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Record W2954817069 · doi:10.1063/1.5112528

Analysis of inter-ply friction in consolidation process of thermoset woven prepregs

2019· article· en· W2954817069 on OpenAlexaff
Armin Rashidi, C. Keegan, Abbas S. Milani

Bibliographic record

VenueAIP conference proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsThermosetting polymerMaterials scienceComposite materialConsolidation (business)Process (computing)Woven fabricComputer scienceBusiness

Abstract

fetched live from OpenAlex

A dedicated custom-built experimental setup and testing methodology were designed and manufactured for the investigation of the inter-ply friction behavior of Carbon/Epoxy fabric prepregs at varying interfacial temperatures, rates, and compaction pressures. The experimental parameters were selected in the ranges corresponding with those in the pregelation phase of the consolidation processes in autoclave processing of laminates and sandwich panels. A thorough understanding of the inter-ply frictional shear forces during the consolidation was obtained, suggesting a strong dependence on the process conditions and the dominance of mixed lubrication regime. Continuous experiments were conducted to determine the state of friction over the course of full-cure cycle. The observed differences were shown to be primarily dependent on both the resin viscosity and the distribution of the resin on the prepreg surface. Identification of dominating trends also suggested the high sensitivity of the results to the surface roughness of the prepreg.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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 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

Citations8
Published2019
Admission routes1
Has abstractyes

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