Austenite Nucleation and Growth as a Function of Starting Microstructure for a Fe–0.15C–5.56Mn–1.1Si–1.89Al Medium‐Mn Steel
Bibliographic record
Abstract
The effects of starting microstructure and intercritical annealing temperature on the phase‐transformation kinetics and microstructural evolution of a prototype Fe–0.15C–5.56Mn–1.1Si–1.89Al medium‐Mn third‐generation advanced high‐strength steel are examined. The starting microstructures comprise 1) an as‐received cold‐rolled (CR) microstructure containing a significant fraction of ferrite and tempered martensite and 2) an austenitized and quenched martensite–ferrite (MF) microstructure. Based on the microstructural observations, two different scenarios for austenite formation during intercritical annealing are proposed. For the CR starting microstructure, austenite can nucleate at ferrite/cementite interfaces, and at ferrite grain boundaries. In the case of the MF starting microstructure, which contains thin films of interlath retained austenite (RA), austenite forms on the martensite lath boundaries or grows directly from the existing interlath RA. The studies are interpreted using the DICTRA module of Thermo‐Calc. At 665 °C, the austenite reversion kinetics of the MF samples are faster than the transformation kinetics in the CR structure. At higher temperatures where most of the cementite has dissolved, the austenite fraction in both starting microstructures is very similar.
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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".