Application of Taguchi Based ANFIS Approach in Wire Electrical Discharge Machining of Haste Alloy C276 for Automobile Applications

Abstract

Superalloys, also known as nickel alloys, are widely employed in a wide variety of engineering applications, including the creation of parts for the chemical processing industry and appliances for the food processing industry. Their high heat conductivity and strength, among other characteristics, make them challenging to machine using traditional techniques. Instead, cutting-edge techniques are typically created for the milling of such tougher materials. In this study, we use a modern method called wire electrical discharge machining, which is typically used for working with tougher materials. In order to anticipate WEDM variables, this paper aims to create a Grey-based Artificial Neural Network (ANN) Model and Adaptive Neuro Fuzzy Inference System. The paper uses a Taguchi method to investigate the model’s varying inputs. The purpose of this model is to visualize the process’s varying performance characteristics. The model’s projected values have been compared to the experimental data, and it has been shown that the two sets of numbers are quite closely related. Developed model’s capabilities have been validated through rigorous testing, allowing the maker to make informed judgments.