Phase formation and microstructural evolution during sintering of Al–Zn–Mg–Cu alloys
Bibliographic record
Abstract
Al–5·6Zn, Al–5·6Zn–2·5Mg and Al–5·6Zn–2·5Mg–1·6Cu (wt-%) powder blends were compacted at 350 MPa and sintered in nitrogen at different temperatures to study microstructural evolution during sintering. Densification, dimensional changes and mechanical properties of the Al alloys were investigated. Microstructural analyses were performed by scanning electron microscopy (SEM) coupled with energy dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and simultaneous thermal analysis (STA) in order to determine the temperature and chemical composition of the phases formed during sintering. It was shown that various liquids and intermetallic phases including Al0·71Zn0·29 at 438°C, Al0·58Mg0·42 and Al32(Mg,Zn)49 at 450°C, Al3Mg3Zn3 at 500°C, and most probably η and S phases at 600°C were formed during the heating cycle. Magnesium was found to be the most effective alloying element on the densification and microstructural development during sintering, and eventually on the mechanical properties of the investigated systems sintered below 550°C. At higher temperatures, copper also becomes effective by the formation of a liquid phase. In the present paper, the phase formation during the sintering of the 7075 Al alloy prepared from elemental powders is addressed.
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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".