基于LS-SVM与遗传算法的数控机床热误差辨识温度传感器优化策略  

Optimal sensor placement for thermal error identification of NC machine tool based on LS-SVM and genetic algorithm

作　　者：林伟青[1] 傅建中[1] 许亚洲[1] 陈子辰[1] 

机构地区：[1]浙江大学机械工程学系

出　　处：《光学精密工程》2008年第9期1682-1687,共6页Optics and Precision Engineering

基　　金：国家自然基金资助项目(No.50675199);浙江省科技计划资助项目(No.2005E10049)

摘　　要：提出了一种在数控机床热误差辨识建模过程中利用最小二乘支持向量机结合遗传算法对温度传感器进行筛选与优化的新方法,对布置在一台数控车床上的温度传感器进行了优化。根据热模态理论,对传感器进行分组,利用最小二乘支持向量机方法构建数控机床热误差辨识模型,再根据遗传算法对其进行传感器优化布置。结果表明,遗传算法与最小二乘支持向量机方法的结合,很好地避免了温度测点的相互影响,保证了模型精度。该台数控车床的轴向建模平均绝对百分比误差为1.89%,径向建模平均绝对百分比误差为2.04%。传感器使用数量减少,节约了硬件成本,提高了辨识建模速度。A novel method based on Least Square Support Vector Machine（LS-SVM） and genetic algorithm to select the temperature sensors of a Numerical Control（NC） machine tool was presented. The measurement points in a CNC lathe were grouped based on the thermal mode theory, Then, the genetic algorithm was used to determine the positions of optimum sensors. Finally,a thermal error regression model was established by the LS-SVM and a compensation model for the machine tool was given also. The results show that the novel method combined genetic algorithms and LS-SVM well avoids the correlation of the temperature sensors and ensures the accuracy of the model. In the experiments of the CNC lathe, the mean absolute percentage error of the LS-SVM model is 1.89% in axial direction and 2.04% in radial direction, it also can reduce costs and shorten modeling time for less temperature sensors.

关 键 词：数控机床 温度传感器 最小二乘支持向量机 遗传算法 

分 类 号：TP212.11[自动化与计算机技术—检测技术与自动化装置]