Systematic errors in digital volume correlation due to the self-heating effect of a laboratory x-ray CT scanner

Self-Adaptive Digital Volume Correlation for Unknown Deformation Fields

Digital volume correlation (DVC) extracts full-field internal deformations of solid objects or biological tissues by correlating volumetric images reconstructed using dense scans. However, due to the limitations of reconstruction methods for volumetric images, when enhancing the efficiency of DVC experiments by sparse scans, severe image artefacts occur and hence substantially reduce the accuracy of DVC measurements. To address this issue, a recently established implicit Neural Representation learning with Prior embedding (NeRP) network is introduced to reconstruct volumetric images with fewer projections. By using a reference volume and 30 projection images as network inputs, high quality deformed volume can be obtained that enhance the accuracy of DVC measurements. For validation, both numerical simulation and real uniaxial compression experiments were conducted to evaluate the displacement measurement accuracy of the proposed method. The experimental results demonstrate that the proposed method significantly reduces displacement errors compared to conventional reconstruction methods under sparse scan conditions. Specifically, the errors in the u and v directions decrease by at least an order of magnitude, while the w direction error is reduced by a factor of two. This improvement enables more accurate DVC measurements with sparse scans.

On the calibration of elastoplastic parameters at the microscale via X-ray microtomography and digital volume correlation for the simulation of ductile damage

Digital volume correlation (DVC) is a powerful technique for quantifying interior deformation within solid opaque materials and biological tissues. In the last two decades, great efforts have been made to improve the accuracy and efficiency of the DVC algorithm. However, there is still a lack of a flexible, robust and accurate version that can be efficiently implemented in personal computers with limited RAM. This paper proposes an advanced DVC method that can realize accurate full-field internal deformation measurement applicable to high-resolution volume images with up to billions of voxels. Specifically, a novel layer-wise reliability-guided displacement tracking strategy combined with dynamic data management is presented to guide the DVC computation from slice to slice. The displacements at specified calculation points in each layer are computed using the advanced 3D inverse-compositional Gauss–Newton algorithm with the complete initial guess of the deformation vector accurately predicted from the computed calculation points. Since only limited slices of interest in the reference and deformed volume images rather than the whole volume images are required, the DVC calculation can thus be efficiently implemented on personal computers. The flexibility, accuracy and efficiency of the presented DVC approach are demonstrated by analyzing computer-simulated and experimentally obtained high-resolution volume images.

Digital volume correlation (DVC) is a powerful experimental tool for quantitative 3D internal deformation measurement throughout the interior of materials or biological tissues. By comparing the volumetric images acquired at the reference and deformed states by a volumetric imaging facility (e.g., X-ray CT), DVC provides full-field displacements with subvoxel accuracy and full-field strain maps. When using DVC method for internal deformation measurement, the quality of internal speckle pattern of a test sample has an important impact on the accuracy and precision of the measured displacements. In this work, theoretical analysis and numerical simulation experiments are firstly performed to assess the quality of internal speckle patterns. The results show that the displacement measurement error of DVC is negative correlation with the sum of square of subvolume intensity gradient (SSSIG) of a subvolume. Thus, the SSSIG value of a subvolume can be used as a simple and effective metric for quality assessment of its internal speckle pattern. In practical DVC analyses, increasing the SSSIG parameter of a subvolume helps to improve the measurement accuracy and precision of DVC. Despite the SSSIG in a subvolume can be increased by enlarging its subvolume size, this simple approach, however, would lead to increased calculation burden. With a purpose to increase SSSIG without significantly increase the amount of calculation, this paper further proposes a method for optimal selection of calculation voxel points in subvolumes. Specifically, the voxel points with small gray gradients in a subvolume are excluded from DVC calculation to decrease the amount of calculation when increasing the size of the subvolume. The efficacy of the presented voxel point optimal selection method is validated by analyzing computer simulated and experimentally obtained volume images. Experimental results reveal that the proposed voxel point selection method is capable of reducing the calculation burden when increasing the subvolume size to increase the SSSIG parameters.

