Contact of Rough Surfaces in Ankle Implants Under Combined Normal and Twist Loading
Bibliographic record
Abstract
The medical application of implant replacements to remedy the pain in joints has necessitated a comprehensive study of wear due to contact of implant surfaces. Excessive wear can lead to toxicity and other implant associated medical issues such as patient discomfort and decreased mobility. Since implant wear is the result of contact between surfaces of tibia and talus implant, it is important to establish a model that can address implant surface contact mechanics with roughness effects. In this research, a statistical contact model is developed for the interaction of tibia and talus including normal and lateral contact in which surface roughness effects are included. The model accounts for the elastic–plastic interaction of the implant surface with roughness. For this purpose, tibia and talus implants are considered as macroscopic surfaces containing micron-scale roughness. Approximate equations are obtained that relate the contact force to the mean surface separation explicitly. Closed-form equations are obtained for hysteretic energy loss in implant using the approximate equations. Such a function can serve as a very useful tool for implant designers and manufacturers. Natural frequencies of both adduction-abduction and planter-dorsiflexion rotations are obtained using nonlinear vibration analyses.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".