ANN-based predictive modelling of the effect of abrasive water-jet parameters on the surface roughness of AZ31 Mg alloy

In today’s world, there is an acute need to increase the usage of ecologically sustainable materials like AZ31 magnesium (Mg) alloy, possessing high strength-to-weight ratio and biocompatibility. However, its machinability through conventional machining techniques remains a challenge due to its high flammability. AWJM of Mg alloys is a promising method in this scenario. The present study investigated the effects of three important operating parameters, viz., stand-off distance (SOD), feed rate, and number of passes on the surface roughness parameters (R_a, R_q and R_z). Experiments were conducted based on Taguchi’s L9 orthogonal array, and the effects of parameters on R_a, R_q and R_z were analysed statistically using analysis of variance (ANOVA). The results demonstrated that SOD and number of passes together have significant influence on the surface roughness (between 60% and 80% contribution). The individual and interaction results effects of parameters revealed that, SOD of 1–2 mm, feed rate of 130 mm/min and two cutting passes resulted in the best surface quality with least roughness (R_a, R_q < 3 mm and R_z < 12 mm). Finally, an artificial neural network model was developed with 7 neurons in the hidden layer, which simultaneously predicted R_a, R_q and R_z with high accuracy (R > 0.99).