Skip to main content

Advertisement

SpringerLink
Log in

Machinability evaluation of Al–4%Cu–7.5%SiC metal matrix composite by Taguchi–Grey relational analysis and NSGA-II

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

Machinability evaluation of Al–4%Cu–7.5%SiC metal matrix composite (MMC) prepared by powder metallurgy (P/M) process is presented. Specimens are prepared with 99.85% pure aluminum added with 4% copper and 7.5% silicon carbide particles by volume fraction. Scanning electron microscope image shows even distribution of particles in Al-MMC. Turning operation is performed by varying machining parameters and experiments are designed using Taguchi’s Design of Experiments (DoE), an L9 Orthogonal Array (OA) is chosen. A hybrid Taguchi–Grey relational approach is used to determine the optimum parameters over measured responses flank wear, roughness, and material removed. Analysis of Variance (ANOVA) result shows that the depth of cut is the influential parameter that contributes toward output responses. A metaheuristic evolutionary algorithm nondominated sorting genetic algorithm (NSGA-II) is applied to optimize the machining parameters for minimizing wear and maximizing metal removal. Experiments with optimum conditions show a better improvement in the output conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18

Similar content being viewed by others

References

  1. DeGarmo E P, Black J T and Ronald A Kohser 2008 Nonmetallic materials: Plastics, elastomers, ceramics, and composites, materials and processes in manufacturing. Tenth edition, Wiley, New York

    Google Scholar 

  2. Akhlaghi F, Lajevardi A and Maghanaki H M 2004 Effects of casting temperature on the microstructure and wear resistance of compocast A356/SiCp composites: A comparison between SS and SL routes. J. Mater. Process. Technol. 155–156: 1874–1880

    Article  Google Scholar 

  3. Ipek R 2005 Adhesive wear behaviour of B4C and SiC reinforced 4147 Al matrix composites (Al/B4C–Al/SiC). J. Mater. Process. Technol. 162–163: 71–75

    Article  Google Scholar 

  4. Monaghan J M and O’Reilly 1992 Machinability of aluminum alloy: Silicon carbide metal matrix composite. Process Adv. Mater. 2: 37–46

    Google Scholar 

  5. Kalpakjian S, Steven R Schmid and Musa H 2013 Manufacturing engineering and technology. Sixth edition, Prentice Hall: London

    Google Scholar 

  6. Pal S, Mitra R and Bhanuprasad V V 2008 Aging behaviour of Al–Cu–Mg alloy–SiC composites. Mater. Sci. Eng. A 480: 496–505

    Article  Google Scholar 

  7. Rajmohan T, Palanikumar K and Ranganathan S 2013 Evaluation of mechanical and wear properties of hybrid aluminium matrix composites. Trans. Nonferrous Metallic Soc. China 23: 2509–2517

    Article  Google Scholar 

  8. Shorowordi K M, Laoui T, Haseeb A S M A, Celis J P and Froyen L 2003 Microstructure and interface characteristics of B4C, SiC and Al2O3 reinforced Al matrix composites: A comparative study. J. Mater. Process. Technol. 142: 738–743

    Article  Google Scholar 

  9. Rajaram G, Kumaran S and Rao T S 2011 Influence of graphite and copper in mechanical properties of aluminum silicon alloy. Trans. Indian Inst. Metals 64(1–2): 53–56

    Article  Google Scholar 

  10. Senthilkumar N, Kalaichelvan K and Elangovan K 2012 Mechanical behavior of aluminum particulate epoxy composite-experimental study and numerical simulation. Int. J. Mech. Mater. Eng. 7(3): 214–221

    Google Scholar 

  11. Pawade R S and Suhas S Joshi 2011 Multi-objective optimization of surface roughness and cutting forces in high-speed turning of Inconel 718 using Taguchi grey relational analysis (TGRA). Int. J. Adv. Manuf. Technol. 56: 47–62

