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Record W2063565163 · doi:10.1063/1.4914638

Dynamic ECA lift-off compensation

2015· article· en· W2063565163 on OpenAlexaff
Benoît Lepage, Charles Brillon

Bibliographic record

VenueAIP conference proceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsQuébec Metro High Tech Park (Canada)
Fundersnot available
KeywordsLift (data mining)CalibrationEddy-current testingElectromagnetic coilEddy-current sensorComputer scienceFalse positive paradoxEddy currentElectronic engineeringAcousticsEngineeringElectrical engineeringArtificial intelligencePhysicsData mining

Abstract

fetched live from OpenAlex

Good control on lift-off is crucial in Eddy Current Testing (ECT) as the signal amplitude, directly affected by lif-toff changes, can potentially lead to reduced detection performance and/or false positives. This is especially true in automated inspections with Eddy Current Array (ECA) technology, where lift-off cannot be mechanically compensated for at each coil position. Here, we report on a novel method for compensating sensitivity variations induced by varying lift-off for an ECA probe. This method makes use of a single ECA probe operated in two different ways: One is to create a set of detection channels and the other is to create a set of lift-off measurement channels. Since a simple relationship exists between the two measurements, an improved calibration process can be used which combines the calibration of both detection and lift-off measurement channels on a simple calibration block exhibiting a reference indication, thus eliminating the need for a predefined lift-off condition. In this work, we will show results obtained on a weld cap, where lift-off condition is known to vary significantly over the scanning area.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.033
GPT teacher head0.258
Teacher spread0.225 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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