Removal of iron from recycled aluminium alloys
Bibliographic record
Abstract
Iron is considered as an impurity in aluminium alloys due to its detrimental impact on strength and ductility. Much work has been carried out to reduce the iron levels in recycled aluminium alloys. In this investigation, the influence of Mn additions for reducing the Fe content in an Al–Si alloy has been studied. The experimental results and thermodynamic calculations revealed that a reduction of up to 62%Fe content can be achieved. The major mechanism of iron removal is the formation of the primary α-AlFeMnSi particles when held at lower temperatures. The characteristics and compositions of these particles, which settle on the bottom of the alloy melts, were confirmed using optical microscopy and X-ray diffraction techniques.Dans les alliages d’aluminium, le fer est considéré comme une impureté en raison de son effet négatif sur la résistance et la ductilité. Des travaux ont été menés pour réduire la quantité de fer dans les alliages d’aluminium recyclé. L’étude menée porte sur l’influence des ajouts de manganèse visant à réduire la teneur en fer dans un alliage Al–Si. Les résultats expérimentaux et les calculs thermodynamiques ont révélé qu’il est possible d’atteindre une réduction de 62% de la teneur en Fe. Le principal mécanisme d’extraction du fer est la formation de particules d’alliage α-AlFeMnSi lorsque des températures plus basses sont maintenues. Les caractéristiques et la composition de ces particules, qui se déposent au bas des fontes d’alliage, ont été confirmées à l’aide de microscopie optique et de techniques de diffraction des rayons X.
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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".