Progressive Fatigue Failure Characterization of Bonded Repaired Sandwich Composites Using Experimental and Multiscale Modelling Approaches
Bibliographic record
Abstract
Abstract Predicting fatigue failure of composite materials is crucial for ensuring the structural integrity of aerospace structures. Both experimental and multiscale modelling (MSM) approaches were developed to characterize the failure of the bonded-repaired sandwich composites. A four-point bending setup under fatigue loading conditions was employed for experimental characterization of failure behaviors, while the MSM approach bridged micro, meso and macro length scales to analyze these behaviors. The experimental and MSM results were compared at each scale to validate the proposed MSM-based fatigue failure prediction. At the microscale level, the experimental specimen exhibited cusp-like failures due to fatigue loading. The MSM analysis showed complete failure of matrix elements after 100 cycles, while the fibre elements remained intact. At the mesoscale, both the experimental and MSM results showed steaks-like failures with distinct staggered patterns, stopping at the edge of the warp tows. Additionally, extensive inter-two failures were seen across large surface areas of the weft and warp tows. At the macroscale, the failure surfaces were wide with deep cracks resulting from progressive matrix cracking as the fatigue cycles increased. Though the focus was on critical point B, similar behaviours were also observed on critical point A as well.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".