基于模糊C均值聚类算法的温度测点优化与建模研究

Temperature Measuring Points Optimization and Modeling Research Based on Fuzzy C-Means Clustering Algorithm

在数控机床热误差补偿技术中,温度测点的选择与优化是一个难点。通过热成像仪获得了某立式铣床的温度场,根据温度场的分布情况,在机床上布置多个温度传感器。根据测量的温度和热变形数据,采用FCM模糊聚类和相关分析对温度测点进行了分组优化,然后利用多元回归分析建立了关键测温点的热误差模型,并通过实验进行了验证。结果表明:该方法能有效减少测温点,测温点由13个减少到5个,所建立模型预测精度较好,Y,Z方向热误差由50μm减少到9μm以内。

The optimization and selection of temperature measuring points is a difficulty in the thermal error compensation technology for numerical control( NC) machine tool. The temperature field of a vertical milling machine was obtained through the thermal imager,according to the distribution of temperature field,some temperature sensors were installed on the machine tool. Using FCM fuzzy clustering method and the correlation analysis,the temperature measuring points were grouped and optimized on the basis of the measurement data of the temperature and thermal deformation,and then the thermal error model of the key temperature measuring point was established by using multiple regressions analysis,and verified by the experiment. The results show that this method can effectively reduce the measuring points,temperature measuring point is reduced from 13 to 5,and the precision of built model is better. Y,Z direction of the thermal error is reduced from 50 μm to 9 μm.