Moisture Effect on Properties of Out-of-Autoclave Laminates with Different Void Content
Bibliographic record
Abstract
Fabrication of large structures using out-of-autoclave prepreg materials will lead to a great amount of savings in manufacturing costs. In the out-of-autoclave processing method, the presence of voids inside the laminate has been an issue due to the lack of high pressure during manufacturing. This study aims primarily to observe the moisture absorption response of composite samples containing different levels of void. By changing the vacuum level inside the bag during the manufacturing process, three different unidirectional laminates at three levels of void have been manufactured. After immersing the samples in warm water at 60°C for about one year, the moisture absorption level was monitored and then diffusion coefficients were calculated using Fick’s law. Results show that the moisture absorption coefficient changes by %8 within the experimental range of void contents. The mechanical behaviour of these laminates has been studied at four different moisture levels by performing dynamic mechanical analysis (DMA) and short beam shear tests. Empirical results indicate that, in general, interlaminar shear strength and glass transition temperature decrease by moisture build-up inside the samples. DiBenedetto equation is proposed to make a correlation between the moisture content and glass transition temperature.
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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".