Digital Image Correlation Algorithm Research Articles

Algorithm Analysis and Optimization of a Digital Image Correlation Method Using a Non-Probability Interval Multidimensional Parallelepiped Model

Digital image correlation (DIC), a widely used non-contact measurement technique, often requires empirical tuning of several algorithmic parameters to strike a balance between computational accuracy and efficiency. This paper introduces a novel uncertainty analysis approach aimed at optimizing the parameter intervals of a DIC algorithm. Specifically, the method leverages the inverse compositional Gauss–Newton algorithm combined with a prediction-correction scheme (IC-GN-PC), considering three critical parameters as interval variables. Uncertainty analysis is conducted using a non-probabilistic interval-based multidimensional parallelepiped model, where accuracy and efficiency serve as the reliability indexes. To achieve both high computational accuracy and efficiency, these two reliability indexes are simultaneously improved by optimizing the chosen parameter intervals. The optimized algorithm parameters are subsequently tested and validated through two case studies. The proposed method can be generalized to enhance multiple aspects of an algorithm’s performance by optimizing the relevant parameter intervals.

Increased virtual resolution for sub-pixel displacement algorithm optimization in digital image correlation for AISI 1020 steel

The development of the digital image correlation (DIC) technique has required the use of cameras with increasingly higher resolution, as well as requiring complex sub-pixel tracking algorithms with exponentially increasing computational cost. This article proposes a DIC algorithm based on the correlation coefficient curve-fitting method, minimizing SSD or ZNSSD correlation criteria, with the help of increasing the virtual resolution of the images through bicubic interpolation, for obtain an accurate, robust and reliable algorithm according to the highest computational efficiency. The developed algorithm was virtually validated using synthetic images and experimentally using AISI 1020 steel samples test in tension, performed with strain gauges. The results showed a reduction in the error observed in synthetic images with increased magnification and good agreement with the deformation data obtained using the conventional extensometry technique.

StrainNet-LD: Large Displacement digital image correlation based on deep learning and displacement-field decomposition

First, a displacement-field decomposition strategy is proposed that effectively enhances the accuracy of digital image correlation (DIC) algorithms under large displacement or deformation conditions. The implementation methodologies of displacement-field decomposition under both Lagrangian and Eulerian descriptions are derived and presented. Second, displacement-field decomposition is applied to speckle image correlation algorithms based on deep learning, resolving the contradiction between registration capability of large displacement and the accuracy. A CUDA-accelerated and residual learning-based strain fitting algorithm is proposed for high-precision and real-time strain computation. This method is named “StrainNet for Large Displacement” (StrainNet-LD). StrainNet-LD achieves a calculation speed of 768 × 768 × 6 fps (768 × 768 × 3 fps with strain calculated simultaneously) on GPU with displacement MAE < 0.05 pixels and strain MAE < 0.002ε. Finally, rubber tensile tests, 3D motion measurement of composite shells, and morphology reconstruction experiments of speckle-free composite repair structures are conducted to validate the algorithm's robustness and efficiency. Some vital discussions of displacement-field decomposition are given in the Appendixes. The code and dataset are available at https://github.com/GW-Wang-thu/StrainNet-LD.

Resolving localized geometrically necessary dislocation densities in Al-Mg polycrystal via in situ EBSD

The distribution of geometrically necessary dislocation (GND) densities is critical to understanding the heterogeneous plastic deformation at intragranular scales in polycrystals. In this work, we performed an in situ electron backscatter diffraction (EBSD) measurement during the tensile test on a polycrystalline Al-Mg alloy. The EBSD patterns were processed through the integrated digital image correlation algorithm to enhance angular resolution. Based on the Nye dislocation density tensor, GND densities of 18 dislocation types in fcc crystals were resolved and mapped at macroscopic strains ranging from 5% to 16%. The evolution of GND distribution showed that GNDs were generated near grain boundaries at small strains and later localized at grain interiors along subgrain boundaries and slip bands at large strains. The inhomogeneous increase in GND densities of different dislocation types, representing dislocation substructures, along the subgrain boundaries was disclosed. Meanwhile, high GND densities were observed along the slip bands when a double noncoplanar slip system was activated inside the grains. The obstructed movement of dislocations by Lomer junctions explained the GND storage in different {111} slip planes. The existence of Lomer junctions was proven by Burgers circuit analysis with high-resolution transmission electron microscopy, which explained the increase in [101] screw dislocation density according to the dislocation reaction.

