Advertisement

Investigation of drilling parameters on hybrid polymer composites using grey relational analysis, regression, fuzzy logic, and ANN models

  • G. Anand
  • N. Alagumurthi
  • R. Elansezhian
  • K. Palanikumar
  • N. Venkateshwaran
Technical Paper

Abstract

Among many machining operations, drilling has become one of the important machining operations performed in polymer composites. The quality of the drilled hole is closely associated with the drilling parameters and conditions. The current work focuses on the optimization of multiple response characteristics during drilling of hybrid glass fiber reinforced polymeric nanocomposites. Taguchi’s L25, orthogonal array is used to conduct the experiments and for optimization of the process parameters. The machining parameters such as spindle speed, feed rate, and drill diameter are optimized for the response which includes delamination, thrust force and torque via grey relational analysis technique. From the grey relational grade analysis, it is clear that the drill diameter is the most influencing factor followed by the feed rate and the spindle speed. The optimized process parameter settings were found as spindle speed of 2700 rpm, the feed rate of 30 mm/min and drill diameter of 4 mm, respectively, for lower delamination, torque and thrust force. Among the various modeling techniques used, ANN is found to be suitable for the process with minimum error percentage of 0.526.

Keywords

Composite Delamination Thrust force Torque GRG 

Abbreviations

Ni–P/GF

Nickel Phosphorus coated glass fiber

Al2O3

Aluminum oxide

GFRP

Glass fiber reinforced plastic

References

  1. 1.
    Rahman M, Ramakrishnan S, Prakash JRS, Tan DCG (1999) Machinability study of carbon fiber reinforced composite. Mater Process Technol 89–90:292–297CrossRefGoogle Scholar
  2. 2.
    Lubin G (1982) Handbook of composites. Van Nostrand Reinhold, New YorkCrossRefGoogle Scholar
  3. 3.
    Palanikumar K, Prakash S, Shanmugam K (2008) Evaluation of delamination in drilling GFRP composites. Mater Manuf Process 23(8):858–864CrossRefGoogle Scholar
  4. 4.
    Arul S, Vijayaraghavan L, Malhotra SK, Krishnamurthy R (2006) The effect of vibratory drilling on hole quality in polymeric composites. Mach Tools Manuf 46:250–259CrossRefGoogle Scholar
  5. 5.
    Liu DF, Tang YJ, Cong WL (2012) A review of mechanical drilling for composite laminates. Compos Struct 94:1265–1279CrossRefGoogle Scholar
  6. 6.
    Lazar MB, Xirouchakis P (2011) Experimental analysis of drilling fiber reinforced composites. Mach Tools Manuf 51:937–946CrossRefGoogle Scholar
  7. 7.
    Zitoune R, Collombet F (2007) Numerical prediction of the thrust force responsible of delamination during the drilling of the long-fibre composite structures. Compos Part A 38:858–866CrossRefGoogle Scholar
  8. 8.
    Mohan NS, Ramachandra A, Kulkarni SM (2005) Influence of process parameters on cutting force and torque during drilling of glass- fiber polyester reinforced composites. Compos Struct 71:407–413CrossRefGoogle Scholar
  9. 9.
    Khashaba UA (2004) Delamination in the drilling GFR-thermoset composites. Compos Struct 63:313–332CrossRefGoogle Scholar
  10. 10.
    Palanikumar K, Prakash S, Shanmugam K (2008) Evaluation of delamination in drilling GFRP composite. Mater Manuf Process 8:858–864CrossRefGoogle Scholar
  11. 11.
    Davim JP, Reis P (2003) Study on the delamination in drilling carbon fiber reinforced plastic using design of experiments. Compos Struct 59:481–487CrossRefGoogle Scholar
  12. 12.
    PalaniKumar K, Latha B, Senthilkumar VS, Davim P (2012) Analysis on drilling of glass fiber–reinforced polymer (GFRP) composite using grey relational analysis. Mater Manuf Process 27:297–305CrossRefGoogle Scholar
