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Simulation of Abnormal Grain Growth Using the Cellular Automaton Method.
Murata, Kenji; Fukui, Chihiro; Sun, Fei; Chen, Ta-Te; Adachi, Yoshitaka.
Affiliation
  • Murata K; Engineering Steel Research Sect., Corporate Research & Development Center, Daido Steel Co., Ltd., 30, Daido-cho 2-chome, Minami-ku, Nagoya 457-8545, Japan.
  • Fukui C; Department of Material Design Innovation Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.
  • Sun F; Department of Material Design Innovation Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.
  • Chen TT; Department of Material Design Innovation Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.
  • Adachi Y; Department of Material Design Innovation Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.
Materials (Basel) ; 17(1)2023 Dec 27.
Article in En | MEDLINE | ID: mdl-38203990
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Materials (Basel) Year: 2023 Document type: Article Affiliation country: Japan

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Materials (Basel) Year: 2023 Document type: Article Affiliation country: Japan