Aircraft assembly is a critical phase in the manufacturing process, where the accuracy and efficiency heavily rely on the performance of posture adjustment mechanisms. For components with point features and linear features, traditional multi-point adjustment technologies based on numerical control positioners are limited by spatial constraints, heavy equipment, and high costs. This paper addresses the challenges of posture adjustment for point-feature and linear-feature aircraft components in confined spaces during assembly (e.g., wing fuselage assembly). We propose a cooperative control strategy using dual-Stewart platforms as core executive units. The approach integrates robust position control with admittance-based force–position coordination to achieve high-precision posture adjustment and internal force–moment suppression. We demonstrate that the system achieves a positioning accuracy of within ±0.05 mm±0.05°
The capture of irregular, dimension-variable non-cooperative space debris remains a critical challenge for on-orbit servicing. This paper proposes a continuum gripper based on modified right-angle Miura-ori tessellation, integrating deployable folding and controllable large-range bending. Geometric relations of crease parameters are derived to build a parametric model mapping two-dimensional fold patterns to three-dimensional deployed configurations. An improved Denavit–Hartenberg (D–H) method provides closed-form kinematic solutions, with workspace evaluated via Monte Carlo simulation. A tendon-driven three-finger prototype is tested. Kinematic experiments verify position prediction accuracy and workspace positioning capability. Grasping tests on typical debris simulants confirm passive adaptation and stable enclosure. Load experiments achieve a 265.8 g payload and 100 % grasping success rate, validating the mechanism's controllability and adaptability for on-orbit grasping applications.
The burrs remaining on switch rails are prone to cracking or even fracturing during operation, thereby diminishing their service life. Moreover, the complex profile of the switch rail makes stable robotic milling difficult, with constantly changing posture and milling force. Therefore, this paper proposes a feed–displacement dual-channel adaptive force control (FDAFC) framework comprising a tangential force–speed loop and a normal-force–displacement loop. The tangential loop employs feed-per-tooth normalization combined with an engagement-aware force–speed mapping and first-order gain scheduling. This design adaptively corrects the feed to regulate tangential force and compensate for engagement-dependent nonlinearities, thereby reducing cross-coupling with the normal channel. The normal loop uses position-based impedance control with radial basis function (RBF)-scheduled inertia, damping, and stiffness to track the desired normal force under time-varying loads. A Lyapunov-guided adaptation law guarantees uniform boundedness, asymptotic tracking-error convergence, and closed-loop stability. Finally, integrated co-simulation and experiments substantiate the efficacy of the compliant milling force-tracking controller for switch rail deburring. The long-distance milling method for robots proposed in this study offers a new approach for automatic burr removal from the switch rail.
Optimizing marine equipment is crucial for enhancing its overall performance, and numerous studies have explored the structural optimization of various marine systems. However, few investigations have focused on the design optimization of submarine cable pallets, despite their importance in marine operations. In this study, a static analysis of a submarine cable pallet under both lifting and transportation conditions was conducted using the finite-element method (FEM). The dimensions of key structural components were sampled using a design of experiment (DOE) approach. The resulting data were utilized to perform a sensitivity analysis on the pallet's performance indicators and to establish a surrogate model. This surrogate model was subsequently combined with a multi-objective particle swarm optimization (MOPSO) algorithm to optimize the pallet design. Specifically, the pallet base and the pallet fence were selected as the optimization targets under lifting and transportation conditions, respectively. The findings reveal significant improvements in the pallet's design, providing a valuable reference for the future structural engineering of submarine cable pallets.
