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Injection Molding of Wood–Fiber/Plastic Composite Foams

2009· article· en· W2012919150 on OpenAlex
Jae D. Yoon, Takashi Kuboki, P.U. Jung, J. Wang, Chul B. Park

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

VenueComposite Interfaces · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHigh-density polyethyleneMaterials scienceComposite materialComposite numberFiberMolding (decorative)PolyethyleneBlowing agentWood-plastic compositePolyurethane

Abstract

fetched live from OpenAlex

This paper investigates the feasibility of injection-molded wood–fiber/high-density polyethylene (HDPE) composite foams that can replace injection-molded HDPE solids in industrial applications. The study applies injection foam molding technology using a physical blowing agent to a wood–fiber/HDPE composite, and examines the effects of the processing parameters on the dimensional and mechanical properties and cell density of the composite foams. In addition, the physical properties and cost of wood–fiber/HDPE composite foams are compared with those of solid HDPE. The experimental results show that wood–fiber/HDPE composite foams that have a 20% weight reduction have superior physical properties, such as density, dimensional properties (68% decrease of shrinkage and 91% decrease of warpage) and mechanical properties (28% increase of Young's modulus). Furthermore, the cost analysis confirms that wood–fiber/HDPE composite foams are much less expensive (by 40%) than HDPE. Therefore, it is concluded that wood–fiber/HDPE composite foams are strong candidates for replacing current injection-molded HDPE products.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.985

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 science0.0010.000
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 head0.012
GPT teacher head0.248
Teacher spread0.237 · 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