How Do 3D Printing Parameters Affect the Dielectric and Mechanical Performance of Polylactic Acid–Cellulose Acetate Polymer Blends?
Bibliographic record
Abstract
Three-dimensional printing is a prototyping technique that is widely used in various fields, such as the electrical sector, to produce specific dielectric objects. Our study explores the mechanical and dielectric behavior of polylactic acid (PLA) and plasticized cellulose acetate (CA) blends manufactured via Fused Filament Fabrication (FFF). A preliminary optimization of 3D printing parameters showed that a print speed of 30 mm·s−1 and a print temperature of 215 °C provided the best compromise between print quality and processing time. The dielectric properties were very sensitive to the three main parameters (CA content WCA, infill ratio, and layer thickness). A Taguchi L9 3^3 experimental design revealed that the infill ratio and WCA were the main parameters influencing dielectric properties. Increasing the infill ratio and WCA increased the dielectric constant ε′ and electrical conductivity σAC. It would, therefore, be possible to promote the integration of CA in the dielectric domain through 3D printing while counterbalancing its greater polarity by reducing the infill ratio. The dielectric findings are promising for an electrical insulation application. Furthermore, the mechanical findings obtained through dynamic mechanical analysis are discussed.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".