Digital Volume Correlation Method Research Articles

Particle tracking-aided digital volume correlation for clay–sand soil mixtures

In this study, a novel, interdisciplinary method is introduced that merges fundamental geomechanics with computer vision to develop an advanced hybrid feature-aided digital volume correlation (DVC) technique. This technique is specifically engineered to measure and compute the full-field strain distribution in fine-grained soil mixtures. A clay–sand mixture specimen composed of quartz sand particles and kaolinite was created. Its mechanical properties and deformation behaviour were then tested using a mini-triaxial apparatus, combined with micro-focus X-ray computed tomography (μCT). The CT slices underwent image processing for denoising, segmentation of distinct phases, reconstruction of sand particles and feature extraction within the soil specimen. The proposed approach incorporated a two-step particle tracking method, which initially uses particle volume and surface area features to establish a preliminary matching list for a reference particle and then use the iterative closest point method for precise target particle matching. The soil specimen's initial displacement field was then mapped onto the DVC method's grid, and further refined through sub-voxel registration by way of a three-dimensional inverse compositional Gauss–Newton algorithm. The proposed method's effectiveness and efficiency were validated by accurately calculating the displacement and strain fields of the soil mixture sample, and comparing the results with those from a traditional DVC method. Given the soil's compositional and microstructural characteristics, these image-matching techniques can be integrated to create a versatile, efficient, and robust DVC system, suitable for a variety of soil mixture types.

Multimodal deep learning framework to predict strain localization of Mg/LPSO two-phase alloys

This study proposes a method for predicting three-dimensional (3D) local strain distribution under compressive deformation of as-cast Mg/LPSO two-phase alloys from 3D microstructure images. The 3D local strain distribution was obtained by applying the digital volume correlation method to X-ray CT images before and after compression tests. Three microstructure descriptors were extracted from the 3D microstructure images around each strain measurement point: volume fractions of the phases, persistent diagrams that can express the connectivity of the phases, and two-phase spatial correlation that can express the spatial distribution of the phases. A deep learning model was then constructed to predict local strain from the three microstructure descriptors. Since two types of descriptors were used in this study, numerical data and image data, multimodal deep learning was employed to make predictions. Thus, the use of multiple microstructure descriptors enabled predictions to be made with higher accuracy than when predictions were made from a single descriptor. Feature importance of the descriptors was assessed through correlation analysis and occlusion sensitivity analysis. The results revealed that high strain tended to occur in the region where the hard phase, LPSO phase, had a large elongated phase oriented at a 45° direction to the loading direction. This result is consistent with other previous studies and indicates that the proposed method is effective in elucidating the relationship between the microstructure and the deformation behavior of the material.

In-situ CT scan-based analysis of damage evolution of coral reef limestone under cyclic loads

The subtropical hurricane, ocean circulation, and excavation disturbance by deep engineering of island reefs bring many challenges for the insight into the mechanical behaviors of coral reef limestone (CRL) under cyclic loads. This paper investigated the damage characteristics of coral gravel limestone (CGL) under triaxial cyclic loads based on the in-situ computed tomography (CT) technique and digital volume correlation method (DVC). The results suggested that the plastic strain of CGL still existed after unloading in the stage of linear elastic deformation. Crack initiation within the specimen occurred at the stress level of about 0.72, and accelerated damage occurred when the stress level exceeded 0.86. Subsequently, we utilized DVC technique to visualize the evolution process of 3-D strain field under cyclic loads. The strain field of the specimen generated strain release in the locality of crack initiation, and the heterogeneous degree of strain field firstly increase and then decrease with the increase of upper limit pressure. Moreover, the secant modulus exhibits a trend consistent with the heterogeneous degree of strain field. Therefore, we introduced the concept of self-information entropy to characterized the heterogeneous degree of strain field, attempting to describe the process of compression and damage of sample. The evolving relationship between secant modulus and axial strain of CGL under cyclic loads was established based on the heterogeneity of strain field. The proposed model provides promising way to predict secant modulus of porous rock materials based on-site CT testing and in-situ measurement in practical engineering.

“Subvolume Splitting” DVC applied for accurate discontinuous deformation measurement and fracture parameter extraction

As a powerful experimental tool for quantifying internal full-field mechanical response, digital volume correlation (DVC) has been increasingly used for extracting crucial fracture parameters. Nevertheless, due to the limitations of its regularly-used shape functions in describing discontinuous deformation, routine subvolume-based DVC encounters challenges in accurately capturing displacement maps around discontinuities, thus impeding reliable and accurate extraction of fracture parameters. To address this issue, a “subvolume splitting” DVC (SS-DVC) method is proposed. SS-DVC first splits subvolumes into two subblocks along a pre-calculated fitted crack plane and then only tracks the subblock with continuous deformation, thereby circumventing the limitations associated with discontinuities. Additionally, a straightforward and effective computational strategy is devised to ensure correct displacement measurements around discontinuities. For validation, both numerical simulation and real three-point bending tests were conducted to examine the performance of SS-DVC in terms of displacement measurement accuracy and extraction of fracture parameters. The experimental results demonstrate that, in comparison to routine DVC, SS-DVC exhibits a significant reduction in measurement errors of displacement around discontinuities, thus enabling more accurate determination of material fracture mechanical properties.

High-accuracy optical coherence elastography digital volume correlation methods to measure depth regions with low correlation.

The decreasing correlation of optical coherence tomography (OCT) images with depth is an unavoidable problem for the depth measurement of the digital volume correlation (DVC) based optical coherence elastography (OCE) method. We propose an OCE-DVC method to characterize biological tissue deformation in deeper regions. The method proposes a strategy based on reliability layer guided displacement tracking to achieve the OCE-DVC method for the deformation measurement in deep regions of OCT images. Parallel computing solves the computational burden associated with the OCE-DVC method. The layer-by-layer adaptive data reading methods are used to guarantee the parallel computing of high-resolution OCT images. The proposed method shown in this study nearly doubles the depth of quantitative characterization of displacement and strain. At this depth, the standard deviation of displacement and strain measurements is reduced by nearly 78%. Under nonuniform deformation field, OCE-DVC method tracked the displacement with large strain gradient in depth region.

Damage Detection in a Polymer Matrix Composite from 4D Displacement Field Measurements.

Standard Digital Volume Correlation (DVC) approaches enable quantitative analyses of specimen deformation to be performed by measuring displacement fields between discrete states. Such frameworks are thus limited by the number of scans (due to acquisition duration). Considering only one projection per loading step, Projection-based Digital Volume Correlation (P-DVC) allows 4D (i.e., space and time) full-field measurements to be carried out over entire loading histories. The sought displacement field is decomposed over a basis of separated variables, namely, temporal and spatial modes. In the present work, the spatial modes are constructed via scan-wise DVC, and only the temporal amplitudes are sought via P-DVC. The proposed method is applied to a glass fiber mat reinforced polymer specimen containing a machined notch, subjected to in situ cyclic tension and imaged via X-ray Computed Tomography. The P-DVC enhanced DVC method employed herein enables for the quantification of damage growth over the entire loading history up to failure.

Mapping internal strain fields of fused filament fabrication metal filled polylactic acid structure using digital volume correlation.

With the advancement in Fused Filament Fabrication (FFF), its application is increasing widely across different industries such as aeronautical, biomedical, robotics, etc. The internal structure is becoming more complex and intricate with varying materials of reinforcement which are used to improve mechanical properties. Current measurement techniques like Digital Image Correlation (DIC) are non-destructive testing methods that do not provide enough information on the behaviour of internal microstructure for anisotropic FFF materials. Digital Volume Correlation (DVC) is non-destructive testing technique which provides full field internal 3D deformation and strain fields. Copper particle filled PLA samples manufactured using FFF method with 20, 40, 60 and 80 infill percentages were loaded in tension inside Micro-CT. X-rays were passed through the sample to get a volumetric dataset for different loadings. Using DVC method on the dataset, internal displacement and strain fields were generated for 20, 40, 60 and 80 infill percentage FFF sample.

Investigation of strain fields and anisotropy in triaxial tests on Callovo-Oxfordian claystone by X-ray micro-tomography and digital volume correlation

The objective of the present study is to investigate deformation behavior of anisotropic claystone in relation with material heterogeneity. For this purpose, a set of five triaxial compression tests are performed on samples of Callovo-Oxfordian (COx) claystone under in-situ X-ray micro-tomography monitoring. Complete 3D images of the samples full loading history were recorded and analyzed by digital volume correlation method. Full non-uniform strain fields were determined and averaged global strains were calculated. As novelty, we have particularly investigated the effects of bedding planes, initial cracks and confining stress on strain fields. The results indicate that the structural anisotropy of COx claystone has a strong influence on the local strain distribution and cracking process. The confining pressure has also an important impact on the strain fields and failure modes of samples in the triaxial tests. The cracking process of samples is strongly correlated with radial strain distribution.

