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Record W2989785547 · doi:10.18280/i2m.180504

Nondestructive Eddy Current Measurement of Coating Thickness of Aeronautical Construction Materials

2019· article· en· W2989785547 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2019
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsEddy currentCoatingNondestructive testingMaterials scienceCurrent (fluid)AcousticsForensic engineeringEngineeringComposite materialElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper puts forward a novel nondestructive method to measure the coating thickness of aeronautical construction materials (e.g. Al, AU4G, Ti and Inox 304L) using the eddy current. First, the forward model of the coupled electric field method was adopted to facilitate the eddy current measurement the coating thickness. The forward model was applied in Matlab simulation. Based on the simulation results, the authors examined the effects of nonconductive coating thickness on sensor impedance component such as amplitude, resistance and reactance. On this basis, an inverse algorithm was developed and coupled with the forward model, and verified through experiments. The results show that our method can measure the coating thickness of aeronautical construction materials rapidly at a high precision. The method has great potential for automated industrial applications.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.031
GPT teacher head0.282
Teacher spread0.251 · 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