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Modification of tundish design to improve quality of slabs, casted at CCM No. 4 of EVRAZ NTMK

2020· article· en· W3045037678 on OpenAlexaff
I. M. Zakharov, В. А. Смирнов, D. V. Sushnikov, A. G. Lyzhin, E. A. Lavrova, E. V. Shekhovtsov, P. V. Ekkert

Bibliographic record

VenueFerrous Metallurgy Bulletin of Scientific Technical and Economic Information · 2020
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsTundishContinuous castingMetallurgyMaterials scienceSlabDrop (telecommunication)Slag (welding)CastingMoldMetalMechanical engineeringComposite materialEngineeringStructural engineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.225
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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