ISSN 1009-0134
CN 11-4389/TP
主管：中国机械工业联合会
主办：北京机械工业自动化研究所有限公司
CN 11-4389/TP
主管：中国机械工业联合会
主办：北京机械工业自动化研究所有限公司
中国科技核心期刊
中国期刊方阵双效期刊
机械行业优秀期刊
中国期刊方阵双效期刊
机械行业优秀期刊
Volume 47 Issue 12
25 December 2025
25 December 2025
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
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.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
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.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
The appearance dimension measurement of foamed plastic blocks requires both high efficiency and high precision. In response to the issues of low efficiency and poor precision in manual measurement of the linear dimensions of foamed plastic samples， a three-dimensional vision-based method for measuring the dimensions of foam blocks is developed. The point cloud is plane-segmented， and edge extraction is performed to obtain the edge model of the foam block， followed by the calculation of the linear dimensions. An adaptive compensation method for edge length calculation is proposed to address the defects in the edge point cloud， enabling compensation for different defective edges. This method realizes the automated， non-contact measurement of the linear dimensions of foamed plastic samples. Experimental results show that both the range error and the absolute error of the linear dimension measurements for foamed plastic samples are below 0.1 mm， with a single measurement time of less than 15 seconds. The measurement accuracy and efficiency are significantly superior to traditional manual methods， making it highly suitable for meeting the practical application needs in industrial settings.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
In advanced intelligent manufacturing， the detection of surface defects on spray-painted components is of paramount importance for ensuring product quality. Conventional photometric stereo vision method， grounded in the Lambertian reflectance assumption， is prone to significant reconstruction inaccuracies when applied to non-Lambertian surfaces， such as high-gloss spray-painted finishes， due to the presence of strong specular reflections. To address this limitation， this paper presents an enhanced photometric stereo vision method that integrates polarization theory. The proposed approach employs orthogonally polarized images to decouple the diffuse and specular reflection components inherent in the reflected light. The isolated diffuse component is then utilized to formulate an overdetermined linear system of equations for solving the surface normal vector field. Experimental evaluations demonstrate that， in comparison to traditional methods， this improved method effectively suppresses highlight interferences while achieving superior reconstruction fidelity for surface morphology. Consequently， it significantly enhances the reconstruction precision and robustness for non-Lambertian surfaces， offering a more reliable solution for surface defect inspection in precision manufacturing scenarios.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
In order to solve the problem of various types and scales of surface defects of cylindrical batteries，so as to quickly and accurately detect the defective products， a lightweight cylindrical battery surface defect detection model with complex feature enhancement is proposed. Firstly， a self-designed ESCA（Efficient Spatial and Channel Attention） attention mechanism is incorporated into the feature extraction part to enhance the capability of complex feature extraction of the model. Secondly， the C3_SC module is embedded into the feature fusion part to compress the redundant feature information in the network and reduce the consumption of computing resources by the model. Finally， the FL（Focal Loss） function is enabled and optimized as GFL（Generalized Focal Loss） to enhance the learning capability of the model for positive samples and further improve the detection accuracy of surface defects of batteries. The experimental results show that the average accuracy of the improved model is 92.6%，which is 6.3 percentage points higher than that of the original model. Additionally， the model parameters are reduced to 6.9%， and the reasoning speed reaches 89 F·S-1，which can meet the practical application of surface defects detection of cylindrical batteries.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
Flat wire motor solder joint defects have various categories and complex characteristics.Aiming at the problem of low detection accuracy of existing visual inspection models for some hard-to-classify defects， a solder joint appearance defect inspection model based on an improved RTDETR-r50 model is proposed. First， 995 images in the dataset are classified according to the actual welding process and defect characteristics. Then， the RTDETR-r50 model is improved as follows： the AFGC attention mechanism is introduced into the Backbone's BottleNeck module to strengthen multi-scale context representation through feature adaptive grouping and cross-group interaction； the RetblockC3 module is used to replace the original RepC3 structure to optimize multi-scale feature fusion； the Inner MPD IoU is adopted as the loss function to improve target positioning accuracy through multi-view geometric distance measurement and standardization. Experimental results show that， compared with the basic model， the improved model increases the average mAP75 by 4.1 %， with significant improvement in some difficult-to-classify defect categories. Meanwhile， it maintains a high real-time inference speed of 93.72 FPS， which can meet the requirements of high-precision detection in industrial environments with complex and variable features.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
To address the challenges of limited transient response and actuator saturation in teleoperated robotic systems， an improved prescribed performance control scheme is proposed. First， a time-varying error constraint function and a transformation function are designed to explicitly regulate the error convergence process， thereby enhancing both the transient response speed and steady-state accuracy. Second， a nonlinear saturation function is integrated with an adaptive tuning mechanism to dynamically constrain and adjust the control torques on both master and slave sides， effectively mitigating discontinuities and performance degradation caused by actuator saturation， and ensuring torque smoothness. Furthermore， a radial basis function neural network is employed to estimate system uncertainties， thereby improving the robustness and adaptability of the control framework. Without relying on accurate system models， the stability of the closed-loop system under the proposed control strategy is rigorously proven via Lyapunov-based analysis. Finally， comparative simulation results validate the superior performance of the proposed method in terms of error convergence， saturation suppression， and position tracking accuracy.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
