Modification of tundish design to improve quality of slabs, casted at CCM No. 4 of EVRAZ NTMK
Bibliographic record
Abstract
A technology of continuous casting of steel has a large effect on its contamination. In particular, proper organization of metal flows in tundish and mold is very important. After completion a series of casting through a tundish and drop of metal level in it, it possible, that the slag flows from the surface layers of the tundish to capture the metal. An analysis of results of ultrasonic control of finished strip showed, that the basic number of the revealed defects was obtained during strip rolling out of the last slabs of the last heat in a series for a tundish. Metallographic studies determined, that the defects were located in the slab axis zone and filled by macro-inclusions of complex composition. To determine the actual distribution of metal flows, a water simulation of them was accomplished for the existing design of the EVRAZ NTMK tundish. It was determined, that at the drop of metal level in a tundish, a capture of slag from the metal reservoir and its transfer into the main bath of the tundish takes place due to whirligig flows. Following the weight metal consumption, the zone of slag inclusions distribution enlarges. Besides, denudation of the metal mirror in the metal reservoir takes place, resulted in metal secondary oxidation. Based on the results of the simulation, it was proposed to modify the design of the “turbostop”, which is installed in the metal reservoir of the tundish. Besides, it was proposed to remove the dividers installation in the tundish. It was noted, that the proposed steps enable to ensure a minimal level of rejections.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".