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Record W4404733109 · doi:10.1177/00219983241289496

Fatigue life prediction of hybrid carbon-flax-epoxy laminates using progressive failure analysis and cohesive zone model

2024· article· en· W4404733109 on OpenAlexaff
Kamal Kolasangiani, Braedon Hoekstra, D. C. D. Oguamanam, Habiba Bougherara

Bibliographic record

VenueJournal of Composite Materials · 2024
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceEpoxyComposite materialCarbon fibersComposite laminatesFatigue testingStructural engineeringComposite number

Abstract

fetched live from OpenAlex

A 3D progressive fatigue damage model was incorporated into ABAQUS software to predict the fatigue life of hybrid carbon-flax-epoxy composites. A cohesive zone method was employed to simulate delamination damage at carbon/flax interface. To evaluate the accuracy of the finite element model, two different layups, namely, unidirectional [0–90 C2 /0 F6 ] S (UD) and angle-ply [0–90 C2 /(±45) F6 ] S (AP), consisting of 12 flax fibers layers (F) sandwiched between four woven carbon fibers layers (C) were created using hand lay-up technique and then placed in compression molding. There is a good agreement between experimental and predicted fatigue life. It was observed that the predicted fatigue life is reduced by about 14% by changing the void content from 0.38% to 3.83%. The finite element analysis revealed that the matrix in the flax region of the AP layup failed at 12% of fatigue life, followed by delamination at the carbon/flax interface, consistent with SEM results.

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 score0.198
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.018
GPT teacher head0.251
Teacher spread0.233 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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