In order to adapt to the characteristics of complex electronic equipment， such as multi-variety， variable batch， multi-level blind matching and vertical interconnection， a six-degree of freedom heterogeneous assembly robot arm has been independently developed and applied to the assembly line of complex electronic equipment. Firstly， the structure composition， configuration advantages and problems in practical application of the heterogeneous six-axis manipulator are introduced. Secondly， the forward and inverse kinematics algorithm of the heterogeneous six-axis manipulator is established by using D-H parameter method， and the kinematics model of the heterogeneous six-axis manipulator is constructed. Then the calibration algorithm， trajectory planning algorithm， collision control algorithm and other methods of the heterogeneous six-axis manipulator are studied. Finally， the field calibration experiment and MATLAB simulation analysis are used to verify the motion planning method， which proves the rationality and practicability of the relevant methods.

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.

Aiming at the pain point of slow traditional rescue response in water areas， this paper proposes a structural design scheme of an amphibious rescue equipment that integrates the functions of unmanned aerial vehicles （UAVs） and rescue boats. The UAV adopts a lightweight fuselage and NACA4412 airfoil aerodynamic design. Through the rotation of wings and elescopic mechanism of the counter-rotating propellers， a rapid cross-medium form switching can be achieved. Meanwhile， the dynamic performance is analyzed by establishing the mathematical model and state-space model of the drone， and a fuzzy PID controller is designed. MATLAB/Simulink is used to carry out dynamic response simulation verification for the mathematical model of the drone and the designed fuzzy PID controller. The results show that when the input is a square wave and a step signal， the fuzzy PID controller designed in this paper has a faster response speed and better stability compared with the traditional PID controller.

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.

Facing the requirements for diverse starting and ending positions as well as velocity in the field of robotic motion control， this paper proposes a novel universal S-curve velocity planning algorithm aiming at adapting to arbitrarily specified starting and ending positions and velocity conditions. The paper first introduces the velocity-to-velocity S-curve velocity planning algorithm， then elaborates on the general 7-segment S-curve velocity planning algorithm. Based on this， a more versatile S-curve velocity planning algorithm is proposed. For different input parameters， this paper classifies the S-curve （s-t curve） into ten types and provides detailed segmented planning strategies for each type. Through simulation tests， it is verified that the algorithm not only excels in efficiency but also demonstrates outstanding performance in the smoothness and positional accuracy of the planned curve. Furthermore， tests conducted on actual robotic platforms further confirm that this algorithm can effectively reduce shocks and vibrations during robotic operations， significantly enhancing the operational performance of robots while demonstrating good practicality and broad application prospects.

To address the problems of scattered knowledge systems in ship assembly and ineffective mining and utilization of massive process data， this paper proposes an automatic knowledge graph construction technology for the shipbuilding domain based on large language models. This method uses large language models to convert unstructured and semi-structured ship data into structured data to build a ship process corpus. It models ship ontology knowledge structure with the assistance of large language models， designs an instruction prompting framework for ship assembly domain， and achieves efficient entity-relationship extraction， to complete the automatic construction of knowledge graphs. Additionally， the method uses triple sets constructed by general large language model instruction prompts as fine-tuning training sets to further fine-tune specialized small language models， ensuring the security of specific private ship data while reducing computational resources. Experimental results show that this method outperforms traditional baseline models in key metrics such as accuracy， providing a new technical approach for knowledge management and intelligent upgrading in the shipbuilding domain.

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.

The quality of screw tightening greatly affects the safety of mechanical products， whereas traditional diagnosis approaches are time-consuming and imprecise， and the implementation of effective fault diagnosis， therefore， bears significant engineering value. In this paper， an innovative method of fault diagnosis for screw-tightening based on LSTM and Expert knowledge is proposed. Firstly， tightening process curve under specific failure mode was studied and several expert knowledge rules were established. Secondly， a data pre-processing algorithm was established based on the characteristics of sequential data such as noise clipping， stage segmentation， fitting and sampling to improve the quality of data. After that， the feature vector obtained through LSTM was used as the input of the expert knowledge model to obtain the expert knowledge vector， and the two vectors were combined as the input of the classifier. Finally， compared with SVM and LSTM， the results show that the method has higher diagnostic accuracy in multiple failure modes.

