Tailoring microstructure and mechanical properties of an AA5454 extruded aluminum alloy with Sc/Zr microalloying and processing conditions
Bibliographic record
Abstract
Microalloying with Sc and Zr offers significant potential to enhance the strength, microstructural stability, and corrosion resistance of aluminum alloys. This study investigates the effects of individual Sc and combined Sc/Zr additions on the mechanical performance and microstructural evolution of AA5454 extrusions subjected to two homogenization treatments (350°C/24h and 575°C/4h). Homogenization at 350 °C promoted the formation of fine, coherent Al 3 Sc and Al 3 (Sc,Zr) precipitates, enhancing dispersion strengthening and recrystallization resistance. At the higher homogenization temperature of 575°C, Al 3 Sc precipitation was suppressed in the Sc-containing alloy, while coarse Al 3 (Sc,Zr) precipitates formed in the alloy containing both Sc and Zr. The results revealed a significant increase in yield strength (YS), ranging from 132–139 MPa in the Sc-containing alloy to 132–164 MPa in the Sc- and Zr-containing alloy, with yield strength increments of 49–77 MPa per 0.1 wt.% Sc addition, depending on the processing route. Transmission electron microscopy and electrical conductivity measurements confirm the evolution and reprecipitation behavior of Al 3 Sc/Al 3 (Sc,Zr) precipitates in the Sc- and Sc/Zr-containing alloys. A predictive strength model combining solid solution, grain boundary, and dispersion/precipitation strengthening showed good agreement with the experimentally measured YS values, identifying precipitation strengthening as the primary contributor, particularly after post-aging. These findings reveal pathways for improving the performance of 5xxx-series extrusions.
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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".