Effect of <scp>non‐woven</scp> flax mat manufacturing parameters and consolidation pressure on properties of composites manufactured using vacuum‐assisted resin transfer molding
Bibliographic record
Abstract
Abstract Composite parts, used in transportation industries, are manufactured using vacuum‐assisted resin transfer molding (VARTM) and non‐woven glass fiber mats that are optimized for impregnation, fiber volume fraction (Vf), and composite properties. However, such optimized hemp and flax mats are not available. Extending the research on hemp mats manufactured using air‐laying, the effect of needle depth (2 or 8 mm) and punch density (0–72 punches/cm2) used to bind the fibers in the mat together, as well as consolidation pressure (101–560 kPa) applied during manufacturing, on mat permeability and composite properties were studied. Non‐woven flax mats exhibited heterogeneity in spatial distribution of areal density (GSM) and fiber distribution. This, together with the distribution in flax fiber diameter and properties, resulted in large scatter in the measured composite properties. The out‐of‐plane permeability and the consolidation of the mat decreased with increase in punch density and depth. This, together with the variation of Vf in the starting mat, resulted in complex variation in the Vf in the composite. 30‐P mat, with tightly bound fibers, resulted in optimal composite properties at VARTM (101 kPa) pressure while 0‐P and 72‐P mats, with loosely bound fibers, resulted in optimal properties at 560 kPa.
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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) | 0.001 | 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 | 0.001 |
| 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".