Skip to main content
SpringerLink
Log in

Employment of cylindrical electrolytic copper grade electrode under EDMed Inconel 825 super alloy: emphasis on machining behavior accompanied with surface topography for sustainability

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

Abstract

The present study embraces two-fold sustainability benefits where in first-fold, minimum power consumption (Pc) and machining time (Mt) escort one pillar of sustainability named as environmental sustainability. Moreover, the highest material removal rate (MRR) reduces manufacturing costs and embraces the second pillar of sustainability named as economic sustainability. The study is experimentally conducted on globally emerging Inconel 825 super alloy with a copper electrode in EDM. The sustainable machining is evaluated under a cluster of 8 input process parameters, i.e., spark gap (Sg), gap voltage (Vg), pulse on time (Ton), pulse off time (Toff), peak current (Ip), servo feed (Sf), depth of cut (Dc), and difficulty index (Di) under multi objective optimization (MOO) domain. The study has originally devised two novel approaches based on Taguchi, i.e., simple additive weighting (SAWTAG) and grey relational analysis (GRATAG). Toff is found as the chief significant parameter having p values of 0.005 and 0.002 respectively. Dc is found as the second significant parameter with p values of 0.061 and 0.071 respectively. Significant improvements of 0.16262 and 0.48371 are recognized in conformity experiment under GRATAG and SAWTAG approaches; optimal parametric setting is found as Sg2Vg3Ton1Toff1Ip3Sf1Dc1Di1, which is experimentally validated as robust decision. Furthermore, the proportional contribution of process parameters is estimated where GRATAG reflected 41.11% and SAWTAG reflected 47.29% of the contribution of pulse off time respectively. The study investigated surface topography for highlighting the effects of process parameters on heat-affected layers, machining debris, and surface cracks. It is examined with an increase in Ip and Vg; MRR increases and machining time decreases. Moreover, it is also noticed that an increase in Vg prevents irregularities such as micro voids and re-solidified droplets in machining.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Golshan A, Gohari S, Ayob A (2011) Modeling and optimization of cylindrical wire electro discharge machining of AISI D3 tool steel using non-dominated sorting genetic algorithm. Int Conf Graph Image Process (ICGIP 2011), SPIE 82853V. https://doi.org/10.1117/12.914614

  2. Kung KY, Chiang KT (2008) Modeling and analysis of machinability evaluation in the wire electrical discharge machining (WEDM) process of aluminum oxide-based ceramic. Mater Manuf Process 23:241–250. https://doi.org/10.1080/10426910701860616

    Article  CAS  Google Scholar 

  3. Yang SH, Srinivas J, Mohan S, Lee DM, Balaji S (2009) Optimization of electric discharge machining using simulated annealing. J Mater Process Technol 209:4471–4475. https://doi.org/10.1016/j.jmatprotec.2008.10.053

    Article  Google Scholar 

  4. Singh NK, Singh Y, Kumar S, Sharma A (2019) Comparative study of statistical and soft computing-based predictive models for material removal rate and surface roughness during helium-assisted EDM of D3 die steel. SN Appl Sci 1:1–12. https://doi.org/10.1007/s42452-019-0545-x

    Article  CAS  Google Scholar 

  5. Gaikwad V, Jatti VKS (2018) Optimization of material removal rate during electrical discharge machining of cryo-treated NiTi alloys using Taguchi’s method. J King Saud Univ - Eng Sci 30:266–272. https://doi.org/10.1016/j.jksues.2016.04.003

    Article  Google Scholar 

  6. 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 

  7. Singh AK, Mahajan R, Tiwari A, Kumar D, Ghadai RK (2018) Effect of dielectric on electrical discharge machining: a review. IOP Conf Ser Mater Sci Eng 377:1–9. https://doi.org/10.1088/1757-899X/377/1/012184

    Article  Google Scholar 

  8. Kumar DNM, Rao MPC (2019) Optimization of EDM process parameters using response surface methodology for AISI D3 steel. Int J Trend Sci Res Dev 3:1651–1656. https://doi.org/10.31142/ijtsrd23535

