关键词: 小波阈值降噪, 卷积神经网络, 双向长短时记忆网络, 刀具磨损状态监测

Abstract: In the automatic cutting processes， accurate and reliable detecting of tool wear states was the key to ensure the processing quality and efficiency. Aiming at the problems of tedious feature extraction with low accuracy and traditional deep learning network could not extract the hidden information of data comprehensively， an improved model was proposed based on integration of CNN and BiLSTM by adding batch standardization layer to CNN and using two BiLSTM layers. The model could automatically extract the spatial and temporal features of cutting forces， vibration and sound signals after wavelet threshold denoising and down sampling to realize tool wear detection. Compared with CNN-BiLSTM model and traditional deep learning model， the accuracy and stability of the improved model are greatly improved. The proposed method provides technical support for accurately detecting the tool wear states in the automatic machining processes and improves the production efficiency and machining quality. 

Key words: wavelet threshold denoising, convolutional neural network（CNN）, bidirectional long short-term memory（BiLSTM） network, tool wear detection