Optimization of Low-Pressure Die Casting Process with Soft Computing

The paper presents a hybrid strategy in a soft computing paradigm for the optimization of the low-pressure die casting process. Casting process parameters, such as various parts temperatures of die, pouring temperature are considered. The hybrid strategy combines numerical simulation software, a genetic algorithm and a multilayer neural network to optimize the process parameters. An approximate analysis model is developed using a BP neural network in order to avoid the expensive computation resulting from the numerical simulation software. According to the characteristic of the optimization problem, a real-code genetic algorithm is applied to solve the optimization model. The effectiveness of the improved strategy is shown by an A356 automotive wheel.