Cold chain transport is the process of keeping goods at low temperatures from one location to another throughout the supply chain， used mainly in food， pharmaceutical， biomedical and other industries to ensure the quality and safety of goods， and the reasonable planning of transport is thus crutial for reducing logistics costs and carbon emissions and improving transport efficiency. This paper establishes a mathematical model for the optimization of cold chain logistics transport paths， taking into account the distance between different customer points， service time window， cargo demand and other factors， in order to achieve the goal of minimizing the total cost of transport， including fixed costs， transport costs， refrigeration costs and so on. The model is then solved using the improved seagull algorithm， which is a bionics-based optimization algorithm that simulates the behavior of seagulls when foraging， and the algorithm has global search capability and adaptivity. During the implementation of the algorithm， this paper considers the iterative parameter setting and convergence control of the algorithm to avoid falling into the local optimal solution and to ensure the reliability and the efficiency of the experimental results. The effectiveness of the cold chain logistics path optimization method based on the improved seagull algorithm is verified through the application of experimental examples and the comparative analysis of different algorithms.

In astronautics manufacturing industries， muti-robot cooperation for transporting massive and large-sized workpieces improves transportation flexibility and adaptability. By combining global and cooperative locating information， a multi-robot cooperative transport system based on leader-follower formation control is proposed. The leader robot tracks the trajectory according to the points searched in path set， and speed is planned by the cubic curve to reduce the impact caused by pose deviation. Speed commands sent to followers are calculated according to the relative deviation and speed feedback from the leader. The relative deviation is calculated by using the data measured by lidars installed on the followers. Considering the dynamic input of the follower at each control period， back propagation neural network （BPNN） is used to calculate the speed control parameters. Formation control data are transmitted by 5th generation mobile networks （5G） due to its large bandwidth， low latency， ultrareliable connection， and multiservice slicing capacities. An experimentation platform with three omnidirectional mobile robots is established， and the experiment result shows the high stability of entire formation work by using the control method.

To improve the path planning efficiency of mobile robots and solve the shortcomings of Gray Wolf Optimization algorithm such as low convergence efficiency and suscepitability to falling into local optimal in path planning obstacle avoidance， a Particle Swarm Optimization improved Gray Wolf Optimization algorithm is proposed. Firstly， Tent chaotic mapping is first added to initialize the initial population to increase the diversity of the population and thus improve the convergence speed. Secondly， the nonlinear convergence factor improvement strategy is added to improve the efficiency of global search while reducing the local optimal solution. And then， the particle swarm location updating strategy is applied to the gray wolf population location updating to enhance the autonomous searching ability of individual gray wolves. Finally， compared with PSO and GWO， the improved algorithm has superior convergence performance and optimization accuracy. Simulation results show that the improved algorithm is superior to other algorithms in average path length， iteration times and search time.

The stable control problem of aerial robots under uncertainties arising from modeling errors and environmental disturbances is investigated. A novel fuzzy adaptive non-singular terminal sliding mode controller is proposed for an aerial robot to achieve stable motion through the use of a fuzzy rule interpolation estimator. Particularly， uncertainty modeling and environmental disturbance are implemented by a fuzzy estimator， where fuzzy rule interpolation （FRI） is adopted to improve the accuracy and converge rate of estimator with a sparse fuzzy rule base. Based on the fuzzy rules interpolation estimator， a novel fuzzy adaptive non-singular terminal sliding mode controller （NTSMC） is introduced to ensure stable control of aerial robots under uncertainties. The stability of the proposed estimator and controller is analyzed through the Lyapunov theory. The feasibility and the performance of the proposed controller are validated via comparative experimental simulations， demonstrating its stable control ability and good robustness of aerial robots working under uncertainties.

Aiming at the mechanical resonance problem in the dual inertia system， a fuzzy sliding mode control method based on unknown input observer is proposed in this paper. This method mainly includes two parts ： the unknown input observer and the fuzzy sliding mode controller. The observer part is designed based on the principle of invariant manifold to observe and compensate the transmitted torque. On this basis， the sliding mode controller is designed to ensure the tracking accuracy of the output signal. In order to eliminate the chattering problem of the traditional sliding mode control itself， a combination of fuzzy control and sliding mode control is proposed at the controller part， which greatly improves the dynamic responsive performance of the system. Finally， the effectiveness of the proposed method is verified by simulation.

