基于PSO-GPR的数控机床热误差建模及泛化性研究  

Research on thermal error modeling and generalization of CNC machine tools based on PSO-GPR

作　　者：张蕾 黄美发[2] 陈琳[3] 杨瑞兆 ZHANG Lei;HUANG Meifa;CHEN Lin;YANG Ruizhao(School of Physics and Electronic Information,Yulin Normal University,Yulin 537000,CHN;School of Mechanical and Electrical Engineering,Guilin University of Electronic Technology,Guilin 541004,CHN;School of Mechanical Engineering,Guangxi University,Nanning 530004,CHN)

机构地区：[1]玉林师范学院物理与电子信息学院,广西玉林537000 [2]桂林电子科技大学机电工程学院,广西桂林541004 [3]广西大学机械工程学院,广西南宁530004

出　　处：《制造技术与机床》2022年第2期135-139,共5页Manufacturing Technology & Machine Tool

基　　金：国家自然科学基金资助项目(51365009);2021年广西高校中青年教师基础能力提升项目资助(2021KY0585)。

摘　　要：数控机床在加工过程中会因发热而造成主轴热变形,为减小热变形对加工零件精度的影响,以1台数控车床为研究对象,提出一种基于粒子群算法优化的高斯过程回归(PSO-GPR)热误差建模与预测方法。通过采集测量5种不同工况下的实验数据,进行主轴轴向的热误差建模,同时与未优化的GPR建模方法进行比较,并在此基础上研究了训练数据增强对热误差模型泛化性的影响。实验结果表明:PSO-GPR模型预测热变形量的最大残差为0.49μm,均方根误差RMSE为0.11μm,优于未优化的GPR模型。经过数据增强的PSO-GPR模型在工况四、工况五为验证数据的热误差预测中最大残差分别下降35%和33.7%,表明训练数据增强可提高热误差模型的泛化能力。Numerical control machine tool in the process of processing will cause thermal deformation of spindle due to heat.In order to reduce the influence of thermal deformation on the precision of machining parts,a numerical control lathe was taken as the research object.A Gaussian process regression(PSO-GPR)thermal error modeling and prediction method based on particle swarm optimization was proposed.By collecting and measuring the experimental data under five different working conditions,the thermal error model of the axial axis of the main shaft was built,and compared with the unoptimized GPR modeling method.On this basis,the influence of training data enhancement on the generalization of the thermal error model was studied.The experimental results show that the maximum residual error of PSO-GPR model for predicting thermal deformation is 0.49μm,and the root mean square error(RMSE)is 0.11μm,which is better than the unoptimized GPR model.The PSO-GPR model with data enhancement reduces the maximum residual in thermal error prediction of verified data by 35%and 33.7%in operating conditions 4 and 5 respectively,indicating that the enhancement of training data can improve the generalization ability of thermal error model.

关 键 词：数控机床 热误差 高斯过程回归 粒子群优化算法 

分 类 号：TH161[机械工程—机械制造及自动化]