Improving Corrosion Resistance of Aluminosilicate Refractories towards Molten Al-Mg Alloy Using Non-Wetting Additives: A Short Review
Bibliographic record
Abstract
The corrosion of refractories in contact with high temperature aluminum-magnesium alloys leads to contamination of the Al-Mg alloy products by solid impurities from degraded refractories. Where both the spinel and corundum phases form in the refractories, cracks are generated and propagated by diffusion of molten Al-Mg, resulting in severe corrosion. In this review paper, the corrosion phenomenon is discussed, and published work is summarized, supplemented by our recent experimental results. Using the Alcan immersion test, materials based on white-fused mullite (WFM) were evaluated for their corrosion resistance and interfacial behavior. WFM was modified using different 2-wt.% of non-wetting additives (NWAs), such as BaSO4, CaF2, Secar®71 cement and wollastonite to improve their performance when in contact with molten Al-Mg alloy at 850 °C for 96 h. The mechanical properties of the samples such as flexural and compressive strength were evaluated, in addition to X-ray diffraction and microscopic analysis (optical and scanning electron microscopy coupled with X-ray elemental mapping). It was observed that cracks formed in samples were promoted with only BaSO4, CaF2, Secar®71 cement or wollastonite. However, cracks did not appear in the sample promoted with both 1-wt.% CaF2 and 1-wt.% BaSO4, because of improved anti-wetting properties in addition to inhibiting spinel (MgAl2O4) formation, which is the main cause of the cracks. This is a significant finding in the prevention of cracks and improvement of the refractory corrosion resistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".