When a turboshaft engine operates in a sand-laden environment, it is prone to erosive wear, which leads to the continuous evolution of surface roughness on compressor blades and consequently alters particle impact behaviour. However, existing studies have mainly focused on the erosion process of smooth surfaces, and there is still a lack of in-depth understanding of the influence of surface roughness on the erosive wear of multi-stage compressor blades. To address these issues, this paper theoretically derives the intrinsic relationship between blade surface roughness and wear rate. An erosion experimental setup for titanium alloy with adjustable impact angles is established to accurately characterize key parameters of the erosive wear model for titanium alloys with different surface roughness values. A dynamic model of blade erosive wear based on gas–solid two-phase flow is constructed, and computational fluid dynamics is employed to analyse the effects of sand particles on the distribution characteristics of erosive wear on compressor blades with varying surface roughness. The research reveals that in the erosion wear experiments conducted at impact angles ranging from 0 to 90°, titanium alloys with different surface roughness exhibited the highest wear rate at an impact angle of 30°. At this specific impact angle, the maximum erosion wear depth of the titanium alloy with Ra =6 µm increased by 88.9 % and 183.3 % compared to those with Ra =3=0.1 µm, respectively. Roughness has the most significant impact on erosive wear of rotor blades, followed by stator blades, and the least on guide blades. As roughness increases, the maximum wear rate concentration on the blades rises, while the location of the erosion-concentrated area does not significantly shift with changes in roughness. The results can provide a basis for the erosion wear assessment of compressor blades at different service stages.
Establishing a comprehensive error model that encapsulates all kinematic error parameters constitutes a critical foundation for achieving satisfactory kinematic calibration performance. In this study, a 5-DOF 5PRR+5PUS-PRPU hybrid perfusion mechanism with a variable structure is selected as the research object, and error modeling is conducted using inverse kinematics and the product of exponentials (POE) method, respectively. Comparative analysis of these two error modeling approaches is performed through kinematic calibration simulations of the hybrid mechanism. Firstly, error models of the 5-DOF hybrid perfusion mechanism are established via inverse kinematics and the POE formula method. To replicate real-world kinematic calibration scenarios, the actual kinematic parameters of the mechanism and the actual pose of the moving platform are defined. Owing to the presence of the 5PRR variable base, kinematic calibration simulations of the hybrid perfusion mechanism are executed separately when the base is positioned at different locations. The results demonstrate that, in comparison to the calibration results obtained via the traditional inverse kinematics modeling method, the mean position and orientation errors of the moving platform after kinematic calibration based on the POE error model are reduced by 89.04 % and 63.79 %, respectively. This verifies the correctness and effectiveness of the proposed POE-based error modeling method and kinematic calibration simulation approach, which can be extended to the error analysis of most parallel mechanisms.
Sparse-path Lamb-wave imaging remains challenging because the available pitch–catch paths contribute unequally to defect localisation, and simple equal-weight fusion often produces diffuse and unstable hotspots. This study proposes a reliability-aware physics-guided framework for sparse-path Lamb-wave defect imaging. The method combines unified preprocessing and scattering-envelope extraction, delay-constrained single-path elliptical imaging, two-stage path-reliability weighting, and lightweight image refinement under a soft physics prior. Experimental validation is performed using paired intact–damaged measurements from an aluminium plate with a controlled through-hole defect, which serves as a representative compact scattering source for evaluating the sparse-path imaging chain. Physically aligned scattering-envelope model (SEM)-assisted auxiliary datasets are used for refinement learning and statistical assessment under the same geometry and delay-mapping convention. The results show that single-path imaging is strongly underdetermined, whereas multi-path fusion reduces localisation error from over 100 mm to the order of tens of millimetres. On the SEM-100 benchmark, the trained refinement stage further reduces the mean localisation error from 19.04.8 mm while substantially improving image quality. The proposed framework therefore provides a practical balance between physical interpretability, sparse sensing, and data-assisted enhancement for guided-wave inspection with limited reliable paths and scarce labelled data.
Under varying flight conditions, helicopter turboshaft engines generate pronounced torque fluctuations that can intensify torsional vibration in the power input chain, accelerating fatigue damage of critical transmission components and threatening flight safety. A lumped-parameter coupled dynamic model of the power turbine and power input chain is developed with time-varying torque excitation. Measured torque signals from three representative flight conditions – hover, high-altitude climb and descent maneuver, and aggressive vertical maneuvers – are applied as inputs to compare torsional responses. Results show that the torque variation rate dominates vibration intensity, as rapid changes promote transient energy accumulation and inertia mismatch within the transmission system. Aggressive vertical maneuvers produce the highest variation rate and the largest torsional response; the torsional angle at the strut-type overrunning clutch is about 10.3 % higher than that in the high-altitude climb and descent maneuver and 280.0 % higher than in hover. These findings clarify the mechanism by which realistic engine torque fluctuations affect torsional vibration and provide theoretical support for the dynamic design and reliability evaluation of helicopter transmission systems.
