Artificial intelligence-based stress prediction in glass fiber reinforced composites

Basit Öğe Kaydı
dc.authorid0000-0001-5223-1976
dc.contributor.authorFerati, Kajs
dc.contributor.authorAdar, Nurettin Gokhan
dc.date.accessioned2026-02-08T15:15:02Z
dc.date.available2026-02-08T15:15:02Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThis study addresses the challenge of predicting stress values in glass fibre reinforced epoxy composites under tensile loading using advanced AI methods. Traditional experimental and finite element analysis (FEA) approaches are time consuming and costly. To overcome this, a dataset was generated from FEA simulations covering different lamination sequences and material properties. Three AI models-narrow neural network (NNN), squared exponential Gaussian process regression (GPR) and support vector machine (SVM)-were developed and evaluated. GPR and SVM achieved superior prediction accuracies of 96.83 % and 95.04 %, respectively, outperforming NNN. Experimental validation confirmed these results and demonstrated the robustness of the proposed models. This study provides a cost-effective framework for stress prediction in composites that reduces the reliance on extensive testing and simulation, and advances AI-driven solutions for materials design and analysis.
dc.identifier.doi10.1007/s12206-025-0724-1
dc.identifier.endpage4526
dc.identifier.issn1738-494X
dc.identifier.issn1976-3824
dc.identifier.issue8
dc.identifier.scopus2-s2.0-105012406452
dc.identifier.scopusqualityQ2
dc.identifier.startpage4519
dc.identifier.urihttps://doi.org/10.1007/s12206-025-0724-1
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5570
dc.identifier.volume39
dc.identifier.wosWOS:001543395400001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherKorean Soc Mechanical Engineers
dc.relation.ispartofJournal of Mechanical Science and Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectFiber reinforced composites
dc.subjectGauss process regression
dc.subjectNeural network
dc.subjectSupport vector machine
dc.titleArtificial intelligence-based stress prediction in glass fiber reinforced composites
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu