Focusing on the need for stable and reliable face gear transmissions, this study develops a high-order topology modification strategy to precisely control meshing behavior. Guided by contact path optimization, four modification functions including fourth-order, fourth-order segmented, second-order, and double-crowned are established along the contact path and instantaneous contact line. Comprehensive tooth contact analysis (TCA) and loaded TCA simulations reveal that the method effectively controls the contact path and shapes the transmission error curve. The fourth-order segmented modification proves particularly robust, significantly improving contact distribution and eliminating edge contact. Consequently, this approach effectively increases transmission stability and contact pattern percentage while reducing deviation. This research provides a practical framework for designing high-performance face gear pairs, especially for critical applications such as helicopter transmissions.
Bone tissue is more accurately removed by micro-grinding, which is more conducive to postoperative recovery and healing. However, the anisotropy of the bone material is not taken into account in current research on bone tissue micro-grinding, and experiments to optimize the parameters of the bone grinding process are lacking, leaving a gap in reference for the selection of parameters in clinical surgery. Based on this, the bone micro-grinding force is first analyzed by considering the anisotropy of bone tissue, and a single-factor experiment investigates the effects of machining parameters on the micro-grinding process under the three directions of vertical, cross, and parallel to the feed direction of the grinding head and the bone unit. Orthogonal experiments are then used for optimization to evaluate the effect of each machining parameter on the machining quality based on the magnitude of micro-grinding force in the x- and y-directions and the surface morphology of the machined bone tissue. The results show that the minimum Fx and Fy and the best surface quality are obtained at a feed rate of 100 mm/min, a grinding head rotational speed of 16000 r/min, and a grinding depth of 15 µm. Finally, the results of the orthogonal experiments are analyzed by signal-to-noise ratio and analysis of variance, and the results show that the grinding depth factor has the greatest effect on Fx and Fy in the vertical direction, the grinding head rotational speed factor has the greatest effect on Fx and Fy in the cross direction, the grinding depth in the parallel direction has the greatest effect on Fx, and the grinding head rotational speed has the greatest effect on Fy. The aim of this study is to provide theoretical guidance and technical support to improve the processing quality of biological bone micro-grinding.
Minimum quantity lubrication (MQL) machining has gained widespread attention in both academic and industrial research fields as a beneficial technology for improving machining performance and sustainability, due to its low cost and environmental protection. Nevertheless, there is still room for improvement in MQL, such as the lack of research and analysis on the development history of MQL, key authors, and research hotspots. This may be one of the reasons limiting the development of MQL. Based on this, this paper proposes a new bibliometric analysis of MQL research, with the aim of describing current research trends and visualizing the development history and emerging trends of MQL to support researchers in conducting in-depth studies. First, a bibliometric analysis was conducted on 1842 publications related to MQL in the Web of Science (WoS) Core Collection database from 2008 to 2023. Secondly, bibliometric analysis software such as VOSviewer and bibliometrix were used to visualize the annual growth of publications, distribution of research fields, regional distribution, distribution of research institutions, author distribution, highly cited articles, and keywords. The results show that from 19 publications annually in 2008 to 292 publications annually in 2023, there has been a 15-fold increase, with India (550 publications) being the country with the most publications and China (19420 citations) having the highest number of citations. Furthermore, an analysis was conducted on the research hotspot directions represented by keyword classification, summarizing the current research achievements. This paper reveals the development trend, global cooperation pattern, basic knowledge, research hotspots, and emerging frontiers of MQL.
