Estimation of Three-Dimensional Fibre-Orientation Distribution in Short-Fibre Composites by a Two-Section Method
Bibliographic record
Abstract
The overall fibre orientation in a composite can be statistically characterized by a fibre-orientation distribution. Fibre-orientation studies have concentrated on two major areas: those dealing with continuous fibres and those dealing with short fibres. This paper presents a novel two-section based method for statistical characterization of the fibre- orientation distribution within short-fibre composites. This method produces unbiased distribution data for the near-zero misalignment angles and simultaneously resolves the orientation-duality problem. The method’s novelty lies in its ability to determine accurately (within 1 µ m) the relative positions of two sections. While taking advantage of the extra information provided by the two sections, the proposed method also makes use of the single-section method’s main principles, namely: (1) intersection of a plane and a cylindrical fibre is an ellipse and (2) the ellipse’s shape is a function of fibre’s orientation relative to the intersecting plane. Therefore, by sectioning a specimen, acquiring images of the cross-sectional surfaces, and collecting the fibre-ellipse data from these images, the orientation of every fibre intersecting the sectioning plane can be determined. The probability distribution for fibre orientations within the sectioning plane is then related to that within the specimen volume.
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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) | 0.000 | 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 | 0.000 | 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".