The rate and quality of microscale meniscus confined electrodeposition represent the key to micromanipulation based on electrochemistry and are extremely susceptible to the ambient relative humidity, electrolyte concentration, and applied voltage. To solve this problem, based on a neural network and genetic algorithm approach, this paper optimizes the process parameters of the microscale meniscus confined electrodeposition to achieve high-efficiency and -quality deposition. First, with the COMSOL Multiphysics, the influence factors of electrodeposition were analyzed and the range of high efficiency and quality electrodeposition parameters were discovered. Second, based on the back propagation (BP) neural network, the relationships between influence factors and the rate of microscale meniscus confined electrodeposition were established. Then, in order to achieve effective electrodeposition, the determined electrodeposition rate of 5 × 10⁻⁸ m/s was set as the target value, and the genetic algorithm was used to optimize each parameter. Finally, based on the optimization parameters obtained, we proceeded with simulations and experiments. The results indicate that the deposition rate maximum error is only 2.0% in experiments. The feasibility and accuracy of the method proposed in this paper were verified.

Microcomponent manipulation (MCM) technology plays a decisive role in assembling complex systems at the micro- and nanoscale. However, the existing micromanipulation methods are difficult to widely apply in the manufacturing of microelectromechanical systems (MEMSs) due to the limited manipulation space and complex application objects, and the manipulation efficiency is relatively low, which makes it difficult to industrialize these micromanipulating systems. To solve the above problems, this paper proposes an efficient metal MCM strategy based on the electrochemical method. To verify the feasibility and repeatability of the strategy, the finite element model (FEM) incorporating the hydrodynamic and electrochemical theories is used to calculate the local stress distribution of the contact position during the dynamic pick-up process. Based on the simulation results, we defined the relationship between the parameters, such as the optimal manipulating position and angle for picking, transferring and releasing. The failure behaviors of pick-up are built to realize the efficient three-dimensional manipulation of microcopper wire of 300 μm. By establishing a theoretical model and experimental verification, it was concluded that the middle point was the best manipulating position when picking up the microcopper wire, the most efficient picking angle was between 45 and 60 degrees for the pipette, and the average time was 480 s in three sets of picking–release manipulation experiments. This paper provides an achievable idea for different types of micro-object manipulations and promotes the rapid application of micromanipulation techniques in MEMSs.

This article presents a new manipulating method of metal microcomponents based on electrochemistry aiming at the problems of inaccurate release and easy damage to manipulated objects by conventional micro gripping tools and vacuum adsorption tools. This method uses a small-sized micropipette to manipulate large-sized metal microcomponents. The reliable picking up is achieved by forming a strong chemical bond between the small-sized pipette and the manipulation object. The accurate and nondestructive release is achieved by converting the chemical bonding force into a more easily overcome capillary force through electrolysis. The electrochemical and the force models of the metal microcomponents based on electrochemistry are simulated by COMSOL and Abaqus, to obtain optimal electrochemical parameters and manipulation strategies. The experiments of picking up and releasing were performed to demonstrate the performance of the proposed method. The results show that this method can enable reliable and accurate manipulation of large-size objects with small-size tools and have a high success rate and efficiency.

Soft modular grippers play a significant role in multiple fields due to their excellent adaptability and flexibility. This paper proposes a modular soft modular gripper driven by pneumatically actuated multi-chambers. The designed soft modular gripper features three operational modes, with its modular fingers employing independently controlled dual chambers. The distal and proximal dual-chamber structure enhances the fingertip force of the modular fingers. Based on classical laminated plate theory and incorporating the large deformation characteristics of soft materials, a relationship between the bending centerline of the fingers and the driving pressure is established, providing a theoretical foundation for grasping tasks executed by the soft modular gripper. The Denavit-Hartenberg (D-H) parameter method is utilized to develop the coordinate system of the soft modular gripper, thereby defining its operational workspace. Visual sensing technology is introduced, incorporating improvements to the YOLOv8-based object recognition and localization framework, which enhances recognition accuracy for target objects and ensures grasping stability.

Architectured soft structures have unlocked new possibilities for designing continuum manipulators with tailored mechanical performance. This work introduces a bioinspired soft manipulator based on modular wave spring units, leveraging the high elasticity of wave spring structures to enable compliant deformation and axial extensibility of the soft manipulator. To achieve precise control of the soft manipulator, a neural network-based inverse kinematics framework is developed to establish an efficient mapping from desired end poses to tendon actuation lengths. An iterative learning control strategy is further integrated to compensate for material hysteresis, friction, and external disturbances. A prototype is fabricated using flexible 3D-printable material (TPU), and comprehensive experiments are conducted to validate extensible performance, point positioning accuracy, trajectory-tracking accuracy, and compliant interaction capability. The results demonstrate that the proposed wave spring-based soft manipulator achieves a high extension ratio and reliable control precision.