Multicondition Tool Wear Assessment for Cutting Tools Based on Kernel Principal Component Analysis and Integrated Transfer Learning

Accurate identification of tool wear status during machining is an important part of intelligent manufacturing, whose health state directly affects processing quality, production costs, and overall productivity. Aiming at the problems of the high dimensionality of features extracted by tool wear characterization sensors and the high similarity of the same tool wear state, a tool wear state recognition model based on kernel principal component analysis (KPCA) and integrated transfer learning is proposed in this article. First, KPCA was used to reduce the dimensionality of features; then, the optimal tool wear state identification model for different working conditions was determined based on integrated transfer learning. Finally, a multicondition cutting experiment was carried out to verify the effectiveness of TrAdaBoost in improving the accuracy of model identification and the efficiency of prediction. The experimental results show that when using the features reduced by KPCA to predict tool wear, the regression model ${R} ^{2}$ of integrated migration using the support vector machine (SVM)-K-nearest neighbor (KNN) classifier is up to 99.691%, and the accuracy of the classification model is up to 99.01%. This article assures accurate identification of tool wear states under multiple operating conditions. In addition, the method solves the challenge of data scarcity in tool wear prediction and proves its adaptability to new working condition data in tool wear prediction.