To accurately identify the dynamic parameters of a six-degree-of-freedom collaborative robot， an identification algorithm based on physical consistency called Iteratively Reweighted Least Squares-Semidefinite Programming （IRLS-SDP） has been proposed. Firstly， the Newton-Euler dynamic model of the 6-DoF collaborative manipulator is established， employing a modified Coulomb-viscous friction model. Then， this dynamic model is linearized. To address the lack of consideration for physical meaningfulness in identified parameters during the identification process， constraints enforcing physical consistency are imposed. An excitation trajectory combining Fourier series with quintic polynomials is designed and optimized with the condition number as the optimization objective. Finally， dynamic parameter identification experiments are conducted： the collaborative robot executes the excitation trajectory， and experimental data is acquired and processed to obtain the regression matrix and actual joint torques. A comparative analysis of the identification results between the IRLS-SDP method and the conventional Least Squares （LS） method is performed. Experimental results demonstrate that the IRLS-SDP algorithm achieves superior overall identification performance. Specifically， it yields higher-precision torque predictions compared to LS， exhibits better model tracking fidelity， and identifies parameters that exhibit significantly better physical consistency.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
To address the issues of poor feature fusion， low diagnostic accuracy， and weak generalization in rolling bearing fault diagnosis， this paper proposes a fault diagnosis method based on PCC-GCN-MHSA feature fusion. The proposed approach constructs a dual-channel feature extraction framework that integrates both time-series signals and image-based information. Specifically， one-dimensional time-series features are extracted using RIME-BiLSTM， while two-dimensional image features are obtained via GADF-CNN-BiLSTM. Based on the combined features from both channels， a fixed graph topology is constructed using the Pearson correlation coefficient （PCC） matrix along with threshold filtering， enabling the mapping of multi-source features into graph nodes. A graph convolutional network （GCN） is then employed to capture local structural information. In addition， a multi-head self-attention （MHSA） mechanism is introduced to model global dependencies among nodes， thereby compensating for the limitations of fixed graph structures in capturing long-range and weakly correlated features. Finally， a gradient-boosted classification tree （GBDT） is used to perform fault classification. Based on the bearing fault datasets from Case Western Reserve University and the University of Paderborn， Germany， model training and validation are conducted under multiple working conditions. A comprehensive evaluation of the model's performance is carried out through t-SNE feature visualization， robustness analysis， comparative analysis of different models， and ablation studies. Experimental results demonstrate that the proposed method improves average accuracy by 0.7%~2.1% and 0.5%~0.8% on the two datasets respectively， and can effectively distinguish between different fault types and exhibits strong generalization capabilities.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
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.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
Aiming at the deficiencies of the traditional fault diagnosis methods for the main bearings of wind turbine units in the extraction of multi-modal features， an integrated fault diagnosis method based on the multi-modal two-dimensional Kolmogorov-Arnold network （2DKAN）， bidirectional gated recurrent unit （BiGRU）， and adaptive CBAM attention mechanism is proposed. This method converts one-dimensional vibration signals into two-dimensional time-frequency images while retaining the time-domain signals. It uses 2DKAN to extract the spatial features of the images and BiGRU to extract the time-domain features. Moreover， the feature weights are weighted and allocated through the adaptive CBAM attention mechanism to optimize feature fusion. The engineering verification results show that this method can effectively extract the fault characteristics of the main bearing， and its diagnostic accuracy is significantly higher than that of the traditional methods， thus having high application value.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
The accurate prediction of the remaining useful life （RUL） of rolling bearings is crucial for ensuring the reliable operation of machinery and implementing effective maintenance measures. It plays a significant role in safeguarding the lives and property of workers. Currently， in the field of rolling bearing RUL prediction， the lack of analysis and extraction of the interrelationships between different samples， as well as the insufficient flexibility in processing vibration signals， leads to less accurate predictions. Therefore， a model based on squeeze-and-excitation graph convolutional networks （SEGCN） and gated recurrent units （GRU） is proposed. First， the bearing data is preprocessed using adaptive variational mode decomposition （AVMD）. Then， considering the complexity and nonlinearity of vibration data in both the temporal and feature space dimensions， the squeeze-and-excitation network （SEN） is used to improve the graph convolutional network （GCN）. Through the squeeze-and-excitation mechanism， the model can aggregate and fully capture channel-related dependencies， and by combining GCN， it can integrate data from different time points to extract the interrelationship features among the data. Finally， the GRU is used to identify different features of the rolling bearing and obtain the RUL prediction results. The research results show that this model can extract the interrelationships between samples and demonstrate good predictive accuracy and generalization capability for rolling bearing RUL prediction under different working conditions.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
