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

  • R. R. ChakuleEmail author
  • S. S. Chaudhari
  • P. S. Talmale
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 949)


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.


Grinding Jaya algorithm Modeling NanoMQL Optimization 



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.


  1. 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). Scholar
  2. 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). Scholar
  3. 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). Scholar
  4. 4.
    Brinksmeier, E., Meyer, D., Huesmann-Cordes, A.G., Herrmann, C.: Metalworking fluids-mechanisms and performance. J. CIRP Ann. 64(2), 605–628 (2015). Scholar
  5. 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). Scholar
  6. 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). Scholar
  7. 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). Scholar
  8. 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). Scholar
  9. 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). Scholar
  10. 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). Scholar
  11. 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). Scholar
  12. 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). Scholar
  13. 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). Scholar
  14. 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). Scholar
  15. 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). Scholar
  16. 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). Scholar
  17. 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). Scholar
  18. 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). Scholar
  19. 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). Scholar
  20. 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). Scholar
  21. 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)CrossRefGoogle Scholar
  22. 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). Scholar
  23. 23.
    Rao, R.V., Kalyankar, V.D.: Parameter optimization of machining processes using a new optimization algorithm. Mater. Manuf. Proc. 27, 978–985 (2012). Scholar
  24. 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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • R. R. Chakule
    • 1
    Email author
  • S. S. Chaudhari
    • 1
  • P. S. Talmale
    • 2
  1. 1.Yeshwantrao Chavan College of EngineeringNagpurIndia
  2. 2.Late G. N. Sapkal College of EngineeringNashikIndia

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