Thermal error modeling and analysis of CNC machine tools based on wavelet neural network

Abstract

Thermal error of CNC machine tools has become an important factor affecting its accuracy and it is of great significance to establish a thermal error prediction model with high prediction accuracy and good robustness. At present, the main way to reduce the thermal error is software compensation, which consists of the model of temperature and thermal error, and the model of temperature and thermal error curve's coefficients. However, most of the current models of temperature and coefficients only consider the relationship between nut temperature and thermal error, ignoring the bearing temperature, ambient temperature and speed. In addition, most of the models only consider the slope but ignore the intercept. Above all, the prediction accuracy of the present models is affected. In view of this problem, a prediction model of Z-axis thermal deformation is proposed, using nut temperature, ambient temperature, bearing temperature and speed to establish the relationship between them and both intercept and slope. First, a thermal error prediction model is established by wavelet neural network according to the slope and intercept of thermal error curve and bearing temperature, ambient temperature and speed. Finally, the prediction effect of the model is verified. The result shows that the prediction accuracy of this method is 97.1%, which greatly improves the prediction accuracy of thermal error and has good engineering practice significance.