To address the challenges of high human intervention risks， insufficient stiffness and poor environmental adaptability of conventional automated equipment， in the sealing operations of nuclear waste containers， this study proposes a high-stiffness modular 3-DOF robotic system for closure grasping and sealing. The mechanical system comprises a base frame， locomotion mechanism， lifting mechanism， and an integrated end-effector （combining rotary， gripping， and tightening functions）. Innovations include a dual closed-loop linkage lifting mechanism and a worm-gear-planetary compound reduction system， enabling precise and reliable power transmission under heavy-load conditions. A closed-loop vector equation for the lifting mechanism is established， and a genetic optimization algorithm is employed for multi-objective optimization design of structural dimensions， thereby determining the key structural dimension parameters. The key structural dimensions and parameters have been determined using a genetic optimization algorithm. Parametric finite element models were established to evaluate the mechanical performance of critical components： the base frame exhibits a maximum von Mises stress of 6.445 MPa （safety factor： 36.4）， the mobile chassis shows a deformation of 0.055 mm， and the tie-rod system achieves a maximum stress of 3.479 MPa （safety factor： 67.5）， all significantly below the allowable stress of Q235 carbon steel. The integrated end-effector platform demonstrates a maximum stress of 6.747 MPa （safety factor： 34.8） and a deformation of 0.0139 mm. This research provides a numerical simulation based structural design methodology and mechanical evaluation framework for automated sealing operations in radioactive environments， ensuring compliance with the stringent demands of nuclear industrial applications.

To address the challenges of limited transient response and actuator saturation in teleoperated robotic systems， an improved prescribed performance control scheme is proposed. First， a time-varying error constraint function and a transformation function are designed to explicitly regulate the error convergence process， thereby enhancing both the transient response speed and steady-state accuracy. Second， a nonlinear saturation function is integrated with an adaptive tuning mechanism to dynamically constrain and adjust the control torques on both master and slave sides， effectively mitigating discontinuities and performance degradation caused by actuator saturation， and ensuring torque smoothness. Furthermore， a radial basis function neural network is employed to estimate system uncertainties， thereby improving the robustness and adaptability of the control framework. Without relying on accurate system models， the stability of the closed-loop system under the proposed control strategy is rigorously proven via Lyapunov-based analysis. Finally， comparative simulation results validate the superior performance of the proposed method in terms of error convergence， saturation suppression， and position tracking accuracy.

With the widespread adoption of ultra-high voltage switchgear， the requirements for assembly accuracy and efficiency continue to increase. Traditional manual assembly suffers from high labor intensity， high centering errors， and insufficient consistency， necessitating the development of intelligent and automated assembly methods. To address the challenges of ultra-high voltage disconnectors， such as the complex spatial structure and the fact that the assembly plane and the centering axis are not in the same coordinate system， this paper proposes a robotic assembly method based on 3D point clouds and vision guidance. First， the moving end intermediate conductor is scanned using a 3D point cloud to obtain its spatial pose in the basin coordinate system. Hand-eye calibration is then used to achieve coordinate system transformation. Subsequently， the pose estimated from the point cloud is error-corrected using vision guidance technology to obtain a high-precision assembly alignment reference. Furthermore， inverse kinematics and path planning are used to generate the assembly trajectory. Force/position hybrid control is then introduced to achieve smooth contact and stable insertion during the assembly process. Experimental results demonstrate that this method achieves an overall assembly accuracy of 97%， meeting the efficiency and precision requirements of the assembly process and enabling automated assembly of high-voltage disconnectors.

In March 2026, Academician JIAO Zongxia, a member of the Chinese Academy of Engineering, a professor at the School of Automation Science and Electrical Engineering at Beihang University, and a leading expert in airborne electromechanical systems and flight control systems, was invited to deliver a distinguished presentation titled“Discussion on Future Development of Airborne Electromechanical Systems” at the 14th National Conference on Fluid Power Transmission and Control held in Wuhan, Hubei Province. The report begins with an overview of aircraft composition and the positioning of airborne systems, highlighting the core role of electromechanical systems in aircraft energy flow, actuation and support functions. It then focuses on three major technological threads of high-temperature and high-pressure hydraulics, multi-electrification and intelligence, and analyzes the new requirements imposed by next-generation aviation platforms on power density, reliability, maintainability and intelligent control capabilities. Finally, from the perspective of the integration of “energy-information-control”, the report offers forward-looking insights into the development trends of airborne systems in the contexts of green aviation, unmanned operations, swarm cooperation, and extreme-environment applications. The following text has been transcribed and edited from Academician JIAO's lecture.

As an important component of intelligent manufacturing and intelligent operation and maintenance systems， industrial inspection robots are playing a key role in various complex industrial scenarios. With the continuous progress of deep learning， multi-sensor fusion， and autonomous navigation technologies， industrial inspection robots have significantly been improved in terms of accuracy， efficiency， and adaptability. This article systematically reviews the concept， key technologies， and typical applications of industrial inspection robots， and focuses on analyzing the research status of core technologies such as perception and recognition， autonomous positioning and navigation， advanced control， and intelligent decision-making. It also assesses the maturity and industrialization progress of current technologies by combining practical applications in fields such as power， workshops， and special environments. Despite significant achievements in this field， challenges still exist in perception accuracy， dynamic environment adaptability， and task execution intelligence. The development of key technologies is expected to continue in the directions of multi-source data fusion， autonomous learning， and collaborative operation. The article aims to provide a systematic reference and guidance for future research and industrial development of industrial inspection robot technology.

