Thermal error modeling of machine tool based on fuzzy c-means cluster analysis

Thermal errors of the machine tools have a significant effect on the machining precision. In this paper, temperature variables selection based on the fuzzy c-means cluster analysis is studied, robust regression theory is utilized to establish the relationship between the thermal errors and the temperature variables, and large residuals are given small weights and leave the residuals associated with extreme points. Pt thermal resistances are used to measure the temperature change and the eddy current sensors are used to monitor the thermal shifts of the spindle, the test results show that robust regression method can predict the thermal errors of the machine accurately. The coupling among the variables is also solved, which can be used for the error compensation of the machine tool so as to meet the accuracy demands of the precision machining.