An adaptive neuro-fuzzy inference system (ANFIS) model for high pressure die casting

Abstract

The focus of this research was to create a reliable and effective methodological tool for the selection of the optimal conditions in the high pressure die casting (HPDC) process. An adaptive neuro-fuzzy inference system (ANFIS) was applied to study the effect of die casting parameters on porosity formation in AlSi9Cu3 pressure die castings. Metal temperature, die temperature, piston velocity in slow phase, die gate velocity, and solidification pressure were considered as the model’s input features. An orthogonal array was used in the experimental design. The experimental results were then used to establish the predictive model based on a neuro-fuzzy approach. The model combined the modelling function of fuzzy inference with the learning ability of an artificial neural network. Α set of rules was generated directly from the experimental data. Trapezoidal and Gauss membership functions were used for the training of the data. 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 percentage.