The dexterous multi-fingered robotic hand， serving as a key end-effector， is pivotal for enabling robots to perform fine-grained grasping and compliant manipulation. Its advancement holds significant importance for promoting automation in manufacturing， enhancing the intelligence of service robots， and expanding applications in specialized environments. Focusing on humanoid multi-fingered dexterous hand technologies， this paper systematically reviews the current state-of-the-art and future trends. It begins by elucidating the fundamental concepts， system architecture， and typical characteristics of dexterous hands. This is followed by a comprehensive of research achievements from domestic and international teams and commercially available mainstream multi-fingered dexterous hand products， covering various degrees-of-freedom designs and their respective hardware and software implementations. Key technologies， including core hardware components， multi-modal sensory fusion， and control strategies， are critically analyzed. The paper subsequently summarizes practical applications across domains such as industrial assembly， daily life assistance， and operations in extreme environments. Current challenges， particularly in reliability， multi-modal coordination， generalization capability， human-robot safety， and integration and application， are identified. Finally， future research directions are prospected from multiple perspectives， including standard establishment， novel mechanical structures， advanced multi-modal perception and fusion， bionic evolution， and embodied intelligence， aiming to provide valuable insights for in-depth research and groundbreaking applications of dexterous hands.

Facing the critical strategic demand for enhancing the resilience and security of industrial and supply chains at the national level， the paper focuses on digital twin technology as a key enabler for driving the digital and intelligent transformation of warehousing and logistics systems. A three-stage evolutionary trajectory is systematically outlined， progressing from static modeling to dynamic synchronization and ultimately to intelligent decision-making. In view of the structural challenges in traditional warehousing and logistics systems such as data silos， lagging equipment maintenance， rigid processes， insufficient flexibility， and lack of holistic optimization， this study constructs a systematic empowerment pathway encompassing five dimensions： “omni-domain data integration—intelligent operations and maintenance reconstruction—process simulation optimization—flexible collaborative scheduling—global decision simulation.” The construction of a unified data foundation enables standardized access and high-quality governance of multi-source heterogeneous data； Deployment of a predictive maintenance platform significantly enhances equipment reliability and system continuity； Application of virtual simulation and dynamic optimization technologies achieves intelligent restructuring of warehouse operations and efficiency multiplication； The construction of an elastic resource scheduling mechanism enhances the adaptability of the system to dynamic demands； By building a full-chain simulation decision-making sandbox， the system is boosted from empirically driven local decisions to data- and model-driven global autonomous optimization. In the practical application within the chemical fiber industry， this technology system has increased production efficiency by 5%， reduced operational costs by 15%， improved equipment utilization by 10%， and shortened fault-handling time by 30%. Looking ahead， digital twin technology will evolve toward “holistic coordination and intelligent symbiosis，” providing critical support for constructing autonomous as well as controllable modern warehousing and logistics systems and cultivating new productive forces.

New-generation intelligent manufacturing is not simply the addition of artificial intelligence， the industrial Internet， or digital tools onto existing manufacturing systems. Rather， it is centered on the deep integration of artificial intelligence technologies with advanced manufacturing technologies， driving manufacturing systems from “connectivity and visibility” toward closed-loop optimization involving “perception， decision-making， execution， and feedback”. Compared with the earlier concept of “Internet Plus Manufacturing”， “AI Plus Manufacturing” no longer focuses merely on the interconnection of equipment， data， and business processes. Instead， it reconstructs product research and development， production and manufacturing， quality control， supply chain collaboration， and service models， through the coordination of industrial data， model algorithms， manufacturing equipment， process knowledge， and organizational capabilities. Based on existing policy contexts and research foundations， this paper reviews the connotations， key technologies， implementation scenarios， and practical constraints of new-generation intelligent manufacturing， focusing on directions such as smart factories， intelligent products， the industrial Internet， digital twins， trustworthy industrial AI applications， and manufacturing servitization. The study concludes that the key to new-generation intelligent manufacturing lies not in the advancement of individual technologies， but in the formation of sustainable system integration capabilities among manufacturing knowledge， data governance， equipment capabilities， industrial software， standards systems， and organizational processes.

To address the inefficiency of traditional manual visual inventory counting and the high deployment costs of existing automated solutions in Automated Storage and Retrieval Systems （AS/RS）， this paper proposes an intra-warehouse visual inventory system based on modular visual devices. A retrofit-free stacker-accessible modular visual inventory device is designed. On the basis of a YOLOv8-powered visual inventory algorithm for multi-surface information fusion from a single view， the accurate counting of complex stack patterns （e.g.， non-full stacks and staggered stacks） is effectively solved by combining front pallet layer identification with top pallet layer counting. The system also features a non-intrusive integration architecture between the Warehouse Visual Stock System （WVSS） and the existing Warehouse Control System （WCS） via a database， enabling dynamic task scheduling and data closed-loop. Experimental results on four palletized cargo datasets demonstrate a stack quantity recognition accuracy of 96.3% with a processing time of 0.11 seconds per storage location. This solution provides a new engineering path for automated warehousing， characterized by high precision， low deployment cost， and minimal operational disruption.

