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On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning

Author

Listed:
  • Yanan Pan

    (Dalian University of Technology)

  • Renke Kang

    (Dalian University of Technology)

  • Zhigang Dong

    (Dalian University of Technology)

  • Wenhao Du

    (China Academy of Engineering Physics)

  • Sen Yin

    (Dalian University of Technology)

  • Yan Bao

    (Dalian University of Technology)

Abstract

The surface quality of tungsten heavy alloy parts has an important influence on its service performance. The accurate on-line prediction of surface roughness in ultra-precision cutting of tungsten heavy alloy has always been the difficulty of research. In this paper, the ultrasonic elliptical vibration cutting technology is used for ultra-precision machining of tungsten heavy alloy. Based on the idea of deep learning, the surface roughness is discretized, and the fitting problem in surface roughness is transformed into a classification problem. The generalization ability of the prediction model is improved by introducing batch standardization and Dropout. The relationship between the vibration signal and the surface roughness is established. Experimental results show that the model can achieve on-line prediction of cutting surface roughness. The prediction accuracy rate can be improved by more than 10% compared with the direct fitting method.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:3:d:10.1007_s10845-020-01669-9
    DOI: 10.1007/s10845-020-01669-9
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    References listed on IDEAS

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    1. 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.
    2. Xiang Li & Xiaodong Jia & Qibo Yang & Jay Lee, 2020. "Quality analysis in metal additive manufacturing with deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2003-2017, December.
    3. 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.
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    Cited by:

    1. Zhicheng Xu & Vignesh Selvaraj & Sangkee Min, 2024. "State identification of a 5-axis ultra-precision CNC machine tool using energy consumption data assisted by multi-output densely connected 1D-CNN model," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 147-160, January.

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