Skip to main content
SpringerLink
Log in

Improvement of dry EDM process characteristics using artificial soft computing methodologies

  • Machine Tool
  • Published:
Production Engineering Aims and scope Submit manuscript

Abstract

Dry electrical discharge machining (EDM) is an environmentally-friendly alternative of die-sinking EDM process, which it uses gaseous medium instead of liquid as a dielectric. Due to contribution of too many parameters in this process, selection of optimal parameters to increase the process performances is a really crucial concern. In this work, a predictive model based on back-propagation neural network has been applied to correlate the inputs and outputs of dry EDM process. Herein, the inputs were gap voltage, pulse current, pulse on time, duty factor, air intake pressure and rotational speed of tool, and also the main outputs were material removal rate (MRR) and surface roughness (SR). Firstly a back-propagation (BP) and radial basis function neural network have been developed based on data generated from literature [Saha and Choudhary Int J Mach Tools Manuf 49:297–308 (2009)]. Then, the accuracy of proposed models has been checked by their values of error percent via testing data. Hereafter, the most accurate model was served as an objective function to optimize the process using artificial bee colony (ABC) algorithm. In optimization stage, firstly a single objective optimization was fulfilled to determine the optimal factors related to each output separately. Then a multi-objective optimization was implemented to calculate the best solutions in the case of higher MRR and lower SR simultaneously. Results indicated that the predictive model can estimate the dry EDM process precisely, and also the ABC algorithm could find the optimal solution sets logically.

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

Similar content being viewed by others

Abbreviations

Vg (V):

Gap voltage

Id (A):

Discharge current

Ton (μs):

Pulse on time

D (%):

Duty factor

N (rpm):

Tool rotational speed

P (kPa):

Air intake pressure

MRR (mm3):

Material removal rate

SR (μm):

Surface roughness

References

  1. Ramani V, Cassidenti ML (1985) Inert-gas electrical discharge machining. NASA Technical Brief Number NPO 15660

  2. Kunieda M, Furuoya S, Taniguchi N (1991) Improvement of EDM efficiency by supplying oxygen gas into gap. CIRP Ann Manuf Technol 40:215–218

    Article  Google Scholar 

  3. Kunieda M, Yoshida M, Taniguchi N (1997) Electrical discharge machining in gas. CIRP Ann Manuf Technol 46:143–146

    Article  Google Scholar 

  4. Saha SK, Choudhury SK (2009) Experimental investigation and empirical modeling of the dry electric discharge machining process. Int J Mach Tools Manuf 49(3–4):297–308

    Article  Google Scholar 

  5. Govindan P, Joshi SS (2010) Experimental characterization of material removal in dry electrical discharge drilling. Int J Mach Tools Manuf 50:431–443

    Article  Google Scholar 

  6. Kuneida M, Miyoshi Y, Takaya T, Nakajima N, Bo YZ, Yoshida M (2003) High speed 3D milling by dry EDM. CIRP Ann Manuf Technol 52:147–150

    Article  Google Scholar 

  7. Yu Z, Jun T, Masanori K (2004) Dry electrical discharge machining of cemented carbide. In: 14th international symposium on electromachining (ISEM XIV), J Mater Process Technol 149 (1–3):353–357

  8. Zhanbo Y, Takahashi J, Nakajima N, Sano S, Karato K, Kuneida M (2005) Feasibility of 3-D surface machining by dry EDM. Int J Electr Mach 10:15–20

    Google Scholar 

  9. Soni JS, Chakraverti G (1994) Machining characteristics of titanium alloy with rotary electro-discharge machining. Wear 171:51–58

    Article  Google Scholar 

  10. Chattophaday KD, Veram S, Satsangi PS, Sharma PC (2009) Development of empirical model for different process parameters during rotary electrical discharge machining of copper-steel (EN-8) system. J Mater Process Technol 209:1454–1465

    Article  Google Scholar 

  11. Mohan B, Rajadurai A, Satyanarayana KG (2002) Effect of SiC and rotation of tool electrode on electric discharge machining of Al–SiC composite. J Mater Process Technol 124:297–304

    Article  Google Scholar 

  12. Kuppan P, Rajadurai A, Narayanan S (2008) Influence of EDM process parameters in deep hole drilling of Inconel 718. Int J Adv Manuf Technol 38:74–84

    Article  Google Scholar 

  13. Sohani MS, Gaitonde VN, Siddeswarappa B, Deshpande AS (2009) Investigations into the effect of tool shapes with size factor consideration in sink electrical discharge machining (EDM) process. Int J Adv Manuf Tech 45:1131–1145

    Article  Google Scholar 

  14. Tsai KM, Wang PJ (2001) Comparisons of neural network models on material removal rate in electrical discharge machining. J Mater Process Technol 117:111–124

    Article  Google Scholar 

  15. Kumar S, Chodhury SK (2007) Prediction of wear and surface roughness in electro-discharge diamond grinding. J Mater Process Technol 191:206–209

    Article  Google Scholar 

  16. Mandal D, Pal SK, Saha P (2007) Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II. J Mater Process Technol 186:154–162

    Article  Google Scholar 

  17. Yang SH, Srinivas J, Mohan S, Lee DM, Blajee S (2009) Optimization of electric discharge machining using simulated annealing. J Mater Process Technol 209:4471–4475

    Article  Google Scholar 

  18. Akay B, Karaboga D Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf. doi:10.1007/s10845-010-0393-4

  19. Basturk B, Karaboga D (2006) An artificial bee colony (ABC) algorithm for numeric function optimization. In: Proceedings of the IEEE swarm intelligence symposium, Indianapolis, IN, USA, May 12–14

  20. Rao RV, Pawar PJ (2009) Modelling and optimization of process parameters of wire electrical discharge machining. Proc IMechE, Part B J Eng Manuf 223(11):1431–1440

  21. Samanta S, Chakraborty S (2011) Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Eng Appl Artif Intell 24:946–957

    Article  Google Scholar 

  22. Chen HC, Lin JC, Yang YK, Tsai CH (2010) Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach. Expert Syst Appl 37:7147–7153

    Article  Google Scholar 

  23. Joshi SN, Pande SS (2011) Intelligent process modeling and optimization of die-sinking electric discharge machining. Appl Soft Comput 11:2743–2755

    Article  Google Scholar 

  24. Singh PN, Raghukandan N, Pai BC (2004) Optimization by Grey relational analysis of EDM parameters on machining Al–10 % SiCP composites. J Mater Process Technol 155–156:1658–1661

    Article  Google Scholar 

  25. Hagan MT, Demuth HB, Beale M (1996) Neural network design. PWS Publishing Company, Boston, MA

    Google Scholar 

  26. Rao SS (2009) Engineering optimization: theory and practice. Wiley, Hoboken, NJ

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mechanical Engineering Department, Babol University of Technology, Babol, Iran

    Reza Teimouri & Hamid Baseri

Authors
  1. Reza Teimouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Baseri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Teimouri.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Teimouri, R., Baseri, H. Improvement of dry EDM process characteristics using artificial soft computing methodologies. Prod. Eng. Res. Devel. 6, 493–504 (2012). https://doi.org/10.1007/s11740-012-0398-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11740-012-0398-2

Keywords

Search

Navigation