Individual and interactive influence of temperature, stress, physical aging and moisture on creep, creep rupture and fracture of epoxy matrix and its composite
Bibliographic record
Abstract
Two major concerns in using polymer composites for load bearing structural applications are time dependent degradation in modulus and strength, measured by creep and creep rupture tests. Previous studies on creep of polymer composites focused on the individual and interactive effect of one or more of factors such as Stress-Temperature, Stress-Temperature-Physical Aging and Stress-Temperature-Moisture. While the effect of stress, temperature, and moisture on creep rupture is well documented, limited studies have focussed on the effect physical aging on creep rupture of polymer composites. No effort has so far been made to study the combined effect of the above factors on creep and creep rupture of polymer composites. Hence, the present investigation was undertaken to experimentally study the individual and interactive effect of temperature, stress, physical aging, and moisture on creep (linear and non-linear region), creep rupture and fracture of a thermoset epoxy resin (F263) and its composite (Hexcel Corporation's F263 epoxy resin reinforced with T 300 carbon fibers). (Abstract shortened by UMI.)
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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".