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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2007-02

AUTHORS

A. García-Crespo, B. Ruiz-Mezcua, D. Fernández-Fdz, R. Zaera

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. More... »

PAGES

147-154

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00521-006-0050-1

DOI

http://dx.doi.org/10.1007/s00521-006-0050-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041919704


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