Effect of Intercritical Annealing Parameters and Starting Microstructure on the Microstructural Evolution and Mechanical Properties of a Medium-Mn Third Generation Advanced High Strength Steel
Bibliographic record
Abstract
A prototype medium-Mn TRIP steel (0.2 C–6 Mn–1.7 Si–0.4 Al–0.5 Cr (wt %)) with a cold-rolled tempered martensite (CR) and martensitic (M) starting microstructures was subjected to continuous galvanizing line (CGL) compatible heat treatments. It was found that the M starting microstructures achieved greater than 0.30 volume fraction of retained austenite and target 3G properties (UTS × TE ≥ 24,000 MPa%) using an intercritical annealing temperature (IAT) of 675 °C with an IA holding time of 60–360 s, whereas the CR microstructure required an IAT of 710 °C and annealing times of 360 s or greater to achieve comparable fractions of retained austenite and target 3G properties. This was attributed to the rapid austenite reversion kinetics for the M starting microstructures and rapid C partitioning from the C supersaturated martensite, providing chemical and mechanical stability to the retained austenite, thereby allowing for a gradual deformation-induced transformation of retained austenite to martensite—the TRIP effect—and the formation of nano-scale planar faults in the retained austenite (TWIP effect), such that a high work-hardening rate was maintained to elongation of greater than 0.20. Overall, it was concluded that the prototype steel with the M starting microstructure is a promising candidate for CGL processing for 3G AHSS properties.
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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 | 0.000 | 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".