机床主轴热误差通用型温度敏感点组合选取  

General-purpose temperature sensitive point combination selection for thermal error of machine tool spindle

作　　者：周琳丰 付国强 李正堂[4] 雷国强 邓小雷 ZHOU Linfeng;FU Guoqiang;LI Zhengtang;LEI Guoqiang;DENG Xiaolei(School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China;State Key Laboratory of Fluid Power and Mechatronics,Zhejiang University,Hangzhou 310027,China;School of Mechanical Engineering,Sichuan University,Chengdu 610065,China;Chongqing Well Primus Technology Research Institute Co.Ltd.,Chongqing 400050,China;Key Laboratory of Air-driven Equipment Technology of Zhejiang Province,Quzhou University,Quzhou 324000,China)

机构地区：[1]西南交通大学机械工程学院,四川成都610031 [2]浙江大学流体动力与机电系统国家重点实验室,浙江杭州310027 [3]四川大学机械工程学院,四川成都610065 [4]重庆遨博智能科技研究院有限公司,重庆400050 [5]衢州学院浙江省空气动力装备技术重点实验室,浙江衢州324000

出　　处：《光学精密工程》2022年第12期1462-1477,共16页Optics and Precision Engineering

基　　金：国家自然科学基金项目(No.52175486,No.51805457);四川省科技计划项目(No.2022YFG0218);中国博士后科学基金面上资助项目(No.2020M673211);流体动力与机电系统国家重点实验室开放基金课题资助项目(No.GZKF-202104);四川省重大科技专项项目(No.2020ZDZX0003)。

摘　　要：本文提出一种基于数量自动确定的机床主轴热误差通用型温度敏感点组合选取方法以解决敏感点数量的选取依赖人工经验的问题。首先,计算各温度变量与热误差之间的绝对均相关系数以评估各温度点对主轴热误差的相关程度。其次,将绝对均相关系数最大的温度点作为K-Means++聚类算法的首个初始聚类中心,进一步选取一系列数量不同的温度敏感点组合。然后,将所得的一系列敏感点组合和热误差作为输入,建立反向传播(Back Propagation,BP)神经网络热误差模型,并通过评价指标选取预测性能最优的温度敏感点组合。最后,在VMC850数控机床上进行了最优温度敏感点组合在不同工况相同误差项、相同工况不同误差项中的有效性以及在不同热误差模型中的通用性验证。结果表明,本文提出的温度敏感点组合选取法适用于不同工况下的误差预测,且在不同的热误差模型中具有良好的通用性。A general temperature-sensitive point combination selection method based on the automatic determination of the points is proposed in this paper to solve the problem of selecting sensitive points depending on manual experience. First,the absolute mean correlation coefficients between temperature variables and thermal errors are calculated for selecting the temperature points most related to the thermal errors as sensitive points. Second,the temperature point with the largest absolute mean correlation coefficient is considered as the initial clustering center of the K-Means++ clustering algorithm,and a series of temperature-sensitive points with different numbers is selected. Subsequently,a backpropagation neural network thermal error model is established by using a series of sensitive point combinations and thermal errors as input,and the temperature-sensitive point combinations with the best prediction performance are selected based on evaluation indexes. Finally,the validity of the optimal temperature-sensitive point combination for the same error terms under different working conditions and different error terms under the same working conditions and the universality of different thermal error models are verified by employing the VMC850CNC machine tool. The results show that the combined selection method of temperature-sensitive points proposed in this paper is suitable for experimental data under different working conditions and exhibits good versatility in different thermal error models.

关 键 词：温度敏感点组合选取 主轴热误差建模 数量自动确定 有效性与通用性 温度敏感点评价指标 

分 类 号：TH161[机械工程—机械制造及自动化]