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A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment

Author

Listed:
  • Pai Zheng

    (Nanyang Technological University
    University of Auckland
    Nanyang Technological University)

  • Xun Xu

    (University of Auckland)

  • Chun-Hsien Chen

    (Nanyang Technological University)

Abstract

The rapid development of information and communication technology enables a promising market of information densely product, i.e. smart, connected product (SCP), and also changes the way of user–designer interaction in the product development process. For SCP, massive data generated by users drives its design innovation and somehow determines its final success. Nevertheless, most existing works only look at the new functionalities or values that are derived in the one-way communication by introducing novel data analytics methods. Few work discusses about an effective and systematic approach to enable individual user innovation in such context, i.e. co-development process, which sets the fundamental basis of the prevailing concept of data-driven design. Aiming to fill this gap, this paper proposes a generic data-driven cyber-physical approach for personalised SCP co-development in a cloud-based environment. A novel concept of smart, connected, open architecture product is hence introduced with a generic cyber-physical model established in a cloud-based environment, of which the interaction processes are enabled by co-development toolkits with smartness and connectedness. Both the personalized SCP modelling method and the establishment of its cyber-physical product model are described in details. To further demonstrate the proposed approach, a case study of a smart wearable device (i.e. i-BRE respiratory mask) development process is given with general discussions.

Suggested Citation

  • Pai Zheng & Xun Xu & Chun-Hsien Chen, 2020. "A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 3-18, January.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:1:d:10.1007_s10845-018-1430-y
    DOI: 10.1007/s10845-018-1430-y
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    References listed on IDEAS

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    Cited by:

    1. Sihan Huang & Guoxin Wang & Shiqi Nie & Bin Wang & Yan Yan, 2023. "Part family formation method for delayed reconfigurable manufacturing system based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2849-2863, August.
    2. Han Cheng & Xianguang Kong & Qibin Wang & Hongbo Ma & Shengkang Yang & Gaige Chen, 2023. "Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 587-613, February.
    3. Wai Sze Yip & Suet To & Hongting Zhou, 2022. "Current status, challenges and opportunities of sustainable ultra-precision manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2193-2205, December.
    4. Yongjun Ji & Zuhua Jiang & Xinyu Li & Yongwen Huang & Fuhua Wang, 2023. "A multitask context-aware approach for design lesson-learned knowledge recommendation in collaborative product design," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1615-1637, April.

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