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. Conse...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 thetemperature rise of the Spindle fromaffecting the accuracy during themachining process, typically, the factory willwarm up themachine before themanufacturing process.However, if there is noway to understand the tool spindle’sthermal deformation, the machining quality will be greatly affected. In order to solve the above problem, thisstudy aims to predict the thermal displacement of the machine tool by using intelligent algorithms. In the practicalapplication, only a few temperature sensors are used to input the information into the prediction model for realtimethermal 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 tointegrate Long Short-TermMemory (LSTM) with Support VectorMachine (SVM). The experimental results showthat the prediction performance of LSTM-SVM is higher than that of other machine learning algorithms.展开更多
基金supported by the Ministry of Science and Technology,Taiwan,under Grant MOST 110-2218-E-194-010。
文摘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 thetemperature rise of the Spindle fromaffecting the accuracy during themachining process, typically, the factory willwarm up themachine before themanufacturing process.However, if there is noway to understand the tool spindle’sthermal deformation, the machining quality will be greatly affected. In order to solve the above problem, thisstudy aims to predict the thermal displacement of the machine tool by using intelligent algorithms. In the practicalapplication, only a few temperature sensors are used to input the information into the prediction model for realtimethermal 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 tointegrate Long Short-TermMemory (LSTM) with Support VectorMachine (SVM). The experimental results showthat the prediction performance of LSTM-SVM is higher than that of other machine learning algorithms.
