Strengthening mechanisms in vanadium-microalloyed medium-Mn steels
Bibliographic record
Abstract
In this work, the impact of adding vanadium, ranging from 0 to 2 wt%, on the microstructure and mechanical properties of as-cast medium manganese steel was explored. A dual phase microstructure consisting of martensite and retained austenite was observed in the 0 V, 0.05 V and 0.8 V conditions. The 2 V condition contained retained austenite, lath martensite, and δ-ferrite bands. The retained austenite fraction and prior austenite grain size initially increased at lean vanadium concentrations but significantly dropped at the highest vanadium concentration. The element distribution in the constituent phases was investigated in detail. Mn, C and Si partitioning to austenite was observed in the 0 V, 0.05 V and 0.8 V conditions. V and C segregation to the grain boundaries and significant grain refinement were evident in the 2 V condition. The findings also revealed that increasing the vanadium content led to an increase in the hardness of the steel. This assessment was validated by tensile testing, which showed an improvement in yield and tensile strength of the steel with increasing vanadium content, and were supported by reconstruction of the parent austenite grains employing martensitic structures. Finally, the influence of different strengthening mechanisms on the yield strength of as-cast, microalloyed medium-manganese steels was also discussed in terms of simulated stacking fault energy, as well as the quantitative contributions from solid solution, grain boundary, and precipitation strengthening mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".