Simultaneous characterization of preform expansion and permeability in vacuum assisted resin infusion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it