High-performance and geometrically complex parts via co-extrusion additive manufacturing of multi-scale continuous carbon fiber-reinforced thermoplastic composites
Bibliographic record
Abstract
Continuous Fiber-Reinforced Polymer Additive Manufacturing (CFRP-AM) often aims to significantly improve the mechanical properties of 3D printed parts. In this paper, we develop a CFRP-AM infrastructure able to print continuous carbon fiber-reinforced polylactic acid (PLA-SCCF) via co-extrusion (i.e., extrusion-based in-situ combination of the thermoplastic matrix and the continuous fibers reinforcement). This infrastructure uses a 6-axis robot to move a co-extrusion printhead over a heated printing bed, and is controlled using a custom-made slicing process. A curved thin-walled vase and a multi-material sandwich panel are made in a single manufacturing step to demonstrate the capabilities of the proposed infrastructure. Their geometrical fidelity is measured and their deviations from the reference model are both < 1%. Micro-computerized tomography scans ( μ CT) are performed to evaluate the micro and meso-structure of printed composite flat beams. Continuous fibers represent ∼ 44 vol.% ( ∼ 58 wt.%) of the composite while voids and porosities represent 0.4 vol.% and 7.9 vol.%, respectively. The ultimate tensile strength (UTS) and stiffness along the principal direction ( E 1 ) are tested for unidirectional flat beams and measured at 854 MPa and 29.5 GPa, representing 16 × and 6.4 × increases when compared to a part reinforced with ∼ 3.4 vol.% ( ∼ 4.5 wt.%) short carbon fibers only, of an average aspect ratio of ∼ 21. The developed co-extrusion CFRP-AM infrastructure could find applications in load-bearing applications where complex part geometries are a requirement, such as the automotive and aerospace industries.
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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 | 0.000 | 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".