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Record W2087319436 · doi:10.1177/0021955x14566208

Asymmetric microcellular composites: Mechanical properties and modulus prediction

2015· article· en· W2087319436 on OpenAlexaff
Cédric Tissandier, Rubén González‐Núñez, Denis Rodrigue

Bibliographic record

VenueJournal of Cellular Plastics · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceComposite materialUltimate tensile strengthFlexural strengthComposite numberBlowing agentFlexural modulusIzod impact strength testReinforcementModulusFiberTorsion (gastropod)Elastic modulusMoldNatural fiberMolding (decorative)

Abstract

fetched live from OpenAlex

In the first part of this study, asymmetric microcellular composites were prepared by injection molding to study their morphological properties as a function of temperature gradient inside the mold (0–60℃), as well as foaming agent (0–1%) and natural fiber (0–30%) contents. High-density polyethylene, flax fiber, and azodicarbonamide were used for the matrix, reinforcement, and chemical blowing agent, respectively. From the samples produced, mechanical properties (tensile, flexion, torsion, impact) are analyzed in this second part. Mechanical properties were found to be strongly influenced by density reduction and natural fiber content. It was also found that fiber addition provides higher reinforcement in flexion than torsion and tension. Also, flexural modulus and impact strength were relatively unaffected by foaming agent content for the range of parameters studied. From the experimental data obtained, a simple mechanical model based on density profile is presented to predict the elastic moduli of asymmetric structural composite foams.

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)0.001
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 score0.062
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.198
Teacher spread0.168 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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