Mechanical, Thermal, and Morphological Analysis of 3D-Printed Polylactic Acid–Polyester Urethane Blends with Varied Infill and Material Compositions
Bibliographic record
Abstract
Multimaterial 3D printing allows for the production of intricate parts with customized mechanical properties, enhancing the versatility of material extrusion additive manufacturing. Typically, 3D printing machines are fed with commercially available filament feedstock, which limits the 3D printing of multiple materials. Hence, this study introduces in-house prepared filaments for creating polymer blend structures with improved properties. In this study, polylactic acid and thermoplastic poly ester urethane (PEU) blends with different composition ratios were processed by varying the infill densities to evaluate their impacts on their thermal, mechanical and morphological properties. The effects of infill percentage on the mechanical, thermal and morphological behaviour were investigated. The results indicate that increasing the infill percentage tends to significantly increase the elastic modulus and tensile strength. The maximum strain increased as the infill percentage increased. Overall, the mechanical results indicated that, without sacrificing any tensile strength, the composite with 25% PEU exhibited better toughness than did the neat PLA and could be printed similarly to that of PLA. Furthermore, scanning electron images revealed that the blends had a homogeneous structure with a fibrillar morphology. These results indicate that 3D printing is an effective technique for creating next generation 4D materials.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".