Metallographic observations of<i>β</i>-AlFeSi phase and its role in porosity formation in Al–7%Si alloys
Bibliographic record
Abstract
The present study was carried out to investigate by metallographic observation, the effect of alloy composition on the stability of the β-AlFeSi phase and the role of the latter in porosity formation in Al–7%Si alloys. The results showed that Mn is more efficient in stabilising the Chinese-script morphology phases (i.e. α-AlFeSi phases) in the 356 alloys than in 319 alloys. The number of β-AlFeSi particles increases at the expense of the α-AlFeSi particles with increasing Mg level, indicating that Mg counters the effect of Mn. Sr is effective in reducing the number and fraction of β-AlFeSi phase through fragmentation and dissolution of the latter, counteracting the effect of Mg on the same. SrO and Al2Si2 Sr particles were observed to have frequent physical contact with the β-AlFeSi platelets in the microstructure. The effect of P in promoting the β-AlFeSi platelets is believed to be in part due to the role of (Al, P)O2 in the nucleation of β-phase. Stirring for long time, addition of Sr, and melt superheating all favour the formation of co-eutectic β-AlFeSi particles and reduce their volume fraction. The β-phase particles are potential sites for porosity formation regardless of the alloy composition and the type or size of the β particles, whereas the α-AlFeSi particles do not seem to be frequently associated with pores under the same conditions. With the addition of Sr, the porosity profile changes so that finer, better distributed pores appear in the microstructure. The SrO particles play a role in pore formation, and this is probably a reason for the appearance of widely distributed porosity in the Sr-modified Al–Si alloys.
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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".