Characterizing and modeling of low twist yarn mechanics
Bibliographic record
Abstract
Twist in yarns can be used for handling or testing purposes, but it is not necessary when using continuous multifilament yarns as compared to the spinning required for a short fiber yarn. Small amounts of twist have shown to increase the strength of the yarn while decreasing the longitudinal stiffness. Previous models, including Gegauff’s cos 2 θ model and Rao and Farris’ model, are compared and discussed. A para-aramid (Kevlar 49) and a regenerated cellulose (BioMid) yarn are tested at various levels of twist to compare with these models. Twist is manually applied, and the samples are tested under continuous rate of extension to determine chord modulus, breaking tenacity, and elongation and break. The results are then fit to existing prediction models using a minimization of the standard error of the regression. Finally, a linear regression is also applied to the data to contrast the fit compared to traditional models. It was found that while the Gegauff model and the Rao and Farris model may capture the overall trend and decrease in longitudinal stiffness over a large range of twist, the small range over which twist can practically be used is not well represented by these models and is better represented by a simple linear relationship.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".