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Record W4311995930 · doi:10.1177/07316844221145559

Role of discontinuous fiber core material on the mechanical behavior of hybrid sandwich polyester composites

2022· article· en· W4311995930 on OpenAlexaff
Richard Larson, Von Clyde Jamora, Siavash Sattar, Sergii G. Kravchenko, Oleksandr G. Kravchenko

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceComposite materialFlexural strengthCompression moldingComposite numberScrapCore (optical fiber)Sheet moulding compoundMolding (decorative)Compressive strengthFiberPolyesterGlass fiberCompression (physics)

Abstract

fetched live from OpenAlex

This study investigated the potential for improving the mechanical behavior of glass fiber polyester composites by using symmetric hybrid sandwich configurations that combine continuous woven composite and different discontinuous reinforcements used as a core. The discontinuous reinforcements included sheet molding compound (SMC), bulk molding compound (BMC), and the discontinuous prepreg based platelets that were chopped from the woven trim scrap. Hybrid sandwich configurations using different core materials exhibited improvements in flexural mechanical properties, while providing more progressive failure under both flexural and compressive loading. Compressive strength in polyester SMC and woven laminate was significantly lower than flexural strength, while BMC and chopped platelets exhibited similar levels of strength in flexure and compression. Overall, hybridized compression molding produced sandwich structures can be used to control the failure progression and to improve the structural characteristics of composite, especially under flexural loads. The trim scrap core sandwich configuration had mechanical properties comparable with the BMC core hybrid. Therefore, trim scrap upcycling can be an efficient approach to reducing the amount of material waste during the manufacturing.

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 score0.004
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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