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Thermal error prediction model of a motorized spindle considering variable preload

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Abstract

To predict the thermal error of the motorized spindle during machining and improve the accuracy of the error compensation system of the motorized spindle, a method for predicting the thermal error of the motorized spindle under different preload forces is proposed. Based on the theory of Palmgren, the relationship between the preload force and the heat generation of bearings was established. Through thermal-solid coupling analysis, the temperature measuring points of the motorized spindle were optimized, and regularity for changes of axial thermal elongation of the motorized spindle was analyzed. The temperature data of the motorized spindle with 800 N preload was used as input to construct the thermal error prediction model of the differential evolution-Gray Wolf Optimization-support vector regression (DE-GWO-SVR). The thermal error prediction model was established to predict the thermal error of the motorized spindle with different preload forces. The results showed that the error of the thermal error prediction model was less than 2 μm, and the accuracy was higher than 95%. This study provides a new modeling idea for thermal error compensation in precision machining of the high-speed motorized spindle for computer numerical control (CNC) machine tools.

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Abbreviations

NC:

Numerical control

CNC:

Computer numerical control

MAE:

Mean absolute error

MSE:

Mean square error

RMSE:

Root mean square error

DE-GWO-SVR:

Differential evolution-Gray Wolf Optimization-support vector regression

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Funding

This research was funded by the National Natural Science Foundation of China (grant number [52075134]), the Opening Project of the Key Laboratory of Advanced Manufacturing and Intelligent Technology (Ministry of Education), Harbin University of Science and Technology (grant number [KFKT202105]), the Joint Guidance Project of Natural Science Foundation of Heilongjiang Province (grant number [LH2019E062]), and the Special Funding for Postdoctoral Fellows in Heilongjiang Province (grant number [LBH-Q20097]).

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing and Intelligent Technology (Ministry of Education), Harbin University of Science and Technology, Harbin, 150080, China

    Ye Dai, Xueshi Tao, Liyu Xuan & Hang Qu

  2. Ningbo Tiankong Five-Axis CNC Technology Co., Ltd, Yuyao, 315400, China

    Gang Wang

Authors
  1. Ye Dai
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  2. Xueshi Tao
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  3. Liyu Xuan
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  4. Hang Qu
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  5. Gang Wang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Ye Dai, Xueshi Tao, Liyu Xuan, Hang Qu, and Gang Wang. The first draft of the manuscript was written by Ye Dai and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ye Dai.

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Dai, Y., Tao, X., Xuan, L. et al. Thermal error prediction model of a motorized spindle considering variable preload. Int J Adv Manuf Technol 121, 4745–4756 (2022). https://doi.org/10.1007/s00170-022-09679-y

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  • DOI: https://doi.org/10.1007/s00170-022-09679-y

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