数控机床热误差的动态自适应加权最小二乘支持矢量机建模方法

Modeling of NC Machine Tool Thermal Error Based on Adaptive Best-fitting WLS-SVM

为消除数控机床热误差对加工精度的影响,提出基于动态自适应加权最小二乘支持矢量机的数控机床热误差建模方法。为构建机床热误差模型,对一台XK713数控铣床进行建模试验,采用智能温度传感器与激光位移传感器分别获取机床温度值与主轴变形量。运用动态自适应算法,优化选择建模过程中的参数;对采样数据进行初始最小二乘支持矢量机建模,根据误差变量确定权重系数,得到基于加权最小二乘支持矢量机的数控铣床热误差模型。试验结果表明,基于动态自适应最小二乘支持矢量机的数控机床热误差建模方法精度高,泛化能力强,优于未加权最小二乘支持矢量机方法与传统最小二乘法。获得的模型可用于数控机床热误差补偿,以提高数控机床的加工精度。

In order to eliminate the influence of thermal error of NC machine on the machining precision, a novel method based on adaptive best-fitting weighted least squares support vector machine （WLS-SVM） is utilized to implement error compensation. In order to construct the thermal error model of machine tool, a series of experiments are carried out to acquire the data of a XK713 NC milling machine, including temperature on different positions and the thermal deformation of spindle. By smart temperature sensors and laser position sensors, the temperature and thermal error of the machine tool are collected respectively. First the parameters of WLS-SVM are optimized by a method called adaptive best-fitting parameter search algorithm. Then the samples are trained and the weighted coefficients are calculated according to the error variables. The regression model is constructed finally. Test results show that WLS-SVM is an effective method for error modeling which can be used for the thermal error compensation. It is superior to unweighted least squares support vector machine （LS-SVM） method and traditional least squares （LS） method.