Prediction of high temperature deformation characteristics of an Fe-based shape memory alloy using constitutive and artificial neural network modelling

Abstract

The high temperature deformation characteristics of an Fe-28Ni-17Co-11.5Al-2.5Ta-0.05B (at.%) shape memory alloy (SMA) were studied by high temperature compression testing under large temperature (1323–1473 K) and strain rate (0.01–10 s−1) ranges. These were predicted by applying the Arrhenius-type and strain-compensated Arrhenius-type constitutive models, and the artificial neural network (ANN) model to the results obtained from the experiments. The capability of the models for prediction was assessed as a function of the correlation coefficient (R) and the relative percentage error. The results reveal that the true stress prediction by the strain-compensated Arrhenius-type constitutive model is more precise at a lower strain rate (0.01 s−1) than at a higher strain rate (10 s−1). Moreover, it yields better results in comparison with those obtained from Arrhenius-type model. They further reveal ANN model shows higher efficiency and preciseness in forecasting the high temperature flow characteristics of the SMA as compared to the strain-compensated Arrhenius-type and Arrhenius-type models.