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Record W1991138149 · doi:10.4188/jte.56.173

Yarn Posture in an Air Suction Gun

2010· article· en· W1991138149 on OpenAlexaboutno aff
Yonggui Li, Yoshiyuki Iemoto, Shuichi Tanoue, Satoshi Takasu

Bibliographic record

VenueJournal of Textile Engineering · 2010
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsnot available
Fundersnot available
KeywordsYarnSuctionTube (container)Compressed airMaterials scienceFalling (accident)DragComposite materialMechanicsMechanical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The geometrical effects of an air suction gun on yarn motion were clarified by measuring postures of a running yarn with a still camera in order to promote the suction performance by controlling the yarn motion appropriately. The relation between the yarn posture and yarn suction force was also discussed. The yarn posture is a helix. This helical motion greatly improves the suction efficiency of the air suction gun owing to high concentration of air drag on the yarn. Helix diameter of yarn posture dy and helix pitch of yarn posture py decrease with an increase in the compressed-air inflow angle and a decrease in the throat diameter of de Laval tube, and are almost independent of the passage diverging angle of nozzle and the converging angle of de Laval tube. Unfavorable values of the geometrical parameters cause large fluctuations in yarn postures, i.e. violent yarn motion. A stable yarn posture with appropriately small dy decreases the friction between the yarn and the wall of tube, and an appropriately small py increases the contact area between the yarn and the air. Hence the yarn suction force is promoted.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.007
GPT teacher head0.248
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2010
Admission routes1
Has abstractyes

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