Currently， wall plastering operations primarily rely on manual labor. Although single-degree-of-freedom rail-guided smoothing plastering equipment has emerged， it still requires human assistance， resulting in low efficiency and high labor costs. To enhance the automation level of wall plastering， a wall-mounted dual-arm plastering robot is designed. The robot adopts a design scheme where dual robotic arms handle spraying and smoothing tasks separately， effectively addressing the issues of uneven spraying thickness and low smoothing efficiency in traditional equipment. The system consists of an autonomous navigation chassis， a concrete mixing tow truck， a dual-arm lifting mechanism， and an adaptive force-controlled smoothing tool. A force-position hybrid control algorithm is proposed， which decouples the force control and position control subspaces through a selection matrix， achieving stable force control and precise position tracking in complex environments. In experimental validation， the robot demonstrates superior flatness and high consistency under various wall conditions， significantly improving construction efficiency and surface quality. Compared to traditional manual methods and single-arm equipment， it respectively enhances operational accuracy and production efficiency by notable margins.

As the core actuator in the turbine governing system, the main distributing valve is prone to a series of problems such as local high pressure, uneven force on the valving element, and vibration under different operating conditions. During the research process, CFD is employed to analyze the internal flow field characteristics of the main distributing valve of a turbine governor under start-up and shutdown conditions. Numerical simulation is carried out on the valve models with different valving element openings to obtain the opening-flow characteristic curve, and the adjustment strategies of the valving element position under different operating conditions are analyzed. The research founds that the annular structure of the valve body causes a large number of backflow and vortex areas in the main distributing valve. Ths results in significant uneven force distribution on the valve core, compromising the main distributing valve's safe and stable operation. Furthermore, the flow characteristic curve of main distributing valve exhibits a single-hump pattern, with the 25%~40% opening range constituting the primary hump zone.

The effectiveness of digital twin systems relies on the dynamic consistency between virtual models and physical entities. As the core power component of hydraulic systems, hydraulic pumps exhibit strongly non-stationary operating signals, such as pressure pulsations and vibration shocks. Traditional consistency assessment methods based on mean squared error or frequency-domain statistics struggle to effectively capture structural dynamic deviations like transient impacts and parameter drifts. To address this, a consistency assessment method integrating continuous wavelet transform and a multimodal large model is proposed. This method involves collecting simulation and measured signals from the hydraulic pump, constructing residual and real noise signals, and converting them into time-frequency images via continuous wavelet transform to highlight dynamic features. Subsequently, the image encoder of a domain-adapted multimodal large model is utilized to extract deep semantic features, and consistency is quantified using feature cosine similarity. Experimental results demonstrate that the proposed method possesses a significant ability to discriminate differences in non-stationary dynamic responses, outperforming traditional evaluation metrics. It can accurately identify model structural deviations under working conditions such as internal leakage and bearing wear, providing reliable technical support for the verification, optimization, and engineering application of hydraulic pump digital twin models.

To address the issues of suboptimal performance， and high rates of missed detection and false detection in steel surface defect detection technology in industrial production environments， an improved YOLO11 algorithm called GCI-YOLO11 has been proposed. Firstly， in the feature extraction part， the GC-C3k2 module based on the GCNet attention mechanism was designed to enhance the algorithm’s capability to extract contextual feature information from images. Secondly， the CARAFE upsampling algorithm was introduced in the neck part to enable the algorithm to aggregate contextual information within a large receptive field， reducing the loss of feature information during the upsampling process. Finally， Inner-CIoU was used to replace CIoU for loss function optimization， and auxiliary regression box was introduced to improve detection accuracy and model generalization capability. Experimental results show that， GCI-YOLO11 achieved improvements of 2.9% and 2.3% in mAP ₅₀ and mAP _50-95 on the NEU-DET dataset， and 1.6% and 0.3% in mAP₅₀ and mAP_50-95 on the GC10-DET dataset， showing better detection performance.

During the inbound phase of warehouse logistics， it is necessary to inspect， count the incoming goods， and update the inventory records. Traditional inbound inventory counting methods require manual visual inspection and the use of handheld terminal devices to input inventory information. Based on this scenario and aligning with the trend of automation and intelligence transformation in modern logistics， an automated visual stocktaking algorithm for pallet-loads using multi-camera collaboration was designed. Multi-angle images of the pallet-load are captured， and deep learning models are used to detect cartons and the pallet. Specifically， for the front， rear， left， and right side view images， an object detection model is used to calculate the quantity， types， and arrangement of the cartons. For the top view image， an instance segmentation model combined with depth information is used to calculate the number of cartons on the top layer of the pallet-load. The detection results from the five surfaces of the pallet-load are comprehensively processed， and the total number of cartons in the entire pallet-load is calculated through spatial logic reasoning. During the counting process， anomaly detection is performed on the pallet-load based on the positional relationships and type information among cartons， achieving full automation of the entire inventory stocktaking task. An experimental setup was built on a conveyor for testing， and it was found that this stocktaking algorithm achieves an average accuracy of 98%. The calculation process of this algorithm is traceable， solving the problems of traditional manual counting， namely high labor cost and the susceptibility to errors caused by fatigue and distraction. This research， starting from the warehouse inbound process， has realized an automated workflow for pallet-load stocktaking， providing both solutions and theoretical foundations for logistics automation.

The rapid advancement of Unmanned Aerial Vehicle （UAV） technology has spurred its widespread application across military reconnaissance， civil monitoring， and logistics delivery. However， as mission requirements grow in diversity and complexity， traditional avionics system architectures struggle to meet the demands for rapid functional expansion and dynamic reconfiguration. In response， a service-oriented avionics architecture has emerged， which enhances the mission flexibility， system maintainability， and functional scalability of UAV systems by decomposing complex functions into independent， minimal， and reusable atomic service units. This paper systematically reviews the evolution of UAV avionics system architectures， provides an in-depth analysis of the theoretical foundations， modeling approaches， and core principles of service decompositions. Building on this foundation， the paper discusses the challenges of the service-oriented transformation for UAVs and explores future development trends in service model standardization， intelligent decomposition， and real-time governance， aiming to offer theoretical references and technical guidance for this field.

