Micro-computed tomography analysis of natural fiber and bio-matrix tubular-braided composites
Bibliographic record
Abstract
There is an increasing demand for the use of “green”-based materials as reinforcement and matrix materials in composites. However, the ability of these natural-based materials to perform as consistently and reliably as conventional materials is still relatively unknown. A key importance in the viability of these materials is the evaluation of the content of voids and imperfections, which may affect the properties of the entire composite. In this study, the microstructure of tubular-braided composites manufactured from cellulose fibers and a partially bio-derived resin was studied with the use of micro-computed tomography. These methods were used to determine the effect of modifying braid angle, resin type, and curing method on fiber volume fraction, void volume, and void distribution. It was determined that the void content increased with the increase in braid angle, and vacuum-bagging reduced the total void content. The sample with the smallest braid angle produced with vacuum-bagged curing contained a void fraction of 1.5%. The results of this study proved that the materials used could be viable for further testing and development and that micro-computed tomography imaging is valuable for identifying how to improve consistency and minimize imperfections to create more accurate and reliable natural fiber-braided composites.
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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 | 0.000 | 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".