Classical Digital Image Correlation Research Articles

Three-dimensional shear angle determination with application to shear-frame test

The shear frame test is very often used in the context of plastics engineering to examine only the fiber construction for its deformability. Here, it is common to assume a homogeneous deformation, which apparently exists as a global deformation due to the shear frame. This results in the assumption of a constant overall shear angle. In this paper, we compare two analytical approaches to describe the deformation and the resulting strains in detail, particularly, the local shear angle calculation. As a second step, the extension of the shear angle computation based on curvilinear, three-dimensional surface deformations is applied to optical results of the experimental deformation behavior. For this purpose, a three-dimensional image correlation system is drawn on to determine the local, tangential shear angle even for folded fabrics. This requires an approach which is different to the classical digital image correlation method since the speckle varnish influences the deformation of the very compliant fiber bundles. In a third step, the same procedure is applied to the results of three-dimensional finite element computations since the programs do not provide the shear angle measure. The shear angle determination is discussed at various examples. As a side result of the investigations, the material parameters for orthotropic elasticity are determined using a representative volume element approach exploiting μ-CT data. Although the wrinkling prediction calculations depend on the pre-deformation, finite element discretization, and uncertainties of the specimens, calculation results might be used as an indicator to improve the specimen geometries.

Architecture-Driven Digital Image Correlation Technique (ADDICT) for the measurement of sub-cellular kinematic fields in speckle-free cellular materials

Measuring displacement and strain fields at low observable scales of complex microstructures still remains a challenge in experimental mechanics often because of the combination of low definition images with poor texture at this scale. This is the case for cellular materials, for which complex local phenomena can occur. The aim of this paper is to design and validate numerically and experimentally a Digital Image Correlation (DIC) technique for the measurement of local displacement fields of samples with complex cellular geometries (i.e. samples presenting multiple random holes). It consists of a DIC method assisted with a physically sound weak regularization using an elastic B-spline image-based model. This technique introduces a separation of scales above which DIC is dominant and below which it is assisted with image-based modeling. Several in silico experimentations are performed in order to finely analyze the influence of the introduced regularization lengths for different input mechanical behaviors (elastic, elasto-plastic and geometrically non-linear) and in comparison with true error quantification. We show that the method can estimate complex local displacement and strain fields with speckle-free low definition images, even in non-linear regimes such as local buckling or plasticity. Finally, an experimental validation is performed in 2D-DIC to allow for the comparison of the proposed method on low resolution speckle-free images with a classic DIC on speckled high resolution images.

A pre-processing method for digital image correlation on rotating structures

Inter-frame structural motions are required to be small for classic Digital Image Correlation (DIC) algorithms, which severely limits the application of the technique in large rotation cases. To solve the problem, multiple methods to obtain initial guesses of deformation parameters have been introduced over recent years. However, the success rate is still limited by rotation angles and the efficiency is low due to extra calculations with these methods. To further develop the application of DIC in large rotation cases, Phase Mapping Method (PMM) is introduced in this paper. The principle of the proposed method is to rearrange the obtained images to be sorted by the structural rotating phases, which are previously mapped into the range of 0 to 2π. As a result, for an image set of n frames, the average inter-frame rotating angle is reduced to 2πn-1, and the original image set is transferred into an equivalent semi-static one by PMM. Additionally, a fast phase-detection approach is introduced to automate PMM in cases that the structural rotating speeds are unknown. By performing multiple tests, it is proved that PMM, as a pre-processing method, can significantly improve the success rate and the efficiency of DIC, whether used alone or concurrently with other algorithms. The first natural frequencies and mode shapes of a rotating beam under different rotating speeds are extracted through vibration test, from which the hardening effect is observed.

Fluorescent digital image correlation applied for macroscale deformation measurement

Fluorescent digital image correlation (DIC) has been implemented on monocular and binocular fluorescence microscopes for shape and deformation measurements at the microscale and even the nanoscale by tracking fluorescent particles. This technique, however, has not been used with regular optical imaging systems for DIC measurements at the macroscale so far. In this Letter, by combining the photoluminescence nature of fluorescent materials and bandpass filtering imaging, we discover that fluorescent DIC also features some distinct advantages in macroscale characterizations, e.g., specular reflection elimination, high speckle contrast, and the capability to simultaneously observe superficial/internal surfaces of transparent structures. To show these advantages and reveal the potential applications of fluorescent DIC, comparative experiments, including shape, displacement, and deformation measurements, were carried out. Furthermore, some issues accompanied by employing fluorescent imaging in DIC were discussed. This study indicates that upgrading classic DIC to fluorescent DIC can significantly improve its performance, enhance its functions, and expand its applications without introducing additional problems.

