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Record W3032370456 · doi:10.3390/met10060704

A New Method for Plasticization of Inclusions in Saw-Wire Steel by NaF Addition

2020· article· en· W3032370456 on OpenAlexaffabout
Liangjun Chen, Yong Wan, Jie Li, Weiqing Chen, Yindong Yang, Alexander McLean

Bibliographic record

VenueMetals · 2020
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceInclusion (mineral)Melting pointScanning electron microscopeMetallurgyTube (container)PlasticizerComposite materialMineralogyChemistry

Abstract

fetched live from OpenAlex

In this study, a new method is proposed for the plasticization of inclusions by taking advantage of the behavior of alkali metals in lowering the inclusion melting point. A series of experiments with NaF additions to molten steel were carried out using a carbon tube furnace followed by simulated rolling tests using the solidified ingots and characterization of inclusions with the help of automated scanning electron microscopy. Compositional changes of the steel and evolution of gaseous species were evaluated using thermodynamic software FactSage 7.2 (ThermFact Inc., Montreal, QC, Canada). Based on this approach, NaF/steel/inclusion interactions and the effects of NaF addition on the melting point, size and deformability of inclusions were clarified. The modification of MnO-SiO2 inclusions by NaF also promoted the removal of inclusions and improved the cleanliness of steel. The results show that with the addition of NaF, the melting point of inclusions is greatly reduced, the deformability is improved, and the removal of inclusions is enhanced, all of which indicates a good prospect for industrial application.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.260
Teacher spread0.245 · 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
GenreMethods

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

Citations5
Published2020
Admission routes2
Has abstractyes

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