The artificial neural network method (ANN) is widely used in both
modeling and optimization of manufacturing processes. Determination of
optimum processing parameters plays a key role as far as both cost and
time are concerned within the manufacturing sector. The burnishing
process is simple, easy and cost-effective, and thus it is more common
to replace other surface finishing processes in the manufacturing
sector. This study investigates the effect of burnishing parameters such
as the number of passes, burnishing force, burnishing speed and feed
rate on the surface roughness and microhardness of an AZ91D magnesium
alloy using different artificial neural network models (i.e. the
function fitting neural network (FITNET), generalized regression neural
network (GRNN), cascade-forward neural network (CFNN) and feed-forward
neural network (FFNN). A total of 1440 different estimates were made by
means of ANN methods using different parameters. The best average
performance results for surface roughness and microhardness are obtained
by the FITNET model (i.e. mean square error (MSE): 0.00060608, mean
absolute error (MAE): 0.01556013, multiple correlation coefficient (R):
0.99944545), using the Bayesian regularization process (trainbr)). The
FITNET model is followed by the FFNN (i.e. MAE: 0.01707086, MSE:
0.00072907, R: 0.99932069) and CFNN (i.e. MAE: 0.01759166, MSE:
0.00080154, R: 0.99924845) models with very small differences,
respectively. The GRNN model has noted worse estimation results

(i.e.
MSE: 0.00198232, MAE: 0.02973829, R: 0.99900783) as compared with the
other models. As a result, MSE, MAE and R values show that it is
possible to predict the surface roughness and microhardness results of
the burnishing process with high accuracy using ANN models.