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Record W1966521082 · doi:10.3139/217.1728

Measurement and Prediction of Temperature Distribution in an Injection Molding Cavity

2003· article· en· W1966521082 on OpenAlexafffund
F. Manero, Musa R. Kamal, R. A. Lai‐Fook, Alfredo Varela, W I Patterson

Bibliographic record

VenueInternational Polymer Processing · 2003
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMoldThermocoupleTemperature measurementMolding (decorative)Measure (data warehouse)Composite materialHeat transferCooling curveTransient (computer programming)MechanicsThermodynamicsMetallurgy

Abstract

fetched live from OpenAlex

Abstract A method to measure the melt temperature distribution in an injection mold cavity is developed. A thermocouple is used in the construction of a sensor with a tip that can be adjusted at different depths of a mold cavity, to measure temperature profiles at different positions in the mold. Polymer temperature distributions are measured and factors affecting temperatures and the key variables that influence distributions are determined. Measurements are compared with temperatures obtained from numerical simulation of the injection molding process using a three-dimensional heat transfer analysis. Although showing lower values and generally higher cooling rates, temperature data measured from the mold cavity indicate similar behavior to predicted transient temperature distributions.

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 studies0.0000.000
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 head0.018
GPT teacher head0.227
Teacher spread0.209 · 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

Citations13
Published2003
Admission routes2
Has abstractyes

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