A displacement field approach based on FEM-ANN and experiments for identification of elastic properties of composites

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Abstract

An inverse approach combining numerical and experimental results with full-field displacement measurements will allow the identification of all the in-plane elastic properties from experimental tests. Instead of the use of a non-destructive technique, an alternative hybrid approach is proposed to obtain the field displacement. The experimental displacement field was replaced by the nodal displacement values numerically determined using the finite element method (FEM). The use of a surrogate model based on artificial neural network (ANN) enables to establish the relationship between the elastic properties and the displacement field avoiding the exhaustive calculations based on FEM. The Uniform Design Method is used to select the input values for ANN learning procedure. The optimal estimation of the model parameters is performed by minimizing an error functional defined as the difference between the experimental measurements and the simulated output results from ANN approximation model. Two examples were chosen to validate the proposed approach. The first one uses the off-axis tensile test in order to calibrate the numerical method eight-harness satin weave glass fiber reinforced phenolic composite. The second example is a hinged cross-ply laminated plate supporting a vertical load. The numerical results show strong agreement with the experimental values.

Keywords

Elastic properties Composite materials Inverse formulation Genetic algorithm Displacement field measurements 

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Notes

Acknowledgments

The authors acknowledge the financial support provided by the Fundação para a Ciência e a Tecnologia (FCT), Portugal, through the funding of “The Associate Laboratory of Energy, Transports and Aeronautics (LAETA).”

References

  1. 1.
    Bruno L, Felice G, Pagnotta L, Poggialini A, Stigliano G (2008) Elastic characterization of orthotropic plates of any shape via static testing. Int J Solids Struct 45(3–4):908–920.  https://doi.org/10.1016/j.ijsolstr.2007.09.017 CrossRefMATHGoogle Scholar
  2. 2.
    Rahmani B, Mortazavi F, Villemure I, Levesque M (2013) A new approach to inverse identification of mechanical properties of composite materials: regularized model updating. Compos Struct 105:116–125.  https://doi.org/10.1016/j.compstruct.2013.04.025 CrossRefGoogle Scholar
  3. 3.
    Lauwagie T, Sol H, Roebben G, Heylen W, Shi Y, Van der Biest O (2003) Mixed numerical–experimental identification of elastic properties of orthotropic metal plates. NDT & E Int 36(7):487–495.  https://doi.org/10.1016/S0963-8695(03)00048-3 CrossRefGoogle Scholar
  4. 4.
    Vishnuvardhan J, Krishnamurthy CV, Balasubramaniam K (2009) Blind inversion method using Lamb waves for the complete elastic property characterization of anisotropic plates. J Acoust Soc Am 125(2):761–771.  https://doi.org/10.1121/1.3050253 CrossRefGoogle Scholar
  5. 5.
    Grediac M (2004) The use of full-field measurement methods in composite material characterization: interest and limitations. Compos A: Appl Sci Manuf 35(7-8):751–761.  https://doi.org/10.1016/j.compositesa.2004.01.019 CrossRefGoogle Scholar
  6. 6.
    Avril S, Bonnet M, Bretelle A, Grediac M, Hild F, Ienny P et al (2008) Overview of identification methods of mechanical parameters based on full-field measurements. Exp Mech 48(4):381–402.  https://doi.org/10.1007/s11340-008-9148-y CrossRefGoogle Scholar
  7. 7.
    Chamekh A, Salah HBH, Hambli R (2009) Inverse technique identification of material parameters using finite element and neural network computation. Int J Adv Manuf Technol 44(1-2):173–179.  https://doi.org/10.1007/s00170-008-1809-6 CrossRefGoogle Scholar
  8. 8.
    Hwang S-F, Wu J-C, He R-S (2009) Identification of effective elastic constants of composite plates based on a hybrid genetic algorithm. Compos Struct 90(2):217–224.  https://doi.org/10.1016/j.compstruct.2009.03.021 CrossRefGoogle Scholar
  9. 9.
    Ahmad S, Irons BM, Zienkiewicz OC (1970) Analysis of thick and thin shell structures by curved finite elements. Int J Numer Methods Eng 2(3):419–451.  https://doi.org/10.1002/nme.1620020310 CrossRefGoogle Scholar
  10. 10.
    Figueiras JA (1983) Ultimate load analysis of anisotropic and reinforced concrete plates and shells. Ph.D. thesis, University College of Swansea, UKGoogle Scholar
  11. 11.
    Ferreira AJM, Barbosa JT, Marques AT, De Sá JC (2000) Non-linear analysis of sandwich shells: the effect of core plasticity. Comput Struct 76(1-3):337–346.  https://doi.org/10.1016/S0045-7949(99)00156-X CrossRefGoogle Scholar
  12. 12.
    Fang L, Wang Y (1994) Number-theoretic methods in statistics. CRC Press, Boca Raton.  https://doi.org/10.1007/978-1-4899-3095-8 CrossRefMATHGoogle Scholar
  13. 13.
    Zhang L, Liang Y-Z, Jiang J-H, R-Q Y, Fang K-T (1998) Uniform design applied to nonlinear multivariate calibration by ANN. Anal Chim Acta 370(1):65–77.  https://doi.org/10.1016/S0003-2670(98)00256-6 CrossRefGoogle Scholar
  14. 14.
    Gupta MM, Jin L, Homma N (2003) Static and dynamic neural networks: from fundamentals to advanced theory. IEEE Press, Wiley-Interscience, Hoboken.  https://doi.org/10.1002/0471427950 CrossRefGoogle Scholar
  15. 15.
    Conceição António CA, Paulo Davim J, Lapa V (2008) Artificial neural network based on genetic learning for machining of polyetheretherketone composite materials. Int J Adv Manuf Technol 39(11-12):1101–1110.  https://doi.org/10.1007/s00170-007-1304-5 CrossRefGoogle Scholar
  16. 16.
    Conceição António CA (2001) A hierarchical genetic algorithm for reliability based design of geometrically non-linear composite structures. Compos Struct 54(1):37–47.  https://doi.org/10.1016/S0263-8223(01)00068-X CrossRefGoogle Scholar
  17. 17.
    Paiva RMM, António CAC, da Silva LFM (2016) Optimal design of adhesive composition in footwear industry based on creep rate minimization. Int J Adv Manuf Technol 84(9-12):2097–2111.  https://doi.org/10.1007/s00170-015-7746-2 CrossRefGoogle Scholar
  18. 18.
    Choudhry RS, Khan KA, Khan SZ, Khan MA, Hassan A (2016) Micromechanical modeling of 8-harness satin weave glass fiber-reinforced composites. J Compos Mater 51(5):705–720.  https://doi.org/10.1177/0021998316649782 CrossRefGoogle Scholar
  19. 19.
    Cheng J, Li Q-S, Xiao R-C (2008) A new artificial neural network-based response surface method for structural reliability analysis. Probab Eng Mech 23(1):51–63.  https://doi.org/10.1016/j.probengmech.2007.10.003 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Carlos Conceição António
    • 1
  • Shummaila Rasheed
    • 2
  1. 1.INEGI/LAETA, Faculty of EngineeringUniversity of PortoPortoPortugal
  2. 2.Erasmus Mundus student, Faculty of EngineeringUniversity of PortoPortoPortugal

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