五轴机床旋转轴误差的在机测量与模糊径向基神经网络建模

On-machine measurement and fuzzy RBF neural network modeling for geometric errors of rotary axes of five-axis machine tools

考虑五轴机床中的旋转轴误差会影响加工精度和在机测量结果,本文研究了旋转轴误差的在机测量与建模方法。介绍了基于标准球和机床在机测量系统的旋转轴综合误差测量方法,采用随机Hammersely序列分组规划旋转轴的测量角位置,通过自由安放策略确定标准球初始安装位置。然后,引入模糊减法聚类和模糊C-均值聚类(Fuzzy C-means,FCM)建立旋转轴误差的径向基(Radial basis function,RBF)神经网络预测模型。最后,进行数学透明解析,从而为误差的精确解析建模提供新途径。利用曲面的在机测量实例验证了提出的旋转轴误差测量与建模方法。结果表明:利用所建模型计算的预测位置与实测位置的距离偏差平均值为9.6μm,最大值不超过15μm;利用所建模型补偿工件的在机测量结果后,其平均值由32.5μm减小到13.6μm,最大误差也由62.3μm减小到18.6μm。结果显示,提出的测量方法操作简单,自动化程度高;模糊RBF神经网络的学习速度快、适应能力强、鲁棒性好,能满足高度非线性、强耦合的旋转轴误差建模要求。

As geometric errors of rotary axes of a five-axis machine impact on its machining accuracy,the on-machine measurement of rotary axes and their error modeling were investigated.Firstly,the measurement method for comprehensive errors of rotary axes was presented based on a standard ball and an on-machine measurement system.The measurement angular positions of the rotation axes were planned by the random Hammersely sequence and the initial position of the standard ball was determined with a free installation strategy.Then,a Radial Basis Function（RBF）neural network model for predicting comprehensive errors of the rotary axes was built based on subtractive clustering andFuzzy C-means（FCM）cluster.Finally,mathematical analysis was carried out to provide a new way to model accurately for geometric errors of rotary axes.A cambered measuring example was used to verify the proposed on-machine measurement method and modeling method.The experimental results indicate that the average deviation between the predicted points from the mathematics model and measured points is 9.6μm and the maximum deviation is not more than 15μm.After the measuring results are corrected by the mathematic model with a 3Dcoordinate measuring machine established by this paper,the average value is reduced to 13.6μm from 33.5μm and the maximum value is reduced to 18.6μm from 62.3μm.It concludes that the measurement method is simple to operate and has high automation.The neural network＇s training is not only fast speed,adaptable and good robust,but also can meet highly linear and strongly coupling of modeling of the geometric errors of rotary axes.