Search

The article proposed approach paves a new way to realize multifunctional soft devices and machines by bonding hydrogel with other polymers in three-dimensional forms. It mentions that hydrogel-polymer hybrids have been widely used for various applications such as biomedical devices and flexible electronics.

In this study, metallic pyramidal lattice truss core sandwich structures, including single-layered as well as multi-layered ones, are fabricated from EOS Aluminium AlSi10 Mg, utilizing additive manufacturing method. Out-of-plane and in-plane compression as well as shear tests were carried out in order to measure the stiffness and strength properties of the structures. The out-of-plane compressive strength is determined by buckling of struts, and specimens fail in plastic yielding of face sheets for in-plane tests. And for the shear performance of the specimens, the strength is dominated by the node failure. In addition to experimental testing, finite element models have been developed for simulating the stiffness and strength properties and the numerical results agree well with experimental performance. [ABSTRACT FROM AUTHOR]

Among all three-dimensional (3D) printing materials, thermosetting photopolymers claim almost half of the market, and have been widely used in various fields owing to their superior mechanical stability at high temperatures, excellent chemical resistance as well as good compatibility with high-resolution 3D printing technologies. However, once these thermosetting photopolymers form 3D parts through photopolymerization, the covalent networks are permanent and cannot be reprocessed, i.e., reshaped, repaired, or recycled. Here, we report a two-step polymerization strategy to develop 3D printing reprocessable thermosets (3DPRTs) that allow users to reform a printed 3D structure into a new arbitrary shape, repair a broken part by simply 3D printing new material on the damaged site, and recycle unwanted printed parts so the material can be reused for other applications. These 3DPRTs provide a practical solution to address environmental challenges associated with the rapid increase in consumption of 3D printing materials. [ABSTRACT FROM AUTHOR]

4D printing enables 3D printed structures to change shape over "time" in response to environmental stimulus. Because of relatively high modulus, shape memory polymers (SMPs) have been widely used for 4D printing. However, most SMPs for 4D printing are thermosets, which only have one permanent shape. Despite the efforts that implement covalent adaptable networks (CANs) into SMPs to achieve shape reconfigurability, weak thermomechanical properties of the current CAN-SMPs exclude them from practical applications. Here, we report reconfigurable 4D printing via mechanically robust CAN-SMPs (MRC-SMPs), which have high deformability at both programming and reconfiguration temperatures (>1400%), high Tg (75°C), and high room temperature modulus (1.06 GPa). The high printability for DLP high-resolution 3D printing allows MRC-SMPs to create highly complex SMP 3D structures that can be reconfigured multiple times under large deformation. The demonstrations show that the reconfigurable 4D printing allows one printed SMP structure to fulfill multiple tasks.. [ABSTRACT FROM AUTHOR]

Recently, design for additive manufacturing has been proposed to maximize product performance through the rational and integrated design of the product, its materials, and their manufacturing processes. Searching design solutions in such a multidimensional design space is a challenging task. Notably, no existing design support method is both rapid and tailored to the design process. In this study, we propose a holistic approach that applies data-driven methods in design search and optimization at successive stages of a design process. More specifically, a two-step surrogate model-based design method is proposed for the embodiment and detailed design stages. The Bayesian network classifier is used as the reasoning framework to explore the design space in the embodiment design stage, while the Gaussian process regression model is used as the evaluation function for an optimization method to exploit the design space in detailed design. These models are constructed based on one dataset that is created by the Latin hypercube sampling method and then refined by the Markov Chain Monte Carlo sampling method. This cost-effective data-driven approach is demonstrated in the design of a customized ankle brace that has a tunable mechanical performance by using a highly stretchable design concept with tailored stiffnesses. [ABSTRACT FROM AUTHOR]