Skip to main content
SpringerLink
Log in

A hybrid Grey-TOPSIS based quantum behaved particle swarm optimization for selection of electrode material to machine Ti6Al4V by electro-discharge machining

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Electro-discharge machining is an extensively used process for machining of hard-to-cut materials. The process necessitates a conducting tool electrode; however, selection of right material for preparing the tool continues to remain an engineering challenge. This work makes use of a hybrid intelligent algorithm for selecting the right electrode out of three tool electrodes such as composite tool manufactured by laser sintering process (AlSi10Mg), copper and graphite for efficient electro-discharge machining of Ti6Al4V. The work began by constructing a Taguchi’s L27 experimental design and then collecting the output data such as the material removal rate, tool wear rate, surface roughness, surface crack density, white layer thickness and micro-hardness. A multi-objective optimization is proposed to maximise the work piece material removal rate while minimize the remaining output responses. For this purpose, a hybrid grey-TOPSIS based quantum-behaved particle swarm optimization is chosen. Additional data gathered from scanning electron microscopy and energy dispersive spectroscopy techniques reveal new insights into the post-machining material behaviour such as the use of graphite electrode makes the machined surface far harder due to the dissociated carbon.

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

References

  1. Shabgard M, Khosrozadeh B (2017) Investigation of carbon nanotube added dielectric on the surface characteristics and machining performance of Ti–6Al–4V alloy in EDM process. J Manuf Process 25:212–219. https://doi.org/10.1016/j.jmapro.2016.11.016

    Article  Google Scholar 

  2. Dewangan S, Gangopadhyay S, Biswas CK (2015) Multi-response optimization of surface integrity characteristics of EDM process using grey-fuzzy logic-based hybrid approach. Eng Sci Technol Int J 18:361–368. https://doi.org/10.1016/j.jestch.2015.01.009

    Article  Google Scholar 

  3. Uhlmann E, Bergmann A, Bolz R, Gridin W (2018) Application of additive manufactured tungsten carbide tool electrodes in EDM. Proc CIRP 68:86–90. https://doi.org/10.1016/j.procir.2017.12.027

    Article  Google Scholar 

  4. Stucker BE, Bradley WL, Norasetthekul S, Eubank PT (1995) The production of electrical discharge machining electrodes using SLS: preliminary results, International Solid Freeform Fabrication Symposium.

  5. Czelusniak T, Amorim FL, Higa CF, Lohrengel A (2014) Development and application of new composite materials as EDM electrodes manufactured via selective laser sintering. Int J Adv Manuf Technol 72:1503–1512. https://doi.org/10.1007/s00170-014-5765-z

    Article  Google Scholar 

  6. Liu C, Goel S et al (2019) Benchmarking of several material constitutive models for tribology, wear, and other mechanical deformation simulations of Ti6Al4V. J Mech Behav Biomed Mater 97:126–137. https://doi.org/10.1016/j.jmbbm.2019.05.013

    Article  Google Scholar 

  7. Stolf P, Paiva JM et al (2019) The role of high-pressure coolant in the wear characteristics of WC-Co tools during the cutting of Ti–6Al–4V. Wear 440–441:203090. https://doi.org/10.1016/j.wear.2019.203090

    Article  Google Scholar 

  8. Bhaumik M, Maity K (2018) Effect of different tool materials during EDM performance of titanium grade 6 alloy. Eng Sci Technol Int J 21:507–516. https://doi.org/10.1016/j.jestch.2018.04.018

    Article  Google Scholar 

  9. Opoz TT, Yasar H, Ekmekci N, Ekmekci B (2018) Particle migration and surface modification on Ti6Al4V in SiC powder mixed electrical discharge machining. J Manuf Process 31:744–758. https://doi.org/10.1016/j.jmapro.2018.01.002

    Article  Google Scholar 

  10. Kumar S, Batish A, Singh R, Singh TP (2015) A mathematical model to predict material removal rate during electric discharge machining of cryogenically treated titanium alloys. Proc Inst Mech Eng Part B: J Engneering Manuf 229(2):214–228. https://doi.org/10.1177/0954405414527955

    Article  Google Scholar 

  11. Sivam SP, Michaelraj AL, Satish KS, Varahamoorthy R, Dinakaran D (2012) Effects of electrical parameters, its interaction and tool geometry in electric discharge machining of titanium grade 5 alloy with graphite tool. Proc Inst Mech Eng Part B: J Eng Manuf 227(1):119–131. https://doi.org/10.1177/0954405412466213

