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A reference framework for the digital twin smart factory based on cloud-fog-edge computing collaboration

Author

Listed:
  • Zhiyuan Li

    (Xi’an Jiaotong University
    State Key Laboratory for Manufacturing Systems Engineering)

  • Xuesong Mei

    (Xi’an Jiaotong University
    State Key Laboratory for Manufacturing Systems Engineering)

  • Zheng Sun

    (Xi’an Jiaotong University
    State Key Laboratory for Manufacturing Systems Engineering)

  • Jun Xu

    (Xi’an Jiaotong University)

  • Jianchen Zhang

    (Xi’an Jiaotong University
    State Key Laboratory for Manufacturing Systems Engineering)

  • Dawei Zhang

    (Xi’an Jiaotong University
    State Key Laboratory for Manufacturing Systems Engineering)

  • Jingyi Zhu

    (Xi’an Jiaotong University
    State Key Laboratory for Manufacturing Systems Engineering)

Abstract

Digital twin (DT) is an important approach for the factory to achieve intelligence. Due to the different scenarios and definitions, the generalization of frameworks for DT-based smart factories is weak, slowing down the overall process of industrial intelligence. Meanwhile, the pressure of data transmission and processing increases dramatically because of data explosion, which poses a challenge to the rational allocation of computing resources. In addition, more advanced strategies for training and running models are needed to support more sophisticated services. This paper proposes a reference framework that combines DT and cloud-fog-edge computing collaboration (CFE). First, the DT fuses physical and virtual spaces. The virtual-real fusion provides more information for operations, and the virtual space gives more accurate and timely decisions based on the constantly refreshed state. Secondly, by introducing CFE, suitable operating platforms for each layer of the DT-based smart factory are set, which enhances data interaction and reduces the dependence on cloud computing. The DT-CFE framework is well generalized. This paper first introduces the definition of the DT-based smart factory and its components. Then the methodology of the DT-CFE-based smart factory is proposed, and the network topology and operation mechanism are introduced. In this framework, the transmission and response performance of its data interaction is tested, and the interference of dynamic events occurring through scheduling is studied to illustrate the effectiveness and superiority of the framework.

Suggested Citation

  • Zhiyuan Li & Xuesong Mei & Zheng Sun & Jun Xu & Jianchen Zhang & Dawei Zhang & Jingyi Zhu, 2025. "A reference framework for the digital twin smart factory based on cloud-fog-edge computing collaboration," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3625-3645, June.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:5:d:10.1007_s10845-024-02424-0
    DOI: 10.1007/s10845-024-02424-0
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    References listed on IDEAS

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    1. Min, Qingfei & Lu, Yangguang & Liu, Zhiyong & Su, Chao & Wang, Bo, 2019. "Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry," International Journal of Information Management, Elsevier, vol. 49(C), pages 502-519.
    2. Luo, Hao & Du, Bing & Huang, George Q. & Chen, Huaping & Li, Xiaolin, 2013. "Hybrid flow shop scheduling considering machine electricity consumption cost," International Journal of Production Economics, Elsevier, vol. 146(2), pages 423-439.
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