Skip to main content

Advertisement

SpringerLink
Log in

Modeling of Electrical Discharge Machining Process Using Conventional Regression Analysis and Genetic Algorithms

  • Published:
Journal of Materials Engineering and Performance Aims and scope Submit manuscript

Abstract

An attempt was made to model input-output relationships of an electrical discharge machining process based on the experimental data (collected according to a central composite design) using multiple regression analysis. Three input parameters, such as peak current, pulse-on-time and pulse-duty-factor, and two outputs, namely, material removal rate (MRR) and surface roughness (SR) had been considered for the said modeling. The value of regression coefficient was determined for each model. The performances of the developed models were tested with the help of some test cases collected through the real experiments and were found to be satisfactory. It had been posed as an optimization problem and solved using a genetic algorithm to determine the set(s) of optimal parameters for ensuring the maximum MRR and minimum SR. It was also formulated as a multi-objective optimization problem and a Pareto-optimal front of solutions had been obtained.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. G.F. Benedict, Nontraditional Manufacturing Process, Marcel Dekker, New York, 1987

    Google Scholar 

  2. J.H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, MI, 1975

    Google Scholar 

  3. D.K. Pratihar, Soft Computing, Narosa Publishing House, New Delhi, 2008

    Google Scholar 

  4. G. Petropoulos, N.M. Vaxevanidis, and C. Pandazaras, Modeling of Surface Finish in Electro-Discharge Machining Based Upon Statistical Multi-Parameter Analysis, J. Mater. Process. Technol., 2004, 155–156, p 1247–1251

    Google Scholar 

  5. M.G. Her and F.T. Weng, A Study of the Electrical Discharge Machining of Semi-Conductor BaTiO3, J. Mater. Process. Technol., 2002, 122, p 1–5

    Article  CAS  Google Scholar 

  6. I. Puertas and C.J. Luis, A Study on the Machining Parameters Optimization of Electrical Discharge Machining, J. Mater. Process. Technol., 2003, 143–144, p 521–526

    Article  Google Scholar 

  7. I. Puertas, C.J. Luis, and L. Alvarez, Analysis of the Influence of EDM Parameters on Surface Quality. MRR and EW of WC-Co, J. Mater. Process. Technol., 2004, 153–154, p 1026–1032

    Article  Google Scholar 

  8. K. Kanlayasiri and S. Boonmung, Effects of Wire-EDM Machining Variables on Surface Roughness of Newly Developed DC 53 Die Steel: Design of Experiments and Regression Model, J. Mater. Process. Technol., 2007, 192–193, p 459–464

    Article  Google Scholar 

  9. D. Kanagarajan, R. Karthikeyan, K. Palanikumar, and J. Davim, Optimization of Electrical Discharge Machining Characteristics of WC-Co Composites Using Non-Dominated Sorting Genetic Algorithm (NSGA-II), Int. J. Adv. Manuf. Technol., 2008, 36, p 1124–1132

    Article  Google Scholar 

  10. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II, IEEE Trans. Evol. Comput., 2002, 6, p 182–197

    Article  Google Scholar 

  11. P. Matoorian, S. Sulaiman, and M.M.H.M. Ahmad, An Experimental Study for Optimization of Electrical Discharge Turning (EDT) Process, J. Mater. Process. Technol., 2008, 204, p 350–356

    Article  CAS  Google Scholar 

  12. A. Mohammadi, A.F. Teharani, E. Emanian, and D. Karimi, Statistical Analysis of Wire Electrical Discharge Turning on Material Removal Rate, J. Mater. Process. Technol., 2008, 205, p 283–289

    Article  Google Scholar 

  13. M.J. Haddad and A.F. Tehrani, Investigation of Cylindrical Wire Electrical Discharge Turning (CWEDT) of AISI, D3 Tool Steel Based on Statistical Analysis, J. Mater. Process. Technol., 2008, 198, p 77–85

    Article  CAS  Google Scholar 

  14. S. Sarkar, M. Sekh, S. Mitra, and B. Bhattacharya, Modeling and Optimization of Wire Electrical Discharge Machining of γ-TiAl in Trim Cutting Operation, J. Mater. Process. Technol., 2008, 205, p 376–378

    Article  CAS  Google Scholar 

  15. D.C. Montgomery, Design and Analysis of Experiments, Wiley, New York, 2001

    Google Scholar 

  16. Minitab 14, Statistical Software, www.minitab.com

Download references

Acknowledgments

The authors gratefully acknowledge the help and cooperation of Mr. S. Bag and Mr. S. Patra of IIT Kharagpur, India, in carrying out the real experiments.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, Kharagpur, 721 302, India

    Kuntal Maji & Dilip Kumar Pratihar

Authors
  1. Kuntal Maji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dilip Kumar Pratihar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dilip Kumar Pratihar.

Appendices

Appendices

Appendix A Experimental data collected as per CCD
Full size table
Appendix B Data collected for testing the models of MRR and SR
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Maji, K., Pratihar, D.K. Modeling of Electrical Discharge Machining Process Using Conventional Regression Analysis and Genetic Algorithms. J. of Materi Eng and Perform 20, 1121–1127 (2011). https://doi.org/10.1007/s11665-010-9754-6

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11665-010-9754-6

Keywords

Search

Navigation