Effect of additives on the microstructure and tensile properties of Al–Si alloys
Bibliographic record
Abstract
The present work was undertaken with the aim of studying the microstructural changes as well as variations in the tensile properties of 413.0 alloy. The ultimate tensile strength (UTS), elastic limit (YS) and elongation to fracture (%El) resulting from the addition of alloying elements – strontium (Sr), magnesium (Mg), copper (Cu), silver (Ag), nickel (Ni), zinc (Zn), cerium (Ce) and lanthanum (La) to the base alloy, and heat treatment were measured. Furthermore, the effect of the addition of phosphorus (P) as well as heat treatment on the microstructure and properties of the base alloy 413.0 modified with Sr was studied from the point of view of the interaction between phosphorus and strontium during the solidification process. The findings revealed that the addition of Mg, Cu, Ag, Ni, Zn, and Sr cause an increase in the values of UTS and YS coupled with a decrease in the values of %El of the base alloy 413.0 following the heat treatment. The hardening effect produced by the addition of ∼0.4% Mg is more or less equal to that obtained from the addition of ∼3% Cu. Alloys modified with Sr show high tensile properties. In addition, the results demonstrate that alloys modified with Sr in which P was added possess ductility values of the order of 4–12%, which is much higher compared to the 2% obtained for the non-modified 413.0 base alloy.
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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".