Simulation insights into strengthening mechanisms of short-range order and Ni segregated twin boundaries in CoCrNi alloys
Source abstract
Abstract
The interaction between chemical short range ordering (SRO) and twins plays a key role in controlling the mechanical properties of medium entropy alloys. In this study, mixed Monte Carlo/molecular dynamics (MC/MD) simulation method was used to study the synergistic strengthening mechanism of SRO and nano-twin (NT) in CoCrNi alloy with different twin spacing. The results show that the segregation of Ni atoms to the twin boundary (TB) during annealing is beneficial in terms of energy, which fundamentally changes the local atomic stress state of the interface and makes it change from compressive stress to tensile stress. This chemical ordering transforms the twin boundary from a “soft” channel that promotes dislocation transfer to a “hard” barrier that effectively pins dislocation movement. The mechanical property analysis shows that the introduction of SRO significantly improves the yield stress of the alloy, and its strengthening effect is far more than the gain brought by simply reducing the twin spacing, showing the dominant position of SRO in the strength design. From the microscopic mechanism, the coupling of SRO and NT not only induces the formation of dense entangled dislocation network and enhances the dislocation storage capacity, but also promotes the transformation of the material from local plastic deformation to cross lamellar collaborative deformation mode, which effectively inhibits the stress concentration and delays the failure process. This study reveals the physical nature of interface strengthening from the atomic scale, and provides a theoretical basis for the design of a new generation of high-performance nano-twin materials.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Shape Memory Alloy Transformations · High Temperature Alloys and Creep · Magnetic properties of thin films
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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