Mostrar o rexistro simple do ítem

dc.contributor.authorRodríguez-Dopico, Francisco J.
dc.contributor.authorÁlvarez García, Ana
dc.contributor.authorTarrío-Saavedra, Javier
dc.contributor.authorMeneses Freire, Antonio
dc.contributor.authorNaya, Salvador
dc.date.accessioned2024-10-23T13:09:22Z
dc.date.available2024-10-23T13:09:22Z
dc.date.issued2024-12-01
dc.identifier.citationRodríguez-Dopico, F.J., Álvarez García, A., Tarrío-Saavedra, J., Meneses, A., Naya, S., 2024. Predicting lifetime of adhesive bonds for naval steel by time-temperature superposition. Ocean Engineering 313, 119482. https://doi.org/10.1016/j.oceaneng.2024.119482es_ES
dc.identifier.issn1873-5258
dc.identifier.urihttp://hdl.handle.net/2183/39741
dc.description.abstract[Abstract] There is a lack of knowledge about the long-term behaviour of adhesive joints in the marine environment, and for hence, its reliability. The life cycle expected for a ship is twenty-five years and conduct duration tests would last decades. The importance and originality of this study are that it provides a methodology for predicting the durability of adhesive bonds. For this purpose, three adhesives for bonding naval steel were tested at several temperatures for determination of their shear strength on standard single-lap-joint specimens. Subsequently, using the time temperature superposition principle, these results were combined into one master curve for each adhesive that can be considered as a prediction method of the durability in a long-term period. The usual parametric models of Arrhenius and Williams-Landel-Ferry were used to obtain the master curve for each adhesive by manual shifting. The results were compared with those obtained applying an open-source software developed by the authors in R language, which specifically implements a non-parametric methodology based on the shift of the first derivative curves, following parameters of Explainable Artificial Intelligence. It was found a good correlation between both methodologies, supporting the durability lifetime obtained and therefore the applicability on ships of this adhesive technology.es_ES
dc.description.sponsorshipThe authors wish to acknowledge and thank the support provided by Krafft S.L.U. Spain (ITW Performance Polymers), Sarsch Adhesives, S.L. Spain (Engineered Bonding Solutions, LLC, Florida, United States) and Masterbond Inc. United States. The research of Javier Tarrío Saavedra and Salvador Naya has been supported by the Ministerio de Ciencia e Innovación grant PID2020-113578RB-100 and PID2023-147127OB-I00, the Ministry for Digital Transformation and Civil Service under Grant TSI-100925-2023-1, the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14 and ED431C 2024/014), and by the CITIC, also funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centers of the Galician University System, CIGUS, with reference ED431G 2023/01.es_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2020-14es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2024/014es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2023/01es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.urihttps://doi.org/10.1016/j.oceaneng.2024.119482es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacionales_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAdhesiveses_ES
dc.subjectLifetimees_ES
dc.subjectShipbuildinges_ES
dc.subjectTime-temperature superpositiones_ES
dc.subjectMachine learninges_ES
dc.subjectExplainable artificial intelligencees_ES
dc.titlePredicting lifetime of adhesive bonds for naval steel by time-temperature superpositiones_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
UDC.volume313es_ES
UDC.issue119482es_ES
UDC.startPage1es_ES
UDC.endPage11es_ES
dc.identifier.doihttps://doi.org/10.1016/j.oceaneng.2024.119482
UDC.conferenceTitleOcean Engineeringes_ES
UDC.coleccionInvestigaciónes_ES
UDC.departamentoEnxeñaría Naval e Industriales_ES
UDC.departamentoMatemáticases_ES
UDC.grupoInvPropiedades Térmicas e Reolóxicas de Materiais (PROTERM)es_ES
UDC.grupoInvModelización, Optimización e Inferencia Estatística (MODES)es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-147127OB-I00es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MTDPF//TSI-100925-2023-1/ES/CÁTEDRA UDC-INDITEX DE IA EN ALGORITMOS VERDESes_ES


Ficheiros no ítem

Thumbnail
Thumbnail

Este ítem aparece na(s) seguinte(s) colección(s)

Mostrar o rexistro simple do ítem