Elimination of systematic error in digital image correlation caused by intensity interpolation by introducing position randomness to subset points

A strategy called random subset is presented to reduce systematic error in digital image correlation caused by intensity interpolation. In classical digital image correlation techniques, the subset points fall at integer locations; by contrast, the proposed random subset strategy imposes random offsets on the conventional locations, and consequently the subset points are located at non-integer positions. The introduction of position randomness to subset points makes the random subset strategy distinctly different from the classical digital image correlation technique. The random offsets can follow various probability distributions. In this work, two types of random offset were studied as examples, and the maximum of the random offsets was adjusted to modify performance. Using the random subset strategy, random-subset-based subpixel registration algorithms were developed in this study, including the random-subset-based forward additive Gaussian–Newton algorithm and the random-subset-based inverse compositional Gauss–Newton algorithm. The measurement accuracy of the random subset-based subpixel registration algorithms was investigated. Numerical simulations and real-world experiments demonstrated that as the maximum of the random offsets increases, the amplitude of the sinusoidal-shaped systematic error changes from positive to negative. Hence, if an appropriate random offset is selected, systematic error can be considerably reduced. In fact, the accuracy enhancement is greater than one order of magnitude, and systematic error is virtually eliminated.

A q-Factor-Based Digital Image Correlation Algorithm (qDIC) for Resolving Finite Deformations with Degenerate Speckle Patterns

Digital image correlation (DIC) has become a widely utilized non-contact, full-field displacement measurement technique for obtaining accurate material kinematics. Despite the significant advances made to date, high resolution reconstruction of finite deformations for images with intrinsically low quality speckle patterns or poor signal-to-noise content has not been fully addressed. In particular, large image distortions imposed by materials undergoing finite deformations create significant challenges for most classical DIC approaches. To address this issue, this paper describes a new open source DIC algorithm (qDIC) that incorporates cross-correlation quality factors (q-factors), which are specifically designed to assess the quality of the reconstructed displacement estimate during the motion reconstruction process. A q-factor provides a robust assessment of the uniqueness and sharpness of the cross-correlation peak, and thus a quantitative estimate of the subset-based displacement measure per given image subset and level of applied deformation. We show that the incorporation of energy- and entropy-based q-factor metrics leads to substantially improved displacement predictions, lower noise floor, and reduced decorrelation even at significant levels of image distortion or poor speckle quality. Furthermore, we show that q-factors can be utilized as a quantitative metric for constructing a hybrid incremental-cumulative displacement correlation scheme for accurately resolving very large homogeneous and inhomogeneous deformations, even in the presence of significant image data loss.

Modified digital image correlation for balancing the influence of subset size choice

A modified digital image correlation (DIC) method is presented to balance the influence of subset size choice, improve the calculation efficiency, and enhance the measurement accuracy. The relations between pixels and the central pixel are investigated. A relatively large subset size is selected in the method and all the pixels in a subset are treated nonuniformly. Weight is assigned to each pixel dependent on the significance of the pixel to track a subset from deformed images, and weights are computed by Butterworth function based on the distances between the pixels and the central pixel in the subset. Compared with classical DIC, the proposed method is a compromise between a small and a large subset. The choice of subset size for classical DIC is displaced by choice of parameters of Butterworth function in the presented method. The influence of subset size is reduced, and the calculation efficiency is improved with enhanced measurement accuracy by the modified method. Computer-simulated speckle patterns and real experiments are applied to verify the proposed method.

Deformation-induced speckle-pattern evolution and feasibility of correlational speckle tracking in optical coherence elastography.

Feasibility of speckle tracking in optical coherence tomography (OCT) based on digital image correlation (DIC) is discussed in the context of elastography problems. Specifics of applying DIC methods to OCT, compared to processing of photographic images in mechanical engineering applications, are emphasized and main complications are pointed out. Analytical arguments are augmented by accurate numerical simulations of OCT speckle patterns. In contrast to DIC processing for displacement and strain estimation in photographic images, the accuracy of correlational speckle tracking in deformed OCT images is strongly affected by the coherent nature of speckles, for which strain-induced complications of speckle “blinking” and “boiling” are typical. The tracking accuracy is further compromised by the usually more pronounced pixelated structure of OCT scans compared with digital photographic images in classical DIC applications. Processing of complex-valued OCT data (comprising both amplitude and phase) compared to intensity-only scans mitigates these deleterious effects to some degree. Criteria of the attainable speckle tracking accuracy and its dependence on the key OCT system parameters are established.

