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Record W4408542721 · doi:10.1016/j.eng.2025.03.015

Rotary Three-Dimensional Braider Design Method Based on the Average Cutting Circle Strategy

2025· article· en· W4408542721 on OpenAlexaff
Xin Yang, Siyi Bi, Huiqi Shao, Chenglong Zhang, Jinhua Jiang, Frank Ko, Nanliang Chen

Bibliographic record

VenueEngineering · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEngineering drawingMechanical engineeringComputer scienceMathematicsEngineeringGeologyStructural engineering

Abstract

fetched live from OpenAlex

Three-dimensional (3D) braided composites have significant potential for use in engineering structural materials. However, conventional 3D braiding machines are insufficient for designing composites with complex geometries. This paper proposes a programmable design methodology for 3D rotary braiding machines using circle-cutting and combination strategies. By introducing varying numbers of incisions on the circle, a diverse range of horn gears can be designed. Different combinations of these cut-circles allow the horn gears to be assembled into various 3D rotary braiders. The parametric equation for the braider plate is derived, showing that a combination strategy involving two cut-circles is feasible for braider design, whereas integrating three cut-circles simultaneously is impossible for a single machine. The construction of an automatic 6-3 type 3D braiding machine demonstrates the effectiveness of the proposed design strategy. This flexible braider design approach provides a practical solution for producing 3D braided composites with complex geometries.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.225
Teacher spread0.209 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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