Analysis of the grain size evolution for ferrite formation in Fe-C-Mn steels using a 3D model under a mixed-mode interface condition

Journal article (2018)

Authors

H. Fang Novel Aerospace Materials - Aerospace Engineering
M.G. Mecozzi (OLD) MSE-3
E.H. Brück RST/Fundamental Aspects of Materials and Energy - AS
S. van der Zwaag Novel Aerospace Materials - Aerospace Engineering
N.H. van Dijk RST/Fundamental Aspects of Materials and Energy - AS
Research Group
Novel Aerospace Materials (Aerospace Engineering) (TU Delft)
Copyright: © 2018 H. Fang, M.G. Mecozzi, E.H. Brück, S. van der Zwaag, N.H. van Dijk
DOI: https://doi.org/10.1007/s11661-017-4397-y
More Info
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Published Date

2018

Language

English

Faculty

Aerospace Engineering

Department

Aerospace Structures & Materials

Research Group

Novel Aerospace Materials

Abstract

A 3D model has been developed to predict the average ferrite grain size and grain size distribution for an austenite-to-ferrite phase transformation during continuous cooling of an Fe-C-Mn steel. Using a Voronoi construction to represent the austenite grains, the ferrite is assumed to nucleate at the grain corners and to grow as spheres. Classical nucleation theory is used to estimate the density of ferrite nuclei. By assuming a negligible partition of manganese, the moving ferrite–austenite interface is treated with a mixed-mode model in which the soft impingement of the carbon diffusion fields is considered. The ferrite volume fraction, the average ferrite grain size, and the ferrite grain size distribution are derived as a function of temperature. The results of the present model are compared with those of a published phase-field model simulating the ferritic microstructure evolution during linear cooling of an Fe-0.10C-0.49Mn (wt pct) steel. It turns out that the present model can adequately reproduce the phase-field modeling results as well as the experimental dilatometry data. The model presented here provides a versatile tool to analyze the evolution of the ferrite grain size distribution at low computational costs.