Effect of Aluminum Content on the Dynamic Recrystallization of Fe18MnxAl0.74C Steels During Hot-Forging Treatments
Bibliographic record
Abstract
Abstract In the present work, the dynamic recrystallization and microstructural evolution of the family of advanced high-strength steels Fe18MnxAl0.74C are studied, varying the aluminum content in 0, 3, 6, and 9 wt pct subjected to hot-forging treatments through three consecutive heating-deformation cycles. For characterization, X-ray diffraction (XRD), Mössbauer absorption spectroscopy (MAS), and electron backscattering diffraction (EBSD) were used. It was determined that for the steels under study, dynamic recrystallization occurs due to strain-induced boundary migration (SIBM) and is strongly influenced by the aluminum content of the alloy and its stacking failure energy (SFE), increasing that the aluminum content will generate greater nucleation sites, favoring the refinement of grains in the material and achieving a crystalline structure of random crystallographic orientation. The results are discussed throughout the article, allowing us to determine potential processing routes for advanced high-strength steels with predominantly plastic deformation mechanisms such as transformation-induced plasticity (TRIP), twinning-induced plasticity (TWIP), and microband-induced plasticity (MBIP).
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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".