粒度
材料科学
铝
合金
冶金
晶界强化
微观结构
晶界
作者
Siwei Ma,Zhibo Zhang,Zhuming Huang,Dongfu Song,Yiwang Jia,Nan Zhou,Kai Wang,Kan Zheng,Huijing Du
出处
期刊:Crystals
[MDPI AG]
日期:2022-03-29
卷期号:12 (4): 474-474
被引量:1
标识
DOI:10.3390/cryst12040474
摘要
Grain refinement of cast alloys, especially aluminum–silicon and magnesium-based alloys, is an effective approach to improve the strength of alloys. Grain size is the most representative parameter used to characterize grain refinement in the industry, thereby attracting increasing attention for developing accurate grain size prediction models. In this paper, several important grain size prediction models under different adaptation conditions are reviewed. These models are obtained either by regression of experimental data or by physical/mathematical inference under certain assumptions of specified cases, focusing on the effects of alloy composition, solidification temperature gradient, grain growth rate, and fining agent composition, among others. The trends of grain size prediction models were also discussed. The results revealed machine learning as an effective tool to establish a data-driven prediction model of grain size in cast aluminum alloys.
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