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Record W2079253359 · doi:10.7735/ksmte.2015.24.1.023

Improvement of Optical 3D Scanner Performance Using Atomization-Based Spray Coating

2015· article· en· W2079253359 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.

Bibliographic record

VenueJournal of The Korean Society of Manufacturing Technology Engineers · 2015
Typearticle
Languageen
FieldEngineering
TopicSurface Roughness and Optical Measurements
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsScannerMaterials scienceCoatingMicrometer3d scanningProfilometerTransparency (behavior)OpticsSpray nozzleSpray characteristicsComputer scienceComposite materialMechanical engineeringEngineeringSurface finishComputer visionPhysics

Abstract

fetched live from OpenAlex

The scanning quality can be influenced by reflective abilities of a surface. Transparency and glossiness of a surface can highly limit the scanning results. Various techniques have been developed to solve problems of reflective and transparent surfaces. As one of the most feasible and convenient solutions, a thin layer of coating with proper specifications is sprayed on surface for eliminating the problems of the surfaces. As the main goal is to keep the object geometry unchanged, then it is important to coat the surface with layers less than one micrometer in thickness. For this purpose, a newly designed atomization-based spray system has been developed and tested in sets of experiments to study its efficiency on scanning results while objects with the surface are in use. This paper presents the spray design process and then studies and compares the 3D scanning results of the surfaces coated with atomization-based and aerosol sprays.

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)0.001
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 score0.779
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.213
Teacher spread0.196 · 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