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Record W3047303225 · doi:10.1080/00325899.2020.1802558

Water atomisation of metal powders: effect of water spray configuration

2020· article· en· W3047303225 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePowder Metallurgy · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceNozzleSuperheatingFinenessGeometric standard deviationMetal powderParticle sizeSpray nozzleMetallurgyPowder metallurgyParticle (ecology)MetalParticle-size distributionStandard deviationAnalytical Chemistry (journal)ThermodynamicsSinteringChemical engineeringChromatographyChemistry

Abstract

fetched live from OpenAlex

We consider the effect of water spray configuration on the fineness and uniformity of a metal powder produced by water atomization of a melt stream. The effects of water spray travel distance, nozzle design, water pressure, melt superheat, and apex angle on the particle size distribution of a metal powder is studied via a laboratory-scale water atomizer; the main focus is on the first two, which are usually fixed parameters of the atomizer. Correlations are proposed relating the mass median size and standard deviation of the powder to the parameters cited. Similar correlations for water pressure, melt superheat, and apex angle have been reported elsewhere; we present data on these effects to confirm the validity of our results, especially as Bi-42%Sn powder has not been studied before. What is new are results on the effect of water spray travel distance and nozzle design on the mass median size and standard deviation of powder.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.763

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)0.0010.000

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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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