Machine learning and sequential subdomain optimization for ultrafast inverse design of 4D-printed active composite structures

Machine learning and sequential subdomain optimization for ultrafast inverse design of 4D-printed active composite structures

Shape transformations of active composites (ACs) depend on the spatial distribution and active response of constituent materials. Voxel-level complex material distributions offer a vast possibility for attainable shape changes of 4D-printed ACs, while also posing a significant challenge in efficient...

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Bibliographic Details
Main Authors: Sun, Xiaohao, Yu, Luxia, Yue, Liang, Zhou, Kun, Demoly, Frédéric, Zhao, Renee Ruike, Qi, H. Jerry
Other Authors: School of Mechanical and Aerospace Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Engineering
Active composites
Morphing structures
Online Access:https://hdl.handle.net/10356/180317

Internet

https://hdl.handle.net/10356/180317

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