Digital Image Correlation Software Research Articles

Assessments of polymerization shrinkage by optical coherence tomography-based digital image correlation analysis—Part I: Parameter identification

ObjectiveTo investigate the feasibility of optical coherence tomography (OCT)-based digital image correlation (DIC) analysis and to identify the experimental parameters for measurements of polymerization shrinkage. MethodsClass I cavities were prepared on bovine incisors and filled with Filtek Z350XT Flowable (Z350F). One OCT image of the polymerized restoration was processed to generate virtually displaced images. In addition, the tooth specimen was physically moved under OCT scanning. A DIC software analyzed these virtual and physical transformation sets and assessed the effects of subset sizes on accuracy. The refractive index of unpolymerized and polymerized Z350F was measured via OCT images. Finally, different particles (70–80 µm glass beads, 150–212 µm glass beads, and 75–150 µm zirconia powder) were added to Z350F to inspect the analyzing quality. ResultsThe analyses revealed a high correlation (>99.99%) for virtual movements within 131 pixels (639 µm) and low errors (<5.21%) within a 10-µm physical movement. A subset size of 51 × 51 pixels demonstrated the convergence of correlation coefficients and calculation time. The refractive index of Z350F did not change significantly after polymerization. Adding glass beads or zirconia particles caused light reflection or shielding in OCT images, whereas blank Z350F produced the best DIC analysis results. SignificanceThe OCT-based DIC analysis with the experimental conditions is feasible in measuring polymerization shrinkage of RBC restorations. The subset size in the DIC analysis should be identified to optimize the analysis conditions and results. Uses of hyper- or hypo-reflective particles is not recommended in this method.

The Development of a 3D-Printed Compliant System for the Orientation of Payloads on Small Satellites: Material Characterization and Finite Element Analysis of 3D-Printed Polyetherketoneketone (PEKK)

This article focuses on the development of a 3D-printed 2-degree-of-freedom (DOF) joint for the payloads’ orientation on small satellites. This system is a compliant mechanism, meaning that this monolithic system composed of cross-axis flexural pivots (CAFPs) produces complex movements through the elastic deformation of its structure. Using fused filament fabrication (FFF), a demonstrator made of Polyetherketoneketone (PEKK) is printed to determine its potential compatibility with space conditions. Focusing on a segment of the joint, the CAFP, this study aims for an enhancement of its mechanical behavior through the study of its printing direction and the creation of an accurate finite element model of this compliant mechanism. First, material characterization of 3D-printed PEKK is achieved through differential scanning calorimetry tests of the filament and flexural and tensile tests of specimens printed in different printing directions. Then, these data are used to perform a finite element analysis of different CAFP designs and compare their mechanical response of their 3D-printed twin using digital image correlation software. Finally, the CAFP structures were observed by X-ray tomography. The results show that printing direction greatly influences both flexural and tensile strength. Voids induced by the FFF process could impact the mechanical behavior of 3D-printed parts as the simple CAFP design has a better test/model correlation than complex ones. This could influence its resistance to space environment.

A DIC-UAV based displacement measurement technique for bridge field testing

Load rating of old or deteriorated bridges is performed either analytically or experimentally to determine their safe passing loads. Due to high costs of field operations and testing, analytical load rating is currently the preferred method of practice. To promote experimental load rating through bridge field testing, a novel displacement measurement technique as an alternative to conventional sensor-based measurements is proposed in the present study incorporating digital image correlation (DIC), unmanned aerial vehicle (UAV or drone), opensource computer programs, and cost-effective cameras. The development of the proposed DIC-UAV displacement measurement method including mission strategy, camera system trade-off, DIC software, drone, and payload configuration is discussed. Furthermore, the accuracy of the proposed technique is evaluated by performing more than 70 tests on a scaled truss bridge in a laboratory. The proposed DIC-UAV based displacement measurement strategy and computational tools were found feasible with submillimeter accuracies, significantly advancing the state-of-the-art methodologies.

