Characterization of superplastic deformation behavior for a novel al-mg-fe-ni-zr-sc-based alloy: Arrhenius-based modeling and artificial neural network approach


Mosleh A.O. Kotov A.D. Kishchik A.A. Rofman O.V. Mikhaylovskaya A.V.
1 March 2021MDPI AG

Applied Sciences (Switzerland)
2021#11Issue 51 - 18 pp.

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.

Aluminum alloys , Artificial neural network , Constitutive equations , Cross-validation , Superplasticity

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National University of Science and Technology “MISiS”, Leninsky Prospekt, 4, Moscow, 119049, Russian Federation
Shoubra Faculty of Engineering, Benha University, Shoubra St. 108, Shoubra, P.O. 11629, Cairo, Egypt
Institute of Nuclear Physics, Ibragimov St. 1, Almaty, 050032, Kazakhstan

National University of Science and Technology “MISiS”
Shoubra Faculty of Engineering
Institute of Nuclear Physics

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