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Record W2899873836 · doi:10.1088/1361-665x/aaefcd

Interlaminar prestressing reinforcement of epoxy/glass fiber composites

2018· article· en· W2899873836 on OpenAlexafffund
Eric S. Kim, Dryver R. Huston, Patrick Lee

Bibliographic record

VenueSmart Materials and Structures · 2018
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsComposite materialEpoxyMaterials scienceReinforcementGlass fiberFiberComposite epoxy material

Abstract

fetched live from OpenAlex

Abstract This paper describes an innovative through-thickness fiber reinforcement technology that employs in situ shrinking fibers to provide supplemental strength-enhancing interlaminar prestresses for fiber-reinforced polymeric laminate structures. Interlaminar stitched fibers shrink and provide prestress. The shrinkage is heat-activated and timed to coincide with epoxy-curing steps. This new technology includes the design and fabrication of in situ shrinking fibers to improve the peel strength of epoxy/glass fiber composite layers. The epoxy specimens were shrink-reinforced under four different conditions; the fibers were activated when the epoxy matrix was cured for 4, 12, and 20 h, or without having any curing time beforehand. Then, the peel strengths and flexural strengths were compared. Also, in-plane tensile tests were conducted under identical conditions to investigate whether the through-thickness shrinking fibers affect the in-plane properties. The results indicated that the maximum improvement from the fiber activation was shown when the epoxy was cured for 4 h, while there was no significant effect from the in-plane tensile test.

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 score0.010
Threshold uncertainty score0.915

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)0.0010.000

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 head0.228
Teacher spread0.218 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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