A multiscale computational framework for wear prediction in knee replacement implants
Wear damage represents a long-term material failure that affects the service life of joint implants. Prediction of wear in bearing inserts under arbitrary joint kinematics and kinetics holds an important key to improving the performance of entire joint implant device through new material development and customized geometry design. In this work, a hierarchical multiscale computational framework that predicts the wear rate of ultra-high molecular weight polyethylene (UHMWPE) insert by resolving the microstructure details and microscale deformation mechanisms is developed. A fully dynamic finite element simulation is carried out at the macroscale level to reproduce the interactions between femoral component and UHMWPE insert according to the knee simulator input profiles. The stress evolution history from the initial wear site is extracted and applied back to the representative volume element (RVE) to further study the microscale deformation mechanisms that are related to wear damage. Activation of different slip systems in the crystalline phase as well as the interplay between the crystalline phase and amorphous phase are captured through a semi-crystal plasticity model. Results shown that improving wear resistance requires minimizing the plastic deformation upon the same external loading while maintaining a load-sharing balance between the crystalline phase and the amorphous phase. A combination of high crystallinity, well-dispersed crystallite with small size and texture alignment along the axial load direction can promote activation of chain pull slip that leads to improved wear resistance.
Mechanics of Materials
Digital Object Identifier (DOI)
Yan Li, Chi Ma, A multiscale computational framework for wear prediction in knee replacement implants, Mechanics of Materials, Volume 175, 2022, 104480, ISSN 0167-6636, https://doi.org/10.1016/j.mechmat.2022.104480.
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