工件圆度误差测量不确定度评定

为了实现工件圆度误差的不确定度评定,对基于三坐标测量机的工件圆度轮廓数据的采样策略、圆度评定方法及不确定度评定方法进行研究。首先,根据工件圆度轮廓特征进行实验测量,获取不同工件的多个样本。接着,基于最小二乘法和微分进化优化算法对样本的圆度误差进行了误差评定。然后,在分析比较误差大小的基础上,说明了采用的采样策略和微分进化评定算法。最后,基于圆度误差评定结果运用了测量不确定度表示指南(GUM)和蒙特卡洛方法(MCM)进行不确定度评定。实验结果表明:微分进化算法与最小二乘法相比均值差最大达到1.1μm, MCM方法比GUM方法得到的标准不确定度均值小0.02μm。合理的采样点数、微分进化算法及MCM不确定度评定方法可以得到更稳定可靠、精度高的评定结果。

Estimation of uncertainty in measuring the workpiece circularity error

In order to realize the uncertainty evaluation of the workpiece circularity error, the sampling strategy, error evaluation method, and uncertainty of the circular outline of the workpiece were investigated based on the Coordinate Measuring Machine (CMM). First, to achieve many samples from different workpieces, circular outlines were measured. Next, the sample circularity errors were evaluated according to the Differential Evolution (DE) algorithm. Then, by comparing the errors, the adopted sampling strategies and the DE algorithm were explained. Finally, based on the results of the circularity error, the uncertainty was evaluated by applying the GUM and MCM methods. The maximum average difference is 1.1 μm, and the average standard uncertainty of the MCM method is 0.02 μm less than the GUM method. More stable, reliable, and accurate results can be obtained using reasonable sampling points, DE algorithm, and MCM evaluation method.