PERFORMANCE ANALYSIS OF ELECTROCHEMICAL MICROMACHINING USING SIMPLE ADDITIVE WEIGHTING, CRITERIA IMPORTANCE THROUGH INTERCRITERIA CORRELATION, AND ARTIFICIAL NEURAL NETWORK METHODS Original scientific paper
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Electrochemical micromachining (ECMM) finds application in various industries, especially in surface finishing processes in aerospace industries. In this research, the workpiece made from aluminum scrap metal matrix reinforced with alumina is subjected to wear, surface profile, and machinability studies. To analyze the ECMM performance, simple additive weighting (SAW) CRiteria Importance Through Intercriteria Correlation (CRITIC) and Artificial Neural Network (ANN) were used. The wear studies show that at high loads the height wear loss is less and frictional force is more. The L18 mixed orthogonal array experiments were conducted and analysis of experiments shows that the most crucial parameter values for high MRR and low OC are 28g/lit NaNO3+0.05M HNO3, 10 V, and 80% duty cycle. The weight values of the performance metrics obtained using the SAW method are 0.549 and 0.45. The optimal output performance predicted by ANN is MRR of 0.520 µm/sec and OC of 23.8 µm.
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