OPTIMIZATION DESIGN OF ARMOR STRUCTURE WITH METAL-COMPOSITE HYBRID MATERIALS FOR PROTECTION OF ARMOR COMBAT VEHICLES FROM BALLISTIC IMPACT USING MACHINE LEARNING METHODS

• Main Author: Naufal Taqi, Muhammad
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/73098
• Institution: Institut Teknologi Bandung

Armored Fighting Vehicle (AFV)’s armor is one of the most important safety components on combat vehicles to protect the personnel inside from external threats. The development of armor-piercing bullets as anti-armor weapon systems encourages the development of armor systems that are more complex, lighter, and have better protection capabilities. Metal and composite based hybrid armor has the potential to be a replacement for conventional metal armor, with higher mechanical strength, resistance to bullet penetration, and a lighter weight. This study will use a simulation using the finite element method of ballistic impacts on the armor structure using the LS-DYNA software. The armor structure is composed of metal and composite layers which will be optimized by varying the geometric parameters (plate arrangement, number of composite layers, and metal thickness) as well as the material of the armor structure (composite and metal types) to achieve parameters of resistance and ballistic response according to STANAG 4569 standard. This study aims to optimize the parameters of the structure to obtain the most optimum results. This goal will be achieved by using Machine Learning (ML) with Artificial Neural Network (ANN) and NSGA-II by providing training test data to obtain the optimum structure with the parameters of maximum SEA. According to the armor structure optimization results, the optimum configuration of the metal-composite hybrid armor has a composite plate arrangement followed by a metal plate (second arrangement) with Weldox 700E metal material with a thickness of 5,211 mm and Cellulose Nanocrystal composite material with 24 layers. This configuration provides a 238.277% increase in SEA performance compared to the metal base model of the same thickness.