Towards an austenite decomposition model for TRIP steels
Bibliographic record
Abstract
The current status of developing a fundamental model for describing the overall austenite decomposition kinetics to ferrite and carbide‐free bainite in low carbon TRIP steels alloyed with Mn and Si is reviewed. For ferrite growth, a model is proposed where both interface and carbon diffusion‐controlled ferrite formation are considered in a mixed‐mode approach. The kinetic model is coupled with Thermocalc to obtain necessary thermodynamic information. Spherical geometry with an outer ferrite shell is assumed to capture in a simple way the topological conditions for growth. The mixed‐mode modelling philosophy has been identified to permit a rigorous incorporation of the solute drag effect of substitutional alloying elements, in particular Mn. The Purdy‐Brechet solute drag theory is adopted to characterize the interaction of Mn with the moving austenite‐ferrite interface. The challenges of quantifying the required solute drag parameters are discussed with an emphasis on a potential solute drag interaction of Mn and Si. The model is extended to non‐isothermal processing paths to account for continuous and stepped cooling occurring on the run‐out table of a hot strip mill or on a continuous annealing line. The transformation start temperature during cooling is predicted with a model combining nucleation and early growth which had previously been validated for conventional low carbon steels. The overall model is evaluated by comparing the predictions with experimental data for the ferrite growth kinetics during continuous cooling of a classical TRIP steel with mass contents of 0.19 % C, 1.49 % Mn and 1.95 % Si. Extension of the model to include bainite formation remains a challenge. Both diffusional and displacive model approaches are discussed for the formation of carbide‐free bainite. It is suggested to develop a combined nucleation and growth model which would enable to capture a potential transition from a diffusional to a displacive transformation mode with decreasing temperature.
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".