基于灰色线性回归组合模型的机床热误差建模方法

摘要

为了控制机床热误差和提高机床加工精度,考虑到测得的热误差数据同时存在着线性和非线性因素,提出了采用具有处理线性和非线性能力的灰色线性回归组合热误差模型的建模方法.用此方法对某卧式加工中心热误差进行了建模和预测,并引入BP神经网络对热误差模型的残差进行修正,从而获得了比较准确的热误差预测值.与用指数函数来模拟生成数据的灰色模型所获得的预测值进行了比较,证明了灰色线性回归组合及BP神经网络模型在机床热误差补偿建模应用中的优越性.%Considering that some linear and nonlinear factors to thermal error data exist when a machine tool works,this paper proposes a modeling method for prediction of machine tools' thermal errors by using a grey linear regression combination thermal error model.This method has an ability to deal with the linear and nonlinear problems.To obtain predictive values of thermal errors,its residual error is corrected by the BP neural network.The predictive value obtained from a grey model using an exponential function to simulate the data,is compared with the one obtained above,and the result proves the superiority of the grey linear regression combination and the BP neural network model for machine tools' thermal error compensation modeling.