Abstract

Although huge research efforts have been devoted to wear analysis of ultra-high molecular weight polyethylene (UHMWPE) in hip and knee implants, shoulder prostheses have been studied only marginally. Recently, the authors presented a numerical wear model of reverse total shoulder arthroplasties (RTSAs), and its application for estimating the wear coefficient k from experimental data according to different wear laws. In this study, such model and k expressions are exploited to investigate the sensitivity of UHMWPE wear to implant size and dimensional tolerance. A set of 10 different geometries was analysed, considering nominal diameters in the range 36–42mm, available on the market, and a cup dimensional tolerance of +0.2, −0.0mm (resulting in a diametrical clearance ranging between 0.04–0.24mm), estimated from measurements on RTSAs. Since the most reliable wear law and wear coefficient k for UHMWPE are still controversial in the literature, both the Archard law (AR) and the wear law of UHMWPE (PE), as well as four different k expressions were considered, carrying out a total of 40 simulations.Results showed that the wear volume increases with the implant size and decreases with the dimensional tolerance for both the wear laws. Interestingly, different trends were obtained for the maximum wear depth vs. clearance: the best performing implants should have a high conformity according to the AR law but low conformity for the PE law. However, according to both laws, wear is highly affected by both implant size and dimensional tolerance, although it is much more sensitive to the latter, with up to a twofold variation of wear predicted. Indeed, dimensional tolerance directly alters the clearance, and therefore the lubrication and contact pressure distribution in the implant. Rather surprisingly the role of dimensional tolerance has been completely disregarded in the literature, as well as in the standards. Furthermore, this study notes some important issues for future work, such as the validation of wear laws and predictive wear models and the sensitivity of k to implant geometry.

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