Microstructure Thermal Stability and Superplastic Behavior of Al-6%Mg-0.12%Sc-0.10%Zr-0.10%(Yb, Er, Hf) Ultrafine-Grained Alloys
Bibliographic record
Abstract
Superplastic behavior of ultrafine-grained (UFG) Al-6Mg-0.12Sc-0.10Zr-0.1X alloys, where X = Yb (Alloy #1-Yb), Er (Alloy #2-Er), and Hf (Alloy #3-Hf), has been studied. The total content of Sc, Zr, Yb, Er, Hf in the alloys was 0.32 wt.% (0.117-0.118 at.%). The alloys used for benchmarking were Al-6Mg-0.12Sc-0.20Zr (Alloy #4-Zr) and Al-6Mg-0.22Sc-0.10Zr (Alloy #5-Sc). Their UFG microstructure was formed with ECAP. Two different types of deformation behavior during superplasticity were demonstrated. A simultaneous increase in yield stress and elongation to failure during superplastic deformation was discovered. High deformation temperatures were shown to cause a competition between dynamic (strain-induced) grain growth and dynamic recrystallization, leading to a finer grain microstructure. The values of strain hardening factor (n), strain rate sensitivity factor (m), and superplastic deformation threshold stress (Sp) were determined. The impact of the type and concentration of alloying elements on the deformation behavior and dynamic grain growth of Al-6%Mg alloys was investigated. It was established that the maximum elongation to failure in Alloy #1-Yb and Alloy #2-Er is observed at lower deformation temperatures than in Alloy #4-Zr and Alloy #5-Sc. The superplastic properties of Alloy #3-Hf are superior to those of Alloy #4-Zr and Alloy #5-Sc with high content of alloying elements (in at.%). Alloy #1-Yb manifests good elongation to failure (910%) at low temperatures (400 oC). The satisfiability of Hart's criterion for calculating uniform deformation value under superplastic conditions was verified. It was demonstrated that cavitation when pores are formed in large Al3X particles at high temperatures causes early failure of aluminum alloys.
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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".