Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique

Abstract

The effective study of hybrid machining processes (HMPs), in terms of modeling and optimization has always been a challenge to the researchers. The combined approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) has attracted attention of researchers for modeling and optimization of the complex machining processes. In this paper, a hybrid machining process of Electrical Discharge Face Grinding (EDFG) and Diamond Face Grinding (DFG) named as Electrical Discharge Diamond face Grinding (EDDFG) have been studied using a hybrid methodology of ANN-NSGA-II. In this study, ANN has been used for modeling while NSGA-II is used to optimize the control parameters of the EDDFG process. For observations of input-output relations, the experiments were conducted on a self developed face grinding setup, which is attached with the ram of EDM machine. During experimentation, the wheel speed, pulse current, pulse on-time and duty factor are taken as input parameters while output parameters are material removal rate (MRR) and average surface roughness (R_a). The results have shown that the developed ANN model is capable to predict the output responses within the acceptable limit for a given set of input parameters. It has also been found that hybrid approach of ANN-NSGA-II gives a set of optimal solutions for getting appropriate value of outputs with multiple objectives.

Keywords

hybrid machining processes (HMPs) / electrical discharge diamond grinding (EDDG) / artificial neural network (ANN) / genetic algorithm / modeling and optimization

References

[1] Konig W, Cronjager L, Spur G, Tonshoff H K, Vigneau M, Zdeblick W J. Machining of new materials. CIRP Annals- Manufacturing Technology, 1990, 39(2): 673–681

[2] Rajurkar K P, Gu L. Resent research and developments in hybrid machining processes, Proc. 3rd Int. 24th AIMTDR Conf. Vishakhapatnam. 2010, 39–44

[3] Kozak J, Oczos K E. Selected problems of abrasive hybrid machining. Journal of Materials Processing Technology, 2001, 109(3): 360–366

[4] Aoyama T, Inasaki I. Hybrid machining-combination of electrical discharge machining and grinding, Proc. 14th N. Am. Manuf. Res. Conf. Annu. Meeting, Minnesota. 1986, 654–661

[5] Wei B, Rajurkar K P. Abrasive electro discharge grinding of super alloys and ceramics, Proc. 1st Int. Mach. Grind. Conf. Dearborn, Michigan. 1995, 188–196

[6] Kozak J. Abrasive electrodischarge grinding (AEDG) of advanced materials. Archives of Civil and Mechanical Engineering, 2002, 2: 83–101

[7] Koshy P, Jain V K, Lal G K. Mechanism of material removal in electrical discharge diamond grinding. International Journal of Machine Tools & Manufacture, 1996, 36(10): 1173–1185

[8] Koshy P, Jain V K, Lal G K. Grinding of cemented carbide with electrical spark assistance. Journal of Materials Processing Technology, 1997, 72(1): 61–68

[9] Choudhury S K, Jain V K, Gupta M. Electrical discharge diamond grinding of high speed steel. Machining Science and Technology, 1999, 3(1): 91–105

[10] Jain V K, Mote R G. On the temperature and specific energy during electrodischarge diamond grinding (EDDG). International Journal of Advanced Manufacturing Technology, 2005, 26(1-2): 56–67

[11] Yadav S K S, Yadava V, Narayana V L. Experimental study and parameter design of electro-discharge diamond grinding. International Journal of Advanced Manufacturing Technology, 2008, 36(1-2): 34–42

[12] Yadav S K S, Yadava V. Multi-objective optimization of electrical discharge diamond cutoff grinding (EDDCG) using Taguchi method. Int. J. Manuf. Technol. Ind. Eng., 2010, 1: 193–198

[13] Singh G K, Yadava V, Kumar R. Robust parameter design and multi-objective optimization of electro-discharge diamond face grinding process of HSS. Int. J. Mach. Mach. Mater., 2012, 11: 1–19

[14] Singh G K, Yadava V, Kumar R. Diamond face grinding of WC-Co composite with spark assistance: Experimental study and parameter optimization. Int. J. Precis. Eng. Manuf., 2010, 11(4): 509–518

[15] Singh G K, Yadava V, Kumar R. Experimental study and parameter optimization of electro-discharge diamond face grinding. Int. J. Abras. Technol., 2011, 4: 14–40

[16] Agrawal S S, Yadava V. Artificial neural network modeling of electrical discharge diamond surface grinding (EDDSG), Proc. 7th Int. Conf. Precis. Meso, Micro and Nano Eng. Pune. 2011,265–269

[17] Joshi S N, Pande S S. Development of an intelligent process model for EDM. International Journal of Advanced Manufacturing Technology, 2009, 45(3-4): 300–317

[18] Jain R K, Jain V K, Kalra P K. Modelling of abrasive flow machining process: A neural network approach. Wear, 1999, 231(2): 242–248

[19] Yousef B F, Knopf G K, Bordatchev E V, Nikumb S K. Neural network modeling and analysis of the material removal process during laser machining. International Journal of Advanced Manufacturing Technology, 2003, 22(1-2): 41–53

