1,720,984 research outputs found
Ultra-long-term knee wear testing: an in-silico perspective
No full textWear is one of the many factors which concern total knee replacement (TKR) designers. With younger patients & increasing life expectancy, new TKR designs face a longer service life[1]. This has implications for conventional wear testing, typical in-vitro tests of 10 million cycles (MCycles) may represent only a fraction of this extended lifespan. Ultra-long term wear tests may reveal the extent to which TKR mechanics adapt over time; e.g. kinematics, contact area (CA), contact pressure (CP) and cross-shear (CS). It is unclear to what extent this long-term adaptation would be dependent on the test design (e.g. control method or input waveforms), as well as implant geometry. A 50MCycle test would take >1year to run in-vitro; hence in-silico methods offer a valuable complementary ‘screening tool’, to determine whether such ultra-long-term tests would be justifie
Probabilistic computational modeling of total knee replacement wear
No full textin both clinical retrieval and experimental wear studies. Recently, computational wear simulations have been shown to predict similar results to in
vitro and retrieval studies. The objectives of this study were to develop a probabilistic wear prediction model capable of incorporating uncertainty
in component alignment, constraint and environmental conditions, to compare computational predictions with experimental results from a knee
wear simulator, and to identify the most significant parameters affecting predicted wear performance during simulated gait. The current study
utilizes a previously verified wear model; the Archard’s law-based wear formulation represents a composite measure, incorporating the effects and
relative contributions of kinematics and contact pressure. Predicted wear was in reasonable agreement in trend and magnitude with experimental
results. After 5 million cycles, the predicted ranges (1–99%) of variability in linear wear penetration and gravimetric wear were 0.13mm and
25 mg, respectively, for the input variability levels evaluated. Using correlation-based sensitivity factors, the coefficient of friction, insert tilt and
femoral flexion–extension alignment, and the wear coefficient were identified as the parameters most affecting predicted wear. Comparisons of
stability, accuracy and efficiency for the Monte Carlo and advanced mean value (AMV) probabilistic methods are also described. The probabilistic
wear prediction model provides a time and cost efficient framework to evaluate wear performance, including considerations of malalignment and
variability, during the design phase of new implants
Influence of kinematics and wear path on surface wear of total knee replacements
No full textIntroduction: Reduction of ultra high molecular weight polyethylene (UHMWPE) wear in total knee replacement (TKR) bearings may delay the onset of osteolysis and subsequent loosening of components. This study used finite element (FE) modelling and in vitro simulator testing to investigate the effect of wear path geometry on UHMWPE surface wear.Methods: The wear of PFC Sigma fixed bearing TKRs (DePuy) was investigated using a six-station force/ displacement controlled knee simulator (frequency 1 Hz) using previously developed methods [1]. High, intermediate and low kinematic inputs were simulated for up to five million cycles (Table 1) with identical flexion-extension and axial loading for all components. This kinematic data was also applied to a FE model of the PFC Sigma TKR and run using PAM-CRASH-SAFE software. The anterior-posterior (AP), medial-lateral (ML) and inferior-superior data were recorded and the resulting wear paths generated by selecting nodes from the contacting surface of the polyethylene relative to the femoral.Results and Discussion: The mean wear rates with 95% confidence limits on the simulator when subjected to high, intermediate and low kinematics were 22.75 ± 5.95, 9.85 ± 3.7 and 5.2 ± 3.77 mm3 per million cycles, respectively. All FE models exhibited looped wear paths. An example wear path for the first 60% of the gait cycle for a lateral node is displayed in Figure I. The high kinematics model generated the greatest ML displacement and similar AP displacement to the intermediate kinematics model. The low kinematics model showed least ML and AP displacements. The AP displacements for medial wear paths differed little when subjected to the different kinematics. A looped wear path on the surface of UHMWPE results in greater cross shear transverse to the principal direction of motion, which is parallel to AP displacement in TKR and is the axis along which strain hardening occurs. This study revealed that increased AP displacement and tibial rotation kinematics generate more looped wear paths, increase ML and AP displacements on the surface of fixed bearing TKR and result in greater cross shear which ultimately increases UHMWPE surface wear
- …
