Artificial neural networks for characterising Whipple shield performance

Abstract An artificial neural network model has been developed for predicting the perforation limits of spaced aluminium armour (aka Whipple shield) under impact of aluminium 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 was found to accurately predict 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.