A human cyber physical system framework for operator 4.0 – artificial intelligence symbiosis

Abstract The emergence of Artificial Intelligence (AI) reveals new opportunities in Industry 4.0 environments. However, the lack of appropriate data and the requirements for trustworthiness pose significant challenges in the applicability and the effectiveness of AI systems in manufacturing environments. On the other hand, Industry 4.0 enables new types of interactions between humans and AI, but also between digital and physical worlds in the context of Cyber Physical Systems (CPS). In this paper, a Human Cyber Physical System (HCPS) framework for Operator 4.0 – Artificial Intelligence Symbiosis is proposed and its main architectural building blocks are described.

[1]  Seung Ho Hong,et al.  Toward Industry 4.0 Components: Insights Into and Implementation of Asset Administration Shells , 2019, IEEE Industrial Electronics Magazine.

[2]  Yanhong Zhou,et al.  Human–Cyber–Physical Systems (HCPSs) in the Context of New-Generation Intelligent Manufacturing , 2019, Engineering.

[3]  Johan Stahre,et al.  The Operator 4.0: Towards socially sustainable factories of the future , 2020, Comput. Ind. Eng..

[4]  Xun Xue,et al.  A Survey of Data-Driven and Knowledge-Aware eXplainable AI , 2020, IEEE Transactions on Knowledge and Data Engineering.

[5]  Sarah R. Fletcher,et al.  Adaptive automation assembly: Identifying system requirements for technical efficiency and worker satisfaction , 2020, Comput. Ind. Eng..

[6]  Apostolos P. Fournaris,et al.  Enabling the human in the loop: Linked data and knowledge in industrial cyber-physical systems , 2019, Annu. Rev. Control..

[7]  Erwin Rauch,et al.  Anthropocentric perspective of production before and within Industry 4.0 , 2020, Comput. Ind. Eng..

[8]  Qiang Liu,et al.  Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop , 2018, Journal of Ambient Intelligence and Humanized Computing.

[9]  J. C. R. Licklider,et al.  Man-Computer Symbiosis , 1960 .

[10]  Norbert Link,et al.  Model-free Adaptive Optimal Control of Episodic Fixed-horizon Manufacturing Processes Using Reinforcement Learning , 2018, International Journal of Control, Automation and Systems.

[11]  Paola Fantini,et al.  Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber-physical systems , 2018, Comput. Ind. Eng..

[12]  Abdulmotaleb El Saddik,et al.  C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems , 2017, IEEE Access.

[13]  Maya Cakmak,et al.  Power to the People: The Role of Humans in Interactive Machine Learning , 2014, AI Mag..

[14]  Daniela Fogli,et al.  A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications , 2019, IEEE Access.

[15]  Glenn F. Wilson,et al.  Human-Automation Interaction Research , 2013 .

[16]  Iveta Zolotova,et al.  Smart and cognitive solutions for Operator 4.0: Laboratory H-CPPS case studies , 2020, Comput. Ind. Eng..

[17]  Ikjin Lee,et al.  Deep Generative Design: Integration of Topology Optimization and Generative Models , 2019, Journal of Mechanical Design.

[18]  Päivi Heikkilä,et al.  Empowering and engaging industrial workers with Operator 4.0 solutions , 2020, Comput. Ind. Eng..

[19]  Hussein A. Abbass,et al.  Corrections to “Guest Editorial: Special Issue on Human–Machine Symbiosis” , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.