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Record W2950451986 · doi:10.1063/1.5100257

A simplified semi-analytical model for the filling and cooling process in plastic molding

2019· article· en· W2950451986 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhysics of Fluids · 2019
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsHusky Injection Molding Systems (Canada)Université Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMolding (decorative)Blow moldingMechanicsMoldThermalProcess (computing)LubricationMechanical engineeringComposite materialPhysicsMaterials scienceThermodynamicsEngineeringComputer science

Abstract

fetched live from OpenAlex

The effects of the operational conditions on the filling and cooling process in plastic molding are studied. First, a semianalytical model is developed to study the effects of thermal boundary conditions and imposed pressure on the velocity profile and blocking time in the filling stage of the molding process. To do so, a lubrication approximation type model is developed in which the shear viscous heating terms are neglected to simplify the governing equations. Using an equipped mold, molding experiments are conducted to validate the model predictions by using a well-controlled injection molding machine. Comparing the model and experimental results shows a reasonable agreement among them. Second, regarding the cooling process, a modified Avrami equation is used to capture the polymer crystallization in variable cooling rates. The model results show that the cooling time and the final solid fraction decrease by increasing the cooling rate.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.028
GPT teacher head0.255
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