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1769814 
Journal Article 
A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method 
Caydas, U; Hascalik, A 
2008 
Yes 
Journal of Materials Processing Technology
ISSN: 0924-0136 
202 
1-3 
574-582 
WOS:000257095200076 
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. 
abrasive waterjet machining; surface roughness; artificial neural network; regression analysis