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.