button is available on the GUI of the Raspberry Pi, which writes the time and calibrated readings of the flex sensors in a CSV file, which can be processed in a wide variety of application. A sample of a CSV file is visualized in a graph in Fig. 11. It is apparent that noises do occur with the sensors, as a single peak at around 240 can be observed on the line for the little finger, where each reading should be Table 1 Loop time of the Raspberry Pi. The values for 3000 loops are the average of 5 runs for each program

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Fig. 11 Graph generated with a CSV file

calibrated within 0 to 100. However, the peaks do not hinder an analysis of the finger movements due to the sheer amount of data taken compared to actual finger movements. The CSV file showed consists of 918 data across 5.694 s, which gave an average of 6.203 ms per loop, implying that writing to a CSV file does not impose noticeable performance reduction. There are also readings existing beyond the 0–100 range, which can be attributed to difficulties in calibrating according to minimum and maximum finger flexes, as these points are less definite. The UDP connection is capable of transmitting the JSON encoded readings through a router, broadcasting the data to all devices on the local area network, which allows multiple devices to access the same data simultaneously. UDP is chosen instead of TCP due to its smaller overhead, simplicity due to lack of handshaking process, as well as real time capability. In the Raspberry Pi program, the JSON datagram is broadcasted to all local IPs on the port 9033, while an Android application made with MIT App Inventor [14] is programmed to access the data through the use of a UDP extension [15] (see Fig. 6). The application listens to the port 9033 on the press of the button and receives the datagram present on the port. The JSON code is then decoded and the values visualized on the sliders, similar to the Raspberry Pi GUI. A JSON encoded datagram allows alternative programs that can access the data to be developed easily. While the datagram can be broadcasted to devices on the same network, modifications must be made to transmit data due to the usage of routers and modems, where most devices are given a private IP address on the network due to Network Address Translation. Therefore, in order to realize transmission across the Internet, port forwarding must be performed on the router of the receiver, where the Android

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Raspberry Pi Datagram sent to IP of Router 2 and external port for receiver

Router 1

Internet

Datagram to external port translated to internal IP and port of Receiver

Router 2

Receiver

Port forwarding on Router 2 Fig. 12 Diagram of datagram across Internet

application is. The Raspberry Pi can then send the datagram to the IP address of the router with the specified port. This setup works for a static receiver and dynamic transmitter, where the Raspberry Pi can be at different location with a different IP address, however the Android device or alternative programs acting as the physiotherapist’s terminal must be connected to the port forwarded router and assigned a static IP address. Another option is to implement a server with a public IP address as the target of the transmission as illustrated in Fig. 12.

5 Conclusion The usage of both Arduino and Raspberry Pi compensates each other, where the Arduino provides electronics interface, while the Raspberry Pi focuses on processing and information technology capabilities. Performance degradation has been observed due to the communication bridge required between the Arduino and Raspberry Pi, as the loop time has been increased from 1.785 to 5.421 ms. Nevertheless, this setup allows a smaller number of components in a system. The system has shown satisfactory results for usage in physiotherapy, as displayed by its ability to poll sensor data on the glove, processed and displayed on the GUI of the Raspberry Pi. Data can also be recorded in CSV for further analysis or accessed remotely through an Android application in real-time.

References 1. Chun Fai C (2019) Call for better emergency stroke treatment. New Straits Times, Penang 2. Recovering From Stroke | cdc.gov. https://www.cdc.gov/stroke/recovery.htm. Accessed 31 Jan 2020 3. Intel: Flex Force Smart Glove: Use Intel® SoC FPGA to Measure Sensorimotor Data. https:// software.intel.com/en-us/articles/flex-force-smart-glove-use-intel-soc-fpga-to-measure-sen sorimotor-data

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4. Connolly J, Condell J, O’Flynn B, Sanchez JT, Gardiner P (2018) IMU sensor-based electronic goniometric glove for clinical finger movement analysis. IEEE Sens J 18:1273–1281 5. Köseoglu M, Celik OM, Pektas O (2018) Design of a smart glove for monitoring finger injury rehabilitation process via MQTT server. In: 2nd international symposium on multidisciplinary studies and innovative technologies (ISMSIT), Ankara 6. Che-Min C, Shuo Wen C, Yu-Ping P, Ming-Zheng H, Shuen-Wen C, Zong-Hong L (2019) A smart glove with integrated triboelectric nanogenerator for self-powered gesture recognition and language expression. Sci Technol Adv Mater 20:964–971 7. Zhu M, Sun Z, Zhang Z, Shi Q, He T, Liu H, Chen T, Lee C (2020) Haptic-feedback smart glove as a creative human-machine interface (HMI) for virtual/augmented reality applications. Sci Adv 6(19):eaaz8693 8. Leneel RS (2016) Flexible smart glove. United States of America Patent US9529433B2, 27 December 2016 9. Hadwan HH, Reddy YP (2016) Smart home control by using Raspberry Pi & Arduino UNO. Int J Adv Res Comput Commun Eng 5(4):283–288 10. Angal S (2016) Raspberry pi and Arduino based automated irrigation system. Int J Sci Res 5(7):1145–1148 11. Kadiyala E, Meda S, Basani R, Muthulakshmi S (2017) Global industrial process monitoring through IoT using Raspberry Pi. In: International conference on Nextgen electronic technologies: silicon to software (ICNETS2), Chennai 12. Raspberry Pi 4 Model B product brief. https://static.raspberrypi.org/files/product-briefs/200 521+Raspberry+Pi+4+Product+Brief.pdf. Accessed 24 May 2020 13. Arduino Mega 2560 Rev3 | Arduino Official Store. https://store.arduino.cc/usa/mega-2560-r3. Accessed 20 Feb 2020 14. MIT App Inventor. https://appinventor.mit.edu/. Accessed 29 May 2020 15. Ullis Roboter Seite/AI2 UDP. https://ullisroboterseite.de/android-AI2-UDP.html. Accessed 29 May 2020

Cross-Platform Appliance Management and Remote-Control Mobile Application Using REST API Communication Eizzat Ayman Zaikuan, Mohamad Khairi Bin Ishak, and Ahmad Afif Ahmarofi

Abstract It is vital for us to efficiently use the electric energy in our everyday life. The project is based on three main cores which are the Mobile Application, Server and Micro-Controller that can be installed to their everyday appliances. The solution helps to point out the usage of every appliance and highlight the most consuming appliance. The three main core that are used is Xamarin Form as the Platform for the mobile application, Raspberry Pi 4 as the micro-controller and a Database Server for data storing. The three main cores communicate with each other using the REST API interfaces. The Xamarin Form permits the application to be used in three most used operating systems which are Android, IOS and Windows. The test result of the system assists users to manage their home routine more efficiently to avoid wastage and provides an affordable product that can be used by all layers of community. The project has undergone a user experience test and has proven to alert wastage of appliances in home and thus reduce the electrical usage. Keywords Smart home · Appliance management · Xamarin · REST API

1 Introduction Electrical consumption in our everyday life is unavoidable. Through recent studies shows a house will waste 19.97 kWh per day [1]. Therefore, the government and big companies are now actively participating to war the wastage of electrical energy [2]. A good habit in managing and controlling electrical consumption has been thought in schools [3]. This is to spread awareness on the importance to saves electrical energy. Big companies also have started to invent and innovate Smart Home Appliances. These appliances can help to auto-manage themselves. However, the appliances are E. A. Zaikuan (B) · M. K. B. Ishak School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia A. A. Ahmarofi Faculty of Industrial Management, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Kuantan, Pahang, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_45

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sold separately and not everyone could afford to buy one of these appliances as they are expensive. This project mainly discusses the innovation of a Cross Platform Mobile Application and a micro-controller that can be installed in normal home appliances. The application will act an intermediary between the appliance and the user. The user can use the remote-control feature to control their appliances. Apart from these, the main feature of this application is it could interact with the web database server. The server will keep track the duration that every appliance is turned on. The database will store these records and will show it in the report that can be assessed by the user from the mobile application. The data that are stored in the database could help point out to the user which appliance is consuming the most electric and thus help the user manage the electrical consumption. The aims of this work is mainly focused to create a solution that help user to easily manage the electrical consumption of their everyday appliances through Big Data and home automation. Apart from the main objective, we also wish to innovate a solution that is affordable and easy to use so that every layer of community can benefit from our product and reduce the wastage of electrical energy.

2 System Design Development The system is separated into three main core which are: • Cross-Platform Mobile Application • REST Database Server • Micro-controller

2.1 Cross-Platform Mobile Application The mobile application is developed using the Xamarin Form. It has been programmed using the C# language using the Visual Studio platform. A Xamarin Forms application is architected in the same way as a traditional cross-platform application. The developed application in Xamarin Form can be installed in different operating systems such as Windows, Android and IOS [4, 14] (Fig. 1). The advantages of using the Xamarin Form in comparison with another crossplatform is [5]: 1.

Using Single Technological Stack – The development of a Xamarin application only involves programming using the C# language. The whole development process can be done effectively using the Visual Studio. This make the development process more convenient and time effective.

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Fig. 1 Xamarin form and supported operating systems

2.

Enhanced Time-to-Market – The development life cycle will be effectively reduced with a sharable codebase. A simplified development procedure and the IDE built in cross-platform tools helps to reduce the cost of developing an application.

3.

Simplified Maintenance – Xamarin Form application is easy to maintain apps as they share code. Hence to fix an issue or update an application, developers can update it in the source codes, and it will automatically change in all solutions.

4.

Testing – Xamarin Form provides a top-notch solution for testing and monitoring which are the Xamarin Test Cloud and the Xamarin Test Recorder. These two solutions allow developer to test and monitor applications performance and UI. Furthermore, developers could develop an automated test on multiple real devices in the cloud and see the performance issues by using these tools.

The button in the Main Page that is shown in the Fig. 2 is representing some of the features of the application. The functionality of the button are as follows: • Dashboard Button: Shows the summary report that the user can set. • Remote Button: Permits the user to control the electrical appliances remotely with their mobile phone. • Schedule: This feature permits the user to schedule time to turn off appliances of their chose. • Report Button: User can get detailed report on each appliance electrical consumption. • Settings Button: Add and Remove appliances • About Button: Shows the version of the application and other details. Xamarin Form is considerably new platform for mobile developers. Many advanced functionality and application that can still be explored in this platform.

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Fig. 2 Mobile application main page UI

2.2 Database Server In this project we choose a web Database Server to store data from the user’s appliances. Some of the advantages of using the database server is [6, 13]: 1.

Performance and Scalability – In comparison with Access database, the database server provides better performance. The server also could support 500 × larger sized database than the 2 GB current limit of the Access Database.

2.

Accessibility and Availability – The server can be accessed anywhere. The user can interact with the appliances in their home through the web-based database.

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Fig. 3 RestDB.io is a NoSql type database

3.

Better Security – One of the security features of the database server is that it includes a robust security system. This system will help to prevent unauthorized user to use our data. Furthermore, it will also run through an Account-Based security. The Account-Based security will totally prevent access by unauthorized users.

We have employed the restdb.io as our primary database for the projects prototype. We could communicate with the server by using the REST API. The restdb.io is an easy to use server and its perfect for the projects prototype as it has a free account for a limited size of database. The current memory size of the database server is enough to save 3 months of data from one house. The downsize is we have to create another server for another house (Fig. 3).

2.3 Raspberry Pi 4 A Raspberry is a small sized computer. It could be programmed to vast numbers of application and functionalities. The functionality of the Raspberry Pi is only limited to the developers’ imagination (Fig. 4). The Raspberry Pi 4 Model B is the latest version of Raspberry Pi computer. There is variation version of the Raspberry Pi. It varies from sizes and functionality. The cheapest form of the Raspberry Pi is as small as a memory card that can be found in a computer [7]. Developers from professionals to students used the Pi boards as media centers, file servers, retro games consoles, routers, and network-level adblockers [8]. In comparison to the previous versions, the Raspberry Pi 4 is faster and provides faster network connections. Furthermore, the Pi 4 can decode 4 K video. The Raspberry Pi 4 has 40 Pins that can be used. The pins are described on Fig. 5: We chose the Raspberry Pi 4 as our micro-controller because of its size and its built-in functionality which are the ethernet port for internet connection, the GPIO pins. It also has tons of other features. This will facilitate us when we are making upgrades and improvement on the project in the future. Bonus advantage is the

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Fig. 4 Raspberry Pi 4

Fig. 5 Raspberry Pi 4 pins

Raspberry Pi is very affordable and help us to achieve the objective of making an affordable solution.

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3 Communication Interfaces Once we have identified the three cores to the project, the next most important thing is to bring up a communication link between the three main core. The proposed communication flow is as shown in the Fig. 6. As shown in Fig. 6, we need a full duplex communication between the Raspberry Pi, Xamarin Application and Database Server. Since all three core supports the REST API. We have decided to explore the REST API in this project. In our research we have found that the REST API is more effective to use as an internet communication between devices compared to the other methods [9]. The REST API is acronyms to: RE: Representational S: State T: Transfer Sharing and transferring data between systems is an important feature that is required when developing a software. In the year 2000, REST was introduced by Roy Fielding. It’s not a standard but a set of recommendations and constraints for RESTful web services [10]. These include the list in Table 1.

Fig. 6 Communication between three main cores

Table 1 Restful API services Client server

Stateless

Cacheable

Layered

Identical to browser functionality. Application sends a request to a specific URL which will be routed to a web server. HTML page will be return by the server

All the information necessary in responding to a request must be included in the client request. Hence, it is possible to send more than one HTTP requests and still receiving the same response

Response need to be defined if it is cacheable or not cacheable

The requesting client should know to whom it is communicating with. Whether it is a actual server, proxy or other intermediary

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Fig. 7 Multi-dimensional data structure

The data that is transferred between devices is in Json format. This is useful if we want to make a multi-dimensional database rather than single dimensional database. An example of a multi-dimensional data is as shown in Fig. 7. The Json format in Fig. 7 gives information of a class named Area has two different class in it which are Living Room and Master Bedroom. Inside of each of these class has another class which is named Appliances. The Appliance class contains the object which is representing the electrical appliance inside each room. Three-dimensional database is used in this project. The database is divided with the class “Area” and inside these class will list the devices, and their “On/Off” state. The multi-dimensional also helps to save space inside the storage since we will only create one database for each home instead of one database for each “Area” in each home. The main API in the REST API are as follows [11]: 1.

2.

3.

GET: The GET API purpose is to read data from the resource. This API will return the data in XML or JSON format and a HTTP response code. The response code is 200(OK), 404(NOT FOUND and 400 (BAD REQUEST) POST: This API is utilized to create new data. It is used to create subordinate to a parent data. For a successful POST request, it will return status code of 201 and the header with a link to the created data. PUT: In order to update the capabilities, the usage of PUT API is needed. This can also be used to create a resource. This scenario is only allowed when the

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data ID is chosen by the client instead of the server. On successful update, return 200 or 204. For a successful create by using the PUT, will return status code 201. DELETE: Used to delete a specified data identified by a URI. Successful deletion will return a status code 201(OK). PATCH: It is used for modifying data. The PATCH request needs to contain the needed changes. PATCH API is similar to PUT, but the it contains a set of instructions describing how a data currently residing on the server should be modified to produce a new version.

4 Hardware Systems The hardware system is mainly already integrated to the Raspberry Pi 4. From the internet adapter to the output GPIO pins. As we are connecting the RPi 4 with ordinary home appliances, we have added relays and other components so it can safely install to the appliance without causing hazard and short-circuit. The components are connected to the RPi 4 according to the schematics on Fig. 8. The connections that are made in the above schematic can be seen more clearly in the connection table that is shown in Fig. 9. The 5 V and Ground (GND) of the Raspberry Pi are connected to the 5 V and GND of relay modules. We then connect GPIO 26, 19,13 and 6 to the Input pins which are IN1, IN2, IN3 and IN4 respectively. Since we want to make the relay to Normally Open (NO) we connect the GPIO Pins of 21, 20, 16 and 12 to each N pins of the relay. A relay is basically a switch which is operated by an electromagnet. The electromagnet only requires a small voltage to get activated. Once the relay is activated it will pull the contact to make the high voltage circuit [12]. The relay is needed as the Raspberry Pi can only control appliances which runs on up to 3.3 V. The appliances

Fig. 8 Raspberry Pi–relay connection schematics

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Fig. 9 Week two report

that we want to control runs on more than 3.3 V. In order to output a higher bigger voltage, the use of the relay is needed and connected as in Table 2.

5 Results and Discussion We have installed our prototype in a house into three different areas which are the Living Room, Master Bedroom and Room 2. The residents of the house had been asked to test out our application in their Android Phones. Installation of our solution has been done to all the fans and lamps in each of the room. We monitor the database daily to ensure that the server is storing all the required information correctly. The objectives of the test are as listed in Table 3.

Cross-Platform Appliance Management and Remote-Control Mobile Application … Table 2 Connection table

Raspberry pin

535

Relay pin

5V

5V

GND

GND

26

IN1

19

IN2

13

IN3

6

IN4

21

NO Relay 1

20

NO Relay 2

16

NO Relay 3

12

NO Relay 4

Table 3 Test objectives Test objective

Description

User experience

Gather feedback from user on their Positive feedback and improvement experience with the mobile suggestion to be added on application

Expectation

Data gathered

Observe the data that been Data gathered is correctly stored gathered from the user’s appliances and transferred

Contribution

Identify if the solution contributes in helping the user to manage his appliances

User appliance managing skill improve

Consumption report

Take consumption report from the data gathered for four weeks

Data stored able to demonstrate correctly in the mobile application

Each week, we asked the owner of the house to check the report that is provided by our mobile application. After week two, the owner starts noticing a high electrical consumption on Fan 1 in the Room 1. The results from the report that can be viewed from the Mobile Application is shown in Fig. 9. The electrical consumption of the Fan 1 from Room 1 is unusual as the consumption should be lower than the Fan 1 in the Living Room. This is due to the Room 1 is used less by the family if compared to the Living Room. An explanation for this is the fan is not switched off when there are no people in the room. Our volunteered tester alerted his family to switch off the fan when it is not in used. On week three report we can see that the electrical consumption on Fan 1 in the Room 1 has been drastically reduced. The results of the reports can be seen in Figs. 10 and 11. This is due to our project had alerted the owner to manage the appliance more efficiently. Our test is still ongoing and will take a report for two to three months. Generally, all test has met our expectation. The feedbacks from the user are as follows:

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Fig. 10 Week three report

Fig. 11 Electric usage comparison of Fan 1 in Week Two and Three

1.

2. 3. 4.

Add more automation on solution such as using capacitive sensor and light sensor. This can ensure that the appliances are smart to turn on and off automatically when it is day or nighttime. Add a schedule feature that permits the user to plan when the appliance will turn off at a time that is set by them. Enhance the application UI Create a local server in case internet connection is not available.

Feedback 1 and 2 are already included in our next feature upgrade plan. The other two feedbacks and have added them in our plan for future upgrades. The feedbacks help us to make our solution more effective in combatting electrical waste. It is also ensuring us to make a more user-friendly solution. The overall test results from test objectives are listed in Table 4.

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Table 4 Test results Test objective

Description

User experience

The feedbacks had been taken and will include in future upgrade

Data gathered

The data that has been saved in the server is enough in showing the electrical consumption of each appliances

Contribution

The solution helps to identify that the user is not using an appliance effectively. There was a lot of electric waste for Fan 1 in Room 1. The user manages to reduce the waste after being alerted

Consumption report Consumption report from UI has been recorded and had helped the user

6 Future Feature and Improvements Our project is still in prototype phase. There are many features that are planned to be implemented on this project. Some of the features planned to be implemented in the future products is to add capacitive sensor. The sensor will be used to automatically turn off appliances when it detects no person in the area. Next, adding “Schedule Feature” to the mobile application which will enable users to schedule when to turn off or on the appliances automatically. Deep Learning technology will also be implemented in the final solution so that it will make a good use of the data that have been stored. Many other features will be corporate to the solution in order to ensure that the products are able to completely transform normal affordable electrical appliances into the expensive electrical appliances that are currently in the market. Apart from the features, many improvements also need to be done. Some of the improvements are improving the database server storage size and improving the UIs.

7 Conclusion From this project, we have achieved our main objective which is transforming a normal affordable electrical appliance into a smart home appliance. Based on the test’s results, we can observe that the data from our database and the report that is accessible in the mobile application helps the user to manage their home appliances more efficiently. The project helped the user to monitor which appliances requires high power consumption. It also points to which time does the appliance used the most.

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References 1. Mohammed N, Danapalasingam KA, Saif AM (2018) Design, control and monitoring of an offline mobile battery energy storage system for a typical malaysian household load using PLC. Int J Power Electron Drive Syst 9(1):180–188 2. Zurer R (2016) Three companies reinventing the energy efficiency business. Conscious Company Media, Boulder 3. Moktar NH (2019) Semai Budaya Jimat Elektrik dari Bangku Sekolah. UTM Newshub, Johor Bharu 4. Nagel C (2018) Xamarin.Forms. Professional C# 7 and .NET Core 2.0, pp 1291–1325 5. Grzmil P, Paszkowska MS, Lukasik E, Smolka J (2017) Performance analysis of native and cross-platform mobile applications. Inform Control Meas Econ Environ Prot 7:50–53 6. Hajoui O, Talea M (2018) Which NoSQL database to combine with spark for real time big data analytics? Int J Comput Sci Inf Secur 16:1–2 7. Maksimovic M, Vladimir V, Davidovi´c N, Milosevic V (2014) Raspberry Pi as Internet of Things hardware: performances and constraints. In: Conference for the society for electronics, telecommunications, computing, automatics and nuclear engineering of Serbia (IcETRAN), Vrnjacka Banja 8. Petrov N, Dobrilovic D, Kavalic M, Stanisavljev S (2016) Examples of Raspberry Pi usage in Internet of Things. In: International conference on applied internet and information technologies, Novi Sad 9. Sohan SM, Maurer F, Anslow C, Robillard MP (2017) A study of the effectiveness of usage examples in REST API documentation. In: Symposium on visual languages and human-centric computing, pp 53–61 10. Rest Architectural Constraints. https://restfulapi.net/rest-architectural-constraints. Accessed 01 May 2020 11. Liew Z (2018) Understanding and Using REST APIs 12. Bredhold D (2009) The basics of control relays, 108 13. Ali O, Ishak MK (2020) Bringing intelligence to IoT edge: machine learning based smart CityImage classification using Microsoft Azure IoT and custom vision. J Phys Conf Ser 1529:042076 14. Ali O, Ishak MK, Zawawi MAMd, Seman MTA, Bhatti MKL, Yusoff ZYM (2020) A MAC protocol for energy efficient wireless communication leveraging wake-up estimations on sender data. In: 2020 17th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), Phuket, Thailand, pp 45–50

Hybrid Design of Model Reference Adaptive Controller and PID Controller for Lower Limb Exoskeleton Application Norazam Aliman, Rizauddin Ramli, and Sallehuddin Mohamed Haris

Abstract An adaptive control with a reference model provides a solution for nonlinearity and uncertainty in dynamics characteristics of rehabilitation lower limb exoskeleton (RLLE). Robustness and compliance are two important challenges for employing joint trajectory control in RLLE application. Therefore, this paper presents the development of RLLE hip-knee joints controllers based on proportional integral derivative (PID), model reference adaptive controller (MRAC) and a combination of both (MRAC-PID). The model consists of hip-knee structure and DC motor model which has been designed in ADAMS and Matlab. Meanwhile, the reference model for MRAC are obtained using the recursive least-squares estimation method. The PID gain tuning is carried out using Ziegler-Nichols (Z-N), while the MRAC parameters are determined using trial-and-error method. The performances of the PID, MRAC and MRAC-PID are evaluated via the co-simulation which show that each tracking error performance. As conclusion, it can be ascertained that the proposed controllers are efficient and gave significant effect in reducing trajectory error during gait training of RLLE. Keywords Lower limb exoskeleton · PID · Adaptive control · Co-simulation

1 Introduction A rehabilitation lower limb exoskeleton (RLLE) is a device that has been aiding spinal cord injury patients recover their walking ability [1]. The rehabilitation program starts with passive training, followed by assistive exercises and resistive exercises. In passive training, a patient does not actively participate in rehabilitation sessions since it is realized via predetermined trajectory algorithms using a tracking control N. Aliman (B) · R. Ramli · S. M. Haris Department of Mechanical and Manufacturing Engineering, Faculty of Engineering and Built Environment, National University of Malaysia (UKM), 43600 Bangi, Selangor, Malaysia N. Aliman Department of Mechanical Engineering, Politeknik Sultan Azlan Shah, Behrang Stesen, 35950 Behrang, Perak, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_46

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system. These conditions causing RLLE to deal with physical human interaction and disturbance. Thus, robustness and compliance are important challenges in RLLE tracking trajectory control methodology. The proportional integral derivative (PID) controller has been widely used in RLLE devices because it is simple and easy to develop [2]. It has been used for a immobile RLLE called CORBYS, in which predetermined trajectories are tracked during rehabilitation exercises [3]. The PID controller has also been developed for an overground RLLE called NaTUre-Gaits [4]. CORBYS and NaTUre-Gait used PID controller in the passive training mode. Recently, the PID controller has been used to control the hips and knees of RLLE whose parameters were tuned using a hybrid of genetic algorithms and particle swarm optimization [5]. The results show that PID controller can track predetermined trajectories with reduced errors without considering disturbances or various operation situations. Trajectory tracking using PID controller, with the human model attached to RLLE, has been developed previously [6]. Since, the RLLE is a nonlinear system which deal with physical human dynamics. Its optimal PID gains were difficult to be tuned correctly. These challenges fundamentally degraded the performance of the controller in tracking a desired trajectory in a real situation. To overcome this problem, the PID controller need to be optimised in real time to maintain optimal performance of the controller [7]. The MRAC was first developed at Massachusetts Institute of Technology, USA which takes the form of adjustment of some or all of the controller coefficients in real time to force the response of the resulting close-loop control system to that of the reference model. This method is relatively simple and easy to use. Various kind of real time optimizing PID controller strategies had been proposed as model reference adaptive control (MRAC). Recently, the MRAC to optimise PID controller was present by Amiri et al. [8] for lower limb exoskeleton trajectory tracking control. The PID controller parameters are tuned using a gradient-based method which show controller can track input trajectories with low errors. However, no disturbances or various actual situations were included during the controller performance in their study. The adaptive PID controller have been proposed by Belkadi et al. [9] where particle swarm optimisation is used to find the best controller parameter and its algorithm does not require prior knowledge of the dynamic model. Hou et al. [10] developed Model-Free Adaptive Control in which the results to be used are compared with data-driven PID control method. The results proved the effectiveness of Model-Free Adaptive Control by numerical analysis. Therefore, there is significant needs of designing a optimization controller that can achieve consistently good tracking performance. This paper focuses on the development of a graphical RLLE model in which the hip-knee joints are controlled by three types of controllers: PID, model reference adaptive control (MRAC) and a parallel combination of both (MRAC-PID). This paper evaluates the controllers’ performance under disturbances via the co-simulation of ADAMS and Matlab.

