Mechanical Performance and Precipitation Behavior in Al-Si-Cu-Mg Cast Alloys: Effect of Prolonged Thermal Exposure
Bibliographic record
Abstract
Al-Si-Cu-Mg cast (354) alloys are used in the automotive sector owing to their remarkable properties which are achievable after applying appropriate thermal treatments. Zirconium, Nickel, and Manganese were added to this category of Al-alloys to preserve good mechanical properties while being exposed to elevated temperatures for long times. The ultimate and yield strength values obtained at room temperature for the stabilized (thermally-exposed) T5-treated condition were comparable to those of the stabilized T6-treated condition, whereas the same properties for T5-treated alloys were higher than those of T6-treated ones for elevated-temperature tensile testing. Interestingly, the results showed that the addition of 0.75 wt.% Mn was competitive with the addition of 2 and 4 wt.% Ni with respect to the elevated-temperature and ambient temperature strength values, respectively. In addition, the Mn-containing alloy M3S exhibited improved ductility values at ambient temperature and at 250 °C, compared to the Ni-containing alloys. Examination of the fracture surface of tested samples revealed the advantageous role of sludge particles in enhancing the performance of Mn-containing alloys through their resistance to the propagation of cracks that developed in many intermetallic phases. This finding is considered to be economically significant in view of the lower price of manganese compared to that of nickel.
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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".