基于改进松鼠搜索算法优化神经网络的数控机床进给系统热误差预测  

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

作　　者：杨赫然[1,2] 李帅 孙兴伟 董祉序[1,2] 刘寅 Yang Heran;Li Shuai;Sun Xingwei;Dong Zhixu;Liu Yin(School of Mechanical Engineering,Shenyang University of technology,Shenyang 110870,China;Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province,Shenyang 110870,China)

机构地区：[1]沈阳工业大学机械工程学院,沈阳110870 [2]辽宁省复杂曲面数控制造技术重点实验室,沈阳110870

出　　处：《仪器仪表学报》2024年第1期60-69,共10页Chinese Journal of Scientific Instrument

基　　金：辽宁省应用基础研究计划项目(2022JH2/101300214);2022年度辽宁省教育厅高等学校基本科研项目面上项目资助。

摘　　要：为探究数控机床进给系统中各因素对热误差的影响规律,建立精准的热误差预测模型。在进给速度为10 m/min、环境温度20℃的条件下进行进给系统热误差测量实验,获得进给系统关键点的温升及热误差。为提高预测精度,采用Tent混沌改进松鼠搜索算法,并利用改进的算法对神经网络进行优化,建立热误差预测模型。利用热误差测量实验获得的数据进行验证,结果表明改进前的神经网络预测误差为12.23%,改进后的模型预测误差为8.92%,精度有较大提升。利用预测模型针对不同进给速度下相同位置处热误差进行分析,结果表明,进给系统中关键测温点的温度和丝杠各点的热误差随着进给速度的增加而增加。因此提出的预测模型可实现进给系统热误差的准确预测,为误差补偿提供理论依据。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.In order 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 established.Conduct thermal error measurement experiments on the feed system at a feed speed of 10m/min and an ambient temperature of 20°C 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,and the improved algorithm is used 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 showed that the prediction error of t

关 键 词：进给系统 热误差 松鼠搜索算法 神经网络 

分 类 号：TH161.1[机械工程—机械制造及自动化]