To solve the problems of small molding size and low printing efficiency of traditional desktop-level single-nozzle FDM 3D printer， a medium-sized FDM multi-nozzles collaborative 3D printer is designed and built. The printer adopts a Cartesian （XYZ） structure and is equipped with three side-by-side composite printing nozzles， and the materials can be selectively extruded according to the demand. The control system is divided into three parts according to the functions： main motion control module，embedded auxiliary measurement and control module and upper computer software module，while the software and hardware of these three parts are developed.Two printing modes of multi-nozzles synchronous forming and multi-nozzles stackable co-filling are designed and the corresponding path planning algorithms are proposed.After printing verification， compared with single-nozzle printing， the synchronous forming efficiency of the composite multi-nozzles printer is increased by 3 times， whereas the stackable co-filling printing time is reduced by 41%. The printing efficiency is significantly improved under the premise of ensuring the printing quality.

By analyzing the existing gangue sorting manipulator claw and its use， it is concluded that the existing claw has a large weight， is susceptive to wear and tear as well as higher cost of the overall replacement. A method is hence adopted to install replaceable wear-resistant shims and to select lighter quality materials for improvement. The finger force analysis is made before and after the improvement through the Ansys Workbench， and the improved finger effect proves to be better， verifying the feasibility of the installation of replaceable wear-resistant shims， while determining the replaceable wear-resistant shims material being 20CrMnSi， and finger base material being TC4. The fatigue life analysis is made for the finger before and after the improvement using fatigue analysis tools， and the conclusion is drawn that the fatigue life of improved finger matrix is longer， and the replaceable wear-resistant shims begin to fail after being used 4.3794×105 times， and are therefore needed to be replaced after about two months of use.

In the industrial software ecosystem， Computer-Aided Design（CAD） interfaces play a pivotal role. This study outlines the composition and collaborative mechanisms of the industrial software ecosystem， reviews the evolutionary trajectory of CAD interface technologies， and summarizes their core roles within the ecosystem from the perspectives of data transmission， functional integration， and innovation-driven development.Building on this foundation， an in-depth analysis of the application bottlenecks and challenges faced by CAD interfaces is conducted， including data interface standards， the depth of system integration， and the convergence with emerging technologies. Furthermore，future development trends for CAD interfaces are explored， emphasizing key directions such as data standardization and semantic enrichment， multi-user collaborative design with real-time interaction， and the deep integration of artificial intelligence technologies. This work aims to provide theoretical insights and practical guidance for the research and application of CAD interfaces within the industrial software ecosystem.

Heat exchangers play a crucial role in improving the energy efficiency of industrial processes， reducing fuel consumption， and decreasing greenhouse gas emissions. This paper addresses the innovative design problem of heat exchangers with numerous parameters， variable structures and complex medium flow characteristics by proposing a generative design method for spiral tube heat exchangers. Firstly， it analyzes the design principles， the structural advantages， and the performance characteristics of spiral tube heat exchangers， introduces the application process of the generative design method， the design optimization logic， and the automated parametric model generation method. Then， through computational fluid dynamics simulation， it evaluates the thermal transfer efficiency and fluid dynamics performance advantages of the spiral tube heat exchangers. Finally， through structural mechanics simulation， it assesses the risk resistance performance advantages of the spiral tube structure under various operating conditions. The proposed generative design method achieves rapid optimization iteration of design solutions and rapid generation of heat exchanger models， providing the possibility for rapid exploration and design of high-performance spiral tube heat exchangers.

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.

Aiming at the aviation complex rotary parts manufacturing workshop in the material distribution often faced by the material supply is not timely， difficult path planning and other difficult problems， analysis of the workshop material distribution characteristics and constraints， with a time window of the vehicle path optimization problem as the basic model， to minimize the integrated trolley call number， the total distance travelled， the delivery time penalty cost of the distribution cost as the optimization objective， to build the material distribution path planning model， design a hybrid adaptive large neighborhood search genetic algorithm to solve the problem. A hybrid adaptive large neighborhood search genetic algorithm is designed to solve the problem， and the actual shortest feasible path between two workstations is obtained in advance considering the complexity of the logistics channel in the workshop， and then the distribution path planning between workstations is carried out. The optimization effect and performance of the proposed algorithm on different scale distribution problems are evaluated through the validation and comparative analysis of the actual cases in the workshop and the classical standard cases.