A meshfree approach for analyzing strain fields near crack tips from Digital Image Correlation displacement data

AbstractThe element‐free Galerkin (EFG) meshfree method is employed in this research to compute strain fields based on discrete displacement data obtained from digital image correlation (DIC). To validate the method's accuracy, both simulated images with highly heterogeneous deformations and real tests conducted on notched specimens with known solutions are initially employed. The study then investigates the strain field near the crack tip in an AISI 1020 steel specimen through a multiscale experiment. From the macroscale experiment, the strain data are used to determine the crack‐tip plastic zone size, and the solution is compared with predictions from linear‐elastic fracture mechanics. The stress intensity factor is also determined. Furthermore, the high strain gradient ahead of the crack tip is determined from the microscale experiment and compared against commercial DIC software. Experimental results confirm the effectiveness of the proposed formulation in crack tip analysis.

Application of digital image correlation to derive Paris' law constants in granite specimens

This study investigates the effectiveness of Digital Image Correlation (DIC) as a powerful tool for deriving Paris' law constants in black granite specimens subjected to cyclic loading. A series of monotonic and cyclic loading tests on rectangular granite specimens yields valuable insights into geological material fatigue behavior, establishing monotonic tests as foundational references for subsequent cyclic loading experiments. Innovative DIC software application enables precise tracking and analysis of Crack Mouth Opening Displacement (CMOD) values, revealing distinct, observable patterns in crack growth during cyclic loading. A notable contribution lies in the implementation of a compliance calibration technique integrating Finite Element Method (FEM), facilitating the direct linkage between CMOD values and effective crack lengths, resulting in highly accurate Paris' law constants. With an average of 1.04 E −7 for C and 8.95 for m, these constants are essential for predicting fatigue crack propagation in granite specimens. This research underscores DIC's efficacy as a pivotal fatigue analysis tool, enhancing predictive capabilities for structural safety and durability assessments in geological materials, while emphasizing the crucial role of advanced imaging and numerical techniques in material science and structural engineering. It lays a robust foundation for future geological material studies and comprehensive structural integrity assessments.

Identification of Location and Geometry of Invisible Internal Defects in Structures using Deep Learning and Surface Deformation Field

On‐site inspection of invisible subsurface defects in multiscale structural materials by conventional nondestructive testing (NDT) methods, such as X‐ray and ultrasound, requires complex sample preparation and data acquisition processes. Moreover, the inspected area is very small. Herein, a simple, inexpensive, and ultrasensitive NDT method for identifying and classifying the geometries of subsurface defects using commercial cameras, digital image correlation software, and object detection (OD) algorithms is developed. Three OD algorithms—Faster region‐based convolutional neural network (Faster R‐CNN), Mask R‐CNN, and you‐only‐look‐once (YOLO)v3—are evaluated for their ability to locate defects and identify defect geometries. Specifically, bounding boxes of two sizes (large and small) are applied to the regions of defect‐induced perturbations in strain tensors, which serve as virtual representatives of invisible subsurface defects. The performance of the proposed approach is validated on test datasets of known and unknown defect types. The experimental results confirm that the proposed approach can effectively utilize the surface deformation field information to accurately and reliably locate and identify subsurface defects. The method is nondestructive and low cost, enables real‐time detection, is robust against noise‐dominated deformation fields, and can be applied to various structural deformations. The method is therefore suitable for multiscale structural health monitoring and characterization of internal defects in materials.

A Preliminary Assessment of Cerebral Surface Strain

Background: Craniotomies are commonly performed neurosurgical procedures. Quantitating cerebral surface strain may facilitate the identification of intracerebral pathology and improve intraoperative management of underperfused brain tissue. Objective: The aim of this study was to test the use of digital image correlation software to quantify cerebral surface strain of intraoperative cerebral tissue during craniotomies. Methods: Ncorr, an open-source software program, was used to perform digital image correlation analysis from publicly available craniotomy videos. Mann-Whitney U tests were then utilized to assess statistical differences between craniotomy datasets. Results: Four different craniotomies were retrospectively examined, and the acquired cerebral surface strain data were subsequently extracted and analyzed. Statistically significant cerebral surface strain values were identified when comparing the four craniotomies. Additional prospective research is needed to establish baseline ranges of cerebral surface strain and to further understand the potential utility and limitations of this non-invasive intraoperative monitoring technique. Conclusion: This preliminary study successfully demonstrated the use of computer-based image analysis for the non-invasive quantification of cerebral surface strain during neurosurgery.