In general, the tool center point (TCP) accuracy of machine tools can be enhanced by minimizing geometric error (GE) and tracking error (TE). However, five-axis machining for sculptured surfaces has led to increased dynamic error (DE), driven by vibrations and deformations under the real-time influence of machine dynamics and motion parameters. These characteristics in DE align closely with the core concept of digital twins, which involve real-time interactions between physical objects and their virtual models to map system state changes. Hence, a TCP trajectory prediction model (TTPM) of five-axis machine tools (FAMTs) is proposed to achieve precise trajectory prediction based on a digital twin, integrating DE with GE and TE. Firstly, a TCP dynamic error model (TDEM) is established to estimate DE considering multi-axis coupling and varying structural dynamics in FAMTs. Simultaneously, a forward kinematic model (FKM) is constructed using screw theory to account for GE and TE. Then, by integrating the TDEM and FKM, the proposed TTPM predicts TCP trajectories considering DE, GE, and TE. Finally, the TTPM is verified through the R-test. The results reveal that the proposed model exhibits an average deviation of 3.80 µm and a maximum deviation of 6.53 µm in high-speed and high-acceleration trajectories, resulting in a 14.81
Walking excavators are all-terrain multifunctional excavators that are often utilized in operations on complex unstructured terrain. Due to structural complexity and the diversity of the terrain on which these excavators operate, ride comfort is a crucial and challenging factor. To address these issues, this paper proposes a nested posture planning strategy for unmanned walking excavators (UWEs) based on multi-objective optimization. First, a 7-degree-of-freedom kinematic model of the chassis is established based on closed-loop vector equations, and a machinery–terrain coupling dynamical model is established based on the Lagrange method. Subsequently, the radial basis function (RBF) is employed to characterize the motion trajectories of the supporting hydraulic cylinders. Furthermore, a nonlinear trajectory planning model that integrates multiple objectives, multiple constraints, and terrain information is constructed to minimize attitude deviations and energy consumption associated with active adjustments during operation. To accelerate model solving, a two-stage nested optimization strategy is proposed. Finally, a high-fidelity mathematical-physics environment and real-world experiments are constructed to investigate the performance of the proposed method. The results demonstrate that the UWE can successfully traverse challenging terrain with excellent chassis posture in several scenarios.
Reliability testing is essential for detecting early failures in computer numerical control (CNC) machine tools and enhancing their operational reliability. However, traditional ex-factory run-in tests require prolonged cutting of raw materials to simulate real-world conditions, leading to high costs, time consumption, and environmental impact. To address these challenges, this paper proposes a novel simulated cutting force loading device based on a parallel mechanism. The kinematics and dynamics of the device are thoroughly analyzed, and a unique force allocation method for redundant controlled variables is developed to improve the smoothness of pneumatic servo output by exploiting the characteristics of pneumatic actuation. Based on real-time kinematic and dynamic calculations, a force feedforward proportional integral derivative controller is designed. Loading experiments on a simulated spindle demonstrate the device’s ability to accurately apply static and low-to-medium-frequency dynamic loads to non-rotating spindles. Furthermore, experiments conducted on the rotating spindle of a CNC machine tool show that the proposed device can effectively simulate cutting forces, offering a cost-effective and environmentally friendly alternative to conventional cutting-based reliability tests.
Leash-connected quadruped guide robots offer a flexible assistance solution for blind and visually impaired people. Existing methods for robots often depend on force sensors and lack effective motion generation for complex scenarios. This paper presents an extensible reactive motion generation framework, specifically developed to enhance the locomotion of quadruped robots through the implementation of Riemannian motion policy (RMP). A motion model is presented for a human-robot system featuring a flexible leash, suitable for geometric motion policy. Based on this model, an extensible reactive motion generation RMPflow framework tailored for guide tasks is presented. Within this framework, the functionalities required for typical guide tasks are decomposed into five subtasks: goal-reaching and path-tracking, leash-tensioning and mode-switching, robot posture constraint, obstacle avoidance, and tactile paving tracking. Each subtask is equipped with a specifically designed RMP controller. To validate the approach, we conducted simulation experiments, confirming the effectiveness of the subtask RMP controllers and the extensibility of the framework. Additionally, we implemented the framework on a quadruped guide robot platform equipped with a simultaneous localization and mapping system and a panoramic camera. Real-world experiments were designed to test the integrated subtasks in a complex environment, including a maze, multiple goal locations, static and dynamic obstacles, and tactile paving. The system successfully guided three participants through the environment. Experimental results highlight the framework’s effectiveness, adaptability, and robustness.
This paper investigates how to further enhance the dynamic running performance of electrically actuated quadruped robots (e-QRs) under structural, actuation, and load constraints. While existing model predictive control frameworks typically rely on pre-defined gait sequences, we propose a gait sequence optimization method that adapts to variable motor limits and payload conditions to better exploit the robot’s motion capabilities. Experiments on a 518 kg battery-powered e-QR demonstrate a 27
Aero-engines are critical industrial assets whose failures can lead to severe consequences, highlighting the necessity of effective Prognostics and Health Management (PHM). However, existing approaches suffer from limitations in data availability and model accuracy, particularly when real fault samples are scarce or absent, hindering reliable diagnostics. This study develops a novel physics-informed network, named Generative Data-Simulation Adversarial Network (GDSAN), to generate labeled fault vibration signals for reliable aero-engine rotor systems health monitoring. This model introduces a learnable modifying matrix to systematically reconcile discrepancies between simulated and measured data across four error dimensions. After that, physics-informed spectral and energy constraints are embedded into the loss function to enhance both model training stability and the physical plausibility of generated signals. Furthermore, a hybrid-driven PHM framework is constructed, leverages former generated labeled fault data to realize zero-shot fault diagnosis, thereby reducing reliance on high-fidelity simulation models or extensive measured fault samples. The following experimental validation on an aero-engine test bench demonstrates that the proposed framework successfully generates labeled fault signals closely aligned with experimental measurements in both the feature space and frequency spectrum, and eliminates the desperate need for enormous but expensive measured fault samples in model training process. Moreover, the proposed physics-informed terms in the loss function significantly improve the physical plausibility of generated signals.
