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 46 Issue 11
25 November 2024
25 November 2024
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Traditional ant colony algorithms generally suffer from low convergence efficiency， insufficient global search capabilities， and a tendency to fall into local optima when dealing with large-scale complex scenarios. The method enhances the guidance and adaptability of the heuristic function to improve the algorithm's optimization ability. By utilizing a pseudo-random transfer strategy to improve state transition rules and dynamically adjusting the pheromone heuristic factor and the expected heuristic factor， the global search capability is enhanced， and the issue of local optima is avoided. At the same time， a multi-strategy approach is adopted to optimize the pheromone update mechanism， combined with an adaptive adjustment of the evaporation coefficient， significantly improving convergence efficiency and search performance. Additionally， through secondary optimization by removing redundant points， the path quality is further optimized. The simulation experiments on grid maps demonstrate that the ant colony algorithm， after multiple strategy improvements， achieves significant improvements in search ability and convergence speed， providing theoretical support for solving path planning problems for AGVs in complex environments.
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To improve the stability of freight AGV， the response of AGV suspension system under random road excitation conditions was studied， and stability analysis of freight AGV was conducted. Firstly， based on the time domain and spatial domain variables and their derivative transformation relationships， a vehicle vibration coupled response model and a road excitation model were derived. Then， based on BP neural network optimization， orthogonal experiments were introduced to optimize the stability evaluation indicators， and the optimal vibration response combination scheme was obtained. Finally， simulations and experiments were conducted to analyze the impact of state changes on driving stability of freight AGVs under non-stationary excitation， verifying the rationality of the model and optimization. The general laws of AGV time domain and driving state changes were summarized， providing new ideas for studying AGV driving stability.
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To address the issue of the active actuation used mostly by variable stiffness joints， a lower-extremity exoskeleton with passive variable stiffness is proposed. Firstly， a variable stiffness joint is designed based on the basic walking principle of the lower limbs， which can change the output stiffness of the joint according to the walking gait. Then the theoretical model of that joint is established to analyze the joint torque and stiffness properties. Finally， a human-machine coupling model is established for dynamics simulation， and the effect of different initial angles of the joint on the exoskeleton's torque and stiffness properties is studied to obtain the optimal initial angle of the joint. The results show that the optimal output torque of the joint is achieved at an initial joint angle of 2.5°， and also has the maximum range of stiffness variation， in which case the exoskeleton can carry more loads.
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The simulated moving bed chromatography separation process exhibits characteristics such as strong coupling， nonlinearity and periodicity. The control of this separation process has always been a complex and challenging issue. In this study， Type I fuzzy control strategy and Type II fuzzy control strategy methods are employed to control the purity and recovery rates of components A and B in the simulated moving bed separation process， and comparisons are made with conventional fuzzy control. The research results indicate that both Type I fuzzy control strategy and Type II fuzzy control strategy can achieve the expected control effects for multiple objectives. In comparison， Type II fuzzy control strategy demonstrates higher precision in recovery rate control. Through simulation experiments， the robustness of the two controllers is studied when parameters such as switching time and porosity change， as well as the disturbance resistance of the two controllers under the application of disturbances. Experimental results show that Type II fuzzy control strategy has good control performance， further improving the reliability of the SMB system in unstable environments.
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The flexible linkage mechanism driven by permanent magnet synchronous motor is a complex and strongly coupled nonlinear system， which is widely used in the applications with lightweight structure and high self-weight ratio， such as flexible manipulators， solar panels， and automated production lines. The existence of flexible links can produce obvious deformation， cause resonance， reduce the accuracy of the system and even lead to instability， all of which increases the difficulty of the system modeling and control. At the same time， since the model parameters of the flexible linkage system are difficult to fully obtain， a Multiple Capacity Process PI （MCP-PI） control method based on parameter identification of multiple information models is therefore proposed in this paper. Firstly， a dynamic model of the flexible linkage system is established according to its structural characteristics. Secondly， the model of the flexible linkage servo system is discretized， and the multi-innovation stochastic gradient algorithm is used to identify the parameters of the flexible linkage. Then， the multi-capacity process theory is used to design the PI control parameters of the current loop and speed loop. Finally， the rapidity and accuracy of the proposed method are demonstrated by numerical simulation， and the superiority of the proposed MCP-PI control method is verified by comparing it with engineering design method and pole placement method.
