Design of metastable complex-concentrated alloys through composition tailoring
[Display omitted] •We proposed alloy exploration approach to develop CCAs with customized properties.•A series of CoCrFeNi-based alloy thin films were produced via solid-state alloying.•Contour maps for composition-dependent property provided a guideline for alloy design.•The ratio of Co/Ni was the...
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Veröffentlicht in: | Materials & design 2022-12, Vol.224, p.111391, Article 111391 |
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Sprache: | eng |
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•We proposed alloy exploration approach to develop CCAs with customized properties.•A series of CoCrFeNi-based alloy thin films were produced via solid-state alloying.•Contour maps for composition-dependent property provided a guideline for alloy design.•The ratio of Co/Ni was the key point to improve mechanical properties of alloys.•Newly developed MSCCAs revealed mechanical properties of good work-hardening ability.
To develop alloys with high strength and reasonable ductility, CoCrFeNi-based metastable complex-concentrated alloys were designed using composition–property contour maps. The map was constructed by exploring the phase stability and mechanical behaviors of a series of CoCrFeNi alloy thin films synthesized via solid-state alloying of multilayer thin films. The concentrations of Co and Ni were key to activate metastable deformation behaviors by reducing the stacking fault energy of alloys and improve solid-solution strengthening, as expected from the atomic-level complexity related to the electronegativity difference. By optimizing the Co and Ni concentrations based on the composition-phase/mechanical property contour maps, we activated the combined deformation behavior of mechanical twinning and phase transformation. This resulted in a Co33Cr25Fe25Ni17 metastable complex-concentrated alloy with excellent tensile properties—yield strength of 234 MPa, ultimate tensile strength of 720 MPa, and elongation to failure of 80%. The proposed approach provides a useful guideline for the design of complex-concentrated alloys with customized properties through property predictive control. |
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ISSN: | 0264-1275 1873-4197 |
DOI: | 10.1016/j.matdes.2022.111391 |