A Validated Open-Source Multisolver Fourth-Generation Composite Femur Model
Synthetic biomechanical test specimens are frequently used for preclinical evaluation of implant performance, often in combination with numerical modeling, such as finite-element (FE) analysis. Commercial and freely available FE packages are widely used with three FE packages in particular gaining popularity: abaqus (Dassault Systèmes, Johnston, RI), ansys (ANSYS, Inc., Canonsburg, PA), and febio (University of Utah, Salt Lake City, UT). To the best of our knowledge, no study has yet made a comparison of these three commonly used solvers. Additionally, despite the femur being the most extensively studied bone in the body, no freely available validated model exists. The primary aim of the study was primarily to conduct a comparison of mesh convergence and strain prediction between the three solvers (abaqus, ansys, and febio) and to provide validated open-source models of a fourth-generation composite femur for use with all the three FE packages. Second, we evaluated the geometric variability around the femoral neck region of the composite femurs. Experimental testing was conducted using fourth-generation Sawbones® composite femurs instrumented with strain gauges at four locations. A generic FE model and four specimen-specific FE models were created from CT scans. The study found that the three solvers produced excellent agreement, with strain predictions being within an average of 3.0% for all the solvers (r2 > 0.99) and 1.4% for the two commercial codes. The average of the root mean squared error against the experimental results was 134.5% (r2 = 0.29) for the generic model and 13.8% (r2 = 0.96) for the specimen-specific models. It was found that composite femurs had variations in cortical thickness around the neck of the femur of up to 48.4%. For the first time, an experimentally validated, finite-element model of the femur is presented for use in three solvers. This model is freely available online along with all the supporting validation data.