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.

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 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.

Clustering Regression Modeling for Gear Hobbing Machine Thermal Error Compensation

2009 ◽  
Vol 416 ◽  
pp. 401-405
Author(s):  
Qian Jian Guo ◽  
Xiao Ni Qi
Keyword(s):  
Error Compensation ◽  
Thermal Error ◽  
Regression Modeling ◽  
Error Model ◽  
Error Modeling ◽  
Machining Accuracy ◽  
Thermal Error Modeling ◽  
Gear Hobbing ◽  
Thermal Error Model ◽  
Thermal Error Compensation

This paper proposes a new thermal error modeling methodology called Clustering Regression Thermal Error Modeling which not only improves the accuracy and robustness but also saves the time and cost of gear hobbing machine thermal error model. The major heat sources causing poor machining accuracy of gear hobbing machine are investigated. Clustering analysis method is applied to reduce the number of temperature sensors. Least squares regression modeling approach is used to build thermal error model for thermal error on-line prediction of gear hobbing machine. Model performance evaluation through thermal error compensation experiments shows that the new methodology has the advantage of higher accuracy and robustness.

Thermal Error Modeling and Compensation for an INDEX-G200 Turning Center

2011 ◽  
Vol 215 ◽  
pp. 53-55
Author(s):  
Qian Jian Guo ◽  
Lei He ◽  
Guang Ming Zhu
Keyword(s):  
Prediction Accuracy ◽  
Back Propagation ◽  
Thermal Error ◽  
Error Modeling ◽  
Thermal Drift ◽  
Thermal Error Modeling ◽  
Thermal Errors ◽  
Whole Process ◽  
Turning Center ◽  
Bp Model

The purpose of this research is to fulfill thermal error modeling and compensation of an INDEX-G200 turning center. This paper presents the whole process of thermal error modeling and compensation by using back propagation neural networks. Results show that the BP model improves the prediction accuracy of thermal errors on the turning center and the thermal drift has been reduced from 39 to 11 after compensation.

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