Comparison of metal removal rate and surface roughness optimization for AISI 316L using sunflower oil minimum quantity lubrication and dry turning processes

Authors

  • S.Y. Martowibowo Faculty of Manufacturing Technology, Universitas Jenderal Achmad Yani, Jalan Terusan Jenderal Gatot Subroto, Bandung, 40284, Indonesia. Phone: +62227312741; Fax.: +62227309433
  • I.J. Ariza Carbay Services Indonesia, Menara Citicon 10th Floor, Jalan Letnan Jenderal S. Parman Kav. 72, Slipi, Palmerah, Jakarta Barat, 11410, Indonesia.
  • B.K. Damanik Alliance Manchester Business School, University of Manchester, Booth Street West, Manchester, M15 6PB, United Kingdom.

DOI:

https://doi.org/10.15282/jmes.16.3.2022.01.0710

Keywords:

Material removal rate, MQL, Minimum quantity lubrication, Optimization, Sunflower oil, Surface roughness

Abstract

The turning process is one of the significant machining processes widely applied in manufacturing industries. The study compared the minimum quantity lubrication turning process using sunflower oil lubrication and the dry turning process for AISI 316L material. In this study, a genetic algorithm was used to optimize material removal rate and surface roughness. Tool nose radius, cutting speeds, feed rates, and depth of cut was chosen as process parameters. The result of the process was a fitness function, which reflects the correlation between process parameters and material removal rate or surface roughness. The genetic algorithm uses the fitness function to yield optimum process parameters with the highest material removal rate and lowest surface roughness in a separate optimization process. The optimization method developed in the study can be applied to predict optimum material removal rate and surface roughness values for minimum quantity lubrication or dry turning process. The study concluded that the minimum quantity lubrication technique could yield favorable machining results with a higher material removal rate and lower surface roughness.

References

K. Palanikumar, L. Karunamoorthy, and R. Karthikeyan, “Assessment of factors influencing surface roughness on the machining of glass fiber-reinforced polymer composites,” Mater. Des., vol. 27, no. 10, pp. 862–871, 2006.

C. Ahilan, S. Kumanan, N. Sivakumaran, and J. Edwin Raja Dhas, “Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools,” Appl. Soft Comput. J., vol. 13, no. 3, pp. 1543–1551, 2013.

S. Y. Martowibowo and B. K. Damanik, “Optimization of material removal rate and surface roughness of AISI 316L under dry turning process using genetic algorithm,” Manuf. Technol., vol. 21, no. 3, pp. 373–380, 2021.

G. M. Krolczyk, P. Nieslony, R. W. Maruda, and S. Wojciechowski, “Dry cutting effect in turning of a duplex stainless steel as a key factor in clean production,” J. Clean Prod., vol. 142, pp. 3343–3354, Jan. 2017.

E. A. Rahim and H. Sasahara, “A study of the effect of palm oil as MQL lubricant on high speed drilling of titanium alloys,” Tribol. Int., vol. 44, no. 3, pp. 309–317, 2011.

Ş. Karabulut, U. Gökmen, and H. Çinici, “Optimization of machining conditions for surface quality in milling AA7039-based metal matrix composites,” Arab. J. Sci. Eng., vol. 43, no. 3, pp. 1071–1082, 2017.

S. A. Lawal, I. A. Choudhury, and Y. Nukman, “A critical assessment of lubrication techniques in machining processes: A case for minimum quantity lubrication using vegetable oil-based lubricant,” J. Clean. Prod., vol. 41, pp. 210–221, 2013.

A. Panday, G. S. Goindi, and N. Singh, “Evaluation of effect of oil viscosity in MQL turning of aluminium 6061,” in Materials Today: Proceedings, vol. 48, pp. 1740–1747, 2021.

B. Arsene, C. Gheorghe, F. A. Sarbu, M. Barbu, L. I. Cioca, and G. Calefariu, “MQL-assisted hard turning of AISI D2 steel with corn oil: Analysis of surface roughness, tool wear, and manufacturing costs,” Metals, vol. 11, no. 12, pp. 1–22, Dec. 2021.

