Rapid prediction and tailoring on compressive behavior of origami-inspired hierarchical structure

Abstract

Thin-walled structures with tailorable compressive behavior offer a promising solution for achieving desired mechanical properties across multi-scenario applications. Therefore, this paper develops a novel thin-walled structure with high programmability through an origami-inspired hierarchical strategy. The origami-inspired hierarchical structure (OIHS) is fabricated using Laser Powder Bed Fusion. The compressive testing reveals that the deformation of OIHS strictly adheres to the pre-set crease, resulting in a stable load-bearing process. Numerical simulations are further conducted to investigate the programmable capacity of OIHS. The results display that the folding angle θ can enhance the deformation stability of OIHS, but is not conducive to the load-bearing level. The module number M effectively tailors the number and wavelength of folding lobes in OIHS, thus improving the energy absorption and load-bearing stability. As the M increases from 4 to10, the SEA and CFE of OIHS increase by 36.55 % and 17.81 %, respectively. The increasing edge length of sub-cell and wall thickness contribute to the interactive effect and material utilization, respectively, which facilitate its energy absorption. Compared to the vertex-based hierarchical structures, the OIHS demonstrates a 15.78 % increase in load-bearing stability without compromising its energy absorption capacity. Ultimately, artificial neural network-based machine learning models are developed to establish forward and inverse relationships between the mechanical curves and configuration parameters of OIHS, enabling rapid prediction and tailoring of the desired compressive behavior with an error of less than 8 %.