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
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We analysed the nonlinear variation of parameters exhibited by the liquid rocket engine test stand under the action of engine thrust， and we have analysed the results of the static loading-unloading experiments and concluded that this non-linear variation is due to the effect of friction between the moving and static frames of the test stand. We derived the explicit relationship between the loading force F and the friction coefficient μ by fitting the experimental data， and used the LuGre friction model in conjunction with the simulation results to provide a theoretical explanation for the variation of the friction coefficient. In this paper， we propose a correction method for the nonlinear model of the test frame friction based on the static response surface method， by constructing a static response surface， taking the friction coefficient of the friction pair of the test frame as the optimisation variable， and taking the minimisation of the error between experimental and simulated static responses as the optimisation objective to correct the finite element model. The results show that the correction of the friction nonlinear model of the test stand by this method not only improve the optimization efficiency， but also keep the maximum error of the corrected finite element model no more than 2.57 kN， and the relative error with the experimental value is 0.43429%， which is a big improvement compared with the relative error of the initial model which is 1.271%； and the friction coefficients of the corrected model further verify the correctness of the experimental fitting and theoretical interpretation. This paper can provide engineering practice value for the subsequent correction of nonlinear changes in the thrust transmission of the test stand.
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To address the insufficient machining accuracy of turbine-disc fir-tree tenon slots caused by the lack of reverse adjustment， correction， and compensation methods for machining parameters in 5-axis CNC broaching machines， this study traces the sources of motion displacement and angular errors in the broaching process. Time- and position-dependent displacement and angular deviation functions are established， and a dynamic machining parameter compensation method， adaptive to positional variations， is proposed. Experimental validation conducted through CNC broaching tests on turbine-disc tenon slots confirms the effectiveness of this research by comparing results before and after parameter compensation. The results demonstrate that the proposed method significantly reduces dimensional and angular deviations in tenon slot machining. Specifically， the roller spacing error is reduced to 27.7% of the original value， the angular deviation between tenon slot centerlines is decreased to 13.3% of the original value， and the angular difference between the tenon slot profile and its symmetric plane is reduced to 23.5% of the original value. This methodology is scalable for enhancing the machining accuracy of other broaching systems， offering substantial theoretical significance and practical engineering value.
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To improve the forming accuracy and dynamic stability of rope-driven 3D printers， this paper designs and verifies a prototype of a rope-driven 3D printer. Through theoretical analysis of the kinematics， statics， and dynamics of the mechanism， an experimental platform is established to validate its performance. Experimental results from trajectory tracking tests and multi-layer structure printing tests show that the trajectory tracking error of the end effector in both the X and Y directions is controlled within 5%， with positioning accuracy reaching the sub-millimeter level， and the system operating smoothly. The optimal nozzle movement speed is determined to be 16 mm/s， at which the root mean square error of the centerline offset of printed lines is minimized， balancing forming accuracy and dynamic stability. This provides a data foundation and theoretical basis for subsequent improvements in control strategies， development of error compensation mechanisms， and robust control design for robust CDPR 3D printing systems.
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Precision linear rolling guides are core functional components of high-end equipment represented by robots， and the drag force performance is an important technical indicator for measuring their motion smoothness. Existing devices can only test the drag force performance of linear rolling guide rail pairs under a fixed preload state， unable to achieve stepless quantitative adjustment and comparison， and thus cannot provide fine quantitative guidance for preload force adjustment during the assembly process. This paper designs a novel drag force testing device for linear guides. It configures dual sliders in a zero-preload state on two rails， which are relatively and parallelly assembled on a fixed base. Between the dual sliders， a guiding mechanism， a force loading mechanism， and sensors for preload force and preload amount are installed. A dragging mechanism and a drag force sensor perform dragging operations and real-time measurement of the drag force on the aforementioned modules. This enables stepless adjustment of the preload state without replacing the built-in balls or reassembling the components， thereby obtaining the data relationship among the internal preload force， preload amount， and corresponding drag force between the slider and the rail in real time. The research provides a new device for drag force testing and offers support for further improving the performance of linear guides as core functional components.
