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Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning

Author

Listed:
  • Jingjing Li

    (Xi’an Jiaotong University)

  • Guanghui Zhou

    (Xi’an Jiaotong University
    Xi’an Jiaotong University)

  • Chao Zhang

    (Xi’an Jiaotong University)

  • Junsheng Hu

    (Xi’an Jiaotong University)

  • Fengtian Chang

    (Xi’an Jiaotong University
    Chang’an University)

  • Andrea Matta

    (Politecnico di Milano)

Abstract

The booming development of emerging technologies and their integration in process planning provide new opportunities for solving the problems in traditional trial-and-error process planning. Combining digital twin with 3D computer vision, this paper defines a novel feature-level digital twin process model (FL-DTPM) by extracting machining features from model-based definition models. Firstly, a multi-dimensional FL-DTPM framework is defined by fusing on-site data, quality information, and process knowledge, where the synergistic mechanism of its virtual and physical processes is revealed. Then, 3D computer vision-enabled machining features extraction method is embedded into the FL-DTPM framework to support the reuse of process knowledge, which involves the procedures of data pre-processing, semantic segmentation, and instance segmentation. Finally, the effectiveness of the proposed features extraction method is verified and the application of FL-DTPM in machining process is presented. Oriented to the impeller process planning, a prototype of FL-DTPM is constructed to explore the potential application scenarios of the proposed method in intelligent process planning, which could provide insights into the industrial implementation of FL-DTPM for aerospace manufacturing enterprises.

Suggested Citation

  • Jingjing Li & Guanghui Zhou & Chao Zhang & Junsheng Hu & Fengtian Chang & Andrea Matta, 2025. "Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3227-3248, June.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:5:d:10.1007_s10845-024-02406-2
    DOI: 10.1007/s10845-024-02406-2
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    References listed on IDEAS

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    1. Chao Zhang & Guanghui Zhou & Qi Lu & Fengtian Chang, 2017. "Graph-based knowledge reuse for supporting knowledge-driven decision-making in new product development," International Journal of Production Research, Taylor & Francis Journals, vol. 55(23), pages 7187-7203, December.
    2. Peizhi Shi & Qunfen Qi & Yuchu Qin & Paul J. Scott & Xiangqian Jiang, 2020. "A novel learning-based feature recognition method using multiple sectional view representation," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1291-1309, June.
    3. Jingjing Li & Guanghui Zhou & Chao Zhang, 2022. "A twin data and knowledge-driven intelligent process planning framework of aviation parts," International Journal of Production Research, Taylor & Francis Journals, vol. 60(17), pages 5217-5234, September.
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