For heavy-duty bearings with low-viscosity lubricant, asperities may contact near the minimum nominal film thickness, while turbulence may develop in the area with large film thickness. When bearings encounter transient impact or unsteady loads, the influence of turbulence and surface roughness is relatively complex. To investigate transient lubrication and dynamic characteristics of mixed-lubricated bearings with turbulent flow, a transient mixed-lubrication model considering turbulence is proposed in this paper. A transient generalized average Reynolds equation is derived based on the Ng-Pan turbulence model. The transient journal center positions are obtained by solving the journal's dynamic equation. The numerical procedure is established. Based on the proposed model, the effect of turbulence, surface roughness, and transient impact load direction as well as the magnitude on transient lubrication and dynamic characteristics of mixed-lubrication bearings is analyzed. The results show that turbulence increases the transient minimum nominal film thickness and may decrease the transient friction force in the mixed-lubrication regime. Surface roughness modifies the dynamic trajectory of the journal center and increases both the transient minimum nominal film thickness and the friction force in the mixed-lubrication regime. The impact load direction significantly affects transient characteristics of the bearing. An increase in the load-deflection angle destabilizes the bearing operation state.

Stroke and related complications such as hemiplegia and disability create huge burdens for human society in the 21st century, which leads to a great need for rehabilitation and daily life assistance. To address this issue, continuous efforts are devoted in human–machine interaction (HMI) technology, which aims to capture and recognize users’ intentions and fulfil their needs via physical response. Based on the physiological structure of the human hand, a dimension-adjustable linkage-driven hand exoskeleton with 10 active degrees of freedom (DoFs) and 3 passive DoFs is proposed in this study, which grants high-level synergy with the human hand. Considering the weight of the adopted linkage design, the hand exoskeleton can be mounted on the existing up-limb exoskeleton system, which greatly diminishes the burden for users. Three rehabilitation/daily life assistance modes are developed (namely, robot-in-charge, therapist-in-charge, and patient-in-charge modes) to meet specific personal needs. To realize HMI, a thin-film force sensor matrix and Inertial Measurement Units (IMUs) are installed in both the hand exoskeleton and the corresponding controller. Outstanding sensor–machine synergy is confirmed by trigger rate evaluation, Kernel Density Estimation (KDE), and a confusion matrix. To recognize user intention, a genetic algorithm (GA) is applied to search for the optimal hyperparameters of a 1D Convolutional Neural Network (CNN), and the average intention-recognition accuracy for the eight actions/gestures examined reaches 97.1% (based on K-fold cross-validation). The hand exoskeleton system provides the possibility for people with limited exercise ability to conduct self-rehabilitation and complex daily activities.

Due to the relatively low load-carrying capacity, low-viscosity lubricated bearings can easily operate in a mixed-lubrication regime. Furthermore, low lubricant viscosity may cause local turbulence in the area with relatively thick film. To analyze the influence of local turbulence on the transient characteristics of mixed-lubrication bearings, a simulation approach for unsteady mixed-lubrication bearings considering local turbulence effects is proposed. Transient journal center locations are solved via journal dynamic equations and numerical integration. The ratio of the film thickness to the roughness determines whether each node of the bearing is in the mixed-lubrication area or the pure hydrodynamic lubrication area. The liquid film in the mixed-lubrication area is analyzed by a transient average Reynolds equation to account for the surface roughness effect. A liquid film in the hydrodynamic lubrication area is analyzed by a transient generalized Reynolds equation to consider local turbulence. The transient average Reynolds equation and transient generalized Reynolds equation are derived in a form convenient for coupling with the governing equations of the journal center. The proposed model is experimentally validated. The influence of local turbulence on unsteady mixed-lubrication bearings is analyzed. The results show that local turbulence and asperity contact can coexist in low-viscosity lubricated bearings, even if the journal speed is relatively low. In the mixed-lubrication regime, local turbulence increases the minimum film thickness and decreases the friction coefficient as well as the journal speed at which the mixed-lubrication transitions to hydrodynamic lubrication.

Soft Pneumatic-Network (Pneu-Net) Actuators (SPAs) have been used extensively in making soft grippers, due to their simple driving forms and large bending deformation. However, the capabilities of the regular SPAs in complex soft gripping application environments are alone insufficient. This work, thus, proposes a modular soft gripper that combines the functionalities of regular and herringbone actuators. The bending deformation characteristics of the two actuators under pneumatic pressures are verified by finite element (FE) simulations and experiments. The functional characteristics of the two actuators are investigated experimentally through a series of methods including the blocking force test, lifting test, grasping strength test, and suction force test. The experimental results show that the regular actuator has the advantages of greater longitudinal bending deformation and higher blocking force; while, the herringbone actuator has better lifting stability and grasping strength due to its conformal deformations both in longitudinal and transverse directions. In addition, the vacuum experiments demonstrate that the actuators can lift heavy plate-like objects through vacuum suction. Based on the functional behaviors of the two actuators, the proposed modular gripper is loaded onto automatic equipment, and the gripper is tested to hook, grasp, or lift various objects with different shapes, sizes, and weights. In essence, the modular and multi-functional characteristics of the design make it a promising candidate for relatively complex and advanced gripping applications.

Marine water-lubricated polymer bearings support the propeller shaft that stretches out of the ship. The bending deformation of propeller shaft often leads to journal misalignment, significantly impacting the bearing performance. In this paper, a mixed lubrication model for water-lubricated polymer bearing is established considering the effect of journal misalignment, liner elastic deformation, and local turbulence. The influence of key structural and operating parameters on the performance of water-lubricated bearing with journal misalignment is investigated. The results show that the journal misalignment significantly reduces the load-carrying capacity, deteriorates the bearing tribological characteristics, and may cause different axial sections of the bearing to be in different lubrication regimes. Compared with aligned bearings, increasing the length–diameter ratio to improve the load-carrying capacity is significantly weakened for misaligned bearings. Increasing the liner thickness and the load improves axial uniformity of pressure distribution of misaligned bearings. A relatively large clearance ratio decreases the load-carrying capacity and increases the friction coefficient of bearings in the mixed lubrication regime.

Under low-speed and heavy-load conditions, the liquid film thickness in journal bearings is often on the same order as the surface roughness. Conventional analytical methods for bearings considering rough surfaces require high computational costs. A rapid prediction method for lubrication characteristics based on physics-informed neural networks (PINNs) is presented in this paper to improve the efficiency of rough bearing analysis in engineering applications. For conventional eccentricity conditions, an extended PINN architecture is used to solve the average Reynolds equation, with the eccentricity ratio as an input parameter to predict bearing characteristics under rough-surface conditions. For extremely high eccentricity-ratio conditions, based on a residual-based adaptive sampling strategy, physical constraints are combined with data fitting loss to improve the prediction accuracy and stability using a hybrid-driven PINN method. The extended PINN and the hybrid-driven PINN are combined to achieve lubrication performance prediction of rough bearings over a wide eccentricity ratio range. The model's validity is verified by comparing its results with experimental data and with those obtained using the finite difference method. Finally, the influence of surface roughness on the bearing lubrication characteristics is analyzed. The results show that the proposed PINNs-based bearing performance prediction method considering rough surfaces has high prediction accuracy and computational efficiency, providing an effective engineering analysis tool for the rapid prediction of rough bearing performance.