الفهرس 
الغلافOnly 14 pages are availabe for public view
 |  | from | 139 |  |  |
| | | from | 139 | | |
Abstract
The aim of this study is to the effect of the cutting variables in the numerical control
devices by controlling the cutting parameter that are causing resulting in a poor surface
finish, high-pitch noise and accelerated tool wear which in turn reduces machine tool
life, reliability and safety of the machining operation. A face milling process was
applied to Aluminum alloy (AA5083) material which used in many different
Engineering application such as, the main factor affecting chatter are (surface cutting
speed N, feed rate f, depth of cut a) five values for each cutting parameter were selected
the different levels of the surface cutting speed are (19, 21, 23, 25and 27 m/ruin), the
feed rate values are (150, 200, 250,300 and 350 mm/min) and the depth of cut values
are (0.7, 1, 1.5, 2 and 2.3 mm). The experiments were carried out and vibration
measurement was done using the Sensor Accelerometer and installed in surface cutting
speed, the readings were recorded directly on the Mat lab program and then the surface
roughness of the device was measured, Experiments were for fifteen test specimens.
Chatter can be reduced by the control of surface cutting speed, feed rate and
machining depth of cut, the tests were conducted to determine the effect of vibration on
the surface roughness; the results showed that the depth of cut and surface cutting speed
were the most important in measuring the Chatter vibrations. The Lower the depth of
cut, the higher the surface cutting speed and the lower the vibrations, lower surface
roughness was obtained at a surface cutting speed of 23 m/min, feed rate of 200
mm/ruin and depth of cut of 1.5 mm. Similarly, high surface roughness was obtained at
surface cutting speed of 23 m/min. feed rate of 350 mm/min and depth of cut of 1.5
mm. The tests were conducted in the case of coolant to determine the ratio of the effect
of the coolant on both the vibration and the roughness of the surface.
An artificial neural network (ANN) model was used to predict the vibration chatter
of Aluminum alloy (AA5083) material. The grid is based on an experimental cIata set
of the vibration chatter and surface roughness measured during operation. (Surface
cutting speed N, feed rate f and depth of cut a) are taken input data in the ANN model,
while surface roughness and vibration are taken output data.