Skip to main content
SpringerLink
Log in

A hybrid process model for EDM based on finite-element method and Gaussian process regression

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This paper proposed a hybrid intelligent process model, based on finite-element method (FEM) and Gaussian process regression (GPR), for electrical discharge machining (EDM) process. A model of single-spark EDM process has been constructed based on FEM method, considering the latent heat, variable heat distribution coefficient of cathode (f c ), and plasma flushing efficiency (PFE), to predict material removal rate (MRR) and surface roughness (Ra). This model was validated using reported analytical and experimental results. Then, a GPR model was proposed to establish relationship between input process parameters (pulse current, pulse duration, and discharge voltage) and the process responses (MRR and Ra) for EDM process. The GPR model was trained, tested, and tuned using the data generated from the numerical simulations. Through the GPR model, it was found that responses of EDM process can be accurately predicted for the chosen process conditions. Therefore, for the selection of optimum parameters, the hybrid intelligent model proposed in this paper can be used in EDM process.

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. Ho KH, Newman ST (2003) State of the art electrical discharge machining (EDM). Int J Mach Tool Manuf 43(13):1287–1300

    Article  Google Scholar 

  2. DiBitonto DD, Eubank PT, Patel MR, Barrufet MA (1989) Theoretical models of the electrical discharge machining process. I. A simple cathode erosion model. J Appl Phys 66(9):4095–4103

    Article  Google Scholar 

  3. Patel MR, Barrufet MA, Eubank PT, DiBitonto DD (1989) Theoretical models of the electrical discharge machining process. II. The anode erosion model. J Appl Phys 66(9):4104–4111

    Article  Google Scholar 

  4. Eubank PT, Patel MR, Barrufet MA, Bozkurt B (1993) Theoretical models of the electrical discharge machining process. III. The variable mass, cylindrical plasma model. J Appl Phys 73(11):7900–7909

    Article  Google Scholar 

  5. Chen Y, Mahdivian SM (2000) Analysis of electro-discharge machining process and its comparison with experiments. J Mater Process Technol 104(1):150–157

    Article  Google Scholar 

  6. Salah NB, Ghanem F, Atig KB (2006) Numerical study of thermal aspects of electric discharge machining process. Int J Mach Tool Manuf 46(7):908–911

    Article  Google Scholar 

  7. Marafona J, Chousal JAG (2006) A finite element model of EDM based on the Joule effect. Int J Mach Tool Manuf 46(6):595–602

    Article  Google Scholar 

  8. Yeo SH, Kurnia W (2008) Critical assessment and numerical comparison of electro-thermal models in EDM. J Mater Process Technol 203(1):241–251

    Article  Google Scholar 

  9. Beck JV (1981a) Transient temperatures in a semi-infinite cylinder heated by a disk heat source. Int J Heat Mass Transf 24(10):1631–1640

    Article  MATH  Google Scholar 

  10. Beck JV (1981b) Large time solutions for temperatures in a semi-infinite body with a disk heat source. Int J Heat Mass Transf 24(1):155–164

    Article  Google Scholar 

  11. Jilani ST, Pandey PC (1982) Analysis and modelling of EDM parameters. Precis Eng 4(4):215–221

    Article  Google Scholar 

  12. Jilani ST, Pandey PC (1983) Analysis of surface erosion in electrical discharge machining. J Wear 84(3):275–284

    Article  Google Scholar 

  13. Snoeys R, Van Dijck FS (1971) Investigation of electro discharge machining operations by means of thermo-mathematical model. CIRP Ann 20(1):35–37

    Google Scholar 

  14. Van Dijck FS, Dutre WL (1974) Heat conduction model for the calculation of the volume of molten metal in electric discharges. J Phys D Appl Phys 7(6):899–910

    Article  Google Scholar 

  15. Kansal HK, Singh S, Kumar P (2008) Numerical simulation of powder mixed electric discharge machining (PMEDM) using finite element method. Math Comput Model 47(11):1217–1237

    Article  MATH  Google Scholar 

  16. Joshi SN, Pande SS (2009) Development of an intelligent process model for EDM. Int J Adv Manuf Technol 45(3–4):300–317

    Article  Google Scholar 

  17. Joshi SN, Pande SS (2010) Thermo-physical modeling of die-sinking EDM process. J Manuf Process 12(1):45–56

    Article  Google Scholar 

  18. Salonitis K, Stournaras A, Stavropoulos P, Chryssolouris G (2009) Thermal modeling of the material removal rate and surface roughness for die-sinking EDM. Int J Adv Manuf Technol 40(3–4):316–323

    Article  Google Scholar 

  19. Shabgard M, Ahmadi R, Seyedzavvar M, Oliaei SNB (2012) Mathematical and numerical modeling of the effect of input-parameters on the flushing efficiency of plasma channel in EDM process. Int J Mach Tool Manuf 65:79–87

    Article  Google Scholar 

  20. Shankar P, Jain VK, Sundarajan T (1997) Analysis of spark profiles during EDM process. Mach Sci Technol 1(2):195–217

    Article  Google Scholar 

  21. Yeo SH, Kurnia W, Tan PC (2007) Electro-thermal modelling of anode and cathode in micro-EDM. J Phys D Appl Phys 40(8):2513

    Article  Google Scholar 

  22. Xia H, Hashimoto H, Kunieda M (1996) Measurement of energy distribution in continuous EDM process. Int J Jpn Soc Precis Eng 62(8):1141–1145

    Article  Google Scholar 

  23. Yadav V, Jain VK, Dixit PM (2002) Thermal stresses due to electric al discharge machining. Int J Mach Tool Manuf 42:877–888

