Effect of CaO/Al <sub>2</sub> O <sub>3</sub> ratio on viscosity and crystallisation behaviour of mould flux for high Al non-magnetic steel
Bibliographic record
Abstract
In order to facilitate the development of CaO–Al2O3 based mould flux for casting high aluminium, non-magnetic steel, the effect of CaO/Al2O3 ratios from 0.6 to 3.2 on viscosity and crystallisation characteristics were investigated with the aid of a rotational viscometer, Fourier transform infrared spectroscopy, a single hot thermocouple technique and X-ray diffraction analysis. The results showed that, at temperatures above 1543 K (1270°C), the viscosity first decreased and then became stable with increase in the CaO/Al2O3 ratio. At temperatures below 1543 K (1270°C), the viscosity again first decreased but then increased, with the CaO/Al2O3 ratio. This viscosity behaviour can be attributed to changes in the network structure characteristics and the precipitation of solid particles within the liquid flux. Increase in the CaO/Al2O3 ratio also first inhibited and then enhanced crystallisation as demonstrated by the changes in initial crystallisation temperatures and incubation times. The X-ray diffraction results confirmed that, at both low and high CaO/Al2O3 ratios, the dominant precipitates were compounds with high melting points. On the other hand, with CaO/Al2O3 ratios in the midrange, the dominant precipitates were compounds with relatively low melting temperatures. It is concluded that mould flux with a CaO/Al2O3 ratio in the range 1.1–1.6 is the most appropriate for casting high aluminium, non-magnetic steels.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".