Predicting knee replacement damage in a simulator machine using a computational model with a consistent wear factor.

Area of Science:

• Biomaterials Engineering
• Computational Mechanics
• Orthopedic Surgery

Background:

• Ultrahigh molecular weight polyethylene wear is a key limitation in total knee replacement (TKR) longevity.
• Current wear testing methods are time-consuming and costly, hindering iterative design processes.

Purpose of the Study:

• To assess the accuracy of a computational model in predicting TKR damage compared to simulator testing.
• To investigate the impact of surface evolution methods and material models on prediction accuracy.

Main Methods:

• An iterative computational damage model was developed for a commercial knee implant using a dynamic contact and surface evolution model.
• The model was validated using a cylinder-on-plate system with a known analytical solution.
• Implant damage was simulated for 5 million gait cycles and compared to experimental data.

Main Results:

• The computational model predicted tibial insert wear volume within 2% error using a pin-on-plate wear factor.
• Damage depths and areas were predicted with 18% and 10% error, respectively.
• Surface evolution method significantly impacted damage depth and area predictions, particularly during the initial cycles.

Conclusions:

• Accurate TKR damage predictions are achievable with computational models using constant wear factors from pin-on-plate tests.
• Surface evolution methods are critical for capturing initial geometry changes but less so for long-term wear volume prediction.