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Record W1621037735 · doi:10.1063/1.1766516

Experimental And Numerical Study Of The Melt Behavior During The Injection Molding Process

2004· article· en· W1621037735 on OpenAlexaff
F. Ilinca

Bibliographic record

VenueAIP conference proceedings · 2004
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSprueNozzleMaterials sciencePressure dropShear stressMechanicsMoldFinite element methodShear (geology)Molding (decorative)Isothermal processStress (linguistics)Composite materialStructural engineeringMechanical engineeringEngineeringThermodynamics

Abstract

fetched live from OpenAlex

In this paper the non‐isothermal flow of a melt polymer during the filling of a rectangular plate is investigated both experimentally and numerically. Two pressure sensors were mounted flush on the slightly tapered rectangular sprue of a center gated plate mold to monitor the time evolution of the wall shear stress prior to entering the cavity. For large injection speeds the pressure drop in the sprue presents a maximum shortly after the beginning of the injection. After this point, the wall shear stress in the sprue drops remaining relatively constant during the late stage of the filling. At low injection speed, the wall shear stress in the sprue increases in time continuously because of the cooling. The filling of the plate is computed using a 3D finite element code and the predicted pressure drop in the sprue is compared with the measurements. Solutions obtained with and without considering the flow in the nozzle illustrate that the nozzle induces important thermal effects and thus need to be taken into account in the simulation. The need for three‐dimensional simulation is also illustrated.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.017
GPT teacher head0.245
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 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

Citations0
Published2004
Admission routes1
Has abstractyes

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Same venueAIP conference proceedingsSame topicInjection Molding Process and PropertiesFrench-language works237,207