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
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
基金Projects(51701061,51705129) supported by the National Natural Science Foundation of ChinaProject(17391001D) supported by the Department of Science and Technology of Hebei Province,ChinaProject(2017-Z02) supported by the State Key Lab of Advanced Metals and Materials,China
文摘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.
基金supported by the National Natural Science Foundation of China (Grant No.51701061)the Natural Science Foundation of Hebei Province (Grant Nos.E2019202059, E2020202124)the foundation strengthening program (Grant No. 2019-JCJQ-142)。
文摘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.