Performance evaluation and multi-objective optimization of EDM parameters for Ti6Al4V using different tool electrodes

Ti6Al4V alloy is widely used in aerospace and biomedical applications due to its excellent mechanical and thermal properties, but its poor machinability makes it a difficult-to-cut material. Electrical Discharge Machining (EDM) offers an effective non-conventional machining approach for such materials, where tool electrode selection and process parameters critically influence performance. This study presents a comprehensive experimental investigation into the effect of three tool electrodes-graphite, copper, and brass-on the EDM performance of Ti6Al4V alloy. Key input parameters, including pulse-on time (T_on), pulse-off time (T_off), and current, were selected based on equipment limits and prior studies. Taguchi's L9 orthogonal array was used for experimental design, and analysis of variance (ANOVA) was employed to determine the statistical significance of each factor. Output responses-material removal rate (MRR), tool wear rate (TWR), surface roughness (SR), and dimensional deviation (DD)-were measured and optimized using the Teaching-Learning-Based Optimization (TLBO) algorithm. Among the electrodes, graphite achieved the highest MRR (31.03 mm³/min), lowest TWR (0.4648 mm³/min), and minimal DD (101.76 μm), while brass produced the smoothest surface (SR = 3.19 μm). A collection of non-dominated responses was also found using Pareto optimal points. A minor adequate deviance was observed between the TLBO algorithm's predicted and actual findings. Scanning electron microscopy (SEM) analysis was conducted to evaluate surface morphology. The qualitative SEM results confirmed fewer defects and better surface integrity for graphite electrodes. The findings validate TLBO as an effective tool for EDM process optimization and provide practical guidance for electrode selection in machining Ti6Al4V.