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Tool health monitoring and prediction via attention-based encoder-decoder with a multi-step mechanism

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Abstract

In metal cutting processing, prediction of the tool wear or VB value can help early warning and timely tool replacement before the tool reaches the service life. Although the deep neural network is an effective method to predict the tool wear, the existing research predicts the tool wear only at its next time moment without considering the tool states at different time points. It ignores important information of the tool wear. In this paper, we propose a comprehensive model, which consists of a monitoring module and a prediction module, to monitor and predict the tool wear for the first time. In the monitoring module, a DenseNet model is constructed to monitor the tool wear via sensor signals. In addition, the prediction module based on attention mechanism is developed by simulating the human brain attention to selectively focus on the important part of processing sequence information. An encoder-decoder structure is introduced for a multi-step prediction of the tool wear. Several VB values in the nearest future are predicted by using sequential VB values monitored in the latest past. Experimental studies show that the short-term information has more influence on the tool wear prediction than the long-term information. The proposed method has been used to predict multi-step VB values in milling operations.

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Funding

This work was supported in part by the National Natural Science Foundation of China under grant 51605422, in part by the Natural Science Foundation of Hebei Province under grant E2017203156, in part by Beijing-Tianjin-Hebei Cooperation Project of Hebei Province Natural Science Foundation under grant E2017203372, in part by the Chinese National Key Research and Development Program under grant 2016YFC0802900, in part by S&T Program of Hebei under grant 20311001D, and in part by S&T Program of Hebei under grant 20310401D.

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Authors and Affiliations

  1. School of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, China

    Baosu Guo & Fenghe Wu

  2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China

    Qin Zhang & Quan Zhang

  3. Department of Mechanical Engineering, University Manitoba, Winnipeg, MB, R3T 5V6, Canada

    Qinjing Peng

  4. School of Mechanical Engineering, Southeast University, Nanjing, 211189, China

    Jichao Zhuang

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  1. Baosu Guo
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  2. Qin Zhang
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Correspondence to Quan Zhang.

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Guo, B., Zhang, Q., Peng, Q. et al. Tool health monitoring and prediction via attention-based encoder-decoder with a multi-step mechanism. Int J Adv Manuf Technol 122, 685–695 (2022). https://doi.org/10.1007/s00170-022-09894-7

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