Tool wear state recognition based on fuzzy neural network

HaoMing Liu; YongHe Wei; Ningning Wang

doi:10.1117/12.2679720

18 July 2023 Tool wear state recognition based on fuzzy neural network

HaoMing Liu, YongHe Wei, Ningning Wang

Proceedings Volume 12722, Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023); 127223D (2023) https://doi.org/10.1117/12.2679720
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023), 2023, Hangzhou, China

Abstract

Aiming at the non-stationary and nonlinear characteristics of tool wear signal in cutting process and the problem that it is difficult to effectively identify the wear state, a tool wear state recognition method based on feature fusion and fuzzy neural network is proposed Multi-domain features were extracted from frequency domain and time-frequency domain, and then several features with higher tool wear information were selected according to pearson correlation coefficient. Then, the optimal feature set was obtained by feature fusion using PCA algorithm. Finally, the multi-dimensional optimal features are input into the fuzzy neural network for training, and the mode state model is established to identify the tool wear state. The experimental results show that the method can accurately identify the tool wear state, which verifies the superiority of the method.

Citation Download Citation

HaoMing Liu, YongHe Wei, and Ningning Wang "Tool wear state recognition based on fuzzy neural network", Proc. SPIE 12722, Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023), 127223D (18 July 2023); https://doi.org/10.1117/12.2679720

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