Modelling the Precipitation of Al<sub>3</sub>X Dispersoids in Aluminium Alloys and their Effect on Recrystallization
Bibliographic record
Abstract
A combined model is presented that predicts the non-uniform distribution of Al3X dispersoid particles in commercial aluminium alloys containing zirconium and scandium and uses these predictions as inputs to a simple recrystallization model. The recrystallization model relies on knowledge of the stored energy in the sub-structure after deformation and this has been measured using electron backscattered diffraction (EBSD) techniques. The recrystallization model is based on the concept that partial recrystallization results from the non-uniform distribution of dispersoid particles due to their precipitation from a segregated cast structure. The model has been used to devise an improved homogenization treatment for AA7050, which uses an isothermal hold during heat up to maximize dispersoid nucleation. It has also been applied to predict the effect of scandium additions on recrystallization, investigate the factors that control the through thickness variation in recrystallized fraction, and interpret the results of experiments where the effect of strain rate have been studied.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".