基于模糊聚类测点优化与向量机的坐标镗床热误差建模

Thermal Error Modeling of a Coordinate Boring Machine Based on Fuzzy Clustering and SVM

为了研究电主轴系统热特性对机床精度的影响,建立了主轴轴向及径向热误差模型.以精密坐标镗床为对象,采用五点法对主轴热误差进行测量,并分析了转速对主轴热误差及温度场的影响规律.利用模糊聚类分析法对温度变量进行分组优化,选出对热误差敏感的温度变量,建立主轴轴向热伸长及径向热倾角的最小二乘支持向量机(LS-SVM)以及多元线性回归(MLRA)的综合热误差模型,并设定了预测优度评价标准.结果表明:模糊聚类分组法能有效降低温度变量间的多重共线性,并提高模型的稳定性;LS-SVM模型具备全局寻优的特点,可实现不同工况的高精度预测,预测精度可达90%,且比传统的MLRA模型有更好的通用性以及更强的泛化能力,可作为后期热误差的补偿模型.

To investigate the effect of the thermal characteristics of a motorized spindle system on the precision of a machine tool,two thermal error modeling of a CNC boring machine spindle were proposed,and a five-point method was used to measure the thermal errors of the spindle.The relationships between the spindle speed and temperature field,and thermal errors were analyzed.Then the method combining fuzzy clustering and correlation analysis was presented to optimize temperature variables and select the variables sensitive to thermal error.Subsequently,the least square support vector machine（LS-SVM）and multivariable linear regression analysis（MLRA）models were established for axial elongation and radial declinations.The results indicate that the fuzzy cluster can reduce the multicollinearity among temperature variables and improve the stability of the model.Moreover,the LS-SVM has better generalization than MLRA under different cutting conditions,and the prediction accuracy could reach up to 90%,which could be used to compensate thermal errors of the machine.