    Article  Google Scholar 

  9. Chakraborty S, Chatterjee P, Das PP (2019) A DoE–TOPSIS method-based meta-model for parametric optimization of non-traditional machining processes. J Model Manag 14:430–455. https://doi.org/10.1108/JM2-08-2018-0110

    Article  Google Scholar 

  10. Das PP, Chakraborty S (2020) Parametric analysis of a green electrical discharge machining process using DEMATEL and SIR methods. Opsearch 57:513–540. https://doi.org/10.1007/s12597-019-00410-2

    Article  Google Scholar 

  11. Aich U, Pal AK, Laha D, Banerjee S (2013) Searching for a pareto optimal solution set of edm responses applying multi-objective simulated annealing on rsm model. Adv Mater Res 622:51–55. https://doi.org/10.4028/www.scientific.net/AMR.622-623.51

    Article  Google Scholar 

  12. Werner A (2016) Method for enhanced accuracy in machining curvilinear profiles on wire-cut electrical discharge machines. Precis Eng 44:75–80. https://doi.org/10.1016/j.precisioneng.2015.10.004

    Article  Google Scholar 

  13. Arrabiyeh PA, Dethloff M, Müller C, Kirsch B, Aurich JC (2019) Optimization of micropencil grinding tools via electrical discharge machining. J Manuf Sci Eng Trans ASME 141:1–9. https://doi.org/10.1115/1.4042110

    Article  Google Scholar 

  14. Gunay M, Korkmaz ME, Yasar N (2020) Performance analysis of coated carbide tool in turning of nimonic 80A superalloy under different cutting environments. J Manuf Process 56:678–687. https://doi.org/10.1016/j.jmapro.2020.05.031

    Article  Google Scholar 

  15. Sharma V, Misra JP, Singhal P (2019) Multi-optimization of process parameters for Inconel 718 while die-sink EDM using multi-criterion decision making methods. J Phys Conf Ser 1240:1–8. https://doi.org/10.1088/1742-6596/1240/1/012166

    Article  CAS  Google Scholar 

  16. Gunen A, Doleker KM, Korkmaz ME, Gok MS, Erdogan A (2021) Characteristics, high temperature wear and oxidation behavior of boride layer grown on nimonic 80A Ni-based superalloy. Surf Coatings Technol 409:126906. https://doi.org/10.1016/j.surfcoat.2021.126906

    Article  CAS  Google Scholar 

  17. Sahu BK, Datta S, Mahapatra SS (2018) On electro-discharge machining of Inconel 718 super alloys: an experimental investigation. Mater Today Proc 5:4861–4869. https://doi.org/10.1016/j.matpr.2017.12.062

    Article  CAS  Google Scholar 

  18. Jafarian F (2020) Electro discharge machining of Inconel 718 alloy and process optimization. Mater Manuf Process 35:95–103. https://doi.org/10.1080/10426914.2020.1711919

    Article  CAS  Google Scholar 

  19. Erdogan A, Yener T, Doleker KM, Korkmaz ME, Gok MS (2021) Low-temperature aluminizing influence on degradation of nimonic 80A surface: microstructure, wear and high temperature oxidation behaviors. Surf Interfaces 25:101240. https://doi.org/10.1016/j.surfin.2021.101240

    Article  CAS  Google Scholar 

  20. Balasubramaniyan S, Selvaraj T (2017) Application of integrated Taguchi and TOPSIS method for optimization of process parameters for dimensional accuracy in turning of EN25 steel. J. Chinese Inst. Eng Trans Chinese Inst Eng A 40:267–274. https://doi.org/10.1080/02533839.2017.1308233

    Article  CAS  Google Scholar 

  21. Ahmed N, Ishfaq K, Moiduddin K, Ali R, Shammary NA (2019) Machinability of titanium alloy through electric discharge machining. Mater Manuf Process 34:93–102. https://doi.org/10.1080/10426914.2018.1532092

