Development of high performance composites for Fused Filament Fabrication for aerospace applications
Bibliographic record
Abstract
This study investigates the development of novel formulations for high temperature thermoplastic polymer composites by mixing either amorphous polyetherimide (PEI) or semicrystalline polyphenylene sulfide (PPS) matrices with incorporation of recycled carbon fibers (rCFs) and thermal black (TB) particles followed by processing them into filaments for Fused Filament Fabrication (FFF) 3D printing. Different formulations of composites were prepared using combination of rCF and TB in which rCF content is gradually replaced by TB to investigate the possible synergistic effect on the performance of the composites. The effect of rCF and TB contents on mechanical, rheological and thermal properties of composites were investigated. With the incorporation of 20 wt.% rCF, tensile modulus and strength of the composites enhanced up to 7 and 2 folds respectively. The incorporation of TB provides flexibility for the composite filaments while maintaining thermal and mechanical performance of the composites. The rheological characterization showed that melt viscosity of thermoplastic matrices are preserved even at high loadings of TB (up to 20 wt.%). The differential scanning calorimetry (DSC) characterization results showed that incorporation of TB shifted the crystallization temperature to lower degrees and hindered the crystal formation of semicrystalline PPS matrix which could possibly provide better layer adhesion of the printed parts. Moreover,the use of TB and rCF provides significant cost saving compared to virgin CF. The obtained results will allow us to understand the developed composites behavior and optimize the FFF printing parameters for better mechanical performance of the printed parts for aerospace applications
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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 | 0.001 |
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".