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Record W3047669220 · doi:10.1002/srin.202000203

Horizontal Single Belt Casting of Thin Strips of an Advanced High Strength Steel (Fe–21%Mn–2.5%Al–2.8%Si–0.08%C wt%)

2020· article· en· W3047669220 on OpenAlexaff
Usman Niaz, Mihaiela Isac, R. I. L. Guthrie

Bibliographic record

Venuesteel research international · 2020
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceSTRIPSNozzleCastingProfilometerSurface finishMetallurgyMicrostructureFree surfaceSurface roughnessComposite materialMechanicsMechanical engineering

Abstract

fetched live from OpenAlex

This research presents numerical modeling and experimental results on thin strips of Fe–21%Mn–2.5%Al–2.8%Si–0.08%C wt% steel, obtained using the horizontal single belt casting (HSBC) process. The free stream of the molten metal, exiting from a nozzle slot, was observed to be highly unstable and nonuniform, after interacting with a 30° inclined refractory plane of a delivery system. However, increasing the inclination of the refractory plane to 45°–60° allows the falling molten metal free stream to become much more stable and less fluctuating. In addition, the molten metal can undergo a hydraulic jump when impacting and flowing down these inclined refractory planes. These hydraulic jumps result in the generation of free surface waves, which travel further downstream. Fortunately, these instabilities are not usually detrimental to the surface quality of the casting, as they are rapidly damped, to disappear within a short distance, prior to solidification. The types and numbers of solid phases then forming for this steel, under the relevant Scheil cooling conditions, are determined using FactSage software. The surface roughness of the cast strip was evaluated using a Nanovea 3D surface profilometer. Microstructures of the cast and heat‐treated strips were determined using Optical and Electron microscopes.

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)0.001
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 score0.824
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.318
Teacher spread0.264 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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