Effect of V and Zr microalloying, and heat treatment on microstructure and mechanical properties of secondary Al-7Si-3Cu-0.3Mg alloy
Bibliographic record
Abstract
Abstract The Al–Si–Cu alloys, which are widely used in automotive powertrains, exhibit limited high-temperature strength properties; the high diffusivity of the main strengthening elements Cu and Mg in α-Al at temperatures between 200 and 300 °C is a dominating factor in alloy softening. In this study, effects of slow-diffusing elements (Zr and V) and heat treatment on the microstructure and mechanical properties of secondary Al-7Si-3Cu-0.3Mg alloy were investigated. Majorities of both Zr and V were retained inside the α-Al matrix during solidification. T6 heat treatment induced the solid-state precipitation of multiple, nano-sized particles in α-Al grain interiors. Unlike Cu/Mg-rich strengthening precipitates that form during aging, the Zr/V-rich precipitates formed during solution heat treatment, which indicates high potential for high-temperature strengthening in Al–Si alloys via transition metal addition. Other transition metals, such as Mn, Fe, Cr and Ti, which were present as impurities in the base alloy significantly promoted the formation of nano-sized Zr/V-rich precipitates inside α-Al grains. These thermally more stable precipitates were credited for the enhanced high-temperature strength properties of Al-7Si-3Cu-0.3Mg alloy by ∼20 %.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".