Enabling Industry 4.0 through Advances in Mechatronics: Selected Articles from iM3F 2021, Malaysia

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ISBN 10: 9811920974 / ISBN 13: 9789811920974

Published by Springer, 2023

2023. Paperback. . . . . . Seller Inventory # V9789811920974

Synopsis:

This book presents part of the iM3F 2021 proceedings from the mechatronics track. It highlights key challenges and recent trends in mechatronics engineering and technology that are non-trivial in the age of Industry 4.0. It discusses traditional as well as modern solutions that are employed in the multitude spectra of mechatronics-based applications. The readers are expected to gain an insightful view on the current trends, issues, mitigating factors as well as solutions from this book.

About the Author:

Dr. Ismail Mohd Khairuddin is a lecturer at Universiti Malaysia Pahang. He received his Bachelor’s Degree in Mechatronics Engineering from Universiti Teknikal Malaysia Melaka (UTeM) in 2010 and was awarded with a Master’s Degree in Mechatronics and Automatic Control from Universiti Teknologi Malaysia in 2012. Then, he pursued his Ph.D. studies in Biomechatronics Engineering at the International Islamic University Malaysia. His research interests include rehabilitation robotics, mechanical and mechatronics design, mechanisms, control and automation, bio-signal processing as well as machine learning.

Muhammad Amirul bin Abdullah is a researcher at Innovative Manufacturing, Mechatronics & Sports Laboratory (iMAMS), Faculty of Manufacturing and Mechatronic Engineering Technology in Universiti Malaysia Pahang (UMP). He was awarded a Master’s Degree and received his Bachelor’s Degree, both in Mechatronics Engineering, from International Islamic University Malaysia (IIUM). His research interest includes machine learning, robotics, control and automation, and sports engineering.

Dr. Ahmad Fakhri bin Ab. Nasir received his Bachelor’s Degree in Information Technology from Universiti Malaya. He enrolled as a full-time master student at the Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, and received his Master's Degree in Engineering (Manufacturing). He pursued his Ph.D. specialized in Pattern Recognition at the Universiti Sultan Zainal Abidin. He joined Universiti Malaysia Pahang as a senior lecturer at the midst of 2016. He has published several articles and actively doing research related to computer vision, pattern recognition, image processing, machine learning, as well as parallel computing.

Dipl. Ing. (FH) Jessnor Arif Mat Jizat is a researcher at Innovative Manufacturing, Mechatronics & Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology in Universiti Malaysia Pahang (UMP). He is currently pursuing his Ph.D. in Mechatronic Engineering in UMP. Prior to that, he completed his Master's Degree at UMP and Diplom(FH) at Hochschule Karlsruhe, Germany. His research interest includes machine learning, robotics, robotic vision, and sports engineering. He is currently involved in wafer defect detection research in collaboration with Ideal Vision Integration Sdn Bhd and Dzuki Consultancy and Training. He had been appointed as a reviewer for the Malaysian Technical Universities Conference on Engineering and Technology (MUCET) 2019, The International Conference of Robotics Intelligence and Applications (RiTA) 2018, Malaysian Journal of Movement, Health & Exercise, and SN Applied Sciences Journal. He also had been appointed as an editor for Lecture Notes in Electrical Engineering 678 Embracing Industry 4.0 - Selected Articles from MUCET 2019 and Lecture Notes in Mechanical Engineering RITA 2018: Proceedings of the 6th International Conference on Robot Intelligence Technology and Applications and as a guest editor for SN Applied Sciences Topical Collection: Engineering - Recent Trends in Electrical & Electronics Engineering.

Dr. Mohd Azraai Mohd Razman graduated his first degree from the University of Sheffield, UK, in Mechatronics Engineering. He then obtained his M.Eng. from Universiti Malaysia Pahang (UMP) in Mechatronics Engineering as well. He then completed his Ph.D. at UMP specifically in the application of machine learning in aquaculture. His research interest includes optimization techniques, control systems, signal processing, instrumentation in aquaculture, sports engineering as well as machine learning. He is currently serving as a guest editor for SN Applied Sciences in a number of topical collections. He has also edited a number of volume in Springer’s LNEE and AISC series. He is currently serving as the editor-in-chief for MEKATRONIKA: Journal of Mechatronics and Intelligent Manufacturing under UMP Press.

Dr. Ahmad Shahrizan Abdul Ghani is currently a senior lecturer at Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang. He obtained his Master of Engineering (M.Eng.) in Mechatronics from University of Applied Sciences Augsburg, Germany, in 2009. In 2015, he completed his doctoral study in Image Processing and Computer Vision at Universiti Sains Malaysia (USM). He is registered as a member of Board of Engineer Malaysia (BEM) and Malaysia Board of Technologists (MBOT). His primary research interest and activities involve the field of mechatronics, image processing, computer vision, sensor, instrumentation, and automation.

Dr. Muhammad Aizzat Zakaria earned his first degree in Electrical-Mechatronics Engineering at the Universiti Teknologi Malaysia. After finishing his first degree, he pursued a career as a process R&D engineer at Intel Microelectronics. He pursued a Doctoral’s Degree in Mechatronic Engineering specialized in the Autonomous Vehicle Development at the Universiti Teknologi Malaysia in Kuala Lumpur.  He has published many research articles related to autonomous vehicle development in Malaysia. His research interests include intelligent vehicle navigation, vehicle stability control, robotics system modeling, and mechatronics system.

Wan Hasbullah Bin Mohd Isa graduated in BEng of Mechatronics (Precision Engineering) from the Hochschule Furtwangen University, Germany, in 2009. He later completed his M.Sc. of Mechatronics study at Fachhochschule Aachen, Germany, and graduated in 2012. Currently, he is pursuing his Ph.D. degree at Delft University of Technology, Netherlands, in the field of Smart Materials/Transducers. Since 2012, he is a lecturer at Faculty of Manufacturing and Mechatronic Engineering Technology of UMP, focusing her research on the miniaturized energy harvesting systems, artificial intelligence, and smart material-based actuation/sensing applications.

Anwar P.P. Abdul Majeed graduated with a first-class honors B.Eng. in Mechanical Engineering from Universiti Teknologi MARA (UiTM), Malaysia. He obtained M.Sc. in Nuclear Engineering from Imperial College London, UK. He then received his Ph.D. in Rehabilitation Robotics from the Universiti Malaysia Pahang (UMP). Dr. Anwar is a chartered engineer (CEng) with the Institution of Mechanical Engineering (IMechE), UK. He is currently serving as a senior lecturer at the Faculty of Manufacturing and Mechatronics Engineering Technology, UMP. He is an active research member at the Innovative Manufacturing, Mechatronics and Sports Laboratory (iMAMS), UMP. His research interest includes rehabilitation robotics, computational mechanics, applied mechanics, sports engineering, renewable and nuclear energy, sports performance analysis as well as machine learning. Anwar has authored over 60 papers in different journals, conference proceedings as well as books. He serves as a reviewer in a number of prolific journals such as IEEE Access, Frontiers in Bioengineering and Biotechnology, PeerJ Computer Science, SN Applied Sciences, Applied Computing and Informatics, among others. He has also served as a guest editor for SN Applied Sciences as well as an editor for several Springer book series. He has recently been appointed as an affiliate member to the Young Scientist Network, Academy of Sciences Malaysia (YSN-ASM).

Bibliographic Details

Title: Enabling Industry 4.0 through Advances in ...
Publisher: Springer
Publication Date: 2023
Binding: Soft cover
Condition: New