Skip to main content
SpringerLink
Log in

PSO-Based Single Objective Optimization of WEDM Process on SKD 11 Material

  • Conference paper
  • First Online:
Recent Advances in Manufacturing Modelling and Optimization

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

The reason for the current investigation is to build precision, profitability, and diminish the expense of wire EDM machining of high chromium high carbon SKD 11. The feed rate of wire, pulse off time, tension of wire, servo voltage, and pulse on time were considered input-controlled parameters for performing the experimental work designed by response surface methodology as DoE techniques. Regardless, the determination of machining conditions for effective machining of material is exceptionally troublesome. Therefore, a problem of single objective optimization for maximizing the material removal rate of the WEDM process has been developed and solved by the particle swarm optimization technique. The result shows that feed rate of wire, servo voltage, and pulse on time are the most critical parameters to affect the material removal rate. Ton-118 mu, Toff-52 mu, IP-190 A, WT-6 mu, SV-20 V, WF-8 m/min, and MRR9.4234 mm3/min, the best global result obtained by using the PSO relates to the favorable consequences of MRR.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Abbreviations

MRR:

Material removal rate

P, WF:

Feed rate of wire

Q, Ton:

Pulse on time

R, IP:

Peak current

S, Toff:

Pulse off time

T, WT:

Tension of wire

U, SV:

Spark gap set voltage

DF:

Degree of freedom

MS:

Mean square

RSM:

Response surface methodology

φ:

Diameter

CCD:

Central composite design

SS:

Sum of squares

References

  1. Dave HK (2019) Optimization of orbital electro discharge machining parameters using TLBO and PSO algorithms. Int J Mod Manuf Technol XI(2):1–6

    Google Scholar 

  2. Mathew B, Babu J et al (2014) Multiple process parameter optimization of WEDM on AISI304 using Taguchi grey relational analysis. Procedia Mater Sci 5:1613–1622

    Article  Google Scholar 

  3. Rajyalakshmi G, Venkata Ramaiah P (2012) A parametric optimization using Taguchi method: effect of WEDM parameters on surface roughness machining on Inconel 825. Elixir Mechanical Engineering 43:6669–6674

    Google Scholar 

  4. Tosun N, Cogun C, Tosun G (2004) A study on kerf and material removal rate in wire electrical discharge machining based on Taguchi method. J Mater Process Technol 152(3):316–322

    Article  Google Scholar 

  5. Mahapatra SS, Patnaik A (2007) Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method. Int J Adv Manuf Technol 34(9–10):911–925

    Article  Google Scholar 

  6. 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 Tools Manuf 48(1):47–60

    Article  Google Scholar 

  7. Ikram A, Mufti NA, Saleem MQ, Khan AR (2013) Parametric optimization for surface roughness, kerf and MRR in wire electrical discharge machining (WEDM) using Taguchi design of experiment. J Mech Sci Technol 27(7):2133–2141

    Article  Google Scholar 

  8. Khan NZ, Khan ZA, Siddiquee AN, Chanda AK (2014) Investigations on the effect of wire EDM process parameters on surface integrity of HSLA: a multi-performance characteristics optimization. Prod Manuf Res 2(1):501–518

    Google Scholar 

  9. Routara BC, Nanda BK, Patra DR (2009) Parametric optimization of CNC wire cut EDM using grey relational analysis. In: International conference on mechanical engineering, ICME09-RT-24

    Google Scholar 

  10. Majhi SK, Mishra TK, Pradhan MK, Soni H (2014) Effect of machining parameters of AISI D2 tool steel on electro discharge machining. Int J Curr Eng Technol 4(1):19–23

    Google Scholar 

  11. Lin KW, Wang CC (2010) Optimizing multiple quality characteristics of wire electrical discharge machining via Taguchi method-based gray analysis for magnesium alloy. J CCIT 39(1):23–34

    Google Scholar 

  12. Lal S, Kumar S, Khan ZA, Siddiquee AN (2015) Multi-response optimization of wire electrical discharge machining process parameters for Al7075/Al2O3/SiC hybrid composite using Taguchi-based grey relational analysis. Proc Inst Mech Eng Part B: J Eng Manuf 229(2):229–237

    Article  Google Scholar 

  13. Bobbili R, Madhu V, Gogia AK (2015) Multi response optimization of wire-EDM process parameters of ballistic grade aluminium alloy. Eng Sci Technol Int J 18(4):720–726. https://doi.org/10.1016/j.jestch.2015.05.004