A novel limit-type solenoid valve structure is proposed to address the issues of overheating and low efficiency in traditional limit-type solenoid valve under continuous power operation. An accurate magnetic-circuit modeling method considering magnetic saturation effects is established. By analyzing and accounting for core saturation at the stator tip and the central hole, the precision of the theoretical mathematical model is significantly enhanced. Finite element simulation and experimental platform testing are employed to systematically verify the structural performance and magnetic force output. The results indicate that the limit-type solenoid valve model, which considers saturation effects, achieves a high degree of fit with the simulation curve within a stroke range of 0~10 mm, with a maximum relative error of only 5.86%, representing a substantial improvement in computational accuracy compared to traditional models. Moreover, the design maintains attractive force performance while significantly reducing copper power consumption to only 19.9 W, approximately 75% lower than existing limit-type solenoid valve structures, demonstrating excellent thermal stability and continuous operation capability. This study provides theoretical support and engineering design references for electromagnetic valve structures intended for low-speed, long-duration operation scenarios.

Aiming at the critical problem that real-time industrial defect detection systems are difficult to balance detection speed， accuracy and computational resource constraints in edge computing environments， a fast lightweight industrial defect detection architecture based on an efficient hybrid state space model is proposed. The architecture designs a C2f_EfficientViM_CGLU fast feature extraction module that deeply integrates the global sequence modelling capability of the visual state space model with the efficient local feature enhancement mechanism of convolutional gated linear units， achieving fast and efficient extraction of complex defect features. The HSM-SSD （Hidden State Mixer based State Space Duality） efficient state space modeling mechanism is introduced to process long sequence dependencies with O（n） linear complexity， significantly improving the fast recognition capability for irregularly shaped and sparsely distributed defects. A Slimneck fast lightweight feature fusion network is constructed through GSConv （Ghost Shuffle Convolution） sparse convolution and VoV-GSCSP （Variance of Variance Ghost Shuffle Cross Stage Partial） efficient feature fusion strategies， achieving significant improvements in inference speed and extreme model compression while ensuring detection accuracy. Comparative experimental results on NEU-DET and APDDD standard datasets show that the proposed network architecture achieves mAP50 of 92.13% on NEU-DET dataset， improving 9.77 percentage points compared to the baseline model YOLOv8n， with only 2.9 M parameters and 7.7 GFLOPs computational complexity， reducing parameters by more than 93% compared to the traditional Faster-RCNN method. The mAP50 on APDDD dataset reaches 89.68%， validating the good generalization performance and fast detection capability of the method. This study provides a theoretical foundation and an efficient and feasible fast detection technical solution for real-time quality control in Industry 4.0 intelligent manufacturing environments.

To address the common challenges in industrial equipment bearing fault diagnosis， including cross-operational condition transfer difficulties and target domain label scarcity， this study proposes an intelligent diagnostic method integrating feature enhancement and domain-adversarial learning. By constructing a Symmetric Dot Pattern feature map based on wavelet transform （WT-SDP）， the original one-dimensional vibration signals are mapped into two-dimensional geometric-semantic feature representations， effectively addressing the limitations of traditional methods in translation invariance and inefficient modeling of long-range dependencies. This approach significantly improves feature separability and noise robustness. Furthermore， a domain-adversarial neural network （DANN） framework is designed， incorporating a gradient reversal layer to achieve multi-scale alignment of feature distributions between source and target domains. This eliminates reliance on target domain labels while mitigating domain shift issues， thereby enhancing model generalization under heterogeneous operating conditions. Experimental validation on cross-domain transfer tasks from the Case Western Reserve University bearing dataset （CWRU） and the Dynamic Diagnostic System testbench （DDS） demonstrates that the proposed method achieves an average diagnostic accuracy of 95% on target domains， representing a 20% improvement over baseline models. This research provides a novel solution for cross-domain fault diagnosis in industrial equipment under complex operating conditions， with the proposed method showcasing significant advantages in domain adaptation efficiency.

Due to the multi-degree-of-freedom， strong coupling， and nonlinear characteristics of wheeled humanoid robots， their motion control， especially whole-body motion control， is highly challenging. To address this issue， a whole-body motion control scheme based on robot kinematics and Model Predictive Control （MPC） is proposed. A simplified kinematic model of the wheeled humanoid robot is established， and a quadratic cost function related to the end-effector's pose is defined. The Relaxed Barrier Functions （RBF） are employed to set the constraints for robots to follow during operation， such as joint position limits， control input constraints， collision avoidance constraints， and tip-over prevention constraints. A simulation experiment for end-effector pose planning is designed， and the results demonstrate that the proposed control scheme not only enables the robot to achieve whole-body motion for end-effector control but also satisfies various constraints.

The multi-way valve is a critical component in engineering machinery for flow distribution and actuator coordination. Its performance directly affects the machine's controllability. To address insufficient flow control accuracy and multi-parameter coupling in existing multi-way valves, this study focuses on a 16-diameter load-sensing multi-way valve. An electro-hydraulic-mechanical multidisciplinary co-simulation model is developed and validated through experiments. The research examines how the compensator spool flow force, spring parameters, damping holes, and the load-holding throttle edge influence the main valve static and dynamic flow characteristics. The results reveal that in static performance, flow force on the compensator is the main cause of flow error. This can be compensated by matching spring stiffness, though improper compensated stiffness may lead to flow “make a big bends”. In dynamic performance, appropriately increasing the damping hole diameter and spring stiffness of the compensator improves flow response speed. Although the load-holding throttle edge enhances safety and control accuracy, it also limits flow capacity and introduces pressure loss, requiring a trade-off in design.

