Selection of an ideal MQL-assisted milling condition: an NSGA-II-coupled TOPSIS approach for improving machinability of Inconel 690

  • Binayak Sen
  • Syed Abou Iltaf Hussain
  • Mozammel MiaEmail author
  • Uttam Kumar Mandal
  • Sankar Prasad Mondal


Through minimum quantity lubrication (MQL) technology, small droplets of cutting fluid accompanied by compressed air are sprayed into the tool-workpiece interface. Hence, it offers effective lubrication/cooling and advances machining performance without using the significant amount of cutting fluids. Conversely, due to the biodegradable and non-pollutant properties of vegetable oils, these are widely employed as a base fluid in MQL technology. Considering the benefits of MQL-vegetable oil synergy, this paper aims to determine the best possible sequence of MQL milling parameters of Inconel 690 using castor oil as a lubricant. Here, response surface methodology (RSM) has been exploited to make a correlation between input and machining responses. To deal with the optimization problem, a two-stage computational approach was adopted. The first theory was non-dominated sorting genetic algorithm-II (NSGA-II) and the second method was technique for order preference by similarity to ideal solution (TOPSIS). NSGA-II has been exploited to explore for the candidate solution, and TOPSIS has been deployed to find out the best compromise solution. Finally, this manuscript compares the outcomes of the adopted approach with experimental outcomes to determine the efficacy of the proposed model. It was revealed from the comparison that the average error obtained between the predicted and the experimental response is less than 1%.


