Conference Proceedings of the Society for Experimental Mechanics Series
Reu, Phillip L.; Toussaint, E.; Jones, E.; Bruck, H.; Iadicola, M.; Balcaen, R.; Turner, Daniel Z.; Siebert, T.; Lava, P.; Simonsen, M.; Grewer, M.
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.
A major and often unrecognized error source in digital image correlation (DIC) is the influence of the intervening air between the cameras and sample. Minute differences in air temperature, composition, or both can cause index of refraction changes that act as a lens and cause distortions in the DIC displacement and strain results (Jones and Reu, Exp Mech, 2017). There are limited options to correct this problem as it is both spatial and temporal in nature. One method is to use X-rays for imaging that are not affected by air refraction, but this requires costly equipment. A second method uses a vacuum chamber to minimize the intervening air to remove the distortions, but unfortunately this requires inconvenient setups.
“Heat waves” is a colloquial term used to describe convective currents in air formed when different objects in an area are at different temperatures. In the context of Digital Image Correlation (DIC) and other optical-based image processing techniques, imaging an object of interest through heat waves can significantly distort the apparent location and shape of the object. There are many potential heat sources in DIC experiments, including but not limited to lights, cameras, hot ovens, and sunlight, yet error caused by heat waves is often overlooked. This paper first briefly presents three practical situations in which heat waves contributed significant error to DIC measurements to motivate the investigation of heat waves in more detail. Then the theoretical background of how light is refracted through heat waves is presented, and the effects of heat waves on displacements and strains computed from DIC are characterized in detail. Finally, different filtering methods are investigated to reduce the displacement and strain errors caused by imaging through heat waves. The overarching conclusions from this work are that errors caused by heat waves are significantly higher than typical noise floors for DIC measurements, and that the errors are difficult to filter because the temporal and spatial frequencies of the errors are in the same range as those of typical signals of interest. Therefore, eliminating or mitigating the effects of heat sources in a DIC experiment is the best solution to minimizing errors caused by heat waves.
Traditionally, material identification is performed using global load and displacement data from simple boundary-value problems such as uni-axial tensile and simple shear tests. More recently, however, inverse techniques such as the Virtual Fields Method (VFM) that capitalize on heterogeneous, full-field deformation data have gained popularity. In this work, we have written a VFM code in a finite-deformation framework for calibration of a viscoplastic (i.e. strain-rate dependent) material model for 304L stainless steel. Using simulated experimental data generated via finite-element analysis (FEA), we verified our VFM code and compared the identified parameters with the reference parameters input into the FEA. The identified material model parameters had surprisingly large error compared to the reference parameters, which was traced to parameter covariance and the existence of many essentially equivalent parameter sets. This parameter non-uniqueness and its implications for FEA predictions is discussed in detail. Finally, we present two strategies to reduce parameter covariance – reduced parametrization of the material model and increased richness of the calibration data – which allow for the recovery of a unique solution.
Reu, Phillip L.; Toussaint, E.; Jones, E.; Bruck, H.A.; Iadicola, M.; Balcaen, R.; Turner, Daniel Z.; Siebert, T.; Lava, P.; Simonsen, M.
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.
Modeling material and component behavior using finite element analysis (FEA) is critical for modern engineering. One key to a credible model is having an accurate material model, with calibrated model parameters, which describes the constitutive relationship between the deformation and the resulting stress in the material. As such, identifying material model parameters is critical to accurate and predictive FEA. Traditional calibration approaches use only global data (e.g. extensometers and resultant force) and simplified geometries to find the parameters. However, the utilization of rapidly maturing full-field characterization tech- niques (e.g. Digital Image Correlation (DIC)) with inverse techniques (e.g. the Virtual Feilds Method (VFM)) provide a new, novel and improved method for parameter identification. This LDRD tested that idea: in particular, whether more parameters could be identified per test when using full-field data. The research described in this report successfully proves this hypothesis by comparing the VFM results with traditional calibration methods. Important products of the research include: verified VFM codes for identifying model parameters, a new look at parameter covariance in material model parameter estimation, new validation tech- niques to better utilize full-field measurements, and an exploration of optimized specimen design for improved data richness.