<scp>3D</scp> ‐printing continuous plant fiber/polylactic acid composites with lightweight and high strength
Bibliographic record
Abstract
Abstract Continuous plant yarn‐reinforced polylactic acid (PLA) composites were produced through in situ 3D printing, focusing on how plant fiber attributes influence the crystallization, mechanical, and rheological properties of the printed composites. The aim of this study was to assess the viability of plant fibers as substitutes for synthetic ones in engineering additive manufacturing. Plant fibers promoted the crystallization of PLA due to their shear induction and nucleation agent induction effects. The inherent triangular void defect during printing decreased with increasing plant fiber‐volume fraction. Rheological analysis revealed a transition to more elastic behavior post‐fiber addition, indicating solid‐like properties. The tensile strength of flax fiber‐yarn/PLA composite (volume fraction of 50.79%) was 342.37% higher than that of pure PLA, with a 22.2% lower density than pure PLA. Flax fiber demonstrated a superior reinforcement effect than carbon fiber in compressive strength for 3D printed honeycomb sheets with lower energy consumption and footprint. Optimizing fiber characteristics holds promise for high‐performance 3D‐printed natural fiber composites, particularly in vehicle applications. Highlights Plant fiber/polylactic acid (PLA) composites were in situ printed with a volume fraction of 50.79%. Plant fibers promoted the crystallization of PLA during the printing process. The tensile strength of flax fiber/PLA increased by 342.3% with a 22.2% lower density than PLA. Flax fiber showed a better reinforcement than carbon fiber in a compressive test of printed honeycomb.
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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".