Optimal image correlation in experimental mechanics

Kahn‐Jetter, Zella; Jha, Nand K.; Bhatia, Harmeet

doi:10.1117/12.166931

Cited by 15 publications

(11 citation statements)

References 6 publications

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“…Since the early 1990s, significant technological advances in the field of optical measuring instruments, such as digital cameras equipped with Charge-Coupled Device (CCD) or Complementary MetalOxideSemiconductor (CMOS) image sensors and microscope objectives, have widely contributed to the emergence of imaging techniques such as two-dimensional (2D) or three-dimensional (3D) digital image correlation (DIC) for identification purposes. DIC techniques [7][8][9] are now commonly used in solid mechanics and material sciences for experimental measurements of elastic displacement fields of samples under external loading [10][11][12][13][14][15][16] in order to identify mechanical properties of complex microstructures for heterogeneous materials [13,[17][18][19][20][21][22][23][24] with different classes of material symmetries. The recent milestones achieved around data acquisition systems and processing softwares for 3D images obtained for example by X-ray computed microtomography (µCT) [25][26][27][28][29][30], magnetic resonance imaging (MRI) [31][32][33][34], optical coherence tomography (OCT) [35][36][37][38][39] or any other non-invasive and non-destructive testing technique for the reconstruction of 3D images in high resolution, have allowed the development of three-dimensional measurements of displacement fields by digital volume correlation (DVC) [9,15,[40]…”

Section: Overview Of Inverse Methods For the Mechanical Characterization Of Micro/meso-structural Propertiesmentioning

confidence: 99%

Robust Multiscale Identification of Apparent Elastic Properties at Mesoscale for Random Heterogeneous Materials with Multiscale Field Measurements

2020

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The aim of this work is to efficiently and robustly solve the statistical inverse problem related to the identification of the elastic properties at both macroscopic and mesoscopic scales of heterogeneous anisotropic materials with a complex microstructure that usually cannot be properly described in terms of their mechanical constituents at microscale. Within the context of linear elasticity theory, the apparent elasticity tensor field at a given mesoscale is modeled by a prior non-Gaussian tensor-valued random field. A general methodology using multiscale displacement field measurements simultaneously made at both macroscale and mesoscale has been recently proposed for the identification the hyperparameters of such a prior stochastic model by solving a multiscale statistical inverse problem using a stochastic computational model and some information from displacement fields at both macroscale and mesoscale. This paper contributes to the improvement of the computational efficiency, accuracy and robustness of such a method by introducing (i) a mesoscopic numerical indicator related to the spatial correlation length(s) of kinematic fields, allowing the time-consuming global optimization algorithm (genetic algorithm) used in a previous work to be replaced with a more efficient algorithm and (ii) an ad hoc stochastic representation of the hyperparameters involved in the prior stochastic model in order to enhance both the robustness and the precision of the statistical inverse identification method. Finally, the proposed improved method is first validated on in silico materials within the framework of 2D plane stress and 3D linear elasticity (using multiscale simulated data obtained through numerical computations) and then exemplified on a real heterogeneous biological material (beef cortical bone) within the framework of 2D plane stress linear elasticity (using multiscale experimental data obtained through mechanical testing monitored by digital image correlation).

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Section: Overview Of Inverse Methods For the Mechanical Characterization Of Micro/meso-structural Propertiesmentioning

confidence: 99%

Robust Multiscale Identification of Apparent Elastic Properties at Mesoscale for Random Heterogeneous Materials with Multiscale Field Measurements

2020

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“…It is first applied to select regions of the sample in order to determine the rotation axis precisely. It is then applied to the full projection sequences for the unloaded and loaded sample configurations to generate the voxel data sets (typically 1024 3 8-bit voxels). Correlation code developed in-house is then used to estimate sample deformation fields.…”

