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Record W1973357901 · doi:10.1063/1.4864848

Laser-ultrasonic inspection of hybrid laser-arc welded HSLA-65 steel

2014· article· en· W1973357901 on OpenAlexaff
Daniel Lévesque, Gilles Rousseau, Priti Wanjara, X. Cao, J.‐P. Monchalin

Bibliographic record

VenueAIP conference proceedings · 2014
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceWeldingLaserButt weldingLaser beam weldingUltrasonic sensorUltrasonic testingArc weldingElectric arcEquiaxed crystalsMetallurgyOpticsAcousticsMicrostructureElectrode

Abstract

fetched live from OpenAlex

The hybrid laser-arc welding (HLAW) process is a relatively low heat input joining technology that combines the synergistic qualities of both the high energy density laser beam for deep penetration and the arc for wide fit-up gap tolerance. This process is especially suitable for the shipbuilding industry where thick-gauge section, long steel plates have been widely used in a butt joint configuration. In this study, preliminary exploration was carried out to detect and visualize the welding defects using laser ultrasonics combined with the synthetic aperture focusing technique (SAFT). Results obtained on 9.3 mm thick butt-welded HSLA-65 steel plates indicated that the laser-ultrasonic SAFT inspection technique can successfully detect and visualize the presence of porosity, lack of fusion and internal crack defects. This was further confirmed by X-ray digital radiography and metallography. The results obtained clearly show the potential of using the laser-ultrasonic technology for the automated inspection of hybrid laser-arc welds.

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 score0.276
Threshold uncertainty score0.823

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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