Intelligent Human-Centric Cyber-Physical Systems for Industry 5.0 Smart Manufacturing

  • Authors

    • Andrey Ershov Professor, Siberian Division Academy of Sciences, Russia. Author
    • Alexey Lyapunov Professor, Moscow State University, Russia. Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2024PI4D9T

    Published 03-03-2024

  • Industry 5.0, Human-Centric Cyber-Physical Systems (HC-CPS), Smart Manufacturing, Artificial Intelligence (AI), Industrial Internet Of Things (Iiot), Digital Twin, Edge Computing, Cloud Computing, Collaborative Robots (Cobots), Human–Machine Collaboration, Explainable Artificial Intelligence (XAI), Predictive Maintenance, Industrial Automation, Sustainable Manufacturing, Cyber-Physical Systems (CPS), Human–Machine Interface (HMI), Manufacturing Intelligence, Industry 5.0 Architecture, Real-Time Monitoring, Intelligent Decision Support

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    Intelligent Human-Centric Cyber-Physical Systems for Industry 5.0 Smart Manufacturing. (2024). International Journal of Modern Research in Science & Engineering, 7(1), 01-15. https://doi.org/10.67228/30716357/IJMRSE-2024PI4D9T

  • Abstract

    The transition from Industry 4.0 to Industry 5.0 emphasizes human-centric, sustainable, and intelligent manufacturing by integrating human expertise with advanced technologies such as Artificial Intelligence (AI), Industrial Internet of Things (IIoT), Cyber-Physical Systems (CPS), Digital Twins (DT), Edge Computing, Cloud Computing, Collaborative Robots (Cobots), and Explainable AI (XAI). This paper proposes an Intelligent Human-Centric Cyber-Physical System (HC-CPS) framework comprising six interconnected layers for real-time monitoring, predictive maintenance, adaptive production scheduling, quality optimization, and human-centered decision support. A multi-objective optimization model and AI-driven closed-loop decision-making algorithm enhance production efficiency, equipment reliability, energy utilization, product quality, and human–machine collaboration while reducing downtime and operational costs. Human operators remain actively involved through collaborative interfaces that validate or modify AI recommendations. Comparative evaluation demonstrates significant improvements over conventional Industry 4.0 systems in Overall Equipment Effectiveness (OEE), predictive maintenance, operational safety, and manufacturing flexibility, providing a scalable, resilient, and sustainable foundation for future Industry 5.0 smart factories.

  • References

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