Design of a Compact Composite Prepreg Tape Dispensing Device
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The design of a compact composite prepreg tape dispensing device is presented. This device supplies composite prepreg plies to manual and robotic composite prepreg layup processes for producing composite laminates. It is able to de-reel a spool of prepreg tape, remove the backing paper, cut the tape into plies at specified lengths, and place the plies for pickup. It provides a compact and economic tape supplying solution to the composite manufacturing industry. A prototype device was created and tested. With an overall footprint of 20 3/8 inches (517.5 mm) by 50 5/8 inches (1285.9 mm) and a mass of 35 lb (15.9 kg), the device was compact enough and functioned well in a tabletop layup workspace. The prepreg tape dispensing process was successfully carried out. The tape cutting accuracy of the prototype device achieved 1/22 inches (1.2 mm) on average with a standard deviation of 1/13 inches (2.0 mm). The tape delivery positioning accuracy achieved 1/32 inches (0.8 mm) on average with a standard deviation of 3/68 inches (1.1 mm) in the longitudinal direction, and 1/80 inches (0.3 mm) on average with a standard deviation of 1/38 inches (0.7 mm) in the transverse direction. The test results verify the capability of the presented technique in producing prepreg plies with sufficient accuracy and consistency.</div></div>
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".