A Novel Approach to Model and Optimize Qualities of Castings Produced by Differential Pressure Casting Process

Abstract

The present study attempts to promote the quality of differential pressure casting component by two-stage optimization, orthogonal virtual casting and BP neural network. In the first stage, the critical parameters determined by the numerical model of casting procedure indicate that the qualities of castings, including casting solidification time, secondary dendrite spacing and porosity, are highly affected by the die temperature, pouring temperature and cooling medium temperature. In the second stage, the input–output relationship developed by utilizing BP neural network is found to be statistically adequate and yielded better prediction accuracy. Artificial fish swarm algorithm (AFSA) performs multi-directional search in multi-dimensional space simultaneously and performs desirability function approach. The results of nonlinear neural network-based models and the performance of artificial fish swarm algorithm optimization technique are summarized.