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.
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Acknowledgements
This research was done with the financial support of the Comunidad Autónoma de Madrid under Project GR/MAT/0507/2004.
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García-Crespo, A., Ruiz-Mezcua, B., Fernández-Fdz, D. et al. Prediction of the response under impact of steel armours using a multilayer perceptron. Neural Comput & Applic 16, 147–154 (2007). https://doi.org/10.1007/s00521-006-0050-1
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DOI: https://doi.org/10.1007/s00521-006-0050-1