机床主轴热关键点选择与典型转速热误差预测  

Thermal key point select and error prediction under typical speed of machine tool spindle

作　　者：郭世杰[1,2] 张学炜 张楠[1,2] 乔冠 唐术锋[1,2] GUO Shi-jie;ZHANG Xue-wei;ZHANG Nan;QIAO Guan;TANG Shu-feng(College of Mechanical Engineering,Inner Mongolia University of Technology,Hohhot 010051,China;Inner Mongolia Key Laboratory of Special Service Intelligent Robotics,Inner Mongolia Autonomous Region,Hohhot 010051,China;School of Mechanical Engineering,Zhejiang University,Hangzhou 310063,China)

机构地区：[1]内蒙古工业大学机械工程学院,呼和浩特010051 [2]内蒙古自治区特殊服役智能机器人重点实验室,呼和浩特010051 [3]浙江大学机械工程学院,杭州310063

出　　处：《吉林大学学报（工学版）》2023年第1期72-81,共10页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(52065053);中央引导地方科技发展专项项目(2020ZY0002);内蒙古关键技术攻关项目(2020GG0255);内蒙古自然科学基金项目(2019BS05008;2020030058);内蒙古自治区高等学校科学研究项目(NJZY21308);内蒙古自治区直属高校基本科研业务费项目(JY20220046);内蒙古自治区高等学校青年科技英才支持计划资助项目(NJYT23043)。

摘　　要：针对机床主轴热误差对准静态精度影响的关键问题,提出了一种基于改进鸡群优化(MCSO)算法及支持向量(SVM)的热误差预测模型。利用基于非监督学习的谱聚类与Spearman关联分析辨识主轴关键敏感温度测点,降低温度数据分布于数量的依赖,削弱温度变量间的多重共线性。引入Levy飞行策略至母鸡个体局部搜索过程,构建了非线性动态自适应惯性权重更新雏鸡策略,基于MCSO-SVM进行核函数、罚因子以及偏差量的全局优化,分别采用MCSO-SVM、BP-GA、GA-SVM和CSO-SVM热误差建模,同时对不同转速下的模型预测能力进行对比分析。热误差实验测量与预测结果表明:谱聚类与Spearman关联分析可有效降低温度变量共线性导致的耦合作用;MCSO-SVM可实现典型转速下主轴五项热误差的高精度预测,模型具备较好的泛化能力和鲁棒性。Aiming at the key problem of the influence of thermal error on the static accuracy of machine tool spindle,a thermal error prediction model based on modified chicken swarm optimization algorithm(MCSO)and support vector machine(SVM)is proposed.Spectral clustering based on unsupervised learning and Spearman correlation analysis are used to identify the key sensitive temperature measuring points of the spindle,so as to reduce the number dependence of temperature data distribution and weaken the multicollinearity among temperature variables.Levy flight strategy is adopted in the local search of hens,and a nonlinear dynamic adaptive inertia weight for updating chick strategy is constructed to realize the global optimization of kernel function,and then MCSO is used to optimize the kernel function,penalty factor and deviation of SVM.MCSO-SVM,BP-GA,GA-SVM and CSO-SVM are used to establish thermal error models respectively.Meanwhile,the prediction ability of models under different working conditions is compared and analyzed.The experimental results of thermal error show that spectral clustering and Spearman correlation analysis can effectively reduce the coupling effect caused by collinearity of temperature variables;MCSO-SVM can achieve high-precision prediction of spindle thermal errors under typical speed,and the model has good generalization ability and robustness.

关 键 词：机床 热误差 谱聚类 改进鸡群算法 支持向量机 

分 类 号：TH161[机械工程—机械制造及自动化]