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Record W3037879896 · doi:10.1177/0021998320935486

An energy-based model for the wear of unidirectional carbon fiber reinforced epoxy

2020· article· en· W3037879896 on OpenAlexafffund
Billy Cheng, Mark T. Kortschot

Bibliographic record

VenueJournal of Composite Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialEpoxyAbrasiveDissipationVolume fractionFiberVolume (thermodynamics)Carbon fibersComposite number

Abstract

fetched live from OpenAlex

The wear resistance of unidirectional carbon fiber reinforced epoxy under severe abrasive sliding conditions was studied. It was found that unidirectional laminates tested with the fibers parallel to the sliding direction (UDp) were more wear resistant than the same laminates tested with fibers transverse to the sliding direction (UDap) under the same set of test conditions. A novel energy-based model was developed to explain the difference in the wear rates. It was found that the difference in wear rates between the two orientations was due to differences in the average volume to surface area ratio of the debris, the energy required to generate new surfaces, and a new k factor that represents the fraction of the total friction energy used for creating wear particles. Furthermore, wear volume per sliding distance was found to be linearly proportional the total frictional energy dissipation for both orientations. These findings can be used to simplify wear predictions for industrial applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.220
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations5
Published2020
Admission routes2
Has abstractyes

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