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A hybrid information model based on long short-term memory network for tool condition monitoring

Author

Listed:
  • Weili Cai

    (Shanghai Jiao Tong University)

  • Wenjuan Zhang

    (University of Warwick)

  • Xiaofeng Hu

    (Shanghai Jiao Tong University)

  • Yingchao Liu

    (Shanghai Jiao Tong University)

Abstract

Excessive tool wear leads to the damage and eventual breakage of the tool, workpiece, and machining center. Therefore, it is crucial to monitor the condition of tools during processing so that appropriate actions can be taken to prevent catastrophic tool failure. This paper presents a hybrid information system based on a long short-term memory network (LSTM) for tool wear prediction. First, a stacked LSTM is used to extract the abstract and deep features contained within the multi-sensor time series. Subsequently, the temporal features extracted are combined with process information to form a new input vector. Finally, a nonlinear regression model is designed to predict tool wear based on the new input vector. The proposed method is validated on both NASA Ames milling data set and the 2010 PHM Data Challenge data set. Results show the outstanding performance of the hybrid information model in tool wear prediction, especially when the experiments are run under various operating conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:6:d:10.1007_s10845-019-01526-4
    DOI: 10.1007/s10845-019-01526-4
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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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