Evolution of Microstructure and Elevated-Temperature Properties during Thermal Exposure with Transition Elements (V, Zr and Mo) in Al–Si 356 Type Cast Alloys
Bibliographic record
Abstract
Precipitation-hardened Al–Si 356 cast alloys are widely used in fabricating the automotive engine parts due to their excellent castability and strength/weight ratio. However, with the increasing demand of the engine service temperature at 250–350°C, the mechanical properties of Al–Si 356 alloys greatly deteriorate owing to the rapid coarsening of precipitates at elevated temperatures. In this study, individual and combined transition elements (V, Zr and Mo) were introduced into Al–Si 356 type cast alloys to form thermally stable dispersoids, and their influences on the strength and creep resistance at 300°C was investigated. During thermal exposure (300°C/100 h) after T7 treatment, the nano-scale β′ and Q′ precipitated during aging were transformed to the equilibrium coarse β and Q phases, loosing their contribution to mechanical strength. However, different types of dispersoids formed during the solution treatment were stable during the thermal exposure, resulting in the different but promising contribution to the elevated-temperature properties. The combined additions of V, Zr and Mo showed the highest mechanical and second highest creep properties at 300°C, which possess 22% and 100% improvement on the strength and creep resistance compared to the 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".