Abstract
Aim of this study was the development of a dynamic FE-framework to identify worst-case size combinations and kinematics in a virtual wear simulator setup covering five daily activities and high, dynamic loads.
Two cruciate sacrificing knee designs (D1 & D2) were tested physically on a wear-testing machine prior the model development using a high demanding, daily activity protocol (HDA) [1]. A simplified FE-setup was generated, reduced to the 3D geometries of the assembly whereas the representation of the mechanical wear simulator conditions and the load transmission was achieved by joint elements. Inertial and other time-related effects of the physical situation were compensated by a system of spring- and damper elements.
Using a time-series signal optimization approach on the anterior-posterior translation and the internal-external rotation results for each activity, 38 variable parameters were varied in between pre-defined limits in a semiautomatic workflow. For each design, two consecutive cycles of a single activity were analysed and the results of the second cycle were used for the optimization. Based on the determined values, a single set of averaged parameter settings was identified that covers all activity cycles sufficiently.
A total of 1010 dynamic analyses were carried out in order to find a sharable set of parameter values.
In this study, an efficient simulation workflow for design evaluation was developed. Therefore, a HDA wear-testing machine was simplified to boundary conditions and stabilizing elements, using a single set of parameters for all activities. The calculated kinematics were in a comparable range to the machine output. Further applications of the method were found in systematic analyses of entire implant systems to achieve consistent kinematics over the size compatibility range in the design process of new implant systems.
