Effect of Ni and Sr on the microstructure and tensile properties of the squeeze cast Al‐Si‐Cu alloy at elevated temperatures
Bibliographic record
Abstract
Abstract The influence of the transition alloying element nickel and the alkaline earth element strontium on the microstructure and tensile properties of squeeze cast Al−Si‐Cu alloy under as‐cast condition at elevated temperature of 100 °C, 200 °C and 300 °C is investigated in comparison with the conventional Al−Si‐Cu alloy (A380). Aluminum alloy A380 is alloyed and modified with 2 wt% additional nickel (Ni) and 0.02 wt% strontium (Sr). Squeeze casting is employed to cast the modified A380 under an applied pressure of 90 MPa. The results of tensile testing at the selected elevated temperatures indicate that the Ni and Sr containing A380 alloy exhibits a significantly improvement on tensile properties, specifically ultimate tensile strength and yield strength with 10 %‐30 % increases. The as‐cast microstructures of both the conventional and Ni and Sr‐containing alloys are observed by an optical microscope and further analyzed with scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The presence of Ni‐containing intermetallic phases with Ni addition, and the modified eutectic Si phase with refined morphologies due to the Sr addition should be responsible for the considerable enhancement on the as‐cast strengths of the squeeze cast Ni‐ and Sr‐containing alloy over those of the conventional A380 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".