Additive manufacturing of multifunctional composites for aerospace applications
Bibliographic record
Abstract
Our research focuses on the development of high-productivity additive manufacturing (AM) processes through several R&D projects in collaboration with our aerospace industrial partners located in Canada and in France. We have developed expertise in the design of advanced AM processes (see Figure 1) that enable large-scale non-planar printing of various polymer-based composites mainly for aerospace, but also for biomedical and energy-harvesting applications. One of our unique AM processes involves a six-axis robotic arm on which various types of AM printheads can be installed. This infrastructure enabled the rapid nonplanar multinozzle direct deposition of thermosetting abradable materials for sound absorption of aircraft engines. For the printing of thermoplastics, we integrated the prevalent Fused Filament Fabrication (FFF) process into the same 6-axis robotic platform for non-planar printing of geometrically-optimized sandwich structures using high-temperature-resistant thermoplastic composites such as carbon fiber-reinforced polyetherimide (PEI) and polyetheretherketone (PEEK). Our very recent efforts are focused on the development of a high-throughput AM process based on Fused Granulate Fabrication (FGF) using a pellet-extrusion printhead. Our experimental works on the manufacturing side are supported by numerical simulations. For example, we have created finite element models with element activation based on the manufacturing G-code instructions to predict the heat exchanges during the printing process. In addition, the phase-field modeling approach is used for the crack initiation and propagation predictions within 3D printed composites. We are also investigating the circular economy of materials in the FFF and FGF processes. Our preliminary results are providing comprehensive insights into the sustainability of fiber-reinforced thermoplastic composites for future 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".