Prediction of Surface Quality Using Artificial Neural Network for the Green Machining of Inconel 718

Abstract

Inconel 718 is a nickel-based heat resistant super-alloy (HRSA) that is widely used in many aerospace and automotive applications. It possesses good properties like corrosion resistance, high strength, and exceptional weld-ability but it is considered as one of the most difficult alloys to cut. Recently researchers have focused on employing many machining strategies to improve machinability of Inconel 718. This research work presents the experimentation of wet milling of Inconel 718 using a carbide tool with biodegradable oil. Surface quality is the major aspect of machinability. Hence input parameters such as depth of cut, cutting speed, and feed rate are considered to study their effect on surface quality. Nine experimental runs based on an L9 orthogonal array are performed. Additionally, analysis of variance (ANOVA) is applied to identify the most significant factors among cutting speed, feed rate, and depth of cut. Moreover, this research work presents the Artificial Neural Network (ANN) model for predicting the surface roughness based on experimental results. The ANN based-decision-making model is trained by using acquired experimental values. Visual Gene Developer 2.0 software package is used to study the efficiency of ANN. The presented ANN model demonstrates a very good statistical performance with a high correlation and extremely low error ratio between the actual and predicted values of surface roughness and tool wear