Prediction of the response under impact of steel armours using a multilayer perceptron

Authors: García-Crespo, A.; Ruiz-Mezcua, B.; Fernández-Fdz, D.; Zaera, R.¹

Source: Neural Computing & Applications, Volume 16, Number 2, February 2007 , pp. 147-154(8)

Publisher: Springer

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Abstract:

This article puts forward the results obtained when using a neural network as an alternative to classical methods (simulation and experimental testing) in the prediction of the behaviour of steel armours against high-speed impacts. In a first phase, a number of impact cases are randomly generated, varying the values of the parameters which define the impact problem (radius, length and velocity of the projectile; thickness of the protection). After simulation of each case using a finite element code, the above-mentioned parameters and the results of the simulation (residual velocity and residual mass of the projectile) are used as input and output data to train and validate a neural network. In addition, the number of training cases needed to arrive at a given predictive error is studied. The results are satisfactory, this alternative providing a highly recommended option for armour design tasks, due to its simplicity of handling, low computational cost and efficiency.

Keywords: Neural network; Numerical simulation; Steel armour; Ballistic impact

Document Type: Research article

DOI: 10.1007/s00521-006-0050-1

Affiliations: 1: Email: rzaera@ing.uc3m.es

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