Material Fracture Life Prediction Using Linear Regression Techniques Under High Temperature Creep Conditions

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dc.contributor.authorFernandez Martinez, Roberto
dc.contributor.authorJimbert, Pello
dc.contributor.authorBarbero, Jose Ignacio
dc.contributor.authorCallejo, Lorena M.
dc.contributor.authorSomocueto, Igor
dc.contributor.editorNyström, Ingela
dc.contributor.editorHernández Heredia, Yanio
dc.contributor.editorMilián Núñez, Vladimir
dc.contributor.institutionPROMETAL
dc.contributor.institutionCIRMETAL
dc.contributor.institutionTecnalia Research & Innovation
dc.date.issued2019-10-22
dc.descriptionPublisher Copyright: © Springer Nature Switzerland AG 2019.
dc.description.abstract9–12% Cr martensitic steels are widely used for critical components of new, high-efficiency, ultra-supercritical power plants because of their high creep and oxidation resistances. Due to the time consuming effort of obtaining creep properties for new alloys under high temperature creep conditions, in both short-term and long-term testing, it is often dealt with simplified models to assess and predict the future behavior of some materials. In this work, the total time to produce the material fracture is predicted according to models obtained using several linear techniques, since this property is really relevant in power plants elements. These models are obtained based on 344 creep tests performed on modified P92 steels. A multivariate analysis and a feature selection were applied to analyze the influence of each feature in the problem, to reduce the number of features simplifying the model and to improve the accuracy of the model. Later, a training-testing validation methodology was performed to obtain more useful results based on a better generalization to cover every scenario of the problem. Following this method, linear regression algorithms, simple and generalized, with and without enhanced by gradient boosting techniques, were applied to build several linear models, achieving low errors of approximately 6.75%. And finally, among them the most accurate model was selected, in this case the one based on the generalized linear regression technique.en
dc.description.statusPeer reviewed
dc.format.extent10
dc.format.extent424991
dc.identifier.citationFernandez Martinez , R , Jimbert , P , Barbero , J I , Callejo , L M & Somocueto , I 2019 , Material Fracture Life Prediction Using Linear Regression Techniques Under High Temperature Creep Conditions . in I Nyström , Y Hernández Heredia & V Milián Núñez (eds) , unknown . vol. 11896 , 0302-9743 , Springer Nature , pp. 535-544 , 24th Iberoamerican Congress on Pattern Recognition, CIARP 2019 , Havana , Cuba , 28/10/19 . https://doi.org/10.1007/978-3-030-33904-3_50
dc.identifier.citationconference
dc.identifier.doi10.1007/978-3-030-33904-3_50
dc.identifier.isbn9783030339036
dc.identifier.otherresearchoutputwizard: 11556/1421
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85075650927&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherSpringer Nature
dc.relation.ispartofunknown
dc.relation.ispartofseries0302-9743
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsLinear regression
dc.subject.keywordsGeneralized linear regression
dc.subject.keywordsEnhanced linear regression
dc.subject.keywordsLinear regression
dc.subject.keywordsGeneralized linear regression
dc.subject.keywordsEnhanced linear regression
dc.subject.keywordsTheoretical Computer Science
dc.subject.keywordsGeneral Computer Science
dc.subject.keywordsFunding Info
dc.subject.keywordsThe authors wish to thanks to the Basque Government through the KK-2018/00074 METALCRO.
dc.subject.keywordsThe authors wish to thanks to the Basque Government through the KK-2018/00074 METALCRO.
dc.titleMaterial Fracture Life Prediction Using Linear Regression Techniques Under High Temperature Creep Conditionsen
dc.typeconference output
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