Probabilistic computational modeling of total knee replacement wear
Probabilistic computational modeling of total knee replacement wear
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.
TKR, computational wear simulation, probabilistic, kinematics, knee mechanics
7pp
Pal, S.
73189e00-256d-484a-a86e-e81a00f5981d
Haider, H.
350a6250-16d7-45bb-b74f-15b71fcc832a
Laz, P.
d9056f7e-296c-408b-87bc-5153b64dbc16
Knight, L.A.
1c1cf1d5-d4ad-4152-983c-d967a399a767
Rullkoetter, P.J.
50a400a0-53d4-4230-81e5-9c7c5d97e5c6
2007
Pal, S.
73189e00-256d-484a-a86e-e81a00f5981d
Haider, H.
350a6250-16d7-45bb-b74f-15b71fcc832a
Laz, P.
d9056f7e-296c-408b-87bc-5153b64dbc16
Knight, L.A.
1c1cf1d5-d4ad-4152-983c-d967a399a767
Rullkoetter, P.J.
50a400a0-53d4-4230-81e5-9c7c5d97e5c6
Pal, S., Haider, H., Laz, P., Knight, L.A. and Rullkoetter, P.J.
(2007)
Probabilistic computational modeling of total knee replacement wear.
Wear, .
(doi:10.1016/j.wear.2007.06.010).
Abstract
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.
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More information
Published date: 2007
Keywords:
TKR, computational wear simulation, probabilistic, kinematics, knee mechanics
Identifiers
Local EPrints ID: 49361
URI: http://eprints.soton.ac.uk/id/eprint/49361
ISSN: 0043-1648
PURE UUID: dbdf0ff0-e997-428b-9937-266a09beaad2
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Date deposited: 02 Nov 2007
Last modified: 15 Mar 2024 09:55
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Contributors
Author:
S. Pal
Author:
H. Haider
Author:
P. Laz
Author:
L.A. Knight
Author:
P.J. Rullkoetter
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