A novel compaction roller with variable pressure distribution and contact time for automated fiber placement: Experimental and numerical analysis
Bibliographic record
Abstract
Automated fiber placement (AFP) technology has gained significant traction due to its adaptability in processing large composite parts with complex geometry. However, a key challenge remains in reducing defects during the layup process to enhance the quality of AFP-manufactured components. This study aims to reduce defect formation in the AFP by introducing a new approach that involves altering the pressure distribution and contact length exerted by the compaction roller during the AFP process. To demonstrate the effectiveness of this approach, the research focuses on out-of-plane defects, commonly known as wrinkle and tape folding deformations, which occur during fiber steering. To address this issue, a new designed compaction roller has been designed and manufactured to provide variable pressure distributions and contact length based on the geometry of the part, unlike traditional rollers. This new roller features a concave shape that adjusts pressure application and contact duration along its length, applying higher pressure for extended periods at the towpreg edges. Finite element (FE) analysis was employed to simulate the roller deformation and pressure distribution, helping to determine the dimensions, particularly the concave radius. A three-part PLA mold was manufactured using the dimensions obtained from the simulations, and polyurethane rollers were produced through casting into the molds. AFP trials were conducted to compare the performance of the new roller against standard compaction rollers. The results demonstrated a 24 percent reduction in wrinkle length with the new roller, highlighting its effectiveness in improving the AFP process.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".