This study presents a simulation model, optimization strategy and the experimental validation of a solenoid electromagnetic actuator that is widely used in industry components, especially in pneumatic/hydraulic valves. In the preliminary design, a two-dimensional magnetostatic finite element analysis (FEA) model is proposed and verified by static push-force comparisons between the two-dimensional FEA model, the three-dimensional FEA model and the experimental results. Then, a static and dynamic performance improvement strategy is proposed. To optimize the static push-force of the actuator, a static multi-objective optimization strategy for actuator structure parameters was developed based on a genetic algorithm. An experimental analysis of dynamic performance was carried out to improve the dynamic response of the actuator. By using a high-precision measuring device in the static-push-force test and dynamic direct current-input-signal tests, the comparisons results between the manufactured conventional actuator and the manufactured optimal actuators showed that the proposed optimization strategy was feasible. Through the static optimization strategy, the average static push-force in the working stroke was improved by 21.8%. Moreover, through the dynamic optimization strategy, the cutoff frequency of the push force response was improved by 129.1%, 79.6% and 74.3%, respectively, at three key positions in the working stroke.

Nanomaterial-based self-healing electrodes have demonstrated significant potential in sensing and energy storage applications due to their ability to withstand electrical breakdowns at high electric fields. However, such electrodes often face mechanical challenges, such as cracking under stress, compromising stability and reliability. This review critically examines nanomaterial-based self-healing mechanisms, focusing on properties and applications in health monitoring, motion sensing, environmental monitoring, and energy storage. By comprehensively reviewing research conducted on dimension-based nanomaterials (OD, 1D, 2D, and 3D) for self-healing electrode applications, this paper aims to provide essential insights into design strategies and performance enhancements afforded by nanoscale dimensions. This review paper highlights the tremendous potential of harnessing dimensional nanomaterials to develop autonomously restoring electrodes for next-generation sensing and energy devices.

Linear proportional solenoid (LPS) is widely applied in different linear motion control systems as the electromagnetic actuator since its high reliability and low cost. LPS is difficult to optimize by changing a single variable due to amounts of structural design parameters, and each design parameter has a nonlinear relationship with the static electromagnetic force. This paper aims to improve LPS’s push force and response performance through magnetostatic finite element analysis (FEA) by ANSYS MAXWELL. This study compares FEA 2D model, 3D model and measurement results underrated coil current to verify the accuracy of FEA 2D model. In order to reveal the nonlinear relationship between shape design parameters and electromagnet design objectives, this study compares the influence degree of each variable on each design objective by conventional type LPS 2D FEA model. And for the purpose of improving LPS’s push force and response performance, a multi-objective optimization method has been proposed in this study based on genetic algorithm (GA) and magnetostatic FEA 2D model for optimizing the shape design parameters. All the study results were validated in both static conditions and dynamic conditions. The comparison between manufactured optimal type and conventional type results shows that the static push force in working stroke is improved 30.1%, displacement step response rise time is reduced 5.2% and 43.4%, and force step response rise time is reduced 20.5% and 44.6% with different return spring stiffness. Above all, LPS static and dynamic performance has been improved directly and the validation of proposed optimization method is verified in this paper.

In order to achieve the optimal design of the hexapod walking robot leg structure, a combined index of energy consumption and workspace is raised. By deriving the energy consumption functions and analyzing the target workspace, a mathematical model of nonlinear programming with inequality constraints is established. The genetic algorithm coupled with inverse kinematics and trajectory planning in a gait period is utilized to solve the optimization problem. The analysis verifies that the requirements of turning and obstacle overcoming can be satisfied, and the total energy consumption can be reduced. The results show that the optimal parameters not only satisfy the requirement of the target workspace, but also achieve the minimum energy consumption and lower joint torques.