Abstract
ABSTRACT. Electrochemical micromachining (ECM) is a nontraditional method used for machining operations in hard and light materials with fixed or varying parameters. In this study, magnesium AZ31 alloy was micro machined using two types of electrolyte supply systems, namely electrolyte flooding and minimum quantity electrolyte (MQE). Experimental investigations were performed using TOPSIS and artificial neural network (ANN) techniques with types of electrolyte supply system, electrolyte concentration (EC), duty cycle (%), and machining voltage (V) as the input parameters, and material removal rate (MRR) and over cut (OC) as the outputs. Single and multi-objective parameter optimization was performed using Taguchi, TOPSIS, and ANN techniques. The machined microholes were analyzed using scanning electron microscopy. According to the TOPSIS results, under optimal conditions, a high MRR value and minimum OC of 1.282 μm/s and 66 μm, respectively, were obtained. The results of TOPSIS were verified using the developed ANN architecture.
 
 KEY WORDS: Magnesium, AZ31 alloy, Electrochemical micromachining, Optimization, TOPSIS, ANN
 Bull. Chem. Soc. Ethiop. 2023, 37(5), 1263-1273. 
 DOI: https://dx.doi.org/10.4314/bcse.v37i5.17
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