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Record W2895328906 · doi:10.1117/12.2500627

Non-invasive method of car wheel rim examination

2018· article· en· W2895328906 on OpenAlexaff
M. Borecki, Arkadiusz Rychlik, Michael L. Korwin-Pawlowski

Bibliographic record

VenuePhotonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018 · 2018
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsVibrationRotation (mathematics)Point (geometry)Structural engineeringChartPosition (finance)Natural frequencyGeologyAcousticsComputer scienceEngineeringPhysicsGeometryMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents the concept of a non-invasive method to determine the technical state of passenger car wheel rims. The method consists of a series of vibration and dimension measurement on a rim mounted in a diagnostic station. The measurements are taken on four positions of the rim of rotation versus constant excitation point angular position. The wheel rim’s natural frequencies distribution versus time and rotation angle are examined as diagnostic tool of rim fit for use classification from materials fatigue point of view. These characteristics are also inspected to determine the condition of joints of rim elements and to identify the cracks or loss of integrity in the wheel rim structure. The wheel rim dimensions as a series of specific diameters are examined for wheel rim radial run-out and for axial run-out. The proposed method was evaluated for a new and worn out rims. Performed experiments show that the natural frequency values of rim, the damping factor of natural rim vibration and rim diameters course grouped in a spider chart allows an effective visual classification of car rim fit for use.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.029
GPT teacher head0.322
Teacher spread0.294 · 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.

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
Published2018
Admission routes1
Has abstractyes

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