Deep-sea gear transmission systems face critical lubrication challenges due to the combined effects of extreme hydrostatic pressure and cryogenic temperatures. These environmental stressors cause exponential increases in lubricant viscosity, leading to poor fluidity, high start-up torque, and lubrication starvation. Seawater intrusion induces lubricant emulsification, additive deactivation, and electrochemical corrosion at meshing interfaces, collectively increasing the risk of lubrication failure and compromising long-term reliability. This study investigates lubrication degradation mechanisms in deep-sea environments and proposes targeted mitigation strategies. Through comprehensive characterization of deep-sea environmental parameters and their effects on lubricant rheological behaviour, we critically evaluate the applicability and limitations of conventional thermal elasto-hydrodynamic lubrication (TEHL) theory under extreme conditions. Our analysis reveals that established TEHL frameworks require substantial modification to accurately capture pressure–viscosity–temperature coupling effects and seawater contamination kinetics. Meshing interface texturing, as an effective strategy for friction reduction and wear mitigation, is examined to understand its mechanisms for enhancing lubricant film formation and tribological performance under starved lubrication conditions. Key findings demonstrate that optimized micro-texture architectures can effectively compensate for viscosity-induced fluidity loss and mitigate against the harmful effects of seawater ingress. Critical knowledge gaps are identified, and future research directions are outlined: (i) multi-physics coupling models integrating thermo-hydrodynamic, chemo-physical, and mechanical degradation processes; (ii) synergistic texture-coating design approaches; (iii) high-pressure, low-temperature experimental validation protocols; and (iv) engineering implementation frameworks for deep-sea gear transmission systems. This review establishes theoretical foundations and provides technical guidelines for robust lubrication design and long-term operational stability of deep-sea transmission equipment.
The rod-type stirred mill is the core equipment used in the grinding of potassium feldspar. However, the grinding process involves numerous parameters that influence performance, various evaluation metrics, and prolonged single-cycle durations, collectively making it difficult to identify optimal operating conditions. To address this challenge, a simulation model of the rod-type stirred mill was developed based on the discrete element method (DEM). This study investigates the effects of rotational speed, grinding media size, and bar spacing on milling performance. By integrating energy efficiency, stirrer wear, collision frequency, and average normal and tangential collision forces, the comprehensive index of milling performance was established. Using Box–Behnken experimental design and analysis of variance, the relative influence of rotational speed, grinding media size, and bar spacing on grinding performance was ranked. With weight coefficients assigned as 0.4 for energy efficiency, 0.2 for stirrer wear, 0.2 for collision frequency, 0.1 for average normal collision force, and 0.1 for average tangential collision force, response surface optimization was conducted. Under the current weighting scheme, the optimal operating parameters of the rod-type stirred mill are determined as follows: a rotational speed of 398 r min−1, a grinding media size of 6 mm, and bar spacing of 21.2 mm. Under these conditions, the predicted comprehensive grinding performance indicator is 0.781, and the error between the discrete element method (DEM) simulation validation results and the predicted value is only 0.77 %.
In this paper, a novel 4-degree-of-freedom (DOF) parallel mechanism (PM) limb is proposed. The properties of the degrees of freedom are verified based on the screw theory. The inverse position, velocity, and acceleration models of the mechanism are developed. The position workspace of the mechanism is generated based on the inverse kinematic model. The singular configurations are identified within the workspace. The distribution of the stiffness index in the workspace is visualized. The inverse dynamic model of the mechanism is developed based on the Lagrangian method. The kinematic and dynamic simulations of the mechanism are carried out in Adams to verify the correctness of the theoretical model. Most of the kinematic joints of the mechanism are revolute joints and form the sub-closed-loop parallelogram structure, which makes this mechanism exhibit promising application prospects for application in high-speed or heavy-load fields.
