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Multistable shells have been proposed for a variety of applications; however, their actuation is almost exclusively addressed through embedded piezoelectric patches. Additional actuation techniques are needed for applications requiring high strains or where remote actuation is desirable. Part of the reason for the lack of research in this area is the absence of appropriate models describing the detailed deformation and energetics of such shells. This work presents a bistable spherical cap made of iron carbonyl-infused polydimethylsiloxane. The magnetizable structure can be actuated remotely through permanent magnets while the transition is recorded with a high-speed camera. Moreover, the experiment is reproduced in a finite element (FE) dynamic model for comparison with the physical observations. High-speed footage of the physical cap inversion together with the FE modeling gives valuable insight on preferable intermediate geometries. Both methods return similar values for the magnetic field strength required for the snap-through. High-strain multistable spherical cap transformation is demonstrated, based on informed material selection. We discover that non-axisymmetric transition shapes are preferred in intermediate geometries by bistable spherical caps. We develop the methods for design and analysis of such actuators, including the feasibility of remote actuation methods for multistable shells.

Sheet metal forming is a critical component of modern manufacturing. The procedure for selecting a suitable manufacturing process to achieve the final geometry of a metal part is unstructured and heavily reliant on human expertise. Similarly, classification and design of new metal forming processes has yet to be automated. In this study, a Machine Learning approach was used for the first time to identify the manufacturing process that formed a part solely from the final geometry. Several Neural Network configurations were tested with different geometry representation methods. The best performing classifier employed a deep Convolutional Neural Network and achieved an accuracy of 89%, namely when the geometry was given through a mapping of the Mean and Gaussian curvatures. The high accuracy rate establishes that automated methods can perform this step between design and manufacture, thus eliminating the need for human experts in matching each product to a suitable forming method.

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