4D printing has attracted tremendous interest since its first conceptualization in 2013. 4D printing derived from the fast growth and interdisciplinary research of smart materials, 3D printer, and design. Compared with the static objects created by 3D printing, 4D printing allows a 3D printed structure to change its configuration or function with time in response to external stimuli such as temperature, light, water, etc., which makes 3D printing alive. Herein, the material systems used in 4D printing are reviewed, with emphasis on mechanisms and potential applications. After a brief overview of the definition, history, and basic elements of 4D printing, the state‐of‐the‐art advances in 4D printing for shape‐shifting materials are reviewed in detail. Both single material and multiple materials using different mechanisms for shape changing are summarized. In addition, 4D printing of multifunctional materials, such as 4D bioprinting, is briefly introduced. Finally, the trend of 4D printing and the perspectives for this exciting new field are highlighted.

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Two-dimensional lattice structures with specific geometric features have been reported to have a negative Poisson's ratio, termed as auxetic metamaterials, that is, stretching-induced expansion in the transversal direction. In this paper, we designed a novel auxetic metamaterial; by utilizing the shape memory effect of the constituent materials, the in-plane moduli and Poisson's ratios can be continuously tailored. During deformation, the curved meshes ensure the rotation of the mesh joints to achieve auxetics. The rotations of these mesh joints are governed by the mesh curvature, which continuously changes during deformation. Because of the shape memory effect, the mesh curvature after printing can be programmed, which can be used to tune the rotation of the mesh joints and the mechanical properties of auxetic metamaterial structures, including Poisson's ratios, moduli, and fracture strains. Using the finite element method, the deformation of these auxetic meshes was analyzed. Finally, we designed and fabricated gradient/digital patterns and cylindrical shells and used the auxetics and shape memory effects to reshape the printed structures.

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Hard‐magnetic soft active materials (hmSAMs), embedding hard‐magnetic particles in soft polymeric matrices, have attracted a great number of research interests due to their fast‐transforming, untethered control, as well as excellent programmability. However, the current direct‐ink‐write (DIW) printing‐based fabrication of hmSAM parts and structures only permits programmable magnetic direction with a constant magnetic density. Also, the existing designs rely on the brute‐force approach to generate the assignment of magnetization direction distribution, which can only produce intuitional deformations. These two factors greatly limit the design space and the application potentials of hmSAMs. Herein, a “voxel‐encoding DIW printing” method to program both the magnetic density and direction distributions during hmSAM printing is introduced. The voxel‐encoding DIW printing is then integrated with an evolutionary algorithm (EA)‐based design strategy to achieve the desired magnetic actuation and motion with complex geometry variations and curvature distributions. With the new EA‐guided voxel‐encoding DIW printing technique, the functional hmSAMs that produce complicated shape morphing with desired curvature distributions for advanced applications such as biomimetic motions are demonstrated. These demonstrations indicate that the proposed EA‐guided voxel‐encoding DIW printing method significantly broadens the application potentials of hmSAMs.

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Active composites are a class of materials that have environmentally responsive components within them. One key advantage of active composites is that through mechanics design, a variety of actuation can be achieved. The development of active composites has been significantly enhanced in recent years by multimaterial 3D printing where different materials can be precisely placed in 3D space, enabling the achievement of shape-shifting of 3D printed parts, or 4D printing. In practical applications, it is highly desirable that the part shape can change in a predescribed manner, which requires the careful design of where to place different materials. However, designing an active composite structure to achieve a target shape change is challenging because it requires solving an inverse problem with spatially heterogenous, highly nonlinear (active) material behavior within a potentially complex boundary value problem. In this paper we present a machine learning approach to the design of active composite structures that can achieve target shape shifting responses. Our strategy is to combine the finite element method with an evolutionary algorithm. In order to achieve a target shape, we compose the structures of equally sized voxel units that are made of either a passive or an active material and optimize the distribution of these two material phases. The optimization method is tested against several illustrative examples in active composite design to show the agreement between the target shape and the best machine learning solution obtained.