Identification and characterization of damaged fiber-reinforced laminates in a Bayesian framework - CentraleSupélec Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

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

Résumé

Non-destructive testing of damaged composite laminates modeled from the homogenization of fiber-reinforced polymers has always been and still is a challenge, both by underlying complexity and the difficulties encountered in the quantification of uncertainties related to the identification and characterization of defects. In order to provide a rigorous framework that accepts data from different modalities and allows data fusion as well, we propose here a Bayesian neural network (BNN) with two input streams, with a focus on local inter-layer delaminations identification and characterization.
Fichier principal
Vignette du fichier
Abstract_ENDE_2023-Noel-etal.pdf (57.25 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04108648 , version 1 (20-11-2023)

Identifiants

  • HAL Id : hal-04108648 , version 1

Citer

Valentin Noël, Thomas Rodet, Dominique Lesselier. Identification and characterization of damaged fiber-reinforced laminates in a Bayesian framework. 26th International Workshop on Electromagnetic Nondestructive Evaluation (ENDE) 2023, Theodoros Theodoulidis, Jun 2023, Thessaloniki, Greece. ⟨hal-04108648⟩
89 Consultations
10 Téléchargements

Partager

Gmail Facebook X LinkedIn More