Estimation of the Deformation Gradient Tensor by Particle Tracking Near a Free Boundary with Quantified Error

BackgroundObtaining accurate displacement measurements for large material deformation and/or rotation presents a distinct challenge to digital image correlation (DIC) due to cumulative and decorrelation errors, particularly near material boundaries.ObjectiveWe aim to accurately measure the deformation gradient tensor near boundary discontinuities in situations of large deformation and large deformation gradients.MethodsTo achieve this goal, the locations of randomly distributed particles are tracked using an open-source particle-tracking software, Trackpy. A least-squares estimate of the deformation gradient tensor field uses nearest-neighbor material vectors and a first-order Finite Difference (FD) approximation, circumventing common errors in other methods. The error caused by FD approximation and that incurred by measurement are derived and tested with exhaustive numerical simulations. Furthermore, a uniaxial tensile test and mode-I fracture experiment are conducted with particle-embedded hydrogels to validate the method.ResultsNumerical results corroborate a theoretical expression of measurement error. They show that the FD error increases while the measurement error decreases for a growing estimating radius. Moreover, measurement error is linearly correlated to displacement noise. A benchmark uniaxial tensile test validates the accuracy of the proposed estimator, and the near-crack-tip measurements in a tensile fracture experiment demonstrate the estimator’s capabilities near a free surface, when a material undergoes large deformation and rotation. The results of the displacement and strain data are benchmarked against kinematic data obtained using an open-source DIC software, Ncorr. Computation time for both methods is compared.ConclusionsA deformation gradient tensor estimator is developed based on a particle tracking technique and a least squares routine. Theoretical error bounds on the estimator are verified by numerical simulations, and the method’s capability is confirmed by physical experiments in evaluating large deformation and rotation near a free boundary. The proposed estimator is expected to open a door towards future material tests and experimental mechanics studies, especially in large deformation and large rotation scenarios.

Examining the Effect of MnS Particles on the Local Deformation Behavior of 8MnCrS4-4-13 Steel by In Situ Tensile Testing and Digital Image Correlation

In this study, the behavior of MnS particles in a steel matrix is investigated through in situ tensile testing and digital image correlation (DIC) analysis. The goal of this research is to understand the mechanical behavior of MnS inclusions based on their position in the steel matrix. To accomplish this, micro-dog bone-shaped samples are prepared, tensile tested, and analyzed. Macro-mechanical results reveal that the material yields at a stress of 350 MPa and has an ultimate tensile strength of 640 MPa, with a total elongation of 17%. For micro-mechanical analysis, scanning electron microscopy (SEM) images are taken at incremental strains and processed using DIC software to visualize the local strain evolution. The DIC analysis quantifiably demonstrates that the local strain is highest in the ferrite matrix, and while lowest in the pearlite matrix, the MnS particles and the interfaces between different materials experienced intermediate strains. The research provides new insights into the micro-mechanical deformation behavior of MnS particles in a steel matrix and has the potential to inform the optimization of the microstructure and properties of materials containing MnS inclusions.

STrain Analysis and Mapping of the Plantar Surface (STAMPS): A novel technique of plantar load analysis during gait.

