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Modeling and optimization of tool wear in MQL-assisted milling of Inconel 718 superalloy using evolutionary techniques

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A Correction to this article was published on 26 November 2022

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Abstract

The Inconel 718 alloy, a difficult-to-cut superalloy with an extensive demand on aircraft and nuclear industries, being a low thermally conductive material exhibits a poor machinability. Consequently, the cutting tool is severely affected, and the tool cost is increased. In this context, an intelligent solution is presented in this paper—investigation of minimum quantity lubrication (MQL) and the selection of best machining conditions using evolutionary optimization techniques. A series of milling experiments on Inconel 718 alloy was conducted under dry, conventional flood, and MQL cooling modes. Afterward, the particle swarm optimization (PSO) and bacteria foraging optimization (BFO) were employed to optimize the cutting speed, feed rate, and depth-of-cut to minimize the flank wear (VBmax) parameter of a cutting tool. Though both the PSO and BFO models performed well, the validated results showed the superiority of PSO. Furthermore, it was found that the MQL performed better than the dry and flood cooling condition with respect to the reduction of the tool flank wear.

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References

  1. Jafarian F, Umbrello D, Golpayegani S, Darake Z (2016) Experimental investigation to optimize tool life and surface roughness in inconel 718 machining. Mater Manuf Process 31(13):1683–1691

    Article  Google Scholar 

  2. Pusavec F, Deshpande A, Yang S, M'Saoubi R, Kopac J, Dillon OW, Jawahir I (2014) Sustainable machining of high temperature nickel alloy–Inconel 718: part 1–predictive performance models. J Clean Prod 81:255–269

    Article  Google Scholar 

  3. Amini S, Fatemi M, Atefi R (2014) High speed turning of Inconel 718 using ceramic and carbide cutting tools. Arab J Sci Eng 39(3):2323–2330

    Article  Google Scholar 

  4. Mia M, Razi MH, Ahmad I, Mostafa R, Rahman SMS, Ahmed DH, Dey PR, Dhar NR (2017) Effect of time-controlled MQL pulsing on surface roughness in hard turning by statistical analysis and artificial neural network. Int J Adv Manuf Technol 91(9):3211–3223. https://doi.org/10.1007/s00170-016-9978-1

    Article  Google Scholar 

  5. 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(1):675–690. https://doi.org/10.1007/s00170-016-9080-8

    Article  Google Scholar 

  6. Al Bashir M, Mia M, Dhar NR (2016) Effect of pulse jet MQL in surface milling of hardened steel. J Mech Eng 45(2):67–72

    Article  Google Scholar 

  7. Bashir MA, Mia M, Dhar NR (2016) Investigations on surface milling of hardened AISI 4140 steel with pulse jet MQL applicator. J Inst Eng (India) Ser C:1–14. doi:https://doi.org/10.1007/s40032-016-0277-2

  8. Gupta MK, Sood PK, Sharma VS (2017) Performance evaluation of cubic boron nitride tool in machining of titanium (grade-II) under minimum quantity lubrication

  9. Gupta MK, Sood PK, Singh G, Sharma VS (2017) Sustainable machining of aerospace material–Ti (grade-2) alloy: modeling and optimization. J Clean Prod 147:614–627

    Article  Google Scholar 

  10. Gupta MK, Sood P, Sharma VS (2016) Optimization of machining parameters and cutting fluids during nano-fluid based minimum quantity lubrication turning of titanium alloy by using evolutionary techniques. J Clean Prod 135:1276–1288

    Article  Google Scholar 

  11. Jawaid A, Koksal S, Sharif S (2001) Cutting performance and wear characteristics of PVD coated and uncoated carbide tools in face milling Inconel 718 aerospace alloy. J Mater Process Technol 116(1):2–9

    Article  Google Scholar 

  12. Sharman A, Dewes RC, Aspinwall DK (2001) Tool life when high speed ball nose end milling Inconel 718™. J Mater Process Technol 118(1):29–35

    Article  Google Scholar 

  13. Li H, Zeng H, Chen X (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):296–304

    Google Scholar 

  14. Ucun I, Aslantas K, Bedir F (2013) An experimental investigation of the effect of coating material on tool wear in micro milling of Inconel 718 super alloy. Wear 300(1):8–19

