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Optimization of atomized spray cutting fluid eco-friendly turning of Inconel 718 alloy using ARAS and CODAS methods

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Abstract

Atomized spray cutting fluid (ASCF) is a complex machining technology that results in increased productivity, improved surface quality, longer tool life, and cost savings. The purpose of this study is to investigate the effect of cutting process parameters on Inconel 718 alloy turning in dry and ASCF cutting environments. These two cooling environments’ essential machining indices, such as surface roughness, machining cost, power consumption, and tool life, were investigated. The cutting parameters were adjusted using desirability functional analysis, and two types of multicriteria decision-making (MCDM) methods were investigated: additive ratio assessment method (ARAS) and combinative distance-based assessment technique (CODAS). Both MCDM approaches yielded identical results, with the best settings being a cutting speed of 200 m/min, a feed rate of 0.08 mm/rev, and a depth of cut of 0.2 mm in an ASCF environment. ASCF machining considerably minimises the surface roughness, machining cost, power consumption and maximises the tool life by around 16%, 51%, 17%, and 48%, respectively, compared with dry machining.

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References

  1. Marques A, Suarez MP, Sales WF, Machado ÁR (2019) Turning of Inconel 718 with whisker-reinforced ceramic tools applying vegetable-based cutting fluid mixed with solid lubricants by MQL. J Mater Process Technol 266:530–543

    Article  Google Scholar 

  2. Li L, He N, Wang M, Wang Z (2002) High speed cutting of Inconel 718 with coated carbide and ceramic inserts. J Mater Process Technol 129(1–3):127–130

    Article  Google Scholar 

  3. Gaitonde V, Karnik S, Figueira L, Davim JP (2009) Machinability investigations in hard turning of AISI D2 cold work tool steel with conventional and wiper ceramic inserts. Int J Refract Metal Hard Mater 27(4):754–763

    Article  Google Scholar 

  4. Gupta MK, Jamil M, Wang X, Song Q, Liu Z, Mia M, Hegab H, Khan AM, Collado AG, Pruncu CI (2019) Performance evaluation of vegetable oil-based nano-cutting fluids in environmentally friendly machining of inconel-800 alloy. Materials 12(17):2792

    Article  Google Scholar 

  5. Singh G, Pruncu CI, Gupta MK, Mia M, Khan AM, Jamil M, Pimenov DY, Sen B, Sharma VS (2019) Investigations of machining characteristics in the upgraded MQL-assisted turning of pure titanium alloys using evolutionary algorithms. Materials 12(6):999

    Article  Google Scholar 

  6. Krolczyk G, Maruda R, Krolczyk J, Wojciechowski S, Mia M, Nieslony P, Budzik G (2019) Ecological trends in machining as a key factor in sustainable production–a review. J Clean Prod 218:601–615

    Article  Google Scholar 

  7. Zhang B, Jessee NM, Yoshiki S (2018) High-speed turning of Inconel 718 by using TiAlN-and (Al, Ti) N-coated carbide tools. Int J Adv Manuf Technol 96(5–8):2141–2147

    Article  Google Scholar 

  8. Çelik A, Alağaç MS, Turan S, Kara A, Kara F (2017) Wear behavior of solid SiAlON milling tools during high speed milling of Inconel 718. Wear 378:58–67

    Article  Google Scholar 

  9. Nath C, Kapoor SG, DeVor RE, Srivastava AK, Iverson J (2012) Design and evaluation of an atomization-based cutting fluid spray system in turning of titanium alloy. J Manuf Process 14(4):452–459

    Article  Google Scholar 

  10. Korkmaz ME, Gupta MK, Boy M, Yaşar N, Krolczyk GM, Günay M (2021) Influence of duplex jets MQL and nano-MQL cooling system on machining performance of Nimonic 80A. J Manuf Process 69:112–124

    Article  Google Scholar 

  11. Sivalingam V, Zan Z, Sun J, Selvam B, Gupta MK, Jamil M,Mia M (2020) Wear behaviour of whisker-reinforced ceramic tools in the turning of Inconel 718 assisted by an atomized spray of solid lubricants. Tribology Int 148:106235

  12. Khanna N, Agrawal C, Pimenov DY, Singla AK, Machado AR, da Silva LRR, Gupta MK, Sarikaya M, Krolczyk GM (2021) Review on design and development of cryogenic machining setups for heat resistant alloys and composites. J Manuf Process 68:398–422

    Article  Google Scholar 

  13. Sankaranarayanan R, Krolczyk G (2021) A comprehensive review on research developments of vegetable-oil based cutting fluids for sustainable machining challenges. J Manuf Process 67:286–313

    Article  Google Scholar 

  14. Sartori S, Ghiotti A, Bruschi S (2018) Solid lubricant-assisted minimum quantity lubrication and cooling strategies to improve Ti6Al4V machinability in finishing turning. Tribol Int 118:287–294

    Article  Google Scholar 

  15. Rajeswari B, Amirthagadeswaran K (2017) Experimental investigation of machinability characteristics and multi-response optimization of end milling in aluminium composites using RSM based grey relational analysis. Measurement 105:78–86

