A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method

In the present study, artificial neural network (ANN) and regression model were developed to predict surface roughness in abrasive waterjet machining (AWJ) process. In the development of predictive models, machining parameters of traverse speed, waterjet pressure, standoff distance, abrasive grit size and abrasive flow rate were considered as model variables. For this purpose, Taguchi's design of experiments was carried out in order to collect surface roughness values. A feed forward neural network based on back propagation was made up of 13 input neurons, 22 hidden neurons and one output neuron. The 13 sets of data were randomly selected from orthogonal array for training and residuals were used to check the performance. Analysis of variance (ANOVA) and F-test were used to check the validity of regression model and to determine the significant parameter affecting the surface roughness. The statistical analysis showed that the waterjet pressure was an utmost parameter on surface roughness. The microstructures, of machined surfaces were also studied by scanning electron microscopy (SEM). The SEM investigations revealed that AWJ machining produced three distinct zones along the cut surface of AA 7075 aluminium alloy and surface striations and waviness were increased significantly with jet pressure. (C) 2007 Elsevier B.V. All rights reserved.