Steering of carbon fiber/PEEK tapes using Hot Gas Torch-assisted automated fiber placement
Bibliographic record
Abstract
In-situ manufacturing of thermoplastic composites using Hot Gas Torch (HGT)-assisted Automated Fiber Placement (AFP) has the potential to produce laminates in an efficient manner by avoiding a secondary process, like autoclave consolidation. One of the advantages of AFP technique is its capability to steer fiber path and to manufacture Variable Angle Tow (VAT) laminates which have shown to have improved mechanical performance. This study investigates the process parameters that affect steering of carbon fiber reinforced thermoplastic tapes (AS4/polyether ether ketone) using an HGT-assisted AFP machine. The effect of the steering radius, laydown speed, number of repasses, and substrate angle on the geometry and bond strength of steered tape was investigated through observation and testing. A modified lap shear test was devised and used to study the bond strength between the steered tape and the substrate and the results were compared with autoclave treated samples which served as a reference. It was found that with a decrease in the steering radius of the tape, there was a decrease in the tape width and an increase in the tape thickness. A significant reduction in the steering-induced defects was observed at higher laydown speeds where the defects were intermittent unlike in the case of lower laydown speeds. Performing a repass over the steered tape smoothed some of the tape defects caused by steering. Furthermore, the lap shear strengths of the steered tapes were found to be functions of laydown speed and substrate angle.
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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 | 0.000 |
| 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 | 0.000 |
| 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".