While carbon-based nanocomposites are widely used for electromagnetic interference (EMI) shielding and flexible sensing, achieving uniform dispersion and structural continuity within flexible matrices remains challenging due to the intrinsic agglomeration of carbon nanomaterials. Furthermore, maintaining material flexibility in composites with 3D continuous structures is difficult. In this study, we synthesized a carbon-based nanocomposite featuring a 3D continuous network to fabricate flexible composite films. Morphological characterizations revealed a hollow, 3D tube-network utilizing a graphite nanosheet framework, densely decorated with surface-grown carbon nanotubes. Upon infiltrating this network with flexible matrices, its microscopic and macroscopic structural integrity was exceptionally preserved. Consequently, the flexible film achieved a maximum EMI shielding effectiveness (SE) of 28.1 dB in the X-band, predominantly driven by absorption loss. Specifically, the composite utilizing a polydimethylsiloxane (PDMS) matrix exhibited optimal EMI SE while closely mirroring the stress-strain behavior of pure PDMS. Mechanical testing demonstrated an elongation at break of 51.9
Aerostatic bearings are extensively applied in the motion stage systems of cutting-edge equipment such as lithography machines. In this paper, a novel aerostatic bearing with non-coplanar orifice and groove (NCOG) is proposed, which effectively addresses the issue that the air supply tubes affect the high-speed motion accuracy of aerostatic guideways. Based on the gas lubrication theory, a three-dimensional computational fluid dynamics (CFD) model is established to analyze the pressure distribution and flow field status of the aerostatic bearing with NCOG. This reveals the lubrication mechanism as well as the static and dynamic characteristics of the aerostatic bearing with such a structure. The results indicate that the aerostatic bearing with NCOG can achieve the same functionality as traditional bearings with identical structural dimensions. The research on static characteristics shows that increasing the orifice diameter and the groove depth can enhance the pressure within the groove. The results of the dynamic characteristics study demonstrate that increasing the orifice diameter can reduce the micro-vibrations of the bearing. Additionally, when the groove depth is less than 0.04 mm, the turbulent kinetic energy (TKE) of the bearing increases with the increase in groove depth, while when it is greater than 0.04 mm, the TKE decreases with the deepening of the groove. Notably, when the groove depth exceeds 0.1 mm, the TKE of the bearing decreases sharply. The effectiveness of the CFD model and the accuracy of the conclusions regarding the static and dynamic characteristics are verified through experiments.
The effect of fiber orientation angle on the fiber fracture mechanism has not been fully explored in 2D ultrasonic vibration-assisted milling (UVAM) of CF/PEEK. A model for ironing surface quality was established by integrating the motion trajectory of the tool tip with variations in ultrasonic amplitude and fiber orientation angle. Milling experiments were then conducted with conventional milling and 2D UVAM at variable amplitudes under the same parameters to compare surface morphology across different fiber orientation angles and to investigate the fiber fracture mechanism. Subsequently, the effects of these mechanisms and milling parameters on surface quality were analyzed in conjunction with milling force and surface roughness data, validating the accuracy of the ironing surface quality model. Tool wear analysis was performed alongside the optimal milling parameters, revealing that the best milling force, surface quality, and tool wear were observed at 90°, followed by 45°, then 0°, while the worst results were seen at 135°. By combining macroscopic and microscopic characteristics, a surface quality enhancement model was constructed to elucidate the coupling relationship between force and surface quality under the ironing effect at the microscopic level. To generalize the findings of this paper, the surface quality is predicted using the XGBoost algorithm, and the model’saccuracy is validated.
