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Digital twin model-driven capacity evaluation and scheduling optimization for ship welding production line

Author

Listed:
  • Jinfeng Liu

    (Jiangsu University of Science and Technology)

  • Qiukai Ji

    (Jiangsu University of Science and Technology)

  • Xiaohu Zhang

    (Jiangsu University of Science and Technology)

  • Yu Chen

    (Jiangsu University of Science and Technology)

  • Yiming Zhang

    (Jiangsu University of Science and Technology)

  • Xiaojun Liu

    (Southeast University)

  • Mingming Tang

    (Jiangsu University of Science and Technology)

Abstract

Approximately 45% of ship delivery delays are due to welding quality. To solve the problematic control of production tempo and process sequence optimization in the welding process, it is urgent to combine the characteristics of the digital twin for dynamic simulation and optimization. Therefore, the capacity evaluation and scheduling optimization for the ship welding production line (WPL) based on the digital twin is proposed. Firstly, to describe the construction method of the digital twin model and digital twin data, a strategy is proposed for the construction of a digital twin ship component WPL model. Based on the fusion mapping of model and data, the construction of the digital twin model for WPL (DTM-WPL) is achieved. Secondly, by using equipment failure rate, processing time and buffer capacity as evaluation indicators, an WPL optimization model based on digital twins is constructed to solve the WPL production sequencing problem. Thirdly, to illustrate the welding quality traceability and prediction process, a DTM-WPL synchronous mapping for quality prediction and adjustment method is proposed. Finally, taking small and medium-sized WPL as the research object, the capacity evaluation and scheduling optimization system of ship components is developed to evaluate the production capacity of ship components. The validation results indicate that the optimized process scheme has increased production efficiency by 7.27%.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:7:d:10.1007_s10845-023-02212-2
    DOI: 10.1007/s10845-023-02212-2
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    References listed on IDEAS

    as
    1. 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.
    2. Chengjun Xu & Guobin Zhu, 2021. "Intelligent manufacturing Lie Group Machine Learning: real-time and efficient inspection system based on fog computing," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 237-249, January.
    3. Adil Baykasoğlu & Fatma S. Karaslan, 2017. "Solving comprehensive dynamic job shop scheduling problem by using a GRASP-based approach," International Journal of Production Research, Taylor & Francis Journals, vol. 55(11), pages 3308-3325, June.
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