Aiming at the problems of many parameters and large amount of calculation in the existing gear surface defect detection algorithm， a surface defect detection method for lightweight gears based on improved YOLOv8s is proposed. Firstly， part of the ordinary convolution in the YOLOv8s network model is replaced by the Adown convolution module， which improves the capability of the model to capture image features and reduces the parameter amount. Secondly， the lightweight module C2f-Faster and the channel mixer CGLU are integrated to construct a new C2f-Faster-CGLU module， which reduces the model size and calculation cost. Finally， the LSCSBD detection head is designed to further reduce the number of model parameters. The experimental results show that compared with the original model， the improved YOLOv8s model has a 58.6% reduction in the number of model parameters， a 46.1% reduction in GFLOPs， a 57.3% reduction in model size， and an average accuracy of 98.8%. The improved algorithm effectively reduces the memory occupation of the model， and the model is lighter， which provides a reference for the real-time detection of gear surface defects in small mobile devices.

In order to address the problems encountered during glass defect detection， such as reliance on manual intervention， strong interferences from complex backgrounds， and difficulties in distinguishing categories， an improved algorithm based on YOLOv8s is proposed. The detection performance is improved by integrating omni-dimensional dynamic convolution （ODConv） and Squeeze-and-Excitation attention mechanism （SEAttention）. This algorithm replaces traditional convolution with ODConv in the backbone feature extraction network of YOLOv8， and enhances the feature capture capability for subtle defects on glass surfaces by dynamically adjusting the overlap and receptive field of convolution kernels； In the feature fusion stage， SEAttention is introduced to enhance effective defect features and suppress background noise by reallocating the weights of channel dimensions. The experimental results showed that the improved YOLOv8s achieved a mAP of 96.8% on a self-made industrial glass defect dataset， an increase of 1.9% compared with that before improvement. The comprehensive mAP of different IOU thresholds increased by 1.8%， and the detection speed met the real-time detection requirements of industry. The performance is comprehensively improved， providing an efficient solution for automated detection and classification of glass surface defects.

To address the severe wear and leakage of conventional combined seals in servo motors under dynamic sealing conditions, a novel combined seal configuration is proposed by exploiting the complementary characteristics of an X-ring, an O-ring and a rectangular ring. A two-dimensional axisymmetric finite-element model is established in ANSYS and used to compare the proposed design with the conventional structure under identical parameters. Based on the contact-stress distribution, the Reynolds equation is inversely solved to obtain the oil-film thickness distribution, from which the leakage rate and friction force are calculated. In addition, an L₉(3⁴) orthogonal design is conducted, with leakage rate as the response to quantitatively evaluate the effects of compression ratio, medium pressure and friction coefficient. The results indicate that the proposed structure yields a more reasonable stress distribution, with a 6.9% reduction in leakage and a 2.5% reduction in friction. The orthogonal analysis further shows that medium pressure is the dominant factor, followed by compression ratio, whereas the friction coefficient has the weakest influence.

Piezoelectric ceramics are functional materials that convert electrical energy into mechanical energy, and vice versa. Piezoelectric proportional flow valves, driven by piezoelectric bimorphs, offer advantages such as fast response, low power consumption and high precision. However, the low output force of piezoelectric bimorphs limits their application in high-flow applications. To improve the output flow rate of the piezoelectric bimorph-driven proportional valve, we use ANSYS and COMSOL software to create three-dimensional and two-dimensional axisymmetric models of the piezoelectric valve and bimorph. The flow field characteristics of the valve and the fluid-structure coupling behavior of the piezoelectric element are analyzed. The effects of inlet and outlet positions, valve opening and chamber pressure on the output flow rate are also explored. The relationship between the force at the end of the piezoelectric bimorph and the output flow rate is studied, and its performance is compared with existing piezoelectric proportional valves of the same type. The results show that the optimized parameters significantly improve the valve's output flow rate. When the valve chamber's inlet pressure reaches 0.5 MPa (with an outlet radius of 1.1 mm), the output flow rate approaches 160 L/min, while the force on the free end of the piezoelectric bimorph is only 0.9 N. It provides guidance for designing nozzle-baffle pneumatic piezoelectric proportional valves and is expected to expand their application range.

With the widespread application of high-speed motors, electro-hydrostatic actuator impose high-speed requirements on piston pumps. We focus on a 60 ml/r swashplate variable-displacement axial piston pump and conduct a high-speed redesign. We address issues such as large pressure pulsations in the piston chambers, poor suction performance, and severe cavitation at high rotational speeds. An integrated booster impeller is designed inside the pump to increase the suction pressure of the piston chambers and to solve the insufficient oil intake problem. A pre-depressurization chamber is added to reduce negative pressure overshoot during high-to-low pressure transitions thereby mitigating cavitation risks. The port plate relief grooves are optimized by configuring the pressurization zone with damping holes and triangular grooves and the depressurization zone with damping holes and U-shaped grooves. The optimization of the port plate damping grooves reduces pressure pulsation during pressure transitions. The proposed design is validated through CFD flow-field simulations. A prototype of the high-speed piston pump is developed and experimentally tested. Testing results show good agreement with the simulation results. The prototype reaches a maximum rotational speed of 5000 r/min, and its volumetric efficiency exceeds 96% under load pressures of 5~15 MPa within the speed range of 1000~5000 r/min.

