Parameter self-tuning of servo control systems is crucial for optimizing automation processes, especially in complex systems such as permanent magnet synchronous motors. In this paper, a nonlinear adaptive whale optimization algorithm (NAWOA) is proposed and applied to parameter self-tuning, which improves the traditional whale optimization algorithm (WOA) by nonlinearly adaptively adjusting two parameters during optimization to enhance fast convergence and global search capabilities. A servo control system with three parameters to be tuned is constructed using both simulation and physical methods. Simulation and experimental results show that the NAWOA outperforms the genetic algorithm, particle swarm optimization, and WOA in parameter self-tuning of the servo control system with lower error indicators and fast convergence speed. Although it still faces the challenge of initial condition dependency, the proposed NAWOA provides a powerful solution for real-time industrial applications.

Hopper scales are critical dynamic metering equipment in industrial production, yet their metrological performance is often compromised by wear on weighing units over long-term service. This study proposes a wear state assessment method based on the evolution of vibration features. Focusing on the rocker-column weighing unit, we analyzed the mechanism by which geometric changes in the spherical indenter—caused by fretting wear—alter the system’s constraint state. A global-to-local coupled Discrete Element Method and Finite Element Method (DEM–FEM) model was constructed to account for material-structure interactions, alongside a dynamic simulation model considering wear evolution. The simulation accuracy was validated through a dedicated experimental platform. The results indicate that as spherical wear intensifies, the low-frequency swaying of the indenter is suppressed, causing the system’s vibration mode to transition from a flexible, swaying-dominated state to a high-frequency, rigid-impact-dominated state. In the frequency domain, this manifests as energy migration, characterized by attenuation of the low-frequency main peak and an elevation of the high-frequency broadband noise floor. Crucially, as a key innovation for wear diagnosis, this study reveals the directional sensitivity of statistical indicators. While the Root Mean Square (RMS) exhibits a non-monotonic V-shaped trend, the Kurtosis and Margin factors of the tangential vibration demonstrate superior monotonic sensitivity. Under severe wear conditions, these two indicators increase by 14 and 11 times, respectively. These findings provide highly effective diagnostic criteria and hold significant engineering application value for the predictive maintenance of industrial dynamic weighing systems.

Due to its excellent high-temperature resistance and fatigue properties, 8Cr4Mo4V high-temperature bearing steel has become a critical material for aero-engine main shaft bearings. Consequently, the surface integrity of this material after grinding directly determines the service performance and fatigue life of the bearings. To address the lack of clarity regarding the correlation between grinding process parameters and defect characteristics under strong thermal–mechanical coupling, systematic grinding experiments were conducted in this study. Scanning Electron Microscopy (SEM) and white light interferometry were employed to observe the ground surface morphology and subsurface damage characteristics under various process parameters. On this basis, the influence of key parameters—such as wheel linear speed and grinding depth—on the formation and distribution of defects, including micro-cracks and material spalling, was analyzed in depth. Through qualitative and quantitative analyses, this study aims to elucidate the mechanisms by which grinding parameters affect machining defects, thereby providing experimental data and references for the optimization of the grinding process for this type of material.

Reliability is a critical indicator for evaluating the safety and performance of mechanical structures. Surrogate models are widely employed in reliability analysis to reduce computational costs; however, their accuracy strongly depends on the quality of training samples. This paper proposes an uncertainty and distance-driven sequential sampling deep neural network method (UDDS-DNN) for structural reliability analysis. First, initial training samples were generated from the Monte Carlo population (MCP) using the Minimax sampling strategy. This ensured global coverage of the random variable space. Then, the predictive uncertainty of the DNN was quantified using the Jackknife-based method. The estimated uncertainty, inter-sample distance, and probabilistic characteristics of the input random variables were considered. The adaptive learning function was constructed to guide sequential sample enrichment. The proposed method was validated through two numerical examples and three engineering case studies. Results demonstrated that UDDS-DNN achieves high accuracy and computational efficiency in multivariate, nonlinear, and complex structural reliability problems. The method provided an effective solution for reliability analysis of complex structures using DNN-based sequential sampling.

Path planning for high-DOF robotic manipulators in highly constrained environments (e.g., narrow passages) remains challenging due to poor configuration-space (C-space) connectivity, low computational efficiency, and susceptibility to local minima. This paper proposes a hybrid planner, termed ASCON, which couples the directional guidance of an improved Artificial Potential Field (APF) with the global exploration capability of RRT-Connect to achieve robust planning in non-convex, strongly constrained workspaces. A smoothed potential-field formulation is introduced to suppress oscillations and improve motion smoothness, while a link-radius-based envelope collision-checking strategy is incorporated to ensure safety margins for real deployment. The evaluation is conducted in two benchmark scenarios—dual-layer stacked obstacles and a 100 mm narrow passage—with 50 independent trials per method per scenario; a run is considered successful only if a collision-free feasible path is found within preset iteration/time limits using fixed hyperparameters. Results show that, compared with conventional APF, ASCON reduces average planning time by 66.0%, decreases iteration count by 80.5%, shortens path length by 13.5%, and lowers peak jerk by 40.3%. Physical experiments further validate practical feasibility by guiding a real manipulator through a 100 mm narrow passage in a collision-free manner, demonstrating efficient, smooth, and robust planning under extreme constraints.