    Article  CAS  Google Scholar 

  22. Jafarian F (2018) A modified non-dominated sorting genetic algorithm for multi-objective optimization of machining process. J Eng Sci Technol 13:4078–4093

    Google Scholar 

  23. Dang XP (2018) Constrained multi-objective optimization of EDM process parameters using kriging model and particle swarm algorithm. Mater Manuf Process 33:397–404. https://doi.org/10.1080/10426914.2017.1292037

    Article  CAS  Google Scholar 

  24. Yurtkuran H, Korkmaz ME, Gunay M (2016) Modelling and optimization of the surface roughness in high speed hard turning with coated and uncoated CBN insert. Gazi Univ J Sci 29:987–995

    Google Scholar 

  25. Shah P, Khanna N, Maruda RW, Gupta MK, Krolczyk GM (2021) Life cycle assessment to establish sustainable cutting fluid strategy for drilling Ti-6Al-4V. Sustain Mater Technol 30:e00337. https://doi.org/10.1016/j.susmat.2021.e00337

    Article  CAS  Google Scholar 

  26. Nadolny K, Kieras S (2020) New approach for cooling and lubrication in dry machining on the example of internal cylindrical grinding of bearing rings. Sustain Mater Technol 24:e00166. https://doi.org/10.1016/j.susmat.2020.e00166

    Article  Google Scholar 

  27. Pellow MA, Ambrose H, Mulvaney D, Betita R, Shaw S (2020) Research gaps in environmental life cycle assessments of lithium ion batteries for grid-scale stationary energy storage systems: end-of-life options and other issues. Sustain Mater Technol 23:e00120. https://doi.org/10.1016/j.susmat.2019.e00120

    Article  CAS  Google Scholar 

  28. Agarwal V, Khalid MK, Porvali A, Wilson BP, Lundström M (2019) Recycling of spent NiMH batteries: integration of battery leach solution into primary Ni production using solvent extraction. Sustain Mater Technol 22:e00121. https://doi.org/10.1016/j.susmat.2019.e00121

    Article  CAS  Google Scholar 

  29. Thunman H, Vilches TB, Seemann M, Maric J, Vela IC, Pissot S, Nguyen HNT (2019) Circular use of plastics-transformation of existing petrochemical clusters into thermochemical recycling plants with 100% plastics recovery. Sustain Mater Technol 22:e00121. https://doi.org/10.1016/j.susmat.2019.e00124

    Article  CAS  Google Scholar 

  30. Abhilash PM, Chakradhar D (2020) ANFIS modelling of mean gap voltage variation to predict wire breakages during wire EDM of Inconel 718. CIRP J Manuf Sci Technol 31:153–164. https://doi.org/10.1016/j.cirpj.2020.10.007

    Article  Google Scholar 

  31. Abhilash PM, Chakradhar D (2021) Failure detection and control for wire EDM process using multiple sensors, CIRP. J Manuf Sci Technol 33:315–326. https://doi.org/10.1016/j.cirpj.2021.04.009

    Article  Google Scholar 

  32. Ozkavak HV, Sofu MM, Duman B, Bacak S (2021) Estimating surface roughness for different EDM processing parameters on Inconel 718 using GEP and ANN. CIRP J Manuf Sci Technol 33:306–314. https://doi.org/10.1016/j.cirpj.2021.04.007

    Article  Google Scholar 

  33. Bucker M, Bartolomeis AD, Oezkaya E, Shokrani A, Biermann D (2020) Experimental and computational investigations on the effects of deep-temperature emulsion on the turning of Inconel 718 alloy. CIRP J Manuf Sci Technol 31:48–60. https://doi.org/10.1016/j.cirpj.2020.10.001

    Article  Google Scholar 

  34. Chakraborty S, Das PP (2018) A multivariate quality loss function approach for parametric optimization of non-traditional machining processes. Manag Sci Lett 8:873–884. https://doi.org/10.5267/j.msl.2018.6.001

