Impact of hot rolling temperature on the mechanical properties and microstructural evolution of hot/cold-rolled AA5083 with Sc and Zr microalloying
Bibliographic record
Abstract
The impact of hot rolling temperature on the microstructure evolution and mechanical properties of hot/cold-rolled AA5083 alloy containing Sc and Zr was studied. The results revealed that low hot-rolling temperatures (LHRT: 400–425 oC) resulted in superior tensile properties compared to high hot-rolling temperatures (HHRT: 500–525 oC). The yield strength (YS) of the LHRT samples reached 450 MPa in the H18-temper and 291 MPa in the O-temper, which was respectively 20% and 39% higher relative to the HHRT samples. Microalloying with Sc/Zr and two-step homogenization promoted the formation of dispersed particles in the form of Mn-dispersoids and nanosized Al3(Sc,Zr) precipitates. The rolling temperature was found to have a profound impact on the characteristics of these dispersed particles and on the recrystallization resistance. The HHRT caused the coarsening of both dispersed particles during rolling. The LHRT preserved the deformed elongated grain structure due to a stronger retardation of recovery and recrystallization during hot rolling and post-annealing. The strengthening mechanisms of the Mn-dispersoids and Al3(Sc,Zr) precipitates were quantitatively analyzed based on particle characteristics. The predicted YS contributions in the O-temper were in good agreement with the measured YS difference between the LHRT and HHRT samples.
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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".