Impact of layup rate on the quality of fiber steering/cut-restart in automated fiber placement processes
Bibliographic record
Abstract
Abstract Developing reliable processes is one of the key elements in producing high-quality composite components using an automated fiber placement (AFP) process. In this study, both simulation and experimental studies were carried out to investigate fiber steering and cut/restart under different processing parameters, such as layup rate and compaction pressure, during the AFP process. First, fiber paths were designed using curved fiber axes with different radii. Fiber placement trials were then conducted to investigate the quality of the steered fiber paths. Furthermore, a series of sinusoidal fiber paths were fiber placed and investigated. Moreover, a six-ply laminate with cut-outs in it was manufactured in the cut/restart trials. The accuracy of the fiber cut/restart was compared at different layup rates for both one- and bi-directional layups. Experimental results show that it was possible to layup steered fiber paths with small radii of curvature (minimum 114 mm) designed for this study when the proper process condition was used. It was observed from the cut/restart trials that the quality of tow cut was independent of layup speed; however, the accuracy of tow restart was related to the layup speed. The faster the layup speed, the less accurate was the tow restart.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".