MétaCan
Menu
Back to cohort
Record W4394793663 · doi:10.31857/s0130308223040036

Possibilities of manual eddy current testing for measuring the depth of contact-fatigue cracks of the surface of rolling rails

2023· article· en· W4394793663 on OpenAlexaff
S. P. Shlyakhtenkov, D. B. Nekrasov, S. V. Palagin, O. V. Bessonova, Artem Popkov, S. A. Becher

Bibliographic record

VenueДефектоскопия · 2023
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsEddy currentPerpendicularSIGNAL (programming language)Materials scienceTransducerAmplitudeAcousticsEddy-current testingCurrent (fluid)Eddy-current sensorCurvaturePhase (matter)MechanicsOpticsGeometryElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The possibility of using the eddy current method to assess the depth of cracks in the surface of rolling rails in operation for planning the work of a rail grinding train is investigated. Experimental studies of the influence of the excitation frequency and the angle of inclination of the eddy current converter, the state of the surface, the depth and angle of inclination of the surface crack on the amplitude and phase of the eddy current signal are carried out. The possibilities of the amplitude-phase method of detuning from interfering factors associated with the slope of the transducer and the curvature of the controlled surface are determined. The resolution of the flaw detector was investigated when evaluating the characteristics of two or more closely spaced cracks - a defect of the "grid" type of cracks. The depth of surface cracks in the range from 0.1 to 1.4 mm was determined by metallographic examination after eddy current control. Optimal control parameters have been experimentally substantiated and a correlation between the crack depth and the projection of the signal amplitude in the direction perpendicular to the direction of change of the interfering factor - the angle of inclination of the transducer - has been established.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.086
GPT teacher head0.311
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 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueДефектоскопияSame topicNon-Destructive Testing TechniquesFrench-language works237,207