Skip to main content
SpringerLink
Log in

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

  • Research Article
  • Published:
Frontiers of Mechanical Engineering Aims and scope Submit manuscript

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-NSGAII gives a set of optimal solutions for getting appropriate value of outputs with multiple objectives.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  12. Yadav S K S, Yadava V. Multi-objective optimization of electrical discharge diamond cutoff grinding (EDDCG) using Taguchi method. International Journal of Manufacturing Technology and Industrial Engineering, 2010, 1: 193–198

    Google Scholar 

  13. Singh G K, Yadava V, Kumar R. Robust parameter design and multi-objective optimization of electro-discharge diamond face grinding process of HSS. International Journal of Machining and Machinability of Materials, 2012, 11: 1–19

    Article  MATH  Google Scholar 

  14. Singh G K, Yadava V, Kumar R. Diamond face grinding of WC-Co composite with spark assistance: Experimental study and parameter optimization. International Journal of Precision Engineering and Manufacturing, 2010, 11(4): 509–518

    Article  Google Scholar 

  15. Singh G K, Yadava V, Kumar R. Experimental study and parameter optimization of electro-discharge diamond face grinding. International Journal of Abrasive Technology, 2011, 4: 14–40

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  40. Ali R, Nejad M. Modeling and optimization of electrical discharge machining of SiCparameters using neural network and nondominating sorting genetic algorithm (NSGA-II). Materials Sciences and Applications, 2011, 2: 669–675

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  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 April 20, 2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad, 211004, India

    Ravindra Nath Yadav, Vinod Yadava & G. K. Singh

  2. Department of Mechanical Engineering, Galgotias University, Gr. Noida, India

    G. K. Singh

Authors
  1. Ravindra Nath Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vinod Yadava
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. K. Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ravindra Nath Yadav.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yadav, R.N., Yadava, V. & Singh, G.K. Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique. Front. Mech. Eng. 8, 319–332 (2013). https://doi.org/10.1007/s11465-013-0269-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11465-013-0269-3

Keywords

Search

Navigation