摘要
为了提高数控机床主轴系统热误差建模的精度和鲁棒性,提出一种基于时间特性的热关键点选取方法。该方法结合模糊C均值聚类和基于时间特性的排序标准,完全依赖于热误差实验获得的温度测点的温度,避免了基于相关性的热关键点选取方法在不同热误差实验下的不稳定性。通过在周边磨床主轴系统上进行热误差实验,将该方法应用于主轴系统热误差建模。研究结果表明:基于时间特性的热关键点选取方法对多元线性回归、BP神经网络、支持向量机等回归模型的建模精度都有不同程度提升;在3种模型的9组预测中,均方根误差降幅最低为6%,最高为40%,证明了基于时间特性的热关键点选取方法能有效提高热误差建模的精度和鲁棒性。
In order to improve the accuracy and robustness of thermal error modeling of computer numerical control(CNC)machine tool spindle system,a thermal key points selection method based on time characteristic was proposed.This method combines the fuzzy C-means clustering and the ranking criterion based on time characteristic.It relies entirely on the temperature of temperature measurement points obtained from thermal error experiments,and avoids the instability of the thermal key points selection method based on correlation in different thermal error experiments.Through the thermal error experiments on the spindle system of the peripheral grinding machine,the method was applied to the thermal error modeling of the spindle system.The results show that the thermal key point selection method based on time characteristic can improve the modeling accuracy of multiple linear regression,BP neural network,and support vector machine to varying degrees.Among the 9 predictions of the three models,the root mean square error(RMSE)is reduced by 6%to 40%,which proves the effectiveness of the thermal key points selection method based on time characteristic in improving the accuracy and robustness of thermal error modeling.
作者
廖启豪
王玲
殷国富
谢政峰
LIAO Qihao;WANG Ling;YIN Guofu;XIE Zhengfeng(School of Mechanical Engineering,Sichuan University,Chengdu 610065,China)
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2020年第6期1501-1508,共8页
Journal of Central South University:Science and Technology
基金
国家科技重大专项(2017ZX04020001-005)。
关键词
主轴系统
热关键点
热误差建模
spindle system
thermal key points
thermal error modeling
