Material Testing 2.0 (MT2.0) couples full‑field deformation measurements (Digital Image Correlation, DIC) with inverse identification methods (Virtual Fields Method, VFM) to extract constitutive parameters from a small number of heterogeneous experiments. This paper presents the Cut‑Clamp‑Play concept: an integrated industrial MT2.0 solution that unifies specimen design, automated testing hardware, and a computationally efficient VFM identification chain to deliver fast, user‑friendly sheet‑metal characterization. A perforated cruciform specimen is optimized for parameter identifiability of the Yld2000‑2d anisotropic yield function and used in a single biaxial test. A working prototype has been built at KU Leuven and used to collect representative DIC data; the measured displacement/strain response is double‑symmetric, confirming correct mechanical operation. Projected and early prototype results indicate that the Cut‑Clamp‑Play approach can reduce operator actions by roughly 70% and produce identification results within one hour for typical sheet‑metal cases, while further work is required to make the fully automated “Play” stage robust for industrial deployment.

This paper revisits the long-standing question of how to fully characterise the in-plane plastic anisotropy of sheet metals without assembling evidence from multiple standardised tests. The central idea is pragmatic: a single, well-designed heterogeneous biaxial experiment can replace the conventional combination of uniaxial and equibiaxial tests if the specimen and the inverse identification method are co-designed to (i) activate informative stress states and (ii) maintain low strain gradients for accurate digital image correlation measurements. The proposed cruciform specimen is deliberately conceived as a benchmark configuration for full-field inverse identification, with known locations and stress-strain states at which relevant material information is embedded. The approach is coupled with a Finite Element Model Updating framework, enabling all anisotropy parameters of the YLD2000-2d model to be identified from a single full-field dataset. Sensitivity and identifiability analyses demonstrate that a physically based parameter formulation significantly improves the conditioning of the inverse problem. Virtual experimentation confirms the robustness and accuracy of the proposed “one-test” identification strategy.

Accurate uncertainty quantification (UQ) in digital image correlation (DIC) deformations is essential for quantitative DIC-based finite element (FE) model validation. DIC UQ is well-studied in the current literature, both from a theoretical as well as experimental point-of-view, but rarely from the model validation perspective. Moreover, the DIC uncertainties are usually considered as spatial averages over the whole field of view while local contrast variations generally lead to spatially-varying noise floors. This paper investigates how DIC UQ should be performed when validating FE models. UQ was performed using experimental stationary images of a test sample. Spatial maps of point-wise temporal standard deviation (noise) and mean (bias) were constructed to be used in the model validation process. The effectiveness of reference image averaging at reducing bias and noise was also studied. Specular reflection ('hotspots') was given special attention, an important additional source of uncertainty not simulated by the Digital Twin (DT) used to perform the validation. As expected, image noise was found to be the most dominant source of DIC uncertainty. The spatially-random noise on the reference stationary image was found to be responsible for the temporal bias of the displacement distribution, as the copy of noise from that initial image affects all displacement maps since this image is used for all displacement maps. Spatially-random noise on the deformed stationary images was found to be responsible for the temporal standard deviation (noise). Both temporal noise and bias were found to be comparable in magnitude, highlighting the necessity for a spatially heterogeneous model validation criterion that accounts for both. The impact of specular reflection was difficult to quantify and exhibits potential for significantly increasing DIC uncertainties. The use of polarized lights and polarizing filters can mitigate this issue but more work is needed to allow for a realistic error budget to be established for this. Heat haze (refraction from warm air flow between camera and object) and camera heating are additional effects that are difficult to error-budget for. Finally, the effect of stereo-DIC calibration errors needs to be studied further.

