Results of the 2013 UT modeling benchmark obtained with models implemented in CIVA

Toullelan, Gwénaël; Raillon, R.; Chatillon, Sylvain; Lonne, Sébastien

doi:10.1063/1.4865081

Cited by 6 publications

(3 citation statements)

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“…The set-up is more than the one used in the DPS experiments: both the top surface and blackwall are more indulated and there is a backwall breaking flaw, creating additional response, see Figures 15b and 16a. The numerical experiments conducted with CIVA [22] (a commercial package for analyzing and simulating NDT [23][24][25][26][27][28], in particular, NDT of components with irregular surfaces [7,8,28,29]) showed that the notch fabricated for the purposes of this experiment was best imaged using the half-skip LTT mode, with the L (longitudinal) transmitted signal converting at the backwall to the T (transverse) and then reflecting from the notch, so that the received signal is also T, see Figure 16a. The set-up is more challenging than the one used in the DPS experiments: both the top surface and blackwall are more indulated and there is a backwall breaking flaw, creating additional response, see Figures 15b and 16a.…”

Section: Testing Autonde On Cea Datamentioning

confidence: 99%

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Fradkin¹,

Altinbasak²,

Darmon

2021

Applied Sciences

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Crack characterization is one of the central tasks of NDT&E (the Non-destructive Testing and Evaluation) of industrial components and structures. These days data necessary for carrying out this task are often collected using ultrasonic phased arrays. Many ultrasonic phased array inspections are automated but interpretation of the data they produce is not. This paper offers an approach to designing an explainable AI (Augmented Intelligence) to meet this challenge. It describes a C code called AutoNDE, which comprises a signal-processing module based on a modified total focusing method that creates a sequence of two-dimensional images of an evaluated specimen; an image-processing module, which filters and enhances these images; and an explainable AI module—a decision tree, which selects images of possible cracks, groups those of them that appear to represent the same crack and produces for each group a possible inspection report for perusal by a human inspector. AutoNDE has been trained on 16 datasets collected in a laboratory by imaging steel specimens with large smooth planar notches, both embedded and surface-breaking. It has been tested on two other similar datasets. The paper presents results of this training and testing and describes in detail an approach to dealing with the main source of error in ultrasonic data—undulations in the specimens’ surfaces.

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Section: Testing Autonde On Cea Datamentioning

confidence: 99%

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Fradkin¹,

Altinbasak²,

Darmon

2021

Applied Sciences

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“…To validate the accuracy of the new parallel model, the results of both of its implementations have been studied. As the CIVA 11.0 software is validated against real experiments through benchmarking, especially during the QNDE 2013 conference, its simulations will serve as a point of reference for result validation [7].…”

Section: Results Validationsmentioning

confidence: 99%

A fast ultrasonic simulation tool based on massively parallel implementations

Lambert¹,

Rougeron²,

Lacassagne³

et al. 2014

AIP Conference Proceedings

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This paper presents a CIVA optimized ultrasonic inspection simulation tool, which takes benefit of the power of massively parallel architectures : graphical processing units (GPU) and multi-core general purpose processors (GPP). This tool is based on the classical approach used in CIVA : the interaction model is based on Kirchoff, and the ultrasonic field around the defect is computed by the pencil method. The model has been adapted and parallelized for both architectures. At this stage, the configurations addressed by the tool are : multi and mono-element probes, planar specimens made of simple isotropic materials, planar rectangular defects or side drilled holes of small diameter. Validations on the model accuracy and performances measurements are presented.

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“…The number of mesh points of the rough surface along the local axes x and y has then to be automatically fixed by the chosen mesh algorithm. The previously described Gaussian rough surface model is currently implemented for planar and cylindrical surfaces in 3D computations, and for their linear cross-sections in 2D, in the CIVA software [28] (platform for non-destructive evaluation simulation [29][30][31][32][33]). Examples of generated surfaces for small specimens with relatively high roughness are shown in Figure 1.…”

Section: Implementation Example For Generation Of An Individual Gaussian Rough Surfacementioning

confidence: 99%

Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances

Darmon

Dorval

Baqué³

2020

Applied Sciences

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This paper proposes a brief review of acoustic wave scattering models from rough surfaces. This review is intended to provide an up-to-date survey of the analytical approximate or semi-analytical methods that are encountered in acoustic scattering from random rough surfaces. Thus, this review focuses only on the scattering of acoustic waves and does not deal with the transmission through a rough interface of waves within a solid material. The main used approximations are classified here into two types: the two historical approximations (Kirchhoff approximation and the perturbation theory) and some sound propagation models more suitable for grazing observation angles on rough surfaces, such as the small slope approximation, the integral equation method and the parabolic equation. The use of the existing approximations in the scientific literature and their validity are highlighted. Rough surfaces with Gaussian height distribution are usually considered in the models hypotheses. Rather few comparisons between models and measurements have been found in the literature. Some new criteria have been recently determined for the validity of the Kirchhoff approximation, which is one of the most used models, owing to its implementation simplicity.

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Results of the 2013 UT modeling benchmark obtained with models implemented in CIVA

Cited by 6 publications

References 0 publications

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

A fast ultrasonic simulation tool based on massively parallel implementations

Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances

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