AI-Enhanced finite element analysis for optimal material selection in total knee replacement

The increasing prevalence of total knee replacement (TKR) surgery and its association with aseptic loosening present a significant research challenge. This issue can be mitigated by selecting optimal materials for TKR components. This paper presents a hybrid approach combining Finite Element Method (FEM) with Artificial Intelligence (AI) techniques for optimal material selection in TKR. Simulations of four normal movements—compression, distraction, medial bending, and lateral bending—were performed on a 3D model of the artificial knee. Four materials were evaluated: Cobalt-Chromium (Co-Cr), Zirconium (Zr), Stainless Steel, and Titanium (Ti). To simulate ligamentous constraints during distraction loading, a model of a nonlinear spring element was included, furnishing more physiologically realistic boundary conditions. An Artificial Neural Network (ANN) was trained on FEM simulation data to predict material performance with 98.7% accuracy for stress prediction (R 2 = 0.987), while displacement prediction achieved R 2 = 0.985. Multi-criteria decision-making using TOPSIS ranked Co-Cr as the optimal material under stress-prioritized weighting (0.6/0.4); though, sensitivity analysis revealed that Stainless Steel converts optimal when displacement/compliance is prioritized (0.4/0.6). Under axial compression of 1300 N, maximum stress for Co-Cr and Zr was 4.207 × 10 8 N/m 2 and 3.921 × 10 8 N/m 2 , respectively. Under distraction: 4.321 × 10 8 N/m 2 and 4.142 × 10 8 N/m 2 . Under medial bending: 1.101 × 10 8 N/m 2 and 1.088 × 10 8 N/m 2 . Under lateral bending: 1.119 × 10 8 N/m 2 and 1.102 × 10 8 N/m 2 . The AI-based approach reduced computational time by 73% compared to full FEM simulations while maintaining high prediction accuracy. This time-saving shows the promise of the surrogate model for large scale materials screening and patient specific implant optimization, but further biocompatibility and tribological validation will be required for clinical translation of these findings.