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Article

Evaluation of Austenite–Ferrite Phase Transformation in Carbon Steel Using Bayesian Optimized Cellular Automaton Simulation

1
Department of Material Design Innovation Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
2
Department of Mechanical Engineering, Aichi Institute of Technology, 1247 Yachigusa, Yakusa Cho, Toyota 470-0392, Japan
3
Department of Materials Science, Akita University, 1-1 Tegata-Gakuenmachi, Akita 010-8502, Japan
*
Authors to whom correspondence should be addressed.
Materials 2023, 16(21), 6922; https://doi.org/10.3390/ma16216922
Submission received: 5 October 2023 / Revised: 25 October 2023 / Accepted: 26 October 2023 / Published: 28 October 2023
(This article belongs to the Special Issue Phase Transformation Theory and Microstructure Simulation of Alloys)

Abstract

Austenite–ferrite phase transformation is a crucial metallurgical tool to tailor the properties of steels required for particular applications. Extensive simulation and modeling studies have been conducted to evaluate the phase transformation behaviors; however, some fundamental physical parameters still need to be optimized for better understanding. In this study, the austenite–ferrite phase transformation was evaluated in carbon steels with three carbon concentrations during isothermal annealing at various temperatures using a developed cellular automaton simulation model combined with Bayesian optimization. The simulation results show that the incubation period for nucleation is an essential factor that needs to be considered during austenite–ferrite phase transformation simulation. The incubation period constant is mainly affected by carbon concentration and the optimized values have been obtained as 10−24, 10−19, and 10−21 corresponding to carbon concentrations of 0.2 wt%, 0.35 wt%, and 0.5 wt%, respectively. The average ferrite grain size after phase transformation completion could decrease with the decreasing initial austenite grain size. Some other parameters were also analyzed in detail. The developed cellular automaton simulation model combined with Bayesian optimization in this study could conduct an in-depth exploration of critical and optimal parameters and provide deeper insights into understanding the fundamental physical characteristics during austenite–ferrite phase transformation.
Keywords: phase transformation; cellular automaton; Bayesian optimization; microstructure phase transformation; cellular automaton; Bayesian optimization; microstructure

Share and Cite

MDPI and ACS Style

Sun, F.; Mino, Y.; Ogawa, T.; Chen, T.-T.; Natsume, Y.; Adachi, Y. Evaluation of Austenite–Ferrite Phase Transformation in Carbon Steel Using Bayesian Optimized Cellular Automaton Simulation. Materials 2023, 16, 6922. https://doi.org/10.3390/ma16216922

AMA Style

Sun F, Mino Y, Ogawa T, Chen T-T, Natsume Y, Adachi Y. Evaluation of Austenite–Ferrite Phase Transformation in Carbon Steel Using Bayesian Optimized Cellular Automaton Simulation. Materials. 2023; 16(21):6922. https://doi.org/10.3390/ma16216922

Chicago/Turabian Style

Sun, Fei, Yoshihisa Mino, Toshio Ogawa, Ta-Te Chen, Yukinobu Natsume, and Yoshitaka Adachi. 2023. "Evaluation of Austenite–Ferrite Phase Transformation in Carbon Steel Using Bayesian Optimized Cellular Automaton Simulation" Materials 16, no. 21: 6922. https://doi.org/10.3390/ma16216922

APA Style

Sun, F., Mino, Y., Ogawa, T., Chen, T.-T., Natsume, Y., & Adachi, Y. (2023). Evaluation of Austenite–Ferrite Phase Transformation in Carbon Steel Using Bayesian Optimized Cellular Automaton Simulation. Materials, 16(21), 6922. https://doi.org/10.3390/ma16216922

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