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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 OpenAlexafffund
Emanuel B. F. Dos Santos, Rob Pistor, A.P. Gerlich

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.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
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

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

Citations41
Published2017
Admission routes2
Has abstractyes

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