Human–Cyber–Physical Systems (HCPSs) in the Context of New-Generation Intelligent Manufacturing

Abstract An intelligent manufacturing system is a composite intelligent system comprising humans, cyber systems, and physical systems with the aim of achieving specific manufacturing goals at an optimized level. This kind of intelligent system is called a human–cyber–physical system (HCPS). In terms of technology, HCPSs can both reveal technological principles and form the technological architecture for intelligent manufacturing. It can be concluded that the essence of intelligent manufacturing is to design, construct, and apply HCPSs in various cases and at different levels. With advances in information technology, intelligent manufacturing has passed through the stages of digital manufacturing and digital-networked manufacturing, and is evolving toward new-generation intelligent manufacturing (NGIM). NGIM is characterized by the in-depth integration of new-generation artificial intelligence (AI) technology (i.e., enabling technology) with advanced manufacturing technology (i.e., root technology); it is the core driving force of the new industrial revolution. In this study, the evolutionary footprint of intelligent manufacturing is reviewed from the perspective of HCPSs, and the implications, characteristics, technical frame, and key technologies of HCPSs for NGIM are then discussed in depth. Finally, an outlook of the major challenges of HCPSs for NGIM is proposed.

[1]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[2]  Dazhong Wu,et al.  Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.

[3]  刘 子熠,et al.  Hybrid-augmented intelligence: collaboration and cognition , 2017, Frontiers of Information Technology & Electronic Engineering.

[4]  Xiong Youlun The Theory and Modeling for Next Generation Manufacturing System , 2000 .

[5]  Pradeep Kumar Jha,et al.  Developments in investment casting process—A review , 2012 .

[6]  Fred Fonseca,et al.  Cyber‐human systems of thought and understanding , 2018, J. Assoc. Inf. Sci. Technol..

[7]  Ahmad-Reza Sadeghi,et al.  Security and privacy challenges in industrial Internet of Things , 2015, 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC).

[8]  Lei Ren,et al.  Cloud manufacturing: a new manufacturing paradigm , 2014, Enterp. Inf. Syst..

[9]  William B Bonvillian,et al.  Advanced Manufacturing Policies and Paradigms for Innovation , 2013, Science.

[10]  Peter Gorm Larsen,et al.  From Embedded to Cyber-Physical Systems: Challenges and Future Directions , 2014, Collaborative Design for Embedded Systems.

[11]  Edward A. Lee Cyber-physical Systems -are Computing Foundations Adequate? Position Paper for Nsf Workshop on Cyber-physical Systems: Research Motivation, Techniques and Roadmap , 1998 .

[12]  Dimitris Mourtzis,et al.  Digital manufacturing: History, perspectives, and outlook , 2009 .

[13]  Jürgen Teich,et al.  Towards the co-evolution of industrial products and its production systems by combining models from development and hardware/software deployment in cyber-physical systems , 2017, Prod. Eng..

[14]  J.D. Sterman,et al.  System Dynamics Modeling: Tools for Learning in a Complex World , 2001, IEEE Engineering Management Review.

[15]  Christos G. Cassandras,et al.  Smart Cities as Cyber-Physical Social Systems , 2016 .

[16]  Fernando Boavida,et al.  A Practical Introduction to Human-in-the-Loop Cyber-Physical Systems , 2018 .

[17]  Liu Daxin Liu Zhenyu Cheng Jin Tan Jianrong Research on Key Technical Approaches for the Transition from Digital Manufacturing to Intelligent Manufacturing , 2017 .

[18]  Åsa Fast-Berglund,et al.  The Operator 4.0: Human Cyber-Physical Systems & Adaptive Automation Towards Human-Automation Symbiosis Work Systems , 2016, APMS.

[19]  Jorge Sá Silva,et al.  A Survey on Human-in-the-Loop Applications Towards an Internet of All , 2015, IEEE Communications Surveys & Tutorials.

[20]  Hiroyuki Yoshikawa,et al.  Manufacturing and the 21st century — Intelligent manufacturing systems and the renaissance of the manufacturing industry , 1995 .

[21]  In Lee,et al.  The Internet of Things (IoT): Applications, investments, and challenges for enterprises , 2015 .

[22]  Kevin I-Kai Wang,et al.  Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues , 2020, Robotics Comput. Integr. Manuf..

[23]  Klaudia Frankfurter As Time Goes By From The Industrial Revolutions To The Information Revolution , 2016 .

[24]  Roland Rosen,et al.  About The Importance of Autonomy and Digital Twins for the Future of Manufacturing , 2015 .

[25]  Soundar R. T. Kumara,et al.  Cyber-physical systems in manufacturing , 2016 .

[26]  Dirk Lindebaum Sapiens: A Brief History of Humankind - A Review , 2015 .

