Ballistic Performance Evaluation of Multi-layered Armors using Neural Network Algorithm

Y.-H. Yoo and O. Kum (Korea)


neural network, multi-layered armor, ballistic evaluation, back propagation algorithm


For a design of multi-layered armors, extensive full scale and sub-scale penetration tests are required. These test ex periments are time consuming, expensive, and highly dan gerous. However, the applications of numerical and analyt ical methods are yet very limited because of the poor un derstanding of penetration mechanisms. In this paper, we developed an object oriented neural network algorithm for new armor design. The neural network algorithm is easy to use and predicted the penetration depths within maximum ± error for most of the new metallic and ceramic armors based on the pre-existing penetration database. Among different combinations of material properties, density was the best material property for the evaluation of penetration depth of ceramics.

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