Examination of voids and geometry of bio-based braided composite structures
Bibliographic record
Abstract
Braided composites are formed by interlacing continuous fibers into a textile pre-form and then impregnating the pre-form within a matrix material. Braid mechanical properties are manipulated through the selection of matrix, fiber and braid geometry. Braided composites are produced with conventional materials like carbon, aramid and glass fibers; however, they can also be produced using natural fibers such as jute, hemp, flax or regenerated cellulose. The mechanical properties of conventional and natural braided composites are highly affected by voids within the braided structure. The effect of voids on braided structures must be investigated to improve braided composite performance. A high-resolution micro-computed tomography (μCT) measurement method was utilized to quantify the size and distribution of voids within natural fiber-bio resin braided composite structures. Image processing techniques were employed to quantify void, matrix and fiber content within 35° and 45° braid samples. Accurate quantification of fiber, matrix and void volumes are crucial for evaluating the quality and repeatability of the braided composite manufacturing process. Reduction of voids and pores will improve braided composite mechanical properties and performance. Measurement of the constituents within braided composites is also necessary for the development of accurate models for predicting braid mechanical properties. Measurement of the internal microstructure of braided composites will allow for the development of improved analytical and numerical braided composite models.
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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 | 0.001 | 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".