This paper presents the design, analytical modeling, and prototype-level experimental assessment of a variable stiffness omnidirectional chain (VSOC) based on positive-pressure fiber jamming. To address the intrinsic pressure limitation (∼ 1 atm) of conventional vacuum-based jamming, an internal inflatable bladder is used to compact a fiber bundle in a rigid chain link, thereby providing a broader tunable pressure range for stiffness modulation. A mechanics-based model is developed for a fiber jamming rod under bending, defining the jamming, transition, and slipping states; deriving the critical shear forces associated with state transitions; and introducing a pressure-dependent equivalent area moment of inertia to describe the variation in stiffness. This framework is then adapted to the two primary bending modes of VSOC. Three-point bending experiments over 0–300 kPa are used to evaluate whether the model can capture the observed pressure-dependent behavior of the present prototype. Effective parameters, including an inter-fiber friction coefficient (μ = 0.3665) and an effective fiber modulus (E = 4.85 GPa), identified from the jamming pressure p = 0 kPa bending response, are used in the present structure-level model. Within the tested pressure range, the results indicate that critical loads and slipping state stiffness increase approximately linearly with jamming pressure, whereas jamming state stiffness is comparatively insensitive to pressure and is primarily governed by geometry. This work bridges design, theory, and experiment to develop a high-performance variable stiffness structure with significant potential for soft robotics and wearable devices.
Farm robots currently struggle in scattered fields due to a lack of precise map data. To solve this, we created a system using satellite imagery and AI to automatically generate accurate field boundaries. Our tests showed 93 % accuracy across different regions. This technology serves as "digital eyes" for machinery, replacing slow manual inputs with automated data. It enables robots to navigate and harvest continuously in complex smallholder farms, unlocking the full potential of smart agriculture.
Traditional autonomous agricultural systems face significant challenges in performing continuous operations within fragmented field regions. To address this issue, it is essential to upgrade these systems to automatically acquire high-precision field boundaries. This study tests the hypothesis that fragmented tobacco parcels can be reliably mapped using a cloud-based, multi-source remote sensing framework and that the resulting products can directly support autonomous field operations. Using Xuchang City, Henan Province, China, as a case study, we developed a cloud-edge-integrated tobacco mapping workflow on the Google Earth Engine (GEE) platform by fusing Sentinel-2 optical imagery, Sentinel-1 synthetic-aperture radar data, and topographic variables. A comprehensive feature set, including spectral bands, vegetation indices, radar backscatter, texture metrics, and terrain attributes, was used to train and compare three machine learning classifiers: random forest (RF), gradient boosting decision tree (GBDT), and classification and regression tree (CART). RF achieved the highest performance, with an overall accuracy of 93.08 % and a kappa coefficient of 0.92, outperforming GBDT (90.60 %, 0.89) and CART (87.60 %, 0.85). The RF-derived tobacco planting area showed the closest agreement with official statistics, with a consistency ratio of 94.12 %. Model robustness was further demonstrated by direct transfer to the adjacent Pingdingshan City without re-training, yielding a 97.70 % consistency with reported acreage. By shifting field-boundary extraction from manual delineation to automated cloud-based processing, this study provides a scalable solution for mapping fragmented tobacco fields and delivering parcel-level geospatial data to autonomous agricultural systems, with broader applicability to other cash crops in fragmented landscapes.
