Optimal Design of Die Casting Process Parameters of A713 Cast Alloy with Grain Refinement by Using Genetic Algorithm Approach for Automobile Industries

Die casting with aluminium alloy has potential applications but often results in castings with poor properties due lagging in density, and it is mandatory to predict the nature of the output responses before manufacturing. Genetic algorithm (GA) approach prediction model helps in optimal output responses before the actual production in die casting. In this research, GA approach for optimising the theoretical and experimental density of an A713 alloy with Al-3.5Ti-1.5C and Al-3Cobalt as grain refiners is carried out. The selected die casting process parameters are molten metal temperature, Al-3.5Ti-1.5C, Al-3.0 Cobalt, die temperature and injection pressure. Theoretical and experimental densities are considered as outputs for the GA modelling. GA was performed for two cases, it was observed that the optimal prediction model for the theoretical and experimental density of A713 alloy with grain refiners have been accomplished using GA approach.