A hybrid FEM-ANN framework for armor design optimization
Shaima Alhosani, Rauda Saeed, Miyra Aljabri, Wadima Ali, Sanan H. Khan
Abstract. Optimizing lightweight armor involves a high-dimensional design space and costly evaluations. This study presents a hybrid framework combining Finite Element Method (FEM) simulations with Artificial Neural Networks (ANNs) to accelerate and guide design. A dataset of 249 high-fidelity FEM simulations of ballistic impacts on multi-layered 2024 aluminum armor revealed that a simple two-layer configuration outperforms more complex designs within the same dimensional constraints. A multi-output ANN, trained on this dataset (2 > 0.90), served as an accurate surrogate model for predicting residual velocity, absorbed energy, and impact duration. Permutation feature importance confirmed thickness-related parameters as the dominant drivers of ballistic performance, independently validating FEM insights. A techno-economic analysis further identified the optimal trade-off between manufacturing complexity and protective efficacy. This hybrid approach offers a powerful tool for rapid design optimization, physical validation, and cost-effective engineering solutions.
Keywords
Surrogate Modeling, Machine Learning, Finite Element Analysis, Armor Design, Cost-Benefit Analysis, Feature Importance, Ballistic Impact
Published online 6/20/2026, 7 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Shaima Alhosani, Rauda Saeed, Miyra Aljabri, Wadima Ali, Sanan H. Khan, A hybrid FEM-ANN framework for armor design optimization, Materials Research Proceedings, Vol. 67, pp 502-508, 2026
DOI: https://doi.org/10.21741/9781644904176-66
The article was published as article 66 of the book Climate Action and Sustainability
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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