Automatic crack tip detection and stress intensity factors estimation of curved cracks from digital images

Réthoré, Julien

doi:10.1002/nme.4905

Cited by 34 publications

(33 citation statements)

References 34 publications

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“…It is assumed that the search for a crack tip is restricted to a potential crack path, similarly to the approach of Réthoré . This potential crack path can be known in advance, e.g.…”

Section: Crack Tip Detectionmentioning

confidence: 99%

“…It is assumed that the search for a crack tip is restricted to a potential crack path, similarly to the approach of Réthoré. [21] This potential crack path can be known in advance, e.g. if the crack path is supposed to propagate along a straight line in the same direction as a notch (see Figure 8), or it can be determined thanks to a crack path tracking algorithm for more complex cases.…”

Section: Potential Crack Pathmentioning

confidence: 99%

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Crack path tracking from full field measurements: A novel empirical methodology

Feld-Payet

Besnerais

Bonnand

et al. 2020

Strain

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The identification of crack growth models requires the knowledge of the crack path geometry and, in particular, of the crack tip position. The objective of the present paper is to provide a criterion for the detection of the crack tip position regardless of the loading conditions or the material behavior. The novelty of the proposed approach is its ability to provide a good estimation of the crack tip even when large plastic deformations occur. The proposed empirical criterion uses the evolution of the displacement gradient standard deviation along a potential crack path to locate the crack tip. It thus relies on full field measures that are relatively smooth while providing accurate displacement vectors even close to the lips. We successfully apply the proposed criterion in two cases of fatigue crack growth: A straight crack at high temperature and a curved crack at room temperature involving large plastic deformations.

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“…It is assumed that the search for a crack tip is restricted to a potential crack path, similarly to the approach of Réthoré . This potential crack path can be known in advance, e.g.…”

Section: Crack Tip Detectionmentioning

confidence: 99%

Section: Potential Crack Pathmentioning

confidence: 99%

Crack path tracking from full field measurements: A novel empirical methodology

Feld-Payet

Besnerais

Bonnand

et al. 2020

Strain

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“…This way of regularizing DIC leads to a trade-off between FE interpolation error and the so-called ultimate random error [6,9]. Another great interest of using FEs in DIC is that it is possible to regularize the DIC problem more softly, based on the knowledge of a FE mechanical model [10][11][12][13][14][15][16]. It can be seen as a mechanical low pass filter whose cutoff 124 R. BOUCLIER AND J.-C. PASSIEUX wavelenght plays the role of the aforementioned regularization length [13].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in [15], two different mechanical models are used in the same measurement, a Williams' series expansion close to the crack tip and a more classical FE elastic model in the remainder of the domain.…”

Section: Introductionmentioning

confidence: 99%

A domain coupling method for finite element digital image correlation with mechanical regularization: Application to multiscale measurements and parallel computing

Bouclier

Passieux

2016

Numerical Meth Engineering

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To cite this version:Robin Bouclier, Jean-Charles Passieux. A domain coupling method for finite element digital image correlation with mechanical regularization: Application to multiscale measurements and parallel computing. International Journal for Numerical Methods in Engineering, Wiley, 2017, 111 (2) SUMMARYA general method is proposed to couple two sub-regions analysed with finite element digital image correlation (FE-DIC) even when using a mechanical regularization (R-DIC). A Lagrange multiplier is introduced to stitch both displacements fields in order to recover continuity over the full region of interest. Another interface unknown is introduced to ensure, additionally, the equilibrium of the mechanical models used for regularization. As a first application, the method is used to perform a single measurement from images at two different resolutions. Secondly, the method is also extended to parallel computing in R-DIC. The problem is formulated at the interface and solved with GMRes. A dedicated preconditioner is proposed to significantly accelerate convergence. The resulting method is a good candidate for the analysis of large data-sets. DraftReceived . . .

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“…They are the cut-off wavelength of low-pass filters [10,8,21]. Usually, this kind of parameters are set between 0 and 1 (eq.…”

Section: Regularisation Coefficientsmentioning

confidence: 99%

Finite Element Stereo Digital Image Correlation: Framework and Mechanical Regularization

2016

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The use of Finite Element meshes in Digital Image Correlation (FE-DIC) is now widespread in experimental mechanics. Up to now FE have been much less used in Stereo-DIC. The first goal of this paper is to explain in details how to use FE in Stereo-DIC using a formulation in the physical coordinate system. More precisely, it is shown how to perform the calibration of possibly nonlinear models, shape and displacement measurement based on a FE mesh. In addition it is shown that with such a framework it is possible to regularise the measurement with a FE model based on the same mesh. For instance, using this technique, it is shown that it is possible to measure the rotation field of a bending plate in addition to its displacement.

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Automatic crack tip detection and stress intensity factors estimation of curved cracks from digital images

Cited by 34 publications

References 34 publications

Crack path tracking from full field measurements: A novel empirical methodology

Crack path tracking from full field measurements: A novel empirical methodology

A domain coupling method for finite element digital image correlation with mechanical regularization: Application to multiscale measurements and parallel computing

Finite Element Stereo Digital Image Correlation: Framework and Mechanical Regularization

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