The effects of mechanical degradation on the quasi static and dynamic stiffness of polyester yarns
Bibliographic record
Abstract
Polyester fibers are the most used in the manufacture of ropes for mooring systems and offshore operation, thus being constantly subjected to different situations. Such requests are implicated in a variety of load conditions, and their effects must be studied. This work presents data referring to an experimental study on the behavior of the quasi-static and dynamic stiffness of polyester yarns considering different mechanical levels of degradation and use. The study is performed with five different types of multifilament samples, these were extracted from a virgin spool and sub-ropes tested for tension and fatigue. The experimental procedure is carried out through an initial characterization where the linear density, the Yarn Break Load - YBL and the linear tenacity of the samples are determined. Continuing with the experimental tests, a procedure standardized by ISO 18962-2 is then carried out, consisting of three quasi-static stages and three dynamic stages, where the data acquired in the tests allow the determination of a dimensionless stiffness value. The results showed an increase in the quasi-static stiffness, tending to a plateau, and a linear increase in the dynamic stiffness, but with somewhat similar behavior between the samples. The results related to the total quasi-static stiffness also show that the specimens extracted from sub-ropes that underwent fatigue present greater total non-dimensionalized stiffness, this is indicative of the mechanical fatigue procedure as an improvement of the specimens, giving them greater stiffness, and consequently greater stresses rupture, a behavior that should be explored in future studies.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".