Full-field optical experimental techniques such as Digital Image Correlation (DIC) enable measurement of information-rich heterogeneous strain states. The aim of Material Testing 2.0 is to capitalise on this and design experimental setups for efficient material characterisation using inverse identification techniques such as the Virtual Fields Method (VFM). In that case, however, a priori knowledge of the constitutive model is required, which sometimes is not a trivial task. This limitation can be overcome by identifying multiple constitutive models using the same DIC strain fields and comparing their performance. In this work, we present a methodology to evaluate model performance and compare different constitutive models based on quantitative metrics. DIC experiments were performed on an S-shaped high density polyethylene test sample under uniaxial load. The heterogeneous DIC strain fields were used in combination with the VFM to identify parameters of hyperelastic constitutive models with increasing complexity. Two deterministic metrics, the equilibrium gap indicator (EGI) and the reconstructed axial force ratio (RAFR), were defined and used to compare the performance of these constitutive models. The impact of spatial smoothing on DIC strain fields was studied by calculating the EGI and RAFR using a digital twin of the DIC experiments. EGI and RAFR metrics provide complementary information regarding the material behaviour, and both are necessary to make an informed choice. In this case study, hyperelastic constitutive models were found not to capture the material stiffness well in the small strain regime. A linear-elastic contribution exponentially decaying with the equivalent shear stress was added to the hyperelastic models and the corresponding model parameters identified using the VFM. The elastic-hyperelastic constitutive models were found to perform better than their purely hyperelastic counterparts.

Stereo-DIC is a widely used optical measurement technique that provides a dense full-field 3D measurement of the shape, displacement, and strain of a solid sample. When compared with 2D-DIC, Stereo-DIC provides greater flexibility and expands its use beyond flat, planar specimens. Furthermore, the widespread availability of commercial systems has led to the adoption of the technique throughout industry, academia, and government research labs. Even though some research has been done to understand the effects of different experimental and stereo-DIC parameters, no reference is available to benchmark and compare the performance of current stereo-DIC algorithms to each other. This paper provides the description and analysis of a carefully controlled 3D experiment and associated images used to compare the results from five subset based DIC software packages. Both the images and analysis codes used in this paper to compare the results are described here and are available for download and use for continued research. We show that over a very large range of motion, the 3D errors are very small, less than 80 μ m over a travel of ±20 mm out-of-plane and ±20 mm in-plane. While all codes performed similarly, there are important differences noted in the paper. The image sets and results comparison software are hosted by the International DIC Society ( www.iDICs.org ) and are freely available for download and analysis for comparison with results in this paper. Furthermore, it is hoped that this set of images can be used for future research in improving stereo-DIC by future authors.

This paper demonstrates a methodology to discriminate between the performances of different material models within the framework of Material Testing 2.0, which consists in coupling heterogeneous test configurations, full-field measurements using for instance Digital Image Correlation (DIC) and inverse identification like the Virtual Fields Method (VFM). The methodology relies on using a set of different virtual fields for parameter identification with a selected model, and to evaluate the performance of the model. The paper illustrates this methodology on anisotropic metal plasticity.

The success of inverse material model identification depends on the interaction between the adopted material model, the design of the heterogeneous specimens, the quality of the full-field measurements and the employed inverse identification method. Although inverse identification with full fields usually uses either FEMU or nonlinear VFM algorithms, a range of specimen designs and heterogeneity indicators have been proposed to assess the quality of the measured field and specimen design. While many studies investigate the effects of strain field heterogeneity on material model identification, few of them address the comprehensive interaction of all the above features and investigate their interactions during inverse identification through identifiability analysis. In this study, we analyze the identifiability of the parameters of the YLD2000-2d model used to describe the plastic anisotropy of steel sheet DC04 using a perforated biaxial specimen with the nonlinear VFM method. For this purpose, we performed a virtual DIC experiment with known material parameters by simulating the test in ABAQUS/Standard, generating synthetic images and reconstructing the strains via stereo DIC. Before inverse identification with a nonlinear sensitivity-based VFM, we analyzed the sensitivity of the virtual work to parameter changes and performed an identifiability analysis.

Digital image correlation has recently seen a growing interest in both the research and industrial community thanks to the possibility to measure full-field information, with high confidence, and with a very limited instrumentation. Furthermore, advances in camera technology, particularly on resolution and data transfer rate, are now opening the door to new application, such as modal analysis and testing on rotating components. In this chapter, we give an overview of industrial applications of digital image correlation, ranging from the more classical characterization of material samples up to modal analysis and dynamic characterization of several components in stationary as well as operating conditions and show how the same instrumentation can be reused to cover multiple scenarios at the same time, without the need for changing instrumentation or data acquisition as is the case for other experimental techniques. We also show how DIC is used to characterize the static behavior of lattice structures, characterize both statically and dynamically mechanical components to validate numerical models, extract the modal behavior of rotating components, such as tires and fans, and finally understand the behavior of huge machines where the size and stiffness pose great limitations to the use of optical techniques.

