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Artificial neural networks for characterizing whipple shield performance

journal contribution
posted on 2022-12-01, 03:37 authored by Shannon RyanShannon Ryan, S Thaler
An artificial neural network model has been developed for predicting the perforation limits of spaced aluminum armor (aka Whipple shield) under impact of aluminum projectiles at hypervelocity. The network, utilizing a multilayer perceptron architecture, was trained on data from 769 impact tests, for which it accurately predicted the perforation of the shield rear wall (or lack thereof) 92% of the time. Comparatively, the leading empirical approach is capable of accurately predicting the outcome of 71% of the impact tests. The network output is an analogue probability of perforation, which is adjusted to a binary "Pass"/"Fail" result via a sigmoid fit that also provides physically plausible confidence bounds (i.e. error bars). Interrogation of the network was performed to identify the input parameters that most heavily correlated with the output prediction. Although the majority of the traditional parameters (i.e. those identified in the empirical ballistic limit equation) were amongst the most influential, some unexpected (and potentially spurious) parameters were also identified. A more widely sampled set of training data incorporating increased diversity in projectile and target materials would likely improve the network internal weighting for material properties and avoid accidental identification of biases in the training exemplars. © 2013 The Authors.

History

Journal

Procedia Engineering

Volume

58

Pagination

31 - 38

ISSN

1877-7058

Publication classification

C1.1 Refereed article in a scholarly journal

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