重型数控机床的热误差已经成为影响其加工精度的一个关键问题。针对一台典型的重型落地铣镗床,以机床热误差测量试验为依据,分析该类机床温度场的特点;据此提出一种旨在完成高效温度测点优化的改进系统聚类方法,该方法使用一种兼顾欧氏距离和相关系数的系统聚类准则,可以有效地降低优化后温度测点之间的共线性。基于优化后的温度测点,利用多元线性回归分析,构建了机床的热误差预测模型。现场试验数据表明,该方法可以将热误差预测的均方根误差降低到10 μm以下,相较于其他方法有着更高的热误差预测精度,有望在其他重型数控机床的热误差建模和预测研究中得到更大的推广应用。
Thermal error has been a significant factor influencing the accuracy of heavy CNC machine tools. A thermal experiment is performed on a typical heavy CNC machine tool. According to the temperature field of the machine tool, a new hierarchical clustering method is proposed to optimize temperature variables efficiently. Euler distance and correlation coefficient are both considered in the method, by which the collinearity of temperature variables is reduced. Thermal error prediction model is built based on optimized temperature variables utilizing MRA. Results show that the RMSE of thermal error prediction can be reduced to lower than 10 μm, which has higher accuracy compared to other methods. This method is expected to be used in thermal error modeling and prediction of other heavy CNC machine tools.
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