Digital image and volume correlation with X-ray micro-computed tomography for deformation and damage characterisation of woven fibre-reinforced composites

Complete mechanical regularization applied to digital image and volume correlation

Deformation bands, or tabular zones of localised strain, are a common manifestation of deformation in upper crustal sedimentary rocks. Any mining or energy-related engineering applications must consider the possibility of reactivating these pre-existing failure planes because doing so can cause seismicity and compartmentalise the reservoir. However, there has only been a small amount of research done on laboratory-induced deformation in rocks with natural deformation features.On a low porosity bioclastic calcarenite from the Cotiella Basin, Spanish Pyrenees, our current experimental work aims to capture, for the first time to our knowledge, the dominant failure mechanisms during the reactivation of natural deformation bands oriented at different angles to the principal stress direction. At the I12-JEEP beamline at the synchrotron facility of Diamond Light Source, UK, we carried out triaxial compression experiments using a modified version of the Mjolnir cell used by Cartwright-Taylor et al., (2022) to examine how these highly heterogeneous rocks respond to additional mechanical deformation. During the deformation experiments, 4D (time and space) x-ray tomography images (8 m voxel size resolution) were acquired. We tested confining pressures between 10 MPa and 30 MPa.The mechanical data demonstrate that the existence of natural deformation features within the tested samples weakens the material. For instance, solid samples of the host rock subjected to the same confining pressures had higher peak differential stresses. Additionally, our findings demonstrate that new deformation bands form as their angle, θ, to σ1 increases, while the reactivation of pre-exiting deformation bands in this low porosity carbonate only occurs for dipping angles close to 70o. The spatio-temporal relationships between the naturally occurring and laboratory-induced deformation bands and fractures were investigated using time-resolved x-ray tomography and Digital Volume Correlation (DVC). Volumetric and shear strain fields were calculated using the SPAM software (Stamati et al., 2020). The orientation of the recently formed failure planes is influenced by the orientation of the pre-existing bands, as well as their width and the presence (or absence) of porosity along their length. Additionally, pre-existing secondary deformation features found in the tested material trigger additional mechanical damage that either promotes the development or deflects the new failure planes.ReferencesCartwright-Taylor et al. 2022, Nature Communications 13, 6169, https://doi.org/10.1038/s41467-022-33855-zStamati et al. 2020, Journal of Open Source Software, 5(51), 2286, https://doi.org/10.21105/joss.0228

Digital volume correlation (DVC) is a powerful technique to measure the deformation inside the material or a structure. In the DVC working steps, the displacements of points of interest (POIs) are first calculated by tracking the correspondences between the volumes acquired before and after deformation using speckle correlation, which is then followed by strain calculation using the derivative of these displacements. Generally, displacement accuracy of the POIs heavily depends on the speckle quality. Unlike the speckles on the surface of test object for digital image correlation analysis can be painted or fabricated, however, the speckles inside the material or a structure for DVC analysis are often not controllable. In addition, the volume acquired by the common volumetric imaging equipment generally has limited resolution and large image noise. As a result, the poor speckle quality and large image noise introduce great displacement outliers. Strain calculation is vulnerable to displacement noise because of the high sensitivity of differential calculation to noise. Therefore, it is critical to discard the displacement outliers to guarantee the strain calculation accuracy. In this work, I proposed a ticket voting method to detect displacement outliers and then exclude them in the strain calculation. In the ticket voting method, each POI is voted by its neighboring POIs based on the residual errors in strain calculation. If this POI obtains more than half of the total tickets, it will be included in strain calculation, otherwise excluded. Both simulation and experiment results showed that the proposed method had significantly higher accuracy than the conventional method; and our DVC method can avoid the miscalculated strain concentration.

We develop speed, efficiency, and accuracy improvements to a three-dimensional (3D) digital volume correlation (DVC) algorithm, which measures displacement and strain fields throughout the interior of a material. Our goal is to perform DVC with resolution comparable to that achieved in 2D digital image correlation, in time that is commensurate with the image acquisition time. This represents a significant improvement over the current state-of-the-art available in the literature. Using an X-ray micro-CT scanner, we can resolve features at the 5 micron scale, generating 3D images with up to 36 billion voxels. We utilize linear and quadratic shape functions with tricubic spline interpolation to achieve high accuracy. We improve the algorithm’s speed and robustness through an improved coarse search, efficient implementation of spline interpolation, and using smoothing splines to address noisy image data. For DVC, the volume of data, number of correlation points, and work to solve each correlation point all grow cubically. We therefore employ parallel computing to handle this tremendous increase in computational and memory requirements. We study how various parameters affect the accuracy of the solution, and how to refine the solution to achieve improved accuracy at reduced computational cost. We demonstrate the effectiveness of our improved DVC implementation using simulated deformations of 3D micro-CT scans of polymer and ceramic foam samples.

