Enhancing strength and ductility in a near medium Mn austenitic steel via multiple deformation mechanisms through nanoprecipitation
Source abstract
Abstract
High-Mn austenitic steels, usually deformed via the TWIP (twinning induced plasticity) mechanism, suffer high cost and other technical issues (e.g., hot-dip galvanizing, welding, etc.) from the high Mn content. Nevertheless, decreasing the Mn content would result in a shift of deformation mechanisms from TWIP to TRIP (transformation induced plasticity), usually causing quasi-cleavage brittle fracture. Herein, we report that by massive nanoprecipitation of coherent disordered particles, the grain sizes of a near medium Mn austenitic steel are successfully refined to 0.9 ± 0.4 μ m, leading to a significant stacking fault energy increment of 6.4–7.9 mJ m –2 and accordingly, the transition of deformation mechanism from TRIP to multiple deformation mechanisms, namely, stacking faults, dislocation slip, nanotwinning and ԑ-martensite transformation. Moreover, the high-density of coherent nanoprecipitates effectively refines ԑ martensite and nanotwins from 20–500 nm and 10–50 nm to a few atomic columns and 1–15 nm, respectively. More importantly, these deformation mechanisms were sequentially activated at different deformation stages, resulting in a consistently high work hardening rate. Based on the synergistic refinement effects of grains, twins and martensite and the transition of deformation mechanism, a near medium Mn ultrafine-grained (UFG) austenitic steel with 15 wt.% Mn is developed, which shows a unique combination of high tensile strength (1210 ± 19 MPa) and large elongation (72 ± 6 %). These findings provide a new route to addressing the trade-off between the Mn content and mechanical performance of high-Mn austenitic steels which could facilitate their widespread applications.
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Research context
OpenAlex topics: Microstructure and Mechanical Properties of Steels · Microstructure and mechanical properties · Metal and Thin Film Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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