Articles written in Sadhana
Volume 47 All articles Published: 7 July 2022 Article ID 0136
Multi response optimization of EDM process parameters for biodegradable AZ31 magnesium alloy using TOPSIS and grey relational analysis
M SOMASUNDARAM J PRADEEP KUMAR
Non-conventional machining is one of the uprisings in the contemporary manufacturing scenario. Electric Discharge Machining (EDM) is a thermo-electrical disintegration process, chiefly used to cut hard materials into intricate shapes. By the machining capabilities, the EDM process is vividly used for machining biodegradable AZ31 Magnesium alloy, which is considered as an extremely potential material for biodegradable implant applications. However, during electric discharge machining of this magnesium alloy, rapid erosion ofelectrodes and improper selection of process parameters tends to decrease productivity and increase the machining cost. To overcome these issues, it is essential to understand the effect of significant process parameters such as pulse-ON time (TON), pulse-OFF time (TOFF), discharge current (IP), and electrode material (M) on critical quality features such as surface roughness (Ra), form tolerances (overcut (OC), taper cut (TC), circularity (CIR), cylindricity (CYL)) and machining characteristics such as material removal rate (MRR), toolwear rate (TWR). An attempt is made in this work to machine AZ31 alloy on EDM to optimize process parameters by integrating multi-attribute optimization and Taguchi techniques. Multi-Criteria Decision Making (MCDM) methodologies such as Techniques for Order Preferences by Similarity to Ideal Solution (TOPSIS)approach and Grey Relational Analysis (GRA) are implemented in this work for multi-response optimization. The effect of process parameters is evaluated by ANOVA and contour plots. Based on the investigational results, the Pulse-ON time is noticed as the most significantly influencing process parameter in both GRA and TOPSIS, whose contribution percentage is 40.63% and 62.49%, respectively. SEM analysis is performed to observe the microstructure of the machined surface, revealing the effect of process parameters on surface roughness and corrosion resistance.
Volume 48, 2023
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