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. 2023 Dec 27;17(1):138.
doi: 10.3390/ma17010138.

Simulation of Abnormal Grain Growth Using the Cellular Automaton Method

Kenji Murata  1   2 , Chihiro Fukui  2 , Fei Sun  2 , Ta-Te Chen  2 , Yoshitaka Adachi  2
Affiliations

Simulation of Abnormal Grain Growth Using the Cellular Automaton Method

Kenji Murata et al. Materials (Basel). .

Abstract

The abnormal grain growth of steel, which is occurs during carburization, adversely affects properties such as heat treatment deformation and fatigue strength. This study aimed to control abnormal grain growth by controlling the materials and processes. Thus, it was necessary to investigate the effects of microstructure, precipitation, and heat treatment conditions on abnormal grain growth. We simulated abnormal grain growth using the cellular automaton (CA) method. The simulations focused on the grain boundary anisotropy and dispersion of precipitates. We considered the effect of grain boundary misorientation on boundary energy and mobility. The dispersion state of the precipitates and its pinning effect were considered, and grain growth simulations were performed. The results showed that the CA simulation reproduced abnormal grain growth by emphasizing the grain boundary mobility and the influence of the dispersion state of the precipitate on the occurrence of abnormal grain growth. The study findings show that the CA method is a potential technique for the prediction of abnormal grain growth.

Keywords: carburizing; cellular automaton; grain growth.

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Conflict of interest statement

Author Kenji Murata is employed by the company Daido Steel Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Schematic of the range of cells considered in the box count method.
Figure 2
Figure 2
Distribution of grain boundary misorientation (dashed line represents ideal random misorientation distribution) (a) using grain numbers and (b) random crystallographic orientations.
Figure 3
Figure 3
The overall flow of the CA simulation.
Figure 4
Figure 4
Microstructures during specific steps.
Figure 5
Figure 5
Time evolution of average grain radius.
Figure 6
Figure 6
Microstructures during specific steps in the case of enhanced mobility.
Figure 7
Figure 7
Effect of mobility coefficient p on the microstructure after 1000 steps.
Figure 8
Figure 8
Effect of mobility coefficient on Rmax/<Rg> after 1000 steps (Dotted line is abnormal grain growth criteria in Equation (15)).
Figure 9
Figure 9
Effect of the number of enhanced grains on Rmax/<Rg> after 1000 steps (Dotted line is abnormal grain growth criteria in Equation (15)).
Figure 10
Figure 10
Effect of the volume fraction of precipitates on the microstructures after 1000 steps.
Figure 11
Figure 11
Effect of volume fraction of precipitates on Rmax/<Rg> after 1000 steps (Dotted line is abnormal grain growth criteria in Equation (15)).
Figure 12
Figure 12
Distribution of f0 (upper row) and microstructures after 1000 steps (lower row).
See this image and copyright information in PMC

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