The traditional external fixation has problems such as a single fixed dimension, limited application range, long fixation time, and heavy weight. A single-function external fixation is unable to meet the various needs of fracture fixation in scenarios involving a large number of injured patients. This paper proposes a reconfigurable-configuration comprehensive method based on a seven-link mechanism. A new type of reconfigurable external-fixation frame with multi-dimensional, multi-posture, and multi-fracture scenario applicability is formed, able to achieve rapid and stable external fixation for fractures in the femur, tibia, elbow joint, knee joint, and ankle joint. A reconfigurable mechanism configuration design is proposed based on the comprehensive configuration of the seven-link mechanism and the analysis of the working space. Human–fixator coupled biomechanical model is established, and the mechanical and stability performance of fractures under five reconfigurable configurations are analysed based on finite-element analysis. Compared with the corresponding single-function external-fixation frame in terms of performance, mechanics, and fixation time, a comparative experiment was finally conducted based on the cross-knee joint of dogs for verification. The results confirmed that the external fixation has the advantages of light weight, stable structure, multiple dimensions, multiple postures, and applicability to various fracture scenarios in addition to the fact that it shortens the fixation time significantly (< 10 min). Compared with other unilateral external fixation, the new external fixation is lighter in weight (weighing 950 g), with a fracture displacement of 3 mm, and the load is 274.2 N, the stiffness is 95.2 N mm−1, and the stability is higher than that of the unilateral external fixator (237.63 N, 72.4 N mm−1). When the relative rotation angle of the fracture end reaches 30°, the torque is 14.66 N m. This external fixation can be widely used in temporary fracture fixation in emergency medical scenarios.
With the rapid increase in the number of spacecraft, the number of various non-cooperative targets and failed spacecraft has risen sharply. Consequently, dealing with these targets requires the actuator to possess high flexibility, high enveloping ability, and structural stability. Following bionic principles, this paper proposes a multi-segment serial–parallel spatial closed-chain capture mechanism. The mechanism is formed by coupling a single parallel capture mechanism composed of four-URU four-branch parallel mechanism and double-loop folding expansion platform. First, the configuration design of the single parallel capture mechanism with the core structure of the double-loop folding expansion platform plus the four-URU four-branch parallel mechanism is carried out. Secondly, the motion screw system and screw-constraint topological diagram of the mechanism at the initial position are obtained through the null-space method, and then the DOF of the mechanism is calculated. Subsequently, a kinematic analysis of the capture mechanism is performed. Finally, simulations and a prototype model are developed to verify the motion feasibility of the multi-segment serial–parallel spatial closed-chain capture mechanism.
This study employs a thermo-mechanical coupled simulation to investigate the tapping process, analyzing the mechanical behavior, thermodynamic characteristics, and variations in the tap pressure field during thread formation. The simulation results reveal that tapping constitutes a progressive material removal process accompanied by severe plastic deformation and friction. The three-dimensional cutting force curves indicate that the axial force follows a three-stage variation pattern, whereas the radial force exhibits periodic alternating loads, reflecting the dynamic nature of multi-edge cutting. Furthermore, contact pressure fields and torque curves demonstrate that friction between the tap and the machined surface accounts for a substantial portion of the overall torque. Temperature field analysis further identifies localized high-temperature zones at the cutting edges. Through multiphysics simulation, this research elucidates the stress and temperature distributions within the tap, providing a theoretical foundation for optimizing tap geometry, selecting suitable process parameters to reduce cutting loads, control machining temperatures, and enhance tool life and thread quality.
Traditional laser processing of micro-textures directly impacts the work surface, leading to significant crack propagation and pore formation around textured pits. These defects result in poor surface quality and insufficient hardness, which in turn deteriorate the tool's milling performance and surface hardness. To enhance the surface properties of the texture and improve the milling performance of the ball-end milling cutter, this paper integrates the prefabricated powder-feeding laser cladding method with the laser preparation micro-texture technique to establish a modified textured ball-end milling cutter milling titanium alloy test platform. The milling performance of cemented carbide modified textured ball-end milling cutters, both with and without coating deposition treatment, was investigated. The findings indicate that at an average laser energy density of 70.8 W cm−2, the grain structure on the surface of the modified textured tool has a fine-grain-strengthening effect, enhancing surface hardness and reducing vibration. During milling, aluminum in the coating oxidizes to form an Al2O3 film with a high melting point and solid lubrication properties, effectively lowering the friction coefficient and improving cutting stability. The surface micro-hardness of the modified textured tool increases by approximately 35 %, accompanied by a substantial presence of hard phases within the strengthening layer. As wear progresses, the detachment of these hard phases further decreases the friction coefficient, resulting in an average reduction of frictional force by about 13.1 %. Concurrently, the lifespan of the strengthening layer is extended, which effectively mitigates wear on the rake face of the ball-end milling cutter. Concurrently, the passivation of the tool edge during the cutting process is mitigated, leading to reduced material adhesion phenomenon in the cutting process, which ultimately enhances the machined surface quality of the workpiece. Moreover,the deposition of the coating not only establishes a dense alumina structure that resists wear at the tool–chip interface but also enhances the bonding strength of the tool surface, curbing the formation of built-up edges and promoting better surface roughness in the machining of titanium alloys. Overall, this study achieves a synergistic enhancement of surface wear resistance and cutting performance in cemented carbide tools, providing valuable insights for the efficient machining of difficult-to-cut materials in aerospace and shipbuilding applications.
