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 10
25 October 2025
25 October 2025
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In recent years， indigenously developed， aerospace-specific 3D structural design systems in China have undergone robust development， with notable achievements in the R&D of core components. However， with the widespread adoption of Large Language Models （LLMs）， establishing an effective interface between 3D structural design and AI-driven methodologies remains a central challenge. Furthermore， existing LLMs lack the capacity for precise reasoning over 3D geometry and complex physical fields， such as aerodynamics， which precludes their direct application in the intelligent design of aircraft structures. Among aerospace structural components， the aircraft wing is critical for generating lift. Its design process is highly complex， heavily reliant on expert experience， and tightly coupled with aerodynamic performance. Consequently， traditional design paradigms are characterized by lengthy iteration cycles and substantial costs. To address this challenge， this paper presents Airfoil-LLM， an intelligent design interface for the 3D modeling of aircraft wings， using the wing as a representative case study. Based on the Transformer architecture， this interface integrates natural language encoding with the decoding of CAD modeling sequences to enable intelligent and automated 3D wing generation. To support model training and validation， we have constructed a large-scale 3D wing design dataset. This comprehensive dataset comprises parameterized 3D CAD models， a wide spectrum of flight conditions from subsonic to supersonic regimes， key aerodynamic performance metrics， and multi-level textual descriptions. Experimental results demonstrate that Airfoil-LLM is capable of deeply comprehending textual descriptions ranging from simple geometric attributes to complex， coupled "geometry-performance" requirements. The system generates 3D models that align closely with the design targets in both geometric shape， achieving a maximum Intersection over Union （IoU） of 0.831， and aerodynamic performance.
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Difficult-to-cut materials are widely used in the aerospace and aviation industries. These materials have characteristics such as high cutting difficulty， high material cost， and difficult calibration experiments. In the finite element simulation modeling process of difficult-to-cut materials， the setting of material mechanical properties and tool chip friction performance， will significantly affect the prediction accuracy of the simulation model. How to achieve efficient acquisition of mechanical property parameters of difficult-to-cut materials， is of great significance to study the rapid and accurate uncertainty calibration strategy of simulation models. Taking the milling process of Ti2AlNb intermetallic compound as an example， a finite element simulation model uncertainty calibration method for difficult-to-cut materials under the Bayesian framework is proposed. Firstly， an uncertainty analysis of the model is conducted， and a Bayesian based model uncertainties quantification method is proposed. The uncertainty coefficients are solved using the Markov chain Monte Carlo method. Secondly， finite element modeling， simulation experiment design， and simulation dataset construction for milling process are carried out based on finite element simulation software. A surrogate modeling method based on Gaussian process regression and supporting vector regression is proposed. Finally， Ti2AlNb milling experimental design is carried out， and the working condition dataset is constructed to quantify the JC constitutive parameters and tool chip friction coefficient within the finite element model. The experimental results show that the uncertainty-calibrated finite element simulation model has significantly improved the prediction accuracy， and the relative error in predicting the cutting force of three-dimensional has decreased from 21.47% before calibration to 12.17%.
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In precision assembly， research on the contact state and resulting non-ideal assembly pose variations during the mating of part surfaces primarily focuses on algorithm prediction， with less attention paid to detection methods for actual minor pose variations and part contact states. This paper proposes an opto-electronic collaborative detection method for contact state and pose inprecision assembly. It employs a light signal device to detect the contact state of parts during assembly through circuit on/off states， and utilizes three laser displacement sensors to measure non-ideal assembly pose variations caused by parts in contact. Compared with related prediction algorithms， this method effectively detects part contact states and assembly pose variations.