To further improve the working efficiency and smoothness of the loading and unloading robot， a time-optimal composite polynomial interpolation trajectory planning method based on the improved zebra algorithm is proposed with the cigarette loading and unloading robot as the research object. The trajectory is interpolated by a composite polynomial of fifth degree and sinusoidal acceleration； the zebra algorithm is optimised by Tent chaotic mapping and zebra dynamic guidance strategy， and the trajectory planned by the composite polynomial is optimised by the improved zebra algorithm with the minimum time as the optimisation objective. Validation is carried out through simulation experiments， and the results show that the optimised motion time is reduced by about 34.30%. The convergence speed of the improved Zebra algorithm is significantly improved， which is conducive to quickly jumping out of the local optimal trap； the robot motion is continuous and smooth， and the curves of angular displacements， angular velocities， and angular accelerations of the joints are smooth and without sudden changes. The smoothness of robot motion is maintained while the motion time is optimal. The working efficiency and stability of the cigarette loading and unloading robot are improved.

Axial piston pump works for a long time in the harsh environment of high speed and high pressure， and its key parts inside often fail， resulting in the failure of the whole hydraulic system. In this paper， fault noise source location method and fault characteristics analysis of axial piston pump are proposed using a self-made non-contact microphone array （acoustic array）. Firstly， a signal source location and spatial filtering model based on array are established. Secondly， four kinds of faults such as plunger fault， plate fault， swash plate fault and return plate fault are designed and studied experimentally. Finally， the time domain index is extracted by the filtered signal using the location results. The experimental results show that the proposed method can locate the fault source， and the noise signal index after spatial filtering can reflect the fault characteristics of different parts and guide the fault diagnosis of axial piston pump effectively.

To improve the accuracy and efficiency of detection in discrete manufacturing workshops and solve the problems such as difficulties in both detecting multi variety and variable batch data and capturing time series existed in the traditional anomaly detection methods， this paper proposes a quantum particle swarm optimization algorithm weighted Transformer GAN （QPSO-TGAN） model for anomaly detection in discrete workshops. In this model， the Transformer in the generator simulates the normal mode of time series data， while the Transformer in the discriminator captures the intrinsic characteristics of time series data to learn the difference between normal and abnormal modes. It is combined with quantum particle swarm optimization algorithm to iteratively optimize parameters and improve the ability of discrete workshop anomaly detection. The proposed model is tested using real data from discrete workshops， and the results are compared with KNN， RNN， VLSTM， LSFL， DAGAN， and Transformer models. The model has higher accuracy， recall and F1 score than the comparison model. Exhibiting an excellent performance in anomaly detection， the model can be effectively applied in discrete workshop anomaly detection scenarios.

Compared with mechanical bearings， magnetic bearings have the advantages of non-contact， no wear， no lubrication， long life， suitable for high-speed movement and active control of performance parameters. When maglev rotor rotates at high speed with gyroscopic effect， the coupling of position and attitude presents complex dynamic characteristics. The accuracy of dynamic modeling directly affects the control accuracy and stability of the control system. Based on the dynamic modeling of the maglev rotor in the static lift-off and stable suspension stage， this paper proposes a parameter adaptive control scheme for maglev rotor to address the problems that the actual dynamic parameters of maglev rotor are quite different from the design parameters， and the traditional PID control is difficult to ensure the robustness of the system. This scheme aims to adaptively identify the real model parameters in the control process. The stability of the system is proved by Lyapunov criterion. The simulation results show that the improved algorithm can effectively improve the control performance of maglev rotor.

Tension control is a critical technology in the production line of biaxially stretched film， as it directly influences the quality of the final product. To address the issue of tension fluctuations in thin films caused by tower rotation during roll changes—an effect that compromises finished film quality—this paper introduces a feedforward speed compensation strategy. This approach involves calculating the cumulative speed imparted to the film due to tower rotation during roll transitions and designing a tension control system based on a feedforward-PID framework informed by this calculated relationship. Comparative simulation experiments demonstrate that this system outperforms traditional PID controllers， yielding significant improvements in film production efficiency.

