Non-planar material-extrusion additive manufacturing of multifunctional sandwich structures using carbon-reinforced polyetheretherketone (PEEK)
Bibliographic record
Abstract
Additive Manufacturing (AM), and especially Material-Extrusion Additive Manufacturing (MEAM), can provide light structures with high mechanical performances for aerospace applications using fiber reinforced high-temperature resistant thermoplastics (HTRT). Non-planar AM allows to manufacture curved parts with a better approximation of their curvature than cartesian AM. In this paper, we additively manufactured a proof of concept of multi-functional non-planar sandwich panel for aircraft casing using a 6-degree of freedom (6-DOF) robotic platform in a custom heating enclosure, using 30 wt.% carbon-reinforced polyetheretherketone (PEEK). Curved beams manufactured using planar and non-planar deposition techniques were manufactured using a custom heating enclosure and reinforced HTRT. The measured surface deviation of the non-planar curved beam (NPCB) was 31% lower than the planar curved beam (PCB). Due to the non-planar deposition that led to a better alignment of the layers along the specimen, the effective stiffness measured during 3-point bending tests was also increased by ~28% compared to PCB, showing the importance of a non-planar deposition. Additively manufactured non-planar sandwich panels containing an acoustic core and two types of microscaffolds (30% infill microscaffold and 78% infill walled microscaffold) were characterized through optical microscopy and 3-dimension (3D) scanner measurements. The 6-DOF robotic platform, combined to the heating enclosure, allowed to print a portion of a non-planar carbon fiber reinforced PEEK sandwich panel to present the versatility of the structures that could be printed with this setup. The use of non-planar reinforced HTRT structures could find applications in the aerospace field for aircraft noise reduction structures, but also in the biomedical domain for the manufacturing of biocompatible PEEK scaffolds.
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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 | 0.000 |
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".