Voxel Design of Grayscale DLP 3D‐Printed Soft Robots

        Grayscale digital light processing (DLP) printing is a simple yet effective way to realize the variation of material properties by tuning the grayscale value. However, there is a lack of available design methods for grayscale DLP 3D-printed structures due to the complexities arising from the voxel-level grayscale distribution, nonlinear material properties, and intricate structures. Inspired by the dexterous motions of natural organisms, a design and fabrication framework for grayscale DLP-printed soft robots is developed by combining a grayscale-dependent hyperelastic constitutive model and a voxel-based finite-element model. The constitutive model establishes the relationship between the projected grayscale value and the nonlinear mechanical properties, while the voxel-based finite-element model enables fast and efficient calculation of the mechanical performances with arbitrarily distributed material properties. A multiphysics modeling and experimental method is developed to validate the homogenization assumption of the degree of conversion (DoC) variation in a single voxel. The design framework is used to design structures with reduced stress concentration and programmable multimodal motions. This work paves the way for integrated design and fabrication of functional structures using grayscale DLP 3D printing.

Multimaterial DLP Printing with Centrifugal-Assisted Rinsing and Inverse Design of Voxel Structures

Voxel structures provide precise control over material distribution at the microscale. The integrated material–structure design paradigm offers a broader design space than traditional approaches based solely on geometry or homogeneous materials. A fundamental question in voxel structures is how to accurately print each voxel with designed sizes while avoiding interfacial contamination. To address it, we develop a multimaterial DLP 3D printer that integrates centrifugal forces and liquid rinsing, significantly reducing interfacial contamination, especially for high-viscosity resins. Additionally, we establish a multiphysics model considering chemical reactions, diffusion effects, and Gaussian light fields to predict voxel sizes accurately. This model is applicable to multiple resin types and is used to optimize printing parameters for individual voxels. By combining the fabrication method and theoretical model, voxel structures can be fabricated with high precision, facilitating the design of diverse functional behaviors. Hence, a machine learning-evolution algorithm method is developed to inversely design voxel distribution across a vast design space, and voxel structures with tailored buckling behaviors are demonstrated. The proposed fabrication, modeling, and design framework pave the way for developing voxel structures.