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.

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.

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.

To address the challenges of frequent bearing failures under complex working conditions， as well as the low real-time performance and strong dependence on manual feature extraction in traditional diagnostic methods， this paper proposes a bearing fault diagnosis method based on a deep learning model combining a Multi-Scale Convolutional Neural Network （MSCNN） and Long Short-Term Memory （LSTM）， and develops an intelligent bearing health management system. The system adopts an end-to-end diagnostic workflow， directly taking raw time-domain vibration signals as input. It extracts hierarchical local features across different frequency domains through MSCNN， and captures the temporal evolution of fault characteristics using LSTM， thereby achieving high-accuracy automated fault classification. To enhance the interpretability of diagnostic results and support intelligent maintenance decisions， the system integrates the Chinese large language model iFLYTEK Spark， which generates natural language diagnostic reports and maintenance suggestions through standardized prompts. The system is deployed on a domestically developed Phytium quad-core processor platform， ensuring full autonomy and reliability of both hardware and software components for industrial applications. Experimental results show that the proposed system achieves an average classification accuracy of 98.46% on the CWRU bearing dataset， and 96.73% on the AITHE bearing fault dataset， demonstrating strong robustness and cross-dataset generalization under complex and noisy conditions. With real-time visualization of diagnostic results and maintenance recommendations through a human-machine interface （HMI）， this system provides a reliable and intelligent solution for equipment health management and predictive maintenance.

The machining quality of electric spindles critically determines precision， efficiency， and stability in precision manufacturing. However， the machining process faces challenges due to diverse product types， multiple operating conditions and scarce target-condition data， making consistent quality of electric spindle difficult to guarantee. To address this， this paper proposes a transfer-learning-based method for multi-operating-condition quality prediction. The method first extracts spindle time-series signals and employs the Synthetic Minority Over-sampling Technique to balance historical and target-condition data distributions. Subsequently， constructs a two-stage regression model， TrAdaboost.R2， and leverages knowledge transfer to predict spindle quality under target conditions. Finally， the proposed method is validated with electric spindle data， demonstrating its superior prediction performance. This approach provides an effective framework for the precise quality prediction of electric spindles across varying operating conditions.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

To address issues such as low detection accuracy， slow speed， missed and false detections， and large model parameter sizes in complex scenarios from a UAV perspective， this paper proposes an improved RBGE-YOLO algorithm model. Firstly， RFAConv is introduced in the backbone network to replace the original Conv， enhancing the model's ability to extract and fuse image features. Secondly， the neck network is reconstructed using BiFPN-GLSA to improve feature fusion and spatial feature utilization efficiency. Thirdly， a dual-layer small target detection structure is designed to strengthen the feature information of small targets. Finally， the Inner-EIoU loss function is utilized to address the limitations of IoU. Experiments on the VisDrone2019 dataset show that RBGE-YOLO improves Precision， Recall， mAP@0.5， and mAP@0.5：0.95 by 4.7%， 2%， 3.6%， and 2.5%， respectively， compared to the original YOLOv8s， while reducing the number of parameters by 16.4%. This achieves model lightweighting while significantly enhancing detection performance.

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°.

In order to reduce the monitoring and operating pressure of operators during deep peak shaving， an intelligent desulfurization control system is proposed to address the problems of poor measurement accuracy and large inertia and delay in the controlled objects that prevented long-term stable automatic operations. By using BP neural network， a mapping relationship is constructed between signals such as flue gas flow rate， SO₂ concentration in the raw flue gas and slurry pH to achieve soft measurement of slurry pH value； Replacing conventional PID with variable structure predictive control and combining it with more accurate and reasonable feedforward signals ensures the control effect of the desulfurization system under rapidly changing load and coal quality conditions. Later， utilizing the unit ICS system， the desulfurization intelligent control system is successfully applied to a 650 MW unit. The operation results show that after the system is put into operation， the SO₂ concentration at the outlet is stably controlled within 25 mg/m³， and the deviation between the pH value of the slurry and the set value is kept within 0.2， and there are no significant fluctuations during the variable load and pH meter flushing process. The desulfurization is automatically put into operation for a long time， effectively reducing the operating pressure of the operators.

