Wrinkle Formation and Initial Defect Sensitivity of Steered Tow in Automated Fiber Placement
Bibliographic record
Abstract
This paper aims to study the wrinkle formation of a prepreg with initial defect during steering in automated fiber placement (AFP). Wrinkle formation has a detrimental effect on the mechanical properties of the final product, limiting the AFP applications. A theoretical model for wrinkle formation has been developed in which a Pasternak foundation and a Koiter imperfection model are adapted to model viscoelastic characteristics of the prepreg tack and initial defect of the prepreg, respectively. The initial defect is defined as a slight deviation of the tow’s mid-plane from a horizontal shape. The initial defect is generated in the tow by moving the tow through the guidance system, pressure of the roller, and resin tackiness. Galerkin method, along with the finite difference method (FDM), are employed to solve the wrinkle problem equation. The proposed method is able to satisfy the different boundary conditions for the wrinkle problem completely. The numerical results show that increasing the initial defect leads to a decrease in critical load and an increase in critical steering radius. To validate the theoretical model, experimental results are presented and compared with model-predicted results. It is shown that the model is well able to capture the trends and values of wrinkle formation wavelengths obtained from the experiment.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".