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Record W2018201255 · doi:10.1002/pen.23465

Real‐time diagnosis of gas‐assisted hot embossing process by ultrasound

2013· article· en· W2018201255 on OpenAlexaff
Chin‐Chi Cheng, Chia-Lin Wu, Kuo-Ting Wu, Sen-Yeu Yang

Bibliographic record

VenuePolymer Engineering and Science · 2013
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsNational Research Council Canada
FundersNational Science Council
KeywordsEmbossingMaterials scienceUltrasonic sensorProcess (computing)Composite materialUltrasoundGas pressureTransducerAcousticsComputer science

Abstract

fetched live from OpenAlex

An integrated high temperature ultrasonic transducer (HTUT) sensor has been utilized for diagnosis of gas‐assisted hot embossing process to fabricate V‐cut patterns real‐time, nonintrusively and nondestructively. The progression of the process, including plastic plate soften, gas introducing, cooling, and plate detachment inside the chamber, was clearly observed using ultrasound. Ultrasonic velocity could not only indicate the four steps of process, but also evaluate the replicating degree of the V‐cut patterns on surface of plastic plate under various temperature and gas pressure settings. The experimental results indicate that the heights of V‐cut patterns increase with temperature and gas pressure. POLYM. ENG. SCI., 53:2175–2182, 2013. ©2013 Society of Plastics Engineers

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 score0.035
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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
Published2013
Admission routes1
Has abstractyes

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