    Article  Google Scholar 

  12. Senthilkumar N and Tamizharasan T 2014 Effect of tool geometry in turning AISI 1045 steel: Experimental investigation and FEM analysis. Arabian J. Sci. Eng. 39(6): 4963–4975

    Article  Google Scholar 

  13. Yang S H and Natarajan U 2009 Multi-objective optimization of cutting parameters in turning process using differential evolution and nondominated sorting genetic algorithm-II approaches. Int. J. Adv. Manuf. Technol. 49: 773–784

    Article  Google Scholar 

  14. Srinivas N and Deb K 1994 Multi-objective optimization using nondominated sorting in genetic algorithms. Evolut. Comput. 2(3): 221–248

    Article  Google Scholar 

  15. Palanikumar K, Latha B, Senthilkumar V S and Karthikeyan R 2009 Multiple performance optimization in machining of GFRP composites by a PCD tool using nondominated sorting genetic algorithm (NSGA-II). Metals Mater. Int. 15(2): 249–258

    Article  Google Scholar 

  16. Ranjith K Roy 2001 Design of experiments using the Taguchi approach: 16 steps to product and process improvement. Wiley-Interscience Publication: New York

    Google Scholar 

  17. Raviraj Shetty, Raghuvir B Pai, Shrikanth S Rao and Rajesh Nayak 2009 Taguchi’s technique in machining of metal matrix composites. J. Brazilian Soc. Mech. Sci. Eng. 31: 12–20

    Article  Google Scholar 

  18. Ross P J 1996 Taguchi techniques for quality engineering: Loss function, orthogonal experiments, parameter, and tolerance design. Second edition, McGraw-Hill, NewYork

    Google Scholar 

  19. Senthilkumar N, Ganapathy T and Tamizharasan T 2014a Optimization of machining and geometrical parameters in turning process using Taguchi method. Australian J. Mech. Eng. 12(2): 233–246

    Article  Google Scholar 

  20. Ramanujam R, Muthukrishnan N and Raju R 2011 Optimization of cutting parameters for turning Al–SiC(10p) MMC using ANOVA and Grey relational analysis. Int. J. Precision Eng. Manuf. 12: 651–656

    Article  Google Scholar 

  21. Ranganathan S and Senthilvelan T 2011 Multi-response optimization of machining parameters in hot turning using Grey analysis. Int. J. Adv. Manuf. Technol. 56: 455–462

    Article  Google Scholar 

  22. Wang M Y and Lan T S 2008 Parametric optimization on multi-objective precision turning using Grey relational analysis. Inform. Technol. J. 7: 1072–1076

    Article  Google Scholar 

  23. Gopalsamy B M, Mondal B and Ghosh S 2009 Optimization of machining parameters for hard machining: Grey relational theory approach and ANOVA. Int. J. Adv. Manuf. Technol. 45: 1068–1086

    Article  Google Scholar 

  24. Suhail A H, Ismail N, Wong S V and Jalil N A A 2012 Surface roughness identification using the grey relational analysis with multiple performance characteristics in turning operations. Arabian J. Sci. Eng. 37(4): 1111–1117

    Article  Google Scholar 

  25. Tzeng C J, Lin Y H, Yang Y K and Jeng M C 2009 Optimization of turning operations with multiple performance characteristics using the Taguchi method and Grey relational analysis. J. Mater. Process. Technol. 209: 2753–2759

    Article  Google Scholar 

  26. Senthilkumar N, Tamizharasan T and Anandakrishnan V 2014b A Hybrid Taguchi–Grey relational technique and Cuckoo search algorithm for multi-criteria optimization in hard turning of AISI D3 steel. J. Adv. Eng. Res. 1(1): 16–31

    Google Scholar 

  27. Gamst G, Meyers L S and Guarino A J 2008 Analysis of variance designs – A conceptual and computational approach with SPSS and SAS. Cambridge University Press: Cambridge, London

