Finite Element and Density Functional Theory Modeling Effectively Predict Pitting Degradation of...

Area of Science:

• Biomaterials Science
• Computational Modeling
• Orthopedic Engineering

Background:

• Degradable orthopedic implants offer an alternative to permanent fixtures, potentially reducing pain and the need for removal surgery.
• Magnesium (Mg) and its alloys are promising biomaterials for implants due to bone-like mechanical properties, but pure Mg degrades too quickly.
• Hydroxyapatite (HA) coatings can control the degradation rate of Mg-based implants, mimicking bone's mineral component.

Purpose of the Study:

• To develop and implement a finite element (FE) model to predict the degradation of pure Mg and HA-coated pure Mg implants.
• To utilize a damage evolution law for pitting corrosion within the FE model to simulate physiological degradation.
• To calibrate the FE model with experimental data and extend its application to predict surface roughness changes.

Main Methods:

• Finite element analysis (FEA) was performed using Abaqus/Standard software on cylindrical Mg specimens.
• A Fortran user-material (UMAT) subroutine incorporated a pitting parameter to control degradation rate and material properties.
• Simulations tracked element damage and removal over 120 days; density functional theory (DFT) provided physical meaning for model parameters.

Main Results:

• The FE model successfully visualized degradation trends for both pure Mg and HA-coated pure Mg over 120 days.
• HA-coated Mg exhibited significantly lower corrosion rates compared to non-coated pure Mg, with degradation propagating inward.
• Simulation results were calibrated against experimental mass loss data, validating the model's predictive accuracy.

Conclusions:

• A calibrated computational model for Mg and HA-coated Mg degradation was established, advancing Mg-based biomaterial research.
• The model accurately predicts degradation behavior, offering a tool to guide the development of next-generation orthopedic implants.
• FEA simulations provide insights into controlling implant degradation for optimized bone regeneration and healing.