ABSTRACT
It is difficult to make the optimum choice of a grinding wheel since many factors affect grinding wheel performance. The main factors affecting grinding wheel performance are the material to be ground and its hardness, the stock to be removed and the finish required, the nature of the coolant (if any), wheelspeed, the area of grinding contact and equivalent diameter, the severity of the operation, and the power available for grinding, King and Hahn (1986). Each of these factors affect the selection of the optimal grinding wheel. Considerable knowledge and experience is required to select a suitable grinding wheel for a
given grinding operation. Artificial intelligence is therefore appropriate for an optimal approach. Midha, Zhu and Trmal (1990), Zhu, Midha and Trmal (1992) developed a rule-based system for wheel selection. The system works well but a very large number of rules are required. The rules represent the domain knowledge of one or more experts. Sometimes these rules are difficult to express explicitly and their development requires much time and effort. Often, reasoning is weak and slow in a rulebased system.