    Book  MATH  Google Scholar 

  28. Montgomery D C 2013 Design and analysis of experiments. Eight edition, Wiley, New York

    Google Scholar 

  29. Chen J 2009 Multi-objective optimization of cutting parameters with improved NSGA-II. In: Proceedings of International Conference on Management and Service Science, Wuhan, 1–4

  30. Datta R and Deb K 2009 A classical-cum-evolutionary multi-objective optimization for optimal machining parameters. World Congress on Nature and Biologically Inspired Computing, Coimbatore, India, 607–612

    Google Scholar 

  31. Datta R and Majumder A 2010 Optimization of turning process parameters using multi-objective evolutionary algorithm. IEEE Congress on Evolutionary Computation, Barcelona, Spain, 1–6

    Google Scholar 

  32. Deb K and Datta R 2011 Hybrid evolutionary multi-objective optimization of machining parameters. KanGAL Report. 1–23

  33. Deb K, Pratap A, Agarwal S and Meyarivan T 2002 A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evolut. Comput. 6(2): 182–197

    Article  Google Scholar 

  34. Deb K and Goel T 2001 Controlled elitist nondominated sorting genetic algorithms for better convergence. Evolutionary Multi-Criterion Optimization 1993: 67–81

    Article  Google Scholar 

  35. Palanikumar K and Karthikeyan R 2006 Optimal machining conditions for turning of particulate metal matrix composites using Taguchi and response surface methodologies. Mach. Sci. Technol. 10(4): 417–433

    Article  Google Scholar 

  36. Senthilkumar N, Tamizharasan T and Gobikannan S 2014d Application of response surface methodology and firefly algorithm for optimizing multiple responses in turning AISI 1045 steel. Arabian J. Sci. Eng. 39(11): 8015–8030

    Article  Google Scholar 

  37. Hocheng H 2012 Machining technology for composite materials: Principles and practice. Woodhead Publishing Limited: UK

    Book  Google Scholar 

  38. Edward M Trent, Paul K Wright and Paul K Wright 2000 Metal cutting. Fourth edition, Butterworth–Heinemann: Woburn, MA

  39. Chadwick G and Froyen L 1991 Metal matrix composites, North Holland, New York, USA

    Google Scholar 

  40. Brun M K and Lee M 1985 Wear characteristics of various hard materials for machining SiC-reinforced aluminum alloy. Wear 104: 21–29

    Article  Google Scholar 

  41. Senthilkumar N, Tamizharasan T and Anandakrishnan V 2014c Experimental investigation and performance analysis of cemented carbide inserts of different geometries using Taguchi-based Grey relational analysis. Measurement 58: 520–536

    Article  Google Scholar 

  42. Li J, Yang X, Ren C, Chen G and Wang Y 2015 Multi-objective optimization of cutting parameters in Ti–6Al–4V milling process using nondominated sorting genetic algorithm-II. Int. J. Adv. Manuf. Technol. 76(5): 941–953

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mahalakshmi Engineering College, Tiruchirapalli, Tamil Nadu, 621213, India

    V Selvakumar

  2. K. Ramakrishnan College of Technology, Tiruchirappalli, Tamil Nadu, 621112, India

    S Muruganandam

  3. Periyar Maniammai University, Thanjavur, Tamil Nadu, 613403, India

    T Tamizharasan

  4. Adhiparasakthi Engineering College, Melmaruvathur, Tamil Nadu, 603319, India

    N Senthilkumar

Authors
  1. V Selvakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S Muruganandam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T Tamizharasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. N Senthilkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N Senthilkumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Selvakumar, V., Muruganandam, S., Tamizharasan, T. et al. Machinability evaluation of Al–4%Cu–7.5%SiC metal matrix composite by Taguchi–Grey relational analysis and NSGA-II. Sādhanā 41, 1219–1234 (2016). https://doi.org/10.1007/s12046-016-0546-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12046-016-0546-z

Keywords

Search

Navigation