(BQ) The objective of this paper is to find out the combination of process parameters for optimum surface roughness and material removal rate (MRR) in electro discharge machining (EDM) of EN31 tool steel using artificial bee colony (ABC) algorithm. | Available online at ScienceDirect EN 31 tool steel Procedia Materials Science 6 (2014) 741 – 751 3rd International Conference on Materials Processing and Characterisation (ICMPC 2014) Application of Artificial bee Colony Algorithm for Optimization of MRR and Surface Roughness in EDM of EN31 tool steel Milan Kumar Dasa, Kaushik Kumarb, Tapan Kr. Barmana*and Prasanta Sahooa a Department of Mechanical Engineering, Jadavpur University, Kolkata 700032, India b Department of Mechanical Engineering, BIT Mesra, Ranchi 835215, India Abstract The objective of this paper is to find out the combination of process parameters for optimum surface roughness and material removal rate (MRR) in electro discharge machining (EDM) of EN31 tool steel using artificial bee colony (ABC) algorithm. For experimentation, machining parameters viz., pulse on time, pulse off time, discharge current and voltage are varied based on central composite design (CCD). Second order response equations for MRR and surface roughness are found out using response surface methodology (RSM). For optimization, both single and multi-objective responses (MRR and surface roughness: Ra) are considered. From ABC analysis, the optimum combinations of process parameters are obtained and corresponding values of maximum MRR and minimum Ra are found out. Confirmation tests are carried out to validate the analyses and it is seen that the predicated values show good agreement with the experimental results. This study also investigates the influence of the machining parameters on machining performances. It is seen that with an increase in current and pulse on time, MRR and surface roughness increase in the experimental regime. Finally, surface morphology of machined surfaces is studied using scanning electron microscope (SEM) images. © 2014 The Authors. Published by Elsevier Ltd. © 2014 Elsevier Ltd. This is an open access article under the CC BY-NC-ND .
