Estimation of uncertainty in measuring the workpiece circularity error

Dong-xia WANG; Xiu-lan WEN; Gui-fang QIAO

doi:10.3788/OPE.20182610.2438

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Estimation of uncertainty in measuring the workpiece circularity error

Micro/Nano Technology and Fine Mechanics | 更新时间：2020-07-05

- Estimation of uncertainty in measuring the workpiece circularity error
- Optics and Precision Engineering Vol. 26, Issue 10, Pages: 2438-2445(2018)
- 作者机构：
  
  南京工程学院自动化学院, 江苏南京 211167
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182610.2438
  CLC： TH161.11
- Received：12 March 2018，
  
  Accepted：16 April 2018，
  
  Published：25 October 2018
- 稿件说明：
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Dong-xia WANG, Xiu-lan WEN, Gui-fang QIAO. Estimation of uncertainty in measuring the workpiece circularity error[J]. Optics and precision engineering, 2018, 26(10): 2438-2445.
DOI：

Dong-xia WANG, Xiu-lan WEN, Gui-fang QIAO. Estimation of uncertainty in measuring the workpiece circularity error[J]. Optics and precision engineering, 2018, 26(10): 2438-2445. DOI： 10.3788/OPE.20182610.2438.

摘要

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1.1

m，MCM方法比GUM方法得到的标准不确定度均值小0.02

m。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。

Abstract

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

and the average standard uncertainty of the MCM method is 0.02

m less than the GUM method. More stable

reliable

and accurate results can be obtained using reasonable sampling points

DE algorithm

and MCM evaluation method.

关键词

Keywords

references

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