Machine Learning‐Evolutionary Algorithm Enabled Design for 4D‐Printed Active Composite Structures
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 21, 2021
- Source ID
- 10.1002/adfm.202109805
Entities
People
- Frédéric Demoly
- H. Jerry Qi
- Han Shao
- Kun Zhou
- Liang Yue
- Luxia Yu
- Ruike Zhao
- Xiaohao Sun
- Xirui Peng
Organizations
- Air Force Office of Scientific Research
- Georgia Tech
- King Abdullah University of Science and Technology
- Nanyang Technological University
- Stanford University