Titanium alloy serves as a critical structural material for aircraft and engine components. During the manufacturing of these titanium parts, machining, particularly turning, is a fundamental process. However, continuous turning faces a significant bottleneck: severe tool wear caused by insufficient lubricant infiltration at the tool-workpiece interface and excessive cutting forces. The nanobiolubricant minimum quantity lubrication (NMQL) turning process of biomimetic textured cutting tools empowered by ultrasound is considered to have the potential to solve the problem of tool wear during titanium alloy cutting. Nevertheless, the lubricant infiltration dynamics mechanism and tribological properties under the new process are unclear. Based on this, the synergistic effect of ultrasonic vibration on lubricant infiltration and migration was first analyzed. Subsequently, research has been conducted on the frictional properties and surface damage characteristics of four working conditions: dry cutting, NMQL, textured tool assisted NMQL (T-NMQL), and ultrasonic vibration empowered T-NMQL (UVT-NMQL). Surface roughness, surface morphology, cutting specific energy, chip morphology, and tool wear analysis have also been carried out. Furthermore, wavelet analysis has been introduced to decompose surface roughness signals into high and low frequencies, enriching the quantitative evaluation system for surface damage of titanium alloy cutting workpieces. The average cutting specific energies under dry cutting, NMQL, T-NMQL, and UVT-NMQL conditions were determined to be 2.32, 2.18, 2.01, and 0.78 J/mm3, respectively. Based on the wavelet decomposition results of surface roughness signals, it was found that the surface damage energy of NMQL, T-NMQL, and UVT-NMQL conditions decreased by 28.84
With the development of nanofabrication technologies, decreasing structural sizes, feature miniaturization, three-dimensional stacking, and concurrent increasing dimension characterize the measurement tasks for nano-measuring systems. Atomic Force Microscopy (AFM) and Scanning Electron Microscopy (SEM) are the most used metrology methods in nanometrology. However, each of the techniques has its inherent strengths and limitations; no single technique can provide the full capabilities, such as resolution, accuracy, and speed, to tackle the challenges of increasingly complex measurement tasks in nanometrology. In this study, a hybrid metrology approach using an Artificial Neural Network (ANN) is proposed to combine the advantages of AFM and SEM for the accurate and efficient measurements of geometrical parameters. To improve measurement efficiency, an automated measurement process utilizing deep learning has also been proposed. AFM and SEM measurement models are established to simulate training data for the ANN. This network can predict geometrical parameters more accurately with high efficiency, which can be achieved through individual techniques. Finally, the effectiveness of this method is validated by exemplary measurements for the determination of step height and pitch. This proposed approach also provides a promising solution for the laboratory-to-fab transition of metrology for semiconductors, for which automation and hybrid metrology are necessary.
Non-uniform layers are a common and unavoidable phenomenon in the fabrication of pixel organic light-emitting diodes (OLEDs), particularly in inkjet printing (IJP), which often exhibits pronounced coffee-ring effects. However, accurately simulating these non-uniform features in pixel OLEDs remains a significant challenge for existing methods. In this work, a two-step domain decomposition method was proposed to accurately and efficiently analyze pixel OLEDs with non-uniform layers. In the first step, the whole pixel was divided into several non-overlapping regions according to the dipole radiation range, and the classical dipole radiation model combined with the scattering-matrix method was applied. In the second step, each radiation region was subdivided into uniform and nonuniform parts (quasi-uniform parts), and a modified physical model was introduced to correct the reflection coefficient, transmission coefficient, and phase difference caused by non-uniform layers. The proposed method was verified through both numerical simulations and experiments on a typical IJP OLED. The results showed excellent agreement between the simulated and experimental data, with computational efficiency improved by a factor of 182 compared with COMSOL Multiphysics®. In addition, the analysis of the Purcell effect of a single dipole in a truncated Gaussian microcavity revealed the influence of non-uniformity on the microcavity effect. It explains the physical mechanism of the optical effect caused by non-uniformity, providing a theoretical fundament for non-uniform OLED optimization and manufacturing. This method breaks through the limitations of the traditional uniform model and facilitates the optical simulation and analysis of large-area pixel OLEDs with non-uniform layers.
The requirements for isolating outer vibration and suppressing inner disturbances are increasingly stringent and even approaching extreme limits in integrated circuit manufacturing, precision measurement, scientific experiments, etc. In comparison with passive isolation, active control methods can significantly enhance vibration isolation performance. However, different control strategies are mainly effective in different frequency domains, and performance may deteriorate in some frequency domains due to sensor noises. Active vibration isolation based on absolute-relative dynamic stiffness control via multi-sensor information fusion is proposed in this paper. This method can substantially improve vibration attenuation capability and position stability performances in broad bandwidth, with a particular focus on improving the resonance peak suppression capability in the ultra-low frequency domain. First, the effects of different control strategies on vibration isolation in different frequency domains are analyzed, and the hybrid control strategy is proposed by using both absolute relative signal feedback. Considering the noise characteristics of absolute velocity sensors and relative displacement sensors, different filters are accordingly adopted to improve vibration isolation performance. A one-dimensional experimental platform is established to conduct vibration control experiments under different configurations. The results demonstrate that vibration isolation performance across a wide frequency range can be significantly improved, and the proposed method further proves effective for micro-vibration systems. Typically, transmissibility can be reduced to as low as −30 dB at 1 Hz and −48 dB at 2 Hz, with guarantee of less than −50 dB within 10–50 Hz. Additionally, compliance results show 10–40 dB performance improvements across the broad frequency range (0.1–100 Hz) compared with the passive system.
