الفهرس 
LITERATURE REVIEWOnly 14 pages are availabe for public view
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Abstract
ding processes is one of the most popular means in the machining of parts,
particularly in fabricating products from brittle materials such as ceramics and composites.
When fine surface finish and high geometrical accuracy are required, grinding appears to be
the most practical and economical means. In the finishing machining operations, grinding
determines the major portion of the machining cost.
This research introduces an artificial intelligent system for monitoring and controlling
surface grinding and dressing operations. The aim of this system is to achieve the desired
workpiece surface roughness and to reduce the production cycle under grinding wheel
surface topography variations. Multi sensor system is used for monitoring. A high response
pressure sensor (HRPS) is used for monitoring of wheel topography condition. A cutting
force dynamometer is used for monitoring grinding force components. The core of the
system consists of two multi-layer feed forward artificial neural networks based on back
error propagation learning algorithm. The first one is used for process design to achieve the
desired surface roughness. It extracts the suitable process variables such as grinding wheel
speed and feed rate. The second one monitors the cutting operation using the sensors
readings. It extracts the different controlling decisions; accept the process, redesign the
process and start dressing operation. According to these decisions, a PC master control
program generates the appropriate control codes and sends them to the machine controllers
to take the required actions. The neural network efficiency can be enhanced by continuous
learning, adding new learning features. A special hybrid neural network is established for
detecting wheel-workpiece contact event. In addition, an automatic dressing system is
developed to conduct the dressing operation whenever it is needed.
The system testing assessment shows its ability to keep the workpiece surface
roughness within the desired range by selecting different proper process variables during
the grinding wheel life. The desired workpiece surface roughness is achieved with an
acceptable tolerance. The accuracy of wheel-workpiece contact detection has been
improved. In addition, the grinding cycle time has been decreased due to using the wheel
direct approach rather than the conventional reciprocating approach. The automated
dressing system decreases the total process cycle time and increases the role of automation
in the system. The developed system can be used for conducting researches for different
types of grinding and dressing operations. The neural networks efficiency can be enhanced
by continuous learning for new examples.