材料科学技术(英文版)2022,Vol.120Issue(25) :99-107.

Chemical-element-distribution-mediated deformation partitioning and its control mechanical behavior in high-entropy alloys

Jia Li Baobin Xie Quanfeng He Bin Liu Xin Zeng Peter K.Liaw Qihong Fang Yong Yang Yong Liu
材料科学技术(英文版)2022,Vol.120Issue(25) :99-107.

Chemical-element-distribution-mediated deformation partitioning and its control mechanical behavior in high-entropy alloys

Jia Li 1Baobin Xie 1Quanfeng He 2Bin Liu 3Xin Zeng 1Peter K.Liaw 4Qihong Fang 1Yong Yang 2Yong Liu3
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作者信息

  • 1. College of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,China
  • 2. College of Science and Engineering,City University of Hong Kong,Hong Kong,China
  • 3. State Key Laboratory of Powder Metallurgy,Central South University,Changsha 410083,China
  • 4. Department of Materials Science and Engineering,The University of Tennessee,Knoxville 37996,USA
  • 折叠

Abstract

The chemical element distributions always strongly affect the deformation mechanisms and mechani-cal properties of alloying materials.However,the detailed atomic origin still remains unknown in high-entropy alloys(HEAs)with a stable random solid solution.Here,considering the effect of elemental fluc-tuation distribution,the deformation behavior and mechanical response of the widely-studied equimolar random CoCrFeMnNi HEA are investigated by atomic simulations combined with machine learning and micro-pillar compression experiments.The elemental anisotropy factor is proposed,and then used to evaluate the chemical element distribution.The experimental and simulation results show that the local variations of chemical compositions exist and play a critical role in the deformation partitioning and me-chanical properties.The high strength and good plasticity of HEAs are obtained via tuning the chemical element distributions,and the optimal elemental anisotropy factor ranges from 2.9 to 3 using machine learning.This trend can be attributed to the cooperative mechanisms depending on the local variational composition:massive partial dislocation multiplication at an initial stage of plastic deformation,and the inhibition of localized shear banding via the nucleation of deformation twinning at a later stage.Using the new insights gained here,it would be possible to create new metallic alloys with superior properties through thermal-mechanical treatment to tailoring the chemical element distribution.

Key words

Machine learning/High-entropy alloy/Plasticity/High strength/Atomic simulation

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基金项目

国家自然科学基金(51871092)

国家自然科学基金(11902113)

国家自然科学基金(11772122)

湖南省自然科学基金(2019JJ50068)

湖南省自然科学基金(2021JJ40032)

出版年

2022
材料科学技术(英文版)
中国金属学会 中国材料研究学会 中国科学院金属研究所

材料科学技术(英文版)

CSTPCDCSCDSCI
影响因子:0.657
ISSN:1005-0302
参考文献量48
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