Abstract
In the present work, experimental research was conducted on a Magnesium alloy (AZ61) to determine the effect of machining variables on output such as cutting speed and roughness. Studies were conducted on a commercially accessible Spark Erosion Machining. During early tests, it was discovered that Pulse off time, Pulse on time, Servo Feed and Servo Voltage had a considerable impact on the CS and Ra. In addition, a statistical model was built utilising the response surface approach to predict the CS and Ra in the SEM process. Twenty-nine runs were performed using the Box Behnken Design approach, and a statistical model was developed using the analysis of variance method with a degree of confidence of 95% (p = 0.05). It was found that among the various variables 
plays a major role (78%) in affecting the CS during machining, followed by Servo Voltage (8.3%) and 
(7%). However, in case of Ra, it was determined that the impact of Ton and Toff among the chosen parameters have a greater influence on Ra of AZ61 Mg Alloy. The empirical models were developed for both CS and Ra. These empirical models were further processed using non-dominated sorting genetic algorithm-II. Here, the AZ61 alloys may be considered for biomedical applications after controlling the corrosion rate. Furthermore, a strong correlation between surface roughness and corrosion rate was established, indicating a critical roughness threshold beyond which rapid degradation occurs. The optimized parameter set ensures improved surface integrity, thereby enabling controlled corrosion behavior suitable for biodegradable implant applications.