Increasing accuracy and precision of digital image correlation through pattern optimization

Bomarito, Geoffrey F.; Hochhalter, Jacob; Ruggles, Timothy; Cannon, Andrew

doi:10.1016/j.optlaseng.2016.11.005

Cited by 106 publications

(87 citation statements)

References 27 publications

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“…Strains are then calculated based on the obtained information on displacements, as in Figure 1c. Among many options, the standardized covariance correlation function [22,23] defines…”

Section: Working Principle Of the Dic Techniquementioning

confidence: 99%

Prediction of Fracture Damage of Sandstone Using Digital Image Correlation

et al. 2020

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Featured Application: A non-contact digital image correlation technique is used to predict sandstone failure. Failure strain for the tested sandstone is estimated to be around 0.004. Finite element analysis verifies the accuracy of prediction results based on experiments.Abstract: Investigation on the deformation mechanism of sandstone is crucial to understanding the life cycle patterns of pertinent infrastructure systems considering the extensive adoption of sandstone in infrastructure construction of various engineering systems, e.g., agricultural engineering systems. In this study, the state-of-the-art digital image correlation (DIC) method, which uses classical digital photography, is employed to explore the detailed failure course of sandstone with physical uniaxial compression tests. Four typical points are specifically selected to characterize the global strain field by plotting their corresponding strain-time relationship curves. Thus, the targeted failure thresholds are identified. The Hill-Tsai failure criterion and finite element simulation are then used for the cross-check process of DIC predictions. The results show that, though errors exist between the experimental and the theoretical values, overall, they are sufficiently low to be ignored, indicating good agreement. From the results, near-linear relationships between strain and time are detected before failure at the four chosen points and the failure strain thresholds are almost the same; as low as 0.004. Failure thresholds of sandstone are reliably determined according to the strain variation curve, to forecast sandstone damage and failure. Consequently, the proposed technology and associated information generated from this study could be of assistance in the safety and health monitoring processes of relevant infrastructure system applications.

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“…Strains are then calculated based on the obtained information on displacements, as in Figure 1c. Among many options, the standardized covariance correlation function [22,23] defines…”

Section: Working Principle Of the Dic Techniquementioning

confidence: 99%

Prediction of Fracture Damage of Sandstone Using Digital Image Correlation

et al. 2020

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“…Digital Image Correlation (DIC), which consists in iteratively minimizing the optical residual over small subsets with respect to the sought displacement, is the most popular one. Finding optimal patterns to achieve the best possible metrological performance is a problem, which has attracted much attention in recent papers [1,2,3]. In [3], it is recalled that checkerboards are theoretically optimal patterns for DIC in terms of sensor noise propagation because their images maximize the sum of square of subset intensity gradient (SSSIG), and noise in final displacement and strain maps is inversely proportional to this quantity [4,5].…”

Section: Introductionmentioning

confidence: 99%

Comparing several spectral methods used to extract displacement fields from checkerboard images

Grédiac

Blaysat

Sur

2020

Optics and Lasers in Engineering

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Checkerboard represents the best pattern for in-plane displacement measurement in terms of sensor noise propagation because this pattern maximizes image gradient. It also exhibits other interesting properties in terms of pattern-induced bias for instance. Digital Image Correlation (DIC) is not the best option to extract displacement fields from such periodic patterns, and spectral methods should be used instead. In this paper, it is shown that three different spectral techniques initially developed for classic bidimensional grids can be adapted to process checkerboard images. These three techniques are the Geometric Phase Analysis (GPA), the windowed version of the Geometric Phase Analysis (WGPA), and the Localized Spectrum Analysis (LSA), which can be regarded as the ultimate version of WGPA. The main features of these three techniques as well as the link between them are given in this paper. Their metrological performance are compared in terms of displacement resolution, spatial resolution and bias. Synthetic checkerboard images deformed with a suitable reference displacement field are considered for this purpose. It is shown that GPA is the fastest method. According to the metric used in this paper, the best metrological performance is obtained with WGPA with suitable settings. LSA followed by a deconvolution algorithm is just behind, but the calculation time is approximately 10 times lower than that of WGPA for the examples considered in this paper, which makes it a reasonable choice for the determination of in-plane displacement fields from checkerboard images.

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“…Creating a pattern for DIC is very straightforward because only a random pattern of speckles is required. However, this random pattern can be optimised based on many different metrics, and the imaging analysis of these speckles can be done with a variety of methods . However, DIC does have some limitations, namely, that resolution issues can be encountered when analysing deformations that are both small and nonuniform, which are common in the fracture of brittle materials .…”

Section: Introductionmentioning

confidence: 99%

“…However, this random pattern can be optimised based on many different metrics, and the imaging analysis of these speckles can be done with a variety of methods. [7,[11][12][13] However, DIC does have some limitations, namely, that resolution issues can be encountered when analysing deformations that are both small and nonuniform, which are common in the fracture of brittle materials. [8] When addressing this issue, the grid method (another noninterferometric technique) acts as a compromise in that it provides consistent measurement resolution of small and inhomogeneous strain while being relatively simple to implement.…”

Section: Introductionmentioning

confidence: 99%

A nonconstraining templated powder grid for measurement of strain

Brodnik

Hsueh

Johnson

2018

Strain

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A method was developed and validated for mounting grid patterns with pitches on the order of tens to hundreds of micrometres onto specimens for the purposes of mechanical characterisation and fracture analysis. The method uses a low stiffness, low‐toughness photopolymer to create a textured pattern of pillars on the sample, and the spaces between these pillars are then filled in with an opaque powder to provide optical contrast. This type of grid is desirable both because of its relatively quick preparation time and because of its minimal influence on the mechanical properties of the sample. The method for mounting this powder‐based grid onto samples is discussed. Then, the accuracy of this new type of grid is validated against current state‐of‐the‐art epoxy‐mounted lithography grids as well as digitally fabricated sinusoidal grids using synthetic digital manipulation. Finally, the experimental performance of the powder‐based grid is assessed in both uniaxial tension on a poly(methyl methacrylate) specimen with a stress concentrator and fracture of stereolithographically printed photopolymer specimens. Synthetic testing shows that the powder grids are of comparable accuracy to lithography grids, but additional processing variability leads to strain errors of about 3% in powder grids compared with the 2.1% seen in lithography grids. In experiments, the powder‐based grids produce deformation patterns comparable with those expected from analytical results, and fracture toughness values for homogeneous stereolithographically printed specimens are uniform and consistent with expected values.

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Increasing accuracy and precision of digital image correlation through pattern optimization

Cited by 106 publications

References 27 publications

Prediction of Fracture Damage of Sandstone Using Digital Image Correlation

Prediction of Fracture Damage of Sandstone Using Digital Image Correlation

Comparing several spectral methods used to extract displacement fields from checkerboard images

A nonconstraining templated powder grid for measurement of strain

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