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An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion

Author

Listed:
  • Xianli Liu

    (Harbin University of Science and Technology)

  • Bowen Zhang

    (Harbin University of Science and Technology)

  • Xuebing Li

    (Harbin University of Science and Technology)

  • Shaoyang Liu

    (Harbin University of Science and Technology)

  • Caixu Yue

    (Harbin University of Science and Technology)

  • Steven Y. Liang

    (Georgia Institute of Technology)

Abstract

An accurate prediction of the machining tool condition during the cutting process is crucial for enhancing the tool life, improving the production quality and productivity, optimizing the labor and maintenance costs, and reducing workplace accidents. Currently, tool condition monitoring is usually based on machine learning algorithms, especially deep learning algorithms, to establish the relationship between sensor signals and tool wear. However, deep mining of feature and fusion information of multi-sensor signals, which are strongly related to the tool wear, is a critical challenge. To address this issue, in this study, an integrated prediction scheme is proposed based on deep learning algorithms. The scheme first extracts the local features of a single sequence and a multi-dimensional sequence from DenseNet incorporating a heterogeneous asymmetric convolution kernel. To obtain more perceptual historical data, a “dilation” scheme is used to extract features from a single sequence, and one-dimensional dilated convolution kernels with different dilation rates are utilized to obtain the differential features. At the same time, asymmetric one-dimensional and two-dimensional convolution kernels are employed to extract the features of the multi-dimensional signal. Ultimately, all the features are fused. Then, the time-series features hidden in the sequence are extracted by establishing a depth-gated recurrent unit. Finally, the extracted in-depth features are fed to the deep fully connected layer to achieve the mapping between features and tool wear values through linear regression. The results indicate that the average errors of the proposed model are less than 8%, and this model outperforms the other tool wear prediction models in terms of both accuracy and generalization.

Suggested Citation

  • Xianli Liu & Bowen Zhang & Xuebing Li & Shaoyang Liu & Caixu Yue & Steven Y. Liang, 2023. "An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 885-902, February.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-022-01954-9
    DOI: 10.1007/s10845-022-01954-9
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    References listed on IDEAS

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    1. 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.
    2. Zoran Jurkovic & Goran Cukor & Miran Brezocnik & Tomislav Brajkovic, 2018. "A comparison of machine learning methods for cutting parameters prediction in high speed turning process," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1683-1693, December.
    3. Yu, Wennian & Kim, II Yong & Mechefske, Chris, 2020. "An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
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    Cited by:

    1. Liang Xi & Wei Wang & Jingyi Chen & Xuefeng Wu, 2024. "Appending-inspired multivariate time series association fusion for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3259-3272, October.
    2. Bowen Zhang & Xianli Liu & Caixu Yue & Shaoyang Liu & Xuebing Li & Steven Y. Liang & Lihui Wang, 2025. "An imbalanced data learning approach for tool wear monitoring based on data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 399-420, January.
    3. Hanting Zhou & Wenhe Chen & Jing Liu & Longsheng Cheng & Min Xia, 2024. "Trustworthy and intelligent fault diagnosis with effective denoising and evidential stacked GRU neural network," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3523-3542, October.

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