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This work harnesses interpretable machine learning methods to address the challenging inverse design problem of origami-inspired systems. We established a work flow based on decision tree-random forest method to fit origami databases, containing both design features and functional performance, and to generate human-understandable decision rules for the inverse design of functional origami. First, the tree method is unique because it can handle complex interactions between categorical features and continuous features, allowing it to compare different origami patterns for a design. Second, this interpretable method can tackle multi-objective problems for designing functional origami with multiple and multi-physical performance targets. Finally, the method can extend existing shape-fitting algorithms for origami to consider non-geometrical performance. The proposed framework enables holistic inverse design of origami, considering both shape and function, to build novel reconfigurable structures for various applications such as metamaterials, deployable structures, soft robots, biomedical devices, and many more.

 

Abstract Origami-inspired systems are attractive for creating structures and devices with tunable properties, multiple functionalities, high-ratio packaging capabilities, easy fabrication, and many other advantageous properties. Over the past decades, the community has developed a variety of simulation techniques to analyze the kinematic motions, mechanical properties, and multiphysics characteristics of origami systems. These various simulation techniques are formulated with different assumptions and are often tailored to specific origami designs. Thus, it is valuable to systematically review the state-of-the-art in origami simulation techniques. This review presents the formulations of different origami simulations, discusses their strengths and weaknesses, and identifies the potential application scenarios of different simulation techniques. The material presented in this work aims to help origami researchers better appreciate the formulations and underlying assumptions within different origami simulation techniques, and thereby enable the selection and development of appropriate origami simulations. Finally, we look ahead at future challenges in the field of origami simulation.