الفهرس 
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Abstract
Electrochemical Machining (ECM) has established itself as one of the
major alternatives to traditional methods of machining difficult to cut
materials and generating complex contours, without inducing residual
stress and tool wear. Owing to the complexity of ECM process, it is very
difficult to study the effect of various predominant process cutting
conditions on resulting process performance measuring parameters and
also, predict their values.
To decrease this difficulty, many researchers have so far concentrated on
the process improvement in ECM as will be seen in literature review.
However, this review showed that no effort has been put on the
development of multi input- multi output models to correlate the effect of
various machining parameters, on the predominant electrochemical
machining criteria. Keeping this is in consideration, the present thesis has
attempted to develop a new multi input- multi output model using
artificial neural networks (ANN). As an efficient approach to predict the
values of resulting process performance measuring parameters such as
material removal rate and surface roughness. And also, study the effect of
variation of these cutting conditions on performance measuring
parameters.
The proposed model was trained using experimental data available from a
previous experimental work conducted and will be discussed in later
chapters. The network was built and trained using MATLAB Neural
Networks Toolbox. To verify the accuracy and generalization of the
proposed model, a new set of experimental data that haven’t been used
during training phase, were introduced to the network as a new input.
ANOVA test was performed in order to measure the degree of fitness
between experimental data and ANN predicted data. And also, to
determine the degree of contribution of cutting conditions considered on
material removal rate and surface roughness respectively. The ANOVA
test was conducted using MiniTab software.