Critical Assessment of Bainite Models for Advanced High Strength Steels
Bibliographic record
Abstract
Bainite is an essential constituent in the microstructure of many advanced high strength steels, e.g. ferrite-bainite dual-phase, transformation induced-plasticity (TRIP) and complex phase (CP) steels. A complex thermo-mechanical processing is employed in industry such that following ferrite formation a desired fraction of bainite can be obtained during austenite decomposition. In order to evaluate robust processing routes it would be very useful to have a bainite transformation model with predictive capabilities. In this work a transformation start criterion for bainite is proposed by defining a critical driving pressure concept. Subsequent bainite formation kinetics from a mixture of ferrite-austenite is described using phenomenological modelling methodologies. In particular, the predictive capabilities of two approaches will be critically discussed, i.e. (i) the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model in conjunction with Rios treatment of the additivity rule and (ii) a nucleation-growth based model that describes simultaneous formation of bainitic ferrite and carbides. Using experimental transformation data for TRIP and CP steels, status and limitations of these models will be delineated.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".