Assessment of Digital Image Correlation Measurement Accuracy in the Ultimate Error Regime: Main Results of a Collaborative Benchmark. (24th September 2013)

• Record Type: Journal Article
• Authors: Amiot, F.; Bornert, M.; Doumalin, P.; Dupré, J. ‐C.; Fazzini, M.; Orteu, J. ‐J.; Poilâne, C.; Robert, L.; Rotinat, R.; Toussaint, E.; Wattrisse, B.; Wienin, J. S.
• Is Part Of: Strain. Volume 49:Number 6(2013:Dec.)
• Page Start: 483
• Page End: 496
• Publication Date: 2013-09-24
• Subjects: Strains and stresses -- Measurement -- Periodicals; 624.176
• DOI: 10.1111/str.12054
• Languages: English
• ISSNs: 0039-2103

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>We report on the main results of a collaborative work devoted to the study of the uncertainties associated with Digital image correlation techniques (DIC). More specifically, the dependence of displacement measurement uncertainties with both image characteristics and DIC parameters is emphasised. A previous work [Bornert <italic>et al</italic>. (2009) Assessment of digital image correlation measurement errors: methodology and results. Exp. Mech. 49, 353–370] dedicated to situations with spatially fluctuating displacement fields demonstrated the existence of an 'ultimate error' regime, insensitive to the mismatch between the shape function and the real displacement field. The present work is focused on this ultimate error. To ensure that there is no mismatch error, synthetic images of in‐plane rigid body translation have been analysed. Several DIC softwares developed by or in use in the French community have been used to explore the effects of a large number of settings. The discrepancies between DIC evaluated displacements and prescribed ones have been statistically analysed in terms of random errors and systematic bias, in correlation with the fractional part τ of the displacement component expressed in pixels. Main results are as follows: (i) bias amplitude is almost always insensitive to subset size, (ii) standard deviation of random error increases with noise level and decreases with subset size and (iii) DIC formulations can be split up into two main families regarding bias sensitivity to noise. For the first one, bias amplitude increases with noise while it remains nearly constant for the second one. In addition, for the first family, a strong dependence of random error with τ is observed for noisy images.</p> </abstract>