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Record W2948594831 · doi:10.4050/f-0074-2018-12887

Development of Techniques for Composite Parts Failure Analysis in Static and Fatigue Mode - Application on Thermoset and Thermoplastic Materials for Rotorcraft Main Rotor Hub Analysis

2018· article· en· W2948594831 on OpenAlexaff
Mélanie Herman, Christian Hochard, J. M. Besson, Jean-Paul Charles

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsCanadian Nautical Research Society
Fundersnot available
KeywordsEpoxyMaterials scienceComposite materialThermosetting polymerComposite laminatesPultrusionWoven fabricPeekFailure mode and effects analysisStructural engineeringRotor (electric)Composite numberTension (geology)Compression (physics)Mechanical engineeringEngineeringPolymer

Abstract

fetched live from OpenAlex

A model defined at the ply scale to predict the failure of laminated composites for static or fatigue loading is proposed. The model describes the loss of strength in the fiber direction for a significant level of transverse damage. This meso-scale model has been characterized on woven ply laminates used for rotorcraft dynamic components, such as glass/epoxy of Starflex®, carbon fiber/epoxy, and carbon fiber/PEEK of H160 main rotor hub. Failure behavior prediction at coupon level has been validated regarding static and fatigue failure mode in tension for epoxy resin woven ply laminates. Characterizations have been also provided for PEEK resin in balanced woven ply laminate, regarding static or fatigue failure mode. Those activities are crucial to increase the level of confidence in failure model, to rely on virtual testing at coupons level, and to better predict damage and failure at component level. This work intends to support the building block approach during development and certification of such critical applications for rotorcraft.

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 score0.282
Threshold uncertainty score0.621

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 head0.016
GPT teacher head0.281
Teacher spread0.265 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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