Paper Title:
Thermal Error Modeling and Compensating of Motorized Spindle Based on Improved Neural Network
  Abstract

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, 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
$32.00
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