An artificial neural network model to characterize porosity defects during solidification of A356 aluminum alloy

In this study, a data-driven multilayer perceptron-based neural network model has been developed to predict the percentage of total porosity and mechanical properties, namely yield strength, ultimate tensile strength and percentage of elongation during the solidification of A356 aluminum alloy. Some of the important processing parameters such as cooling rate, solidus velocity, thermal gradient and initial hydrogen content have been considered as inputs to this model. The network training architecture has been optimized using the gradient-based Broyden–Fletcher–Goldfarb–Shanno training algorithm to minimize the network training error within few training cycles. Parametric sensitivity analysis is carried out to characterize the influence of processing parameters (inputs) on the behavior of porosity formation and simultaneously, the tensile properties of A356 alloy castings. It has been observed that initial hydrogen content in the melt and cooling rate has significant influence on the porosity formation and eventually on the tensile properties of the cast product. There has been an excellent agreement between artificial neural network predictions and the target (measured) values of porosity and the tensile properties as depicted by the regression fit between these values.