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Record W2619368441 · doi:10.1002/mop.30670

A high precision scheme for micro‐channel processing in quartz glass using femtosecond laser

2017· article· en· W2619368441 on OpenAlexafffund
Pan Zhang, Lei Chen, Xinghuan Wang, Yiliu Tu

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Calgary
FundersChina Postdoctoral Science FoundationCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceFemtosecondOpticsQuartzLaserLaser ablationSurface micromachiningAblationBorosilicate glassLaser power scalingOptoelectronicsFabricationComposite materialEngineering

Abstract

fetched live from OpenAlex

Abstract Ultra‐short pulse laser with unique advantages in micromachining, especially on the hard brittle material becomes the focus of study on micro‐structure processing. Theoretical and experimental researches are taken from the ablation properties of micro‐channel in the quartz glass by femtosecond laser. As the ablation depth of quartz glass has not relationship obviously with defocusing distance within LAV effective range, a new way to control precisely the ablation depth of quartz glass is present in the study. With fixed laser power, the ablation depth could be known easily by the length of the ablation streak while moving the femtosecond laser focus along the straight line with a 45‐degree angle from the quartz glass surface within the LAV range. Research is carried out to study laser pulse power, scanning velocity and step distance to do something with micro‐channel ablation quality. According to the parameters of test, the micro‐channel with rectangle cross section of the quartz glass is processed, and then we analyze the ablation morphology and processing efficiency. The results provided a important suggestion to process micro‐structure precisely for quartz glass by femtosecond laser.

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: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.951

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 head0.016
GPT teacher head0.250
Teacher spread0.235 · 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

Citations8
Published2017
Admission routes2
Has abstractyes

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