International Journal of Plastics Technology

, Volume 20, Issue 2, pp 424–450 | Cite as

Simultaneous prediction of delamination and surface roughness in drilling GFRP composite using ANN

  • Rasmi Ranjan Behera
  • Ranjan Kr. Ghadai
  • Kanak Kalita
  • Simul Banerjee
Research Article


Delamination in the drilling of polyester composite reinforced with chopped fiberglass is a problematic phenomenon. The material’s structural integrity is reduced by delamination, which results in poor tolerance during assembly and is a primary reason for decreased performance. Surface roughness is another important factor to consider when drilling fiber-reinforced plastics, as surface roughness causes failures by inducing high stresses in rivets and screws. Due to the random orientation of fiberglass and the non-homogenous, anisotropic properties of this material, an exact mathematical model has not been developed yet. Instead, modelling by artificial neural networks (ANNs) is adopted. In the present work, a multilayer perception ANN architecture has been developed with a feed-forward back-propagation algorithm. The algorithm uses material thickness, drill diameter, spindle speed, and feed rate as input parameters and delamination factor (Fd) at the entrance of the drilled hole, average surface roughness (Ra), and root mean square surface roughness (Rq) as the output parameters. The ANN model is then used to develop response surfaces to examine the influence of various input parameters on different response parameters. The developed model predicts that surface roughness increases with increases in feed rate and that a smaller-diameter drill will be advantageous in reducing surface roughness. A reduced feed rate will minimize delamination as well.


Delamination Glass fibre reinforced polyester GFRP Surface roughness Artificial neural network ANN 


