Nano‐yttrium‐containing precipitates of T6 heat‐treated A356.2 alloy when trace yttrium (Y less than 0.100 wt%) added
Bibliographic record
Abstract
Abstract To investigate the effect of yttrium (Y) on microstructure refinement and mechanical properties of aluminum alloy A356.2, the different trace contents of Y (0 wt%, 0.025 wt%, 0.050 wt%, 0.075 wt%, or 0.100 wt%) were introduced into the liquid alloy. The alloys were fabricated in a preheated permanent mold, and subsequently treated by a T6 heat treatment. The results of tensile testing indicate that the yield strength (YS), the ultimate tensile strength (UTS) and the elongation (El) of the A356.2 alloy are improved by the Y additions. The YS dependence on grain size for the test alloys follows the Hall–Petch equation, which gives with a correlation of R 2 = 0.83. As 0.050 wt% Y is added, the optimum values of the YS, UTS and El are achieved after T6 heat treatment. The secondary phases were identified by X‐ray diffraction (XRD) which mainly consisted as Si, Mg 2 Si and Al 3 Y. The scanning electron microscope (SEM) and energy‐dispersive spectrometer (EDS) analyses reveal the presence of the nano‐sized Al 3 Y particles on the surface of the Si phase. The A356.2 alloy with the Y addition is strengthened by the dendritic refinement, and the presence of the micron‐ and nano‐sized Al 3 Y precipitates.
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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".