Prediction of the flow stress of 6061 Al–15% SiC – MMC composites using adaptive network based fuzzy inference system

Silicon carbide reinforced aluminium composite materials are increasingly used in many engineering fields. Flow stress prediction for these materials is increasingly important. In the present work, flow stress of 1.0Mg – 0.6% Si – 0.3% Cu – 0.2% Cr rest Al with 15% SiCp during hot deformation is carried out using the conventional regression method, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) method. The temperature at which the aluminium is compressed are 300– 500 °C with strain rates ranging from 0.00857 to 2.7 s−1 and for the strains of 0.1–0.5. Simulation studies are carried out for analysis. By comparing the performances of various modeling techniques, ANFIS modeling can effectively be employed for prediction of flow stress of 6061 Al–15% SiC composites. The convergence speed of this algorithm is higher than that of the ANN.