A Milling Cutter Wear Monitoring Method Based on ABiGRU
LIU Chao1,WANG Chen1,2,ZHANG Xiu-feng1,LU Xu-xiang1
1. Hubei University of Automotive Technology, Shiyan,Hubei, 442002, China
2. Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai 200072, China
Abstract：In order to accurately monitor tool wear in the milling process, a tool wear monitoring model based on attention and bidirectional gated recurrent unit (ABiGRU) was proposed. In the monitoring model, firstly, the time series data collected by vibration, force, and acoustic emission sensors were analyzed in the time domain, frequency domain, and time-frequency domain, and spearman coefficient was used to extract 20-dimensional features strongly correlated with the average rear tool surface wear. Secondly, the ELU activation function was introduced to optimize the BiGRU network and solve the gradient disappearance problem, in the meantime, the internal attention mechanism was used to improve the models ability to capture important feature information and quickly realize the mapping from feature to tool wear value. Finally, compared with RNN, LSTM, GRU, BiLSTM, and BiGRU, the results shown that the model could not only accurately characterize the tool wear degree, but also the accuracy and efficiency is improved greatly.
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2023, Vol. 44 