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This paper addresses the path tracking control problem of 4WID vehicles under uncertain upper bound disturbance and proposes a sliding mode control method suitable for mismatched uncertain systems. Firstly， considering that disturbances do not necessarily satisfy matching conditions， this paper designs a linear sliding mode surface that is robust to mismatched disturbances based on the Backstepping method and LMI. Secondly， for disturbances that are bounded but with unknown upper bounds， this paper designs a sliding mode control law that adaptively changes the gains of the SMC based on a positive semidefinite barrier function. This method does not require any prior knowledge about the disturbance. Thirdly， a vehicle road tracking model with uncertain sideslip stiffness and road curvature is established， and a lateral motion controller is designed based on this model. Finally， the robustness and stability of the proposed controller are verified by CarSim-Simulink joint simulation. The simulation results show that the proposed controller is robust to uncertain parameters and can effectively eliminate chattering.
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Most of the existing anti swing control methods of overhead crane are based on the design of single crane models. When the load is too large and needs to be transported by two cranes together， a closed kinematic chain is formed between the two cranes， which makes the coupling relationship more complex. The offset of the center of gravity of a large load will increase the difficulty of swing angle control. A nonlinear coupling anti swing controller is proposed to solve the problems of positioning accuracy and eccentric large load swing in the overhead crane system with double cranes. First of all， the dynamic model of eccentric center of gravity is established for the lifting system of dual trolley bridge crane， and the equation of large size load swing angle is derived； Then， based on the characteristic structure of the lifting system of the dual trolley bridge crane， the coupling signal containing the trolley displacement and the swing angle of the two ropes is constructed， and the nonlinear coupling anti swing controller is designed based on the energy method. The simulation results show that the controller can effectively suppress and eliminate the swing angles of the lifting rope and the load， and its control performance is superior to traditional control methods and has strong robustness.
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The decision-making process of CNC machining parameters relies heavily on the engineers' experience， leading to long preparation and unstable machining quality. The oil distributor cap， oil distributor and shell parts are critical components of the hydraulic pump system. Their internal slot features often suffer from low machining efficiency and high costs， which are the key targets for production enterprises' cost reduction and efficiency improvement. Therefore， establishing the relationship between milling process parameters and process indicators is essential for evaluating and optimizing machining parameters， thus improving machining efficiency and ensuring process quality. Due to the complex thermo-mechanical coupling involved in the machining process， the relationship between machining parameters and process indicators is highly nonlinear， making it difficult to model effectively using traditional fitting or data-driven methods. To address this issue， this paper proposes a cutting parameter evaluation model based on a Variational Autoencoder （VAE）， which establishes a predictive model between machining parameters and key physical quantities that reflect cutting characteristics. This model serves as a foundation for optimizing machining parameters in production enterprises. The validation results show that the prediction error of the proposed model is merely 1.2%， fulfilling the requirements for machining parameter optimization.
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To address the issues of high cutting force and elevated cutting temperatures during the milling of 1Cr11Ni2W2MoV heat-resistant stainless steel， optimization of milling parameters was conducted. A 3D milling simulation model was established using AdvantEdge FEM simulation software to investigate the influence of milling parameters on cutting force and cutting temperature. The optimization of milling parameters was achieved through orthogonal experiments. The results indicate that the optimal cutting parameters， based on minimizing cutting force， are a spindle speed of 4000 r/min， a feed per tooth of 0.08 mm/r， and a cutting depth of 0.3 mm. The milling parameter optimization plan based on the lowest cutting temperature is as follows： the spindle speed is 1000 r/min， the feed rate per tooth is 0.12 mm/r， and the cutting depth is 0.3 mm. Adopting a larger spindle speed， smaller feed rate per tooth， and cutting depth is beneficial for reducing cutting force while not affecting production efficiency； Adopting a smaller spindle speed， moderate feed rate per tooth， and smaller cutting depth can lower the cutting temperature.