The pressure-flow characteristic is a key performance indicator for hydraulic valves, among which spool valves represent one of the three most widely used types of hydraulic control valves. Typical hydraulic spool valves include L-type, U-type, and V-type configurations. To refine the theoretical framework for their pressure-flow characteristics, this study firstly develops a universal hydraulic model based on the structural features and fluid dynamics principles of typical spool valves. MATLAB programs are written to analyze how spool opening influences different hydraulic models. Furthermore, commercial CFD simulation software is employed to conduct numerical calculations of the flow field inside a typical hydraulic spool valve. The analysis focuses on the variation patterns of the velocity and pressure fields, the pressure-flow characteristics, and factors influencing the flow coefficient of the valve port. The results indicate that both the equivalent diameter and the flow area of typical hydraulic spool valves increase synchronously with the spool opening. Under identical boundary conditions, the L-type spool valve exhibits the smallest pressure loss and the lowest flow coefficient. The variation of the flow coefficient depends solely on the spool structure and the spool opening, decreasing as the opening increases.

This study focuses on the bolted connection structure between the pump flange and the housing, and establishes a finite element model of the piston pump. The model is validated through both modal analysis experiments and vibration response experiments. Based on the validated model, the influence of four design parameters—bolt position, installation radius, bolt quantity, and bolt size—on modal frequencies and vibration responses is systematically analyzed. The results indicate that a symmetrical bolt arrangement increases certain modal frequencies by up to 12.7% and reduces vibration velocity by up to 21.5%. The installation radius has a relatively minor effect on vibration characteristics. An increase in bolt size leads to higher modal frequencies, but has limited effectiveness in suppressing vibration. Increasing the number of bolts significantly improves the modal frequencies, with a maximum increase of 10.7%, and reduces vibration velocity by as much as 36.1%. An orthogonal experimental design is used to evaluate the relative significance of each parameter. The analysis confirms that bolt position and bolt quantity have the most substantial impact on the modal characteristics and vibration response of the piston pump. Bolt size has a moderate influence, while installation radius has the least effect.

To address the challenges faced by mobile robots in overcoming obstacles in unstructured environments such as agricultural inspections and disaster rescue， this study proposes a design scheme for a four-wheeled mobile chassis that integrates a rocker-steering suspension with a crank-slider mechanism. First， kinematic and dynamic models of the walking mechanism were established to analyze the influence of key configuration parameters （e.g.， support wheel center distance， hinge distance） on terrain adaptability and load platform posture. A multi-objective optimization method was employed to determine the optimal parameter combination （L_F =200 mm，k ₁=0.9）. Second， a three-dimensional virtual prototype was developed by integrating a crank-slider mechanism and symmetric frame design. Dynamic simulations conducted on the RecurDyn platform validated the chassis performance in traversing 18 mm speed bumps and 20 mm semi-cylindrical obstacles， showing pitch angle fluctuations within ±3°and peak torque demand ≤15 N·m. Finally， prototype tests demonstrated that the chassis can stably cross 90 mm speed bump-type obstacles under a 75 kg load， with a linear motion speed of 1.8 m/s and a path deviation of less than 20 mm/5 m. The results indicate that this design significantly enhances the terrain adaptability of mobile robots in unstructured environments， providing a reliable mobile platform for agricultural inspection， logistics， disaster rescue， and similar scenarios.

In recent years， indigenously developed， aerospace-specific 3D structural design systems in China have undergone robust development， with notable achievements in the R&D of core components. However， with the widespread adoption of Large Language Models （LLMs）， establishing an effective interface between 3D structural design and AI-driven methodologies remains a central challenge. Furthermore， existing LLMs lack the capacity for precise reasoning over 3D geometry and complex physical fields， such as aerodynamics， which precludes their direct application in the intelligent design of aircraft structures. Among aerospace structural components， the aircraft wing is critical for generating lift. Its design process is highly complex， heavily reliant on expert experience， and tightly coupled with aerodynamic performance. Consequently， traditional design paradigms are characterized by lengthy iteration cycles and substantial costs. To address this challenge， this paper presents Airfoil-LLM， an intelligent design interface for the 3D modeling of aircraft wings， using the wing as a representative case study. Based on the Transformer architecture， this interface integrates natural language encoding with the decoding of CAD modeling sequences to enable intelligent and automated 3D wing generation. To support model training and validation， we have constructed a large-scale 3D wing design dataset. This comprehensive dataset comprises parameterized 3D CAD models， a wide spectrum of flight conditions from subsonic to supersonic regimes， key aerodynamic performance metrics， and multi-level textual descriptions. Experimental results demonstrate that Airfoil-LLM is capable of deeply comprehending textual descriptions ranging from simple geometric attributes to complex， coupled "geometry-performance" requirements. The system generates 3D models that align closely with the design targets in both geometric shape， achieving a maximum Intersection over Union （IoU） of 0.831， and aerodynamic performance.

To address issues such as poor stability, low coordination, high labor intensity and significant drill pressure fluctuations associated with manual drilling operations in automatic drilling systems, we design a hydraulic drawworks system for automatic drilling. This system includes power, drive, execution and control units. It enables control over the drill string hoisting speed and surface drill pressure by adopting a composite controller that combines the conventional PID with self-adjusting fuzzy control. Using the AMESim-Simulink co-simulation platform, simulation tests verify that the hydraulic drawworks system can effectively control the hoisting speed and surface drill pressure. The controller improves the system's response speed, reduces overshoot and enhances its stability. The research shows that this composite controller delivers high accuracy in controlling the automatic drilling hydraulic drawworks system, enables real-time monitoring, improves both intelligence and drilling efficiency and reduces drilling costs. These findings hold practical application value for the future of automatic drilling operations.