Diabetic foot ulceration is driven by peripheral neuropathy, resulting in abnormal foot biomechanics and elevated plantar load. Plantar load comprises normal pressure and tangential shear stress. Currently, there are no in-shoe devices measuring both components of plantar load. The STAMPS (STrain Analysis and Mapping of the Plantar Surface) system was developed to address this and utilises digital image correlation (DIC) to determine the strain sustained by a plastically deformable insole, providing an assessment of plantar load at the foot-surface interface during gait. STAMPS was developed as a multi-layered insole, comprising a deformable mid-layer, onto which a stochastic speckle pattern film is applied. A custom-built imaging platform is used to obtain high resolution pre- and post-walking images. Images are imported into commercially available DIC software (GOM Correlate, 2020) to obtain pointwise strain data. The strain and displacement data are exported and post-processed with custom analysis routines (MATLAB, Mathworks Inc.), to obtain the resultant global and regional peak strain (SMAG), antero-posterior strain (SAP) and medio-lateral strain (SML). To validate the core technique an experimental test process used a Universal Mechanical Tester (UMT) system (UMT TriboLab, Bruker) to apply controlled vertical and tangential load regimes to the proposed multi-layer insole. A pilot study was then conducted to assess the efficacy of using the STAMPS system to measure in-shoe plantar strain in three healthy participants. Each participant walked 10 steps on the STAMPS insole using a standardised shoe. They also walked 10 m in the same shoe using a plantar pressure measurement insole (Novel Pedar®) to record peak plantar pressure (PPP) as a gold-standard comparator. The results of the experimental validation tests show that with increased normal force, at a constant shear distance, SMAG increased in a linear fashion. Furthermore, they showed that with increased shear distance, at a constant force, SMAG increased. The results of the pilot study found participant 1 demonstrated greatest SMAG in the region toes 3-5 (15.31%). The highest mean SMAG for participant 2 was at the hallux (29.31%). Participant 3 exhibited highest strain in the regions of the first and second metatarsal heads (58.85% and 41.62% respectively). Increased PPP was strongly associated with increased SMAG with a Spearman's correlation coefficient 0.673 (p < 0.0001). This study has demonstrated the efficacy of a novel method to assess plantar load across the plantar surface of the foot. Experimental testing validated the sensitivity of the method to both normal pressure and tangential shear stress. This technique was successfully incorporated into the STAMPS insole to reliably measure and quantify the cumulative degree of strain sustained by a plastically deformable insole during a period of gait, which can be used to infer plantar loading patterns. Future work will explore how these measures relate to different pathologies, such as regions at risk of diabetic foot ulceration.

Inelastic zone around crack tip in polyacrylamide hydrogel identified using digital image correlation

Prior to fracture, a polyacrylamide hydrogel has a stress-stretch curve of nearly perfect elasticity, but it has been suggested that an inelastic zone exists around a crack tip. This inelastic zone, however, has never been observed directly in a polyacrylamide hydrogel. Here we identify the inelastic zone using digital image correlation (DIC). We prepare a polyacrylamide hydrogel with a precut crack. While a sample of the hydrogel is stretched, the speckle patterns are recorded using a microscope or a camera, with pixel size 2.3 μm and 22.7 μm, respectively. The speckle patterns recorded by the microscope and camera are processed using the DIC software, and merged to provide the deformation field over the entire sample. The measured field of deformation is used to calculate the field of energy density according to the neo-Hookean model. When the body is perfectly elastic, the field of energy density around the crack tip is inversely proportional to the distance from the crack tip. The difference between the measured field and the predicted elastic field identifies the inelastic zone. The measured size of the inelastic zone is ∼ 0.6 mm. We further confirm that, when a sample is much larger than the inelastic zone, an annulus exists, in which the elastic crack tip field prevails.

An evaluation method for experimental necking detection of automotive sheet metals

In sheet metal stamping, the occurrence of strain localization in a deformed sheet is considered a failure. As so, sheet metal’s formability is conventionally evaluated using the Forming Limit Diagram (FLD), which separates the principal strain space into safety and unsafety regions by a Forming Limit Curve (FLC). This study presents an evaluation method for detecting strain localization based on Digital Image Correlation (DIC) during the experiment. The commercial DIC software ARAMIS is adopted to monitor the strain-field distribution on the deformed specimen’s surface. A detailed analysis of the proposed method is presented considering Nakajima tests conducted for two automotive sheet metals: AA6016 and DP800. The identified FLC based on the proposed method is compared with that of well-established methods such as ISO 12004:2-2008 and time-dependent methods. For both investigated materials, the proposed method presents a lower FLC than the others.

