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ANFIS Modeling for Higher Machining Performance of Aluminium Tempered Grade 6061 Using Novel SIO2 Nanolubrication

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Transactions on Engineering Technologies

Abstract

Aluminum Al6061-T6 is a common alloy which is used for many purposes due to its superior mechanical properties such as hardness and weldability. Implementation of CNC milling machine in processing Al6061-T6 would be a good process especially in producing varieties shape of products to adapt with different applications. However, the demand for high quality focuses attention on product quality, especially the roughness of the machined surface, because of its effect on product appearance, function, and reliability. Introducing correct lubrication in the machining zone could improve the product quality. Due to complexity and uncertainty of the machining processes, soft computing techniques are being preferred for predicting the performance of the machining processes and. In this chapter, a new application of ANFIS to predict the performance of machining AL-6061-T6 using SiO2 nanolubricant is presented. The parameters of SiO2 nanolubrication include SiO2 concentration, nozzle angle and air carrier pressure are investigated for the lowest cutting force, cutting temperature and surface roughness with the 96.195, 98.27 and 91.37 % accuracy obtained between experimental and numerical measurement, respectively.

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References

  1. Sayuti M, Sarhan AAD, Hamdi M (2014) Performance predictions of using novel SiO2 nanolubrication in end milling of aerospace AL6061-T6—ANFIS modeling approach. In: Proceedings of the World Congress on Engineering and Computer Science 2014, WCECS 2014, 22–24 Oct 2014. Lecture notes in engineering and computer science, San Francisco, USA, pp 985–991

    Google Scholar 

  2. Li Y, Liang SY (1999) Cutting force analysis in transient state milling processes. Int J Adv Manuf Technol 15(11):785–790

    Article  MathSciNet  Google Scholar 

  3. Reddy NSK, Rao PV (2005) A genetic algorithmic approach for optimization of surface roughness prediction model in dry milling. Int J Mach Sci Technol 9(1):63–84

    Article  MathSciNet  Google Scholar 

  4. Lee J, Cho S, Hwang Y, Cho H-J, Lee C, Choi Y, Ku B-C, Lee H, Lee B, Kim D, Kim SH (2009) Application of fullerene-added nano-oil for lubrication enhancement in friction surfaces. Tribol Int 42:440–447

    Article  Google Scholar 

  5. Tao X, Jiazheng Z, Kang X (1996) The ball-bearing effect of diamond nanoparticles as an oil additive. J Phys D Appl Phys 29:2932–2937

    Article  Google Scholar 

  6. Lathkar GS, Bas USK (2000) Clean metal cutting process using solid lubricants. In: Proceeding of the 19th AIMTDR conference, Narosa Publishing House IIT Madras, pp 15–31

    Google Scholar 

  7. Reddy NSK, Nouari M, Yang M (2010) Development of electrostatic solid lubrication system for improvement in machining process performance. Int J Mach Tools Manuf 50:789–797

    Article  Google Scholar 

  8. Ezugwu EO, Bonney J (2005) Finish machining of nickel-base inconel 718 alloy with coated carbide tool under conventional and high-pressure coolant supplies. Tribol Trans 48(1):76–81

    Article  Google Scholar 

  9. Sharma VS, Dogra M, Suri NM (2009) Cooling techniques for improved productivity in turning. Int J Mach Tools Manuf 49(6):435–453

    Article  Google Scholar 

  10. Nkeki CI, Nwozo CR (2013) Optimal investment under inflation protection and optimal portfolios with stochastic cash flows strategy. IAENG Int J Appl Math 43(2):54–63

    Google Scholar 

  11. Sreejith PS, Ngoi BKA (2000) Dry machining: machining of the future. J Mater Process Technol 101(1–3):287–291

    Article  Google Scholar 

  12. Masuko M, Aoki S, Suzuki A (2005) Influence of lubricant additive and surface texture on the sliding friction characteristics of steel under varying speeds ranging from ultralow to moderate. Tribol Trans 48(3):289–298

    Google Scholar 

  13. Klocke F, Eisenblätter G (1997) Dry cutting. CIRP Ann Manuf Technol 46(2):519–526

    Article  Google Scholar 

  14. He S (1982) Solid lubrication materials for high temperature a review. Tribol Int 15:303–314

    Article  Google Scholar 

  15. Zalnezhad E, Sarhan AAD, Hamdi M (2013) Adhesion strength predicting of Cr/CrN coated Al7075 using fuzzy logic system for fretting fatigue life enhancement. In: Proceedings of the world congress on engineering and computer science 2013, WCECS 2013, 23–25 Oct 2013. Lecture notes in engineering and computer science, San Francisco, USA, pp 589–595

