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Record W2150200394 · doi:10.1115/1.1991876

Optimized Inspection of Thin-Walled Pipe Welds Using Advanced Ultrasonic Techniques

2005· article· en· W2150200394 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsSizingUltrasonic testingUltrasonic sensorPlanarWeldingMaterials scienceFabricationAcousticsNondestructive testingPhased arrayComputer scienceEngineeringComposite materialElectrical engineering

Abstract

fetched live from OpenAlex

In this paper an effective way to optimize the inspection of welds in thin-walled pipe less than 6 mm (0.24 in.) thick using automated ultrasonic testing (AUT) is described. AUT offers a better solution than radiography for detecting and sizing of planar defects. However, cap width, weld shrinkage and defect sizing put constraints on the actual ultrasonic approach for inspection of pipes with wall thickness less than 6 mm (0.24 in.). The applications of high-frequency single/multiprobe techniques and phased-array technology for inspection of thin-walled pipe welds have been investigated in this paper. It has been demonstrated that combining an advanced ultrasonic phased-array technique with a novel approach for modeling and simulation of ultrasonic inspection have potentially significant advantages for enhanced detectability, better sizing and improved flaw characterization of randomly oriented planar fabrication imperfections in thin-walled pipe 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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.254
Teacher spread0.245 · 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