机床热误差非线性组合预测模型研究

在精密及超精密加工过程中,数控机床热误差是影响加工精度的一项重要误差源,最经济和有效地减少热误差的方法是热误差补偿技术。针对热误差补偿预测模型的预测精度问题,提出一种非线性组合预测模型。该预测模型利用灰色关联度方法对单项预测模型进行筛选,对筛选出的单项预测模型基于不同优化准则进行线性组合,通过广义回归神经网络对该线性组合模型进行非线性组合,得到非线性组合预测模型。误差预测结果表明:对比典型的BP神经网络预测模型,非线性组合预测模型的预测精度更高,最大误差由4.78μm减小到0.7μm。

Research on Nonlinear Combination Prediction Model for Thermal Error of Machine Tools

In the process of precision and ultra-precision machining,thermal error of CNC machine tool is a main error source that affects machining accuracy,and thermal error compensation technology is the most economical and effective method to reduce thermal error.For the prediction accuracy problem of thermal error compensation prediction model,a nonlinear combination prediction model was proposed.The single prediction model was screened by using the grey correlation method,and the selected single prediction models were combined linearly based on different optimization criteria.The generalized regression neural network was used to combine the linear combination model,and the nonlinear combination prediction model was obtained.The error prediction results show that compared with the typical BP neural network prediction model,the nonlinear combination prediction model has higher prediction accuracy,and the maximum error decreases from 4.78μm to 0.7μm.