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Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools

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dc.contributor.authorEKİCİ, BÜLENT
dc.contributor.authorsKilic, Namik; Ekici, Bulent; Hartomacioglu, Selim
dc.date.accessioned2022-03-14T11:01:44Z
dc.date.available2022-03-14T11:01:44Z
dc.date.issued2015-06
dc.description.abstractDetermination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods (FEM) in this research field. The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort, therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time. This study aims to apply a hybrid method using FEM simulation and artificial neural network (ANN) analysis to approximate ballistic limit thickness for armor steels. To achieve this objective, a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition. In this methodology, the FEM simulations are used to create training cases for Multilayer Perceptron (MLP) three layer networks. In order to validate FE simulation methodology, ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569. Afterwards, the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor. Results show that even with limited number of data, FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy. Copyright (C) 2015, China Ordnance Society. Production and hosting by Elsevier B.V. All rights reserved.
dc.identifier.doi10.1016/j.dt.2014.12.001
dc.identifier.issn2214-9147
dc.identifier.urihttps://hdl.handle.net/11424/245756
dc.identifier.wosWOS:000365991300003
dc.language.isoeng
dc.publisherELSEVIER SCIENCE BV
dc.relation.ispartofDEFENCE TECHNOLOGY
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectFinite element method (FEM)
dc.subjectArtificial neural network (ANN)
dc.subjectMultilayer perceptron (MLP)
dc.subjectGeneralized feed forward (GFF)
dc.subjectBallistics
dc.subjectHigh hardness armor
dc.subjectSTEEL
dc.subjectDEFORMATION
dc.subjectPREDICTION
dc.subjectPROJECTILE
dc.subjectDESIGN
dc.subjectPERFORMANCE
dc.subjectBEHAVIOR
dc.subjectTARGETS
dc.subjectMODELS
dc.subjectPLATES
dc.titleDetermination of penetration depth at high velocity impact using finite element method and artificial neural network tools
dc.typearticle
dspace.entity.typePublication
local.avesis.id39e6fd99-bbe9-48a6-a6a8-503de45e8418
local.import.packageSS16
local.indexed.atWOS
local.indexed.atSCOPUS
local.journal.numberofpages13
oaire.citation.endPage122
oaire.citation.issue2
oaire.citation.startPage110
oaire.citation.titleDEFENCE TECHNOLOGY
oaire.citation.volume11
relation.isAuthorOfPublicationbfa2c25f-3c5b-4469-a10f-34f036177bc1
relation.isAuthorOfPublication.latestForDiscoverybfa2c25f-3c5b-4469-a10f-34f036177bc1

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