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Record W2601345674 · doi:10.1177/1099636217716059

Mechanical performance of repaired sandwich panels: Experimental characterization and finite-element modelling

2017· article· en· W2601345674 on OpenAlexaff
Emna Ghazali

Bibliographic record

VenueJournal of Sandwich Structures & Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsOrthotropic materialFinite element methodMaterials scienceComposite materialStructural engineeringComposite numberUltimate tensile strengthSandwich-structured compositeAdhesiveEpoxyCore (optical fiber)Shear (geology)Linear elasticityJoint (building)EngineeringLayer (electronics)

Abstract

fetched live from OpenAlex

This paper describes the static performance of adhesively bonded repairs on sandwich panels made with carbon-epoxy composite skins and a Nomex core. First, the mechanical behaviour of pristine and repaired panels under tensile loading was studied. All tests were conducted under room temperature conditions. Then, finite-element analyses were performed to predict the behaviour of repaired panels. Two material models were developed for the adhesive joint: one was linear elastic and the second was elastic-plastic with a shear failure criterion. For the composite skins, an orthotropic linear elastic model was used. Numerical model predictions are in good agreement with the experimental results. It was found also that the strength recovery of the repaired structure increases with the decrease of the scarf angle.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.258
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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