Three-dimensional surface temperature measurement using lifetime-based phosphor thermometry

Abstract In this study, we propose a three-dimensional (3D) surface temperature measurement method based on the principle of stereoscopic 3D reconstruction and the dependence of phosphorescence lifetime on temperature. A 385-nm UV(Ultraviolet) light was used as the excitation light, and two high-speed cameras were used as the detectors. The phosphor MFG (Mg4FGeO6: Mn4+) was mixed with the binder HPC and sprayed onto the tested 3D surface. The natural texture generated by the surface roughness of the phosphor coating was used as a feature for cross-correlation calculations. The digital image correlation (DIC) algorithm was used to match these feature positions in the phosphorescent images from the two cameras. The effects of the excitation angle and detecting angle were analyzed. The results indicate that the temperature measurement based on phosphorescent lifetime was not affected by the excitation and detecting angle. The method was validated on a turbine blade as an example of a 3D surface to demonstrate the capability. A comparison of the measurement results with the thermocouples proves that the current method can successfully measure the temperature on 3D surfaces with a maximum difference of 1.63°C. The spatial accuracy of the method was obtained by comparing with the measurement results of a 3D scanner, which shows that the maximum absolute error of the 3D reconstruction was 0.350 mm. The current study proposes a promising 3D surface temperature measurement method, which is expected to be widely used in gas turbine blades, Internal Combustion (IC) engine cylinders, complex curved heat exchangers, and other fields due to its non-contact measurement, low susceptibility to infrared radiation interference, high measurement accuracy, and ability to withstand harsh environments.&amp;#xD;

Experimental Study on Rock Deformation Localization Using Digital Image Correlation and Acoustic Emission

In this study, the digital image correlation (DIC) method and acoustic emission (AE) technology were combined to study the evolution of rock deformation localization in detail. The second-order spatial–temporal subset DIC (STS-DIC) algorithm was proposed and used for measuring strongly heterogeneous deformation fields of red sandstone specimens under uniaxial compression. The evolution of the deformation field was analyzed with a focus on the deformation localization stage. The length and width of the deformation localization band (DLB) were measured, and the relationships between the relative sliding rate of the DLB, the relative opening rate of the DLB, and the AE counts were identified. Deformation localization was found to result from the rapid evolution of the strain concentration before the peak stress. The complete development of the DLB is an inducing factor for catastrophic rock failure, and the failure modes of the rock specimens were consistent with the final state of the DLB. A good correlation was identified between the AE counts and the relative displacement rate of the DLB, and the sliding rate was found to have a significant influence on the AE counts.

A Technique for In-Situ Displacement and Strain Measurement with Laboratory-Scale X-Ray Computed Tomography

Abstract Purpose: Establish a technique for simultaneous interrupted volumetric imaging of internal structure and time-resolved full-field surface strain measurements during in-situ X-ray micro-computed tomography (XCT) experiments. This enables in-situ testing of stiff materials with large forces relative to the compliance of the in-situ load frame, which might exhibit localization (e.g., necking, compaction banding) and other inhomogeneous behaviors.Methods: The system utilizes a combination of in-situ XCT, 2D X-ray imaging, and particle tracking to conduct volumetric imaging of the internal structure of a specimen with interrupted loading and surface strain mapping during loading. Critically, prior to the laboratory-scale XCT experiments, specimens are speckled with a high-X-ray-contrast powder that is bonded the surface. During in-situ loading, the XCT system is programmed to capture sequential 2D X-ray images orthogonal to the speckled specimen surface. A single particle tracking (SPT) or digital image correlation (DIC) algorithm is used to measure full-field surface strain evolution throughout the time-sequence of images. At specified crosshead displacements, the motion and 2D image sequence is paused for volumetric XCT image collection. Results: We show example results on a micro-tensile demonstration specimen additive manufactured from Inconel 718 nickel-chrome alloy. Results include XCT volume reconstructions, crosshead-based engineering stress, and full-field strain maps. Conclusion: We demonstrate an in-situ technique to obtain surface strain evolution during laboratory-scale XCT testing and interrupted volumetric imaging. This allows closer investigation of, for example, the effect of micro-pores on the strain localization behavior of additive manufactured metal alloys. In addition to describing the method using a representative test piece, the dataset and code are published as open-source resources for the community. Graphical abstract

Experimental Examination of Additively Manufactured Patterns on Structural Nuclear Materials for Digital Image Correlation Strain Measurements

