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 6
25 June 2025
25 June 2025
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In the industrial software ecosystem， Computer-Aided Design（CAD） interfaces play a pivotal role. This study outlines the composition and collaborative mechanisms of the industrial software ecosystem， reviews the evolutionary trajectory of CAD interface technologies， and summarizes their core roles within the ecosystem from the perspectives of data transmission， functional integration， and innovation-driven development.Building on this foundation， an in-depth analysis of the application bottlenecks and challenges faced by CAD interfaces is conducted， including data interface standards， the depth of system integration， and the convergence with emerging technologies. Furthermore，future development trends for CAD interfaces are explored， emphasizing key directions such as data standardization and semantic enrichment， multi-user collaborative design with real-time interaction， and the deep integration of artificial intelligence technologies. This work aims to provide theoretical insights and practical guidance for the research and application of CAD interfaces within the industrial software ecosystem.
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In response to the challenges posed by complex scenes featuring engineering vehicles with intricate backgrounds and relatively small targets， an enhanced engineering vehicle localization and segmentation algorithm based on YOLOv8x-seg is proposed. Firstly， the improved C2f feature fusion module （C2f_Faster） is introduced into the backbone network to extract rich spatial and channel information. Subsequently， the Coordinate Attention Mechanism （CA） is incorporated into the neck network to augment the focus on critical regions in the image， thereby enhancing the model's precision in localizing and segmenting engineering vehicles in complex scenes. Furthermore， the enhanced Feature Pyramid Network （GBiFPN） is utilized to construct a shallow segmentation head， facilitating multi-scale feature representation to effectively address the issue of small target scales. Finally， leveraging field-collected images， the ENNG-3K dataset of engineering vehicles under transmission lines is established， and comparative experiments are conducted. The results demonstrate that the proposed algorithm outperforms YOLOv8x-seg， with an improvement of 2% in mAP50 and 0.8% in mAP50-95， showcasing superior performance.
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Due to factors such as environmental corrosion and working load， the gantry crane （gantry crane） of hydropower station is susceptive to deformation， damage and other failure forms during operation， and the health management and life prediction research of the gantry crane are， therefore，crutial， with its structural stress monitoring being an important part. In recent years， with the development of digital intelligence， digital twin and artificial intelligence have become effective technical means to realize the stress monitoring of door operator. In this paper， by analyzing the stress generation mechanism of the door operator， a stress field construction method based on the mechanism model and a stress field reconstruction method based on Transformer are proposed， and the corresponding digital twin system of the door operator equipment is developed. In the twin system， the stress field is constructed by combining multiple feature points and stress mechanism models， and the Transformer algorithm is used to combine multiple stress observation data to realize the real-time deduction of the stress field of the door operator， so as to achieve the purpose of dynamic reconstruction of the stress field of the door operator， and the method is displayed in the form of the three-dimensional stress contour diagram of the door operator. It can be learned that the method in this paper effectively solves the difficulty in covering the whole pysical field of the stress monitoring of the gantry crane， the cumbersome stress analysis process， and the insufficient computing power of stress simulation in terms of meeting the real-time requirements of the engineering.
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The quality of screw tightening greatly affects the safety of mechanical products， whereas traditional diagnosis approaches are time-consuming and imprecise， and the implementation of effective fault diagnosis， therefore， bears significant engineering value. In this paper， an innovative method of fault diagnosis for screw-tightening based on LSTM and Expert knowledge is proposed. Firstly， tightening process curve under specific failure mode was studied and several expert knowledge rules were established. Secondly， a data pre-processing algorithm was established based on the characteristics of sequential data such as noise clipping， stage segmentation， fitting and sampling to improve the quality of data. After that， the feature vector obtained through LSTM was used as the input of the expert knowledge model to obtain the expert knowledge vector， and the two vectors were combined as the input of the classifier. Finally， compared with SVM and LSTM， the results show that the method has higher diagnostic accuracy in multiple failure modes.
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Facing the requirements for diverse starting and ending positions as well as velocity in the field of robotic motion control， this paper proposes a novel universal S-curve velocity planning algorithm aiming at adapting to arbitrarily specified starting and ending positions and velocity conditions. The paper first introduces the velocity-to-velocity S-curve velocity planning algorithm， then elaborates on the general 7-segment S-curve velocity planning algorithm. Based on this， a more versatile S-curve velocity planning algorithm is proposed. For different input parameters， this paper classifies the S-curve （s-t curve） into ten types and provides detailed segmented planning strategies for each type. Through simulation tests， it is verified that the algorithm not only excels in efficiency but also demonstrates outstanding performance in the smoothness and positional accuracy of the planned curve. Furthermore， tests conducted on actual robotic platforms further confirm that this algorithm can effectively reduce shocks and vibrations during robotic operations， significantly enhancing the operational performance of robots while demonstrating good practicality and broad application prospects.
