Genetic algorithm-enabled mechanical metamaterials for vibration isolation with different payloads

Mechanical vibration isolation with adaptable payloads has always been one of the most challenging topics in mechanical engineering. In this study, we address this problem by introducing machine learning method to search for quasi-zero stiffness metamaterials with arbitrarily predetermined payloads and by employing multi-material 3D printing technology to fabricate them as an integrated part. Dynamic tests demonstrate that both the single- and multi-payload metamaterials can effectively isolate mechanical vibration in low frequency domain. Importantly, the payload of the metamaterial could be arbitrarily designed according to the application scenario and could function at multiple payloads. This design strategy opens new avenues for mechanical energy shielding under various scenarios and under variable loading conditions. • A modular method to search for the optimal zero stiffness metamaterials with predetermined payloads is introduced. • The metamaterials are fabricated by multi-material 3D printing technology as one integrated sample. • Dynamic experiments demonstrate the relatively low frequency vibration isolation performance of the metamaterials. • This work makes a contribution to construct a mechanical vibration isolation system with adaptable payloads.