Many malfunctions of current total knee replacements are related to the failure of the ultra-high-molecular weight polyethylene (UHMWPE) tibial component; therefore developing an understanding of the deterioration mechanisms experienced by these components is essential for the improvement of these artificial joints. Far too often these components will sustain premature failure and require replacement.
Characterization of UHMWPE retrieved tibial inserts with implantation times of up to 20 years was performed in order to identify some of the factors that affect the performance of these elements. The surface damage was assessed with a semi-quantitative wear damage scoring. Additionally, the surface morphology of the retrievals was examined microscopically using stereo- and low voltage scanning electron microscopy to explore the relationship between in vivo surface damage mechanisms and large-deformation plasticity. The semi-quantitative wear scoring method revealed that the damage experienced by the five studied retrieval ranged from 19 to 134; nonetheless, the score damage could not be correlated to the implantation time due to the lack of information provided on the patients' background. Two out of the 5 retrieved components displayed severe delamination, pitting, surface deformation, burnishing, and abrasion. Nevertheless, a larger group of retrieved inserts should be studied in order to achieve a deeper understanding of the failure of these components.
Additionally, computer-aided design (CAD) was used to study analytically the effect of different activities on the bearing surface. The study was solved using linear and non-linear models in order to identify the best approach. It was found that the linear model displayed a higher value for both, Von Mises stress and displacement. The non-linear solution showed displacements up to 0.062 mm while the linear solution showed values up to 0.14 mm. The change for the Von Mises stress was more severe with values up to 1.1 MPa for the linear model and 12.06 MPa for the non-linear model.
