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Metallurgical and Materials Transactions A
Erschienen in: Metallurgical and Materials Transactions A 7/2021

19.05.2021 | Original Research Article

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

verfasst von: Xin Li, Xiao-Guang Zhou, Guang-Ming Cao, Shao-Hua Xu, Yong Wang, Zhen-Yu Liu

Erschienen in: Metallurgical and Materials Transactions A | Ausgabe 7/2021

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Abstract

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.
Vorheriger Artikel The High-Strain-Rate Constitutive Behavior and Shear Response of Pure Magnesium and AZ31B Magnesium Alloy
Nächster Artikel Observation of Transformation Strain Arrest During Partial Thermomechanical Cycling of an NiTi Shape Memory Alloy

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Metadaten
Titel
Machine Learning Hot Deformation Behavior of Nb Micro-alloyed Steels and Its Extrapolation to Dynamic Recrystallization Kinetics
verfasst von
Xin Li
Xiao-Guang Zhou
Guang-Ming Cao
Shao-Hua Xu
Yong Wang
Zhen-Yu Liu
Publikationsdatum
19.05.2021
Verlag
Springer US
Erschienen in
Metallurgical and Materials Transactions A / Ausgabe 7/2021
Print ISSN: 1073-5623
Elektronische ISSN: 1543-1940
DOI
https://doi.org/10.1007/s11661-021-06315-4

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