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Prediction of the behaviour of CFRPs against high-velocity impact of solids employing an artificial neural network methodology

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dc.affiliation.dptoUC3M. Departamento de Mecánica de Medios Continuos y Teoría de Estructurases
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Dinámica y Fractura de Elementos Estructuraleses
dc.contributor.authorFernández Fernández, David
dc.contributor.authorLópez Puente, Jorge
dc.contributor.authorZaera, Ramón
dc.date.accessioned2010-03-12T11:14:34Z
dc.date.available2010-03-12T11:14:34Z
dc.date.issued2008-06
dc.description8 pages, 9 figures.
dc.description.abstractA new methodology based on artificial neural networks has been developed to study the high velocity oblique impact of spheres into CFRP laminates. One multilayer perceptron (MLP) is employed to predict the occurrence of perforation of the laminate and a second MLP predicts the residual velocity, the obliquity of trajectory of the sphere after perforation and the damage extension in the laminate. In order to train and test the networks, multiple impact cases have been generated by finite-element numerical simulation covering different impact angles and impact velocities of the sphere for a given system sphere/laminate.
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationComposites Part A, 2008, vol. 39, n. 6, p. 989-996
dc.identifier.doi10.1016/j.compositesa.2008.03.002
dc.identifier.issn1359-835X
dc.identifier.urihttps://hdl.handle.net/10016/7296
dc.language.isoeng
dc.publisherElsevier
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.compositesa.2008.03.002
dc.rights© Elsevier
dc.rights.accessRightsopen access
dc.subject.ecienciaIngeniería Mecánica
dc.subject.ecienciaIngeniería Industrial
dc.subject.otherCarbon fibre
dc.subject.otherArtificial neural network
dc.subject.otherImpact behaviour
dc.subject.otherNumerical analysis
dc.titlePrediction of the behaviour of CFRPs against high-velocity impact of solids employing an artificial neural network methodology
dc.typeresearch article*
dc.type.reviewPeerReviewed
dspace.entity.typePublication
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