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.

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.

The effect of fiber orientation angle on the fiber fracture mechanism has not been fully explored in 2\mathrm{D} 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 2\mathrm{D} 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{}^{\circ }, followed by 45{}^{\circ }, then 0{}^{\circ }, while the worst results were seen at 135{}^{\circ }. 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.

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.

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%, 67.71%, and 82.12%, respectively, compared to dry cutting conditions. The infiltration enhancement effect of ultrasonic vibration is considered important reasons for reducing damage signal energy. These results provide novel research insights for evaluating surface properties in ultrasonic vibration-assisted machining processes.

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.

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 non-uniform 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.