The Optimal Processing Parameters of Radial Ultrasonic Rolling Electrochemical Micromachining—RSM Approach

• Main Authors: Kailei He, Xia Chen, Minghuan Wang
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Micromachines
• Subjects: microstructure; radial ultrasonic rolling electrochemical micromachining (RUR-EMM); material removal amount; surface roughness; response surface methodology (RSM)
• Online Access: https://www.mdpi.com/2072-666X/11/11/1002

Radial ultrasonic rolling electrochemical micromachining (RUR-EMM) is a new method of electrochemical machining (ECM). By feeding small and rotating electrodes aided by ultrasonic rolling, an array of pits can be manufactured, which is called microstructures. However, there still exists the problem of choosing the optimal machining parameters to realize the workpiece machining with high quality and high efficiency. In the present study, response surface methodology (RSM) was proposed to optimize the machining parameters. Firstly, the performance criteria of the RUR-EMM are measured through investigating the effect of working parameters, such as applied voltage, electrode rotation speed, pulse frequency and interelectrode gap (IEG), on material removal amount (MRA) and surface roughness (<i>R</i>_a). Then, the experimental results are statistically analyzed and modeled through RSM. The regression model adequacies are checked using the analysis of variance. Furthermore, the optimal combination of these parameters has been evaluated and verified by experiment to maximize MRA and minimize <i>R</i>_a. The results show that each parameter has a similar and non-linear influence on the MRA and <i>R</i>_a. Specifically, with the increase of each parameter, MRA increases first and decreases when the parameters reach a certain value. On the contrary, <i>R</i>_a decreases first and then increases. Under the combined effect of these parameters, the productivity is improved. The experimental value of MRA and <i>R</i>_a is 0.06006 mm² and 51.1 nm, which were 0.8% and 2.4% different from the predicted values.