Advertisement

OPSEARCH

pp 1–28 | Cite as

Parametric analysis of a green electrical discharge machining process using DEMATEL and SIR methods

  • Partha Protim Das
  • Shankar ChakrabortyEmail author
Application Article
  • 14 Downloads

Abstract

To achieve safer machining environment, and minimize emission of harmful and toxic substances during electrical discharge machining (EDM) process along with improvement in its performance, this paper emphasizes on identifying the best parametric combination of a green EDM process using superiority and inferiority ranking (SIR) method. Decision making trial and evaluation laboratory method is also employed to visualize the interrelationships between the responses of the said process while splitting them into cause and effect groups. In this process, peak current, pulse duration, dielectric level and flushing pressure are the input parameters, while process time, relative tool wear ratio, process energy, concentration of aerosol and dielectric consumption are considered as the responses. The optimal parametric combination as derived employing the SIR method is validated with the help of developed regression equations for each of the responses, which show that the adopted approach outperforms the other popular optimization techniques in obtaining the best mix of the green EDM process parameters for having improved machining performance and less hazardous effects on the environment.

Keywords

Green EDM DEMATEL SIR method Process parameter Response Optimization 

Notes

References

  1. 1.
    Abbas, N.M., Solomon, D.G., Bahari, M.F.: A review on current research trends in electrical discharge machining (EDM). Int. J. Mach. Tools Manuf. 47(7–8), 1214–1228 (2007)CrossRefGoogle Scholar
  2. 2.
    Bhuyan, R., Routara, B.: Optimization the machining parameters by using VIKOR and entropy weight method during EDM process of Al-18% SiCp metal matrix composite. Decis. Sci. Lett. 5(2), 269–282 (2016)CrossRefGoogle Scholar
  3. 3.
    Brans, J.P., Vincke, P., Mareschal, B.: How to select and how to rank projects: the PROMETHEE method. Eur. J. Oper. Res. 24(2), 228–238 (1986)CrossRefGoogle Scholar
  4. 4.
    Chakraborty, S., Das, P.P.: A multivariate quality loss function approach for parametric optimization of non-traditional machining processes. Manag. Sci. Lett. 8(8), 873–884 (2018)CrossRefGoogle Scholar
  5. 5.
    Chakraborty, S., Das, P.P., Kumar, V.: Application of grey-fuzzy logic technique for parametric optimization of non-traditional machining processes. Grey Syst.: Theory Appl. 8(1), 46–68 (2018)CrossRefGoogle Scholar
  6. 6.
    Chen, Y.C., Lien, H.P., Tzeng, G.H.: Measures and evaluation for environment watershed plans using a novel hybrid MCDM model. Expert Syst. Appl. 37(2), 926–938 (2010)CrossRefGoogle Scholar
  7. 7.
    Choi, A.C.K., Kaebernick, H., Lai, W.H.: Manufacturing processes modelling for environmental impact assessment. J. Mater. Process. Technol. 70(1–3), 231–238 (1997)CrossRefGoogle Scholar
  8. 8.
    Das, P.P., Chakraborty, S.: Parametric optimization of non-traditional machining processes using Taguchi method and super ranking concept. Yugosl. J. Oper. Res. (2018).  https://doi.org/10.2298/YJOR180821033D Google Scholar
  9. 9.
    Dewangan, S., Gangopadhyay, S., Biswas, C.K.: Multi-response optimization of surface integrity characteristics of EDM process using grey-fuzzy logic-based hybrid approach. Eng. Sci. Technol., Int. J. 18(3), 361–368 (2015)CrossRefGoogle Scholar
  10. 10.
    El-Taweel, T.A.: Multi-response optimization of EDM with Al–Cu–Si–TiC P/M composite electrode. Int. J. Adv. Manuf. Technol. 44(1–2), 100–113 (2009)CrossRefGoogle Scholar
  11. 11.
    Fontela, E., Gabus, A.: The DEMATEL Observer, DEMATEL 1976 Report. Battelle Geneva Research Center, Geneva (1976)Google Scholar
  12. 12.
