Optimization of Nanofluid Minimum Quantity Lubrication (NanoMQL) Technique for Grinding Performance Using Jaya Algorithm
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.
KeywordsGrinding 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.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.2009.06.015CrossRefGoogle 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.2012.01.001CrossRefGoogle 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/s12541-014-0390-1CrossRefGoogle 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.2016.09.013CrossRefGoogle 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.2016.01.031CrossRefGoogle 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.2016.10.110CrossRefGoogle 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.2016.06.184CrossRefGoogle Scholar