An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion
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DOI: 10.1007/s10845-022-01954-9
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References listed on IDEAS
- 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.
- 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.
- 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:
- 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.
- 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.
- 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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Keywords
Heterogeneous asymmetric convolution kernel; DenseNet; Depth-gated recurrent unit; Feature extraction; Tool wear prediction; Dilated convolution kernel;All these keywords.
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