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Predicting lifetime of adhesive bonds for naval steel by time-temperature superposition
dc.contributor.author | Rodríguez-Dopico, Francisco J. | |
dc.contributor.author | Álvarez García, Ana | |
dc.contributor.author | Tarrío-Saavedra, Javier | |
dc.contributor.author | Meneses Freire, Antonio | |
dc.contributor.author | Naya, Salvador | |
dc.date.accessioned | 2024-10-23T13:09:22Z | |
dc.date.available | 2024-10-23T13:09:22Z | |
dc.date.issued | 2024-12-01 | |
dc.identifier.citation | Rodríguez-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.2024.119482 | es_ES |
dc.identifier.issn | 1873-5258 | |
dc.identifier.uri | http://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.sponsorship | The 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.sponsorship | Xunta de Galicia; ED431C-2020-14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2024/014 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2023/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.oceaneng.2024.119482 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Adhesives | es_ES |
dc.subject | Lifetime | es_ES |
dc.subject | Shipbuilding | es_ES |
dc.subject | Time-temperature superposition | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Explainable artificial intelligence | es_ES |
dc.title | Predicting lifetime of adhesive bonds for naval steel by time-temperature superposition | es_ES |
dc.type | journal article | es_ES |
dc.rights.accessRights | open access | es_ES |
UDC.volume | 313 | es_ES |
UDC.issue | 119482 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 11 | es_ES |
dc.identifier.doi | https://doi.org/10.1016/j.oceaneng.2024.119482 | |
UDC.conferenceTitle | Ocean Engineering | es_ES |
UDC.coleccion | Investigación | es_ES |
UDC.departamento | Enxeñaría Naval e Industrial | es_ES |
UDC.departamento | Matemáticas | es_ES |
UDC.grupoInv | Propiedades Térmicas e Reolóxicas de Materiais (PROTERM) | es_ES |
UDC.grupoInv | Modelización, Optimización e Inferencia Estatística (MODES) | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-147127OB-I00 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MTDPF//TSI-100925-2023-1/ES/CÁTEDRA UDC-INDITEX DE IA EN ALGORITMOS VERDES | es_ES |
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