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Record W2102030520 · doi:10.1002/pen.23947

Fatigue properties of vibration‐welded postindustrial waste nylon with glass fibers at room and elevated temperatures

2014· article· en· W2102030520 on OpenAlexafffund
Y. Zhang, K.T. Lockwood, P. J. Bates, D.L. DuQuesnay

Bibliographic record

VenuePolymer Engineering and Science · 2014
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsQueen's UniversityRoyal Military College of Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceUltimate tensile strengthComposite materialFatigue limitGlass fiberWeldingNylon 66Nylon 6Tension (geology)FiberPolymer

Abstract

fetched live from OpenAlex

The effect of temperature on the tensile and fatigue strength of vibration‐welded and unwelded postindustrial waste nylon 6 reinforced with 30 wt% glass fiber (PIWGF) was experimentally examined, and the results were compared to those obtained from a 30 wt% glass fiber reinforced prime nylon 6 compound (PAGF) from a previous study. Fatigue tests were performed under sinusoidal constant amplitude tension‐tension load at a stress ratio of R = 0.1 and within the frequency range of 2–10 Hz at temperatures from 24 to 120°C. Stress levels from just under the tensile strength down to the run‐out point at 5 million cycles were used. It was found that increasing temperature led to a significant decrease in both tensile strength and fatigue life. For PIWGF, there was ∼20% strength reduction under both static tensile and cyclic loading as compared to PAGF. For both welded and unwelded PIWGF, the endurance ratio; i.e., the ratio of fatigue strength to static tensile strength, was ∼45% regardless of the temperature. The fatigue notch factor ( K f ) was between 1.4 and 1.8 for all test temperatures examined. POLYM. ENG. SCI., 55:799–806, 2015. © 2014 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 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 score0.165
Threshold uncertainty score0.418

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 head0.013
GPT teacher head0.192
Teacher spread0.180 · 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

Citations4
Published2014
Admission routes2
Has abstractyes

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