Thermal Error Modelling of the Spindle Using Data Transformation and Adaptive Neurofuzzy Inference SystemReport as inadecuate

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Mathematical Problems in Engineering - Volume 2015 2015, Article ID 130253, 10 pages -

Research ArticleState Key Laboratory for Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Received 10 January 2015; Accepted 29 April 2015

Academic Editor: Jean-Charles Beugnot

Copyright © 2015 Yanlei Li 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 method for predicting spindle deformation based on temperature data. The method introduces the adaptive neurofuzzy inference system ANFIS, which is a neurofuzzy modeling approach that integrates the kernel and geometrical transformations. By utilizing data transformation, the number of ANFIS rules can be effectively reduced and the predictive model structure can be simplified. To build the predictive model, we first map the original temperature data to a feature space with Gaussian kernels. We then process the mapped data with the geometrical transformation and make the data gather in the square region. Finally, the transformed data are used as input to train the ANFIS. A verification experiment is conducted to evaluate the performance of the proposed method. Six Pt100 thermal resistances are used to monitor the spindle temperature, and a laser displacement sensor is used to detect the spindle deformation. Experimental results show that the proposed method can precisely predict the spindle deformation and greatly improve the thermal performance of the spindle. Compared with back propagation BP networks, the proposed method is more suitable for complex working conditions in practical applications.

Author: Yanlei Li, Youmin Hu, Bo Wu, and Jikai Fan



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