Machine learning for 3D shaped morphable materials

A research team from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a platform that utilises machine learning to program the transformation of 2D stretchable surfaces into specific 3D shapes.

Flat materials that can morph into 3D shapes

Flat materials that can morph into 3D shapes have potential applications in architecture, medicine, robotics, space travel, and much more. But programming these shapes has been a challenge for scientists, as changes require complex and time-consuming computations.

Katia Bertoldi, senior author of the study and the William and Ami Kuan Danoff Professor of Applied Mechanics at SEAS, explained: “While machine learning methods have been classically employed for image recognition and language processing, they have also recently emerged as powerful tools to solve mechanics problems.

“In this work we demonstrate that these tools can be extended to study the mechanics of transformable, inflatable systems.”

The study

The research team began by dividing an inflatable membrane into a 10×10 grid of 100 square pixels that can either be soft or stiff. The soft or stiff pixels can be combined in an almost infinite variety of configurations, making manual programming extremely difficult. Which is where machine learning is considered beneficial in the construction of the 3D shapes.

Bertoldi and her team used what is known as ‘finite element simulations’ to sample this infinite design space. Then neural networks used that sample to learn how the location of soft and stiff pixels controls the deformation of the membrane when it is pressurised.

“Once the machine learning model was trained, we came up with an arbitrary 3D shape and passed it to the model,” commented Elia Forte, first author of the paper, and a former postdoctoral fellow at SEAS. “The neural network then outputs the membrane design and the pressure at which we should inflate such membrane to obtain the desired 3D shape.”

The research team used this new design method to build and test a device for mechanotherapy that can stimulate tissue around a scar to enhance healing and reduce recovery time. The research has revealed how this technology can be utilised to design morphable surfaces at multiple scales for applications from medical devices to architecture.

“This platform has potential to quickly and effectively design patient-specific devices for mechanotherapy and beyond. Before this research, we didn’t know how to use machine learning to unravel nonlinear mappings in inflatable systems, but it turns out that they are very powerful for these purposes.

“Machine learning could push the boundaries of currently known design strategies and allow us to design and build fully reconfigurable shape-morphing material,” Forte concluded.

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