Optimization of high-pressure die-casting process parameters using artificial neural network

High-pressure die casting is a versatile process for producing engineered metal parts. There are many attributes involved which contribute to the complexity of the process. It is essential for the engineers to optimize the process parameters and improve the surface quality. However, the process parameters are interdependent and in conflict in a complicated way, and optimization of the combination of processes is time-consuming. In this work, an evaluation system for the surface defect of casting has been established to quantify surface defects, and artificial neural network was introduced to generalize the correlation between surface defects and die-casting parameters, such as mold temperature, pouring temperature, and injection velocity. It was found that the trained network has great forecast ability. Furthermore, the trained neural network was employed as an objective function to optimize the processes. The optimal parameters were employed, and the castings with acceptable surface quality were achieved.