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Intelligent recognition of tool wear in milling based on a single sensor signal

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Abstract

A major problem in the high-speed cutting process of machine tools is tool wear. Tool wear directly affects the surface quality and machining accuracy of the workpiece. However, the limits of fusing multiple sensing signals to indirectly monitor tool wear are rarely concerned in real manufacturing environments. In this paper, a tool wear identification method based on a single sensor signal is proposed. To solve the limits of less obtained information and poor anti-interference ability of single sensor, multi-domain feature fusion strategy is established. By establishing a hybrid model of deep convolutional neural network and stacked long short-term memory network, the complex mapping relationship between fusion features and tool wear is constructed. Specifically, the spatial features of the input data set are extracted by the convolution kernel of the deep convolutional neural network. Then, a stacked double-layer long short-term memory neural network is established to capture sequence features with long-term dependence, thereby identifying tool wear. Finally, the superiority of the developed method is verified by tool wear experiments. The results show that the method can be effectively applied to tool wear identification from single sensor signals, and the mean RMSE and MAE of the identification results are 9.43 and 7.15, respectively. Compared with four other traditional multiple regression methods, RMSE and MAE are reduced by 73.0% and 78.7% on average. This study provides a reference value for the industrial implementation of tool wear monitoring system.

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Funding

The authors are grateful to the financial supports of the National Natural Science Foundation of China (No. 52275445) and the Shandong Provincial Key Research and Development Program (Nos. 2020CXGC010204, 2021JMRH0301).

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Authors and Affiliations

  1. Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan, 250061, People’s Republic of China

    Yezhen Peng, Qinghua Song, Runqiong Wang & Zhanqiang Liu

  2. National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan, 250061, People’s Republic of China

    Yezhen Peng, Qinghua Song, Runqiong Wang & Zhanqiang Liu

  3. School of Information Science and Engineering, Shandong University, Qingdao, 266237, People’s Republic of China

    Zhaojun Liu

Authors
  1. Yezhen Peng
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Contributions

All authors contributed to this research. Yezhen Peng is responsible for the design of the algorithm, analysis and validation of the experimental data and writing of the manuscript. Qinghua Song and Zhanqiang Liu are responsible for the discussion of ideas and methods, review of the manuscript, and financial support. Runqiong Wang is responsible for signal pre-processing and algorithm review. Zhaojun Liu is responsible for the editing and review of the paper.

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Correspondence to Qinghua Song.

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Peng, Y., Song, Q., Wang, R. et al. Intelligent recognition of tool wear in milling based on a single sensor signal. Int J Adv Manuf Technol 124, 1077–1093 (2023). https://doi.org/10.1007/s00170-022-10404-y

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