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To address the issues of high-frequency random jitter and low-frequency drift in laser collimation signals caused by air disturbances during laser straightness measurement， this paper proposes a combined suppression method based on Variational Mode Decomposition-Kalman Filter （VMD-KF）. VMD precisely separates and eliminates higher-frequency modes affected by air disturbances through frequency-domain decomposition， while KF tracks and compensates for low-frequency drift in real-time using a state-space model. Experiments were conducted under artificially created air disturbance conditions to evaluate the proposed method's effectiveness in suppressing disturbance effects. Results demonstrate that at a transmission distance of 400 mm， the method reduces the standard deviation of laser collimation signals by 47% in the horizontal direction and 46% in the vertical direction. This approach effectively mitigates the impact of random air disturbances on the measurement reference in laser straightness measurement.
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Aiming at the critical problem that real-time industrial defect detection systems are difficult to balance detection speed， accuracy and computational resource constraints in edge computing environments， a fast lightweight industrial defect detection architecture based on an efficient hybrid state space model is proposed. The architecture designs a C2f_EfficientViM_CGLU fast feature extraction module that deeply integrates the global sequence modelling capability of the visual state space model with the efficient local feature enhancement mechanism of convolutional gated linear units， achieving fast and efficient extraction of complex defect features. The HSM-SSD （Hidden State Mixer based State Space Duality） efficient state space modeling mechanism is introduced to process long sequence dependencies with O（n） linear complexity， significantly improving the fast recognition capability for irregularly shaped and sparsely distributed defects. A Slimneck fast lightweight feature fusion network is constructed through GSConv （Ghost Shuffle Convolution） sparse convolution and VoV-GSCSP （Variance of Variance Ghost Shuffle Cross Stage Partial） efficient feature fusion strategies， achieving significant improvements in inference speed and extreme model compression while ensuring detection accuracy. Comparative experimental results on NEU-DET and APDDD standard datasets show that the proposed network architecture achieves mAP50 of 92.13% on NEU-DET dataset， improving 9.77 percentage points compared to the baseline model YOLOv8n， with only 2.9 M parameters and 7.7 GFLOPs computational complexity， reducing parameters by more than 93% compared to the traditional Faster-RCNN method. The mAP50 on APDDD dataset reaches 89.68%， validating the good generalization performance and fast detection capability of the method. This study provides a theoretical foundation and an efficient and feasible fast detection technical solution for real-time quality control in Industry 4.0 intelligent manufacturing environments.
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The performance of the rolling bearing fault diagnosis model is highly dependent on the training dataset. During the process of constructing the model training dataset， experimental data possesses high precision but limited operational coverage， while simulation data has broad operational coverage but insufficient precision. To address this， this paper proposes a compensatory neural network method that integrates experimental and simulation data. This method utilizes a feature prediction module trained on simulation data to broaden operational coverage and corrects errors through a residual compensation module driven by experimental data， effectively reducing the deviation between simulation and experimental data. Experiments demonstrate that this method not only generates multi-condition samples but also reduces the root mean square error of feature values between experimental and simulation data from 8.56 to 0.53， significantly enhancing sample precision and providing an effective technical approach to constructing a highly reliable training dataset that covers a wide range of operational conditions.
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The recoil impact generated during the firing process of light weapons significantly shortens the service life of the weapon station's servo platform and reduces shooting accuracy. To effectively mitigate recoil impact while ensuring shooting accuracy， a compliant buffer mechanism composed of multiple modular compliant units has been designed. To address the issue of guidance errors caused by parasitic motion in compliant buffer mechanisms， which reduces shooting accuracy， this study investigates the impact of different layouts of compliant buffer mechanisms on shooting accuracy. Using a combined approach of finite element simulation and experimental testing， the modal characteristics and parasitic pitch error angles of the mechanisms were analyzed. The results show that both the two-unit and four-unit parallel compliant layouts share identical first three modal shapes： the first-order mode is translational motion along the working direction， while the second and third-order modes are rotational motion in non-working directions， inducing parasitic errors in pitch and yaw directions. Layout two significantly outperforms layout one in suppressing parasitic pitch motion. Under an operating height of 80 mm and a maximum thrust of 1200 N， the pitch error of layout two is 0.83 mrad， which is 43.15% lower than that of layout one （1.46 mrad）. Based on the requirements of weapon station applications， it is recommended to use compliant buffer mechanisms composed of four or more compliant units in parallel.
