Phase Field Modelling of Austenite Formation in Low Carbon Steels
Bibliographic record
Abstract
There is renewed interest in the investigation of austenite formation due to the development and increased use of advanced high strength steels for automotive applications. Intercritical annealing is an essential processing step for cold rolled and coated steel products with multi-phase microstructures. During intercritical annealing the initial ferrite-pearlite microstructure transforms partially to austenite. Models for the austenite formation are critical to predict the austenite fraction as a function of the thermal cycle thereby facilitating the design and control of robust processing paths. Modelling the austenite formation is challenging because of the morphological complexity of this transformation. Phase field models are a powerful tool to describe the evolution of microstructures with complex morphologies, e.g. formation of finger-type features during austenite formation. The present paper gives an overview of model approaches for the austenite formation. Phase field simulations are presented for two scenarios: (i) austenite formation from a fully pearlitic structure with a lamellar arrangement of carbide aggregates and (ii) austenite formation from ferrite-pearlite microstructures. Simulation results are compared with experimental observations for pearlitic steels. The challenges are delineated for the development of austenite formation models with predictive capabilities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".