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Enhancing wisdom manufacturing as industrial metaverse for industry and society 5.0

Author

Listed:
  • Xifan Yao

    (South China University of Technology)

  • Nanfeng Ma

    (South China University of Technology)

  • Jianming Zhang

    (Haixi Institutes, Chinese Academy of Sciences
    Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System)

  • Kesai Wang

    (South China University of Technology)

  • Erfu Yang

    (University of Strathclyde)

  • Maurizio Faccio

    (University of Padova)

Abstract

Industry 4.0 focuses on the realization of smart manufacturing based on cyber-physical systems (CPS). However, emerging Industry 5.0 and Society 5.0 reaches beyond CPS and covers the entire value chain of manufacturing, and faces economic, environmental, and social challenges. To meet such challenges, we regard Industry 5.0 as a socio-technical revolution based on the socio-cyber-physical system (SCPS), and propose a socio-technically enhanced wisdom manufacturing architecture and framework beyond CPS-based Industry 4.0/smart manufacturing with especially concerning transition enabling technologies such as artificial intelligence, social Internet of Things (SIoT), big data, machine learning, edge computing, social computing, 3D printing, blockchains, digital twins, and cobots. Finally we address the roadmap to blockchainized value-added SCPS-based Industrial Metaverse for Industry/Society 5.0, which will achieve high utilization of resources and provide products and services to satisfy experience-driven individual needs via metamanufacturing cloud services towards smart, resilient, sustainable, and human-centric solutions.

Suggested Citation

  • Xifan Yao & Nanfeng Ma & Jianming Zhang & Kesai Wang & Erfu Yang & Maurizio Faccio, 2024. "Enhancing wisdom manufacturing as industrial metaverse for industry and society 5.0," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 235-255, January.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:1:d:10.1007_s10845-022-02027-7
    DOI: 10.1007/s10845-022-02027-7
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    References listed on IDEAS

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    1. Fukuda, Kayano, 2020. "Science, technology and innovation ecosystem transformation toward society 5.0," International Journal of Production Economics, Elsevier, vol. 220(C).
    2. Jianming Zhang & Xifan Yao & Yun Li, 2020. "Improved evolutionary algorithm for parallel batch processing machine scheduling in additive manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(8), pages 2263-2282, April.
    3. Urbinati, Andrea & Bogers, Marcel & Chiesa, Vittorio & Frattini, Federico, 2019. "Creating and capturing value from Big Data: A multiple-case study analysis of provider companies," Technovation, Elsevier, vol. 84, pages 21-36.
    4. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
    5. Xifan Yao & Jiajun Zhou & Yingzi Lin & Yun Li & Hongnian Yu & Ying Liu, 2019. "Smart manufacturing based on cyber-physical systems and beyond," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2805-2817, December.
    6. Kiel, Daniel & Arnold, Christian & Voigt, Kai-Ingo, 2017. "The influence of the Industrial Internet of Things on business models of established manufacturing companies – A business level perspective," Technovation, Elsevier, vol. 68(C), pages 4-19.
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