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Robust anomaly detection in high-dimensional industrial data is crucial for ensuring equipment safety and production quality. However， existing methods often fail due to insufficient adaptability of feature importance， the vulnerability of single-perspective detection mechanisms to noise interference， and limited generalization capability for anomaly patterns. To address these issues， an end-to-end multi-perspective anomaly detection architecture named the Integrated Reconstruction and Adaptive Selection - Knowledge Distillation is proposed. This architecture innovatively integrates three core components： The reconstruction verification module， which learns compact representations of normal data through multi-scale auto-encoders to capture structural deviation； The knowledge distillation module， which transfers semantic knowledge using a teacher-student network to provide an independent verification perspective so to enhance generalization capability； The adaptive feature selection module， which dynamically learns feature importance weights through a gated attention mechanism to focus on discriminative information. These three modules are jointly optimized through a multi-objective dynamic weighted loss function that fuses reconstruction error， knowledge alignment difference， and attention regularization， achieving complementary verification of multi-level information.
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Due to the discontinuities between the design and assembly stages， split structural components are prone to uneven stress distribution and local stress concentration around bolt holes， which can reduce the stiffness and reliability of the assembled structure. Therefore， it is necessary to establish an accurate stress calculation model around holes to guide hole placement design in assemblies.In this study， based on contact theory and the principle of superposition coupling， the stress field around the hole is decomposed into the uniform contact stress induced by bolt preload and the two-dimensional far-field stress， with finite-size correction and three-dimensional thickness-direction modification applied， thereby constructing a theoretical model for calculating the stress distribution around holes under biaxial loading. A margin parameter factor is introduced to quantify the edge effect， and the influences of the margin parameter， bolt preload， and tensile load on the hole-edge stress are analyzed.The results show that the proposed theoretical model can accurately predict the hole-edge stress in split structural components under biaxial loading， with the calculation error within 10% compared to finite element simulation. An increase in the margin parameter factor significantly raises both the peak stress around the hole and its distribution range； a higher preload generates compressive stress that offsets part of the tensile stress， slowing the growth rate of the maximum tensile stress， whereas an increased tensile load leads to an accelerated growth trend in the maximum tensile stress. The margin parameter factor provides a quantitative basis for optimizing hole placement and contributes to improving the reliability and load-bearing capacity of assembled structures.
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In response to the characteristics of large geometric dimensions， thick billets， high difficulty in integrated forming of billets， and uncontrollable internal quality of the conical section components of large lightweight aluminum alloy cabin， research on reverse engineering machining and manufacturing of large conical section surfaces is carried out. A split manufacturing scheme is proposed， which includes "flat plate cutting， plate rolling forming， offline CNC machining， combined ring welding， and overall machining". Based on the physical model of conical section billets， optical scanning technology is used to measure the curved rolling accuracy of conical section billets in reverse. Through offline CNC machining technology， the linear dimension accuracy of the welding end face of conical section billets with a large end diameter of 4000 mm is achieved to ± 0.3 mm， laying a technical foundation for the integrated high-quality manufacturing of large and ultra large lightweight cabin parts.
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To reduce the engagement response time of the electro-pneumatic converter， a new design incorporating a spring plate and an embedded permanent magnet is proposed. By adjusting the spring plate and its initial preload， the influence of friction on the response time can be minimized， thereby accelerating the engagement of the armature. A multi-physics coupled dynamic simulation model of the electro-pneumatic converter was established. Using Fluent software， the backpressure at the nozzle and the aerodynamic forces acting on the armature were estimated and simulated. Using Maxwell software， the effects of various parameters （driving voltage， coil turns， working air gap， reed leaf stiffness， and initial preload of the reed leaf） on the response time of the electro-pneumatic converter were analyzed. The grey correlation method was used to quantify the correlation between each parameter and the engagement time. The study indicated that the spring plate stiffness had the highest correlation with the engagement time. An experimental platform was established to validate the accuracy of the simulation model， providing a basis for further improving the structural design and performance of the electro-pneumatic converter.