    Article  Google Scholar 

  12. Sivam SP, Michaelraj AL, Satish KS, Prabhakaran G, Dinakaran D, Ilankumaran V (2014) Statistical multi-objective optimization of electrical discharge machining parameters in machining titanium grade 5 alloy using graphite electrode. Proc Inst Mech Eng Part B: J Eng Manuf 228(7):736–743. https://doi.org/10.1177/0954405413511073

    Article  Google Scholar 

  13. Gohil V, Puri YM (2018) Statistical analysis of material removal rate and surface roughness in electrical discharge turning of titanium alloy (Ti-6Al-4V). Proc Inst Mech Eng Part B: J Eng Manuf 232(9):1603–1614. https://doi.org/10.1177/0954405416673104

    Article  Google Scholar 

  14. Kolli M, Kumar A (2017) Surfactant and graphite powder–assisted electrical discharge machining of titanium alloy. Proc Inst Mech Eng Part B: J Eng Manuf 231(4):641–657. https://doi.org/10.1177/0954405415579019

    Article  Google Scholar 

  15. Sahu AK, Mahapatra SS (2019) Optimization of electrical discharge machining of titanium alloy (Ti6Al4V) by grey relational analysis based firefly algorithm. In: AlMangour B (ed) Additive manufacturing of emerging materials. Springer, Cham, pp 29–53

    Chapter  Google Scholar 

  16. Czelusniak T, Amorim FL, Lohrengel A, Higa CF (2014) Development and application of copper–nickel zirconium diboride as EDM electrodes manufactured by selective laser sintering. Int J Adv Manuf Technol 72:905–917. https://doi.org/10.1007/s00170-014-5728-4

    Article  Google Scholar 

  17. Amorim FL, Lohrengel A, Schafer G, Czelusniak T (2013) A study on the SLS manufacturing and experimenting of TiB2-CuNi EDM electrodes. Rapid Prototyp J 19(6):418–429. https://doi.org/10.1108/RPJ-03-2012-0019

    Article  Google Scholar 

  18. Amorim FL, Lohrengel A, Neubert V, Higa CF, Czelusniak T (2014) Selective laser sintering of Mo-CuNi composite to be used as EDM electrode. Rapid Prototyp J 20(1):59–68. https://doi.org/10.1108/RPJ-04-2012-0035

    Article  Google Scholar 

  19. Amorim FL, Lohrengel A, Müller N, Schäfer G, Czelusniak T (2013) Performance of sinking EDM electrodes made by selective laser sintering technique. Int J Adv Manuf Technol 65:1423–1428. https://doi.org/10.1007/s00170-012-4267-0

    Article  Google Scholar 

  20. Durr H, Pilz R, Eleser NS (1999) Rapid tooling of EDM electrodes by means of selective laser sintering. Comput Ind 39:35–45

    Article  Google Scholar 

  21. Meena VK, Nagahanumaiah (2006) Optimization of EDM machining parameters using DMLS electrode. Rapid Prototyp J, 12(4):222–228. https://doi.org/10.1108/13552540610682732

  22. Zhao J, Li Y, Zhang J, Yu C, Zhang Y (2003) Analysis of the wear characteristics of an EDM electrode made by selective laser sintering. J Mater Process Technol 138:475–478. https://doi.org/10.1016/S0924-0136(03)00122-5

    Article  Google Scholar 

  23. Tang Y, Fuh JYH, Lu L, Wong YS, Loh HT, Gupta M (2002) Formation of electrical discharge machining electrode via laser cladding. Rapid Prototyp J 8(5):315–319. https://doi.org/10.1108/13552540210451787

    Article  Google Scholar 

  24. Tay FEH, Haider EA (2001) The potential of plating techniques in the development of rapid EDM tooling. Int J Adv Manuf Technol 18:892–896

    Article  Google Scholar 

  25. Rashid WB, Goel S, Davim JP, Joshi SN (2016) Parametric design optimization of hard turning of AISI 4340 steel (69 HRC). Int J Adv Manuf Technol 82:451–462. https://doi.org/10.1007/s00170-015-7337-2

