Fail-Safe Prediction for Bonded Composite Structures Using Discrete Damage Modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-1318.vid Modern aircraft design uses composites for their outstanding strength, stiffness, and lightweight properties of these materials. The bonded composites require a fail-safe design, where the structure retains adequate strength for the service period between inspections or maintenance. This may take the form of alternate load paths and crack arrestment features. Since adhesive debonding is a critical failure method in bonded unitized structural components, understanding the behaviors of the bondline in both pure mode and mixed mode fracture is vital for understanding and predicting mechanical performance. The FASTBUCS program aims to develop a validation methodology for such structures through inspection methods, structural testing, and progressive damage analysis. This work uses Discrete Damage Modeling within the finite element code BSAM to capture structural elements with and without crack-arresting fasteners in a Pi-Joint stiffened panel under various types of loading. The results show that BSAM can replicate crack arrestment using fasteners in mode II loading. In addition, models without crack arresting features for Pi Cantilever Beam and a combined loading test article are also discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it