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In response to the problems of multiple parameter settings， high repetitive labor intensity， long programming time， and strong experience dependence in the CNC machining programming of carbon fiber reinforced resin based polymer （CFRP） plate components with simple geometric features and a large quantity， research on feature-based automatic programming technology is carried out. Based on the feature definitions of panel， structural plate， and substrate components， a method based on attribute adjacency graph matching is used to complete the feature recognition of the 3D model. The processing operation generation algorithm based on the production rule and processing element priority map is used to achieve automatic feature process decision-making. The improved genetic algorithm is used to optimize the processing route. Then， the intelligent programming of CNC machining programs is realized. Finally， a prototype system was developed on the NX platform and validated using CFRP board components as test objects. The test results showed that the system improved programming efficiency by more than 22 times compared to traditional manual programming operations， with an accuracy rate of 100%. It has been applied in engineering in aerospace manufacturing enterprises.
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In the production of rubber products， the intensive temperature fluctuations and pressure instability during the mixing and extrusion processes not only adversely affect the mixing quality and the plasticization uniformity of the compound， but also significantly impact equipment energy consumption and production efficiency. To address the issues of large temperature fluctuations and poor pressure stability during rubber compounding and extrusion production， an improved fuzzy PID intelligent cooperative control strategy is proposed. By integrating real-time data acquisition from multiple sensors， including temperature， pressure， rotor speed， and torque sensors， an adaptive parameter tuning mechanism based on process characteristics and a pressure-temperature dynamic coupling compensation model are designed. Through this adaptive adjustment process， precise cooperative control of the mixer rotor and extruder screw is achieved， which deeply analyzes and compensates for the mutual interference between pressure and temperature parameters caused by the strong shear heating of the mixer rotor and the conveying compression process of the extruder screw， thereby overcoming the limitations of single-variable control. Experimental results demonstrate that the improved algorithm reduces the overshoot by 11%， shortens the settling time， and achieves a temperature control accuracy of ±0.8 ℃ in the temperature control process. The interference of pressure fluctuations on temperature is reduced from 35% to 12.7%. This system enhances temperature control precision， reduces pressure fluctuations， and significantly improves product uniformity and production efficiency.
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In view of the problems of unsmooth speed transition， significant impact and low processing efficiency of the traditional S-curve acceleration and deceleration algorithm in the process of gantry robot material transportation. The traditional five-segment S-curve acceleration and deceleration algorithm is improved， and a five-segment S-curve acceleration and deceleration algorithm for gantry robots is proposed. Firstly， the maximum acceleration is taken as the parameter， and the acceleration curve is combined with the sinusoidal function to simplify the five-segment S-curve acceleration and deceleration algorithm model. Secondly， in the process of velocity planning under short path， the midpoint Newtonian iteration method was used to speed up the iterative solution and complete the velocity planning. Finally， through Matlab simulation and gantry robot material transportation experiments， the acceleration and jerk curves of the improved algorithm are smoothly transitioned， the constant speed running time is extended by 13.92%， and the overall running time is shortened by 6.06%， which effectively improves the stability and transportation efficiency of material transportation.
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Aiming at the problems of insufficient coordinated control and poor adaptability due to the single-arm design of the traditional pork cutting robot， a dual-arm segmentation supple control method based on six-dimensional force sensors is proposed， in which the right arm decomposes the cutting task into the orthogonal subspace of force and position control by constructing a master-slave force-position hybrid control framework， adjusting the cutting force and motion trajectory in real time to ensure the smooth cutting， while the left arm tracks the change of the master arm position and attitude in real time based on the Jacobi matrix mapping and acceleration constraint transfer， to ensure the balance and stability of the two arms when they move together. The simulation results show that the control method has good robustness and flexibility， and is able to accomplish the high-precision pork cutting task.
