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.

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% reduction in sliding distance and maintains a zero tip-over rate (compared with 13.9% for the baseline). These results demonstrate enhanced adaptability to multi-object parameters and robust performance in complex nonprehensile transportation scenarios.

Magnetorheological jet polishing is a non-contact, deterministic technique for ultra-precision fabrication of hard and brittle optical materials. To address efficiency and scalability, this work develops a dual-parameter material removal model through dimensional analysis, coupling jet pressure and dwell time for flexible tool influence function (TIF) adaptation. A parameter planning algorithm is then established to allocate dwell times among multiple TIFs, enabling fabrication of microlens arrays (MLAs) with varying feature-to-spot ratios. Experiments reveal controllable subunit geometry, stable optical parameters, and edge effects evolving from amplification in small arrays to attenuation or reversal in larger ones, in agreement with model predictions. The proposed model and algorithm provide stable topography control and optical optimization across multiple array scales, demonstrating effectiveness for large-scale MLA fabrication and potential applicability to planar, spherical, and aspherical optical surfaces.

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 3\mathrm{D} continuous structures is difficult. In this study, we synthesized a carbon-based nanocomposite featuring a 3\mathrm{D} continuous network to fabricate flexible composite films. Morphological characterizations revealed a hollow, 3\mathrm{D} 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% and an ultimate tensile strength of 1.25 MPa. The composite film displayed extraordinary mechanical and piezoresistive stability, maintaining uncompromised performance after extensive stretch-release cycling and prolonged static strain. For human physiologic monitoring, the PDMS-based film successfully tracked articular movements—including the fingers, elbows, and cervical spine—accurately distinguishing subtle variations in joint flexion angles.

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.

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% improvement in outdoor running speed—from 1.8 m/s to 2.3 m/s—compared with a baseline using a fixed gait under the same controller.

Femtosecond photoacoustic characterization offers a high-resolution, non-destructive approach for probing metal nanofilms; however, high-repetition-rate laser excitation can induce thermal or mechanical damage, compromising measurement reliability. While existing studies primarily focus on high-energy laser processing, predictive modeling of damage thresholds under low-fluence, multi-pulse excitation remains limited. This study establishes a multiphysics-based framework for predicting laser-induced damage thresholds, encompassing single-pulse failure mechanisms, including thermal melting and stress-induced yielding, as well as thermal accumulation effects under repetitive pulses. A curve-fitting strategy is proposed to efficiently estimate steady-state lattice temperatures during continuous excitation. Using Cu nanofilms as a case study, the influence of film thickness on damage thresholds is systematically analyzed. The predicted thresholds are compared with experimental data to validate the model’s applicability and accuracy. This work provides theoretical guidance for assessing damage risks and optimizing photoacoustic measurements across varying operating conditions.

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.

Machine learning is advantageous for the online monitoring of tool wear conditions. However, current algorithms encounter limitations in proper extraction and application of features in high-dimensional data, which degrade accuracy and efficiency in the identification of tool wear states. In this study, a Kernel Principal Component Analysis-Sparse Principal Component Analysis (KPCA-SPCA) feature is proposed, which overcomes the limitations of conventional statistical models in managing high-dimensional nonlinear data. A Deep-Kernel Gaussian Process Regression (DKGPR) method is proposed for online tool wear monitoring, which integrates long short-term memory into radial basis function, extracts critical time-dependent features, and reduces sensitivity to short-term abnormal fluctuations. The performance of the DKGPR is compared with different machine-learning-based algorithms, and results show that the proposed algorithm has higher accuracy. The average Root Mean Squared Error (RMSE) of the DKGPR model is 1.686, which is 41.8% less than that of the conventional Gaussian process regression; the KPCA-SPCA reduces RMSE by over 55% and compresses the average confidence interval width by more than 70%. The KPCA-SPCA effectively improves the identification of multi-scale features and robustness to non-stationary signals, and the DKGPR is capable of learning via small-batch data. The combination of modified data-processing and machine-learning algorithms provides a highly efficient solution for tool wear monitoring.