Interaction between Steel and Distinct Gunning Materials in the Tundish
Bibliographic record
Abstract
Gunning materials are used as a dispensable protective layer to extend the tundish life. Their use however, affects the steel cleanliness by introducing steel reoxidation and exogenous inclusions. In the present work, the interaction between molten steel and three types of refractory, i.e. MgO, Al2O3, and MgO + 2MgO∙SiO2 based gunning materials (GM) have been investigated. Compared to MgO and MgO + 2MgO∙SiO2 GM, the Al2O3 GM exhibited better infiltration resistance to molten steel. The two phase MgO + 2MgO∙SiO2 GM was found to be less prone to steel infiltration than single-phase MgO GM. The compositional evolution of steel influenced by gunning material was experimentally measured and also predicted with thermodynamic equilibrium calculations. The steel cleanliness in terms of inclusion size, number density, area fraction and morphology was measured and evaluated. Although large Al2O3/AlTiOX inclusions were formed through the erosion of Al2O3 GM, the use of Al2O3 GM resulted in an improved steel cleanliness due to its lower content of reducible compounds compared to that of MgO and MgO + 2MgO∙SiO2 GM.
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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".