Understanding Uncertainties in Thermographic Imaging

7 p.The present article proposes a workflow based on free/open-source software solutions for the acquisition of competences in engineering courses related to the use of thermographic images. The approach is aimed to three-dimensional visualization techniques over thermographic images to improve the comprehension and interpretation of the different error sources that affects the measurements, and therefore the conclusions and analysis derived from them. The present work is framed inside the virtual laboratories discipline, as the new learning material can be employed for the acquisition of competences and skills. Additionally, it can be used for the evaluation of competences in asynchronous and e-learning programs. The learning materials could be easily deployed in a learning management system, allowing the students to work with the models by means of open-source solutions easily, both in asynchronous and face-to-face courses. Consequently, the present approach will improve the application of professional techniques, so the future professionals will reach the working market better prepared.S

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

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.</p