Effect of Zr and Ti Addition and Aging Treatment on the Microstructure and Tensile Properties of Al-2%Cu-Based Alloys
Bibliographic record
Abstract
The present work investigated the effect of aging treatment on the microstructure and tensile properties of an Al-2%Cu base alloy containing various additions of Zr and other alloying elements. Aging was carried out at temperatures of 180-300 °C for different aging times at each temperature. The tensile properties indicated that Zr additions improved the strength of the base alloy, especially at high Zr levels at 180 °C. At the 220 °C aging temperature, however, while Zr addition did not have a beneficial effect on the alloy strength, the ductility was found to improve. Zr-Ti combined additions had a significant effect on the microstructure of the base alloy, as the morphology of the α-Al grains transformed into a non-dendritic morphology, and the grain size decreased sharply. These effects were at their maximum at 180 °C and 0.5 wt% Zr addition. Moreover, the Zr-containing alloys aged at higher temperatures, such as 220 °C and 240 °C, maintained a noticeably higher level of strength over the base alloy aged at the same temperatures. Quality index charts based on the tensile test data also reflected an improvement in alloy quality and strength with Zr-Ti combined additions.
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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".