In this paper we show some different concepts for the use of Artificial Neural Networks (ANNs) in modelling of composites and hierarchical structures. By using virtual testing, a proper set of corresponding input-output data can be created to train neural networks to identify the effective properties. Furthermore, ANN based procedures can be exploited in a multiscale analysis as a tool for the stress recovery at lower levels of the hierarchical structure and/or to estimate the state of yielding of the materials. Finally, ANNs may be used to define the homogenized properties for a class of parameterized unit cells or when material characteristics depend upon a parameter (e.g. temperature, damage etc.).

A combined FE-ANN approach for multiscale numerical modeling of composites

BOSO, DANIELA;SCHREFLER, BERNHARD
2012

Abstract

In this paper we show some different concepts for the use of Artificial Neural Networks (ANNs) in modelling of composites and hierarchical structures. By using virtual testing, a proper set of corresponding input-output data can be created to train neural networks to identify the effective properties. Furthermore, ANN based procedures can be exploited in a multiscale analysis as a tool for the stress recovery at lower levels of the hierarchical structure and/or to estimate the state of yielding of the materials. Finally, ANNs may be used to define the homogenized properties for a class of parameterized unit cells or when material characteristics depend upon a parameter (e.g. temperature, damage etc.).
2012
Multi-Physics Numerical Modeling and Computational Strategies
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2532832
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact