3D printing is a versatile technology capable of rapidly fabricating intricate geometric structures and enhancing the performance of flexible devices in comparison to conventional fabrication methods. However, 3D-printed devices are susceptible to failure as a result of minuscule structural impairments, thereby impacting their overall durability. The utilization of self-healing, biodegradable materials in 3D printing holds immense potential for increasing the longevity and safety of devices, thereby expanding the application prospects for such devices. Nevertheless, enhancing the self-repairing capability of devices and refining the 3D printing performance of self-healing materials are still considerable challenges that need to be addressed to achieve optimal outcomes. This paper reviews recent developments in the field of advancements in 3D printing using self-healing and biodegradable materials. First, it investigates self-healing and biodegradable materials that are compatible with 3D printing techniques, discussing their printability, material properties, and factors that influence print quality. Then, it explores practical applications of self-healing and biodegradable 3D printing technology in depth. Finally, it critically offers practical perspectives on this topic.
The deployable telescopic boom, whose mass and stiffness play crucial roles, is extensively used in the design of space-deployable structures. However, the most existing optimal design that neglects the influence of the locking mechanisms in boom joints cannot raise the whole stiffness while reducing the boom mass. To tackle this challenge, a novel optimization model, which utilizes the arrangement of the locking mechanisms to achieve synchronous improvement of the stiffness and mass, is proposed. The proposed optimization model incorporates a novel joint stiffness model developed based on an equivalent parallel mechanism that enables the consideration of multiple internal stiffness factors of the locking mechanisms and tubes, resulting in more accurate representations of the joint stiffness behavior. Comparative analysis shows that the proposed stiffness model achieves more than at least 11% improved accuracy compared with existing models. Furthermore, case verification shows that the proposed optimization model can improve stiffness while effectively reducing mass, and it is applied in boom optimization design.
A key challenge is using bionic mechanisms to enhance aerodynamic performance of hover-capable flapping wing micro air vehicle (FWMAV). This paper presented a new lift system with high lift and aerodynamic efficiency, which use a hummingbird as a bionic object. This new lift system is able to effectively utilize the high lift mechanism of hummingbirds, and this study innovatively utilizes elastic energy storage elements and installs them at the wing root to help improve aerodynamic performance. A flapping angle of 154° is achieved through the optimization of the flapping mechanism parameters. An optimized wing shape and parameters are obtained through experimental studies on the wings. Consequently, the max net lift generated is 17.6% of the flapping wing vehicle’s weight. Moreover, energy is stored and released periodically during the flapping cycle, by imitating the musculoskeletal system at the wing roots of hummingbirds, thereby improving the energy utilization rate of the FWMAV and reducing power consumption by 4.5% under the same lift. Moreover, strength verification and modal analyses are conducted on the flapping mechanism, and the weight of the flapping mechanism is reduced through the analysis and testing of different materials. The results show that the lift system can generate a stable lift of 31.98 g with a wingspan of 175 mm, while the lift system weighs only 10.5 g, providing aerodynamic conditions suitable for high maneuverability flight of FWMAVs.
The dynamic motion of quadrupedal robots on challenging terrain generally requires elaborate spatial–temporal kinodynamic motion planning and accurate control at higher refresh rate in comparison with regular terrain. However, conventional quadrupedal robots usually generate relatively coarse planning and employ motion replanning or reactive strategies to handle terrain irregularities. The resultant complex and computation-intensive controller may lead to nonoptimal motions or the breaking of locomotion rhythm. In this paper, a kinodynamic optimization approach is presented. To generate long-horizon optimal predictions of the kinematic and dynamic behavior of the quadruped robot on challenging terrain, we formulate motion planning as an optimization problem; jointly treat the foot’s locations, contact forces, and torso motions as decision variables; combine smooth motion and minimal energy consumption as the objective function; and explicitly represent feasible foothold region and friction constraints based on terrain information. To track the generated motions accurately and stably, we employ a whole-body controller to compute reference position and velocity commands, which are fed forward to joint controllers of the robot’s legs. We verify the effectiveness of the developed approach through simulation and on a physical quadruped robot testbed. Results show that the quadruped robot can successfully traverse a 30° slope and 43% of nominal leg length high step while maintaining the rhythm of dynamic trot gait.
The quality of the exposed avionics solder joints has a significant impact on the stable operation of the in-orbit spacecrafts. Nevertheless, the previously reported inspection methods for multi-scale solder joint defects generally suffer low accuracy and slow detection speed. Herein, a novel real-time detector VMMAO-YOLO is demonstrated based on variable multi-scale concurrency and multi-depth aggregation network (VMMANet) backbone and “one-stop” global information gather-distribute (OS-GD) module. Combined with infrared thermography technology, it can achieve fast and high-precision detection of both internal and external solder joint defects. Specifically, VMMANet is designed for efficient multi-scale feature extraction, which mainly comprises variable multi-scale feature concurrency (VMC) and multi-depth feature aggregation-alignment (MAA) modules. VMC can extract multi-scale features via multiple fix-sized and deformable convolutions, while MAA can aggregate and align multi-depth features on the same order for feature inference. This allows the low-level features with more spatial details to be transmitted in depth-wise, enabling the deeper network to selectively utilize the preceding inference information. The VMMANet replaces inefficient high-density deep convolution by increasing the width of intermediate feature levels, leading to a salient decline in parameters. The OS-GD is developed for efficacious feature extraction, aggregation and distribution, further enhancing the global information gather and deployment capability of the network. On a self-made solder joint image data set, the VMMAO-YOLO achieves a mean average precision mAP@0.5 of 91.6%, surpassing all the mainstream YOLO-series models. Moreover, the VMMAO-YOLO has a body size of merely 19.3 MB and a detection speed up to 119 frame per second, far superior to the prevalent YOLO-series detectors.
The failure types in gear systems vary, with typical ones mainly including pitting, cracking, wear, and broken teeth. Different modeling and stiffness calculation methods have been developed for various gear failure types. A unified method for typical gear failure modeling and stiffness calculation is introduced in this study by considering the deviations in the time-varying meshing stiffness (TVMS) of faulty gears resulting from the use of different methods. Specifically, a gear tooth is discretized into a large number of microelements expressed with a matrix, and unified models of typical gear failures are built by adjusting the values of the matrix microelements. The values and positions of the microelements in the tooth failure model matrix have the same physical meaning as the parameter variables in the potential energy method (PEM), so the matrix-based failure model can be perfectly matched with PEM. Afterward, a unified method for TVMS is established. Modeling of healthy and faulty gears with pitting, wear, crack, and broken tooth is performed with the matrix equation, and the corresponding TVMS values are calculated by incorporating the matrix models with PEM. On the basis of the results, the mechanism of typical fault types that affect TVMS is analyzed, and the conclusions are verified through the finite element method. The developed unified method is a promising technique for studying the dynamic response characteristics of gear systems with different failure types because of its superiority in eliminating stiffness deviations.
