Skip to main content
SpringerLink
Log in

Prediction of temperature distribution over cutting tool with alumina-MWCNT hybrid nanofluid using computational fluid dynamics (CFD) analysis

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The accurate measurement of tool tip temperature in turning has been a challenging task for researchers. The present study covers the computational fluid dynamics (CFD) analysis of a single point carbide cutting tool dissipating heat and subjected to streams of nanofluid mist. This study is an attempt to numerically analyze the thermal behavior of the cutting tool having constant temperature heat source at its tip. To determine the optimized experimental value of nodal temperature, machining of AISI 304 steel has been carried out using alumina and alumina-MWCNT hybrid nanofluid mist with minimum quantity lubrication (MQL) system. Furthermore, the conjugate heat transfer (CHT) analysis has been performed to investigate the temperature distribution over the rake and flank face of carbide cutting insert. An overview of the mathematical model, complete geometry, and mesh generation of the carbide cutting tool is presented. Additionally, the CHT simulation results for the mist of alumina and alumina-MWCNT hybrid nanofluids were compared and validated successfully with the experimental data. The maximum variation of 5.79% has been observed between experimental and simulation data, which in turn proves the proposed conjugate heat transfer-based CFD model as an alternative approach to investigate the influence of cooling media on cutting tool under MQL technique.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Abukhshim NA, Mativenga PT, Sheikh MA (2005) Investigation of heat partition in high speed turning of high strength alloy steel. Int J Mach Tools Manuf 45(15):1687–1695

    Article  Google Scholar 

  2. Carvalho SR, Lima e Silva SMM, Machado AR, Guimarães G (2006) Temperature determination at the chip–tool interface using an inverse thermal model considering the tool and tool holder. J Mater Process Technol 179(1–3):97–104

    Article  Google Scholar 

  3. Liang L, Xu H, Ke Z (2013) An improved three-dimensional inverse heat conduction procedure to determine the tool-chip interface temperature in dry turning. Int J Therm Sci 64:152–161

    Article  Google Scholar 

  4. Shaw MC (2006) Metal cutting principles, 1st edn. Oxford University Press Inc., New Delhi

    Google Scholar 

  5. Astakhov VP (2006) Tribology of metal cutting. Tribology and Interface Engineering Series, No. 52. UK: Elsevier Limited

  6. Chow JG, Wright PK (1998) On-line estimation of tool/chip interface temperatures for a turning operation. T-ASME 110:56–64

    Google Scholar 

  7. Perri GM, Bräunig M, Gironimo GD, Putz M, Tarallo A, Wittstock V (2016) Numerical modelling and analysis of the influence of an air cooling system on a milling machine in virtual environment. Int J Adv Manuf Technol 86(5–8):1853–1864

    Article  Google Scholar 

  8. Norouzifard V, Hamedi M (2014) A three-dimensional heat conduction inverse procedure to investigate tool–chip thermal interaction in machining process. Int J Adv Manuf Technol 74(9–12):1637–1648

    Article  Google Scholar 

  9. Kottenstette JP (1968) Measuring tool-chip interface temperatures. J Eng Indus ASME 108:101–104

    Article  Google Scholar 

  10. Sullivan DO, Cotterell M (2001) Temperature measurement in single point turning. J Mater Proces Technol 118:301–308

    Article  Google Scholar 

  11. Feng Y, Zheng L, Wang M, Wang B, Hou J, Yuan T (2015) Research on cutting temperature of work-piece in milling process based on WPSO. Int J Adv Manuf Technol 79(1–4):427–435

    Article  Google Scholar 

  12. Lavisse B, Lefebvre A, Sinot O, Henrion E, Lemarié S, Tidu A (2017) Grinding heat flux distribution by an inverse heat transfer method with a foil/workpiece thermocouple under oil lubrication. Int J Adv Manuf Technol 92(5–8):2867–2880

