基于CNN-GRU组合神经网络的数控机床进给系统热误差研究  

Research on thermal error of CNC machine tool feed system based on CNN-GRU combined neural network

作　　者：孙兴伟[1,2] 杨铜铜 杨赫然 董祉序[1,2] 刘寅 Sun Xingwei;Yang Tongtong;Yang Heran;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》2023年第10期219-226,共8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(LJKMZ20220459)资助。

摘　　要：热变形引起的误差是影响数控机床精度的主要因素之一。为了减小热误差对数控机床精度的影响,提出一种基于CNN-GRU组合神经网络的热误差预测方法。通过热误差实验,采集螺旋曲面专用数控机床直线进给系统的温升数据和热误差数据;利用模糊C均值聚类和灰色关联度分析筛选进给系统温度敏感点;以温度敏感点的温升数据和进给系统热误差为数据样本,建立CNN-GRU热误差预测模型。为验证模型的准确性和实用性,与基于CNN-LSTM和基于LSTM的传统热误差预测模型进行预测对比分析,结果表明CNN-GRU模型预测结果的平均绝对误差、均方根误差和决定系数均优于CNN-LSTM模型和LSTM模型,具有较高的预测精度和鲁棒性。提供的热误差模型可为后续误差补偿奠定基础,为数控机床的热误差预测提供思路。The error caused by thermal deformation is one of the main factors affecting the accuracy of CNC machine tools.Correspondingly a thermal error prediction method based on CNN-GRU combined neural network is proposed to reduce the impact of thermal error on the accuracy of CNC machine tools.By conducting thermal error experiments,the temperature rise data and thermal error data of the linear feed system of a specialized CNC machine tool are collected for spiral surfaces.Then the fuzzy c-means clustering and grey relation analysis are carried out to screen temperature sensitive points in the feed system,and a CNN-GRU thermal error prediction model is established using temperature rise data of temperature sensitive points and thermal error of feed system as data samples.To verify the accuracy and practicality of model,a comparative analysis is conducted with traditional thermal error prediction models based on CNN-LSTM and LSTM.The results showed that the CNN-GRU model possesses the high prediction accuracy and robustness,whose average absolute error,root mean square error,and determination coefficient of the prediction results are better than those of the CNN-LSTM model and LSTM model.The proposed thermal error model can lay the foundation for subsequent error compensation and provide ideas for predicting thermal errors in CNC machine tools.

关 键 词：模糊C均值聚类 灰色关联度分析 进给系统 温度敏感点 误差预测 

分 类 号：TH161.1[机械工程—机械制造及自动化] TN05[电子电信—物理电子学]