Simultaneous characterization of preform expansion and permeability in vacuum assisted resin infusion
Bibliographic record
Abstract
Abstract Vacuum assisted resin infusion (VARI) is a composite manufacturing process, in which a fibrous reinforcement is laid out in a mold, and then sealed under a vacuum bag. The preform is compressed under vacuum and a liquid polymer resin is infused into the mold cavity. A characterization of compressibility and permeability is required to get accurate predictions of infusion times and thickness of final parts. A new experimental methodology is developed to simultaneously characterize the preform expansion and permeability of fibrous reinforcements during infusion. It is implemented in a one‐dimensional rectangular workbench by impregnating the preform with silicone oil. Pressure sensors measure the liquid pressure, and the reinforcement thickness is acquired by linear variable displacement transducers (LVDTs). The flow rate is also recorded with a scale. The expansion of the wetted reinforcement and permeability can be modeled by power laws as a function of pressure and fiber volume content, respectively. For isotropic preforms, a single experiment provides all the information needed to simulate the flow and predict the infusion time, the thickness and pressure. To validate this new characterization approach, the results of two infusions are successfully compared with numerical simulations.
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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) | 0.000 | 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) | 0.000 | 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".