Machine Learning-Driven Design Optimization of Buckling-Induced Quasi-Zero Stiffness Metastructures for Low-Frequency Vibration Isolation.

Metastructures, artificial arrangements of micro/macrostructures, possess unique properties and are of significant interest in aerospace, stealth technology, and various other applications. Recent studies have focused on quasi-zero stiffness metastructures, providing an outstanding vibration isolation capability. However, existing methods are constrained to low preloads and lack the consideration of structural analysis, despite their intended use in practical structures. This study introduces metastructures with quasi-zero stiffness characteristics under high preloads by inducing local buckling. An optimization framework combining deep reinforcement learning and finite-element analysis is employed to derive an optimal model that considers both structural safety and quasi-zero stiffness characteristics. To validate the optimization results, quasi-zero stiffness metastructures are fabricated via 3D printing, and compression and vibration experiments are conducted. The fabricated metastructures exhibit quasi-zero stiffness characteristics under a high target preload along with outstanding vibration reduction performance, even in the low-frequency range.