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Record W2091818944 · doi:10.1002/pen.22143

Morphology development in the gate region of microinjection‐molded thermoplastics

2011· article· en· W2091818944 on OpenAlexaff
Jingsong Chu, Musa R. Kamal, A. Derdouri

Bibliographic record

VenuePolymer Engineering and Science · 2011
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsMcMaster UniversityNational Research Council CanadaMcGill UniversityMicromolding Solutions (Canada)
Fundersnot available
KeywordsPolyoxymethyleneMaterials sciencePolarized light microscopyDifferential scanning calorimetryComposite materialMolding (decorative)MicroinjectionOptical microscopeScanning electron microscopeMorphology (biology)PolyethyleneShear (geology)PolymerOptics

Abstract

fetched live from OpenAlex

Abstract The melt experiences extremely high shear rates as it travels through the very small gate, in the microinjection molding process. The combination of the high shear rates, extensive viscous heating, and the large thermal gradients has a profound influence on the characteristics of the moldings. However, many of these interactions are not clearly understood. In this study, the morphology of moldings in the gate region was observed using a polarized light microscope. Moreover, the specimens from different regions of micromoldings were analyzed using a differential scanning calorimeter (DSC). Two materials were selected for the study: polyoxymethylene (POM) and high‐density polyethylene. Three special morphological features were observed in the gate region for POM but not for polyethylene. The results obtained using the DSC were explained in light of the microstructural features observed using polarized light microscopy. POLYM. ENG. SCI., 2011. © 2011 Society of Plastics Engineers

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: Bench or experimental · Consensus signal: Bench or experimental
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 head0.024
GPT teacher head0.189
Teacher spread0.165 · 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

Citations21
Published2011
Admission routes1
Has abstractyes

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