Analytical and computational sliding wear prediction of ultrahigh molecular weight polyethylene UHMWPE in block-on-ring (BOR) tribometer

In knee joint replacement, wear of Ultrahigh Molecular Weight Polyethylene (UHMWPE) can be a significant factor in shortening the implant life span. With advancements in computational technology, virtual testing has become more reliable at a lower cost compared to physical testing. This paper evaluates the wear coefficient, k_D, from physical tests as a reliable predictor of wear volume in the computational method. The physical test run with a block-on-ring (BOR) configuration of UHMWPE on a steel counterface with 225N load for wear coefficient, k_D acquisition and 130N load for computational prediction validation using the same wear coefficient, k_D. The computational methodology involved the use of an Abaqus solver incorporating the UMESHMOTION subroutine to implement Archard's law. The maximum FEA result error was 14% in the 225N load test, and FEA prediction for the 130N load test was 17%. The results show that the wear coefficient,k_D produced by coupling UMESHMOTION in the computational method, is reliable for predicting wear volume in BOR physical test.