Predictive Modeling and Optimization of Abrasive Waterjet Drilling (AWJD) Parameters on AZ31B-Mg Alloy using Neural Networks and Grey Relational Analysis

Abstract

Magnesium alloys are lightweight structural materials recognized for their exceptional strength-to-weight ratio, corrosion resistance, and superior bio compatibility, rendering them suitable for aircraft components, gearboxes, computers, mobile devices, automotive, biomedical, and electronic devices. This study investigates the modelling and prediction of abrasive waterjet drilling (AWJD) process parameters on Magnesium Alloy (AZ31B-Mg) using Artificial Neural Networks (ANN) and Grey Relational Analysis (GRA). The L9 Taguchi orthogonal array was used to examine the effects of Abrasive Waterjet Pressure (Awjp), Stand-off Distance (Sd), and Abrasive Flow Rate (Afr) on two critical responses: Surface Roughness (Sr) and Kerf Taper Angle (Kta). The optimum parameter setting (Awjp = 220 MPa, Sd = 2 mm, Afr = 230 ġmin-1) minimized Sr and Kta. Experimental validation demonstrated that the ANN model obtained greater prediction accuracy with an average error of 1.2154%, compared to 12.18114% for the GRA model. Regression analysis produced R2 = 95.05% and R2(adj) = 80.19%. The study demonstrates the effectiveness of ANN in optimizing AWJD processes and enhancing the machining performance of AZ31B-Mg alloy, that supports greater adoption of the alloys in high-performance engineering applications.