Gelişmiş Arama

Basit öğe kaydını göster

dc.contributor.authorGulsen, Abdulkadir
dc.contributor.authorKolukisa, Burak
dc.contributor.authorCaliskan, Umut
dc.contributor.authorBakir-Gungor, Burcu
dc.contributor.authorGungor, Vehbi Cagri
dc.date.accessioned2024-12-04T08:14:34Z
dc.date.available2024-12-04T08:14:34Z
dc.date.issued2024en_US
dc.identifier.issn1438-1656
dc.identifier.urihttps://doi.org/10.1002/adem.202400317
dc.identifier.urihttps://hdl.handle.net/20.500.12573/2399
dc.description.abstractAcoustic emission (AE) serves as a noninvasive technique for real-time structural health monitoring, capturing the stress waves produced by the formation and growth of cracks within a material. This study presents a novel ensemble feature selection methodology to rank features highly relevant with damage modes in AE signals gathered from edgewise compression tests on honeycomb-core carbon fiber-reinforced polymer. Two distinct features, amplitude and peak frequency, are selected for labeling the AE signals. An ensemble-supervised feature selection method ranks feature importance according to these labels. Using the ranking list, unsupervised clustering models are then applied to identify damage modes. The comparative results reveal a robust correlation between the damage modes and the features of counts and energy when amplitude is selected. Similarly, when peak frequency is chosen, a significant association is observed between the damage modes and the features of partial powers 1 and 2. These findings demonstrate that, in addition to the commonly used features, other features, such as partial powers, exhibit a correlation with damage modes.en_US
dc.description.sponsorshipThis work was supported by TÜBITAK ULAKBIM; through its agreement with Wiley, the open access fee for this publication has been covered.en_US
dc.language.isoengen_US
dc.publisherJohn Wiley and Sons Incen_US
dc.relation.isversionof10.1002/adem.202400317en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectacoustic emissionen_US
dc.subjectcarbon fiber-reinforced polymer compositesen_US
dc.subjectclusteringen_US
dc.subjectdamage characterizationen_US
dc.subjectensemble feature selectionen_US
dc.subjectindustrial innovationen_US
dc.subjectmachine learningen_US
dc.titleEnsemble Feature Selection for Clustering Damage Modes in Carbon Fiber-Reinforced Polymer Sandwich Composites Using Acoustic Emissionen_US
dc.typearticleen_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.authorID0000-0002-4250-2880en_US
dc.contributor.authorID0000-0003-0423-4595en_US
dc.contributor.authorID0000-0002-2272-6270en_US
dc.contributor.institutionauthorGulsen, Abdulkadir
dc.contributor.institutionauthorKolukisa, Burak
dc.contributor.institutionauthorBakir-Gungor, Burcu
dc.identifier.volume22en_US
dc.identifier.issue22en_US
dc.identifier.startpage1en_US
dc.identifier.endpage11en_US
dc.relation.journalAdvanced Engineering Materialsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


Bu öğenin dosyaları:

Thumbnail

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster