Progressive Fatigue Damage Modeling of Composite Materials, Part II: Material Characterization and Model Verification
Bibliographic record
Abstract
To validate the fatigue progressive damage model, developed in the first part of this paper, an experimental program was conducted using graphite/epoxy AS4/3501-6 material. As the input for the model, the material properties (residual stiffness, residual strength and fatigue life) of unidirectional AS4/3501-6 graphite/epoxy material are fully characterized under tension and compression, for fiber and matrix directions, and under in-plane and out-of-plane shear in static and fatigue loading conditions. An extensive experimental program, by using standard experimental techniques, is performed for this purpose. Some of the existing standard testing methods are necessarily modified and improved. To evaluate the progressive fatigue damage model, fatigue behaviour of pin/bolt-loaded composite laminates is simulated as a complicated example. The model is validated by conducting an experimental program on pin/bolt-loaded composite laminates and by comparison with experimental results from other authors. Different capabilities of the model are examined by conducting different types of experiments. The comparison between the analytical results and the experiments shows the successful simulation capability of the model.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".