A magnetorheological damper, known for rapid response, wide adjustment range of damping coefficient and low energy consumption, is a significant research direction in the field of vehicle engineering. By dynamically controlling the excitation current of the magnetorheological damper, we minimize the vibrations transmitted to the vehicle body, ultimately improving vehicle running smoothness. This research conducts a theoretical analysis of the hybrid-mode damping characteristics of a certain magnetorheological damper model. A multi-physics simulation model of the magnetorheological damper is developed using ANSYS to analyze the relationships among piston velocity, damping force and control current. A semi-active suspension and full-vehicle dynamics model is established, and a sliding mode control algorithm is applied in a Simulink numerical simulation to investigate the impact of control system on suspension and vehicle vibration performance. The research shows that the finite element analysis of the electromagnetic field provides a clear visualization of magnetic flux distribution, offering guidance for magnetic fluid flow path structural design. Additionally, the sliding mode control significantly enhances the performance of the semi-active suspension, effectively improving critical indicators such as vehicle body acceleration and suspension stroke. Ultimately the vibration characteristics, as well as ride smoothness and ride comfort of vehicles are significantly improved.

Against the backdrop of Industry 4.0， the demands for device collaboration and real-time communication continue to rise. Traditional industrial Automated Guided Vehicle （AGV） systems face critical challenges such as inconsistent protocol standards， poor compatibility among heterogeneous devices， and insufficient stability in wide-area communication. To address these issues， this paper proposes a collaborative communication scheme for AGVs based on 5G-VXLAN and OPC UA/DDS protocol integration. The goal is to enable cross-platform interaction and multi-protocol adaptation of AGV control information within an ROS 2 distributed system. By employing VXLAN technology to build a scalable logical Layer 2 overlay network atop a Layer 3 physical infrastructure， the solution effectively overcomes the traditional VLAN's limitation of 4094 identifiers and network scalability bottlenecks， forming a logically unified communication plane. Combined with OPC UA and DDS transmission protocols， AGV PLC data can be integrad into the ROS 2 distributed control system， enabling cross-platform command delivery and status monitoring. Experimental results demonstrate that 5G-VXLAN technology ensures stable and real-time data interaction during AGV mobility， while the integration of OPC UA and DDS enables conversion and transmission of data across different protocols.

To address the limitations in control precision and robustness of hydraulic position servo systems, an intelligent adaptive control strategy combining the deep deterministic policy gradient algorithm with sliding mode control is proposed. A coupled electro-hydraulic asymmetric cylinder system model is established on the AMESim-Simulink platform, and the integration of the sliding mode control module with the reinforcement learning module is validated. The designed controller, combining deep deterministic policy gradient and sliding mode control, enables online self-tuning of sliding surface gains and chattering suppression factors. Simulation scenarios under three typical operating conditions—step input, sinusoidal input, and composite disturbances—are constructed. Results show that the proposed strategy achieves rise and settling times of 0.82 s and 0.83 s, respectively, in step tracking, outperforming radial basis function-based sliding mode control and conventional sliding mode control; under disturbance conditions, the maximum tracking error remains below 0.003 m, effectively suppressing system chattering. These findings demonstrate the proposed method's superior dynamic response and robustness in complex environments, providing significant implications for enhancing the intelligence and control performance of hydraulic servo systems.

A large amount of process knowledge accumulated during industrial programmable logic controller （PLC） development exists in tacit form， embedded in historical control logic and dependent on engineers' experiential judgment， making it difficult for large language models to directly utilize. To address this problem， this paper proposes a systematic engineering method that transforms tacit process knowledge into reusable process templates with hierarchical structure and control topology details， through a dual-layer knowledge graph schema and a bidirectional fusion construction strategy. Using PLC code generation as a downstream evaluation task， we target two structural barriers — knowledge accessibility and functional association completeness — and validate the effectiveness of the proposed method through a knowledge granularity gradient experiment and a graph-versus-vector retrieval recall comparison experiment， respectively. Experimental results demonstrate that， on code-segment-level tasks， the process templates automatically extracted by the proposed method approach the performance of manually written pseudocode-level fine-grained knowledge specifications in terms of control requirement information supply； on functional-block-level tasks， the graph-structured knowledge organization successfully mitigates the systematic deficiency of vector retrieval in preserving functional association completeness.

Aiming at the problems of cumbersome deployment and poor adjustment flexibility associated with QR code navigation， which is widely used in current warehouse robotics， this paper designs a global path planning method based on predefined paths and a secondary positioning and pose adjustment method based on the PL-ICP （Point-to-Line Iterative Closest Point） algorithm. The system utilizes ROS2 （Robot Operating System 2） with 2D LiDAR， IMU， and wheel odometry sensors. The methodology employs the Cartographer laser SLAM algorithm to construct a 2D grid obstacle map and robot localization data files. A self-developed RViz2 plugin is utilized to create navigation map files， upon which the A* algorithm generates global paths. The Navigation2 framework is then implemented for local path planning and motion control. Upon arrival at a designated target point， the robot calculates its pose deviation using laser feature data and corrects its position via a PID control algorithm. Simulation tests conducted on the Gazebo platform demonstrate that， after secondary positioning and pose adjustment， the robot achieves a positional accuracy of ±3 mm in the x and y directions， and a heading angle （θ） accuracy of ±0.1°.