    Article  Google Scholar 

  15. Aramcharoen A, Chuan SK (2014) An experimental investigation on cryogenic milling of Inconel 718 and its sustainability assessment. Proc CIRP 14:529–534

    Article  Google Scholar 

  16. Hoier P, Klement U, Tamil Alagan N, Beno T, Wretland A (2017) Flank wear characteristics of WC-Co tools when turning Alloy 718 with high-pressure coolant supply. J Manuf Process 30(Supplement C):116–123. https://doi.org/10.1016/j.jmapro.2017.09.017

    Article  Google Scholar 

  17. Kuppuswamy R, Zunega J, Naidoo S (2017) Flank wear assessment on discrete machining process behavior for Inconel 718. Int J Adv Manuf Technol 93(5):2097–2109. https://doi.org/10.1007/s00170-017-0623-4

    Article  Google Scholar 

  18. Feyzi T, Safavi SM (2013) Improving machinability of Inconel 718 with a new hybrid machining technique. Int J Adv Manuf Technol 66(5):1025–1030. https://doi.org/10.1007/s00170-012-4386-7

    Article  Google Scholar 

  19. Mia M, Khan MA, Dhar NR (2017) Performance prediction of high-pressure coolant assisted turning of Ti-6Al-4V. Int J Adv Manuf Technol 90(5):1433–1445. https://doi.org/10.1007/s00170-016-9468-5

    Article  Google Scholar 

  20. Mia M, Khan MA, Dhar NR (2017) Study of surface roughness and cutting forces using ANN, RSM, and ANOVA in turning of Ti-6Al-4V under cryogenic jets applied at flank and rake faces of coated WC tool. Int J Adv Manuf Technol 93(1):975–991. https://doi.org/10.1007/s00170-017-0566-9

    Article  Google Scholar 

  21. Mia M, Khan MA, Rahman SS, Dhar NR (2017) Mono-objective and multi-objective optimization of performance parameters in high pressure coolant assisted turning of Ti-6Al-4V. Int J Adv Manuf Technol 90(1):109–118. https://doi.org/10.1007/s00170-016-9372-z

    Article  Google Scholar 

  22. Thakur DG, Ramamoorthy B, Vijayaraghavan L (2010) Investigation and optimization of lubrication parameters in high speed turning of superalloy Inconel 718. Int J Adv Manuf Technol 50(5):471–478. https://doi.org/10.1007/s00170-010-2538-1

    Article  Google Scholar 

  23. Jafarian F, Amirabadi H, Fattahi M (2014) Improving surface integrity in finish machining of Inconel 718 alloy using intelligent systems. Int J Adv Manuf Technol 71(5):817–827. https://doi.org/10.1007/s00170-013-5528-2

    Article  Google Scholar 

  24. Krain H, Sharman A, Ridgway K (2007) Optimisation of tool life and productivity when end milling Inconel 718TM. J Mater Process Technol 189(1):153–161

    Article  Google Scholar 

  25. Senthilkumaar JS, Selvarani P, Arunachalam RM (2012) Intelligent optimization and selection of machining parameters in finish turning and facing of Inconel 718. Int J Adv Manuf Technol 58(9):885–894. https://doi.org/10.1007/s00170-011-3455-7

    Article  Google Scholar 

  26. Gupta MK, Sood P (2017) Machining comparison of aerospace materials considering minimum quantity cutting fluid: a clean and green approach. Proc Inst Mech Eng C J Mech Eng Sci 231(8):1445–1464

    Article  Google Scholar 

  27. Zhou J, Ren J, Yao C (2017) Multi-objective optimization of multi-axis ball-end milling Inconel 718 via grey relational analysis coupled with RBF neural network and PSO algorithm. Measurement 102:271–285

    Article  Google Scholar 

  28. Pimenov DY, Bustillo A, Mikolajczyk T (2017) Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. J Intell Manuf. https://doi.org/10.1007/s10845-017-1381-8

  29. Sarıkaya M, Güllü A (2015) Multi-response optimization of minimum quantity lubrication parameters using Taguchi-based grey relational analysis in turning of difficult-to-cut alloy Haynes 25. J Clean Prod 91:347–357

