Delamination and thrust force analysis in GLARE: Influence of tool geometry and prediction with machine learning models

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dc.authoridEkici, Ergün / 0000-0002-5217-872X
dc.contributor.authorEkici, Ergün
dc.contributor.authorPazarkaya, İbrahim
dc.contributor.authorUzun, Gültekin
dc.date.accessioned2025-01-27T20:35:22Z
dc.date.available2025-01-27T20:35:22Z
dc.date.issued2024
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe multi-layered (fiber/metal) structure of glass fibre aluminium reinforced epoxy (GLARE) makes it difficult to obtain acceptable damage-free holes that meet aerospace standards. This paper investigated the effects of tool geometry and drilling parameters on reducing delamination damage and uncut fibers at the hole exit surface in drilling GLARE. The hole surfaces were examined by scanning electron microscope (SEM) at various magnifications. In addition, deep neural network (DNN) and long-short-term memory (LSTM) machine learning models were used to predict delamination (Fda), uncut fiber (UCF), and thrust forces using experimental data. No positive contribution of the special geometry tool was observed, while the standard geometry tool was found to be ideal for drilling conditions. Analysis of variance (ANOVA) revealed that feed rate contributed 57.83% to delamination damage, while tool geometry contributed 74.31% and 92.33% for uncut fiber and thrust force, respectively. SEM analysis revealed high deformation zones in the aluminum layers and fiber fracture and separation in the glass fibre reinforced polymer (GFRP) layers. DNN and LSTM models were found to provide accurate predictions with R2 values greater than 95% and 98%, respectively.
dc.identifier.doi10.1177/00219983241305706
dc.identifier.issn0021-9983
dc.identifier.issn1530-793X
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85211907924
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1177/00219983241305706
dc.identifier.urihttps://hdl.handle.net/20.500.12428/23642
dc.identifier.volume59
dc.identifier.wosWOS:001377116400001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSage Publications Ltd
dc.relation.ispartofJournal of Composite Materials
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectGlass fibre aluminium reinforced epoxy
dc.subjectdelamination
dc.subjectuncut fiber
dc.subjectanalysis of variance
dc.subjectmachine learning
dc.subjectlong-short-term memory
dc.titleDelamination and thrust force analysis in GLARE: Influence of tool geometry and prediction with machine learning models
dc.typeArticle

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