Investigation of the mechanical behavior of AL7075 plate supported hybrid composite plates using artificial neural networks algorithm

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dc.contributor.authorTepehan, Burhan
dc.contributor.authorSulu, Ismail Yasin
dc.date.accessioned2026-04-04T13:33:34Z
dc.date.available2026-04-04T13:33:34Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractThe mechanical behavior of the hybrid structure formed by placing an AL7075 plate as the middle layer between different composite fibers was examined. Glass fiber and carbon fiber were preferred as fibers. Epoxy was used as matrix material. Four different cases with different fiber material layer alignments were examined. The produced plates were cut according to ASTM standards suitable for the tests to be performed, and samples were created. The samples were subjected to tensile tests, three-point bending tests, and ballistic tests. It has been determined that samples produced in sequential order with different layers reached high stress values in tensile tests and bending tests. It was observed that all alignments gave successful results in ballistic tests. In layered hybrid structures, the mechanical effects of the layer order and the type of material used on the hybrid structures have been demonstrated. The Levenberg-Marquardt algorithm with artificial neural networks was applied to investigate the appropriateness of the results. The results were presented to be appropriate in the graphs created with artificial neural networks, and it could be said that they were compatible. It can be said that more effective results are obtained in the combinations of carbon/glass/carbon and glass/carbon/glass composite fibers in layer arrangements.
dc.description.sponsorshipInonu University Scientific Research Coordination Unit [FYL-2021-2656]
dc.description.sponsorshipWe would like to thank Inonu University Scientific Research Coordination Unit [INONU-BAP, Project Code: FYL-2021-2656] for their support to this study.
dc.identifier.doi10.1080/1023666X.2025.2491030
dc.identifier.endpage562
dc.identifier.issn1023-666X
dc.identifier.issn1563-5341
dc.identifier.issue5
dc.identifier.scopus2-s2.0-105003276801
dc.identifier.scopusqualityQ2
dc.identifier.startpage543
dc.identifier.urihttps://doi.org/10.1080/1023666X.2025.2491030
dc.identifier.urihttps://hdl.handle.net/11616/109256
dc.identifier.volume30
dc.identifier.wosWOS:001474227100001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofInternational Journal of Polymer Analysis and Characterization
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectHybrid composites
dc.subjectartificial neural networks
dc.subjectmaterials testing
dc.subjectvacuum infusion
dc.subjectlayered composites
dc.titleInvestigation of the mechanical behavior of AL7075 plate supported hybrid composite plates using artificial neural networks algorithm
dc.typeArticle

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