基于差分融合长短期记忆神经网络数控机床热误差建模  

Modeling for CNC Machine Tool Thermal Error Based on DF-LSTM

作　　者：刘占广 张云[1] 刘晴雨 LIU Zhanguang;ZHANG Yun;LIU Qingyu(Department of Mechanical Engineering,Tsinghua University,Beijing 100084)

机构地区：[1]清华大学机械工程系,北京100084

出　　处：《机械工程学报》2024年第7期249-257,共9页Journal of Mechanical Engineering

基　　金：工信部2021年面向产业集群中小型数控机床关键加工装备资助项目。

摘　　要：数控机床的热效应是制约机床加工精度的主要因素,为了进一步提高机床热误差预测的精度,针对热误差表征参数温升和位移的相对量特性,引入差分预测,综合直接预测和差分预测的优点,提出一种基于差分融合长短期记忆神经网络的数控机床热误差预测模型。以某型号卧式外圆磨床为实验对象,按照不同电机频率设计四组实验,连续采集温度和刀具(砂轮)轴向伸长量数据,基于随机森林和特征交叉递归移除方法确定四个温度敏感点,随机选定训练集与测试集进行训练和预测,通过与现有普遍使用的人工神经网络方法对比验证,提出的热误差模型能够在数据趋势性和波动性间取得较好平衡,且预测结果有更高的精度和更好的鲁棒性,印证了引入差分预测的合理性,对于实际数控机床热误差预测补偿具有一定的参考意义。Thermal effect is the key factor restricting the machining accuracy of machine tools,in order to further improve the accuracy of thermal error prediction of machine tools,considering the relative characteristics of thermal error parameters,temperature and deformation,a thermal error prediction model for machine tools based on differential fusion long short-term memory neural network,which combines the advantages of direct prediction and differential prediction,is proposed.Taking a certain model of cylindrical grinding machine as the experimental object,four sets of experiments are designed according to different motor frequencies,the data including temperature and axial deformation of the tool(grinding wheel)are continuously collected.Then 4 temperature sensitive points are selected using random forest and recursive feature elimination with cross-validation.Training datasets and testing datasets are randomly selected,compared to conventional widely used artificial neural networks.The proposed model gets a better balance between trendiness and volatility and achieves better prediction accuracy and has stronger robustness,which demonstrates the rationality of introducing differential prediction.Thus,it has certain reference significance to the actual thermal error prediction and compensation of CNC machine tools.

关 键 词：热误差预测 相对量 差分融合 长短期记忆神经网络 

分 类 号：TG502[金属学及工艺—金属切削加工及机床]