Detection on the surface defect of Pouch Cells is a key procedure of the production process. Aiming at the problems of low detection accuracy and difficult imaging of large-sized batteries in the existing detection methods， a detection method based on photometric stereo imaging and deep learning was proposed. Firstly， a Multi-Source Time-Sharing Exposure Imaging System （MSTIS） was established by combining photometric stereo and line scan camera imaging technology. After obtaining the surface images of batteries under different light sources through time-sharing exposure， photometric stereo calculation was conducted to obtain the curvature map with 3D information. Then， to solve the problem of missed detection of minor target and multi-scale defects， the YOLOv8 algorithm was improved. An edge information enhancement module （EIEM） was developed using a dual-channel convolution structure， which incorporated Sobel convolution and conventional convolution to improve feature edge extraction capabilities. The semantic and detail information fusion method （SDI） was integrated with the bidirectional feature pyramid module to boost the recognition accuracy of tiny defects. A lightweight shared convolution detection head was also implemented to reduce the algorithm's computational load.The experimental results show that the average detection accuracy of this method reaches 94.2% and the detection speed reaches 116 FPS， which can effectively detect the surface defects of pouch cells.

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.

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 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.

To solve the dynamic job shop scheduling problem in scenarios with variable job and machine quantities， a solution approach called Dense-D3QN， combining DenseNet， a densely connected convolutional network， with Dueling Double Deep Q-Learning （D3QN） is proposed. The disjunctive graph model is utilized to construct a single-objective job shop scheduling model aiming to minimize the maximum processing time， representing the scheduling state in the form of multi-dimensional matrices while designing a dense-sparse reward function. To validate the effectiveness of the proposed algorithm， both public benchmarks and real data are used to construct common and actual scheduling environments. The Dense-D3QN model is trained and tested in the common environment. In the actual environment， the Dense-D3QN model is trained and tested in both static and dynamic settings. The experimental results demonstrate that the Dense-D3QN model is more capable of handling dynamic job shop scheduling problems with variable scales.

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 need for both independent control of Continuous Damping Control （CDC） dampers and coordinated control of the entire vehicle semi-active suspension system， while also improving upon the issues present in traditional semi-active suspension controller software design such as challenges in meeting real-time requirements and low CPU utilization in bare-metal development environments， this study proposes an innovative approach. Initially， the study establishes separate models for the seven-degree-of-freedom semi-active suspension system and the forward-inverse models of CDC dampers. Building upon the skyhook control strategy， the study integrates a vehicle-coordinated parallel fuzzy controller based on the Mamdani fuzzy control method. Subsequently， by transplanting the FreeRTOS-SMP multicore real-time operating system and utilizing the Infineon AURIX series 32-bit triple-core microcontroller TC275 as the main control chip， the study designs the software and hardware system for the CDC damper control unit. Furthermore， the study conducts task scheduling verification of the multicore real-time operating system and validates the effectiveness of the designed control unit and proposed strategy through hardware-in-the-loop testing using typical random road surfaces to demonstrate the improvement in overall ride comfort of the vehicle.

Manual hammer riveting is widely used in the assembly of aircraft skin and truss. During the process of hammer riveting， the strong vibration and noise caused by the repeated impact of the riveting gun on the rivet seriously affects the physical and mental health of the operator and the precision of precision instruments. Therefore， a viscous damping vibration reduction fixture and a riveting gun sound insulation cover are designed to maximize the vibration and noise reduction effect and realize the efficient suppression of vibration and noise during the riveting process of thin-walled skin parts. The principle of viscous damping and sound absorption of perforated plate is deduced， the structural design of viscous damping vibration reduction fixture and sound insulation cover of riveting gun is carried out， and the modal test and hammer riveting experiment are carried out respectively to verify their effectiveness. The experimental results show that the vibration of thin-walled parts presents multi-modal characteristics， and the peak value of each mode of frequency response function is reduced by more than 52.87% after using the vibration reduction and noise reduction device. The maximum acceleration in the process of hammer riveting is reduced by 59.34%， and the adjustment time of sound pressure attenuation in hammer riveting is reduced by 32.45%. The sound pressure level of noise is decreased by 3.2dB compared with that of conventional hammer riveting， showing good vibration and noise reduction effect.

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 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.

