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A thermal deformation prediction method for grinding machine’ spindle

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Abstract

Thermal deformation is the main factor of the machining accuracy for grinding machines, which seriously restricts the precision improvement of grinding machines. However, at present, there are little researches on thermal error prediction, and the accuracy of the prediction model is comparatively low. Thus, a novel approach for thermal deformation prediction of grinding machine spindle based on heat energy conduction principle and neural network is proposed in this paper. Firstly, the temperature sensors’ pairs are applied to measure the temperature deviation between the spindle surface and its adjacent ambient which are directly related to the heat energy exchange. Secondly, the temperature deviations of each segment of the spindle are taken as inputs, which will exist and accumulate in the form of heat energy subsequently in the convolutional neural network. Meanwhile, the accumulated heat energy is mixed and transferred to the different segments of the spindle in the convolutional neural network. Thirdly, the thermal deformation caused by the increment of heat energy is considered as the output of thermal error prediction result based on the principle of heat energy conduction. Finally, the simulations and experiments are implemented to validate the feasibility and effectiveness of the proposed method.

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The authors confirm that the data supporting the findings of this study are available within the article.

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Code availability

The authors confirm that the codes supporting the findings of this study are available within the article.

Funding

This work is financially supported by the National Natural Science Foundation of China (grant no. 51775010 and 51705011), the National Science and Technology Major Project of China (grant no. 2019ZX040 060 01).

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Authors and Affiliations

  1. Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, 100124, PR China

    Jinwei Fan, Peitong Wang, Haohao Tao & Ri Pan

Authors
  1. Jinwei Fan
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  2. Peitong Wang
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  3. Haohao Tao
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  4. Ri Pan
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Contributions

Fan Jinwei and Wang Peitong provided ideas for this study and wrote codes and manuscripts. Haohao Tao and Ri Pan were responsible for the experiment in this study. All authors contributed to this study.

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Correspondence to Jinwei Fan.

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Fan, J., Wang, P., Tao, H. et al. A thermal deformation prediction method for grinding machine’ spindle. Int J Adv Manuf Technol 118, 1125–1139 (2022). https://doi.org/10.1007/s00170-021-07931-5

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