Experimental Design of Sustainable 3D-Printed Poly(Lactic Acid)/Biobased Poly(Butylene Succinate) Blends via Fused Deposition Modeling
Bibliographic record
Abstract
A mixture design of experiment (DoE) was used to guide the fabrication and analysis of sustainable poly(lactic acid) (PLA) and biobased poly(butylene succinate) (BioPBS) 3D-printing filaments. The statistical DoE approach was employed to investigate the correlation between the mechanical properties of the PLA/BioPBS blends at different PLA and BioPBS wt % and to obtain the linear regression models of the mechanical properties. The statistical models help to design PLA/BioPBS blends with the desired mechanical properties. The PLA/BioPBS filaments with different composition ratios were 3D-printed via fused deposition modeling (FDM). The 3D-printability of the polymer blends was determined by the flowability and dimensional stability of the filaments, provided by fundamental rheological and coefficient of linear thermal expansion (CLTE) studies. Preliminary research found that the 3D-printability of PLA/BioPBS filaments with BioPBS content higher than 50 wt % was unsuccessful due to high viscosity and low thermal stability. These findings were verified with rheological tests for a range of PLA/BioPBS blend ratios and thermomechanical studies. Rheological results show a significant increase of the blend viscosity when BioPBS content in the blend was >50%. Additionally, the CLTE drastically increased with higher contents of BioPBS, making the PLA/BioPBS filaments thermally unstable during FDM processing. These results confirmed that the 3D-printability of PLA/BioPBS filaments is greatly influenced by the blend viscosity and the printing temperature. Rheological studies revealed that the viscosity range of a 3D-printable PLA/BioPBS filament lies within 1000–100 Pa·s. Scanning electron microscopy (SEM) and polarized optical microscopy (POM) images confirmed that PLA and BioPBS are immiscible. However, the addition of BioPBS improved the ductility and the crystallinity of PLA. The 3D printed PLA/BioPBS (90/10) blend showed an interesting result in that it obtained higher tensile and impact strengths than the neat PLA, which was attributed to crystallinity and morphological factors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".