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Record W2027915061 · doi:10.1177/0731684413509427

A 3D micromechanical energy-based creep failure criterion for high-temperature polymer–matrix composites

2013· article· en· W2027915061 on OpenAlexaff
Alireza Sayyidmousavi, Habiba Bougherara, Seyed Reza Falahatgar, Zouheir Fawaz

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2013
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMicromechanicsMaterials scienceCreepComposite materialViscoelasticityComposite numberMatrix (chemical analysis)Polymer

Abstract

fetched live from OpenAlex

In the present study, a generalized three-dimensional (3D) energy-based criterion for the creep failure of viscoelastic materials is developed. Unlike the existing approaches which are restricted to uniaxial loading, the proposed criterion can predict failure under any combination of loads. This criterion is then incorporated into a simplified unit cell micromechanical model to predict the time-delayed failure of unidirectional polymer–matrix composites at elevated temperatures. The composite material used in this study is T300/934 which is suitable for service at high temperatures in aerospace applications. The use of micromechanics can give a more accurate insight into the failure mechanisms of the composite materials, in particular at high temperatures, where the general behavior of the polymer–matrix composite is governed by matrix viscoelasticity and matrix time-dependent failure due to creep is a localized phenomenon. The micromechanical model is also used to estimate the ultimate strength of the constituents from the knowledge of the allowable strengths of the unidirectional composite in the principal material directions. The obtained creep failure stresses are found to be in reasonable agreement with the experimental data particularly for the 90° unidirectional laminate, where failure is totally matrix dominated.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.198
Teacher spread0.194 · 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
Published2013
Admission routes1
Has abstractyes

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