Machine Learning‐Evolutionary Algorithm Enabled Design for 4D‐Printed Active Composite Structures. (21st November 2021)

• Record Type: Journal Article
• Authors: Sun, Xiaohao; Yue, Liang; Yu, Luxia; Shao, Han; Peng, Xirui; Zhou, Kun; Demoly, Frédéric; Zhao, Ruike; Qi, H. Jerry
• Is Part Of: Advanced functional materials. Volume 32:Number 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-21
• Subjects: 4D printing -- active composites -- evolutionary algorithms -- machine learning; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.202109805
• Languages: English
• ISSNs: 1616-301X

Abstract

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