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A single contact sensor only provides feedback during a collision and is unable to predict obstacles in advance， resulting in low obstacle avoidance efficiency. Therefore， in order to improve the obstacle avoidance efficiency of industrial robots， a minimum safe obstacle avoidance distance control method for industrial robots based on laser distance sensing is proposed. The HG-C1000 laser displacement sensor is selected. The laser triangulation method is adopted， combined with the double-buffer mechanism and the interrupt transmission protocol to achieve intensive data collection， thereby accurately obtaining the distance of obstacles. Firstly， the equation of state of motion is established to describe the planar motion of the obstacle. Then， the Lagrange interpolation method is used to construct a polynomial to predict its trajectory. Finally， the feedback correction mechanism is introduced to reduce the error， thereby improving the accuracy of the prediction. Based on the prediction of the obstacle distance and its motion trend obtained by laser distance sensing， the obstacle is first approximated as an ellipsoid to calculate the minimum safe obstacle avoidance distance. Then， the artificial potential field method is adopted to control the direction of the obstacle avoidance path， and the dynamic obstacle avoidance strategy is designed in combination with the dynamic window method， thereby achieving the dynamic minimum safe obstacle avoidance of industrial robots. The experimental results show that this method performs excellently in terms of the minimum safe distance， response speed and trajectory smoothness， and can efficiently avoid obstacles.
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To address multi-source interference issues， including partial occlusion， perspective distortion， large dimensions， and spatial curvature， in machine vision measurement of multi-specification rebar profiles， a machine vision measurement system tailored for the geometric parameters of rebar is designed and constructed. Firstly， a dual-camera near-far vertical layout resolves the conflict between wide field-of-view and high precision. Secondly， an object plane elevation method combined with dual-camera coupling calibration corrects perspective distortions induced by varying rebar specifications. Thirdly， an image preprocessing framework is developed to identify contour regions and locate sub-pixel coordinates， incorporating Bézier interpolation-based occlusion repair. Fourthly， Euclidean clustering-based feature extraction is adopted for deriving geometric parameters. Finally， back-projection registration and geometric scaling converts pixel dimensions to real-world measurements via Zhang’s calibration. Experimental results demonstrated 0.1 mm accuracy validated through manual measurements and 0.24 s per image processing speed， meeting rapid measurement requirements for six rebar profile parameters.
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To address potential limitations in existing attention mechanisms， such as insufficient learning capability and inadequate focus on critical targets， as well as problems in traditional steel surface defect detection networks like low detection accuracy and single-scale feature extraction， a novel iterative Recurrent Channel Attention （RCA） mechanism is designed. This mechanism independently calculates attention weights along horizontal and vertical directions， enhancing the model's positional awareness of features. By employing a recursive strategy to iteratively refine the fused results and reapplying the generated weights to input features， RCA significantly strengthens the network's ability to capture key feature information within similar targets. This substantially improves detection capability for objects at various locations and scales. The RCA mechanism is integrated into the YOLOv8 network architecture， resulting in an improved steel surface defect detection network based on YOLOv8n. Firstly， Switchable Atrous Convolution （SAConv） is introduced to expand the model's receptive field and enhance its perception and adaptability to multi-scale features， enabling more precise feature extraction. Secondly， an adaptive weighted feature fusion module is incorporated into the Neck section of the network， effectively combining global and local features to strengthen multi-level feature fusion capabilities. Finally， the designed RCA mechanism is implemented， followed by extensive ablation and comparative experiments. The experiments demonstrate that the YOLOv8 model with only RCA achieves an mAP@0.5 of 81.1% on the NEU-DET steel surface defect dataset， representing a 3.4% improvement over the baseline model. On the GC10-DET dataset， it achieves an mAP@0.5 of 63.2%， a 1.7% improvement. The complete YOLO-SCR network reduces computational complexity by 12% compared to the baseline model. It achieves an mAP@0.5 of 84.0% on NEU-DET （a 6.3% improvement） and 64.1% on GC10-DET （a 2.6% improvement）， achieving a better balance between detection accuracy and inference speed.
