Abstract:Aiming at the problems of scarce machine tool wear data and low tool wear status recognition accuracy, a tool status recognition method based on Sample Expansion and Improved Domain Adversarial Training of Neural Networks (SE-IDANN) is proposed.First, perform two feature extractions on the machine tool data, and expand the sample through the Smooth algorithm to solve the problem of scarce machine tool wear data;Secondly, add residual blocks to the DANN model feature extractor to further extract effective feature information to solve the problem of weak tool wear characteristics;Finally, the Wasserstein distance is used as the data distribution similarity standard between the target domain and the source domain to introduce the DANN model to realize the accurate identification of tool wear. Through the analysis and test verification of machine tool data, it is proved that this method can better identify tool wear.