Background 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. Objective 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. Methods 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). Results 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. Conclusions 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.

Over the past few years, the application fields for digital image correlation (DIC) have significantly broadened and are now extensively used not only for material characterization and deformation analysis but also for vibration and dynamic testing. The key advantage over more traditional technologies such as accelerometer- or laser-based measurement is the fact that all points of interest can be measured at once or over a limited number of repetitions without mass loading the structure or having to deal with complex cabling scenarios. While qualitative analysis, such as operational deflection shapes, proved to be very successful and straightforward, performing classical modal analysis on frequency response functions between a load cell signal and the deformations obtained by applying digital image correlation still requires extra care when setting up the experiments. In this paper, the effect of different cameras, camera settings, excitation levels, and speckle pattern on the modal parameters of a simple plate structure will be investigated and compared with classical acceleration measurement. The results will also be compared with numerical simulations where the accuracy of the mode shapes can be easily and quantitatively assessed.

Recently, the issue of automatically defining virtual fields for non-linear constitutive models has been resolved (Marek et al., Comput Mech 60:409–431, 2017), relying on a new sensitivity-based approach hereby reducing the influence of noise on the parameter identification. These new set of fields act as weighting factors in the identification process emphasizing the impact of measurement regions with high signal to noise ratios. Conclusions in (Marek et al., Comput Mech 60:409–431, 2017) were drawn based on a numerical example involving small strain plasticity. In this presentation, the performance of these newly defined fields is studied when applied to digital image correlation (DIC) measurement data, hereby including the DIC filtering effects through synthetic image deformation (Rossi et al., Strain 51:206–222, 2017). Results are presented for both large strain isotropic plasticity and hyperelastic material models.

Digital Image Correlation (DIC) is a non-contact full-field image analysis technique which allows to retrieve strains and displacements in three dimensions at the surface of any type of material and under arbitrary loading. In recent years, high-speed and high-resolution cameras have been developed for static as well as for dynamic applications. As consequence, the application fields for DIC have broadened and it has proven to be a flexible and very accurate measurement solution for deformation analysis and material characterization. In this work DIC technique is used to get the full-field displacement of the structure under test. This information could then be used to derive the modal characteristic of the structure (e.g. natural frequencies, damping ratios and full-field mode shapes). These results can be validated by using classical sensors (e.g. strain gauges, accelerometers) or other optical methods (e.g. laser doppler vibrometers). Several test cases are discussed, and two different approaches are used for combining the data obtained during the vibration tests. The most obvious approach would be the alignment of the time histories of input (shaker) and output based on reference signals for Frequency Response Functions (FRFs) calculation prior to perform any further processing. An alternative in case of broadband excitation, requires processing time data into auto and crosspowers and identify the modal parameters by using Operational Modal Analysis (OMA).

Full-field data from digital image correlation (DIC) provide rich information for finite-element analysis (FEA) validation. However, there are several inherent inconsistencies between FEA and DIC data that must be rectified before meaningful, quantitative comparisons can be made, including strain formulations, coordinate systems, data locations, strain calculation algorithms, spatial resolutions and data filtering. In this paper, we investigate two full-field validation approaches: (1) the direct interpolation approach, which addresses the first three inconsistencies by interpolating the quantity of interest from one mesh to the other, and (2) the proposed DIC-levelling approach, which addresses all six inconsistencies simultaneously by processing the FEA data through a stereo-DIC simulator to 'level' the FEA data to the DIC data in a regularisation sense. Synthetic 'experimental' DIC data were generated based on a reference FEA of an exemplar test specimen. The direct interpolation approach was applied, and significant strain errors were computed, even though there was no model form error, because the filtering effect of the DIC engine was neglected. In contrast, the levelling approach provided accurate validation results, with no strain error when no model form error was present. Next, model form error was purposefully introduced via a mismatch of boundary conditions. With the direct interpolation approach, the mismatch in boundary conditions was completely obfuscated, while with the levelling approach, it was clearly observed. Finally, the 'experimental' DIC data were purposefully misaligned slightly from the FEA data. Both validation techniques suffered from the misalignment, thus motivating continued efforts to develop a robust alignment process. In summary, direct interpolation is insufficient, and the proposed levelling approach is required to ensure that the FEA and the DIC data have the same spatial resolution and data filtering. Only after the FEA data have been 'levelled' to the DIC data can meaningful, quantitative error maps be computed.