    Article  Google Scholar 

  35. Tamiloli N, Venkatesan J, Ramnath BV (2016) A grey-fuzzy modeling for evaluating surface roughness and material removal rate of coated end milling insert. Meas J Int Meas Confed 84:68–82. https://doi.org/10.1016/j.measurement.2016.02.008

    Article  Google Scholar 

  36. Masoudi S, Mirabdolahi M, Dayyani M, Jafarian F, Vafadar A, Dorali MR (2019) Development of an intelligent model to optimize heat-affected zone, kerf, and roughness in 309 stainless steel plasma cutting by using experimental results. Mater Manuf Process 34:345–356. https://doi.org/10.1080/10426914.2018.1532579

    Article  CAS  Google Scholar 

  37. Jafarian F (2019) 3D modeling of recrystallized layer depth and residual stress in dry machining of nickel-based alloy. J Brazilian Soc Mech Sci Eng 41:1–10. https://doi.org/10.1007/s40430-019-1707-x

    Article  CAS  Google Scholar 

  38. Hribersek M, Pusavec F, Rech J, Kopac J (2018) Modeling of machined surface characteristics in cryogenic orthogonal turning of inconel 718. Mach Sci Technol 22:829–850. https://doi.org/10.1080/10910344.2017.1415935

    Article  CAS  Google Scholar 

  39. Behera BC, Ghosh CS, Paruchuri VR (2019) Study of saw-tooth chip in machining of Inconel 718 by metallographic technique. Mach Sci Technol 23:431–454. https://doi.org/10.1080/10910344.2019.1575397

    Article  CAS  Google Scholar 

  40. Lin YC, Yan BH, Huang FY (2001) Surface improvement using a combination of electrical discharge machining with ball burnish machining based on the Taguchi method. Int J Adv Manufact Technol 18:673–682. https://doi.org/10.1007/s001700170028

    Article  Google Scholar 

  41. Lin YC, Yan BH, Huang FY (2001) Surface modification of Al-Zn-Mg aluminum alloy using the combined process of EDM with USM. J Mater Process Technol 115:359–366. https://doi.org/10.1016/S0924-0136(01)01017-2

    Article  CAS  Google Scholar 

  42. Furutania K, Saneto A, Takezawa H, Mohri N, Miyake H (2001) Accretion of titanium carbide by electrical discharge machining with powder suspended in working fluid. Precis Eng 25:138–144. https://doi.org/10.1016/S0141-6359(00)00068-4

    Article  Google Scholar 

  43. Scott D, Boyina S, Rajurkar KP (1991) Analysis and optimization of parameter combinations in wire electrical discharge machining. Int J Prod Res 29:2189–2207. https://doi.org/10.1080/00207549108948078

    Article  Google Scholar 

  44. Tarng YS, Ma SC, Chung LK (1995) Determination of optimal cutting parameters in wire electrical discharge machining. Int J Mach Tools Manuf 35:1693–1701. https://doi.org/10.1016/0890-6955(95)00019-T

    Article  Google Scholar 

  45. Jeswani ML (1981) Effect of the addition of graphite powder to kerosene used as the dielectric fluid in electrical discharge machining. Wear 70:133–139. https://doi.org/10.1016/0043-1648(81)90148-4

    Article  CAS  Google Scholar 

  46. Mohri N, Saito N, Higashi MA (1991) A new process of finish machining on free surface by EDM methods. Ann CIRP 40:207–210. https://doi.org/10.1016/S0007-8506(07)61969-6

    Article  Google Scholar 

  47. Ming QY, He LY (1995) Powder-suspension dielectric fluid for EDM. J Mater Process Tech 52:44–54. https://doi.org/10.1016/0924-0136(94)01442-4

    Article  Google Scholar 

  48. Wong YS, Lim LC, Rahuman I, Tee WM (1998) Near-mirror-finish phenomenon in EDM using powder-mixed dielectric. J Mater Process Technol 79:30–40. https://doi.org/10.1016/S0924-0136(97)00450-0