  13. 13.
    Tosun N (2006) Determination of optimum parameters for multi performance characteristics in drilling by using grey relational analysis. Int J Adv Manuf Technol 28:450–455CrossRefGoogle Scholar
  14. 14.
    Kuo Y, Yang T, Huang GW (2008) The use of a grey based Taguchi method for optimizing multi-response stimulation problems. Eng Optim 23(1):51–58Google Scholar
  15. 15.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefzbMATHGoogle Scholar
  16. 16.
    Palanikumar K (2009) Surface roughness model for machining glass fiber reinforced plastics by PCD tool using fuzzy logics. J Reinf Plast Compos 28(18):2273–2286CrossRefGoogle Scholar
  17. 17.
    Singh RVS, Latha B, Senthilkumar VS (2009) Modeling and analysis of thrust force and torque in drilling GFRP composites by multi-facet drill using fuzzy logic. Int J Recent Trends Eng 1(5):66–70Google Scholar
  18. 18.
    Krishnamoorthy A, Boopathy SR, Palanikumar K, Davim JP (2012) Application of grey fuzzy logic for the optimization of drilling parameters for CFRP composites with multiple performance characteristics. Measurement 45(5):1286–1296CrossRefGoogle Scholar
  19. 19.
    McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Chang-Xue F, Wang X, Zhiguang Y (2002) Neural networks modeling of honing surface roughness parameters defined by ISO 13565. J Manuf Syst 21(5):395CrossRefGoogle Scholar
  21. 21.
    Krishnamoorthy A, Boopathy SR, Palanikumar K (2011) Delamination prediction in drilling of CFRP composites using artificial neural network. J Eng Sci Technol 6(2):191–203Google Scholar
  22. 22.
    Lin CL (2004) Use of the Taguchi method and grey relational analysis to optimize turning operations with multiple performance characteristics. Mater Manuf Process 19:209–220CrossRefGoogle Scholar
  23. 23.
    Park SH, Antony J (2008) Robust design for quality engineering and six sigma. World Scientific, SingaporeCrossRefzbMATHGoogle Scholar
  24. 24.
    Palanikumar K (2008) Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling. Adv Manuf Technol 36:19–27CrossRefGoogle Scholar
  25. 25.
    Fung CP (2003) Manufacturing process optimization for wear property of fiber-reinforced polybutylene terephthalate composites with grey relational analysis. Wear 254:298–306CrossRefGoogle Scholar
  26. 26.
    Hsiao YF, Tarng YS, Huang WJ (2008) Optimization of plasma arc welding parameters by using the Taguchi method with the grey relational analysis. Mater Manuf Process 23(1):51–58CrossRefGoogle Scholar
  27. 27.
    Anand G, Alagumurthi N, Elansezhian R, Venkateshwaran N (2017) Ni–P coated glass fiber/Al2O3 nanowire reinforced vinyl ester composite. Polym Korea 41(3):443–451CrossRefGoogle Scholar
  28. 28.
    Palanikumar K (2011) Experimental investigation and optimization in drilling of GFRP composites. Measurement 44:2138–2148CrossRefGoogle Scholar
  29. 29.
    Yilmaz O, Eyercioglu O, Gindy NNZ (2006) A user-friendly fuzzy-based system for the selection of electro discharge machining process parameters. J Mater Process Technol 172(3):363–371CrossRefGoogle Scholar
  30. 30.
    Latha B, Senthilkumar VS, Palanikumar P (2011) Modelling and optimization of process parameters for delamination in drilling glass fiber reinforced plastic (GFRP) composites. Mach Sci Technol 15(2):172–191CrossRefGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, Pondicherry Engineering CollegePondicherry UniversityPuducherryIndia
  2. 2.Department of Mechanical EngineeringSri Sai Ram Institute of TechnologyChennaiIndia
  3. 3.Department of Mechanical EngineeringRajalakshmi Engineering CollegeChennaiIndia

Personalised recommendations