    Article  Google Scholar 

  26. Deng JL (1989) Introduction to grey system theory. J Grey Syst 1:1–24

    MathSciNet  MATH  Google Scholar 

  27. Datta S, Mahapatra SS (2010) Modeling, simulation and parametric optimization of wire EDM process using response surface methodology coupled with grey-Taguchi technique. Int J Eng Sci Technol 2(5):162–183

    Article  Google Scholar 

  28. Mohanty CP, Satpathy MP, Mahapatra SS, Singh MR (2018) Optimization of cryo-treated EDM variables using TOPSIS-based TLBO algorithm. Sadhana 43(51):1–18. https://doi.org/10.1007/s12046-018-0829-7

    Article  MathSciNet  MATH  Google Scholar 

  29. Jadidi O, Hong TS, Firouzi F, Yusuff RM (2009) An optimal grey based approach based on TOPSIS concepts for supplier selection problem. Int J Manag Sci Eng Manag 4(2):104–117

    Google Scholar 

  30. Nyaoga R, Magutu P, Wang M (2016) Application of Grey-TOPSIS approach to evaluate value chain performance of tea processing chains. Decision Sci Lett 5:431–446

    Article  Google Scholar 

  31. Sadeghi M, Razavi SH, Saberi N (2013) Application of Grey TOPSIS in preference ordering of action plans in balanced scorecard and strategy map. Informatica 24(4):619–635

    Article  Google Scholar 

  32. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95 - International conference on neural networks, Perth, WA, Australia, 4:1942–1948. https://doi.org/10.1109/ICNN.1995.488968.

  33. Mohanty CP, Mahapatra SS, Singh MR (2016) A particle swarm approach for multi-objective optimization of electrical discharge machining process. J Intell Manuf 27:1171–1190. https://doi.org/10.1007/s10845-014-0942-3

    Article  Google Scholar 

  34. Sun J, Feng B, Xu W (2004) Particle swarm optimization with particles having quantum behavior. In: Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No.04TH8753), Portland, OR, USA, 1:325–331. https://doi.org/10.1109/CEC.2004.1330875.

  35. Nayak BB, Mahapatra SS (2015) A quantum behaved particle swarm approach for multi-response optimization of WEDM process. In: Panigrahi B, Suganthan P, Das S. (eds) Swarm, evolutionary, and memetic computing. SEMCCO 2014. Lecture Notes in Computer Science, Springer, Cham, 8947:62–73. https://doi.org/10.1007/978-3-319-20294-5_6

  36. Mohanty CP, Mahapatra SS, Singh MR (2017) An intelligent approach to optimize the EDM process parameters using utility concept and QPSO algorithm. Eng Sci Technol Int J 20:552–562. https://doi.org/10.1016/j.jestch.2016.07.003

    Article  Google Scholar 

  37. Omkar SN, Khandelwal R, Ananth TVS, Naik GN, Gopalakrishnan S (2009) Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures. Expert Syst Appl 36:11312–11322. https://doi.org/10.1016/j.eswa.2009.03.006

    Article  Google Scholar 

Download references

Acknowledgements

Marco Leite would like to acknowledge the support provided by FCT, through IDMEC, under LAETA, Project UIDB/50022/2020. Saurav Goel greatly acknowledges the financial support provided by the UKRI via Grant No. EP/T001100/1 and EP/T024607/1 and Royal Academy of Engineering for assisting with the Grant No. IAPP18-19\295 and TSP1332.

Author information

Author notes

  1. Saurav Goel

    Present address: School of Engineering, London South Bank University, 103 Borough Road, London, SE1 0AA, UK

Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, 769008, India

    Anshuman Kumar Sahu & Siba Sankar Mahapatra

  2. IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Marco Leite

  3. University of Petroleum and Energy Studies, Dehradun, 248007, India

    Saurav Goel

Authors
  1. Anshuman Kumar Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siba Sankar Mahapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marco Leite
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Saurav Goel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anshuman Kumar Sahu.

Additional information

Technical Editor: Adriano Fagali de Souza.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahu, A.K., Mahapatra, S.S., Leite, M. et al. A hybrid Grey-TOPSIS based quantum behaved particle swarm optimization for selection of electrode material to machine Ti6Al4V by electro-discharge machining. J Braz. Soc. Mech. Sci. Eng. 44, 188 (2022). https://doi.org/10.1007/s40430-022-03494-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-022-03494-y

Keywords

Search

Navigation