Machine Learning Hot Deformation Behavior of Nb Micro-alloyed Steels and Its Extrapolation to Dynamic Recrystallization Kinetics

被引：11
|
作者：
Li, Xin [1 ]
Zhou, Xiao-Guang [1 ]
Cao, Guang-Ming [1 ]
Xu, Shao-Hua [1 ]
Wang, Yong [2 ,3 ]
Liu, Zhen-Yu [1 ]
机构：
[1] Northeastern Univ, State Key Lab Rolling & Automat, Shenyang 110819, Peoples R China
[2] Ansteel Iron & Steel Res Inst, Anshan 114001, Peoples R China
[3] State Key Lab Met Mat Marine Equipment & Applicat, Anshan 114001, Peoples R China
来源：
METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE | 2021年 / 52卷 / 07期
基金：
中国国家自然科学基金;
关键词：
ARTIFICIAL NEURAL-NETWORK; FLOW-STRESS; GRAIN-GROWTH; PREDICTION; AUSTENITE; CURVES; DESIGN; MODEL; TRANSFORMATION; SOLUTE;
D O I：
10.1007/s11661-021-06315-4
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In the present paper, a novel method was developed to predict the dynamic recrystallization (DRX) behavior during hot rolling based on machine learning in combination with physical models of niobium (Nb) micro-alloyed steels. It has been demonstrated that the comprehensive modeling could accurately predict the DRX behavior under different compositional and processing conditions, which not only exhibited higher precisions but also was in better agreement with physical metallurgical principles than traditional methods.
引用
收藏
页码：3171 / 3181
页数：11
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