基于加权融合矩阵系统聚类的多机床温度测点选择方法  

Temperature measurement point selection method of multi-machine tool based on weighted fusion matrix system clustering

作　　者：邓小雷[1] 陈昱珅 方诚至 门大厦 林晓亮 姜少飞[2] DENG Xiao-lei;CHEN Yu-shen;FANG Cheng-zhi;MEN Da-sha;LIN Xiao-liang;JIANG Shao-fei(Key Laboratory of Air-driven Equipment Technology of Zhejiang Province,Quzhou University,Quzhou 324000,China;College of Mechanical Engineering,Zhejiang University of Technology,Hangzhou 310023,China)

机构地区：[1]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 [2]浙江工业大学机械工程学院,浙江杭州310023

出　　处：《浙江大学学报（工学版）》2023年第6期1147-1156,共10页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(52175472);浙江省自然科学基金资助项目(LZY21E050002);浙江省公益技术应用研究资助项目(LGG22E050031);浙江省教育厅科研资助项目(Y201942770)。

摘　　要：针对不同数控机床热误差建模中温度关键点的选择和优化问题,提出有效减少温度测点并提高热误差预测精度的新方法.所提方法通过建立各个温度信号序列之间的相关性和差异性,对温度测点进行有效分类和选择.在系统聚类的基础上,将变量间的相关系数与欧氏距离矩阵进行权重赋值后加以融合,以优化各测点之间的共线性.利用灰色关联分析判断测点与热误差间的紧密程度并提取各类子集中的最优测点,以优化后的测点为输入构建基于支持向量回归的热误差预测模型.为了确定权重系数、聚类数、支持向量回归的参数,采用遗传算法进行参数寻优得到最优的预测模型.在多台机床上进行实验,列举出3台不同型号机床的验证分析结果,使用所提方法的预测均方根误差与原始测点的相比分别下降了24%、71%和62%.结果表明,所提方法在不同机床上均能实现在大幅减少传感器数量的前提下有效提高热误差模型预测精度;所提方法具有良好的通用性,与其他方法相比,所提方法的预测性能更强.Aiming at the selection and optimization of temperature key points in thermal error modeling of different CNC machine tools,a new method was proposed to effectively reduce the temperature measurement points and improve the prediction accuracy of thermal error.The method could effectively classify and select temperature measuring points by establishing the correlation and difference between various temperature signal sequences.On the basis of system clustering,the correlation coefficients between variables and Euclidean distance matrix were weighted and fused to optimize the collinearity between measurement points.Then,grey correlation analysis was used to determine the degree of closeness between measuring points and thermal errors,and the optimal measuring points in various subsets were extracted.A thermal error prediction model based on support vector regression was constructed with the optimized measuring points as input.In order to determine the weight coefficient,the number of clusters and the parameters of support vector regression,the optimal prediction model was obtained by using genetic algorithm for parameter optimization.Finally,the proposed method was validated on several machine tools,and the verification and analysis results of three different machine tools were given.The root mean square error of predictions using the proposed method decreased by 24%,71%and 62%compared with those using the original measurement points,respectively.The results showed that the proposed method can effectively improve the prediction accuracy of the thermal error model on different machine tools under the premise of greatly reducing the number of sensors.The proposed method has good versatility and the predictive performance of the method is stronger compared to other methods.

关 键 词：系统聚类 测点关联性 多机床通用性 热误差建模 灰色关联度法 

分 类 号：TH161[机械工程—机械制造及自动化]