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dc.contributor.authorSourmail, Thomas-
dc.contributor.authorGarcía Mateo, Carlos-
dc.date.accessioned2008-03-10T14:51:31Z-
dc.date.available2008-03-10T14:51:31Z-
dc.date.issued2005-
dc.identifier.citationComputational Materials Science 34 (2005) 323-334en_US
dc.identifier.urihttp://hdl.handle.net/10261/3191-
dc.description.abstractDifferent approaches to predicting the Ms temperatures of steels are reviewed and discussed with the objective of summarising the main characteristics, advantages and difficulties of each method, mostly from a practical point of view. Empirical methods, and methods based on thermodynamics are then assessed against published data.en_US
dc.description.sponsorshipNPL for provision of MTDATA and Neuromat for provision of the Model Manageren_US
dc.format.extent246969 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsopenAccessen_US
dc.subjectMartensite; Thermodynamics; Bayesian neural networks; Linear regressionen_US
dc.titleCritical assessment of models for predicting the Ms temperature of steelsen_US
dc.typeartículoen_US
dc.identifier.doi10.1016/j.commatsci.2005.01.002-
dc.description.peerreviewedPeer revieweden_US
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.commatsci.2005.01.002-
dc.type.coarhttp://purl.org/coar/resource_type/c_6501es_ES
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeartículo-
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