Search

The soft hand allows for flexible and safe object grasping by utilizing tactile sensitivity to gather information about the object, thereby minimizing potential damage to its surface. However, the integration design of the drive and sensor faces significant challenges due to variations in driving mode and substrate material properties. This research presents a soft hand design based on a liquid crystal elastomer (LCE) integrated with a capacitive pressure sensor in the finger. The finger-bending part of the soft hand is composed of an LCE driven by a low-pressure (6 V) electric heating film, and the finger sensor component consists of a flexible capacitive pressure sensor with a grid-like structure of electrodes and a PU sponge medium. Among them, the electric heating film is prepared from polyimide (PI) with imprinted gate electrodes, and the flexible capacitive sensor is prepared from an electrode composed of LCEs with an imprinted grid structure and filled with multiwalled carbon nanotubes (MWCNTs), with a porous PU sponge as the medium. The flexible capacitive pressure sensor has a sensitivity of up to $0.06875\,\,\text {kPa}^{-{1}}$ in the low-pressure range. This method of integrating the sensor on an LCE film achieves pressure sensing and demonstrates the potential of LCE films for gripping objects and sensing recognition applications.

Various complex and minor defects exist inside 3-D-printed lattice structures that are difficult to detect using traditional detection methods. This study focuses on enhancing the accuracy of YOLO V7 in identifying these intricate defects. Initially, a convolutional block attention module (CBAM) was integrated into the YOLO V7 network to emphasize key image details and enhance detection precision. Then, adaptively spatial feature fusion (ASFF) was used to enable the model to select and fuse features of different scales, as needed, to improve the detection performance of the model. These improvements address the issue of inconsistent attention to features at different scales in the classic feature pyramid network (FPN) of the YOLO V7. To assess the performance of the proposed model, this study utilized two datasets—the face-centered cubic (FCC) dataset and the combined body-centered cubic (BCC) and FCC datasets. The improved YOLO V7 demonstrated an average accuracy of 96.9%, a 2.4% enhancement compared to the unmodified model.

4-D printed structures have excellent application prospects in defense, aerospace, medical, and other fields due to their alterable shape and function. Current 4-D printed structures are limited to make predefined shape changes in response to particular external stimuli by the design of the materials and structures. However, it is crucial to achieve adaptive accurate control of its deformation in a dynamic environment, particularly in highly sensitive tasks. Therefore, we propose a deformation regulation strategy for a 4-D printed flexible intelligent structure based on an adaptive integral terminal sliding mode controller. The effectiveness of the algorithm is verified using a lab-made finger structure made of Liquid Crystal Elastomer as a 4-D printed structural part. Considering the condition of disturbed and nonpredetermined external excitation, the bending angle of the finger joint can be stable within $\mathbf {\pm 2.1\%}$ of the expected value based on the feedback of the self-sensing information, which enables collaborative tracking of the deformation state of the external human finger. This controller-based, adaptive, and accurate deformation regulation is not possible with the existing 4-D printed deformation structures and has not yet been reported nationally or internationally.

With the accelerating of electrification of ship power system, it enables a more efficient and flexible operation to alleviate the pressure on environment pollution and energy consumption. Hitherto, many existing literatures have achieved the optimal electric energy management on board, with the application of diesel generator and electric energy storage, while the thermal energy scheduling has been barely studied. In this paper, the electric energy and thermal energy scheduling are studied simultaneously, thus expanding the ship power system into hybrid power ship system (HPSS). Considering the total operating cost and Greenhouse Gases (GHG) emissions, the energy scheduling optimization problem is defined. To improve the quality of solution, an adaptive moth flame optimization based on multi-populations (AMFOMP) is proposed. Specifically, moth sorting strategy based on logistic mapping, nonlinear flames number update mechanism and modified update formula of moth are carried out to improve the exploration and exploitation ability in iterations. Moreover, modified opposition-based learning and population diversity strategy are implemented to prevent algorithm from stagnation. AMFOMP is carried out into electric and thermal energy scheduling and the simulation results show the economic and environmental benefits of the proposed HPSS.

Coplanar capacitive testing (CCT) is a new nondestructive testing (NDT) technique that has been developed in recent years. There is no consensus on the sensing depth of coplanar capacitive sensors (CCSs). The sensing depths of regularly used rectangular, circular, and triangular probes were examined in this work. Using the sample lifting method, the sensing depths of the probes were calibrated using samples of various thicknesses made of wood, glass, and acrylic. The experimental findings showed that the sensing depth of the probe varied depending on the sample material and thickness. A novel Jacobian matrix (JM) method for calculating the sensing depth was presented and tested, and its validity and accuracy were verified by comparing the sample lifting results and analyzing the defect detection results. Further discussion focused on the effect of grid division on JM results and the calibration of JM to probe sensing depths of different sizes and separations. With matched fine grid divisions, JM provided unique sensing depths, which may serve as a reference for the consensus CCS sensing depth.

Lattice structures created using additive manufacturing technology inevitably produce inherent defects that seriously affect their mechanical properties. Predicting and analysing the effect of defects on the maximum stress is very important for improving the lattice structure design and process. This study mainly used the finite element method to calculate the lattice structure constitutive equation. The increase in defect type and quantity leads to difficulty in modelling and reduces calculation accuracy. We established a data-driven extreme gradient enhancement (XGBoost) with hyperparameter optimization to predict the maximum stress of the lattice structure in additive manufacturing. We used four types of defect characteristics that affect the mechanical properties—the number of layers, thick-dominated struts (oversize), thin-dominated struts (undersizing), and bend-dominated struts (waviness)—as the input parameters of the model. The hyperparameters of the basic XGBoost model were optimised according to the diversity of the inherent defect characteristics of the lattice structure, while the parameters selected by experience were replaced using the Gaussian process method in Bayesian optimization to improve the model’s generalisation ability. The prediction datasets included the type and number of defects obtained via computer tomography and the calculation results of the finite element model with the corresponding defects implanted. The root mean square error and R-squared error of the maximum stress prediction were 17.40 and 0.82, respectively, indicating the effectiveness of the model proposed in this paper. Furthermore, we discussed the influence of the four types of defects on the maximum stress, among which the thick strut defect had the greatest influence.