    Gabus, A., Fontela, E.: Perceptions of the world problematique: communication procedure, communicating with those bearing collective responsibility (No. 1). DEMATEL Report (1973)Google Scholar
  13. 13.
    Gopalakannan, S., Senthilvelan, T.: Optimization of machining parameters for EDM operations based on central composite design and desirability approach. J. Mech. Sci. Technol. 28(3), 1045–1053 (2014)CrossRefGoogle Scholar
  14. 14.
    Govindan, K., Rajendran, S., Sarkis, J., Murugesan, P.: Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. J. Clean. Prod. 98, 66–83 (2015)CrossRefGoogle Scholar
  15. 15.
    Hamidi, N., Yousefi, P., Rahimi, A., Jabari, F.: A hybrid of Borda and DEMATEL for productivity improvement. Manag. Sci. Lett. 2(8), 2757–2764 (2012)CrossRefGoogle Scholar
  16. 16.
    Ho, K.H., Newman, S.T.: State of the art electrical discharge machining (EDM). Int. J. Mach. Tools Manuf. 43(13), 1287–1300 (2003)CrossRefGoogle Scholar
  17. 17.
    Ho, W., Xu, X., Dey, P.K.: Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. Eur. J. Oper. Res. 202(1), 16–24 (2010)CrossRefGoogle Scholar
  18. 18.
    Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making Methods and Applications. Springer, Berlin (1981)CrossRefGoogle Scholar
  19. 19.
    Jagadish, Ray, A.: Multi-objective optimization of green EDM: an integrated theory. J. Inst. Eng. (India): Ser. C 96(1), 41–47 (2015)Google Scholar
  20. 20.
    Jagadish, Ray, A.: Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA). Int. J. Adv. Manuf. Technol. 87(5–8), 1299–1311 (2016)CrossRefGoogle Scholar
  21. 21.
    Janic, M., Reggiani, A.: An application of the multiple criteria decision making (MCDM) analysis to the selection of a new hub airport. Eur. J. Transp. Infrastruct. Res. 2(2), 113–141 (2002)Google Scholar
  22. 22.
    Joshi, S.N., Pande, S.S.: Intelligent process modeling and optimization of die-sinking electric discharge machining. Appl. Soft Comput. 11(2), 2743–2755 (2011)CrossRefGoogle Scholar
  23. 23.
    Kung, K.Y., Horng, J.T., Chiang, K.T.: Material removal rate and electrode wear ratio study on the powder mixed electrical discharge machining of cobalt-bonded tungsten carbide. Int. J. Adv. Manuf. Technol. 40(1–2), 95–104 (2009)CrossRefGoogle Scholar
  24. 24.
    Kuo, T.C., Chang, S.H., Huang, S.H.: Environmentally conscious design by using fuzzy multi-attribute decision-making. Int. J. Adv. Manuf. Technol. 29(3–4), 209–215 (2006)CrossRefGoogle Scholar
  25. 25.
    Liu, F., Zhang, H.: A decision-making framework model of green manufacturing. Chin. J. Mech. Eng. 35, 11–15 (1999)Google Scholar
  26. 26.
    Marzouk, M.: A superiority and inferiority ranking model for contractor selection. Constr. Innov. 8(4), 250–268 (2008)CrossRefGoogle Scholar
  27. 27.
    Mukherjee, R., Chakraborty, S.: Selection of EDM process parameters using biogeography-based optimization algorithm. Mater. Manuf. Process. 27(9), 954–962 (2012)CrossRefGoogle Scholar
  28. 28.
    Rebai, A.: BBTOPSIS: a bag based technique for order preference by similarity to ideal solution. Fuzzy Sets Syst. 60(2), 143–162 (1993)CrossRefGoogle Scholar
  29. 29.
    Rebai, A.: Canonical fuzzy bags and bag fuzzy measures as a basis for MADM with mixed non cardinal data. Eur. J. Oper. Res. 78(1), 34–48 (1994)CrossRefGoogle Scholar
  30. 30.
    Reddy, V.V., Valli, P.M., Kumar, A., Reddy, C.S.: Multi-objective optimization of electrical discharge machining of PH17-4 stainless steel with surfactant-mixed and graphite powder-mixed dielectric using Taguchi-data envelopment analysis-based ranking method. Proc. Inst. Mech. Eng., Part B: J. Eng. Manuf. 229(3), 487–494 (2015)CrossRefGoogle Scholar
  31. 31.
    Roy, B., Slowinski, R., Treichel, W.: Multicriteria programming of water supply systems for rural areas. J. Am. Water Resour. Assoc. 28(1), 13–31 (1992)CrossRefGoogle Scholar
  32. 32.
    Roy, B.: Multicriteria Methodology for Decision Aiding, vol. 12. Springer, Berlin (2013)Google Scholar
