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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".