Chunyang Liu

Q420 high-strength steel exhibits pronounced anisotropy due to its rolling process, and conventional uniaxial tensile testing is incapable of acquiring strain field evolution information during the local necking stage. In this study, quasi-static uniaxial tensile tests were conducted on Q420 cold-rolled high-strength steel sheets at six orientations (0°, 15°, 30°, 45°, 60°, and 90°) using Digital Image Correlation (DIC) technology. The evolution of the strain field and the corresponding stress–strain responses at different orientations were systematically investigated. The results show that the DIC technique effectively captured the full-field strain evolution of the specimens from uniform deformation to local necking and final fracture in all directions. Taking the 0° direction as an example, the local maximum engineering strain prior to fracture reached 35.866%, whereas the average fracture strain within the gauge section was only approximately 22.5%, corresponding to a ratio of approximately 1.6 and clearly demonstrating the severe strain concentration within the necking zone. The stress–strain curves corresponding to different rolling directions exhibited pronounced anisotropy. The tensile strength was highest in the 90° direction and lowest in the 0° direction; however, the 0° direction exhibited the best ductility, whereas the 45° direction showed the poorest ductility. Among the six orientations, the midpoint transverse engineering strain exhibited the largest absolute value in the 45° direction, further indicating that this orientation is the most susceptible to plastic instability. In this work, DIC-based full-field measurement was combined with multi-directional tensile testing to quantitatively characterize the relationship between local strain concentration and anisotropy. The findings provide high-precision experimental data for the calibration of anisotropic constitutive models and the optimization of forming processes.

Path planning for high-DOF robotic manipulators in highly constrained environments (e.g., narrow passages) remains challenging due to poor configuration-space (C-space) connectivity, low computational efficiency, and susceptibility to local minima. This paper proposes a hybrid planner, termed ASCON, which couples the directional guidance of an improved Artificial Potential Field (APF) with the global exploration capability of RRT-Connect to achieve robust planning in non-convex, strongly constrained workspaces. A smoothed potential-field formulation is introduced to suppress oscillations and improve motion smoothness, while a link-radius-based envelope collision-checking strategy is incorporated to ensure safety margins for real deployment. The evaluation is conducted in two benchmark scenarios—dual-layer stacked obstacles and a 100 mm narrow passage—with 50 independent trials per method per scenario; a run is considered successful only if a collision-free feasible path is found within preset iteration/time limits using fixed hyperparameters. Results show that, compared with conventional APF, ASCON reduces average planning time by 66.0%, decreases iteration count by 80.5%, shortens path length by 13.5%, and lowers peak jerk by 40.3%. Physical experiments further validate practical feasibility by guiding a real manipulator through a 100 mm narrow passage in a collision-free manner, demonstrating efficient, smooth, and robust planning under extreme constraints.

To address the challenge of achieving high-precision and ordered calibration of strong-scatter points in inverse synthetic aperture radar (ISAR) images, this paper proposes a collaborative framework that integrates YOLOv12-pose with Peak-Constrained Watershed (PCW). The method first employs the YOLOv12-pose model to produce an initial localization of scatter points. PCW is then applied to fine-segment individual points. Finally, a three-stage global optimal matching strategy is introduced to achieve high-precision fusion between index labels and their geometric positions. Experimental results on a microwave photonic radar ISAR dataset demonstrated that the proposed method achieved an average error of 1.89 pixels, with accuracy, recall, and F1 scores exceeding 95%. The approach significantly outperformed standalone YOLO, Mask R-CNN, and traditional SVM-based methods while maintaining label consistency and substantially improving precision and robustness for the recognition, localization, and tracking of strong scatter points in ISAR imagery.

To address the challenges of low automation in tunnel wet-spraying jumbos and the heavy reliance on manual expertise for ensuring the spraying quality, this study proposes a novel task planning method for tunnel spraying operations. First, the tunnel surface to be sprayed is aligned with the designed contour using a vehicle navigation method, enabling the estimation of the overbreak and underbreak volumes. These volumes are then utilized to hierarchically plan the spraying tasks (e.g., patching, filling, and surface smoothing). A concrete coating thickness prediction method is developed, incorporating static and dynamic coating accumulation models with key process parameters—spraying flow rate Q, air pressure P, and spraying distance H—as independent variables. Based on the required thickness for each task layer, operational parameters such as the spraying duration t and nozzle movement speed v are optimized. By analyzing the spray gun action combinations and integrating hierarchical task planning with parameter optimization, a Planning Domain Definition Language (PDDL) domain file and problem file are designed to generate the spray gun action sequences and paths via a planner. The experimental results demonstrate that the overbreak volume on the sprayed tunnel surface is reduced to approximately 3 cm after applying the planned sequences. The proposed method autonomously generates the task hierarchies and the corresponding spray gun actions based on the 3D morphology of the tunnel surface, effectively ensuring the spraying quality while significantly reducing the dependence on manual intervention. This approach provides a practical solution for enhancing automation and precision in tunnel spraying operations.

