Experimental study of saturation by visible light transmission in dual-scale fibrous reinforcements during composite manufacturing
Bibliographic record
Abstract
A new in situ monitoring strategy is proposed to study void formation during real-time impregnation of dual-scale fibrous reinforcements in liquid composite molding. Void content data from burn-off tests are used to calibrate a refractive index matching approach based on two optical principles: Beer–Lambert and Fresnel laws. Once calibrated, this approach based on visible light transmission is used to study the impact of key process parameters on the saturation footprint of dual-scale fibrous reinforcements during and after mold filling. The injection parameters investigated are the flow front velocity, the pressure distribution inside the mold cavity, the bleeding flow rate, and the mold packing pressure. The experimental setup is a computer-assisted injection system and a transparent resin transfer molding mold is used to perform unidirectional injections. A vinyl ester resin is injected through E-glass bidirectional non-crimp fabrics under various manufacturing conditions. This investigation not only confirms the decreasing trend in void formation by mechanical entrapment of air with the decrease in impregnation velocity, as it converges toward the optimal impregnation conditions for this fibrous reinforcement reported in previous studies, but it also brings insights on void dissolution and transport in liquid composite molding.
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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".