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Due to their inherent properties and superior performance over titanium-based materials, nickel-based superalloys are widely utilized in the manufacturing industry. Monel 400 is among them. This nickel-copper alloy possesses exceptional corrosion resistance and mechanical properties. Monel 400 is primarily utilized in the chemical industry, heat exchangers, and turbine component manufacturing. Due to the properties of Monel 400, it is deemed as hard to machine materials with the aid of conventional methods. For investigating the performance of this process, a three-level analysis was carried out. Pulse on duration and applied current at three levels are the independent parameters used for designing the experiments. In this present article, a single-response analysis technique is used which is known as Taguchi to investigate the impact of the various process parameters on the output variables.

Nickel-based superalloys are frequently adopted in various engineering applications, such as the production of food processing equipment, aerospace parts, and chemical processing equipment. Because of higher strength and thermal conductivity, they are often regarded as difficult-to-machine materials in certain processes. Various methods were evolved for machining the hard materials such as Nickel-based superalloys more effective. One of these is wire electrical discharge machining. In this paper, we will discuss the development of an artificial neural network model and an adaptive neuro-fuzzy inference system that can be used to predict the future performance of Wire Electrical Discharge Machining (WEDM). The paper uses the Taguchi and Analysis of Variance (ANOVA) design techniques to analyze the model’s variable input. It aims to simulate the various characteristics of the process and its predicted values.