Artificial intelligence methodologies for the design of armour and protection systems

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Predicting the mechanical and dynamic response of materials and structures to a high energy ballistic impact is a fundamental component of the design of safe and fit-for-purpose armour and protective systems. The UK's Defence Science and Technology Laboratory (Dstl) has world-leading experts in ballistic protection but often the design and optimisation of protection systems is reliant on expensive and time-consuming experimental testing.To increase efficiency and reduce costs in-house modelling capabilities are often used to support trials and armour Subject Matter Expert (SME) assessments. This model development, however, particularly when testing multiple configurations, can be time consuming and costly.To understand and support the development of complex multi-layered armour systems this research will develop advanced Machine Learning (ML) architectures that learn directly from open-source ballistic test data and Finite Element Analyses (FEA) simulations. FEA models will be developed to predict the ballistic response of multi layered armour plates, comprising a diverse range of materials, to a range of ballistic impacts, representative of real-life threat scenarios.This research will develop, implement and test ML approaches that can be used to supplement sparse ballistic data-sets. This will allow substantially larger data-sets to be generated, which is not feasible through conventional methods. Additionally, these methods can be reversed to predict key design parameters for armour systems, including the Ballistic Limit Velocity (BLV). This research proposal aims to develop a robust methodology of combining legacy ballistic analytical approaches, terminal ballistics testing and high-fidelity finite element numerical modelling and simulation in an AI/ML framework to rapidly predict ballistic performance, improving on existing AI-based approaches and methods.The overarching aim of this work is to ultimately build on the current design capability to propose, implement, validate and test a set of tools that can be used to optimise and design layer-based armour systems for personal and vehicle protection against high energy threats. Consequently, the main objectives of the proposed research are to:Generate and/or source relevant ballistic data to train the ML models and autonomously handle high volumes of complex data for use in Machine Learning (ML).Develop ML-based procedures and methods to aid the design of armour and protection systems, incorporating physics-based assumptions, constraints and probabilistic uncertainties, without compromising their efficiency.Validate the proposed networks, ensuring they deliver improved and optimised protection systems (e.g. higher BLV and lower areal density) and are computationally feasible.Propose a design approach for better (i.e. more mass/bulk efficient) armour systems, ensuring efficient coupling with FEA