Pal, S., Haider, H., Laz, P., Knight, L.A. and Rullkoetter, P.J.
Probabilistic computational modeling of total knee replacement wear
Wear, . (doi:10.1016/j.wear.2007.06.010).
Full text not available from this repository.
in both clinical retrieval and experimental wear studies. Recently, computational wear simulations have been shown to predict similar results to in
vitro and retrieval studies. The objectives of this study were to develop a probabilistic wear prediction model capable of incorporating uncertainty
in component alignment, constraint and environmental conditions, to compare computational predictions with experimental results from a knee
wear simulator, and to identify the most significant parameters affecting predicted wear performance during simulated gait. The current study
utilizes a previously verified wear model; the Archard’s law-based wear formulation represents a composite measure, incorporating the effects and
relative contributions of kinematics and contact pressure. Predicted wear was in reasonable agreement in trend and magnitude with experimental
results. After 5 million cycles, the predicted ranges (1–99%) of variability in linear wear penetration and gravimetric wear were 0.13mm and
25 mg, respectively, for the input variability levels evaluated. Using correlation-based sensitivity factors, the coefficient of friction, insert tilt and
femoral flexion–extension alignment, and the wear coefficient were identified as the parameters most affecting predicted wear. Comparisons of
stability, accuracy and efficiency for the Monte Carlo and advanced mean value (AMV) probabilistic methods are also described. The probabilistic
wear prediction model provides a time and cost efficient framework to evaluate wear performance, including considerations of malalignment and
variability, during the design phase of new implants.
Actions (login required)