Modeling the Rheological Behavior of a Novel Hot Isostatic Pressed Powder Metallurgy Superalloy

The rheological behavior and deformation mechanisms of a new powder metallurgy (P/M) superalloy at various deformation conditions are researched. The deformation conditions have significant influence on the rheological stress. Increasing the deformation temperature or decreasing the strain rate can decrease the rheological stress. The discontinuous hardening and softening phenomena are observed at the strain rate of 1 s(-1), resulting from the complex phase transformation and dynamic recrystallization. Besides, the deformation activity energy (Q) declines with increasing the strain. The phenomenon is attributed to the spheroidization of gamma ' phase and the decreased content/aspect ratio of gamma ' phase. The deformation mechanisms of the researched superalloy are the accumulation of dislocation, stacking faults shearing, dislocations pinned by gamma ' phase, and the formation of microtwins during hot deformation. The strain-compensated Arrhenius and particle swarm optimization-based backpropagation artificial neural network (PSO-BP ANN) models are established to predict the rheological behavior. Compared to the strain-compensated Arrhenius equation, the developed PSO-BP ANN model presents the higher accuracy in predicting the rheological behavior of the researched alloy. Furthermore, for the developed PSO-BP ANN model, the correlation coefficient is 0.9995, and the root mean square error is 1.224 MPa. So, the forecasted rheological stresses are consistent with the measured ones.

Automated summary

The rheological behavior and deformation mechanisms of a new powder metallurgy (P/M) superalloy were investigated under different deformation conditions. It was found that the rheological stress can be reduced by increasing the deformation temperature or decreasing the strain rate. The deformation mechanisms of the superalloy include dislocation accumulation, stacking faults shearing, dislocations pinned by gamma ' phase, and the formation of microtwins during hot deformation. A particle swarm optimization-based backpropagation artificial neural network (PSO-BP ANN) model was developed to accurately predict the rheological behavior of the alloy.