A hybrid tool wear prediction model based on JDA

Abstract Tool wear monitoring plays a vital role in improving product quality and reducing production costs. Aiming at the problems of low prediction accuracy and poor generalization caused by the difference in tool wear data distribution and the fixation of single global model parameters, a hybrid prediction modeling method for tool wear based on joint distribution adaptation (JDA) is proposed. Firstly, JDA is exploited to adapt the data features under different data distributions. Then, the adapted data features are identified by the KNN classifier. Finally, according to the tool state classification results, different regression prediction models are assigned to different wear stages to complete the whole tool wear prediction task. The results of milling experiments show that the maximum prediction accuracy of this method can reach 91.15%, and it has good recognition accuracy and generalization performance. Through the analysis of the tool wear hybrid prediction modeling method, the research can improve the prediction accuracy and generalization performance of the model and realize tool on-line monitoring. The research results can provide solutions and a theoretical basis for the application of tool wear monitoring technology in practical production.