Advances in Nano Research

Volume 20, Number 6, 2026, pages 787-819

DOI: 10.12989/anr.2026.20.6.787

Bridging mechanical theory and biomedical application: Stability analysis of functionally graded microstructures in sports medicine and implant design

Yuan Wen , Yanfeng Dong , Defang Chen , Mostafa Habibi

Abstract

The clinical success of orthopedic implants in sports medicine depends on mechanical stability, yet conventional designs often trade off strength against stress shielding. We analyze functionally graded microstructure implants made of titanium and hydroxyapatite, with material properties varying through the thickness. To capture small-scale effects in additively manufactured microstructures, we adopt the modified couple stress theory. The governing equations come from energy method and are solved using a finite element method based on a higher-order shear deformation theory. A parametric study examines how the power law index, material length scale, boundary conditions, and porosity distribution affect the critical buckling load. Beyond traditional simulation, we implement an artificial neural network model to perform rapid stability analysis across the design space. The neural network, trained on finite element results, predicts critical buckling loads with high accuracy and significantly reduced computational cost. This allows real-time stability assessment for patient-specific implant geometries. Our combined approach shows that an optimal material gradation with porosity concentrated at the bioactive surface preserves mechanical integrity while promoting osseointegration. The findings offer a quantitative design framework that balances mechanical stability and biocompatibility, with direct relevance to load-bearing implants in sports medicine and orthopedics.

Key Words

artificial neural networks; buckling analysis; finite element method; functionally graded materials; implant stability; orthopedic biomechanics; optimization

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