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Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process

Author

Listed:
  • Yuqing Zhou

    (Wenzhou University)

  • Bintao Sun

    (Wenzhou University)

  • Weifang Sun

    (Wenzhou University)

  • Zhi Lei

    (Wenzhou University)

Abstract

Tool condition monitoring (TCM) in numerical control machines plays an essential role in ensuring high manufacturing quality. The TCM process is conducted according to the data obtained from one or more of a variety of sensors, among which acoustic sensors offer numerous practical advantages. However, acoustic sensor data suffer from strong noise, which can severely limit the accuracy of predictions regarding tool condition. The present work addresses this issue by proposing a novel TCM method that employs only a few appropriate feature parameters of acoustic sensor signals in conjunction with a two-layer angle kernel extreme learning machine. The two-layer network structure is applied to enhance the learning of features associated with complex nonlinear data, and two angle kernel functions without hyperparameters are employed to avoid the complications associated with the use of preset hyperparameters in conventional kernel functions. The proposed TCM method is experimentally demonstrated to achieve superior TCM performance relative to other state-of-the-art methods based on sound sensor data.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:1:d:10.1007_s10845-020-01663-1
    DOI: 10.1007/s10845-020-01663-1
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    References listed on IDEAS

    as
    1. 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.
    2. Kamran Javed & Rafael Gouriveau & Xiang Li & Noureddine Zerhouni, 2018. "Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1873-1890, December.
    3. C. K. Madhusudana & Hemantha Kumar & S. Narendranath, 2017. "Face milling tool condition monitoring using sound signal," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1643-1653, November.
    4. 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.
    5. Weili Cai & Wenjuan Zhang & Xiaofeng Hu & Yingchao Liu, 2020. "A hybrid information model based on long short-term memory network for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1497-1510, August.
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    Cited by:

    1. Rishi Kumar & Kuldip Singh Sangwan & Christoph Herrmann & Rishi Ghosh, 2024. "Development of a cyber physical production system framework for smart tool health management," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3037-3066, October.

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