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 48 Issue 5
25 May 2026
25 May 2026
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When deploying inspection robots in chemical production， storage and other scenarios， ensuring operational safety is of paramount importance. However， traditional path planning methods suffer from issues such as planned paths being too close to obstacles， and having difficulties in balancing global path optimization and dynamic obstacle avoidance. To address these issues， a path planning method combining improved Dijkstra algorithm and artificial potential field（APF） method is proposed. Firstly， a penalty term based on the repulsive potential field of obstacles is introduced into the cost function of the Dijkstra algorithm， enabling the planned global path to maintain a certain distance from obstacles； meanwhile， an obstacle repulsive potential field function is designed to control the magnitude of the distance. Secondly， to overcome the shortcomings of the APF algorithm， such as unreachable targets and local minima， the repulsive function is improved and an optimization algorithm of obstacle neighborhood space is proposed to assist the robot in escaping local minima. Simulation results show that the proposed method can effectively plan safe paths and has good obstacle avoidance capabilities， thereby enhancing the safety of inspection robots in chemical plants.
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For automated guided vehicles （AGVs） in complex industrial logistics scenarios， traditional single-modal perception methods suffer from significant interference from lighting and oil stains and have a high missed detection rate for small obstacles. A visual-tactile road obstacle recognition method is proposed that integrates dual attention mechanisms and temporal dynamic modeling. First， based on a self-developed visual-tactile roller， this method uses a ResNet34 backbone network to extract spatial features， embeds an AFT-Local module to enhance shallow-layer local perception， introduces a CBAM module to calibrate channel and spatial features， and utilizes an LSTM network to capture the temporal evolution patterns of deformations， thereby constructs the ResNet34-A-C-LSTM fusion model. Second， model training and validation are completed based on the self-built AGV-RoadTact multi-condition visual-tactile dataset， and an end-to-end recognition system is deployed on a real AGV platform. Experimental results show that this method achieved a classification accuracy of 93.51% on the test set， with a sequence inference time of only 128 ms， and under oil-contaminated steel plate roads， the recognition confidence improved by more than 50% on average compared with pure visual methods， providing a highly reliable real-time perception solution for the safe autonomous operation of AGVs in complex environments.
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As a critical load-bearing component for train coupling， the railway knuckle coupler's casting quality directly affects operational safety. Removing risers at the curved-surface edge of the coupler shank is a long-standing automation bottleneck： sand casting introduces stochastic riser positions and morphologies， so neither CAD-based offline programming nor teach-pendant programming works reliably. Adopting a hybrid view that combines geometric priors with data-driven learning， this paper proposes a Slice-aware Geometric Transformer Network （SaGT-Net） for end-to-end generation of plasma-cutting trajectories from raw 3-D point clouds. The principal axis slicing—a physically meaningful structural prior—is preserved， decomposing the intractable 3-D feature identification problem into several sets of 2-D sub-problems with sequential structures. Within each slice， 1-D convolutions and a Transformer encoder jointly extract local-global geometric context， while a detection head outputs point-wise inflection point confidence scores， replacing the noise-sensitive curvature rules. Across slices， a permutation-invariant Set-Transformer is introduced to perform weighted aggregation of all high-confidence inflection points， and a singular value decomposition on the centred data matrix directly regresses the cutting plane parameters， allowing inflection point detection， inlier weighting， and plane fitting to be jointly optimised under a unified loss. The continuous robot-executable path is finally generated by intersecting the regressed plane with the Alpha-Shape mesh and applying cubic B-spline smoothing. Experiments on 30 E-grade-steel knuckle couplers （240 point cloud samples） show that SaGT-Net outperforms classical geometric methods （Harris3D， FPFH， ISS） and mainstream learning-based baselines （PointNet++， DGCNN， Point Transformer） in terms of cutting plane normal-angle error， plane position error and F1-score， retaining 93.4% accuracy at a 2.0 mm noise level with 0.81 s average inference time per piece. The method has been validated on the production line of a railway rolling-stock manufacturer， where the cutting pass rate meets the process requirement.