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2 Dynamic Modelling of RLLE 2.1 RLLE Structure Langrange’s method which is based on the kinetic and potential energy has been used to determine dynamic equation of the multi-rigid-body RLLE [11–14]. The RLLE is a 3-degree-of-freedom (DoF) linkage model restricted in the sagittal plane, as shown in Fig. 1. The origin (0, 0) point is connected to RLLE waist. By using Lagrange equation, the dynamic equation in the single leg supporting phase of the 3 DOF model is obtained as follows:   M I (θ )θ¨ + C θ, θ˙ + Mg (θ ) + Td = T j

(1)

where θ , θ˙ and θ¨  R n is  the angular positions, velocities and accelerations   of the RLLE respectively; M I θ, θ¨  R n is the inertial effect of RLLE; C θ, θ˙  R n×n is the centrifugal and Coriolis forces; Mg (θ ) R n is the gravitational components of RLLE; T j is the torque required on the RLLE joint; and Td (t)  R n is unknown parameters, such as friction, disturbance and parameter changes. The joint torque of RLLE structure with gear ratio n can be expressed as, T j = nTm

(2)

where Tm is DC motor torque. The RLLE, as shown in Fig. 1(a), consists of eight segments: the supporter structure, feet, shanks, thighs and waist. The model is designed with 1 DoF at the hip, knee and ankle joints, rotating only in the sagittal plane; a 1-DoF translation joint at the waist and supporter for up and down motions during walking; and a 1-DoF translation joint between the supporter and ground. The DC motor is used to drive the joints in the hips and knees, while the spring is used in the ankle joints. The RLLE structure made by aluminium due to its strength, anti-rust, light weight and ability to carry and transfer the patient’s weight to the ground. The ground reaction force was created using contact force as shown in Fig. 1(b). The normal forces vector at point I is represented in the equation below, Fig. 1 RLLE a Three DoF b ground reaction force

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  FNi = FN ,x FN ,y , FN ,x

(3)

In ADAMS, it is calculate using impact function as discussed as follows: I M P AC T ( p, p, ˙ p1 , k, e, cmax , d)

(4)

where p is distance; p˙ is time derivative of p; p1 is variable that is represent the free length of p, which positif force value if p is less than p1 , otherwise is zero; k is stiffness of the boundary surface interaction; cmax is maximum damping coefficient; and d is boundary penetration at which ADAMS applies full damping. In ADAMS, when two solid bodies have contact with each other, the nonlinear spring damper system is introduced to determine normal force vector of point I . Therefore, normal forces vector, FNi can be obtained as follows:  FNi

=

˙ p, 0, 0, d, 1) i f x > 0 kx e − cmax pStep( 0 if x ≤ 0

(5)

The tangential friction force vector of FTi shown in Fig. 1(b) is calculated using coulomb friction model in ADAMS, which can be expressed as, FTi = FNi μ(Vs )

(6)

Where μ(Vs ) is friction coefficient defined as a function of slip velocity vector at point I . From Eq. (5) and (6), the ground reaction force of point I can be illustrated as equation below: F j = FNi + FTi

(7)

The ground reaction force parameters shown in Table 1 are used in this study for calculating normal and tangential force. Simulations are performed repeatedly to obtain the appropriate parameters value for the ground reaction force. According to [15], the stiffness parameter should be Table 1 Ground reaction force

Parameter

Symbol

Value

Stiffness

k

10 × 108 N/m

Damping

cmax

1.0E + 004N -s/m

Force exponent

e

2.1

Max. Penetration Depth

d

1 × 10−4 m

Static coefficient

μs

0.3

Dynamic coefficient

μd

0.1

Stiction transition velocity

Vs

0.1 m/s

Friction transition velocity

Vd

5.0 m/s

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sufficient to avoid large penetration, which causing numerical simulation problems. Exponents represent the properties of materials in which soft (e ≈ 1.1), soft iron (e ≈ 1.5) and hard material (e ≈ 2.2). Damping is needed to maintain the damping properties every time a contact begins. The static coefficient μs , dynamic coefficient μd , transition velocity Vs and friction transition velocity Vd are coulomb friction which are specified by the user. Static coefficients and dynamic coefficients range from 0 to 1. Dynamic coefficients are usually smaller than static coefficients to facilitate movement on horizontal surfaces and initiate movement from rest. Stiction transition velocity is an object from a state of rest to moving in a domain of low velocity, to explain the characteristics of a stationary object. In practice, stiction transition velocity is greater than friction transition velocity.

2.2 DC Motor Model The RLLE joints are driven by DC motors in both the hips and knees which demonstrate the working principle of kinetic energy. The dynamic modelling based on Kirchhoff’s law equation shown as, Va (t) = Ra i a (t) + L a

di a (t) + K b ω(t) dt

(8)

where Va is the voltage input to the DC motor, Ra is the armature’s resistance, i a is the armature’s current of the DC motor, ω is the angular velocity, L a is the armature’s inductance and K b is the back electromotive force (EMF) constant. Newton’s law equation is represented by, Tm (t) − TL (t) = Jm

dω(t) + Bm ω(t) dt

(9)

where Tm is motor torque; Jm is rotor inertia; TL is load torque; and Bm is the friction constant. Since the output torque of the DC motor is proportional to the current, the motor torque, Tm , can be obtained as follows,

Tm (t) = K t i a (t)

(10)

where K t is the constant torque. By applying the Laplace transform and considering the motor gear ratio n, the transfer function of the input voltage Va (s) to angular velocity ω(s) is,

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Fig. 2 Hardware setup for DC motor parameter estimation

 Kt × 1 n ω(s) = Gm = Va (s) L a Jm s 2 + (Ra Jm + L a bm )s + (Ra bm + K b K t )

(11)

According to Eq. (8), the parameters are defined using parameter estimation tool in Matlab Simulink. This approach consists of three steps: experiment setup; data collection and parameter estimation; and model validation. In step 1, the hardware was set up based on the schematic diagram shown in Fig. 2. The five bits pseudo random binary sequence (PRBS) generator as the signal for the DC motor driver. The DC motor speeds were measured by an encoder, thus the PWM on encoder output was converted to angular velocity in analog signal by electronic circuit. In step 2, the data were collected and parameter are estimated. From Eq. (8), the value of Ra and L a can be identify by fully locking the rotor DC motor shaft then ω(t) = 0 rad/s. Therefore, Eq. (8) becomes: Va (t) = Ra i a (t) + L a

di a (t) dt

(12)

With an Eq. (12), the transfer function is: G(s) =

Ia (s) 1 = Va (s) Ls + R

(13)

According to Eq. (13), the gain of steady state output per input and time constant are K = 1/R and τ = L/R, respectively. With the DC motor rotor locked and the input

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Table 2 Parameters of the DC motor Parameter

Unit

Left hip

Right hip

Armature resistance (Ra )



1.030

1.042

Armature inductance (La )

H

0.0196

0.0072

Back emf constant (Kb )

V/(rad/s)

0.185

0.1852

Torque constant (Kt )

N.m/A

1.865

1.953

Friction coefficient (Bm )

Nm/(rad/s)

0.0275

0.0259

Motor inertia (Jm )

Kg.m2

0.0453

0.0314

Fig. 3 Validation DC motor model a Left hip b Right hip

voltage Va = 5V , the current response i a (t) were obtained. Therefore, the value of K and L for four DC motor were identified based on current response. The value of Jm , Bm , K b and K t were identified with parameter estimation toolbox in Matlab Simulink. The identified data for all parameter are shown in Table 2. In step 3, the parameters is validates by comparing the transfer function and the actual speed via real time experiment. The pulse generator signal with amplitude of 24 V and period of 10 s is used as input to the estimation model and actual model. The response of each DC motor is illustrated in Fig. 3.

2.3 Model Reference of RLLE The model consists of physical features of the RLLE and DC motor. Figure 4 shows a block diagram of the parameter estimation process. The transfer function of joints is established using recursive least-squares estimation, in which one joint is moving while the other is fixed. The estimation parameters are updated at every sampling interval. The discrete-time transfer function is represented as,

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G(z) =

b0 + b1 z −1 + b2 z −2 θk (k) = θr (k) 1 + a1 z −1 + a2 z −2 + a3 z −3

(14)

where θk is the output angle of joint i (hips, knees) of model reference and θr is an input trajectory. The algorithm for parameters estimation using recursive least-squares is shown in Table 3. In this work, the forgetting factor, ρ is set to 1, which no data will be forgotten. If the parameters to be identified are time varying, the value of ρ should be less than 1. The smaller of ρ, the faster old data is discarded. The result of parameter estimation process is shown in Fig. 5. By combining of three difference sin wave frequency and amplitude as input signal θr , the transfer function obtained for hip and knee are shown as follows, θk(hi p) (k) =

0.00007128 + 0.08628z −1 − 0.08179z −2 θr (hi p) (k) 1 − 1.355z −1 − 0.181z −2 + 0.5408z −3

Fig. 4 Parameter estimation process

Table 3 Recursive least-squares algorithms Algorithm Initial value:

1 2

While true do is current estimation is least-squares weighting factor is current estimation error

3 4 5

6 7

is forgetting factor = 1 End while

(15)

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Fig. 5 Parameters estimation process for a hips b knees

Fig. 6 Validation of transfer function for a hip, b knee

θk(knee) (k) =

0.0000807 + 0.09286z −1 − 0.08801z −2 θr (knee) (k) 1 − 1.363z −1 − 0.1571z −2 + 0.525z −3

(16)

Figure 6 shows the validation of the RLLE model and the hip and knee transfer function. For both joints, the results show that the transfer function follows the graphical RLLE model.

3 Controller Design 3.1 PID Controller The closed-loop transfer function of the PID control system is shown as, dθi G(s)C(s) = dθr 1 + G(s)C(s)

(17)

where G(s) is the transfer function of the hips and knees of the RLLE, and C(s) is the transfer function of the PID controller. G(s) can be represented as [8],

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PID

DC motor

LLE Fig. 7 PID structure

G(s) =

b s 3 + a1 s 2 + a2 s + a3

(18)

and C(s) is the PID controller, shown as, C(S) =

K P s + K I + K D s2 s

(19)

Thus,θi /θr becomes,   b K D s2 + K P s + K I θi = 4 θr s + a1 s 3 + (a2 + b + a3 K D )s 2 + (a3 + bK P )s + bK I

(20)

where θi is the output angle of joint i (hips, knees) of LLE. Figure 7 shows the structure of the PID controller in a RLLE model.

3.2 Model Reference Adaptive Controller In this study, the gradient method of the MRAC is used with the output of the closed loop RLLE follow the output of the reference model. Therefore, the e is minimized by designing an adjustment mechanism in real time such that a cost function is minimized. The cost function is formulated as, J (K v ) =

1 2 e 2 m

(21)

where K v is an adjustable controller parameter of the MRAC and em is the output difference between the reference and the graphical RLLE model. The em is represented as,

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em = θi − θk

549

(22)

θk is the output model reference of joint i (hips, knees). K v is changed in the direction of the negative gradient of J where the negative sign implies that K v is changed such that J becomes small. Thus, d K v /dt becomes ∂J dkv ∂ J ∂em ∂em = −γ = −γ = −γ e dt ∂ Kv ∂em ∂ K v ∂ Kv

(23)

where γ is the speed of adaption, and ∂e/∂ K v is the sensitivity derivatives of the system, indicating how error is influenced by the adjustable parameter K v . From Eq. (22), ∂em /∂ K v is shown as ∂em = kG(s)θr ∂kv

(24)

Substitute Eq. (24) into Eq. (23) to produce, dkv = −γ em kG(s)θr dt

(25)

The reference model is formulated as, θk = ko G(s) θr

(26)

Substitute G(s)θr in Eq. (26) into Eq. (25) to obtain the dkv /dt as, k dkv = −γ θk em = −γ  θk em dt ko

(27)

where γ  is the adaptation parameter. Figure 8 shows the structure of the MRAC control in the RLLE model. Equations (15) and (16) are used as reference models ko G(s). LLE =

DC motor MRAC

Fig. 8 MRAC structure

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Fig. 9 MRAC-PID structure

3.3 MRAC-PID Control The MRAC-PID control is designed using two control schemes i.e. MRAC and PID in which combined in a parallel connection. The controller structure in a closed-loop control system is shown in Fig. 9. The initial values of PID are set using the Z-N method. The MRAC-PID controller structure is represented as, Vu = θr



K P s + K I + K D s2 − γ  θk em e s

(28)

where Vu is the MRAC-PID controller output.

4 Result and Discussion Figure 10(a) and Fig. 11(a) show the controller performances of trajectory tracking of the hips and knees of RLLE. Two operation conditions are considered drug simulation, i.e. without DC motor gear backlash and with DC motor gear backlash. The PID parameters is tuning by the Z-N, while the MRAC γ  gain are set using the trial-and-error method. The proportional, integral, derivative and γ  gain for the hips are 0.91, 0.0802, 0.0046 and −0.006 respectively. Similarly, these values for the knees are 0.85, 0.0831, 0.0042 and −0.006. The backlash signal is generated using white noise in Matlab Simulink, where noise power, sample time and seed are set to 0.00005, 0.2 and [1 2 1 2] respectively. The respective control performances for the angle tracking errors, integral of absolute error (IAE) is obtained using, ∞

I AE = ∫ |e(t)|dt 0

(29)

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Table 4 Controller performance Operating condition

Left hip (IAE) PID

MRAC

MRAC-PID

PID

Left knee (IAE) MRAC

MRAC-PID

Without backlash

293.1

245.9

289.2

289.2

224.7

289

With backlash

295.1

214.0

262.9

299.0

253.9

322.7

Fig. 10 Hip a controller performance including noise, b convergence performance for the adjustable Kv

The comparisons performance of respective controller are shown in Table 4. The IAE of angle tracking errors shows that the MRAC has a higher performance compared to the PID and MRAC-PID for both operation condition, i.e. without backlash and with backlash. Figure 10(b) and Fig. 11(b) show the convergence rate for the adjustable parameter K v of the MRAC and MRAC-PID, in which depends on the value of the γ  gain.

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Fig. 11 Knee a controller performance with noise, b convergence performance for the adjustable Kv

5 Conclusion This paper investigates the development of hybrid of model reference adaptive controller and PID controller for joint trajectory tracking control. The tracking controller performance of the PID, MRAC and MRAC-PID is evaluated on the RLLE. The gradient based method is used as an adjustment mechanism to adapt the parameters of the MRAC. The RLLE model is achieved via ADAMS, while the DC motor model is determined using Kirchhoff’s equation via Matlab. The reference model of the MRAC is obtained using the recursive least-squares algorithms. A comparison of performances between each of them via the co-simulation of ADAMS and Matlab shows that the MRAC provides great compliance, the lowest cost function and robustness against DC motor gear backlash. This study is useful for designing exoskeleton controllers used in passively repetitive training.

References 1. Aliman N, Ramli R, Haris SM (2019) Design of locomotive lower limb exoskeleton with Malaysian anthropometric characteristics. Int J Mech Eng Robot Res 8:304–309 2. Aliman N, Ramli R, Haris S (2017) Design and development of lower limb exoskeletons: a survey. Rob Auton Syst 95:102–116

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3. Akdo˘gan E, Adli MA (2011) The design and control of a therapeutic exercise robot for lower limb rehabilitation: physiotherabot. Mechatronics 21:509–522 4. Luu TP, Low KH, Qu X, Lim HB, Hoon KH (2014) Hardware development and locomotion control strategy for an over-ground gait trainer: NaTUre-gaits. IEEE J Transl Eng Heal Med 2:1–9 5. Amiri MS, Ramli R, Ibrahim MF (2019) Hybrid design of PID controller for four DoF lower limb exoskeleton. Appl Math Model 72:17–27 6. Aliman N, Ramli R, Haris SM (2018) Modeling and co-simulation of actuator control for lower limb exoskeleton. In: 2018 3rd international conference on control and robotics engineering, pp 94–98. IEEE 7. Han S, Wang H, Tian Y (2018) Model-free based adaptive nonsingular fast terminal sliding mode control with time-delay estimation for a 12 DOF multi-functional lower limb exoskeleton. Adv Eng Softw 119:38–47 8. Amiri MS, Ramli R, Ibrahim MF (2019) Initialized model reference adaptive control for lower limb exoskeleton. IEEE Access 7:167210–167220 9. Belkadi A, Oulhadj H, Touati Y, Khan SA, Daachi B (2017) On the robust PID adaptive controller for exoskeletons: a particle swarm optimization based approach. Appl Soft Comput 60:87–100 10. Hou Z, Xiong S (2019) On model-free adaptive control and its stability. IEEE Trans Automat Control 64:4555–4569 11. Lu R, Li Z, Su C-Y, Xue A (2014) Development and learning control of a human limb with a rehabilitation exoskeleton. IEEE Trans Ind Electron 61:3776–3785 12. Sado F, Yap HJ, Ghazilla RAR, Ahmad N (2019) Design and control of a wearable lower-body exoskeleton for squatting and walking assistance in manual handling works. Mechatronics 63:102272 13. Sun H, Zhang L, Li C (2009) Dynamic analysis of horizontal lower limbs rehabilitative robot. In: 2009 IEEE international conference on intelligent computing and intelligent systems, vol. 2, pp 656–60. IEEE 14. Baluch TH, Masood A, Iqbal J, Izhar U, Khan US (2012) Kinematic and dynamic analysis of a lower limb exoskeleton 6:904–908 15. Alaci S, Ciornei FC, Romanu IC, Ciornei MC (2018) The importance of correct specification of tribological parameters in dynamical systems modelling. IOP Conf Ser Mater Sci Eng 294:012039

Nature Driven IOT Based Automation of Aquaponic System S. Zakaria, M. A. A. Ahmad Jafri, E. A. REngku Ariff, R. Hamidon, Z. A. Zailani, M. S. Bahari, and H. Azmi

Abstract Automation of Aquaponic with (Internet of Things) IoT system model is designed with the integration of hydroponics (growing plant or vegetable without soil), aquaculture (fish farming) and vertical farming. The main purpose of the designed model is to ultimately support the survival and healthy growth of bacteria in plants, fish as well as saving water by increasing the productivity. The development of the system with IoT to monitor and control parameters such as water level, water temperature and water pH. The control system based on Arduino Uno, multiple sensors for water pH, water level and water temperature as well as automatic feeder dispenser are used in this project. This system is using IoT as a platform supported by Wi-Fi module ESP8266 Wi-Fi module in Blynk Software. System settings and controls can be carried out manually on the main control unit or remotely through mobile phone commands. This system also used to collect different data parameters and those data will be compared with the optimal range as the data will be transferred to Blynk for analysis. Therefore, the design system can operate independently without manually monitoring and utilized IoT to control and monitor the system systematically. Keywords Aquaponics · Aquaculture · Agriculture · Internet of Things

1 Introduction Aquaponics is a combination of the aquaculture fish production and the hydroponic plants production into a sustainable agriculture system. But, according to [1], aquaponics is an integrated system that connects hydroponic production [2] with recirculating aquaculture which uses natural biological cycles to produce nutrients S. Zakaria (B) · M. A. A. A. Jafri · R. Hamidon · Z. A. Zailani · M. S. Bahari · H. Azmi Faculty of Mechanical Engineering Technology, Pauh Putra Campus, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia e-mail: [email protected] E. A. R. Ariff Faculty of Electrical Engineering Technology, Pauh Putra Campus, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_47

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and optimum the usage of nonrenewable resources. Thus, gradually providing benefits to the economy [3]. Aquaponics can also be defined as the most beneficial integration of hydroponics (e.g. soilless systems for plant production) and aquaculture (e.g. aquatic animal production) to synchronize the production of plant and animal waste. In this system, aquatic animal discharge waste will be converted into nutrients by bacteria and those nutrients will be absorbed by the plants to improve water quality for the aquatic animal [2]. This is also known as a combination of aquatics system and the development of plants, which both are connected to the integrated system and produce a symbiotic relation between them [4] following the principle of: I. II. III. IV.

one of biological system serve as nutrients from the waste products for a biological system. the combination of fish and plants results in a polyculture that increases the diversity and yields numerous products. water as an element to be re-used through biological filtration and recirculation. local food production gives access to healthy foods and improves the local economy.

Therefore, the system consists of bio-integrated farming system advancement concept combined with IoT-based electronic technology will be designed to automatically monitor the source of nutrition and hydroponic growing medium by feeding nutrients from the aquaculture ponds. The efficiency and quality of the feeding and plant nutrition can therefore be improved [5].

2 Methodology 2.1 Integration an Aquaponic System with IoT Figure 1 shows the work process of aquaponic system involving fish, bacteria, plant, water and oxygen. The ammonia rich waste is produced from the fish waste. Then, the bacteria grown in the beds and fish tank form ammonia which further divides into nitrites and nitrates. Thus, the water is refresh by the conversion from the waste substance into a good nutrient for the plants. The purpose of automation for the aquaponics system is to develop an IoT monitoring system to continuously observe parameters such as water pH level, water temperature and water level. Actual time observations of the water parameters inside the tank needs to be closely monitored so that the biological system is balanced. Figure 2 shows the flowchart of the overall system and Arduino Uno will be used to control the feeding rate, water pH, water temperature and water circulating system.

