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Thermal error modeling and prediction analysis based on OM algorithm for machine tool’s spindle

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Abstract

The manufacturing precisions of machine tools are seriously affected by high-speed spindle thermal error, especially the axial thermal deformation. A thermal error prediction model is an effective and economical approach to enhance the accuracy of machine tools, which is based on various artificial intelligence algorithms. This paper puts forward a novel optimal effective composite model (OM) for spindle thermal error prediction, which integrates the advantages of both gray model (GM(1,n)) and least squares support vector machine (LS-SVM) in terms of the experiment sample data. In the first place, the GM(1,n) and the LS-SVM are borrowed to establish the spindle thermal error prediction model, respectively. Then, the OM model is built by optimizing and adjusting the weighting coefficient of GM(1,n) and LS-SVM model, which is predicted by practical thermal error sample data. Finally, the prediction accuracy of the OM model is better than GM(1,n) model and LS-SVM by comparing the above models. After compensation, the maximum spindle thermal error, dropping from 16.4 to 3.5 μm, is significantly reduced with a droop rate of 78.7%. Therefore, the results show that, comparing with traditional GM(1,n) and LS-SVM method, the OM presented in this paper is more accurate and robust for thermal error prediction and compensation under complex machining conditions, which has preliminarily industrial application prospect.

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Acknowledgements

The authors sincerely thanks Professor Guofu Yin of Sichuan University for the outstanding guidance on research and reading during manuscript preparation.

Funding

This paper was financially supported by the National Key Technology R&D Program of China under grant No.2017ZX04020001-005, Young Scholars Development Fund of SWPU under grant No.201499010023, and Applied Basic Research Program of Southwest Medical University under No. 2017-ZRZD-019.

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Author notes

  1. Xiaopeng Yao and Chuanhua Cheng contributed equally to this work.

Authors and Affiliations

  1. School of Medical Information and Engineering, Southwest Medical University, Luzhou, 646000, China

    Xiaopeng Yao

  2. The Affiliated Chinese Medicine Hospital of Southwest Medical University, No.8 Xindu Road, Xindu District,, Luzhou, 646000, China

    Chuanhua Cheng

  3. School of Mechatronic Engineering, Southwest Petroleum University, No.8 Xindu Road, Xindu District,, Chengdu, 610500, China

    Teng Hu

  4. School of Manufacturing Science and Engineering, Sichuan University, Chengdu, 610065, China

    Guofu Yin

Authors
  1. Xiaopeng Yao
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  2. Teng Hu
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  3. Guofu Yin
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  4. Chuanhua Cheng
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Correspondence to Teng Hu or Chuanhua Cheng.

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Yao, X., Hu, T., Yin, G. et al. Thermal error modeling and prediction analysis based on OM algorithm for machine tool’s spindle. Int J Adv Manuf Technol 106, 3345–3356 (2020). https://doi.org/10.1007/s00170-019-04767-y

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