    Google Scholar 

  16. Miaosun S (2000) Solid lubrication materials. China Chemistry Press, Beijing

    Google Scholar 

  17. Nakamura T, Tanaka S, Hayakawa K, Fukai Y (2000) A study of the lubrication behavior of solid lubricants in the upsetting process. J Tribol 122:803–808

    Article  Google Scholar 

  18. Deshmukh SD, Basu SK (2006) Significance of solid lubricants in metal cutting, in 22nd AIMTDR

    Google Scholar 

  19. Alberts M, Kalaitzidou K, Melkote S (2009) An investigation of graphite nanoplatelets as lubricant in grinding. Int J Mach Tools Manuf 49:966–970

    Article  Google Scholar 

  20. Peng DX, Kang Y, Hwang RM, Shyr SS, Chang YP (2009) Tribological properties of diamond and SiO2 nanoparticles added in paraffin. Tribol Int 42:911–917

    Article  Google Scholar 

  21. Keblinski P, Phillpot SR, Choi SUS, Eastman JA (2002) Mechanisms of heat flow in suspensions of nano-sized particles (nanofluids). Int J Heat Mass Transf 45:855–863

    Article  MATH  Google Scholar 

  22. Shamshirband S, Kalantari S, Bakhshandeh Z (2010) Designing a smart multi-agent system based on fuzzy logic to improve the gas consumption pattern. Sci Res Essays 5(6):592–605

    Google Scholar 

  23. Ager P, Oseni MI, Yanshio ET (2014) Fuzzy system algorithm on processing methods rheological properties of oil lubricants. In: Proceedings of the world congress on engineering and computer science 2014, WCECS 2014, 22–24 Oct 2014. Lecture notes in engineering and computer science, San Francisco, USA, pp 992–997

    Google Scholar 

  24. Tootoonchy H, Hashemi HH (2013) Fuzzy logic modeling and controller design for a fluidized catalytic cracking unit. In: Proceedings of the world congress on engineering and computer science 2013, WCECS 2013, 23–25 Oct 2013. Lecture notes in engineering and computer science, San Francisco, USA, pp 982–987

    Google Scholar 

  25. Kasabov NK (1997) Foundations of neural networks, fuzzy systems, and knowledge engineering, vol 33. Computers & mathematics with applications, vol 7. MIT Press, Cambridge. doi:http://dx.doi.org/10.1016/S0898–1221(97)84600-7

  26. Tu-Chieh H, Yaw-Terng S (2006) A method for reducing tool wear in a polishing process. Int J Mach Tools Manuf 46:413–423

    Article  Google Scholar 

  27. Reza Yousefi YI (2000) A study on ultra—high-speed cutting of aluminium alloy: formation of welded metal on the secondary cutting edge of the tool and its effects on the quality of finished surface. J Int Soc Precisi Eng Nanotechnol 24:371–376

    Google Scholar 

  28. Lin YC, So H (2004) Limitations on use of ZDDP as an antiwear additive in boundary lubrication. Tribol Int 37:25–33

    Article  Google Scholar 

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Acknowledgments

This work was supported by the high impact research (HIR) grant number: HIR-MOHE-16001-00-D000001 from the Ministry of Higher Education, Malaysia.

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Authors and Affiliations

  1. Centre of Advanced Manufacturing and Material Processing, Department of Engineering Design and Manufacturing, Engineering Faculty, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Mohd Sayuti Ab Karim & Ahmed Aly Diaa Mohammed Sarhan

Authors
  1. Mohd Sayuti Ab Karim
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  2. Ahmed Aly Diaa Mohammed Sarhan
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Corresponding author

Correspondence to Ahmed Aly Diaa Mohammed Sarhan .

Editor information

Editors and Affiliations

  1. Computer & Communication, Catholic University of DaeGu Engineering College, DaeGu, Korea, Republic of (South Korea)

    Haeng Kon Kim

  2. College of Engineering, California State Polytechnic University, Pomona, California, USA

    Mahyar A. Amouzegar

  3. IAENG Secretariat, International Association of Engineers, Hong Kong, Hong Kong SAR

    Sio-long Ao

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Karim, M.S.A., Sarhan, A.A.D.M. (2015). ANFIS Modeling for Higher Machining Performance of Aluminium Tempered Grade 6061 Using Novel SIO2 Nanolubrication. In: Kim, H., Amouzegar, M., Ao, Sl. (eds) Transactions on Engineering Technologies. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7236-5_39

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  • DOI: https://doi.org/10.1007/978-94-017-7236-5_39

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-7235-8

  • Online ISBN: 978-94-017-7236-5

  • eBook Packages: EngineeringEngineering (R0)

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