Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2010
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dc.contributor.authorSeyhan, Abdullah Tuğrul-
dc.contributor.authorTayfur, Gökmen-
dc.contributor.authorKarakurt, Murat-
dc.contributor.authorTanoğlu, Metin-
dc.date.accessioned2016-07-28T13:30:32Z
dc.date.available2016-07-28T13:30:32Z
dc.date.issued2005-08
dc.identifier.citationSeyhan, A. T., Tayfur, G., Karakurt, M., and Tanoǧlu, M. (2005). Artificial neural network (ANN) prediction of compressive strength of VARTM processed polymer composites. Computational Materials Science, 34(1), 99-105. doi:10.1016/j.commatsci.2004.11.001en_US
dc.identifier.issn0927-0256
dc.identifier.issn0927-0256-
dc.identifier.urihttp://doi.org/10.1016/j.commatsci.2004.11.001
dc.identifier.urihttp://hdl.handle.net/11147/2010
dc.description.abstractA three layer feed forward artificial neural network (ANN) model having three input neurons, one output neuron and two hidden neurons was developed to predict the ply-lay up compressive strength of VARTM processed E-glass/ polyester composites. The composites were manufactured using fabric preforms consolidated with 0, 3 and 6 wt.% of thermoplastic binder. The learning of ANN was accomplished by a backpropagation algorithm. A good agreement between the measured and the predicted values was obtained. Testing of the model was done within low average error levels of 3.28%. Furthermore, the predictions of ANN model were compared with those obtained from a multi-linear regression (MLR) model. It was found that ANN model has better predictions than MLR model for the experimental data. Also, the ANN model was subjected to a sensitivity analysis to obtain its response. As a result, the ANN model was found to have an ability to yield a desired level of ply-lay up compressive strength values for the composites processed with the addition of the thermoplastic binder.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltd.en_US
dc.relation.ispartofComputational Materials Scienceen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural network (ANN)en_US
dc.subjectCompressive strengthen_US
dc.subjectMulti-linear regression (MLR)en_US
dc.subjectPolymer compositesen_US
dc.subjectPreforming binderen_US
dc.subjectNeural networksen_US
dc.titleArtificial neural network (ANN) prediction of compressive strength of VARTM processed polymer compositesen_US
dc.typeArticleen_US
dc.authoridTR2054en_US
dc.authoridTR30837en_US
dc.institutionauthorSeyhan, A. Tuğrul-
dc.institutionauthorTayfur, Gökmen-
dc.institutionauthorKarakurt, Murat-
dc.institutionauthorTanoğlu, Metin-
dc.departmentİzmir Institute of Technology. Mechanical Engineeringen_US
dc.departmentİzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume34en_US
dc.identifier.issue1en_US
dc.identifier.startpage99en_US
dc.identifier.endpage105en_US
dc.identifier.wosWOS:000228943700009en_US
dc.identifier.scopus2-s2.0-17444388869en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.commatsci.2004.11.001-
dc.relation.doi10.1016/j.commatsci.2004.11.001en_US
dc.coverage.doi10.1016/j.commatsci.2004.11.001en_US
dc.identifier.wosqualityQ1-
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept03.03. Department of Civil Engineering-
crisitem.author.dept03.10. Department of Mechanical Engineering-
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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