数控机床热误差的混合预测模型及应用

The Application of Hybrid Prediction Model to Thermal Error Modeling on NC Machine Tools

基于机床热变形误差的产生机理及其表现形式的复杂性,综合时序分析方法建模和灰色系统理论建模的优点,研究了一种智能混合预测模型.将该模型应用于一台数控车削加工中心进行热误差趋势预测,以进行机床热误差补偿研究.结果表明,混合预测模型预测精度高于时序分析模型和灰色系统模型,其优异的预测性能可使数控机床进行实时补偿更加有效,从而大大提高机床热误差的补偿精度.

Based on the generation mechanism and anfractuosity of machine tool thermal errors, a new hybrid prediction model, synthesizing the advantages of time series analysis and grey system theory, was applied to the trend prediction of thermal errors in a spot NC turning center. The testing results show that the prediction performance of hybrid prediction model outperforms any one of the two single prediction methods. The prediction precision of the hybrid prediction model for machine tool thermal errors was the highest among three kinds of prediction models. Therefore, hybrid prediction model can highly improve machine tool＇s processing precision and make it more effective for real-time compensation of NC thermal error.