This research investigates the application of spark electro-discharge machining for fabricating a stir-cast composite reinforced with silicon carbide, based on the Al7050 alloy. Employing a response surface methodology with a central composite design, the study explores 20 combinations of control parameters to investigate their collective influence. Specifically, it focuses on understanding how three key machining parameters—current, pulse-on time, and pulse-off time—affect material removal rates, electrode wear, and surface roughness. A novel teaching-learning-based optimization strategy, integrating response surface methodology with grey relational analysis, is utilized to optimize multiple responses. The optimized parameters derived through response surface methodology are a current of 10 amps, a pulse-on time of 6 µsec, and a pulse-off time of 5 µsec, resulting in significant improvements. These optimized settings correspond to material removal rates, electrode wear rates, and surface roughness values of 0.01074 g/min, 0.0040 g/min, and 4.9395 µm, respectively. Additionally, the teaching-learning-based optimization method employs grey relational analysis initially to rank the input factors. With the optimized process variables obtained using GRA-TLBO—8.48 amps for current, 6.22 µsec for pulse-on time, and 3.34 µsec for pulse-off time—the material removal rate, electrode wear rate, and surface roughness are further enhanced to 0.01159 g/min, 0.00408 g/min, and 3.7202 µm, respectively

Keywords: Al7050, Ceramics processing, RSM, MCDM, GRA-TLBO.