0 The Optimization of the Electro-Discharge Machining Process Using Response

(BQ) In this paper, working current, working voltage, oil pressure, spark gap Pulse On Time and Pulse Off Time on Material Removal Rate (MRR) and Surface Finish (Ra) has been studied. Empirical models for MRR and Ra have been developed by conducting a designed experiment based on the Grey Relational Analysis. Genetic Algorithm (GA) based multi-objective optimization for maximization of MRR and minimization of Ra has been done by using the developed empirical models. Optimizationresults have been used for identifying the machining conditions. For verification of the empirical models and the optimization results, focused experiments have been conducted in the rough and finish machining regions.
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0 The Optimization of the Electro-Discharge Machining Process Using ResponseAvailable online at www.sciencedirect.comProcedia Engineering 38 (2012) 3941 – 3950International Conference on Modelling, Optimization and Computing(ICMOC - 2012)Al alloyThe Optimization of the Electro-Discharge Machining Process Using ResponseSurface Methodology and Genetic AlgorithmsR.Rajesha and M. Dev AnandbaAssistant ProfessorbProfessor & Deputy Director Academic Affairs,Department of Mechanical Engineering,Noorul Islam Centre for Higher Education,Kumaracoil - 629180Kanyakumari District, Tamilnadu, IndiaAbstractElectric Discharge Machining (EDM) is a thermo-electric non-traditional machining process in whichmaterial removal takes place through the process of controlled spark generation between a pair ofelectrodes which are submerged in a dielectric medium. Due to the difficulty of EDM, it is verycomplicated to determine optimal cutting parameters for improving cutting performance. So, optimizationof operating parameters is an important action in machining, particularly for unconventional electricaltype machining procedures like EDM. A proper selection of machining parameters for the EDM processSince for an arbitrary desired machining time for a particular job, they do not provide the optimalconditions. To solve this task, multiple regression model and modified Genetic Algorithm model aredeveloped as efficient approaches to determine the optimal machining parameters in electric dischargemachine. In this paper, working current, working voltage, oil pressure, spark gap Pulse On Time andPulse Off Time on Material Removal Rate (MRR) and Surface Finish (Ra) has been studied. Empiricalmodels for MRR and Ra have been developed by conducting a designed experiment based on the GreyRelational Analysis. Genetic Algorithm (GA) based multi-objective optimization for maximization ofMRR and minimization of Ra has been done by using the developed empirical models. Optimizationresults have been used for identifying the machining conditions. For verification of the empirical modelsand the optimization results, focused experiments have been conducted in the rough and finish machiningregions.© 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul IslamCentre for Higher Education Open access under CC BY-NC-ND license.Keywords: Electro Discharge Machining, Grey Relational Analysis, Genetic Algorithm, RegressionModel, Taguchi Method.E-mail ID : rajesh200345@yahoo.co.inContact No : +91 94888820731877-7058 © 2012 Published by Elsevier Ltd. Open access under CC BY-NC-ND license.doi:10.1016/j.proeng.2012.06.4513942R. Rajesh and M. Dev Anand / Procedia Engineering 38 (2012) 3941 – 39501. INTRODUCTIONElectric Discharge Machining (EDM) is now a well known process particularly used inprecise machining for complex shaped work pieces, as an alternative to more traditionalapproaches. EDM is a thermal erosion process in which an electrically generated sparkvaporizes electrically conductive material. EDM is one of the most extensively usednon-conventional material removal processes [2]. Both electrode (tool) and workpiecemust be electrically conductive [3]. The spark occurs in a gap filled with dielectricsolution between the tool and workpiece. The process removes metal via electrical andthermal energy, having no mechanical contact with the workpiece [4]. Its unique featureof using thermal energy is to machine electrically conductive parts regardless of theirhardness; its distinctive advantage is in the manufacture of mould, die, automotive,aerospace and other applications. In addition, EDM does not make direct contactbetween the electrode and the workpiece, eliminating mechanical stresses, chatter andvibration problems during machining [2]. Today, an electrode is as small as 0.1mm canbe used to make hole into curved surface s at steep angles without drill [2]. The spark isgenerated due to a gap between the workpiece and a tool. The smaller the spark gapbetter the accuracy and the slower the MRR [1]. Figure 1 shows the classification of thespark erosion machining processes [5].Figure 1: Classification of the Spark Erosion Machining Processes.2. LITERATURE SURVEYDifferent researchers did various investigations about EDM. The results weresummarizes as follows: Ho and Newman (2003) [2] studied the research work carriedout from the inception to the development of die-sinking EDM. They reported on theEDM arch related to improving performance measures, optimizing the processvariables, monitoring and control the sparking processes, simplifying the electrodedesign and manufacture. Figure 2 presents the classification of the various researchareas and possible future research directions. Margaret (2004) [4] showed the analysisof the various inputs into EDM and the resulting outputs into the environment. Asimplified mo ...