Characterization of Superplastic Deformation Behavior for a Novel Al-Mg-Fe-Ni-Zr-Sc Alloy: Arrhenius-Based Modeling and Artificial Neural Network Approach
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Metallurgy and Material Forming · Microstructure and mechanical properties · Metal Forming Simulation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Physical Metallurgy of Non-Ferrous Metals, National University of Science and Technology “MISiS”, Leninsky Prospekt, 4, 119049 Moscow, Russia
Department of Mechanical Engineering, Shoubra Faculty of Engineering, Benha University, Shoubra St. 108, Shoubra, P.O. 11629 Cairo, Egypt
Laboratory of Radiation Materials Science, Institute of Nuclear Physics, Ibragimov St. 1, Almaty 050032, Kazakhstan
Funding
Russian Science Foundation
References (81)
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