Prediction of density, porosity and hardness in aluminum-copper-based composite materials using artificial neural network

The potential of using feed forward backpropagation neural network in prediction of some physical properties and hardness of aluminium-copper/silicon carbide composites synthesized by compocasting method has been studied in the present work. Two input vectors were used in the construction of proposed network; namely weight percentage of the copper and volume fraction of the reinforced particles. Density, porosity and hardness were the three outputs developed from the proposed network. Effects of addition of copper as alloying element and silicon carbide as reinforcement particles to Al-4 wt.% Mg metal matrix have been investigated by using artificial neural networks. The maximum absolute relative error for predicted values does not exceed 5.99%. Therefore, by using ANN outputs, satisfactory results can be estimated rather than measured and hence reduce testing time and cost. (C) 2008 Published by Elsevier B.V.