Full field modeling of austenite grain growth using the level set method
Bibliographic record
Abstract
Microstructural modeling plays a crucial role in reducing experimental workload and accelerating the development of advanced materials. Among the various modeling approaches, full-field models have gained significant attention due to their ability to more accurately represent metallurgical phenomena across a broader range of conditions. This study focuses on the investigation of pure grain growth in polycrystalline material with an initial lognormal grain size distribution. The DIGIMU® software, which employs the level-set method, is utilized to simulate grain growth. To calibrate and validate the model, heat treatment experiments were conducted at temperatures of 1150°C, 1175°C, 1200°C, and 1260°C, with holding times ranging from 5 to 25 minutes. The results demonstrate that the level-set method provides an accurate prediction of grain size evolution, showing good agreement with experimental measurements, with a difference between experimental and simulated results ranging from 1.4% to 4.9%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".