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The vigorous development of the e-commerce industry has given birth to the "multi-variety and small -batch" order characteristics and the customer demand for delivery timeliness， while the key issue facing warehousing lies in how to develop in the direction of intelligence， reasonably optimize the warehouse layout， and efficiently execute order picking. Based on this， a storage location assignment strategy considering the task sequence was proposed in order to improve the efficiency of order picking and optimize the running time of the climbing robot in the rack-climbing robotic storage and retrieval system （CRSRS）， while a joint optimization strategy considering and jointly optimizing the task scheduling of the climbing robot and the dynamic storage location assignment of the container was considered. With the optimization goal of minimizing the maximum picking time of the climbing robot， a mixed integer programming model was established， and an simulated annealing algorithm was designed to solve the problem. Finally， numerical experiments verified the effectiveness of the proposed storage location assignment strategy and algorithm. The results show that under the SKU decentralized storage strategy， the storage location assignment strategy considering task order is improved by 12.48% and 11.6% on average compared with the fixed storage location and nearby assignment strategies in the randomly generated examples， and the simulated annealing algorithm exerts better optimization effects than the genetic algorithm and the variable neighborhood search algorithm， which are improved by 6.04% and 9.5% on average in the medium and large-scale examples. The proposed storage location assignment strategy， mathematical model and algorithm can effectively save order picking time， and provide a decision-making basis for improving operational efficiency and reducing logistics costs in practical applications.
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At present， close range manual handles are still used at many bridge crane workplaces. To address the potential safety risks faced by operators， a remote path planning bridge crane experimental device is designed to improve the stability and gripping efficiency of the bridge crane and determine the mechanical structure scheme of the device； By studying the working characteristics of the experimental platform in three-dimensional space， the three-dimensional path planning of the device is designed； the ant colony algorithm is improved by storing pheromones on path nodes， and a search method that combines layer by layer advancement with grid plane method is used， optimizing path nodes using pruning algorithm， and updating pheromones using a combination of global and local path planning. Through the above improvement strategies， the improved ant colony algorithm obtained through simulation in MATLAB software has 3 fewer iterations， 46 fewer inflection points， 22.6874 s shorter algorithm time， and 4.4043 units shorter shortest path compared to the traditional ant colony algorithm. Finally， the operating system of the experimental platform is designed， and an experimental prototype is built. The results show that the device meets the actual work requirements， verifying the feasibility and effectiveness of improving ant colony algorithm.
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In ABAQUS simulation analysis， the Young's modulus and Poisson's ratio are input as fixed values with temperature changes， which affects the reliability and accuracy of the results. Therefore， the temperature variation function is embedded into the ultrasonic vibration-assisted cutting （UVAC） simulation of titanium alloy TC4 by using the secondary development function of ABAQUS. The analysis results show that the secondary development carried out is practical and instructive. Compared with the ordinary cutting， the ultrasonic vibration-assisted cutting can effectively achieve chip breaking and reduce the cutting temperature， with the maximum reduction reaching 34%. With the increase of amplitude， the chip length gradually decreases， and the influence depth of amplitude on residual compressive stress shows a trend of first decreasing and then increasing； in contrast， the frequency has a smaller impact on the chip morphology. As the frequency increases， both the chip length and residual compressive stress decrease.
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To address the need for both independent control of Continuous Damping Control （CDC） dampers and coordinated control of the entire vehicle semi-active suspension system， while also improving upon the issues present in traditional semi-active suspension controller software design such as challenges in meeting real-time requirements and low CPU utilization in bare-metal development environments， this study proposes an innovative approach. Initially， the study establishes separate models for the seven-degree-of-freedom semi-active suspension system and the forward-inverse models of CDC dampers. Building upon the skyhook control strategy， the study integrates a vehicle-coordinated parallel fuzzy controller based on the Mamdani fuzzy control method. Subsequently， by transplanting the FreeRTOS-SMP multicore real-time operating system and utilizing the Infineon AURIX series 32-bit triple-core microcontroller TC275 as the main control chip， the study designs the software and hardware system for the CDC damper control unit. Furthermore， the study conducts task scheduling verification of the multicore real-time operating system and validates the effectiveness of the designed control unit and proposed strategy through hardware-in-the-loop testing using typical random road surfaces to demonstrate the improvement in overall ride comfort of the vehicle.
