| Summary: | 博士 === 國立中央大學 === 機械工程研究所 === 88 === Electrical discharge machining (EDM) has been used effectively in machining hard, high-strength, and temperature-resistant materials. Material is removed by means of rapid and repetitive spark discharges across the gap between the electrode and workpiece. In the electrical discharge machining process, the shape of the electrode determines the shape of the workpiece due to the electrode sunk into the workpiece. However, electrode wear takes place as well during the electrical discharge machining process. This is because each spark discharge removes material not only from the workpiece but also from the electrode. In time the electrode will become unusable because an excessive electrode wear on the electrode affects dimensional accuracy and the shape produced of the workpiece. Therefore, the study of the electrode reliability for wear is important to ensure the dimensional accuracy and the geometry of the workpiece.
In electrical discharge machining, it is important to select machining parameters for achieving optimal machining performance. Usually, the desired machining parameters are determined based on experience or handbook values. However, it does not ensure that the selected machining parameters result in optimal or near optimal machining performance for that particular electrical discharge machine and environment. The use of the grey-based and fuzzy-based Taguchi method for optimizing the multi-response process has been reported. Both the grey relational analysis and the fuzzy logic analysis are applied to the Taguchi method for solving the multiple responses in the electrical discharge machining process. Experimental results have been shown that both approaches can optimize the machining parameters (workpiece polarity, pulse on time, duty factor, open discharge voltage, discharge current and dielectric fluid) with considerations of the multiple responses (electrode wear ratio and material removal rate) effectively. The similarity and the difference between these two approaches for handling with the optimization of the multi-response process are illustrated in this study.
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