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Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations

Author

Listed:
  • Zhiwen Huang

    (University of Shanghai for Science and Technology)

  • Jianmin Zhu

    (University of Shanghai for Science and Technology)

  • Jingtao Lei

    (Shanghai University)

  • Xiaoru Li

    (University of Shanghai for Science and Technology)

  • Fengqing Tian

    (University of Shanghai for Science and Technology)

Abstract

Tool wear monitoring has been increasingly important in intelligent manufacturing to increase machining efficiency. Multi-domain features can effectively characterize tool wear condition, but manual feature fusion lowers monitoring efficiency and hinders the further improvement of predicting accuracy. In order to overcome these deficiencies, a new tool wear predicting method based on multi-domain feature fusion by deep convolutional neural network (DCNN) is proposed in this paper. In this method, multi-domain (including time-domain, frequency domain and time–frequency domain) features are respectively extracted from multisensory signals (e.g. three-dimensional cutting force and vibration) as health indictors of tool wear condition, then the relationship between these features and real-time tool wear is directly established based on the designed DCNN model to combine adaptive feature fusion with automatic continuous prediction. The performance of the proposed tool wear predicting method is experimentally validated by using three tool run-to-failure datasets measured from three-flute ball nose tungsten carbide cutter of high-speed CNC machine under dry milling operations. The experimental results show that the predicting accuracy of the proposed method is significantly higher than other advanced methods.

Suggested Citation

  • Zhiwen Huang & Jianmin Zhu & Jingtao Lei & Xiaoru Li & Fengqing Tian, 2020. "Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 953-966, April.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:4:d:10.1007_s10845-019-01488-7
    DOI: 10.1007/s10845-019-01488-7
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    References listed on IDEAS

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    1. Jeff Morgan & Garret E. O’Donnell, 2018. "Cyber physical process monitoring systems," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1317-1328, August.
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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. Ammar H. Elsheikh & Taher A. Shehabeldeen & Jianxin Zhou & Ezzat Showaib & Mohamed Abd Elaziz, 2021. "Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1377-1388, June.
    3. Shanmugasivam Pillai & Prahlad Vadakkepat, 2022. "Deep learning for machine health prognostics using Kernel-based feature transformation," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1665-1680, August.
    4. Raouf Zerrougui & Amel B. H. Adamou-Mitiche & Lahcene Mitiche, 2023. "A novel machine learning algorithm for interval systems approximation based on artificial neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2171-2184, June.
    5. Chia-Yu Hsu & Wei-Chen Liu, 2021. "Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 823-836, March.
    6. Yuqing Zhou & Bintao Sun & Weifang Sun & Zhi Lei, 2022. "Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 247-258, January.

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