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Record W3093171641 · doi:10.3390/ma13204637

Creep Testing of Thermoplastic Fiber-Reinforced Polymer Composite Tubular Coupons

2020· article· en· W3093171641 on OpenAlexafffund
Hai Giang Minh Doan, Pierre Mertiny

Bibliographic record

VenueMaterials · 2020
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesAlberta Innovates - Technology Futures
KeywordsCreepMaterials scienceComposite materialUltimate tensile strengthContext (archaeology)Digital image correlationFibre-reinforced plasticDeformation (meteorology)ThermoplasticComposite numberLateral strainStrain gaugeViscoplasticityStructural engineeringConstitutive equationFinite element method

Abstract

fetched live from OpenAlex

Thermoplastic fiber-reinforced polymer composites (TP-FRPC) are gaining popularity in industry owing to characteristics such as fast part fabrication, ductile material properties and high resistance to environmental degradation. However, TP-FRPC are prone to time-dependent deformation effects like creep under sustained loading, which can lead to significant dimensional changes and affect the safe operation of structures. Previous research in this context has focused, mainly, on testing of flat coupons. In this study, a creep testing method for TP-FRPC tubular coupons was developed. Specimens were fabricated using tape winding and subjected to well-defined loading conditions, i.e., pure hoop tensile and pure axial compressive stress. Strain gauges and digital image correlation were both employed for strain measurements and were found to be in good agreement. The evolution of strain rate, Poisson's ratio and creep compliance were investigated. The prediction of experimental data by the Burgers model and the Findley's power law model were explored. The research findings suggest that the developed experimental and analysis approach provides valuable information for the design of material systems and structures.

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 categoriesInsufficient payload (model declined to judge)
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 score0.002
Threshold uncertainty score0.999

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)0.0020.000

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 head0.023
GPT teacher head0.216
Teacher spread0.193 · 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

Citations24
Published2020
Admission routes2
Has abstractyes

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