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A reasonable inventory control strategy for spare parts has direct influence on the stable production and the economic benefit of manufacturing enterprises， especially on the spare parts of class A with large production influence and large consumption. This kind of spare parts tends to have a long lead time and complicated demand changes. Therefore， two multi-objective dynamic inventory control strategies considering cost， satisfaction rate and turnover rate are proposed based on future prediction and historical demand. The effect of the model is verified by the real data of one cigarette factory. It is proved that， compared with the enterprise’s existing strategy， the dynamic model based on future prediction has the best effect for the spare parts whose demand occurrence time is unstable and less， with the comprehensive performance of each index improved by 50.74%. For the spare parts whose demand occurrence time is relatively stable， the dynamic model based on historical demand is more advantageous with comprehensive performance of each index increased by 4.31%. Meanwhile， the sensitivity analysis of each target’s weight is also carried out. The analysis shows that the dynamic model based on future prediction should be prioritized when the importance of the fill rate increases， whereas the dynamic model based on historical demand should be considered when the enterprise is more concerned about the total cost and turnover rate. The research results provide important management guidance for manufacturing enterprises to choose appropriate inventory control strategy under different demand characteristics and different target requirements.
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Person re-identification technology plays an important role in intelligent surveillance system. Aiming at the problem of the insuficient extraction of pedestian features by visual transformer in scenes such as occlusion and similar appearance， resulting in low accuracy of person re-identification， a person re-identification method fusing multi-granularity features and Transformer architecture is proposed. Firstly， the channel interaction attention module is embedded into the Transformer network to enhance the ability of the network to capture key features in the channel dimension； Secondly， the multi-granularity features that delineate the image are fed into the last layer of the Transformer architecture to generate local classification tokens， which mine attribute dependencies by augmenting local information with global information to capture more robust local features.The method achieves 95.5% and 90.6% of Rank-1 metrics and 88.9% and 81.9% of mAP metrics on Market-1501 and DukeMTMC-ReID datasets， respectively. The experimental results show that this method possesses a better person re-identification ability.
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The replacement rate of freight train parts is an important data source for freight train remanufacturing enterprises to formulate pre-investment and pre-purchase plans， while the prediction of a more accurate replacement can help enterprises shorten delivery time and reduce inventory. At present， enterprises that fail to fully consider the impact of various factors on the replacement rate during estimation find it difficult to effectively formulate accurate pre-investment and pre-purchase plans， thereby shortening the delivery time and reducing the inventory costs. This paper analyzes the actual business scenarios， and proposes a dynamic model-based method for predicting the replacement rate of freight train parts. This method combines the multi-dimensional factors affecting the replacement rate of freight trains， and uses the support vector machine regression model to construct a dynamic model of the parts replacement rate. By validating the model against historical replacement data of freight train bogie components， the experimental results demonstrate that the model achieves a mean absolute error of less than 15% on the testing set， confirming its effectiveness and applicability in real-world scenarios. This method provides data support for enterprises' pre-investment and pre-purchase decision-making as well as inventory control in the remanufacturing process， which helps enterprises to reduce costs and improve competitiveness.
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The high fidelity of digital twin models is crucial for the performance of machine tools in intelligent applications. The electric spindle encounters problems of complex coupling relationships that are difficult to describe and sensitive parameters that are susceptible to change during digital twin modeling， which causes certain errors between the model's response and the real physical system， resulting in limitations in simulation analysis of various stages based on twin models. This article proposes a twin model updating method based on Bayesian inference. Firstly， a twin model of electric spindle based on multi domain unified modeling language is constructed by integrating knowledge from various disciplines； Secondly， the mechanism of parameter changes is anylized during operation， while Bayesian inference method is introduced to construct a twin model update strategy， and solve Bayesian posterior distribution is solved through variational inference by combining actual operation data and prior knowledge； Finally， the effectiveness of the model update method is verified through an example of the machine tool electric spindle. After the update， the simulation error of the model reaches below 5%， effectively improving the fidelity of the twin model of the electric spindle.