BackgroundThere are a limited number of commercially available sensors for monitoring the deformation of materials in-situ during harsh environment applications, such as those found in the nuclear and aerospace industries. Such sensing devices, including weldable strain gauges, extensometers, and linear variable differential transformers, can be destructive to material surfaces being investigated and typically require relatively large surface areas to attach (> 10 mm in length). Digital image correlation (DIC) is a viable, non-contact alternative to in-situ strain deformation. However, it often requires implementing artificial patterns using splattering techniques, which are difficult to reproduce.ObjectiveAdditive manufacturing capabilities offer consistent patterns using programmable fabrication methods.MethodsIn this work, a variety of small-scale periodic patterns with different geometries were printed directly on structural nuclear materials (i.e., stainless steel and aluminum tensile specimens) using an aerosol jet printer (AJP). Unlike other additive manufacturing techniques, AJP offers the advantage of materials selection. DIC was used to track and correlate strain to alternative measurement methods during cyclic loading, and tensile tests (up to 1100 µɛ) at room temperature.ResultsThe results confirmed AJP has better control of pattern parameters for small fields of view and facilitate the ability of DIC algorithms to adequately process patterns with periodicity. More specifically, the printed 100 μm spaced dot and 150 μm spaced line patterns provided accurate measurements with a maximum error of less than 2% and 4% on aluminum samples when compared to an extensometer and commercially available strain gauges.ConclusionOur results highlight a new pattern fabrication technique that is form factor friendly for digital image correlation in nuclear applications.

On the Compositional Step of the IC-GN Digital Image Correlation Algorithm

Digital Image Correlation (DIC) is a well-known experimental optical technique for measuring the displacement field based on the assumption that pixel intensity does not change with motion.DIC can be implemented using various alternative approaches. The most used in the Experimental Mechanics field are the Forward-Additive Gauss-Newton (FA-GN) and the Inverse-Compositional Gauss-Newton (IC-GN) formulations. The former corresponds to the original formulation proposed by Lucas and Kanade, while the latter was proposed by Baker and Matthews twenty years later. Although both formulations give the same results at the first order, their speed, convergency characteristic and noise robustness differ considerably. Nowadays, the IC-GN method is usually preferred because of its lower computational load and the significantly better noise sensitivity. However, the Inverse Compositional approach, as its name states, requires the inversion and composition of the displacement field, thus enforcing the use of invertible displacement fields. This can be a significant limitation because it may introduce an under-matching error in the solution.This work shows that a simple modification of the viewpoint makes the compositional step simpler, thus giving a DIC formulation as fast as that of the IC-GN, with the same noise-bias characteristic and without requiring an invertible displacement field.

Augmented-Resolution Digital image correlation algorithm for vibration measurements

Digital Image Correlation (DIC) is a widespread technique, and many different implementations were developed in the literature to optimize the algorithm performance. In this paper, a new DIC algorithm is optimized for vibration measurements, where the displacement amplitude drastically decreases with the increase in frequency. Even if high-resolution cameras are used, it is mandatory to use a DIC algorithm with reliable sub-pixel performance. DIC algorithms are generally optimized for large complex displacement fields, and they solve the sub-pixel problem by fitting correlation data through a second-order polynomial function. An innovative solution is proposed to push the sub-pixel accuracy and increase the signal-to-noise ratio of the measurement, by exploiting image resizing to achieve virtual higher resolutions and by adopting a quartic polynomial function to interpolate the displacement map with an increased number of interpolation points. The proposed algorithm is validated through synthetic and experimental data and compared to literature DIC algorithms.

Using digital image correlation (DIC) to measure railway ballast movement in full-scale laboratory testing of sleeper lateral resistance

A fast and accurate method is described for determining the surface movement of railway ballast during a full-scale lateral resistance test. The proposed method utilises commodity camera equipment and open-source Digital Image Correlation (DIC) algorithms, to track individual ballast particles. It does not require time intensive ballast preparation. Tests have been performed under ambient and floodlit conditions, using colour and greyscale processing routes. The results are compared against direct measurements from lateral resistance tests to assess the accuracy of the proposed method, with a range of absolute maximum error between 0.9% and 3.4% under different laboratory conditions. The study shows that this technique is a viable way to track and measure ballast and sleeper movement over wide areas in near real time, which will lead to an increased understanding of the way ballast interactions influence track behaviour.

