RESEARCH ARTICLE

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

  • Ravindra Nath YADAV , 1 ,
  • Vinod YADAVA 1 ,
  • G.K. SINGH 1,2
Expand
  • 1. Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad-211004, India
  • 2. Department of Mechanical Engineering, Galgotias University, Gr. Noida, India

Received date: 17 Apr 2013

Accepted date: 12 May 2013

Published date: 05 Sep 2013

Copyright

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

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 (Ra). 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.

Cite this article

Ravindra Nath YADAV , Vinod YADAVA , G.K. SINGH . Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique[J]. Frontiers of Mechanical Engineering, 0 , 8(3) : 319 -332 . DOI: 10.1007/s11465-013-0269-3

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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

DOI

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)

Outlines

/