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Fig. 1 Work process of Aquaponic system [6]

2.2 Design Automation Aquaponic Process The Fig. 3 shows an automation of aquaponic process that involved the application of IoT to install and configure the electronic devices that are used, such as water temperature sensor and pH sensor. Thus, the measured water parameters facilitate the aquaponics system. The data obtained from the sensors are sent to Blynk, an application service that offering real-time data visualization analysis. Continuous tracking and recording of the data, and requisite changes would make it easier to sustain a balanced ecosystem that leads to the growth of fish and plants. It is also used approximately 90% less water compared to traditional farming method. Therefore, the process of the aquaponic is the best alternative with an impressive feature which can overtake traditional farming technique in a near future. The approach of the automation into this process to control and monitor the system requires a balance in parameters for farming in different type agriculture. The system starts with fabrication of fish tank and plant grower which are connected and behaved as a continuous flow system. The water from the fish tank which contain ammonia from the fish waste is supplied to the plant grower bed to fertilize the plants using a water pump. The plant roots will change ammonia into other molecules which become the nutrient for the plants’ growth. Thus, the plant grower bed i.e. plants roots also act as a water filter which directly filter the fish waste and improves the quality of water.

558 Fig. 2 Flow chart of overall project

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Fig. 3 Automation Aquaponic system

3 Result and Discussion 3.1 Setup for Automation of Aquaponic System Fig. 4 shows that a mini aquaponic system for fish and plants growth with installation of water monitoring testing kit which. The sensors are used to check the condition of water which relates to the ammonia concentration and temperature of the water in the circulation water tank system. The water pump is triggered within certain allocated time throughout the day. Fig. 4 Mini aquaponic system

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Fig. 5 Plant watering system

3.2 Setup for Automation of Aqaponic System Figure 5 shows the flow of the water from the fish tank to the plants using the water pump which act as a fertilizer for a plant’s growth. In the plants system, clay pebbles are used as medium for growth which help to provide space for the roots as well as the base for farming the plants. In addition, the clay pebbles also help to filter the water that flow through in order to increase the quality of water for the fish tank by removing or blocking the excess of fish waste which are not yet absorbed by the plants.

3.3 Control and Monitoring System of Aquaponic A monitoring device for the aquaponic system will collect all the data and send directly through a software and being recorded for a duration of 10 days. Table 1 shows the result of water testing and was recorded 4 times per day. This is to ensure the pH level at both fish tank and plant bed are properly maintain at the right level. The data is recorded at 8:00–9:00 am, 12:00–1:00 pm, 5:00–6:00 pm and 10:00–11:00 pm. Referring to Table 1, at 8:00 am until 9:00 am, the pH level is within the range of 6.9 up to 7.4 which is the best pH level for aquaponic system. At 12:00 to 1:00 pm, the pH level is within the range of 7.4 up to 7.6. Similar pH level can be observed at 5:00 pm until 6:00 pm. Lastly, at 10:00 to 11:00 pm, the pH level quality with a first selection time at 8.00 to 9.00 am. The variation of measured pH level is not significant. Nevertheless, the common value for pH level in aquaponic system is 6.5 until 8, however, the best value is in the range of 6.8 until 7.2, which gives more efficient growth for the plants. It is important to keep the pH level at a desirable range

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Table 1 Measured pH level for 10 days Day

8.00–9.00 AM

12.00–1.00 PM

5.00–6.00 PM

10.00.11.00 PM

1

7.2

7.5

7.5

7.2

2

7.2

7.5

7.5

7.15

3

7.2

7.5

7.5

7.2

4

7.4

7.4

7.5

7.4

5

7.4

7.6

7.6

6.9

6

6.9

7.5

7.6

6.9

7

7.2

7.5

7.45

7.2

8

7.2

7.5

7.45

7.2

9

7.2

7.6

7.5

7.2

10

7.2

7.6

7.6

6.9

for the fish reproduction as well as to properly maintain the system to support plant growth. Table 2 tabulate the measured water temperature at the allocated time for a duration of 10 days. At 8:00 to 9:00 am, the measured water temperature is within a range of 28 °C up to 28.6 °C. This range is the best range for the measured water temperature. As for within 12:00 to 1:00 pm, the measured water temperature is in the range of 29.5 to 30 °C. This range is also measured at 5:00 to 6:00 pm. At 10:00 pm until 11:00 pm, the measured water temperature range is the same as at 8:00 am to 9:00 am. The best range of water temperature for the aquaponics system to properly sustain maintain the growth for the fish and plant is within 28 °C until 32 °C. Since the variation of water temperature within the duration of 10 days is very small, it can be said the water temperature does not affecting the system in general. Table 2 Measured water temperature for 10 days Day

8.00–9.00 AM

12.00–1.00 PM

5.00–6.00 PM

10.00.11.00 PM

1

28.5

29.5

29.5

28.5

2

28.5

29.5

29.5

28.2

3

28.5

29.5

29.5

28.5

4

28.6

29

29.5

28.6

5

28.6

30

30

28

6

28

29.5

30

28

7

28.5

29.5

29.3

28.5

8

28.5

29.5

29.3

28.5

9

28.5

30

29.5

28.5

10

28.5

30

30

28

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4 Conclusion The implementation of IoT into the aquaponic to monitor and maintain several of system parameters such as water pH level and temperature are discussed in this paper. By pumping the water onto the plant bed, the accumulated substances such as ammonia, nitrite and nitrate which can change the water pH level are successfully filter by the plants’ roots and clay pebbles, thus later become the nutrient for the plants. Obtained results show that the water pH level and temperature for a duration of 10 days remain relatively constant and within proper range for fish and plants growth.

References 1. Nguyen NT, McInturf SA, Mendoza-Cózatl DG (2016) Hydroponics: a versatile system to study nutrient allocation and plant responses to nutrient availability and exposure to toxic elements. J Vis Exp 2016(113):1–9 2. Bakiu R, Tafaj C, Taci J (2017) First study about aquaponic systems in Albania. J Mar Biol Aquaculture Res 1(1), 1–7 3. Tyson RV, Treadwel DD, Simonne EH (2011) Opportunities and challenges to sustainability in aquaponic systems. Horttechnology 21(1):1–13 4. Campanhola C, Pandey S (2019) Integrated Aquaculture and Aquaponics. Sustainable Food and Agriculture, pp 251–257 (2019) 5. Haryanto MU, Ibadillah AF, Alfita R, Aji K, Rizkyandi R (2019) Smart aquaponic system-based Internet of Things (IoT). J Phys Conf Ser vol 1211, no 1 6. Sawyer J (2013) Growing Fish and Plants Together, Color. Aquaponics, pp 1–76

Implementation of PID Controller for Solar Tracking System S. Zakaria, J. Q. Ong, E. A. R. Engku Ariff, R. Hamidon, Z. A. Zailani, M. S. Bahari, and H. Azmi

Abstract Proportional integral derivative (PID) controllers are widely used in industrial processes cue to their simplicity and effectiveness for linear and nonlinear systems. Solar tracking system is one of the most direct approaches adopted to harvest more solar energy from photovoltaic (PV) system compared to stationary solar system. Hence, the PV panels able to receive maximum sunlight and generate more energy. Arduino based prototype dual axis (Azimuth-Altitude) solar tracking system is constructed with the implementation of PID controller. The performance of dual axis tracking system and stationary solar system are compared and discussed. Types of tuning methods for PID constant will be determine with use of Arduino IDE. Comparative results depicted that performance in terms of current, voltage and power value. According to results, dual axis solar tracking system with implement of PID controller is shown better performance compare to stationary solar system. Keywords PID controller · Solar tracking system · Stepper motor · Arduino

1 Introduction Renewable energy is a type of energy that is generated from natural processes that can be recycles naturally. Solar energy has gain massive attention in recent year due to the massive usage of fossil fuels which leads to climate changes by increased the emission of carbon, and, solar energy is classified as the most distinguished ways of harvesting energy by generating electricity using photovoltaic (PV) cells [1]. Solar panels harnessed the sunlight and convert the solar irradiation into electricity. Solar cells or known as photovoltaic (PV), which operate as a conductor, are used to track the sun rays and transform solar radiation into electrical energy. A solar panel S. Zakaria (B) · J. Q. Ong · R. Hamidon · Z. A. Zailani · M. S. Bahari · H. Azmi Faculty of Mechanical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia e-mail: [email protected] E. A. R. Engku Ariff Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_48

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only receives a maximum power when the sunlight is perpendicularly exposure on the panel [2]. Thus, solar tracking system are designed to enhance the ability of photovoltaic to receive a maximum solar radiation, by the process of maintaining the solar panel’s optimum angle so that its produce a best power output, with the idea of tracking the motion of sun’s position changes from time to time within a day so that received the maximum amount of sunlight [3]. In 2011, Li et al. [4] undergo an optical performance of vertical single-axis solar tracking system, compare with fixed and dual axis solar tracking system by using a mathematical procedure to calculate the annual collectible radiation. In 2014, Ferdaus et al. [5] designed an energy efficient hybrid dual axis solar tracking system by implementing various evolutional algorithms and methodologies from a normal mechanical single axis. However, the single axis tracking system does not sense the movement of sun from another axis while the dual axis tracking system shows some error on tracking the correct position whenever there is a cloudy day which is blocked the sunlight partially or fully. Thus, the implementation of Arduino based PID controller will be further study and dual axis solar tracking system is chosen hence comparison with be make between static solar system and dual axis solar tracking system.

2 Methodology A Proportional-Integral-Derivative (PID) controller is a control loop feedback mechanism that attempts to correct the error between a desired set point & a measured process variable by calculating & then output of a corrective action that can adjust the process according. In 2016, Klyak and Gol [6] investigated the energy generation and maximum efficiency of both PID controller and a fuzzy logic controller and compared with each other in single axis solar tracking system by using an Atmel microcontroller. The main purposed of this study is to rinse the energy obtained from solar panels by giving the sunlight specular reflection to the solar panel. In 2017, Hanwate and Hote [7] proposed a direct formula that use for solar tracking system for implementation of PID controller by using quadratic regulator approach with compensating pole (QRAWCP). Performance comparison is made between the proposed systems and other tuning application of solar tracking system, in terms of domain of time and frequency, and also integral indices of performances. Therefore, in this project, a prototype will be build, as well as tuning method that will be used to tune the parameter of Kp, Ki, and Kd is trial and error method as shown in Fig. 1. By using this method, a simple predetermination of these constant value will be made. First, the value that need to first set is the values of the Ki and Kd terms to zero, and then increase the Kp term until the system starts to reach the oscillation behaviour. Once oscillating occurs, adjust the Ki term to stop the oscillation, and finally adjust the Kd term to obtain a quick response. At last, adjust these three terms repeatedly until a most satisfy PID controller system is achieved.

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Fig. 1 Block diagram of of dual axis solar tracker system

Fig. 2 Graph of Kp = 80, Ki = 0 and Kd = 0 in serial plotter

In Arduino IDE sketch, a PID library is created by Brett Beauregard. A library is a file that also written in C++ version which provide a extra functionality for use in sketches. The existing of PID library in Arduino IDE sketch was making the PID controller can be applied easily with the same function as theory of PID controller. The system is design with Arduino microcontroller and the fabrication part is designed by using Solidworks.

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3 Result and Discussion In this system, the method to tuning the constant K parameters of PID controller is trial and error method. First of all, integral gain, Ki and derivative gain, Kd is set to be zero, and then proportional gain, Kp until the output of the control loop oscillates at a constant rate. Therefore, Kp as 80 is chosen as the first parameter constant value as in Fig. 2. Furthermore, the second parameter need to be tuned is derivative gain, Kd. At this step, the previous Kp value is used which is 80, while Ki will be remain as zero. Thus, Kd is equal to 50 is chosen as the second parameter value as shown in Fig. 3. Lastly, the last parameter that needs to be tune is integral gain, Ki. At this step, the previous Kp and Kd value will be keep which is Kp equal to 80 and Kd equal to 50 as in Fig. 4 (Table 1). In this experiment, both prototype model in Fig. 5 and stationary solar panel in Fig. 6 has been placed under the sunlight at the outdoor. The testing is only done in the good weather which is sunny day. The collection of data is taken hourly from morning 8am until evening 6 pm, while the value of voltage, current and power is collected for both stationary solar panel and dual axis solar tracker system as shown in Table 2 and Table 3.

Fig. 3 Graph of Kp = 80, Ki = 0 and Kd = 50 in serial plotter

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Fig. 4 Graph of Kp = 80, Ki = 15 and Kd = 50 in serial plotter Table 1 Final value of each parameter

Parameters

Proportional gain, Kp

Integral gain, Ki

Derivative gain, Kd

Value

80

15

50

Fig. 5 Dual axis solar tracking system prototype model placed under the sun

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Fig. 6 Stationary solar panel placed under the sun

Table 2 Result of dual axis solar tracking system output value

Time (Hour)

Current (A)

Voltage (V)

Power (W)

8.00 am

0.463

16.23

7.51

9.00 am

0.490

16.34

8.01

10.00 am

0.528

16.71

8.82

11.00 am

0.543

16.94

9.19

12.00 pm

0.575

17.21

9.89

1.00 pm

0.571

17.01

9.71

2.00 pm

0.556

16.86

9.37

3.00 pm

0.564

15.91

8.97

4.00 pm

0.523

16.35

8.55

5.00 pm

0.497

15.85

7.87

6.00 pm

0.487

15.92

7.75

According to graphs in Fig. 7, Fig. 8, and Fig. 9, the trends of both systems in the all types of graph were the same, the value increased slowly until the peaks at around 12 and 1 pm, and then slowly fall down from the peak until the end of the days. Thus, the power generation of dual axis solar tracking system is greater than stationary solar panel. The dual axis solar tracking system has better performance because the solar panel received larger sunlight intensity. There is a range of power generation difference that up to maximum 20% different between both systems where dual axis solar tracking system shown a higher power generation up to 20%.

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Table 3 Result of stationary solar panel output value Time (Hour)

Current (A)

Voltage (V)

Power (W)

8.00 am

0.351

13.80

4.84

9.00 am

0.364

14.68

5.34

10.00 am

0.462

15.20

7.02

11.00 am

0.512

16.86

8.63

12.00 pm

0.572

17.18

9.83

1.00 pm

0.575

17.16

9.87

2.00 pm

0.525

16.70

8.77

3.00 pm

0.485

15.98

7.75

4.00 pm

0.464

15.76

7.31

5.00 pm

0.442

14.58

6.44

6.00 pm

0.316

13.16

4.16

Fig. 7 Current versus time graph for both systems

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Fig. 8 Voltage versus time graph for both systems

Fig. 9 Power versus time graph for both systems

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4 Conclusion In short, dual axis solar tracking system was built successfully with the implementation of PID controller. The purpose of dual axis solar tracking system with the implementation of PID controller is to control and monitor a more accurate solar panel movement based on the light intensity. The solar panel will always keep its position that perpendicular to the sun in order to receive a maximum sunlight exposure. This implementation will maximize the output of power generation during the day while stationary solar panel only generated a full power in some certain period. Therefore, the performance of implemented PID controller in dual axis solar tracking system was presented a better result.

References 1. Kazici M et al (2018) Solar Cells, Comprehensive Energy Systems 2. Kaur T, Mahajan S, Verma S, Priyanka, Gambhir J (2017) Arduino based low cost active dual axis solar tracker. In: 1st IEEE international conference on power electronics, intelligent control and energy systems, ICPEICES 2016, pp 2–6 3. Al-Rousan N, Isa NAM, Desa MKM (2018) Advances in solar photovoltaic tracking systems: a review. Renew Sustain Energy Rev 82:2548–2569 4. Li Z, Liu X, Tang R (2011) Optical performance of vertical single-axis tracked solar panels. Renew Energy 36(1):64–68 5. Ferdaus RA, Mohammed MA, Rahman S, Salehin S, Mannan MA (2014) Energy efficient hybrid dual axis solar tracking system. J Renew Energy 2014:629717 6. Kiyak E, Gol G (2016) A comparison of fuzzy logic and PID controller for a single-axis solar tracking system. Renewables Wind Water Sol 3(1):7 7. Hanwate SD, Hote YV (2018) Design of PID controller for sun tracker system using QRAWCP approach. Int J Comput Intell Syst 11(1):133–145

Mechanical and Design

Analysis of Vibration for Grass Trimmer W. H. Tan, A. S. N. Amirah, S. Ragunathan, N. A. N. Zainab, A. M. Andrew, and W. Faridah

Abstract Grass trimmer is identified as a type of machine which contributes high vibration level and can cause hand-arm vibration. Hand-arm vibration syndrome (HAVS) can cause musculoskeletal disorder, neurology and complex vascular. The risk of developing HAVS is depending on the magnitude of vibration transmitted to the tool handle, the duration of vibration exposure and the user sensitivity to HAVS. In this study, a prototype handle is designed to reduce the vibration level. Three rubber mounts were used as isolator in the experiment. There are three isolators was selected to measure the different acceleration between the specimen. In addition, the transmissibility of engine was selected to compare between the original and three different isolators but on transmissibility of handle was compared between three rubber mounts. Every isolator has different value of stiffness and damping. When stiffness and damping are decrease in value; the vibration level was decreases. However, decrement of stiffness and damping, the value of transmissibility were decreases. In addition, the dynamic behavior as natural frequency and mode shapes of free analysis was determined between the original handle and the prototype handle. According to the analysis, the local and global vibration were found in the vibration mode of the grass trimmer. Keywords Hand-arm Vibration Syndrome (HAVS) · Grass trimmer · Rubber mount

W. H. Tan (B) Faculty of Mechanical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Main Campus Pauh Putra, 02600 Arau, Perlis, Malaysia e-mail: [email protected] A. S. N. Amirah · S. Ragunathan · N. A. N. Zainab · W. Faridah Faculty of Civil Engineering Technology, Universiti Malaysia Perlis, Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia A. M. Andrew Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Main Campus Pauh Putra, 02600 Arau, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_49

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1 Introduction Hand-arm vibration syndrome (HAVS), white finger disease or Raynaud’s phenomenon that can cause damage to hands and fingers. It can affect and damage blood vessels, nerves and joints in the fingers [1, 2]. One of the symptoms is affected fingers may turn into white, especially when exposed to cold. The syndrome is caused by the vibration that is delivered from the machine to the hand-workers. Based on statistics by the Department of Statistics, Malaysia in 2017, 15.0 million workers were employed in the field of labours [3]. Then, the health of employees is important, at the same time ensuring that the vibration rate is appropriate to the machine used. The BS EN ISO 11,806 (2008) has set a limitation for machine with engine displacement of less than 35 cc. The vibration total value (a_hv ) is set below 15 m/s2 for handle grass trimmer [4]. The Musculoskeletal Disorders (MSDs) was recorded as the common workrelated disorder suffered by employees, which has it increased since 2006. The statistics are collected by the Social Security Organization (SOCSO). People with MSDs suffer from joints, nerves and muscle injuries. 80% of population are having MSDs, it will rise to 84% if people are aware of it [5]. Vibration located at the engine makes the whole-body shake (WBV). Both of the syndrome can affect the health of workers [6]. The original handle of the grass trimmer does not have any insulation to reduce the vibration which transmitted from the engine to the handle. In this study, the operating frequency is important to redesign the new handle because as it influenced the HAVS. The original isolator of the engine is not good in specifications to minimize the vibration level which causes musculoskeletal disorder and whole-body vibration (WBV). The isolator will be developed by adding new prospect such as test a new material of isolator in this study.

2 Theoretical Background 2.1 Hand-arm Vibration (HAV) HAV is a form of vibration that is delivered to the hands and arms. There are three sources of HAV are hand-held power tools, hand-guided machinery and materials that are held whilst being worked upon by a vibrating process. Hand-arm vibration (HAV) has been known as a significant hazard for the health and safety of workers. International and national standards related to workplace safety, working conditions and exposure to physical agents also know hand-arm vibrations as bad health hazards or the specific name is hand-arm vibration syndrome (HAVS). Based on the study of Ko et al. [7], it showed that not all the rubber mounts are incapable with handles to reduce vibration at the hand-arm. This is because, each material has their own mechanical properties that can influence the device

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or machine. The material and the distance between rubber mounts influence the reduction of vibration and transmissibility of handle. To check the capability of vibration isolation, one of the parameters that can be used is transmissibility. This parameter is most important to the handle that can affect the grass trimmer system [8]. To reduce the vibration on chassis, the rubber mount has been selected as isolator [9, 10].

2.2 Vibration Isolation There are three vibration optimization approaches: isolation (buffers system from excitation), design modification (modifies the system) and control (senses the vibration and applies a counteracting force (used passive/active control)). Based on previous study [7], the rubber mount can be used to isolate the vibrating system. The role of the rubber mount can minimize the vibration between the handle with the vibrating engine. The single degree of freedom system is considered in this case. The transmissibility can be lower when the natural frequency of the mounts is below than exciting frequency of the engine. Thus, the frequency of engine and the natural frequency ratio should be maximized. Then, the engine mount stiffness coefficient and damping should be as low as possible to obtain a low transmissibility for the high frequency range.

2.3 Health Effect of Vibration Hand-arm vibration syndrome (HAVS) is a serious condition that can affect employee’s health. At the same time, it can affect the performance of works and also might disturb their social activities. According to medical condition, HAVS can be divided into 3 parts of human body such as vascular (affecting the blood vessels (white fingers), neurological (affecting the nerves) and musculoskeletal (affecting the muscles and joints). Figure 1 shows musculoskeletal pain on human body. The source of whole-body vibration (WBV) is caused by the position of the worker between vibration that is transmitted nearby the vibrating machine. Whole-body vibration (WBV) can also cause musculoskeletal disorder such as a very strong acceleration (over 10 m/s2 ) may cause acute injury to the spine. Furthermore, it will cause low back pain when a worker is exposed to shock WBV events and awkward body posture.

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Fig. 1 Musculoskeletal pain

3 Material and Methods 3.1 Grass trimmer Description The strimmer or grass trimmer is powered by 2-stroke engines engine, which is mostly used in Malaysia. Flexible strings or metal blades are normally used as the cutter. The type of cutter used is depend on the land surface. In this study, the grass trimmer used is powered by a 2-stroke engine, where it is located to the chassis, and shouldered with a strap. The engine output is connected to a 1.8 m drive and rigid shaft. The function of drive shaft is to deliver the output of engine. Figure 2 shows the heavy-duty grass trimmer (Mitsubishi TL 33) that used for this study. The grass trimmer can generate the revolution between 4000–6200 rpm. The machine showed the efficiency when the operating system is full throttle (6200 rpm). The kinetic energy produced by the movement of piston and block then produce the

Fig. 2 Grass trimmer (Mitsubishi TL 33)

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Fig. 3 YV400 vibrometer

rotation energy to the grass trimmer cutter. The engine mounting system for the heavy-duty petrol engine consisting of three metal-to-metal bonded engine mounts located at specific coordinates. The positions of engine mounts are fixed as usual on the chassis. The selected isolator will be tested on the engine mount of chassis.

3.2 Vibrometer The YV400 vibrometer meter is designed to measure conventional vibration especially the vibration test in the rotating and reciprocating machine as shown in Fig. 3. The acceleration, velocity and displacement of vibration as well as rotational speed (RPM) can be measured by this device. Technically, the measurement range for this device as; acceleration (0.1 –200 m/s2 ) (peak), velocity (0.1–400 mm/s) (RMS) and displacement (0.001–10.0 nm) (peak-peak). In addition, this device can perform in simple failure diagnosis. There are three modes can be displayed, which are common mode, spectrum mode and rev mode. This device can set the warning limit and alarm limit. In this study, the frequency was set up in 0–200 Hz for this device as the RPM range of used grass trimmer is always in this frequency range.

3.3 Rubber Mount The rubber mount where can also be called vibration isolator as shown in Fig. 4. It is used as the elastic mounting of machine where it has capability characteristics to

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Fig. 4 Rubber mount

Table 1 Specification of rubber mount [13]

Parameter

A

B

C

Diameter outer, mm

–

–

–

Diameter inner (D), mm

19.0

15.0

15.0

Height (H), mm

25.0

20.0

25.0

Thread, G

M6

M6

M6

Length (L), mm

18.0

15.0

18.0

mount a grass trimmer engine and prototype of handle [11]. The rubber mount can accommodate load either in compression load and shear load [12]. In this engine mounting system, three mountings will be used for each corner of engine chassis. For the handle prototype, only two mountings were used only. The specification of rubber mount used in this study as shown in Table 1.

