Theoretical and Experimental Investigation of out-of-plane Wrinkle Formation during Steering in Automated Fiber Placement
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, steering is limited by process-induced defects such as out-of-plane wrinkles, which occur when the steering radius exceeds its critical limit. This thesis 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. \nIn this context, Normal stiffness of the foundation (prepreg tackiness) is demonstrated an important role in the AFP process and steering limitations. The second stage of this work aims to measure the prepreg tackiness at different AFP processing conditions to study the effect of the process parameters on prepreg tackiness. Finally, this experimental work is used to predict the optimum parameters for high tackiness levels. Two in-house setups were developed for layup and measuring tackiness. The first setup was designed to simulate the AFP process and precisely control layup speed, compaction force, and temperature. It is used to layup the prepreg under different conditions and with different rollers. The second setup performs a peel-off test to measure the sample’s tackiness. Taguchi method is applied to optimize the layup process parameters and find the optimal combination for high resultant tackiness. It is also applied to study the effect of the placing roller. Analysis of results shows that prepreg tack is affected by the interaction among the process parameters rather than the individual effect of these parameters. The study shows that the Taguchi method is suitable to solve the stated problem with a minimum number of trials as its results are experimentally validated.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".