Mechanistic insights into martensitic transformation and DSA coupling in medium-Mn steel: A multiscale experimental and modeling approach
Source abstract
Abstract
This study provides a mechanistic framework to elucidate the interaction between deformation-induced martensitic transformation (DIMT) kinetics and dynamic strain aging (DSA) manifestations through elevated-temperature tensile testing with digital image correlation, multiscale microstructural characterization, and full-field crystal plasticity (CP) simulations. A pronounced temperature dependence is identified: both DIMT and DSA are simultaneously activated at intermediate temperatures and suppressed at higher temperatures, resulting in a distinct transition in macroscopic flow behavior. The ε martensitic transformation, controlled by the motion of leading partial dislocations, accommodates strain without dislocation multiplication, thereby attenuating DSA. In contrast, α ′ martensite exhibits a mechanically size-dependent role, i.e. , acting as a hard, non-deformable phase when submicron, but transitioning into a plastically active constituent when coarsened. CP simulations quantitatively capture this transition and determine a critical grain size threshold of ∼0.37 μm, beyond which dislocation transmission and strain accommodation occur within α ′ martensite. Additionally, atom probe tomography reveals that DIMT induces carbon supersaturation and directional solute flux toward γ / α ′ interfaces. This transformation-induced redistribution accelerates solute-dislocation interactions and reactivates DSA, independent of Mn content. These findings establish a transformation-controlled mechanism for strain aging, integrating microstructural evolution with thermomechanical instability in TRIP-assisted steels.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Microstructure and Mechanical Properties of Steels · Advanced Materials Characterization Techniques · High Temperature Alloys and Creep
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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