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Load partitioning between the bcc-iron matrix and NiAl-type precipitates in a ferritic alloy on multiple length scales.


ABSTRACT: An understanding of load sharing among constituent phases aids in designing mechanical properties of multiphase materials. Here we investigate load partitioning between the body-centered-cubic iron matrix and NiAl-type precipitates in a ferritic alloy during uniaxial tensile tests at 364 and 506 °C on multiple length scales by in situ neutron diffraction and crystal plasticity finite element modeling. Our findings show that the macroscopic load-transfer efficiency is not as high as that predicted by the Eshelby model; moreover, it depends on the matrix strain-hardening behavior. We explain the grain-level anisotropic load-partitioning behavior by considering the plastic anisotropy of the matrix and elastic anisotropy of precipitates. We further demonstrate that the partitioned load on NiAl-type precipitates relaxes at 506 °C, most likely through thermally-activated dislocation rearrangement on the microscopic scale. The study contributes to further understanding of load-partitioning characteristics in multiphase materials.

SUBMITTER: Sun Z 

PROVIDER: S-EPMC4793255 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Load partitioning between the bcc-iron matrix and NiAl-type precipitates in a ferritic alloy on multiple length scales.

Sun Zhiqian Z   Song Gian G   Sisneros Thomas A TA   Clausen Bjørn B   Pu Chao C   Li Lin L   Gao Yanfei Y   Liaw Peter K PK  

Scientific reports 20160316


An understanding of load sharing among constituent phases aids in designing mechanical properties of multiphase materials. Here we investigate load partitioning between the body-centered-cubic iron matrix and NiAl-type precipitates in a ferritic alloy during uniaxial tensile tests at 364 and 506 °C on multiple length scales by in situ neutron diffraction and crystal plasticity finite element modeling. Our findings show that the macroscopic load-transfer efficiency is not as high as that predicte  ...[more]

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