Prediction of tangential force and maximum temperature generation at the tool tip using ANFIS model during CNC turning operations for an intricate shape

Prediction of tangential force and maximum temperature generation at the tool tip using ANFIS model during CNC turning operations for an intricate shape

Goutam PaulPritam Patra 

Department of Mechanical Engineering, MCKV Institute of Engineering, 243 G.T. Road Liluah (N), Howrah, 711204, India

Corresponding Author Email: 
goutamju04@gmail.com
Page: 
106-112
|
DOI: 
https://doi.org/10.18280/mmep.040208
Received: 
|
Accepted: 
|
Published: 
30 June 2017
| Citation

OPEN ACCESS

Abstract: 

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.

Keywords: 

CNC Turning, Tangential Force, Tool Tip Temperature, L8 Orthogonal Array.

1. Introduction
2.Experimental Procedure and Measurements
3. Results and Discussion
4.Prediction of Optimum TMAX by ANFIS Approach
5. Conclusions
Nomenclature
  References

[1] Weiser C.R., Vijayraghavan A., Dornfeld D. (2008).Metrics for sustainable manufacturing, Proceeding ofthe 2008 International Manufacturing Science andEngineering Conference MSEC2008, Illinois, USA.

[2] 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

[3] 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

[4] 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

[5] 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

[6] 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

[7] 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

[8] 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

[9] 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

[10] Madhav S.P. (1989). Matrix experiments usingorthogonal arrays, quality engineering using robustdesign, Prentice Hall, Englewood Cliffs, New Jersey,pp. 41-59.

[11] Kumar S. (2015). Soft computing goes hybrid, neuralnetworks a class room approach, Mc GrawHillEducation, India, 2nd Edition, pp. 610-614.

[12] 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