Prediction of critical fraction of solid in low-pressure die casting of aluminum alloys using artificial neural network

Abstract

Casting simulation programs are the computer programs that digitally model the casting of an alloy in the sand, shell or permanent mold and then the cooling and solidification processes. However, obtaining consistent results from the casting modeling depends on providing many parameters and boundary conditions accurately. Critical fraction of solid (CFS), which is one of the most important of these parameters, is defined as the point where the solid dendrites do not allow any flow of the liquid metal in the mushy zone. Since the CFS value varies depending on many factors, inconsistent results can be experienced in the modeling applications. In this study, the CFS value obtained during the solidification of various commercial aluminum alloys' casting process carried out using low pressure die casting method, is predicted by using artificial neural network (ANN) method based on alloy type, grain refiner and modifier additions, initial mold temperature, pressure level parameters. In the scope of the study, 162 experiments are conducted. The results obtained from the low pressure die casting experiments using a special model designed for the study are validated by using SOLIDCast casting simulation. The CFS values obtained from this validation range from 33% to 61%.