Development of entropy embedded COPRAS-ARAS hybrid MCDM model for optimizing EDM parameters while machining high carbon chromium steel plate

Abstract

The purpose of this paper is to discuss the optimization of electrical discharge machining (EDM) parameters in a green production environment. The primary goal of this work is to create a novel hybrid multi-criteria decision making (MCDM) model that combines COPRAS and ARAS to determine the best input EDM parameters for machining high carbon chromium tool steel plate. To carry out the selection process, a total of nine tests were carried out using four input parameters, including dielectric level, peak current, flushing pressure, and pulse duration at three different levels of magnitude, and the readings of the respective five output parameters were measured. The weights of these five criterions are evaluated using entropy, while COPRAS-ARAS hybrid model is implemented to choose the best alternative experiment among these nine availabilities. Experiment 5 is found to be the best choice, with the machining parameters 261 µs pulse length, 0.3 kg/cm2 flushing pressure, 4.5 Å current, and 80 mm dielectric level being the optimal input values. The primary result demonstrates that the developed hybrid model is sufficiently accurate and provides the best choice when compared to six existing MCDM tools. Finally, sensitivity analysis is used to justify the hybrid model’s stability and consistency.