主成分算法在数控机床主轴热误差补偿中的应用  

Application of principal component algorithm in spindle thermal error modeling of CNC machine tools

作　　者：魏新园 陈雨尘 苗恩铭 冯旭刚 潘巧生 WEI Xin-yuan;CHEN Yu-chen;MIAO En-ming;FENG Xu-gang;PAN Qiao-sheng(School of Electrical and Information Engineering,Anhui University of Technology,Ma’anshan 243032,China;School of Mechanical Engineering,Chongqing University of Technology,Chongqing 400054,China;School of Instrument Science and Opto-electronics Engineering,Hefei University of Technology,Hefei 230009,China)

机构地区：[1]安徽工业大学电气与信息工程学院,安徽马鞍山243032 [2]重庆理工大学机械工程学院,重庆400054 [3]合肥工业大学仪器科学与光电工程学院,安徽合肥230009

出　　处：《光学精密工程》2021年第11期2649-2660,共12页Optics and Precision Engineering

基　　金：国家重点研发计划资助项目(No.2019YFB1703700);安徽省自然科学基金青年项目(No.1908085QF294);重庆市技术创新与应用发展专项项目(No.cstc2019jscx-mbdxX0045,No.cstc2019jscx-mbdxX0016)。

摘　　要：为了提高数控机床热误差补偿模型的预测精度与稳健性,对主成分算法在数控机床主轴热误差建模中的应用进行了研究。首先,根据主成分算法原理,提出基于主成分分析的温度敏感点选择算法和热误差建模算法。然后,以一台三轴立式加工中心为对象进行全年温度范围内的主轴热误差测量实验,并基于实验数据建立主轴热误差主成分回归(Principal Component Regression,PCR)模型。进而,将所建立的PCR模型与多元线性回归模型、BP神经网络模型和岭回归模型的预测精度与稳健性进行比对分析,实验结果表明PCR模型在该四种模型中具有最高的预测精度和稳健性,分别达到6.8μm和2.4μm。最后,使用所建立的PCR模型对按照转速图谱运行的机床主轴热误差进行预测,预测精度和稳健性分别为6.12μm和3.43μm。并将PCR模型嵌入到热误差补偿控制器中进行热误差补偿实验,以验证本文建模算法的有效性。To improve the prediction accuracy and robustness of the spindle thermal error compensation model of computer numerical control(CNC)machine tools,this study investigates the application of the principal component algorithm to the thermal error modeling of CNC machine tools.First,a selection algorithm of the temperature sensitive point and thermal error modeling algorithm based on principal component algorithm are proposed.Second,a three-axis vertical machining center is used to measure the spindle thermal error over an entire year.Thereafter,the principal component regression(PCR)model of the spindle thermal error is established based on the experimental data obtained.Then,the prediction accuracy and robustness of the PCR model are compared with those of the multivariate linear regression,back propagation(BP)neural network,and ridge regression models.The experimental results show that the PCR model has the highest prediction accuracy(6.8μm)and robustness(2.4μm).Finally,the developed PCR model is used to predict the thermal errors of machine spindles that operate according to the speed spectrum.In this case,the model exhibits a prediction accuracy and robustness of 6.12μm and 3.43μm,respectively.Finally,the PCR model is embedded into the thermal error compensation controller for performing thermal error compensation experiments to verify the effectiveness of the proposed modeling algorithm.

关 键 词：机床主轴热误差 主成分 建模算法 模型稳健性 

分 类 号：TH161[机械工程—机械制造及自动化] TG659[金属学及工艺—金属切削加工及机床]