Optimization Scheme for 3D Printing of PLA–PHBV–PCL Biodegradable Blends for Use in Orthopedic Casting
Bibliographic record
Abstract
Three-dimensional printing technology offers significant advantages in the production of orthopedic casts, providing a promising alternative to conventional plaster and fiberglass materials. Polylactic acid (PLA) is widely used for this purpose; however, its adoption is limited due to poor mechanical properties, including high brittleness, low thermal stability, and limited elongation. These challenges can be mitigated by blending PLA with other biodegradable polymers. This study investigated a blend of PLA with poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), a type of polyhydroxyalkanoate (PHA), and polycaprolactone (PCL) for the development of 3D printed orthopedic casts. The key mechanical properties-tensile strength, percent elongation at break, Young's modulus, flexural strength, flexural modulus, and impact strength-were evaluated as a function of the printing parameters, including nozzle temperature, layer height, and raster angle. The grey relational analysis (GRA) approach was applied to optimize these mechanical properties. The optimal printing parameters were found to be a nozzle temperature of 180 °C, a layer height of 0.18 mm, and a raster angle of 0°, resulting in a tensile strength of 44.4 ± 4.4 MPa, an elongation at break of 68.5 ± 11.6%, a Young's modulus of 948.7 ± 25.1 MPa, a flexural strength of 54.6 ± 8.9 MPa, a flexural modulus of 1549.3 ± 141 MPa, and an impact strength of 80.77 ± 5.6 J/m. Statistical analysis using analysis of variance (ANOVA) revealed that for tensile strength, 50.18% was influenced by the raster angle, 26.38% by the layer height, and 18.92% by the nozzle temperature; for flexural strength, 69.81% was influenced by the raster angle, 20.67% by the layer height, and 3.53% by the nozzle temperature; and for impact strength, 75.11% was influenced by the raster angle, 13.16% by the layer height, and 4.45% by the nozzle temperature.
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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 | 0.000 | 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 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".