Harnessing Interpretable Machine Learning for Origami Feature Design and Pattern Selection

Engineering design of origami systems is challenging because comparing different origami patterns requires using categorical features and evaluating multi-physics behavior targets introduces multi-objective problems. This work shows that a decision tree machine learning method is particularly suitable for the inverse design of origami. This interpretable machine learning method can reveal complex interactions between categorical features and continuous features for comparing different origami patterns, can tackle multi-objective problems for designing active origami with multi-physics performance targets, and can extend existing origami shape fitting algorithms to further consider non-geometrical performances of origami systems. The proposed framework shows a holistic way of designing active origami systems for various applications such as metamaterials, deployable structures, soft robots, biomedical devices, and many more.

Rapid Multi-Physics Simulation for Electro-Thermal Origami Robotic Systems

Electro-thermally actuated origami provides a novel method for creating 3-D systems with advanced morphing and functional capabilities. However, it is currently difficult to simulate the multi-physical behavior of such systems because the electro-thermal actuation and large folding deformations are highly interdependent. In this work, we introduce a rapid multi-physics simulation framework for electro-thermal origami robotic systems that can capture: thermo-mechancially coupled actuation, inter panel contact, heat transfer, large deformation folding, and other complex loading applied onto the origami. Comparisons with finite element simulations validate the proposed framework for capturing origami heat transfer with different system geometries, materials, and surrounding environments. Verification against physical electro-thermal micro origami further demonstrates the validity of the proposed model. Simulations of more complex origami patterns and a case study for origami optimization are provided as application examples to show the capability and efficiency of the model. The framework provides a novel simulation tool for analysis, design, control, and optimization of active origami robotic systems, pushing the boundary for feasible morphing and functional capability.