Inversion Technique for Quantitative Infrared Thermography Evaluation of Delamination Defects in Multilayered Structures

Abstract

Inverse analysis is a promising tool for quantitative evaluation offering informative model-based prediction and providing accurate reconstruction results without preinspections for characterization criteria. For the traditional defect inverse reconstruction, a large number of parameters are required to reconstruct a complex defect, and the corresponding forward modeling simulation is very time-consuming. Such issues result in ill-posed and complex inverse reconstruction results, which further reduce its practical applicability. In this article, we propose and experimentally validate an inversion technique for the reconstruction of complexly shaped delamination defects in a multilayered metallic structure using signals derived from infrared thermography (IRT) testing. First, we employ a novel defect parameterization strategy based on Fourier series fitting to represent the profile of a complicated delamination defect with relatively few coefficients. Second, the multimedium element modeling method is applied to enhance a finite element method (FEM) fast forward simulator, in order to solve the mismatching mesh issue for mesh updating during inversion. Third, a deterministic inverse algorithm based on a penalty conjugate gradient algorithm is employed to realize a robust and efficient inverse analysis. By reconstructing delamination profiles with both numerically simulated IRT signals and those obtained through laser IRT experiments, the validity, efficiency, and robustness of the proposed inversion method are demonstrated for delamination defects in a double-layered plate. Based on this strategy, not only is the feasibility of the proposed method in IRT nondestructive testing is validated, but the practical applicability of inversion reconstruction analysis is significantly improved.