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Record W2118861564 · doi:10.5539/mas.v4n9p90

Texturing Parameters and Characteristics of Multi-head CO2 Laser Surface Texturing

2010· article· en· W2118861564 on OpenAlexvenueno aff
Le Zhou, Peifeng Chen

Bibliographic record

VenueModern Applied Science · 2010
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceSurface finishSurface roughnessOpticsPolishingFormabilityHead (geology)AbrasivePolygon (computer graphics)Surface (topology)LaserComposite materialComputer scienceGeologyGeometry

Abstract

fetched live from OpenAlex

Cold-rolled steel sheets with specially designed surface topographies are widely in applications in car and fabrication industry. Laser texturing is a novel technique used to texture the cold roller surface to produce high quality steel sheets with good formability and image clarity. Principle of multi-head CO2 laser surface texturing technique using polygon scanner for beam modulation is introduced. Texturing parameters and their influence are investigated. The obtained craters are of volcano shape with diameter of 120~230?m, depth of 7~21?m and bump height of 5~17?m. The surface average roughness Ra is in the range of 1.03~5.86?m. The textured surface is abrasive resistant and prolongs the roller service life.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.512

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 head0.012
GPT teacher head0.218
Teacher spread0.207 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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