Multi-objective parametric optimization of powder mixed electro-discharge machining using response surface methodology and non-dominated sorting genetic algorithm

Powder mixed electro-discharge machining (EDM) is being widely used in modern metal working industry for producing complex cavities in dies and moulds which are otherwise difficult to create by conventional machining route. It has been experimentally demonstrated that the presence of suspended particle in dielectric fluid significantly increases the surface finish and machining efficiency of EDM process. Concentration of powder (silicon) in the dielectric fluid, pulse on time, duty cycle, and peak current are taken as independent variables on which the machining performance was analysed in terms of material removal rate (MRR) and surface roughness (SR). Experiments have been conducted on an EZNC fuzzy logic Die Sinking EDM machine manufactured by Electronica Machine Tools Ltd. India. A copper electrode having diameter of 25 mm is used to cut EN 31 steel for one hour in each trial. Response surface methodology (RSM) is adopted to study the effect of independent variables on responses and develop predictive models. It is desired to obtain optimal parameter setting that aims at decreasing surface roughness along with larger material removal rate. Since the responses are conflicting in nature, it is difficult to obtain a single combination of cutting parameters satisfying both the objectives in any one solution. Therefore, it is essential to explore the optimization landscape to generate the set of dominant solutions. Non-sorted genetic algorithm (NSGA) has been adopted to optimize the responses such that a set of mutually dominant solutions are found over a wide range of machining parameters.

[1]  Yoke San Wong,et al.  Effects of flushing on electro-discharge machined surfaces , 1995 .

[2]  W. Zhao,et al.  The application of research on powder mixed EDM in rough machining , 2002 .

[3]  Jose Mathew,et al.  Optimization of Material Removal Rate in Micro-EDM Using Artificial Neural Network and Genetic Algorithms , 2010 .

[4]  Nixon Kuruvila,et al.  PARAMETRIC INFLUENCE AND OPTIMIZATION OF WIRE EDM OF HOT DIE STEEL , 2011 .

[5]  Norliana Mohd Abbas,et al.  A review on current research trends in electrical discharge machining (EDM) , 2007 .

[6]  B. Yan,et al.  Study of added powder in kerosene for the micro-slit machining of titanium alloy using electro-discharge machining , 2000 .

[7]  Tao Yu,et al.  Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression , 2008 .

[8]  Y. Wong,et al.  Near-mirror-finish phenomenon in EDM using powder-mixed dielectric , 1998 .

[9]  H. S. Shan,et al.  Electro jet drilling using hybrid NNGA approach , 2007 .

[10]  B. Schumacher,et al.  About the Role of Debris in the Gap During Electrical Discharge Machining , 1990 .

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  Yan Cherng Lin,et al.  Surface Improvement Using a Combination of Electrical Discharge Machining with Ball Burnish Machining Based on the Taguchi Method , 2001 .

[13]  Pradeep Kumar,et al.  PARAMETRIC OPTIMIZATION OF POWDER MIXED ELECTRICAL DISCHARGE MACHINING BY RESPONSE SURFACE METHODOLOGY , 2005 .

[14]  C. L. Lin,et al.  The use of grey-fuzzy logic for the optimization of the manufacturing process , 2005 .

[15]  Biing-Hwa Yan,et al.  Surface modification of Al–Zn–Mg aluminum alloy using the combined process of EDM with USM , 2001 .

[16]  Wei Liu,et al.  A hybrid model using supporting vector machine and multi-objective genetic algorithm for processing parameters optimization in micro-EDM , 2010 .

[17]  Yih-fong Tzeng,et al.  A simple approach for robust design of high-speed electrical-discharge machining technology , 2003 .

[18]  Qing Gao,et al.  Parameter optimization model in electrical discharge machining process , 2008 .

[19]  Yang Dayong,et al.  The study of high efficiency and intelligent optimization system in EDM sinking process , 2004 .

[20]  R. K. Bhoi,et al.  Artificial Neural Network Prediction of Material Removal Rate in Electro Discharge Machining , 2005 .

[21]  Naotake Mohri,et al.  A new process of finish machining on free surface by EDM methods , 1991 .

[22]  Dong-Mok Lee,et al.  Optimization of electric discharge machining using simulated annealing , 2009 .

[23]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[24]  N. Mohri,et al.  Accretion of titanium carbide by electrical discharge machining with powder suspended in working fluid , 2001 .

[25]  G. Krishna Mohana Rao,et al.  Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm , 2009 .

[26]  Zhanbo Yu,et al.  Dry electrical discharge machining of cemented carbide , 2004 .

[27]  M. L. Jeswani,et al.  Effect of the addition of graphite powder to kerosene used as the dielectric fluid in electrical discharge machining , 1981 .

[28]  S. S. Pande,et al.  Intelligent process modeling and optimization of die-sinking electric discharge machining , 2011, Appl. Soft Comput..

[29]  Yi Wang,et al.  A hybrid intelligent method for modelling the EDM process , 2003 .

[30]  Quan Ming,et al.  Powder-suspension dielectric fluid for EDM , 1995 .

[31]  Yusuf Keskin,et al.  An experimental study for determination of the effects of machining parameters on surface roughness in electrical discharge machining (EDM) , 2006 .

[32]  Elsa Henriques,et al.  Effect of the powder concentration and dielectric flow in the surface morphology in electrical discharge machining with powder-mixed dielectric (PMD-EDM) , 2008 .

[33]  E. Henriques,et al.  Influence of silicon powder-mixed dielectric on conventional electrical discharge machining , 2003 .

[34]  Y. S. Tarng,et al.  Optimisation of the electrical discharge machining process using a GA-based neural network , 2003 .

[35]  K. Palanikumar,et al.  Optimization of electrical discharge machining characteristics of WC/Co composites using non-dominated sorting genetic algorithm (NSGA-II) , 2008 .

[36]  Fu-Chen Chen,et al.  Multi-objective optimisation of high-speed electrical discharge machining process using a Taguchi fuzzy-based approach , 2007 .

[37]  Surjya K. Pal,et al.  Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II , 2007 .

[38]  M. S. Shunmugam,et al.  Multi-objective optimization of wire-electro discharge machining process by Non-Dominated Sorting Genetic Algorithm , 2005 .