MétaCan
Menu
Back to cohort
Record W2911664900 · doi:10.3390/ma12030335

Application of Cold Wire Gas Metal Arc Welding for Narrow Gap Welding (NGW) of High Strength Low Alloy Steel

2019· article· en· W2911664900 on OpenAlexafffund
Rafael Ribeiro, P. D. C. Assunção, Emanuel B. F. Dos Santos, Ademir Angelo Castro Filho, Eduardo de Magalhães Braga, A.P. Gerlich

Bibliographic record

VenueMaterials · 2019
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsFord Motor Company (Canada)University of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsWeldingMaterials scienceGas metal arc weldingHeat-affected zoneMetallurgyElectric resistance weldingShielding gasArc weldingComposite materialCold weldingSubmerged arc weldingUpset welding

Abstract

fetched live from OpenAlex

Narrow gap welding is a prevalent technique used to decrease the volume of molten metal and heat required to fill a joint. Consequently, deleterious effects such as distortion and residual stresses may be reduced. One of the fields where narrow groove welding is most employed is pipeline welding where misalignment, productivity and mechanical properties are critical to a successful final assemblage of pipes. This work reports the feasibility of joining pipe sections with 4 mm-wide narrow gaps machined from API X80 linepipe using cold wire gas metal arc welding. Joints were manufactured using the standard gas metal arc welding and the cold wire gas metal arc welding processes, where high speed imaging, and voltage and current monitoring were used to study the arc dynamic features. Standard metallographic procedures were used to study sidewall penetration, and the evolution of the heat affected zone during welding. It was found that cold wire injection stabilizes the arc wandering, decreasing sidewall penetration while almost doubling deposition. However, this also decreases penetration, and incomplete penetration was found in the cold wire specimens as a drawback. However, adjusting the groove geometry or changing the welding parameters would resolve this penetration issue.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.597

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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 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

Citations29
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueMaterialsSame topicWelding Techniques and Residual StressesFrench-language works237,207