[27]  Yubao Chen,et al.  Integrated and Intelligent Manufacturing: Perspectives and Enablers , 2017 .

[28]  Wen Gao,et al.  Cross-media analysis and reasoning: advances and directions , 2017, Frontiers of Information Technology & Electronic Engineering.

[29]  Christian Brecher,et al.  Industrial Internet of Things and Cyber Manufacturing Systems , 2017 .

[30]  William E. Frazier,et al.  Metal Additive Manufacturing: A Review , 2014, Journal of Materials Engineering and Performance.

[31]  Yang Shuzi Network Manufacturing and Enterprise Integration , 2000 .

[32]  Lihui Wang,et al.  Intelligent manufacturing systems : A review , 2016 .

[33]  Chun-Wei Yang,et al.  Applications of artificial intelligence in intelligent manufacturing: a review , 2017, Frontiers of Information Technology & Electronic Engineering.

[34]  Eric Simmon,et al.  Cyber-Physical-Human Systems: Putting People in the Loop , 2016, IT Professional.

[35]  Andrew Kusiak,et al.  Data-driven smart manufacturing , 2018, Journal of Manufacturing Systems.

[36]  Zhou Yuan,et al.  Brief Analysis on Three Basic Paradigms of Intelligent Manufacturing , 2018 .

[37]  F. Richard Yu,et al.  Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges , 2017, IEEE Communications Surveys & Tutorials.

[38]  Johan Stahre,et al.  TOWARDS AN OPERATOR 4.0 TYPOLOGY: A HUMAN-CENTRIC PERSPECTIVE ON THE FOURTH INDUSTRIAL REVOLUTION TECHNOLOGIES , 2016 .

[39]  A. Busnaina,et al.  Nanomanufacturing and sustainability: opportunities and challenges , 2013, Journal of Nanoparticle Research.

[40]  Andrew Y. C. Nee,et al.  Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison , 2019, Engineering.

[41]  Andrew Kusiak,et al.  Intelligent Manufacturing Systems , 1990 .

[42]  Chai Xu-dong,et al.  Cloud manufacturing:a new service-oriented networked manufacturing model , 2010 .

[43]  Yoram Koren,et al.  The Global Manufacturing Revolution: Product-Process-Business Integration and Reconfigurable Systems , 2010 .

[44]  Andrew Kusiak,et al.  Smart manufacturing must embrace big data , 2017, Nature.

[45]  S. Zapperi,et al.  Dislocation Avalanches, Strain Bursts, and the Problem of Plastic Forming at the Micrometer Scale , 2007, Science.

[46]  Laine Mears,et al.  A complementary Cyber-Human Systems framework for Industry 4.0 Cyber-Physical Systems , 2018 .

[47]  Han-Xiong Li,et al.  Control for Intelligent Manufacturing: A Multiscale Challenge , 2017 .

[48]  Deniz Erdogmus,et al.  The Future of Human-in-the-Loop Cyber-Physical Systems , 2013, Computer.

[49]  Anne Lauscher Life 3.0: being human in the age of artificial intelligence , 2019, Internet Histories.

[50]  Fei Tao,et al.  CCIoT-CMfg: Cloud Computing and Internet of Things-Based Cloud Manufacturing Service System , 2014, IEEE Transactions on Industrial Informatics.

[51]  Katharine Armstrong,et al.  Big data: a revolution that will transform how we live, work, and think , 2014 .

[52]  Damien Trentesaux,et al.  Towards human-based industrial cyber-physical systems , 2018, 2018 IEEE Industrial Cyber-Physical Systems (ICPS).

[53]  Rolf Steinhilper,et al.  The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0☆ , 2017 .

[54]  Xun Xu,et al.  Machine Tool 4.0 for the new era of manufacturing , 2017 .

[55]  Patrick M. Pilarski,et al.  Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning , 2016 .

[56]  Osvaldo N Oliveira,et al.  A Future with Ubiquitous Sensing and Intelligent Systems. , 2018, ACS sensors.

[57]  Norbert Wiener,et al.  Cybernetics, or control and communication in the animal and the machine, 2nd ed. , 1961 .

[58]  Rolf H. Weber,et al.  Internet of Things - New security and privacy challenges , 2010, Comput. Law Secur. Rev..

[59]  Ben Wang,et al.  The Future of Manufacturing: A New Perspective , 2018, Engineering.

[60]  Shancang Li,et al.  5G Internet of Things: A survey , 2018, J. Ind. Inf. Integr..

[61]  Norbert Wiener,et al.  Cybernetics. , 1948, Scientific American.

[62]  Chun Chen,et al.  Challenges and opportunities: from big data to knowledge in AI 2.0 , 2017, Frontiers of Information Technology & Electronic Engineering.

