Ultra-Fine Grain Size by Dynamic Recrystallization in Strip Rolling of Nb Microalloyed Steel
Bibliographic record
Abstract
The design of base chemistry and optimization of rolling schedule are the two important factors that influence large strain accumulation in multi-pass rolling in order to obtain ultra-fine grain size by dynamic recrystallization. A base chemistry of 0.03C-0.003N-0.08Nb-0.015Ti-1.8Mn (all in weight %) of HTP steel design was chosen in order to control the time evolution of strain induced precipitation of NbC and the strain accumulation through precipitate interaction with recovery and recrystallization at short inter-pass times characteristic of strip rolling. Experimental data on the critical strain for static and dynamic recrystallisation for HTP steel are used in a quantitative model to predict strain accumulation pass by pass and to achieve grain refinement by dynamic recrystallisation through large strain accumulation. The model is used to optimize the time-temperature-deformation schedule to prevent static recrystallization during the inter-pass times and to target ultra-fine grain size through dynamic recrystallization by large strain accumulation. The model predictions are validated by simulation of strip rolling of HTP steel on the thermo-mechanical simulator (WUMSI) to obtain a uniform ultra-fine ferrite grain size of about 1.5 micrometer diameter in final ferrite microstructure.
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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) | 0.000 | 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".