Optimization of porosity formation in AlSi9Cu3 pressure die castings using genetic algorithm analysis

In this investigation, an effective approach based on multivariable linear regression (MVLR) and genetic algorithm (GA) methods has been developed to determine the optimum conditions leading to minimum porosity in AlSi9Cu3 aluminium alloy die castings. Experiments were conducted by varying holding furnace temperature, die temperature, plunger velocities in the first and second stage, and multiplied pressure in the third stage using L27 orthogonal array of Taguchi method. The experimental results from the orthogonal array were used as the training data for the MVLR model to map the relationship between process parameters and porosity formation of the die cast parts. With the fitness function based on this model, genetic algorithms were used for the process conditions optimization. By comparing the predicted values with the experimental data, it was demonstrated that the proposed model is a useful and efficient method to find the optimal process conditions in pressure die casting associated with the minimum porosity percent.