Thermally assisted consolidation of out-of-autoclave prepreg for concave parts
Bibliographic record
Abstract
Vacuum Bag Only, Out-of-Autoclave prepreg processing offers a sustainable, cost-efficient alternative to conventional autoclave manufacturing of high strength composite parts.Flat or low curvature parts made with Out-of-Autoclave prepregs have demonstrated comparable mechanical properties to their autoclave counterparts, yet challenges still remain with the fabrication of parts with more complex features.One area of concern is corner thickening in concave corners.Best practices for reducing this defect exist; however, a more robust processing technique is desirable.In this work, a manufacturing process is proposed that involves heating the laminate during the vacuum bag compaction at regular intervals in the layup process.The aim of these heated debulks is to reduce corner thickening early on in the manufacturing process by encouraging inter-ply slipping, precipitating air removal, and increasing the degree of impregnation.Before the process could be designed, its potential was assessed through material characterization and demonstrator studies.Inter-ply friction was characterized for debulked samples using pull-out tests, allowing for a better understanding of the forces required to encourage prepreg slip.Dynamic mechanical analysis was used to assess changes in the degree of cure caused by debulking, and found a slight increase in curing with increased debulk temperature and duration.Investigating the removal of air from a tool-part interface showed no impact of heating on air removal rate.It was observed that air returned to the plies if vacuum was released when the resin viscosity was low.Initial trials were run on flat plates.There was a decrease in short beam shear strength as the debulk dwell temperature increased.Meanwhile, low temperature hot debulks led to an increase in voids.L-brackets were manufactured on a concave tool.The hot debulk led to a 25% reduction in corner thickening and a 80% reduction in void content compared to the part debulked at room temperature.Such improvements in quality motivated the development of a more robust design for a hot debulk process.This procedure consisted of using an infrared lamp to heat the prepreg, dry carbon fibre fabric as breather to absorb the heat while evacuating gases, and a bath of dry ice used to cool the tool before the vacuum bag was vented.In writing this, I have come to the end of more than five years of formation in the McGill Structures and Composite Materials Laboratory.During that time, I have had the privilege of working with some of the most admirable individuals and gained a wealth of experience.I am very fortunate to have had Pascal Hubert take me on as a masters student.Through him, I have been able to learn and experience so much.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".