"The text highlights the importance of intelligent decision-making in advanced production systems, and optimization of process parameters using fuzzy-based multi-criteria decision-making tools. It discusses the decision-making aspects of Industry 4.0 using machine learning and optimization techniques and helps in moving towards the digitalization of manufacturing systems. It further covers several important topics including the role of digital twins in advanced manufacturing processes, machine learning-based prediction of overall equipment effectiveness, intelligent quality control tools, and life cycle assessment models in Industry 4.0. Key Features: Presents a conceptual framework to measure the readiness of adopting Industry 4.0 in advanced manufacturing systems. Discusses the impact of smart manufacturing on sustainable development and integration of Industry 4.0 and additive manufacturing. Covers topics such as intelligent automation systems, machine learning-based preventive maintenance, and the Internet of Things-enabled additive manufacturing in Industry 4.0. Explains cyber-physical system integration with Industry 4.0 technologies, cyber-physical systems in industrial robotics, and green cyber-physical systems. Illustrates optimization of process parameters using fuzzy-based multi-criteria decision-making tools and life cycle assessment models in Industry 4.0. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of industrial engineering, production engineering, mechanical engineering, supply chain management, and manufacturing engineering"-- Provided by publisher.
The book highlights the importance of intelligent decision-making in advanced production systems, and optimization of process parameters using fuzzy-based multi-criteria decision-making tools. It discusses the decision-making aspects of Industry 4.0 using machine learning and optimization techniques and helps in moving toward the digitalization of manufacturing systems. It further covers several important topics including the role of digital twins in advanced manufacturing processes, machine learning-based prediction of overall equipment effectiveness, intelligent quality control tools, and life cycle assessment models in Industry 4.0.
Key features:
- Presents a conceptual framework to measure the readiness of adopting Industry 4.0 in advanced manufacturing systems.
- Discusses the impact of smart manufacturing on sustainable development and integration of Industry 4.0 and additive manufacturing.
- Covers topics such as intelligent automation systems, machine learning-based preventive maintenance, and the Internet of Things-enabled additive manufacturing in Industry 4.0.
- Explains cyber-physical system integration with Industry 4.0 technologies, cyber-physical systems in industrial robotics, and green cyber-physical systems.
- Illustrates optimization of process parameters using fuzzy-based multi-criteria decision-making tools and life cycle assessment models in Industry 4.0.
This book is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of industrial engineering, production engineering, mechanical engineering, supply chain management, and manufacturing engineering.
The text comprehensively discusses tools, techniques, design principles, and benefits of Industry 4.0. It further covers information flow, advanced manufacturing systems, intelligent automation systems, and the role of data in Industry 4.0. The book explains the integration of Industry 4.0 with additive manufacturing and circular economy.
1. Industry 4.0: Transforming communication, sustainability, and collaboration in manufacturing
2. 2. Integrating Industry 4.0 Technologies in Manufacturing Systems
3. The Smart Manufacturing Concept
4. Advanced Manufacturing Systems and Industry 4.0
5. Cyber-Physical System for Advanced Manufacturing
6. Digital Twins for Advanced Manufacturing
7. Decision-making in Industry 4.0
8. Navigating the Legal Landscape of Sustainable Industry 4.0: Challenges and Considerations
9. Industry 4.0 Performance Measurement Using Key Performance Indicators for Effective Digital Transformation
10. Machine Learning applications in Inventory Management: A Case Study
11. Research Issues in Industry 4.0
Vishal Ashok Wankhede is an Assistant Professor in the Department of Operations Management and Quantitative Techniques at the Indian Institute of Management, Bodh Gaya, India, since July 2023. Previously, he served as an Assistant Professor in the Department of Mechanical Engineering at Pandit Deendayal Energy University (PDEU), Gandhinagar, Gujarat, from March 2022 to June 2023, and in the Department of Industrial Engineering at PDEU from July 2016 to June 2019. Dr. Wankhede earned his Ph.D. in Industry 4.0 from the Department of Production Engineering, National Institute of Technology (NIT), Tiruchirappalli, in July 2022. He also holds an M.Tech in Cloud Manufacturing (2016) and a B.Tech in Mechanical Engineering (2014). He was recognized with the "Budding Researcher Award" in 2022 and an academic proficiency prize for his M.Tech at NIT Trichy. He has published over 50 peer-reviewed articles and book chapters with over 1300 citations and an h-index of 20. He is a member of several professional organizations, including the Institution of Engineers (India) and the American Society of Mechanical Engineers. His research interests include Industry 4.0, Smart Manufacturing, Additive Manufacturing, Circular Economy, Advanced Manufacturing Processes, Optimization, and Multi-Criteria Decision Making.
K.E.K. Vimal received a Ph.D. degree in Sustainable Manufacturing in 2016 and an M. Tech degree in Industrial Engineering in 2012 from the National Institute of Technology Tiruchirappalli, India, and a Production Engineering degree in 2010 from the PSG College of Technology, India. He has more than 10 years of teaching and research experience in Mechanical Engineering with a special emphasis on Circular supply chain management, Lean Six Sigma, and sustainability in manufacturing. He has worked as a Guest Editor for a few journals. Presently, he acts as a reviewer for more than 20 prestigious Web of Science journals. In addition, he has also published as an author (and co-author) 4 books with Publisher, 20 book chapters, and more than 60 articles in Journals and Conferences. He is currently working as Assistant Professor, Department of Production Engineering, National Institute of Technology Tiruchirappalli, India.
Pankaj Sahlot is an Assistant Professor in the Mechanical Engineering Department at the National Institute of Technology (NIT) Kurukshetra, India. He also worked as an Assistant Professor at Pandit Deendayal Energy University Gandhinagar (PDEU) for around 4 years. He completed his Ph.D. from IIT Gandhinagar, India. He also visited the University of North Texas Denton, USA, for six months as a visiting researcher during his PhD. His research work has been published in various reputable journals and conferences. He did his M.Tech from IIT Hyderabad in the Department of Materials Science and Engineering and received the Academic Excellence award from Prof. C. N. R. Rao. He completed his B.Tech. in Mechanical Engineering at Ideal Institute of Technology in Ghaziabad, U.P. India. Currently, his research of interest focuses on additive manufacturing (AM) and Friction Stir Welding (FSW) of different metals. He has also collaborated with reputed national and international institutes and published several articles.
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