In-situ testing capabilities of lab-scale industrial computed tomography are rapidly improving. In the present study, the compressive deformation behaviour of a flax fibre-reinforced epoxy composite is studied in an in-situ compression test with stepwise loading, during 3D imaging with X-ray Computed Tomography (XCT). The 3D volumes are processed with Digital Volume Correlation (DVC) to identify the deformation of each loading step. The natural variability of the flax texture served as an adequate speckle pattern for DVC. The precision and accuracy of the DVC were investigated prior to the analysis of the deformation volumes by digitally deforming the reference undeformed volume with a known strain of 2 %. The calculated global strain was in good agreement with the digitally applied strain. Subsequently, DVC was applied to the acquired reference and deformed volumes and the analysis was focused on the 3D strain field evolution between the consecutive loading steps. Strain localization regions were observed in the axial strain εzz and the in-plane strain εxx. In the subsequent loading steps, the onset of compressive fibre-kinking is observed as a rotating fibre inside the magnified strain region. The 3D strain field evolution allows for a better understanding of the deformation and failure mechanisms at the meso/micro-scale in natural fibre reinforced composites under compressive loads.

Mapping soil deformation around plant roots using in vivo 4D X-ray Computed Tomography and Digital Volume Correlation

Damage development during flexural loading of a 5-directional braided C/C-SiC composite, characterized by X-ray tomography and digital volume correlation

This study focuses on microstructural changes that occur within the mammalian lung when subject to blast and how these changes influence strain distributions within the tissue. Shock tube experiments were performed to generate the blast injured specimens (cadaveric Sprague-Dawley rats). Blast overpressures of 100 kPa and 180 kPa were studied. Synchrotron tomography imaging was used to capture volumetric image data of lungs. Specimens were ventilated using a custom-built system to study multiple inflation pressures during each tomography scan. This data enabled the first digital volume correlation (DVC) measurements in lung tissue to be performed. Quantitative analysis was performed to describe the damaged architecture of the lung. No clear changes in the microstructure of the tissue morphology were observed due to controlled low to moderate level blast exposure. However, significant focal sites of injury were observed using DVC, which allowed detection of bias and concentration in the patterns of strain level. Morphological analysis corroborated the findings, illustrating that the focal damage caused by a blast can give rise to diffuse influence across the tissue. It is important to characterise the non-instantly fatal doses of blast, given the transient nature of blast lung in the clinical setting. This research has highlighted the need for better understanding of focal injury and its zone of influence (alveolar inter-dependency and neighbouring tissue burden as a result of focal injury). Digital volume correlation techniques show great promise as a tool to advance this endeavour, providing a new perspective on lung mechanics post-blast.

BackgroundThe objective study was to compare medial tibial stress variations in three different situations: (1) on native knees, (2) after performing medial meniscus ramp lesions then (3) after their arthroscopic treatments with all-inside suture or total menisectomy.MethodsWe performed a biomechanical cadaveric study on specimens with a normal knee joint line alignment and without meniscal and/or cartilaginous injuries. The comparison of medial tibial stress variations was measured by a digital volume correlation (DVC) method using 7 Tesla MRI images at different loadings as close as possible to physiological conditions (1500 N).ResultsWe observed that medial tibial cartilage deformation and mechanical response, and thus the thickness of the cartilage, are restored in the case of all-inside arthroscopic suture whereas they increase in the case of menisectomy.ConclusionMedial menisectomy increases medial tibial cartilage displacement fields in physiological compression, whereas all-inside arthroscopic suture allows to get closer to the initial conditions before medial meniscus ramp lesion. This biomechanical study suggests that ramp lesions are potentially at risk of osteoarthritic evolution and that its repair would make it possible to reduce the risks associated with increased stress on the medial tibial cartilage.