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Record W2048876684 · doi:10.3139/217.1695

Three-dimensional Numerical Modeling of Co-injection Molding

2002· article· en· W2048876684 on OpenAlexaff
F. Ilinca, J.‐F. Hétu

Bibliographic record

VenueInternational Polymer Processing · 2002
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceCore (optical fiber)Isothermal processMolding (decorative)Work (physics)Finite element methodPolymerMechanicsComposite materialNewtonian fluidFlow (mathematics)Mechanical engineeringThermodynamicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract In this work, a three-dimensional finite element flow analysis code is used to solve sequential co-injection molding problems. Non-Newtonian, non-isothermal flow solutions are obtained by solving the momentum, mass and energy equations. Two additional transport equations are solved for tracking polymer/air and skin/core polymers interfaces. Solutions are shown for a center gated rectangular plate. The effect of varying the melt/mold temperature and the ratio between the skin and core materials is investigated. The solution obtained for the same skin and core materials is compared with those in which viscosities of core and skin materials are much different. Finally, the solution for the co-injection of a C-shaped plate is presented. The three-dimensional modeling of co-injection molding provides the complete shape of the core polymer as well as the skin polymer thickness at any location. This is a major improvement over the traditional mid-plane approach, which is unable to recover the material behavior in critical regions as near corners, obstacles, and changes in part thickness.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.247
Teacher spread0.218 · 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

Citations10
Published2002
Admission routes1
Has abstractyes

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Same venueInternational Polymer ProcessingSame topicInjection Molding Process and PropertiesFrench-language works237,207