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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 8281490, 10 pages
Research Article

Thermal Error Modelling of the Spindle Using Neurofuzzy Systems

1School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
3Laser Institute of Shandong Academy of Sciences, Jinan 250000, China

Received 10 November 2015; Revised 11 February 2016; Accepted 21 February 2016

Academic Editor: Mohammed Nouari

Copyright © 2016 Jingan Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a new combined model to predict the spindle deformation, which combines the grey models and the ANFIS (adaptive neurofuzzy inference system) model. The grey models are used to preprocess the original data, and the ANFIS model is used to adjust the combined model. The outputs of the grey models are used as the inputs of the ANFIS model to train the model. To evaluate the performance of the combined model, an experiment is implemented. Three Pt100 thermal resistances are used to monitor the spindle temperature and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that the combined model can better predict the spindle deformation compared to BP network, and it can greatly improve the performance of the spindle.