Evaluation of Sample Preparation Importance for Digital Image Correlation during Cold Deformation

In this paper, tests were performed by monitoring changes of strain amounts and their distribution by digital image correlation during static tensile testing of steel. The influence of different combination of speckles application on the sample surface (white speckles on the black sample surface and black speckles on the white sample surface) during cold deformation of steel was tested with the non-contacting digital image correlation. Strain amounts obtained by digital image correlation analysis of tested samples, with white on black and black on white painted speckle patterns, were compared. Different analysis for determining and monitoring strain amounts, provided by digital image correlation software, were used in this paper. The strain amounts were compared during cold deformation until the occurrence of sample fracture in both combinations of speckles application on the sample surface. Research has shown the importance of proper sample preparation for determination strain amounts and insignificant minor differences of strain amounts, between different sample preparations for digital image correlation, during cold deformation of tested steel.

OpenCorr: An open source library for research and development of digital image correlation

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.

On the use of the Gleeble® test as a heterogeneous test: sensitivity analysis on temperature, strain and strain rate

The development of hot and warm forming processes for the manufacture of complex-shaped alloy components has been increasing. At the same time, the knowledge of the effect of certain variables involved in such processes such as temperature and strain rate is essential for an accurate modelling of the materials and processes. This work provides a sensitivity study on the effect of different testing conditions variables on beta titanium-molybdenum (Ti-Mo) alloys. Data from quasi-static uniaxial tensile tests performed on a dog bone shaped specimen using a Gleeble® machine is post-processed with Aramis digital image correlation (DIC) software and numerical models of the tests are developed using Abaqus® finite element analysis (FEA) software. Results considering different temperature conditions, different applied strain rates and different gauge lengths for the determination of the mechanical properties of the material are discussed.

High resolution and real-time measurement of 2D fatigue crack propagation using an advanced digital image correlation

This paper developed a real-time and non-destructive 2D fatigue crack propagation tracing method based on an optimized digital image correlation (DIC) approach by introducing an image in-situ processing algorithm and a fatigue crack propagation monitoring algorithm. The in-situ processing algorithm coupled with optimizing the speeded up robust features (SURF) was applied to correct the shifted images during fatigue crack propagation tests, while the fatigue crack propagation monitoring algorithm was developed to detect the true crack tip and measure crack length. By optimizing SURF algorithm, the construction time of feature descriptor for interest point matching was reduced to 0.0066 s, 55 times shorter than the case using traditional SURF. In conjunction with the fatigue crack identification, this enables the real-time measurement of fatigue crack length with the resolution as high as 5.2 μm for a measurement region of 15.85 × 10.57 mm2. Compared to current DIC, the proposed algorithm either enables characterizing the crack tip and crack propagation path, or can be programmed as user-defined subroutine and integrated into commercial DIC software for example MatchID. As a result, the experimental measurements obtained by the proposed approach on the fatigue crack propagation tests of an Aluminium alloy exhibited a good agreement with simulation results based on the extended finite element method (XFEM), indicated its promising industrial prospect.

Deep DIC: Deep learning-based digital image correlation for end-to-end displacement and strain measurement

Digital image correlation (DIC) has become an industry standard to retrieve accurate displacement and strain measurement in tensile testing and other material characterization. Though traditional DIC offers a high precision estimation of deformation for general tensile testing cases, the prediction becomes unstable at large deformation or when the speckle patterns start to tear. In addition, traditional DIC requires a long computation time and often produces a low spatial resolution output affected by filtering and speckle pattern quality. To address these challenges, we propose a new deep learning-based DIC approach--Deep DIC, in which two convolutional neural networks, DisplacementNet and StrainNet, are designed to work together for end-to-end prediction of displacements and strains. DisplacementNet predicts the displacement field and adaptively tracks a region of interest. StrainNet predicts the strain field directly from the image input without relying on the displacement prediction, which significantly improves the strain prediction accuracy. A new dataset generation method is developed to synthesize a realistic and comprehensive dataset, including the generation of speckle patterns and the deformation of the speckle image with synthetic displacement fields. Though trained on synthetic datasets only, Deep DIC gives highly consistent and comparable predictions of displacement and strain with those obtained from commercial DIC software for real experiments, while it outperforms commercial software with very robust strain prediction even at large and localized deformation and varied pattern qualities. In addition, Deep DIC is capable of real-time prediction of deformation with a calculation time down to milliseconds.

