Instrumented linear cutting device for the analysis of fiber severing process
Bibliographic record
Abstract
In recent years, stringent governmental regulations along with falling carbon fiber prices have pushed high-volume composite manufacturing somewhere at the top of the “to do” list for the majority of carmakers. However, a careful survey of the literature reveals that little is available on the topic of high-throughput severing of the carbon fibers, one of the principal constituents of carbon fiber reinforced polymers. To address this relative paucity of valid scientific information, the major goal of this study was to develop a robust and accurate experimental apparatus capable of establishing correlations between the amount of cutting force developed at the blade/fiber tow/deformable backing interface and various process parameters such as fiber material, tow and backing characteristics, blade geometry/material, and so on. The characteristic cutting force–position curves obtained by means of the developed device suggest that (1) the cutting forces require to cut glass fibers are typically larger (up to 27% observed in this study), (2) softer backings tend to have a positive effect on the fiber severing process both in terms of peak cutting force and total work to cut, and (3) dull blades require either larger severing forces (60 µm blade edge radius was associated with a 21% increase in cutting force) or are simply unable to produce severing of the fibers (80 µm blade edge radius). Future extensions of this work will focus on the determination of systematic correlations between the parameters of fiber severing 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".