Thermal Error Prediction of the Spindle Using Adaptive Neuro-Fuzzy Inference System

Abstract:

This paper proposes a new method to predict the spindle deformation based on temperature data. The method introduces ANFIS (adaptive neuro-fuzzy inference system). For building the predictive model, we first extract temperature data from sensors in the spindle, and then they are used as the inputs to train ANFIS. To evaluate the performance of the prediction, an experiment is implemented. Three Pt-100 thermal resistances is used to monitor the spindle temperature, and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that our prediction model can better predict the spindle deformation and improve the performance of the spindle.

• Periodical: Applied Mechanics and Materials (Volumes 789-790)
• Pages: 263-267
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.789-790.263
• Online since: September 2015
• Authors: Yan Lei Li*, Ming Yan Wang, You Min Hu, Bo Wu
• Keywords: Adaptive Neuro-Fuzzy Inference System, Error Compensation, Thermal Error Prediction

References

[1] L. Uriarte, M. Zatarain, D. Axinte, J. Yage-Fabra, S. Ihlenfeldt, J. Eguia and A. Olarra, Machine tools for large parts. CIRP Annals – Manufacturing Technology, (2013) (0).

DOI: 10.1016/j.cirp.2013.05.009

[2] M. Weck, P. McKeown, R. Bonse and U. Herbst, Reduction and compensation of thermal errors in machine tools. CIRP Annals - Manufacturing Technology, 44 (2) (1995) 589–598.

DOI: 10.1016/s0007-8506(07)60506-x

[3] J. Mayr, J. Jedrzejewski, E. Uhlmann, M Alkan Donmez, Thermal issues in machine tools. CIRP Annals - Manufacturing Technology, 61 (2) (2012) 771–791.

DOI: 10.1016/j.cirp.2012.05.008

[4] J. Bryan, International status of thermal error research (1990). CIRP Annals - Manufacturing Technology, 39 (2): (1990) 645–656.

DOI: 10.1016/s0007-8506(07)63001-7

[5] D. Shi, J. Zurada and J. Guan, An adaptive neurofuzzy inference system for predicting the risks of low back disorders due to manual material lifting jobs. Expert Systems with Applications, 40 (14) (2013) 5490–5500.

DOI: 10.1016/j.eswa.2013.04.007

[6] G. N. Marichal, M. Arts, J. C. Garca Prada and O. Casanova, Extraction of rules for faulty bearing classification by a neuro-fuzzy approach. Mechanical Systems and Signal Processing, 25 (6) (2011) 2073–(2082).

DOI: 10.1016/j.ymssp.2011.01.014

[7] G. N. Marichal, J. Toledo, L. Acosta, E. J. Gonzlez and G. Coll, A neuro-fuzzy method applied to the motors of a stereovision system. Engineering Applications of Artificial Intelligence, 20 (7) (2007) 951–958.

DOI: 10.1016/j.engappai.2006.12.010

[8] M. -J. Huang, Y. -L. Tsou and S. -C. Lee, Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowledge-Based Systems, 19 (6) (2006) 396–403.

DOI: 10.1016/j.knosys.2006.04.003

[9] J. S. R. Jang, Anfis: adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions on, 23 (3) (1993) 665–685.

DOI: 10.1109/21.256541