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Record W2896602220 · doi:10.2351/1.5061540

Effect of joint gap on Nd: YAG laser welded Ti-6Al-4V

2009· article· en· W2896602220 on OpenAlexaff
X. Cao, G. Debaecker, E. Poirier, Surendar Marya, J. Cuddy, A. Birur, Priti Wanjara

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsResearch ManitobaNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceJoint (building)WeldingUltimate tensile strengthComposite materialMicrostructurePorosityLaser beam weldingAlloyMetallurgyStructural engineering

Abstract

fetched live from OpenAlex

The effect of joint gap on the butt joint quality of Ti-6Al-4V alloy welded using 4 kW Nd:YAG laser was evaluated in terms of welding defects, microstructure, hardness and tensile properties. Fully penetrated welds were obtained up to a joint gap of 0.5 mm. No cracks were detected. The main defects observed in the welds are porosity and underfill. The porosity area increases with increasing joint gap but remains less than 1% of the fusion zone area. Large underfill defects appear at the welds in the absence of a joint gap but the use of filler wire can reduce this defect in the presence of a joint gap. The weld hardness is slightly decreased with increasing joint gap but the tensile properties seem to be optimized at an intermediary joint gap, probably due to the compromise between the low underfill (after the use of filler wire) and a limited amount of porosity.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.010
GPT teacher head0.240
Teacher spread0.229 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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