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Record W4311635215 · doi:10.6000/1929-5995.2022.11.06

Fatigue and Failure Analysis of Sandwich Composites using Two Types of Cross-Ply Glass Fibers Laminates and Epoxy Resin

2022· article· en· W4311635215 on OpenAlexvenueno aff
João Pedro Monteiro Cheloni, Márcio Eduardo Silveira, Eder Sócrates Najar Lopes, Leandro José da Silva

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2022
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMaterials scienceEpoxyComposite materialStiffnessGlass fiberComposite laminatesBendingComposite number

Abstract

fetched live from OpenAlex

Sandwich structures have become effective structural elements for engineering applications due to their good design flexibility. Understanding the material behavior under static and dynamic loads, as well as the failure mechanisms of these sandwich structures, is of great importance. This work evaluates the fatigue and static bending behavior of epoxy resin specimens and sandwich composites composed of an epoxy resin core with glass fiber laminated faces. The fatigue life, failure modes, and stiffness degradation of these specimens are determined experimentally. The specimens were cycled under constant amplitude and monitored by a data acquisition system that allowed continuous data collection. Three stages of failure were identified using microscope analyses and stiffness degradation curves. In the case of an imposed displacement of 2 mm, the sandwich structures were shown to have a significantly lower fatigue life than the epoxy resin specimens.

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)0.003
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 score0.094
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.043
GPT teacher head0.391
Teacher spread0.349 · 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

Same venueJournal of Research Updates in Polymer ScienceSame topicMechanical Behavior of CompositesFrench-language works237,207