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Record W2599462877 · doi:10.1080/13621718.2017.1306014

Residual stresses in cold-wire gas metal arc welding

2017· article· en· W2599462877 on OpenAlexaff
Ednelson Silva Costa, P. D. C. Assunção, Emanuel B. F. Dos Santos, Luiza Gaspar Feio, M. S. Q. Bittencourt, Eduardo de Magalhães Braga

Bibliographic record

VenueScience and Technology of Welding & Joining · 2017
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of Waterloo
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsResidual stressMaterials scienceWeldingBase metalGas metal arc weldingMetallurgyArc weldingShielded metal arc weldingSubmerged arc weldingComposite materialArc (geometry)Gas tungsten arc weldingManganeseHeat-affected zoneMechanical engineering

Abstract

fetched live from OpenAlex

This work compares the welding residual stresses of the cold-wire gas metal arc welding and conventional gas metal arc welding processes. Two techniques were used to measure the residual stresses: X-ray diffraction and acoustic birefringence. The base metal used was carbon manganese steel plates of 9.5- mm thickness. The results showed that the introduction of the cold-wire tends to decrease the residual stresses, suggesting that the introduction of the cold wire decreases the amount of heat given to the base metal, and consequently lowers residual stresses.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.258
Teacher spread0.243 · 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

Citations46
Published2017
Admission routes1
Has abstractyes

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