Machine learning-based approach for ballistic performance prediction of hybrid armors
Yazarlar (1)
Dr. Öğr. Üyesi Halil Burak MUTU
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Materials Today Communications |
| Dergi ISSN | 2352-4928 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | Türkçe |
| Basım Tarihi | 07-2025 |
| Cilt No | 47 |
| Sayı | 1 |
| Sayfalar | 1 / 22 |
| DOI Numarası | 10.1016/j.mtcomm.2025.113226 |
| Makale Linki | https://doi.org/10.1016/j.mtcomm.2025.113226 |
| Özet |
| In this study, numerical simulation and machine learning-based analyses of multilayered armor structures consisting of ceramic and composite layers were performed to increase ballistic armor systems' performance. Five different alumina ceramic thicknesses were used as the front layer of the armor; this layer was supported by Kevlar-29 and ultra-high molecular weight polyethylene (UHMWPE) composite layers in different ratios to have a total thickness of 10 mm. Ballistic impact analyses were performed with a 7.62 mm armor-piercing bullet in the 700--1000 m/s velocity range with 50 m/s increments using LS-DYNA software as a result of numerical simulations created a data set for the residual velocities that occur depending on the ceramic thickness, composite configuration, and bullet velocity. The obtained data were used to train the Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Decision ... |
| Anahtar Kelimeler |
| Ballistic performance | Finite element method | Hybrid armor | Machine Learning |
Science Direct
Machine learning-based approach for ballistic performance prediction of hybrid armors
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| SCOPUS | 6 |
| Google Scholar | 6 |