User:Bill Flanagan/docs/Prediction of the response under impact of steel armours using a multilayer perceptron

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

Journal	Neural Computing & Applications

Link

Publisher	Springer London

ISSN	0941-0643 (Print) 1433-3058 (Online)

Issue	Volume 16, Number 2 / February, 2007

Category	Original Article

DOI	10.1007/s00521-006-0050-1

Pages	147-154

Subject Collection	Computer Science

SpringerLink Date	Thursday, March 30, 2006 PDF (417.5 KB)HTML

Original Article

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

A. García-Crespo1, 3, B. Ruiz-Mezcua1, 3, D. Fernández-Fdz2 and R. Zaera2, 3 Contact Information

(1) 	Computer Science Department, University Carlos III of Madrid, Avda. de la Universidad 30, 28911 Leganés, Madrid, Spain

(2) 	Department of Continuum Mechanics and Structural Analysis, University Carlos III of Madrid, Avda. de la Universidad 30, 28911 Leganés, Madrid, Spain

(3) 	Research Institute “Pedro Juan de Lastanosa”, University Carlos III of Madrid, Avda. de la Universidad 30, 28911 Leganés, Madrid, Spain

Received: 11 August 2005 Accepted: 17 February 2006  Published online: 30 March 2006 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