Application of Partial Least Squares Neural Network in Thermal Error Modeling for CNC Machine Tool |
| Journal |
Key Engineering Materials (Volumes 392 - 394) |
| Volume |
Manufacturing Automation Technology |
| Edited by |
Guanglin Wang, Huifeng Wang and Jun Liu |
| Pages |
30-34 |
| DOI |
10.4028/www.scientific.net/KEM.392-394.30 |
| Online since |
October, 2008 |
| Authors |
J.H. Shen,
Jian Guo Yang
|
| Keywords |
CNC Machine Tool, Neural Network (NN), Partial Least Squares, Thermal Error |
| 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. |
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