Towards Optimal Design of Dielectric Elastomer Actuators Using a Graph Neural Network Encoder

Dielectric elastomer actuators (DEAs), a type of “artificial muscles”, can generate significant deformations and offer speedy responses when exposed to voltage. Owing to their high electromechanical conversion efficiency and great flexibility, they have been extensively used in soft robot applications, such as soft grippers, walking robots, crawling robots, climbing robots, swimming robots, etc. Although previous research has explored the use of DEAs in soft robot locomotion, achieving optimal behavior is challenging due to the complexity of the constituent materials and the highly nonlinear nature of the problem. In this study, a simulation-based design optimization approach is proposed to address this challenge. The proposed approach involves developing a computational modeling framework that evaluates the electromechanical behavior of the DEA. A graph neural network (GNN) is employed as an encoder to extract the latent representation of the geometry in a low dimensional space, which is further used to construct a surrogate model for fast prediction of target responses. To achieve an optimal actuation capability under design constraints, a multi-objective optimization function is formulated to balance the actuation distance and the actuator size, where the Pareto front demonstrates the trade-off between the actuation distance and design constraints. Finally, three optimized designs are fabricated and tested, demonstrating a performance improvement of over 140% compared to an intuitive design. This framework can greatly benefit the design of DEA-based soft robotics.