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A new tool wear monitoring method based on multi-scale PCA

Author

Listed:
  • Guofeng Wang

    (Tianjin University)

  • Yanchao Zhang

    (Tianjin University)

  • Chang Liu

    (Tianjin University)

  • Qinglu Xie

    (Tianjin University)

  • Yonggang Xu

    (Tianjin University)

Abstract

A multi-scale principal component analysis (MSPCA) method is presented to realize online tool wear monitoring of milling process. In this method, the training sample set of normal operational condition is decomposed into different scales using wavelet multi resolution analysis. The statistical indices and the corresponding control limits are constructed to monitor the tool wear based on principal component analysis (PCA). By integration of PCA with wavelet transformation, the accuracy and robustness of tool wear monitoring model can be improved greatly. To test the effectiveness of the proposed method, a Ti–6Al–4V milling experiment was carried out. Force and vibration signals during the machining process were collected simultaneously to depict the characteristics of the tool wear variation. Based on the extracted root mean square and kurtosis features, the tool wear monitoring is realized by MSPCA and PCA respectively. The analysis and comparison results show that MSPCA can produce higher accuracy in comparison with PCA.

Suggested Citation

  • Guofeng Wang & Yanchao Zhang & Chang Liu & Qinglu Xie & Yonggang Xu, 2019. "A new tool wear monitoring method based on multi-scale PCA," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 113-122, January.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:1:d:10.1007_s10845-016-1235-9
    DOI: 10.1007/s10845-016-1235-9
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    Citations

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    Cited by:

    1. Yanning Sun & Wei Qin & Zilong Zhuang & Hongwei Xu, 2021. "An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 2007-2021, October.
    2. Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
    3. Joanna Kossakowska & Sebastian Bombiński & Krzysztof Ejsmont, 2021. "Analysis of the Suitability of Signal Features for Individual Sensor Types in the Diagnosis of Gradual Tool Wear in Turning," Energies, MDPI, vol. 14(20), pages 1-23, October.
    4. Wo Jae Lee & Gamini P. Mendis & Matthew J. Triebe & John W. Sutherland, 2020. "Monitoring of a machining process using kernel principal component analysis and kernel density estimation," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1175-1189, June.
    5. Lucas Costa Brito & Márcio Bacci Silva & Marcus Antonio Viana Duarte, 2021. "Identification of cutting tool wear condition in turning using self-organizing map trained with imbalanced data," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 127-140, January.
    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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