Mechanical cleavage of complex microstructured fibers
Bibliographic record
Abstract
If microstructured optical fibers are to find widespread use in photonics technology, they will have to be easily cleavable using mechanical cleavers, since more sophisticated cleaving techniques add complexity. Conventional mechanical cleavers are the preferred laboratory and production tools because they are both simpler to use and more time- and cost-effective compared to techniques such as laser cutting. When designing complex microstructured fibers (MSF) with exciting novel optical characteristics, it is therefore important to favor those geometries that allow high-quality cleavage using standard mechanical cleavers. In this paper, we present an analytical model for fracture propagation during the cleaving process in a complex MSF. The model is based on experimental observations. Three samples of high air-fraction double-clad MSFs were used. Although that they all feature the same structural profiles (but differing in certain geometrical dimensions), they give totally different cleavage patterns. The cleaved surfaces were studied and analyzed. Analysis of the cleaved surfaces allowed to establish a criterion for smooth fracture propagation in a high air-fraction double clad MSF and to suggest a novel design approach for these specific structures.
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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 | 0.001 |
| 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".