Uncertainty quantification in DIC with Kriging regression

Wang, Dezhi; DiazDelaO, F.A.; Wang, Weizhuo; Lin, Xiaoshan; Patterson, E. A.; Mottershead, John E.

doi:10.1016/j.optlaseng.2015.09.006

Cited by 16 publications

(9 citation statements)

References 40 publications

(68 reference statements)

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“…image noise, speckle size, but possibly also image imperfections, etc.) [7,17,[21][22][23][24][25][26][27][28][29]. To generate speckle painting representative contrasts, methods based on the construction of a continuous speckle pattern in 2D space from the random spatial distribution of individual Gaussian marks or from the definition of a continuous luminance field based on a modified Perlin noise function have been proposed.…”

Section: Shifted Image Generationmentioning

confidence: 99%

Shortcut in DIC error assessment induced by image interpolation used for subpixel shifting

Bornert

Doumalin

Dupré

et al. 2017

Optics and Lasers in Engineering

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International audienceIn order to characterize errors of Digital Image Correlation (DIC) algorithms, sets of virtual images are often generated from a reference image by in-plane sub-pixel translations. This leads to the determination of the well-known S-shaped bias error curves and their corresponding random error curves. As images are usually shifted by using interpolation schemes similar to those used in DIC algorithms, the question of the possible bias in the quantification of measurement uncertainties of DIC softwares is raised and constitutes the main problematic of this paper. In this collaborative work, synthetic numerically shifted images are built from two methods: one based on interpolations of the reference image and the other based on the transformation of an analytic texture function. Images are analyzed using an in-house subset-based DIC software and results are compared and discussed. The effect of image noise is also highlighted. The main result is that the a priori choices to numerically shift the reference image modify DIC results and may lead to wrong conclusions in terms of DIC error assessmen

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Section: Shifted Image Generationmentioning

confidence: 99%

Shortcut in DIC error assessment induced by image interpolation used for subpixel shifting

Bornert

Doumalin

Dupré

et al. 2017

Optics and Lasers in Engineering

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“…An important remark is the fact that we deal here with a measurement issue. However, despite many efforts on this subject, see for instance, one standard only: ASTM‐E2208 seems to be available to help users estimate the metrological performance of full‐field measurement techniques, as well as a draft standard for dynamic measurements . Ref.…”

Section: Introductionmentioning

confidence: 99%

The Grid Method for In‐plane Displacement and Strain Measurement: A Review and Analysis

Grédiac

Sur

Blaysat

2016

Strain

139

162

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The grid method is a technique suitable for the measurement of in-plane displacement and strain components on specimens undergoing a small deformation. It relies on a regular marking of the surfaces under investigation. Various techniques are proposed in the literature to retrieve these sought quantities from images of regular markings, but recent advances show that techniques developed initially to process fringe patterns lead to the best results. The grid method features a good compromise between measurement resolution and spatial resolution, thus making it an efficient tool to characterise strain gradients. Another advantage of this technique is the ability to establish closed-form expressions between its main metrological characteristics, thus enabling to predict them within certain limits. In this context, the objective of this paper is to give the state of the art in the grid method, the information being currently spread out in the literature. We propose first to recall various techniques that were used in the past to process grid images, to focus progressively on the one that is the most used in recent examples: the windowed Fourier transform. From a practical point of view, surfaces under investigation must be marked with grids, so the techniques available to mark specimens with grids are presented. Then we gather the information available in the recent literature to synthesise the connection between three important characteristics of full-field measurement techniques: the spatial resolution, the measurement resolution and the measurement bias. Some practical information is then offered to help the readers who discover this technique to start using it. In particular, programmes used here to process the grid images are offered to the readers on a dedicated website. We finally present some recent examples available in the literature to highlight the effectiveness of the grid method for in-plane displacement and strain measurement in real situations.

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“…However, strain errors increases if the specimen’s dimensions decrease [ 25 ]. Moving on to DIC, experimental studies have defined effective procedures to calibrate DIC systems and/or to process the acquired images [ 26 , 27 , 28 , 29 ], thereby improving measurement accuracy. Unfortunately, although DIC has been demonstrated to be accurate in measuring medium-large strain levels [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ], non negligible errors have been found when measuring small (<0.1% or 1000 microstrain) strain values [ 37 , 38 , 39 , 40 ], i.e., of the same order of magnitude of strain that CPCs can withstand before fracture.…”

Section: Introductionmentioning

confidence: 99%

Experimentally Achievable Accuracy Using a Digital Image Correlation Technique in measuring Small-Magnitude (<0.1%) Homogeneous Strain Fields

2018

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Measuring small-magnitude strain fields using a digital image correlation (DIC) technique is challenging, due to the noise-signal ratio in strain maps. Here, we determined the level of accuracy achievable in measuring small-magnitude (<0.1%) homogeneous strain fields. We investigated different sets of parameters for image processing and imaging pre-selection, based on single-image noise level. The trueness of DIC was assessed by comparison of Young’s modulus (E) and Poisson’s ratio (ν) with values obtained from strain gauge measurements. Repeatability was improved, on average, by 20–25% with experimentally-determined optimal parameters and image pre-selection. Despite this, the intra- and inter-specimen repeatability of strain gauge measurements was 5 and 2.5 times better than DIC, respectively. Moreover, although trueness was also improved, on average, by 30–45%, DIC consistently overestimated the two material parameters by 1.8% and 3.2% for E and ν, respectively. DIC is a suitable option to measure small-magnitude homogeneous strain fields, bearing in mind the limitations in achievable accuracy.

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Uncertainty quantification in DIC with Kriging regression

Cited by 16 publications

References 40 publications

Shortcut in DIC error assessment induced by image interpolation used for subpixel shifting

Shortcut in DIC error assessment induced by image interpolation used for subpixel shifting

The Grid Method for In‐plane Displacement and Strain Measurement: A Review and Analysis

Experimentally Achievable Accuracy Using a Digital Image Correlation Technique in measuring Small-Magnitude (<0.1%) Homogeneous Strain Fields

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