Skip to main content
SpringerLink
Log in

Optimization of Nanofluid Minimum Quantity Lubrication (NanoMQL) Technique for Grinding Performance Using Jaya Algorithm

  • Conference paper
  • First Online:
Advanced Engineering Optimization Through Intelligent Techniques

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 949))

Abstract

The machining performance and the surface quality are the basic requirements of industries. At the same time, the machining process should be clean, economical and eco-friendly to sustain in globalized competitive environments. The wet technique consumes large amount of cutting fluid to minimize temperature and friction generates during grinding process. The recent NanoMQL technique of cutting fluid can substitute over wet grinding due to better cooling and lubrication obtained using nanofluid and better penetration using compressed air at contact zone. The experiments were conducted as per the design matrix using response surface methodology (RSM). The modeling and multi-objective optimization of NanoMQL process are carried out for minimizing the surface roughness and cutting force using Jaya algorithm. The study demonstrates the validity of regression models by comparing the experimental test results conducted at optimized parameters value obtained from Jaya algorithm with predicted values and is observed the close.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Tawakoli, T., Hadad, M.J., Sadeghi, M.H., Daneshi, A., Stockert, S., Rasifard, A.: An experimental investigation of the effects of workpiece and grinding parameters on minimum quantity lubrication-MQL grinding. Int. J. of Machine Tools and Manufacture. 49 (12–13), 924–932 (2009). https://doi.org/10.1016/j.ijmachtools.2009.06.015

    Article  Google Scholar 

  2. Sinha, M.K., Madarkar, R., Ghosh, S., Rao, P.V.: Application of eco-friendly nanofluids during grinding of Inconel 718 through small quantity lubrication. J. Cleaner Prod. 141, 1359–1375 (2017). https://doi.org/10.1016/j.jclepro.2016.09.212

    Article  Google Scholar 

  3. Kalita, P., Malshe, A.P., Arun Kumar, S., Yoganath, V.G., Gurumurthy, T.: Study of specific energy and friction coefficient in minimum quantity lubrication grinding using oil-based nanolubricants. J. Manuf. Proc. 14(2), 160–166 (2012). https://doi.org/10.1016/j.jmapro.2012.01.001

    Article  Google Scholar 

  4. Brinksmeier, E., Meyer, D., Huesmann-Cordes, A.G., Herrmann, C.: Metalworking fluids-mechanisms and performance. J. CIRP Ann. 64(2), 605–628 (2015). https://doi.org/10.1016/j.cirp.2015.05.003

    Article  Google Scholar 

  5. Kim, H.J., Seo, K.J., Kang, K.H., Kim, D.E.: Nano-lubrication: a review. Int. J. Precision Eng. Manuf. 17(6), 829–841 (2016). https://doi.org/10.1007/s12541-016-0102-0

    Article  Google Scholar 

  6. Chakule, R.R., Chaudhari, S.S., Talmale, P.S.: Evaluation of the effects of machining parameters on MQL based surface grinding process using response surface methodology. J. Mech. Sci. Technol. 31(8), 3907–3916 (2017). https://doi.org/10.1007/s12206-017-0736-6

    Article  Google Scholar 

  7. Tawakoli, T., Hadad, M.J., Sadeghi, M.H.: Influence of oil mist parameters on minimum quantity lubrication-MQL grinding process. Int. J. Mach. Tools Manuf. 50(6), 521–531 (2010). https://doi.org/10.1016/j.ijmachtools.2010.03.005

    Article  Google Scholar 

  8. Huang, X., Ren, Y., Jiang, W., He, Z., Deng, Z.: Investigation on grind-hardening annealed AISI5140 steel with minimal quantity lubrication. Int. J. Adv. Manuf. Technol. 89(1–4), 1069–1077 (2017). https://doi.org/10.1007/s00170-016-9142-y

    Article  Google Scholar 

  9. Lee, J., Yoon, Y.-J., Eaton, J.K., Goodson, K.E., Bai, S.J.: Analysis of oxide (Al2O3, CuO, and ZnO) and CNT nanoparticles disaggregation effect on the thermal conductivity and the viscosity of nanofluids. Int. J. Prec. Eng. Manuf. 15(4), 703–710 (2014). https://doi.org/10.1007/s12541-014-0390-1

    Article  Google Scholar 

  10. Mao, C., Zou, H., Zhou, X., Huang, Y., Gan, H., Zhou, Z.: Analysis of suspension stability for nanofluid applied in minimum quantity lubricant grinding. Int. J. Adv. Manuf. Technol. 71(9–12), 2073–2081 (2014). https://doi.org/10.1007/s00170-014-5642-9

    Article  Google Scholar 

  11. Chiam, H.W., Azmi, W.H., Usri, N.A., Mamat, R., Adam, N.M.: Thermal conductivity and viscosity of Al2O3 nanofluids for different based ratio of water and ethylene glycol mixture. Exp. Thermal Fluid Sci. 81, 420–429 (2017). https://doi.org/10.1016/j.expthermflusci.2016.09.013

    Article  Google Scholar 

  12. Zhang, D., Li, C., Jia, D., Zhang, Y., Zhang, X.: Specific grinding energy and surface roughness of nanoparticle jet minimum quantity lubrication in grinding. Chinese J. Aeronautics 28(2), 570–581 (2015). https://doi.org/10.1016/j.cja.2014.12.035

