Uncertainty evaluation of straightness in coordinate measuring machines based on error ellipse theory integrated with Monte Carlo method

2019 ◽  
Vol 31 (3) ◽  
pp. 035008 ◽  
Author(s):  
Mengrui Zhu ◽  
Guangyan Ge ◽  
Yun Yang ◽  
Zhengchun Du ◽  
Jianguo Yang
Author(s):  
Magnus Hölle ◽  
Christian Bartsch ◽  
Peter Jeschke

The subject of this paper is a statistical method for the accurate evaluation of the uncertainties for pneumatic multi-hole probe measurements. The method can be applied to different types of evaluation algorithms and is suitable for steady flowfield measurements in compressible flows. The evaluation of uncertainties is performed by a Monte Carlo method (MCM), which is based on the statistical law of large numbers. Each input quantity, including calibration and measurement quantities, is randomly varied on the basis of its corresponding probability density function (PDF) and propagated through the deterministic parameter evaluation algorithm. Other than linear Taylor series based uncertainty evaluation methods, MCM features several advantages. On the one hand, MCM does not suffer from lower-order expansion errors and can therefore reproduce nonlinearity effects. On the other hand, different types of PDFs can be assumed for the input quantities and the corresponding coverage intervals can be calculated for any coverage probability. To demonstrate the uncertainty evaluation, a calibration and subsequent measurements in the wake of an airfoil with a 5-hole probe are performed. MCM is applied to different parameter evaluation algorithms. It is found that the MCM approach presented cannot be applied to polynomial curve fits, if the differences between the calibration data and the polynomial curve fits are of the same order of magnitude compared to the calibration uncertainty. Since this method has not yet been used for the evaluation of measurement uncertainties for pneumatic multi-hole probes, the aim of the paper is to present a highly accurate and easy-to-implement uncertainty evaluation method.


Metrologia ◽  
2007 ◽  
Vol 44 (5) ◽  
pp. 319-326 ◽  
Author(s):  
T J Esward ◽  
A de Ginestous ◽  
P M Harris ◽  
I D Hill ◽  
S G R Salim ◽  
...  

2017 ◽  
Vol 13 ◽  
pp. 585-592 ◽  
Author(s):  
S. Aguado ◽  
P. Pérez ◽  
J.A. Albajez ◽  
J. Velázquez ◽  
J. Santolaria

2010 ◽  
Vol 2010.16 (0) ◽  
pp. 447-448
Author(s):  
Shinichi Naito ◽  
Zenichi Miyagi ◽  
Youichi Bitou ◽  
Kensei Ehara

2013 ◽  
Vol 684 ◽  
pp. 429-433 ◽  
Author(s):  
Hong Li Li ◽  
Xiao Huai Chen ◽  
Hong Tao Wang

There is presented a complete uncertainty evaluation process of end distance measurement by CMM. To begin with, the major sources of uncertainty, which would influence measurement result, are found out after analyzing, then, the general mathematic model of end distance measurement is established. Furthermore, Monte Carlo method (MCM) is used, and the uncertainty of the measured quantity is obtained. The complete results are given out, so the value of CMM is enhanced. Moreover, seen from the evaluation example, the results of uncertainty evaluation obtained from MCM method and from GUM method are compared, the comparison result indicates that the mathematic model is feasible, and using MCM method to evaluate uncertainty is easy and efficient, having practical value.


MAPAN ◽  
2019 ◽  
Vol 34 (3) ◽  
pp. 295-298
Author(s):  
P. Rachakonda ◽  
V. Ramnath ◽  
V. S. Pandey

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4472 ◽  
Author(s):  
Mingotti ◽  
Peretto ◽  
Tinarelli ◽  
Ghaderi

The paper addresses the evaluation of the uncertainty sources of a test bed system for calibrating voltage transformers vs. temperature. In particular, the Monte Carlo method has been applied in order to evaluate the effects of the uncertainty sources in two different conditions: by using the nominal accuracy specifications of the elements which compose the setup, or by exploiting the results of their metrological characterization. In addition, the influence of random effects on the system accuracy has been quantified and evaluated. From the results, it emerges that the choice of the uncertainty evaluation method affects the overall study. As a matter of fact, the use of a metrological characterization or of accuracy specifications provided by the manufacturers provides respectively an accuracy of 0.1 and 0.5 for the overall measurement setup.


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