Analysis of Mn Effect on Recrystallization Kinetics in High Nb Steels
Bibliographic record
Abstract
A physically based model is used to analyze quantitatively, the relative contributions of solute Nb and strain-induced NbC precipitation to the retardation of static recrystallization during the interpass time. The model explicitly takes into account the time evolution of strain-induced precipitation and its interaction with recovery and recrystallization. It is thus possible to quantitatively model the recrystallization kinetics taking into account: i) the effect of solute drag on the boundary mobility, ii) the effect of particle pinning (Zener drag) on the driving force for boundary motion, and iii) the effect of dislocation pinning by strain-induced precipitates, on the recovery kinetics and the nucleation of recrystallization. The analysis shows that there is an optimum partitioning of Nb between matrix solute and strain induced precipitates. This optimum partitioning maximizes particle pinning while ensuring an adequate solute drag effect to prevent the boundary from breaking away from solute atmosphere. The optimum partitioning of Nb between the matrix and the precipitates is shown to depend upon the temperature window of rolling, pass reduction and interpass time. The effect of delaying the kinetics of strain-induced precipitation of NbC through large Mn addition is shown to be an advantage for ensuring adequate solute drag in the low temperature, large pass deformation schedule used in near-net shape processing of thin slab or thick strip castings.
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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".