Theoretical and Experimental Investigation of out-of-plane Wrinkle Formation during Steering in Automated Fiber Placement
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».