Microstructure Evolution in Fine-Grained Microalloyed Steels
Bibliographic record
Abstract
There is an increasing emphasis to develop novel hot-rolled high strength steels with fine and ultra fine grain sizes for structural and other applications. Traditionally the concept of microalloying has been employed to refine microstructures thereby obtaining increased strength levels. For example, employing an alloying strategy with Nb, Ti and Mo is promising to attain yield strength levels of 700MPa and beyond. In the present study, the transformation behaviour is investigated for a HSLA steel containing 0.05wt%C-1.65wt%Mn-0.20wt%Mo-0.07wt%Nb- 0.02wt%Ti. The ferrite formation from work-hardened austenite has been studied for simulated run-out table cooling conditions employing a Gleeble 3500 thermomechanical simulator equipped with a dilatometer. The effects of cooling rate and initial austenite microstructure, i.e. austenite grain size and degree of work hardening, on the austenite decomposition kinetics and resulting ferrite grain size have been quantified. Based on the experimental results, a phenomenological transformation and ferrite grain size model is proposed for run-out table cooling conditions. The transformation model includes submodels for transformation start and ferrite growth. The latter is described using a Johnson-Mehl-Avrami-Kolmogorov approach. The degree of work hardening is incorporated by introducing an effective austenite grain size as a function of the strain applied under no-recrystallization condition. The ferrite grain size can be predicted as a function of the transformation start temperature. Increasing both cooling rate and amount of work hardening can optimize ferrite grain refinement. In the present steel, ferrite grain sizes of as low as 2µm have been obtained in this way. The results observed for the present steel are compared to the transformation behaviour in previously studied Nb-Ti HSLA steels of similar strength levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".