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To address missed and false detections caused by the scale variations and morphological randomness of transformer magnet surface defects， alongside limited edge computing resources， a lightweight and high-precision detection algorithm RMEC-YOLO based on YOLO11n is proposed. First， a Rolling Rotational Convolution （RRC） module is designed to extract multi-angle edge features for directionally random slender defects like knife marks. Second， the backbone is reconstructed using MobileViTv2 and the Efficient Channel Attention （ECA） mechanism， significantly reducing parameters while enhancing global context perception. Finally， the Convolutional Block Attention Module （CBAM） is integrated into the feature fusion network to suppress background noise and improve the localization of tiny defects like cracks and blowholes. Experimental results demonstrate that RMEC-YOLO achieves a mean average precision （mAP） of 87.7% （a 3.4% improvement over the baseline）， requires only 2.0 M parameters （a 20% reduction）， and operates at 147 frames/s. This algorithm effectively balances model complexity and detection accuracy， fulfilling the requirements for real-time， precise defect detection on industrial assembly lines.
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The key to automated compliance reviews of substation fire safety design is to transform fire code provisions into review rules that can be understood and executed by computers. To solve the problems of low efficiency in traditional manual translation， and difficulties in maintaining generated results due to dependence on paired sample data in existing automated translation methods， this paper proposes a method for automatically generating "Skills" for fire safety reviews of substations. First， based on summarizing the semantic differences between design codes and modeling specifications， a three-layer organizational architecture for Skills is constructed， and the representation forms of the resource layer， operation instruction layer， and metadata layer are defined. Then， a Skills Agent based on the ReAct mechanism is developed to automatically generate fire safety review Skills. Finally， a 110 kV substation is used as an example to verify the feasibility and practicality of the aforementioned Skills. The results show that the proposed method can automatically generate stable and easy-to-maintain fire safety review Skills， and significantly improve review efficiency and the degree of automation.
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In four-way shuttle systems， factors such as diverse rack layouts， varying numbers of shuttles， and adjustable shuttle speeds make system efficiency evaluation both challenging and time-consuming. Traditional simulation-based methods suffer from high computational cost and long iteration cycles， making them unsuitable for rapid design optimization. To address this issue， a neural network-based approach for fast system efficiency evaluation is proposed. In this method， a discrete-event simulation model is used as the data source to generate a large number of labeled training samples. Through data normalization， dataset partitioning， and hyperparameter optimization， a multi-layer neural network model is constructed. The model takes key parameters—including the number of shuttles， shuttle speeds， and storage layout configurations—as input features， and system efficiency as the output label. By leveraging supervised learning， it effectively captures the complex nonlinear relationships between inputs and outputs， enabling rapid system efficiency evaluation. Experimental results show that the proposed model achieves a coefficient of determination （R²） of 0.995 on the test set， demonstrating a high degree of linear consistency between predicted values and actual values. Furthermore， across 594 experimental scenarios involving 22 different layout configurations and storage scales ranging from 400 to 1，800 locations per layer， the model maintains good convergence and prediction stability （ ）， validating its strong generalization capability.
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In order to solve the problem of accuracy limitations caused by complex noise contamination of PMU measurement data and insufficient robustness of traditional methods against noise in parameter estimation of three-phase transmission lines， a hybrid estimation method combining Kalman filter preprocessing and Bayesian multi-output regression is proposed in this study. The Kalman filter algorithm is used to preprocess the original PMU voltage and current measurement data at one end of the line. The filtering algorithm effectively suppresses the random noise and error in the data by simultaneously considering the noise in both voltage and current phasors and using the analytically derived measurement noise covariance matrix， which provides a cleaner and more reliable input sequence for subsequent estimation. On this basis， a data-driven Bayesian multi-output regression model is used. The model takes the above-mentioned filtered sequence data as input， and can directly learn the robust probability mapping relationship from data to multi-dimensional parameters （such as resistance and reactance） of the line without the need for the system to accurately analyze the model. Through simulation， under different sample sizes， load levels and strong noise conditions， the proposed method is superior to the traditional regression method in both estimation accuracy and robustness， which proves the effectiveness of solving parameter estimation problems in complex noise environments.
