Identification of Johnson-Cook model parameters for human cortical bone using inverse finite element method to practice and rehearse surgical operations.

Abstract

Constitutive models are used to predict material-specific relationships, such as stress and strain, through computer simulation. Although constitutive models, such as Johnson-Cook, Cowper-Symonds, modified Johnson-Cook, and Arrhenius were used to simulate the behavior of animal bones and surrogate materials, the outcomes predicted from such models cannot be directly applied to human bone, as they were developed from animal and bone surrogate materials. Therefore, this is the first study to identify the Johnson-Cook model parameters for human cortical bone using the inverse finite element method. As a procedure, the initial value with upper and lower bounds for each of the parameters involved in the Johnson-Cook model was assigned for the simulation, and then the parameter values that could best represent the human cortical bone were determined using the Levenberg-Marquardt optimization algorithm. To evaluate the results, tensile test simulations were carried out at various strain rates (0.00001/s ~ 1/s); the results obtained from the simulations were shown to agree well with the experiments. A case study to demonstrate the orthogonal bone cutting was also conducted, which justified the demand for the Johnson-Cook model parameters of the human cortical bone. The findings of this study could be used to simulate complex surgical operations, and thus, the surgical rehearsal and practice could be carried out in silico without conducting experiments on human or animal bones.