3.4 Design of Handle The original and prototype handle was designed to their own natural frequency. The natural frequency can influence the resonance. The original and prototype handle are tested in this study. There are 3 different sizes of rubber mounts mounted on the prototype handle with named as A, B and C. The handle prototype as shown in Fig. 5 is designed based on the standard ISO 11,896 (2008). The natural frequency of handle will affect the transmissibility. If the transmissibility is low, it means that the handle is optimized to be designed. The excitation frequency of vibrating source should be higher than natural frequency of the handle in order to obtain the transmissibility less than one. For this phenomenon, it is called as maximum isolation.

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Fig. 5 Handle prototype

3.5 Measurement of Vibration Level using Vibrometer Piezoelectric acceleration transducer converts vibration signal into electrical signal. Then, the vibrometer analyzing input signal, results including RMS of velocity values, peak-peak value of displacement, peak values of acceleration or real-time spectral charts. This accelerometer is able to measure vibration within 10–1000 Hz. When the accelerometer is mounted on the handle/engine chassis, the vibration that is transmitted to piezoelectric accelerometer will have a small weight towards the face of a crystal. From this, the electrical voltage will generate the crystal element. So, the compression of weights is proportional to the electrical voltage. In addition, the electrical voltage is proportional to the acceleration. The working principle of accelerometer obeys the principle of Newton’s second law, F = ma, and generates a charge. Then, by using vibrometer, the charge output is then converted to low impedance voltage output. The vibrometer generate the vector quantity of acceleration. It required to measure the vibration in triaxial orthogonal axis. Figure 6 and Fig. 7 show that the manner X, Y and Z axis are oriented at the chassis grass trimmer and handle. In triaxial orthogonal axis the vibration measurement should always be at or as close as possible to the surfaces of the vibration handle and chassis where the maximum vibration transfer of energy to the hand and spine. Hence, the accelerometer is attached to engine chassis using a magnet, however, M5 bolt is used to mount piezoelectric accelerometer on the handle. Figure 8 and Fig. 9 show that the foam was placed below the chassis, plastic debris shield and trigger. It can decrease the vibration energy slightly only and has high accuracy of the measured vibration results.

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Fig. 6 The manner of X, Y and Z axis oriented at chassis engine

Rubber mounts

Fig. 7 The accelerometer was attached to the Z-axis on the handle prototype

4 Result and Discussion 4.1 Vibration Measurement for Engine The acceleration was selected as parameter to compare the specimens. The measurement of acceleration was measured in x, y and z axes. The magnitude of accelerations for original mounting were recorded as 1.893, 2.099 and 0.541m/s2 for x, y and z axes respectively. The respected excitation frequency was 29, 37 and 28 Hz for x, y and z axes respectively. These frequencies were selected for engine vibration acceleration measurement when mounting A, B and C were installed. According to the

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Foam

Fig. 8 Foam is placed at chassis engine

Foam

Foam

Fig. 9 Foam is placed at the trigger and plastic debris shield

measurements, the vibration acceleration measurement was recorded and tabulated in Table 2. Based on Table 2, vibration acceleration of mounting C was recorded as 0.1314,0.1705 and 0.1445 m/s2 respect to x, y and z axes. The mounting A showed the peak acceleration of 0.3116 m/s2 at x-axis, 0.1727 m/s2 for y-axis and 0.02515 m/s2 for z-axis while mounting B showed the acceleration of 0.2717 m/s2 at x-axis, 0.0099 m/s2 for y-axis and 0.0443 m/s2 for z-axis. The mounting A in x-axis observed has the highest peak among the three mountings. Table 2 Vibration acceleration measurement for the engine Mounting

Revolution per minute = 2397 rpm Acceleration (m/s2 ) X-axis (29 Hz)

Y-axis (37 Hz)

Z-axis (28 Hz)

Original

1.893

2.099

0.541

A

0.3116

0.1727

0.0252

B

0.2717

0.0099

0.0443

C

0.1314

0.1705

0.1445

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Table 3 Vibration acceleration measurement for the handle Mounting

Revolution per minute = 2397 rpm Acceleration (m/s 2 ) at 55 Hz X-axis

Y-axis

Z-axis

Original

0.137

0.337

0.443

A

0.0695

0.0514

0.0089

B

0.0258

0.0532

0.0086

C

0.0073

0.0751

0.0149

4.2 Vibration Measurement for Handle The vibration acceleration measurements for handle was tabulated in Table 3. The excitation frequency for vibration acceleration measurement of the handle is 55 Hz. The measured accelerations for mounting B at x, y and z axis were recorded as 0.0258, 0.0532 and 0.00806 m/s2 respectively. The original handle shows the peak acceleration of 0.137 m/s2 at x-axis, 0.337 m/s2 at y-axis, and 0.443 m/s2 at z-axis. For mounting A, vibration acceleration was recorded as 0.0695 m/s2 (x-axis), 0.0514 m/s2 (y-axis), and 0.0089 m/s2 (z-axis). However, x-axis acceleration of mounting C is 0.0073 m/s2 which showed the lowest acceleration among three mountings, while 0.0751 m/s2 is for y-axis and 0.0149 m/s2 is for z-axis.

5 Conclusion In conclusion, this study has achieved the objectives to minimize the vibration level of engine by changing from spring mounting to rubber mount. It is found that the vibration acceleration of engine and handle is significantly decreased when mounting A, B and C were replaced and compared with the original mounting. The decrement of vibration acceleration at x-axis, y-axis and z-axis. Thus, if the mounting is optimized to be designed, it can reduce the vibration of grass trimmer effectively. Acknowledgements The authors gratefully acknowledge tbe financial support from UniMAP and also like to acknowledge the School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP) for the equipment and technical assistance. The authors are much gratitude to the dedicated staff for fruitful discussions and input to the project.

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References 1. Bovenzi M, Hulshof CT (1999) An updated review of epidemiologic studies on the relationship between exposure to whole-body vibration and low back pain (1986–1997). Int Arch Occup Environ Health 72(6):351–365 2. Fridén J (2001) Vibration damage to the hand: Clinical presentation, prognosis and length and severity of vibration required. J Hand Surg Am 26 B(5):471–474 3. Department of Statistics , Malaysia Press Release Labour Force Survey Report, Malaysia , 2017, Dep. Stat. Malaysia Press Release Labour Force Surv. Rep., Malaysia , 2017, April, pp. 6–8 (2016) 4. László HE (2010) The vibration exposure of small horticultural tools and its reduction. Dr. Diss. Budapesti Corvinus Egy, p 21 5. Shariat A, Bahri S, Tamrin M, Arumugam M, Danaee M, Ramasamy R (2016) Review musculoskeletal disorders and their relationship with physical activities among office workers: a review. Malaysian J Public Heal Med 16(1):62–74 6. B. Rehn: Musculoskeletal Disorders and Whole-body Vibration Exposure, no. 852 (2004). 7. Ko YH, Ean OL, Ripin ZM (2011) The design and development of suspended handles for reducing hand-arm vibration in petrol driven grass trimmer. Int J Ind Ergon 41(5):459–470 8. Yu Y, Naganathan NG, Dukkipati RV (2001) A literature review of automotive vehicle engine mounting systems, vol 36, pp 123–142 9. Tu YQ, Zheng GT (2007) On the vibration isolation of flexible structures. J Appl Mech 74(3):415 10. Tewari VK, Dewangan KN (2009) Effect of vibration isolators in reduction of work stress during field operation of hand tractor. Biosyst Eng 103(2):146–158 11. Östberg M, Coja M, Kari L (2013) Dynamic stiffness of hollowed cylindrical rubber vibration isolators - the wave-guide solution. Int. J. Solids Struct. 50(10):1791–1811 12. Lin J, Ran T, Farag NH, Pan (2005) Evaluation of frequency dependent rubber mount stiffness and damping by impact test. Appl Acoust 66(7):829–844 13. I. AV Products (2019) Male/Male Cylindrical Bobbin Mounts | Type A Metric | Rubber Vibration

Acoustical Analysis and Optimization for Micro-Perforated Panel Sound Absorber W. H. Tan, A. S. N. Amirah, S. Ragunathan, N. A. N. Zainab, A. M. Andrew, W. Faridah, and E. A. Lim

Abstract The main objective of this study is to maximize the rate of sound absorption by applying the parameters of micro-perforated plate (MPP) such as the holes diameter, holes spacing, thickness of MPP, and air cavity depth of MPP. In this study, an optimization algorithm – Firefly algorithm is adopted to determine an optimum set of four parameters for MPP. There are four pieces of MPP with different holes of diameter and spacing was used as specimens. The two-microphone impedance tube method was used to measure sound absorption coefficient (SAC) of MPP sound absorber according to ASTM E1050-12 standard. From the experiments, MPP C (hole diameter = 0.5 mm, hole spacing = 7 mm) for both air cavity depth (30 & 60 mm) score the highest SAC which is 1.00 while MPP B (hole diameter = 0.9 mm, hole spacing = 5 mm) obtain the lowest SAC for air cavity of 30 and 60 mm, which are 0.62 and 0.54 respectively. Then, the firefly algorithm is applied to obtain the optimal solution the set of parameters for MPP sound absorber to reduce the noise level. Hence, it is concluded that by increasing the air cavity depth, holes spacing, and decreasing holes diameter size can increase the rate of sound absorption for MPP. The optimal set of parameters obtained from this study for MPP sound absorber for air cavity, hole diameter and holes spacing are 30, 0.71 mm and 0.5 respectively. Keywords Micro-Perforated Plate (MPP) · Impedance tube · Sound Absorption Coefficient (SAC) W. H. Tan (B) Faculty of Mechanical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Main Campus Pauh Putra, 02600 Arau, Perlis, Malaysia e-mail: [email protected] A. S. N. Amirah · S. Ragunathan · N. A. N. Zainab · W. Faridah Faculty of Civil Engineering Technology, Universiti Malaysia Perlis, Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia A. M. Andrew Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Main Campus Pauh Putra, 02600 Arau, Perlis, Malaysia E. A. Lim Institute of Engineering Mathematics, Faculty of Applied and Human Sciences, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_50

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1 Introduction Micro-perforated panel (MPP) is kind of sound absorbers normally are used as replacement for porous sound absorption material such as glass wool and mineral wool. Normally, a porous sound absorption material produced unwanted floating dust particles in inhibit buildings that impair to our health. MPP was first proposed by Maa in 1975 [1], that demonstrated many excellent performances, such as fireproofing, sound proofing, moisture-proofing, and ability to adapt high-speed air flow [2]. An MPP sound absorber is an environmentally friendly sound absorption system where it consists of a lot of sub-millimeter holes that functioned to increase viscous and thermal losses inside the perforations [3]. They provide high acoustic resistance and low acoustic mass reactance to tune the sound absorption peak frequency [3]. In addition, firefly algorithm is used to determine the optimal solution for micro-perforated in this study. Firefly algorithm is one of the evolutionary computation methods used for optimization problems that was created by Xin-She Yang in 2007 [4]. It is inspired by the behavior of firefly and is a kind of nature-inspired algorithm that was applied for solving the hardest optimization problem such as travelling salesman problem [4], and optimization of MPP sound absorber to deliver an optimum sound absorption performance in this study. This study starts by applying all identified parameters such as holes diameter, holes spacing, thickness of MPP, and air cavity depth of MPP to increase sound absorption coefficient (SAC) by using two microphones impedance tube measurement method. Firefly Algorithm was coded in MATLAB. The Firefly Algorithm was then tuned and executed to obtain an optimum set of parameters for MPP sound absorber.

2 Methodology 2.1 Two-Microphone Impedance Tube The two-microphone impedance tube is showed in Fig. 1, was used for sound absorption coefficient (SAC) measurement of MPP sound absorber according to ASTM E1050-12 standard [5]. The experiment was conducted in laboratory under the room temperature of 25 °C. The impedance tube inner diameter is 10 cm and can measure the SAC of specimens for the frequency range 200 to 1800 Hz. In the measurement, one of the ends of impedance tube is attached with a loudspeaker that served as the source to generate random sound signal. The complex sound reflection coefficient of R for a test specimen was calculated from the corrected complex acoustic transfer function H12 between the two-microphone positions [6, 7]. Hence, the normal incidence sound absorption coefficient α n was calculated by α n = 1−|Rˆ2| [3]. In this study, SAC of MPP sound absorber were measured with different air cavity depth which are, 30 and 60 mm for different perforation ratio of MPP.

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Fig. 1 Two-microphone impedance tube

Fig. 2 Setup of two-microphone impedance tube measurement

Fig. 3 LMS SCADAS MOBILE-4 channels

2.2 Sound Absorption Coefficient Measurement Setup There are four main equipment that are requires to conduct the sound absorption coefficient measurement. The impedance tube is shown in Fig. 2, while the microphone is shown in Fig. 3, the DAQ device is shown in Fig. 4. DAQ device acted as bridge to connect between the electric signals from sensor to computer and presented the numerical results in LMS software [8, 9]. Data acquisition (DAQ) is a device that functioned as a converter that processes the changes of signal received to numerical values. The conversion processes start

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Fig. 4 Microphone as a sensor

with acoustic sensor. In this measurement, the acoustic sensors are the microphones. The noise received in impedance tube is converted to electrical signals by sensors and sending into DAQ device. DAQ device acts as medium connection between computer and sensor. Hence, they can be analyzed using specialized software tools such as LMS Test Lab Absorption Testing software [8–10]. There are four pieces of MPPs are used with different diameter and spacing holes in this measurement as tabulated in Table 1. The MPPs are made from aluminum with 1 mm of thickness.

2.3 Firefly Algorithm Initially, the sound absorption coefficient (SAC) measurement result for MPP sound absorber is fitted using nonlinear model proposed by Saber et al. [11] due to the measurement results behaved in nonlinear relationship. After the nonlinear model is fitted using the measurement results, a cost function was built and it is then used in the Firefly Algorithm for determined optimal solution. From flow chat in Fig. 5, Firefly Algorithm started with randomly generated an initial population of fireflies X i = (1, 2, 3, 4…n). Then, it will evaluate the cost function from nonlinear fitting on the measurement results by seeking the fireflies with highest brightness values. Next, the fireflies ranked according to the brightness values and the current location firefly will move to firefly with highest brightness values. If X j is greater than X i , firefly-i will move toward firefly-j. This is because the concept of firefly algorithm is based on biological behavior of fireflies that are attracted to light. Fireflies that shine brighter attracted more others fireflies to it. Hence, for each of iteration for the computation in the algorithm, a new solutions and ranking are updated. This process will be repeated until the generation is greater or exceed than maximum generation, and it will stop the loop. The new generation will be the best solution.

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Table 1 Holes diameter and holes spacing of micro-perforated plate

MPP C (d = 0.5mm, b = 7mm)

MPP D (d = 0.3mm, b = 5mm)

MPP B (d = 0.9mm, b = 5mm)

MPP A (d = 0.5mm, b = 3mm)

3 Result and Discussion In this study, all the data obtained from the measurement of two-microphone that installed in an impedance tube are displayed in the graph via Sound Absorption Coefficient (SAC) against frequency. The thickness of MPP is maintained to 1 mm throughout the whole study. This section discusses the SAC measurement results obtained from LMS Test Lab absorption software. The graph of SAC against frequency will show the perforation ratio and air cavity depth influences on the performance of SAC. Finally, Firefly Algorithm is applied to the model cost function and determined the optimum combination parameters for MPP for higher rate of SAC.

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Fig. 5 Flow chart of firefly algorithm [11]

3.1 Sound Absorption Coefficient of MPP Sound Absorber Based on Fig. 6 (a), the graph indicates the highest Sound Absorption Coefficient (SAC) is obtained by MPP C (d = 0.5 mm b = 7 mm). The highest peak of SAC is 1.00 which drops at 662.5 Hz. Then, the lowest SAC is obtained by MPP B (d = 0.9 mm, b = 5 mm) which is 0.62 at 1300 Hz. Although MPP C reaches higher SAC for 30 mm of air cavity, the frequency band is relatively narrow compared to the frequency band for MPP B due to the different in perforation ratio. Therefore, is it discovered that, using different perforation ratio will definitely affect SAC for MPP.

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Fig. 6 Comparison between MPP of different perforation ratio for a 30 mm and b 60 mm of air cavity

Figure 6(b) shows the graph of SAC against the frequency of different perforation of MPP with similar backing air cavity depth of 60 mm. The highest SAC point reached at 1.00, with drops at 462.5 Hz when using MPP C (d = 0.5 mm, b = 7 mm). Meanwhile, the lowest point of SAC is 0.54, drops at 462.5 Hz when using MPP B (d = 0.9 mm, b = 5 mm). However, MPP B generated a wider band of frequency compared to MPP C even though MPP C obtained the highest value of SAC of 60 mm of backing air cavity depth. In summary, different perforation ratio can create difference frequency band that affect SAC for MPP. Figure 7 (a) shows the graph of SAC against frequency for constant perforation ratio with different air cavity which are, 30 and 60 mm. Constant perforation ratio means that the MPP had similar holes diameter size and holes spacing. Furthermore, it is observed that the SAC for MPP A is getting higher with a deeper air cavity depth. From Fig. 7 (a), it is found that MPP A with 60 mm air cavity depth gives the SAC peak of 0.76 at the frequency 809.38 Hz. However, MPP A with 30 mm air cavity depth, the SAC peak dropped to 0.62 at frequency of 1290.625 Hz. Thus, it is observed that the peak of SAC for MPP sound absorber can shifted to a lower frequency range by increasing the air cavity depth. From Fig. 7 (b), it is observed that the sound absorption performance for MPP C reached the highest peak at 1.00 by increasing the depth of air cavity. However, the frequency to achieve the peak is different. Hence, it is discovered that MPP C with 60 mm of air cavity depth drops at 462.5 Hz compared to MPP C with air cavity of 30 mm, the frequency increase to 662.5 Hz. So, it is observed the increment of air cavity depth can affect the peak of amplitude to shift to the left, and creates lower frequency peak with same SAC with constant perforation ratio. From Fig. 8 (a), it is observed that the performance of sound absorption coefficient getting higher by decreasing the holes diameter size of MPP. MPP B has bigger size

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Fig. 7 Comparison between different air cavity of a MPP A and b MPP C but same perforation ratio

Fig. 8 Comparison between biggest and smallest size holes diameter for a 30 mm and b 60 mm air cavity

holes diameter which is, 0.9 mm and it obtained a lower SAC which is, 0.62 that drops at 1300 Hz. However, MPP D has smaller size holes diameter of 0.3 mm, it obtained higher SAC which is, 0.95 at frequency of 553.125 Hz. Hence, it is observed that MPP D has narrow frequency bands compared to MPP B. Therefore, decreasing the size of holes diameter can increase SAC but, it narrows the frequency band in condition of using same holes spacing and distance of air cavity.

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Figure 8 (b) shows the comparison of MPP D and MPP B using 60 mm of air cavity depth. It can be observed that using smaller size of holes diameter can increased the performance of sound absorption coefficient of MPP. MPP D obtained higher SAC which is, 0.97 when frequency reached 393.75 Hz using smaller diameter size compared to MPP B with its SAC reached 0.54 at 842.19 Hz when using larger holes diameter. However, the peak of SAC of MPP D shifted to the left, thus create a lower frequency and narrow the frequency band, while MPP B that has wider frequency band but obtained lower SAC value. Hence, reducing the size of holes diameter can increase the SAC, but at same time narrowing the band of frequency. Figure 9 (a) shows comparison between MPP A and MPP C with different holes spacing but constant holes diameter size and air cavity depth. It is observed that the sound absorption coefficient for MPP A and MPP C getting higher by increasing the distance between hole of MPP. MPP C obtained highest SAC for 30 mm air cavity depth which is 1.00 dropped at 662.5 Hz compared to MPP A that obtained lower SAC which is 0.81 at 1270.31 Hz. Furthermore, MPP C reaches peak of amplitude faster than using MPP A. However, MPP C obtained narrow frequency band compared to MPP A. It can be concluded that by larger holes spacing for MPP can obtained highest SAC but narrow the band of frequency. Additionally, it is can observed in Fig. 9 (b), the MPP C obtained highest SAC which is 1.00 while MPP A get a value of SAC which is 0.75, achieve at the frequency of 845.31 Hz. Although MPP C obtained highest SAC for 60 mm air cavity depth, it has narrow frequency band compared to MPP A. This situation occurs due to the different size of holes spacing. Thus, by increasing the length of spacing between holes, it can increase the performance of SAC with narrower of frequency band.

Fig. 9 Comparison between different hole spacing of MPP A and MPP C for a 30 mm and b 60 mm air cavity

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3.2 Firefly Algorithm (FA) Firefly algorithm is one of optimization algorithm for getting optimal solution for cost function without trapping into local minima problem. Since FA is a global search algorithm, hence it would search whole population and can automatically subdivide into subgroups for comparing the most minimum value [12]. However, since the data is nonlinear, hence, the cost function must be obtained using nonlinear model for fitting purpose as proposed by Saber et al. [11]. The fitted nonlinear cost function is then used by FA for computing the optimal solution for this study. The output is shown in Fig. 10. So, nonlinear regression model is: y = −0.62967 + 0.23067x1−0.26874 + 89.33x2264.227 − 26.924x383.211 + 0.8867x40.0019072 By substitute all the estimated coefficients in nonlinear regression model and applyinto firefly algorithm, the results appear as shown in Table 2 below. FA mimics the behaviour of firefly that seeks the brightest firefly for best mate. FA search the optimal combination that minimize the cost function, the algorithm

Fig. 10 Variation of step over 500 iterations

Table 2 Result from firefly algorithm when apply nonlinear regression model

The optimization parameters for micro-perforated plate (mm) Air cavity

30

Hole diameter

0.71

Hole spacing

0.5

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begins with randomly assigned initial combination of the four parameters, then iterate using pre-result until the best solution with minimum cost function value is obtained. From figure above, it can conclude that the best character of micro-perforated plate for backing cavity, diameter of hole and holes spacing are 30, 0.71 and 0.5 mm respectively. FA is very fast and less complicated algorithms, it obtained optimal solution using less than 500 iterations.

4 Conclusion In this study, there are four major parameters affect sound absorption coefficient (SAC) of MPP; thickness of MPP, holes diameter size, holes spacing and air cavity. Thickness of MPP is maintained 1 mm for SAC measurement using two-microphone impedance tube method. Based on measurement, MPP C (hole diameter = 0.5 mm, hole spacing = 7 mm) for both air cavity (30 & 60 mm) score the highest SAC while MPP B (hole diameter = 0.9 mm, hole spacing = 5 mm) obtain lowest SAC. Hence, it can conclude that by increasing distance of air cavity, holes spacing and decreasing holes diameter size can increase rate of sound absorption for MPP sound absorber. Finally, Firefly Algorithm is applied to obtain the optimum parameters (d = 0.71 mm, b = 0.5 mm, D = 30 mm) for the optimized MPP sound absorber to absorb the noise level. Using Firefly Algorithm ability to seek optimal combination of parameters can ensure to obtain best solution for SAC. Acknowledgement The authors sincerely express the gratitude to School of Mechatronic Engineering, UniMAP for providing the equipment and site to success this study. It is blessing to Mr. Muhd Aliff, the technician of Solid Mechanic Lab, who willing to share precious knowledge and experience about the machine and method to make an experiment. Finally, authors would like to thank all those who involved and shares knowledge in this study.

