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Thermal error prediction of machine tool spindle using segment fusion LSSVM

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A Correction to this article was published on 10 June 2021

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Abstract

The key temperature points are the input variables of the thermal error model for prediction and compensation of thermal errors for precision CNC machine tools. However, the revealed time-varying characteristics of the key temperature points may jeopardize the robust prediction. To this end, the segment fusion least squares support vector machine (SF-LSSVM) thermal error modeling method is proposed. Firstly, the temperature data and thermal error data are divided into different segments according to time. Then, using the LSSVM with excellent nonlinear mapping capabilities as the basic model, the sub LSSVM thermal error model building and the corresponding key temperature points selection in each segment are fulfilled with genetic algorithm (GA) in a wrapper manner to preserve the corresponding local prediction characteristics. Finally, pick some of or all the sub LSSVM thermal error models to fuse together as the final thermal error model which may incorporate both the local and global prediction characteristics. The modeling and prediction experiment results on the spindle thermal error of a horizontal machining center demonstrate that the mean root-mean-square error (RMSE) on 5 spindle speeds after compensation is only 3.1 μm. Comparing with two traditional thermal error models, the prediction performance of the present model is improved by up to 51%. This research casts new light on both the mechanism of key temperature points and the prediction method of thermal errors.

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The data and materials generated or analyzed during this study cannot be shared at this time as the data also forms part of an ongoing study.

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Funding

This research was financially supported by the Chongqing Research Program of Basic Research and Frontier Technology (cstc2019jcyj-msxmX0540), the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202000614), and the National Natural Science Foundation of China (51905065).

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Authors and Affiliations

  1. School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, People’s Republic of China

    Feng Tan, Kai Zheng & Xin Wang

  2. School of Mechanical Engineering, Sichuan University, Chengdu, 610065, People’s Republic of China

    Guofu Yin

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  1. Feng Tan
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  2. Guofu Yin
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  3. Kai Zheng
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  4. Xin Wang
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Contributions

Feng Tan: conceptualization, methodology, software, writing—original draft. Guofu Yin: supervision, writing—review and editing. Kai Zheng: data curation, visualization, investigation, software, validation. Xin Wang: investigation, validation.

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Correspondence to Feng Tan.

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Tan, F., Yin, G., Zheng, K. et al. Thermal error prediction of machine tool spindle using segment fusion LSSVM. Int J Adv Manuf Technol 116, 99–114 (2021). https://doi.org/10.1007/s00170-021-07066-7

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  • DOI: https://doi.org/10.1007/s00170-021-07066-7

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