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The riserless mud recovery （RMR） system is a key technology for the efficient development of deep-sea oil and gas resources. As the core power unit for mud return in this system， the health status of the mud lift pump directly affects the continuity and safety of drilling operations. However， under complex operating conditions such as deep-sea high pressure and dual-pump switching， the strong nonlinear characteristics of solid-liquid two-phase flow inside the pump and transient flow surges can easily induce typical faults like excessive axial force and abnormal torque. Traditional diagnostic methods based on experience or single mechanisms struggle to meet the engineering demands for high accuracy and real-time performance. To address the difficulties in extracting fault features and the low diagnostic accuracy of mud lift pumps under complex operating conditions， this study proposes a fault diagnosis method based on LightGBM optimized by the Jellyfish Search （JS） algorithm， namely the JS-LightGBM model. The method first constructs a simulation model of the multi-stage mud lift pump based on the Fluent-EDEM fluid-solid coupling method. Combined with orthogonal experimental design， multi-condition simulations are conducted on key parameters including mud density， viscosity， rotational speed， flow rate， turbulence intensity， and turbulence viscosity ratio. A fault dataset containing three states， namely normal， excessive axial force， and excessive torque，is established， and the Synthetic Minority Over-sampling Technique （SMOTE） is employed to alleviate class imbalance. Subsequently， the Jellyfish Search algorithm is used to adaptively optimize key hyperparameters of LightGBM， including learning rate， number of leaves， and tree depth， thereby constructing a high-precision and high-efficiency fault classification model. Experimental results demonstrate that， under ten-fold cross-validation， the JS-LightGBM method achieves a fault diagnosis accuracy of 95.06% on the test set. This represents a significant improvement over methods such as GBDT， JS-XGBoost， JS-CatBoost， and the standard LightGBM. Furthermore， the proposed method achieves the shortest average single-diagnosis time， exhibiting excellent computational efficiency and generalization capability.
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To address the issues of poor anti-noise capability of single-modal features and insufficient distribution alignment in cross-domain bearing fault diagnosis under varying operating conditions and complex background noise， a diagnostic method based on an Adaptive Dual-Branch Fusion Dynamic Adversarial Network is proposed. This method introduces the Fast Kurtogram and Continuous Wavelet Transform to construct a dual-branch network for physical feature complementarity. An adaptive attention mechanism is designed to dynamically allocate feature weights， enhancing anti-noise robustness. Furthermore， a globally and locally coordinated dynamic adversarial module is embedded to achieve dual dynamic fine-grained alignment between the source and target domains. Extensive cross-condition and cross-load transfer experiments were conducted on both the Paderborn University （PU） and Case Western Reserve University （CWRU） datasets. The results show that the proposed method achieves an average cross-domain diagnostic accuracy of 96.40% on the PU dataset and 98.87% on the CWRU dataset.
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In the turbine system of gas-steam combined cycle units， bearings in turbines often face the problem of scarce fault samples. Traditional diagnostic methods struggle to balance generalization capability and diagnostic accuracy under complex scenarios. To address this， a fault diagnosis scheme that combines the ResNet-ViT joint network with transfer learning in few-shot scenarios is proposed： After preprocessing the bearing vibration signals， one-dimensional time-domain signals are converted into two-dimensional time-frequency images using continuous wavelet transform； a hybrid network is constructed that combines the advantages of the local feature extraction of ResNet with the global correlation modeling strengths of ViT， and is pretrained on a publicly available bearing dataset as the source domain. Transfer learning is then applied to fix the lower-layer weights and fine-tune the upper-layer parameters， enabling the model to adapt to the turbine bearing target domain. Experimental results show that this method achieves diagnostic accuracies of 99.2% and 84.5% on gas turbine and steam turbine bearing fault datasets， respectively， effectively addressing the challenges of limited turbine bearing fault samples and cross-condition diagnostics， providing practical technical support for rapid fault diagnosis in industrial settings.
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To address the difficulty of timely identifying abnormal temperature rise in variable frequency drives （VFDs） of production-site auxiliary equipment， a health assessment method based on random forest prediction residuals is proposed. First， a VFD temperature prediction model is established using normal operating data to learn the nonlinear mapping relationship between equipment operating parameters and VFD temperature under different operating conditions. Then， a healthy reference residual distribution is constructed based on the out-of-bag prediction residuals of the healthy training set， and a prediction deviation index （PDI） and a prediction variation index （PVI） are developed to quantify the current temperature deviation and the recent accumulation trend of abnormal deviations， respectively. Finally， PDI and PVI are fused to obtain a health index （HI）， and warning and alarm decisions are implemented by combining HI thresholds， recent-mean gating of PDI， and a continuous-triggering mechanism. The method is validated using actual operating data from a suction blower numbered BB15EC at a textile production site. The results show that the random forest temperature prediction model achieves an R² of 0.997 on the healthy test set. The alarm triggering rate of the health assessment method is 0 among 3，341 healthy test samples， and a stable alarm is triggered 86.63 h before shutdown in the pre-fault dataset. The proposed method can effectively identify the abnormal temperature-rise process of the VFD and distinguish short-term fluctuations under healthy conditions from persistent anomalies under fault conditions， providing a reference for condition monitoring and predictive maintenance of production auxiliary equipment.
