Abstract
The employment of aluminum based metal matrix composites are exponentially growing up in wide range of applications in automobiles. The prevailing demand to produce refined pore free and near net shape composites, is sufficed by squeeze casting process. The main intent of this work is to predict the experimental results and to analyze the effects of process variables on squeeze cast A413-B4C composites. The experimental work is carried out based on Full Factorial Design (FFD) and Artificial Neural Network (ANN) model has been developed based on feed forward back propagation to map the mechanical properties with different architectures. Optimal model shows appropriate results that can be estimated rather than measured, thereby reducing the testing time and cost. Further, quantitative and statistical analyses were performed in order to evaluate the effect of process parameters on the mechanical properties of the composites.