Paper Title:
Application of Partial Least Squares Neural Network in Thermal Error Modeling for CNC Machine Tool
  Abstract

This paper presents a partial least squares neural network modeling method for CNC machine tool thermal errors. This method uses the neural network learning rule to obtain the PLS parameters instead of the traditional linear method in partial least squares regression so as to overcome the multicollinearity and nonlinearity problem in thermal error modeling. The basic principle and architecture of PLSNN is described and the new method is applied on the thermal error modeling for a CNC turning center. After model validation with two groups of new testing data and performance comparison with other five different modeling methods, PLSNN performs better than the others with better robustness.

  Info
Periodical
Key Engineering Materials (Volumes 392-394)
Edited by
Guanglin Wang, Huifeng Wang and Jun Liu
Pages
30-34
DOI
10.4028/www.scientific.net/KEM.392-394.30
Citation
J.H. Shen, J. G. Yang, "Application of Partial Least Squares Neural Network in Thermal Error Modeling for CNC Machine Tool ", Key Engineering Materials, Vols. 392-394, pp. 30-34, 2009
Online since
October 2008
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Price
$32.00
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