    Article  Google Scholar 

  14. Jangra K, Jain A, Grover S (2010) Optimization of multiple-machining characteristics in wire electrical discharge machining of punching die using grey relational analysis

    Google Scholar 

  15. Haddad MJ, Tehrani AF (2008) Material removal rate (MRR) study in the cylindrical wire electrical discharge turning (CWEDT) process. J Mater Process Technol 199(1–3):369–378

    Article  Google Scholar 

  16. Hewidy MS, El-Taweel TA, El-Safty MF (2005) Modelling the machining parameters of wire electrical discharge machining of Inconel 601 using RSM. J Mater Process Technol 169(2):328–336

    Article  Google Scholar 

  17. Ghodsiyeh D, Davoudinejad A, Hashemzadeh M, Hosseininezhad N, Golshan A (2013) Optimizing finishing process in wedming of titanium alloy (Ti6Al4V) by brass wire based on response surface methodology. Res J Appl Sci Eng Technol. https://doi.org/10.17485/IJST/2012/V5I10/30915

  18. Rajesh R, Anand MD (2012) The optimization of the electro-discharge machining process using response surface methodology and genetic algorithms. Procedia Eng 38:3941–3950

    Article  Google Scholar 

  19. Sharma N, Khanna R, Gupta R (2013) Multi quality characteristics of WEDM process parameters with RSM. Procedia Eng 64:710–719

    Article  Google Scholar 

  20. El-Taweel TA, Hewidy AM (2013) Parametric study and optimization of WEDM parameters for CK45 steel. Int J Eng Pract Res 2(4):156–169

    Google Scholar 

  21. Shandilya P, Jain PK, Jain NK (2013) RSM and ANN modeling approaches for predicting average cutting speed during WEDM of SiCp/6061 Al MMC. Procedia Eng 64:767–774

    Article  Google Scholar 

  22. Yang SH, Srinivas J, Mohan S, Lee DM, Balaji S (2009) Optimization of electric discharge machining using simulated annealing. J Mater Process Technol 209(9):4471–4475

    Article  Google Scholar 

  23. Guven O, Esme U, Kaya IE, Kazancoglu Y, Kulekci MK, Boga C (2010) Comparative modeling of wire electrical discharge machining (Wedm) process using Back propagation (BPN) and general regression neural networks (GRNN). Mater Technol 44(3):147–152

    Google Scholar 

  24. Joshi SN, Pande SS (2011) Intelligent process modeling and optimization of die-sinking electric discharge machining. Appl Soft Comput 11(2):2743–2755

    Article  Google Scholar 

  25. Shandilya P, Tiwari A (2014) Artificial neural network modeling and optimization using genetic algorithm of machining process. J Autom Control Eng 2(4)

    Google Scholar 

  26. Rao TB, Krishna AG (2014) Compliance modelling and optimization of Kerf during WEDM of Al7075/SiCP metal matrix composite. Int J Mech Mechatron Eng 7(2):324–333

    Google Scholar 

  27. Diantoro M, Soepangkat BOP (2016) Optimization of multiple response characteristics in the WEDM process of Buderus 2379 ISO-B tool steel using Taguchi-Grey-Fuzzy logic method. In: Applied mechanics and materials, vol 836. Trans Tech Publications Ltd., pp 185–190

    Google Scholar 

  28. Sengottuvel P, Satishkumar S, Dinakaran D (2013) Optimization of multiple characteristics of EDM parameters based on desirability approach and fuzzy modeling. Procedia Eng 64:1069–1078

    Article  Google Scholar 

  29. Maher I, Ling LH, Sarhan AA, Hamdi M (2015) Improve wire EDM performance at different machining parameters-ANFIS modeling. IFAC-PapersOnLine 48(1):105–110

    Article  Google Scholar 

  30. Manjaiah M, Laubscher RF, Kumar A, Basavarajappa S (n.d.) Parametric optimization of MRR and surface roughness in wire electro discharge machining (WEDM) of D2 steel using Taguchi-based utility approach. https://doi.org/10.1186/s40712-016-0060-4

  31. Zhang Z, Ming W, Huang H, Chen Z, Xu Z, Huang Y, Zhang G (2015) Optimization of process parameters on surface integrity in wire electrical discharge machining of tungsten tool YG15. Int J Adv Manuf Technol 81(5):1303–1317

    Article  Google Scholar 

  32. Sarkar S, Sekh M, Mitra S, Bhattacharyya B (2008) Modeling and optimization of wire electrical discharge machining of γ-TiAl in trim cutting operation. J Mater Process Technol 205(1–3):376–387