Meso-scale damage characteristics of low carbon concrete with recycled aggregate based on in-situ CT test

Construction and industrial solid waste have caused some severe effects to the environment. The collaborative and effective disposals of these wastes not only have the positive impact on the usage of waste, but also could protect the environment. In this paper, low carbon concrete with recycled aggregate (LCCRA) mainly made of phosphogypsum-based cementitious material and recycled aggregate is studied. Uniaxial compression test was used to investigate the basic mechanical properties of LCCRA. In-situ computerized tomography (CT) and digital volume correlation (DVC) were used to investigate meso-scale damage characteristics of LCCRA under uniaxial compression. The results demonstrated that this concrete has a higher compressive strength than the ordinary recycled aggregate concrete (ORAC). Gray mean value and gray distribution variance from CT gray scale image can characterize the meso-scale damage process of LCCRA. Interfacial transition zone (ITZ) is the main factor that affects the meso-scale damage characteristics, which could be revealed by the in-situ CT test and DVC method.

Multi-frame DVC for temporal image sequences

Digital volume correlation (DVC) is a 3D image-based technique for displacement and strain computation. Traditionally, both (digital image correlation) DIC and DVC are methods based on two individual time frames; the estimation of the displacement and strain field is done using one reference and one moving frame as input. However, dynamic experiments generate more than two temporal frames. Therefore, with classical DVC techniques, only a subset of the available data is used. In this study, we propose a novel DVC method that can rely on more than two frames for the displacement and strain computation. The proposed method aims to be as general as possible; there is no constraint regarding the nature or the rate of the displacement (e.g., cyclic or linear). The aim of this method is to impose a temporal regularization that improves the self-consistency of the algorithm. The multi-frame DVC improves the quality of the registration in challenging situations. As an example, we investigate the dissolution of a pharmaceutical tablet in water, which undergoes three processes: swelling, gel formation, and material erosion. The accuracy of the registration—quantified by the sum of square differences (SSD)—has improved by 23% on an average with respect to the classical two-frame method. Classical DVC methods fail in registering images with structures that change appearance through time, such as the tablet that, in contact with water, reacts chemically, changing phase and becoming a gel. Moreover, we proved that multi-frame DVC is more robust in registering images with severe but realistic motion artefacts. As an example for this case, we apply the method to a series of μ-CT datasets of aluminum foam during a compression experiment. As seen with the tablets, we are in a situation where the appearance of the structures in the images changes through time, but in this case it is because of motion artefacts. Finally, the use of more than two frames makes the method more robust against noisy images, with an average improvement of 35% in registration accuracy obtained using the three-frame DVC method compared to the classical two-frame DVC method.

Study on Failure Mechanism of Mudstone Based on Digital Core and Digital Volume Correlation Method

In order to study the damage evolution law and failure mechanism of mudstone under different stress states, with the help of high-resolution CT scanning equipment, in situ CT scanning experiments of mudstone under uniaxial compression were carried out. Combined with digital core technology and the digital volume image correlation method, the 3D characterization of meso-structure and the evolution process of localized damage in mudstone were analyzed. The research shows that brittle minerals such as quartz in mudstone often exist in the form of agglomerated strips, resulting in the formation of weak structural planes at the contact surfaces of different minerals. There are a large number of primary intergranular pores near the mineral accumulation zone. With the increase in axial load, the connectivity of pores will gradually increase, cracks will gradually emerge, internal pores will develop abnormally, and rocks will reach the critical state of failure; at this time, the throat number and coordination number of pores increase obviously. There was no obvious difference found in the distribution of mineral particles of different sizes, and the slip between mineral zones was mainly dominated by small particles. The accumulated mineral zone was able to easily form a weak surface, and the aggregated mineral zone under loading was easily able to produce local deformation, which is related to the mechanical properties of the mineral zone and its surrounding rock matrix, with the rock failure easily occurring along the junction of the two minerals. The displacement in the polymeric mineral zone was small, the deformation displacement of the rock skeleton dominated by clay minerals near the quartz mineral zone was larger, and the stronger quartz minerals restrained the rock skeleton deformation in the region.

