关键词: 多域特征, 刀具磨损, 联合分布适配, K-最近邻分类器

Abstract: Aiming at the modeling problem of tool wear under different machining parameters,a tool state recognition method based on joint distribution adaptation of multi-domain features was proposed to improve the tool state recognition model's generalization performance and recognition accuracy.For the data of sensor signals which under different machining parameters in the cutting process,the features were extracted in time domain,frequency domain as a well as time-frequency domain.Then the differences between the features were reduced by joint Distribution Adaptation Algorithm (JDA),and the adapted features were input to K-Nearest Neighbor (KNN) classifier to identify the wear state.The experimental results showed that the proposed method could identify the tool wear state under different machining parameters effectively,and the average recognition accuracy could be improved by more than 12%.It has great generalization and identification accuracy.

Key words: multi-domain feature, tool wear, joint distribution adaptation, K-nearest neighbor classifier