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Record W2102207750 · doi:10.1177/0021955x06060950

Manufacture of Integral Skin PP Foam Composites in Rotational Molding

2006· article· en· W2102207750 on OpenAlexaff
Remon Pop‐Iliev, Kyoung Ho Lee, Chul B. Park

Bibliographic record

VenueJournal of Cellular Plastics · 2006
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of TorontoOntario Tech University
Fundersnot available
KeywordsMaterials scienceComposite materialPolypropyleneMoldHomogeneity (statistics)Molding (decorative)Rotational speedCore (optical fiber)Blowing agentProcess windowMechanical engineeringPolyurethaneComputer science

Abstract

fetched live from OpenAlex

The feasibility of applying the single-charge rotational foam molding processing principle to the fabrication of integral skin polypropylene (PP) foams comprising a PP solid skin and a PP foamed core is investigated in this study. A systematic process interruption and sample evaluation approach was used to quantify the experimental results and explore possibilities for improving the process control strategies to ultimately achieve a desired homogeneity and thickness uniformity of the solid PP skin layer that would be fully encapsulating the PP foamed core of a desired cell population density and average cell size. The experimental results revealed that this is quite a challenging task, not only because of the well-known intrinsically poor foaming nature of PP due to its low melt strength at elevated temperatures, but also because, in single-charge rotational molding, the processing parameters are often conflicting with each other and therefore, have to be optimized within a very narrow processing window. However, simultaneous, single-charge, quality PP integral skin and foamed PP core formation in rotational foam molding is feasible. Optimizing the heating profile, heating rate, heating time, and the mold rotational speed as well as careful selection of PP resins (or resin blends), chemical blowing agents (CBA), and their composition formulations is strongly recommended.

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 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 score0.009
Threshold uncertainty score0.371

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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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 teacher head, 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

Citations13
Published2006
Admission routes1
Has abstractyes

Explore more

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