    Article  Google Scholar 

  33. Sarkar S, Mitra S, Bhattacharyya B (2005) Parametric analysis and optimization of wire electrical discharge machining of γ-titanium aluminide alloy. J Mater Process Technol 159(3):286–294

    Article  Google Scholar 

  34. Maan V, Chaudhary A (2013) Optimization of wire electric discharge machining process of D-2 steel using response surface methodology. Int J Eng Res Appl (IJERA) 3(3):206–216

    Google Scholar 

  35. Sahu J, Mohanty CP, Mahapatra SS (2013) A DEA approach for optimization of multiple responses in electrical discharge machining of AISI D2 steel. Procedia Eng 51:585–591

    Article  Google Scholar 

  36. Mohapatra KD, Sahoo SK (2015) Micro-structural analysis and multi-objective optimization in gear cutting process for AISI 304 stainless steel using wire EDM 26:978–993

    Google Scholar 

  37. Bobbili R, Madhu V, Gogia AK (2015) Modelling and analysis of material removal rate and surface roughness in wire-cut EDM of armour materials. Eng Sci Technol Int J 18(4):664–668

    Google Scholar 

  38. Mukherjee R, Chakraborty S, Samanta S (2012) Selection of wire electrical discharge machining process parameters using non-traditional optimization algorithms. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2012.03.053

    Article  Google Scholar 

  39. Singh S, Garg D, Kumar S (2018) Investigation for obtaining the optimal solution for improving the performance of WEDM of super alloy Udimet-L605 using particle swarm optimization. Eng Sci Technol Int J 21(2):261–273. https://doi.org/10.1016/j.jestch.2018.03.005

    Article  Google Scholar 

  40. Patel SS, Prajapati JM (2018) Experimental investigation of surface roughness and kerf width during machining of blanking die material on wire electric discharge machine. Int J Eng 31(10):1760–1766

    Google Scholar 

  41. Mohapatraa K, Satpathya M, Sahooa S (2017) Comparison of optimization techniques for MRR and surface roughness in wire EDM process for gear cutting. Int J Ind Eng Comput 8(2):251–262

    Google Scholar 

  42. Ingh S, Isra M (2016) A critical review of wire electric discharge machining, pp 249–266. https://doi.org/10.2507/daaam.scibook.2016.23

  43. Guo WY, Chen LG, Cao Y (2013) Study on the optimization of absorption baffle with coating material. Adv Res Intell Syst Mech Eng 644:165–170. https://doi.org/10.4028/www.scientific.net/AMR.644.165

    Article  Google Scholar 

  44. Kumar P, Gupta M, Kumar V (2019) Experimental analysis of WEDM machined surface of Inconel 825 using single objective PSO. J Phys Conf Ser 1240(1):012053 (IOP Publishing, July)

    Google Scholar 

  45. Shayan AV, Afza RA, Teimouri R (2013) Parametric study along with selection of optimal solutions in dry wire cut machining of cemented tungsten carbide (WC-Co). J Manuf Process 15(4):644–658

    Article  Google Scholar 

  46. Zhang HC, Tang L, Yang QL (2011) Optimization of the ultrasonic wave-assisted extraction condition of peanut protein isolate. Adv Mater Res 189:3904–3911 (Trans Tech Publications Ltd.)

    Google Scholar 

  47. Sreebalaji VS, Kumar KR (2016) Artificial neural networks and multi response optimisation on EDM of aluminium (A380)/fly ash composites. Int J Comput Mater Sci Surf Eng 6(3–4):244–262

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Government Engineering College, Patan, Gujarat, India

    Sandip S. Patel

  2. Department of Mechanical Engineering, Faculty of Technology & Engineering, M. S. University of Baroda, Baroda, Gujarat, India

    J. M. Prajapati

Authors
  1. Sandip S. Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. M. Prajapati
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, S. V. National Institute of Technology, Surat, India

    Shailendra Kumar

  2. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, India

    J. Ramkumar

  3. Department of Product and Systems Design Engineering, University of Western Macedonia, Kila Kozani, Greece

    Panagiotis Kyratsis

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Patel, S.S., Prajapati, J.M. (2022). PSO-Based Single Objective Optimization of WEDM Process on SKD 11 Material. In: Kumar, S., Ramkumar, J., Kyratsis, P. (eds) Recent Advances in Manufacturing Modelling and Optimization. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-9952-8_33

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-9952-8_33

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9951-1

  • Online ISBN: 978-981-16-9952-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation