期刊
APPLIED SCIENCES-BASEL
卷 11, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/app11052208
关键词
aluminum alloys; superplasticity; constitutive equations; artificial neural network; cross-validation
类别
资金
- Russian Science Foundation, RF [17-79-20426]
- Russian Science Foundation [20-79-20018] Funding Source: Russian Science Foundation
This study modeled the superplastic behavior of a novel Al-Mg-Fe-Ni-Zr-Sc alloy with high-strain-rate superplasticity, developing an ACE and an ANN model. Comparative analysis showed that the ACE approach had better predictability than the ANN in forecasting the flow stress of the investigated alloy.
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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