Design and analysis of a compliant 3D printed energy harvester housing for knee implants
Bibliographic record
Abstract
Instrumented implants have the potential to detect abnormal loading patterns which could be deleterious to implant longevity, indicating a need for intervention which could reduce the need for more complicated revision surgeries. Reliably powering such devices has been one obstacle preventing widespread usage of instrumented implants in clinical populations. This study presents a 3D-printed titanium interpositional device designed to integrate triboelectric generators (TEGs) into a commercially available total knee replacement (TKR). The device's stiffness, durability, and efficacy as a TEG housing were determined. Surprisingly, the stiffness of the 3D printed prototype was 73% less than what was calculated in a corresponding computational model, and under long-term durability testing failed after approximately 30,000 cycles of simulated gait loading. Under cyclical compressive loading, TEGs embedded in the device were able to generate 10.05 μW of power which is sufficient to run the frontend electronics for a load measurement system. The stiffness discrepancy between the computational and experimental models and the premature fatigue failure are suspected to be a result of internal porosity, unfused material and surface roughness of the 3D printed metal. Further refinements in design and manufacturing of the compliant device are required to improve its durability and TEG power output.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".