Inconel 690 MQL Milling RSM NSGA-II TOPSIS 


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  1. 1.
    Sen B, Mandal UK, Mondal SP (2017) Advancement of an intelligent system based on ANFIS for predicting machining performance parameters of Inconel 690—a perspective of metaheuristic approach. Measurement 109:9–17CrossRefGoogle Scholar
  2. 2.
    Boubekri N, Shaikh V (2015) Minimum quantity lubrication (MQL) in machining: benefits and drawbacks. J Ind Intell Inf 3(3):205–209Google Scholar
  3. 3.
    Klocke FA, Eisenblätter G (1997) Dry cutting. CIRP Ann 46(2):519–526CrossRefGoogle Scholar
  4. 4.
    MaClure TF, Adams R, Gugger MD, Gressel MG (2001) Comparison of flood vs. microlubrication on machining performance. Internet: http://www. Google Scholar
  5. 5.
    Dixit US, Sarma DK, Davim JP (2012) Environmentally friendly machining. Springer Science & Business MediaGoogle Scholar
  6. 6.
    Sharma VS, Dogra M, Suri NM (2009) Cooling techniques for improved productivity in turning. Int J Mach Tools Manuf 49(6):435–453CrossRefGoogle Scholar
  7. 7.
    Sharma VS, Dogra M, Suri NM (2008) Advances in the turning process for productivity improvement—a review. Proc Inst Mech Eng B J Eng Manuf 222(11):1417–1442CrossRefGoogle Scholar
  8. 8.
    Sun J, Wong YS, Rahman M, Wang ZG, Neo KS, Tan CH, Onozuka H (2006) Effects of coolant supply methods and cutting conditions on tool life in end milling titanium alloy. Mach Sci Technol 10(3):355–370CrossRefGoogle Scholar
  9. 9.
    Li HZ, Zeng H, Chen XQ (2006) An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts. J Mater Process Technol 180(1-3):296–304CrossRefGoogle Scholar
  10. 10.
    Da Silva RB, Vieira JM, Cardoso RN, Carvalho HC, Costa ES, Machado AR, De Ávila RF (2011) Tool wear analysis in milling of medium carbon steel with coated cemented carbide inserts using different machining lubrication/cooling systems. 271(9-10):2459–2465Google Scholar
  11. 11.
    Liu ZQ, Cai XJ, Chen M, An QL (2011) Investigation of cutting force and temperature of end-milling Ti–6Al–4V with different minimum quantity lubrication (MQL) parameters. Proc Inst Mech Eng B J Eng Manuf 225(8):1273–1279CrossRefGoogle Scholar
  12. 12.
    Cai XJ, Liu ZQ, Chen M, An QL (2012) An experimental investigation on effects of minimum quantity lubrication oil supply rate in high-speed end milling of Ti–6Al–4V. Proc Inst Mech Eng B J Eng Manuf 226(11):1784–1792CrossRefGoogle Scholar
  13. 13.
    Zhang S, Li JF, Wang YW (2012) Tool life and cutting forces in end milling Inconel 718 under dry and minimum quantity cooling lubrication cutting conditions. J Clean Prod 32:81–87CrossRefGoogle Scholar
  14. 14.
    Kasim MS, Haron CC, Ghani JA, Mohamad N, Izamshah R, Minhat M, Mohamed SB, Saedon JB, Saad NH (2015) Prediction of cutting force in end milling of Inconel 718. J Eng Technol 5(2):63–70Google Scholar
  15. 15.
    Hassanpour H, Sadeghi MH, Rasti A, Shajari S (2016) Investigation of surface roughness, microhardness and white layer thickness in hard milling of AISI 4340 using minimum quantity lubrication. J Clean Prod 120:124–134CrossRefGoogle Scholar
  16. 16.
    Öktem H, Erzurumlu T, Kurtaran H (2005) Application of response surface methodology in the optimization of cutting conditions for surface roughness. J Mater Process Technol 170(1-2):11–16CrossRefGoogle Scholar
  17. 17.
    Palanisamy P, Rajendran I, Shanmugasundaram S (2007) Optimization of machining parameters using a genetic algorithm and experimental validation for end-milling operations. Int J Adv Manuf Technol 32(7-8):644–655CrossRefGoogle Scholar
  18. 18.
    Zain AM, Haron H, Sharif S (2010) Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst Appl 37(6):4650–4659CrossRefGoogle Scholar
  19. 19.
    Reddy BS, Kumar JS, Reddy KVK (2011) Optimization of surface roughness in CNC end milling using response surface methodology and genetic algorithm. Int J Eng Sci Technol 3(8):102–109Google Scholar
  20. 20.
    Kumar SL, Jerald J, Kumanan S, Aniket N (2014) Process parameters optimization for micro end-milling operation for CAPP applications. Neural Comput & Applic 25(7-8):1941–1950CrossRefGoogle Scholar
  21. 21.
    Mia M, Dhar NR (2017) Prediction and optimization by using SVR, RSM and GA in hard turning of tempered AISI 1060 steel under effective cooling condition. Neural Comput & Applic:1–22Google Scholar
  22. 22.
    Chen J (2009) Multi-objective optimization of cutting parameters with improved NSGA-II. In: 2009 International Conference on Management and Service Science. IEEE, pp 1–4Google Scholar
  23. 23.
    Qu S, Zhao J, Wang T (2017) Experimental study and machining parameter optimization in milling thin-walled plates based on NSGA-II. Int J Adv Manuf Technol 89(5-8):2399–2409CrossRefGoogle Scholar
  24. 24.
