Validation of finite‐element models using full‐field experimental data: Levelling finite‐element analysis data through a digital image correlation engine

Abstract

Full-field data from Digital Image Correlation (DIC) provides 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-leveling 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 regularization sense. Synthetic "experimental" DIC data was 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 leveling 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 leveling approach, it was clearly observed. Finally, the "experimental" DIC data was 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 leveling 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 has been "leveled" to the DIC data can meaningful, quantitative error maps be computed.

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“…However, in the case of the DIC technique, the accuracy of strain measurements is limited by many factors such as the resolution of camera and lens, size of pattern feature, subset size, step size, and strain window. [ 45 ]…”

Flat Specimen Shape Recognition Based on Full-Field Optical Measurements and Registration Using Mapping Error Minimization Method

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“…4 also represents the reference (modelling) geometry. The measured geometry is obtained by introducing rigid translation and rotation to the reference one [10].…”

“…The authors also concluded that the level of threshold seemed to be material dependent. Lava et al [10] studied two different methods used to compute full-field error maps between experimental DIC data and FEA data to, among others, investigate the effects of processing parameters, model form error (such as incorrect boundary conditions) and mesh alignment. The authors showed that even a small misalignment can have a surprisingly large effect on the strain error maps and exposed the necessity to develop robust and precise methods of alignment.…”

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