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Record W2794517645 · doi:10.1117/12.2294561

Characterization and optimization of spiral eddy current coils for in-situ crack detection

2018· article· en· W2794517645 on OpenAlexaff
Catalin Mandache

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsFastenerEddy currentSpiral (railway)Eddy-current testingClassification of discontinuitiesInductanceAerospaceStructural engineeringMechanical engineeringCurrent (fluid)EngineeringElectrical engineeringVoltageAerospace engineering

Abstract

fetched live from OpenAlex

In-situ condition-based maintenance is making strides in the aerospace industry and it is seen as an alternative to scheduled, time-based maintenance. With fatigue cracks originating from fastener holes as the main reason for structural failures, embedded eddy current coils are a viable non-invasive solution for their timely detection. The development and potential broad use of these coils are motivated by a few consistent arguments: (i) inspection of structures of complicated geometries and hard to access areas, that often require disassembly, (ii) alternative to regular inspection actions that could introduce inadvertent damage, (iii) for structures that have short inspection intervals, and (iv) for repaired structures where fastener holes contain bushings and prevent further bolt-hole inspections. Since the spiral coils are aiming at detecting radial cracks emanating from the fastener holes, their design parameters should allow for high inductance, low ohmic losses and power requirements, as well as optimal size and high sensitivity to discontinuities. In this study, flexible, surface conformable, spiral eddy current coils are empirically investigated on mock-up specimens, while numerical analysis is performed for their optimization and design improvement.

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 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)0.000
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: none
Teacher disagreement score0.305
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.018
GPT teacher head0.259
Teacher spread0.241 · 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 teacher head, 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

Citations2
Published2018
Admission routes1
Has abstractyes

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