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dc.contributor.authorCapdevila, Carlos-
dc.contributor.authorToda Caraballo, Isaac-
dc.contributor.authorGarcía Caballero, Francisca-
dc.contributor.authorGarcía Mateo, Carlos-
dc.contributor.authorGarcía de Andrés, Carlos-
dc.date.accessioned2013-02-01T12:27:26Z-
dc.date.available2013-02-01T12:27:26Z-
dc.date.issued2012-
dc.identifier.citationMaterials Science and Technology 28(3) : 321-333 (2012)es_ES
dc.identifier.issn0267-0836-
dc.identifier.urihttp://hdl.handle.net/10261/65653-
dc.description.abstractThe work reported in the present paper outlines the use of a combined artificial neural network model capable of fast online prediction of textures in low and extralow carbon steels. We approach the problem by a Bayesian framework neural network model that takes into account as input to the model the influence of 23 parameters describing the chemical composition and the thermomechanical processes, such as austenite and ferrite rolling, coiling, cold working and subsequent annealing, involved in the production route of low and extralow carbon steels. The output of the model is in the form of fibre texture data. The predictions of the network provide an excellent match to the experimentally measured data. The results presented in the present paper demonstrate that this model can help in optimising the normal anisotropy rm of steel productses_ES
dc.description.sponsorshipSpanish Ministerio de Ciencia e Innovacio´n through the Plan Nacional 2009 (grant no. ENE2009 13766-C04-01). The authors are also grateful to Neuromat Ltd for the provision of the neural network softwarees_ES
dc.language.isoenges_ES
dc.publisherInstitute of Materials, Minerals and Mininges_ES
dc.rightsopenAccesses_ES
dc.subjectArtificial Neural Networkes_ES
dc.subjectTexture predictiones_ES
dc.subjectAnisotropyes_ES
dc.subjectHot rollinges_ES
dc.subjectCold rollinges_ES
dc.subjectSteeles_ES
dc.titleDetermination of hot- and cold-rolling texture of steels: A combined Bayesian Neurales_ES
dc.typeartículoes_ES
dc.identifier.doihttp://dx.doi.org/10.1179/1743284711Y.0000000035-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttp://dx.doi.org/10.1179/1743284711Y.0000000035es_ES
dc.identifier.e-issn1743-2847-
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