Mask projection stereolithography is a digital light processing-based additive manufacturing technique that has various advantages, such as high-resolution, scanning-free parallel process, wide material sets available, and support-structure-free three-dimensional (3D) printing. However, multi-material 3D printing with mask projection stereolithography has been challenging due to difficulties of exchanging a liquid-state material in a vat. In this work, we report a rapid multi-material projection micro-stereolithography using dynamic fluidic control of multiple liquid photopolymers within an integrated fluidic cell. Highly complex multi-material 3D micro-structures are rapidly fabricated through an active material exchange process. Material flow rate in the fluidic cell, material exchange efficiency, and the effects of energy dosage on curing depth are studied for various photopolymers. In addition, the degree of cross-contamination between different materials in a 3D printed multi-material structure is evaluated to assess the quality of multi-material printing. The pressure-tight and leak-free fluidic cell enables active and fast switch between liquid photopolymers, even including micro-/nano-particle suspensions, which could potentially lead to facile 3D printing of multi-material metallic/ceramic structures or heterogeneous biomaterials. In addition, a multi-responsive hydrogel micro-structure is printed using a thermo-responsive hydrogel and an electroactive hydrogel, showing various modes of swelling actuation in response to multiple external stimuli. This new ability to rapidly and heterogeneously integrate multiple functional materials in three-dimension at micro-scale has potential to accelerate advances in many emerging areas including 3D metamaterials, tissue engineering, and soft robotics.

 The exotic properties of mechanical metamaterials emerge from the topology of micro-structural elements. Once manufactured, however, the metamaterials have fixed properties without the ability to adapt and adjust. Here, we present geometrically reconfigurable, functionally deployable, and mechanically tunable lightweight metamaterials created through four-dimensional (4D) printing. Using digital micro 3D printing with a shape memory polymer, dramatic and reversible changes in the stiffness, geometry, and functions of the metamaterials are achieved.

 Despite recent advances in additive manufacturing (AM) that shifts the paradigm of modern manufacturing by its fast, flexible, and affordable manufacturing method, the achievement of high-dimensional accuracy in AM to ensure product consistency and reliability is still an unmet challenge. This study suggests a general method to establish a mathematical spatial uncertainty model based on the measured geometry of AM microstructures. Spatial uncertainty is specified as the deviation between the planned and the actual AM geometries of a model structure, high-aspect-ratio struts. The detailed steps of quantifying spatial uncertainties in the AM geometry are as follows: (1) image segmentation to extract the sidewall profiles of AM geometry; (2) variability-based sampling; (3) Gaussian process modeling for spatial uncertainty. The modeled spatial uncertainty is superimposed in the CAD geometry and finite element analysis is performed to quantify its effect on the mechanical behavior of AM struts with different printing angles under compressive loading conditions. The results indicate that the stiffness of AM struts with spatial uncertainty is reduced to 70% of the stiffness of CAD geometry and the maximum von Mises stress under compressive loading is significantly increased by the spatial uncertainties. The proposed modeling framework enables the high fidelity of computer-based predictive tools by seamlessly incorporating spatial uncertainties from digital images of AM parts into a traditional finite element model. It can also be applied to parts produced by other manufacturing processes as well as other AM techniques.

 When a polymer network is exposed to a suitable solvent, the migration of solvent molecules into the network will cause volumetric deformation, known as swelling, but more importantly forms a mixture that is known as a polymeric gel. Despite numerous potential applications, many aspects of the coupled diffusion-deformation behavior in polymeric gels have not yet been thoroughly investigated. Here, we focus our attention on the coupled deformation-diffusion response of fiber-reinforced polymeric gels. The presence of embedded fibers in a swellable polymer matrix leads to anisotropy in the overall behavior. In order to capture this response, we have developed a constitutive model for fiber-reinforced polymeric gels, that explicitly takes into account anisotropy in both the mechanical and diffusive behavior. The constitutive model is implemented as user element subroutine (UEL) in the commercial finite element software package Abaqus/Standard. Numerical simulations are performed to show the behavior of the model, and qualitative comparisons are made to experiments of a soft robotic gripper.

 Integrated circuits or miniaturized portable electronics require adaptive thermal control under certain temperatures. Thermal metamaterials (TMs), which artificially manipulate the heat passing through mediums have shown innovative thermal functions at a continuum scale. However, they cannot implement tunable thermal functions at local spots depending on the operating temperatures. Herein, we introduce temperature-responsive TMs enabled by modular design of thermally tunable unit cells. As ambient temperature changes, tunable thermal shifters can dynamically turn on/off their intrinsic functions to guide anisotropic heat transfer through the transition of thermal conductivities from the inner phase change nanocomposites (PCNCs), and their modular design realizes temperature-responsive thermal shields having switchable functions. The layered structures of stainless steel and the PCNC of n-octadecane embedding carbon nanotubes and copper powder are fabricated as tunable thermal shifters. Their 4 × 4 modular structure confirms the feasibility of temperature-responsive TMs, verified by the disappearance and appearance of thermally shielded regimes at low- and high-temperature ranges. The potential use of the developed concept was demonstrated as tunable interfaces between thermal dissipation and insulation for protecting temperature-sensitive components. This work can offer new capabilities for conventional passive TMs, such as local thermal adaptation, active thermal control interface, and thermal disturbance mitigation.

 Although additive manufacturing (AM) offers great potential to revolutionize modern manufacturing, its layer-by-layer process results in a staircase-like rough surface profile of the printed part, which degrades dimensional accuracy and often leads to a significant reduction in mechanical performance. In this paper, we present a systematic approach to improve the surface profile of AM parts using a computational model and a multi-objective optimization technique. A photopolymerization model for a micro 3D printing process, projection micro-stereolithography (PμSL), is implemented by using a commercial finite element solver (COMSOL Multiphysics software). First, the effect of various process parameters on the surface roughness of the printed part is analyzed using Taguchi’s method. Second, a metaheuristic optimization algorithm, called multi-objective particle swarm optimization, is employed to suggest the optimal PμSL process parameters (photo-initiator and photo-absorber concentrations, layer thickness, and curing time) that minimize two objectives; printing time and surface roughness. The result shows that the proposed optimization framework increases 18% of surface quality of the angled strut even at the fastest printing speed, and also reduces 50% of printing time while keeping the surface quality equal for the vertical strut, compared to the samples produced with non-optimized parameters. The systematic approach developed in this study significantly increase the efficiency of optimizing the printing parameters compared to the heuristic approach. It also helps to achieve 3D printed parts with high surface quality in various printing angles while minimizing printing time.