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Record W2565059179 · doi:10.1063/1.4974684

Finite element model study of the effect of corner rounding on detectability of corner cracks using bolt hole eddy current

2017· article· en· W2565059179 on OpenAlexaff
P. R. Underhill, Thomas W. Krause

Bibliographic record

VenueAIP conference proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsRoundingFinite element methodStructural engineeringRADIUSEddy currentMaterials scienceEddy-current testingEnhanced Data Rates for GSM EvolutionMechanicsGeometryEngineeringMathematicsPhysicsComputer scienceElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

Recent work has shown that the detectability of corner cracks in bolt-holes is compromised when rounding of corners arises, as might occur during bolt-hole removal. Probability of Detection (POD) studies normally require a large number of samples of both fatigue cracks and electric discharge machined notches. In the particular instance of rounding of bolt-hole corners the generation of such a large set of samples representing the full spectrum of potential rounding would be prohibitive. In this paper, the application of Finite Element Method (FEM) modeling is used to supplement the study of detection of cracks forming at the rounded corners of bolt-holes. FEM models show that rounding of the corner of the bolt-hole reduces the size of the response to a corner crack to a greater extent than can be accounted for by loss of crack area. This reduced sensitivity can be ascribed to a lower concentration of eddy currents at the rounded corner surface and greater lift-off of pick-up coils relative to that of a straight-edge corner. A rounding with a radius of 0.4 mm (.016 inch) showed a 20% reduction in the strength of the crack signal. Assuming linearity of the crack signal with crack size, this would suggest an increase in the minimum detectable size by 25%.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.050
GPT teacher head0.308
Teacher spread0.258 · 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 designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

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