Polyolefin Elastomer Toughened Polylactic Acid Composites With Low Extrusion Expansion for Quick Fused Deposition Modeling Additive Manufacturing
Bibliographic record
Abstract
ABSTRACT Polylactic acid (PLA), a degradational plastic, faces challenges in additive manufacturing due to high cost and low toughness. This study developed an innovative PLA random copolymer‐based composite system by incorporating maleic anhydride‐grafted polyethylene‐octene (MA‐POE) and bamboo powder to achieve a composite with low cost and superb mechanical strength for high‐speed 3D printing. Furthermore, a fused deposition modeling 3D‐printing approach was employed with a speed of 200 mm/s to enhance both processing efficiency and mechanical performance. The tensile and impact strength of the resulting composites increased by 33% and 24% after the incorporation of bamboo powders, respectively. The addition of MA‐POE significantly enhanced the toughness up to 17.36 KJ/m 2 , a 50% improvement compared to PLA/bamboo composites. Both bamboo powder and MA‐POE decreased the melt flow index from 67.2 g/10 min of pure PLA to 13.5 g/10 min of the resulting composites with 15% POE and 15% bamboo fiber. The entanglement effect of polymer molecular chains between PLA and bamboo powder, as well as MA‐POE, reduced die swell of the extruded filament, enabling more consistent strand dimensions, thereby improving printing accuracy and quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".