Design, Optimization, and Mechanical Properties Evaluation of 3D-Printed Auxetic Structures from a Talc-Filled PLA/BioPBS/PBAT Composite for Advanced Engineering Applications
Bibliographic record
Abstract
This study investigates the development and optimization of talc-filled PLA, BioPBS, and PBAT composites for 3D printing of high-performance auxetic structures. A Taguchi L9 design of experiments, combined with Grey relational analysis and principal component analysis, was employed to optimize printing parameters, including the nozzle temperature, print speed, and shell number. The optimized 3D-printed composite exhibited strength and stiffness comparable to injection-molded samples, while the impact resistance remained comparatively lower. A star-shaped auxetic structure was printed using the optimized printing conditions and found to exhibit transversely isotropic properties. Compression in the vertical build direction ( XZ plane) resulted in the best performance among the orientations tested with a specific energy absorption of 0.36 J/g and an equivalent plateau stress of 0.61 MPa. The crushing force efficiency varied between 0.52 and 0.58 depending on the load direction. These results demonstrate the potential of the composite for 3D printing of multifunctional, energy-absorbing parts.
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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) | 0.000 | 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 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".