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Digital twin-based decision making paradigm of raise boring method

Author

Listed:
  • Fuwen Hu

    (North China University of Technology)

  • Xianjin Qiu

    (North China University of Technology)

  • Guoye Jing

    (China Coal Research Institute)

  • Jian Tang

    (Quzhou University)

  • Yuanzhi Zhu

    (North China University of Technology)

Abstract

Raise boring is an important method to construct underground shafts of mines and other underground infrastructures by drilling down the pilot hole and then reaming up to the desired diameter. As a typical cyber-physical system, the raise boring construction project is full of high heterogeneity, complexity and intrinsic uncertainty. Currently, its decision making loop is mainly based on the document-based system engineering and expertise experience. Regarding the intrinsic invisibility and uncertain risks in the underground engineering, especially for the remotely underground constructions on the extraterrestrial planets, it is absolutely required to shift the document-based and experience-dependent decision making paradigm into a digital and smart way. To this end, a systematic framework of the digital twin-driven process planning system for the raise boring method was conceived and presented. Then following the principles of open architecture, modularization and extensibility, a five-dimension architecture of digital twinning was built comprehensively that contained physical entity, digital representation, service entity, cross-systems entity and connection entity. Furthermore, a digital twin-driven decision making prototype system for the raise boring process was developed by the hybrid modeling of data-based model, visual geometric models, domain knowledge-based model and physics-based model. System verification indicated that the presented system had great potentials to facilitate the already very complicated process planning via the planning recommendation, visual simulation and models fusion. Finally, the contributions, novelty and limitations of this endeavour to extend the current digital twin practice were discussed.

Suggested Citation

  • Fuwen Hu & Xianjin Qiu & Guoye Jing & Jian Tang & Yuanzhi Zhu, 2023. "Digital twin-based decision making paradigm of raise boring method," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2387-2405, June.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-022-01941-0
    DOI: 10.1007/s10845-022-01941-0
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    References listed on IDEAS

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    1. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
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