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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 OpenAlexvenueno aff
Abdelhak Abdou, Tarik Bouchala, Bachir Abdelhadi, Amor Guettafi, Azzedine Benoudjit

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.

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)0.001
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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

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

Citations8
Published2019
Admission routes1
Has abstractyes

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