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To effectively address the difficulty in identifying the fault type in distribution networks， a fault type identification method based on phase voltage images is proposed. First， the three-phase voltage signals before and after a fault are extracted for one cycle， and the acquired three-phase voltage signals are converted into line-mode components through a modal transformation matrix. Next， the line-mode components of the voltage signal are transformed into images rich in fault characteristics using Gram angle and field， as well as Gram angle difference field. These two feature maps are then fused in the spatial domain. The fused image， which contains information such as points， lines， and surfaces， can fully reflect the operating conditions of the system. Finally， the fused image is input into a convolutional neural network， and the fault type of the distribution network is output through the Softmax function. The experimental results show that the proposed method can accurately diagnose the fault type of the distribution network under complex conditions such as high-resistance grounding， noisy signals and unsynchronized sampling times， showcasing the high robustness of the diagnostic method.
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Interface debonding is one of the main failure modes of solid rocket motors （SRM） and a weak link limiting the lifespan of SRM. Accurate and reliable monitoring of the debonding damage is of great significance to ensure SRM reliability. However， the identification accuracy of debonding damage is still low. A debonding damage monitoring method based on probabilistic neural network （PNN） using electromechanical impedance is hence proposed in this paper. A PNN for disbonding monitoring is established， and the electromechanical impedance curve is directly used as input to realize the "end to end" identification of disbonding damage location. The proposed method is verified experimentally. The results show that the proposed method can achieve more than 90% damage monitoring accuracy under 12 training samples for each class. The sensor position has little influence on the monitoring accuracy， while overcoming the drawbacks of the existing method which relies on the artificially constructed damage index. The proposed method provides a technical basis for the SMR debonding monitoring.
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In factory automation， pallet recognition is often conducted in multi-object scenarios， and relying solely on the detection confidence provided by neural networks in terms of the selection of targets can lead to misidentification. Additionally， pose estimation for pallets with significant angular deviations or displacements is often ineffective， potentially misleading downstream tasks and wasting computational resources. To address these issues， a pallet recognition method is proposed based on a screening strategy and confidence evaluation in multi-object scenarios. First， leveraging the geometric properties of a depth camera mounted at the center of the forklift， combined with the pixel coordinates， the confidence scores， and the depth information of the neural network's predicted bounding boxes， the target pallets are selected from multiple detection results. Second， the pallet planes are extracted using normal vector estimation and the RANSAC algorithm. Finally， the extracted results and the original pallet plane point cloud are integrated to evaluate the confidence of the current results. Compared to that of the baseline model， the pixel screening strategy effectively filters out non-target pallets in multi-object scenarios， significantly reducing the chance of misidentification. When pallets have significant deviations or poor poses， the confidence evaluation strategy assesses the quality of the extracted planar point clouds， allowing timely identification of unsuitable data and preventing unnecessary pose estimation and resource waste. The integration of these two strategies significantly enhances the system's reliability and robustness in complex environments.
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An integrated FMEA method based on the cloud model and evidence theory is proposed to address the uncertainty and information loss problems of failure mode risk assessment. In this method， the cloud generator is used to quantify the risk assessment information， solve the problems of ambiguity and randomness in the assessment process， and integrate the cloud quantitative risk assessment information under the framework of evidence theory to improve the quality of information. In addition， risk factor weights are also handled with the help of a subjective-objective game combination approach， which takes into account the differences between failure modes and risk factors. The distance between each failure mode and the positive and negative ideal solutions of cloud evidence is calculated according to cloud evidence fusion information， and the TOPSIS method is then used to prioritize the risk of failure modes. Finally， the risk assessment of painting processes of a certain company is used as an example to validate the applicability of the proposed method.
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Currently， the fault diagnosis methods for the equipment in the insulation board production line lack systematic organization and effective utilization of the historical fault data. To address this issue， a method for constructing a fault knowledge graph for insulation board production lines is proposed. Firstly， the characteristics of selected fault knowledge were analyzed to define the ontology of fault knowledge for insulation board production lines. Secondly， multi-source fault data was preprocessed at the data level and sequentially annotated to form a dataset suitable for experimentation. Based on this data， deep learning models such as BERT-BiLSTM-CRF were introduced on entity recognition. Entities were matched based on syntactic trees to generate triplet data. Finally， the Neo4j graph database was employed to store the triplet information， completing the construction of the production line fault knowledge graph. The research results indicate that the entity recognition model achieved an accuracy of 84.84%， a recall rate of 89.88%， and an F 1 score of 87.29%. Leveraging this， the fault knowledge graph for the insulation board production line was established， enabling effective utilization of fault knowledge and precise explanation of fault causes. These findings can provide knowledge support for fault diagnosis in insulation board production lines.
