Identification and characterization of damaged fiber-reinforced laminates in a Bayesian framework

Issue title: 26th International Workshop on Electromagnetic Nondestructive Evaluation

Guest editors: Theodoros Theodoulidis, Christophe Reboud and Christos Antonopoulos

Article type: Research Article

Authors: Noël, Valentin^{a; b;} | Rodet, Thomas^a | Lesselier, Dominique^b;

Affiliations: [a] Systèmes et Applications des Technologies de l’Information et de l’Energie, Ecole Normale Supérieure Paris-Saclay, Gif-sur-Yvette, France | [b] Laboratoire des Signaux et Systèmes, Université Paris-Saclay, Gif-sur-Yvette, France

Correspondence: [*] Corresponding authors: Valentin Noël, Laboratoire des Signaux et Systèmes, Université Paris-Saclay, CNRS, CentraleSupélec, Gif-sur-Yvette, France. E-mail: [email protected]. Dominique Lesselier, Laboratoire des Signaux et Systèmes, Université Paris-Saclay, CNRS, CentraleSupélec, Gif-sur-Yvette, France. E-mail: [email protected]

Abstract: Non-destructive thermographic testing of damaged composite laminates modeled from the homogenization of fiber-reinforced polymers is a challenge, both because of its underlying complexity and because of the difficulties encountered in the quantification of uncertainties related to the identification and characterization of defects. To provide a rigorous framework that accepts data from different modalities and allows data fusion as well, a Bayesian neural network (BNN) [I. Kononenko, Biological Cybernetics 61(5) (1989), 361–370] with two input streams is proposed, with a focus on local inter-layer delaminations identification and characterization.

Keywords: Non-destructive testing, fiber-reinforced laminates, infra-red thermal imaging, Bayesian neural network, uncertainty quantification

DOI: 10.3233/JAE-230140

Journal: International Journal of Applied Electromagnetics and Mechanics, vol. 74, no. 4, pp. 379-386, 2024