Deep Learning Approaches for Dynamic Mechanical Analysis of Viscoelastic Fiber Composites - Université de Lorraine Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2023

Deep Learning Approaches for Dynamic Mechanical Analysis of Viscoelastic Fiber Composites

Résumé

The increased adoption of reinforced polymer (RP) composite materials, driven by eco-design standards, calls for a fine balance between lightness, stiffness, and effective vibration control. These materials are integral to enhancing comfort, safety, and energy efficiency. Dynamic Mechanical Analysis (DMA) characterizes viscoelastic behavior, yet there's a growing interest in using Machine Learning (ML) to expedite the design and understanding of microstructures. In this paper we aim to map microstructures to their mechanical properties using deep neural networks, speeding up the process and allowing for the generation of microstructures from desired properties.
Fichier principal
Vignette du fichier
HoffmannNahmed2023_Hal.pdf (1.4 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04250557 , version 1 (19-10-2023)

Identifiants

Citer

Victor Hoffmann, Ilias Nahmed, Parisa Rastin, Guénaël Cabanes, Julien Boisse. Deep Learning Approaches for Dynamic Mechanical Analysis of Viscoelastic Fiber Composites. 2023. ⟨hal-04250557⟩
193 Consultations
56 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More