A weighted adaptive transfer learning for tool tip dynamics prediction of different machine tools

Abstract

In the batch machining of manufacturing enterprises, there are always different machine tools of the same type used. Due to the influence of the uncertain degree of deterioration during the service period and other factors, there are great differences between different machine tools of the same type. These factors lead to different tool tip dynamics even for the same tool-tool holder assembly under the same conditions, thus affecting the prediction of stability in the milling process. In this paper, a weighted adaptive joint distribution adaptation transfer learning method was proposed to predict the position-speed dependent tool tip dynamics of different machine tools that had different service time. Firstly, a machine tool was selected as the source machine tool, and the corresponding tool tip dynamics were identified as the source data through different position-speed milling tests. Then, for the new same type of machine tools, that was, the target machine tools, only a few different position-speed combinations milling tests need to be carried out for the same tool, so as to identify the tool tip dynamics as the target data. Subsequently, a Kriging regression model for predicting the position-speed dependent tool tip dynamics of the target machine tool was trained by the proposed transfer learning method. Compared with the prediction results of the current transfer learning method, it was found that the average test errors of the natural frequency and damping ratio of the proposed method were minimum, which were 0.48% and 7.2% respectively. Finally, the accuracy and effectiveness of the proposed method were verified by the actual milling experiments.