Dynamic prediction model for tool wear based on a multi-channel one-dimensional convolutional neural network

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (2) : 60-67.

HUANG Hua1，YAO Jiajing1，WANG Yonghe1，L Yanjun2

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Abstract

Aiming at the failure of historical model caused by different data distribution in tool wear prediction modeling under the same working condition, a tool wear dynamic prediction modeling method based on multi-channel one-dimensional convolutional neural network is proposed. The multi-channel one-dimensional convolutional neural network trained on historical tool wear data serves as historical model of the initial tool wear prediction. The maximum mean difference method (MMD) was used to detect the similarity of different tool wear data. When the similarity difference is large, the iterative update is carried out on the basis of the historical model, and then the updated model is used to predict the wear data. The results of milling experiments show that the method can accurately predict the wear values of different tools and has great adaptive ability.

Key words

Tool wear;Dynamic model / one-dimensional convolutional neural network / maximum mean difference

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HUANG Hua1，YAO Jiajing1，WANG Yonghe1，L Yanjun2. Dynamic prediction model for tool wear based on a multi-channel one-dimensional convolutional neural network[J]. Journal of Vibration and Shock, 2023, 42(2): 60-67

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