Predicting implant UHMWPE wear in-silico: A robust, adaptable computational–numerical framework for future theoretical models

Computational methods for pre-clinical wear prediction for devices such as hip, knee or spinal implants are valuable both to industry and academia. Archardʹs wear model laid the basis for the first generation of theoretical wear estimation algorithms, and this has been adapted to account for the importance of multi-directional sliding. The resulting second generation cross-shear algorithms are useful, but they leave room for improvement. In this paper, we outline a adaptable framework for a ‘third generation’ wear model. The essential feature of this proposed approach is that it removes the acausality and scale-independence of current second-generation algorithms. The methodology is presented in such a way that any existing second-generation model could be adapted using this framework. Using this approach, the predictive power against pin-on-disc and implant tests is shown to be improved; however, the model is still a purely adhesive-abrasive wear predictor, accounting for only a limited number of factors as part of the tribological process. Further ongoing work is needed to expand and improve upon the current capabilities of in-silico UHMWPE wear prediction capabilities.