This paper presents the characterization of Lüders bands by digital image correlation on niobium microalloyed steel during a static tensile testing. Digital image correlation with the qualitative and quantitative analysis of the Lüders bands on the microalloyed steel was proved as a very precise and suitable method for determining the strain amount in the deformation zone. In this research was determined that the strain amount is the highest in the area behind the Lüders band front and the lowest in the area in front of the Lüders band.

The 2D-DIC Challenge is organized by an international committee working to understand the accuracy of digital image correlation (DIC) through standardized image sets. The DIC Challenge is run under the auspices of the Society for Experimental Mechanics (SEM) and the International DIC Society (iDICs). The 2D-Challenge incorporates 19 image sets that can be used in evaluating 2D-DIC algorithms. The full results of the study and description of the image sets may be found in Reu et al. (Exp Mech, 2017). A new round of the 2D Challenge is being launched at SEM 2018 and will seek to probe the concept of spatial resolution.

Camera rotation during stereo digital image correlation (DIC) measurements is always present in some amount and is caused by environmental vibrations during testing (e.g. blast testing, testing in industrial environments, vibration testing Helffrick et al., Mech Syst Signal Process 25(3):917–927, 2011, etc.). The impact of changing extrinsic parameters on stereo-DIC measurements was investigated in this paper by simulating several DIC experiments with a shaking camera system. To obtain an appropriate order of magnitude of the camera motion, the actual camera rotation during a test was observed and reported. The impact hereof was investigated by analyzing static images and by simulating camera rotation with a stereo-DIC simulator presented in Balcaen et al. (Exp Mech 57(5):703–718, 2017). Insight on this underestimated error source is explained by investigating which rotations are more critical to the resulting displacements and strains. Two possible compensation methods were investigated and their capability of compensating camera motion was evaluated.

Digital Image Correlation (DIC) is a non-contact full-field image analysis technique which allows to retrieve strains and displacements in three dimensions at the surface of any type of material and under arbitrary loading. In recent years, high-speed and high-resolution cameras have been developed for static as well as for dynamic applications. As consequence, the application fields for DIC have broadened and it has proven to be a flexible and very accurate measurement solution for deformation analysis and material characterization. Nevertheless, nowadays DIC is often used in a qualitative manner rather than as a metrological tool. This is especially due to the time-consuming task related to the post-processing of the images. When compared to other vibration testing techniques, full-field approaches (such as DIC) allow a greater flexibility by providing a very dense number of experimental data over a single measurement. Another advantage is related to the fact that the geometry is automatically extracted from the images. In this paper, the possibility to combine global acceleration measurements on a small component with local full-field standard machine vision quasi-static camera measurements is investigated. In particular, the regularization properties of DIC and their impact on modal analysis will be studied in detail. Strains and displacements could be used in a second stage for modal analysis purpose in order to characterize the dynamic behaviour of the specimen in a certain frequency range. Different approaches could be used for combining together the data obtained during the tests. The most obvious approach would be the alignment of the time histories based on reference signals for Frequency Response Functions (FRFs) calculation prior to perform any further processing. Unfortunately this is not always possible because of synchronization issues. An alternative possibility, in case broadband random excitation is used, requires to process time data into auto and crosspowers and identify the modal parameters by using Operational Modal Analysis (OMA).

With the rapid spread in use of Digital Image Correlation (DIC) globally, it is important there be some standard methods of verifying and validating DIC codes. To this end, the DIC Challenge board was formed and is maintained under the auspices of the Society for Experimental Mechanics (SEM) and the international DIC society (iDICs). The goal of the DIC Board and the 2D–DIC Challenge is to supply a set of well-vetted sample images and a set of analysis guidelines for standardized reporting of 2D–DIC results from these sample images, as well as for comparing the inherent accuracy of different approaches and for providing users with a means of assessing their proper implementation. This document will outline the goals of the challenge, describe the image sets that are available, and give a comparison between 12 commercial and academic 2D–DIC codes using two of the challenge image sets.