Tool wear prediction based on domain adversarial adaptation and channel attention multiscale convolutional long short-term memory network

Intelligent real-time monitoring of tool wear is significant to ensure the quality of workpieces and the efficiency of machining. However, various factors in the machining process can cause large variations in the monitoring signals, making it difficult to accurately predict tool wear values. To solve this, a tool wear prediction method based on domain adversarial adaptation and squeeze-and-excitation channel attention multiscale convolutional long short-term memory network (SE-DAAMSCLSTM) is proposed. A feature extractor combining multiscale convolution and channel attention with the introduction of domain adversarial mechanism was constructed to extract domain-independent multiscale spatiotemporal features that characterize tool wear, thus enabling accurate prediction of tool wear values. By validating the model on milling datasets and comparing it with conventional prediction methods, the results show that the model enables accurate prediction with variation in tool monitoring signals, demonstrating the superiority of the method in predicting tool wear. • A “domain adaptation + feature extraction” tool wear prediction method is proposed. • The proposed deep learning model can extract multiscale spatiotemporal features. • The proposed method adaptively reduces the impact of domain changes on prediction. • The method was validated on the same and variable working condition datasets. • The proposed model showed better prediction accuracy and performance.