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Parametric simulation study of a wavy slot channel thermoplastic melt impregnator for carbon fibre-reinforced prepreg tape production

2025· article· en· W4415494084 on OpenAlexaff
Maximilian Pitto, Pascal Hubert, Tom Allen, Casparus J. R. Verbeek, Simon Bickerton

Bibliographic record

VenueComposites Part A Applied Science and Manufacturing · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsMcGill UniversityAS Composite (Canada)
FundersMinistry of Business, Innovation and Employment
KeywordsPressure gradientStatic pressureParametric statisticsThermoplasticInletChannel (broadcasting)Melt flow indexViscosityDie (integrated circuit)

Abstract

fetched live from OpenAlex

Compared to pin-driven thermoplastic melt impregnation for fibre-reinforced tape manufacture, a cross-head slot channel die with crests can be used to reduce polymer degradation and tape porosity. In a slot channel, polymer melt is driven from the gate to the die exit by a static pressure gradient and drag at the fibre-tow interface. To inform the wavy slot die design, this paper augments pin-assisted melt impregnation models to introduce a process model combining static pressure in the melt, wedge-driven pressure, and crest pressure. Between crests, pressure develops in the wavy channel, analogous to theoretical pin-driven models. The static pressure gradient in the slot with a moving tow was examined by computational fluid dynamics. Parametrically studying the pulling speed, number of crests, crest length, melt viscosity, and melt inlet velocity shows that the interactive trends significantly change depending on the static pressure. When static pressure dominates, parameters that raise driving pressure, such as melt viscosity and melt inlet velocity, substantially improve the impregnation. Additionally, extended residence time by increasing the crest length and the number of crests improves the degree of impregnation. Meanwhile, wedge-driven impregnation is principally enhanced by the number of crests. The model is calibrated to fit experimental results using a single theoretical parameter when applying (i) Kozeny-Carman’s general permeability or (ii) Bruschke and Advani’s theoretical transverse permeability. Since static pressure in the wavy slot channel strongly benefits impregnation, the introduced model can inform the die design and operating conditions needed to elevate static pressure for minimum porosity. • Combined assessment of simulated static and theoretical contracting wedge pressure. • Impregnation at leading crests is dominantly driven by static pressure. • Impregnation at trailing crests is driven by pressure developed in the contracting wedge. • Fibre tow separation and spreading at crests increases transverse permeability. • Model informs die design and process parameter selection for effective impregnation.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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