Microstructural evolution during solidification of Al–Cu-based alloys
Bibliographic record
Abstract
Abstract The present study focuses on the effect of alloying elements, namely, strontium, titanium, zirconium, scandium and silver, individually or in combination, on the performance of a newly developed Al-2 %Cu-based alloy. A total of thirteen alloy compositions were used in the study. Castings were prepared employing the low pressure die casting technique. The microstructures of selected samples were examined using optical microscopy and electron probe microanalysis. Hardness measurements were also carried out on these alloys using a Brinell hardness tester. The results showed that adding Ti in the amount of 0.15 wt.% in the form of Al-5 %Ti-1 %B master alloy is sufficient to refine the grains in the cast structure in the presence of 200 ppm Sr (0.02 wt.%). Addition of Zr and Sc did not contribute further to the grain refining effect. The main role of addition of these two elements appeared in the formation of complex compounds with Al and Ti. The results obtained from the low pressure die casting samples were compared with those produced using traditional gravity die casting. No heat treatment was applied.
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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".