Y. Touggui, A. Uysal, U. Emiroglu, S. Belhadi, and M. Temmar, “Evaluation of MQL performances using various nanofluids in turning of AISI 304 stainless steel,” Int. J. Adv. Manuf. Technol., vol. 115, no. 11–12, pp. 3983–3997, 2021.

J. R. Davis, Handbook of Materials for Medical Devices. Ohio: ASM International, 2003.

J. A. Disegi and L. Eschbach, “Stainless steel in bone surgery,” Injury, vol. 31, no. 4, pp. 2–6, 2000.

I. Asiltürk and H. Akkuş, “Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method,” Measurement, vol. 44, no. 9, pp. 1697–1704, Nov. 2011.

N. J. Fox and G. W. Stachowiak, “Vegetable oil-based lubricants-A review of oxidation,” Tribol. Int., vol. 40, no. 7, pp. 1035–1046, Jul. 2007.

E. E. G. Rojas, J. S. R. Coimbra, and J. Telis-Romero, “Thermophysical properties of cotton, canola, sunflower and soybean oils as a function of temperature,” Int. J. Food Prop., vol. 16, no. 7, pp. 1620–1629, Oct. 2013.

K. P. E. Chong and S. H. Zak, An Introduction to Optimization, 2nd ed. New York: John Wiley and Sons, 2001.

A. A. Krimpenis, N. A. Fountas, I. Ntalianis, and N. M. Vaxevanidis, “CNC micromilling properties and optimization using genetic algorithms,” Int. J. Adv. Manuf. Technol., vol. 70, no. 1–4, pp. 157–171, 2014.

S. Y. Martowibowo and A. Kaswadi, “Optimization and simulation of plastic injection process using genetic algorithm and moldflow,” Chin. J. Mech. Eng. (English Edition), vol. 30, no. 2, pp. 398–406, 2017.

B. Senthilkumar, T. Kannan, and R. Madesh, “Optimization of flux-cored arc welding process parameters by using genetic algorithm,” Int. J. Adv. Manuf. Technol., vol. 93, no. 1–4, pp. 35–41, 2017.

D. C. Montgomery, Design and Analysis of Experiments. New York: John Wiley and Sons, 2008.

J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press, 2008.

D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Massachusetts: Addison-Wesley, 1989.

Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs. Berlin: Springer Berlin Heidelberg, 1992.

M. Hadad and B. Sadeghi, “Minimum quantity lubrication-MQL turning of AISI 4140 steel alloy,” J. Clean Prod,, vol. 54, pp. 332–343, Sep. 2013.

O. Y. Teräskontorri, “Mitsubishi materials technical data,” 2019. (accessed Jul. 19, 2019).

A. Hazra, “Using the confidence interval confidently,” J. Thorac. Dis., vol. 9, no. 10, pp. 4125–4130, Oct. 2017.

A. Mishra and A. Shukla, “Analysis of the effect of elite count on the behavior of genetic algorithms: A perspective,” in Proc. IEEE 7th International Advance Computing Conference (IACC), 5-7 January 2017, Hyderabad, India; pp. 835-840, 2017.

S. Imade, R. Mori, Y. Uchio, and S. Furuya, “Effect of implant surface roughness on bone fixation: The differences between bone and metal pegs,” J. Orthop. Sci., vol. 14, no. 5, pp. 652–657, 2009.

S. Masoudi, M. J. Esfahani, F. Jafarian, and S. A. Mirsoleimani, “Comparison the effect of MQL, wet and dry turning on surface topography, cylindricity tolerance and sustainability,” Int. J. Precis. Eng. Manuf. - Green Technol., 2019.

A. T. Abbas, S. Anwar, E. Abdelnasser, M. Luqman, J. E. Abu Qudeiri, and A. Elkaseer, “Effect of different cooling strategies on surface quality and power consumption in finishing end milling of stainless steel 316,” Materials, vol. 14, no. 4, pp. 1–15, Feb. 2021.

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Published

2022-09-28

How to Cite

[1]
S. Y. MARTOWIBOWO, I.J. Ariza, and B.K. Damanik, “Comparison of metal removal rate and surface roughness optimization for AISI 316L using sunflower oil minimum quantity lubrication and dry turning processes”, J. Mech. Eng. Sci., vol. 16, no. 3, pp. 8976–8986, Sep. 2022.