Computational Materials Science2022,Vol.20313.DOI:10.1016/j.commatsci.2021.111131

Prediction of grain-size transition during solidification of hypoeutectic Al-Si alloys by an improved three-dimensional sharp-interface model

Ren, Zhe Pu, Zhenpeng Liu, Dong-Rong
Computational Materials Science2022,Vol.20313.DOI:10.1016/j.commatsci.2021.111131

Prediction of grain-size transition during solidification of hypoeutectic Al-Si alloys by an improved three-dimensional sharp-interface model

Ren, Zhe 1Pu, Zhenpeng Liu, Dong-Rong
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作者信息

  • 1. Harbin Univ Sci & Technol
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Abstract

Control of grain size during solidification is an important issue according to the desired usage properties. In this study, both microstructure characterizations and numerical simulations are performed for as-cast hypoeutectic Al-Si alloys to understand the mechanism of grain-size transition with initial Si contents. An improved threedimensional (3D) sharp-interface model is developed that couples cellular automaton (CA) approach with a deterministic mesh-anisotropy reduction (DMAR) algorithm. The improved sharp-interface model is able to accurately calculate interface curvature and reproduce reasonable dendrite morphology within a wide range of cooling rates. It is found that grain size first decreases with increasing the initial Si content to 3 wt% and then increases with further Si additions. The nucleation undercooling significantly increases with increasing the Si content from 3 wt% to 10 wt% due to a Si-poisoning effect. The grain-size transition is mainly determined by the variations in the nucleation undercooling. Analysis using the Interdependence model also supports that a wide nucleation-free-zone is formed during solidification of Al-10 wt% Si alloy induced by a large nucleation barrier and a decreased growth velocity.

Key words

Al-Si alloy/3D sharp-interface model/Mesh-anisotropy reduction/Grain-size transition/Solidification/FRONT TRACKING MODEL/CELLULAR-AUTOMATON/DENDRITIC GROWTH/REFINEMENT/ALUMINUM/SIMULATION/NUCLEATION/EVOLUTION/BORIDES/PHASE

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出版年

2022
Computational Materials Science

Computational Materials Science

EISCI
ISSN:0927-0256
被引量3
参考文献量46
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