In recent years, artificial neural networks were used in many applications in the field of machining operations. This is because of its capability to represent the multi variable systems and its ability for learning by examples. Grinding operation is the most sophisticated cutting operation and its mode is very hard to achieve accurately. This renders grinding operation control using conventional methods very complicated. Therefore, the aim of this search is to use artificial neural networks as the brain of grinding machine controller. The target of this controller is to achieve the desired workpiece surface roughness under grinding wheel surface topography variations. The core of the system consists of two multilayer 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.
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