Implant positioning represents a significant challenge for total hip replacements. Implants need to be well-fitted and positioned within the medullary canal, and should ideally maximise the femoral bone-implant contact area. Experimental testing of implant positions is limited by cost. An alternative is to use computational modelling to comprehensively analyse implant position at an early stage of product development. While this approach is not intended to replace experiments, it can help surgeons to better understand the effect of implant position on primary or secondary stability. Simpleware software and ANSYS were combined to create an automated workflow to integrate a CAD-designed implant into a CT scan of a femur, generating Finite Element (FE) models for micromotion analysis. Simulation results were used to generate response surfaces, demonstrating the effect a change in position can have on micromotion.
Simpleware modules ScanIP and +CAD were used to combine a segmented femur model taken from a CT scan with a CAD-designed implant. An FE mesh was then generated in the Simpleware +FE module and exported to ANSYS Workbench for micromotion simulation. The Simpleware API was used to automate the process of generating multiple implant positions.
FE meshes were generated for each implant position with approximately 10,000 nodes and 38,000 elements for the femur, and approximately 2,000 nodes and 6,000 elements for the implant, which was modelled as titanium. Standard material properties, node sets and loading conditions were also added, and refinements made at interfaces.
The initial FE models were exported to ANSYS Workbench for micromotion simulation based on successful simulations generated from a thousand potential candidates. A Response Surface Model (RSM) was generated by interpolating the 425 successful simulation points with the Kriging regression method.
ANSYS Workbench simulation was then able to use the RSM to determine the implant position that leads to the highest and lowest possible values of micromotion. These results can be interpreted by a surgeon to predict what the best and worst positions are for an implant.