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논문 기본 정보

자료유형
학술저널
저자정보
H. R. Rezaei Ashtiani (Arak University of Technology) A. A. Shayanpoor (Arak University of Technology)
저널정보
대한금속·재료학회 Metals and Materials International Metals and Materials International Vol.27 No.12
발행연도
2021.12
수록면
5,017 - 5,033 (17page)
DOI
10.1007/s12540-020-00943-y

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In this investigation, processing maps and artificial neural network (ANN) models were carried out to describe and predictthe flow behavior of pure aluminum at various initial grain sizes in the hot working conditions. The elevated temperatureflow behavior of AA1070 aluminum was done through isothermal hot compressive tests in a large range of initial grain size(IGS) (50?450 μm), strain rate (0.005?0.5 s?1) and temperature (623?773 K). Consequences showed that the flow stress canbe remarkably influenced by the initial grain size at high temperatures. Based on the results, the ANN model trained witha feed-forward back-propagation learning algorithm which was prepared to describe the flow behavior of pure aluminumat the elevated temperatures. In which the initial grain size, strain, temperature and strain rate were taken as input data andtrue stress was used as target data. The results showed that the developed ANN model was a powerful method to predictthe complex non-linear of the hot flow behavior of pure aluminum. The processing map was plotted and analyzed via thedynamic material model as “stable” and “unstable” regions were determined by observing the microstructure evolution. Based on this, The optimum ranges for temperature and strain rate were 623?773 K and 0.05 s?1 respectively, for fine-grainedmicrostructure (lower than 50 μm) and were 650?720 K and 0.005?0.5 s?1 respectively, for coarse-grained microstructures(over than 50 μm).

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