To address the issues of suboptimal performance， and high rates of missed detection and false detection in steel surface defect detection technology in industrial production environments， an improved YOLO11 algorithm called GCI-YOLO11 has been proposed. Firstly， in the feature extraction part， the GC-C3k2 module based on the GCNet attention mechanism was designed to enhance the algorithm’s capability to extract contextual feature information from images. Secondly， the CARAFE upsampling algorithm was introduced in the neck part to enable the algorithm to aggregate contextual information within a large receptive field， reducing the loss of feature information during the upsampling process. Finally， Inner-CIoU was used to replace CIoU for loss function optimization， and auxiliary regression box was introduced to improve detection accuracy and model generalization capability. Experimental results show that， GCI-YOLO11 achieved improvements of 2.9% and 2.3% in mAP 50 and mAP 50-95 on the NEU-DET dataset， and 1.6% and 0.3% in mAP50 and mAP50-95 on the GC10-DET dataset， showing better detection performance.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
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.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
To enhance vehicle yaw stability under challenging maneuvers such as high-speed cornering and obstacle avoidance， this study proposes a cooperative control strategy integrating Active Stabilizer Bar （ASB） and Direct Yaw-moment Control （DYC）. A two-degree-of-freedom vehicle dynamics model is developed to estimate the front and rear wheel slip angles， and a fuzzy observer of steering characteristics is designed to determine the instability trade-off variables. Leveraging the ASB’s capability to influence load transfer between the front and rear axles， an ASB-based yaw stability controller is formulated， which optimizes the torque distribution of the front and rear active stabilizer bars using steering characteristics， lateral acceleration， and longitudinal acceleration as inputs. Furthermore， a DYC controller employing single-wheel braking is designed based on the relationship between differential braking and yaw moment generation. The final ASB-DYC cooperative control strategy is derived according to the steering characteristic values. Hardware-in-the-loop simulation results confirm that the proposed approach effectively suppresses excessive yaw rate， maintains a stable vehicle sideslip angle at the center of mass， and improves both steering response and overall handling stability.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
A collaborative stochastic optimization method based on an improved scenario generation strategy is proposed to address the problem of source-grid-load-storage coordinated optimization caused by the strong randomness of photovoltaic output and load demand when a high proportion of renewable energy is connected to the distribution network. Firstly， a two-stage stochastic programming framework is established： in the current stage， an improved Kantorovich distance scenario reduction technique is used to generate a robust scheduling scheme， combined with virtual energy storage （VES） mechanism and adaptive weighting strategy to enhance extreme scenario adaptability； In the real-time stage， build a minute-level dynamic correction model to ensure the real-time security of the system through power balance dynamic correction constraints and dynamic voltage security boundary mechanisms. On this basis， an improved Hippopotamus Optimization （IHO） algorithm is developed， which compresses the decision space through three-dimensional coding mapping， designs a dynamic learning factor mechanism to balance exploration and exploitation capabilities， and innovates branch-and-bound ensemble strategies to handle discrete constraints. Experiments are conducted on an extended IEEE 33 node system， and the results show that， in terms of computational efficiency， IHO reduces total computational time to 49.6 seconds， 72.9% faster than traditional deterministic optimization； In terms of economy， the objective function value of 108530 CNY represents a reduction of 7.2% compared with the simulated annealing-genetic combination algorithm； In terms of safety， IHO has reduced the energy storage switching over-limit rate and voltage over-limit duration by 66.5% and 65.9%， respectively， compared with the gray wolf optimization algorithm. This method provides a new paradigm for optimizing high randomness distribution systems and technical support for high resilience power grid scheduling.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
This paper addresses vibration-induced machining surface defects and severe tool wear in large overhang-tool boring by proposing a design method for a dual-stage series tuned-mass-damped boring-bar based on a three-degree-of-freedom series dynamics model. The model establishes motion differential equations and amplitude ratio expressions of the system. Following the principle of 3 lowest equally high peaks， MATLAB optimizer is employed to select the optimal parameters for the model under different total mass ratios， and data fitting is adopted to establish the functional relationships between total mass ratio and such key parameters as mass distribution ratio， natual frequency ratio， and damping ratio. The derived relationships guide the design of a dual-stage damper. Comparative harmonic and acceleration response analyses demonstrate significantly lower resonance amplitudes and broader， flatter resonance peaks versus single tuned-mass dampers at identical total mass ratios. The dual-stage design also exhibits faster energy dissipation rates and shorter vibration decay time， providing an effective solution for enhancing vibration resistance in large overhang boring tools.
Abstract (
)
Download PDF (
)
HTML
(
)
Knowledge map
Save
With the rapid development of the nuclear power industry， intelligence and digitization have become important trends in the manufacturing of nuclear power equipment. Three-dimensional MBD （Model-Based Definition） technology， as an advanced digital design method， can integrate a product’s geometric information， manufacturing information， and management information into a 3D model， providing a new solution for the design and manufacturing of nuclear power equipment. This paper takes nuclear power plant steam generators as the research object， explores the application of 3D MBD design technology in the design and manufacturing of nuclear power equipment， and analyzes its advantages in improving design efficiency， optimizing manufacturing processes， and enhancing product quality. This paper elaborates in detail the technical concepts， methods， implementation paths， and applications of 3D MBD design in nuclear power equipment manufacturing， providing theoretical support and practical references for the intelligent design and manufacturing of nuclear power equipment.