Artificial Intelligence in numerical modeling of nano sized ceramic particulates reinforced metal matrix composites

Artificial neural network models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the inter-relationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Aluminum is the best metal for producing metal matrix composites which are known as one of the most useful and high-tech composites in our world. Combining aluminum and nano Al2O3 particles will yield a material with high mechanical and tribological properties. In this investigation, the accuracy of various artificial neural network training algorithms in FEM modeling of Al2O3 nano particles reinforced A356 matrix composites has been studied.