Additive manufacturing (AM) can be advanced by the diverse characteristics offered by thermoplastic and thermoset polymers and the further benefits of copolymerization. However, the availability of suitable polymeric materials for AM is limited and may not always be ideal for specific applications. Additionally, the extensive number of potential monomers and their combinations make experimental determination of resin compositions extremely time-consuming and costly. To overcome these challenges, we develop an active learning (AL) approach to effectively choose compositions in a ternary monomer space ranging from rigid to elastomeric. Our AL algorithm dynamically suggests monomer composition ratios for the subsequent round of testing, allowing us to efficiently build a robust machine learning (ML) model capable of predicting polymer properties, including Young's modulus, peak stress, ultimate strain, and Shore A hardness based on composition while minimizing the number of experiments. As a demonstration of the effectiveness of our approach, we use the ML model to drive material selection for a specific property, namely, Young's modulus. The results indicate that the ML model can be used to select material compositions within at least 10% of a targeted value of Young's modulus. We then use the materials designed by the ML model to 3D print a multimaterial "hand" with soft "skin" and rigid "bones". This work presents a promising tool for enabling informed AM material selection tailored to user specifications and accelerating material discovery using a limited monomer space.

Fabrication of structures in unstructured conditions is a promising area of bolstering the application spaces of additive manufacturing (AM). One emerging application is appending structures on existing ones that may have nonplanar surfaces in unconventional orientations. However, extrusion‐based AM techniques are limited to printing on structured, planar environments with a fixed single‐nozzle direction. Herein, the authors present a dexterous conformal material extrusion printing method using a six‐axis robotic arm capable of constructing complex parts onto highly unstructured surfaces with rough topographies. The manufacturing method employs a custom algorithm that generates layers consisting of 3D spatial coordinates of print path as well as the extrusion nozzle oriented in the normal direction of the substrate, thereby enabling conformal motion of the extrusion nozzle to the unstructured surface. The capabilities of the surface‐informed robotic conformal 3D printing method to fabricate structures on surfaces with a variety of topographies in unconventional orientations are demonstrated. Finally, via addition of deposited conductive paths, a high‐strength, functional reinforcement capable of in situ deformation monitoring is appended. This work has the potential for reconstructing, repairing, and reinforcing existing structures in human‐limited or inaccessible spaces. Integration of functional elements can also enable in situ sensing, monitoring, and self‐diagnosis.

Shape transformations of active composites (ACs) depend on the spatial distribution of constituent materials. Voxel-level complex material distributions can be encoded by 3D printing, offering enormous freedom for possible shape-change 4D-printed ACs. However, efficiently designing the material distribution to achieve desired 3D shape changes is significantly challenging yet greatly needed. Here, we present an approach that combines machine learning (ML) with both gradient-descent (GD) and evolutionary algorithm (EA) to design AC plates with 3D shape changes. A residual network ML model is developed for the forward shape prediction. A global-subdomain design strategy with ML-GD and ML-EA is then used for the inverse material-distribution design. For a variety of numerically generated target shapes, both ML-GD and ML-EA demonstrate high efficiency. By further combining ML-EA with a normal distance-based loss function, optimized designs are achieved for multiple irregular target shapes. Our approach thus provides a highly efficient tool for the design of 4D-printed active composites. Researchers have developed a machine learning-empowered voxel-level inverse design approach for complicated shape changes of 4D-printed plates, which paves the way for intelligent design and fabrication for 4D printing and shape-morphing structures

Fiber reinforced polymer composites (FRPCs) are valued for their high strength and lightweight and have found applications from aerospace to renewable energy. Additive manufacturing (AM, or 3D printing) of FRPCs is of great interest in recent years due to the manufacturing flexibility offered by AM. Direct ink write (DIW) 3D printing is a popular choice for FRPC 3D printing due to its low cost and open framework for broad material choices. However, previously explored techniques for AM of FRPCs are hindered by heavy reliance on fiber orientation dictated by extrusion toolpath and often require specialized hardware to print FRPCs with low viscosity and long cure time thermoset epoxy matrices. In this paper, we introduce a single-stream hybrid DIW AM technique for the creation of mechanically robust FRPC functional structures where the matrix is DIW 3D printed, and pre-epoxy impregnated (prepreg) woven carbon fiber (CF) fabrics are robotically placed. Additionally, functional components, such as conductive elements, can be readily integrated. We investigated the impact of prepreg woven CF reinforcement on a two-stage photo-thermal thermoset resin matrix on the mechanical characteristics of manufactured FRPCs. We also combined these efforts to fabricate functional structures, including strain sensors for in-situ deformation monitoring, heating elements, and embedded light sensors. This study found that the proposed single-stream hybrid DIW AM process could be a facile approach to fabricate high-strength FRPC functional structures.