References 1. Maa DY (1975) Theory and design of microperforated panel sound-absorbing construction. Sci Sin 18:55–71 2. Guo Wencheng, Mina Hequn (2015) A Compound micro-perforated panel sound absorber with partitioned cavities of different depths. Energy Procedia 78:1617–1622 3. Liu Z et al (2017) Acoustic measurement of a 3D printed micro-perforated panel combined with a porous material. Measurement 104:233–236 4. Shuhao Yu, Ma Shenglong Zhu Yan, Mao Demei (2015) A variable step size firefly algorithm for numerical optimization. Appl Math Comput 263:214–220 5. ASTM E1050-12 (2012) Standard test method for impedance and absorption of acoustical materials using a tube, two microphones and a digital frequency analysis system. New York: American National Standards Institution 6. Chung JY, Blaser DA (1980) Transfer function method of measuring in-duct acoustic properties. II. experiment. J Acoust Soc Am 68:914–921

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7. Koruk H (2014) An assessment on the performance of impedance tube method. Noise Control Eng J 62:264–274 8. What is data acquisition? - national instruments (2017) 9. What is data acquisition and Why is it important? (2017) 10. What is a data acquisition system? - definition from techopedia (2017) 11. Seber GAF, Wild CJ (2003) Nonlinear regression. Wiley Interscience, Hoboken, NJ 12. Yang X, He X (2013) Firefly algorithm: recent advances and applications. Int J Swarm Intell 1(1):36

Rehabilitation Progress of Arm VR Game Based on Hand Trajectory B. N. Cahyadi, Wan Khairunizam, S. Diny Syarifah, Wan Azani Mustafa, A. B. Shahriman, and M. R. Zuradzman

Abstract Long-term disability can reduce someone’s performance in activities or jobs. Although stroke is not the leading cause of disability, 75% of stroke survivors have decreased activity caused by disability. Serious long-term disability can be treated by using active movements, repetitive tasks, and task-oriented or movement sequences. Evaluation and monitoring the rehabilitation after stroke is the most crucial element to prevent the injury and determine the next step rehabilitation. This study will discuss monitoring arm movement for virtual reality (VR) game rehabilitation based on the trajectory movements. Five participants have contributed to data collection during three sessions and five repetition. Their movement recorded by using Kinect Xbox One sensor with data sampling 10 Hz. The mean absolute trajectory error (ATE) and hand speed movement methods are used to analyze the arm movement during the VR game. Although this study uses healthy subjects, 80% of them have an improvement in the movements, and this condition is proven by the reduced ATE value in each session. Trajectory data provides useful information about arm movements during the rehabilitation of VR games, including movement errors, hand position errors and hand speed to reach targets. Moreover, the mean ATE and hand speed movement able to provide clear information about the development of hand movements in completing the game. Keywords Absolute trajectory error · Virtual reality · Kinect xbox one

1 Introduction 2016 World Health Organization (WHO) informed that two-thirds of stroke patients have a permanent disability [1]. Although stroke is not the leading cause of disability, B. N. Cahyadi (B) · W. Khairunizam · W. A. Mustafa · A. B. Shahriman · M. R. Zuradzman School of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Arau, Malaysia S. Diny Syarifah Department of Informatics Engineering, University of Suryakancana, Cianjur, Indonesia B. N. Cahyadi Department of Electronic Engineering, University of Muhammadiyah Malang, Malang, Indonesia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_51

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long-term disability caused by a stroke can reduce someone’s performance in activities or jobs and decreasing confidence in life. Disability after stroke can be caused by muscle weakness and lost motor control. To regain muscle weakness and motor control after stroke have to start as possible as soon [2]. Recovery after stroke aims to increase motor function, lessen physical impairment, rebuild patient confidence, and restore the quality of life. Motor and muscle impairment could be treated with wellplanned and organised treatment modules, including the use of active movement in repetitive tasks and task oriented. Besides, rehabilitation movements must be safe to prevent decreased fibre length, muscle atrophy, and increased tendon compliance [3]. VR is a digital environment, which can be entirely controlled by a particular party. VR is a popular platform in the world game that can manipulate the virtual environment become more interesting. Researchers and stroke therapists have applied this platform to replace conventional stroke rehabilitation. It is an effective way of establishing a variable and stimulating environment, allowing the patient to engage in meaningful and motivating therapeutic activities [4]. Rehabilitation by using VR games may have some advantages than traditional therapy. VR games can allow people to practice daily activities that are cannot be practice within the hospital environment. In addition, several VR game features might be able to attract patients to spend more time in therapy. Studies in arm rehabilitations reported that the most crucial element in the arm treatment was to monitor the progress of rehabilitation [5–7]. To monitor and evaluate the arm rehabilitation, most therapists still rely on clinical assessments. These methods are manually performed by therapists by using chart-based ordinal scales and only monitors in terms of movements [8, 9]. An effective and accurate physiological assessment tool is essential to achieve the rehabilitation goal. Currently, there are lots of assessment tools designed to assess rehabilitation. Conventionally, clinical assessments are manually performed by the therapists by using chart-based ordinal scales such as Fugl-Meyer Assessment (FMA), Barthel Index (BI), Wolf Motor Function (WMF), Functional Independence Measure (FIM), Edmonton Symptom Assessment Scale (ESAS) and National Institutes of Health Stroke Scale (NIHSS) [8, 9]. This study will focus on the investigation of VR games for arm rehabilitation by using absolute trajectory error and hand speed movements. The key points of this study consist of four section. The first section is an introduction and related researchers. The second section is the methodologies of the study. The third section is result, and discussion and the last section is the conclusion.

2 Methodologies 2.1 VR Games Rehabilitation A VR game with a square shape movement is used for data collection in this study. This game is designed based on non-immersive VR game because this method has

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Fig. 1 Users interface for VR game rehabilitation [12]

not required the highest level of graphics performance, no specialised hardware such as a head-mounted display (HMD), low cost, and can be used for many applications. Movement in this game has been analyzed in previous studies regarding muscle activity [10–13]. The VR environment for arm movement was designed with a space area of 3 × 3 m. The system consists of a Kinect sensor, screen display, personal computer (PC), projector, and audio (built-in PC). The Kinect Xbox One sensor placed on the floor with a high 75 cm and the distance of the subject to the Kinect sensor is 150 cm. Because previous researchers declared that’s the optimal distance for the Kinect Xbox sensor is 150 cm to 200 cm [14, 15] (Fig. 1).. The VR game user interface consists of the virtual hand, square-shaped target, the path for the hand movement, capture of the subject, time, and coordinate of the virtual hand .

2.2 Data Collections A total of five subjects participated in this study for three sessions. At each session, all subjects have to perform the game with five repetitions. All the participants, including males and females, were students of Universiti Malaysia Perlis (UniMAP), and they were in a healthy condition. The VR game movements are shown in Fig. 2. The first step is put the hand in free position. The second step is put the hand in start position with the hand conditions is open with the elbow bend upwards at 60 degree angle. The next step is move the hand follow the line in the game. The subject needs

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Fig. 2 The direction of movements for VR game rehabilitation [13]

2.3 Data Processing Trajectory data were recorded by using the Kinect Xbox One sensor integrated with the VR game rehabilitation. The hand movement of the subjects and the trajectory of hand movement will be saved in the VR games software. The frequency sampling was

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used for recording the hand movements is 10 Hz. The data processing and analysing were done by using MATLAB software. Absolute Trajectory Error (ATE). Generally, trajectory error is the deviation of positions of the object coordinates with the actual coordinate of movements. In this study, the hand movement error is calculated by using mean absolute trajectory error (ATE). The absolute trajectory error calculation is by dividing the movement coordinate by a reference coordinate. The reference coordinate was generated by the game. The mean absolute trajectory error movement is the average of absolute trajectory error from each coordinate [16, 17]. mean AT E =

1 n |a − a| i=1 n

(1)

where: a¯ = Reference coordinate. A = Virtual hand coordinate. Hand Speed Movement. The basic calculation of hand speed is a distance divided by the time. The distance between the two coordinates can be calculated by using Pythagoras’ Theorem. If the number of coordinates is n, then the total distance (d total ) is d 1 + d 2 + d 3 + …… + d [18].  d=

(Y1 − Y0 )2 +(X 1 − X 0 )2 v=

dtotal t

(2) (3)

where: d = distance between coordinate X 0 ,Y 0 = Initial coordinate X 1 ,Y 1 = First coordinate v = Hand speed d total = The total distance t = time

3 Results and Discussions The objective of this study is to analyses the arm movement during VR game rehabilitation by using trajectory movement. Figure 3 and Table 1 show trajectory and data of arm movements from Subject #1. From the pattern of the arm movements, the subject has an improvement movements at each session. The pattern of trajectory movements at Session #3 is smoothness

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Trajectory of Arm Movements 80.00 60.00 40.00 20.00 0.00 -100.00

-80.00

-60.00

-40.00

-20.00

0.00

-20.00

Reff

Session #1

Session #2

Session #3

Fig. 3 Trajectory of arm movements from Subject #1

Table 1 Data of arm movements from Subject #1 Session

Time of movements (s)

Distance of hand movements (cm)

Hand speed movements (cm/s)

Absolute Trajectory Error

Length of track(cm)

#1

8.1

302.29

37.32

0.387

280

#2

7.2

300.46

41.73

0.346

280

#3

5.2

271.49

52.21

0.278

280

than Session #2 and Session #1. Moreover, the time to complete the game increase at each session and the error movement also reduce at each session. While the absolute trajectory error value to be small, the pattern of arm movements will be smoothness. The arm movement subjects called normal if the pattern of arm movements is same with the reference pattern. Absolute Trajectory Error. Figure 4 shows the graph of the ATE changes between sessions. The improvement of hand movement is showed by decreasing of ATE value between sessions. Decreasing ATE value caused by improvement of the movements which performed by the subject at each session. If the subject make mistake during arm movements including not focus in a game or moving the hand with fast, the absolute trajectory error will be increase. Generally, participants will try to improve their movements until they get lower error values. The mean absolute trajectory error can present the accuracy of hand movements between sessions. Although this study uses healthy subjects, most of them have an improvement in the movements. The task-oriented and repetitive task methods for arm movement treatment can help the subject to improve the accuracy of hand movements. This condition is proven by decreasing ATE values between sessions.

Absolute Trajectory Error

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Absolute Trajectory Error

0.50 0.40 0.30 0.20 0.10 0.00 Session #1

#1

Session #2

#2

Session #3

#4

#3

#5

Fig. 4 The mean absolute trajectory error values between sessions

Hand Speed Movement. yFigure 5 shows the graph of hand speed movement between sessions. An increase in value indicates an improvement in hand speed movements between sessions. Most of the subjects have experienced an increase in the speed of movements at each session. At Session #1, the average of hand speed is 43.52 cm/s, Session #2 is 56.32 cm/s and Session #3 is 62.08 cm/s. Participants will move slowly during the first session and speed will increase in the next session if they continue to practice, as shown in the graph. The analysis shows, most of subjects have an improvement in the hand speed movements. Motor function and muscle strength can affect the hand movements to complete the game. If a subject does exercise in much time or using repetitive method than the movement will be better including hand speed movement and error movements. If a subject does rarely exercise than the graph of progress rehabilitation will be decrease or inconsistence. Hand Speed Movements Hand speed movement (cm/s)

90.00 80.00 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00 Session #1

#1

Session #2

#2

#3

Fig. 5 The hand speed movement values between sessions

Sesion #3

#4

#5

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4 Conclusion Trajectory data provides useful information about arm movements during the rehabilitation of VR games, including movement errors, hand position errors and hand speed to reach targets. In this study, the mean ATE and hand speed movement able to provide clear information about the development of hand movements in completing the game. The errors and speeds of hand movements during arm treatment can be improved by using a repetitive task and task-oriented. Acknowledgements The authors gratefully acknowledge the financial support from UniMAP.

References 1. Johnson W, Onuma O, Owolabi M, Sachdev S (2016) Stroke: a global response is needed. Bull World Health Organ 94(9):633–708 2. Clinic M (2017) Stroke rehabilitation: What to expect as you recover. https://www.mayoclinic. org/stroke-rehabilitation/art-20045172 3. Vicky G, Charles RL, Garland SJ (2012) Factors that influence muscle weakness following stroke and their clinical implications: a critical review. Physiother Can 64(4):415–426 4. Trombetta M, Henrique PPB, Brum MR, Colussi EL, Marchi ACBD, Rieder R (2017) Motion Rehab AVE 3D: A VR-based exergame for post-stroke rehabilitation. Computer Methods and Programs in Biomedicine, pp. 15–20 5. Kultu M, Freeman CT, Hallewell E, Hughes A-M, Laila DS (2016) Upper-limb stroke rehabilitation using electrode-array based functional electrical stimulation with sensing and control innovations 6. Krabben T, Molier BI, Houwink A, Rietman JS, Buurke JH, Prange GB (2011) Circle drawing as evaluative movement task in stroke rehabilitation: an explorative study. Neuroeng Rehabil 8(15):1–11 7. Caby B, Stamatakis J, Laloux P, Macq B, Vandermeeren Y (2011) Multi modal movement reconstruction for stroke rehabilitation and performance assessment. Multimod User Interface 4(3):119–127 8. Zhang Z, Fang Q, Gu X (2015) Objective assessment of upper limb mobility for post-stroke rehabilitation. IEEE Trans Biomed Eng 63(4):859–868 9. Yeh SC, Lee SH, Wang JC, Chen S, Chen YT, Yang YY, Chen HR, Hung YP (2012) Virtual reality for post-stroke shoulder-arm motor rehabilitation: training system & assessment method. In: 14th International Conference on e-Health Networking, Applications and Services (Healthcom), pp 190–195 (2012) 10. Cahyadi BN, Khairunizam W, Ibrahim Z, Razlan ZM, Bakar SA, Mustafa WA, Majid SH (2018) Analysis of EMG based arm movement sequence using mean and median frequency. In: International Conference in Electronics, Electrical, Computer, Science and Informatics, Malang - Indonesia 11. Cahyadi BN, Khairunizam W, Majid SH, Ibrahim Z, Bakar SA, Razlan ZM (2018) Investigation of Upper Limb Movement for VR based Post Stroke Rehabilitation Device. International Colloquium on Signal Processing & its Applications, Penang - Malaysia 12. B. N. Cahyadi, W. Khairunizam, D. S. Sanny, Z. Ibrahim, L. H. Ling, S. A. Bakar, Z. M. Razlan and W. A. Mustafa: Arm Games for Virtual Reality Based Post-stroke Rehabilitation. Lecture Note in Mechanical Engineering, pp. 91–101 (2020).

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13. Rasidah SN (2017) Design of Arm Movement Sequence for Virtual Reality-Based Upper Limb Management After-Stroke. University Malaysia Perlis, Perlis 14. Pedraza-Hueso M, Martín-Calzón S, Díaz-Pernas FJ, Martínez-Zarzuela M (2015) Rehabilitation using kinect-based game and virtual reality. Procedia Comput Sci 75:161–168 15. Shahrbanian S, Ma X, Aghaei N, Korner-Bitensky N, Moshiri K, Simmonds MJ (2012) Use of virtual reality ( immersive vs non-immersive) for pain management in children and adults: a systematic review of evidence from randomized controlled trial. Exp Biol 2(5):408–1422 16. Sturm J, Engelhard N, Endres F, Burgard W, Cremers D (2012) A benchmark for the evaluation of RGB-D SLAM system. In: Intelligent Robots and Systems, Vilamoura 17. Zhang C, Liu Y, Wang F, Xia Y, Zhang W (2018) VINS-MKF: a tightly-coupled multi-keyframe visual-inertial odometry for accurate and robust state estimation. Sensors 18(11):1–29 18. Jhon, D (2011) Introduction to Coordinate Geometry. https://www.mathopenref.com/coordd ist.html. Accessed 26 June 2019

Development and Design Humidity Controller for Hybrid Refrigerator System Mohd Saifizi Saidon, Wan Azani Mustafa, Mohd Hanif Ismail, and Muzammil Jusoh

Abstract This paper studies on control the humidity in the refrigerator for a vaccine. For the vaccine to be effective, it must be stored at a specific temperature for a vaccine to maintain its potency. The required range of temperature for vaccines is between +2 to +8ºC, while the relative humidity is 20 to 60%. The main problem is that vaccines are quickly diminished and cannot stay if they exposed to the extreme temperature and humidity from the range required. Nowadays, refrigerators are used to store vaccines in the hospital, and manufacturers are not able to control the humidity inside the refrigerator. Therefore, this paper aims to develop and design a humidity controller for a hybrid refrigerator system. This paper will consist of three main parts: integrate vapour refrigerator with thermoelectric, analysis of hybrid refrigerator characteristics, and design humidity controller. Based on the hybrid refrigerator response, the 10 to 50% duty cycyle of pulse width modulation (PWM) current is injected to maintain the desired temperature range. Among the controller tested, the value of PWM with a minimum of 20% to a maximum of 30% - this is deemed to be the most desirable range that would be able to maintain the desired humidity range. The thermoelectric used in this paper will be able to maintain temperature 5 ºC and also to control humidity as desired, which ranged from 20 to 60% to ensure that the vaccine performance would last longer. Keywords Vaccine · Humidity · Thermoelectric

1 Introduction Vaccine as described in The Basic Concept of Vaccination by Pharmaceutical Research and Manufacturers of America (PhRMA), is a biological preparation that M. S. Saidon (B) · W. A. Mustafa · M. H. Ismail Faculty of Electrical Engineering, Universiti Malaysia Perlis, Perlis, Malaysia e-mail: [email protected] W. A. Mustafa · M. Jusoh Bioelectromagnetics Research Group (BioEM), School of Microelectronic, Universiti Malaysia Perlis (UniMAP), Arau, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_52

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enhances immunity against disease by stimulating the production of antibodies [1, 2]. The guidelines provided by World Health Organization (WHO) and the vaccine labels state that the use of hepatitis B, diphtheria – tetanus – pertussis, diphtheria – tetanus, and tetanus toxoid vaccines must be stored at a temperature of 2–8 °C to ensure the quality of vaccine [3]. The fact that the vaccines are fragile must be taken into consideration. They must be maintained at the temperatures recommended by vaccine manufactures and protected from light at every link in the cold chain [4–6]. In the short-lived sustenance industry, the usage of a chilly stockpiling to keep nourishment new has drawn much consideration [7]. Refrigerator, given the aggressiveness of the market, which clarifies the way that industry incline toward less expensive and less difficult gadgets. All inactivated vaccine requires a refrigerator storage temperature between 35°F and 46°F (2 and 8 °C) with a desired average temperature of 40 °F (5 °C). The refrigeration is a process where removing heat from a particular surrounding will make its temperature lower than the surrounding temperature [8, 9]. A refrigerator (colloquially fridge) is a common household appliance that consists of a thermally insulated compartment and a heat pump (mechanical, electronic, or chemical) that transfers heat from inside of the fridge to its external environment [10, 11]. A refrigerator maintains a temperature a few degrees above the freezing point of water, called a freezer. The refrigerant gas is utilized as coolant. To ensure that the temperature of warmth source beneath than the encompassing temperature, the refrigeration must exchange the concentrated heat and any expected contribution to a warmth sink, barometrical air, or surface water [12, 13]. The air temperature may dip under the set an incentive to accomplish the ideal stickiness level. In this manner, a warm curl is utilized to build a reasonable temperature of the air back to its set esteem. Vapour refrigerator has been commonly utilized in-car applications, despite antagonistically influencing the eco-friendliness because of the fuel utilization for the blower activity [14, 15]. Thermoelectric refrigeration offers a few points of interest as for regular vapour pressure innovation since thermoelectric gadgets are progressively conservative, free of clamours and vibrations, providing astounding temperature control that requires far less upkeep [16, 17]. Thermoelectric can be utilized in cooling and warming applications. Moreover, power can be produced by keeping up a temperature contrast between the surfaces of the thermoelectric device by using See Beck Effect [18]. Module makers indicate module cooling execution as a component of the temperature distinction between the hot and the virus face of the (thermoelectric) TE module and the working voltage. The WHO recommends that all vaccines except oral polio vaccine should be store in a range of temperatures for vaccines is between +2 to +8 ºC while the relative humidity between 20 to 60% [19–21]. A hybrid refrigerator is developed which is integration of vapour compression system with thermoelectric cooler (TEC) to maintain desired temperature and humidity.

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2 Methodology 2.1 Development of Hybrid Refrigerator A minibar fridge type with refrigerant R600a is used in this study. The dimension of refrigerator is 44 × 47 × 51 cm for width, depth and height. Voltage and current for the power supply of the refrigerator are 220–240 V and 0.6 A (Fig. 1). The regulator of vapour refrigerator will evacuate to change over into a new circuit with include a couple of parts, which include solid state relay (SSR), Arduino Mega Microcontroller, resistance temperature detectors (RTD) sensor, thermoelectric, and DHT11 sensor as shown above.

2.2 Input–Output Response of Hybrid Refrigerator A black-box model is introduced, which is based on the input and output of the system in order to simplify the system analysis. The system is analysed to describe the behaviour of the hybrid refrigerator based on the step response recorded data. Any underlying physic of the system is ignored for the modelling process of the hybrid refrigerator.

Fig. 1 The integrated system of hybrid refrigerator

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The temperature of the hybrid refrigerator must be set at 5ºC by using a close loop system (on–off controller). An open-loop system in Fig. 2 has been used to observe the humidity behaviour of the system in relation to the design controller. An open-loop system should follow its input command (10, 20, and 30% of PWM) to observe the humidity response. The desired temperature of the refrigerator is settled as 5 ◦ C by controlling the compressor using SSR.

2.3 Design of Controller for Hybrid Refrigerator The controller of the hybrid refrigerator is designed as shown in Fig. 3 based on the previous experiment that is the behaviour of the open-loop system. An on–off

Fig. 2 Open loop system for humidity of hybrid refrigerator

Fig. 3 Close loop system for humidity of hybrid refrigerator

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controller is designed by setting different minimum and maximum control efforts to get the best control performance of humidity. The humidity range suitable for vaccine storage is 20–60%. Therefore, the average of desired humidity with 35% is chosen to maintain in this system. When activating the refrigerator, the controller will sense the humidity difference via the humidity feedback, and the system will inject current into Thermoelectric Cooler (TEC). Since the TEC has been turned on, the humidity of the refrigerator is gradually decreasing. The decrease in humidity is not abrupt but slow until the temperature reaches the setpoint value. When the humidity is less than 35%, the controller sliding down the PWM value of TEC until the humidity is above 35%. With the usage of a set point at 35% humidity, the test of PWM has been performed by the control of a relay. The value of percentage of PWM duty cycle has been test is 20–40%, 10–30%, 0–30%, and 20–30%.

2.4 Performance of Controller Lastly, the performance of the controller will be tested. The load test has been running by using 250 ml, and 500 ml of mineral water has been placed in a refrigerator. From the previous experiment design of the controller, the best controller has been chosen for the performance test that is 20–30% duty cycle of the PWM setting controller.

3 Results 3.1 Input–Output Analysis of Hybrid Refrigerator In order to choose the best duty cycle of PWM input for the thermoelectric device, four different input is tested, which is 40, 50, 70, and 80%. Table 1 shows the temperature reaction of the hybrid refrigerator with various input values of PWM at thermoelectric. The responses show that 40 to 50% able to maintain the desired temperature for vaccine storage. Figure 4 shows that the implementation of thermoelectric can reach a temperature of 5 ◦ C. However, the temperature will be increased to over 5 ◦ C when the duty cycle of PWM is up to 70% above. Therefore, the maximum duty cycle of PWM input that Table 1 Temperature response of hybrid refrigerator

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able to use is 50% to maintain the required temperature storage of the vaccine with 5 ◦ C.

3.2 Humidity Response of Hybrid Refrigerator In order to achieve the desired humidity (20–60%), the response of humidity due to current input injected to thermoelectric is recorded. There are four different duty cycle of PWM tested, which are 10, 20, and 30% to thermoelectric. In addition, the temperature has to maintain at 5 ◦ C by refrigerator compressor using an on–off controller. Furthermore, the humidity recorded based on an open-loop system by injecting constant input to the thermoelectric. Figure 5 shows the steady-state response of humidity due to a 10 to 30% duty cycle of PWM current to the thermoelectric, and temperature is controlled at 5 ◦ C by the on–off controller. Regarding Fig. 5 above show that, when a 10% duty cycle of PWM current is injected into the thermoelectric, the percentage of humidity is increased to 70% humidity. This will cause the vaccine to diminish. It is because of the humidity range for the vaccine to sustain performance is in the range of 20–60%. Based on Fig. 5 and Table 1, the best range of duty cycle of PWM current is 30% to 60%, which is able to maintain 5 ◦ C and range of humidity 20–60%.

