Application of Artificial bee Colony Algorithm for Optimization of MRR and Surface Roughness in EDM of EN31 tool steel

(BQ) The objective of this paper is to find out the combination of process parameters for optimum surface roughness and materialremoval rate (MRR) in electro discharge machining (EDM) of EN31 tool steel using artificial bee colony (ABC) algorithm.
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Application of Artificial bee Colony Algorithm for Optimization of MRR and Surface Roughness in EDM of EN31 tool steelAvailable online at www.sciencedirect.comScienceDirectEN 31 tool steelProcedia Materials Science 6 (2014) 741 – 7513rd International Conference on Materials Processing and Characterisation (ICMPC 2014)Application of Artificial bee Colony Algorithm for Optimization ofMRR and Surface Roughness in EDM of EN31 tool steelMilan Kumar Dasa, Kaushik Kumarb, Tapan Kr. Barmana*and Prasanta SahooaaDepartment of Mechanical Engineering, Jadavpur University, Kolkata 700032, IndiabDepartment of Mechanical Engineering, BIT Mesra, Ranchi 835215, IndiaAbstractThe objective of this paper is to find out the combination of process parameters for optimum surface roughness and materialremoval rate (MRR) in electro discharge machining (EDM) of EN31 tool steel using artificial bee colony (ABC) algorithm. Forexperimentation, machining parameters viz., pulse on time, pulse off time, discharge current and voltage are varied based oncentral composite design (CCD). Second order response equations for MRR and surface roughness are found out using responsesurface methodology (RSM). For optimization, both single and multi-objective responses (MRR and surface roughness: Ra) areconsidered. From ABC analysis, the optimum combinations of process parameters are obtained and corresponding values ofmaximum MRR and minimum Ra are found out. Confirmation tests are carried out to validate the analyses and it is seen that thepredicated values show good agreement with the experimental results. This study also investigates the influence of the machiningparameters on machining performances. It is seen that with an increase in current and pulse on time, MRR and surface roughnessincrease in the experimental regime. Finally, surface morphology of machined surfaces is studied using scanning electronmicroscope (SEM) images.© 2014 The Authors. Published by Elsevier Ltd.© 2014 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/3.0/). the Gokaraju Rangaraju Institute of Engineering and Technology (GRIET).Selection and peer-review under responsibility ofSelection and peer review under responsibility of the Gokaraju Rangaraju Institute of Engineering and Technology (GRIET)Keywods: EDM, MRR, Surface Roughness, Optimization, ABC algorithm1. IntroductionElectrical discharge machining (EDM) is a well-established machining option for manufacturing geometricallycomplex parts or hard materials that are extremely difficult-to-machine by conventional machining processes. Its* Corresponding author. Tel.: +91 33 2457 2661; fax: +91 33 2414 6890.E-mail address:tkbarman@gmail.com2211-8128 © 2014 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/3.0/).Selection and peer review under responsibility of the Gokaraju Rangaraju Institute of Engineering and Technology (GRIET)doi:10.1016/j.mspro.2014.07.090742Milan Kumar Das et al. / Procedia Materials Science 6 (2014) 741 – 751unique feature of using thermal energy to machine electrically conductive parts regardless of hardness has been itsdistinctive advantage in the manufacture of mould, die, automotive, aerospace and surgical components (Ho andNewman, 2003). It uses preciously controlled sparks that occur between an electrode and a work piece in presence ofa dielectric fluid (Jameson, 2001).EDM parameter selection is done in the industry based on experience. In some cases, selected parameters areconservative and far from the optimum, and at the same time selecting optimized parameter requires many costlyand time consuming experiments. Many researchers tried to optimize the machining performance by adaptingdifferent optimization techniques. Pradhan and Biswas (2008) have presented a neuro-fuzzy model to predict MRRof AISI D2 tool steel with current (Ip), pulse on time (Ton) and duty cycle (τ) as process parameters. The modelpredictions are found to be in good agreement with the experimental results. Pradhan et al. (2009) have alsoproposed two neural network models for the prediction of surface roughness and compared with the experimentalresults. Kanagarajan et al. (2008) have chosen I p, Ton, electrode rotation, and flushing pressure as design factors tostudy the process performance such as surface roughness and MRR on tungsten carbide/cobalt cemented carbide andthe most influential parameters for minimizing surface roughness have been identified using RSM. Jaharah et al.(2008) have investigated the machining performance such as surface roughness, electrode wear rate and MRR withcopper electrode and AISI H3 tool steel workpiece. Kuppan at el. (2007) have derived mathematical model for MRRand average Ra in deep hole drilling of Inconel 718. It revealed that MRR is more in ...