Elevated-temperature performances of Al-Si-Cu casting alloys for cylinder head applications
Bibliographic record
Abstract
In this study, high-temperature properties of two newly developed Al-Si-Cu alloys (2Cu and 3.5Cu alloys) were investigated and compared to the commercial-grade A356 + 0.5Cu alloy (R alloy). 3.5Cu alloy exhibited the highest strength, outperforming R alloy by over 50 MPa at room temperature and by more than 20 MPa at elevated temperatures in ultimate tensile strength. However, R alloy demonstrated three to four times higher elongation than 3.5Cu alloy at room temperature, though this difference diminished at high temperatures. The minimum creep rate of 3.5Cu alloy was 2.4 times lower than that of 2Cu alloy and 14.5 times lower than that of R alloy, showing the superior creep resistance. Under low cycle fatigue loadings, the fatigue lifetimes of R and 2Cu alloys were similar, and slightly longer than that of 3.5Cu alloy. Conversely, in the high cycle fatigue regime, 3.5Cu alloy exhibited the highest fatigue resistance, followed by 2Cu and R alloys. The superior high-temperature performances of 3.5Cu alloy were attributed to the enhanced thermal stability of θ' precipitates compared to β' precipitates in R alloy, as confirmed during long-term thermal exposures at 250 and 300 °C. These findings suggest that 3.5Cu alloy is a promising candidate to replace the traditional A356 + 0.5Cu alloy for cylinder head applications. • High-temperature performances of two newly developed Al-Si-Cu alloys were investigated. • Tensile and creep properties, low and high cycle fatigue properties at elevated temperatures were studied in details. • Long-term thermal stability of strengthening precipitates at 300 °C was characterized. • Al-9Si-3.5Cu alloy exhibited the best comprehensive properties for cylinder head applications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".