2012, Volume 7, Issue 1
Frontiers of Mechanical Engineering >> 2012, Volume 7, Issue 1 doi: 10.1007/s11465-012-0307-6
Identification of thermal error in a feed system based on multi-class LS-SVM
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
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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.
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