Thermal error prediction of machine tool spindle based on WOA-Elman network

Chunmiao Tian; Zeping Ji; Guan Qiao; Biao Liu; Shijie Guo

doi:10.1117/12.2655663

7 October 2022 Thermal error prediction of machine tool spindle based on WOA-Elman network

Chunmiao Tian, Zeping Ji, Guan Qiao, Biao Liu, Shijie Guo

Proceedings Volume 12344, International Conference on Intelligent and Human-Computer Interaction Technology (IHCIT 2022); 123441J (2022) https://doi.org/10.1117/12.2655663
Event: International Conference on Intelligent and Human-Computer Interaction Technology (IHCIT 2022), 2022, Zhuhai, China

Abstract

In order to solve the shortcomings of the spindle thermal error prediction model established by ELMAN neural network, such as low precision, slow convergence speed and easy to fall into the local optimal solution, k-means ++ algorithm and correlation analysis were used to optimize temperature measurement points and extract thermal sensitive points. Whale Optimization Algorithm (WOA) was used to determine the optimal node number, weight and threshold of hidden layer. The thermal error prediction models of spindle based on ELMAN and WOA-ELMAN networks were established respectively. The thermal error of spindle was measured by five-point method with a five-axis machining center of double turntable as the research object. The thermal error experiment results show that the k-means++ algorithm combined with Person, Sperman and Kendall correlation analysis can effectively reduce the multicollinearity between temperature variables. WOA-ELMAN model can predict spindle thermal error with higher prediction accuracy.

Citation Download Citation

Chunmiao Tian, Zeping Ji, Guan Qiao, Biao Liu, and Shijie Guo "Thermal error prediction of machine tool spindle based on WOA-Elman network", Proc. SPIE 12344, International Conference on Intelligent and Human-Computer Interaction Technology (IHCIT 2022), 123441J (7 October 2022); https://doi.org/10.1117/12.2655663

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