Aluminum-zinc alloy squeeze casting technological parameters optimization based on PSO and ANN

This paper presents a kind of ZA27 squeeze casting process parameter optimization method using artificial neural network (ANN) combined with the particle swarm optimizer (PSO). Regarding the test data as samples and using neural network create ZA27 squeeze casting process parameters and mechanical properties of nonlinear mapping model. Using PSO optimize the model and obtain the optimum value of the process parameters. Make full use of the non-neural network mapping capabilities and PSO global optimization capability. The network uses the radial direction primary function neural network, using the clustering and gradient method to make use of network learning, in order to enhance the generalization ability of the network. PSO takes dynamic changing inertia weights to accelerate the convergence speed and avoid a local minimum.