Optimizing Design Parameters of PLA 3D-Printed Scaffolds for Bone Defect Repair
Bibliographic record
Abstract
Current materials used to fill bone defects (ceramics, cement) either lack strength or do not induce bone repair. The use of biodegradable polymers such as PLA may promote patient healing by stimulating the production of new bone in parallel with a controlled degradation of the scaffold. This project aims to determine the design parameters maximising scaffold mechanical performance in such materials. Starting from a base cylindrical model of 10 mm height and of outer and inner diameters of 10 and 4 mm, respectively, 27 scaffolds were designed. Three design parameters were investigated: pore distribution (crosswise, lengthwise, and eccentric), pore shape (triangular, circular, and square), and pore size (surface area of 0.25 mm2, 0.5625 mm2, and 1 mm2). Using the finite element approach, a compressive displacement (0.05 mm/s up to 15% strain) was simulated on the models and the resulting scaffold stiffnesses (N/mm2) were compared. The models presenting good mechanical behaviors were further printed along two orientations: 0° (cylinder sitting on its base) and 90° (cylinder laying on its side). A total of n = 5 specimens were printed with PLA for each of the retained models and experimentally tested using a mechanical testing machine with the same compression parameters. Rigidity and yield strength were evaluated from the experimental curves. Both numerically and experimentally, the highest rigidity was found in the model with circular pore shape, crosswise pore distribution, small pore size (surface area of 0.25 mm2), and a 90° printing orientation. Its average rigidity reached 961 ± 32 MPa from the mechanical testing and 797 MPa from the simulation, with a yield strength of 42 ± 1.5 MPa. The same model with a printing orientation of 0° resulted in an average rigidity of 515 ± 7 MPa with a yield strength of 32 ± 1.6 MPa. Printing orientation and pore size were found to be the most influential design parameters on rigidity. The developed design methodology should accelerate the identification of effective scaffolds for future in vitro and in vivo studies.
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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) | 0.000 | 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".