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Record W2963425566 · doi:10.1016/j.promfg.2019.06.150

Effect of initial surface topography during laser polishing process: Statistical analysis

2019· article· en· W2963425566 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProcedia Manufacturing · 2019
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsNational Research Council CanadaWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolishingSurface roughnessSurface (topology)SmoothingSurface finishMaterials scienceSurface finishingLaserStatistical analysisOpticsMechanical engineeringMetallurgyComposite materialGeometryMathematicsEngineeringPhysicsStatistics

Abstract

fetched live from OpenAlex

Surface finish is one of the most important quality characteristics of fabricated components. Laser polishing (LP) is one of the advanced manufacturing surface finishing techniques that has been recently developed and successfully employed for improving surface quality without deteriorating the overall structural form through surface smoothing by melting and redistributing a thin layer of molten material. This paper advances the statistical analysis of the LP process emphasizing aspects of the effect of the initial surface topography. Flat and ground initial surfaces are used for comparative statistical analysis of initial and polished profiles obtained experimentally. Their profile geometries and surface quality characteristics, such as, roughness, were compared and analyzed. In addition, LP process was experimentally investigated as a thermodynamic operator represented by a transfer function and it was examined by means of a coherence function.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.905

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 head0.003
GPT teacher head0.239
Teacher spread0.237 · 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