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Record W2589472906 · doi:10.1080/13621718.2017.1288889

Pulse profile and metal transfer in pulsed gas metal arc welding: droplet formation, detachment and velocity

2017· article· en· W2589472906 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

VenueScience and Technology of Welding & Joining · 2017
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCurrent (fluid)Dimensionless quantityMass transferPulse (music)MechanicsArc (geometry)Gas metal arc weldingWeldingAnalytical Chemistry (journal)Composite materialOpticsArc weldingThermodynamicsChemistryPhysicsGeometry

Abstract

fetched live from OpenAlex

The influence of current profile and pulse parameters on droplet formation and transfer was investigated. One profile has an exponential ramp up and down in the current pulse shape, while the second is nearly square shaped. High-speed photography, synchronised with a high-speed data acquisition system, was used to monitor the droplet formation and transfer. It was found that for long-tail current profile, most of droplet formation and detachment occurs before background current is reached. While, for the nearly square pulse, most of droplet formation and transfer occurs during background current, giving a stable and smooth metal transfer. The arc attachment position was found to vary for the different profiles. Droplet speed was measured, and it was found that it is proportional to the peak current and inversely proportional to background current. Dimensionless process parameters were defined and used to predict droplet speed using a neural networks algorithm.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.444

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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