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Machine learning assisted design of shape-programmable 3D kirigami metamaterials

Engineering and Technology

Machine learning assisted design of shape-programmable 3D kirigami metamaterials

N. A. Alderete, N. Pathak, et al.

This innovative research by Nicolas A. Alderete, Nibir Pathak, and Horacio D. Espinosa presents a cutting-edge machine learning framework tailored for designing and controlling kirigami-based materials. By utilizing clustering, tandem neural networks, and symbolic regression, the framework predicts optimal kirigami cut layouts to meet specific design criteria, showcasing its effectiveness in developing shape-shifting metamaterials.

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Playback language: English
Abstract
This paper introduces a machine learning framework for the design and control of kirigami-based materials. The framework combines clustering, tandem neural networks, and symbolic regression to predict kirigami cut layouts that achieve specific design constraints. The approach is experimentally validated through various applications, demonstrating its utility in creating shape-shifting kirigami metamaterials.
Publisher
npj Computational Materials
Published On
Authors
Nicolas A. Alderete, Nibir Pathak, Horacio D. Espinosa
Tags
machine learning
kirigami
metamaterials
neural networks
symbolic regression
design constraints

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