Network Modeling Approach to Predict the Effect of the Reinforcement Content on the Hot Strength of Al-based Composites

Abstract

In this investigation, the empirical formula that is used to predict the strength of the composite material has been compared to the predictions of the strength of Al-based composites by the trained artificial neural network (ANN), and trained Neuro-fuzzy network. To achieve this purpose, four schemes of evaluation have been used. The results showed that the Neuro-fuzzy network is the best tool to predict the hot deformation behavior of Al-based composites with different reinforcement contents (5, 15, and 20%) of Al2O3 particles that have an average particle size of 25 mm at different deformation conditions, since the reinforcement content and the deformation conditions have a nonlinear complex effect on the flow stress.