重型数控机床热误差建模及预测方法的研究

发布时间：2018-06-09 14:35

本文选题：重型数控机床 + 温度测点优化　；参考：《机械工程学报》2016年11期

【摘要】：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10μm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。
[Abstract]:The thermal error of heavy-duty NC machine tool has become a key problem affecting its machining accuracy. Based on the thermal error measurement test of a typical heavy-duty ground milling and boring machine, the characteristics of the temperature field of this kind of machine tools are analyzed, and an improved system clustering method is proposed to optimize the temperature measurement points of this kind of machine tools. This method uses a system clustering criterion which takes Euclidean distance and correlation coefficient into account and can effectively reduce the collinearity between the optimized temperature measurement points. Based on the optimized temperature measurement point, the thermal error prediction model of machine tool is constructed by multiple linear regression analysis. Field test data show that this method can reduce the RMS error of thermal error prediction to less than 10 渭 m, and has higher thermal error prediction accuracy than other methods. It is expected to be widely used in thermal error modeling and prediction of other heavy-duty NC machine tools.
【作者单位】：清华大学精密超精密制造装备及控制北京市重点实验室;
【基金】：国家科技重大专项(2013ZX04013011,2015ZX04011002) 国家重点基础研究发展计划(973计划,2011CB302400) 国家自然科学基金(51275260) 清华大学自主科研计划(2014z22068)资助项目
【分类号】：TG659

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