Machine learning assisted design of shape-programmable 3D | ResearchBunny

Index

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

Related Publications

Explore these studies to deepen your understanding of the subject.

Engineering and Technology

Deep learning for the rare-event rational design of 3D printed multi-material mechanical metamaterials

H. Pahlavani, M. Amani, et al.

Engineering and Technology

Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning

C. S. Ha, D. Yao, et al.

Engineering and Technology

Machine intelligence accelerated design of conductive MXene aerogels with programmable properties

S. Shrestha, K. J. Barvenik, et al.

Engineering and Technology

Machine learning enables design automation of microfluidic flow-focusing droplet generation

A. Lashkaripour, C. Rodriguez, et al.

Listen, Learn & Level Up

Over 10,000 hours of research content in 25+ fields, available in 12+ languages.

No more digging through PDFs—just hit play and absorb the world's latest research in your language, on your time.

listen to research audio papers with researchbunny