[20] Briceno J F, Mounayri H E, Mukhopadhyay S. Selecting an artificial neural network for efficient modeling and accurate simulation of the milling process. International Journal of Machine Tools & Manufacture, 2002, 42(6): 663–674

[21] Sanjay C, Neema M L, Chin C W. Modeling of tool wear in drilling by statistical analysis and artificial neural network. Journal of Materials Processing Technology, 2005, 170(3): 494–500

[22] Markopoulos A P, Manolakos D E, Vaxevanidis N M. Artificial neural network models for the prediction of surface roughness in electrical discharge machining. Journal of Intelligent Manufacturing, 2008, 19(3): 283–292

[23] Kumar S, Choudhury S K. Prediction of wear and surface roughness in electro-discharge diamond grinding. Journal of Materials Processing Technology, 2007, 191(1-3): 206–209

[24] Yadav S K S, Yadava V. Artificial neural network modeling of electrical discharge diamond cut-off grinding (EDDCG), Proc. 3rd Int. 24th AIMTDR Conf. Vishakhapatnam. 2010, 271–275

[25] Sharma V, Yadava V, Rao R. Yadava, R. Rao, Optimization of kerf quality characteristics during Nd: YAG laser cutting of nickel based superalloy sheet for straight and curved cut profiles. Optics and Lasers in Engineering, 2010, 48(9): 915–925

[26] Tosun N. Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis. International Journal of Advanced Manufacturing Technology, 2006, 28(5-6): 450–455

[27] Mahapatra S S, Patnaik A. Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method. International Journal of Advanced Manufacturing Technology, 2007, 34(9-10): 911–925

[28] Jung J H, Kwon W T. Optimization of EDM process for multiple performance characteristics using Taguchi method and Grey relational analysis. Journal of Mechanical Science and Technology, 2010, 24(5): 1083–1090

[29] Kansal H K, Singh S, Kumar P. Parametric optimization of powder mixed electrical discharge machining by response surface methodology. Journal of Materials Processing Technology, 2005, 169(3): 427–436

[30] Siddiquee A N, Khan Z A, Mallick Z. Grey relational analysis coupled with principal component analysis for optimisation design of the process parameters in in-feed centreless cylindrical grinding. International Journal of Advanced Manufacturing Technology, 2010, 46(9-12): 983–992

[31] Rajasekaran S, Pai G A V. Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis and Applications. PHI Learning Pvt. Ltd. New Delhi, 2004

[32] Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182–197

[33] Mitra K, Gopinath R. Multiobjective optimization of an industrial grinding operation using elitist nondominated sorting genetic algorithm. Chemical Engineering Science, 2004, 59(2): 385–396

[34] Tavoli M A, Zadeh N N, Khakhali A, Mehran M. Multi-objective optimization of abrasive flow machining processes using polynomial neural networks and genetic algorithms. Machining Science and Technology, 2006, 10(4): 491–510

[35] Su J C, Kao J Y, Tarng J Y S. Optimisation of the electrical discharge machining process using a GA-based neural network. International Journal of Advanced Manufacturing Technology, 2004, 24: 81–90

[36] Kanagarajan D, Karthikeyan R, Palanikumar K, Davim J P. Optimization of electrical discharge machining characteristics of WC/Co composites using non-dominated sorting genetic algorithm (NSGA-II). International Journal of Advanced Manufacturing Technology, 2008, 36(11-12): 1124–1132

[37] Joshi S N, Pande S S. Intelligent process modeling and optimization of die-sinking electric discharge machining. Applied Soft Computing, 2011, 11(2): 2743–2755

[38] Mandal D, Pal S K, Saha P. Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II. Journal of Materials Processing Technology, 2007, 186(1-3): 154–162

[39] Rao G K M, Janardhana G R, Rao D H, Rao M S. Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm. Journal of Materials Processing Technology, 2009, 209(3): 1512–1520

[40] AliR, NejadM. Modeling and optimization of electrical discharge machining of SiCparameters using neural network and non-dominating sorting genetic algorithm (NSGA-II). Materials Sciences and Applications, 2011, 2: 669–675

[41] Wang K, Gelgele H L, Wang Y, Yuan Q, Fang M. A hybrid intelligent method for modelling the EDM process. International Journal of Machine Tools & Manufacture, 2003, 43(10): 995–999

[42] Cochran W G, Cox G M. Experimental Designs, Asia Publishing House, Bombay, 1959.

[43] Moller M F. A scale conjugate gradient algorithm for fast supervised learning. Neural Networks, 1993, 6(4): 525–533

[44] Deb K. Multi-Objective Optimization using Evolutionary Algorithm, First ed., John Wiley and Sons, Ltd, West Sussex, 2002

[45] Song L. NGPS-A NSGA-II Program in Matlab, Version 1.4, Coll. Astronaut. Northwestern Polytech. Univ. China, [on line], 2011, Available from: http://www.mathworks.com/matlabcentral/fileexchange (Accessed 20 April, 2012)

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg