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Record W2062331498 · doi:10.1177/0021955x14525797

Visualization of foaming phenomena in thermoplastic injection molding process

2014· article· en· W2062331498 on OpenAlexaff
Ahmadzai Ahmadzia, Amir Hossein Behravesh, Majid Tabkhpaz Sarabi, Peyman Shahi

Bibliographic record

VenueJournal of Cellular Plastics · 2014
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMoldMaterials scienceMolding (decorative)ThermoplasticComposite materialVisualizationTransfer moldingInjection mouldingCore (optical fiber)Flow (mathematics)Engineering drawingMechanical engineeringMechanics

Abstract

fetched live from OpenAlex

The present study focuses on visualization analysis of foaming mechanisms in foam injection molding process. A novel approach in mold design is introduced to take advantages of concepts such as counter-pressure and mold opening to further extension of expansion range, a visual mold with a rectangular cavity was designed and manufactured to observe the effectiveness of this approach. The mold consists of a main cavity and an over-flow well connected together via a secondary gate of variable sizes. The selected processing parameters were gate width and part thickness. The observation clearly showed the development of melt frontier and gas escape, which were affected by varying the selected parameters. The results showed that the relative bulk density and cell diameter decreased and cell population density increased as the optimum gate width (7 mm) was employed.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.233
Teacher spread0.226 · 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 designObservational
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

Citations15
Published2014
Admission routes1
Has abstractyes

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