Dynamic simulation of a displacement-controlled total knee replacement wear tester
Bibliographic record
Abstract
This paper presents a dynamic finite element method (FEM) model of a commercial displacement-controlled total knee replacement (TKR) wear tester. The first goal of the study was to validate the model, which included both the wear tester and the TKR components. Convergence simulations and experimental testing were performed. These included a novel experimental determination of the coefficient of friction and an evaluation of predicted joint contact areas by comparing simulation results with experimental data collected using pressure-sensitive film. The second goal of this study was to develop a procedure for implementing force-based testing protocols on a displacement-controlled TKR wear tester. A standard force-based cyclic wear-testing protocol was simulated using the FEM model and resulting displacement waveforms were extracted. These were used as control inputs to the physical wear tester and an experimental wear test was performed. Reaction loads on the tibial components were measured and compared with the simulated results. The model was capable of accurately predicting the tibial loads throughout the test cycle, verifying the model's contact mechanics. The study demonstrated the use of computational modelling to convert a force-based testing protocol into displacement-based control parameters for use in a displacement-controlled mechanical testing system.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".