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Acoustic emission as a reliable technique for filiform corrosion monitoring on coated AA7075-T6: Tailored data processing

dc.contributor.authorAbarkane, Chihab
dc.contributor.authorFlorez Tapia, A. M.
dc.contributor.authorOdriozola, J.
dc.contributor.authorArtetxe, A.
dc.contributor.authorLekka, M.
dc.contributor.authorGarcía Lecina, E.
dc.contributor.authorGrande, H. J
dc.contributor.authorVega, J. M.
dc.date.accessioned2026-07-01T07:48:33Z
dc.date.available2026-07-01T07:48:33Z
dc.date.issued2023
dc.identifier.citationAbarkane, C., Florez Tapia, A. M., Odriozola, J., Artetxe, A., Lekka, M., García Lecina, E., Grande, H. J., y Vega, J. M. (2023). Acoustic emission as a reliable technique for filiform corrosion monitoring on coated AA7075-T6: Tailored data processing. Corrosion Science, 214. https://doi.org/10.1016/j.corsci.2023.110964es
dc.identifier.issn0010-938X
dc.identifier.urihttp://hdl.handle.net/20.500.12251/4489
dc.description.abstractAcoustic emission (AE) was used for in-situ filiform corrosion (FFC) monitoring on coated AA77075-T6. The analysis of AE data using DBSCAN as clustering algorithm (validated by Bhattacharyya Coefficients´ evaluation) has revealed the presence of three clusters (out of four) related to phenomena involved in the FFC mechanism: metal-coating interface delamination due to opening (tensile), sliding (shear) and mixed mode enclosing both previous ones. The peak frequency was found to be the most relevant descriptor for clustering by using Random Forest classifier, and the correlation with the dominant frequencies range was validated obtaining the Power Spectrum Density of the AE signals. © 2023 The Authorses
dc.language.isoenges
dc.publisherElsevier Ltdes
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleAcoustic emission as a reliable technique for filiform corrosion monitoring on coated AA7075-T6: Tailored data processinges
dc.typearticle
dc.identifier.doi10.1016/j.corsci.2023.110964
dc.identifier.urlhttps://www.scopus.com/results/results.uri?sort=plf-f&src=s&sid=0aa81cd6208cd1dc003ab2f070016d03&sot=a&sdt=a&sl=18&s=AU-ID%2857198448220%29&origin=searchadvanced&editSaveSearch=&txGid=7b47ae64ba322601b16e986b59b85a95&sessionSearchId=0aa81cd6208cd1dc003ab2f070016d03&limit=10
dc.journal.titleCorrosion Sciencees
dc.rights.accessRightsopenAccesses
dc.subject.keywordAlgoritmoses
dc.subject.keywordMachine Learninges
dc.subject.keywordRedes neuronales artificialeses
dc.subject.keywordBig Dataes
dc.subject.keywordAcústicaes
dc.subject.keywordAbsorción acústicaes
dc.subject.keywordCorrosiónes
dc.subject.keywordAluminioes
dc.subject.keywordInhibidores de corrosiónes
dc.subject.unesco3305 Tecnología de la Construcciónes
dc.subject.unesco1203.17 Informáticaes
dc.subject.unesco1203.26 Simulaciónes
dc.subject.unesco3305.32 Ingeniería de Estructurases
dc.subject.unesco3305.33 Resistencia de Estructurases
dc.subject.unesco2201 Acústicaes
dc.volume.number214


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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