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Loading and unloading operations are one of the labor-intensive scenarios in the logistics field. As a crucial issue in automatic loading and unloading， the three-dimensional packing problem is increasingly receiving attention. In response to the shortcomings of traditional 3D packing algorithms that do not pay attention to the order loading and unloading sequence， this paper proposes an efficient and fast two-stage heuristic algorithm for the 3D packing problem under the premise of considering customer order classification and loading and unloading sequence. The algorithm first stacks goods into a "tower" shape and uses dimensionality reduction to transform it into a two-dimensional rectangular filling problem for optimization. It innovatively combines the skyline algorithm with the BL （Bottom Left） algorithm to decode the optimal packing order and position. The experimental results have shown that this algorithm can maximize the space utilization of the packing strategy.
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A design method for an integrated system for detecting and controlling the thickness uniformity of blown film based on visual servoing is proposed to address issues in the film production process such as uneven discharge leading to quality problems and wastage of resources. The visual detection module is based on machine vision and image semantic segmentation algorithms. By monitoring and recognizing the contour of the formed film using a camera， the image information of the plastic film's outer contour is extracted. Additionally， a midline fitting algorithm is proposed to calculate and analyze the asymmetry of the film， thereby detecting its thickness uniformity. The control module is based on dual-axis interpolation linear motion. With the asymmetry information and position information provided by the detection module， real-time control and feedback are performed on the gap adjustment mechanism of the film mouth on the XY moving platform equipped with two-axis servo motors， ensuring uniform discharge of the film and overall quality. The experimental results demonstrate that the designed system achieves a 97% confidence level in image processing， with a deviation recognition error of 0.485° and an interpolation control error of 0.098 mm， meeting the requirements of the system.
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This study proposes an efficient text recognition algorithm that addresses challenges encountered in text recognition in diverse environments， such as text skew and varying sizes. First， the algorithm incorporates an attention mechanism during the feature extraction phase of a deep convolutional neural network to facilitate interaction between different levels， thereby reducing instances of missed detections caused by text positional diversity. Second， the use of dilated convolutions with variable receptive fields helps to capture detailed information in text regions and to adapt to text variations at different scales. Finally， the research employs a feature pyramid enhancement mechanism to efficiently integrate features of different sizes， channels and depths， which are then integrated into the final features used for segmentation. This not only enhances the accuracy of text detection but also reduces the complexity of the model. The research results show that on the ICDAR 2015 dataset， this improved algorithm achieves a detection accuracy of 88.1%， an improvement over the leading DBNet algorithm currently in use.
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To avoid the influence of human subjective factors and a single time-domain and frequency-domain analysis on the detection accuracy of metal equipment in the process of ultrasonic defect detection， this paper proposes to use time-frequency analysis to obtain ultrasonic signal spectrum information， and combine it with visual Transformer model （Ultra-ViT） to realize the recognition and classification of ultrasonic A-scan defect detection signals. Firstly， ultrasonic A-scan is used to obtain the defect signal of the artificial metal defect plate， make the defect signal data set and extract the spectrum characteristics of the defect signal. Using spectral features as training samples， spectral image blocks are extracted， their positions are encoded， and the Ultra-ViT detection model is utilized for training to realize the safety detection of metal equipment. The Experiments show that the recognition accuracy of ultrasonic spectrogram features using Ultra-ViT model is 97.73%. Compared with the classical CNN model， this model can achieve higher detection accuracy with higher detection speed. It shows the superiority of this method in ultrasonic spectrum detection.
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A YOLOv5s network with fused large convolution kernels is proposed to address the problem of low efficiency and poor accuracy in the detection of surface cracks on anchor bolts. Firstly， the large convolutional kernels are fused in the feature extraction network to obtain a larger effective receptive field and extract more spatial information. Secondly， the Omni-dimensional dynamic convolution with a single convolutional kernel is introduced， and a parallel strategy is used to study four different dimensions of features simultaneously， which not only reduces the computational effort but also improves the feature extraction capability. Finally， a coordinated attention mechanism is added to enhance the extraction of location information. The experimental results show that the algorithm improves mAP by 3%， reduces FLOPs by 21.5% and achieves FPS of 85.0 frames per second compared to that of the original YOLOv5s model on the wind power anchor bolt crack dataset， which can meet the real-time and accuracy requirements of industrial production.
