Abstract
Flow behavior of a metal during hot deformation is influenced by physical phenomena such as work hardening, dynamic recovery and dynamic recrystallization. Effects of these phenomena on flow stress can be expressed through various semi-empirical models. To find unknown parameters of these semi-empirical models, one can use a process such as system identification in the way that the difference between experimental data and model output become minimized. Genetic algorithm is one of reliable and flexible methods in this category that has gained extensive application in different fields of science; so, in this research, Genetic algorithm was used to model flow stress of API-X70 microalloyed steel considering mentioned metallurgical phenomena during hot compression test. Accuracy of the developed models for dynamic recovery and recrystallization was evaluated through statistical methods. Results showed a good agreement between the developed models and experimental data and also indicated that these models are very suitable for predicting flow stress.
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