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Research Papers

Predicting Knee Replacement Damage in a Simulator Machine Using a Computational Model With a Consistent Wear Factor

[+] Author and Article Information
Dong Zhao, W. Gregory Sawyer

Department of Mechanical & Aerospace Engineering, University of Florida, Gainesville, FL 32611

Hideyuki Sakoda

 Nakashima Medical Division, Nakashima Propeller Co., Ltd., Japan

Scott A. Banks

Department of Mechanical & Aerospace Engineering, Department of Biomedical Engineering, Department of Orthopaedics and Rehabilitation, University of Florida, Gainesville, FL 32611

Benjamin J. Fregly1

Department of Mechanical & Aerospace Engineering, Department of Biomedical Engineering, Department of Orthopaedics and Rehabilitation, University of Florida, Gainesville, FL 32611fregly@ufl.edu

1

Corresponding author.

J Biomech Eng 130(1), 011004 (Feb 05, 2008) (10 pages) doi:10.1115/1.2838030 History: Received July 31, 2006; Revised May 11, 2007; Published February 05, 2008

Wear 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 choice 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 5 million 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% error, 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-on-plate 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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Copyright © 2008 by American Society of Mechanical Engineers
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Figure 5

Cross sectional view of worn plate surface after 5 million cycles (mc) as predicted by the computational damage model using different surface evolution methods and update criteria. (a) No evolution (5mc, 1 simulation), (b) fixed step evolution (2.5mc, 2 simulations), (c) fixed step evolution (0.25mc, 20 simulations), (d) variable step evolution (5%, 7 simulations), and (e) variable step evolution (0.5%, 19 simulations).

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Figure 6

Comparison of experimentally measured and computationally predicted wear volumes for the knee replacement system over 5 million cycles of simulated gait. Simulation error bars indicate change in predictions due to ±1 standard deviation in wear factor measurement.

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Figure 7

Damage depth and area predictions for the knee replacement system generated using fixed and variable step surface evolution. Left column: medial side. Right column: lateral side.

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Figure 8

Qualitative comparison of measured and predicted damage scars after 5 million cycles of simulated gait. (a) Experimental damage scars measured by a CMM. (b) Computational damage scars predicted using variable step evolution with a threshold of 0.5% and the nonlinear material model. x’s indicate locations of maximum damage.

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Figure 9

Qualitative comparison of predicted damage contours for different combinations of surface evolution and material model after 5 million cycles (mc) of simulated gait. (a) no evolution with linear material model; (b) no evolution with nonlinear material model; (c) Fixed step evolution with linear material model (0.25mc, 20 simulations); (d) fixed step evolution with nonlinear material model (0.25mc, 20 simulations); (e) variable step evolution with linear material model (0.5%, 13 simulations); and (f) variable step evolution with nonlinear material model (0.5%, 11 simulations).

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Figure 4

Comparison of analytical and computational results for wear depth and area for the cylinder-on-plate system. Left column: computational predictions using fixed step surface evolution. Right column: computational predictions using variable step surface evolution.

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Figure 3

Schematic of the cylinder-on-plate system used for analytical validation of the computational damage prediction framework

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Figure 2

Flowchart of the iterative series of analyses performed to develop computational damage predictions with surface evolution

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Figure 1

Overview of the process used to evaluate computational damage predictions for a total knee replacement. (a) A constant wear factor is obtained from pin-on-plate experiments using the same material pair as in the implant. (b) This wear factor is used in a computational damage prediction that combines a dynamic contact model of the implant in an AMTI simulator machine with a surface evolution model. (c) The computational damage predictions are compared to experimental damage measurements made on the same implant during testing in a physical AMTI simulator machine.

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