3.3 Controller Design The controller will control by relay where the value of PWM has been set up at a certain value. During modelling, the thermoelectric is not automatically on and

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off. However, in terms of the performance of the controller, the thermoelectric will be automatically switched on and off due to the presence of the relay. When the percentage of humidity is higher, the thermoelectric will automatically on to reduce the percentage of humidity in the refrigerator for the vaccine to last longer at the desired percentage of humidity. When the humidity is lower than 35%, the thermoelectric will automatically off. Here, the function of the relay and dht11 sensor is crucial to control the percentage of humidity in the refrigerator. With the usage of the setpoint at 35% humidity, the test of PWM was carried out by the control of a relay. The value of percentage of PWM has been used are 10–30%, 0–30%, and 20–30%. This test has been done to observe the control effort of thermoelectric. Control effort is the amount of energy or power necessary for the controller to perform its duty. Thus control effort performs tasks is like a switch. Therefore, the best value of the percentage of humidity after the test has been done is 20–30%. When the humidity

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is below the setpoint, which is 35%, thus the PWM value will be at 20%, and the value of PWM will be at 30% when the percentage of humidity is higher than the set point. Figure 6 shows a controller response 0–30% PWM. From the observation, the highest and the lowest temperature for controller response 0–30% are 4 and 7 ◦ C. From the graph percentage of humidity, the minimum and maximum humidities are 30 and 70%, while the minimum and maximum control efforts are 0 and 76.5. The conclusion is PWM 0–30% controller response is not suitable to use to store the vaccine because the temperature to store vaccine must be in the range of 2 to 8 ◦ C while, the humidity must be in the range of 20–60%. Both temperature and humidity of PWM 0–30% controller response are not suitable to be used to store vaccine. Figure 7 shows graphs for controller response 10–30% PWM. From the observation, the highest and the lowest temperature for controller response 30–70% are 5 and 6 ◦ C. From the graph percentage of humidity, the minimum and maximum humidities are 30 and 70%, while the minimum and maximum control effort are 25 and 76.5. The conclusion is PWM 10–30% controller response is not suitable to use to store the vaccine because the temperature to store vaccine must be in the range of 2 ◦ C to 8 ◦ C while, the humidity must be in the range of 20–60%. Both temperature and humidity of PWM 10–30% controller response are not suitable to be used to store the vaccine. Figure 8 shows graphs for controller response 20–30% PWM. From the observation, the highest and the lowest temperature for controller response 20–30% are 5 and 6 ◦ C. From the graph percentage of humidity, the minimum and maximum humidities are 30% and 60%, while the minimum and maximum control effort are 51 and 76.5. The conclusion is PWM 20–30% controller response is suitable to use 8 7 6 5 4 3 2 1 0

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3.4 Performance of Controller The thermoelectric module also is used to generate electricity by using a temperature differential between the two sides of the module. Furthermore, to store the vaccine inside the refrigerator, the humidity must be in the range of 20% to 60%. Therefore, Fig. 9 shows small oscillation when thermoelectric is introduced compared to the refrigerator without thermoelectric integration. The hybrid system shows a fast transient response compared to a conventional refrigerator. On top of that, the conventional refrigerator response is not stable and may occur overshoot drop below 20% of humidity. Load 500 ml. The performance of a hybrid refrigerator is tested by putting a bottle of water as a load. Two different loads have been used to be replaced in the refrigerator, which are loads with 250 ml and 500 ml of mineral water. In this experiment, the settling time of the system for each load is recorded. The system is continued running for about 8000 s after the system is settled to observe refrigerator steadystate temperature response. Figure 10 shows the hybrid refrigerator able to maintain at the desired temperature of 5 °C, and the system able to maintain a safety range of humidity, which is 30% to 60%. Load 250 ml In addition, a load of 250 ml water is put into the refrigerator to test. As shown in Fig. 11, the hybrid refrigerator able to maintain at 5 °C and humidity oscillation sustain at 30–60% range humidity. In fact, the response shows the same performance as the load is 250 ml of water.

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Fig. 11 Response of a Temperature, b Humidity with a load 500 ml of water

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4 Conclusion During the open-loop test of the TEC system, the duty cycle of PWM current input 10 to 50% is able to use to maintain 5 °C and sustain at 20–60% relative humidity that required for the vaccine storage. Therefore, the controller of TEC is designed due to the control effort in the range 0 to 30% duty cycle of PWM current. The hybrid refrigerator shows the best response when the limit of control effort is set to 20–30% range. The performance of the controller is measured based on controlling at 5 °C temperature and small oscillation of relative humidity. The hybrid refrigerator with the controller is compared to the conventional refrigerator, and it shows that the hybrid refrigerator gives better performance in controlling humidity response in refrigerator storage. The hybrid refrigerator also shows excellent performance in controlling humidity and temperature when there is a load in storage.

References 1. Harandi AM, Medaglini D, Shattock RJ (2010) Vaccine adjuvants: a priority for vaccine research. In: Vaccine. pp 2363–2366 2. Dubé E, Vivion M, MacDonald NE (2014) Vaccine hesitancy, vaccine refusal and the antivaccine movement: Influence, impact and implications 3. Nasruddin Aliu QH, Djubaedah Taufan A, Gurky RG, Arsyad, A.P. (2018): Performance prediction of vaccine carrier using adsorption process and 13x/cacl2 composite zeolite as adsorbent. In: IOP conference series: earth and environmental science 4. Nelson C, Froes P, Dyck AMV, Chavarría J, Boda E, Coca A, Crespo G, Lima H (2007) Monitoring temperatures in the vaccine cold chain in Bolivia. Vaccine 25:433–437 5. Matthias DM, Robertson J, Garrison MM, Newland S, Nelson C (2007) Freezing temperatures in the vaccine cold chain: a systematic literature review. Vaccine 25:3980–3986 6. Lloyd JS (1981) Monitoring vaccine storage temperatures. WHO Chron. 35:51 7. Ça˘glar A (2018) Optimization of operational conditions for a thermoelectric refrigerator and its performance analysis at optimum conditions. Int J Refrig 96:70–77 8. McColloster PJ, Martin-de-Nicolas A (2014) Vaccine refrigeration. Hum Vaccin Immunothe 10:1126–1128 9. Kilfoyle D, Ventre GG (1988) Test and evaluation of vaccine refrigeration systems. In: Conference record of the IEEE photovoltaic specialists conference, pp 1200–1205 10. Min’an W, Min’a, W (2018) Refrigerator. In: Domestic Spaces in Post-Mao China. pp 13–24 11. Gibson, C., Farbotko, C., Gill, N., Head, L., Waitt, G.: The refrigerator. In: Household Sustainability. pp. 116–124 (2013). 12. Allahverdyan, A.E., Hovhannisyan, K., Mahler, G.: Optimal refrigerator. Phys Rev E - Stat Nonlinear, Soft Matter Phys. 81 (2010) 13. Agarwal I (2020) Intelligent refrigerator. In: Advances in intelligent systems and computing, pp. 241–258 (2020) 14. Narayankhedkar KG, Maiya MP (1985) Investigations on triple fluid vapour absorption refrigerator. Int J Refrig 8:335–342 15. Astrain D, Martínez A, Rodríguez A (2012) Improvement of a thermoelectric and vapour compression hybrid refrigerator. Appl Therm Eng 39:140–150 16. Wright WL (2013) Thermoelectric refrigeration. Electr Eng 79:380–384 17. Goldsmid HJ (2010) Theory of thermoelectric refrigeration and generation. In: Springer Series in Materials Science, pp 7–21

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18. Söylemez E, Alpman E, Onat A, Yükselentürk Y, Hartomacıo˘glu S (2019) Numerical (CFD) and experimental analysis of hybrid household refrigerator including thermoelectric and vapour compression cooling systems. Int J Refrig 99:300–315 19. World Health Organization: WHO | Vaccines. 20. Milstien JB, Galazka AM, Kartoglu U, Zaffran M (2006) Temperature sensitivity of vaccines. World Heal Organ Dept Immunization, Vaccines Biol, pp 1–58 21. Ravisekar CV (2012) Storage of vaccines. Indian J Pract Pediatr 14:82–84

Fabrication of Parallel Ankle Rehabilitation Robot Mohd Khairul Ashraf Bin Ismail, Muhammad Nazrin Shah, and Wan Azani Mustafa

Abstract In this modernization era, we have been showered with multiple of technologies which have leaded a much more comforting lifestyle especially to humankind. This report presents a survey of development of parallel based ankle rehabilitation robot. As a result, robot therapy systems have been developed worldwide for training lower extremities. This work reviews all current robotic systems to date for ankle injury, with the aim of showing a clear starting point in the field. It also discusses numerous challenges faced by engineers in designing this robot, including framework stability, universal evaluation criteria to assess end-user comfort, safety and training performance and the scientific basis on the optimal rehabilitation strategies to improve ankle condition. Nowadays, there is so many treatment machine and it can be use at hospital or physiotherapy unit, clinic, and health centre with expensive cost This development is to reduce a cost for the treatment and also make the model easy to carry anywhere Based on the previously defined constraints, the goal of robotic robot development for ankle recovery is to create a treatment for ankle recovery by integrating software and hardware process, also analyses performance of ankle rehabilitation robot model. This project will consist of two main parts, which are the design and development parts. For the software design, Solidworks are selected to make the dimension and design. For the development part, the material for the framework is an aluminum will be select and use. The result will be including a degree of freedom for the rotation of the platform using load and no load. This project mainly aids the users who is affected by stroke, illness or injuries due to accident and encouraging them to exercise regularly in house without consulting the physiotherapist in the hospital. Keywords Robotics · Rehabilitation · Solidworks M. K. A. B. Ismail · M. N. Shah (B) · W. A. Mustafa Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, Arau, Malaysia e-mail: [email protected] W. A. Mustafa e-mail: [email protected] M. N. Shah · W. A. Mustafa Sports Engineering Research Centre, Universiti Malaysia Perlis, Arau, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_53

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1 Introduction Stroke is one of the first causes that can cause severe disabilities as the third leading cause of death in Asia. In Malaysia, there are almost 30,000 strokes each year, resulting in 3,000 deaths and persistent disability for their survivors and recent increases in disability caused by the increase of the elderly. In addition, strokes are one of the top 10 hospitalization causes in Malaysia and one of the leading 5 death causes in this case. Stroke is also among the top five diseases that have the greatest burden of disease, based on disability-adjusted years [1, 2]. Rehabilitation helps to re-learn skills that are lost by damaging a part of the brain. Rehabilitation also teaches survivors new ways to work on any residual disabilities. In the acute care hospital, rehabilitative therapy begins after the overall condition of the person has been stable, usually 1 to 2 days after the stroke. Robotic rehabilitation has many advantages, one of which is that you can repeat your exercises many times until you can adapt to the movements. There has been more research on robotic rehabilitation in the past [7–9]. Even now, this field of study is progressing enormously, the development phases are still in progress. The various stroke types, current treatments related projects were focused on in our research. There has been a greater amount of past research on robotic rehabilitation. Even now this field of research is making enormous progress and the development stages are still in progress. However, there is more machine of treatment using a highly cost for the development and the weight is very heavy due to the parts that has been applied to the machine [10, 11]. For this model are develop light weight easy to carry and make the treatment just at home or anywhere. Using a new method with a lower price for the components can cut the cost and valuable to buy. This model potential to compete among the others model that has been used until now in modern medical sector because of the advantages on this model. This project called ‘Development of Parallel Based Ankle Rehabilitation Robot’ which is this model can concentrate and helps disabled persons especially focusing at the ankle rehabilitation. The design of the robot is limited and this robot will help and focus more those people to rehabilitate their ankle. Many robotic devices for the motor relearning of stroke rehabilitation have recently been developed. In this section, the recent systems will examine for the rehabilitation of robots supported and focus on the ankle rehabilitation, including that robotics which potential benefits in future stroke rehabilitation applications.

2 Methodology 2.1 Project Workflow For each of the project must have a project workflow to develop and ensure the project be carried out according to become the project running smoothly. Figure 1

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are shown project workflow in this study basically consists of several steps that need to be complete such as several studies of the project, design the framework of the model, construct hardware part by part, hardware and software combination, analyzing result and writing report. Each of phase to build the model must be tested before through the next phase.

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Fig. 2 The motion of ankle structure [10]

Fig. 3 a The foot platform for the rehabilitation with the motion structure b The ball joint mechanism

First of all, the literature review and research is study to gain knowledge about that has been used by the previous researches. All the technique and method is studied and compared for apply to the project. Next phase is a software development process involving the design that has been sketched with the dimension given by using Solidworks. For the third phase is construct the hardware process which involving model and design construction. The model design carried out by using Solidworks software and then built the model by constructed using selected material which is the aluminum. Subsequently, both software and hardware are combined and be tests by using some of the method and data analysis to evaluate the result. Once the analysis result is obtained, the data will be compiled in a report and submitted after completion.

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Fig. 4 a Design the platform for the foot. b Design ball joint with the thread. c Design the framework.d Fully finish design

2.2 Design the Model Using Solidworks Software The design for this model has come from many perspectives of ideas and suggestions. The project design has been decided carefully based on its functionality and safety. This design had been drawn part by part with the dimension. The combination between ball joint with foot platform is important because the ball joint can make a motion for the foot platform as same with the rotation of ankle motion. Compare with the three motions of the rotating ankle, these are foot platform which is considered suitable as a platform for rehabilitation ankle injury based on the analysis ankle joint structure (Figs. 2, 3 and 4).

2.3 Construct the Hardware The main objective of this project is to provide a robot rehabilitation which is economical for the low moderate incomes and to educate the users to use the project in the house and exercise regularly without consulting physiotherapist in the hospital. The

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model is kept a minimum to achieve a low budget of the production. The metal bar is used in the project because it is cheaper compared to stainless steel and it is durable. The model enables the patient to do the rehabilitation session self-initiate without having guidance, the users can be more independent. The design part by part with dimension has been done successfully. Then, the construction phase base on the dimension and design model to build it. Each part must be measured first before a cut. The dimension was measured in millimeter (mm) using measuring tape and elbow ruler. The connection part by part used rivet gun because it is can binding more strong and suitable for the aluminum material that works for this model. For the wall of the model, it covered with the composite panel which are suitable for this model.

2.4 Phases to Construct the Hardware • Phase 1 Measured an aluminum using a measuring tape and marked on the work pieces first. This Fig. 5 below has shown the work pieces has been measured and marked on the surface. • Phase 2 This Fig. 6 below has shown in this phase must alert in safety condition because it used sharp and dangerous equipment cutting machine to cut the work pieces into the part by part. Fig. 5 Measured and marked on the work pieces

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Fig. 6 The work pieces have been cut into part by part

• Phase 3 Refer the Fig. 7 below, this part must be alert in safety first because it used a drill machine to make a hole at the work pieces. • Phase 4 Refer the Fig. 8 below, after part by part that had been drilled, this phase is used as a rivet gun to make combination part by part.

Fig. 7 Part of the work pieces has been drilled

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Fig. 8 The rivet gun has used to make a combination

• Phase 5 Construction part has been recorded to make a reference with a design in Solidworks. This hardware construct which a part by part in the model. Refer the Fig. 9 below.

2.5 Operation Setup Overall, in this operation setup has 2 sections which is hardware and software. For the hardware section has 2 operations which is mechanical setup and wiring setup. For the software section has 2 software which is Solidworks for designed model and Arduino for programming of the operation model.

2.6 Mechanical Setup In this section, the construction of the model has been done by using software Solidworks for modelling. After the modelling has been done, the construction hardware was carried out with the selected material. For this model, the material for aluminum which had been chosen because this material is lightweight, stainless and long-lasting material than others that had been discussed in the methodology. Refer to the Fig. 10 below.

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Fig. 9 a The foot platform construction hardware parts. b The framework construction hardware parts c The base for adjustable thread for the height foot platform

Fig. 10 The top view for the model

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Fig. 11 Front view assembles wiring circuit with model

2.7 Mechanical Setup For the wiring section has been implemented after finished for mechanical section. In this wiring section, the circuit for electrical component must be complete because to collect the data and make analyze from the movement of the model must have electrical power supply. Refer to the Fig. 11 shows the completed implemented wiring at the model.

2.8 Model Testing After overall section has been completely installation, the movements 2 degree of freedom (DOF) for the platform with no load have been measured. The result must have 2 degree of freedom (DOF) movement which is 2 positions in x-axis and 2 positions in y-axis. Figures 12 and 13 below, shows the movements for ankle rehabilitation robot model. The platform for the foot has been made a bigger size due to variety of people size’s foot to use this model and should have a drawing size of foot easier the user to put on the platform. This platform can make the user more comfortable and prevent injured while using this model. The motor is suitable using the stepper motor because this motor can be precise to measure the angle and it can be track which angle of the platform can be rotate while the user’s foot using it. So, this method using this type of motor are suitable for the treatment ankle. There is a reason why stepper motor is chosen to use in this model. This is because the movements of operation at stepper motor it moves or rotate in a condition step by step positions. So, it easier to locate how many turning of the motor and setting in the programming’s software to make the platform smoothly move with a certain angle. For the angle of the platform when it moves can be trace by using the application in

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Fig. 12 a Zero position for the first movement. b X-axis movement platform to the right. c X-axis movement platform to the left

Fig. 13 a Y-axis movement platform to up. b Y-axis movement platform to down

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(a)

(b)

(c)

Fig. 14. a Zero position. b X-axis to the right. c X-axis to the left.

(a) Fig. 15. a Y-axis to up b Y-axis to down

(b)

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mobile phone. The mobile phone will place on the platform at the point of the ball joint under the platform.

3 Result 3.1 Analysis Rotation of Stepper Motor This model has used 4 stepper motors for rotation forward and backward to make a movement for the platform. To run the motors for the desired direction by the platform, the motors required to be run either forward and backward. The Table 1 below has been shown diagram for direction of the motor with the platform moving behavior. The results show that the motor is moved forward and backward works synchronously with the movements of platform. To get synchronously the platform when it moves, tension of string must be emphasized from the motor to the platform. Because the motor will move forward and reverse based on the positioning string at the motor. Refer to the Figure, show the sequentially motions of platform based on the Table 1 above show for rotational motor. From the Tables 2 and 3 can conclude that stepper motor that has been used for this model a not suitable because it comes with low torque even in this model used 4 stepper motors. The motion for the platform rotates absolutely achieved but it difficult to rotate until reach predict angle. Even with load and no load, the motor cannot make the platform reach the predict angle. After the load has been put onto the platform, the actual angle in Table 3 is higher than actual angle with no load in Table 2. It is because the stepper motor with low torque difficult to pull the load and Table 1 Rotational motor for motion of the platform Axes

Name of motion

Motor 1

Motor 2

Motor 3

Motor 4

X

Inversion

Forward

Backward

Backward

Forward

Eversion

Backward

Forward

Forward

Backward

Y

Dorsiflexion

Forward

Forward

Forward

Forward

Plantar flexion

Backward

Backward

Backward

Backward

Table 2 Rotational motor for motion of the platform

Axes

Name of motion

X

Inversion

22

15.6

Eversion

−22

−10.5

35

10.1

−40

−8.0

Y

Dorsiflexion Plantar flexion

Predict angle (°)

Actual angle (°)

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Name of motion

X

Inversion

22

15.6

Eversion

−22

−10.5

Y

Dorsiflexion Plantar flexion

Predict angle (°)

Actual angle (°)

35

10.1

−40

−8.0

the motor will be wobble until the motor accept some light weight to continue pull. Refer to the Fig. 14 and 15 above show 2 degree of freedom (DOF) with load. From Table 3 has shown that the result the model operates with the load of adult foot. Compare with the predict angle, the actual angle cannot achieve the target because the torque power of the motor is weak to pull the string when there has a load on the platform. It’s doesn’t mean cannot be work for any weight, but not suitable for adult foot. The load of foot that has been use in Table 3 is above 6.0 kg (kg). The ideal weight of foot can be put onto the platform is under the 5.0 kg (kg) for one foot only and with the ideal weight that has been mention its suitable for a kid. The string should be a suitable for this model because the motor will roll the string to pull and release for the platform can reach a certain angle precisely. Then, the platform should have site of the size foot and holder for the foot. It is because the foot must be in the correct point at the platform and the holder is to make the foot comfortable while using this model. The user must be in position sit at the chair or place that can be the user in relaxing mood while using it.

4 Conclusion The ultimate intention of the proposed ankle rehabilitation robot to assist the disabled individuals who suffered from lower body motor impairment by giving support to their lower body especially the ankle. The mechanical design of the ankle rehabilitation robot is based on the real concept of the human ankle. the experiment results are presented the ball joint under the foot platform and adjustable height for foot platform. The results didn’t fulfill the expectation as the platform didn’t reach the maximum required movement of the angle. Therefore, there is a need for motor that can support the load from the patients. Additionally, the lack of closed loop mechanism in this project had deterred the platform from reaching the ideal position. Thus, introduction of closed loop mechanism must be introduced in order to improve the performance of the robot significantly. Acknowledgements The authors gratefully acknowledge the financial support from UniMAP.

