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Carbon fiber reinforced aluminum composites with ordered architectures of shear-induced aligned carbon fibers were fabricated by 3D printing. The microstructures of the printed and sintered samples and mechanical properties of the composites were investigated. Carbon fibers and aluminum powder were bonded together with resin. The spatial arrangement of the carbon fibers was fixed in the aluminum matrix by shear-induced alignment in the 3D printing process. As a result, the elongation of the composites with a parallel arrangement of aligned fibers and the impact toughness of the composites with an orthogonal arrangement were 0.82% and 0.41 ​J/cm2, respectively, about 0.4 and 0.8 times higher than that of the random arrangement.

Biological materials generally have better properties than engineered materials due to their intricate architecture, e.g., high strength-to-weight ratio, stiffness, toughness and adaptability. It has proven difficult to synthesize materials with biomimetic architecture. Recently, 3D printing techniques show great promise in bioinspired structural materials. In this paper, we propose an approach to fabricate composite materials with aligned steel fibers using a self-made 3D printer and characterized the mechanical properties of the prepared materials. To do so, we developed a DLP-based 3D printing process that can align short steel fibers in the resin matrix via magnetic assembly during the printing process. Using the developed process and raw materials, samples with ordered fibers were prepared. The mechanical properties of the printed materials, including the strength and friction, as well as morphology, were characterized. The results show that magnetically assisted scraper shear-induced 3D printing can realize the ordered arrangement of fibers. The tensile and compressive strength consistent with the direction of aligned fibers are higher than that of other directions. The friction performance perpendicular to the direction of aligned fibers is better than that of other directions. The mechanical and frictional properties of the composite contenting 5% fibers are better than that of 10% or 15%. This study provides a basis for the manufacture of biomimetic materials. A magnet-assisted assembly 3D print technology is developed to produce steel fiber-resin composites with ordered architecture by programming the traveling paths of a magnet. To demonstrate the feasibility of the proposed method, a helicoidal ladder structure with ordered reinforced fibers is printed. Finally, the mechanical properties of printed composite materials with various patterns of aligned fibers are characterized.

Biocomposite systems evolve to superior structural strategies in adapting to their living environments, using limited materials to form functionality superior to their inherent properties. The synergy of physical-field and 3D printing technologies creates unprecedented opportunities that overcome the limitations of traditional manufacturing methods and enable the precise replication of bio-enhanced structures. Here, we provide an overview of typical structural designs in biocomposite systems, their functions and properties, and highlight the recent advances in bio-inspired composites using mechanical, electrical, magnetic, and ultrasound -field-assisted 3D printing techniques. Finally, in order to realize the preparation of bionic functional devices and equipment with more superior functions, we here provide an outlook on the development of field-assisted 3D printing technology from three aspects: materials, technology and post-processing. This article is protected by copyright. All rights reserved.

The repetitive motion planning (RMP) is an important issue in the study of robotics. In this paper, we provide a further investigation by proposing a new RMP scheme for omnidirectional mobile robot manipulators. On the basis of the gradient descent method, such a scheme depicted in the pseudoinverse-type formulation is theoretically analyzed and proven to realize the RMP of omnidirectional mobile robot manipulators. With two illustrative examples, comparative simulation results under a specific omnidirectional mobile robot manipulator are presented to verify the effectiveness of the proposed RMP scheme.

Shape memory polymers (SMPs) generally change shape from a temporary state to a permanent state, and the permanent shape is only determined by its initial form, which leads to the lack of design freedom for SMPs. In order to enrich morphing behavior and extend applications of SMPs, bioinspired design and fabrication methods need to be developed. Many biological dynamic materials enable shape changes ranging from bending, twisting to spiraling using site-specific aligned cellulose microfibers orientations. Here, we proposed an approach integrating bioinspired fiber architectures and varying 3D printing parameters into SMPs, to achieve tunable permanent shape and shape memory properties. The self-folded flower and sequentially deployed smart robotic hand have been developed to demonstrate the feasibility of our method. The proposed bioinspired SMPs, which is rarely seen in the previous reports, have intriguing fundamental properties and hold great potential for applications in soft actuators, smart textiles, wearable equipment, medical devices, and other intelligent apparatus.

