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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.

Keywords

modeling artificial neural network (ANN)neuro-fuzzy metal matrix composite (MMC)reinforcement

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

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