Ensemble Model for Spindle Thermal Displacement Prediction of Machine Tools

Numerous factors affect the increased temperature of a machine tool, including prolonged and high-intensity usage, tool-workpiece interaction, mechanical friction, and elevated ambient temperatures, among others. Consequently, spindle thermal displacement occurs, and machining precision suffers. To prevent the errors caused by the temperature rise of the Spindle from affecting the accuracy during the machining process, typically, the factory will warm up the machine before the manufacturing process. However, if there is no way to understand the tool spindle's thermal deformation, the machining quality will be greatly affected. In order to solve the above problem, this study aims to predict the thermal displacement of the machine tool by using intelligent algorithms. In the practical application, only a few temperature sensors are used to input the information into the prediction model for realtime thermal displacement prediction. This approach has greatly improved the quality of tool processing. However, each algorithm has different performances in different environments. In this study, an ensemble model is used to integrate Long Short-Term Memory (LSTM) with Support Vector Machine (SVM). The experimental results show that the prediction performance of LSTM-SVM is higher than that of other machine learning algorithms.

Automated summary

Many factors such as prolonged and high-intensity usage, tool-workpiece interaction, mechanical friction, and ambient temperatures can increase the temperature of a machine tool. This leads to spindle thermal displacement and reduced machining precision. To address this, an intelligent algorithm is used to predict the thermal displacement of the machine tool. The ensemble model of LSTM-SVM shows higher prediction performance compared to other machine learning algorithms.