Temperature-sensitive point selection for thermal error modeling of machine tool spindle by considering heat source regions

The suitable and stable temperature-sensitive point combination is closely related to the thermal error modeling and compensation of the machine tool. In this paper, one K-means clustering and correlation analysis-based selection method are proposed by considering the influences of different heat sources to obtain the global temperature-sensitive point combination for thermal error modeling of machine tool spindle. Firstly, machine tool is divided into different heat source regions by analyzing the influences of temperature field distribution on the spindle thermal error comprehensively. Secondly, the synthetical selection method is proposed to obtain the key temperature variable combinations from a group of temperature points by comparing temperature variable combinations corresponding to a series of K values. The key temperature variable combinations of each heat source region are obtained, and the global temperature-sensitive point combination is selected by setting the key temperature variable combinations of all regions as the initial temperature points. Finally, experiments under different conditions are carried out on the machining center to verify the effectiveness of the proposed method, including the comparison with the non-subregion, the application to different error terms of the same speed, and the same error term at different speeds.