Precipitation in Al–Mg–Si Alloys: Modeling
Bibliographic record
Abstract
Different approaches for modeling of precipitation in Al–Mg–Si alloys are reviewed. First of all, the importance of a precipitation modeling and its interrelations with other components in a process model are explained. Then the empirical, statistical, and physically based modeling, with each being a different modeling approach, are introduced. The Kampmann–Wagner numerical (KWN) model, which is a physically based finite difference method, is explained as a model that is able to capture simultaneous nucleation, growth, and coarsening reactions. The growth kinetics in the KWN model is based on the assumption of local equilibrium hypothesis, inferring that there is an immediate thermodynamic equilibrium as soon as two phases are in contact. This assumption implies the diffusion-controlled nature of the transformation. The other extreme approach is the assumption of interface-controlled growth, where the interface reaction (atom transport across the interface) controls the kinetics. In reality, neither of these scenarios can be absolutely true. A modified version of KWN model such that the growth can be treated with a mixed-mode nature (neither pure diffusion-controlled nor pure interface-controlled) is introduced. How the geometry of precipitates can be incorporated into the precipitation model is also explained.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".