Section: Example Applicationsmentioning

confidence: 99%

“…With the development of digital imaging technology in the 1970s and a concomitant growth in computer capabilities, a new strain measurement technique began to emerge -digital image correlation (DIC) [1][2][3][4][5]. With this method a surface need only be marked with an appropriate visible light speckle pattern and a sequence of digital images generated as the object undergoes deformation.…”

Section: Introductionmentioning

confidence: 99%

Methods and applications of digital volume correlation

Bay

2008

The Journal of Strain Analysis for Engineering Design

182

123

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Digital volume correlation, an experimental method for volumetric strain measurement, has experienced a growth in technique development and application since its introduction in 1999. This has largely been the result of more accessible volumetric imaging methods and greater speed and capacity of computational facilities. This paper reviews recent work using the method and presents examples from the author's laboratory. The emerging message is that, although closely related to the surface-oriented method of digital image correlation, the volume method is distinct due to its predominant reliance on naturally occurring image texture for displacement tracking. This requires careful tuning for successful application with different materials, and therefore the appropriate focus should not be on developing the 'best' digital volume correlation method, but on developing a set of tools that can be selected from and adjusted to specific mechanics problems.

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“…The experimental identification of microstructural morphology by image analysis began in the 1980s Jeulin (1987Jeulin ( , 1989Jeulin ( , 2001, and has led to significant advances for the identification of the elastic properties of materials at the macroscale and/or mesoscale. Many works could be cited among which Besnard et al (2006); Bornert et al (2009); Hild (2002); Hild et al (1999); , 2012; Kahnjetter et al (1994); Rethore et al (2008); ; Roux et al (2002; Vendroux & Knauss (1998a,b) for field measurements using digital image correlation (DIC) (see for instance Pan et al (2009Pan et al ( , 2006; Schreier et al (2000); Sutton et al (2008)), including software developments, Bornert et al (2010); Hild et al (2002) for multiscale field measurements, Bonnet & Constantinescu (2005); Constantinescu (1995); Geymonat et al (2002); Geymonat & Pagano (2003) for inverse problems in elasticity, Avril et al (2008a); Avril & Pierron (2007); Avril et al (2008b); Calloch et al (2002); Chevalier et al (2001); Madi et al (2007) for identification methods from full-field measurements in linear and nonlinear elasticity, and Baxter & Graham (2000); Graham et al (2003) for stochastic aspect of random media using a moving-window technique. In this paper, the heterogeneous material is made up of a cortical bone for which the microstructure is very complex, random and presents a hierarchical structure.…”

Section: Introductionmentioning

confidence: 99%

Experimental multiscale measurements for the mechanical identification of a cortical bone by digital image correlation

Nguyen

Allain

Gharbi

et al. 2016

Journal of the Mechanical Behavior of Biomedical Materials

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The implementation of the experimental methodology by optical measurements of mechanical fields, the development of a test bench, the specimen preparation, the experimental measurements, and the digital image correlation (DIC) method, have already been the object of research in the context of biological materials. Nevertheless, in the framework of the experimental identification of a mesoscopic stochastic model of the random apparent elasticity field, measurements of one specimen is required at both the macroscopic scale and the mesoscopic scale under one single loading. The nature of the cortical bone induces some difficulties, as no single speckled pattern technique is available for simultaneously obtaining the displacement at the macroscopic scale and at the mesoscopic scale. In this paper, we present a multiscale experimental methodology based on (i) an experimental protocol for one specimen of a cortical bone, (ii) its measuring bench, (iii) optical field measurements by DIC method, (iv) the experimental results, and (v) the multiscale experimental identification by solving a statistical inverse problem.

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Optimal image correlation in experimental mechanics

Cited by 15 publications

References 6 publications

Robust Multiscale Identification of Apparent Elastic Properties at Mesoscale for Random Heterogeneous Materials with Multiscale Field Measurements

Robust Multiscale Identification of Apparent Elastic Properties at Mesoscale for Random Heterogeneous Materials with Multiscale Field Measurements

Methods and applications of digital volume correlation

Experimental multiscale measurements for the mechanical identification of a cortical bone by digital image correlation

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