In-Plane Buckling Of Steered Tows In Hot Gas Torch-Assisted Automated Fiber Placement (Afp) Of Thermoplastic Composites
Bibliographic record
Abstract
Manufacturing of Variable Angle Tow (VAT) laminates by in-situ consolidation of thermoplastic composite tapes using Automated Fiber Placement (AFP) or Automated Tape Laying (ATL) techniques has gained recent popularity due to its ability to efficiently manufacture composite laminates without the need for secondary processes like curing in an autoclave. VAT lamiates allows one to design laminates with tailored stiffness for a particular application and also helps in reducing stress concentration. However, such techniques that employ steered tows have some inherent defects such as tow buckling and tape folding. The buckling of fibers may result in a reduction in the stiffness of the laminate and tape folding will affect the quality of the laminate. The understanding of these defects will allow us to design laminates with the right allowances thus resulting in a better prediction of stiffness and strength for the entire laminate. This research studies available theoretical formulations that can predict the onset of in-plane tow buckling during AFP process, its wavelength, and amplitude. Verification of the theoretical buckling predictions are performed by comparing the results to the experimental ones obtained by steering a 6.35mm (0.25in) Carbon Fiber/PEEK tow using a Hot Gas Torch-assisted AFP available at Concordia Centre for Composites at different steering radii.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".