Application of High-Pressure Torsion to Al-Si Alloys with and without Scandium Additions
Bibliographic record
Abstract
Al-2 wt. % Si alloys with and without 0.25 wt. % scandium additions were processed by high-pressure torsion up to five turns at room temperature under a pressure of 6.0 GPa. Microstructural examination of the as-cast Al-2Si-0.25Sc alloy revealed the presence of Al3Sc precipitates which refined the Al grain structure, whereas no major changes were observed in the morphology of the Si particles. Processing by HPT of both experimental alloys revealed submicrometer grains with uniformly distributed Si particles. The mechanical properties were obtained using hardness measurements and the ball-indentation technique. The results show the hardness increased in the first turn of HPT and further increased with increasing numbers of turns. In addition, the hardness values were lower at the centers and continuously increased towards the edges of the disks. The difference in hardness values between the centre and the edge decreased with increasing turns, thereby suggesting an increasing homogeneity with increasing processing. The scandium addition and HPT processing of the Al-2Si alloy strongly influences the grain refinement and mechanical properties. The grain size reduction in the Al-2Si alloy was similar to Al whereas the presence of Sc in Al-2Si during HPT processing was responsible for large precipitation networks and a submicrometer grain formation.
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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) | 0.001 | 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) | 0.003 | 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".