Measurement and Modeling of Spindle Thermal Error of Fiveaxis CNC Machine Tool with Double Turntable

• Published in: Journal of Harbin University of Science and Technology
• Main Authors: LIU Xianli, SONG Houwang, WU Shi, YUE Caixu, Steven Y.Liang, LI Rongyi
• Format: Article
• Language: Chinese
• Published: Harbin University of Science and Technology Publications 2019-12-01
• Subjects: five axis cnc machine tool; thermal error of spindle; thermal test piece; k-means++ algorithms; correlation coefficient method
• Online Access: https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1791
• ISSN: 1007-2683

In order to measure the thermal error of the spindle in the actual cutting process of CNC machine tools and optimize the output of the thermal error model, a method of measuring the thermal error of the spindle of machine tools by using the thermal test piece is proposed, and the thermal error is separated by using the error characteristics. In order to optimize the selection of temperature measurement points in the thermal error modeling of machine tools, a method based on the combination of K-means + + algorithm and correlation coefficient method is proposed to select the temperature sensitive points. K-means + + algorithm is used to cluster all temperature measurement points, and correlation coefficient method is used to calculate the correlation between each temperature variable and the thermal error of the spindle, so as to determine the temperature sensitive points, combined with the separated thermal error to establish a multivariate linear regression model of spindle thermal error. The method is tested on VMCC50 double turntable five axis CNC machine. The results show that the number of temperature measuring points is reduced from 8 to 2, and the prediction accuracy and robustness of the model are effectively improved.