Investigating the mechanical behaviour of Fukushima MCCI using synchrotron Xray tomography and digital volume correlation

Paraskevoulakos, C.; Forna-Kreutzer, J. Paul; Hallam, K; Jones, Christopher P.; Scott, Thomas Bligh; Gausse, Clémence; Liu, Dong; Reinhard, Christina; Corkhill, Claire L.; Mostafavi, Mahmoud

doi:10.1038/s41529-022-00264-y

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…Digital image correlation (DIC) is a noncontact photogrammetric technique that has the ability to measure full-field deformation (2D and 3D) from a sequence of images. [29][30][31][32] This method tracks pixel movements through correlated speckle pattern subsets from each image in a sequence to accurately describe the full-field deformation over the specimen surface. This can aid in detecting invisible internal defects by correlating the defect-induced perturbations in the deformation field.…”

Section: Introductionmentioning

confidence: 99%

Identification of Location and Geometry of Invisible Internal Defects in Structures using Deep Learning and Surface Deformation Field

Timilsina,

Jang,

et al. 2023

Advanced Intelligent Systems

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On‐site inspection of invisible subsurface defects in multiscale structural materials by conventional nondestructive testing (NDT) methods, such as X‐ray and ultrasound, requires complex sample preparation and data acquisition processes. Moreover, the inspected area is very small. Herein, a simple, inexpensive, and ultrasensitive NDT method for identifying and classifying the geometries of subsurface defects using commercial cameras, digital image correlation software, and object detection (OD) algorithms is developed. Three OD algorithms—Faster region‐based convolutional neural network (Faster R‐CNN), Mask R‐CNN, and you‐only‐look‐once (YOLO)v3—are evaluated for their ability to locate defects and identify defect geometries. Specifically, bounding boxes of two sizes (large and small) are applied to the regions of defect‐induced perturbations in strain tensors, which serve as virtual representatives of invisible subsurface defects. The performance of the proposed approach is validated on test datasets of known and unknown defect types. The experimental results confirm that the proposed approach can effectively utilize the surface deformation field information to accurately and reliably locate and identify subsurface defects. The method is nondestructive and low cost, enables real‐time detection, is robust against noise‐dominated deformation fields, and can be applied to various structural deformations. The method is therefore suitable for multiscale structural health monitoring and characterization of internal defects in materials.

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Section: Introductionmentioning

confidence: 99%

Identification of Location and Geometry of Invisible Internal Defects in Structures using Deep Learning and Surface Deformation Field

Timilsina,

Jang,

et al. 2023

Advanced Intelligent Systems

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Sensitivity Study Using Synthetic 3D Image Datasets to Investigate the Effect of Noise Artefacts on Digital Volume Correlation

Paraskevoulakos,

Ghosh,

Andriollo

et al. 2024

Exp Mech

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Background The potential effect of image noise artefacts on Digital Volume Correlation (DVC) analysis has not been thoroughly studied and, more particularly quantified, even though DVC is an emerging technique widely used in life and material science over the last decade. Objective This paper presents the results of a sensitivity study to shed light on the effect of various noise artefacts on the full-field kinematic fields generated by DVC, both in zero and rigid body motion. Methods Various noise artefacts were studied, including the Gaussian, Salt & Pepper, Speckle noise and embedded Ring Artefacts. A noise-free synthetic microstructure was generated using Discrete Element Modelling (DEM), representing an idealistic case, and acting as the reference dataset for the DVC analysis. Noise artefacts of various intensities (including selected extreme cases) were added to the reference image datasets using MATLAB (R2022) to form the outline of the parametric study. DVC analyses were subsequently conducted employing AVIZO (Thermo Fisher). A subset-based local approach was adopted. A three-dimensional version of the Structural Similarity Index Measure (SSIM) was used to define the similarity between the compared image datasets on each occasion. Sub-pixel rigid body motion was applied on the DEM-generated microstructure and subsequently “poisoned” with noise artefacts to evaluate mean bias and random error of the DVC analysis. Results When the local approach is implemented, the sensitivity study on zero motion data revealed the insignificant effect of the Gaussian, Salt & Pepper, and Speckle noise on the DVC-computed kinematic field. Therefore, the presence of such noise artefacts can be neglected when DVC is executed. On the contrary, Ring Artefacts can pose a considerable challenge and therefore, DVC results need to be evaluated cautiously. A linear relationship between SSIM and the correlation index is observed for the same noise artefacts. Gaussian noise has a pronounced effect on the mean bias error for sub-pixel rigid body motion. Conclusions Generating synthetic image datasets using DEM enabled the investigation of a variety of noise artefacts that potentially affect a DVC analysis. Given that, any microstructure – resembling the material studied – can be simulated and used for a DVC sensitivity analysis, supporting the user in appropriately evaluating the computed kinematic field. Even though the study is conducted for a two-phase material, the method elaborated in this paper also applies to heterogeneous multi-phase materials also. The conclusions drawn are valid within the environment of the AVIZO DVC extension module. Alternative DVC algorithms, utilising different approaches for the cross-correlation and the sub-pixel interpolation methods, need to be investigated.

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Desiccation and crack behavior of modified waste materials–clay mixture as landfill liner: a systematic review

Puspita,

Budihardjo,

Samadikun

2024

Int. J. Environ. Sci. Technol.

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Investigating the mechanical behaviour of Fukushima MCCI using synchrotron Xray tomography and digital volume correlation

Cited by 4 publications

References 42 publications

Identification of Location and Geometry of Invisible Internal Defects in Structures using Deep Learning and Surface Deformation Field

Identification of Location and Geometry of Invisible Internal Defects in Structures using Deep Learning and Surface Deformation Field

Sensitivity Study Using Synthetic 3D Image Datasets to Investigate the Effect of Noise Artefacts on Digital Volume Correlation

Desiccation and crack behavior of modified waste materials–clay mixture as landfill liner: a systematic review

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