Electrochemical discharge machining is a nonconventional machining method which can be used to machine nonconductive materials such as glass and ceramics. However, machining of the refractory materials such as ceramics requires high voltages to produce the required thermal energy. In this condition, the tool wear would be increased significantly. This paper reports the study of the wear of the different tool materials. The selected tool materials have different melting/boiling temperatures responding to the high voltages in different ways. The possibility of using different tool materials in high voltages along with estimation of tool surface temperature are discussed in this paper.

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This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, and depth of cut) and end mill flutes leading to minimum surface roughness and delamination factor in end milling of glass fibre reinforced plastics (GFRP) by coupling neural network (NN) and genetic algorithm (GA). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and genetic optimization method are exploited in an integrated manner. The genetically optimized neural network system (GONNS) is proposed for the selection of the optimal cutting conditions from the experimental data when an analytical model is not available. GONNS uses back-propagation (BP) type NNs to represent the input and output relations of the considered system. The GA obtains the optimal operational conditions through using the NNs. From this, it can be clearly seen that a good agreement is observed between the predicted values and the experimental measurements.