    Article  Google Scholar 

  13. Wang D, Ge P, Sun S, Jiang J, Liu X (2017) Investigation on the heat source profile on the finished surface in grinding based on the inverse heat transfer analysis. Int J Adv Manuf Technol 92(1–4):1201–1216

    Article  Google Scholar 

  14. Oezkaya E, Beer N, Biermann D (2016) Experimental studies and CFD simulation of the internal cooling conditions when drilling Inconel 718. Int J Mach Tool Manu 108:52–65

    Article  Google Scholar 

  15. Woon KS, Tnay GL, Rahman M, Wan S, Yeo SH (2017) A computational fluid dynamics (CFD) model for effective coolant application in deep hole gundrilling. Int J Mach Tool Manu 113:10–18

    Article  Google Scholar 

  16. Abukhshim NA, Mativenga PT, Sheikh MA (2006) Heat generation and temperature prediction in metal cutting: a review and implications for high speed machining. Int J Mach Tool Manu 46:782–800

    Article  Google Scholar 

  17. Su H, Lu L, Liang Y, Zhang Q, Sun Y (2014) Thermal analysis of the hydrostatic spindle system by the finite volume element method. Int J Adv Manuf Technol 71:1949–1959

    Article  Google Scholar 

  18. Karpat Y, Özel T (2006) Predictive analytical and thermal modeling of orthogonal cutting process—part I: predictions of tool forces, stresses, and temperature distributions. J Manuf Sci Eng 128:435–444

    Article  Google Scholar 

  19. Kundrák J, Gyáni K, Tolvaj B, Pálmai Z, Tóth R, Markopoulos AP (2017) Thermo technical modelling of hard turning: a computational fluid dynamics approach. Simul Model Pract Theory 70:52–64

    Article  Google Scholar 

  20. Tnay GL, Wan S, Woon KS, Yeo SH (2016) The effects of dub-off angle on chip evacuation in single-lip deep hole gun drilling. Int J Mach Tool Manu 108:66–73

    Article  Google Scholar 

  21. Li T, Wu T, Ding X, Chen H, Wang L (2017) Design of an internally cooled turning tool based on topology optimization and CFD simulation. Int J Adv Manuf Technol 91:1327–1337

    Article  Google Scholar 

  22. Obikawa T, Yamaguchi M (2013) Computational fluid dynamic analysis of coolant flow in turning. 14th CIRP conference on modeling of machining operations. Procedia CIRP 8:271–275

    Article  Google Scholar 

  23. Pervaiz S, Deiab I, Wahba E, Rashid A, Nicolescu CM (2015) A novel numerical modeling approach to determine the temperature distribution in the cutting tool using conjugate heat transfer (CHT) analysis. Int J Adv Manuf Technol 80:1039–1047

    Article  Google Scholar 

  24. Pervaiz S, Deiab I, Wahba E, Rashid A, Nicolescu M (2018) A numerical and experimental study to investigate convective heat transfer and associated cutting temperature distribution in single point turning. Int J Adv Manuf Technol 94(1–4):897–910

    Article  Google Scholar 

  25. Setti D, Sinha MK, Ghosh S, Rao PV (2015) Performance evaluation of Ti–6Al–4V grinding using chip formation and coefficient of friction under the influence of nanofluids. Int J Mach Tool Manu 88:237–248

    Article  Google Scholar 

  26. Singh RK, Sharma AK, Dixit AR, Tiwari AK, Pramanik A, Mandal A (2017) Performance evaluation of alumina-graphene hybrid nano-cutting fluid in hard turning. J Clean Prod 162:830–845. https://doi.org/10.1016/j.jclepro.2017.06.104

    Article  Google Scholar 

  27. 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(9–12):3211–3223

    Article  Google Scholar 

  28. Sharma AK, Tiwari AK, Dixit AR (2016) Characterization of TiO2, SiO2 and Al2O3 nanoparticle based cutting fluids. Mater Today Proceed 3:1890–1898