To accurately identify the dynamic parameters of a six-degree-of-freedom collaborative robot， an identification algorithm based on physical consistency called Iteratively Reweighted Least Squares-Semidefinite Programming （IRLS-SDP） has been proposed. Firstly， the Newton-Euler dynamic model of the 6-DoF collaborative manipulator is established， employing a modified Coulomb-viscous friction model. Then， this dynamic model is linearized. To address the lack of consideration for physical meaningfulness in identified parameters during the identification process， constraints enforcing physical consistency are imposed. An excitation trajectory combining Fourier series with quintic polynomials is designed and optimized with the condition number as the optimization objective. Finally， dynamic parameter identification experiments are conducted： the collaborative robot executes the excitation trajectory， and experimental data is acquired and processed to obtain the regression matrix and actual joint torques. A comparative analysis of the identification results between the IRLS-SDP method and the conventional Least Squares （LS） method is performed. Experimental results demonstrate that the IRLS-SDP algorithm achieves superior overall identification performance. Specifically， it yields higher-precision torque predictions compared to LS， exhibits better model tracking fidelity， and identifies parameters that exhibit significantly better physical consistency.

We analysed the nonlinear variation of parameters exhibited by the liquid rocket engine test stand under the action of engine thrust， and we have analysed the results of the static loading-unloading experiments and concluded that this non-linear variation is due to the effect of friction between the moving and static frames of the test stand. We derived the explicit relationship between the loading force F and the friction coefficient μ by fitting the experimental data， and used the LuGre friction model in conjunction with the simulation results to provide a theoretical explanation for the variation of the friction coefficient. In this paper， we propose a correction method for the nonlinear model of the test frame friction based on the static response surface method， by constructing a static response surface， taking the friction coefficient of the friction pair of the test frame as the optimisation variable， and taking the minimisation of the error between experimental and simulated static responses as the optimisation objective to correct the finite element model. The results show that the correction of the friction nonlinear model of the test stand by this method not only improve the optimization efficiency， but also keep the maximum error of the corrected finite element model no more than 2.57 kN， and the relative error with the experimental value is 0.43429%， which is a big improvement compared with the relative error of the initial model which is 1.271%； and the friction coefficients of the corrected model further verify the correctness of the experimental fitting and theoretical interpretation. This paper can provide engineering practice value for the subsequent correction of nonlinear changes in the thrust transmission of the test stand.

Difficult-to-cut materials are widely used in the aerospace and aviation industries. These materials have characteristics such as high cutting difficulty， high material cost， and difficult calibration experiments. In the finite element simulation modeling process of difficult-to-cut materials， the setting of material mechanical properties and tool chip friction performance， will significantly affect the prediction accuracy of the simulation model. How to achieve efficient acquisition of mechanical property parameters of difficult-to-cut materials， is of great significance to study the rapid and accurate uncertainty calibration strategy of simulation models. Taking the milling process of Ti₂AlNb intermetallic compound as an example， a finite element simulation model uncertainty calibration method for difficult-to-cut materials under the Bayesian framework is proposed. Firstly， an uncertainty analysis of the model is conducted， and a Bayesian based model uncertainties quantification method is proposed. The uncertainty coefficients are solved using the Markov chain Monte Carlo method. Secondly， finite element modeling， simulation experiment design， and simulation dataset construction for milling process are carried out based on finite element simulation software. A surrogate modeling method based on Gaussian process regression and supporting vector regression is proposed. Finally， Ti₂AlNb milling experimental design is carried out， and the working condition dataset is constructed to quantify the JC constitutive parameters and tool chip friction coefficient within the finite element model. The experimental results show that the uncertainty-calibrated finite element simulation model has significantly improved the prediction accuracy， and the relative error in predicting the cutting force of three-dimensional has decreased from 21.47% before calibration to 12.17%.

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.

Part machining accuracy inspection is a critical aspect of production， and efficient deviation analysis methods can save costs while ensuring part quality. For three-dimensional point clouds obtained through laser 3D scanning， a 3D part deviation analysis system has been designed. By comparing the scanned data with the CAD design model of the part， the deviation of the part is calculated and visually displayed. The system is divided into three parts： data preprocessing， alignment， and deviation calculation. In the data preprocessing stage， the CAD 3D model is discretized into a point cloud， while points from the scanned data are extracted， and key points from both are identified to facilitate alignment. During the alignment stage， an OBB segmentation box coarse registration algorithm is designed for coarse alignment， followed by the ICP algorithm for fine alignment， effectively preventing the ICP algorithm from falling into local optima. In the deviation calculation stage， a point-surface substitution method is used to compute the initial deviation values， and finally， an outlier processing system is designed to handle anomalies. Compared with professional software， the measurement error is at the micrometer level.

