Influence of transition elements (V, Zr and Mo) and cooling rate on the precipitation of dispersoids in Al-7Si-0.6Cu-0.35Mg foundry alloy
Bibliographic record
Abstract
In the present work, individual/combined additions of transition elements (V, Zr and Mo) were introduced into Al-7Si-0.6Cu-0.35Mg foundry alloy at different cooling rates to study their influence on the precipitation behaviour of dispersoids. Results showed that both individual and combined additions of V, Zr, Mo lead to the formation of dispersoids but with different composition, morphology and number density during solution treatment. The addition of V produces the precipitation of both (Al,Si)3M dispersoids and α-dispersoids, while the Zr addition promotes (Al,Si)3M type dispersoids but inhibits the formation of α-Al(Mn,Fe)Si dispersoids. The addition of Mo effectively promotes α-Al(Mn,Mo,Fe)Si dispersoids and significantly reduces the dispersoid size and increase the number density of dispersoids. The combined addition of V, Zr and Mo produces the largest number of finer dispersoids among all five alloys studied, but the most dispersoids are (Al,Si)3M. The (Al,Si)3M dispersoids and α-dispersoids have the rod-like and block-like morphologies, respectively. High cooling rate can generally refine the dispersoids and increase their number density, while it also increases the proportion of (Al,Si)3M dispersoids.
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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".