    Article  Google Scholar 

  16. Debnath S, Reddy MM, Yi QS (2014) Environmental friendly cutting fluids and cooling techniques in machining: a review. J Clean Prod 83:33–47

    Article  Google Scholar 

  17. Sait AN, Aravindan S, Haq AN (2009) Optimisation of machining parameters of glass-fibre-reinforced plastic (GFRP) pipes by desirability function analysis using Taguchi technique. Int J Adv Manuf Technol 43(5):581–589

    Article  Google Scholar 

  18. Singaravel B, Selvaraj T (2016) Application of desirability function analysis and utility concept for selection of optimum cutting parameters in turning operation. J Adv Manuf Syst 15(01):1–11

    Article  Google Scholar 

  19. Świercz R, Oniszczuk-Świercz D, Chmielewski T (2019) Multi-response optimization of electrical discharge machining using the desirability function. Micromachines 10(1):72

    Article  Google Scholar 

  20. Sharma VK, Rana M, Singh T, Singh AK, Chattopadhyay K (2021) Multi-response optimization of process parameters using Desirability Function Analysis during machining of EN31 steel under different machining environments. Mater Today Proc 44:3121–3126

    Article  Google Scholar 

  21. Poongavanam G, Sivalingam V, Prabakaran R, Salman M, Kim SC (2021) Selection of the best refrigerant for replacing R134a in automobile air conditioning system using different MCDM methods: a comparative study. Case Stud Therm Eng 27:101344

  22. Singaravel B, Shankar DP, Prasanna L (2018) Application of MCDM method for the selection of optimum process parameters in turning process. Mater Today Proc 5(5):13464–13471

    Article  Google Scholar 

  23. Ghenai C, Albawab M, Bettayeb M (2020) Sustainability indicators for renewable energy systems using multicriteria decision-making model and extended SWARA/ARAS hybrid method. Renew Energy 146:580–597

    Article  Google Scholar 

  24. Marichamy M, Babu S (2021) The selection of optimum process parameters on A319 aluminum alloy in friction stir welding MCDM method. Mater Today Proc 37:228–231

    Article  Google Scholar 

  25. Kumar A, Rai RN (2019) Optimisation of EDM process parameters for AA7050-10 (WT)% B4C composite through ARAS, grey and Taguchi technique. Int J Mater Prod Technol 59(2):102–120

    Article  MathSciNet  Google Scholar 

  26. Ramezanali AK, Feizi F, Jafarirad A, Lotfi M (2020) Application of Best-Worst method and Additive Ratio Assessment in mineral prospectivity mapping: a case study of vein-type copper mineralization in the Kuhsiah-e-Urmak Area, Iran. Ore Geol Rev 117:103268

  27. Goswami SS, Behera DK (2021) Implementation of ENTROPY-ARAS decision making methodology in the selection of best engineering materials. Mater Today Proc 38:2256–2262

    Article  Google Scholar 

  28. Balki MK, Erdoğan S, Aydın S, Sayin C (2020) The optimization of engine operating parameters via SWARA and ARAS hybrid method in a small SI engine using alternative fuels. J Cleaner Prod 258:120685

  29. Çolak M, Kaya İ (2020) Multicriteria evaluation of energy storage technologies based on hesitant fuzzy information: a case study for Turkey. J Energy Storage 28:101211

  30. Radović D, Stević Ž, Pamučar D, Zavadskas EK, Badi I, Antuchevičiene J, Turskis Z (2018) Measuring performance in transportation companies in developing countries: a novel rough ARAS model. Symmetry 10(10):434

    Article  Google Scholar 

  31. Ghorabaee MK, Amiri M, Zavadskas EK, Hooshmand R, Antuchevičienė J (2017) Fuzzy extension of the CODAS method for multicriteria market segment evaluation. J Bus Econ Manag 18(1):1–19

    Article  Google Scholar 

  32. Karaşan A, Boltürk E, Kahraman C (2019) A novel neutrosophic CODAS method: Selection among wind energy plant locations. J Intell Fuzzy Syst 36(2):1491–1504

    Article  Google Scholar 

  33. Badi I, Shetwan AG, Abdulshahed AM (2017) Supplier selection using combinative distance-based assessment (CODAS) method for multicriteria decision-making. In Proceedings of The 1st International Conference on Management, Eng Environ (ICMNEE) 395–407

  34. Ren J (2018) Sustainability prioritization of energy storage technologies for promoting the development of renewable energy: a novel intuitionistic fuzzy combinative distance-based assessment approach. Renew Energy 121:666–676

    Article  Google Scholar 

  35. Li H, Wang W, Fan L, Li Q, Chen X (2020) A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR. Appl Soft Comp 91:106207

  36. Ijadi Maghsoodi A, Ijadi Maghsoodi A, Poursoltan P, Antucheviciene J, Turskis Z (2019) Dam construction material selection by implementing the integrated SWARA—CODAS approach with target-based attributes. Arch Civ Mech Eng 19(4):1194–1210