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Aiming at the problem of fault detection of wheel-set bearing in rail vehicles， an improved power-iterative fast independent component analysis（IPowerICA） algorithm is proposed. This method first utilizes the energy-amplitude Hilbert transform to perform noise reduction processing on the fault signals of the wheelset bearings of rail vehicles. Then， the maximum weighted iterative decomposition method is applied to reconstruct the pre-whitening vector of the noise reduction signal； Subsequently， under the guidance of the pre-whitening vector， a power-iterative rapid independent component analysis is conducted and the optimal separation result is selected based on the minimum level of attention entropy. The presents of faults in the wheel-set bearing can be determined by comparing the theoretical fault feature frequencies with the prominent frequency components in the envelope spectrum of the optimal separation result. The results of the experimental data analysis show that the proposed algorithm can distinguish the weak feature information from the original signals of the complex wheel-set bearing， efficiently determine whether the wheel-set bearings are damaged or not， and provide reference value for practical engineering applications.
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To address key challenges in transmission line point cloud classification—namely large-scale variations in components， severe material reflectance interference， loss of edge features， and real-time bottlenecks—this paper proposes an intelligent classification method based on multi-scale adaptive feature fusion. The method integrates three core innovations： 1） a Reflectance-Geometry Joint Compensation Model that dynamically allocates reflectance coefficients via a pre-trained material classifier， effectively resolving reflectance discrepancies between metallic and non-metallic components； 2） a Power-Specific Feature Pyramid Network employing a three-tier parametric structure for hierarchical extraction of conductor details， insulator textures， and tower contours； 3） a Noise-Resistant Edge Real-Time Optimization System combining Hessian-Modified Gradient Magnitude with momentum-accelerated label propagation to enhance edge precision and computational efficiency. Experimental results demonstrate that our method achieves 90.5% overall accuracy on million-scale point cloud datasets， with metallic fittings attaining a 0.93 F1-score. It reduces cross-scale errors by 12.7% while maintaining a processing latency of 198 ms and a throughput of 1，120 points/ms. Compared to state-of-the-art approaches， this solution significantly improves classification accuracy without compromising real-time performance， offering substantial engineering value for transmission line inspection and advancing 3D vision applications in complex industrial scenarios.
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In order to solve the problem of task allocation and worker fatigue in hybrid flow shop， a mixed integer programming model considering worker fatigue is proposed. According to the characteristics of the problem， the real number coding method is used to solve the problem of process sequencing and worker assignment， and an improved nutcracker optimization algorithm （INOA） is proposed to solve the problem. Firstly， reverse learning population initialization is used to increase the diversity of the population. Secondly， the parameter factors and are optimized to improve the search efficiency and convergence speed of the algorithm. Finally， in order to improve the robustness of the algorithm， a population diversity monitoring mechanism is introduced， and Cauchy disturbance is used to avoid stagnation caused by too low population diversity. The INOA and other algorithms are tested on 12 functions. The test results show that the performance of INOA is significantly better than other algorithms. Through the example verification of the connection line assembly line， the results further verify the superiority of the algorithm. The proposed optimization scheme can effectively shorten the completion cycle， prevent excessive fatigue of workers， and promote the rational allocation of tasks.
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Manufacturing overhead constitutes a crucial component of production costs in equipment manufacturing enterprises， and accurate prediction of manufacturing overhead is of significant importance for enhancing enterprises' production cost management capabilities. To improve prediction accuracy， a BP neural network prediction model optimized by an Improved Crayfish Optimization Algorithm （ICOA） is proposed. First， optimized Latin hypercube sampling is employed to initialize the population， improving the uniformity of initial population distribution； the first phase search strategy of Marine Predators Algorithm and temperature adaptive factor are introduced to improve the summer avoidance phase， enhancing global search capability； Lévy flight strategy is integrated to optimize the foraging phase， balancing global exploration and local exploitation； t-distribution perturbation is utilized to update optimal individuals， preventing the algorithm from falling into local optima. Subsequently， the improved crayfish algorithm is used to optimize the initial thresholds and weights of the BP neural network to enhance the model's prediction accuracy. Finally， validation is conducted using manufacturing overhead and related data from heat exchanger tube bundles of a chemical equipment manufacturing enterprise in Shandong Province. Results demonstrate that the ICOA-BP neural network prediction model achieves reductions of at least 20.95% and 20.45% in Mean Absolute Error （MAE） and Root Mean Square Error （RMSE）， respectively， while the coefficient of determination （R²） improves by at least 14.01%， proving the superiority of the constructed model in manufacturing overhead prediction accuracy.
