Effect of Solidification Rate and Rare Earth Metal Addition on the Microstructural Characteristics and Porosity Formation in A356 Alloy
Bibliographic record
Abstract
The present study was performed on A356 alloy with the main aim of investigating the effects of La and Ce additions to 356 alloys (with and without 100 ppm Sr) on the microstructure and porosity formation in these alloys. Measured amounts of La, Ce, and Sr were added to the molten alloy. The results showed that, in the absence of Sr, addition of La and Ce leads to an increase in the nucleation temperature of the α-Al dendritic network with a decrease in the temperature of the eutectic Si precipitation, resulting in increasing the freezing range. Addition of 100 ppm Sr results in neutralizing these effects. The presence of La or Ce in the casting has a minor effect on eutectic Si modification, in spite of the observed depression in the eutectic temperature. It should be noted that Ce is more effective than La as an alternate modifying agent. According to the atomic radius ratio, rLa/rSi is 1.604 and rCe/rSi is 1.559, theoretically, which shows that Ce is relatively more effective than La. The present findings confirm that Sr is the most dominating modification agent. Interaction between rare earth (RE) metals and Sr would reduce the effectiveness of Sr. Although modification with Sr causes the formation of shrinkage porosity, it also reacts with RE-rich intermetallics, resulting in their fragmentation.
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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".