Machine Learning?Evolutionary Algorithm Enabled Design for 4D?Printed Active Composite Structures

نویسندگان

چکیده

Active composites consisting of materials that respond differently to environmental stimuli can transform their shapes. Integrating active and 4D printing allows the printed structure have a pre-designed complex material or property distribution on numerous small voxels, offering enormous design flexibility. However, this tremendous space also poses challenge in efficiently finding appropriate designs achieve target shape change. Here, novel machine learning (ML) evolutionary algorithm (EA) based approach is presented guide process. Inspired by beam deformation characteristics, recurrent neural network (RNN) ML model whose training dataset acquired finite element simulations developed for forward shape-change prediction. EA empowered with then used solve inverse problem optimal design. For multiple shapes different complexities, ML-EA demonstrates high efficiency. Combining computer vision algorithms, new paradigm streamlines process where straight beams be designed hand-drawn lines into drawn profiles under stimulus. The thus provides highly efficient tool 4D-printed composites.

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ژورنال

عنوان ژورنال: Advanced Functional Materials

سال: 2021

ISSN: ['1616-301X', '1616-3028']

DOI: https://doi.org/10.1002/adfm.202109805