Influence of Si on the Elevated-Temperature Mechanical and Creep Properties of Al–Cu 224 Cast Alloys during Thermal Exposure
Bibliographic record
Abstract
The influence of Si content (0.1-0.8 wt.%) on the development of precipitation microstructures and the resultant mechanical and creep properties during thermal exposure, up to 1000 h at 300 °C, in Al-Cu 224 cast alloys, was systematically investigated. The room and elevated temperature yield strength (YS) increased with increasing Si content under the T7 condition, which was attributed to the fact that the Si promoted the precipitation of fine θ'. However, Si increased the coarsening of θ' during thermal exposure at 300 °C, and the alloys with low Si exhibited a higher YS and creep resistance at elevated temperatures than high Si alloys. The mechanical strength and creep resistance were mainly controlled by the precipitation strengthening of the predominant θ' phase. Because of the high mechanical strength and creep resistance of the 0.1Si alloy during long-term thermal exposure, the Si level in Al-Cu alloys should be maintained at a low level of 0.1 wt.% for high-temperature applications. The strengthening mechanisms were quantitatively analyzed, based on the characteristics of the precipitate. The predicted YS values under different exposure conditions agreed well with the experimentally measured values.
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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".