APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR PREDICTION OF SUPERPLASTIC BEHAVIOUR IN NiAl ALLOYS

Abstract

Chemical composition, grain size, and processing conditions such as temperature and strain rate have important influence on superplasticity of NiAl alloys, which would allow the optimization of these parameters in order to achieve the desired combination of properties. In this work, the optimal superplastic deformation conditions of NiAl alloys were studied by using artificial neural networks (ANN). The standard multilayer feedforward networks were trained and tested using comprehensive datasets from previous experimentally works on the as-extruded NiAl, NiAl-25Cr, NiAl-20Fe-Y(Ce), NiAl-30Fe-Y, NiAl-9Mo, NiAl-P alloys. Different effects are modeled, including the influence of the alloying elements on the superplastic, and the influence of deformation temperature, strain rate and grain size on the elongations during the superplastic tensile tests. The artificial neural network models are combined with computer programmers for optimization of the inputs in order to achieve desirable combination of outputs. Good performances of the neural networks are achieved. Results of this research propose a range of strain rate and temperature within which the NiAl alloy possesses superplasticity with larger elongations, although the deformation temperature and strain rate of superplastic alloys alternately influence each other within the range. These models are convenient and powerful tools for practical applications in superplastic prediction in NiAl alloys.