MétaCan
Menu
Back to cohort
Record W1922132145 · doi:10.1177/0731684415588936

A multiscale approach for fatigue life prediction of polymer matrix composite laminates

2015· article· en· W1922132145 on OpenAlexaff
Alireza Sayyidmousavi, Habiba Bougherara, Zouheir Fawaz

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2015
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMicromechanicsMaterials scienceComposite laminatesFinite element methodComposite materialComposite numberFailure mode and effects analysisMultiscale modelingStructural engineeringDamage mechanicsDamage toleranceStress (linguistics)Engineering

Abstract

fetched live from OpenAlex

The present study introduces a progressive fatigue damage model within a multiscale framework by incorporating a Simplified Unit Cell Micromechanical model into a Finite Element program. The use of micromechanics will allow the study of damage at the micro-scale which can therefore identify modes of failure in each of the composite’s constituents, separately. The use of finite element method at the macro-scale enables the model to capture the geometric complexities including regions of stress concentration, which expedites the failure of the material. Damage progression is modeled through the degradation of the material property corresponding to the failure mode detected by the micromechanical model. The results of the model are in good agreement with the experimental data for both unidirectional and multidirectional laminates. The present approach is capable of predicting the fatigue life of composite laminates of any arbitrary geometry and lay-up configuration with minimum dependence on empirical parameters.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.626

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 head0.026
GPT teacher head0.241
Teacher spread0.215 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Reinforced Plastics and CompositesSame topicMechanical Behavior of CompositesFrench-language works237,207