Effect of thermal ratcheting on the mechanical properties of Teflon and fiber based gasket materials
Bibliographic record
Abstract
ABSTRACT This paper discusses the effect of thermal ratcheting on the material properties of expand polytetrafluoroethylene (ePTFE), virgin polytetrafluoroethylene (vPTFE), and compressed nonasbestos fiber (CNA) gasket materials. Comparison between the creep strain at constant temperature and when subjected to thermal ratcheting show a 7.7 and 28% increase in the creep strain of ePTFE and vPTFE, respectively. In addition, thermal ratcheting produces a substantial reduction of creep modulus of these materials. The CNA material does not exhibit significant change in creep strain or in creep modulus with thermal ratcheting. However, all three gasket materials show a momentous raise in the creep strain when the material temperature is reduced. On declining the gasket temperature from 212 to 100 °F at the end of 20th thermal cycle, the materials—ePTFE, vPTFE, and CNA exhibit 27, 48, and 15% increase in creep strain value, respectively. The percentage of thickness reduction raises with the increase of cyclic temperature and with increase of creep pre‐exposure time, except for CNA where only a small variation is observed. The coefficient of thermal expansion of both PTFE materials shows a significant change due to cyclic temperature and initial creep exposure. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47265.
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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 | 0.000 | 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".