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Record W2030148427 · doi:10.1179/174329306x148200

Experimental examination of welding nozzle jet flow at cold flow conditions

2006· article· en· W2030148427 on OpenAlexfundno aff
David A. Johnson, P. A. Orakwe, Elizabeth J. Weckman

Bibliographic record

VenueScience and Technology of Welding & Joining · 2006
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGas metal arc weldingShielding gasNozzleWeldingMaterials scienceFlow (mathematics)MechanicsJet (fluid)Particle image velocimetryFlow visualizationGas tungsten arc weldingElectromagnetic shieldingWeld poolMechanical engineeringMetallurgyLaser beam weldingArc weldingComposite materialTurbulenceEngineeringPhysics

Abstract

fetched live from OpenAlex

A fundamental study of welding nozzle flow under cold flow conditions is presented. The aim is to examine the shielding gas flow characteristics for several gas metal arc welding (GMAW) flow conditions. Experimental investigations are used to predict the flow behaviour of the gas shielding the weld pool. Results are presented for generic GMAW nozzle configurations at typical welding situations under cold flow. Flow visualisation and particle image velocimetry (PIV) gas velocity measurements reveal the various flow characteristics that are crucial to understanding of weld pool protection by the shielding gas. Results may be used to assist in understanding shield gas delivery and the effect of flow and geometry variables on shield gas coverage.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2006
Admission routes1
Has abstractyes

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