Optimizing the properties of PHBV/PBAT blend for additive manufacturing
Bibliographic record
Abstract
In recent years, 3D printing has become increasingly popular for producing biodegradable products and for exploring a variety of applications. The development of polyhydroxy-co-3-butyrate-co-3-valerate (PHBV) and Polybutylene adipate-co-terephthalate (PBAT) blend for 3D printing has been the subject of extensive research. This paper examines the impact of printing parameters on the mechanical properties of 3D-printed components. The study successfully optimized the mechanical properties of 3D-printed PHBV/PBAT blend parts, achieving a tensile strength of 22.96 MPa, a modulus of 767 MPa, and an elongation percentage of 180 %. These results were obtained by identifying the optimal printing parameters through a Taguchi L9 design and Desirability Function Analysis (DFA), specifically a 0.35 mm layer height, 210 degrees C nozzle temperature, and 100 % infill density. The addition of PBAT significantly enhanced the ductility of PHBV, marking a substantial improvement in elongation. This research provides a robust framework for optimizing the performance of biodegradable blends in additive manufacturing, with promising implications for future applications in sustainable material development.
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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) | 0.001 | 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 | 0.000 |
| 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".