Anisotropic mechanical properties and deformation mechanisms of nanotwinned Ni and Ni alloys with extremely fine twin boundary spacings
Source abstract
Abstract
Introducing nanotwins with extremely fine twin boundary spacing (TBSs ≤ 10 nm) in metals with high stacking fault energy (SFE) has posed a longstanding challenge, hindering an in-depth understanding of their strengthening mechanisms. Here, we synthesized a series of columnar-grained nanotwinned structures with extremely fine TBSs in Ni and Ni alloys with high SFEs, and systematically investigated their mechanical responses under various loading directions relative to the coherent twin boundaries (CTBs). Under the loading directions of 90° and 0°, contrary to the softening effect observed in nanotwinned metals with equiaxed grains, continuous strengthening was found to extend to the finest TBS of 1.9 nm in the columnar-grained nanotwinned Ni and Ni alloy. This resulted in an ultra-high strength of 4.46 and 3.80 GPa, respectively. Combined experimental and simulational studies were employed to investigate the mechanism for continuous strengthening. As the TBS decreased to a critical value (3–5 nm), the strengthening mechanism was revealed to transform from CTB obstructing dislocations to the dislocation starvation-dominated mechanism, which led to a shift in deformation behavior from homogeneous to localized plasticity. Distinct from the detwinning-induced softening effect, the dislocation-starvation mechanism resulted in continuous strengthening, with the yield strength following a power-law function concerning TBS. Additionally, our results illustrate that nanotwins deformed via significant detwinning at the loading direction of 45°, leading to strengthening that primarily depends mainly on the column width rather than TBS.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Microstructure and mechanical properties · Aluminum Alloys Composites Properties · Metal and Thin Film Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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