Intelligent computational design of scalene-faceted flat-foldable tessellations

Abstract Origami tessellations can be folded from a given planar pattern into a three-dimensional object with specific geometric properties, inspiring developments in various fields of science and engineering such as deployable structures, energy absorption devices, reconfigurable robots, and metamaterials. However, the range of existing origami patterns with functional properties such as flat-foldability is rather scant, as analytical solutions to constraint equations arising in the design process are generally highly complicated. In this paper, we tackle the challenging problem of automated design of scalene-faceted flat-foldable origami tessellations using an efficient metaheuristic algorithm. To this end, this study establishes constraint curves based on compatibility conditions for all six-fold (i.e., degree-6) vertices. Subsequently, a graphical method and a particle swarm optimization (PSO) method are adopted to produce optimal origami patterns. Moreover, mountain-valley assignments for the obtained geometric designs are determined using a computational approach based on mixed-integer linear programming. It turns out that the flat-foldable internal vertices of each C2-symmetric unit fragment (UF) exist as C2-symmetric pairs about the centroid of the UF. Furthermore, numerical experiments are carried out to examine the feasibility and compare the accuracy, computational efficiency, and global convergence of the proposed methods. The results of numerical experiments demonstrated that, in comparison with the graphical method, the proposed PSO method has not only a higher accuracy but also a significantly lower computational cost, enabling us to develop an intelligent computational platform to efficiently design scalene-faceted flat-foldable origami tessellations.