Enhanced Performance 3‐D Printed PLA Parts through a Photo‐Initiator Mediated UV‐Curing
Bibliographic record
Abstract
Abstract The fused deposition modeling or three‐dimensional (3D) extrusion fabrication of material parts from thermoplastics, including poly(lactic acid) (PLA) usually results in inferior mechanical performance due to weak inter‐layer bonding compared to injection molded parts. In this work, a solvent‐free, effective, and inexpensive method is developed by incorporating photo‐initiators during a 3D extrusion fabrication followed by a post‐process UV treatment to improve the 3D‐printed PLA part's performance. This developed method addresses key challenges of 3D printed PLA parts, such as poor toughness and UV sensitivity, while enabling its use in high‐performance additive manufacturing (AM) processes. The UV‐curing process enhances PLA's suitability for 3D printing, supporting complex geometries and custom designs by enhancing the crosslinking between adjacent PLA chains, under UV exposure. The solvent‐free nature of the developed process aligns with sustainable manufacturing, strengthening PLA's role as an eco‐friendly alternative to petroleum‐based polymers. A standout result of this approach is the 64% improvement in tensile properties of PLA with the photo‐initiator and UV treatment samples compared to untreated PLA, achieved through UV‐curing. This advancement overcomes the persistent issue of weak interlayer bonding in the 3D printing of PLA and positions PLA as a viable material for sustainable and high‐performance applications.
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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) | 0.002 | 0.001 |
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".