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MWear of ultrahigh molecular weight polyethylene remains a primary factor limiting the
longevity of total knee replacements (TKRs). However, wear testing on a simulator machine
is time consuming and expensive, making it impractical for iterative design purposes.
The objectives of this paper were, first, to evaluate whether a computational model
using a wear factor consistent with the TKR material pair can predict accurate TKR
damage measured in a simulator machine and, second, to investigate how choices of
surface evolution method (fixed or variable step) and material model (linear or nonlinear)
affect the prediction. An iterative computational damage model was constructed for
a commercial knee implant in an AMTI simulator machine. The damage model combined
a dynamic contact model with a surface evolution model to predict how wear plus creep
progressively alter tibial insert geometry over multiple simulations. The computational
framework was validated by predicting wear in a cylinder-on-plate system for which an
analytical solution was derived. The implant damage model was evaluated for
5106 cycles of simulated gait using damage measurements made on the same implant
in an AMTI machine. Using a pin-on-plate wear factor for the same material pair as the
implant, the model predicted tibial insert wear volume to within 2% error and damage
depths and areas to within 18% and 10% errors, respectively. Choice of material model
had little influence, while inclusion of surface evolution affected damage depth and area
but not wear volume predictions. Surface evolution method was important only during the
initial cycles, where variable step was needed to capture rapid geometry changes due to
the creep. Overall, our results indicate that accurate TKR damage predictions can be
made with a computational model using a constant wear factor obtained from pin-onplate
tests for the same material pair, and furthermore, that surface evolution method
matters only during the initial “break in” period of the simulation.
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