Use of artificial neural networks to predict the deformation behavior of Zr–2.5Nb–0.5Cu

In this study, artificial neural networks were used to model the hot deformation behavior of Zr–2.5Nb–0.5Cu alloy, in the strain rate range of 10−3 to 10 s−1, temperature range of 650–1050 °C and to a strain of 0.5. Strain, log strain rate and inverse of temperature were used as inputs and stress was taken as the output of the network. The feed-forward network used consisted of two hidden layers containing four and three neurons each with a log-sigmoid activation function and Levenberg–Marquardt training algorithm. The network was successfully trained across phase regimes (α + β) to β and across different deformation domains. This trained network could predict the flow stress better than a constitutive equation of the type image.