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dc.contributor.authorMartinez, Roberto Fernandez
dc.contributor.authorJimbert, Pello
dc.contributor.authorCallejo, Lorena M.
dc.contributor.authorBarbero, Jose Ignacio
dc.date.accessioned2022-11-29T10:36:44Z
dc.date.available2022-11-29T10:36:44Z
dc.date.issued2021
dc.identifier.citationMartinez, Roberto Fernandez, Pello Jimbert, Lorena M. Callejo, and Jose Ignacio Barbero. “Material Fracture Life Prediction Under High Temperature Creep Conditions Using Support Vector Machines And Artificial Neural Networks Techniques.” 2021 31st International Conference on Computer Theory and Applications (ICCTA), December 11, 2021. https://doi.org/10.1109/iccta54562.2021.9916603.en
dc.identifier.isbn978-1-6654-7855-7en
dc.identifier.issn2770-6567en
dc.identifier.urihttp://hdl.handle.net/11556/1441
dc.description.abstractOne of the most applied materials to manufacture critical components in power plants are martensitic steels due to their high creep and oxidation resistance. In this work, the fracture life of martensitic steels that are designed based on the P92 standard is modeled in order to better understand the relation between its service life and its composition and its thermal treatment. This feature is usually studied by performing creep tests, although carrying out tests of this type are really cost and time consuming. To solve this problem, a multivariate analysis and a training-testing model methodology were performed using a dataset formed by 344 creep tests with the final goal of obtaining a model to predict the fracture life of the material based on several nonlinear techniques like support vector machines and artificial neural networks. Once the models were defined based on predicting with the better generalization capability to cover the whole scenario of the problem, those were compared to determine which one was the most accurate among them. Finally, it was concluded that the model’s performance using the proposed methodology based on artificial neural networks got the most accurate results, achieving low errors of approximately 6.14% when predicting creep behavior under long service times.en
dc.description.sponsorshipThe authors wish to thank to the Basque Government for its support through grant KK-2019-00033 METALCRO2.en
dc.language.isoengen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.titleMaterial Fracture Life Prediction Under High Temperature Creep Conditions Using Support Vector Machines And Artificial Neural Networks Techniquesen
dc.typeconferenceObjecten
dc.identifier.doi10.1109/iccta54562.2021.9916603en
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsNonlinear regressionen
dc.subject.keywordsArtificial neural networksen
dc.subject.keywordsSupport vector machinesen
dc.subject.keywordsValidation methodologyen
dc.identifier.essn2770-6575en
dc.page.final132en
dc.page.initial127en
dc.identifier.esbn978-1-6654-7854-0en
dc.conference.title2021 31st International Conference on Computer Theory and Applications (ICCTA)en


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