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Record W2914606935 · doi:10.1177/096369351001900105

Design of a Carbon Fiber Bicycle Stem using a Novel Internal Bladder Resin Transfer Molding Technique

2010· article· en· W2914606935 on OpenAlexaff
Maxime Thouin, Hossein Ghiasi, Larry Lessard

Bibliographic record

VenueAdvanced Composites Letters · 2010
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceTransfer moldingComposite numberStiffnessMolding (decorative)FabricationComposite materialMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The goal of this research is to design, analyze, and manufacture a carbon fibre bicycle stem that maximizes the use of carbon fibre composites. The stem is a part of the bicycle that connects the handlebar to the fork. The design is difficult due to the small size and complex geometry of the part, thus not obvious to conceive with a standard composite manufacturing approach. A novel approach using an inner bladder resin transfer moulding (RTM) technique is used to manufacture the bicycle stem. The design of the moulds incorporates some efficient devices to facilitate various steps in the manufacturing process. The resulting design is successful in terms of weight, stiffness, strength, and aesthetic properties. The developed technique used in this project can be applied as a possible solution to the design and fabrication of small, complex, hollow composite parts.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.256
Teacher spread0.238 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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