    Magabe R, Sharma N, Gupta K, Davim JP (2019) Modeling and optimization of wire-EDM parameters for machining of Ni 55.8 Ti shape memory alloy using hybrid approach of Taguchi and NSGA-II. Int J Adv Manuf Technol:1–15Google Scholar
  25. 25.
    Bhuyan RK, Routara BC, Parida AK (2015) An approach for optimization the process parameter by using TOPSIS method of Al–24% SiC metal matrix composite during EDM. Mater Today: Proc 2(4-5):3116–3124Google Scholar
  26. 26.
    Parida AK, Routara BC (2014) Multiresponse optimization of process parameters in turning of GFRP using TOPSIS method. Int scholarly research notices.
  27. 27.
    Tripathy S, Tripathy DK (2016) Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis. Eng Sci Technol, Int J 19(1):62–70CrossRefGoogle Scholar
  28. 28.
    Nguyen HP, Pham VD, Ngo NV (2018) Application of TOPSIS to Taguchi method for multi-characteristic optimization of electrical discharge machining with titanium powder mixed into dielectric fluid. Int J Adv Manuf Technol 98(5-8):1179–1198CrossRefGoogle Scholar
  29. 29.
    Mannekote JK, Kailas SV (2009) Studies on boundary lubrication properties of oxidized coconut and soybean oils. Lubr Sci 21(9):355–365CrossRefGoogle Scholar
  30. 30.
    Ghani JA, Jamaluddin H, Rahman MN, Deros B (2013) Philosophy of Taguchi approach and method in design of experiment. Asian J Sci Res 6(1):27–37CrossRefGoogle Scholar
  31. 31.
    Gopalsamy BM, Mondal B, Ghosh S (2009) Taguchi method and ANOVA: an approach for process parameters optimization of hard machining while machining hardened steel. J Sci Ind Res 68:686–695Google Scholar
  32. 32.
    Mia M, Dhar NR (2017) Modeling of surface roughness using RSM, FL, and SA in dry hard turning. Arab J Sci Eng 43:1125–1136CrossRefGoogle Scholar
  33. 33.
    Deb K (2014) Multi-objective optimization. In: Search methodologies. Springer, BostonGoogle Scholar
  34. 34.
    Zitzler E (1994) Multi-objective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 2(3):221–248MathSciNetGoogle Scholar
  35. 35.
    Zhang W, Gen M, Jo J (2014) Hybrid sampling strategy-based multi-objective evolutionary algorithm for process planning and scheduling problem. J Intell Manuf 25(5):881–897CrossRefGoogle Scholar
  36. 36.
    Luo G, Wen X, Li H, Ming W, Xie G (2017) An effective multi-objective genetic algorithm based on immune principle and external archive for multi-objective integrated process planning and scheduling. Int J Adv Manuf Technol 91(9-12):3145–3158CrossRefGoogle Scholar
  37. 37.
    Souier M, Dahane M, Maliki F (2018) An NSGA-II-based multi-objective approach for real-time routing selection in a flexible manufacturing system under uncertainty and reliability constraints. Int J Adv Manuf Technol:1–17Google Scholar
  38. 38.
    Yusoff Y, Ngadiman MS, Zain AM (2011) Overview of NSGA-II for optimizing machining process parameters. Procedia Eng 15:3978–3983CrossRefGoogle Scholar
  39. 39.
    Hussain SAI, Mandal UK, Mondal SP (2018) Decision maker priority index and degree of vagueness coupled decision making method: a synergistic approach. Int J Fuzzy Sys 20(5):1551–1566MathSciNetCrossRefGoogle Scholar
  40. 40.
    Deb M, Debbarma B, Majumder A, Banerjee R (2016) Performance–emission optimization of a diesel-hydrogen dual fuel operation: an NSGA II coupled TOPSIS MADM approach. Energy 117:281–290CrossRefGoogle Scholar
  41. 41.
    Deng H, Yeh CH, Willis RJ (2000) Inter-company comparison using modified TOPSIS with objective weights. Comput Oper Res 27(10):963–973CrossRefzbMATHGoogle Scholar
  42. 42.
    Hwang CL, Masud AS (2012) Multiple objective decision making—methods and applications: a state-of-the-art survey. Springer Science & Business MediaGoogle Scholar
  43. 43.
    Jahanshahloo GR, Lotfi FH, Izadikhah M (2006) Extension of the TOPSIS method for decision-making problems with fuzzy data. Appl Math Comput 181(2):1544–1551zbMATHGoogle Scholar
  44. 44.
    Mia M, Bashir MA, Khan MA, Dhar NR (2017) Optimization of MQL flow rate for minimum cutting force and surface roughness in end milling of hardened steel (HRC 40). Int J Adv Manuf Technol 89:675–690CrossRefGoogle Scholar
  45. 45.
    Zhang CY (2008) Study on the mechanism of MQL cutting and its application fundament. Ph.D. Thesis. Department of Mechanical Manufacturing and Automation, Jiangsu UniversityGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Binayak Sen
    • 1
  • Syed Abou Iltaf Hussain
    • 1
  • Mozammel Mia
    • 2
    Email author
  • Uttam Kumar Mandal
    • 1
  • Sankar Prasad Mondal
    • 3
  1. 1.Department of Production EngineeringNational Institute of TechnologyAgartalaIndia
  2. 2.Department of Mechanical and Production EngineeringAhsanullah University of Science and TechnologyDhakaBangladesh
  3. 3.Department of MathematicsMidnapore CollegeWest MidnaporeIndia

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