Multi Response Optimization of ECDM Process Parameters for Machining of Microchannel in Silica Glass Using Taguchi–GRA Technique

• Published in: SILICON 2022-06, Vol.14 (8), p.4249-4263
• Main Authors: Bellubbi, Sadashiv, N, Sathisha, Mallick, Bijan
• Format: Article
• Language: English
• Subjects: Bubbles; Chemistry; Chemistry and Materials Science; Electric potential; Electrolytes; Environmental Chemistry; Hydrogen; Inorganic Chemistry; Lasers; Material removal rate (machining); Materials Science; Microchannels; Multiple objective analysis; Optical Devices; Optics; Optimization; Original Paper; Orthogonal arrays; Photonics; Polymer Sciences; Process parameters; Silica glass; Tool wear; Voltage; Wear rate
• ISSN: 1876-990X; 1876-9918
• DOI: 10.1007/s12633-021-01167-4

In this work, machining of microchannel in silica glass was successfully carried out using electro chemical discharge machining (ECDM) process. The experiments were planned according to L 27 orthogonal array with applied voltage, stand-off distance (SOD), electrolyte concentration, pulse frequency and pulse-on-time (T ON ) as control factors. The material removal rate (MRR), overcut (OC) and tool wear rate (TWR) were considered as response characteristics. In this study the effects of control parameters on MRR, OC and TWR have been investigated. The increase in applied voltage, electrolyte concentration and pulse on time lead to the improvement in the output characteristics which is attributed to formation of heavily crowded hydrogen bubbles and further coalescence of hydrogen bubbles promotes the occurrence of sparks which resulted in higher values of MRR, OC and TWR. The multi-objective optimization of ECDM was carried out through grey relational analysis (GRA) method. Optimal combination of process parameters achieved from GRA was 45 V applied voltage, 25 wt.% electrolyte concentration, 1.5 mm SOD, 400 Hz pulse frequency and 45 μs T ON . ANOVA for GRG study revealed that the applied voltage (70.33%) was most significant factor affecting output responses followed by electrolyte concentration (11.69%), pulse frequency (4.98%) and SOD (4.13%). Furthermore, the regression equations were formulated for the optimum combination to predict the collaboration and higher-order effects of the control parameters. In addition, confirmation test was conducted for the optimal setting of process parameters and the comparison of experimental results exhibited a good agreement with predicted values. The microstructural observation of machined surface for the optimum combination was carried out.