Machine learning-aided prediction and customization on mechanical response and wave attenuation of multifunctional kiri/origami metamaterials

Abstract

Multifunctional materials attract extensive attention for simultaneously satisfying diverse engineering applications, such as protection against mechanical and vibratory intrusions. Here, the mechanical responses and wave attenuation of multi-functional metamaterials at various elastoplastic are custom-designed. An elegant kiri/origami metamaterial is proposed, offering widely tunable mechanical responses and broadband wave attenuation in ultra low-frequencies. The incomparable compression-twist of kresling origami and the prominent local-resonance of kirigami split-rings promote efficient elastic wave polarization and plastic hinges, providing comprehensive protection from elastic to plastic. Kirigami split-rings highlight a fabrication-friendly approach of forming local resonators. Experiments and analyses confirm the reliability and superiority. Leveraging a machine learning-aided framework, optimal and anticipated individual properties and custom multi-performances are achieved for wave attenuation, energy absorption, plateau fluctuations, deformation triggering forces, and load-bearing/plateau forces under various impact levels. The machine learning-aided framework enables rapid multi-objective prediction and customization end-to-end without requiring prior knowledge. This work holds significant potential for the development and application of multi-functional, multi-physical field and multi-scale metamaterials.