Bayesian neural network–based thermal error modeling of feed drive system of CNC machine tool

Abstract

It is well known that thermal error has a significant impact on the accuracy of CNC machine tools. In order to decrease the thermally induced positioning error of machine tools, a novel thermal error modeling approach based on Bayesian neural network is proposed in this paper. The relationship between the temperature rise and positioning error of the feed drive system is investigated by simultaneously measuring the thermal characteristics that include the temperature field and positioning error of the CNC machine tool. Fuzzy c-means (FCM) clustering and correlation analysis are used to select temperature-sensitive points, and the Dunn index is introduced to determine the optimal number of clustering groups, which can inhibit the multicollinearity problem among temperature measuring points effectively. The least-square linear fitting is applied to explore the feature of the positioning error data. The results show that compared with the BP neural network and multiple linear regression model, the Bayesian neural network not only has higher prediction accuracy but also can guarantee excellent prediction performance under different working conditions. The prediction results obtained under different operating conditions indicate that the maximum thermal error can be reduced from around 18.2 to 5.14 μm by using the Bayesian neural network, which represents a 71% reduction in the thermally induced error of the feed drive system of machine tool.