    Article  Google Scholar 

  49. Wong YS, Lim LC, Lee LC (1995) Effects of flushing on electro-discharge machined surfaces. J Mater Process Tech 48:299–305. https://doi.org/10.1016/0924-0136(94)01662-K

    Article  Google Scholar 

  50. Kunieda M, Yoshida M (1997) Electrical discharge machining in gas. CIRP Ann - Manuf Technol 46:143–146. https://doi.org/10.1016/s0007-8506(07)60794-x

    Article  Google Scholar 

  51. Yoshida M, Kunieda M (1999) Study on mechanism for minute tool electrode wear in dry EDM. Seimitsu Kogaku Kaishi/Journal Japan Soc. Precis Eng 65:689–693. https://doi.org/10.2493/jjspe.65.689

    Article  Google Scholar 

  52. Chow HM, Yan BH, Huang FY, Hung JC (2000) Study of added powder in kerosene for the micro-slit machining of titanium alloy using electro-discharge machining. J Mater Process Technol 101:95–103. https://doi.org/10.1016/S0924-0136(99)00458-6

    Article  Google Scholar 

  53. Shao B, Rajurkar KP (2015) Modelling of the crater formation in micro-EDM. Procedia CIRP 33:376–381. https://doi.org/10.1016/j.procir.2015.06.085

    Article  Google Scholar 

  54. Reddy PR, Reddy GJ, Prasanthi G (2020) Mathematical modeling of material removal rate using Buckingham Pi theorem in electrical discharge machining of hastelloy C276. Lect Notes Mech Eng Springer 2020:843–852. https://doi.org/10.1007/978-981-15-1201-8_90

    Article  Google Scholar 

  55. Tzeng YF, Lee CY (2001) Effects of powder characteristics on electrodischarge machining efficiency. Int J Adv Manuf Technol 17:586–592. https://doi.org/10.1007/s001700170142

    Article  Google Scholar 

  56. Tzeng FY, Chen FU (2003) A simple approach for robust design of high speed electrical-discharge machining technology. Int J Mach Tools Manufact 43:217–227. https://doi.org/10.1016/S0890-6955(02)00261-4

    Article  Google Scholar 

  57. Panda DK, Bhoi RJ (2005) Artificial neural network prediction of material removal rate in EDM. Mater Manuf Process 20:645–672. https://doi.org/10.1081/AMP-200055033

    Article  CAS  Google Scholar 

  58. Tanveer A, Kapoor SG, Mujumdar SS (2019) Modeling of material removal in atomized dielectric-based electrical discharge machining (EDM). ASME 2019 14th Int. Manuf Sci Eng Conf MSEC 2019:1–9. https://doi.org/10.1115/MSEC2019-3025

    Article  Google Scholar 

  59. Luan ND, Minh ND, Thanh LTP (2019) Multi-Objective optimization of PMEDM process parameter by topsis method. Int J Trend Sci Res Dev 3:112–115. https://doi.org/10.31142/ijtsrd23169

    Article  Google Scholar 

  60. Jain S, Parashar V (2020) Application of multi-objective BAT algorithm for EDM process parameters optimization. Int J Adv Sci Technol 29:5214–5225

    Google Scholar 

  61. Kansal HK, Singh S, Kumar P (2005) Parametric optimization of powder mixed electrical discharge machining by response surface methodology. J Mater Process Technol 169:427–436. https://doi.org/10.1016/j.jmatprotec.2005.03.028

    Article  CAS  Google Scholar 

  62. Wang H, Zuo D, Miao H, Wang H, Wang M (2010) Effect of discharge parameters on micro-surface topography of NAK80 by mirror-like surface EDM. Key Eng Mater 2010:438–441. https://doi.org/10.4028/www.scientific.net/KEM.431-432.438

