Modelling and optimisation of machining parameters for composite pipes using artificial neural network and genetic algorithm

Technical Paper

Abstract

An integrated approach to determine the influence of cutting parameters on carbon fiber reinforced polymer (CFRP) pipe using Taguchi’s Design of experiments (DOE), artificial neural network (ANN) and optimisation with genetic algorithm (GA) is followed when carrying out turning operation on a bidirectional carbon fabric reinforced bisphenol pipe using a carbide tool at different set of input parameters like feed, speed and depth of cut. Due to the anisotropic nature of composites and inhomogeneity, optimisation of machining parameters for composites is very difficult and largely dependent on wide variety of combinations among various factors such as fiber properties, orientation, resin properties, curing conditions, etc. For successful application of these composites in different fields, knowledge on machining the composites is necessary. Taguchi’s orthogonal array is used for DOE and the output under consideration is forces acting on cutting tool. Genetic algorithm is used to optimize the machining parameters to yield minimum cutting force. ANN with back propagation neural network algorithm has been adopted to model the machining operation. The results were verified using confirmation tests which indicate that the experimental values and observed values are almost equal with minimum error and ANN with GA could be successfully implemented for modelling and optimizing the machining parameters for bidirectional CFRP composite pipes.

Keywords

CFRP ANN DOE Orthogonal array GA 

List of Symbols

V

Cutting speed (m/min)

f

Feed rate (mm/rev)

d

Depth of cut (mm)

DOE

Design of experiments

S/N

Signal-to-noise ratio

GA

Genetic algorithm

ANN

Artificial neural network

MSE

Mean square error

References

  1. 1.
    Naveen Sait, A., Aravindan, S., Noorul Haq, A.: Investigation of surface damages on machining hand lay up GFRP composites. Int. J. Mater. Sci. 3(3), 275–288 (2008)Google Scholar
  2. 2.
    Naveen Sait, A., Aravindan, S., Noorul Haq, A.: Optimisation of machining parameters of GFRP pipes by desirability function analysis using Taguchi Technique. Int. J. Adv. Manuf. Technol. 43(5), 581–589 (2009)CrossRefGoogle Scholar
  3. 3.
    Naveen Sait, A., Aravindan, S.: Experimental investigation on machining of filament wound GFRP pipe by cemented carbide (K20) cutting tool. Int. J. Mach. Mach. Mater. 3(3/4), 364–381 (2008)Google Scholar
  4. 4.
    Palanikumar K.: Application of Taguchi and response surface methodologies for suface roughness in machining glass fiber reinforced plastics by PCD tooling. Int. J. Adv. Manuf. Technol.36(1–2), 19–27 (2008)Google Scholar
  5. 5.
    Naveen Sait, A., Aravindan, S., Noorul Haq, A.: Influence of Machining parameters on surface roughness of GFRP pipes. Adv. Prod. Eng. Manag. 498, 861–869 (2010)Google Scholar
  6. 6.
    Naveen Sait, A.: Optimisation of Machining Parameters of GFRP Pipes using Evolutionary Techniques. Int. J. of Precision Engg. and Manuf. 11(6), 891–900 (2010)CrossRefGoogle Scholar
  7. 7.
    Rajasekaran, T., Palanikumar, K., Vinayagam, B.: Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool. Prod. Eng. Res. Devel. 5, 191–199 (2011)Google Scholar
  8. 8.
    Rajasekaran, T., Palanikumar, B., Vinayagam, K.: Influence of machining parameters on surface roughness and material removal rate in machining carbon fiber reinforced polymer material. J. Mater. Process. Technol. pp. 82–98, 123–158 (2005)Google Scholar
  9. 9.
    Aravindan, S., Naveen Sait, A., Noorul Haq, A.: A machinability study of GFRP pipes using statistical techniques. Int. J. Adv. Manuf. Technol. 37(11), 1069–1081 (2008)Google Scholar
  10. 10.
    Ghani, J.A., Choudhury, I.A., Hasan, H.H.: Application of Taguchi method in optimization of end milling parameters. J. Mater. Process. Technol. 145, 84–92 (2004)CrossRefGoogle Scholar
  11. 11.
    Neelesh Jain, K., Jain, V.K., Kalyanmoy, Deb: Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms. Int. J. Mach. Tools Manuf. 47(6), 900–919 (2007)CrossRefGoogle Scholar
  12. 12.
    Lin, J.T., Bhattacharyya, D., Kecman, V.: Multiple regression and neural networks analyses in composites machining. Compos. Sci. Technol. 63, 539–548 (2003)CrossRefGoogle Scholar
  13. 13.
    Cicek, A., Kivak, T., Smta, G., Cay, Y.: Modelling of thrust forces in drilling of AISI 316 stainless steel using artificial neural network and multiple regression analysis. J. Mech. Eng. 58, 7–8, 492–498 (2012)Google Scholar
  14. 14.
    Krishnamoorthy, A., Rajendra Boopathy, S.: Delamination prediction in drilling of CFRP composites using Artificial Neural Network. J. Eng. Sci. Technol. 6(2), 191–203 (2011)Google Scholar
  15. 15.
    Devarasiddappa, D., Chandrasekaran, M.: Artificial Neural Network for predicting surface roughness in end milling of Al-SiCp metal matrix composite and its evaluation. J. Appl. Sci. 12, 955–962 (2010)Google Scholar
  16. 16.
    Davim, J.P., Mata, F.: A new machinability index in turning fiber reinforced plastics. J. Mater. Process Technol. 170, 436–440 (2005)CrossRefGoogle Scholar
  17. 17.
    Khan, Z., Prasad, B., Singh, T.: Machining condition optimization by genetic algorithms and simulated annealing. Comput. Oper. Res. 24(7), 647–657 (1997)CrossRefMATHGoogle Scholar
  18. 18.
    Krishnamoorthy, A., Rajendra Boopathy, S., Palanikumar, K., Paulo Davim, J.: Application of grey fuzzy logic for the optimization of drilling parameters for CFRP composites with multiple performance characteristics. J. Mater. Process. Technol. 88, 356–375 (2009)Google Scholar
  19. 19.
    Kumar, K.V., Naveen Sait, A., Panneerselvam, K.: Machinability study of hybrid polymer composite pipe using response surface methodology and genetic algorithm. J. Sandw. Struct. Mater. 16(4), 418–439 (2014)CrossRefGoogle Scholar
  20. 20.
    Kumar, K.V., Naveen Sait, A., Panneerselvam, K.: Machining parameter optimisation of bidirectional CFRP composite using Genetic algorithm. Mater. Test. 56(9), 728–736 (2014)CrossRefGoogle Scholar
  21. 21.
    Noorul Haq, A., Guharaja, S., Karuppannan, K.M.: Parameter optimization of \(CO_{2}\) casting process by using Taguchi method. Int. J. Interact. Des. Manuf. 3(1), 41–50 (2008)CrossRefGoogle Scholar
  22. 22.
    Nagesh, D.S., Datta, G.L.: Modeling of fillet welded joint of GMAW process: integrated approach using DOE, ANN and GA. Int. J. Interact. Des. Manuf. 2(3), 127–136 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag France 2014

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringSeshasayee Institute of TechnologyTiruchirappalliIndia
  2. 2.Department of Mechanical EngineeringChendhuran College of Engineering & TechnologyPudukkottaiIndia

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