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Record W2588549236 · doi:10.1080/15440478.2016.1266290

Numerical and Experimental Validation of Natural Fiber Orientation in Viscous Fluid of Injection Cavity

2017· article· en· W2588549236 on OpenAlexafffund
Kanniah Rajasekaran, Jimi Tjong, Sanjay K. Nayak, Mohini Sain

Bibliographic record

VenueJournal of Natural Fibers · 2017
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsUniversity of Toronto
FundersCentre for Bio-composite and Biomaterial ProcessingUniversity of Toronto
KeywordsMaterials scienceFiberOrientation (vector space)Composite materialFlow (mathematics)CurlingNatural fiberMechanicsOpticsGeometryPhysicsMathematics

Abstract

fetched live from OpenAlex

In an injection moulded product, the orientation of short fiber in polymer composite influences the strength of the product. A method was developed to predict orientation of natural fiber in thermoset composite. The purpose of this investigation is to predict the orientation of natural fiber in a viscous fluid and to study fiber flow in mould cavity. An experimental set up was developed on injecting viscous fluid with short natural inside the transparent mould cavity and visualize the orientation of short natural fiber and flow front during filling period of cavity. The proposed model for natural fiber orientation was derived by coupling the tangential orientation of natural fiber in flow front and constant curling factor in angular velocity of fluid element. The orientation angle was predicted through proposed model at specified position and was validated with experimental method through digitized image analysis technique.

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: Simulation or modeling · Consensus signal: none
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 studies0.0000.001
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 head0.258
Teacher spread0.251 · 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 designSimulation or modeling
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

Citations3
Published2017
Admission routes2
Has abstractyes

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