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Dynamic design method of digital twin process model driven by knowledge-evolution machining features

Author

Listed:
  • Jinfeng Liu
  • Peng Zhao
  • Xuwen Jing
  • Xuwu Cao
  • Sushan Sheng
  • Honggen Zhou
  • Xiaojun Liu
  • Feng Feng

Abstract

Machining plan is the core of guiding manufacturing production and is regarded as one of the keys to ensure the quality of product processing. Existing process design methods are inefficient to quickly handle the machining plan changed induced by the unpredictable events in real-time production. It inevitably causes time and economic losses for the enterprise. In order to express the evolutionary characteristics of product processing, the construction method of digital twin process model (DTPM) is proposed based on the knowledge-evolution machining features. Three key technologies include correlation structure of process knowledge, expression method of the evolution geometric features and the association mechanism between two are solved. On this basis, the construction framework of DTPM is illustrated. Then, the organisation and management mechanism of multi-source heterogeneous data is discussed in detail. At last, a case study of the complex machined part is researched, the results show that the processing time reduced by about 7% and the processing stability improved by 40%. Meanwhile, the implementation scheme, application process and effect of this case are described in detail to provide reference for enterprises.

Suggested Citation

  • Jinfeng Liu & Peng Zhao & Xuwen Jing & Xuwu Cao & Sushan Sheng & Honggen Zhou & Xiaojun Liu & Feng Feng, 2022. "Dynamic design method of digital twin process model driven by knowledge-evolution machining features," International Journal of Production Research, Taylor & Francis Journals, vol. 60(7), pages 2312-2330, April.
    • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:7:p:2312-2330
    DOI: 10.1080/00207543.2021.1887531
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    Cited by:

    1. Jinfeng Liu & Qiukai Ji & Xiaohu Zhang & Yu Chen & Yiming Zhang & Xiaojun Liu & Mingming Tang, 2024. "Digital twin model-driven capacity evaluation and scheduling optimization for ship welding production line," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3353-3375, October.

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