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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 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.
fundA Canadian funder is recorded on the work.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.253
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