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.
Domaines
Sciences de l'ingénieur [physics]| Origine | Fichiers produits par l'(les) auteur(s) |
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