Impact of gas film thickness on the performance of RM-ECDM process during machining of glass

• Published in: Materials and manufacturing processes Vol. 37; no. 6; pp. 652 - 663
• Main Authors: Singh, Tarlochan, Dvivedi, Akshay
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 26.04.2022
• Subjects: ECDM; energy channelization behavior; gas film thickness; multi objective optimization; regression modeling; RM-ECDM
• ISSN: 1042-6914; 1532-2475
• DOI: 10.1080/10426914.2021.1945092

Nowadays, the extensive use of glass-based materials for biomedical, micro-fluidic, and MEMS applications grabbed great attention. The use of glass substrate for the above-said applications required subtractive processing to fabricate essential microstructures. Electrochemical discharge machining (ECDM) is an emerging hybrid micromachining process employed to machine the glass work-material. In ECDM, the thermal energy released by the tool electrode in the form of electrochemical discharges due to the electrical breakdown of hydrogen gas film takes away the material from the work surface. The thickness of hydrogen gas film plays a crucial role in deciding the discharge characteristics and subsequent thermal energy amount. Thus, in the present research work, an experimental investigation on hydrogen gas film thickness during the triplex hybrid process of rotary mode (RM) ECDM has been carried out. A high-speed imaging technique was utilized to capture the images of the gas film. These captured images were processed through Matlab's edge detection toolbox to measure its thickness. Central composite design (CCD) of response surface methodology (RSM) was used to design the experimental plan. Applied voltage, tool rotation rate, and electrolyte concentration were taken as input parameters. To investigate the influence of gas film thickness on energy channelization behavior during RM-ECDM process, a material removal rate (MRR) and L/D ratio (diameter to depth ratio of machined holes) were taken as the response characteristics. A desirability approach was used to optimize the input parameters.