Fiber is a crucial element in biological micro-structural materials. Replication of fiber-reinforced composites with analogous architectures of their natural counterparts has caused widespread academic concern. Recent researches indicate 3D printing technology has the potential to produce biomimetic structural materials. The aim of this study is to develop a process to fabricate fiber-reinforced composites with ordered yet spatially tunable fiber arrangement. Specifically, we present a method to align fibers during the 3D printing of fiber-reinforced composites. A modified slurry-based stereolithography process was developed, and the fibers in the fiber–resin mixture were aligned by Shear force produced during the spreading of slurry. We investigated the influence of relative factors on fiber orientation, and two models were used to uncover the internal mechanism. By controlling the speed and the direction of the moving blade, the patterns that fibers were arranged can be freely programmed. Therefore, we have extracted bioinspired sinusoidal and zigzag design motifs to analyze their mechanical properties compared with non-bioinspired motifs. The proposed method is relatively material agnostic, more efficient and more facile. It thus provides a promising route to fabricate fiber-reinforced composites, and has potential to be adopted in biological structures researches and industrial applications.

For prey, seeding, and protection, plants exhibit ingenious adaptive motions that respond autonomously to environmental stimuli by varying cellular organization, anisotropic orientation of cellulose fibers, mechanical instability design, etc. Notably, plants do not leverage muscle and nerves to produce and regulate their motions. In contrast, they harvest energy from the ambient environment and compute through embodied intelligence. These characteristics make them ideal candidates for application in self-morphing devices. Four-dimensional (4D) printing is a bottom-up additive manufacturing method that builds objects with the ability to change shape/properties in a predetermined manner. A versatile motion design catalog is required to predict the morphing processes and final states of the printed parts. This review summarizes the morphing and actuation mechanisms of plants and concludes with the recent development of 4D-printed smart materials inspired by the locomotion and structures of plant systems. We provide analyses of the challenges and our visions of biomimetic 4D printing, hoping to boost its application in soft robotics, smart medical devices, smart parts in aerospace, etc.

Currently, four-dimensional (4D) printing programming methods are mainly structure-based, which usually requires more than one material to endow products with site-specific attributes. Here, we propose a new 4D printing programming approach that enables site-specific shape-morphing behaviors in a single material by regulating the printing parameters. Specifically, a direct ink writing three-dimensional (3D) printer with the ability to change printing parameters (e.g., deposition speed) on the fly is reported. By site-specifically adjusting print speed and print path to control the local nematic arrangements of printed liquid crystal elastomers (LCEs), the shape-morphing behaviors of the LCEs can be successfully programmed. In this way, locally programmed popping-up, self-assembling, and oscillating behaviors can be designed by varying the print speed in specific regions. Snake-like curling is realized by uniformly boosting the print speed in a single line. Furthermore, two theories and an ultrasound image diagnostic apparatus are employed to reveal the mechanism behind this behavior. This work provides a feasible way to realize the gradient transition of material properties through a single material. It broadens the design space and pushes the envelope of 4D printing, which is expected to be helpful in the fabrication of soft robotics and flexible electronics.

Programmable nonuniform deformation is of great significance for self-shape-morphing systems that are commonly seen in biological systems and also has practical applications in drug delivery, biomedical devices and robotics, etc. Here, we present a novel gradient four-dimensional (4D) printing method toward biomimetic nonuniform, dual-stimuli self-morphing. By modeling and printing graded active materials with water swelling properties, we can configure continuously smooth gradients of volume fraction of the active material in bilayer structures. The variation of swelling ratio mismatch between the two layers can be delicately regulated, which results in the programmable nonuniform shape transformation. The shape-shifting results can be predicted by the established mathematical model and computational simulations. Furthermore, we demonstrate dual-stimuli self-morphing structures by printing the graded water-responsive elastomer materials onto a heat-shrinkable shape memory polymer, which could produce different shape changes in response to humidity and different temperatures. This method pioneers a versatile approach to broaden the design space for 4D printing and will be compatible with a wide range of active materials meeting various requirements in diverse potential applications.

In the natural composites, the reinforcement particles are subtly organized into complex structures in matrix, and its various microstructures endow the biomaterials with a variety of excellent mechanical behaviors. Among the various reinforcement building blocks used in biomaterials, short fibers are the most ubiquitous reinforcement elements. Unfortunately, because of the limitation of fabrication technique, replication of these fiber-reinforced biological composites can be extremely difficult in practice. In this paper, we developed a fiber assembly 3D printing process, which can manipulate the orientation of fibers during 3D printing procedure. Benefiting from this technology, the reinforcement particles were manipulated remotely and the arrangement of reinforcement particles with higher degree of freedom was achieved. Subsequently, based on the herringbone-modified helicoidal architecture of mantis shrimp, the bio-inspired composites with various microstructures were reconstructed by 3D magnetic printing. In addition, the influence of microstructure type and parameters of bio-inspired composites on the properties of composites was studied systematically, and the quantitative relationship between microstructure and properties of the composites was established. The results show that the impact resistance and compression resistance of the composites can be significantly improved by the simple internal microstructures design, and the regression model established in this article can be used for accurate prediction of composites properties and the reliability is higher. In a word, this study opens a new route for the design of composites with unusual features.

