Prediction of the response under impact of steel armours using a multilayer perceptron (original) (raw)

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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Authors and Affiliations

  1. Computer Science Department, University Carlos III of Madrid, Avda. de la Universidad 30, 28911, Leganés, Madrid, Spain
    A. García-Crespo & B. Ruiz-Mezcua
  2. Department of Continuum Mechanics and Structural Analysis, University Carlos III of Madrid, Avda. de la Universidad 30, 28911, Leganés, Madrid, Spain
    D. Fernández-Fdz & R. Zaera
  3. Research Institute “Pedro Juan de Lastanosa”, University Carlos III of Madrid, Avda. de la Universidad 30, 28911, Leganés, Madrid, Spain
    A. García-Crespo, B. Ruiz-Mezcua & R. Zaera

Authors

  1. A. García-Crespo
  2. B. Ruiz-Mezcua
  3. D. Fernández-Fdz
  4. R. Zaera

Corresponding author

Correspondence toR. Zaera.

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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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