A computational model for the prediction of articular and backside polyethylene (PE) wear of total knee replacements (TKRs) could enable the optimization of TKRs for the reduction of polyethylene wear, thereby improving the long term success of TKRs. A finite element model was developed for the TKR and the results were implemented in a computational wear model to assess PE wear. The wear factors of Archard’s wear law were identified by implementing the finite element simulation results along with knee simulator wear test results. Archard’s wear law was found to have insufficient accuracy for the purpose of optimization. Therefore, a novel computational wear model was developed by the author based on a theoretical understanding of the molecular behavior of PE. The model predicted result fell within the standard deviation of the independent knee simulator wear test results, indicating a high level of accuracy for the novel computational wear model.

A computational model for the prediction of articular and backside polyethylene (PE) wear of total knee replacements (TKRs) could enable the optimization of TKRs for the reduction of polyethylene wear, thereby improving the long term success of TKRs. A finite element model was developed for the TKR and the results were implemented in a computational wear model to assess PE wear. The wear factors of Archard’s wear law were identified by implementing the finite element simulation results along with knee simulator wear test results. Archard’s wear law was found to have insufficient accuracy for the purpose of optimization. Therefore, a novel computational wear model was developed by the author based on a theoretical understanding of the molecular behavior of PE. The model predicted result fell within the standard deviation of the independent knee simulator wear test results, indicating a high level of accuracy for the novel computational wear model.