    Article  Google Scholar 

  24. Singh H (2012) Experimental study of distribution of energy during EDM process for utilization in thermal models. Int J Heat Mass Transf 55(19):5053–5064

    Article  Google Scholar 

  25. Singh H, Shukla DK (2012) Optimizing electric discharge machining parameters for tungsten-carbide utilizing thermo-mathematical modelling. Int J Therm Sci 59:161–175

    Article  Google Scholar 

  26. Kumar A, Maheswari S, Sharma S, Naveen B (2010) A study of multi-objective parametric optimization of silicon abrasive mixed electrical discharge machining of tool steel. Mater Manuf Process 25:1041–1047

    Article  Google Scholar 

  27. Kao JY, Tsao CC, Wang SS, Hsu CY (2010) Optimization of the EDM parameters on machining Ti-6Al-4V with multiple quality characteristics. Int J Adv Manuf Technol 47(1–4):395– 402

    Article  Google Scholar 

  28. Patel KM, Pandey PM, Rao PV (2010) Optimization of process parameters for multi-performance characteristics in EDM of Al2O3 ceramic composite. Int J Adv Manuf Technol 47(9–12):1137–1147

    Article  Google Scholar 

  29. Lin CL, Lin JL, Ko TC (2002) Optimization of the EDM process based on the orthogonal array with fuzzy logic and grey relational analysis method. Int J Adv Manuf Technol 19(4):271–277

    Article  Google Scholar 

  30. Lin YC, Lee HS (2009) Optimization of machining parameters using magnetic-force-assisted EDM based on gray relational analysis. Int J Adv Manuf Technol 42(11–12):1052–1064

    Article  Google Scholar 

  31. Nikalje AM, Kumar A, Srinadh KVS (2013) Influence of parameters and optimization of EDM performance measures on MDN 300 steel using Taguchi method. Int J Adv Manuf Technol 69(1–4):41–49

    Article  Google Scholar 

  32. Aliakbari E, Baseri H (2012) Optimization of machining parameters in rotary EDM process by using the Taguchi method. Int J Adv Manuf Technol 62(9–12):1041–1053

    Article  Google Scholar 

  33. Manna A, Bhattacharyya B (2006) Taguchi and Gauss elimination method: a dual response approach for parametric optimization of CNC wire cut EDM of PRAlSiCMMC. Int J Adv Manuf Technol 28(1–2):67–75

    Article  Google Scholar 

  34. Azhiri RB, Teimouri R, Baboly MG, Leseman Z (2013) Application of Taguchi, ANFIS and grey relational analysis for studying, modeling and optimization of wire EDM process while using gaseous media. Int J Adv Manuf Tech 71:279–295

  35. Patowari PK, Saha P, Mishra PK (2011) Taguchi analysis of surface modification technique using W-Cu powder metallurgy sintered tools in EDM and characterization of the deposited layer. Int J Adv Manuf Technol 54(5–8):593–604

    Article  Google Scholar 

  36. Wang K, Gelgele HL, Wang Y, Yuan Q, Fang M (2003) A hybrid intelligent method for modeling the EDM process. Int J Mach Tool Manuf 43:995–999

    Article  Google Scholar 

  37. 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(1–3):154– 162

    Article  Google Scholar 

  38. Yuan J, Wang K, Yu T, Fang M (2008) Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression. Int J Mach Tool Manuf 48(1):47–60

    Article  Google Scholar 

  39. Yuan J, Liu CL, Liu X, Wang K, Yu T (2009) Incorporating prior model into Gaussian processes regression for WEDM process modeling. Expert Syst Appl 36(4):8084–8092

    Article  Google Scholar 

  40. Ikai T, Hashigushi K (1995) Heat input for crater formation in EDM. ISEM XI, EPFL, Lausanne, pp 163–170

    Google Scholar 

  41. Yadav V, Jain VK, Dixit PM (2002) Thermal stresses due to electrical discharge machining. Int J Mach Tool Manuf 42(8):877–888

    Article  Google Scholar 

  42. Ross P J (1988) Taguchi techniques for quality engineering. McGraw Hill, New York

    Google Scholar 

  43. Mori T (1991) The new experimental design: Taguchi’s approach to quality engineering. ASI Press, Dearborn

    Google Scholar 

  44. Mason RL, Gunt RF, Hess JL (2003) Statistical design and analysis of experiments. Wiley, New York

    Book  MATH  Google Scholar 

  45. Huang Y, Ming WY, Guo JW, Zhang Z, Liu GD, Li MZ, Zhang GJ (2013) Optimization of cutting conditions of YG15 on rough and finish cutting in WEDM based on statistical analyses. Int J Adv Manuf Technol 69:993–1008

    Article  Google Scholar 

  46. Rasmussen C, Williams C (2006) Gaussian processes for machine learning. MIT Press, USA

    MATH  Google Scholar 

  47. Neal R (1996) Bayesian learning for neural networks. Springer, New York

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electromechanical Science and Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Wuyi Ming

  2. State Key Lab of Digital Manufacturing Equipment, Tech, School of Mechanical Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Guojun Zhang, He Li, Jianwen Guo, Zhen Zhang, Yu Huang & Zhi Chen

  3. Guangdong Province Key Lab of Digital Manufacturing Equipment, Dongguan, 523000, China

    He Li

Authors
  1. Wuyi Ming
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guojun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. He Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianwen Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to He Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ming, W., Zhang, G., Li, H. et al. A hybrid process model for EDM based on finite-element method and Gaussian process regression. Int J Adv Manuf Technol 74, 1197–1211 (2014). https://doi.org/10.1007/s00170-014-5989-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-5989-y

Keywords

Search

Navigation