Simulation of Picking Up Metal Microcomponents Based on Electrochemistry

Li, Dongjie; Xu, Jiyong; Rong, Weibin; Yang, Liu

doi:10.3390/mi11010033

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…Therefore, a new method of manipulating metallic microstructured parts based on localized electrochemical deposition was proposed. [ 15 ] Organic–inorganic metal halide perovskite continues to occupy a leading position in the field of optoelectronics with their excellent material properties and low production costs, and structures made by 3D printing technology are mainly used for ultrahigh‐resolution color displays and multilevel anticounterfeiting. [ 16,17 ] In addition, MEMS switch is an integral part of MEMS.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Deposition Rate in Meniscus‐Confined Electrodeposition Based on Parameter Optimization Algorithm

Wang

Ren

et al. 2023

Adv Eng Mater

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Meniscus‐confined electrodeposition (MCED) is a potential approach to fabricate 3D metallic microstructures and can be used in microelectromechanical systems, optoelectronics, and flexible electrical interconnections. However, deposition efficiency and the quality of the microstructures obtained by this method on optimal parameters are identified by a large number of comparative experiments, resulting in high cost and inefficient practices. For this reason, herein, the fabrication process of metal microstructures by MCED simulation and optimizes parameters affecting deposition quality and rate is investigated. First, the finite‐element method is used to simulate the voltage and copper ion concentration identifies the target range. Then, the electrodeposition rate 1 × 10−7 m s−1 is set as the target value. The best applicable values of 0.29 V and 0.46 m are obtained after optimizing the voltage and copper ion concentration that affects the quality of deposited microstructures and rate of MCED based on the genetic algorithm (GA), cuckoo search (CS) algorithm, and support vector regression (SVR) algorithm. Finally, simulations and experiments are carried out according to the optimized parameters values. The results show that the maximum error of deposition rate is 7.9%. This study pioneers a new approach to achieving efficient and high‐quality deposition by MCED.

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Section: Introductionmentioning

confidence: 99%

Prediction of Deposition Rate in Meniscus‐Confined Electrodeposition Based on Parameter Optimization Algorithm

Wang

Ren

et al. 2023

Adv Eng Mater

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“…The traditional method of picking up microcomponents with a mechanical manipulation tool can easily cause irreversible damage to the object [18,19]. We proposed a reliable and non-destructive manipulation method for metal micro-components based on electrochemistry [20,21]. However, while picking up by electrochemistry, deposition first occurs in the meniscus area between the manipulating tool and the object.…”

Section: Introductionmentioning

confidence: 99%

Study of Microscale Meniscus Confined Electrodeposition Based on COMSOL

Zhang

Rong

et al. 2021

Micromachines

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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.

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“…The dual parallel jet is widely used in fluid energy transmission [ 1 ], atomic energy conversion [ 2 ], dynamics [ 3 ], chemical industry [ 4 ], aviation [ 5 ], and other fields. And as an important actuator of the dual parallel jet, the nozzle has a great influence on electrostatic distribution [ 6 ], three-dimensional (3D) bioprinting [ 7 ], electro-osmotic fluid flow [ 8 ], jetting performance [ 9 ], metal microcomponents picking [ 10 ], energy transport, chemical combustion and pollutant generation [ 11 ]. Thus, it is of great significance to verify the parallel jet characteristics and microfluidic state in the nozzle by means of numerical simulation and experiments.…”

Section: Introductionmentioning

confidence: 99%

Influence of Non-Structural Parameters on Dual Parallel Jet Characteristics of Porous Nozzles

Zhang

Yang

et al. 2020

Micromachines

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As an important actuator of the dual parallel jet, the porous nozzle has some non-structural parameters (such as inlet pressure, nozzle spacing ratio, etc.) which have a significant influence on energy transport, chemical combustion and pollutant generation. The research on the microfluidic state of the porous nozzle dual parallel jet, however, remains insufficient because of its microjet pattern and complex intersection process. In this paper, the authors used numerical simulation and an experimental method to clarify the influence of porous nozzles’ non-structural parameters on dual parallel jet characteristics. The results show that the inlet pressure only changes the pressure peak value on the parallel jet axis; the starting point (SP) and peak point (PP) on the parallel jet axis, which are located at Xsp = 22 mm and Xpp = 75 mm, respectively, are not changed; and with the increase in the nozzle spacing ratio, the merging points (MPs) on the parallel jet axis are Xmp = 25 mm, 32 mm and 59 mm, respectively. The merging point and the combined point move to a farther distance and the inner deflection angle of the jet is weakened.

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Simulation of Picking Up Metal Microcomponents Based on Electrochemistry

Cited by 6 publications

References 21 publications

Prediction of Deposition Rate in Meniscus‐Confined Electrodeposition Based on Parameter Optimization Algorithm

Prediction of Deposition Rate in Meniscus‐Confined Electrodeposition Based on Parameter Optimization Algorithm

Study of Microscale Meniscus Confined Electrodeposition Based on COMSOL

Influence of Non-Structural Parameters on Dual Parallel Jet Characteristics of Porous Nozzles

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