Feed shaft thermal expansion error modeling based on principal component regression

Abstract

In order to further improve the prediction accuracy of the thermal error model of the feed shaft of the gear grinding machine, this paper proposes a feed shaft thermal expansion error modeling method based on principal component regression. The feed shaft positioning error is decoupled by linear fitting to obtain the thermal expansion slope parameter, the position correlation of the feed shaft thermal expansion error is eliminated, and a regression model of the thermal expansion slope parameter and the temperature of all measuring points is established based on the principal component regression algorithm. Different from the traditional method, the principal component regression model does not require additional screening of temperature sensitive points, and the root mean square error mean and standard deviation of the group experimental prediction results can reach 2.0 μm/m、0.9μm/m, which has higher accuracy and stability than conventional methods.