Enhancement of Elevated‐Temperature Strength in Al–Cu–Ce–Mn–Zr Cast Aluminum Alloy Through Ni Alloying
Bibliographic record
Abstract
Herein, to enhance the elevated‐temperature strength of heat‐resistant aluminum alloys to satisfy application requirements, the effect of Ni content (0.5, 1.0, 2.0, 4.0 wt%) on the microstructures and tensile properties of Al–8.4Cu–2.3Ce–1.0Mn–0.2Zr alloy is investigated. The metallographic analysis techniques are used to quantitatively examine the microstructural changes. The skeleton‐like Al7Cu4Ni phase is formed after the addition of Ni and its morphology is gradually transformed into a coarse reticular‐like shape with Ni content increasing. However, the thermally stable Al8CeCu4 and Al24MnCu8Ce3 phases disappear when Ni content exceeds 1.0%. Al–8.4Cu–2.3Ce–1.0Mn–0.2Zr–0.5Ni alloy exhibits the optimal elevated‐temperature tensile performance at 400 °C, and its ultimate tensile strength, yield strength, and elongation at 400 °C reach 105, 85 MPa, and 16.5%, respectively. The optimal tensile performance is attributed to synergistic enhancing action of the thermostable Al8CeCu4, Al24MnCu8Ce3, Al16Cu4Mn2Ce, and Al7Cu4Ni phases at the grain boundaries and the nano‐sized Al20Cu2Mn3 and Al2Cu precipitates inside the grains. The typical brittle fracture is dominating in the five alloys with different Ni contents at ambient temperature, but the fracture mode at 400 °C is changed from ductile fracture to ductile and brittle mixed fracture with the increase of Ni.
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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) | 0.000 | 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 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".