Shufeng Tang

To address the low operational efficiency and suboptimal crushing quality of conventional straw crushers, a serrated hammer blade was designed and optimized. The working mechanism of straw crushing and the force interaction between the hammer blade and straw were theoretically analyzed, and a finite element model was established to simulate straw fragmentation under impact. The crushing performances of serrated, rectangular, and stepped hammer blades were comparatively evaluated, and cutting force and cutting time were selected as key response indicators to investigate the effects of structural parameters. Using Latin hypercube sampling and a Kriging surrogate model, the relative importance of hammer blade parameters was quantified, followed by multi-objective optimization using the NSGA-II algorithm. The results indicate that the significance of the influencing factors follows the order of blade thickness, blade width, tooth spacing, and blade length. The optimal hammer blade configuration was determined as 4 mm in thickness, 39 mm in width, and 4 mm in tooth spacing. Crushing experiments demonstrate that, compared with the conventional rectangular hammer blade, the optimized serrated design increases productivity by 17.49% and improves the pass rate by 5.02%. This study provides practical parameter support and technical guidance for the low-cost upgrading and performance improvement of straw crushing equipment.

Conducting risk assessments on highways is a critical task. This paper introduces a mobile platform designed for guardrail inspection robots to address the gap between the inspection requirements in road traffic management and the current capabilities of existing highway inspection robots. The platform is utilized for random vehicle inspections, road environment assessments, and transportation equipment evaluations. The robot is designed to operate on double-waveform beam guardrails and features an innovative adaptive dual-wheel tensioning mechanism, significantly enhancing its ability to adapt to the guardrail’s shape and joints. A mechanical model of the robot was developed, and the impact of the tension on the robot’s obstacle-crossing performance was analyzed and optimized through theoretical and simulation-based studies. Finally, a prototype of the robot was constructed, and a testing platform for the highway guardrails was established to evaluate the robot’s operational capabilities. The results demonstrate that the robot exhibits excellent performance in both operation and obstacle-crossing tasks.

Inspired by earthworm peristalsis, a novel modular robot suitable for narrow spaces is proposed, capable of elongation, contraction, deflection and crawling. Unlike motor-driven robots, the earthworm-inspired robot achieves extension and deflection in each module through “on–off” control of the SMA springs, utilizing the cooperation of mechanical skeletons and gears to avoid posture redundancy. The return to the initial posture and the maintenance of the posture are achieved through tension and torsion springs. To study the extension and deflection characteristics, we established a model through kinematic and force analysis to estimate the relationship between the length change and tensile characteristics of the SMA on both sides and the robot’s extension length and deflection angle. Through model verification and experiments, the robot’s extension, deflection and movement characteristics in narrow spaces and varying curvature narrow spaces were comprehensively studied. The results show that the earthworm-inspired robot, as predicted by the model, possesses accurate extension and deflection performance, and can perform inspection tasks in complex and narrow space environments. Additionally, compared to motor-driven robots, the robot designed in this study does not require insulation in low-temperature environments, and the cold conditions can improve its movement efficiency. This new configuration design and the extension and deflection characteristics provide valuable insights for the development of new modular robots and robot drive designs for extremely cold environments.

With the escalating demand for exploration within confined spaces, bionic design methodologies have attracted considerable attention from researchers, primarily due to the intrinsic limitations of human access to hazardous environments. However, contemporary bionic robots primarily attain linear motion through the axial radial deformation of their body segments, thereby lacking the upright functionality that is characteristic of these organisms. In response to the limitations associated with current bionic earthworm robots concerning upright capability and stiffness modulation, we propose an innovative bionic robot that incorporates upright functionality and programmable stiffness. Initially, we present a bionic robot unit module that is capable of attaining an upright posture. A mechanical model is established in accordance with the principle of minimum potential energy to facilitate various compression and deflection functionalities. Through comprehensive simulation and experimental studies, we validate the model’s high precision in predicting compression and deformation behaviors. Furthermore, the effects of varying spring stiffness values (k) on device performance are systematically investigated, thereby enabling tailored stiffness adjustments for each module. This programmability empowers the robot to adapt to a broader spectrum of environmental demands. Ultimately, we construct a multi-module robot and successfully evaluate its upright functionality under diverse compression and deformation conditions. The proposed bionic structure, characterized by its enhanced ease of control and programmable stiffness, exhibits considerable potential for applications in complex and unstructured environments.

