Multi-Objective Electric Discharge Machining Process Parameters Optimization of Inconel 718 by using Machine Learning Techniques.

Nickel based Alloys like Inconel 718 are increasingly being used in automotive and aerospace engineering, because of its remarkable properties of high durability, resistance to rust, lightweight, and minimal rate of wear. But Nickel based alloys is limited due to the difficulty in machine by using conventional methods. Electrical Discharge Machining (EDM) is one of the unconventional techniques, which has the ability to machine these Nickel based alloys. The ideal parameters in EDM differ depending on the anticipated outcomes in various materials, It is essential to comprehend the connection between the EDM process parameters and the variables that determine the response, using a specific model, equation, or approach. This understanding is vital for determining the most favorable values for different applications. In this study, we conducted a thorough examination of existing literature. An effort has been made to ascertain the best possible combinations of EDM procedure variables. and the effect of response variables during EDM of Inconel by utilizing copper electrode by Machine Learning Techniques. The process parameters included are Pulse on, Pulse off, Voltage and Current. The Output response variables are Material Removal Rate (MRR), and the work piece's Surface Roughness (SR) can be greatly enhanced by choosing the optimal machining parameters. The primary goal of this research was to create a framework. by using machine learning techniques to forecast the value of process parameter for an optimal value of Material Removal Rate and Surface Roughness and furthermore a mathematical model was formulated for response variables based on the obtained regression models. Regression models were conducted using full factorial method in DOE. ANOVA was used to assess the validity of the developed model. Experiments were performed on an EDMN450CNC die-sinking EDM machine on work piece of Inconel 718 with copper electrode which is utilized in the die and mold fabrication sector. The findings revealed that Pulse-on time contributed 47.76% and most significant parameter affecting surface roughness, followed by peak current with percentage contribution of 26.7%. Peak current had a significant effect on the MRR (78.13%), subsequent step by pulse-on condition time percentages of contribution of 21.344%. From full-factorial design of experiments process optimizer optimum value of Ra was 3.80µm and MRR was 52.0 mm3/min at I, T-on and T-off 15A,100µs and 20µs respectively and From Predictive model MRR was 56.2497mm3/min at I, Pulse-on and Pulse-off conditions were 15A,100 µs and 80 µs respectively and Ra were 3.01 µm, at I, T-on and T-off 6A,100 µs and 20 µs.