Space manipulators are crucial for conducting various space missions. To accurately simulate these operations on Earth, this paper presents a full-physical simulation system and corresponding method based on disturbance moment identification, addressing the issue of incomplete gravity unloading in space dexterous operations. Full-physical simulation is the comprehensive modeling of real-world physical interactions such as motion, forces, and collisions in a virtual environment with high fidelity and accuracy. The system’s hardware configuration is introduced first. Then an innovative full-physical method is proposed mainly consisting of the modeling and optimization of disturbance moment (force). The disturbance moment (force) model is optimized to enhance full-physical simulation accuracy. The control framework gives the system framework and signal flows. Numerical simulations are done to verify the optimization process. Interior point method is utilized to decrease the disturbance moment enormously and to reduce the largest joint moment significantly. Multi-objective particle swarm optimization is then implemented to achieve optimal unloading forces. Finally, experiments confirm the effectiveness of the proposed full-physical methodology from two aspects: the verification of the identification method and that of optimization method.
Nonprehensile transportation represents a fundamental approach in robotic manipulation widely implemented in practical applications, where object dynamics constraints must be strictly maintained. However, existing approaches have certain limitations in operational reliability and control performance, particularly regarding execution efficiency and input adaptation. To address these limitations, we propose a novel shared teleoperation method for nonprehensile object transportation. The method reformulates constraints from object dynamics to robot kinematics level, eliminating the need for direct contact force control. It achieves autonomous orientation control through orientation feedforward smoothing while enabling shared position control based on user teleoperation inputs. Additionally, the coordination between position and attitude is ensured through input command optimization. The effectiveness of this method was evaluated through extensive trajectory tracking simulations and human subject experiments. The results demonstrate superiority over existing methods regarding operational safety, task efficiency, tracking accuracy, and input command adaptability.
Robots have found extremely widespread applications in today’s manufacturing industry. Integrating practical experiments for robotic measurement-machining (RMM) is crucial for cultivating academic and applied engineering professionals in the field of intelligent manufacturing. In this regard, this study proposes an integrated RMM platform for practical training of professionals in robotics. The platform features key characteristics such as modularity, customization, and an open architecture, covering the entire process of RMM, providing students with a comprehensive perspective and enhancing their interest in both theoretical learning and professional skills. The platform achieves threefold objectives: First, it is an interdisciplinary subject that allows students to translate theoretical knowledge into real-world practice. Second, it fosters critical thinking among students and enhances their ability to solve practical problems. Third, it broadens students’ horizons and motivates them to establish personal development goals through practical experience. Teaching practices have been conducted for undergraduate, graduate, and international students. The positive feedback and evaluations received confirm that this integrated RMM platform contributes to the cultivation of robotics professionals in higher engineering education.
Multi-object nonprehensile transportation in teleoperated robotic systems poses a dual control challenge: real-time trajectory tracking and simultaneous tray orientation control to satisfy object dynamic constraints. Existing approaches face limitations, including difficulty satisfying trajectory state constraints, excessive model dependency, inadequate adaptability to multi-object scenarios, and a lack of robust mechanisms for handling uncertain object parameters. To address these limitations, this work proposes a novel shared teleoperation framework for multi-object nonprehensile transportation, which enables shared control between human operators and the robotic system for object positioning; meanwhile, the robot autonomously controls object orientation to satisfy task constraints. The primary contributions are threefold: First, a theoretical analysis of dynamic constraints is developed, incorporating object position, inertial parameters, quantity, friction coefficients, and motion states. Furthermore, a virtual object-based dynamic constraint processing method is proposed for the first time, enabling simplified dynamic constraints to be directly utilized for trajectory planning. Second, a model predictive control-based trajectory smoothing algorithm with real-time dynamic constraint enforcement is designed, enabling dynamic coordination between user input tracking and orientation control. Third, simulation and experimental validation confirm that the proposed method successfully ensures dynamic constraints for all objects and achieves stable manipulation of nine different objects at accelerations up to 2.4 m/s2. Compared with the baseline method, the approach achieves a 72.45
