Overview of β-Al₅FeSi phase in Al-Si alloys
Bibliographic record
Abstract
In aluminum alloys one of the most pervasive and important impurity elements is iron, stemming from the impurities in bauxite ores and the contamination of ferrous metals such as melting tools. Since iron has a very low solid solubility in aluminum (max. 0.05%), almost all iron in aluminum alloys is present in the form of second intermetallic phases. One of the most common Fe-rich intermetallics that form in cast and wrought aluminum alloys upon solidification is the β-Al₅FeSi phase. This phase has long been thought to be brittle and responsible for the inferior mechanical properties (in particular ductility) of aluminum cast alloys. The commonly accepted method to ameliorate the harmful influence of iron is the addition of one or more corrective elements. Such additions generally convert the β-Fe platelets into α-Fe dendrites. Various studies have been carried out by researchers on the modification of β-Al₅FeSi intermetallics in aluminum alloys using Mn, Cr, Co, Mg, Sr, Li and Be. The relative effectiveness of these elements is collected and compared in the present review. The mechanisms for the action of the chemical modifiers are critically reviewed particularly in the light of the modern theory of their nucleation on oxide films present in aluminum melts, probably in large populations. The new insights into the Fe-rich phase in aluminum alloys will aid in better understanding the role of iron in aluminum alloys.
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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 | 0.002 | 0.002 |
| 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) | 0.006 | 0.004 |
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".