Header menu link for other important links
X
Shape-memory-alloy-based smart knee spacer for total knee arthroplasty: 3D CAD modelling and a computational study
A. Gautam, M.A. Callejas, , S.G. Acharyya
Published in Elsevier Ltd
2018
PMID: 29576460
Volume: 55
   
Pages: 43 - 51
Abstract
This study introduced a shape memory alloy (SMA)-based smart knee spacer for total knee arthroplasty (TKA). Subsequently, a 3D CAD model of a smart tibial component of TKA was designed in Solidworks software, and verified using a finite element analysis in ANSYS Workbench. The two major properties of the SMA (NiTi), the pseudoelasticity (PE) and shape memory effect (SME), were exploited, modelled, and analysed for a TKA application. The effectiveness of the proposed model was verified in ANSYS Workbench through the finite element analysis (FEA) of the maximum deformation and equivalent (von Mises) stress distribution. The proposed model was also compared with a polymethylmethacrylate (PMMA)-based spacer for the upper portion of the tibial component for three subjects with body mass index (BMI) of 23.88, 31.09, and 38.39. The proposed SMA -based smart knee spacer contained 96.66978% less deformation with a standard deviation of 0.01738 than that of the corresponding PMMA based counterpart for the same load and flexion angle. Based on the maximum deformation analysis, the PMMA-based spacer had 30 times more permanent deformation than that of the proposed SMA-based spacer for the same load and flexion angle. The SME property of the lower portion of the tibial component for fixation of the spacer at its position was verified by an FEA in ANSYS. Wherein, a strain life-based fatigue analysis was performed and tested for the PE and SME built spacers through the FEA. Therefore, the SMA-based smart knee spacer eliminated the drawbacks of the PMMA-based spacer, including spacer fracture, loosening, dislocation, tilting or translation, and knee subluxation. © 2018
About the journal
JournalData powered by TypesetMedical Engineering and Physics
PublisherData powered by TypesetElsevier Ltd
ISSN13504533