Machine learning-enabled forward prediction and inverse design of 4D-printed active plates

Xiaohao Sun, Liang Yue, Luxia Yu, Connor T. Forte, Connor D. Armstrong, Kun Zhou, Frédéric Demoly, Ruike Renee Zhao and H. Jerry Qi ()
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Xiaohao Sun: Georgia Institute of Technology
Liang Yue: Georgia Institute of Technology
Luxia Yu: Georgia Institute of Technology
Connor T. Forte: Georgia Institute of Technology
Connor D. Armstrong: Georgia Institute of Technology
Kun Zhou: Nanyang Technological University
Frédéric Demoly: UTBM
Ruike Renee Zhao: Stanford University
H. Jerry Qi: Georgia Institute of Technology

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Shape transformations of active composites (ACs) depend on the spatial distribution of constituent materials. Voxel-level complex material distributions can be encoded by 3D printing, offering enormous freedom for possible shape-change 4D-printed ACs. However, efficiently designing the material distribution to achieve desired 3D shape changes is significantly challenging yet greatly needed. Here, we present an approach that combines machine learning (ML) with both gradient-descent (GD) and evolutionary algorithm (EA) to design AC plates with 3D shape changes. A residual network ML model is developed for the forward shape prediction. A global-subdomain design strategy with ML-GD and ML-EA is then used for the inverse material-distribution design. For a variety of numerically generated target shapes, both ML-GD and ML-EA demonstrate high efficiency. By further combining ML-EA with a normal distance-based loss function, optimized designs are achieved for multiple irregular target shapes. Our approach thus provides a highly efficient tool for the design of 4D-printed active composites.

Date: 2024
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DOI: 10.1038/s41467-024-49775-z

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