Variations in liner vibration among cylinders can lead to non-uniform lubrication, accelerated wear, and cavitation in multi-cylinder diesel engines. This study investigated the origin of these variations in a heavy-duty straight-six diesel engine using a transient dynamic model of the cylinder assembly, modal analysis, and VMD. An elastic transient model of the block, liner, and piston system was developed with measured cylinder pressure, cylinder head bolt preload, and piston thermal deformation applied as boundary conditions. The model was validated against modal testing and bench measurements of liner acceleration. Under nominally identical piston excitation across all six cylinders, the computed liner responses were decomposed using VMD to extract intrinsic mode components and dominant frequency bands. The results show that the primary vibration response is concentrated in the upper-middle region of the liner, while the end cylinders exhibit higher vibration levels than the central cylinders. A dominant component centred at approximately 1800 Hz is identified and linked to an engine block mode whose spatial deformation pattern matches the cylinder-to-cylinder distribution of liner vibration and cavitation risk. These findings indicate that the inter-cylinder discrepancy is linked to engine block modal and non-uniformity constraints. The proposed model provides a basis for reliability-oriented mitigation of vibration and cavitation in multi-cylinder diesel engines.

Traditional control strategies for Cable-Driven Parallel Robots (CDPRs) rely heavily on global kinematic modeling and precise calibration, severely limiting their adaptability in unstructured or dynamic environments. This study addresses the challenge of rapid deployment without geometric priors by proposing a reconfigurable CDPR system composed of modular units. A novel heuristic control strategy based on “4+2+1” local rules is introduced, comprising translational, attitude correction, and tension maintenance logic. By utilizing local feedback—including cable tension, attitude, and anchor orientation—this method generates control commands without requiring boundary condition calibration, thereby supporting real-time reconfiguration. Numerical simulations of a façade cleaning scenario demonstrate that the system maintains stability across varying topologies, including anchor position changes and unit failures. Compared to a benchmark kinematic method, the proposed strategy reduces trajectory tracking error by approximately 50.5% and suppresses the pitch Root Mean Square Error (RMSE) from a divergent 42.75° (traditional) to 1.52°, effectively preventing the attitude failure typical of uncalibrated model-based control. These findings confirm that the proposed rule-based approach significantly enhances robustness and adaptability, offering a practical solution for deploying CDPRs in complex environments without pre-existing maps.

To mitigate the high contact forces and noise inherent in traditional hard-impact dampers, this work evaluates the efficacy of a soft viscoelastic vibro-impact interface for passive vibration suppression. This study investigates the nonlinear dynamic behavior of a cantilever beam equipped with a soft vibro-impact interface, combining theoretical modeling and experimental validation to explore energy redistribution and damping enhancement mechanisms. The system is excited under both free and forced vibration conditions, and its response is characterized through tip displacement, acceleration, and impact force measurements. Numerical simulations based on an impact-contact model accurately predict the amplitude-dependent broadening and frequency shift observed in the experiments, demonstrating that the soft impacts introduce nonlinear stiffness and effective damping. The comparison between theoretical and experimental frequency responses confirms that energy is transferred from the primary mode to higher harmonics, leading to broadband vibration attenuation. These findings provide experimental evidence of the nonlinear energy transfer mechanisms previously predicted, including harmonic resonance stimulation and non-resonant energy exchange. The results demonstrate that soft-contact vibro-impact dampers can be effectively tuned to exploit nonlinear dynamics for enhanced passive vibration suppression, bridging the gap between theoretical predictions and practical implementations.

Gravity storage has become an important development direction of physical energy storage technology due to its high energy conversion efficiency and low site selection difficulty. However, the existing gravity energy storage systems based on pure mechanical transmission still have shortcomings, such as low reliability and high operation and maintenance costs, which seriously limit their promotion. To overcome this obstacle, this paper proposes a linear-motor direct-drive gravity energy storage system (LMDD-GESS) with a simpler structure and higher energy conversion efficiency. The cableless flux-switching permanent magnet linear motor (SFPMLSM) is used to replace the traditional wire rope or chain transmission mechanism and fundamentally eliminates frictional losses. Firstly, in terms of the SFPMLSM, the thrust fluctuation is suppressed through the integrated design of the permanent magnet and armature winding on the mover side and the optimization of the end magnetic blocks. In terms of the system grid connection, the double-closed-loop PI control of the machine side and the three-level coordination strategy of the grid side are established, and the in-phase carrier PWM modulation and harmonic feedback compensation algorithm are used to improve the quality of grid connection. The speed curve optimization and multi-machine-switching scheme are designed to achieve smooth power output. The simulation results show that the proposed system significantly improves the operation efficiency and power output stability and provides a reliable gravity energy storage solution for the high proportion of new energy grids.

To investigate the factors affecting the impact performance of hydraulic impact hammers, a numerical calculation model and an experimental system were developed in this study. The consistency between the simulation and experimental results confirmed the accuracy of the numerical model. The results indicated that both the reversing valve stroke and the length of the piston middle-front section are positively correlated with impact energy, impact frequency, and impact power. The accumulator precharge pressure also shows a positive correlation with impact energy, but a negative correlation with impact frequency and impact power. Among these factors, the accumulator precharge pressure exhibits the greatest influence on impact energy, accounting for 26.7% of the variance. The piston middle-front section length has the most significant effect on impact frequency and impact power, contributing 52.3% and 50.2%, respectively. Based on these findings, a set of design criteria is proposed to maximize the impact power of hydraulic impact hammers. An optimization example for a specific model demonstrates an 8.26% increase in impact power after optimization. Overall, this study provides a reliable theoretical and experimental foundation for impact performance research and offers practical guidance for the design and optimization of hydraulic impact hammers.