Pixel-level robust digital image correlation

Digital Image Correlation (DIC) is a well-established non-contact optical metrology method. It employs digital image analysis to extract the full-field displacements and strains that occur in objects subjected to external stresses. Despite recent DIC progress, many problematic areas which greatly affect accuracy and that can seldomly be avoided, received very little attention. Problems posed by the presence of sharp displacement discontinuities, reflections, object borders or edges can be linked to the analysed object's properties and deformation. Other problematic areas, such as image noise, localized reflections or shadows are related more to the image acquisition process. This paper proposes a new subset-based pixel-level robust DIC method for in-plane displacement measurement which addresses all of these problems in a straightforward and unified approach, significantly improving DIC measurement accuracy compared to classic approaches. The proposed approach minimizes a robust energy functional which adaptively weighs pixel differences in the motion estimation process. The aim is to limit the negative influence of pixels that present erroneous or inconsistent motions by enforcing local motion consistency. The proposed method is compared to the classic Newton-Raphson DIC method in terms of displacement accuracy in three experiments. The first experiment is numerical and presents three combined problems: sharp displacement discontinuities, missing image information and image noise. The second experiment is a real experiment in which a plastic specimen is developing a lateral crack due to the application of uniaxial stress. The region around the crack presents both reflections that saturate the image intensity levels leading to missing image information, as well as sharp motion discontinuities due to the plastic film rupturing. The third experiment compares the proposed and classic DIC approaches with generic computer vision optical flow methods using images from the popular Middlebury optical flow evaluation dataset. Results in all experiments clearly show the proposed method's improved measurement accuracy with respect to the classic approach considering the challenging conditions. Furthermore, in image areas where the classic approach completely fails to recover motion due to severe image de-correlation, the proposed method provides reliable results.

A space–time approach in digital image correlation: Movie-DIC

A new method is proposed to estimate arbitrary velocity fields from a time series of images acquired by a single camera. This approach, here focused on a single spatial plus a time dimension, is specialized to the decomposition of the velocity field over rectangular shaped (finite-element) bilinear shape functions. It is therefore assumed that the velocity field is essentially aligned along one direction. The use of a time sequence over which the velocity is assumed to have a smooth temporal change allows one to use elements whose spatial extension is much smaller than in traditional digital image correlation based on successive image pairs. This method is first qualified by using synthetic numerical test cases, and then applied to a dynamic tensile test performed on a tantalum specimen. Improvements with respect to classical digital image correlation techniques are observed in terms of spatial resolution.

Identification of a macroscopic anisotropic damage model using digital image correlation and the equilibrium gap method

The aim of the work is to demonstrate how an anisotropic damage model may be identified from full field measurements retrieved during a heterogeneous test. The example of a biaxial test performed on a 3D C / C composite is used. In a first step, the displacement fields measured by classical Digital Image Correlation are used as input data of a finite difference version of the Equilibrium Gap Method. A benefit from unloadings (assumed to be elastic) is shown to retrieve a damage law. In a second step, inelastic strains can be assessed from the total measured strain and the elastic estimated strains. The constitutive parameters relative to the inelastic part of the model are then identified.

A high-efficiency digital image correlation method based on a fast recursive scheme

Due to computational complexity of correlation searching, the digital image correlation (DIC) method is often extremely time consuming in image processing and optical measurement, which has limited its further applications to a great extent. This paper develops a fast recursive scheme to mathematically reduce the computational burden of the traditional DIC technique and therefore improve its efficiency in deformation calculation. A global sum-table approach is proposed to simplify the computations of all double sums arising in the zero-normalized cross-correlation coefficient (ZNCC). A fast recursive algorithm is established to accelerate the calculation of the cross-correlation term in the ZNCC. Both theoretical analysis and actual displacement acquisition are carried out to validate the performance of the new DIC algorithm, which indicates that the fast recursive scheme can improve computational efficiency of integer-pixel correlation searching by about 10 to 50 times in comparison with the classic DIC algorithm, on the condition of keeping the measurement accuracy.

“Finite-Element” Displacement Fields Analysis from Digital Images: Application to Portevin–Le Châtelier Bands

A new methodology is proposed to estimate displacement fields from pairs of images (reference and strained) that evaluates continuous displacement fields. This approach is specialized to a finite-element decomposition, therefore providing a natural interface with a numerical modeling of the mechanical behavior used for identification purposes. The method is illustrated with the analysis of Portevin–Le Châtelier bands in an aluminum alloy sample subjected to a tensile test. A significant progress with respect to classical digital image correlation techniques is observed in terms of spatial resolution and uncertainty.

The study of fractal correlation method in the displacement measurement and its application

The classical digital speckle, or digital image, correlation method of deformation measurement is based on gray level correlation between unformed and deformed digital images. The pattern of artificial random speckles and the natural textures on some object's surfaces have fractal characteristics, and their fractal dimensions represent both gray and morph information. Furthermore, the fractal dimensions are stable feature parameters of the patterns. The digitized images of the patterns are confirmed to be also fractals. By this fact, a new method of displacement measurement is developed in the paper, based on the fractal dimensions correlation. The in-plane displacement fields of a body can be acquired. In order to verify the validity of the new method, an experiment has been designed and the results have been compared with those obtained from the classical digital image correlation method. The validity of the new method is not less than that of classical method. Further discussions about the traits and the developing vista of the method are given at the end.