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Modifying element diffusion pathway by transition layer structure in high-entropy alloy particle reinforced Cu matrix composites 被引量:2
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作者 Hao-yang YU Wei FANG +2 位作者 ruo-bin chang Pu-guang JI Qing-zhou WANG 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2019年第11期2331-2339,共9页
The Al0.3CoCrFeNi high-entropy alloy(HEA)particles reinforced Cu matrix composites(CMCs)were fabricated by mechanical alloying and sintering.Transition layer structure was obtained by multi-step ball milling to invest... The Al0.3CoCrFeNi high-entropy alloy(HEA)particles reinforced Cu matrix composites(CMCs)were fabricated by mechanical alloying and sintering.Transition layer structure was obtained by multi-step ball milling to investigate the related influence on element diffusion behavior and wear properties of CMCs.The results indicate that a new Cu transition layer is generated,and the thickness is about 5μm.Cr element diffuses into the interface via the transition layer,which forms the complex oxide.Because of the structure of Cu transition layer,the diffusion rates of Ni,Co and Fe increase,especially the Ni element.The wear resistance of CMCs is improved by 30%,which is due to the improvement of interface bonding strength,compared with the CMCs without transition layer.This method is applicable to the development of advanced HEA reinforced metallic matrix composites. 展开更多
关键词 high-entropy alloy copper-matrix composites transition layer structure diffusion WEAR
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Composition design of high yield strength points in single-phase Co-Cr-Fe-Ni-Mo multi-principal element alloys system based on electronegativity,thermodynamic calculations,and machine learning 被引量:1
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作者 Jiao-Hui Yan Zi-Jing Song +6 位作者 Wei Fang Xin-Bo He ruo-bin chang Shao-Wu Huang Jia-Xin Huang Hao-Yang Yu Fu-Xing Yin 《Tungsten》 EI CSCD 2023年第1期169-178,共10页
A method which combines electronegativity difference,CALculation of PHAse Diagrams(CALPHAD) and machine learning has been proposed to efficiently screen the high yield strength regions in Co-Cr-Fe-Ni-Mo multi-componen... A method which combines electronegativity difference,CALculation of PHAse Diagrams(CALPHAD) and machine learning has been proposed to efficiently screen the high yield strength regions in Co-Cr-Fe-Ni-Mo multi-component phase diagram.First,the single-phase region at a certain annealing temperature is obtained by combining CALPHAD method and machine learning,to avoid the formation of brittle phases.Then high yield strength points in the single-phase region are selected by electronegativity difference.The yield strength and plastic deformation behavior of the designed Co_(14)Cr_(30)Ni_(50)Mo_(6)alloy are measured to evaluate the proposed method.The validation experiments indicate this method is effective to predict high yield strength points in the whole compositional space.Meanwhile,the interactions between the high density of shear bands and dislocations contribute to the high ductility and good work hardening ability of Co_(14)Cr_(30)Ni_(50)Mo_(6)alloy.The method is helpful and instructive to property-oriented compositional design for multi-principal element alloys. 展开更多
关键词 High entropy alloys Multi-principal element alloys Yield strength Electronegativity difference CALculation of PHAse Diagrams Machine learning
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