摘要
针对环境温度变化较大时常用的热误差模型预测精度低的问题,提出了一种基于环境温度的模型库分段式加权的热误差建模方法,以UPM120型数控铣床为实验对象,通过跨季度的7批次数据,完成了环境温度15~35℃的分段式加权模型建模和预测精度分析。结果表明,环境温度变化在5℃以内时,多元线性回归模型的预测精度优于BP神经网络模型、分布滞后模型、灰色理论模型和支持向量机模型,可以将其作为分段式加权模型库中的基础模型。当环境温度变化较小时,基于多元线性回归的分段式加权模型预测精度为1.39μm;当环境温度变化较大时,其预测精度为1.51μm,均远高于单一环境温度样本的回归模型、多环境温度样本的回归模型和泛化能力强的支持向量机模型的预测精度。
Aiming at the problem that the common thermal error models have low prediction accuracy when the ambient temperature changes greatly,a piecewise weighted modeling method of thermal error based on model library of ambient temperature was proposed.The UPM120 CNC milling machine was used as the experimental object.Modeling and prediction accuracy analysis of the piecewise weighted model with ambient temperature between 15℃and 35℃were accomplished by using seven batches of data in different quarters.The experimental results showed that when the ambient temperature was varied within 5℃,the prediction accuracy of multiple linear regression model was better than that of BP neural network model,distributed lag model,grey theory model and support vector machine model,so multiple linear regression model was used as the basic model of the piecewise weighted model library.When the ambient temperature had small change,the piecewise weighted model based on multiple linear regression had a prediction accuracy of 1.39μm,which was slightly lower than that of the multiple linear regression model,but higher than that of the other four common thermal error models.When the ambient temperature had great change,the prediction accuracy was 1.51μm,which was much higher than the accuracy of multiple linear regression model of single ambient temperature sample,multiple linear regression model of multi-environment temperature samples and support vector machine model with strong generalization ability.The piecewise weighted model had high prediction accuracy under both large or small changes in ambient temperature.
作者
李兵
苏文超
魏翔
白金峰
蒋庄德
LI Bing;SU Wenchao;WEI Xiang;BAI Jinfeng;JIANG Zhuangde(State Key Laboratory of Mechanism Manufacture System Engineering,Xi’an Jiaotong University,Xi’an 710049,China;International Joint Laboratory for Micro/Nano Manufacturing and Measurement Technology,Xi’an Jiaotong University,Xi’an 710049,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2020年第7期413-419,共7页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家科技重大专项(2017ZX04021001-005-001)
中国博士后科学基金项目(2018M643626)。
关键词
数控机床
热误差
环境温度
分段式加权模型
预测精度
CNC machine tool
thermal error
ambient temperature
piecewise weighted model
prediction accuracy
