Thermal expansion error modeling of feed axis based on principal component regression
To further improve the prediction accuracy of the thermal error model of the feed axis of the gear grinding machine,a thermal expansion modeling method of the feed axis based on principal component regression is proposed in this paper.The slope parameter of thermal expansion is obtained by decoupling the positioning error of the feed axis through a linear fitting,which eliminates the position correlation between the thermal expansion error and the position of feed axis.The regression model between the thermal expansion slope and all the temperature points is established using the principal component regression algorithm.Different from the traditional methods,the principal component regression model does not need additional screening of temperature sensitive points,and the mean value and standard deviation of the root means square errors of the prediction results can reach 2.0 μm/m、0.9 μm/m,which has higher accuracy and stability than conventional methods.
万方数据
维普
