High-performance and geometrically complex parts via co-extrusion additive manufacturing of multi-scale continuous carbon fiber-reinforced thermoplastic composites
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it