Identification of Material Models of Nanocoatings System Using the Metamodeling Approach

  • Magdalena Kopernik
  • Andrzej Stanisławczyk
  • Jan Kusiak
  • Maciej Pietrzyk
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 312)


Hard systems of nanocoatings deposited using PVD (physical vapor deposition) are used in the artificial heart prosthesis. Correct determination of nanomaterial parameters is crucial for accuracy of simulation. The objective of this work is identification of material parameters of nanocoatings in hard system using the inverse analysis based on the artificial neural network metamodeling. The inverse analysis was preceded by the development of the Finite Element Method (FEM) model dedicated to the nanoindentation test of the hard nanocoatings system. The performed sensitivity analysis is focused on determination of parameters, having the highest influence on FEM model response. The obtained, reliable FEM model was used next in the inverse analysis. The objective of that analysis was evaluation of the parameters of the individual layers of the nanocoating system. In order to decrease the computation time connected with the inverse analysis, the metamodeling approach was proposed. The used metamodel was based on the artificial neural network technique. The obtained results confirm the usefulness of the presented method in the identification of the material properties of the complex, nanocoating systems.


  1. 1.
    Albrecht, H.J., Hannach, T., Hase, S., et al.: Nanoindentation: a suitable tool to determine local mechanical properties in microelectronic packages and materials? Arch. Appl. Mech. 74, 728–738 (2005)CrossRefMATHGoogle Scholar
  2. 2.
    Beake, B.D., Ranganathan, N.: An investigation of the nanoindentation and nano/micro-tribological behaviour of monolayer, bilayer and trilayer coatings on cemented carbide. Mat. Sci. Eng. A23, 46–51 (2006)CrossRefGoogle Scholar
  3. 3.
    Beake, B.D., Smith, J.F., Gray, A., et al.: Investigating the correlation between nano-impact fracture resistance and hardness/modulus ratio from nanoindentation at 25-500 C and the fracture resistance and lifetime of cutting tools with Ti1-xAlxN (x=0.5 and 0.67) PVD coatings in milling operations. Surface and Coatings 201, 4585–4593 (2007)CrossRefGoogle Scholar
  4. 4.
    Chollacoop, N., Dao, M., Suresh, S.: Depth-sensing instrumented indentation with dual sharp indenters. Acta Mater 51, 3713–3729 (2003)CrossRefGoogle Scholar
  5. 5.
    Fischer-Cripps, A.C.: Nanoindentation. Springer, New York (2004)CrossRefGoogle Scholar
  6. 6.
    Koker, R., Altincock, N., Demir, A.: Neural network based prediction of mechanical properties of particulate reinforced metal matrix composites using various training algorithms. Materials and Design 28, 616–627 (2007)CrossRefGoogle Scholar
  7. 7.
    Kopernik, M., Pietrzyk, M.: 2D numerical simulation of elasto-plastic deformation of thin hard coating systems in deep nanoindentation test with sharp indenter. Archives of Metallurgy and Materials 52, 299–310 (2007)Google Scholar
  8. 8.
    Kopernik, M., Szeliga, D.: Modelling of nanomaterials – sensitivity analysis to determine the nanoindentation test parameters. Computer Methods in Materials Science 7, 255–261 (2007)Google Scholar
  9. 9.
    Kopernik, M., Stanisławczyk, A., Kusiak, J., et al.: Identification of material models in hard system of nanocoatings using metamodel. In: Korytowski, A., Mitkowski, W., Szymkat, M. (eds.) Abstr., 23rd IFIP TC7 Conference on System Modelling and Optimization, Kraków (2007)Google Scholar
  10. 10.
    Kopernik, M., Stanisławczyk, A., Szeliga, D.: Problems of material models of hard nanocoatings. In: Dems, K. (ed.) Proc. 17th Int. CMM Conf., Łódź-Spała (2007)Google Scholar
  11. 11.
    Kustosz, R., Major, R., Wierzchoń, T., et al.: Designing a new heart. Academia 3, 14–17 (2004)Google Scholar
  12. 12.
    Oliver, C., Pharr, G.M.: An improved technique for determining hardness and elastic modulus using load and displacement sensing indentation experiment. J. Mater. Res. 7, 1564–1583 (1992)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Magdalena Kopernik
    • 1
  • Andrzej Stanisławczyk
    • 1
  • Jan Kusiak
    • 1
  • Maciej Pietrzyk
    • 1
  1. 1.AGH University of Science and TechnologyKrakówPoland

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