Strain Rate-Dependent Compressive Properties of Bulk Cylindrical 3D-Printed Samples from 316L Stainless Steel.

The main aim of the study was to analyse the strain rate sensitivity of the compressive deformation response in bulk 3D-printed samples from 316L stainless steel according to the printing orientation. The laser powder bed fusion (LPBF) method of metal additive manufacturing was utilised for the production of the samples with three different printing orientations: 0, 45, and 90. The specimens were experimentally investigated during uni-axial quasi-static and dynamic loading. A split Hopkinson pressure bar (SHPB) apparatus was used for the dynamic experiments. The experiments were observed using a high-resolution (quasi-static loading) or a high-speed visible-light camera and a high-speed thermographic camera (dynamic loading) to allow for the quantitative and qualitative analysis of the deformation processes. Digital image correlation (DIC) software was used for the evaluation of displacement fields. To assess the deformation behaviour of the 3D-printed bulk samples and strain rate related properties, an analysis of the true stress–true strain diagrams from quasi-static and dynamic experiments as well as the thermograms captured during the dynamic loading was performed. The results revealed a strong strain rate effect on the mechanical response of the investigated material. Furthermore, a dependency of the strain-rate sensitivity on the printing orientation was identified.

DIC Challenge 2.0: Developing Images and Guidelines for Evaluating Accuracy and Resolution of 2D Analyses

The DIC Challenge 2.0 follows on from the work accomplished in the first Digital Image Correlation (DIC) Challenge Reu et al. (Experimental Mechanics 58(7):1067, 1). The second challenge was required to better quantify the spatial resolution of 2D-DIC codes. The goal of this paper is to outline the methods and images for the 2D-DIC community to use to evaluate the performance of their codes and improve the implementation of 2D-DIC. This paper covers the creation of the new challenge images and the analysis and discussion of the results. It proposes a method of unambiguously defining spatial resolution for 2D-DIC and explores the tradeoff between displacement and strain noise (or measurement noise) and spatial resolution for a wide variety of DIC codes by a combination of the images presented here and a performance factor called Metrological Efficiency Indicator (MEI). The performance of the 2D codes generally followed the expected theoretical performance, particularly in the measurement of the displacement. The comparison did however show that even with fairly uniform displacement performance, the calculation of the strain spatial resolution varied widely. This work provides a useful framework for understanding the tradeoff and analyzing the performance of the DIC software using the provided images. It details some of the unique errors associated with the analysis of these images, such as the Pattern Induced Bias (PIB) and imprecision introduced through the strain calculation method. Future authors claiming improvements in 2D accuracy are encouraged to use these images for an unambiguous comparison.

Simplified approach for quantitative inspections of concrete structures using digital image correlation

Periodic visual inspections are typically the primary means by which the existing condition of most infrastructure assets is assessed. The results of these inspections are inherently qualitative and subjective, and unfortunately, the recent catastrophic collapse of structures such as De La Concorde overpass in Canada have underscored the need for quantitative assessment tools to monitor crack growth and deformations over time. Conventional measurement methods present several limitations, including cost, need for a priori knowledge of critical crack locations and orientation, and exposure of sensors to harsh environmental conditions. Digital image correlation (DIC) is an optical full-field non-contact measurement technique that has been shown to be highly accurate for laboratory studies under controlled conditions and has also been successfully used for short term field tests of bridges and other structures. In this study, a simple low-cost approach is proposed to supplement periodic visual inspections with quantitative measurements using images from a handheld digital camera processed with a free DIC software. A method is introduced to correct measurement errors associated with the inevitable camera movements between photographs taken during different inspections. The approach was validated by tracking the shear crack kinematics of seven push-off specimens tested in the laboratory. After correcting for errors due to movement of the handheld camera, 98% of the 462 measurements made during the tests were within the selected tolerance limit of ± 0.1 mm compared with reference measurements made with a stationary camera equipped with a long focal length lens. These results represent a first step towards the use of the DIC technique for quantitative structural inspections for improved infrastructure management.