Wrinkle formation during steering in automated fiber placement: Modeling and experimental verification
Bibliographic record
Abstract
Automated fiber placement is being widely applied in the aerospace industry due to its advantages. This technology has the capability to improve the efficiency of composite structures by steering where properties such as stiffness can vary within the same part. However, such steering is limited by process-induced defects such as out-of-plane wrinkles, which occur when the steering radius exceeds its critical limit. The present paper proposes a theoretical model for wrinkle formation during steering of the autoclave thermosetting prepreg. The Rayleigh–Ritz approach is used to model wrinkle formation based on the critical buckling load. The prepreg tape is considered an orthotropic plate resting on a Pasternak elastic foundation, which consists of one elastic spring layer connected to an elastic shear layer. Closed form solutions for predicting both critical steering radius and buckling wavelength is presented. The two foundation parameters and the required mechanical properties of the prepreg are experimentally characterized. The model-predicted results are validated by the experimental results. The results reveal good agreement between the predicted and experimental values. It is also found that a significant improvement in the model was achieved by adding the shear layer to the foundation.
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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 | 0.001 |
| 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 | 0.001 |
| 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".