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Record W2187850883 · doi:10.3233/jae-141842

Pulsed eddy current detection of cracks in F/A-18 inner wing spar at large lift-off using modified principal component analysis

2014· article· en· W2187850883 on OpenAlexafffund
Peter Francis Horan, Ross Underhil, Thomas W. Krause

Bibliographic record

VenueInternational Journal of Applied Electromagnetics and Mechanics · 2014
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsRoyal Military College of Canada
FundersNatural Sciences and Engineering Research Council of CanadaU.S. NavyMinistère de la Défense Nationale
KeywordsSparWingLift (data mining)Eddy currentPrincipal component analysisStructural engineeringEngineeringComputer scienceElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Stress corrosion cracks may develop between fasteners in the aluminum inner wing spars of F/A-18 (CF188 Hornet) aircraft. These fasteners secure carbon-fibre/epoxy composite wing skin, of varying thickness (8 to 21 mm), to the spar. Inspection of the spar through the wing skin is required in order to avoid wing disassembly. A pulsed eddy current system that uses principal component analysis and discriminant analysis to identify cracks has been field tested at the USN North Island facility. The results show that the system can accurately identify cracks in real time throughout the wing. The method is far faster than X-ray radiography and, because it is very portable, can be readily deployed to second or first line facilities. Issues that need to be addressed to improve the performance of the system are identified and potential solutions are examined.

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)0.000
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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.253
Teacher spread0.240 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

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