    Article  Google Scholar 

  37. Roy J, Das S, Kar S, Pamučar D (2019) An extension of the CODAS approach using interval-valued intuitionistic fuzzy set for sustainable material selection in construction projects with incomplete weight information. Symmetry 11(3):393

    Article  Google Scholar 

  38. Karagoz S, Deveci M, Simic V, Aydin N, Bolukbas U (2020) A novel intuitionistic fuzzy MCDM-based CODAS approach for locating an authorized dismantling center: a case study of Istanbul. Waste Manage Res 38(6):660–672

    Article  Google Scholar 

  39. Harrington EC (1965) The desirability function. Ind Qual Control 21(10):494–498

    Google Scholar 

  40. Costa NR, Lourenço J, Pereira ZL (2011) Desirability function approach: a review and performance evaluation in adverse conditions. Chemom Intell Lab Syst 107(2):234–244

    Article  Google Scholar 

  41. Yazdani M, Torkayesh AE, Santibanez-Gonzalez ED, Otaghsara SK (2020) Evaluation of renewable energy resources using integrated Shannon Entropy—EDAS model. Sustain Oper Comput 1:35–42

    Article  Google Scholar 

  42. Zavadskas EK, Turskis Z (2010) A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technol Econ Dev Econ 16(2):159–172

    Article  Google Scholar 

  43. Özbek A, Erol E (2017) Ranking of factoring companies in accordance with ARAS and COPRAS methods. Int J Acad Res Account Financ Manag Sci 7(2):105–116

    Google Scholar 

  44. Ferreira R, Carou D, Lauro C, Davim J (2016) Surface roughness investigation in the hard turning of steel using ceramic tools. Mater Manuf Processes 31(5):648–652

    Article  Google Scholar 

  45. Sivalingam V, Zhuoliang Z, Jie S, Baskaran S, Yuvaraj N, Gupta MK, Aqib MK (2021) Use of atomized spray cutting fluid technique for the turning of a nickel base superalloy. Mater Manuf Processes 36(3):373–380

    Article  Google Scholar 

  46. Chavan V, Kadam S, Sadaiah M (2019) Performance of alumina-based ceramic inserts in high-speed machining of nimonic 80A. Mater Manuf Processes 34(1):8–17

    Article  Google Scholar 

  47. Gupta S, Venkatesan K, Devendiran S, Mathew AT (2019) Experimental investigation of IN725 under different cooling environments using new tool holder. Mater Manuf Processes 34(6):637–647

    Article  Google Scholar 

  48. Klocke F, Nobel C, Veselovac D (2016) Influence of tool coating, tool material, and cutting speed on the machinability of low-leaded brass alloys in turning. Mater Manuf Processes 31(14):1895–1903

    Article  Google Scholar 

  49. Zhuang K, Zhu D, Zhang X, Ding H (2014) Notch wear prediction model in turning of Inconel 718 with ceramic tools considering the influence of work hardened layer. Wear 313(1–2):63–74

    Article  Google Scholar 

  50. An Q, Cai C, Zou F, Liang X, Chen M (2020) Tool wear and machined surface characteristics in side milling Ti6Al4V under dry and supercritical CO2 with MQL conditions. Tribology Int 151:106511

  51. Pal A, Chatha SS, Sidhu HS (2020) Experimental investigation on the performance of MQL drilling of AISI 321 stainless steel using nano-graphene enhanced vegetable-oil- based cutting fluid. Tribology Int 151:106508

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Funding

This work is supported by Fundamental Research Funds of Shandong University [2019HW040]. Future for Young Scholars of Shandong University, China (31360082064026).

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Author notes

  1. Vinothkumar Sivalingam and Ganeshkumar Poogavanam contributed equally to this work.

Authors and Affiliations

  1. School of Mechanical Engineering, Key Laboratory of High-Efficiency and Clean Mechanical Manufacture, National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan, Shandong, 250061, China

    Vinothkumar Sivalingam & Jie Sun

  2. Research Center for Aeronautical Component Manufacturing Technology & Equipment, Shandong University, Jinan, Shandong, 250061, China

    Vinothkumar Sivalingam & Jie Sun

  3. School of Mechanical Engineering, Yeungnam University, Gyeongbuk, 712-749, Republic of Korea

    Ganeshkumar Poogavanam

  4. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Avadi, Chennai-62, India

    Yuvaraj Natarajan

Authors
  1. Vinothkumar Sivalingam
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  2. Ganeshkumar Poogavanam
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  3. Yuvaraj Natarajan
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  4. Jie Sun
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Contributions

Vinothkumar Sivalingam: conceptualization, experimental work and data curation writing—review and editing, Ganeshkumar Poogavanam: writing—review and editing Yuvaraj Natarajan: writing—review and editing and technical validation, Jie Sun: conceptualization and supervision.

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Correspondence to Vinothkumar Sivalingam or Jie Sun.

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Sivalingam, V., Poogavanam, G., Natarajan, Y. et al. Optimization of atomized spray cutting fluid eco-friendly turning of Inconel 718 alloy using ARAS and CODAS methods. Int J Adv Manuf Technol 120, 4551–4564 (2022). https://doi.org/10.1007/s00170-022-09047-w

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