Identification of Johnson-Cook Model Parameters for Human Cortical Bone Using Inverse Finite Element Method to...

Area of Science:

• Biomechanics
• Computational Material Science
• Orthopedic Surgery

Background:

• Constitutive models (CMs) predict material stress-strain relationships via computer simulation.
• Existing CMs like Johnson-Cook are based on animal or surrogate bone data, limiting human bone application.
• Accurate human bone models are crucial for simulating surgical procedures and developing implants.

Purpose of the Study:

• To determine Johnson-Cook model parameters specifically for human cortical bone.
• To validate the model's accuracy through tensile test simulations and experimental data comparison.
• To demonstrate the model's utility in simulating orthopedic surgical operations.

Main Methods:

• Utilized the inverse finite element method (FEM) to identify Johnson-Cook model parameters.
• Employed the Levenberg-Marquardt optimization algorithm (LMOA) to refine parameter values.
• Conducted tensile test simulations across a range of strain rates (0.00001-1/s).

Main Results:

• Successfully identified Johnson-Cook model parameters for human cortical bone.
• Simulations demonstrated strong agreement with experimental tensile test results.
• A case study on orthogonal bone cutting validated the model's applicability.

Conclusions:

• The derived Johnson-Cook model parameters provide a reliable representation of human cortical bone behavior.
• This research enables accurate in silico simulation of complex surgical operations.
• Facilitates virtual surgical rehearsal and practice, reducing the need for physical experimentation.