Experimental and numerical investigation of damage evolution in carbon fiber reinforced polymer stiffened panel in post-buckling regime
Bibliographic record
Abstract
Stiffened composite panels have been extensively used thanks to their ability to withstand high load and large deflection. However, their nonlinear behavior and complex damage modes during the post-buckling regime remain challenging and still require research work to improve understanding. In the current study, the buckling and post-buckling responses of a hat-stiffened panel made with carbon fiber reinforced polymer composite material are investigated experimentally and numerically with identifying the damage process and its effect on the panel stiffness. The experimental analysis is achieved by performing quasi-static multi-step compressive loading up to failure with full displacement assessment. A Structural Health Monitoring strategy is also deployed for registering and localizing the Acoustic Emission (AE) activities during each run. Matrix data extracted from the AE waveforms are processed and classified with respect to damage mechanism. Additionally, this research proposes predictive Finite Element Model based on Progressive Failure Analysis. Modified Chang-Chang failure criteria is adopted and implemented using Ansys Usermat subroutine. Numerical and experimental data have been compared and good agreement is demonstrated.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".