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Aiming at the problems of many parameters and large amount of calculation in the existing gear surface defect detection algorithm， a surface defect detection method for lightweight gears based on improved YOLOv8s is proposed. Firstly， part of the ordinary convolution in the YOLOv8s network model is replaced by the Adown convolution module， which improves the capability of the model to capture image features and reduces the parameter amount. Secondly， the lightweight module C2f-Faster and the channel mixer CGLU are integrated to construct a new C2f-Faster-CGLU module， which reduces the model size and calculation cost. Finally， the LSCSBD detection head is designed to further reduce the number of model parameters. The experimental results show that compared with the original model， the improved YOLOv8s model has a 58.6% reduction in the number of model parameters， a 46.1% reduction in GFLOPs， a 57.3% reduction in model size， and an average accuracy of 98.8%. The improved algorithm effectively reduces the memory occupation of the model， and the model is lighter， which provides a reference for the real-time detection of gear surface defects in small mobile devices.
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To address the issue of printing quality degradation caused by thermal expansion and resulting pressure fluctuations in the central impression cylinder of satellite-type flexographic printing machines， a double-inlet and double-outlet spiral cooling channel structure was designed to reduce the surface temperature and axial temperature difference of the cylinder. Computational fluid dynamics （CFD） simulations were conducted to investigate the influence of the bidirectional spiral channel configuration on the cylinder's surface temperature distribution. On this basis， an orthogonal experimental design was employed for parameter optimization， aiming to minimize the axial temperature difference on the cylinder surface. Three key structural parameters including spiral pitch， channel width， and channel height were selected as variable factors. The optimal parameter combination was obtained： spiral pitch of 1000 mm， channel width of 200 mm， and channel height of 35 mm， under which the axial temperature difference on the cylinder surface was reduced to 1.02 ℃， representing a 58.5% decrease compared to 2.46 ℃ observed with a conventional unidirectional spiral channel. This study provides valuable guidance for the structural optimization of cooling channels and the design of central impression cylinders in flexographic printing systems.
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To address the problem of the crank-slider mechanism to overturn due to excessive reciprocating frequency of the sliding frame in vibrating membrane bioreactors， the instantaneous center of velocity （ICV） line model was derived through analytical solution research on the crank-slider mechanism. Calculations based on this model reveal that when the crank-connecting rod length ratios are 1⁄5， 1⁄4， and 1⁄3， respectively， the instantaneous center line exhibits the following variation pattern： with the crank length fixed， a longer connecting rod causes the overall ICV line to shift to the right. Except at the midpoint， the connecting rod of ICV line approaches a nearly horizontal straight line. Building upon the ICV line model， the variation patterns of the sliding frame's velocity and acceleration were further derived for the overturning limit frequency of 1.9 Hz and the safe frequency of 1.8 Hz within a 10 minutes interval. Using ADAMS Software to verify the effectiveness of the ICV line model for the crank-slider mechanism， providing crucial design theory references for predicting the stable operation of vibration membrane module mechanisms. Extending the ICV line model， the ICV line of the four-bar linkage mechanism was derived and applied to the design of the human knee joint. The ICV lines at two extreme positions （leg upright and bent） were obtained， which are similar to the J-shaped ICV trajectory of the human knee joint. The proposed ICV line model exhibits excellent alignment with the ideal instantaneous center trajectory of human joints， providing a reference for optimizing designs with human-machine compatibility. The ICV line mode established in this paper holds promising application prospects in the field of biomechanics.