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References 1. Bernhardt J, Godecke E, Johnson L, Langhorne P (2017) Early rehabilitation after stroke. Curr Opinion Neurol 30, 48–54 2. Al-Mohrej O, Al-Kenani N (2016) Chronic ankle instability: Current perspectives. Avicenna J. Med. 30:48–54 (2016) 3. Lee Y, et al. (2017) Robot-guided ankle sensorimotor rehabilitation of patients with multiple sclerosis. Mult Scler Relat Disord 4. Lubbe D, et al. (2015) Manipulative therapy and rehabilitation for recurrent ankle sprain with functional instability: a Short-term, assessor-blind, parallel-group randomized tria. J. Manipulative Physiol Ther 5. Etminan N, et al.: Multidisciplinary consensus on assessment of unruptured intracranial aneurysms: Proposal of an international research group. Stroke (2014) 6. Liu Q, et al. (2017) Development of a novel ankle rehabilitation robotic platform for hemiplegic stroke survivors. Basic Clin Pharmacol Toxicol 7. Lu Z, et al. (2016) Development of an ankle robot MKA-III for rehabilitation training. In: 2016 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2016 (2016) 8. Jamwal PK, Xie SQ, Hussain S, Parsons JG (2014) An adaptive wearable parallel robot for the treatment of ankle injuries. IEEE/ASME Trans. Mechatronics 19(1):64–75 9. Aman MNSS, Basah SN, Wan Ahmad WK, Abu Bakar S (2015) Conceptual design for robotaided ankle rehabilitation device. J Teknol 76(12):45–52 10. Jamwal PK, Hussain S, Mir-Nasiri N, Ghayesh MH, Xie SQ (2018) Tele-rehabilitation using in-house. Wearable ankle rehabilitation robot. Assist Technol 11. Chen B, et al. (2016) Recent developments and challenges of lower extremity exoskeletons. J Orthopaedic Trans. 5: 26–37 (2016) 12. Shahrol MN, Basah SN, Basaruddin KS, Ahmad WKW, Ahmad SA (2018) Modelling of a cable-driven. Ankle rehabilitation robot. J Telecommun Electron Comput Eng 5:26–37 13. Shahrol MN, Basah SN, Basaruddin KS, Ahmad WKW, Ahmad SA (2018) Modelling of a Cable-driven. Ankle Rehabilitation Robot. J Telecommun Electron Comput Eng (2018) 14. Rehabilitation of leg”, Rehabilitat.org (2016)

Development of Fragility Curve of Reinforced Concrete Buildings with Different Height Based on Dynamic Analysis N. A. N. Zainab, N. Amirah, W. H. Tan, W. Faridah, A. M. Andrew, and S. Ragunathan Abstract This study aims to develop the fragility curve of reinforced concrete buildings with different height based on dynamic analysis. There are 4 models of the moment-resisting concrete frame (MRCF) were used, which are 10, 15, 20, and 25-storey. Each of these frames was designed based on Eurocode 2. The SAP2000 program was used as the main analysis tool to obtain the limit state of MRCF. Then, an incremental dynamic analysis (IDA) was carried out with 7 ground motion records obtained from the Pacific Earthquake Engineering Research Center (PEER). The IDA curves were compared with the limit state of the MRCF, which is maximum allowable drift. The calculation of maximum allowable drift is based on the formula from the International Building Code (IBC, 2003). From the IDA results, 10-storey MRCF has better structural performance under the same ground motion as compared to other MRCF (10, 15, 20, and 25-storey). Fragility curves were developed by considering the results of IDA as parameters. Based on the results of the fragility curve, the MRCF that has the highest probability of exceeding the limit state is 25-storey MRCF. This is because 25-storey MRCF has the lowest PGA at 100% probability. Keywords Reinforced concrete · Incremental dynamic analysis · Fragility curve · Ground motions · Drift limit

N. A. N. Zainab (B) · N. Amirah · W. Faridah · S. Ragunathan School of Environmental Engineering, Universiti Malaysia Perlis (UniMAP), Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia e-mail: [email protected] W. H. Tan School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia A. M. Andrew Centre for Diploma Studies (PPD), Universiti Malaysia Perlis (UniMAP), S2-L1-26, KampusUniCITIAlam Sg.Chuchuh, 02100 Arau, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_54

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1 Introduction The sudden release of the energy stored in the Earth’s crust that creates seismic waves is called an earthquake. The earthquake has received much attention in these recent years due to its frequent occurrence. Ranau, Sabah is one of the places that commonly experienced an earthquake as compare to other states in Malaysia. According to news from Malaymail Online [1], Ranau, Sabah experienced an earthquake on 5 June 2015 with a moment magnitude of 6.0 which lasted for 30 s. It is the largest earthquake that origin in Malaysia in recent years, the impact of this earthquake has caused damages to infrastructure and public assets being assessed at RM94.8 million. Besides, the earthquake also leads to slightly or moderately damaged of 136 houses in Kota Belud and 268 houses in Ranau. Billah and Alam [2], incremental dynamic analysis (IDA) is a special type of Nonlinear time history analysis (NTHA) where ground motions are incrementally scaled and a series of analyses is performed at different intensity levels. IDA performed better as the requirement the number of ground motions is much lesser than the NLTHA for fragility assessment. Therefore researchers prefer to use IDA to establish fragility curve. The paper mentioned the method was first developed by Luco and Cornell [3] and has been described in detail in Vamvatsikos and Cornell [4]. The main purpose of an IDA is to establish a curve through the relation of the intensity level with the peak seismic response of the structural system. An intensity measure (IM) and an engineering demand parameter (EDP) is used to describe the intensity level and the seismic response respectively. The peak ground acceleration (high-frequency component), peak velocity (intermediate frequency component), peak displacement (low-frequency component), sustained maximum acceleration and velocity, and the effective design acceleration are the most frequently used for amplitude intensity measures that derived from an accelerogram. The amplitude of intensity measures is used for the derivation of empirical attenuation relationships used in probabilistic hazard analysis because their generation is based on intensity measures’ dependence on the magnitude of the earthquake and the site-to-source distance. Frequency content intensity measures describe through different types of spectra the distribution of the amplitude of a ground motion among different frequencies [5]. An essential aspect of the seismic investigation of the structures is developing fragility curves to define the probability of failure under different earthquake intensities. Fragility curve is a convenient tool to analyse the overall risk from potential earthquakes of structural systems. According to Hosseinpour and Abdelnaby [6], the fragility of the frame structure is indicated as the probability of exceeding the limit of the structure stability at a given ground motion intensity. Fragility curves is a curve function that demonstrates the probability of structural damage in the specific limit state for measuring certain intensity. The main objective of this study is to obtain the IDA curve of reinforced concrete buildings with different height based on dynamic analysis, to develop the fragility curve of reinforced concrete buildings with different

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height based on dynamic analysis and to compare the probability of failure of reinforced concrete buildings with different height at a specific ground motion (PGA) based on dynamic analysis.

2 Methodology 2.1 Models of Designed Frame Structure 4 sets of moment-resisting concrete frames (MRCF) models were analysed with different heights. These frames were designed based on Eurocode 2 [7]. Each frame had 3 bays with measurement of 8 m span and identical height of 3.5 m for 10, 15, 20 and 25-storey concrete frames as illustrated in Fig. 1 (a) and (b) shows the plan view with 6.5 × 8 m span. The designs for moment-resisting concrete frames (MRCF) based on the existing building by using Eurocode 2 [7], Eurocode 8 [8], and MS 1553:2002 [9] standards for concrete design, earthquake design, and wind load design respectively. Several assumptions were made during the design of MRCF. The compressive stress of concrete was 35 N/mm2 and yield stress of reinforcing steel was 460 N/mm2 . Table 1 tabulated the sizes and detailing of the beam and the column for MRCF for 10, 15, 20, and 25-storey respectively. The designs for the frame structures were based on Model 4 (25-storey) by using Eurocode 2 [7]. Several assumptions were made before the design of the frame structure. Characteristic strength of concrete was 35 N/mm2 and characteristic strength of reinforcing steel was 500 N/mm2 . The diameter of reinforcing steel and link was set at 25 and 10 mm respectively. The dead load involving in manually design are slab self-weight, beam self-weight, brick wall, and screed which are 41.74 kN/m. Whereas the live load is considered as 3.00 kN/m2 for the office area, and the total live load is 16.38 kN/m. By assuming the concrete density is 25 kN/m3 , the slab thickness is 150 mm. Furthermore, the design load for MRCF, Wd can be obtained by using the formula from Eurocode 2 [7] as below: Wd = 1.35G k + 1.5Q k

(1)

From Eq. (1), the moment of the beam, F can be obtained as: F = Wd · L which are W d = design load Gk = dead load Qk = live load L = span length

(2)

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a) Elevation view

b) Plan view Fig. 1 2D Model of frame structure Table 1 Size of beam and column for 10, 15, 20 and 25-storey No of storey

Section

Size (mm)

Reinforcement

Shear link

10-storey

Beam

350 × 650

5H 25

3H 10

Column

1000 × 1000

5H 25

3H 12

15-storey

Beam

350 × 650

5H 25

3H 10

Column

1000 × 1000

5H 25

3H 12

20-storey

Beam

350 × 650

5H 25

3H 10

Column

1000 × 1000

5H 25

3H 12

Beam

350 × 650

5H 25

3H 10

Column

1000 × 1000

5H 25

3H 12

25-storey

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2.2 Selection and Scaling of Ground Motion Ground motion records are considered as an important factor that will affect the fragility curve of the buildings. Therefore, to establish accurate results in the fragility curve, the procedures in selecting a suitable ground motion and scaling of ground motions should be taken wisely. If the selected ground motion is scaled up arbitrarily to a certain spectral acceleration level, Sa, at a specific period, T, overly conservative structural responses may be produced. This because a single extreme Sa (T) level of interest for engineering analysis does not imply the occurrence of equally extreme Sa levels at any periods [10, 11]. Based on the statement of Mehdizaded [12], one of the way to obtain the earthquake record is from the Pacific Earthquake Engineering Research Center (PEER) [13] Ground Motion Database. Therefore, in this study, 7 sets of ground motions were used to analyse the models. Refer to that, the ground motions with the peak ground acceleration 0.15 G or above are analysed in SeismoSignal program [14] and the distance of buildings and the source is less than 15 km chosen from the Pacific Earthquake Engineering Research Centre (PEER) [13]. The information about the selected earthquake is tabulated in Table 2. Furthermore, to match the characteristics of the ground motion to the soil type, the ground motions data were scaled according to the developed elastic response spectrum. The scaling was depended on the value of frame fundamental period, T1. After that, the scale factor will be used in SAP2000 program [15] to develop the IDA curve. Figure 2 shows the scaling of ground motion for the soil type respectively. Table 2 Earthquake data [13]

Fig. 2 Scaling of Corinth Greece earthquake for soil type A

Earthquake

PGA (G)

Magnitude (M)

Date

Imperial valley-02

0.28080

6.95

19/05/1940

Corinth Greece

0.23677

6.6

24/02/1981

Landers

0.27358

7.28

28/06/1992

Manjil, Iran

0.51456

7.4

20/06/1990

Iwate, Japan

0.28619

7.2

13/06/2008

Kobe, Japan

0.27578

6.9

17/01/1995

Friuli Italy

0.35713

6.5

06/05/1976

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2.3 Development of Non-linear IDA Curves From Billah and Alam [2], incremental dynamic analysis (IDA) is a special type of Non-linear time history analysis (NTHA) where ground motions are incrementally scaled and a series of analyses is performed at different intensity levels. IDA performed better as the requirement of the number of ground motions is much lesser than the NLTHA for fragility assessment. Therefore researchers prefer to use IDA to establish fragility curve. The paper mentioned the method was first developed by Luco and Cornell [3] and has been described in detail in Vamvatsikos and Cornell [4]. To obtain the dynamic behaviour of the MRCF due to different ground motions, non- linear Incremental Dynamic Analysis (IDA) curve was developed. In the IDA curve, the limit state of the MRCF (maximum drift) is used to identify the maximum intensity of ground motion before the failure of MRCF. The maximum allowable drift is obtained from the International Building Code [16], which is 0.0015 times with the storey height of MRCF. After the analysis, the data of IDA will be used to develop the fragility curve.

2.4 Development of Fragility Curves The fragility curve is developed after the IDA curve is obtained. To develop the IDA curve, the maximum allowable drift of the frame or displacement of the frame is obtained to define the limit state of the frame. From the International Building Code [16], the maximum allowable storey drift is determined. Furthermore, the drift of MRCF used in IDA can be calculated by dividing the maximum storey displacement with the height of the building. Figure 3 shows the procedures to establish the fragility curve from beginning to results. The fragility equation used in this study is stated in Eq. (3) below, which is used by Kircil and Polat [17] to develop the fragility curve for mid-rise reinforced concrete buildings.  P(≤ D) = 

ln X − μ σ



where,  [.] = Standardise normal distribution X = Lognormal distributed ground motion index (PGA) M = Mean of ln X  = Standard deviation of ln X

(3)

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Ground motion selection from PEER 2D RC frame design SeismoSignal Software SAP 2000 Software Scaled response spectrum curve

Incremental Dynamic Analysis (IDA)

Repeat the analysis with different height of frame (25, 20, 15 and 10 storey)

Drift Limit

Fragility Curve

Fig. 3 Fragility curve procedures

3 Results and Discussion 3.1 IDA Curves A total number of 168 number of analyses were conducted and the maximum displacement of MRCF is recorded to obtain the drift of MRCF. The IDA curve was developed based on the analysis data with PGA (g) as Intensity Measure (IM) and drift of MRCF as Damage Measure (DM). Figure 3 presented the non-linear incremental dynamic analysis curve for MRCF (10, 15, 20, and 25-storey) based on the selected and scaled seven ground motions together with the maximum allowable drift as the limit state. As the IDA curves move toward the right, the PGA (g) of ground motions increased, the maximum drift of MRCF increased until the limit state, 0.0525 m. Figure 4 shows the performance of MRCF under incremental dynamic analysis (IDA) for 10, 15, 20, and 25-storey respectively. There are 7 different colour lines in the figures which represented the 7 different selected ground motions that applied in IDA. From this IDA curves, the correlation between the peak ground acceleration (PGA) from the ground motion and the drift of MRCF can be found. It can be seen in Fig. 4(a), the 10-storey MRCF can withstand the highest peak ground acceleration among the other MRCF. It can withstand 7.4 g from Kobe Japan

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(a) 10 Storey

(b) 15 Storey

(c) 20 Storey

(d) 25 Storey

Fig. 4 The IDA curve of MRCF

Table 3 The IDA performance for 10-storey MRCF Ground motion

Imperial valley

Corinth Greece

Friuli Italy

Iwate Japan

Kobe Japan

Landers

Manjil Iran

PGA (g) at maximum drift

1.9

2.5

1.1

1.75

5.1

2.1

1.3

before reaching the maximum drift, follow by Landers, Imperial Valley, Corinth Greece, Friuli Italy, and Manjil Iran, which are 5.8, 3.8, 3.1, 2.0, and 1.7 g respectively. On the other hand, the lowest peak ground acceleration that 10-storey MRCF can withstand before reaching the maximum drift is Iwate Japan, which is 1.5 g. Therefore, we can conclude that the strongest ground motion that leads to the failure of 10-storey MRCF is Iwate Japan, while the weakest ground motion is Kobe Japan. The results of IDA performance for 10-storey MRCF is then tabulated in Table 3. Figure 4(d) shown that the 25-storey MRCF can withstand the highest peak ground acceleration from Kobe Japan, which is 2.2 g before reaching the maximum drift, follow by Iwate Japan, Imperial Valley, Corinth Greece, Manjil Iran, and Landers, which are 2.1, 2.05, 1.8, 1.7, and 1.25 g respectively. On the other hand, the lowest peak ground acceleration that 25-storey MRCF can withstand before reaching the

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Table 4 The IDA performance for 25-storey MRCF Ground motion

Imperial valley

Corinth Greece

Friuli Italy

Iwate Japan

Kobe Japan

Landers

Manjil Iran

PGA (g) at maximum drift

2.05

1.8

0.6

2.1

2.2

1.25

1.7

maximum drift is Friuli Italy, which is 0.6 g. Therefore, we can conclude that the strongest ground motion that leads to the failure of 25-storey MRCF is Friuli Italy, while the weakest ground motion is Kobe Japan. The results of IDA performance for 25-storey MRCF is then tabulated in Table 4.

3.2 Fragility Curves Fragility curve is defined as a function to predict the probability of failure of a building (exceeding certain limit state of damage) under a ground motion. Fragility curve aims to estimate the probability of failure of a structure or under a certain intensity measure. The development of fragility curve is based on two components, which are intensity measure (peak ground acceleration (PGA)) and damage measure (drift of MRCF). The formula of the fragility function is stated in Eq. (3). In this equation, the mean and standard deviation of PGA were calculated and considered as the main parameters to obtain the fragility function. Refer to that, the fragility curve for 10, 15, 20, and 25-storey MRCF is plotted in Fig. 5. Figure 5 shows the fragility curve of MRCF (10, 15, 20, and 25-storey) from different sets of ground motion. Each of the ground motion is represented by the different colour of lines, which can be found at legend. It can be seen in Fig. 5(d) that the 25-storey MRCF has the lowest PGA of ground motion that having 100% probability of exceeding the limit state among other MRCF. The lowest PGA is only 0.5 g from Friuli Italy. Meanwhile, 10-storey MRCF in Fig. 5(a) has the highest PGA of ground motion that having 100% probability of exceeding the limit state among other MRCF. The highest PGA is 4.0 g from Imperial Valley. From the fragility curve, we know that as the fragility curves shift toward the left, the hazard or the risk of MRCF collapsed will be increased. Therefore, we can conclude that the most stable MRCF which having the least chance to collapse is 10-storey MRCF as it has the highest PGA of ground motion that having 100% probability of exceeding the limit state among other MRCF. On the other hand, the most unstable MRCF which having the highest chance to collapse is 25-storey MRCF as it has the lowest PGA of ground motion that having 100% probability of exceeding the limit state among other MRCF.

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(a) 10 Storey

(c) 20 Storey

(b) 15 Storey

(d) 25 Storey

Fig. 5 The fragility curve of MRCF

4 Conclusions Based on the results of IDA, the most stable MRCF is 10-storey MRCF, which can withstand 7.4 g from Kobe Japan, while the most unstable MRCF is 25-storey MRCF, which can only withstand 0.6 g from Friuli Italy. Furthermore, the results from fragility curves also show that the most stable MRCF which having the least chance to collapse is 10-storey MRCF. It has the highest PGA of ground motion that having 100% probability of exceeding the limit state among other MRCF. On the other hand, the most unstable MRCF which having the highest chance to collapse is 25-storey MRCF. It has the lowest PGA of ground motion that having 100% probability of exceeding the limit state among other MRCF. In short, the performance of 10-storey MRCF is the best among the other MRCF, and we can say that 10-storey MRCF is the most sustainable among the other MRCF. Acknowledgements The authors gratefully acknowledge the financial support from UniMAP.

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References 1. Malaymail Online Sabah earthquake (2017) Damages to public assets and infrastructure worth RM94.8m 2. Billah A, Alam M (2014) Seismic fragility assessment of highway bridges: a state-of-the-art review. In: Structure and infrastructure engineering (ahead-of-print), pp 1–29 3. Luco N, Cornell CA (1998) Effects of random connection fractures on the demands and reliability for a 3-story pre-Northridge SMRF structure. In: Proceedings of the 6th US national conference on earthquake engineering 4. Vamvatsikos D, Cornell CA (2001) Incremental dynamic analysis. Earthquake Eng Struct Dyn 31(3):491–514 5. Mitropoulou CC, Papadrakakis M (2011) Developing fragility curves based on neural network IDA predictions. Eng Struct 33(12):3409–3421 6. Hosseinpour F, Abdelnaby AE (2017) Fragility curves for RC frames under multiple earthquakes. Soil Dyn Earthquake Eng 98(February):222–234 7. Eurocode 2 Eurocode 2: Design of concrete structure - Part 1–1: General rules and rules for buildings. London: British Standards Institution (2004). 8. Eurocode 8 (2004) Design Provisions for Earthquake Resistance of Structures: Part 1- 1, General Rules - Seismic Actions and General Requirements for Structures. London: British Standards Institution Eurocode 8 9. Malaysian Standard Code of Practice for Building Structure (2002) 10. Baker JW, Cornell CA (2006) Spectral shape, epsilon and record selection. Earthquake Eng Struct Dyn 35(9):1077–1095 11. Baker JW (2011) Conditional mean spectrum: tool for ground-motion selection. J Struct Eng 137(3):322–331 12. Mehdizadeh M, Mackie KR, Nielson BG (1998) Scaling bias and record selection for fragility analysis. In: Scaling bias and record selection for fragility analysis 13. PEER PEERC (2017) PEER Ground Motion Database 14. SeismoSinal Version (2017) 15. Verification Manual: Integrated Finite Element Analysis and Design of Structures. California (2017). Computer and Structures, Inc. SAP2000, USA 16. IBC 2003 (2003) International Building Code 2003: Section 1618, Dynamic Analysis Procedure for the Seismic Design of Buildings. International Code Consortium, US 17. Kirçil MS, Polat Z (2006) Fragility analysis of mid-rise R/C frame buildings. Eng Struct 28(9):1335–1345

Evaluate the Performance of Regular and Irregular Shape of Building Based on Dynamic Analysis N. A. N. Zainab, W. H. Tan, W. Faridah, A. M. Andrew, S. Ragunathan, and A. S. N. Amirah

Abstract In this paper, the main objective is to develop incremental dynamic analysis (IDA) curve of the regular and irregular shape of the building based on concrete material and ground motions. The regular shape was rectangle shape whereas irregular shape is L-shape. 7 sets of ground motions were used in this research for regular and irregular shape of the building. The structural components of the building were designed based on Eurocode 2 with the aid Eurocode 8 for earthquake loading. Wind load was applied to the building in x-direction and y-direction based on MS 1553. The SAP2000 was used as the main tool to carry out the analysis. The nonlinear dynamic analysis also called incremental dynamic analysis (IDA) was carried out with 7 sets of ground motion records that fulfil the criteria such as the distance of earthquake event to the station is less than 15 km, the earthquake magnitude is equal to or greater than 5.5 M and the PGA is equal to or greater than 0.15 g. 7 sets of ground motion were converted to response spectrum and scaled according to soil type A to develop the elastic response spectrum. The drift limit of the building was determined. The inter-storey drift of regular and irregular building were determined to predict the damage of the building. Form the IDA results, it was proven that regular building performs better as compared to an irregular building. Keywords Regular and irregular shape of building · Incremental dynamic analysis · Ground motions · Drift limit · Inter-storey drift

N. A. N. Zainab (B) · W. Faridah · S. Ragunathan · A. S. N. Amirah School of Environmental Engineering, Universiti Malaysia Perlis (UniMAP), Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia e-mail: [email protected] W. H. Tan School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia A. M. Andrew Centre for Diploma Studies (PPD), Universiti Malaysia Perlis (UniMAP), S2-L1-26, Kampus UniCITI Alam Sg. Chuchuh, 02100 Padang Besar (U), Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_55

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1 Introduction The buildings, having regular and irregular configurations in plan and elevation. The regular shape of building like square and rectangle is the most common building in the construction field which having a uniformly distributed in mass and stiffness through its height behaves normally. The H-shape (+)-shape, T-shape, L-shape, E-shape and C-shape are some of the shapes that have been developed in the construction field [1]. Designing an irregular shape of the building is more challenging compare to the regular shape of the building. One of the challenges faced when designing an irregular configuration building is the buildings with re-entrant corners that caused stress concentration. Stress concentration occurs when there is a difference between stiffness and torsion amplification in the buildings that will cause the building early collapse [2]. Besides re-entrant corners, torsion irregularity, diaphragm discontinuity, out of plan offsets and non-parallel system also needed to be taking into consideration when designing the irregular configuration of buildings. In Malaysia, the magnitude of the earthquake happened is classified in low or moderate. The movement of the plates struck Sabah [3] on 5 June 2015, when 6.0 magnitude earthquake struck Ranau, Sabah at depths of 10 and 15 km epicenter north of Ranau. Approximately 90 aftershock events were reported in Ranau by the Malaysian Meteorological Department [4] on 23 June and the seismic sequences were continued until the end of 2015. The earthquake caused the water supply disrupted and discoloured in Kundasang-Ranau and the structural damage to 23 schools and the other buildings. The behaviour of building during an earthquake depends on overall size, shape and geometry. During an earthquake, the weakest structural member will subject the forces first and the weakness of this structure increased due to geometry of structure, stiffness and discontinuity in mass. It found that the building with irregular structure possesses these types of weakness. Nowadays, the buildings are commonly constructed by using reinforced concrete and steel structures with various height. Moreover, designing towards irregular configurations buildings became common because of the aesthetical needs. Since, Malaysia is classified as a country that with low or moderate earthquake magnitude, therefore different ground motion records must be used to evaluate the performances of the regular and irregular shape of buildings to overcome, reduce and prevent any loss or damage caused by the earthquake. Incremental dynamic analysis (IDA) curve shows the relationship between the maximum inter-storey drift ratio and the intensity of the ground motion [5, 6] also mentioned that incremental dynamic analysis (IDA) is a useful method for evaluating the structural performance under different ground motions. Thus, the principal goal of this study is to evaluate the performances of the regular and irregular shape of buildings under dynamic analysis and determine the drift limit and inter-storey limit of the building with different ground motions to develop incremental dynamic analysis (IDA) curve.

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Fig. 1 Regular shape a Plan view b 3D model

Fig. 2 Irregular shape a Plan view b 3D model

2 Methodology 2.1 Structural Model In this study, 3D model of regular and irregular configuration of 20 stories high-rise building is designed by using reinforced concrete structures under [7]. A rectangle is chosen as a regular shape whereas L-shape is chosen as irregular shape. Both buildings had an identical height of 3.5 m. The dimension of the regular and irregular shape of the building is 27 × 32 m and 32 × 32 m respectively. Figures 1 and 2 shows the plan view and 3D model of the regular and irregular shape of the building. The buildings are constructed at soil that classified as class A. According to [8], soil A is rock or other rock-like geological formation, including at most 5 m of weaker material at the surface.

2.2 Design Load All of the structural components such as beam, column, slab and shear wall were imposed by the dead load, live load, superimposed dead load, wind load and seismic load. Dead load is the self-weight of structures whereas the superimposed dead

654 Table 1 Factor of safety [7]

Table 2 Types of load [7]

N. A. N. Zainab et al. Dead load

1.35

Live load

1.50

Superimposed dead load

1.35

Wind load

1.4

Type of load

Superimposed dead load, Gk (kN/m2 )

Live load, Qk (kN/m2 )

Finishes

1.50

–

Brickwork

2.60

–

Lift

7.50

–

Office

–

4.00

load is the weight of permanent partition such as the brick wall, ceilings, services and finishes. Live load is caused by the actions of people, furniture and equipment. Table 1 shows the value for the factor of safety (FOS) that will be used in this study. The factor of the dead load is the self-weight of the structure meanwhile the superimposed dead load is the finishing of the structure such as tiles, screeding, etc. Table 2 tabulated the types of load that considered at the structural components respectively can be identified in the Eurocode 2 [7].

