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

Bijan Mallick,Sathisha N,Sadashiv Bellubbi

doi:10.1007/s12633-021-01167-4

Bijan Mallick, Sathisha N + Show 1 more

Open Access

https://doi.org/10.1007/s12633-021-01167-4

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Abstract

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 L27 orthogonal array with applied voltage, stand-off distance (SOD), electrolyte concentration, pulse frequency and pulse-on-time (TON) 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 TON. 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.

Highlights

Nowadays, silica glass plays a significant role in potential areas like medical, MEMS, windows, optical lenses, crucibles, metrological instruments etc., attributed to its relatively higher thermal shock resistance and low coefficient of thermal expansion [1]
Several studies have been conducted to investigate the effects of the electro chemical discharge machining (ECDM) process parameters such as pulse frequency, applied voltage, current density, electrolyte concentration, anode to cathode distance, tool feed rate, pulse-on-time, duty cycle, stand-offdistance, different electrolytes, various tool materials etc
Works reported hitherto prove the effectiveness of Taguchi-grey relational analysis (GRA) for optimizing ECDM process parameters; the available literature reveals the scarcity of published works reported on micromachining of silica glass and relevant multi response optimization through GRA

Summary

Introduction

Silica glass plays a significant role in potential areas like medical, MEMS, windows, optical lenses, crucibles, metrological instruments etc., attributed to its relatively higher thermal shock resistance and low coefficient of thermal expansion [1]. The influence of control factors; applied voltage, pulse frequency, pulse-on-time, stand-off-distance and electrolyte concentration on response variables; MRR, OC and TWR have been investigated. Multi response optimization of ECDM of silica glass through Taguchi-GRA has been proposed and the significance of process parameters was determined through ANOVA. The performance of ECDM process in terms of width of cut, surface roughness, machining depth, TWR, MRR, heat affected zone, recast layer is mainly dependent on various factors like SOD, voltage, current density, electrolyte concentration, auxiliary electrode material, tool materials, electrolyte and pulse factors of supply. Instead of having various optimized results, GRA provides single optimum combination of parameters for better MRR, OC and TWR This technique involves normalization of the obtained results to find grey relational coefficients (GRCs) and grey relational grades (GRGs) [27].

Results and Discussion

Normalizing the data

Calculation of deviation sequence

Confirmation test

Findings

Conclusion