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A dynamic resource scheduling method based on enhanced double Q-learning （EDQL） is proposed to address the issues of insufficient efficiency in dynamic resource scheduling and difficulty in ensuring differentiated service quality when multiple services coexist in the cloud radio access network （C-RAN） of the fifth generation mobile communication （5G） system. Firstly， establish a virtualized slicing architecture for ultra reliable low latency communication （uRLLC）， enhanced mobile broadband （eMBB）， and massive machine type communication （mMTC）， and achieve adaptive correction of resource weights through a multi priority preemption mechanism and dynamic queue adjustment factors. On this basis， design an EDQL algorithm that integrates competitive network architecture and dynamic reward scaling， and combine Markov decision processes to jointly model the network load， channel state， and queue delay. The experimental results show that the proposed method reduces the forced termination probability of uRLLC services by 82.3%， improves the eMBB service completion rate by 41.2%， increases system resource utilization by 28.5%， and reduces the average queuing delay of mMTC by 76.9%， compared to traditional polling scheduling， static priority， and heuristic rule algorithms. This study provides a new paradigm for 5G multi service resource scheduling through the deep integration of virtualized slicing and reinforcement learning.
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How to interconnect mature atomic models， achieve agile construction of complex scenario application models， and meet the needs of flexible production， is the key to accelerating the process of industrial intelligence. This paper proposes a model interconnection system based on standardized interfaces and intelligent combination to address the problems of heterogeneous atomic model interfaces and challenging state synchronization and updating. By decoupling complex systems into independent atomic model units， a structured connection paradigm and dynamic collaboration mechanism are established to achieve cross platform model interoperability and scenario based composite modeling. We have developed a web-based graphical agile development environment that supports rapid construction and collaborative simulation of industrial interconnection models， enabling developers to shift their focus from low-level programming to application scenario driven model combination design.
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With the acceleration of intelligent development in rail transit， classifying and grading onboard data for rail transit vehicles has become a core challenge in achieving data asset management and security control. This paper focuses on the on-board data of EMU （Electric Multiple Unit） trains， presents methods for data classification and grading， and in response to the characteristics of onboard data—such as large scale， strong heterogeneity， high real-time requirements， and significant multi-source correlation—analyzes key technologies and implementation paths. It proposes critical steps for conducting data classification and grading， including clarifying regulatory standards， constructing classification and grading templates， assessment and analysis of data classification and grading， and strengthening post-management. The paper also introduces the approach and application of leveraging large models to enhance onboard data classification and grading， demonstrating the feasibility of its large-scale deployment. This work aims to support further improvements in efficient data utilization and safety management within the rail transit industry.
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To enhance the performance stability of smart electricity meters under varying metering environments， a metering performance evaluation method based on big data analysis is proposed. This method utilizes statistical results of measurement errors obtained under standard laboratory conditions， combined with field operation data and environmental factors of the meters， to systematically correct measurement errors. Firstly， the Fuzzy C-Means clustering algorithm is employed to classify laboratory and field environmental data， enabling effective categorization of environmental features. Then， a Convolutional Neural Network model is used to perform deep feature extraction and modeling of multidimensional data such as voltage， current， power factor， and environmental parameters， thereby establishing a nonlinear mapping relationship between meter errors and influencing factors. Finally， the established model is applied to correct measurement errors of smart electricity meters in the field. Experimental results demonstrate that the proposed method significantly improves the accuracy of error correction， verifying its feasibility and effectiveness in the analysis and optimization of electricity meter metering performance.
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To enhance robotic spraying automation and coating quality， this study established a high-precision numerical model of the air spraying film-forming process， systematically revealing the coupling mechanism between atomization characteristics and film formation dynamics， and proposed a process parameter optimization strategy. Based on the Euler-Lagrange method， a numerical model for paint deposition was developed by integrating the KHRT droplet breakup model and a wall-capturing model， incorporating discrete phase motion， continuous phase turbulence， and gas-liquid coupling effects. Grid independence verification ensured computational accuracy， while experimentally calibrated spray cone angles and initial velocities were adopted as boundary conditions. The influence mechanisms of spray distance， initial velocity， and spray cone angle on film thickness distribution， uniformity， and transfer efficiency were comprehensively analyzed. Key findings include： （1） Droplet impact velocity serves as a critical intermediate variable affecting atomization and film quality， decreasing linearly with increasing spray distance， which expands effective coating coverage and improves uniformity but elevates paint escape rates. （2） An optimal range exists for initial velocity—excessive velocities intensify droplet escape， while insufficient velocities cause inadequate atomization， requiring trade-offs between uniformity and efficiency. （3） Larger spray cone angles significantly reduce transfer efficiency and induce uneven particle distribution， whereas overly narrow angles degrade uniformity due to localized kinetic energy concentration， necessitating cone angle optimization to balance coverage and quality. Experimental validation confirmed model reliability. This work pioneers a unified analytical framework integrating atomization dynamics and film formation characteristics， elucidating the nonlinear regulatory mechanisms of process parameters on coating quality. The results provide critical engineering guidance for spray process development.
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In order to reduce the monitoring and operating pressure of operators during deep peak shaving， an intelligent desulfurization control system is proposed to address the problems of poor measurement accuracy and large inertia and delay in the controlled objects that prevented long-term stable automatic operations. By using BP neural network， a mapping relationship is constructed between signals such as flue gas flow rate， SO2 concentration in the raw flue gas and slurry pH to achieve soft measurement of slurry pH value； Replacing conventional PID with variable structure predictive control and combining it with more accurate and reasonable feedforward signals ensures the control effect of the desulfurization system under rapidly changing load and coal quality conditions. Later， utilizing the unit ICS system， the desulfurization intelligent control system is successfully applied to a 650 MW unit. The operation results show that after the system is put into operation， the SO2 concentration at the outlet is stably controlled within 25 mg/m3， and the deviation between the pH value of the slurry and the set value is kept within 0.2， and there are no significant fluctuations during the variable load and pH meter flushing process. The desulfurization is automatically put into operation for a long time， effectively reducing the operating pressure of the operators.
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With the continuous growth of global maritime cargo throughput， ship unloading equipments become the core hub of port logistics， and their automation level directly impacts the efficiency and safety of port operations. Among various types of unloading equipments， grab ship unloaders are widely used due to their advantages of strong adaptability and wide operational range. As a kind of nonlinear underactuated system， the input dimension of a ship unloader is smaller than the dimension of its state variables， and the load mass is enormous， making the suppression of load swing angle particularly difficult. To address these problems， this paper proposes a novel hierarchical fractional-order sliding mode adaptive control method. Based on the analysis to dynamic characteristics of ship unloaders， a multi-degree-of-freedom coupled model incorporating load swing is established. With the designing of a hierarchical sliding mode structure using fractional-order calculus operators， the system model errors are estimated online in combination with the adaptive law， and the asymptotic stability of the closed loop is strictly proved by Lyapunov energy function method. Numerical simulation comparison results demonstrate that the proposed method outperforms traditional sliding mode control and adaptive sliding mode control in swing suppression， offering higher precision and faster convergence. This approach provides a novel theoretical and technical solution for intelligent control in port automation， contributing to safer and more efficient cargo handling operations. The method’s effectiveness in handling nonlinearities and uncertainties highlights its potential for broader industrial applications.
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For the PID parameter optimization problem， this study first compares traditional methods （e.g.， Ziegler-Nichols） with other mainstream reinforcement learning algorithms （e.g.， PPO）， selecting Deep Deterministic Policy Gradient （DDPG） as the foundational framework. However， DDPG poses instability risks during exploration in industrial control systems with time delays （e.g.， second-order lag processes）. To address this safety concern， we integrate the Feasible Policy Iteration （FPI） framework， proposing the DDPG_FPIS method. Furthermore， we independently develop an enhanced version of DDPG_FPIS incorporating mechanisms such as model predictive action safety screening， and construct a delay-tunable second-order lag system for verifying. Experimental verification demonstrates that the proposed method achieves zero constraint violations （absolute safety） throughout training. Its ultimate control performance—significantly suppressing overshoot and shortening settling time—along with training efficiency， substantially outperform all initially compared methods. This provides an effective approach for safe and efficient adaptive PID tuning in time-delay systems.
