Machinability Studies and the Evolution of Hybrid Artificial Intelligent Tools for Advanced Machining of Nickel Alloy for Aerospace Applications

Abstract

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. A comparison of the two was then performed, and it was revealed that the prophesied values are close to the actual results. The findings of the investigation support the manufacturer’s decision-making process and demonstrate the evolved capability of the process.