基于灰关联分析的热误差测点优化

Optimization of Measuring Points for Machine Tool Thermal Error Modeling Based on Grey Relational Analysis Method

针对影响机床热误差建模的机床温度场分布问题,提出了优化热关键点的新方法。借助于灰色系统理论的关联度分析方法,根据现场测得的统计数据序列,建立了灰关联分析模型,通过计算布置于机床上各个温度布点的温度传感器的温度采样序列值同机床定位误差之间的绝对灰色关联度值,最终从32个温度测点当中选择了4个点用于建立热误差补偿模型。最后基于四个测温关键点建模对Z轴的定位误差进行了补偿实验,结果证明补偿效果较好,所提出的热误差测点优化研究可以有效提高热误差模型的鲁棒性。

According to temperature distribution acting on machine tool thermal error modeling,a new method of optimization of thermal critical point is put forward in this paper.With relational analysis method of grey system theory,according to the statistical data sequence measured at site,a grey relational analysis model was established.Via calculating the absolute grey relational grade value between temperature sampling sequence value of temperature sensor arranged in different temperature distribution points on the machine and machine tool positioning error,finally,select 4 points from 32 temperature measuring points to establish a thermal error compensation model.In conclusion,based on the model of 4 temperature measuring key points,Z《Times New Roman》〗 axis positioning error compensation experiments were made,and results show that compensation works well,thereby the proposed optimization of measuring points for thermal error modeling can effectively improve the robustness of thermal error model.