Proposed Use of Monte Carlo Simulated Images to Evaluate the Accuracy of Measurements on X-Ray Computed Tomography

Abstract

X-ray computed tomography (CT) is increasingly recognized as a promising measuring technique for dimensional metrology. Various methods are being developed to improve measurement accuracy. Tests of new methods for such applications include accuracy evaluation with the use of calibrated workpieces; however, the internal algorithms of image acquisition and data processing might influence the experimental error, and then also the comparison of methods at different CTs. The accuracy of the results of tomographic measurements is influenced by many factors, one of which is the setting of the threshold value. The article presents the results of an attempt to use Monte Carlo simulated images to estimate deviations to determine threshold values to improve measurement accuracy and additionally, to estimate the impact of data processing. The differences of the results obtained from the simulated images were up to 4 % larger than those from tomographic images. It was caused by degradation of the image contrast by scattered radiation.

Adaptive quasi-Monte Carlo method for uncertainty evaluation in centroid measurement of planetary rovers

“…Considering the problems of GUM, the Monte Carlo method (MCM) was proposed to evaluate uncertainty based on the propagation distributions (JCGM 101:2008(JCGM 101: , 2008JCGM 102:2011JCGM 102: , 2011. And a considerable amount of papers has been published on uncertainty evaluation for multi-output nonlinear measurement models in many fields (Dadamos et al, 2019;Kowaluk et al, 2018;Tomczyk, 2019;Wang et al, 2018). Some analysts (Moona et al, 2018) have attempted to draw subtle distinctions between MCM and GUM (Huang et al, 2019;Rameba¨ck and Lindgren, 2018;Witkovsky´et al, 2017).…”