Effect of density grading on the mechanical behaviour of advanced functionally graded lattice structures

Lattice structures have found significant applications in the biomedical field due to their interesting combination of mechanical and biological properties. Among these, functionally graded structures sparked interest because of their potential of varying their mechanical properties throughout the volume, allowing the design of biomedical devices able to match the characteristics of a graded structure like human bone. The aim of this works is the study of the effect of the density grading on the mechanical response and the failure mechanisms of a novel functionally graded lattice structure, namely Triply Arranged Octagonal Rings (TAOR). The mechanical behaviour was compared with the same lattice structures having constant density ratio. Electron Beam Melting technology was used to manufacture titanium alloy specimens with global relative densities from 10% to 30%. Functionally graded structures were obtained by increasing the relative density along the specimen, by individually designing the lattice's layers. Scanning electron and a digital microscopy were used to evaluate the dimensional mismatch between actual and designed structures. Compressive tests were carried out to obtain the mechanical properties and to evaluate the collapse modes of the structures in relation to their average relative density and lattice grading. Open-source Digital Image Correlation algorithm was applied to evaluate the deformation behaviour of the structures and to calculate their elastic moduli. The results showed that uniform density structures provide higher mechanical properties than functionally graded ones. The Digital Image Correlation results showed the possibility of effectively designing the different layers of functionally graded structures selecting desired local mechanical properties to mimic the different characteristics of cortical and cancellous bone.

Digital image correlation technique for failure and crack propagation of fibre-reinforced polymer composites – A review

Digital image correlation (DIC) is a powerful technique for determining material deformation and damage and currently is viewed as a potential method to study failure and crack propagation in advanced composite materials. Case studies in concrete, epoxy adhesives, and carbon/epoxy laminates demonstrate the effectiveness of the DIC technique in visualizing crack propagation. This review highlights the applications, challenges, and implementation of the DIC technique in the failure and crack propagation of fibre-reinforced polymer (FRP) composites. The important factors such as field of view, speckle pattern, and algorithm choice are discussed while emphasizing the need for careful selection and calibration. The review concludes by highlighting the significance of crack propagation studies for composite behaviour and the role of DIC in calibrating and validating models of composite failure analysis. Future research directions include investigating mixed-mode delamination, improving calibration procedures, assessing DIC accuracy for large deformations, and comparing different DIC algorithms, which are discussed.

Conditional diffusion model-based generation of speckle patterns for digital image correlation

Generating speckle images is crucial for calibrating errors in digital image correlation (DIC) algorithms, alleviating the scarcity of training data for deep learning-based DIC algorithms, and even for the broader application of digital speckle methods. Traditional methods for speckle image synthesis struggle to accurately replicate the brightness variations in the foreground and background of realistic speckle images, and these methods come with high computational costs. Additionally, there needs to be more scientific and systematic evaluation methods for the quality of generated DIC speckle images. Our study presents a high-fidelity method for generating DIC speckle images with cracks based on a conditional diffusion model. We established evaluation metrics for generated DIC speckle images based on multiscale spatial and spectrum domains. We verified them on an open-source dataset of DIC speckle images depicting cracks in stone masonry walls. The conditional diffusion model excels at forward tasks (DIC crack image segmentation) and inverse tasks (DIC speckle image generation), with particular strength in the latter. The proposed spatial and multiscale spectrum domain metrics allow for the comprehensive and accurate evaluation of the alignment between the synthetic and natural speckle images. The conditional diffusion model for DIC speckle image generation and evaluation proposed in this paper has the potential to enhance further the accuracy and robustness of DIC technology in experimental mechanics and engineering applications.

Investigations on tensile mechanical properties of rigid insulation tile materials at elevated temperatures based on digital image correlation algorithm

This work investigates the tensile mechanical properties of rigid insulation tile (RIT) materials at room temperature (26 °C) and elevated temperatures (700–1000 °C) through experimental and digital image correlation (DIC) methods. Experimental results indicate that the tensile strength of RIT materials changes nonlinearly with increasing temperature and that the primary fracture mode under tension load is fiber root fracture. In terms of the DIC algorithm, this work builds an elevated-temperature DIC test system. A zero-mean Gaussian filtering algorithm and an image gradient difference square sum algorithm (SSDGI) are proposed to improve the precision of the DIC algorithm. The derived tensile stress-strain curve of RIT materials reveals that the elastic modulus changes nonlinearly with increasing temperature. The reliability and precision of the DIC algorithm are confirmed through the tensile test of silicon carbide particle-reinforced aluminum matrix composites. Hence, this work provides guidance for evaluating mechanical properties of RIT materials under elevated temperature environments.

