A 3D micromechanical energy-based creep failure criterion for high-temperature polymer–matrix composites
Bibliographic record
Abstract
In the present study, a generalized three-dimensional (3D) energy-based criterion for the creep failure of viscoelastic materials is developed. Unlike the existing approaches which are restricted to uniaxial loading, the proposed criterion can predict failure under any combination of loads. This criterion is then incorporated into a simplified unit cell micromechanical model to predict the time-delayed failure of unidirectional polymer–matrix composites at elevated temperatures. The composite material used in this study is T300/934 which is suitable for service at high temperatures in aerospace applications. The use of micromechanics can give a more accurate insight into the failure mechanisms of the composite materials, in particular at high temperatures, where the general behavior of the polymer–matrix composite is governed by matrix viscoelasticity and matrix time-dependent failure due to creep is a localized phenomenon. The micromechanical model is also used to estimate the ultimate strength of the constituents from the knowledge of the allowable strengths of the unidirectional composite in the principal material directions. The obtained creep failure stresses are found to be in reasonable agreement with the experimental data particularly for the 90° unidirectional laminate, where failure is totally matrix dominated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".