Effect of autoclave process parameters on quality and performances of PEEK/carbon composite panels
Bibliographic record
Abstract
The use of high performance thermoplastic composite structure in aerospace has seen a great increase in the past decade. Thermoplastic composites present many advantages over thermoset composites in term of processing and performance. Their fast processing time, infinite shelf life and recyclability lead to a decrease in the manufacturing costs compared to traditional autoclaved thermoset composites. Also, their high toughness and fatigue resistance, high temperature performance, chemical resistance and low flammability make them good candidates to replace metallic or thermoset composite aerospace structures. Among the several manufacturing techniques available, autoclave processing of thermoplastic composite is a simple technique allowing the co-consolidation of semi-complex composite structures. In this study, the effect of the autoclave process parameters (processing temperature, temperature cooling rate and consolidation pressure) on the crystallinity, the panel quality and mechanical performances of PEEK/carbon composite panels was investigated. The neat resin crystallinity was first examined by Differential Scanning Calorimetry (DSC) under different cooling rates. Tensile, in-plane shear, and interlaminar shear tests were performed to assess the panel mechanical performance under different processing conditions. Panel quality was defined by observing the panel consolidation and void content via microscopy and X-ray tomography. From these results, the sensitivity of the material performance to the process parameters was determined and the optimal autoclave processing windows of PEEK/carbon tape material was established.
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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) | 0.001 | 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".