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Record W2581180273 · doi:10.1155/2017/5637248

Adhesive Through-Reinforcement Improves the Fracture Toughness of a Laminated Birch Wood Composite

2017· article· en· W2581180273 on OpenAlexafffund
Wenchang He, Philip D. Evans

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of British Columbia
FundersChina Scholarship CouncilFPInnovationsAustralian National University
KeywordsMaterials scienceComposite materialReinforcementAdhesiveComposite numberFracture toughnessVeneerToughnessFracture (geology)Layer (electronics)

Abstract

fetched live from OpenAlex

In this paper we test the hypothesis that adhesive through-reinforcement in combination with glass-fibre reinforcement of adhesive bond lines will significantly improve the fracture toughness of a laminated birch wood composite. We test this hypothesis using a model composite consisting of perforated veneer that allowed a polyurethane adhesive to penetrate and reinforce veneers within the composite. Model composite specimens were tested for mode I fracture properties, and scanning electron microscopy was used to examine the microstructure of fracture surfaces. Our results clearly show that through-reinforcement, and also reinforcing adhesive bond lines with glass-fibre, significantly improved fracture toughness of the birch wood composite. Our results also indicate that improvements in fracture toughness depended on the level of reinforcement. Improvements in fracture toughness were related to the ability of the reinforcement to arrest crack development during fracture testing and the fibre bridging effect of glass-fibre in adhesive bond lines. We conclude that through-reinforcement is an effective way of improving the fracture toughness of laminated wood composites, but further research is needed to develop practical ways of creating such reinforcement in composites that more closely resemble commercial 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.

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 score0.019
Threshold uncertainty score0.442

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 communication0.0000.001
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 head0.008
GPT teacher head0.254
Teacher spread0.246 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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