Analog-Circuit Model of FGH96 Superalloy Hot Deformation Behaviors Based on Artificial Neural Network

At the present time, numerical models (such as, numerical simulation based on FEM) adopted broadly in technological design and process control in forging field can not implement the real-time control of material forming process. It is thus necessary to establish a dynamic model fitting for the real-time control of material deformation processing in order to increase production efficiency, improve forging qualities and increase yields. In this paper, hot deformation behaviors of FGH96 superalloy are characterized by using hot compressive simulation experiments. The artificial neural network (ANN) model of FGH 96 superalloy during hot deformation is established by using back propagation (BP) network. Then according to electrical analogy theory, its analog-circuit (AC) model is obtained through mapping the ANN model into analog circuit. Testing results show that the ANN model and the AC model of FGH96 superalloy hot deformation behaviors possess high predictive precisions and can well describe the superalloy's dynamic flow behaviors. The ideas proposed in this paper can be applied in the real-time control of material deformation processing.