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Superalloys, also known as nickel alloys, are commonly used in various engineering practices such as the manufacture of chemical processing components and food processing equipment. Due to their properties, such as their high thermal conductivity and strength, they are often deemed hard to machine in conventional methods of material removal processes. As an alternative approach, modern methods are generally developed for the machining of this kind of harder material. Wire Electrical Discharge Machining is one amidst the present-day approach which is employed for machining of harder materials, that is adopted in this present investigation. This article aspires to develop a Grey-based Artificial Neural Network Model (ANN) and Adaptive Neuro Fuzzy Inference System that can be adopted for the prediction of WEDM variables. In order to study the variable inputs of the model, the paper utilizes the ANOVA and design by Taguchi. This model is designed to envisage the various performance characteristics of the process. A comparison of the model’s predicted values with the experimental results has been conducted, and it is noticed that there is a close relation between the experimental and prophesied values. The performance investigation proves the capability of evolved which helps the manufacturer to make effective decisions.

Investigations on machinability and evolution of hybrid artificial intelligent tools for contemporary machining of nickel alloy