Material Removal Rate Prediction in Abrasive Water Jet (AWJ) Process Using Artificial Neural Network

Abstract

The abrasive water jet (AWJ) is one of the most cutting-edge unconventional machining methods available today. It can remove metal from both hard and soft metals. During the process of abrasive water jet cutting of carbon steel metal work pieces, this study explored how jet pressure, feed rate, and standoff distance affect the rate of material removal. With the help of a precision balance instrument, a total of sixteen trials were carried out to ascertain the proportion of weight loss to total cutting time. To run experiments and determine which process parameters have the most significant impact on the material removal rate, the Taguchi method was utilized. Feed rate and pressure jet had a significant impact on the amount of material that was removed. The optimum rate of material removal was 4.18 grams per minute when the jet pressure was 350 MPa, the feed rate was 40 millimeters per minute, and the standoff distance was 5 millimeters. The prediction of the material removal rate made by the artificial neural network method was accurate 96.53% of the time