This study explores the potential effect of a cross-sectional shape with an arcuate protruding and depressed features on the performance. The geometric configurations include two feature types (protruding and depressed), each with six distinct perimeter arrangements and three depths per arrangement, yielding thirty-six different cross-sectional shapes for systematic evaluation. The aerodynamic characteristics and electrical performance are numerically analyzed, using a computational fluid dynamics model and a distributed parameter electromechanical coupling model, respectively. A smooth protruding feature on the front, top, or bottom side suppresses the electrical output; however, when located on the rear side, it significantly increases the slope of the power versus wind speed curve. Depressed features on the rear, top, or bottom side only reduce the critical wind speed and the power enhancement positively correlates with the feature depth. Compared to a square, a harvester with depressed feature on both top and bottom sides exhibits a significant jump in power at the critical wind speed, greatly improving the power. These findings provide important design guidelines for structural optimization of galloping piezoelectric energy harvesters, enabling them to match the wind energy distribution characteristics of specific regions with optimal performance.

When a high-speed rolling bearing cage comes into contact with rollers, it experiences random movement due to friction and collision, which significantly impacts the overall performance of the bearing. To further investigate the motion law of cages, a test bench for a 7010C angular-contact bearing cage was constructed. This setup utilized laser sensors to obtain changes in attitude and displacement during operation. After analyzing how cage deflection errors influenced trajectory measurements, corrections were applied to the measurement results. Additionally, an investigation was conducted into the effects of varying rotational speeds on the dynamic performance of the cage. Simulations were performed using ADAMS software, which verified both the effectiveness of the measuring method and the testing results. The findings indicated that within the tested range of rotational speeds, the centroid trajectory stability of the cage gradually improved as rotational speed increased and then began to show a tendency to deteriorate. Furthermore, there existed a negative correlation between the deflection error of the cage and the centroid trajectory stability.

To address the limitations of the Deep Deterministic Policy Gradient (DDPG) in robot path planning, we propose an improved DDPG method that integrates kinematic analysis and D* algorithm, termed D*-KDDPG. Firstly, the current work promotes the reward function of DDPG to account for the robot’s kinematic characteristics and environment perception ability. Secondly, informed by the global path information provided by the D* algorithm, DDPG successfully avoids getting trapped in local optima within complex environments. Finally, a comprehensive set of simulation experiments is carried out to investigate the effectiveness of D*-KDDPG within various environments. Simulation results indicate that D*-KDDPG completes strategy learning within only 26.7% of the training steps required by the original DDPG, retrieving enhanced navigation performance and promoting safety. D*-KDDPG outperforms D*-DWA with better obstacle avoidance performance in dynamic environments. Despite a 1.8% longer path, D*-KDDPG reduces navigation time by 16.2%, increases safety distance by 72.1%, and produces smoother paths.

Robots need to sense information about the external environment before moving, which helps them to recognize and understand their surroundings so that they can plan safe and effective paths and avoid obstacles. Conventional algorithms using a single sensor cannot obtain enough information and lack real-time capabilities. To solve these problems, we propose an information perception algorithm with vision as the core and the fusion of LiDAR. Regarding vision, we propose the YOLO-SCG model, which is able to detect objects faster and more accurately. When processing point clouds, we integrate the detection results of vision for local clustering, improving both the processing speed of the point cloud and the detection effectiveness. Experiments verify that our proposed YOLO-SCG algorithm improves accuracy by 4.06% and detection speed by 7.81% compared to YOLOv9, and our algorithm excels in distinguishing different objects in the clustering of point clouds.

In response to the challenge of timely fault identification in the spindle bearings of machine tools operating in complex environments, this study proposes a method based on a combination of infrared imaging with an Informer and a CNN + Swin Transformer. The aim is to achieve real-time monitoring of bearing faults, precise fault localization, and classification of fault severity. To accomplish this, an angular contact ball bearing was chosen as the research subject. Initially, an infrared image dataset was constructed, encompassing various fault positions and degrees, by simulating different forms of bearing faults. Subsequently, an Informer-based bearing temperature prediction model was established to select faulty bearing data. Lastly, the faulty data were input into the CNN + Swin Transformer model for bearing fault recognition and classification. The results demonstrate that the Informer model accurately identifies abnormal temperature rises during bearing operation, effectively screening out faulty bearings. Under steady-state conditions, the model achieves a classification accuracy of 97.8%. Furthermore, after employing the Informer screening process, the proposed model exhibits a recognition precision of 98.9%, surpassing other models such as CNN, SVM, and Swin Transformer, which are mentioned in this paper.