    Article  Google Scholar 

  29. Sharma AK, Tiwari AK, Dixit AR, Singh RK, Singh M (2018) Novel uses of alumina/graphene hybrid nanoparticle additives for improved tribological properties of lubricant in turning operation. Tribol Int 119:99–111

    Article  Google Scholar 

  30. Tiwari AK, Ghosh P, Sarkar J, Dahiya H, Parekh J (2014) Numerical investigation of heat transfer and fluid flow in plate heat exchanger using nanofluids. Int J Therm Sci 85:93–103

    Article  Google Scholar 

  31. Murshed SMS, Leong KC, Yang C (2008) Investigation of thermal conductivity and viscosity of nano fluids. Int J Therm Sci 47:560–568

    Article  Google Scholar 

  32. Sai SS, Kumar KM, Ghosh A (2015) Assessment of spray quality from an external mix nozzle and its impact on SQL grinding performance. Int J Mach Tool Manu 89:132–141

    Article  Google Scholar 

  33. Najiha MS, Rahman MM (2014) A computational fluid dynamics analysis of single and three nozzles minimum quantity lubricant flow for milling. IJAME 10:1891–1900

    Google Scholar 

  34. Gopal AV, Rao PV (2003) Selection of optimum conditions for maximum material removal rate with surface finish and damage as constraints in SiC grinding. Int J Mach Tool Manu 43:1327–1336

    Article  Google Scholar 

  35. http://www.matweb.com/search/DataSheet.aspx?MatGUID=d4fe3bb60c91416fb508f7ae067f094e (Access date: 29/08/2017)

  36. Tu J, Yeoh GH, Liu C (2008) Computational fluid dynamics: a practical approach. Elsevier Limited, Burlington, MA, US

    MATH  Google Scholar 

  37. Vine LD (2005) A report on the development of a set of tools to visualize CFD data using texture based visualization algorithms. In: 2005–2006 QPSF Summer Internship Project. Queensland University of Technology, Brisbane, Australia

    Google Scholar 

  38. Anderson JD, Degroote J, Degrez G, Dick E, Grundmann R, Vierendeels J (2009) Computational fluid dynamics: an introduction, 3rd edn. Springer-Verlag, Berlin Heidelberg

    Google Scholar 

  39. Sayma A (2009) Computational fluid dynamics. Ventus Publishing Aps, ISBN: 978-87-7681-430-4

  40. ANSYS, Inc (2009) ANSYS CFX solver theory guide. USA

  41. Arrazola PJ, Özel T (2010) Investigations on the effects of friction modeling in finite element simulation of machining. Int J Mech Sci 52:31–42

    Article  Google Scholar 

  42. Hoyne AC, Nath C, Kapoor SG (2013) Characterization of fluid film produced by an atomization-based cutting fluid spray system during machining. J Manuf Sci Eng 135(1–8):051006

    Article  Google Scholar 

  43. Heywood JB (1988) Internal combustion engine fundamentals. McGraw Hill Inc., USA

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge the support from Dr. Sandipan Roy of SRM Institute of Science and Technology Kattankulathur and Rabesh Kumar Singh of IIT (ISM) Dhanbad for his valuable suggestions for preparing the paper in its present form.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India

    Anuj Kumar Sharma

  2. Department of Mechanical Engineering, Institute of Engineering &Technology, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, 226021, India

    Arun Kumar Tiwari

  3. Department of Mechanical Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004, India

    Amit Rai Dixit

Authors
  1. Anuj Kumar Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arun Kumar Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amit Rai Dixit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anuj Kumar Sharma.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, A.K., Tiwari, A.K. & Dixit, A.R. Prediction of temperature distribution over cutting tool with alumina-MWCNT hybrid nanofluid using computational fluid dynamics (CFD) analysis. Int J Adv Manuf Technol 97, 427–439 (2018). https://doi.org/10.1007/s00170-018-1946-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-018-1946-5

Keywords

Search

Navigation