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Record W4244148264 · doi:10.32920/ryerson.14648766.v1

Preliminary Analysis of the Effects of Different Machining Techniques on Carbon Fibre Epoxy Materials

2021· preprint· en· W4244148264 on OpenAlexaff
Diana Mollicone

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMachiningAerospaceEpoxyMaterials scienceThermographyDrillingStrain gaugeComposite materialCarbon fibersUltimate tensile strengthBearing (navigation)Mechanical engineeringStructural engineeringComposite numberInfraredMetallurgyComputer scienceEngineeringOpticsAerospace engineering

Abstract

fetched live from OpenAlex

Composite materials present a potential alternative to traditional metallic alloys in aerospace structural components such that they have desirable mechanical properties while possessing low densities. These components are traditionally joined together through bolts, which require the materials to have machined holes. This inquiry compared the effects of different types of machining on carbon epoxy plates: drilling with a coated bit and waterjet machining, and how they impact the material’s behavior during load-bearing operations. Three forms of material testing were used: tensile testing, strain gauge, and infrared thermography analysis during cyclic loading. The results obtained do not demonstrate consistent patterns that would suggest that one machining method is beneficial over the other.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.008
GPT teacher head0.234
Teacher spread0.225 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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