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Against the backdrop of lean production and digital transformation， the Process Bill of Materials （PBOM） serves as a core information carrier supporting digital management and control in manufacturing. Rational construction of PBOM can effectively address various practical production issues. To meet material management requirements in PBOM development， a twin material construction method is adopted， classifying twin materials into Type AA （with identical technical status to original materials） and Type AB （with certain differences）. Configuring twin materials in PBOM fulfills diverse production needs including loss compensation， spare part provision， error compensation， graded screening and quality traceability. This approach enables dynamic material quantity adjustment， improves material utilization， reduces schedule delays and cost waste， while ensuring consistent product quality and full-process traceability， providing effective technical support for enterprises to achieve refined and digitalized production.
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To address the scheduling optimization problem in cable flow-shop production， arising from the coupling of mold specification matching， limited inventory， lifetime constraints， and multi-state energy consumption of equipment， a mixed-integer programming model is established with the objectives of minimizing the makespan and total energy consumption. The model comprehensively considers mold usage constraints in key processes， including wire drawing， insulation， and sheathing， as well as the energy consumption characteristics of equipment during processing， standby， specification switching， and wear-induced mold replacement. To improve the solution efficiency， an adaptive large neighborhood search enhanced NSGA-II algorithm is designed. A two-segment encoding scheme， constraint-based decoding mechanism， and local search strategy are adopted to improve the quality of the Pareto solution set. Simulation results based on production instances from a cable enterprise show that the proposed algorithm outperforms traditional NSGA-II and MOEA/D in terms of multi-objective performance indicators， convergence speed， and compromise solution performance. Simulation results based on the practical production case of a cable manufacturer show that the proposed method can effectively shorten the makespan， reduce energy consumption， and decrease mold change operations， providing a reference for green and efficient scheduling in cable workshops.
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The Planetary Roller Screw Mechanism （PRSM） is a typical threaded transmission component， featuring strong load-bearing capacity， excellent positioning accuracy， and outstanding overall rigidity. Considering the uneven distribution of load among thread teeth caused by the threaded load-bearing form， which significantly affects the service life of the planetary roller screw， a calculation model for load distribution on the thread teeth of the planetary roller screw is established by comprehensively considering shaft deformation， thread tooth deformation， and thread contact deformation. This model reveals the influence of tooth thickness parameters on load distribution. Based on this， an optimal gradual tooth thickness modification and uniform load design method is proposed using a multi-objective genetic algorithm. The research indicates that thread tooth thickness affects the timing of thread tooth contact， thereby influencing load distribution. The roller thread tooth thickness has a significant impact on the load distribution on both the screw-roller side and the roller-nut side， while the nut thread tooth thickness only significantly affects the load distribution on the roller-nut side. After adopting the optimal tooth thickness modification parameters， the maximum load on the thread tooth is reduced by 30%， and the unevenness of load distribution on the thread tooth is reduced by 63.5%. This uniform load method provides theoretical support for enhancing the load-bearing capacity and service life of the planetary roller screw.
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In response to the high-quality requirements for the surface of the forming molds of transparent aviation parts， the 1.2316 high-end die steel is taken as the research object by using a robotic automated grinding and polishing system. The influence of electric spindle speed， robot feed speed， normal contact pressure， and abrasive type on the surface roughness and material removal rate after polishing was systematically investigated through the combination of single-factor variable method and orthogonal experiments. The surface morphology under the optimal process was characterized using scanning electron microscope and white light interferometer. Two types of optimal process combinations were obtained after orthogonal experiment： the roughness optimal scheme （spindle speed 600 r/min， feed speed 10%， pressure 4.8 N， diamond polishing fluid） can reduce the surface roughness to 0.017 μm and the material removal rate to 202 nm/min； the removal rate optimal scheme （spindle speed 600 r/min， feed speed 3%， pressure 7.2 N， diamond polishing fluid） can achieve a material removal rate of 258.4 nm/min and a surface roughness of 0.0285 μm. The analysis using scanning electron microscope and white light interferometer further confirmed that the surface treated by the roughness optimal scheme is the smoothest and most flat. The research results provide data support and theoretical guidance for the robotic automated mirror polishing process of die steel for transparent aviation parts.
