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On-line part deformation prediction based on deep learning

Author

Listed:
  • Zhiwei Zhao

    (Nanjing University of Aeronautics and Astronautics)

  • Yingguang Li

    (Nanjing University of Aeronautics and Astronautics)

  • Changqing Liu

    (Nanjing University of Aeronautics and Astronautics)

  • James Gao

    (University of Greenwich)

Abstract

Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model is proposed to represent the continuous part geometric information, process information, and monitoring information, which is used as the input to the deep learning model. A deep learning framework with a conventional neural network and a recurrent neural network has been constructed and trained by monitored deformation data and process information associated with interim part geometric information. The proposed method can be generalised for different parts with certain similarities and has the potential to provide a reference for an adaptive machining control strategy for reducing part deformation. The proposed method was validated by actual machining experiments, and the results show that the prediction accuracy has been improved compared with existing methods. Furthermore, this paper shifts the difficult problem of residual stress measurement and off-line deformation prediction to the solution of on-line deformation prediction based on deformation monitoring data.

Suggested Citation

  • Zhiwei Zhao & Yingguang Li & Changqing Liu & James Gao, 2020. "On-line part deformation prediction based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 561-574, March.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:3:d:10.1007_s10845-019-01465-0
    DOI: 10.1007/s10845-019-01465-0
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    References listed on IDEAS

    as
    1. Qiang Cheng & Hongwei Zhao & Yongsheng Zhao & Bingwei Sun & Peihua Gu, 2018. "Machining accuracy reliability analysis of multi-axis machine tool based on Monte Carlo simulation," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 191-209, January.
    2. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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    Citations

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    Cited by:

    1. Yanan Pan & Renke Kang & Zhigang Dong & Wenhao Du & Sen Yin & Yan Bao, 2022. "On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 675-685, March.
    2. Mohamed Elhefnawy & Ahmed Ragab & Mohamed-Salah Ouali, 2023. "Polygon generation and video-to-video translation for time-series prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 261-279, January.
    3. Yunhan Kim & Taekyum Kim & Byeng D. Youn & Sung-Hoon Ahn, 2022. "Machining quality monitoring (MQM) in laser-assisted micro-milling of glass using cutting force signals: an image-based deep transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1813-1828, August.
    4. Tianchi Deng & Yingguang Li & Xu Liu & Lihui Wang, 2023. "Federated learning-based collaborative manufacturing for complex parts," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3025-3038, October.
    5. Hao Sun & Shengqiang Zhao & Fangyu Peng & Rong Yan & Lin Zhou & Teng Zhang & Chi Zhang, 2024. "In-situ prediction of machining errors of thin-walled parts: an engineering knowledge based sparse Bayesian learning approach," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 387-411, January.

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