2.3 Wind Load The wind is essentially the large scale horizontal movement of free air. Wind load will be caused by the bending moment and the shear force to increase. Besides that, the wind is caused by air flowing from high pressure to low pressure. Hence, wind load play an important role in designing a high-rise building. Wind load was applied to the building in two directions which include x-direction and y-direction. In this study, wind load designed with wind speed 33.5 m/s, suburban terrain for all directions and ground slope less than 1 in 20 for greater than 5 km in all directions. The wind load was calculated by using Eqs. (1) to (5). All of the parameters of the wind load were based on [9]. p = 0 · 613[Vdes ]2 Cfig Cdyn

(1)

Vdes = Vsit I

(2)

Vsit = Vs Md Mz,cat Ms Mh

(3)

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Cfig = Cp,c Ka Kc K1 Kp

(4)

  0.5 1 + 2Ih g2v Bs + g2r SEt /ε Cdyn = (1 + 2I h )

(5)

2.4 Nonlinear Dynamic Analysis The nonlinear incremental dynamic analysis is also known as time history analysis which required the ground motions time history for the evaluation on the performance of the building. This analysis needs ground motions for the development of the IDA curve. After that, the drift limit of the building is identified. Then, the performance of regular and irregular shape of the building can be evaluated by observing the IDA curves. Ground motions were one of the significant parameters that needed to perform incremental dynamic analysis. In this study, 7 sets of ground motions were used and all of these ground motions were selected from [10] which in soil type A. Table 3 shows the ground motions that have been selected. All the ground motions should fulfil criteria which the distance, R is less than 15 km from the earthquake sources, the magnitude of the ground motions is greater than 5.5 and the Peak Ground Acceleration (PGA) is greater than 0.15 g. Different ground motions were selected, scaled and applied to the buildings to determine the effect of the buildings. Scaling Ground Motion. All of the ground motions were selected from the [10] that fulfil the requirement as mentioned above. Before scaling, the acceleration response spectrum of seven ground motions was developed from SeismoSignal software. The fundamental period of the building was determined from [11] software after analyzed. The fundamental period, T 1 was used to determine the point for the ground motions to scale up or scale down. The acceleration response spectrum for each of the ground motion was scaled to the same pseudo-spectrum acceleration with the elastic response spectrum. In this study, the fundamental period for the regular building was 1.38 s Table 3 Seven set of ground motions [10] Location

R (km) Magnitude PGA (g)

1. Kobe Japan, 1/16/1995, Amagasaki

11.34

6.9

2. Friuli Italy-01, 5/6/1976, Tolmezzo

14.97

6.5

0.35713

3. Loma Prieta, 10/18/1989, Saratoga - Aloha Ave

7.58

6.93

0.51446

4. Imperial Valley-06, 10/15/1979, Brawley Airport

8.54

0.27578

6.53

0.16261

5. Parkfield-02 CA, 9/28/2004, PARKFIELD - WORK RANCH 10.33

6.0

0.34130

6. Bam Iran, 12/26/2003, Bam

0.05

6.6

0.80766

10.21

6.63

0.86241

7. Niigata Japan, 10/23/2004, NIG021

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Fig. 3 Scaled response spectrum

Fig. 4 IDA curve (Regular building)

whereas irregular building is 1.80 s. Figure 3 shows the scaled response spectrum for one of the ground motion.

3 Results and Discussions Figures 4 and 5 shows the IDA curve for regular and irregular shape of the building that obtained after analysis by using software [11]. Seven ground motions are selected which are Bam, Fruili, Imperial Valley, Kobe, Loma Prieta, Niigata and Parkfield. Each of these ground motions will produce an incremental dynamic analysis (IDA) curve and the following results will be discussed.

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Fig. 5 IDA curve (Irregular building)

From the figures above, the IDA curves for each of the ground motions that produced after dynamic analysis have its maximum PGA values. These PGA values are the intensity measure which can estimate the damage of the buildings. The intensity measure of the ground motions is depending on the drift limit of the building which is equal to 0.0525 m. The PGA values that exceeded that drift limit of the building will be caused damage to the building. Form the graph above, the pattern of the graph for the regular and irregular building is almost the same and the difference between them is the PGA values. The PGA values for Bam, Fruili, Imperial Valley, Kobe, Loma Prieta, Niigata and Parkfield for regular building are 0.72, 0.163, 0.282, 0.322, 0.431, 0.07 and 0.029 g respectively whereas for irregular building, the PGA values are 0.555, 0.083, 0.27, 0.442, 0.357, 0.051 and 0.015 g respectively. So it can be concluded that the range of PGA values for the regular building is between 0.029 and 0.72 g whereas, for irregular building, range of PGA values is between 0.555 and 0.015 g. Besides that, most of the PGA values for the regular building is greater than irregular shape such as Bam, Fruili, Imperial Valley, Loma Prieta, Niigata and Parkfield except Kobe. The PGA values of the regular building are 0.322 g which is smaller than the PGA values of the irregular shape of the building. The reason that PGA values in Kobe for regular building smaller than the irregular building is the scale factor of the response spectrum. The fundamental period for the regular building is 1.38 s while for the irregular building in 1.80 s. The different fundamental period will give a different scale factor. For the scale factor that larger than 1 is called scaled up factor whereas smaller than 1 is scaled down factor. In Kobe, the scale factor for the regular and irregular building is less than 1 which means is a scaled-down factor. With the same time history, the scaled-down factor of the regular building is much greater than irregular building. As the scaled-down factor increases, the PGA values will increase and the effects of the ground motion to the building also increases.

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Therefore, the PGA values in Kobe for the regular building is smaller than irregular building. Seven ground motions are selected for dynamic analysis. According to [12], the design values of engineering demand parameters (EDPs) are the mean of the EDPs that obtained. Figures 6 and 7 show the median and the mean drift. The performance of the building can be evaluated by calculating the IDA curve of seven ground motions for every PGA values to obtain the mean drift and median drift. By referring the graph, the PGA values for the regular building are higher than irregular building. The maximum PGA values for mean drift for the regular building is 0.29 g whereas irregular building is 0.25 g. The maximum PGA values for median drift for the regular building is 0.28 g while the irregular building is 0.27 g. This shows that the performance of the regular building is better than irregular building because regular building subjected higher intensity measure of ground motions than irregular building. According to [13], the irregularities of the building caused the amplification of damage when subjected to seismic loads. Irregularities in the plan will be caused the torsional effects in structural components whereas the irregularities in elevation will be caused by the increase of seismic demand in a specific storey. These two irregularities caused the increase in seismic demand in the structures of the building which will decrease the strength and ductility of the structure. In this study, reentrant corners are one of the irregularities of the building and the results show that the performance of the irregular building is less than a regular building. The performance of the irregular building is affected by the re-entrant corners. Fig. 6 Median drift

Fig. 7 Mean drift

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4 Conclusions i)

ii)

iii)

Based on the inter-storey drift of the building, the range of inter-storey drift for regular building under seven ground motions is between 6.5 to 7.0% whereas for the irregular building is between 6.1 to 7.4%. Besides that, by observing the graph, the conclusion that can be made was the critical inter-storey drift occurs at the lower part of the regular and irregular shape of the building which means that most of the damage will occur at the lower part of the building. Based on dynamic analysis, the IDA curves for regular and irregular shape of building were compared. By observing the IDA curve, it can be concluded that regular building is more stable and provides better performance as compared with the irregular building. With the same drift limit between the regular and irregular shape of the building, the regular building can sustain a maximum PGA value of 0.72 g whereas irregular building can sustain 0.555 g of PGA values. For the mean drift and median drift, the PGA values for the regular building are higher than irregular building. The maximum PGA values for mean drift for the regular building is 0.29 g whereas irregular building is 0.25 g. The maximum PGA values for median drift for the regular building is 0.28 g while the irregular building is 0.27 g. This shows that the performance of the regular building is better than irregular building because regular building subjected higher intensity measure of ground motions than irregular building.

Acknowledgements The authors gratefully acknowledge the financial support from UniMAP.

References 1. Mohod MV (2015) Pushover analysis of structures with plan irregularity. IOSR J Mech Civ Eng 12(4):2278–1684 2. Ahmed MMM, Raheem SEA, Ahmed MM, Abdel-Shafy AGA (2016) Irregularity effects on the seismic performance of L-shaped multi-story buildings. J Eng Sci Assiut Univ Fac Eng 44(5):513–536 3. 7 Things You Should Know About Ranau Earthquake. Astro Awani (2005) 4. MMD (2015) Malaysian Meteorological Department 5. Mansour YEI, Osman Shallan AE-S, Selim M (2016) Assessment of seismic retrofitting techniques of RC structures using fragility curves. Int J Struct Civ Eng Res 6(1):63–72 6. Kircil MSZP (2006) Fragility analysis of mid-rise R/C frame buildings. Eng Struct 28(9):1335– 1345 7. Eurocode 2: design of concrete structures - Part 1-1 : general rules and rules for buildings. British Standards Institution, vol 1, p 230 (2004) 8. Eurocode 8 – design of structures for earthquake resistance. Part 1: General rules, seismic actions and rules for Buildings (2004) 9. MS 1553 (2002) Malaysian Standard Code of Practice for Building Structure

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10. Ancheta TD, Darragh RB, Stewart JP, Seyhan E, Silva WJ, Chiou BSJ, Wooddell KE, Graves RW, Kottke AR, Boore DM, Kishida T, Donahue JL (2015) Pacific Earthquake Engineering Research Center - PEER, Ground Motion Database 11. SAP2000 (2017) Verification Manual: Integrated Finite Element Analysis and Design of Structures. Computer and Structures, Inc. California USA 12. Najafi LH, Tehranizadeh ML (2015) Ground motion selection and scaling in practice. Periodica Polytech Civ Eng 59(2):233–248 13. Prajwal TP, Parvez IA, Kamath K (2017) Nonlinear analysis of irregular buildings considering the direction of seismic waves. Mater Today Proc 4(9):9828–9832

Performance of Concrete Gravity Dam with Different Height of Dam and Water Level Under Seismic Loadings N. A. N. Zainab, A. M. Andrew, S. Ragunathan, A. S. N. Amirah, W. H. Tan, W. Faridah, and C. C. Mah

Abstract This research investigates the performance of the concrete gravity dam with a height of 50, 75, 100 and 125 m with different height of water level using incremental dynamic analysis (IDA). IDA is a method using the scaled ground motions record to evaluate the structure demand of dam. In the selection of ground motions, the following criteria must be achieved: (i) the distance of earthquake event to the station is less than 15 km, (ii) the earthquake magnitude is equal to or more than 5.5 (Mw) and (iii) the peak ground acceleration is equal to or greater than 0.15 g. The seven ground motions histories are converted to response spectrum and it is scaled to obtain the elastic response spectrum. The limit states of the dam were based on the cracking schemes and maximum displacement from IDA. The average maximum crest displacement for yielding and the ultimate limit of the dam with full water level is lower, the same goes to the limit states in terms of PGA is higher. The higher dam, the greater the average maximum crest displacement, the lower the limit states in terms of PGA. The cracking for the dam with full water level initiates at the heel of the dam as the existence of high hydrostatic pressure. Keywords Elastic response spectrum · Eurocode 8 · Incremental dynamic analysis · Maximum crest displacement · Peak ground acceleration

N. A. N. Zainab (B) · S. Ragunathan · A. S. N. Amirah · W. Faridah · C. C. Mah School of Environmental Engineering, Universiti Malaysia Perlis (UniMAP), Kompleks Pusat Pengajian Jejawi 3, 02600 Arau, Perlis, Malaysia e-mail: [email protected] W. H. Tan School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia A. M. Andrew Centre for Diploma Studies (PPD), Universiti Malaysia Perlis (UniMAP), S2-L1-26, Kampus UNICITI Alam Sg. Chuchuh, 02100 Arau, Perlis, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Bahari et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-16-0866-7_56

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1 Introduction Concrete dams are massive structures which are designed to resist the external force exerted on it by using its’ weight. In ancient times, dams were built to supply water and for irrigation purposes. Nowadays, dams are being built not only for water supply and irrigation but also for navigation, sediment control and hydropower is important to design dams with a high factor of safety to resist natural forces. According to Jauhari [11], construction of the dam can cause an earthquake, the phenomenon is known as Reservoir-Induced Seismicity (RIS). There are more than 100 identified cases related to earthquakes that scientists believe were caused by the construction of reservoirs [1]. The accepted explanation for this phenomenon is due to either change in stress of the weight of water, or the increase of groundwater pore pressure reduces the effective strength of the rock under the dam [2]. Statistics show that there are many reasons for the failure of dams, which 35% is due to leakage and piping, 25% of the overturning of the dam, 14% of spillway erosion, 11% of excessive deformation, 10% sliding, 2% gate failure, 2% faulty construction and the least is earthquake instability which comprises of 1% [3]. The failure of dams due to the earthquake is experienced by Koyna dam (1967), Banqiao dam (1975), Shihgang Dam (1999) and Zipingpu Dam (2008). The damage of Shihgang Dam is shown in Fig. 1. Lack of professional and experience in designing earthquakeresistant structure and repetition of mistakes leads to the failure of structure and it brings very high risks to the human lives. Time history analyses are more commonly conducted for design and evaluation of structure against seismic hazards for prediction of the performance level of the existing dam and new system [5]. Incremental Dynamic Analysis (IDA), is developed to evaluate the performance of structures. The structural model is subjected to one (or more) ground motion records, each scaled to a range of levels of intensity, to

Fig. 1 Failure of Shihgang Dam in 1999 due to the Chi-Chi earthquake [4]

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produce IDA curves of response versus intensity level [6]. By using IDA, the limit states of the structure can be identified based on the cracking pattern.

2 Methodology Four models of gravity dam of different height were modelled in ABAQUS version 6.12 using 2D planar. The height of the concrete gravity dams was 50, 75, 100 and 125 m are chosen due to most of the concrete gravity dam height in Malaysia between 50 to 125 m. The dimension of the dam was calculated using the equation proposed by Garg [7] as shown in Fig. 2. The material properties used for the dam is the same as Koyna Dam which adopted from ABAQUS 6.12 Example Problems Manual as shown in Table 1. Fig. 2 Model of the dam [7]

Table 1 Materials properties for Koyna Dam [8]

Young’s modulus, E

31,027 MPa

Poisson’ ratio, v

0.15

Density, ρ

2643 kg/m3

Dilation angle, ψ

36.31o

Compressive initial yield stress, σc0

13.0 MPa

Compressive ultimate stress, σcu

24.1 MPa

Tensile failure stress, σt0

2.9 MPa

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N. A. N. Zainab et al.

a=



Hw

(1)

where a = top width of the dam Hw = height of the water H B=√ (Sc − c)

(2)

where B = base width H = height of the dam Sc = specific gravity of concrete c = 0 (when uplift is ignored) Four models of gravity dam of different ground motions were selected based on the criteria (i) the distance from the epicenter is less than 15 km, (ii) the magnitude is equal to or greater than 5.5 and (iii) the PGA is equal to or greater than 0.15 g [9]. Scaling of respond spectrum according to the developed elastic respond spectrum after it is converted from ground motion to match the characteristic of the ground motion to the soil type. Elastic respond spectrum developed based on soil type B by using Eurocode 8. The seven ground motion was obtained from the Pacific Earthquake Engineering Research (PEER). The ground motions are Bam Iran, Friuli Italy, Imperial Valley, Kobe Japan, Loma Prieta, Niigata Japan and Parkfield California. The details about the ground motions are as shown in Table 2. The time history of Friuli Italy shown in Fig. 3 which is acceleration (g) and time (second). The selected ground motions are scaled according to Eurocode 8 and Draft Malaysia Standard for the future design of the structure in the seismic zone in Sabah. The natural frequency of each dam is obtained by using ABAQUS version 6.12 software to determine the fundamental period by using Eq. 3. The equation is proposed by a German physicist named Heinrich Rudolf Hertz in 1886. Period, T =

1 f r equency, f

(3)

Table 2 Parameters of the selected ground motions Event

R (km)

Magnitude

PGA-H (g)

PGA-V (g)

Bam Iran, 26/12/2003

12.00

6.60

0.80766

0.96951

Friuli Italy, 6/5/1976

14.97

6.50

0.35713

0.27704

8.54

6.53

0.21952

0.15281

11.34

6.90

0.32686

0.34183

Loma Prieta, 18/10/1989

7.58

6.93

0.32686

0.39570

Niigata Japan, 23/10/2004

10.21

6.63

1.74227

0.57301

Parkfield California, 28/9/2004

10.33

6.00

0.34130

0.16974

Imperial Valley, 15/10/1995 Kobe Japan, 16/1/1995

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Fig. 3 Friuli Italy seismic event

Elastic response spectrum was obtained by scaling of the ground motion according to Eurocode 8. The ground motions were scaled including motion in both vertical and horizontal direction. The fundamental period for each dam calculated previously was used to develop the elastic response spectrum. According to the Malaysia Decision for Sabah, the parameter for scaling chosen was on Ground Type B. The parameters are shown in Tables 3 and 4. The equations to develop horizontal elastic response spectrum Eqs. (4) to (7) and equations for vertical elastic response spectrum Eqs. (8) to (11) obtained from Eurocode 8 [10] are used in the scaling process. For horizontal elastic response spectrum,

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Table 3 Parameters of S, TB , TC and TD [10] Ground type

S

TB

TC

TD

A

1

0.1

0.4

2

B

1.4

0.15

0.4

2

C

1.35

0.15

0.6

2

D

1.35

0.2

0.8

2

E

1.4

0.15

0.5

2

Table 4 Parameters of avg / ag , TB , TC and TD [10]

avg / ag

TB

TC

TD

0.90

0.05

0.15

1.0

0 ≤ T ≤ TB : Sve (T ) = ag · S



T TB

· (η · 3.0 − 1)

 (4)

TB ≤ T ≤ TC : Sve (T ) = ag · S · η · 3.0

(5)

  TC ≤ T ≤ TD : Se (T ) = ag · S · η · 2.5 TTC

(6)

TD ≤ T ≤ 4s: Sve (T ) = ag · S · η · 3.0



TC ·TD T2

 (7)

where Se (T ) = elastic response spectrum; T = vibration period of a linear single-degree-of-freedom system; ag = design ground acceleration on type A ground (ag = γI · agR); TB = lower limit of the period of the constant spectral acceleration branch; TC = upper limit of the period of the constant spectral acceleration branch; TD = the value defining the beginning of the constant displacement response range of the spectrum; s = soil factor η = damping correction factor. For vertical elastic response spectrum, 0 ≤ T ≤ TB : Sve (T ) = avg · S



T TB

 · (η · 3.0 − 1)

TB ≤ T ≤ TC : Sve (T ) = avg · S · η · 3.0 TC ≤ T ≤ TD : Sve (T ) = avg · S · η · 3.0 TD ≤ T ≤ 4s Sve (T ) = avg · S · η · 3.0



(9)

 TC  T

TC TD T2

(8)

(10)

 (11)

Performance of Concrete Gravity Dam …

667

Incremental Dynamic Analysis (IDA) is carried out to study the performance for the various height of concrete gravity dam of 50, 75, 100 and 125 m for various water level of 0 m, 1/3 of the dam height, 1/2 of the dam height, 2/3 of the dam height and full height and there were a total of 7 ground motion. There was a total number of 840 non840 non-linear analyses conducted using ABAQUS software. In this research, there are a total of The properties for the concrete dam used was assumed to be the same as Koyna Dam adopted from ABAQUS 6.12 Example Problems Manual [8]. The dams were subjected to its self-weight, water pressure and seismic loadings. The ground motions were converted to acceleration response spectrum by using SeismoSignal software to consider acceleration ranging from 0.10 to 1.10 g with an increment of 0.2 g for each interval. The ground motions were scaled to the same spectrum acceleration at T1 according to Eurocode 8. The model in ABAQUS software was analysed with respect ground motion. The scaling of ground motion was carried out to obtain scale up and scale down factor for different accelerograms in different for different height of dam as well as the increasing peak ground acceleration (PGA) in both vertical and horizontal direction. In IDA curve, peak ground acceleration (PGA) acts as Intensity Measure (IM) and maximum crest displacement as Damage Measure (DM), and hence the limit state of the structure was determined. In ABAQUS software, the model is meshed and analysed with the step time and time history for selected ground motion which the data can be obtained from SeismoSignal software. After the analysis, the results were plotted into IDA curve to obtain the maximum crest displacement with an increment of the PGA for each ground motion. The data obtained from ABABUS were exported out to Excel software for each ground motion and PGA. By doing this, IDA curves were generated. The cracking schemes together with the tensile damage measured using the variable DAMAGET in ABAQUS software. The yielding state and ultimate state were determined from the data.

3 Results and Discussion The cracking scheme is important to study the effect of different water level and different height of the dam to the damage of structure under seismic loadings. With a single ground motion acts as a constant variable, manipulated variable of water level and height of the dam is used to find out the effect of water level and height of the dam to the damage of the structure. Figure 4 shows the cracking scheme of 125 m high dam under Friuli motion with 5 different water levels. The PGA 0.1 g, there are no craking on the concrete gravity dam for all water level. The cracking with red colour at the neck and the body of the dam occurred when the PGA 0.3 g. But at the same PGA for the full water level, the cracking only happened at the neck and the base of the dam with lower displacement compared to the other water level. For the dam with the full water level, the cracking always initiates from the downstream face of the neck and upstream face of the heel of the dam. It can be seen that the yielding limit of 125 m dam under Friuli motion is when the maximum crest

N. A. N. Zainab et al.

Peak Ground Acceleration 0.10g

0.30g

0.50g

0.70g

Full

2/3

1/2

1/3

0

Water Level

668

Fig. 4 Cracking displacement for 125 m dam under Friuli motion

0.90g

1.10g

Performance of Concrete Gravity Dam …

669

displacement reached 60.73 mm while 76.20 mm for yielding limit. When two cracks from upstream and downstream join together, it can be concluded as the collapse of the structure. On the other hand, the cracking for the dam with water level less than 2/3 of the height of the dam, the cracking starts at the downstream face between the neck and the toe of the dam. Li et al. [13] states that when the water level is under the full water level, therefore maximum tensile stress will occur the heel of the dam, while maximum compressive stress will occur at the toe of the dam. Since the maximum crest displacement is not much difference between the dam for the water level of less than 2/3 of the height, the most critical situation is considered for determination of limit states. The minimum value of maximum crest displacement among the dam with water level less than 2/3 of the dam is taken, which can be explained by the minimum displacement that caused the cracking of structure if the most critical condition. In this case, the maximum crest displacement for yielding limit and ultimate limit are 70.42 and 125.78 mm. It can be seen that the limit states of the dam with the full water level in terms of maximum crest displacement is higher than the dam of water level less than 2/3 of dam height. The results are supported by Jansen [12] who stated that the hydrodynamic pressure which causes the inertia force is subjected to the dam structure is considered during the design of the dam. Hence the displacement for yielding and the ultimate limit is lower for a dam with full water level as there are additional inertia forces exerted to the structure. The average maximum crest displacement obtained from 7 ground motions for yielding and ultimate states is listed in Table 5. The higher the dam, the higher the average maximum crest displacement for yielding and limit state of the structure. As the results for the dam with the water level of less than 2/3 of the height of the dam are very close, the most critical condition is considered, in which the maximum crest displacement is the highest. The IDA curve for the dam of 50, 75, 100 and 125 m is as shown in Figs. 5, 6, 7 and 8. The range of PGA for yielding and ultimate limit decreases as the height of the dam increases. For dam with the full water level, the range for yielding limit lies between 0.5 to 0.6 g for 50 m dam, decreases to 0.3 to 0.4 g for 75 m dam, and Table 5 The yielding and ultimate limit for the dam with different height and water level

Height of dam m

Displacement (mm) Yielding

50

Ultimate