Inverse Design of Inflatable Soft Membranes Through Machine Learning. (10th January 2022) - Forte, Antonio Elia; Hanakata, Paul Z.; Jin, Lishuai; Zari, Emilia; Zareei, Ahmad; Fernandes, Matheus C.; Sumner, Laura; Alvarez, Jonathan; Bertoldi, Katia
Abstract: Across fields of science, researchers have increasingly focused on designing soft devices that can shape‐morph to achieve functionality. However, identifying a rest shape that leads to a target 3D shape upon actuation is a non‐trivial task that involves inverse design capabilities. In this study, a simple and efficient platform is presented to design pre‐programmed 3D shapes starting from 2D planar composite membranes. By training neural networks with a small set of finite element simulations, the authors are able to obtain both the optimal design for a pixelated 2D elastomeric membrane and the inflation pressure required for it to morph into a target shape. The proposed method has potential to be employed at multiple scales and for different applications. As an example, it is shown how these inversely designed membranes can be used for mechanotherapy applications, by stimulating certain areas while avoiding prescribed locations. Abstract : Soft devices that can shape morph in response to an external stimulus are becoming increasingly popular across many fields of science. Here, a platform that allows programmable transformation of 2D stretchable surfaces into target 3D shapes upon inflation is presented using machine learning methods. These devices are simple to miniaturize or enlarge enabling applications spanning from medical devices to architecture.