Tensile parameters evaluation of two solid solution super alloys by ANN modeling - Hassan, Muhammad Hasibul; Al Hazza, Muataz Hazza Faizi (Praise Worthy Prize, 2014)
Solid solution nickel base super alloys 617 and 276 possess excellent mechanical properties, oxidation, creep-resistance, and phase stability at high temperatures. These alloys are used in complex and stochastic applications including the structural material of high temperature heat exchanger. Thus, it is difﬁcult to predict their output characteristics mathematically. Therefore, the non-conventional methods for modeling become more effective. These two alloys have been subjected to tensile deformation at high temperatures and different tensile parameters have been used to develop the new models. Artificial neural network (ANN) was applied to predict yield strength (YS), Ultimate Tensile strength (UTS), percent elongation (%El) and percent reduction in area (%RA) for the two alloys. The neural network comprises twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed results which indicates the validity of the models.