Investigating Commercial Filaments for 3D Printing of Stiff and Elastic Constructs with Ligament-Like Mechanics
Bibliographic record
Abstract
The current gold standard technique for treatment of anterior cruciate ligament (ACL) injury is reconstruction with autograft. These treatments have a relatively high failure and re-tear rate. To overcome this, tissue engineering and additive manufacturing are being used to explore the potential of 3D scaffolds as autograft substitutes. However, mechanically optimal polymers for this have yet to be identified. Here, we use 3D printing technology and various materials with the aim of fabricating constructs better matching the mechanical properties of the native ACL. A fused deposition modeling (FDM) 3D printer was used to microfabricate dog bone-shaped specimens from six different polymers-PLA, PETG, Lay FOMM 60, NinjaFlex, NinjaFlex-SemiFlex, and FlexiFil-at three different raster angles. The tensile mechanical properties of these polymers were determined from stress-strain curves. Our results indicate that no single material came close enough to successfully match reported mechanical properties of the native ACL. However, PLA and PETG had similar ultimate tensile strengths. Lay FOMM 60 displayed a percentage strain at failure similar to reported values for native ACL. Furthermore, raster angle had a significant impact on some mechanical properties for all of the materials except for FlexiFil. We therefore conclude that while none of these materials alone is optimal for mimicking ACL mechanical properties, there may be potential for creating a 3D-printed composite constructs to match ACL mechanical properties. Further investigations involving co-printing of stiff and elastomeric materials must be explored.
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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".