Parameter identification of a phase-field fracture model using integrated digital image correlation

Phase-field fracture (PFF) modeling is a popular approach to model and simulate fracture processes in solids. Accurate material parameters and boundary conditions are of utmost importance to ensure a good prediction quality of numerical simulations. In this work, an Integrated Digital Image Correlation (IDIC) algorithm is proposed to calibrate boundary conditions, Poisson’s ratio, fracture energy and internal length, all at once, by using the phase-field model itself and images of a deforming sample. The presented approach is applied to virtual experiments mimicking a single edge notched shear test and implemented in the open-source deal.II-based software pfm-cracks and the digital image correlation library Correli 3.2. The reliability of the results is investigated for different levels of acquisition noise, thereby demonstrating high robustness and accuracy for a wide range of noise levels. The conditioning of the problem is analyzed via sensitivity fields for all parameters and the eigendecomposition of the Hessian matrix used in the IDIC algorithm.

Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring

Accurate and reliable analyses of high-alpine landslide displacement magnitudes and rates are key requirements for current and future alpine early warnings. It has been proved that high spatiotemporal-resolution remote sensing data combined with digital image correlation (DIC) algorithms can accurately monitor ground displacements. DIC algorithms still rely on significant amounts of expert input; there is neither a general mathematical description of type and spatiotemporal resolution of input data nor DIC parameters required for successful landslide detection, accurate characterisation of displacement magnitude and rate, and overall error estimation. This work provides generic formulas estimating appropriate DIC input parameters, drastically reducing the time required for manual input parameter optimisation. We employed the open-source code DIC-FFT using optical remote sensing data acquired between 2014 and 2020 for two landslides in Switzerland to qualitatively and quantitatively show which spatial resolution is required to recognise slope displacements, from satellite images to aerial orthophotos, and how the spatial resolution affects the accuracy of the calculated displacement magnitude and rate. We verified our results by manually tracing geomorphic markers in orthophotos. Here, we show a first generic approach for designing and optimising future remote sensing-based landslide monitoring campaigns to support time-critical applications like early warning systems.

The Contribution of Digital Image Correlation for the Knowledge, Control and Emergency Monitoring of Earth Flows

Earth flows are complex geological processes that, when interacting with linear infrastructures, have the potential to cause significant damage. Consequently, conducting comprehensive investigations and continuous monitoring are becoming paramount in designing effective mitigation measures and ensuring sustainable risk management practices. The use of digital image correlation (DIC) algorithms for the displacement monitoring of earth flows is a relatively new and growing field with a limited number of dedicated works in the literature. Within this framework, the present paper contributes by evaluating DIC techniques at several earth flow sites. These assessments were founded upon data obtained from both low-cost and multi-platform sensors, used to assess the displacement field, pinpointing the most active sectors, and gauging their rates of movement during different kinematic phases. In this regard, the highest localized movement velocities reached in the Pietrafitta (0.01 m/day) and Grillo (0.63 m/day) earth flows experience different ongoing kinematic processes. The versatility of these techniques is notable, manifesting in the ease of installation, cost-effectiveness, and adaptability to various platforms. The proven reliability in terms of maximum accuracy (1 pixel) and sensitivity (0.05 m) lays the foundation for the extensive use of the presented techniques. The amalgamation of these attributes positions DIC techniques as competitive, adaptable, and readily deployable tools in earth flow studies.

The movement pattern of particles on the surface of liquid marble during rupture based on the DIC algorithm

The stability of liquid marbles is critical to their application in sensors, microfluidic control, microreactors, and other fields. A more comprehensive understanding of the rupture kinetics of liquid marble is instructive for studies in improving the stability of liquid marble. However, the mechanism of rupture of liquid marbles is not clear at present, therefore, this paper addressed this issue by calculating the displacement of particles on the surface of a liquid marble using the DIC (Digital Image Correlation) algorithm. The distribution characteristics of the particles on the surface of the liquid marble and their movement patterns were clarified, the causes of cracks on the marble surface were analyzed, the mechanism of liquid marble rupture was derived, and the effect of volume on rupture kinetics was analyzed. Based on the obtained data, a method was proposed to control the maximum compression that the liquid marble can withstand by spraying different sizes of superhydrophobic coatings. This paper contributes to the study of the stability of liquid marble, allowing its wider application in industrial production.

A pointwise optimal subset selection strategy assisted by shape functions in digital image correlation algorithm

A pointwise optimal subset selection strategy assisted by shape functions in digital image correlation algorithm