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Record W2942571895 · doi:10.1177/0021998319844590

Tensile behaviour of hybrid fibre architectures of randomly oriented strands combined with laminate groups

2019· article· en· W2942571895 on OpenAlexaff
Swaroop B Visweswaraiah, Larry Lessard, Pascal Hubert

Bibliographic record

VenueJournal of Composite Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceWavinessUltimate tensile strengthComposite materialStackingStiffnessComposite number

Abstract

fetched live from OpenAlex

In this work, the tensile behaviour of co-moulded hybrid fibre architectures of randomly oriented strands (carbon/PEEK) combined with laminate groups (cross-ply, angle-ply and quasi-isotropic) is studied. The effects of varying the thickness of the laminate group relative to that of randomly oriented strands, stacking sequence of the architectures within a hybrid specimen, and the ply stacking sequences within the laminate group are quantified. Processing benefits of hybridization such as reduction in warpage and strand waviness are discussed. The tensile behaviour of hybrid fibre architectures is quantified and compared with that of randomly oriented strand specimens, base laminate groups and aluminum 7075. In addition, tensile failure modes have been investigated. Significant improvements in the mechanical properties of randomly oriented strands are observed with small proportions of laminate groups in the specimen. In addition, hybrid fibre architectures exhibit a positive synergy or a positive deviation from the rule-of-mixtures in the overall stiffness and strength behaviour when stacked in specific configurations, despite the same fundamental fibre type and matrix system.

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 score0.012
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.004
GPT teacher head0.199
Teacher spread0.195 · 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
Published2019
Admission routes1
Has abstractyes

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