Artificial Neural Network-based Finite Element method for assessing fatigue and stability of an origami-inspired structure

Abstract

In this paper, we present a comprehensive study of mechanical characteristics of a reconfigurable origami-inspired structure using Finite Element and Artificial Neural Network (ANN) approaches. We introduce a design of crease section in the proposed reconfigurable structure, which undergoes enormous and complicated deformation in the folding and unfolding process. Although this crease design can make the deploying process more straightforward, it can jeopardize the stability and life cycle of the structure. We explore how the geometric parameters of the design affect the stability and fatigue failure, including length ratio, total height, story’s height, thickness, crease indexes, and circumscribed circle’s radius. In order to reduce the computational time, we develop an ANN employing the obtained Finite Element method (FEM) results. The ANN results demonstrate that decreasing the circumscribed circle’s radius, the length ratio, and the total height enhance the stability of the origami-inspired structure. It is found that crease indexes affect stability based on the radius of the circumscribed circle. In addition, we investigate how these parameters simultaneously contribute to this design’s buckling load and life cycle. The results provide a detailed design parameter characteristic map that can be used to optimize origami structure performance.