基于过程数据时段特性的数控机床热误差预测研究

Study of Thermal Error Prediction of Numerical Control Machine Based on Process Data Timeslice Properties

准确可靠的热误差预报模型,对提高数控机床的加工精度尤为重要。针对数控加工的过程数据呈现出多时段、多变量、三维特性,基于时间片矩阵的思想,在过程数据标准化处理的基础上,采用偏最小二乘方法提取时间片矩阵与热误差在高维空间的预测关系并进行降维;在低维特征空间中基于K-means算法实现时间片预测模型的聚类,以便于加工过程时段特性的分析和知识发现,藉此构建热误差预报模型。仿真实验结果表明,与BP热误差建模方法相比,所提方法的预测精度、泛化能力均显著提高,为数控机床的热误差预测研究提供一种新思路的同时,也给出行之有效的解决方法。

An accurate thermal error prediction model is crucial to the improvement of machining precision for numerical control machine. Aimed at the idea of modeling data generated during process operation usually presented with the characteristics of multiphase,multi-variables and three dimensional( 3-D),based on standard processing of the data,the partial least square method was employed to derive the predictive relationship between timeslice matrix and thermal error in high dimensional space,and the data space was reduced. K-means cluster algorithm was used to divide the models into different group under low dimensional properties space,next,the whole properties of manufacture process for a part were analyzed and knowledge discovered,therefore the thermal error predication model was established. The simulation experiment results show that as comparing with modeling method of BP thermal error,the proposed method has obviously improved predictive and generalization ability,which provides a novel way of idea for studying of thermal error prediction of NC machine,at the same time,it is an effective and practical solution.