Robust anomaly detection in high-dimensional industrial data is crucial for ensuring equipment safety and production quality. However， existing methods often fail due to insufficient adaptability of feature importance， the vulnerability of single-perspective detection mechanisms to noise interference， and limited generalization capability for anomaly patterns. To address these issues， an end-to-end multi-perspective anomaly detection architecture named the Integrated Reconstruction and Adaptive Selection - Knowledge Distillation is proposed. This architecture innovatively integrates three core components： The reconstruction verification module， which learns compact representations of normal data through multi-scale auto-encoders to capture structural deviation； The knowledge distillation module， which transfers semantic knowledge using a teacher-student network to provide an independent verification perspective so to enhance generalization capability； The adaptive feature selection module， which dynamically learns feature importance weights through a gated attention mechanism to focus on discriminative information. These three modules are jointly optimized through a multi-objective dynamic weighted loss function that fuses reconstruction error， knowledge alignment difference， and attention regularization， achieving complementary verification of multi-level information.

To overcome the limitations of traditional 2D planar grasping and address the challenge of inaccurate position estimation in existing 6D pose estimation algorithms such as Gen6D， this paper proposes an optimized algorithm， Gen6D-Op. For typical robotic grasping scenarios， the algorithm formulates the position estimation error as a constrained optimization problem based on a collinearity assumption， enabling the precise acquisition of object poses. Building on this high-precision pose， we further design two efficient grasping strategies—Vertical Pose and Planar Projection—to enhance grasping efficiency and stability. Experiments demonstrate that Gen6D-Op significantly improves pose estimation accuracy， reducing the total error by 72.3% to 9.48 mm and achieving a multi-object grasping success rate of 94%. Furthermore， applying the designed grasping strategies effectively reduces the robotic arm's joint angle variation and shortens the grasping time.

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.

During automated automotive welding， mechanical vibrations and workpiece movement easily cause motion blur in acquired images， while intense arc light and reflections from metal surfaces lead to local overexposure. These two types of interference affect weld seam tracking accuracy and quality evaluation. To address such problems， a real-time image deblurring algorithm for automotive welding based on an improved NAFNet （nonlinear activation free network） network is proposed. The algorithm uses NAFNet as a basic architecture and introduces three core modules： metal reflection suppression， multi-scale gradient constraints and skip connections. Through dynamic brightness clipping and a gated attention mechanism， it effectively suppresses interference from overexposed regions. Gaussian difference operator is adopted to restore weld seam edges and defects， while it enhances transmission of high-frequency features by optimized skip connections. Experiments show that the improved algorithm achieves image structural similarity and edge preservation indices of 0.879 and 0.872， respectively， which significantly outperforms the original NAFNet algorithm. Furthermore， under a resolution of 1080×720， single-frame processing time is only 63 milliseconds， which meets the strict real-time requirements of vision-guided systems.

As market demands become increasingly diverse and competition intensifies， the limited capacity for product updates and iterations， coupled with the continuous reduction of profit margins， necessitates the transition of traditional manufacturing enterprises towards service-oriented manufacturing as a crucial step for future growth. Product-service modular design serves as a key strategy to facilitate this transformation. This study， grounded in an analysis of existing research findings， develops a theoretical framework for product-service modular design and outlines the criteria for service module segmentation. A service module clustering method based on minimum spanning tree is proposed， utilizing an enhanced closeness value method to optimize the multi-scheme decision for different clustering results. To validate the practical applicability of the theoretical model， a case study exploring the service modular design of CNC machine tools is presented.

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.

Aiming at the collaborative optimization problem of flexible job shop scheduling and AGV material handling in intelligent factory under multi-variety and small-batch production mode， a joint scheduling method of production and handling based on Dueling Double Deep Q-Network （D3QN） is designed to minimize the maximum completion time， and the conflict-free path planning of AGV is realized by combining the A * algorithm with time window. The design algorithm is compared with a variety of rule scheduling methods， and a variety of different scale examples are designed for experimental verification. The results show that the scheduling performance of D3QN algorithm is better， and it has good optimization effect and generalization ability. At the same time， the influence of the number of AGVs on the scheduling performance is analyzed， and finally it is concluded that it conforms to the law of diminishing marginal benefit.

In the production of rubber products， the intensive temperature fluctuations and pressure instability during the mixing and extrusion processes not only adversely affect the mixing quality and the plasticization uniformity of the compound， but also significantly impact equipment energy consumption and production efficiency. To address the issues of large temperature fluctuations and poor pressure stability during rubber compounding and extrusion production， an improved fuzzy PID intelligent cooperative control strategy is proposed. By integrating real-time data acquisition from multiple sensors， including temperature， pressure， rotor speed， and torque sensors， an adaptive parameter tuning mechanism based on process characteristics and a pressure-temperature dynamic coupling compensation model are designed. Through this adaptive adjustment process， precise cooperative control of the mixer rotor and extruder screw is achieved， which deeply analyzes and compensates for the mutual interference between pressure and temperature parameters caused by the strong shear heating of the mixer rotor and the conveying compression process of the extruder screw， thereby overcoming the limitations of single-variable control. Experimental results demonstrate that the improved algorithm reduces the overshoot by 11%， shortens the settling time， and achieves a temperature control accuracy of ±0.8 ℃ in the temperature control process. The interference of pressure fluctuations on temperature is reduced from 35% to 12.7%. This system enhances temperature control precision， reduces pressure fluctuations， and significantly improves product uniformity and production efficiency.

