Identification of thermal error in a feed system based on multi-class LS-SVM

Abstract

Research of thermal characteristics has been a key issue in the development of high-speed feed system. The thermal positioning error of a ball-screw is one of the most important objects to consider for high-accuracy and high-speed machine tools. The research work undertaken herein ultimately aims at the development of a comprehensive thermal error identification model with high accuracy and robust. Using multi-class least squares support vector machines (LS-SVM), the thermal positioning error of the feed system is identified with the variance and mean square value of the temperatures of supporting bearings and screw-nut as feature vector. A series of experiments were carried out on a self-made quasi high-speed feed system experimental bench HUST-FS-001 to verify the identification capacity of the presented method. The results show that the recommended model can be used to predict the thermal error of a feed system with good accuracy, which is better than the ordinary BP and RBF neural network. The work described in this paper lays a solid foundation of thermal error prediction and compensation in a feed system.

Key words： least squares support vector machine (LS-SVM); feed system; thermal error; precision machining

Acknowledgements

The work here is supported by the National Natural Science Foundation of China (Grant Nos. 51175208 and 51075161), the National Key Basic Research Development Program of China (No. 2011CB706803) and the National High Technology Research and Development Program of China (No. 2008AA042802).

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