In addressing the issue of measuring the dimensions of the triangular mating area of automobile body components in the context of automated quality inspection for the entire vehicle， a non-calibration-based measurement method using image processing has been proposed. This method comprises two main components： image distortion correction and dimension measurement. Firstly， tangential distortion is corrected by designing a reference board. Secondly， radial distortion is estimated and corrected using pixel feature information to enhance measurement accuracy. Finally， a method for extracting the contour of the triangular mating area is designed to determine the maximum inscribed circle size， thereby improving measurement stability. Experimental results demonstrate the successful correction of a certain degree of radial and tangential distortion errors. The measured maximum inscribed circle size deviation for the triangular mating area is within 0.2 mm， achieving a success rate of 98%. This research provides a high-precision， efficient， and straightforward measurement method for automated quality inspection of entire vehicles.

To optimize unloading convenience and efficiency， a three-dimensional packing algorithm for combined unloading is proposed. This algorithm defines the adjacency criteria between boxes and the concept of mutually exclusive unloading combinations. It establishes an unloading-oriented packing model with optimization objectives of mutually exclusive unloading combination rate， space utilization rate and packing rate. Using graph theory， sub-algorithms for adjacency detection， undirected graph conversion， BFS-based unloading combination generation， and mutually exclusive combination screening are designed. The Taguchi method is used to configure the improved genetic algorithm， combined with NSGA-II for multi-objective optimization. Tests on various unloading combinations show that the algorithm effectively optimizes cargo grouping， enhancing unloading efficiency and logistics economics.

Aiming at solving the problem of uneven and unstable gas jet impact in the current Mini/Micro LED pneumatic mass transfer， a blowing scheme based on multi-hole was proposed， which used the gas jet to pass through the multi-hole glass plate， and at the same time impact the film to deform and produce microbubbles， so as to reduce the contact area between the chip and the film， so as to realize the transfer of the chip to the target substrate， and the flow field characteristics of the gas jet in the working process were studied from two aspects： simulation and experiment. Convection analysis in the COMSOL Multiphysic software yields gas driving patterns for different numbers of orifice. The test platform was designed and tested， and the test results showed that the jet hole was arranged in the center and the contact area between the jet hole and the chip was larger， the better the driving effect and the high precision of the chip was realized and the high-precision and damage-free transfer of the chip could be realized. In the environment where the air pressure is 1.0 MPa， the gas duration is 0.3 s， and the spacing between the outlet and the through-hole glass plate is 0.1 mm， the actual transfer yield of this scheme reaches 99.9815%.

To improve the quality and efficiency of assembly and ensure the consistency in product quality， the assembly actions of workers are recognized and monitored. This paper proposes a fusion assembly action recognition network with Longformer's spatiotemporal separated attention. Through the spatiotemporal separated attention structure， Longformer attention encoder and Transformer attention encoder are used separately to extract appearance and motion features of the video， effectively integrating spatiotemporal information in long video sequences while reducing the computational complexity and network parameters. Experimental results on an assembly action dataset show that our approach outperforms the convolution-based SlowFast network in extracting global video features， achieving a 2.44% improvement in Top-1 accuracy. Compared to the Transformer-based TimeSformer network， Top-1 accuracy is improved by 0.45%， and parameters are reduced by 65.9%， while enabling effective recognition of worker assembly actions with longer video sequences.

The rotor bearing is subjected to huge axial force and overturning moment in the work， causing the load concentrated often in the channel away from the overturning moment axis， resulting in the channel at these positions prone to plastic deformation or fatigue damage， seriously affecting the bearing capacity and service life. Therefore， a kind of non-planar raceway turntable bearing is proposed. Firstly， the mechanical calculation model of turntable bearing with arbitrary shape raceway is established by vector expression method for theoretical analysis， and the load distribution of traditional circular raceway is optimized for maximum life. The optimal non-plane raceway profile is obtained through decomposition and regulation， and the load distribution under non-plane raceway and traditional circular raceway is compared. Then the non-plane raceway is verified by finite element simulation and corrected considering the ring deformation. The results show that the maximum load of the non-flat raceway turntable bearing is significantly lower than that of the traditional circular raceway turntable bearing， the load distribution is more uniform， and the service life is increased by 8%~15%. This paper provides a new way to even and reduce the contact load of the raceway of the turntable bearing.