This study presents an advanced semi-active vibration control strategy for tractor cab suspension systems based on active disturbance rejection control (ADRC). A novel multi-state adjustable damper, capable of real-time switching between four damping modes via dual solenoid valves, is developed to adapt to varying vibration conditions. A comprehensive 7-degree-of-freedom (7-DOF) dynamic model of the tractor cab, incorporating vertical, pitch, and roll motions, is established to characterize the vibration behavior. Based on this model, an ADRC controller is designed to suppress cab vibrations caused by both road surface irregularities and the engine-induced profile. The control forces computed by ADRC are allocated to four corner dampers through a decoupling-based force distribution method, enabling mode switching through solenoid valve control signals. Simulation results under the ISO Class D road profile demonstrate that the proposed ADRC strategy significantly reduces the RMS values of vertical, pitch, and roll accelerations by up to 57.1 %, 67.1 %, and 65.4 %, respectively, compared with conventional fuzzy PID and skyhook control methods. Furthermore, according to the ISO 2631-1 evaluation, the overall frequency-weighted acceleration was reduced by 60.2 % and 46.7 % compared with fuzzy PID and skyhook control, respectively, confirming a significant improvement in ride comfort classification. Additionally, the proposed system effectively limits dynamic suspension travel and avoids excessive valve switching, demonstrating improved ride comfort and strong system robustness.
This paper introduces an improved adaptive fuzzy sliding-mode control approach for semi-active seat suspension utilizing magnetorheological fluid (MRF) dampers. Firstly, the damping characteristic of the MRF damper was tested, and the dynamics model of MRF damper was established. Secondly, the 5-degree-of-freedom “human-seat” suspension system model was built and adaptively simplified, and a suitable adaptive control law was designed to estimate the perturbations generated during the simplification process of the human-seat model online. Based on the simplified model, a fuzzy algorithm was adopted to optimize the approach rate parameters in the sliding-mode control so as to improve the robustness of the system while guaranteeing the approach rate, and hyperbolic tangent function was employed to replace the sign function in the switching term to make the system more continuous during the switching process, which effectively reduces the “chatter” problem in the sliding-mode control. Thirdly, the dynamics model of the MRF damper is added into the sliding-mode control model to ensure the effectiveness of the MRF damper output control force. Finally, the effectiveness of the improved adaptive fuzzy sliding-mode control method was confirmed through simulation, demonstrating its capability to significantly reduce seat acceleration and suspension dynamic deflection under different working conditions compared with passive damping, skyhook control, and sliding-mode control.
The kart is a high-speed mechanical system that requires real-time and reliable visual perception, while motion blur, occlusion, and limited mobile computing resources pose significant challenges. To address these issues, we propose RIL-YOLO, a lightweight object detection framework based on YOLOv8 and optimized for mobile deployment. The method incorporates motion blur data augmentation, a re-parameterized shared convolutional detection head architecture, an inner-CIoU (complete intersection over union) loss, and LAMP (layer-adaptive sparsity for the magnitude-based pruning)-based pruning to improve robustness, localization accuracy, and inference efficiency. Experimental results show that, compared with YOLOv8n, RIL-YOLO improves mAP@0.5 and mAP@[0.5:0.95]