Simulation and characterization of circular hexagonal braiding fabricstructure
Bibliographic record
Abstract
Hexagonal braiding technology is a kind of state-of-the-art braiding method, which uses hexagonal horngears to driveyarn carriers and make yarns intertwined into fabrics. In terms of hexagonal braiding principles, the braiding parameterslike initial arrangement of yarn carriers, yarn number and horngears sequence were defined, and then the movementpaths of yarn carriers in hexagonal braiding process and stitch length were obtained, which could be converted intocoordinates on the xoy plane and the coordinates along z-axis. In that case, a group of spatial coordinates were got tocreate the yarn trajectories and fabric structures in Matlab. And then, B-spline curve was utilized to fit the yarntrajectories. Considering the compactness of hexagonal fabric, the coordinates conversion algorithm and conversionmatrix were utilized to optimize the fabric structure, so a more compact fabric structure was established. The braidingangle variation and volume fraction of fabric showed that after coordinates conversion the braiding angles became morestable than original fabric model, and the fiber volume fraction of fabric was improved too. So the fabric structure modelwas available to describe hexagonal fabric structure, which can offer the reference for the further study on properties ofhexagonal braiding technology and application of hexagonal braided fabric
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".