Phase Formation and Microstructure Evolution of Al-5Si-0.8Mg Alloys with Different Mn Concentrations
Bibliographic record
Abstract
Mg-containing high-Si aluminum alloys are heat-treatable alloys that are widely used in industry. Substantial attention has been paid to increasing the performance of such alloys by adding a small amount of Mn, which is an effective and common alloying element in aluminum alloys. In the present work, the solidification process of Mn-free Al-5Si-0.8Mg alloy and Al-5Si-0.8Mg-(0.45–1.97)Mn alloys are analyzed by the experimental results combined with thermodynamic calculation. The results showed that α-Al, Si, Mg2Si and π (Al8Mg3FeSi6) are predominant phases in the Mn-free Al-5Si-0.8Mg alloy while the π (Al8Mg3FeSi6) phase are transformed to α-Al(FeMn)Si phase with the addition of 0.45% Mn. With increasing Mn addition to 0.72%, the L→α-Al was replaced by L→α-Al(FeMn)Si and a primary α-Al(FeMn)Si phase appeared. Further increasing the Mn to 1.97%, the solidification reactions remained unchanged. However, the size and number of the primary α-Al(FeMn)Si phase gradually increased, while the divorced eutectic phenomenon of quaternary eutectic structure gradually weakened. Meanwhile, the Mg2Si phase in the quaternary eutectic structure gradually transformed from blocky to fine eutectic lamellar, and the quaternary eutectic structure was significantly refined. Primary blocky α-Al(FeMn)Si began to form when the Mn content was higher than 0.75%.
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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".