Thermal error prediction of CNC machine tool feed system based on neural network optimized by improved squirrel search algorithm

被引：0
|
作者：
Yang H. [1 ,2 ]
Li S. [1 ,2 ]
Sun X. [1 ,2 ]
Dong Z. [1 ,2 ]
Liu Y. [1 ,2 ]
机构：
[1] School of Mechanical Engineering, Shenyang University of technology, Shenyang
[2] Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2024年 / 45卷 / 01期
关键词：
feed system; neural network; squirrel search algorithm; thermal error;
D O I：
10.19650/j.cnki.cjsi.J2210705
中图分类号：
学科分类号：
摘要：
To explore the influence of various factors on thermal error in the feed system of CNC machine tools, an accurate thermal error prediction model is formulated. Thermal error measurement experiments are implemented on the feed system at a feed speed of 10 m/min and ambient temperature of 20℃ to obtain the temperature rise and thermal error of the key points of the feed system. To improve prediction accuracy, Tent chaos is used to improve the squirrel search algorithm. The improved algorithm is utilized to optimize the neural network and establish a thermal error prediction model. The data obtained from thermal error measurement experiments are used for validation, and the results show that the prediction error of the neural network before improvement is 12.23%, while the prediction error of the improved model is 8.92%, indicating a significant improvement in accuracy. The prediction model is used to analyze the thermal error at the same position under different feed speeds. The results show that the temperature of key temperature measurement points in the feed system and the thermal error at each point of the lead screw increased with the increase in feed speed. Therefore, the proposed prediction model can accurately predict the thermal error of the feed system and provide a theoretical basis for error compensation. © 2024 Science Press. All rights reserved.
引用
收藏
页码：60 / 69
页数：9
相关论文
共 21 条
  • [1] LYU X H, GUO Q J, WANG H T, Et al., Summary of key technologies for error compensation of CNC machine tools, Aeronautical Manufacturing Technology, 65, 11, pp. 104-111, (2022)
  • [2] MAN B, GUO Y H, FAN X Y, Et al., Research status and development trend of thermal error of ball screw feed system for machine tool, Machine Tool & Hydraulics, 49, 15, pp. 174-179, (2021)
  • [3] BRYAN J B., International status of thermal error research, CIRP Annals Manufacturing Technology, 39, 2, pp. 645-656, (1990)
  • [4] JIN C, WU B, HU Y M, Et al., Thermal characteristics of a CNC feed system under varying operating conditions, Precision Engineering, 42, pp. 151-164, (2015)
  • [5] ZHU X X, ZHAO L, LEI M H, Et al., Co-training support vector machine regression modeling and compensation for thermal error of precision feed system, Journal of Xi′ an Jiaotong University, 53, 10, pp. 40-47, (2019)
  • [6] TIAN C M, JI Z P, Et al., Thermal error prediction of machine tool spindle base-d on GA-ACO-BP network, Manufacturing Technology & Machine Tool, 2022, 9, pp. 153-161
  • [7] DU L Q, HU J, YU Y W., Modeling for machine tools thermal error based on chaotic evolution and CNN-GRU, Modular Machine Tool & Automatic, 8, pp. 18-20, (2022)
  • [8] GUO SH J, ZHANG X W, ZHANG N, Et al., Thermal key point select and error prediction under typical speed of machine tool spindle, Journal of Jilin University (Engineering and Technology Edition), 53, 1, pp. 72-81, (2023)
  • [9] SHI Y L, TIAN CH M, A Y G, Et al., Research on SFLA-SVM prediction method for feed axis thermal error of CNC machine Tool, Aeronautical Manu-facturing Technology, 64, 22, pp. 48-55, (2021)
  • [10] TAN F, LI CH N, XIAO H, Et al., A thermal error prediction method for CNC machine tool based on LSTM recurrent neural network, Chinese Journal of Scientific Instrument, 41, 9, pp. 79-87, (2020)
123