    Article  Google Scholar 

  30. Sarıkaya M, Yılmaz V, Güllü A (2016) Analysis of cutting parameters and cooling/lubrication methods for sustainable machining in turning of Haynes 25 superalloy. J Clean Prod 133:172–181

    Article  Google Scholar 

  31. Wang C, Li K, Chen M, Liu Z (2015) Evaluation of minimum quantity lubrication effects by cutting force signals in face milling of Inconel 182 overlays. J Clean Prod 108:145–157

    Article  Google Scholar 

  32. Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, pp 760–766

  33. Majumder A (2013) Process parameter optimization during EDM of AISI 316 LN stainless steel by using fuzzy based multi-objective PSO. J Mech Sci Technol 27(7):2143–2151

    Article  Google Scholar 

  34. Yusup N, Zain AM, Hashim SZM (2012) Overview of PSO for optimizing process parameters of machining. Proc Eng 29:914–923

    Article  Google Scholar 

  35. Zhou J, Duan Z, Li Y, Deng J, Yu D (2006) PSO-based neural network optimization and its utilization in a boring machine. J Mater Process Technol 178(1):19–23

    Article  Google Scholar 

  36. Mahapatra SS, Sood AK (2012) Bayesian regularization-based Levenberg–Marquardt neural model combined with BFOA for improving surface finish of FDM processed part. Int J Adv Manuf Technol 60(9):1223–1235. https://doi.org/10.1007/s00170-011-3675-x

    Article  Google Scholar 

  37. Mohamed Imran A, Kowsalya M (2014) Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization. Swarm Evol Comput 15(Supplement C):58–65. https://doi.org/10.1016/j.swevo.2013.12.001

    Article  Google Scholar 

  38. Devi S, Geethanjali M (2014) Application of modified bacterial foraging optimization algorithm for optimal placement and sizing of distributed generation. Expert Syst Appl 41(6):2772–2781. https://doi.org/10.1016/j.eswa.2013.10.010

    Article  Google Scholar 

  39. Mia M, Al Bashir M, Dhar NR (2016) Modeling of principal flank wear: an empirical approach combining the effect of tool, environment and workpiece hardness. J Inst Eng (India) Ser C 97(4):517–526

    Article  Google Scholar 

  40. Groover MP (2007) Fundamentals of modern manufacturing: materials processes, and systems. John Wiley & Sons,

  41. Maruda RW, Krolczyk GM, Feldshtein E, Pusavec F, Szydlowski M, Legutko S, Sobczak-Kupiec A (2016) A study on droplets sizes, their distribution and heat exchange for minimum quantity cooling lubrication (MQCL). Int J Mach Tools Manuf 100:81–92

    Article  Google Scholar 

  42. Gupta MK, Sood P (2017) Surface roughness measurements in NFMQL assisted turning of titanium alloys: an optimization approach. Friction:1–16

  43. Mia M, Dhar NR (2016) Response surface and neural network based predictive models of cutting temperature in hard turning. J Adv Res 7(6):1035–1044

    Article  Google Scholar 

  44. Mia M (2017) Multi-response optimization of end milling parameters under through-tool cryogenic cooling condition. Measurement 111(Supplement C):134–145. https://doi.org/10.1016/j.measurement.2017.07.033

    Article  Google Scholar 

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Acknowledgments

The authors are extremely grateful to Prof Knut Sorby, NTNU Norway for his valuable contribution in this research work.

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Authors and Affiliations

  1. Mechanical Engineering, Lovely Professional University, Phagwara, Punjab, India

    GurRaj Singh

  2. Mechanical Engineering, NIT Hamirpur, Hamirpur, India

    Munish Kumar Gupta

  3. Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka, 1208, Bangladesh

    Mozammel Mia

  4. Industrial & Production Engineering, Dr B R Ambedkar National Institute of Technology, Jalandhar, 144011, India

    Vishal S. Sharma

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  1. GurRaj Singh
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Correspondence to Mozammel Mia.

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Singh, G., Gupta, M.K., Mia, M. et al. Modeling and optimization of tool wear in MQL-assisted milling of Inconel 718 superalloy using evolutionary techniques. Int J Adv Manuf Technol 97, 481–494 (2018). https://doi.org/10.1007/s00170-018-1911-3

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  • DOI: https://doi.org/10.1007/s00170-018-1911-3

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