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Accurate temperature field measurement is very important in the industrial field， and ultrasonic temperature tomography imaging has a broad application prospect and significance. In order to improve the accuracy of the reconstructed temperature field distribution， a two-step temperature field reconstruction method is used in this paper. First， the low-resolution temperature field is solved by combining a cellular automaton （CA） with an equilibrium optimizer （EO）， and then an interpolation method is used to improve the temperature field resolution. In this paper， a series of comparison tests are used to verify the performance of the algorithm， and finally， numerical simulations are carried out using ansys simulation software to verify the practical feasibility of the algorithm. The results show that the CAEO algorithm is able to effectively improve the accuracy of the temperature field reconstruction， and provide a new solution for the temperature field laminar solving.
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To address the limited transferability issue of traditional deep learning in bearing fault diagnosis under variable operating conditions， an innovative approach that combined Weighted Limited Passage Horizontal Visual Graph （WLPHVG） and Graph Isomorphic Network （GIN） was propossed in this paper. The method transformed raw bearing vibration time-series signals into graph-structured data using WLPHVG and employed the Maximum Mean Discrepancy method to weight the edges connecting graph nodes， reducing the impact of noise on model accuracy. At the same time， adjustments were made to the aggregation layer of the Graph Isomorphic Network to better capture the features of graph data. The graph data was processed then by the final model to obtain the classification results for bearing fault diagnosis. Experimental evaluations on multiple bearing datasets using various models demonstrated that the proposed approach achieved superior accuracy （over 97.27%） under different operating conditions and signal-to-noise ratios compared to that of other benchmark models. The deconstructive experiments validated the effectiveness of the weighting method on the noise resistance of the WLPHVG-GIN model， confirming its ability to leverage internal structural relationships in graph domain data and exhibit strong transferability under diverse conditions such as cross-platform and varying speeds.
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To optimize the detection of abnormal shapes of cardboard boxes on conveyor belts in the existing logistics industry， an improved backbone network has been developed based on the YOLOv8 framework， introducing a lightweight backbone network that integrates a shiftconv module. A new detection head， DSDET， has been proposed to maintain relatively high accuracy while keeping the network lightweight. To solve the problem of poor detection performance in case of target occlusion， a Repulsion Loss function has been used. This loss function encourages predictions of the same class to be as distant as possible from each other， thereby increasing the accuracy of the detection network model in tasks involving carton shape anomaly detection with a lot of occlusions. Testing of the object detection network model on a self-made carton dataset has shown an average detection accuracy of 93.8%， which is a 2.4% improvement in detection accuracy compared to the original YOLOv8. This method can be applied to the detection of carton shape anomalies in automated logistics scenarios.
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In this article， a digital twin virtual system of the Rice Processing System is built based on Unreal Engine 5. Aiming at the mutual mapping and integration of physical simulation platform and virtual simulation platform， a digital twin system model of the processing line of preserved rice is constructed.To address the data transmission problem of digital twin five-dimensional system model， a real-time data communication network architecture of digital twin system is constructed， while a new comprehensive network based on Mask RCNN is designed to promote the digital transformation of rice processing to address the issue of detection and segmentation of the fine rice quality with the help of extremely subtle local differences in characteristics. By applying this digital twin system， the detection speed of rice in the production and processing process is greatly improved， the problem of untimely detection data caused by the need for manual inspection in the traditional rice processing industry is solved， and the pain point of the industry where milling accuracy depends on the experience of skilled workers is tackled.
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According to the requirements of the national standard， the frame of a certain type of battery rail vehicle needs to be checked and tested in terms of statics and dynamics. In the experimental analysis， it is difficult to use the original frame in equal proportion to carry out the relevant research due to the large size of the structure， hence the research on the scale is carried out. According to the actual parameters of railcar frame， a set of similarity constants of material， geometry， load and power is established. The similarity constant is used to convert the main design parameters of the original frame into the main design parameters of the scale frame. The finite element analysis software is used to simulate the static and dynamic simulation of the scale frame， and the calculated values of the stiffness， strength， damping and mass of the scale frame are obtained. Comparing it with the design value calculated by the similarity theory， the error is less than 10%. It can be concluded that the designed scale frame is similar to the original frame. The modal analysis method of single-point excitation is used to test the physical scale model. The difference between the modal frequency of the scale model and the theoretical value is less than 10%， which verifies the consistency between the physical model and the theoretical model of the scale frame.