Human-Centric Smart Manufacturing Towards Industry 5.0 (eBook, PDF)

Redaktion: Wang, Baicun; Mourtzis, Dimitris; Wang, Lihui; Zheng, Pai

• Format: PDF

Produktbeschreibung

This book presents a set of innovative solutions to human-centric manufacturing systems, offering critical insights and comprehensive application guidelines for understanding how to realize human-centric smart manufacturing by exerting its power and influence towards Industry 5.0.

While human-centric manufacturing possesses a substantial and growing body of knowledge, there are distinct research gaps that are not sufficiently addressed. With the development of enabling technologies, it is necessary to propose more precise, robust, and practical approaches in support of smart manufacturing towards Industry 5.0. As a response to the new research opportunities, this book presents and highlights the latest development on applying advanced techniques in human-centric manufacturing. The book will be of interest to a broad readership, from academic researchers to practicing engineers

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 369
• Erscheinungstermin: 11. April 2025
• Englisch
• ISBN-13: 9783031821707
• Artikelnr.: 73849454

Autorenporträt

Baicun Wang is currently a Research Professor and the Deputy Dean of School of Mechanical Engineering, Zhejiang University, China. Dr. Wang's research interests focus on research, innovation, and applications of human-centric smart manufacturing based on human-cyber-physical systems (HCPS), which include: (1) HCPS and human-robot collaboration, (2) Human-centric Smart Manufacturing, and (3) Human digital twin. Dr. Wang has secured over 10 research grants to date, with a total of more than ¥ 10 million. He has authored or co-authored over 70 scientific articles in journals, books, and peer-reviewed conference proceedings. He served as the Guest Editor for Journal of Manufacturing Systems, Chinese Journal of Mechanical Engineering, and reviewer for over 20 international scientific journals, including Robotics and Computer-Integrated Manufacturing, Journal of Manufacturing Systems, Nano Energy, and Applied Energy. He is also the Senior Member of Chinese Mechanical Engineering Society (CMES) and Chinese Association for Artificial Intelligence (CAAI).

Pai Zheng is currently an Associate Professor, Wong Tit Shing Endowed Young Scholar in Smart Robotics in the Department of Industrial and Systems Engineering and Director of Human-Machine Symbiotic Design and Manufacturing Laboratory (HMS-DML), at the Hong Kong Polytechnic University, HKSAR. His research interests include smart product-service systems, human-robot collaboration, and smart manufacturing systems. He is a Senior Member of IEEE/CMES, Member of SME/ASME, CIRP Associate Member, SME

Inhaltsangabe

HSM Perspectives.- Human centric Smart Manufacturing and Interoperability towards the Industrial Metaverse.- Human centric Smart Manufacturing for Sustainability.- Human Digital Twin Modelling from a Human Factor Perspective.- Human centric smart design.- User participation in product conceptual design a process model and its iterative methodology.- Designing of a man machine-duplex production line for Human centric Smart Manufacturing towards Industry 5 0.- Smart product service system a systematic framework for engineering product development and service innovation in human-cyber physical system.- Towards Digital Twin for Human Skeleton in Smart Manufacturing with predictive modelling.- Human centric smart production.- Identification and control methods of human computer interaction safety behaviour in digital twin workshop.- Operation tasks assignment optimization of human robot collaboration system based on NSGA II.- Safe Human Robot Collaboration Using Multi Modal Perception and Robot Learning.- Human centric smart assembly.- Digital twin enabled assistance for human centric assembly.- A digital twin enabled approach to reliable human robot collaborative assembly.- Human robot collaboration empowered by digital twin and knowledge graph in an intelligent assembly system.- Deep reinforcement learning based robot adaptive action decision making considering biased operator's actions in the human robot collaborative assembly.- Human machine synchronization for smart assembly in the context of Industry 5 0.- Outlook and Future Trends.