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Transactions of the Indian Institute of Metals

, Volume 72, Issue 1, pp 191–204 | Cite as

An Efficient Approach to Optimize Wear Behavior of Cryogenic Milling Process of SS316 Using Regression Analysis and Particle Swarm Techniques

  • M. C. Karthik Rao
  • Rashmi L. MalghanEmail author
  • S. ArunKumar
  • Shrikantha S. Rao
  • Mervin A. Herbert
Technical Paper
  • 31 Downloads

Abstract

The present work is an endeavor to carry out a machining using LN2 in face milling operations and to produce the milling samples with excellent wear resistance property. The output response (wear rate) depends on appropriate choice of speed, feed, and depth of cut. The experimental data are conducted (collected) for SS316 as per central composite design. The present work exemplifies an employment of conventional and nonconventional strategies for optimizing the milling factors of cryogenically treated samples in face milling to achieve the desired wear (response). The results of nonlinear regression (desirability strategy) and nonconventional [particle swarm optimization, (PSO)] optimization techniques are compared, and PSO is found to outperform the desirability function approach. The present work even highlights the effect and results of LN2 on wear in contrast to wet condition.

Keywords

Cryogenic Optimization Conventional Nonconventional Milling Central composite design Desirability Particle swarm optimization Wear 

Notes

Acknowledgements

I would like to thank NITK, Surathkal, for providing facilities to carry out my research work.

References

  1. 1.
    Cambri B M, J Mat Processing Technology 56 (1996) 786.Google Scholar
  2. 2.
    Shaw M C, Pigott J D, and Richardson L P, Am Soc. Mech. Eng. 71 (1951) 45.Google Scholar
  3. 3.
    Cassin C, and Boothroyd G, J Mech Eng Sci 7 (1965) 67.Google Scholar
  4. 4.
    Baradie M A, J Mater Process Technol 56 (1996b) 798.CrossRefGoogle Scholar
  5. 5.
    Pusavec F, Kramar D, Krajnik P, and Kopac J, J Cleaner Prod 18 (2010) 1211.Google Scholar
  6. 6.
    Hong S Y, and Broomer M, Clean Prod Process 2 (2000) 157.Google Scholar
  7. 7.
    Hong S Y, Ding Y, and Jeong J, Mach Sci Technol 6 (2002) 235.Google Scholar
  8. 8.
    Bordin A, Bruschi S, Ghiotti A, and Bariani P F, Wear 328 (2015) 89.CrossRefGoogle Scholar
  9. 9.
    Jerold B D, and Kumar M P, Cryogenics 52 (2012) 569.Google Scholar
  10. 10.
    Umbrello D, J Adv Manuf Technol 64 (2015) 633.Google Scholar
  11. 11.
    Umbrello D, Int J Adv Manuf Technol 54 (2011) 887.Google Scholar
  12. 12.
    Klocke F, Settineri L, Lung D, Priarone PC, and Arft M, Wear 302 (2013) 1136.Google Scholar
  13. 13.
    Tandon V, Mounayri H E, and Kishawy H, Int J Mach Tools Manuf 42 (2002) 595.Google Scholar
  14. 14.
    Basker N, Asokan P, Saravanna R, and Probhaharan G, Int J Adv Manuf Technol 25 (2005) 10781088.Google Scholar
  15. 15.
    Mukherjee I, and Kumar R P, Comput Ind Eng 50 (2006) 15.Google Scholar
  16. 16.
    Raja S B, and Baskar N, Expert Syst Appl 39 (2012) 5982.Google Scholar
  17. 17.
    Julie Z, Joseph C, and Daniel K, J Mater Process Technol 184 (2007) 233.Google Scholar
  18. 18.
    Rashmi L M, Karthik Rao M C, Arun Kumar S, Shrikantha S Rao, and D’Souza R J, J Braz Soc Mech Sci Eng 39 (2016) 3541.Google Scholar
  19. 19.
    Reddy S K, and Rao P V, Int J Adv Manuf Technol 28 (2006) 463.Google Scholar
  20. 20.
    Rashmi L M, Karthik R M C, Arun Kumar S, Shrikantha S R, D’Souza R J, Mater Manuf Process 33 (2017) 1406.Google Scholar
  21. 21.
    Phadke M S, Quality engineering using robust design, Prentice Hall, New Jersey (1989).Google Scholar
  22. 22.
    Ross P J, Taguchi techniques for quality engineering, McGraw-Hill, New York (1996).Google Scholar
  23. 23.
    Montgomery D C, Design and analysis of experiments, Wiley, New York (2008).Google Scholar
  24. 24.
    Manjunath P, Krishna P, and Parappagoudar B, Int J Adv Technol (2016), http://dx.doi.org/10.1007/s00170-016-8416-8.
  25. 25.
    Manjunath P, Krishna P, and Parappagoudar B, Aust J Mech Eng (2015) http://dx.doi.org/10.1080/14484846.2015.1093231.
  26. 26.
    Rashmi L M, Karthik R M C, Arun Kumar S, Shrikantha S R, and Mervin A H, Int J Precis Eng Manuf 19 (2018) 695.Google Scholar
  27. 27.
    Manjunath P, Arun Kumar S, and Parappagoudar B, J Manuf Process 32 (2018) 199.Google Scholar
  28. 28.
    Kovacevic R, Cherukuthota C, and Mzurkiewiez M, Int J Mach Tools Manuf 35 (1995) 1459.Google Scholar
  29. 29.
    Dhar N R, Paul S, and Chattopadhyay A B, Wear 249 (2002b) 932.Google Scholar
  30. 30.
    Chen Z, Atmadi A, Stephennon D A, and Liang S Y, Ann CIRP 49 (2000) 53.Google Scholar
  31. 31.
    Yakup Y, and Muammer N, Int J Mach Tool Manuf 48 (2008) 947.Google Scholar
  32. 32.
    Barry J, and Byrne G, Ann CIRP 51 (2002) 65.Google Scholar

Copyright information

© The Indian Institute of Metals - IIM 2018

Authors and Affiliations

  • M. C. Karthik Rao
    • 1
  • Rashmi L. Malghan
    • 2
    Email author
  • S. ArunKumar
    • 3
  • Shrikantha S. Rao
    • 1
  • Mervin A. Herbert
    • 1
  1. 1.Department of Mechanical EngineeringNITKSurathkalIndia
  2. 2.Department of Computer Science EngineeringMadanapalle Institue of Technology and ScienceMadanapalleIndia
  3. 3.Department of Mechatronics Engineering, Manipal Institute of TechnologyManipal Academy of Higher EducationManipalIndia

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