, Volume 10, Issue 6, pp 2751–2768 | Cite as

Analysis and Modeling of Cryogenic Turning Operation Using Response Surface Methodology

  • P. SivaiahEmail author
  • D. Chakradhar
Original Paper


In the present scenario, metal cutting industries are looking for alternative cooling techniques to conventional cooling to satisfy the stringent environment regulations as well as lower productivity problems while machining of difficult to cut materials. Cryogenic machining is a novel eco-friendly as well as efficient cooling techniques. In present work, an attempt has been made to study the effect of process parameters on turning performance characteristics and the development of correlation models between the input process parameters and output responses while machining of difficult to cut materials 17-4 precipitated hardened stainless steel (PH SS) using response surface methodology (RSM) under the cryogenic cooling environment. The turning process parameters considered in the present study are cutting velocity (v), feed rate (f) and depth of cut (d) whereas responses are tool flank wear (Vb), surface roughness (Ra) and material removal rate (MRR) respectively. RSM based face centered central composite design (CCD) experimental design has been used to perform the experiments. From the conformation test results, it was observed that very good agreement was found between the actual and predicted values, which represent that the developed predictive models are well effective with a maximum of ± 5% error.


Cryogenic machining Response surface methodology 17-4 PH SS Tool wear Surface roughness Material removal rate (MRR) Modeling 


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  1. 1.
    Kochmański P, Nowacki J (2006) Activated gas nitriding of 17-4 PH stainless steel. Surf Coat Technol 200(22–23):6558–6562CrossRefGoogle Scholar
  2. 2.
    Mohanty A, Gangopadhyay S, Thakur A (2015) On applicability of multilayer coated tool in dry machining of aerospace grade stainless steel. Mater Manuf Process 31(7):869–879CrossRefGoogle Scholar
  3. 3.
    Shaw MC (1984) Metal cutting principles. Clarendon Press, Wotton-under-EdgeGoogle Scholar
  4. 4.
    Hong SY, Broomer M (2000) Economical and ecological cryogenic machining of AISI 304 austenitic stainless steel. Clean Prod Process 2(3):0157–0166CrossRefGoogle Scholar
  5. 5.
    Montgomery DC (1987) Design and analysis of experiments-second edition. Qual Reliab Eng Int 3(3):212–212Google Scholar
  6. 6.
    Gupta MK, Sood PK, Sharma VS (2016) Machining parameters optimization of titanium alloy using response surface methodology and particle swarm optimization under minimum quantity lubrication environment. Mater Manuf Process 31(13):1671– 1682CrossRefGoogle Scholar
  7. 7.
    Gupta MK, Sood PK, 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 135:1276–1288CrossRefGoogle Scholar
  8. 8.
    Makadia AJ, Nanavati JI (2013) Optimisation of machining parameters for turning operations based on response surface methodology. Meas J Int Meas Confed 46(4):1521–1529CrossRefGoogle Scholar
  9. 9.
    Asiltürk I, Neşeli S, Ince MA (2016) Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods. Meas J Int Meas Confed 78:120–128CrossRefGoogle Scholar
  10. 10.
    Bouacha K, Yallese MA, Mabrouki T, Rigal JF (2010) Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool. Int J Refract Met Hard Mater 28(3):349–361CrossRefGoogle Scholar
  11. 11.
    Bouacha K, Yallese MA, Khamel S, Belhadi S (2014) Analysis and optimization of hard turning operation using cubic boron nitride tool. Int J Refract Met Hard Mater 45:160–178CrossRefGoogle Scholar
  12. 12.
    Mandal N, Doloi B, Mondal B (2011) Development of flank wear prediction model of Zirconia Toughened Alumina (ZTA) cutting tool using response surface methodology. Int J Refract Met Hard Mater 29(2):273–280CrossRefGoogle Scholar
  13. 13.
    Aouici H, Yallese MA, Fnides B, Chaoui K, Mabrouki T (2011) Modeling and optimization of hard turning of X38CrMoV5-1 steel with CBN tool: machining parameters effects on flank wear and surface roughness. J Mech Sci Technol 25(11):2843–2851CrossRefGoogle Scholar
  14. 14.
    Chauhan SR Dass K (2012) Optimization of machining parameters in turning of titanium (grade-5) alloy using response surface methodology. Mater Manuf Process 27(5):531–537CrossRefGoogle Scholar
  15. 15.
    Sivaiah P, Chakradhar D (2017) Multi-objective optimisation of cryogenic turning process using Taguchi-based grey relational analysis. Int J Mach Mach Mater 19(4):297–312Google Scholar
  16. 16.
    Sivaiah P, Chakradhar D (2017) Machinability studies on 17-4 PH stainless steel under cryogenic cooling environment. Mater Manuf Process 32(15):1775–1788CrossRefGoogle Scholar
  17. 17.
    Sivaiah P, Chakradhar D (2017) Influence of cryogenic coolant on turning performance characteristics?: a comparison with wet machining. Mater Manuf Process 32(13):1475–1485CrossRefGoogle Scholar
  18. 18.
    Sivaiah P, Chakradhar D (2017) Comparative evaluations of machining performance during turning of 17-4 PH stainless steel under cryogenic and wet machining conditions. Mach Sci Technol 22(1):147–162CrossRefGoogle Scholar
  19. 19.
    Stephan DD, Werner J, Yeater RP (1998) Essential regression and experimental design for chemists and engineers. MS Excel add Softw Packag. 2001Google Scholar
  20. 20.
    Pawade RS, Joshi SS, Brahmankar PK, Rahman M (2007) An investigation of cutting forces and surface damage in high-speed turning of Inconel 718. J Mater Process Technol 193:139–146CrossRefGoogle Scholar
  21. 21.
    Palanikumar K, Karthikeyan R (2006) Optimal machining conditions for turning of particulate metal matrix composites using taguchi and response surface methodologies. Mach Sci Technol 10(4):417–433CrossRefGoogle Scholar
  22. 22.
    Maity KP, Swain PK (2008) An experimental investigation of hot-machining to predict tool life. J Mater Process Technol 198(1–3):344–349CrossRefGoogle Scholar

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringMadanapalle Institute of Technology and ScienceMadanapalleIndia
  2. 2.Mechanical EngineeringIndian Institute of Technology PalakkadPalakkadIndia

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