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In order to solve the problem that the drilling arm takes too long to reach the target blasthole in the case of trolley tunnel drilling operation， this study plans its motion trajectory， and optimizes the trajectory curve using the Differential Evolution-Artificial Bee Colony （DE-ABC） algorithm to enhance motion stability， reduce operation time， and improve efficiency. First， an 8-degree-of-freedom （8-DOF） robotic arm kinematic model is established. Inverse solutions from Cartesian space to joint space are calculated by decomposing degrees of freedom. In joint space， a three-segment quintic polynomial curve （"quintic-quintic-quintic"） is employed for trajectory planning based on the inverse solutions. With trajectory duration and motion stability as optimization objectives， the DE-ABC algorithm incorporating Cauchy perturbation operations optimizes the trajectory curve. Compared with the traditional Modified Artificial Bee Colony （MABC） algorithm， the DE-ABC algorithm mitigates the tendency of MABC to converge on local optima， demonstrating superior adaptability and fitness.
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As market demands become increasingly diverse and competition intensifies， the limited capacity for product updates and iterations， coupled with the continuous reduction of profit margins， necessitates the transition of traditional manufacturing enterprises towards service-oriented manufacturing as a crucial step for future growth. Product-service modular design serves as a key strategy to facilitate this transformation. This study， grounded in an analysis of existing research findings， develops a theoretical framework for product-service modular design and outlines the criteria for service module segmentation. A service module clustering method based on minimum spanning tree is proposed， utilizing an enhanced closeness value method to optimize the multi-scheme decision for different clustering results. To validate the practical applicability of the theoretical model， a case study exploring the service modular design of CNC machine tools is presented.
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To address the challenges faced by mobile robots in overcoming obstacles in unstructured environments such as agricultural inspections and disaster rescue， this study proposes a design scheme for a four-wheeled mobile chassis that integrates a rocker-steering suspension with a crank-slider mechanism. First， kinematic and dynamic models of the walking mechanism were established to analyze the influence of key configuration parameters （e.g.， support wheel center distance， hinge distance） on terrain adaptability and load platform posture. A multi-objective optimization method was employed to determine the optimal parameter combination （LF =200 mm，k 1=0.9）. Second， a three-dimensional virtual prototype was developed by integrating a crank-slider mechanism and symmetric frame design. Dynamic simulations conducted on the RecurDyn platform validated the chassis performance in traversing 18 mm speed bumps and 20 mm semi-cylindrical obstacles， showing pitch angle fluctuations within ±3°and peak torque demand ≤15 N·m. Finally， prototype tests demonstrated that the chassis can stably cross 90 mm speed bump-type obstacles under a 75 kg load， with a linear motion speed of 1.8 m/s and a path deviation of less than 20 mm/5 m. The results indicate that this design significantly enhances the terrain adaptability of mobile robots in unstructured environments， providing a reliable mobile platform for agricultural inspection， logistics， disaster rescue， and similar scenarios.
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To address the inefficiency of traditional manual visual inventory counting and the high deployment costs of existing automated solutions in Automated Storage and Retrieval Systems （AS/RS）， this paper proposes an intra-warehouse visual inventory system based on modular visual devices. A retrofit-free stacker-accessible modular visual inventory device is designed. On the basis of a YOLOv8-powered visual inventory algorithm for multi-surface information fusion from a single view， the accurate counting of complex stack patterns （e.g.， non-full stacks and staggered stacks） is effectively solved by combining front pallet layer identification with top pallet layer counting. The system also features a non-intrusive integration architecture between the Warehouse Visual Stock System （WVSS） and the existing Warehouse Control System （WCS） via a database， enabling dynamic task scheduling and data closed-loop. Experimental results on four palletized cargo datasets demonstrate a stack quantity recognition accuracy of 96.3% with a processing time of 0.11 seconds per storage location. This solution provides a new engineering path for automated warehousing， characterized by high precision， low deployment cost， and minimal operational disruption.