Today, ultrasonic transducers are commonly used in various industries. Most of the ultrasonic transducers used in industry are of the longitudinal and piezoelectric type. With the development of the application of these transducers in recent years, the need for small-sized transducers has arisen. In the present research, the vibration behavior of the Langevin transducer with four piezoelectric layers is studied using the simple theory and finite element model. For this purpose, by extracting the governing equations and the corresponding boundary conditions, the characteristic equation of the system is derived, and the effect of various parameters on the natural frequency of the system is studied analytically. Through the finite element simulation analysis of the modal, natural frequencies, frequency response, and vibration mode shapes of the transducer are obtained. Finally, the optimal values are determined to achieve the minimum length for the transducer. The results show that in the case of using aluminum electrode layers, the minimum transducer length of 87.47 mm is achieved with the electrode thickness equivalent to 6 mm. It is about 6% shorter than the corresponding transducer length with a copper electrode. Aluminum electrodes in the Langevin transducer under study achieve the optimal size for the transducer.

The geometric errors of industrial robots are key factors affecting positioning accuracy. A new compensation method for industrial robots is proposed based on the kinematics characterizing with conformal geometric algebra (CGA) and measurement strategy with double ball bar (DBB) path point optimization. Firstly, a kinematic error model for the industrial robot is established using CGA, the starting point of the CGA model is modified to simplify the modeling process and reduce computational complexity. Secondly, fused observability index is proposed and the relationship between the number of sampling points on the DBB path and the effectiveness of error parameter is obtained. Thirdly, the adaptive golden spiral optimization algorithm for error parameter identification is proposed, achieving efficient and stable identification of error parameters. Finally, a case study is carried out on a six-degree-of-freedom industrial robot. The validity of measurement strategy and error parameter identification algorithm are confirmed by comparing the residuals and uncertainties of predicted point positions in space with different methods. The spatial compensation results show that, after compensation, the average error and root mean square error of measurement paths are reduced by 36.76% and 33.96%, respectively.

To address the thermal deformation of machine tool components, a thermal error prediction model based on the ROA-LSSVM network was proposed. First of all, the heat transfer mechanism of the linear feed system was analyzed. By analyzing temperature distribution characteristics during the heat transfer process, the best temperature measurement point position was determined to ensure that the thermal error could be accurately predicted. Secondly, in order to build a prediction model with high accuracy and strong robustness, Raccoon optimization algorithm (ROA) was proposed to optimize the hyperparameters of the least square support vector machine (LSSVM) network model, which was difficult to determine the kernel function and penalty function. Finally, the experiment was measured on a VDL-600A machining center, and the accuracy and practicability of the proposed thermal error prediction model were verified by the thermal deformation in the measurement process. The experimental results show that The ROA-LSSVM model reduces the RMSE by 42% compared with the LSSVM network and 45% compared with the SVM network.

Circuits on different layers in a printed circuit board (PCB) must be aligned according to high-precision fiducial mark images during exposure processing. However, processing quality depends on the detection accuracy of fiducial marks. Precise segmentation of fiducial marks from images can significantly improve detection accuracy. Due to the complex background of PCB images, there are significant challenges in the segmentation and detection of fiducial mark images. In this paper, the mARU-Net is proposed for the image segmentation of fiducial marks with complex backgrounds to improve detection accuracy. Compared with some typical segmentation methods in customized datasets of fiducial marks, the mARU-Net demonstrates good segmentation accuracy. Experimental research shows that, compared with the original U-Net, the segmentation accuracy of the mARU-Net is improved by 3.015%, while the number of parameters and training times are not increased significantly. Furthermore, the centroid method is used to detect circles in segmentation results, and the deviation is kept within 30 microns, with higher detection efficiency. The detection accuracy of fiducial mark images meets the accuracy requirements of PCB production.