[63]  Jay Lee,et al.  Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation , 2015 .

[64]  Damien Trentesaux,et al.  Designing intelligent manufacturing systems through Human-Machine Cooperation principles: A human-centered approach , 2017, Comput. Ind. Eng..

[65]  Dimitris Mourtzis,et al.  Industrial Big Data as a Result of IoT Adoption in Manufacturing , 2016 .

[66]  Nanning Zheng,et al.  Hybrid-augmented intelligence: collaboration and cognition , 2017, Frontiers of Information Technology & Electronic Engineering.

[67]  Ray Y. Zhong,et al.  Intelligent Manufacturing in the Context of Industry 4.0: A Review , 2017 .

[68]  David Romero,et al.  Smart manufacturing: Characteristics, technologies and enabling factors , 2019 .

[69]  R. G. Brown Driving digital manufacturing to reality , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[70]  Lei Shu,et al.  Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges , 2018, IEEE Access.

[71]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[72]  Damien Trentesaux,et al.  A Human-Centred Design to Break the Myth of the "Magic Human" in Intelligent Manufacturing Systems , 2015, SOHOMA.

[73]  Wenguang Chen,et al.  The future of artificial intelligence in China , 2018, Commun. ACM.

[74]  Behzad Esmaeilian,et al.  The evolution and future of manufacturing: A review , 2016 .

[75]  Berend Denkena,et al.  Advancing Cutting Technology , 2003 .

[76]  Peigen Li,et al.  Toward New-Generation Intelligent Manufacturing , 2018 .

[77]  Peter Marsh,et al.  The New Industrial Revolution: Consumers, Globalization and the End of Mass Production , 2012 .

[78]  P. O'Donovan,et al.  Big data in manufacturing: a systematic mapping study , 2015, Journal of Big Data.

[79]  Stephen T. Newman,et al.  Making CNC machine tools more open, interoperable and intelligent - a review of the technologies , 2006, Comput. Ind..

[80]  Suphunnika Ibbotson,et al.  Direct digital manufacturing: definition, evolution, and sustainability implications , 2015 .

[81]  Masahiko Mori,et al.  Development of Sensing Interface for Preventive Maintenance of Machine Tools , 2017 .

[82]  John W. Fowler,et al.  Grand Challenges in Modeling and Simulation of Complex Manufacturing Systems , 2004, Simul..

[83]  Jeffrey S. Smith,et al.  Simulation for manufacturing system design and operation: Literature review and analysis , 2014 .

[84]  Ping Wang,et al.  Data and Decision Intelligence for Human-in-the-Loop Cyber-Physical Systems: Reference Model, Recent Progresses and Challenges , 2018, J. Signal Process. Syst..

[85]  Fenghua Zhu,et al.  Cyber-physical-social system in intelligent transportation , 2015, IEEE/CAA Journal of Automatica Sinica.

[86]  Hirokazu Taki Society5.0技術イノベーションに向けて;Society5.0技術イノベーションに向けて;Towards Technological Innovation of Society5.0 , 2017 .

[87]  S. Jack Hu,et al.  Evolving paradigms of manufacturing: From mass production to mass customization and personalization , 2013 .

[88]  Nathan W Hartman,et al.  Identified research directions for using manufacturing knowledge earlier in the product life cycle , 2017, Int. J. Prod. Res..

[89]  William B. Bonvillian Advanced Manufacturing: A New Policy Challenge , 2017 .

[90]  S. Liu,et al.  The New Role of Design in Innovation: A Policy Perspective from China , 2018 .

[91]  Edward A. Lee Cyber Physical Systems: Design Challenges , 2008, 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC).

[92]  Wei Li,et al.  Crowd intelligence in AI 2.0 era , 2017, Frontiers of Information Technology & Electronic Engineering.

[93]  Yunhe Pan,et al.  Heading toward Artificial Intelligence 2.0 , 2016 .

[94]  Zheng Chen,et al.  Exploring the Development of Research, Technology and Business of Machine Tool Domain in New-Generation Information Technology Environment Based on Machine Learning , 2019 .

[95]  Peter C. Evans,et al.  Industrial Internet: Pushing the Boundaries of Minds and Machines , 2012 .

[96]  Hua Xiang,et al.  CPS Modeling of CNC Machine Tool Work Processes Using an Instruction-Domain Based Approach , 2015 .

[97]  Fei Tao,et al.  Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.

[98]  Michael Marien,et al.  Book Review: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies , 2014 .

[99]  Åsa Fast-Berglund,et al.  Towards a Human-Centred Reference Architecture for Next Generation Balanced Automation Systems: Human-Automation Symbiosis , 2015, APMS.