Influence of Fe-rich intermetallics on solidification defects in Al–Si–Cu alloys
Bibliographic record
Abstract
To better understand the influence of Fe-rich intermetallics on solidification defect formation, fast in situ synchrotron X-ray tomographic microscopy experiments were performed on a commercial A319 alloy (Al–7.5Si–3.5Cu, wt.%) with 0.2 and 0.6 wt.% Fe. Real-time observations during solidification and semi-solid deformation experiments reveal that β-intermetallics contribute via several different mechanisms to porosity formation and hot tearing susceptibility. While β-intermetallics were not observed to nucleate porosity directly, they do block interdendritic channels, thereby reducing the shrinkage feeding, and increasing pore tortuosity. Pores also grow preferentially along the surface of the β-intermetallics, suggesting that the β-phase has a lower gas–solid interfacial energy than α-Al, thus assisting in increasing pore volume. During uniaxial tension experiments, the ductile failure of the semi-solid, intermetallic-poor, base alloy transitions to a brittle-like failure when a large amount of β-intermetallics are present. In all post-failure microstructures, internal damage was preferentially orientated perpendicular to the loading direction, agreeing with prior experimental and numerical studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".