Effect of Rare Earth Metals, Sr, and Ti Addition on the Microstructural Characterization of A413.1 Alloy
Bibliographic record
Abstract
The present work was performed on A413.1 alloy containing 0.2–1.5 wt% rare earth metals (lanthanum or cerium), 0.05–0.15% Ti, and 0–0.02 wt% Sr. These elements were either added individually or combined. Thermal analysis, image analysis, and electron probe microanalysis were the main techniques employed in the present study. The results show that the use of the depression in the eutectic temperature as a function of alloy modification cannot be applied in the case when the alloy is treated with rare earth metals. Increasing the concentration of RE increases the solidification zone especially in Sr-modified alloys leading to poor feeding ability. This observation is more prominent in the case of Ce addition. Depending upon the amount of added Ti, two RE based intermetallics can be formed: (i) a white phase, mainly platelet-like (approximately 2.5 μm thick), that is rich in RE, Si, Cu, and Al and (ii) a second phase made up of mainly grey sludge particles (star-like) branching in different directions. The grey phase is rich in Ti with some RE (almost 20% of that in the white phase) with traces of Si and Cu. There is a strong interaction between RE and Sr leading to a reduction in the efficiency of Sr as a eutectic Si modifier causing particle demodification.
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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".