Fabrication of structures in unstructured environments is a promising field to expand the application spaces of additive manufacturing (AM). One potential application is to add new components directly onto existing structures. Herein, a versatile, reconfigurable direct ink writing (DIW) manufacturing method is developed in tandem with a two‐stage hybrid ink designed to fabricate high‐strength, self‐supporting parts in unconventional printing spaces such as underneath a build surface or horizontally. This two‐stage hybrid DIW ink combines a photopolymer and a tough epoxy resin. The photopolymer can cure rapidly to enable layer‐by‐layer printing of complex structures. It also possesses adequate adhesion to allow the fabrication of large volume structures on a diversity of substrates including acrylic, wood, glass, aluminum, and concrete. The epoxy component can cure after 72 h in ambient conditions with further increased adhesion strengths. The capabilities of the reconfigurable DIW extrusion nozzle method to print complex structures in inverted and horizontal environments are demonstrated. Finally, via addition of DIW‐deposited conductive paths, a functional 3D‐printed structure capable of in situ deformation monitoring is created. This work has the potential to be used for applications such as appending new parts to existing structures for increasing functionality, repair, and structure health monitoring.

Additive manufacturing using edible feedstock – known as “edible three-dimensional (3D) printing” – offers a unique method of producing visually appealing meals with customizable nutrition profiles. Direct ink write (DIW) 3D printing is a popular choice for edible 3D printing due to its low cost and open framework for broad material choices. However, the breadth of food suitable for DIW 3D printing is hindered due to the unsuitably low viscosity of many potential edible feedstocks such as food purees. In this paper, we present cellulose nanocrystals (CNCs) as a safe and renewable rheological modifier capable of enabling DIW 3D printing of a variety of foodstuffs, specifically spinach puree, tomato puree, and applesauce, and using freeze-dry to obtain final solid structures. We first analyzed the rheological characterization of foodstuffs combined with varying volume fractions of CNCs to produce shear-thinning, printable inks. The print quality of different inks was then analyzed via image processing techniques. Finally, we demonstrated the capability of CNC-laden inks by printing a variety of structures, including multi-material structures with integrated packaging. This study found that CNCs are an effective rheological additive which promoted shear-thinning, viscous behavior in the studied edible feedstocks necessary for DIW 3D printing of self-supporting edible structures.

Mechanical metamaterials are architected manmade materials that allow for unique behaviors not observed in nature, making them promising candidates for a wide range of applications. Existing metamaterials lack tunability as their properties can only be changed to a limited extent after the fabrication. In this paper, we present a new magneto-mechanical metamaterial that allows great tunability through a novel concept of deformation mode branching. The architecture of this new metamaterial employs an asymmetric joint design using hard-magnetic soft active materials that permits two distinct actuation modes (bending and folding) under opposite-direction magnetic fields. The subsequent application of mechanical forces leads to the deformation mode branching where the metamaterial architecture transforms into two distinctly different shapes, which exhibit very different deformations and enable great tunability in properties such as mechanical stiffness and acoustic bandgaps. Furthermore, this metamaterial design can be incorporated with magnetic shape memory polymers with global stiffness tunability, which further enables the global shift of the acoustic behaviors. The combination of magnetic and mechanical actuations, as well as shape memory effects, imbue unmatched tunable properties to a new paradigm of metamaterials.

In article number 2000829, Ryan D. Sochol, and co-workers present a morphing nozzle for additive manufacturing of fiber-filled composite materials. This image shows the nozzle changing shape on demand, which provides new means to control fiber alignment during 3D printing. Their results for dynamically adjusting the fiber orientation, and in turn, the swelling properties of printed composites hold promise for “4D printing” applications.