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To solve the dynamic job shop scheduling problem in scenarios with variable job and machine quantities， a solution approach called Dense-D3QN， combining DenseNet， a densely connected convolutional network， with Dueling Double Deep Q-Learning （D3QN） is proposed. The disjunctive graph model is utilized to construct a single-objective job shop scheduling model aiming to minimize the maximum processing time， representing the scheduling state in the form of multi-dimensional matrices while designing a dense-sparse reward function. To validate the effectiveness of the proposed algorithm， both public benchmarks and real data are used to construct common and actual scheduling environments. The Dense-D3QN model is trained and tested in the common environment. In the actual environment， the Dense-D3QN model is trained and tested in both static and dynamic settings. The experimental results demonstrate that the Dense-D3QN model is more capable of handling dynamic job shop scheduling problems with variable scales.
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To address the difficulty in accurately calculating the effective working hours of workshop workers and the issue of low interpretability， an explainable digital twin-based calculation method for effective working hours in human-centered manufacturing workshops is proposed， inspired by the human-centric concept of Industry 5.0. First， a framework for calculating effective working hours based on digital twins is established. Then， an integrated worker positioning method combining machine vision and UWB is proposed to achieve precise worker positioning. Next， effective working hours are calculated based on the detection of both working and non-working behaviors with corresponding interpretable 3D visualizations provided. Finally， a prototype system is developed on the Unity3D platform， using the Key Laboratory of Intelligent Manufacturing of Mechanical Equipment of Henan province as a case study to validate the feasibility and effectiveness of the proposed method.
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To tackle the problems such as excessive reliance on manual experience and low plan granularity in the production scheduling of multi-station assembly workshops， an intelligent production scheduling scheme is proposed. First， the scheduling problem is modeled as a Markov Decision Process （MDP）， with a deep reinforcement learning approach aiming at minimizing assembly task completion time. Next， a hierarchical multi-agent cooperation strategy is designed based on workshop characteristics， incorporating noisy networks and prioritized experience replay to enhance training efficiency. Finally， a proactive scheduling method is proposed to mitigate frequent adjustments caused by material shortages， assessesing material readiness time using inventory， delivery， and processing data，while integrating the results as a key input for scheduling. The simulation results demonstrate that the proposed algorithm achieves fast convergence， high stability， and optimal scheduling strategies.
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The tender document is the core basis for the configuration design of high-speed trains. Existing methods for extracting technical specifications rely mainly on manual operations， leading to low efficiency and the possibility of omissions. Despite significant progress in document entity and relation extraction technology in recent years， the automatic extraction of technical specifications still fails to meet expectations due to differences in the descriptions of specifications of various stakeholders and the complex structural and interface constraints between train modules. To address this， a method for automatically extracting technical indicators from bidding documents by integrating knowledge graphs and large language models has been proposed. First， a product series configuration design ontology model is established， and the ontology structure and constraints are defined. Next， an automatic extraction framework is designed， wherein a pre-trained large language model is used to automatically extract entities and relations from the tender document. The extraction results are pre-aligned using a product series meta-structure tree and an named dictionary to construct a product technical specification data graph. Additionally， structural， interface， and performance constraints between modules are defined using a shape constraint definition language， and a shape constraint graph is constructed to check and correct the automatic extraction results. Finally， an automatic extraction tool is developed， and the effectiveness of the proposed method is verified using a case study of a high-speed train tender document.
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To solve the problems of small molding size and low printing efficiency of traditional desktop-level single-nozzle FDM 3D printer， a medium-sized FDM multi-nozzles collaborative 3D printer is designed and built. The printer adopts a Cartesian （XYZ） structure and is equipped with three side-by-side composite printing nozzles， and the materials can be selectively extruded according to the demand. The control system is divided into three parts according to the functions： main motion control module，embedded auxiliary measurement and control module and upper computer software module，while the software and hardware of these three parts are developed.Two printing modes of multi-nozzles synchronous forming and multi-nozzles stackable co-filling are designed and the corresponding path planning algorithms are proposed.After printing verification， compared with single-nozzle printing， the synchronous forming efficiency of the composite multi-nozzles printer is increased by 3 times， whereas the stackable co-filling printing time is reduced by 41%. The printing efficiency is significantly improved under the premise of ensuring the printing quality.
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Aiming at the pain point of slow traditional rescue response in water areas， this paper proposes a structural design scheme of an amphibious rescue equipment that integrates the functions of unmanned aerial vehicles （UAVs） and rescue boats. The UAV adopts a lightweight fuselage and NACA4412 airfoil aerodynamic design. Through the rotation of wings and elescopic mechanism of the counter-rotating propellers， a rapid cross-medium form switching can be achieved. Meanwhile， the dynamic performance is analyzed by establishing the mathematical model and state-space model of the drone， and a fuzzy PID controller is designed. MATLAB/Simulink is used to carry out dynamic response simulation verification for the mathematical model of the drone and the designed fuzzy PID controller. The results show that when the input is a square wave and a step signal， the fuzzy PID controller designed in this paper has a faster response speed and better stability compared with the traditional PID controller.