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WANG Zihan, YANG Xiuzhi, DUAN Xianyin, JIANG Yuhui, WANG Xingdong. Research on thermal error modeling of machine tool based on bayesian neural network[J]. Manufacturing Technology & Machine Tool, 2022, (1): 141-145. doi: 10.19287/j.cnki.1005-2402.2022.01.026
Citation: WANG Zihan, YANG Xiuzhi, DUAN Xianyin, JIANG Yuhui, WANG Xingdong. Research on thermal error modeling of machine tool based on bayesian neural network[J]. Manufacturing Technology & Machine Tool, 2022, (1): 141-145. doi: 10.19287/j.cnki.1005-2402.2022.01.026
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Research on thermal error modeling of machine tool based on bayesian neural network

doi: 10.19287/j.cnki.1005-2402.2022.01.026
  • ①.

    School of Mechanics, Wuhan University of Science and Technology, Wuhan 430070, CHN

  • ②.

    Key Laboratory of Intelligent Transportation Technology and Equipment of Hubei, Hubei Polytechnic University, Huangshi 435003, CHN

Funds:

 51875418

 GF201906

 2017YFB1300502

  • Received Date: 2021-05-17
    Available Online: 2022-03-07
    Abstract
  • Thermal error seriously affects the machining accuracy of the machine tool. The thermal characteristic analysis of the key parts of the machine tool is an important link in the development of precision machine tool. Therefore, this paper studies the relationship between temperature rise and positioning error by measuring the thermal characteristics including the temperature and positioning error of special position of CNC machine tool, and proposes a thermal error modeling method based on Bayesian neural network. Using K-means clustering and correlation coefficient method to select temperature sensitive points can effectively suppress multicollinearity between temperature measurement points. The results show that the accuracy of machine tool can be improved by 88.015 9% by using bayesian neural network, which is 15.763 8% higher than BP neural network. Compared with BP neural network model, bayesian neural network has better prediction performance. Bayesian neural network model provides a new idea to reduce the influence of thermal error of machine tool.

     

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