    Article  CAS  Google Scholar 

  63. Li L, Niu Z, Yin F, Liu Y (2012) Surface integrity of sintered NdFeB permanent magnet after EDM. Adv Mater Res 2012:27–30. https://doi.org/10.4028/www.scientific.net/AMR.503-504.27

    Article  CAS  Google Scholar 

  64. Zhang Y, Liu Y, Shen Y, Ji R, Li Z, Zheng C (2014) Investigation on the influence of the dielectrics on the material removal characteristics of EDM. J Mater Process Technol 214:1052–1061. https://doi.org/10.1016/j.jmatprotec.2013.12.012

    Article  CAS  Google Scholar 

  65. Payal H, Maheshwari S, Bharti PS (2019) Parametric optimization of EDM process for Inconel 825 using GRA and PCA approach. J Inf Optim Sci 40:291–307. https://doi.org/10.1080/02522667.2019.1578090

    Article  Google Scholar 

  66. Sahu NK, Sahu AK, Sahu AK (2017) Optimization of weld bead geometry of MS plate (Grade: IS 2062) in the context of welding: a comparative analysis of GRA and PCA–Taguchi approaches. Indian Acad Sci 8:234–259. https://doi.org/10.1007/s12046-016-0589-1

    Article  Google Scholar 

  67. Sahu AK, Sahu NK, Sahu AK, Rajput MS, Narang HK (2019) T-SAW methodology for parametric evaluation of surface integrity aspects in AlMg3 (AA5754) alloy: comparison with T-TOPSIS methodology. Measurement 132:309–323. https://doi.org/10.1016/j.measurement.2018.09.037

    Article  Google Scholar 

  68. Guo X, Sahu AK, Sahu NK, Sahu AK (2022) A novel integrated computational TRIFMRG approach with grey relational analysis toward parametric evaluation of weld bead geometry of MS-Grade: IS 2062. Grey Syst: Theory Appl 12:117–141. https://doi.org/10.1108/GS-09-2020-0124

    Article  Google Scholar 

  69. Sahu NK, Singh MK, Mwanza BGM, Sahu AK (2022) Investigation of machinability characteristics of EDMed Inconel 825 alloy under multidimensional parametric modeling by using holistic grey-PCA statistical models. Adv Mater Sci Eng 2022:1–29. https://doi.org/10.1155/2022/3147586

    Article  CAS  Google Scholar 

  70. He Z, Ma X, Luo J, Sahu AK, Sahu AK, Sahu NK (2021) Exploitation of the advanced manufacturing machine tool evaluation model under objective-grey information: a knowledge-based cluster with the grey relational analysis approach. Grey Syst: Theory Appl 11:394–417. https://doi.org/10.1108/GS-03-2020-0028

    Article  Google Scholar 

  71. Sahu NK, Singh MK, Sahu AK, Sahu AK (2022) Surface topography and optimal machining characteristics investigation for advanced engineering material Inconel 825 alloy exploring statistical TGRA accompanied with T-Topsis methodology. Surf Rev Lett 29:1–46. https://doi.org/10.1142/S0218625X22501499

    Article  Google Scholar 

  72. Muthuramalingam T, Babu LG, Sridharan K, Geethapriyan T, Srinivasan KP (2020) Multi-response optimization of WEDM process parameters of Inconel 718 alloy using TGRA method. Lect Notes Networks Syst 2020:487–492. https://doi.org/10.1007/978-3-030-37497-6_56

    Article  Google Scholar 

  73. Gostimirovic M, Pucovsky V, Sekulic M, Radovanovic M, Madic M (2018) Evolutionary multi-objective optimization of energy efficiency in electrical discharge machining. J Mech Sci Technol 32:4775–4785. https://doi.org/10.1007/s12206-018-0925-y

    Article  Google Scholar 

  74. Choudhury SD, Saharia NJ, Surekha B, Mondal G (2018) Study on the influence of hybridized powder mixed dielectric in electric discharge machining of alloy steels. Mater Today Proc 5:18410–18415. https://doi.org/10.1016/j.matpr.2018.06.181

