ELK Asia Pacific Journals – Special Issue ISBN: 978-81-930411-4-7 OPTIMIZATION OF ELECTRO DISCHARGE MACHINING OF SUPERALLOYS AND COMPOSITES: A REVIEW AMRIT SHIWANI Mechanical Engineering Department Jaypee University of engineering and technology, Raghogarh, Guna, India amritshivani0@gmail.com AMIT SHARMA Mechanical Engineering Department Jaypee University of engineering and technology, Raghogarh, Guna, India amit.sharma@juet.ac.in Abstract—Electro discharge machining (EDM) process is a well-established advanced machining process for producing complex geometry with close tolerances in these materials that are extremely difficult-to-machine by conventional machining processes. EDM process offers a better alternative or sometimes the only alternative for generating accurate three dimensional complex shapes of macro, micro and nano features in difficult–to- machine materials among other advanced machining processes. The success of the EDM process depends upon the selection of appropriate process parameters. The selection of preferred process parameters plays a significant role to ensure quality of product and to reduce the machining cost in computer controlled machining process. Hence, Optimization methods applied for machining process are necessary for continuous improvement of the machining process. The objective of this paper is to offer comprehensive knowledge concerning the optimization of electro discharge machining of superalloys and composites materials. Keywords: Electro discharge machining, Superalloys, Composites, Optimization I. INTRODUCTION In the present scenario, the modern manufacturing industries are required advanced engineering materials (i.e., Composites, superalloys and ceramics) for manufacturing the various components or parts, these types of materials are used in automotive, aerospace, electronics, optics, medical devices and communications industries due to their superior mechanical properties like high strength to weight ratio, high stiffness and resistance to high temperature [1]. These types of materials cannot be machined by using conventional machining process. Machining of such materials required some modified or unconventional machining process. Electro discharge machining (EDM) process is well-accepted advanced machining process among other processes. EDM process was invented by Lazarenko in 1943 in USSR. The spark generator was used many of the years to supply the power in EDM and the circuits which are known as Lazarenko circuits. Some types of problems occurred in the circuits so to reduce these problems, developed a pulse and solid state generator in 1960’s [2]. EDM process is used to machine electrically conductive advanced engineering materials to create complex geometrical shapes with proper accuracy and precision. In EDM process, a repeated electrical spark is generated in between the tool and workpiece with the help of electrical energy in the presence of dielectric fluid so the material is removed from workpiece with the help of thermal energy of sparks [3]. There are using two electrodes one acts as a tool and another acts as workpiece the tool is moved toward the work piece until the gap is small enough so that the impressed voltage is great enough to ionize the dielectric. Short duration discharges are generated in a liquid dielectric gap, which removed the materials from both the electrode (tool and work piece) by melting and vaporization. EDM can eliminate mechanical stresses, chatter and vibration problems during machining due to absence of physical contact between tool and workpiece [4]. This paper presents a brief review about the optimization of electro discharge machining of superalloys and composites materials on the basis of various optimization techniques such as Taguchi method, Gray Relational Analysis, Genetic Algorithm (GA) and Desirability function (DF) approach. A review on the optimization techniques in metal machining processes are focusing on (i) modelling techniques and (ii) conventional and non-conventional (evolutionary) optimization techniques as illustrated in Fig.1.