A hybrid information model based on long short-term memory network for tool condition monitoring
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DOI: 10.1007/s10845-019-01526-4
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Cited by:
- 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.
- M. R. Pavan Kumar & Prabhu Jayagopal, 2023. "Context-sensitive lexicon for imbalanced text sentiment classification using bidirectional LSTM," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2123-2132, June.
- 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.
- Chuanhui Zuo & Jialong Wang & Mingping Liu & Suhui Deng & Qingnian Wang, 2023. "An Ensemble Framework for Short-Term Load Forecasting Based on TimesNet and TCN," Energies, MDPI, vol. 16(14), pages 1-17, July.
- 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.
- Carlos A. Escobar & Megan E. McGovern & Ruben Morales-Menendez, 2021. "Quality 4.0: a review of big data challenges in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2319-2334, December.
- Chia-Yu Hsu & Wei-Chen Liu, 2021. "Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 823-836, March.
- Jianliang He & Yuxin Sun & Chen Yin & Yan He & Yulin Wang, 2023. "Cross-domain adaptation network based on attention mechanism for tool wear prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3365-3387, December.
- Qiwu Zhu & Qingyu Xiong & Zhengyi Yang & Yang Yu, 2023. "A novel feature-fusion-based end-to-end approach for remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3495-3505, December.
- 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.
- Jianwei Wu & Jiaqi Wang & Huanguo Chen, 2024. "A Method for Predicting Tool Remaining Useful Life: Utilizing BiLSTM Optimized by an Enhanced NGO Algorithm," Mathematics, MDPI, vol. 12(15), pages 1-22, August.
- Longhua Xu & Chuanzhen Huang & Chengwu Li & Jun Wang & Hanlian Liu & Xiaodan Wang, 2021. "Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 77-90, January.
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Keywords
Tool condition monitoring; Tool wear; Maintenance; Deep learning; Long short-term memory network; Process information;All these keywords.
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