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Design Innovation Paper

Intuitive Interface for the Quantitative Evaluation of Speckle Patterns for Use in Digital Image and Volume Correlation Techniques

[+] Author and Article Information
Jonathan B. Estrada

School of Engineering,
Brown University,
Providence, RI 02912
e-mail: jonathan_estrada@brown.edu

Christian Franck

Assistant Professor
Mem. ASME
School of Engineering,
Brown University,
Providence, RI 02912
e-mail: franck@brown.edu

1Corresponding author.

Contributed by the Applied Mechanics Division of ASME for publication in the JOURNAL OF APPLIED MECHANICS. Manuscript received May 1, 2015; final manuscript received June 8, 2015; published online June 25, 2015. Editor: Yonggang Huang.

J. Appl. Mech 82(9), 095001 (Sep 01, 2015) (5 pages) Paper No: JAM-15-1216; doi: 10.1115/1.4030821 History: Received May 01, 2015; Revised June 08, 2015; Online June 25, 2015

Digital image correlation (DIC) and digital volume correlation (DVC) are powerful means of resolving local kinematic descriptions of material deformation fields across a variety of material and testing platforms. Their spatial resolution, sensitivity, and accuracy depend in large part on the quality of the intrinsic material speckle pattern. Traditional evaluation of speckle pattern quality, or subset intensity distribution, relies on a set of well-characterized experimental measurements including rigid-body translation and rotation. In order to provide a significantly faster quantitative evaluation process on whether a particular speckle pattern is suitable for DIC or DVC purposes, we present a simple, intuitive DIC and DVC speckle pattern graphical user interface (GUI) tool programmed in matlab. This tool assesses the DIC and DVC robustness of user-supplied speckle patterns via a two-step procedure: The first step involves warping the specific image according to a set of analytically prescribed deformation functions. The second step involves correlating the analytically warped and reference image pairs to recover the prescribed displacement field and its quantitative comparison to the prescribed warping function. Since the accuracy and precision of the recovered solution depend on the characteristics of the intensity distributions encoded in the image, this approach allows for a simple, yet effective, quantification procedure of the correlation suitability in the supplied image speckle pattern. In short, this procedure allows for fast and quantitative evaluation of the quality and suitability of a given speckle pattern to be used in DIC and DVC applications without the need of performing time-consuming experimental measurements. As such, we hope that this free tool will benefit anyone interested in performing DIC- or DVC-based kinematic measurements.

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Figures

Grahic Jump Location
Fig. 3

Example (a) and (f) input images and output displacement contour maps and residual error histograms in (b), (c), (g), and (h) uniaxial tension and (d), (e), (i), and (j) for a point force

Grahic Jump Location
Fig. 2

Screenshot of the matlab-based DIC simulator GUI, with (a) input parameters, (b) input image, and output (c), (d) contour maps and (e), (f) residual deformation recovery error histograms

Grahic Jump Location
Fig. 1

Schematic overview of the DIC and DVC simulators

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