Additive manufacturing of a super toughened biodegradable polymer blend: Structure-property-processing correlation and 3D printed prosthetic part development
Bibliographic record
Abstract
Super toughened polymer materials have garnered significant attention from the scientific and industrial communities due to their vast application potential. However, a notable gap exists in the exploration of additive manufacturing (AM) of biodegradable polymer blends with enhanced impact resistance. This research addresses this gap by investigating the AM of a resilient green blend comprising PLA, BioPBS, and PBAT to achieve an enhanced impact resistance. To characterize the blend, rheological analysis, differential scanning calorimetry, thermomechanical analysis, and mechanical testing were performed. The printing parameters varied were the nozzle temperature, infill density, top and bottom solid layer raster angles, and infill pattern. This study is the first attempt to investigate the impact of the raster angle of top and bottom solid layers, which is important for determining the structural integrity of printed objects. The study revealed that a nozzle temperature of 180 °C, a rectilinear infill pattern, 100% infill density, and solid layers raster angle of 0° resulted in high mechanical properties with a notched Izod impact strength of 489.75 ± 16.6 J/m. Additionally, the study compared the mechanical properties of 3D printed and injection-molded samples. The 3D printed samples demonstrated comparable tensile strength to injection-molded samples, with only a 2% difference, and exhibited higher tensile and flexural moduli, showcasing 8 and 10% increases, respectively. However, there was a 10% decrease in impact strength compared to that of the injection-molded counterparts. These findings indicate that the 3D printed blend offers mechanical properties comparable to injection molding, making it a promising alternative for biomedical applications. A 3D printed leg prosthesis prototype has been developed using the formulated blend at optimized printing conditions.
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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) | 0.001 | 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".