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dc.contributor.authorCaro Carretero, Raqueles-ES
dc.contributor.authorJimenez-Octavio, JRes-ES
dc.contributor.authorCarnicero, Albertoes-ES
dc.contributor.authorGarrido Contreras, Arturoes-ES
dc.contributor.authorSuch, Migueles-ES
dc.date.accessioned2019-12-11T14:35:16Z
dc.date.available2019-12-11T14:35:16Z
dc.date.issued16/12/2019es_ES
dc.identifier.issn0361-0926es_ES
dc.identifier.urihttp://hdl.handle.net/11531/43809
dc.descriptionArtículos en revistases_ES
dc.description.abstractThe field of strength reliability is one of the critical factors restricting wider use of brittle materials in certain structural applications, like ceramics. In this area, the Weibull distribution is widely accepted for lifetime modeling. In essence, the brittleness of ceramic materials leads to poor toughness and low strength reliability. The statistical nature of these flaws results in a significant scatter of the measured macroscopic strength outcomes, which has a number of consequences both in the design and verification of components involving such materials. In the present work, an analysis and evaluation of six existing estimation methods for a Weibull distribution are presented, as well as a new approach for fitting the Weibull distribution using Neural Networkses-ES
dc.description.abstractThe field of strength reliability is one of the critical factors restricting wider use of brittle materials in certain structural applications, like ceramics. In this area, the Weibull distribution is widely accepted for lifetime modeling. In essence, the brittleness of ceramic materials leads to poor toughness and low strength reliability. The statistical nature of these flaws results in a significant scatter of the measured macroscopic strength outcomes, which has a number of consequences both in the design and verification of components involving such materials. In the present work, an analysis and evaluation of six existing estimation methods for a Weibull distribution are presented, as well as a new approach for fitting the Weibull distribution using Neural Networks. The major focus of this work is, however, the implementation of simulations in order to contrast how well the suggested techniques of the Weibull parameter estimation perform. Finally, an important implication of the present study is that it shows how various estimators of the Weibull model work for wide-ranging sample sizes and different parameter values. The simulation results revealed that L-Moment estimator produces more accurate estimates, unlike those using Neural Networks that are more robust with the lowest Root Mean Square Error.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoes-ESes_ES
dc.rightses_ES
dc.rights.uries_ES
dc.sourceRevista: Communications in Statistics-Theory and Methods, Periodo: 4, Volumen: , Número: , Página inicial: 1, Página final: 22es_ES
dc.titleA new approach to fitting the three-parameter Weibull Distribution. An application to glass ceramicses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.holderpublicado onlinees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses_ES
dc.keywordsWeibull strength distribution; brittle materials; ceramics; parameter estimation, Neural Networkses-ES
dc.keywordsWeibull strength distribution; brittle materials; ceramics; parameter estimation, Neural Networksen-GB
dc.identifier.doi10.1080/03610926.2019.1702698es_ES


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