In spite of vigorous research on advanced material processing and advanced manufacturing processes, the conventional processes are essential in building a country’s economy till date. The disadvantage of this process is that machining industry is the most energy consuming and waste spawning industry. The main question is how the energy can be utilized in proper way such that energy consumption will be on lower side and will provide high productivity. The consumption is more whenever we concern the intricate shape of the job. The two factors that are important for the measurement of energy consumption during CNC turning of an intricate shape are tangential force and tool tip temperature generation. In the current research, experiments were conducted based on DOE by developing experiments with three factors i.e. cutting speed at four levels and feed and depth of cut at two levels corresponding to the L8 experimental array to measure maximum tangential force and temperature generation at the tool tip during CNC turning operation. Prediction of maximum tangential force and tool tip temperature during CNC turning operation has been pursued with the help of Taguchi approach. At the end, a verification test was conducted to illustrate the effectiveness of this approach.
CNC Turning, Tangential Force, Tool Tip Temperature, L8 Orthogonal Array.
 Weiser C.R., Vijayraghavan A., Dornfeld D. (2008).Metrics for sustainable manufacturing, Proceeding ofthe 2008 International Manufacturing Science andEngineering Conference MSEC2008, Illinois, USA.
 Jiang Z., Zhang H., Yan W., Zhoiu M., Li G. (2012).A method for evaluating environmental performanceof machining system, Int. Journal of ComputerIntegrated Manufacturing, Vol. 25, No. 6, pp. 488-495.DOI: 10.1080/0951192X.2011.638323
 Bhanot N., Venkateswara R.P., Deshmukh S.G.(2016). An assessment of sustainability for turningprocess in an automobile firm, Procedia CIRP, Vol.48, pp. 538 – 543. DOI: 10.1016/j.procir.2016.03.024
 Smith L., Ball P. (2012). Steps towards sustainablemanufacturing through modelling material, energy andwaste flows, International Journal of ProductionEconomics, Vol. 140, No. 1, pp. 227–238. DOI:10.1016/j.ijpe.2012.01.036
 Muthukrishnan N., Davim J.P. (2009). Optimization ofmachining parameters of Al/Sic-MMC with ANOVAand ANN analysis, Journal of Material ProcessingTechnology, Vol. 209, pp. 225–232. DOI:10.1016/j.jmatprotec.2008.01.041
 Rao C.J., Sreeamulu D., Mathew A.T. (2014).Analysis of tool life during turning operation bydetermining optimal process parameters, ProcediaEngineering, Vol. 97, pp. 241–250. DOI:10.1016/j.proeng.2014.12.247
 Sadílek M., Dubsk´y J., Sadílková Z., Poruba Z.(2016). Cutting forces during turning depth withvariable of cut, Perspectives in Science, Vol. 7, pp.357-363. DOI: 10.1016/j.pisc.2015.11.055
 Wang T.C., Hu X.X., Zhong S.S., Zhang Y.J. (2016).Research on extension knowledge base system forscheme design of mechanical product, MathematicalModelling of Engineering Problems, Vol. 3, No. 3, pp.141-145. DOI: 10.18280/mmep.030305
 Rajemi M.F., Mativenga P.T., Aramcharoen A. (2010).Sustainable machining. selection of optimum turningconditions based on minimum energy considerations,Journal of Cleaner Production, Vol. 18, pp. 1059-1065. DOI: 10.1016/j.jclepro.2010.01.025
 Madhav S.P. (1989). Matrix experiments usingorthogonal arrays, quality engineering using robustdesign, Prentice Hall, Englewood Cliffs, New Jersey,pp. 41-59.
 Kumar S. (2015). Soft computing goes hybrid, neuralnetworks a class room approach, Mc GrawHillEducation, India, 2nd Edition, pp. 610-614.
 Xia J., Xiao L., Wan L.P. (2016). Application ofrandom-fuzzy probability statistics method,Mathematical Modelling of Engineering Problems,Vol. 3, No. 1, pp. 19-24. DOI:10.18280/mmep.030103