Deep learning-based digital volume correlation

With the rapid development of 3D volume imaging technology, digital volume correlation (DVC) has already made great progresses and become an indispensable tool in various engineering kingdoms. Inspired by artificial intelligence-related technologies, we develop a novel deep learning-based DVC methodology for 3D deformation analyses with the aid of a set of well-trained neural networks, hereafter referred to as DVC-Net, to implement accurate, precise and robust 3D deformation characterization in an efficient manner in this work. This proposed DVC-Net comprises three sub-networks, i.e., DVC-NetI, DVC-NetS and DVC-NetD, which are used in turn for integer-voxel searching, sub-voxel registration and displacement field denoising. Unlike traditional DVC methods, e.g., subvolume-based local DVC or finite-element method-based global DVC, the proposed DVC-Net neither explicitly relies on correlation criteria nor requires numerical iterative calculations, which greatly reduces computational complexity of the DVC analyses and therefore improves computational efficiency by at least 2∼3 orders of magnitude. Besides, it is suitable for dealing with various practical problems arising in the DVC-related studies such as volumetric images that experience changes in lighting conditions or noisy images. It is anticipated that the proposed DVC-Net can provide an alternative method for high-performance volumetric deformation characterization.

A high-accuracy and high-efficiency digital volume correlation method to characterize in-vivo optic nerve head biomechanics from optical coherence tomography

In-vivo optic nerve head (ONH) biomechanics characterization is emerging as a promising way to study eye physiology and pathology. We propose a high-accuracy and high-efficiency digital volume correlation (DVC) method to characterize the in-vivo ONH deformation from optical coherence tomography (OCT) volumes. Using a combination of synthetic tests and analysis of OCTs from monkey ONHs subjected to acutely elevated intraocular pressure, we demonstrate that our proposed methodology overcame several challenges for conventional DVC methods: First, a pre-registration technique was used to remove large ONH rigid body motion in OCT volumes which could lead to analysis failure; second, a modified 3D inverse-compositional Gaussian Newton method was used to ensure sub-voxel accuracy of displacement calculations despite high noise and low image contrast of some OCT volumes; third, a tricubic B-spline interpolation method was applied to improve computational efficiency; fourth, a confidence parameter was introduced to guide the searching path in the displacement calculation; fifth, a confidence-weighted strain calculation method was applied to further improve the accuracy. The proposed DVC method had displacement errors smaller than 0.037 and 0.028 voxels with Gaussian and speckle noises, respectively. The strain errors in the three directions were less than 0.0045 and 0.0018 with Gaussian and speckle noises, respectively. Compared with the conventional DVC method, the proposed method reduced the errors of displacement and strain calculations by up to 70% under large body motions, with 75% lower computation time, while saving about 30% memory. Our study demonstrates the potential of the proposed technique to investigate ONH biomechanics. Statement of significanceThe biomechanics of the optic nerve head (ONH) in the posterior pole of the globe play a central role in eye physiology and pathology. The application of digital volume correlation (DVC) to the analysis of optical coherence tomography (OCT) images of the ONH has emerged as a promising way to quantify ONH biomechanics. Conventional DVC methods, however, face several important challenges when analyzing OCT images of the ONH. We introduce a high-accuracy and high-efficiency DVC method to characterize in vivo ONH deformations from OCT volumes. We demonstrate the new method using synthetic tests and actual OCT data from monkey ONHs. The new method also has the potential to be used to study other tissues, as OCT applications continue to expand.

Strain induced crack initiation and the subsequent crack propagation of fiber-reinforced resin composites

Fiber-reinforced composite is a kind of excellent lightweight material that has important applications in the fields of aerospace, automotive, etc. However, due to the strong heterogeneous characteristics of its internal microstructures, material properties dramatically changed interfaces, and the rapid brittle damage process, its internal deformation and failure mechanism is a current hot research topic. Here, the synchrotron X-ray computed tomography technology and digital volume correlation method were adopted to investigate the internal three-dimensional deformation and failure evolution of the representative unit of fiber-reinforced resin composites during loading process. It shows that the deformation parameters including the local strain concentration and strain gradient in this fiber-reinforced heterogeneous system have important prediction and guiding effects on crack initiation and the subsequent propagation. These results may have positive significance for revealing the failure mechanisms, as well as microstructure and interface optimization of fiber-reinforced composites.