For the need of detecting overexposed regions， a saturated pixel detection method for overexposed images based on Principal Component Analysis （PCA） and Logistic Regression is proposed.By analyzing the salient features of overexposed images， the variables such as brightness and color features， saturation features corrected by human vision， spatial neighborhood features， local entropy features， and grayscale contrast features are selected as the initial indexes for detecting overexposure of images； the original index variables are downscaled by using principal component analysis， and then analyzed by using the established logistic regression model with L2 regularization. Finally， it is compared and analyzed with other overexposure detection algorithms， and the effect of overexposed region detection is verified in a security monitoring image.The results show that the detection results of this model are more holistic and the detection area is more compact， which is more in line with human's visual perception of overexposed areas.

Aiming at the issues of both the high accuracy requirements for identification and positioning of the refractory brick in the automated gripping of coke oven masonry robot and the limited computing resources of masonry equipments on site， a lightweight algorithm is proposed for identifying and positioning the refractory bricks. First， to target misdetection or omission due to the tight arrangement of dry-laid refractory bricks， the self-made refractory brick dataset is enhanced by a multi-boundary feature stochastic synthesis method. The global attention module （GAM） and the loss function Wise-IoUv3 are embedded in the YOLOv8 model to improve the feature extraction capability and the anchor frame quality. Second， to address the problem of limited computing resources of the equipment， the model is pruned by a parameter magnitude-based pruning algorithm to reduce the computational and parametric quantities of the model； Then， based on 2D identifying information， the positioning depth information obtained by a lightweight depth information threshold segmentation algorithm based on point cloud data is fused to accomplish accurate identification and positioning of refractory bricks； Finally， the test is validated by building an experimental platform for coke oven masonry robots. The experimental results show that the recognition mAP@0.5 of the algorithm in this paper reaches 98.83%， while the Params and the FLOPs reach 0.211 M and 0.856 G， respectively， which is an increase of 0.21 percentage points compared to the YOLOv8 model mAP@0.5， and a decrease of 92.99% and 89.56% for Params and FLOPs， respectively. The average relative measurement error is 0.28% when measuring the depth information from 800 mm to 1000 mm. The algorithm in this paper can ensure the recognition and localization accuracy while reducing the requirements of the equipment on computational resources， providing technical support for coke oven masonry robots to realize the automation applications.

The measurement of workpiece contour distances significantly influences the ensurance of machining quality. However， traditional measurement methods often exhibit lower efficiency and higher costs. Conventional machine vision measurement methods commonly encounter issues related to insufficient measurement accuracy and limited stability. To enhance the quality of manufacturing， this paper proposes a measurement system for high-precision workpiece contour distance based on dual telecentric imaging. Initially， an analysis， modeling， and overall structural design of the measurement system are conducted in this paper using dual telecentric imaging. Subsequently， an improved adaptive threshold Canny edge detection algorithm is proposed for preliminary edge detection on the target image to enable the initial edge detection on the merged image， followed by coarse edge positioning. Then， edge gradient interpolation information is obtained using a cubic spline interpolation algorithm， and curve fitting is applied to achieve sub-pixel coordinates of the edges. Finally， the RANSAC algorithm is utilized for contour fitting to complete workpiece contour distance detection. The algorithm presented in this paper demonstrates high accuracy and reliability in standard component detection experiments. It accurately performs measurements on standard components of various sizes， while exhibiting minimal repetitive errors during large-scale inspections. This provides a feasible solution for efficient and precise industrial part contour detection.

To improve the accuracy of coal and gangue identification in the process of top coal caving， a multi-channel information fusion method for coal gangue identification combining variational mode decomposition （VMD）， principal component analysis （PCA） and convolutional neural network （CNN） is proposed. Firstly， the coal gangue slip test bench of caving hydraulic support is built， and the vibration data of coal gangue in multiple sensor channels are collected. Secondly， the collected data are decomposed into intrinsic mode function （IMF） using VMD， and the time domain and frequency domain features of each IMF component are calculated respectively. Then， the features are reduced by PCA to obtain the feature vector of coal gangue vibration signal after dimension reduction and construct the feature data set of coal gangue vibration signal. Afterwards， the CNN model is used to train different channel data respectively. Finally， the weighted average method is used for multi-channel information fusion to conduct comprehensive evaluation and identification. The results show that the method has a high accuracy of coal gangue identification， the accuracy rate can reach 97.15 % after information fusion， and the coal and gangue identification effect is good.