Organisms in nature grow with senses, nervous, and actuation systems coordinated in ingenious ways to sustain metabolism and other essential life activities. The understanding of biological structures and functions guide the construction of soft robotics with unprecedented performances. However, despite the progress in soft robotics, there still remains a big gap between man-made soft robotics and natural lives in terms of autonomy, adaptability, self-repair, durability, energy efficiency, etc. Here, the actuation and sensing strategies in the natural biological world are summarized along with their man-made counterparts applied in soft robotics. The development trends of bioinspired soft robotics toward closed loop and embodiment are proposed. Challenges for obtaining autonomous soft robotics similar to natural organisms are outlined to provide a perspective in this field. [Abstract copyright: © 2021.]

Unmanned combat systems, as new weapons to overturn the rules of future war, have been the focus of research and development of military powers. To realize the efficient combat coordination between human and unmanned systems, the task understanding of unmanned combat systems is critical. Concerning the task understanding problems of unmanned combat systems, the basic concepts and significance are first discussed Then the existing methods for the task understanding of unmanned systems are summarized and analyzed A general process of the task understanding of unmanned combat systems and a task understanding framework based on deep learning are proposed The process defines the inputs, outputs, and triggering conditions. The deep learning based framework is capable of dealing with complex combat situations, which has certain reference significance for the task understanding of unmanned combat systems.

Soft actuators, which ensure the safety of robot–human interactions, extend the range of robotic operations to fragile and sensitive objects. Shape memory polymers are one of the building material for soft actuators due to their spontaneous shape memory properties under stimulation. However, the global, discontinuous and imprecise motion put significant limitations on their wide application. Recently, it has been demonstrated that by using motifs in nature, anisotropic, heterogeneous properties of soft actuators can be fabricated. Here, it is shown that soft actuators with local response and continuously varying shape memory properties can be realized through integrating bioinspired arranged building blocks (fibers). The modified 3D printing technique provides the pathway of assembling these fibers as designed. We have revealed the underlying mechanism of the formation of gradient shape memory properties. Simulations successfully demonstrate the feasibility of our approach to manipulate shape memory behaviors. The translation of nature’s design motifs offers synthetic soft actuators the opportunity towards unprecedented applications such as soft robot, drug carrier and other intelligent applications.

Functionally graded materials (FGMs) with spatially varying material properties in 3D space are highly desired for a wide range of applications. Although FGMs are ubiquitous in biological materials, the fabrication of FGMs with continuous non-linear spatial variation of material properties remains a great challenge. Here we present a self-developed extrusion-based additive manufacturing system capable of fabricating FGMs with sophisticated material property distributions. The workflow involves property function modeling, gray-scale representation and control codes generation, digital material feeding and active multi-material mixing. The effectiveness of the technology is demonstrated by the successful printing of a diverse range of objects with complex spatial variations in color and Al2O3 concentration. In addition, by controlling the dynamic change of the different components during reactive 3D printing process, we can fabricate polyurethane (PU) objects with various gradient patterns in material mechanical properties. The results of the cantilever bending test and simulation show that the material gradient can effectively relieve the stress concentration. The proposed gradient 3D printing system opens a new avenue for manufacturing FGMs with exquisite material property distributions thus far only accessible by biological materials grown in nature. Keywords: Continuous gradient, Spatial gradient, Non-linear gradient, Variable properties, Extrusion-based 3D printing

In view of the problem that the global optical flow algorithm cannot acquire accurate motion parameter estimation at a low-gradient value, an improved method has been presented in order to enhance the self-adaptive ability. The proposed method is divided into three steps: firstly, the Gaussian filter is used to restrain the background noise; secondly, the optical basic constraint weighted function is modified to acquire targets area; finally, the morphological filtering and mean shift segmentation algorithm is adopted to reduce the false alarm rate. Compared with other algorithms, the experiment results show that the proposed method can improve the self-adaptive ability and the detection rate.

Under the effect of solar variation, atmospheric attenuation and thermal radiation distribution, the grey value of interference source is close to or equal to the target grey value. With the distance between the imaging system and the target being very long, the target is dotted or mottled in the image. In addition, the target motion is usually unknown and abrupt. All these factors cause the negative effect of small target detection. In this paper, we present a motion estimation and spatial-temporal filter-based algorithm to detect infrared small targets under complex background. The algorithm is divided into three steps: 1 researching the image transformation technology from greyscale pattern to entropy pattern; 2 studying the anisotropic smoothing and local constraint criterion of entropy flow; and 3 introducing spatial-temporal filter for detecting small targets through energy accumulation and trial search of targets. Compared to other methods, experimental results indicate that the proposed method can robustly detect small targets under complicated background.