Complete mechanical regularization applied to digital image and volume correlation

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

Digital image correlation (DIC) is an optical technique that measures displacement and strain by tracking pattern movement in a sequence of captured images during testing. DIC has gained recognition in asphalt pavement engineering since the early 2000s. However, users often perceive the DIC technique as an out-of-box tool and lack a thorough understanding of its operational and measurement principles. This article presents a state-of-art review of DIC as a crucial tool for laboratory testing of asphalt concrete (AC), primarily focusing on the widely utilized two-dimensional DIC and three-dimensional DIC techniques. To address frequently asked questions from users, the review thoroughly examines the optimal methods for preparing speckle patterns, configuring single-camera or dual-camera imaging systems, conducting DIC analyses, and exploring various applications. Furthermore, emerging DIC methodologies such as digital volume correlation and deep-learning–based DIC are introduced, highlighting their potential for future applications in pavement engineering. The article also provides a comprehensive and reliable flowchart for implementing DIC in AC characterization. Finally, critical directions for future research are presented.

OpenCorr is an open source C++ library made for research and development of digital image correlation (DIC) technology. It provides full-function modules of 2D and stereo DIC, as well as digital volume correlation (DVC). In contrast with most of current open source DIC software which is application-oriented, the sophisticated design of OpenCorr makes it developer-friendly and facilitates the method study. Users can create DIC software quickly for specific applications by assembling the modules in this library and realize new algorithms readily by modifying the modules or incorporating additional modules. Three examples are given as the showcases for the capability of OpenCorr. The first one is an image feature guided DIC method, which can accurately measure large strain over 35%. The second is a stereo DIC method combining image feature guided DIC with an epipolar constraint aided algorithm to achieve enhanced robustness. The third is a novel self-adaptive DIC method, which dynamically optimize the size and shape of subset at each point of interest (POI) according to the nearby image features. The computation speed of DIC and DVC modules is evaluated through a few tests, which demonstrate superior running efficiency of the library. OpenCorr has attracted rapidly growing interest from both academic and industrial communities. It is expected to stimulate the advance and spread of DIC technology.

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

Composite laminates possess heterogeneous microstructures which make characterization and modeling a great challenge, particularly in their failure response to different loadings. One of the most emerging research areas involves the development of a robust, high-fidelity, physics-based model to predict the progressive damage response of composites under mechanical loading. In the literature, many failure models have been developed with a view to predicting various failure modes observed in composites, including fiber breakage, fiber kinking, matrix cracking, and delamination between plies. Digital image correlation (DIC) techniques have been widely used to identify hot spots and failure evolution by tracking the surface strain histories. Although this method can capture crack propagation, the application is limited to determining surface intra-ply damage, and the resolution is generally not fine enough to capture the failure at the fiber level. The most viable approach to produce data of value for the formulation and validation of composite material models would need to be fully 3-D and in-situ. In this experiment, a proof of concept approach to study carbon fiber laminates with 3-D X-ray tomography and in-situ tensile loading is proposed and developed. Test results revealed information regarding through-ply cracking and its impact on catastrophic failure of the specimen. Based on the results of this experiment, the implementation of 3-D data correlation (digital volume correlation) can be evaluated as a way to quantify the load- and time-based material changes that lead to failure. Additionally, other types of loadings including temperature, compressive loading, and 3-point/4-point bending can be considered for future studies.

Interactions between multiple stress corrosion cracks (M-SCC) have a major influence on crack growth but are underestimated in models devoted to the evaluation of the lifetime of industrial components. In this study, the growth and interactions between multiple cracks on a sensitized Alloy 600 in a 0.01 M tetrathionate solution, were studied by digital image correlation (DIC). Cracks exceeding 55 µm in length and 0.45 µm in opening were successfully detected by DIC. The emergence and intensification of interactions modify the growth of the crack colony which evolves from a mostly surface crack propagation (lack of interactions) to in-depth propagation controlled by crack shielding. A multiphysics phase field model was jointly developed and successfully implemented to simulate intergranular M-SCC. It coupled a robust algorithm based on brittle fracture and a diffusion model. The resulting modeling allowed simulating the interactions between cracks and the shielding effects observed experimentally. Finally, 3-D quantification of crack propagation was performed by micro-tomography and digital volume correlation (DVC).

DVC analysis of a polymer material subjected to tensile loading with synchrotron radiation tomography

Accurately capturing internal crack evolution and deformation is essential for understanding rock failure and improving disaster prediction. However, traditional methods mainly focus on surface cracks and struggle to reveal the spatial characteristics of crack propagation, limiting insights into rock damage mechanisms. This study proposes a framework for automatically and visually characterizing crack behavior and deformation evolution by integrating in-situ X-ray computed tomography (XCT), deep learning, and digital volume correlation (DVC) techniques to address this limitation. This framework enhances crack identification accuracy and internal deformation field analysis. First, in-situ XCT scanning experiments were conducted to dynamically capture the crack propagation process in sandstone samples with an initial single defect. Next, a U-Net-based image segmentation approach was employed to improve crack extraction accuracy from CT images, enabling precise characterization of internal crack behavior. Finally, DVC technology was utilized to compute three-dimensional (3D) full-field displacement and strain distributions, facilitating the visualization and quantitative analysis of internal rock deformation mechanisms. The results indicate that the displacement difference between monitoring points on both sides of a crack enables the quantitative classification of crack types. Crack behavior in sandstone generally exhibits a mixed combination of opening, shearing, and tearing modes. Moreover, the heterogeneous displacement field distribution within the sandstone influences spatial variations in crack behavior. After crack formation, the internal strain field exhibits significant localization, which largely governs the sample’s failure process. The distribution characteristics of the ε yy and ε xy strain fields closely correspond to the internal crack morphology, demonstrating that their evolution effectively characterizes the internal rock damage process.

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.

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.

Understanding the dynamic fracturing and deformation behaviour of geomaterials, such as concrete and rock, is essential for underground infrastructure safety. This study integrates experimental techniques, including the Triaxial Hopkinson Bar (Tri-HB) system, digital image correlation (DIC), digital volume correlation (DVC), acoustic emission (AE), and high-speed X-ray phase contrast imaging (XPCI), to analyse the mechanical and fracturing properties of geomaterials under high strain rates. The findings reveal the interplay between stress confinement, strain rates, and microcrack evolution. A machine learning-based crack classification method is proposed to distinguish crack types and their evolution. This study provides a foundation for numerical modelling and further engineering applications.

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.

Corrosion damages of reinforced concrete characterized by X-ray CT and DVC techniques

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.