Study on the effects of changes in temperature-sensitive points on thermal error compensation model for CNC machine tool

• Variation of temperature-sensitive points in CNC machine error modeling is revealed.
• Impact of change in sensitive points on model’s accuracy and robustness are analyzed.
• Principal component regression (PCR) modeling is proposed to weaken this impact.
• Traverse method for selecting optimum variables is raised to improve PCR’s accuracy.
• Proposed methods are verified by triaxial thermal error experimental on CNC machine.

In thermal error compensation technology on computer numerical control (CNC) machine tool, selecting appropriate and stable temperature-sensitive points for modeling and compensation, is crucial for improving the accuracy of machine. In this paper, the temperature-sensitive points are changeable is proved by analyzing batches of experiment data of air cutting experiments on Leaderway-V450 machine, so it changes the degree of multi-collinearity among temperature variables, causes a serious impact on linearization and forecasting accuracy of the model, and can’t guarantee the model’s robustness. Based on the above analysis, a modeling method of principal component regression (PCR) algorithm is proposed, which can eliminate the influence of multi-collinearity among temperature variables. On this basis, according to the characteristic of PCR algorithm, traverse optimization method for selecting the optimum temperature measuring points is put forward as well. And both of two methods are given practice tests through triaxial thermal error experiments of actual machine. And the results show, PCR model significantly reduces the effects of changes in temperature-sensitive points on model’s accuracy; what’s more, the model has good forecasting accuracy and robustness by using PCR model combines with traverse optimization method. So that makes real-time compensation for thermal error on CNC machine more applied engineering.