Analysis and prediction for thermal error of precision coordinate boring machine

To predict the effect of heat deviation on positioning accuracy of feed system in a running CNC machine, for a precision coordinate boring machine, the temperature accuracy in every measuring point of the system is measured by infrared thermal imager and the positioning accuracy is measured by laser interferometer simultaneously, then the least squares support vector machine (LS-SVM) prediction method of thermal error in feed system is adopted to establish a prediction model of temperature and location. LS-SVM prediction method is introduced into the prediction model to predict the thermal error in the feed system. The model well describes the nonlinear relationship between the thermal errors of position and temperature with slight dependence on samples and good generalization ability. The difficulty that the prediction error gets greater because the average characteristic temperature is considered instead of the current measuring point temperature in establishing linear prediction model is solved. The results show that the prediction precision of thermal error by LS-SVM model reaches 90%, 30% higher than that by the existing linear prediction model for NC machine.