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Thermal error modeling of electric spindle based on particle swarm optimization-SVM neural network

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Abstract

High-speed electric spindle is an important part of computer numerical control (CNC) machining equipment. The thermal displacement generated by the electric spindle during operation is the main reason that affects the machining stability and machining accuracy of the electric spindle. Compensating the thermal error of the high-speed electric spindle can effectively improve the CNC machining. Improve equipment processing performance. Therefore, it is particularly important to establish the accuracy of the thermal error prediction model. Taking the A02 high-speed electric spindle as the research object, ANSYS is used to analyze the thermal characteristics of the electric spindle, and the temperature and thermal displacement monitoring points of the electric spindle are arranged according to the simulation results, and the temperature and thermal displacement data of the monitoring points under different rotational speeds are collected; using K-means to classify temperature measurement points, uses the gray relation analysis degree to determine the correlation between the temperature measurement point and the thermal displacement data, and selects 4 temperature-sensitive points from 10 temperature measurement points. Finally, particle swarm optimization (PSO) is used to optimize the penalty factor and kernel function of support vector machine (SVM), and the PSO-SVM prediction model is established to compare with the neural network prediction model of SVM and genetic algorithm (GA) optimized SVM. The results show that PSO-SVM has better robustness, stability, and generalization ability.

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Funding

This research was funded by the National Natural Science Foundation of China (grant number [52075134]).

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Authors and Affiliations

  1. School of Mechanical Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China

    Zhaolong Li, Wenming Zhu, Bo Zhu, Baodong Wang & Qinghai Wang

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

    Zhaolong Li

Authors
  1. Zhaolong Li
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  2. Wenming Zhu
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  3. Bo Zhu
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  4. Baodong Wang
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  5. Qinghai Wang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Zhaolong Li, Wenming Zhu, Bo Zhu, Qinghai Wang, and Baodong Wang. The first draft of the manuscript was written by Wenming Zhu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Zhaolong Li.

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Li, Z., Zhu, W., Zhu, B. et al. Thermal error modeling of electric spindle based on particle swarm optimization-SVM neural network. Int J Adv Manuf Technol 121, 7215–7227 (2022). https://doi.org/10.1007/s00170-022-09827-4

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  • DOI: https://doi.org/10.1007/s00170-022-09827-4

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