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Record W2007225480 · doi:10.1063/1.4811696

Shock adhesion test for composite bonded assembly using a high pulsed power generator

2013· article· en· W2007225480 on OpenAlexfundno aff
Elise Gay, Laurent Berthe, E. Buzaud, Michel Boustié, Michel Arrigoni

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsnot available
FundersNational Research Council CanadaCommissariat à l'Énergie Atomique et aux Énergies AlternativesCentre National de la Recherche Scientifique
KeywordsMaterials scienceEpoxyUltimate tensile strengthComposite materialComposite numberPulsed powerContext (archaeology)Shock (circulatory)Stress (linguistics)Paint adhesion testingStructural engineeringTensile testingAdhesionElectrical engineeringVoltageEngineering

Abstract

fetched live from OpenAlex

In a context of the rising use of composite assemblies in aeronautic or defense fields, the assessment of their strength is a key issue. The method developed in this study attempts to provide solutions. A shock adhesion test based on short compressive loads, obtained by a high pulsed power generator, is proposed as a proof test to ensure the quality of composite bonded assemblies. A calibrated load induces a local tensile stress able to damage the bond interface. The high pulsed power source is the GEnerateur de Pression Isentropique device (Isentropic Pressure Generator), used to generate the required stresses, with a 450 ns pulse duration to test assemblies above the mm thickness range. The understanding of the mechanisms of wave propagation and tensile stress generation within these multilayer assemblies are scientific challenges. The ability of the technique to induce a tensile stress able to disbond the laminates and the assemblies is demonstrated. This paper details the response of carbon epoxy laminates and their bonded assemblies to a shock loading near the damage threshold.

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 score0.043
Threshold uncertainty score0.466

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)0.0000.000

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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