    Article  Google Scholar 

  13. Zhang, Y., Li, C., Jia, D., Li, B., Wang, Y., Yang, M., Hou, Y., Zhang, X.: Experimental study on the effect of nanoparticle concentration on the lubricating property of nanofluids for MQL grinding of Ni-based alloy. J. Mater. Proc. Technol. 232, 100–115 (2016). https://doi.org/10.1016/j.jmatprotec.2016.01.031

    Article  Google Scholar 

  14. Wang, Y., Li, C., Zhang, Y., Yang, M., Zhang, X., Zhang, N., Dai, J.: Experimental evaluation on tribological performance of the wheel/workpiece interface in minimum quantity lubrication grinding with different concentrations of Al2O3 nanofluids. J. Cleaner Production 142(4), 3571–3583 (2017). https://doi.org/10.1016/j.jclepro.2016.10.110

    Article  Google Scholar 

  15. Setti, D., Sinha, M.K., Ghosh, S., Rao, P.V.: Performance evaluation of Ti-6Al-4V grinding using chip formation and coefficient of friction under the influence of nanofluids. Int. J. Mach. Tools Manuf. 88, 237–248 (2015). https://doi.org/10.1016/j.ijmachtools.2014.10.005

    Article  Google Scholar 

  16. Mao, C., Zou, H., Huang, X., Zhang, J., Zhou, Z.: The influence of spraying parameters on grinding performance for nanofluid minimum quantity lubrication. Int. J. Adv. Manuf. Technol. 64(9–12), 1791–1799 (2013). https://doi.org/10.1007/s00170-012-4143-y

    Article  Google Scholar 

  17. Rao, R.V., Rai, D.P., Balic, J.: A new optimization algorithm for parameter optimization of nano-finishing processes. Int. J. Sci. Technol. 24(2), 868–875 (2017). https://doi.org/10.24200/sci.2017.4068

    Article  Google Scholar 

  18. Gupta, M.K., Sood, P.K., Sharma, V.S.: Optimization of machining parameters and cutting fluids during nano-fluid based minimum quantity lubrication turning of titanium alloy by using evolutionary techniques. J. Cleaner Production 135, 1276–1288 (2016). https://doi.org/10.1016/j.jclepro.2016.06.184

    Article  Google Scholar 

  19. Rao, R.V., Rai, D.P., Ramkumar, J., Balic, J.: A new multi-objective Jaya algorithm for optimization of modern machining processes. Adv. Prod. Eng. Manag. 11(4), 271–286 (2016). https://doi.org/10.14743/apem2016.4.226

    Article  Google Scholar 

  20. Rao, R.V.: Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7(1), 19–34 (2016). https://doi.org/10.5267/j.ijiec.2015.8.004

    Article  Google Scholar 

  21. Rao, R.V., More, K.C.: Optimal design and analysis of mechanical draft cooling tower using improved Jaya algorithm. Int. J. Refriger. 82, 312–324 (2017)

    Article  Google Scholar 

  22. Rao, R.V., Rai, D.P.: Optimization of submerged arc welding process parameters using quasi-oppositional based Jaya algorithm. J. Mech. Sci. Technol. 31(5), 2513–2522 (2017). https://doi.org/10.1007/s12206-017-0449-x

    Article  Google Scholar 

  23. Rao, R.V., Kalyankar, V.D.: Parameter optimization of machining processes using a new optimization algorithm. Mater. Manuf. Proc. 27, 978–985 (2012). https://doi.org/10.1080/10426914.2011.602792

    Article  Google Scholar 

  24. Rao, R.V., More, K.C., Taler, J., Oclon, P.: Dimensional optimization of a micro-channel heat sink using Jaya algorithm. Appl. Therm. Eng. 103, 572–582 (2016)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thanks the Director, Visvesvaraya National Institute of Technology (VNIT) for providing facility to characterize the nanofluid and Sameeksha industry for extending the experimental facility.

Author information

Authors and Affiliations

  1. Yeshwantrao Chavan College of Engineering, Nagpur, 441110, India

    R. R. Chakule & S. S. Chaudhari

  2. Late G. N. Sapkal College of Engineering, Nashik, 422213, India

    P. S. Talmale

Authors
  1. R. R. Chakule
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. S. Chaudhari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. S. Talmale
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. R. Chakule .

Editor information

Editors and Affiliations

  1. Sardar Vallabhbhai National Institute of Technology, Surat, Surat, Gujarat, India

    R. Venkata Rao

  2. Cracow University of Technology, Kraków, Poland

    Jan Taler

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chakule, R.R., Chaudhari, S.S., Talmale, P.S. (2020). Optimization of Nanofluid Minimum Quantity Lubrication (NanoMQL) Technique for Grinding Performance Using Jaya Algorithm. In: Venkata Rao, R., Taler, J. (eds) Advanced Engineering Optimization Through Intelligent Techniques. Advances in Intelligent Systems and Computing, vol 949. Springer, Singapore. https://doi.org/10.1007/978-981-13-8196-6_20

Download citation

Publish with us

Policies and ethics

Search

Navigation