| Abstract Scope |
The development of architectured materials has revolutionized mechanical design, enabling exceptional properties through the optimization of structural geometry rather than material composition. This research focuses on three innovative approaches to designing high-specific-stiffness materials: strut-based architectures, hybrid triply periodic minimal surface (HTAM) structures, and generative AI-driven shape exploration. For strut-based architectures, multi-objective Bayesian optimization was employed to refine beam shapes, achieving a balance between stiffness and density. HTAM structures, combining multiple TPMS geometries, expanded the design space to yield configurations with superior mechanical performance. Finally, leveraging generative AI, novel architectures were explored, demonstrating unprecedented flexibility in achieving optimized designs. Finite element analysis (FEA) and experimental validation confirmed significant improvements in modulus and strength across all approaches. These methodologies underscore the transformative potential of integrating machine learning, advanced optimization techniques, and additive manufacturing to address critical challenges in aerospace, biomedical, and lightweight structural applications. |