A multiscale deep learning model for elastic properties of woven composites
Artikel i vetenskaplig tidskrift, 2023

Time-consuming and costly computational analysis expresses the need for new methods for generalizing multiscale analysis of composite materials. Combining neural networks and multiscale modeling is favorable for bypassing expensive lower-scale material modeling, and accelerating coupled multi-scale analyses (FE2). In this work, neural networks are used to replace the time-consuming micromechanical finite element analysis of unidirectional composites, representing the local material properties of yarns in woven fabric composites in a multiscale framework. Leveraging the fast multiscale data generation procedure, we presented a second neural networks model to estimate the elastic engineering coefficients of a particular weave architecture based on a broad range of dry resin and fiber properties and yarn fiber volume fraction. As an outcome, this paper provides the user with a generalized, neural network-based approach to tackle the balance of computational efficiency and accuracy in the multiscale analysis of elastic woven composites.

Multiscale analysis

Woven composites

Artificial neural networks

Elastic properties

Författare

E. Ghane

Göteborgs universitet

Martin Fagerström

Chalmers, Industri- och materialvetenskap, Material- och beräkningsmekanik

S. M. Mirkhalaf

Göteborgs universitet

International Journal of Solids and Structures

0020-7683 (ISSN)

Vol. 282 112452

Ämneskategorier

Kompositmaterial och -teknik

DOI

10.1016/j.ijsolstr.2023.112452

Mer information

Senast uppdaterat

2023-09-14