Thermal error modeling of machine tool spindle based on the improved algorithm optimized BP neural network

2019 ◽  
Vol 105 (1-4) ◽  
pp. 1497-1505 ◽  
Author(s):  
Bo Li ◽  
Xitian Tian ◽  
Min Zhang
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Bp Neural Network ◽  
Thermal Error ◽  
Error Modeling ◽  
Thermal Error Modeling ◽  
Machine Tool Spindle ◽  
Improved Algorithm

Application of Partial Least Squares Neural Network in Thermal Error Modeling for CNC Machine Tool

2008 ◽  
Vol 392-394 ◽  
pp. 30-34 ◽  
Author(s):  
J.H. Shen ◽  
Jian Guo Yang
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Least Squares ◽  
Partial Least Squares ◽  
Thermal Error ◽  
Error Modeling ◽  
Neural Network Modeling ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Thermal Error Modeling

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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