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EKİCİ, BÜLENT

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EKİCİ

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BÜLENT

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2013 - 20173
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End date: 2022 × Subject: PROJECTILE ×

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Now showing 1 - 3 of 3
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    Ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition
    (ELSEVIER SCI LTD, 2013) EKİCİ, BÜLENT; Kilic, Namik; Ekici, Bulent
    Although advanced lightweight composite based armors are available, high hardness steels in military vehicles are often used to provide ballistic protection at a relatively low cost and is an interesting material due to its widespread usage in vehicle structure. In this study, ballistic limit of 500 HB armor steel was determined against 7.62 mm 54R B32 API hardened steel core ammunition. Lagrange and smoothed particle hydrodynamics (SPH) simulations were carried out using 3D model of bullet and high hardness armor target. Perforation tests on 9 and 20 mm thickness armor were performed to validate simulation methodology. Also material tests were performed for armor steel and ammunition hardened steel core to develop Johnson-Cook constitutive relations for both strength and failure models. Finally, results from 3D numerical simulations with detailed models of bullet and target were compared with experiments. The study indicates that the ballistic limit can be quantitatively well predicted independent of chosen simulation methodology, but qualitatively some differences are seen during perforation and fragmentation. As shown in results, good agreement between Ls-Dyna simulations and experimental data was achieved by Lagrange formulation with the full bullet model. (c) 2012 Elsevier Ltd. All rights reserved.
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    Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools
    (ELSEVIER SCIENCE BV, 2015-06) EKİCİ, BÜLENT; Kilic, Namik; Ekici, Bulent; Hartomacioglu, Selim
    Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods (FEM) in this research field. The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort, therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time. This study aims to apply a hybrid method using FEM simulation and artificial neural network (ANN) analysis to approximate ballistic limit thickness for armor steels. To achieve this objective, a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition. In this methodology, the FEM simulations are used to create training cases for Multilayer Perceptron (MLP) three layer networks. In order to validate FE simulation methodology, ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569. Afterwards, the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor. Results show that even with limited number of data, FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy. Copyright (C) 2015, China Ordnance Society. Production and hosting by Elsevier B.V. All rights reserved.
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    Optimization of high hardness perforated steel armor plates using finite element and response surface methods
    (TAYLOR & FRANCIS INC, 2017) EKİCİ, BÜLENT; Kilic, Namik; Ekici, Bulent; Bedir, Said
    In this article, finite element simulations and response surface method are used to optimize perforated plate parameters for ballistic protection. After statistically validating the relationship between residual velocity and geometric parameters, a response optimizer was used to find the best combination of design parameters to stop a threat with less areal density. Finally, the optimized solution was checked both numerically and experimentally to show the effectiveness of the developed methodology. The weight is decreased by 28% when compared with monolithic steel armor having the same antiballistic performance.