Fatigue Behavior of a Nanocomposite under a Fighter Aircraft Spectrum Load Sequence
Bibliographic record
Abstract
Two different E-glass fiber reinforced plastic (GFRP) composite laminates having quasi isotropic [(+45/-45/0/90)2]S layup sequence were fabricated viz., GFRP with neat epoxy matrix (GFRP-neat) and GFRP with modified epoxy matrix (GFRP-nano) containing 9 wt. % of CTBN rubber micro-particles and 10 wt.% of silica nanoparticles. Standard fatigue test specimens were machined from the laminates and end-tabbed. Spectrum fatigue tests under a standard fighter aircraft load spectrum, mini-FALSTAFF, were conducted on both the composites at various reference stress levels and the experimental fatigue life expressed as number of blocks to fail, were determined. The stiffness of the specimen was determined from the load-displacement data acquired at regular intervals during the fatigue test. The matrix cracks development in the test specimens with fatigue cycling was determined through optical photographic images. The fatigue life of GFRP-nanocomposite under mini-FALSTAFF load sequence was observed to be enhanced by about four times when compared to that of GFRP-neat composite due to presence of micro-and nanoparticles in the matrix. The stiffness degradation rate and matrix crack density was considerably lower in GFRP-nanocomposite when compared to that of GFRP-neat composite. The underlying mechanisms for improved fatigue performance of GFRP-nanocomposite are discussed.
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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".