Sonlu Elemanlar Yöntemi ve Makine Öğrenme Algoritmaları Kullanılarak Silisyum Karbür Seramik Vücut Zırhının Balistik Performans Analizi

Abstract

Bu çalışma, farklı kalınlıklardaki silisyum karbür (SiC) seramik vücut zırh plakalarına çarpan mermilerin artık hızını tahmin etmek için makine öğrenimine dayalı bir yaklaşım sunmaktadır. Yüksek hızlı darbe altında zırhın balistik tepkisini modellemek için ANSYS sonlu elemanlar yazılımı kullanılarak açık dinamik simülasyonlar gerçekleştirildi. Ana girdi parametreleri arasında mermi tipi, mermi namlu çıkış hızı, seramik kalınlığı ve gözenek boyutu yer almaktadır. Çıkış parametresi, darbeden sonra merminin ölçülen artık hızıdır. Simülasyon verileri, üç farklı makine öğrenimi modelini eğitmek ve değerlendirmek için kullanıldı: Doğrusal Regresyon, ElasticNet ve Çok Katmanlı Algılayıcı (MLP). Her modelin tahmini performansı, hem eğitim hem de test veri kümelerinde belirleme katsayısı (R), ortalama mutlak hata (MAE) ve kök ortalama kare hata (RMSE) ölçümleri kullanılarak değerlendirildi. Test edilen algoritmalar arasında, MLP modeli en yüksek doğruluk ve en düşük hata değerlerine ulaşarak balistik çarpma olaylarını yöneten karmaşık doğrusal olmayan ilişkileri yakalamada üstün bir yetenek gösterdi. Bulgular, yüksek doğruluklu simülasyon verileriyle eğitildiğinde makine öğrenimi tekniklerinin balistik koruma uygulamalarında artık hızı tahmin etmek için etkili tahmin araçları olarak hizmet edebileceğini göstermektedir. Bu yaklaşım, koruyucu zırh sistemlerinin ön tasarım aşamasında kapsamlı fiziksel testlere ve hesaplama açısından pahalı simülasyonlara olan ihtiyacı önemli ölçüde azaltabilir ve böylece malzeme seçimi ve optimizasyon sürecini hızlandırabilir.

Keywords

• Balistik
• Silisyum karbür
• Vücut zırhı
• Makine öğrenmesi
• Simülasyon

Ballistic Performance Analysis of Silicon Carbide Ceramic Body Armor Using Finite Element Method and Machine Learning Algorithms

Abstract

This study presents a machine learning-based approach for predicting the residual velocity of projectiles impacting silicon carbide (SiC) ceramic body armor plates of varying thicknesses. Explicit dynamic simulations were performed using the ANSYS finite element software to model the ballistic response of the armor under high-velocity impact. Key input parameters included projectile type, bullet muzzle velocity, ceramic thickness, and mesh size. The output parameter of interest was the residual velocity of the projectile after impact. Simulation data were used to train and evaluate three different machine learning models: Linear Regression, ElasticNet, and Multilayer Perceptron (MLP). The predictive performance of each model was assessed using the coefficient of determination (R), mean absolute error (MAE), and root mean square error (RMSE) metrics across both training and testing datasets. Among the tested algorithms, the MLP model achieved the highest accuracy and lowest error values, demonstrating superior capability in capturing the complex nonlinear relationships governing ballistic impact phenomena.The findings indicate that machine learning techniques, when trained with high-fidelity simulation data, can serve as efficient predictive tools for estimating residual velocity in ballistic protection applications. This approach can significantly reduce the need for extensive physical testing and computationally expensive simulations during the preliminary design phase of protective armor systems, thereby accelerating the material selection and optimization process.

Keywords

• Ballistic
• Silicon carbide
• Body armor
• Machine learning
• Simulation

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