基于主动构造温差变量的机床温度敏感点选择方法

Temperature-sensitive point selection method of machine tool based on active construction of temperature difference variable

温度敏感点显著影响机床热误差模型的性能。针对现有温度敏感点选择中需要布置多个温度测量点,且可能遗失关键温差信息问题,本文提出了一种基于主动构造温差变量的温度敏感点选择方法。通过从有限个温度测量点中组合并构造温差变量,作为原始温度变量的扩展补充,重新基于模糊聚类和相关系数分析进行温度敏感点选择,并用于热误差建模。该方法可弥补现有方法中潜在关键温度信息缺失问题,且具有更高的精度与稳定性。实验结果表明重构温差变量方法相比传统温度敏感点选择方法,可将模型预测结果的平均均方根误差由11.1、10.3μm降低至3.6μm,效果显著。

Temperature-sensitive points significantly affect the performance of the thermal error model of machine tools.To solve the problem that many measurement points need to be arranged and key temperature difference information may be lost in the existing temperature-sensitive point selection methods,this article proposes a temperature-sensitive point selection method based on active construction of temperature difference variables.The temperature difference variables are obtained from constructing a limited number of the original temperature measurement points,and added as an extension of the original temperature variables.The temperature-sensitive points are selected,which are based on fuzzy clustering and correlation coefficient analysis,and used for the thermal error modeling.This method can make up for the lack of potential key temperature information in existing methods,which has higher accuracy and stability.Compared with the traditional temperature-sensitive point selection methods,experimental results show that the proposed method can reduce the average root mean square error from 11.1、10.3μm to 3.6μm,which shows the remarkable effect.