Tear behavior of polyester‐based coated textiles after thermo‐oxidative aging
Bibliographic record
Abstract
Abstract In this article, a new method to characterize the tear behavior of coated textiles using fracture mechanics is proposed. The energy dissipated in tearing (EDT) of as‐received and thermally aged samples of polyester fabric, polyvinyl acetate rubber coating, and textile‐coating composites was calculated and compared. The EDT of the coated fabric displayed a slightly smaller value than the fabric alone, whereas the EDT of the coating was found to be negligible when compared with the other two. The presence of the coating is believed to have a detrimental effect on the tearing behavior of the coated fabric as it hinders interfilament slippage. A master curve of EDT retention vs. aging time for noncoated and coated fabric samples was constructed using the time–temperature superposition principle and fitted using the Hill equation. Fourier transform infrared analyses carried out on aged fabric samples hinted at a possible chain scission process, whereas the crystallinity of fabric samples, calculated via differential scanning calorimetry, was found to decrease after thermal aging. Scanning electron microscopy images revealed an increase in surface roughness after aging that may reduce interfilament friction. These results, coupled to an increase in the adhesion strength between fabric and coating, are likely the cause of the reduction of EDT noticed in aged coated and noncoated fabrics compared with as‐received ones. POLYM. COMPOS., 2012. © 2012 Society of Plastics Engineers
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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 | 0.000 | 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".