Prediction of physical and Mechanical Properties of aluminum metal matrix composite Using Artificial Neural Networks

Rasha M. Hussien*, Mohsin Abdullah Al-Shammari

Department of Mechanical Engineering, College of Engineering, University of Baghdad, Iraq

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Aluminum metal matrix composites (AMCs) are advanced engineering metal that used for many applications. In this work AMCs consist of aluminum 7075 and silicon carbide (SiC) were manufactured by stir casting for different weight ratio. AMCs were tested to find mechanical and physical properties such as young modulus, ultimate stress, maximum elongation, hardness, density. These properties are trained using artificial neural network to predict the property for different weight ratio. The main point in this research is using ANN with small number of data (only ten) that reducing the cost manufacture, testing and getting better results of prediction. The maximum increasing percentages of young modulus due to adding SiC is 72.8% at weight ratio 1% and the all other properties is became less than AL7075 because of the form of brittle phase aluminum carbide due to manufacturing process.