In-situ deformation imaging of articular cartilage using grating-based phase-contrast X-ray CT at a synchrotron light source

Quantification of deformation behavior of articular cartilage (AC) is crucial for understanding its mechanical function and response to mechanical stimuli. Here, we explored whether grating-based phase-contrast X-ray CT using monochromatic synchrotron light (grating-based msPXCT) enables in-situ quantification of local deformation in AC. Grating-based msPXCT of a porcine AC sample during axial compression test was conducted using a Talbot grating interferometer. Local displacements and strains were computed using a digital volume correlation method. The magnitude of axial strain decreased from the upper to middle sample zones and reached almost constant over the middle-lower zone, consistent with the depth-dependent density increase with compression. Thus, grating-based msPXCT may be suitable for quantitative analysis of AC deformation.

High resolution three-dimensional strain measurements in human articular cartilage

An unresolved challenge in osteoarthritis research is characterising the localised intra-tissue mechanical response of articular cartilage. The aim of this study was to explore whether laboratory micro-computed tomography (micro-CT) and digital volume correlation (DVC) permit non-destructive quantification of three-dimensional (3D) strain fields in human articular cartilage. Human articular cartilage specimens were harvested from the knee, mounted into a loading device and imaged in the unloaded and loaded states using a micro-CT scanner. Strain was measured throughout the cartilage volume using the micro-CT image data and DVC analysis. The volumetric DVC-measured strain was within 5% of the known applied strain. Variation in strain distribution between the superficial, middle and deep zones was observed, consistent with the different architecture of the material in these locations. These results indicate DVC method may be suitable for calculating strain in human articular cartilage.

INTERNAL SPECKLE PATTERN QUALITY ASSESSMENT AND OPTIMAL SELECTION OF VOXEL POINTS FOR DIGITAL VOLUME CORRELATION

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.

Influences of structural anisotropy and heterogeneity on three-dimensional strain fields and cracking patterns of a clay-rich rock

In this study, three-dimensional non-uniform strain fields and cracking patterns of a clay-rich rock are investigated. Uniaxial compression creep tests are performed on samples drilled in five different orientations with respect to bedding planes. A digital volume correlation method is applied to X-ray micro-tomographic images taken during the creep tests to calculate both full local strain fields and average global strains. Both instantaneous and time-dependent strains, respectively, induced by applied stress and creep mechanism are analysed. Strain heterogeneity inside samples is quantified by calculating strain concentration zones, which are correlated with the presence of stiff inclusions. Cracking patterns of samples are characterized through reconstructed X-ray micro-tomographic images. Correlations between major cracking patterns and strain concentration zones are investigated.

A Critical Review of Current Imaging Techniques to Investigate Water Transfers in Wood and Biosourced Materials

This review paper proposes a critical overview of experimental techniques and innovative experimental methods to observe and deeply understand the migration of water inside wood and biosourced materials. The state of the art of the knowledge of water transfer phenomena are first presented, namely liquid and bound water migration, together with shrinkage/swelling. Then, the papers presenting the 3D imaging techniques at high resolution offered by recent technologies, such as magnetic resonance imaging, X-ray tomography, and in situ tomography-mechanical tests. They enable visualization and analysis of water transfer mechanisms in eco-responsible materials such as natural fiber-concretes, wood, and biobased insulating materials. The 2D and 3D images processed with specific software packages allow for an overview of the distribution and orientation of the material components, as well as the moisture-content field which affects most properties of the materials such as mechanical properties (compression and tensile strength), hygro-thermal performance, as well as durability. Finally, the Digital Volume Correlation method allows for the observation and evaluation of deformation, cracks and failure mechanisms resulting from either a mechanical or hydric loading. The paper intends to help the reader to acquire a deep understanding of water imbibition and drying mechanisms in such materials.

Morphology characterization and in-situ three-dimensional strain field monitor of short carbon fiber-reinforced polymer composites under tension

In-situ Micro X-ray computed tomography (μCT) offers a new opportunity to monitor the 3D morphology and damage evolution of short carbon fiber-reinforced polymer (SCFRP) composite. However, the sample size of in-situ μCT is generally limited to achieve high revolution, which resulted in different mechanical behavior compared with that obtained from standard samples. In this study, μCT scans with two resolutions were combined to character the 3D geometrical morphology and monitor the 3D deformation fields of SCFRP composites under tension. High resolution μCT scans with voxel size of 0.68 μm were used to quantify the geometric characteristics of fibers and void defects inside small samples. Low resolution in-situ μCT scans with voxel size of 4 μm and digital volume correlation method were utilized to monitor the 3D deformation fields of standard specimens under tension. The failure behavior of SCFRP was determined by the micro geometric morphology. The fracture surface under tension is oriented along 120° and 240° in the XY plane, which is consistent with the fiber and debonding distribution.