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Record W2022472066 · doi:10.1002/pc.20057

Fatigue resistance of continuous glass fiber/polypropylene composites: Temperature dependence

2004· article· en· W2022472066 on OpenAlexaff
Martin Bureau, J. Denault

Bibliographic record

VenuePolymer Composites · 2004
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceComposite materialPolypropyleneThermosetting polymerComposite numberGlass fiberFlexural strengthFatigue limitSofteningStress (linguistics)FiberReinforcement

Abstract

fetched live from OpenAlex

Abstract The effect of testing temperature on the fatigue resistance of continuous glass fiber/polypropylene (CGF/PP) composites was studied. Fatigue resistance curves (or S‐N curves) were obtained at −40°C, 23°C and 50°C. Both on an absolute stress basis and on a normalized stress basis (with respect to the yield stress at the temperature considered), the S‐N curves showed that CGF/PP composites had excellent fatigue performance at 23°C and that their performance was actually improved at −40°C (below T g of the PP matrix). The S‐N curves at 50°C showed that, although the composite flexural strength was reduced because of PP matrix softening, their fatigue performance remained relatively high, as it is controlled by the CGF reinforcement. Comparison with a CGF/thermoset isophthalic polyester composite of identical fiber architecture and similar flexural strength at 23°C indicated that the properties of the thermoplastic PP matrix provided improved fatigue resistance, both on an absolute and a normalized basis, especially below the glass transition temperature. It was concluded that the fact that the fatigue performance of the CGF/polyester composite is only weakly temperature‐dependent, while that of the CGF/PP composite is strongly temperature‐dependent, does not necessarily mean that it shows superior performance. Polym. Compos. 25:622–629, 2004. © 2004 Society of Plastics Engineers.

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 score0.014
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 science0.0010.000
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 head0.011
GPT teacher head0.226
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.

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

Citations20
Published2004
Admission routes1
Has abstractyes

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