Application of projection pursuit regression to thermal error modeling of a CNC machine tool

2010 ◽  
Vol 55 (5-8) ◽  
pp. 623-629 ◽  
Author(s):  
Guo Qianjian ◽  
Yang Jianguo
Keyword(s):  
Machine Tool ◽  
Projection Pursuit ◽  
Thermal Error ◽  
Error Modeling ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Projection Pursuit Regression ◽  
Thermal Error Modeling

Thermal Error Modeling of a CNC Machine Tool

2013 ◽  
Vol 303-306 ◽  
pp. 1782-1785
Author(s):  
Chong Zhi Mao ◽  
Qian Jian Guo
Keyword(s):  
Machine Tool ◽  
Back Propagation ◽  
Thermal Error ◽  
Error Modeling ◽  
Machining Accuracy ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Thermal Error Modeling ◽  
Thermal Errors ◽  
Thermal Error Model

The purpose of this research is to improve the machining accuracy of a CNC machine tool through thermal error modeling and compensation. In this paper, a thermal error model based on back propagation networks (BPN) is presented, and the compensation is fulfilled. The results show that the BPN model improves the prediction accuracy of thermal errors on the CNC machine tool, and the thermal drift has been reduced from 15 to 5 after compensation.

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.

Research on Thermal Error Modeling of CNC Machine Tool

2010 ◽  
Vol 135 ◽  
pp. 170-173 ◽  
Author(s):  
Qian Jian Guo ◽  
Jian Guo Yang
Keyword(s):  
Machine Tool ◽  
Back Propagation ◽  
Thermal Error ◽  
Back Propagation Neural Network ◽  
Error Modeling ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Daily Production ◽  
Proposed Model ◽  
Thermal Error Model

Four key temperature points of a CNC machine tool were obtained in this paper, and a thermal error model based on the four key temperature points was proposed by using based back propagation neural network. A thermal error compensation system was developed based on the proposed model, and which has been applied to the CNC machine tool in daily production. The results show that the thermal error in workpiece diameter has been reduced from 33 to 6 .

Application of a Bayesian Network to Thermal Error Modeling and Analysis for Machine Tool

2010 ◽  
Vol 455 ◽  
pp. 616-620
Author(s):  
X. Li ◽  
Q. Lei ◽  
Z.H. Li
Keyword(s):  
Machine Tool ◽  
Bayesian Network ◽  
Error Compensation ◽  
Process Condition ◽  
Thermal Error ◽  
Error Modeling ◽  
Machining Process ◽  
Cnc Machine ◽  
Thermal Error Modeling ◽  
Thermal Error Compensation

CNC machine tool dynamic thermal error compensation has always been a hot issue to improving precision. This dissertation proposes a method of machine tool thermal error modeling during processing, based on Bayesian network theory, by describing the correlation between the various factors of generated the heat error, through the sample data, analyzed and simplified the intrinsic correlation between these various factors, established the basic thermal error compensation model, and used the network’s good characteristic of self-studying, combining the result of update collection data, continually modify the model to reflect the machining process condition changes. Finally, the experimental results show the feasibility of Bayesian network model, it was a stronger application for achieving the thermal error compensation.

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