Fiber‐filled composite materials offer a unique pathway to enable new functionalities for systems built via extrusion‐based additive manufacturing (or “3D printing”); however, challenges remain in controlling the fiber orientations that govern ultimate performance. In this work, a multi‐material, shape‐changing nozzle—constructed by means of PolyJet 3D printing—is presented that allows for the spatial distribution of short fibers embedded in polymer matrices to be modulated on demand throughout extrusion‐based deposition processes. Specifically, the nozzle comprises flexible bladders that can be inflated pneumatically to alter the geometry of the material extrusion channel from a straight to a converging–diverging configuration, and in turn, the directional orientation of fibers within printed filaments. Experimental results for printing carbon microfiber‐hydrogel composites reveal that increasing the nozzle actuation pressure from 0 to 100 kPa reduced the proportion of aligned fibers, and notably, prompted a transition from anisotropic to isotropic water‐induced swelling properties (i.e., the ratio of transverse to longitudinal swelling strain decreased from 1.73 ± 0.37 to 0.93 ± 0.39, respectively). In addition, dynamically varying the nozzle geometry during the extrusion of continuous composite filaments effects distinct swelling behaviors in adjacent regions, suggesting potential utility of the presented approach for emerging “4D printing” applications.

Active mechanical metamaterials are emerging materials receiving tremendous attention in the past decades. Additive manufacturing (AM, or 3D printing) techniques empower the rational design and fabrication of complex, multiscale architectures that enable unprecedented mechanical properties and functionality of metamaterials. Moreover, the use of smart or stimuli-responsive materials for AM of active mechanical metamaterials offers new capabilities to program the mechanical, acoustic, or other functional properties. Herein, we present an overview of recent advances in AM of active mechanical metamaterials. The primary AM techniques used for mechanical metamaterial fabrication are discussed first. Several mechanical metamaterial structures and designs enabled by AM are summarized. Active mechanical metamaterial designs utilizing different stimuli, such as solvents, heat, and magnetic field, are introduced. Additionally, some functional applications of active material systems to create transforming mechanical metamaterials are included. Finally, the outlook and challenges for future research in this field are provided.

Additive manufacturing methods currently use solid fillers in the polymer matrix to make composites. Many researchers have studied the effect of die design on solid fillers like glass and carbon fibers but there is no research on incorporating immiscible liquid additives which can lead to additive manufacturing of solid parts with a controllable immiscible liquid morphology internal to the part. To accomplish this, experiments must be done to determine the effect of die design on the deformation induced on an immiscible droplet in the flow field. In this research, the feasibility of using shear flows and extensional flows to deform a droplet is first studied. A channel with walls based on a hyperbolic equation can impose a constant stretch rate at the center and makes it easier to model the flow. Applying this principle, a converging channel is built, filled with Silicone oil (Matrix liquid) and subject to hyperbolic flows. The deformation seen by a droplet of immiscible Castor oil is studied for various positions of injection using high speed imagery. It was observed that droplets injected at the center of the channel did not see any significant stretch but the ones injected closer to the walls stretched and reached the affine state. Simulations studies were performed using ANSYS Fluent to find the velocity gradients along each streamline where the droplet was injected. This data, combined with the droplet widths measured from the images, was used to track the Capillary number changes for a given droplet. It was observed that droplets with a higher initial Capillary number saw a steeper reduction than droplets with lower initial Capillary numbers. Further, close examination of the droplet dimensions from each image showed that the droplet width reduced and reached an asymptote in the channel. These results aid in understanding liquid filler behavior in a polymer flow and helps in incorporating liquids in polymer based additive manufacturing.

Controlled processing of carbon microfiber (CMF) reinforced polymers widens control of material properties of fabricated parts. Continuous transfer from compounding to Fused Filament Modeling (FFM) platform brings this advantage to additive manufacturing. CMF reinforced composites are compounded using a co-rotating twin screw extrusion machine (CoTSE). Controlled, direct transfer from the CoTSE to FFM is accomplished using a mechanical system comprised of interconnected feedback control subsystems. Controlled transfer of CMF reinforced composite polymers is studied over a selected range of temperatures, volumetric flow conditions, and microfiber weight fractions using the system. Characteristics of the produced materials are discussed with respect to CMF weight fractions and processing conditions.

Incorporating liquid fillers in additive manufacturing processes can produce liquid-filled solid parts with unique properties. To develop this, the behavior of immiscible droplets in a polymer matrix subject to different kinds of flows is explored. Castor oil droplets, with a range of capillary numbers much higher than the critical capillary number, were injected in a matrix of Silicone oil and subjected to flows within a converging channel. The rate of change of capillary number as the droplet moves down the channel was measured to illuminate the effect of the die design. The affine state was not reached when the droplets were deployed in the center but was achieved when injected in an offset position. This data is valuable to understand the effect of the die on the deformation induced on immiscible droplets and is one of the preliminary steps to incorporate liquids in additive manufacturing.