http://orcid.org/0000-0001-5997-6823
![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
Abstract
The modern manufacturing industry is investing in new technologies such as the Internet of Things (IoT), big data analytics, cloud computing and cybersecurity to cope with system complexity, increase information visibility, improve production performance, and gain competitive advantages in the global market. These advances are rapidly enabling a new generation of smart manufacturing, i.e., a cyber-physical system tightly integrating manufacturing enterprises in the physical world with virtual enterprises in cyberspace. To a great extent, realizing the full potential of cyber-physical systems depends on the development of new methodologies on the Internet of Manufacturing Things (IoMT) for data-enabled engineering innovations. This article presents a review of the IoT technologies and systems that are the drivers and foundations of data-driven innovations in smart manufacturing. We discuss the evolution of internet from computer networks to human networks to the latest era of smart and connected networks of manufacturing things (e.g., materials, sensors, equipment, people, products, and supply chain). In addition, we present a new framework that leverages IoMT and cloud computing to develop a virtual machine network. We further extend our review to IoMT cybersecurity issues that are of paramount importance to businesses and operations, as well as IoT and smart manufacturing policies that are laid out by governments around the world for the future of smart factory. Finally, we present the challenges and opportunities arising from IoMT. We hope this work will help catalyze more in-depth investigations and multi-disciplinary research efforts to advance IoMT technologies.
Acknowledgements
The authors would like to thank Chen Kan, Rui Zhu, Cheng-bang Chen, Bing Yao for their help in organizing and editing the references used in this paper. Also, the authors thank Dr. Congbo Li for sharing the dataset of power profiles from machining opeartaions, as well as Dr. Yun Chen and Dr. Shijie Su for sharing the power profiles from welding operations.
Additional information
Funding
Notes on contributors
Hui Yang
Hui Yang is the Harold and Inge Marcus Career Associate Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University, University Park, PA. His research interests are sensor-based modeling and analysis of complex systems for process monitoring, process control, system diagnostics, condition prognostics, quality improvement, and performance optimization. He received the NSF CAREER award in 2015, and multiple best paper awards from the international IEEE, IISE and INFORMS conferences. Dr. Yang is the president (2017–2018) of IISE Data Analytics and Information Systems Society, the president (2015–2016) of INFORMS Quality, Statistics and Reliability (QSR) society, and the program chair of 2016 Industrial and Systems Engineering Research Conference (ISERC). He is also an associate editor for IISE Transactions, IEEE Journal of Biomedical and Health Informatics (JBHI), IEEE Transactions on Automation Science and Engineering, IEEE Robotics and Automation Letters (RA-L), Quality Technology & Quantitative Management.
Soundar Kumara
Soundar Kumara is the Allen, E., and Allen, M., Pearce Professor of Industrial and Manufacturing Engineering at Penn State. He holds an affiliate appointment with the school of Information Sciences and Technology. Dr. Kumara is a Fellow of Institute of Industrial and Systems Engineers (IISE), Fellow of the International Academy of Production Engineering (CIRP), Fellow of American Society of Mechanical Engineers (ASME), and a Fellow of the American Association for the Advancement of Science (AAAS). Kumara is a leader in industrial engineering for his pioneering and visionary interdisciplinary research in logistics and manufacturing. His unique approaches integrate mathematics, AI, pattern recognition, advanced computing, statistical physics and operations research, to solve problems in complex networks, product design and real- time monitoring of manufacturing and logistics systems. He has laid the foundations of nonlinear dynamics-based monitoring and diagnosis methodologies in manufacturing process monitoring. One of his papers on clustering in large networks in Physics Reviews – E is designated as a milestone paper for 2007, commemorating 25 years of PRE, which has published more than 50 000 articles since its beginning in 1993.
Satish T.S. Bukkapatnam
Satish T. S. Bukkapatnam serves as Rockwell International Professor with Department of Industrial and Systems Engineering department at Texas A&M University, College Station, TX, USA. He has previously served as an AT&T Professor at the Oklahoma State University and as an assistant professor at the University of Southern California. He is also the Director of the Texas A&M Engineering Experimentation Station (TEES) Institute for Manufacturing Systems. He also holds an affiliate faculty appointment at Ecole Nationale Superior Arts et Metier (ENSAM), France. His research addresses the harnessing of high-resolution nonlinear dynamic information, especially from wireless MEMS sensors, to improve the monitoring and prognostics, mainly of ultraprecision and nanomanufacturing processes and machines, and cardiorespiratory processes. His research has led to 151 peer-reviewed publications (87 published/accepted in journals and 64 in conference proceedings), five pending patents, 14 completed Ph.D. dissertations, $5 million in grants as PI/Co-PI from the National Science Foundation, the U.S. Department of Defense, and the private sector, and 17 best-paper/poster recognitions. He is a fellow of the Institute for Industrial and Systems Engineers (IISE) and the Society of Manufacturing Engineers (SME), and he has been recognized with Oklahoma State University Regents distinguished research, Halliburton outstanding college of engineering faculty, IISE Boeing technical innovation, IISE Eldin outstanding young industrial engineer, and SME Dougherty outstanding young manufacturing engineer awards. He currently serves as the editor of the IISE Transactions, Design and Manufacturing Focused Issue. He received his master's and Ph.D. degrees from the Pennsylvania State University and undergraduate degree from S.V. University, Tirupati, India.
Fugee Tsung
Fugee Tsung is professor of the Department of Industrial Engineering and Decision Analytics (IEDA), Director of the Quality and Data Analytics Lab, at the Hong Kong University of Science & Technology (HKUST), and editor-in-chief of the Journal of Quality Technology. He is a Fellow of the Institute of Industrial and Systems Engineers, Fellow of the American Society for Quality, Fellow of the American Statistical Association, Academician of the International Academy for Quality, and Fellow of the Hong Kong Institution of Engineers. He received both his MSc and Ph.D. from the University of Michigan, Ann Arbor and his BSc from National Taiwan University. He has authored over 100 refereed journal publications and is also the winner of the Best Paper Award for the IISE Transactions in 2003, 2009, 2017. His research interests include industrial big data and quality analytics.