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Prediction of mechanical strength of cork under compression using machine learning techniques

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dc.contributor.authorGarcía, Ángela
dc.contributor.authorAnjos, O.
dc.contributor.authorIglesias, Carla
dc.contributor.authorPereira, Helena
dc.contributor.authorMartínez, Javier
dc.contributor.authorTaboada, Javier
dc.date.accessioned2017-07-22T16:20:02Z
dc.date.available2017-07-22T16:20:02Z
dc.date.issued2015
dc.description.abstractIn this study, the accuracy of mathematical techniques such as multiple linear regression, clustering, decision trees (CART) and neural networks was evaluated to predict Young’s modulus, compressive stress at 30% strain and instantaneous recovery velocity of cork. Physical properties, namely test direction, density, porosity and pore number, as well as test direction were used as input. The better model was achieved when a classification problem was performed. Only compressive stress at 30% strain can be predicted with neural networks with an error rate of about 20%. The prediction of Young’s modulus and instantaneous recovery velocity led to unacceptably high error rates due to the heterogeneity of the material.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGARCÍA, A. [et al.] (2015) - Prediction of mechanical strength of cork under compression using machine learning techniques. Materials and Design. ISSN 0261-3069. 82. P. 304-311.pt_PT
dc.identifier.doi10.1016/j.matdes.2015.03.038pt_PT
dc.identifier.issn0261-3069
dc.identifier.urihttp://hdl.handle.net/10400.11/5612
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCorkpt_PT
dc.subjectMechanical propertiespt_PT
dc.subjectNeural networkpt_PT
dc.subjectMultiple linear regressionpt_PT
dc.subjectCARTpt_PT
dc.subjectClusterpt_PT
dc.titlePrediction of mechanical strength of cork under compression using machine learning techniquespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage311pt_PT
oaire.citation.startPage304pt_PT
oaire.citation.titleMaterials and Designpt_PT
oaire.citation.volume82pt_PT
person.familyNameAnjos
person.givenNameOfélia
person.identifier.ciencia-idC21D-D8C7-3037
person.identifier.orcid0000-0003-0267-3252
person.identifier.ridG-2808-2012
person.identifier.scopus-author-id23395659700
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationdf9191ae-0bbb-4bb8-bbdc-0f79c7365876
relation.isAuthorOfPublication.latestForDiscoverydf9191ae-0bbb-4bb8-bbdc-0f79c7365876

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