    Article  CAS  Google Scholar 

  75. Baldin V, Baldin CRB, Machado AR, Amorim FL (2020) Machining of Inconel 718 with a defined geometry tool or by electrical discharge machining. J Brazilian Soc Mech Sci Eng 42:1–14. https://doi.org/10.1007/s40430-020-02358-7

    Article  CAS  Google Scholar 

  76. Zhou S, Yang Y, Zhou M, Sun H (2020) Electrical discharge machining Inconel 718 with adaptively regulating gap servo-voltage. Int J Adv Manuf Technol 109:2575–2585. https://doi.org/10.1007/s00170-020-05835-4

    Article  Google Scholar 

  77. Suji D, Adesina A, Mirdula R (2021) Optimization of self-compacting composite composition using Taguchi-Grey relational analysis. Materialia 15:101027. https://doi.org/10.1016/j.mtla.2021.101027

    Article  Google Scholar 

  78. Geethapriyan T, Kalaichelvan K, Muthuramalingam T (2016) Multi performance optimization of electrochemical micro-machining process surface related parameters on machining Inconel 718 using Taguchi-grey relational analysis. Metall Ital 108:13–19

    Google Scholar 

  79. Alinezhad A, Amini A, Alinezhad A (2009) Sensitivity analysis of simple additive weighting method (SAW): the results of change in the weight of one attribute on the final ranking of alternatives. J Ind Eng 4:13–18

    Google Scholar 

  80. Afshari A, Mojahed M, Yusuff R (2010) Simple additive weighting approach to personnel selection problem. Int J Innov, Manag Technol 1:511–515. https://doi.org/10.1061/9780784413265.043

    Article  Google Scholar 

  81. Dhakar K, Dvivedi A (2017) Influence of glycerin-air dielectric medium on near-dry EDM of titanium alloy. Int J Addit Subtractive Mater Manuf 1:328–337. https://doi.org/10.1504/ijasmm.2017.10010933

    Article  Google Scholar 

  82. Jafarian F, Masoudi S, Umbrello D, Filice L (2019) New strategies for improvement of numerical model accuracy in machining of nickel-based alloy. Simul Model Pract Theory 94:134–148. https://doi.org/10.1016/j.simpat.2019.02.006

    Article  Google Scholar 

  83. Rajyalakshmi G, Ramaiah PV (2013) Multiple process parameter optimization of wire electrical discharge machining on Inconel 825 using Taguchi grey relational analysis. Int J Adv Manuf Technol 69:1249–1262. https://doi.org/10.1007/s00170-013-5081-z

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial and Production Engineering, Guru Ghasidas (Central) Vishwavidyalaya, Bilaspur, Chhattisgarh, India

    Nitin Kumar Sahu, Mukesh Kumar Singh & Atul Kumar Sahu

  2. Department of Mechanical Engineering, Guru Ghasidas (Central) Vishwavidyalaya, Bilaspur, Chhattisgarh, India

    Anoop Kumar Sahu

Authors
  1. Nitin Kumar Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mukesh Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Atul Kumar Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anoop Kumar Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The first author, i.e., Nitin Kumar Sahu, has developed the theoretical content of the paper. He has also performed the experimental investigations related to the study. Mukesh Kumar Singh has developed the methodological aspects and performed the computations. Atul Kumar Sahu and Anoop Kumar Sahu have assisted in framing decision modeling, descriptions, and findings of this study. All authors have discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to Nitin Kumar Sahu.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors have read and agreed to the published version of the manuscript.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahu, N.K., Singh, M.K., Sahu, A.K. et al. Employment of cylindrical electrolytic copper grade electrode under EDMed Inconel 825 super alloy: emphasis on machining behavior accompanied with surface topography for sustainability. Int J Adv Manuf Technol 131, 2207–2233 (2024). https://doi.org/10.1007/s00170-023-10967-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-10967-4

Keywords

Search

Navigation