  1. 1.
    Guu YH, Hocheng H, Tai NH, Liu SY (2001) Effect of electrical discharge machining on the characteristics of carbon fibre reinforced carbon composites. J Mater Sci 36:2037–2043CrossRefGoogle Scholar
  2. 2.
    Callister WD (2002) Materials science and engineering: an introduction, 6th edn. Wiley, MississaugaGoogle Scholar
  3. 3.
    Sonbaty EI, Khasaba UA, Machaly T (2004) Factors affecting the machinability of GFR/epoxy composites. Compos Struct 63:329–338CrossRefGoogle Scholar
  4. 4.
    Capello E (2004) Workpiece damping and its effects on delamination damage in drilling thin composite laminates. J Mater Process Technol 148:186–195CrossRefGoogle Scholar
  5. 5.
    Khashaba UA (2004) Delamination in drilling GFR-thermoset composites. Compos Struct 63:313–327CrossRefGoogle Scholar
  6. 6.
    Abrao AM, Rubio JC, Faria PE, Davim JP (2008) The effect of cutting tool geometry on thrust force and delamination when drilling glass fibre reinforced plastic. Mater Des 29:508–513CrossRefGoogle Scholar
  7. 7.
    Velayudham A, Krishnamurty R (2007) Effect of point geometry and their influence on thrust force and delamination in drilling of polymeric composites. J Mater Process Technol 185:204–209CrossRefGoogle Scholar
  8. 8.
    Rubio JC, Abrao AM, Faria PE, Correia AE, Davim JP (2008) Effects of high speed in drilling of glass fiber reinforced plastic: evaluation of the delamination factor. Int J Mach Tools Manuf 48:715–720CrossRefGoogle Scholar
  9. 9.
    Hocheng H, Tsao C (2003) Comprehensive analysis of delamination in drilling of composite materials with various drill bits. J Mater Process Technol 140:335–339CrossRefGoogle Scholar
  10. 10.
    Palanikumar K, Prakash S, Shanmugam K (2008) Evaluation of delamination in drilling GFRP composites. Mater Manuf Process 23:858–864CrossRefGoogle Scholar
  11. 11.
    Mohan NS, Kulkarni SM, Ramachandra A (2007) Delamination analysis in drilling process of glass fibre reinforced plastic (GFRP) composite materials. J Mater Process Technol 186:265–271CrossRefGoogle Scholar
  12. 12.
    Babu J, Philip J (2014) Experimental studies on effect of process parameters on delamination in drilling GFRP composites using Taguchi method. Proc Mater Sci 6:1131–1142CrossRefGoogle Scholar
  13. 13.
    Davim JP, Reis P, Antonio CC (2004) Drilling fibre reinforced plastics (FRPs) manufactured by hand lay-up: influence of matrix (Viapalvup 9731 and ATLAC 382-05). J Mater Process Technol 155:1828–1833CrossRefGoogle Scholar
  14. 14.
    Davim JP, Reis P, Antonio CC (2004) Experimental study on drilling glass fibre reinforced plastics (GFRP) manufactured by hand lay-up. Compos Sci Technol 64:289–297CrossRefGoogle Scholar
  15. 15.
    Khashaba UA, Seif MA, Elhamid MA (2007) Drilling analysis of chopped composites. Compos Part A 38:61–70CrossRefGoogle Scholar
  16. 16.
    Haykin S (2007) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall of India Private Ltd, New DelhiGoogle Scholar
  17. 17.
    Rajasekaran S, VijayalakshmiPai GA (2007) Neural networks, fuzzy logic, and genetic algorithms: synthesis and applications. Prentice-Hall of IndiaPrivate Ltd, New DelhiGoogle Scholar
  18. 18.
    Himmel C, May G (1993) Advantages of plasma etch modeling using neural networks over statistical techniques. IEEE Trans Semicond Manuf 6:103–111CrossRefGoogle Scholar
  19. 19.
    Bezerra EM, Ancelotti AC, Pardini LC, Rocco JAFF, Iha K, Ribeiro CHC (2007) Artificial neural networks applied to epoxy composites reinforced with carbon and E-glass fibers: analysis of the shear mechanical properties. Mater Sci Eng A 464:177–185CrossRefGoogle Scholar
  20. 20.
    Hayajneh MT, Hassan AM, Mayyas AT (2009) Artificial neural network modelling of the drilling process of self-lubricated aluminium/alumina/graphite hybrid composites synthesized by powder metallurgy technique. J Alloys Compd. doi: 10.1016/j.jallcom.2008.11.155
  21. 21.
    Kadi H (2006) Modeling the mechanical behavior of fiber-reinforced polymeric composite materials using artificial neural networks—a review. Compos Struct 73:1–23CrossRefGoogle Scholar
  22. 22.
    Karnik SR, Gaitonde VN, Rubio JC, Correia AE, Abrao AM, Davim JP (2008) Delamination analysis in high speed drilling of carbon fibre reinforced plastic (CFRP) using artificial neural network model. Mater Des. doi: 10.1016/j.matdes.2008.03.014
  23. 23.
    Tsao CC, Hocheng H (2007) Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network. J Mater Process Technol. doi: 10.1016/j.jmatprotec.2006.04.126
  24. 24.
    Hansda S, Banerjee S (2012) Multiple performance characteristics optimisation in drilling of glass fibre reinforced polyester composite at different weightage of performance by grey relational analysis. Int J Mach Mach Mater 2 12(1–2):14–27Google Scholar
  25. 25.
    Hansda S, Banerjee S (2014) Optimizing multi characteristics in drilling of GFRP composite using utility concept with Taguchi’s approach. Proc Mater Sci 6:1476–1488CrossRefGoogle Scholar
  26. 26.
    Soren H et al (2013) Analyzing process capability of drilling on glass fiber reinforced polyester (GFRP) composites with Taguchi Loss Function. Adv Mater Res 622. doi: 10.4028/
  27. 27.
    Rajamurugan TV, Shanmugam K, Palanikumar K (2013) Analysis of delamination in drilling glass fiber reinforced polyester composites. Mater Des 45:80–87CrossRefGoogle Scholar
  28. 28.
    Mishra R et al (2010) Neural network approach for estimating the residual tensile strength after drilling in uni-directional glass fiber reinforced plastic laminates. Mater Des 31(6):2790–2795CrossRefGoogle Scholar
  29. 29.
    Tsao CC (2008) Comparison between response surface methodology and radial basis function network for core-center drill in drilling composite materials. Int J Adv Manuf Technol 37:1061–1068CrossRefGoogle Scholar
  30. 30.
    Abrao AM, Faria PE, Rubio JC, Reis P, Davim JP (2007) Drilling of fibre reinforced plastics: a review. J Mater Process Technol 186:1–7CrossRefGoogle Scholar
  31. 31.
    Babu J et al (2015) Assessment of delamination in composite materials: a review. Proc Inst Mech Eng Part B J Eng Manuf 0954405415619343. doi: 10.1177/0954405415619343
  32. 32.
    Canakci A, Varol T, Ozsahin S (2015) Artificial neural network to predict the effect of heat treatment, reinforcement size, and volume fraction on AlCuMg alloy matrix composite properties fabricated by stir casting method. Int J Adv Manuf Technol 78(1–4):305–317CrossRefGoogle Scholar
  33. 33.
    Satapathy A, Tarkes DP, Nayak NB (2010) Wear response prediction of TiO2-polyester composites using neural networks. Int J Plast Technol 14(1):24–29CrossRefGoogle Scholar
  34. 34.
    Varol T, Canakci A, Ozsahin S (2015) Modeling of the prediction of densification behavior of powder metallurgy Al–Cu–Mg/B4C composites using artificial neural networks. Acta Metall Sin (English Letters) 28(2):182–195CrossRefGoogle Scholar
  35. 35.
    Khanlou HM et al (2015) Prediction and characterization of surface roughness using sandblasting and acid etching process on new non-toxic titanium biomaterial: adaptive-network-based fuzzy inference system. Neural Comput Appl 26(7):1751–1761CrossRefGoogle Scholar
  36. 36.
    Khanlou HM et al (2014) Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks. Neural Comput Appl 25(3–4):767–777CrossRefGoogle Scholar
  37. 37.
    Sadollah A et al (2013) Prediction and optimization of stability parameters for titanium dioxide nanofluid using response surface methodology and artificial neural networks. Sci Eng Compos Mater 20(4):319–330CrossRefGoogle Scholar
  38. 38.
    Hemmatian H et al (2013) Optimization of laminate stacking sequence for minimizing weight and cost using elitist ant system optimization. Adv Eng Softw 57:8–18CrossRefGoogle Scholar
  39. 39.
    Kosko B (1994) Neural networks and fuzzy systems. Prentice-Hall of India Private Ltd, New DelhiGoogle Scholar
  40. 40.
    Schalkoff RB (1997) Artificial neural networks. McGraw-Hill, New YorkGoogle Scholar
  41. 41.
    Vankanti VK, Ganta V (2014) Optimization of process parameters in drilling of GFRP composite using Taguchi method. J Mater Res Technol 3(1):35–41CrossRefGoogle Scholar

Copyright information

© Central Institute of Plastics Engineering & Technology 2016

Authors and Affiliations

  • Rasmi Ranjan Behera
    • 1
  • Ranjan Kr. Ghadai
    • 2
  • Kanak Kalita
    • 3
  • Simul Banerjee
    • 4
  1. 1.Department of Mechanical EngineeringIndian Institute of TechnologyGuwahatiIndia
  2. 2.Department of Mechanical EngineeringSikkim Manipal Institute of TechnologyMajitarIndia
  3. 3.Department of Mechanical EngineeringMPSTME, SVKM’s Narsee Monjee Institute of Management Studies (NMIMS)DhuleIndia
  4. 4.Department of Mechanical EngineeringJadavpur UniversityKolkataIndia

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