  33. 33.
    Roy, T., Dutta, R.K.: Integrated fuzzy AHP and fuzzy TOPSIS methods for multi-objective optimization of electro discharge machining process. Soft Comput. (2018).  https://doi.org/10.1007/s00500-018-3173-2 Google Scholar
  34. 34.
    Sheng, P., Srinivasan, M., Kobayashi, S.: Multi-objective process planning in environmentally conscious manufacturing: a feature-based approach. CIRP Ann.—Manuf. Technol. 44(1), 433–437 (1995)CrossRefGoogle Scholar
  35. 35.
    Singh, J., Sharma, R.K.: Green EDM strategies to minimize environmental impact and improve process efficiency. J. Manuf. Sci. Prod. 16(4), 273–290 (2016)Google Scholar
  36. 36.
    Singh, N.K., Pandey, P.M., Singh, K.K., Sharma, M.K.: Steps towards green manufacturing through EDM process: A review. Cogent Eng. 3(1), 13 (2016).  https://doi.org/10.1080/23311916.2016.1272662 Google Scholar
  37. 37.
    Singh, P.N., Raghukandan, K., Pai, B.C.: Optimization by grey relational analysis of EDM parameters on machining Al-10% SiCP composites. J. Mater. Process. Technol. 155, 1658–1661 (2004)CrossRefGoogle Scholar
  38. 38.
    Sivapirakasam, S.P., Mathew, J., Surianarayanan, M.: Multi-attribute decision making for green electrical discharge machining. Expert Syst. Appl. 38(7), 8370–8374 (2011)CrossRefGoogle Scholar
  39. 39.
    Tam, C.M., Tong, T.K., Wong, Y.W.: Selection of concrete pump using the superiority and inferiority ranking method. J. Constr. Eng. Manag. 130(6), 827–834 (2004)CrossRefGoogle Scholar
  40. 40.
    Tan, X.C., Liu, F., Cao, H.J., Zhang, H.: A decision-making framework model of cutting fluid selection for green manufacturing and a case study. J. Mater. Process. Technol. 129(1–3), 467–470 (2002)CrossRefGoogle Scholar
  41. 41.
    Tang, L., Du, Y.T.: Experimental study on green electrical discharge machining in tap water of Ti–6Al–4V and parameters optimization. Int. J. Adv. Manuf. Technol. 70(1–4), 469–475 (2014)CrossRefGoogle Scholar
  42. 42.
    Tang, L., Du, Y.T.: Multi-objective optimization of green electrical discharge machining Ti–6Al–4V in tap water via grey-Taguchi method. Mater. Manuf. Process. 29(5), 507–513 (2014)CrossRefGoogle Scholar
  43. 43.
    Tavana, M., Zareinejad, M., Santos-Arteaga, F.J.: An intuitionistic fuzzy-grey superiority and inferiority ranking method for third-party reverse logistics provider selection. Int. J. Syst. Sci.: Oper. Logist. 5(2), 175–194 (2018)Google Scholar
  44. 44.
    Tönshoff, H.K., Egger, R., Klocke, F.: Environmental and safety aspects of electrophysical and electrochemical processes. CIRP Ann. 45(2), 553–568 (1996)CrossRefGoogle Scholar
  45. 45.
    Wang, X., Chen, L., Dan, B., Wang, F.: Evaluation of EDM process for green manufacturing. Int. J. Adv. Manuf. Technol. 94(1–4), 633–641 (2018)CrossRefGoogle Scholar
  46. 46.
    Wu, X., Zhang, S., Qiu, S., Sun, L.: Decision making method of process parameter selection for green manufacturing based on a DEMATEL-VIKOR algorithm. J. Mech. Eng. 49(7), 91–100 (2013)CrossRefGoogle Scholar
  47. 47.
    Xu, X.: The SIR method: a superiority and inferiority ranking method for multiple criteria decision making. Eur. J. Oper. Res. 131(3), 587–602 (2001)CrossRefGoogle Scholar
  48. 48.
    Yeo, S.H., Neo, K.G., Tan, H.C.: Assessment of health hazards in production of printed paper packages. Int. J. Adv. Manuf. Technol. 14(5), 376–384 (1998)CrossRefGoogle Scholar
  49. 49.
    Yeo, S.H., New, A.K.: A method for green process planning in electric discharge machining. Int. J. Adv. Manuf. Technol. 15(4), 287–291 (1999)CrossRefGoogle Scholar

Copyright information

© Operational Research Society of India 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringSikkim Manipal Institute of Technology, Sikkim Manipal UniversityMajhitarIndia
  2. 2.Department of Production EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations