Prediction of the collapse modes of PVC cylindrical shells under compressive axial loads using Artificial Neural Networks

  • Angelos P. Markopoulos
  • Dimitrios E. Manolakos
  • Nikolaos M. Vaxevanidis
Part of the IFIP The International Federation for Information Processing book series (IFIPAICT, volume 247)


In the present paper Artificial Neural Networks (ANN) are applied in order to predict the buckling modes of thin-walled PVC tubes under compressive axial forces. For the development of the models the neural network toolbox of Matlab® was applied. The results show that these models can satisfactorily face these problems and they constitute not only a fast method, compared to time consuming experiments, but also a reliable tool that can be used for the studying of such parts which are usually employed as structural elements for the absorption of the energy of an impact, in automotive and aerospace applications.


Artificial Neural Network Hide Layer Cylindrical Shell Collapse Mode Compressive Axial Load 
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  1. 1.
    A.G. Mamalis and D.E. Manolakos, Deformation Characteristics of Crashworthy Components (VDI, 1989).Google Scholar
  2. 2.
    K.R.F. Andrews, G.L. England and E. Ghani, Classification of the axial collapse of cylindrical tubes under quasi-static loading, International Journal of Mechanical Sciences, Vol. 25, No. 9–10, 687–696 (1983).CrossRefGoogle Scholar
  3. 3.
    S.R. Guillow, G. Lu and R.H. Grzebieta, Quasi-static axial compression of thin-walled circular aluminium tubes, International Journal of Mechanical Sciences 43, 2103–2123 (2001).MATHCrossRefGoogle Scholar
  4. 4.
    G. Dini, Literature database on applications of artificial intelligence methods in manufacturing engineering, Annals of the CIRP, 46(2), 681–690 (1997).CrossRefGoogle Scholar
  5. 5.
    A. Markopoulos, N.M. Vaxevanidis, G. Petropoulos and D.E. Manolakos, Artificial Neural Networks Modeling of Surface Finish in Electro-Discharge Machining of Tool Steels, Proc. of ESDA 2006, 8 th Biennial ASME Conference on Engineering Systems Design and Analysis, Torino, Italy (July 4–7, 2006).Google Scholar
  6. 6.
    J.M. Alexander, An Approximate analysis of the collapse of thin cylindrical shells under axial loading, Quart. Journal of Mech. And Applied Math., Vol. XIII, Pt. 1 (1960).Google Scholar
  7. 7.
    N. Jones and W. Abramowicz, Static and dynamic axial crushing of circular and square tubes, In: S.R. ReidMetal forming and Impact Mechanics”, Oxford, Pergamon Press, p. 225 (1985).Google Scholar
  8. 8.
    N.K. Gupta and R. Velmurugan, Consideration of internal folding and non-symmetric fold formation axi-symmetric axial collapse round tubes, Int. J. Solid Structures, Vol. 34, 2611–2630 (1997).CrossRefGoogle Scholar
  9. 9.
    W. Johnson, P.D. Soden and S.T.S. Al-Hassani, Inextensional Collapse of thin-walled tubes under axial compression, J. Strain Analysis, Vol. 12, No. 4 (1977).Google Scholar
  10. 10.
    S. Haykin, Neural networks, a comprehensive foundation (Prentice Hall, 1999).Google Scholar
  11. 11.
    H. Demuth and M. Beale, Neural networks toolbox for use with Matlab, (User’s guide 2001).Google Scholar
  12. 12.
    A.A.A. Alghamdi, Collapsible impact energy absorbers: an overview, Thin-Walled Structures, Vol. 34, No. 2, 189–213 (2001).CrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • Angelos P. Markopoulos
    • 1
  • Dimitrios E. Manolakos
    • 1
  • Nikolaos M. Vaxevanidis
    • 2
  1. 1.Manufacturing Technology DivisionNational Technical University of AthensAthensGreece
  2. 2.Department of Mechanical Engineering TeachersInstitute of Pedagogical and Technological EducationN. Heraklion AttikisGreece

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