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Record W1608389454 · doi:10.1109/mwscas.1993.343387

An optical technique for the measurement of 2-D texture of roller bearing surfaces

2002· article· en· W1608389454 on OpenAlexafffund
Vinh-Nam Huynh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSurface Roughness and Optical Measurements
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBidirectional reflectance distribution functionStylusSurface finishSurface roughnessOpticsScatteringSurface (topology)Bearing (navigation)Light scatteringTexture (cosmology)ProfilometerRange (aeronautics)PhysicsMaterials scienceMathematicsComputer scienceArtificial intelligenceGeometryComputer visionReflectivityImage (mathematics)

Abstract

fetched live from OpenAlex

An optical method was developed for the assessment of surface texture of tapered roller bearings. This technique was based on an analysis of the bi-directional scattering pattern of a surface which was illuminated by a coherent light source. The scattering pattern was captured by a CCD camera and then digitized by a frame grabber board on a PC-AT. The Bi-directional Reflective Distribution Function (BRDF) was obtained to derive the zero and second moments of the distribution function. These moments were then correlated to the RMS roughness and slope of the surface profile which was obtained from a stylus instrument. Good correlations were established for these parameters which indicates there exists a relationship between the BRDF of the surface and its surface profile statistics. This method offers a convenient way for the inspection of 2-D surfaces in the range of 0.2 /spl mu/m to 0.8 /spl mu/m RMS roughness.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 score0.727
Threshold uncertainty score0.291

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 head0.046
GPT teacher head0.241
Teacher spread0.194 · 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
Published2002
Admission routes2
Has abstractyes

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