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Record W4400251150 · doi:10.1002/pssb.202400115

Using a Negative Poisson's Ratio to Mitigate Stress Concentrations in Perforated Composite Plates

2024· article· en· W4400251150 on OpenAlexfundno aff
Amine Harkati, Said Chouai, El‐Haddi Harkati, Abderrezak Bezazi, Salah Ellagoune, Fabrizio Scarpa

Bibliographic record

Venuephysica status solidi (b) · 2024
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsnot available
FundersMinistry of Advanced Education and Skills Development
KeywordsMaterials scienceComposite numberComposite materialModulusPoisson's ratioStress concentrationStress (linguistics)Structural engineeringPoisson distributionEpoxyFracture mechanicsMathematicsEngineeringStatistics

Abstract

fetched live from OpenAlex

Stress concentrations pose a significant challenge in designing and optimizing composite components, often resulting in structural alterations and crack formation. This study delves into a detailed numerical and analytical analysis of stress concentration factors in composite materials, with a specific focus on the influence of negative Poisson's ratio and out‐of‐plane modulus. By exploring the interaction of these material properties, the objective is to devise effective strategies for alleviating stress concentrations in composite structures. Through analytical and numerical simulations, a robust correlation between the customized Poisson's ratio and modulus and the mitigation of stress concentration factors, particularly in epoxy/graphite‐reinforced composites are established. This tailored approach has the potential to enhance energy absorption capabilities and consequently reduce the risk of crack propagation in perforated laminated composite plates. This research findings offer valuable insights into composite material design, presenting innovative solutions for enhancing structural integrity and reducing susceptibility to stress‐related issues.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.288
Teacher spread0.261 · 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.

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

Citations6
Published2024
Admission routes1
Has abstractyes

Explore more

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