Thermal error modeling and compensation of spindles based on LS-SVM

Abstract

This paper presents a new modeling methodology for machine tool thermal error. The method uses the least squares support vector machine (LS-SVM) model to track nonlinear time-varying spindle thermal error under certain conditions. Experiments on spindle thermal deformation are conducted to evaluate the model performance in terms of model estimation accuracy and robustness. The comparison indicates that the LS-SVM performs better than other modeling methods, such as multi-variable least squares regression analysis, in terms of model accuracy and robustness. Using the constructed thermal error model, the thermal deformation can be compensated. After compensation, the machine tool accuracy improves greatly.