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Tool wear prediction based on parallel dual-channel adaptive feature fusion

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Abstract

The tool is a component that is easily damaged and wasted in the CNC machining process. Accurate prediction of tool wear is conducive to reducing processing costs and improving processing efficiency. Most of the current research uses deep learning models to mine the degradation characteristics of tool wear. However, a single deep learning model and a simple sequential combination model can only learn some features, resulting in insufficient features extracted by the model, which seriously affects the accuracy of tool wear prediction. To solve the above problems, a tool wear prediction method based on parallel dual-channel adaptive features fusion is proposed. Firstly, the force, vibration, and acoustic emission signals collected by multi-sensors are preprocessed. Based on CNN-GRU and ConvGRU, a new dual-channel parallel structure is established to extract features from the input multi-sensor signal data. Secondly, the attention mechanism is used to fuse the features extracted from the parallel structure, and different weights are adaptively assigned to the tool wear features, thereby suppressing the influence of irrelevant or redundant features. Finally, the tool wear prediction values are output through the linear layer. The experimental results based on the PHM2010 data set show that the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) of the proposed method are 4.24, 6.41, and 0.966, respectively. The prediction performance of the model is better than other deep learning methods, which can accurately predict the wear state of the tool, provide information support for tool change decisions, and improve production efficiency.

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Data availability

The acquisition website of these data sets is provided in this study.

Code availability

It declares that codes are not available for this research.

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Funding

This study was supported by the National Natural Science Foundation of China (No. 51965006) and the Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology (Grant No. 19–050-44-S006).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Guangxi University, Nanning, 530004, China

    Jinfei Yang, Jinxin Wu, Xianwang Li & Xuejing Qin

  2. Guangxi Key Laboratory of Manufacturing Systems and Advance Manufacturing Technology, Guangxi University, Nanning, 530004, China

    Xianwang Li

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  1. Jinfei Yang
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Contributions

Xianwang Li: supervision, project administration, funding acquisition, writing—review and editing. Jinfei Yang: methodology, experiment, software, writing—original draft. Jinxin Wu: validation, investigation. Xuejing Qin: data curation.

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Correspondence to Xianwang Li.

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Yang, J., Wu, J., Li, X. et al. Tool wear prediction based on parallel dual-channel adaptive feature fusion. Int J Adv Manuf Technol 128, 145–165 (2023). https://doi.org/10.1007/s00170-023-11832-0

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