To reduce measurement errors caused by uneven heating of the graphite base in high-temperature contact angle measuring instruments， a Comsol finite element model was first established based on the principle of induction heating of the device. The influence of parameters such as the current frequency， the thickness of the graphite base， and the distance between the lower surface of the graphite base and the center of the coil cross-section on the upper surface temperature difference of the graphite base was investigated using the controlled variable method. The experimental results showed that the standard deviation of the temperature on the upper surface was directly proportional to the current frequency and the base thickness and inversely proportional to the distance between the lower surface of the graphite base and the center of the coil cross-section. Subsequently， response surface analysis was performed on the interactions of multiple parameters using the Design-Expert software. Based on this， the optimal parameters were determined using the optimization module of the software： a base thickness of 2 cm， a current frequency of 306 kHz， and a distance of 3.65 cm between the lower surface of the graphite base and the center of the coil cross-section. Finally， the optimal parameters were imported into the finite element model， and the structure of the coil was optimized. The post-processing results of the optimized finite element simulation indicated that the temperature fluctuation in the working area on the upper surface of the graphite base was a minimum of 0.16% and a maximum of 0.33% under different currents， demonstrating good uniformity of temperature distribution.

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.

This study proposes a novel method for optimizing the scheduling of Iron and Steel Enterprise Energy Systems （ISES）. Given the diversity of energy equipment and the frequent fluctuations in energy demand within iron and steel enterprises， the proposed method coordinates various energy-consuming devices， with particular emphasis on the performance variations of these devices under different operating conditions， aiming to provide a more efficient and economical energy supply. The innovative point of this study lies particularly in its focus on the performance variations of equipment under different operating conditions and its incorporation of carbon emission costs into the objective function for the first time. By employing adaptive piecewise linearization technology to address nonlinear constraints， it effectively balances solution accuracy and computational complexity. Through analysis of a real-life ISES case study， this research demonstrates the significant benefits of the method in improving energy utilization efficiency， reducing operational costs， and mitigating carbon dioxide emissions. This study offers an effective pathway for iron and steel enterprises towards lower-carbon and more cost-effective operations.

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 fully harness wind energy in urban environments， lift-drag hybrid vertical axis wind turbine （known as DCS-VAWT） has been designed. This turbine addresses the issue faced by lift-type vertical axis wind turbines， which struggle to self-start at low wind speeds. Computational fluid dynamics （CFD） simulations have been employed to evaluate both the dynamic self-starting performance and steady-state performance of the DCS-VAWT. Unlike traditional static CFD simulation methods， a passive CFD approach with fluid-solid coupling has been used to simulate the transient self-starting process of the wind turbine. This simulation accurately captures the turbine's transition from a stationary state to steady operation. The results demonstrate that the DCS-VAWT can successfully self-start even at low wind speeds of 5 m/s. The self-starting performance of the DCS-VAWT improves as the diameter of the internal drag-type rotor increases. However， it is worth noting that the steady-state performance decreases with this increase in rotor diameter. Furthermore， the two-bladed DCS-VAWT exhibits better selfstarting performance compared to the three-bladed version due to factors such as rotational inertia and aerodynamic external shape. Through optimization， the two-bladed DCS-VAWT has achieved successful self-starting within 16 seconds under an oncoming wind speed of 5 m/s. The maximum wind energy utilization efficiency was found to be 0.261， achieved at the optimal tip speed ratio of 2.5. These findings contribute to the development of efficient wind energy utilization in urban environments.

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.

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.

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.

The identification of the starting point of the welding is an important step in the intelligent welding process. Due to the influence of fixture positioning error and workpiece deformation， the actual position of the welding starting point often deviates greatly from the theoretical position of the workpiece. Taking the common "three-sided corner joint" weldments as the research object， a two-stage 2S-RANSAC algorithm was proposed by using a line structured light camera， which realized the fast and accurate identification of the welding starting point. In the first stage of the algorithm， the position of the weld corner point was found， while a "three-point sampling method" was proposed to carry out a pre-test of the model so as to improve the accuracy of point cloud sampling and the efficiency of the algorithm. The experiments show that the efficiency and the accuracy of the 2S-RANSAC algorithm were increased by 67% and 43% compared with the traditional RANSAC algorithm. With the recommended point cloud data acquisition scheme， about 10 groups were collected at intervals of 10 mm on both sides of the starting point， and the weight accuracy of the welding starting point fitting could be controlled within 0.34 mm， meeting the process requirements of automatic argon arc welding of general "three-sided corner joint" weldments.

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.

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.

