General Method of Phase Transformation Modeling in Advanced High Strength Steels
Bibliographic record
Abstract
In the present paper, a new modeling approach is proposed for the austenite to ferrite and bainite transformation kinetics in transformation induced plasticity (TRIP) and complex phase (CP) steels. Based on experimental data obtained by dilatometry during continuous cooling, Rios' method has been successfully applied assuming additivity to calculate the parameters for the Johnson–Mehl–Avrami–Kolmogorov (JMAK) model, i.e. exponent n and the rate parameter k. Limitations of the Rios' method have been identified when k is a function of both temperature T and fraction transformed X. For these cases that are in particular relevant for the bainite transformation, a new modeling method has been developed to investigate the exact relationship of k with T and X. The new method has been used to describe the transformation kinetics in a TRIP and a CP steel. Good agreement has been obtained between the calculated and measured transformation data. The proposed new modeling method provides a general modeling approach that promises to be useful in predicting the complex phase transformation kinetics during industrial processing of advanced high strength steels (AHSS).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".