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Voluteless centrifugal fans are widely used in rail transit and large-scale ventilation systems due to their compact structure and ease of installation. However， owing to the absence of the volute structure and its pressurization effect， the aerodynamic performance improvement of such fans mainly depends on the optimization of impeller blade structural parameters. Therefore， the blades of a voluteless centrifugal fan are selected as the research object. To address the issues of significant internal flow loss and relatively low static pressure efficiency， the airflow rate and static pressure efficiency are adopted as the optimization objectives. Experimental measurements combined with numerical simulations are employed to investigate the internal flow field and energy characteristics of the fan under different blade number and blade installation angle conditions. Based on fan performance experiments and the validation of the numerical simulation model， the energy characteristics and flow field distributions of the voluteless centrifugal fan with different blade structural parameters are analyzed.The results indicate that both blade number and blade installation angle have significant effects on the aerodynamic performance of the voluteless centrifugal fan. The optimal comprehensive performance is achieved when the blade number is seven and the blade installation angle is 10°. Compared with the prototype fan， the optimized configuration increased the airflow rate to 11，740.29 m3/h， representing an improvement of 12.75%， while the fan efficiency increased to 70.42%， corresponding to an enhancement of 4.56 percentage points. Flow field comparison results further demonstrate that the optimized fan effectively reduces internal flow losses， promotes a more uniform distribution of high-velocity regions， and extends the high-velocity flow toward the outlet region. Consequently， the internal flow condition and overall aerodynamic performance of the fan are significantly improved.
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To address the issue of high cost in microwave oven heating systems， TRIZ function analysis and trimming tools are applied to identify and trim low-value components， thereby establishing a trimming problem model. By transforming the trimming problem into corresponding TRIZ problem models and matching them with solution models， secondary problems are further addressed， ultimately yielding an optimized structure with a cost reduction of 5.32%. The feasibility of the proposed solution is verified through microwave heating performance tests， noise tests， and simulation experiments. In this process， two improved TRIZ tools are proposed： one is the "improved function-cost diagram" to enhance the conciseness and clarity of function-cost analysis； the other is the "multi-component trimming principle" to optimize the logic and sequence of the trimming process， providing a reference for solving successive and continuous trimming problems. The above practice can provide insights for structural optimization and cost control of mechanical products， particularly microwave ovens.
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A systematic methodology covering temporal modeling， layout scheme evaluation， and engineering implementation is proposed for loading/unloading systems with a single robot serving multiple machine tools. By constructing a temporal model， key time parameters such as machine tool processing， robot movement and interactions are defined， cycle time matching constrains are derived， and quantitative formulas for the maximum number of machine tools a robot can serve， time redundancy， and theoretical production capacity are provided. A comprehensive evaluation and decision-making between two typical layout schemes are conducted based on multidimensional indicators such as production efficiency and maintenance convenience. In engineering practice， an innovative multi-station robotic gripper is designed， and a coordinated workflow is planned. This method has been applied to the retrofit of a grinding unit for the inner diameter of upper shaft caps in a compressor， successfully reducing the number of operators from 2 to 1， increasing the equipment's natural operating rate from 88% to 92%， and shortening the production cycle from 15 s/piece to 13 s/piece， which verifies the method's engineering feasibility and practical value.
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To meet the requirements for fully automated， efficient， and high-quality production lines in modern pharmaceutical industry， and to address the problem of inspection blind spots in containers after drug filling， a machine-vision-based pre-filling inspection system for vials is designed. The system consists of an image acquisition unit， a defect detection unit and a rejection unit. First， when each vial passes through， the image acquisition unit captures images using a high-frame-rate industrial camera， with supplementary illumination from a high-brightness light source. Then the system analyzes the images from the vial mouth station and vial bottom station separately. At the vial mouth station， a randomized recursive algorithm is used for vial mouth positioning， and then image analysis in polar coordinates is performed to determine whether there are defects in the vial mouth. At the vial bottom station， tubular vials and molded vials are distinguished； a mold number shielding module is specially designed for molded vials after positioning， and finally defects in the vial bottom are identified within the positioned region. Finally， the practical application performance of the current system is introduced. Implementation results show that the overall false detection rate of the inspection system is ≤0.1% and the average missed detection rate is ≤0.05%， which meets on-site production requirements. The system can significantly improve the quality of pharmaceutical production， reduce the workload and missed detection rate of downstream lamp inspection， and enhance the utilization rate of pharmaceutical raw materials.