Irregularly shaped objects often appear inside the pipelines of nuclear power systems， and soft grippers with passive deformation ability are hence needed for grabbing operations. While the load capacity of the fixed stiffness soft gripper is usually low， the variable stiffness is an important way to improve the load capacity of the soft gripper. On the other hand， the force perception is very important for grasping fragile objects， but rigid force perception is difficult to integrate with soft grippers. Therefore， it is crucial to develop a flexible force perception function module that can be integrated with the soft gripper. Based on the characteristics of the tendon-air hybrid drive structure and the coordinated working relationship of the muscles on the upper and lower sides of the bionic human fingers， this paper proposes a design idea of a soft gripper that uses antagonistic action to change the stiffness of the soft fingers and integrates a force perception function. The designed soft gripper has three pneumatic chamber fingers， each of which is prepared by the distributed casting silicone rubber process， and the deformation process of a single pneumatic chamber finger is simulated using simulation software. To achieve contact force perception during grasping， a force sensing system based on Hall chip magnetic field intensity sensing is integrated at the tip of the soft finger， and the performance of the sensor is calibrated and tested. Through experiments， the adaptive grasping ability of the soft gripper for different target objects and the load capacity of the tendon-air antagonistic drive are tested. The experiments show that the tendon-air antagonistic drive can effectively improve the load capacity of the soft finger， and the perception module can achieve real-time measurement of the contact force of the grasping target.

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.

To realize the effective continuous target detection and tracking of the insulator flushing robot in the occlusion environment， a detection and tracking method based on computer vision in the occlusion environment is proposed. Firstly， the attention mechanism is added to the YOLOv5 detection algorithm to enhance the recognition accuracy of the detection algorithm， the scale filter in the DSST algorithm is then combined with the KCF tracking algorithm to make the KCF scale adaptive. Thereafter， the Multi-PROSAC-ORB occlusion recognition algorithm is constructed to realize the occlusion recognition. Finally， the above three algorithms are fused and an occlusion judgment condition is proposed to ensure the continuous and stable recognition and real-time performance of the target in the case of occlusion. The experimental results show that the proposed method can effectively avoid the low accuracy of target tracking in the occlusion environment while ensuring the real-time performance， and the target tracking accuracy is increased by 10.9% and the tracking success rate is increased by 13.6% compared with the target tracking accuracy in the unocclusion recognition， which has high accuracy and real-time performance.

In the field of modern logistics warehousing， carton identification is crucial for inventory management and logistics automation. Aiming at the shortcomings of traditional methods and some existing automation schemes in carton detection tasks， a new carton detection method based on improved YOLOv8 network is proposed. Firstly， an Adaptive Batch Normalization（ADBN） mechanism is proposed and introduced to the YOLOv8 backbone network， enhancing the feature extraction ability. The C2f-Faster-CGLU mechanism combining FasterBlock and Convolutional Gated Linear Unit （CGLU） is introduced into the YOLOv8 detection header， which improves the computational efficiency. In addition， a new boundary frame similarity comparison index based on the minimum point distance （MPDIoU） is introduced， which can evaluate the similarity between prediction and real frame more accurately. Finally， the improved network model is applied to Rectagular Stacked Carton Dataset（RSCD）， Online Stacked Carton Dataset（OSCD） and Live Stacked Carton Dataset（LSCD）. Compared with that of the original model， the mAP of the improved model is increased by 1.6%， and the recall rate is increased by 1.3%. The improved model has also improved performance compared with other mainstream detection algorithms，providing more accurate and efficient technical support for the object detection of modern logistics and warehousing industry.

This study addresses the issue of low efficiency in manually operated ship void compartment inspection robots by proposing a position-based visual servo control method for an eight wheel-legged ship void compartment inspection robot. The method utilizes a global threshold segmentation technique to extract real-time geometric features of the ship void compartment doors from depth images， enabling the selection of sampling points for calculating the robot's relative pose to the void compartment. This approach provides the control system with highly responsive visual feedback. A hierarchical controller is designed for the robot's eight steering wheels， with an upper layer comprising decoupled controllers for lateral， longitudinal， and rotational motions， and a lower layer consisting of target velocity tracking controllers for the steering wheels， enabling the omni-directional movement control. The method is validated through experiments in a simulated environment. The results indicate that in the simulated experiment， the robot maintains lateral errors below 2 cm and angular errors below 0.02 radians， accurately executing obstacle avoidance actions upon reaching the predefined poses. In the physical experiments， the method， compared to manual operations， reduces inspection time by 69.74%， and increases the door passage rate by 59.24%， validating the effectiveness of the proposed method.

