Thermal Error Modeling and Compensating of Motorized Spindle Based on Improved Neural Network

Abstract:

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In a lot of factors, thermal deformation of motorized high-speed spindle is a key factor affecting the manufacturing accuracy of machine tool. In order to reduce the thermal errors, the reasons and influence factors are analyzed. A thermal error model, that considers the effect of thermodynamics and speed on the thermal deformation, is proposed by using genetic algorithm-based radial basis function neural network. The improved neural network has been trained and tested, then a thermal error compensation system based on this model is established to compensate thermal deformation. The experiment results show that there is a 79% decrease in motorized spindle errors and this model has high accuracy.

Info:

Periodical:

Advanced Materials Research (Volumes 129-131)

Edited by:

Xie Yi and Li Mi

Pages:

556-560

DOI:

10.4028/www.scientific.net/AMR.129-131.556

Citation:

C. L. Lei and Z. Y. Rui, "Thermal Error Modeling and Compensating of Motorized Spindle Based on Improved Neural Network", Advanced Materials Research, Vols. 129-131, pp. 556-560, 2010

Online since:

August 2010

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Price:

$35.00

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