Vacuum-Bag Manufacturing of Honeycomb Structures: Opportunity for In-Situ Process Monitoring and Non-Destructive Voidage Estimates
Bibliographic record
Abstract
A helicopter crew door was manufactured using out-of-autoclave prepregs and vacuum-bag-only processing techniques. Non-metallic honeycomb core was used to stiffen the structure. Miniature pressure sensors were embedded in the demonstrator to measure the honeycomb core pressure throughout the lay-up, including intermittent de-bulking, the pre-cure vacuum hold, and the elevated temperature cure. The sensors identified two insightful process phenomena: 1) gas evacuation increased with additional plies, implying that de-bulking may increase a skin's air permeability, thereby decreasing total process time, and 2) a non-uniform pressure response was observed in the part during cure, potentially leading to variations in part quality. Visible part quality was acceptable, excluding small radii geometrical features, a known source for defects in bag processing of woven materials. Internal part quality (voidage) was estimated in the honeycomb regions using pulsed infrared thermography non-destructive-evaluation. Calibration curves were generated by comparing thermography to micro-computed tomography images of the same samples. Macro-porosity around fibre bundles was identified as the major voidage source in these structures, and the thermography inspection identified significant local voidage variations over the part.
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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".