الفهرس 
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Abstract
Cutting tool temperature is generally classified as the most important
technological parameters in machining processes due to their significant
impacts on the product quality. A large number of interrelated machining
parameters have a great influence on the cutting tool temperature so it is
quite difficult to develop a proper theoretical model to describe efficiently
and accurately a machining process. In this work, an artificial neural
network (ANN) model using MATLAB program for predicting cutting
tool temperature and surface roughness during hard turning of alloy steel
C45 is proposed. This work is based on an experimental dataset of cutting
tool temperature and surface roughness measured during hard turning
process. Rotational speed values of 900, 1200 and 1500 rpm and a depth
of cut of 1, 1.5 and 2 mm and feed rate values of 0.1, 0.15 and 0.2mm/rev
respectively are taken as input parameters of the ANN model. However,
the surface roughness and the cutting tool temperature are the outputs. It
is found that, the ANN model showed a reasonable agreement with the
experimental results, therefore it is considered to be a trusted means of
modeling and simulating the turning process. It is found also that
increasing the speed of rotation of the turning process increased the
cutting tool temperature. The maximum temperature recorded during the
process was 110°C at a=2 mm, S=0.2 mm/rev and at N=1500 rpm.
Similarly, low surface roughness of 0.72 μm was obtained at a rotational
speed of 1500 rpm, depth of cut of 2 mm and feed rate of 0.15 mm/rev.
However, high surface roughness of 2.78 μm was obtained at a rotational
speed of 900 rpm, depth of cut of 1.5 mm and feed rate of 0.1 mm/rev. In
addition to that, increasing the feed rate and rotational speed gave a low
level of surface roughness. Finally, it showed that both of the rotational
speed and feeding rate have a significant impact on the surface roughness,
however, the cutting depth has not a sufficient effect on the average surface roughness.