An Ensemble Modeling for Thermal Error of CNC Machine Tools

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 946)


Thermal error caused by the thermal deformation of computer numerical control (CNC) machine tools is one of the main factors to affect the machining accuracy. With monitoring data of the temperature field, establishing data-driven thermal error model is considered as a more convenient, effective and cost-efficient way to reduce the thermal error. As a matter of fact, it is very difficult to develop a thermal error model with perfect generalization adapting to different working conditions of machining tools. In this paper, a method of an ensemble modeling (EM) based on Convolution Neural Network (CNN) and Back Propagation (BP) Neural Network for modeling thermal error is presented. This ensemble model takes full advantages of two different neural networks, namely CNN having self-extracting feature to solve collinear problem in temperature field and BP can process heat source to thermal error by mapping nonlinear function, then combined into a EM. To demonstrate the effectiveness of the proposed model, an experiment platform was set up based on a heavy-duty CNC machine tool. The results show that the proposed model achieves better accuracy and strong robustness in comparison with only with BP network and CNN network respectively.


Thermal error Ensemble modeling CNC machine Convolution neural network Back propagation network 



The authors would like to acknowledge funding support from the National Natural Science Foundation Committee of China under Grant No. 51475347 and the Major Project of Technological Innovation Special Fund of Hubei Province Grant No. 2016AAA016, as well as the contributions from all collaborators within the projects mentioned. We would also like to thank Wuhan University of Technology, People’s Republic of China in supporting this work.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Information EngineeringWuhan University of TechnologyWuhanChina

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