To meet the requirements of heavy-duty vehicle suspensions operating under complex road conditions—specifically high load-carrying capacity, wide-band damping, and tunable stiffness—this study investigates the dynamic characteristic modeling and structural parameter optimization of a dual-chamber hydro-pneumatic spring. Based on the operating mechanism of the hydro-pneumatic spring, a fractional-order Zener model is introduced. Frequency-domain representations of equivalent stiffness and equivalent damping are established, and the influence of different fractional-order derivatives on the system's dynamic response is analyzed. Building on this, a vehicle suspension model is developed, with ride comfort and handling stability selected as optimization objectives and dynamic suspension travel imposed as a constraint. Multi-objective optimization of key structural parameters of the hydro-pneumatic spring is performed using the particle swarm optimization algorithm. Bench tests of the hydro-pneumatic spring and quarter-vehicle suspension performance experiments are conducted to validate the proposed model and assess the optimization effectiveness through comparative analysis. The results indicate that the developed model effectively captures the wide-frequency dynamic characteristics of the dual-chamber hydro-pneumatic spring, and that the optimized parameter combination improves overall suspension performance while satisfying the prescribed constraints. This provides a feasible method and practical reference for the design and performance enhancement of hydro-pneumatic suspensions for heavy-duty vehicles.

To address the challenges of leakage in the mechanical seal of high-pressure oil delivery pumps and the insufficient opening force of the seal face, a novel long-arc groove seal face is optimally designed, and its sealing performance is analyzed. Firstly, the L25(5⁶) orthogonal experiment is used to perform multi-objective optimization on five structural parameters, including the groove diameter ratio of the designed long-arc groove seal face. Then, based on the construction of a comprehensive scoring model, the optimal operating condition curve is fitted and the optimal parameter combination is obtained. Finally, the optimal parameter combination is verified under different operating conditions, including rotational speed, medium pressure, and liquid film thickness. Numerical tests show that when the groove diameter ratio, groove width ratio, groove depth ratio, arc radius, and groove number are 0.6, 0.6, 2.5, 5 mm, 8, respectively, the sealing performance of long-arc groove mechanical seal for the high-pressure delivery pump is optimal. Research shows that under the optimal parameter combination, the maximum stress in the flow field at the outlet of the groove area generally decreases, the overall stress distribution along the circumference is more uniform, and the fluid dynamic pressure effect is more significant, greatly improving the opening force of the end-face liquid film while effectively controlling the leakage rate.

The realization of industrialized intelligent construction for steel structures is contingent upon two prerequisites： first， the development of a fully assembled steel structure system that is inherently efficient for repeated disassembly； second， the development of automated assembly robots to address the issues of low efficiency， low precision， and poor quality associated with on-site manual installation. This paper proposes a solution involving an automated assembly robot for the installation of torsion-shear high-strength bolts in Core-tube type steel column joint. Focusing on the assembly process of M16 torsion-shear high-strength bolts， this study emphasizes the mechanism design and structural reliability analysis of the robot end-effector. A hierarchical control system for bolt-hole assembly， based on machine vision， is designed. Simulation experiments demonstrate that the proposed robotic mechanism satisfies the assembly process requirements and significantly enhances the efficiency， precision， quality， and safety of installing torsion-shear high-strength bolts in core-tube column connections.

The stator surface roughness significantly alters the blade wall turbulence structure of the torque converter, thereby affecting its operational performance. Therefore, studying the influence of stator surface roughness on flow field evolution holds important research significance and application value. Considering manufacturing constraints, several groups of stator blade surface roughness levels are selected. Based on CFD, the internal flow field of the torque converter is numerically simulated using the SST k-ω turbulence model, and the three-dimensional vortex structures under different stator surface roughness levels are comparatively analyzed. The results indicate that: the variation in stator surface roughness significantly affects its blade wall turbulence structure. As the roughness increases, the area of the high-velocity region on the blade suction side at the inlets of the stator, pump, and turbine gradually shrinks, the velocity magnitude drops significantly, and the overall vortex system becomes more chaotic. Under stall conditions, with the progressive increase in stator blade surface roughness, the error between the torque converter's torque ratio and the experimental value decrease by approximately 2.7%, 3.6% and 1.24%, respectively, compared to the smooth blade. The pump torque coefficient decreased by approximately 1.6%, 2% and 4.45%, respectively. The research results can provide certain technical guidance for the flow field analysis and blade manufacturing of torque converters.

In the discrete control system of the robotic arm， the traditional sliding mode method often encounters high-frequency chattering problems and control failure caused by input saturation. To break through this bottleneck， this paper proposes a trajectory tracking control method for the robotic arm based on anti-saturation sliding surface and adaptive reaching rate， aiming to address the influence of input saturation and external disturbances on control performance. By directly integrating the anti-saturation suppression factor into the design of the discrete sliding surface， dual constraints control over both input amplitude and input variation rate are achieved， and an adaptive reaching rate is constructed to suppress the chattering in the traditional discrete sliding reaching law， improving the stability and tracking accuracy of the system under disturbances. The hyperbolic tangent function is used instead of the sign function to further eliminate high-frequency chattering phenomena and improve control smoothness. Simulation results show that the proposed control method achieves smaller joint position and velocity errors on a two-degree-of-freedom robotic arm， converging within 0.35 seconds， with steady-state error controlled within 0.01 rad， and the maximum steady-state error is reduced by 74.16% and 68.71% compared with the traditional method. The root mean square error is reduced by 57.47% and 19.07%， respectively， verifying its superiority in anti-saturation and chattering suppression. The research in this paper provides an effective control strategy for the control application of robotic arms in complex environments with input saturation and disturbances， with high application value and engineering significance.