Effect of Fe Concentration and Superheating on the Microstructure and Tensile Properties of High Mg 413.0-Type Alloys: Role of Sr, Be, P, and La
Bibliographic record
Abstract
The present work was undertaken to explore the multiple alloys and process steps that have been suggested to mitigate the harmful effects of high iron content in cast Al-Si alloys. The base alloy used was ommercial 413.0 alloy containing 0.35%Mg. Iron was added at three Fe levels up to 1.8%. The addition of Sr, 1.0%Zn, 0.2%Ti were made to the alloys so prepared, which were melted and maintained at a superheat of 750 °C or 950 °C. The melts were poured in different molds that produced three solidification rates. In total, 40 castings were prepared: half of the castings were used for metallographic examinations in the as-cast condition, while the other half were set aside for tensile testing following T6 treatment. The results show that at a solidification rate of 50 °C/s, 1.8%Fe could be dissolved in the aluminum matrix regardless of other melt treatments. With regard to the other solidification rates, superheating at 950 °C, coupled with Sr addition or Sr + Be, reduces the average β-platelets length by 80% (0.8 °C/s) or 95% (8 °C/s). The addition of P causes a marked drop in the alloy tensile strength due to the precipitation of primary Si, Al2Si2Sr, and β-AlFeSi hard-phase particles. Therefore, reducing the iron content in the castings may be considered a major objective to be recommended for developing alloys with higher strength and optimum quality values. More than 1000 tensile bars were tested in this study.
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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".