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 application scenarios of large-scale equipment data communication in industrial sites requires a data communication solution with low latency， large capacity and high speed. This paper compares mainstream cellular IoT technologies， proposes an industrial site data collection technology solution based on the 5G lite technology RedCap， desigs a data communication terminal based on 5G RedCap technology， and verifies its feasibility through testing. The data communication terminal based on 5G RedCap technology can well meet the needs of industrial site data communication， is in a valuble position to be promoted and applied in the field of IIoT， and will push forward the development and evolution of cellular IoT towards end-network collaboration.

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.

Aiming at the problems of low sample utilization rate， slow training convergence speed， and poor path planning performance in deep reinforcement learning， an improved path planning algorithm based on Dueling DQN is proposed， and directional rewards and filtering strategies are introduced. According to the angle between the line connecting the current state and the next moment state and the line connecting the current state and the target point， the reward function is redesigned to alleviate the problem of sparse rewards. During training， the actions resulting in collisions with obstacles are put into the blacklist， so that the action can be filtered in the next round of action selection， and the exploration speed of the algorithm is improved. The experimental results show that the improved algorithm can effectively improve the efficiency of path planning， and the exploration efficiency of the agent in complex environment is increased by about 95%， so that the agent can reach the target point with fewer exploration steps and less time.

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.

In ABAQUS simulation analysis， the Young's modulus and Poisson's ratio are input as fixed values with temperature changes， which affects the reliability and accuracy of the results. Therefore， the temperature variation function is embedded into the ultrasonic vibration-assisted cutting （UVAC） simulation of titanium alloy TC4 by using the secondary development function of ABAQUS. The analysis results show that the secondary development carried out is practical and instructive. Compared with the ordinary cutting， the ultrasonic vibration-assisted cutting can effectively achieve chip breaking and reduce the cutting temperature， with the maximum reduction reaching 34%. With the increase of amplitude， the chip length gradually decreases， and the influence depth of amplitude on residual compressive stress shows a trend of first decreasing and then increasing； in contrast， the frequency has a smaller impact on the chip morphology. As the frequency increases， both the chip length and residual compressive stress decrease.

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.

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 problem that traditional convolutional neural networks cannot effectively extract global features and some deep learning models are more complex， this paper proposes a lightweight gearbox fault diagnosis method based on multi-scale convolutional neural networks and feature fusion ViT. Firstly， a multi-scale feature extraction module is constructed， which captures the feature information of the data from multiple scales by multi-scale convolutional neural network using different scale convolution kernels， and fully exploits the local features of the input information. Then， the feature fusion ViT module is designed， which utilises an improved multi-attention mechanism to capture the global features of the fault information， and further constructs the D-MLP to reduce the number of parameters in the model using depth-separable convolution. Finally， the experimental validation is given using gearbox data from Southeast University， and the results show that， compared with the comparison methods， the proposed method has high fault diagnosis accuracy and good generalization ability under complex conditions such as variable operating conditions and variable noise.

To tackle the problems such as excessive reliance on manual experience and low plan granularity in the production scheduling of multi-station assembly workshops， an intelligent production scheduling scheme is proposed. First， the scheduling problem is modeled as a Markov Decision Process （MDP）， with a deep reinforcement learning approach aiming at minimizing assembly task completion time. Next， a hierarchical multi-agent cooperation strategy is designed based on workshop characteristics， incorporating noisy networks and prioritized experience replay to enhance training efficiency. Finally， a proactive scheduling method is proposed to mitigate frequent adjustments caused by material shortages， assessesing material readiness time using inventory， delivery， and processing data，while integrating the results as a key input for scheduling. The simulation results demonstrate that the proposed algorithm achieves fast convergence， high stability， and optimal scheduling strategies.

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.

Query command on ship component is generally coded by database maintainers. When the maintainers lack professional background knowledge however， it would be difficult for them to understand query requirements and feedback query results. To solve this problem， database query for ship component based on large language mode and template sentence is proposed. First， the multi-discipline design model data and expertise knowledge are integrated， and the graph database is imported. Second， the Large language model is used to convert query requirements of natural language into template sentences， which are further converted into database query code and get the query result from database. This method does not require costly fine-tuning， but rather inherits the encoder capability of large language model. It replaces the decoder of transformer structure with ontology knowledge and template sentence to improve the controllability and accuracy of code generation. The test result of 408 natural language questions shows that the accuracy of the proposed method is as high as 90%， which can be applied to the ship operation and maintenance.