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Record W2944239507 · doi:10.1063/1.5099770

Friction stir welded lap joint inspection using ultrasonic guided waves

2019· article· en· W2944239507 on OpenAlexaff
Pierre Bélanger, Mohammad Jahazi

Bibliographic record

VenueAIP conference proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsLaser Doppler vibrometerMaterials scienceLap jointFriction stir weldingRotational speedWeldingUltrasonic sensorRivetTraverseAcousticsTransducerUltrasonic testingJoint (building)Lamb wavesComposite materialStructural engineeringMechanical engineeringEngineeringOpticsWave propagation

Abstract

fetched live from OpenAlex

Friction stir welding is a solid state joining process used across a range of industries from shipbuilding to aerospace. However, defects such as kissing bonds or wormholes usually associated with poor control of the rotational and traverse speeds during manufacturing remain difficult to detect using standard nondestructive testing methods. This paper investigates the correlation between the transmission of low-frequency ultrasonic guided waves through a lap joint and the rotational speed used during manufacturing. The aim of the method is therefore to nondestructively assess the manufacturing parameters rather than detect specific defects. The experimental setup comprises a 100 kHz shear transducer used for the excitation of S0, six pairs of 3.2 mm aluminium plates in a friction stir welded lap joint configuration and a 2D laser Doppler vibrometer. The lap joints were manufactured with a rotational speed varying from 600 RPM to 1050 RPM while the traverse speed and the plunge were maintained constant. The experimental results showed excellent correlation between the transmission S0 and the stir zone width. Clear outliers were also identified when looking at the amplitude of the 2nd harmonic of the S0 mode.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.028
GPT teacher head0.254
Teacher spread0.226 · 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.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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