Unmanned aerial vehicles （UAVs） can be used as signal relays， and outdoor UAVs can provide signal coverage for indoor users in emergency situations. The existing research on UAV signal coverage mostly focuses on indoor users in specific locations or outdoor users， and the proposed methods available are not suitable for the signal coverage scenarios of indoor users. In the case of the random distribution of indoor user locations， a single-hovering-plane and multi-hovering-plane particle swarm optimization （PSO） algorithm for UAV deployment is proposed to determine the optimal hovering position and transmission power of a single UAV， thereby optimizing the deployment strategy of a single UAV. Furthermore， to address the issue of limited transmission power of a single UAV， a method combining K-Means and PSO is proposed to achieve multi-UAV signal coverage for indoor users. The simulation experiments show that the proposed algorithm reduces the power required for a single UAV to cover indoor users in single-UAV scenarios and optimizes the transmission power and number of UAVs in multi-UAV scenarios.

Accurate force/position control in the presence of modeling uncertainty in robots is a challenging task. This article is based on improved integral sliding mode control for hybrid visual & force control. To perform visual servoing， an optimized visual feature is adopted to avoid the ill-condition visual Jacobian matrix. An improved super-twisting algorithm is proposed based on time delay estimation for integral sliding mode control， and an improved sliding surface and convergence law is used for the positive definite part of integral sliding mode control. Correspondingly， visual control method and force control method are proposed to apply to the hybrid visual & force control framework. To avoid the impact of noise from the force control part on the control output， a visual admittance framework is used for hybrid control. To optimize the solidification impedance characteristics caused by fixed admittance parameters， a fuzzy adaptive admittance framework is proposed to inherit the advantages of admittance while adaptively adjusting parameters in real-time. Finally， a 6-degree-of-freedom deviation model is used for simulation to verify that the proposed scheme can accurately track visual trajectories and expected forces under relatively small chatting. The performance of the three with different focuses is compared based on direct visual perception hybrid control， visual admittance control framework， and fuzzy adaptive admittance framework. Under the deviation model， the proposed i-CISMC algorithm is validated by surface force tracking to have better tracking accuracy while ensuring smaller control chattering； the proposed algorithm is combined with the fuzzy adaptive admittance framework， and the results of tracking under noise have proved that the framework inherites the suppleness of the admittance framework to the disturbance of the force loop， and at the same time， can adaptively change the parameters of the admittance to obtain better tracking speed and tracking accuracy.

This paper addresses the challenge of inadequate timeliness and accuracy in processing multi-source heterogeneous data within special equipment assembly workshops， which tends to hinder real-time transparent management and control during the assembly manufacturing process of specialized equipment. To tackle this， a method for fusing multi-source heterogeneous data in these workshops is proposed with the approach based on an analysis of the composition and characteristics of operational data in these workshops， and the benefits of Multi-Agent technology is applied to construct a framework for data fusion. The research on the methods involved in data layer fusion and feature layer fusion is also conducted. The feasibility and effectiveness of the proposed method are confirmed through simulation examples， thereby providing reliable， timely， and accurate data support for the intelligent operation and control of special equipment assembly workshops.

Aiming at the problem of poor hole quality caused by excessive axial force in drilling aluminum alloy， the simulation models of drilling 6061 aluminum alloy with step drill and traditional twist drill were established， and drilling experiments were carried out to analyze the drilling quality in the two drilling cases and to verify the simulation model. The tool structure was closely related to the quality of the hole. The step drilling simulation with different diameter ratios \lambda （the ratio of the first diameter to the second diameter） was conducted to compare the axial force and average burr height during the drilling process. On this basis， the response surface method was used to optimize the geometric parameters of the step drill tool. Taking the first apex angle θ ₁， the second apex angle θ ₂ and the spiral angle β as variables， the influence of different factors on the axial force was analyzed and the optimal parameter combination was obtained. The results show that the modified step drill has smaller axial force and better processing performance than the traditional twist drill. When the diameter ratio of the step drill is 0.6， the optimal geometric parameter combination of the tool is θ ₁=90°， θ ₂=106° and β=28°.

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.