Artificial neural network modeling to predict corrective stress of a two-layer composite plate under fully reversed cyclic loading using the finite element method & morrow method
Scientific Reports, cilt.16, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1038/s41598-026-61304-0
- Dergi Adı: Scientific Reports
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, EMBASE, MEDLINE, Directory of Open Access Journals, Zoological Record, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: Artificial neural network, Composite sheet, Corrective stress, Fiber angle, Finite element method, Manufacturing innovation, Morrow method
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- İstanbul Gelişim Üniversitesi Adresli: Evet
Özet
In this study, a single-hidden-layer feedforward Artificial Neural Network was developed to predict the maximum stress of a two-layer composite plate based on the Morrow correction method under fully reversed cyclic loading. A rectangular two-layer plate made of Epoxy Carbon Woven (230 GPa) was analyzed using the Finite Element Method for various fiber orientations of each layer (0°, 15°, 30°, 45°, 60°, 75°, and 90°) relative to the transverse axis. The results indicate that a 0° fiber orientation in both layers produced the maximum stress, potentially weakening the plate under tensile load, whereas a 90° orientation minimized stress and enhanced tensile strength. Increasing the second-layer angle while keeping the first layer fixed reduced stress, whereas decreasing the first-layer angle for a fixed second-layer angle increases stress. Maximum stress regions shifted from localized points to linear distributions, sometimes moving from uniform edge distributions to mid-edge concentrations. When both layers had identical angles, the stress magnitude increased, and the maximum stress shifted from the loaded edge to a corner, indicating stress concentration and a potential reduction in lifespan. The Artificial Neural Network demonstrated excellent predictive performance, achieving an optimal validation Mean Squared Error of 3.2266 × 10⁻4 at iteration 22, with a minimum overall Mean Squared Error of 6.5566 × 10⁻4 across all datasets. The correlation coefficients for the training, validation, test, and entire datasets were 0.99508, 0.98649, 0.99484, and 0.99485, respectively, indicating a strong agreement between the Mean Squared Error predictions and Finite Element Method-simulated values.