Background: Experimental knee implant wear testing according to ISO 14243 is a standard procedure, but it inherently possesses limitations for preclinical evaluations due to extended testing periods and costly infrastructure. In an effort to overcome these limitations, we hereby develop and experimentally validate a finite-element (FE)-based algorithm, including a novel cross-shear and contact pressure dependent wear and creep model, and apply it towards understanding the sensitivity of wear outcomes to the applied boundary conditions.
Methods: Specifically, we investigated the application of in vivo data for level walking from the publicly available "Stan" data set, which contains single representative tibiofemoral loads and kinematics derived from in vivo measurements of six subjects, and compared wear outcomes against those obtained using the ISO standard boundary conditions. To provide validation of the numerical models, this comparison was reproduced experimentally on a six-station knee wear simulator over 5 million cycles, testing the same implant Stan's data was obtained from.
Results: Experimental implementation of Stan's boundary conditions in displacement control resulted in approximately three times higher wear rates (4.4 vs. 1.6 mm³ per million cycles) and a more anterior wear pattern compared to the ISO standard in force control. While a force-controlled ISO FE model was unable to reproduce the bench test kinematics, and thus wear rate, due to a necessarily simplified representation of the simulator machine, similar but displacement-controlled FE models accurately predicted the laboratory wear tests for both ISO and Stan boundary conditions. The credibility of the in silico wear and creep model was further established per the ASME V&V-40 standard.
Conclusions: The FE wear model is suitable for supporting future patient-specific models and development of novel implant designs. Incorporating the Stan data set alongside ISO boundary conditions emphasized the value of using measured kinematics in displacement control for reliably replicating in vivo joint mechanics in wear simulation. Future work should focus on expanding the range of daily activities simulated and addressing model sensitivity to contact mechanics to further enhance predictive accuracy.