1992
DOI: 10.1016/0956-7151(92)90198-n
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Microstructural simulation of dynamic recrystallization

Abstract: A Monte Carlo model for dynamic recrystallization has been developed from earlier models used to simulate static recrystallization and grain growth. The model simulates dynamic recrystallization by adding recrystallization nuclei and stored energy continuously with time. The simulations reproduce many of the essential features of dynamic recrystallization. The stored energy of the system, which may be interpreted as a measure of the flow stress, goes through a maximum and then decays, monotonically under some … Show more

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Cited by 127 publications
(57 citation statements)
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“…In the past, most predictive models are based on either modified Johnson-Mehl-Avrami kinetic equations or probabilistic approaches. [13][14][15][16][17][18][19][20] In this work, a DDE is used for modeling this type of stress-strain behavior: a delay time due to diffusion is taken into consideration, and it is expressed in terms of a critical strain for nucleation for recrystallization.…”
Section: A Oscillatory Flow Behavior During Hot Workingmentioning
confidence: 99%
“…In the past, most predictive models are based on either modified Johnson-Mehl-Avrami kinetic equations or probabilistic approaches. [13][14][15][16][17][18][19][20] In this work, a DDE is used for modeling this type of stress-strain behavior: a delay time due to diffusion is taken into consideration, and it is expressed in terms of a critical strain for nucleation for recrystallization.…”
Section: A Oscillatory Flow Behavior During Hot Workingmentioning
confidence: 99%
“…However, the JMAK equation is not adequate for actual applications due to the heterogeneous nature of the recrystallization process. Recently, many modeling methods have been proposed to solve this problem, such as the vertex model, 3) the Monte Carlo model, 4,5) and the phase field model. 6) Although these models successfully describe microstructural evolution during recrystallization, the CA model is used most often due to its straightforward time and length scale calibrations.…”
Section: Introductionmentioning
confidence: 99%
“…If information on microstructure development during DRX is simultaneously required, the models with adequate temporal and spatial resolution are needed. Therefore, numerical models, such as Q state Monte Carlo (MC) [5,6], phase field (PF) [7] and cellular automaton (CA) [8][9][10][11][12][13][14][15] have been proposed to simulate DRX. Compared with MC and PF method, the CA method is used more because of its more flexible and adaptable to temporal and spatial scale.…”
Section: Introductionmentioning
confidence: 99%