Artificial intelligence-based stress prediction in glass fiber reinforced composites
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Korean Soc Mechanical Engineers
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This 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.
Açıklama
Anahtar Kelimeler
Fiber reinforced composites, Gauss process regression, Neural network, Support vector machine
Kaynak
Journal of Mechanical Science and Technology
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
39
Sayı
8
