| Título: |
MODELING THE YOUNG MODULUS OF NANOCOMPOSITES: A NEURAL NETWORK APPROACH
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| Instituição: |
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| Autor(es): |
LEANDRO FONTOURA CUPERTINO
OMAR PARANAIBA VILELA NETO
MARCO AURELIO CAVALCANTI PACHECO
MARLEY MARIA BERNARDES REBUZZI VELLASCO
JOSE ROBERTO MORAES D ALMEIDA
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| Colaborador(es): |
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| Catalogação: |
18 11:10:20.000000/02/2013 |
| Tipo: |
PAPER
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Idioma(s): |
ENGLISH - UNITED STATES
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| Nota: |
© 2011 IEEE. Reprinted, with permission, IEEE INTERNATIONAL JOINT CONFERENCE ON
NEURAL NETWORKS, JULY 2011. This material is posted here with permission of the IEEE.
Such permission of the IEEE does not in any way imply IEEE endorsement of any of Pontifícia
Universidade Catolica do Rio de Janeiro’s. Internal or personal use of this material is
permitted. However, permission to reprint/republish this material for advertising or
promotional purposes or for creating new collective works for resale or redistribution
must be obtained from the IEEE by writing to pubs-permissions@ieee.org.
By choosing to view this document, you agree to all provisions of the copyrightlaws
protecting it.
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| Referência [en]: |
https://www.maxwell.vrac.puc-rio.br/eletricaonline/serieConsulta.php?strSecao=resultado&nrSeq=21164@2 |
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Resumo:
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Composite materials have changed the way of
using polymers, as the strength was favored by the incorporation
of fibers and particles. This new class of materials allowed
a larger number of applications. The insertion of nanometric
sized particles has enhanced the variation of properties with
a smaller load of fillers. In this paper, we attempt to a
better understanding of nanocomposites by using an artificial
intelligence’s technique, known as artificial neural networks.
This technique allowed the modeling of Young’s modulus of
nanocomposites. A good approximation was obtained, as the
correlation between the data and the response of the network
was high, and the error percentage was low.
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