Oleg V. Rofman

The application of superplastic forming for complex components manufacturing is attractive for automotive and aircraft industries and has been of great interest in recent years. The current analytical modeling theories are far from perfect in this area, and the results deduced from it characterize the forming conditions insufficiently well; therefore, successful numerical modeling is essential. In this study, the superplastic behavior of the novel Al-Mg-Fe-Ni-Zr-Sc alloy with high-strain-rate superplasticity was modeled. An Arrhenius-type constitutive hyperbolic-sine equation model (ACE) and an artificial neural network (ANN) were developed. A comparative study between the constructed models was performed based on statistical errors. A cross validation approach was utilized to evaluate the predictability of the developed models. The results revealed that the ACE and ANN models demonstrated strong workability in predicting the investigated alloy’s flow stress, whereas the ACE approach exhibited better predictability than the ANN.

Al-0.1wt.%Mg has been chosen to explore the effects of deformation on the microstructure of nominally single-phase materials. For these materials, the rate of static grain growth is much higher compared to that of Zener pinned systems and dual-phase alloys, where growth is hindered due to the pinning force exerted by second-phase particles on grain boundaries. Therefore, deformation-induced microstructural changes in single-phase alloys occur without any restricting pinning pressure. This paper illustrates the complex effect of deformation on the microstructural changes mainly associated with dynamic recovery. The process affects the initial microstructure due to an intensive substructure development. Dynamic recrystallisation, associated with the formation of new grains, is considered to be a transient phenomenon that quantitatively influences the mean grain size formed during straining. This work also aims to estimate the stored energy of the deformed regions and texture components to explore their contribution to the migration of high-angle grain boundaries.