Multi-Objective Optimization Technique to Optimize the Process Parameters of EDM for Al Alloys Using Taguchi with GRA and TOPSIS Method

Electrical Discharge Machining (EDM) is a non-traditional machining process which employed to make complex products from hard materials. The machining efficiency and surface integrity are influenced by the selection of parameters. In this research, datasets were collected from earlier experimental research works on EDM process of AL alloy to evaluate machining performance. The input parameters: pulse-on time, pulse-off time, discharge current, gap voltage, flushing pressure, and tool rotational speed and the output responses: material removal rate (MRR), tool wear rate (TWR), surface roughness (SRS), recast layer (RLR), and microhardness (MHS) were considered. MINITAB software was utilized to identify the most significant factors affecting responses through signal-to-noise analysis. For multi-objective optimization, the Taguchi method was integrated with Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). From the results, GRA identified optimal parameters of discharge current of 11 A, pulse-on time of 75 µs, pulse-off time of 25 µs, gap voltage of 50 V, flushing pressure of 0.4 MPa, and tool speed of 600 rpm, yielding the highest productivity with MRR of 4.76 g/min, moderate TWR of 0.478 g/min. In contrast, TOPSIS suggested a discharge current of 7 A, pulse-on time of 175 µs, pulse-off time of 90 µs, gap voltage of 40 V, a flushing pressure of 0.5 MPa, and a tool speed of 900 rpm, which produced superior surface quality with a lower SRS of 7.34 µm, reduced RLR of 19.6 µm, and higher MHS of 120.9 HV and a reduced MRR of 0.78 g/min. Both optimal results were validated using an Adaptive Neuro-Fuzzy Inference System (ANFIS) model, confirming accurate prediction of EDM responses. This study demonstrates that GRA is more suitable for productivity-focused applications, whereas TOPSIS is advantageous when surface integrity and hardness are critical, offering a robust decision-making framework for EDM optimization.