Kinetics of Austenite Formation in a Medium-Carbon, Low-Alloy Steel with an Initial Martensite Microstructure: Influence of Prior Austenite Grain Size
Bibliographic record
Abstract
The impact of prior austenite grain size (PAGS) on the kinetics of austenite formation with an initial martensite microstructure was investigated in a medium-carbon, low-alloy steel. Two distinct PAGS of 117 and 330 μm, representing the range of grain sizes encountered in industries, were considered. In this analysis, high-resolution dilatometry was used to study the formation of austenite during continuous heating experiments. The analysis of the dilatometry results revealed that grain refinement accelerated the rate of austenite formation without impacting its austenite formation temperature. Intermittent quenching tests were conducted to elucidate the nucleation and growth mechanisms of austenite formation using a combination of optical, scanning electron microscopy (SEM), and electron backscatter diffraction (EBSD). The differences in austenite formation kinetics as a function of prior austenite grain size were quantified and modeled in the framework of diffusion-controlled nucleation and growth theories using the genetic algorithm optimization.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".