Thermo-Drifting Error Modeling of Spindle Based on Combination of Principal Component Analysis and BP Neural Network

Abstract

Abstract:

In order to improve the prediction accuracy of thermal error model, a data processing method based on the combination of principal component analysis and BP neural network was presented. By using principal component analysis, the amount of temperature variables will be reduced. Then the principal components are employed to train the BP neural network in order to obtain the thermal error model. As the number of inputs is reduced, the train process can be faster and the iteration time can be reduced. In comparison with the network model which uses the critical point temperatures as the input, the

results show that BP neural network thermal error modeling method based on principal component analysis method has advantages of high fitting accuracy and smaller residual error. So this modeling method has both high efficiency and accuracy of compensation.

Key words: machine tools, thermal error, principal component analysis, neural network, modeling

CLC Number:

TH 161

YANG Yi1，YAO Xiaodong1，YANG Jianguo1,ZHANG Yusheng2，YUAN Feng2. Thermo-Drifting Error Modeling of Spindle Based on Combination of Principal Component Analysis and BP Neural Network[J]. Journal of Shanghai Jiaotong University, 2013, 47(05): 750-753.

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