To solve the problem of accidents caused by damaged crane hooks in current industrial environment， and the low efficiency of crane loading and unloading， an improved YOLOv8n crane hook identification algorithm is proposed. Firstly， AKConv module is introduced to replace the Conv module in the backbone network. This module gives arbitrary parameters and shapes to the convolution kernel， providing rich choices between the convolution cores. Secondly， the ADown downsampling module is embedded in the backbone network， reducing the loss of feature information during the downsampling process. Finally， a CAFMAttention convolution attention fusion module is introduced to enhance the global and local feature extraction of hook recognition. Based on the experimental results， the improved YOLOv8n algorithm increases the precision， recall and mAP50 indicators by 4.6 %、4.2 % and 3.3 % respectively. The improved algorithm enables real time detection of hook positions， assisting operators in timely adjustment and decision-making， avoiding collisions or accidents， thereby improving safety in industrial environments. In addition， automatic hook recognition facilitates faster hook location identification while enabling precise cargo loading and unloading operations， consequently boosting work efficiency.

This paper investigates a vision-based dual-arm peg-in-hole assembly method for a humanoid dual-arm robot platform equipped with dexterous hands. The research addresses the requirements for grasping adaptability and motion anthropomorphism in natural environments. This paper uses steel pipes and pipe caps as assembly objects， employing the YOLOv8n-seg model to detect and generate segmented images； Combines depth images with the FoundationPose model to determine the pose of parts in 3D space， enabling visual recognition and positioning of assembly components. We further propose a pose alignment method that extracts part axes based on recognition results， calculates the target pose of the end-effectors of the robotic arms， and generates corresponding motion waypoints to accomplish vision-based grasping and peg-hole alignment. During the assembly phase， we introduced a helical hole-finding strategy to achieve compliant assembly of peg and hole components to complete the entire dual-arm assembly process. The entire system is integrated using Robot Operating System， achieving full automation from visual perception and grasping alignment to peg-hole insertion.Experimental results demonstrate that the system can effectively and consistently complete the dual-arm peg-in-hole assembly task.

In precision assembly， research on the contact state and resulting non-ideal assembly pose variations during the mating of part surfaces primarily focuses on algorithm prediction， with less attention paid to detection methods for actual minor pose variations and part contact states. This paper proposes an opto-electronic collaborative detection method for contact state and pose inprecision assembly. It employs a light signal device to detect the contact state of parts during assembly through circuit on/off states， and utilizes three laser displacement sensors to measure non-ideal assembly pose variations caused by parts in contact. Compared with related prediction algorithms， this method effectively detects part contact states and assembly pose variations.

To address potential limitations in existing attention mechanisms， such as insufficient learning capability and inadequate focus on critical targets， as well as problems in traditional steel surface defect detection networks like low detection accuracy and single-scale feature extraction， a novel iterative Recurrent Channel Attention （RCA） mechanism is designed. This mechanism independently calculates attention weights along horizontal and vertical directions， enhancing the model's positional awareness of features. By employing a recursive strategy to iteratively refine the fused results and reapplying the generated weights to input features， RCA significantly strengthens the network's ability to capture key feature information within similar targets. This substantially improves detection capability for objects at various locations and scales. The RCA mechanism is integrated into the YOLOv8 network architecture， resulting in an improved steel surface defect detection network based on YOLOv8n. Firstly， Switchable Atrous Convolution （SAConv） is introduced to expand the model's receptive field and enhance its perception and adaptability to multi-scale features， enabling more precise feature extraction. Secondly， an adaptive weighted feature fusion module is incorporated into the Neck section of the network， effectively combining global and local features to strengthen multi-level feature fusion capabilities. Finally， the designed RCA mechanism is implemented， followed by extensive ablation and comparative experiments. The experiments demonstrate that the YOLOv8 model with only RCA achieves an mAP@0.5 of 81.1% on the NEU-DET steel surface defect dataset， representing a 3.4% improvement over the baseline model. On the GC10-DET dataset， it achieves an mAP@0.5 of 63.2%， a 1.7% improvement. The complete YOLO-SCR network reduces computational complexity by 12% compared to the baseline model. It achieves an mAP@0.5 of 84.0% on NEU-DET （a 6.3% improvement） and 64.1% on GC10-DET （a 2.6% improvement）， achieving a better balance between detection accuracy and inference speed.