Skip to main content
SpringerLink
Log in

Prediction of the response under impact of steel armours using a multilayer perceptron

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This article puts forward the results obtained when using a neural network as an alternative to classical methods (simulation and experimental testing) in the prediction of the behaviour of steel armours against high-speed impacts. In a first phase, a number of impact cases are randomly generated, varying the values of the parameters which define the impact problem (radius, length and velocity of the projectile; thickness of the protection). After simulation of each case using a finite element code, the above-mentioned parameters and the results of the simulation (residual velocity and residual mass of the projectile) are used as input and output data to train and validate a neural network. In addition, the number of training cases needed to arrive at a given predictive error is studied. The results are satisfactory, this alternative providing a highly recommended option for armour design tasks, due to its simplicity of handling, low computational cost and efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Goldsmith W (1960) Impact. The theory and physical behaviour of colliding solids. Edward Arnold Publishers, London

    MATH  Google Scholar 

  2. Zaera R, Sánchez-Gálvez V. (1998) Analytical modelling of normal and oblique ballistic impact on ceramic/metal lightweight armours. Int J Impact Eng 21:133–48

    Article  Google Scholar 

  3. Arias A, Zaera R, López-Puente J, Navarro C (2003) Numerical modeling of the impact behavior of new particulate-loaded composite materials. Comp Struct 61:151–59

    Article  Google Scholar 

  4. Awerbuch A, Bodner SR (1974) Analysis of the mechanics of perforation of projectiles in metallic plates. Int J Solids Struct 10:671–684

    Article  Google Scholar 

  5. Ravid M, Bodner SR (1983) Dynamic perforation of viscoplastic plates by rigid projectiles. Int J Eng Sci 21:577–591

    Article  Google Scholar 

  6. Anderson CE (1987) An overview of the theory of hydrocodes. Int J Impact Eng 5:33–59

    Article  Google Scholar 

  7. Minsky M, Papert S (1969) Perceptrons. MIT Press, Cambridge

    MATH  Google Scholar 

  8. ABAQUS/Explicit v6.4 User_s manual (2003) ABAQUS Inc., Richmond

  9. Johnson GR, Cook WH (1983) A constitutive model and data for metals subjected to large strains, high strain rates, and temperatures. In: Proceedings of seventh international symposium ballistics, The Hague, The Netherlands, pp 1–7

  10. Kirraine DE (1990) Machine learning, Training and Development. J 40:24–29

    Google Scholar 

  11. Rumelhart DE, McCleland JL (1986) Parallel distributed processing. MIT Press, Cambridge

    Google Scholar 

  12. Rumelhart DE, McCleland JL (1988) Parallel distributed processing VI: foundations. Bradford Books, New York

    Google Scholar 

  13. Hopfield JJ (1987) Neurons with graded response have collective computational properties like those of two state neurons. Proc Natl Acad Sci 81:4947–4960

    MathSciNet  Google Scholar 

  14. Waszczyszyn Z, Ziemiański L (2001) Neural networks in mechanics of structures and materials—new results and prospects of applications. Comput Struct 79(22–25):2261–2276

    Article  Google Scholar 

  15. Ince R (2004) Prediction of fracture parameters of concrete by artificial neural networks. Eng Fract Mech 71(15):2143–2159

    Article  Google Scholar 

  16. Liu SW, Huang JH, Sung JC, Lee CC (2002) Detection of cracks using neural networks and computational mechanics. Comput Methods Appl Mech Eng 191(25–26):2831–2845

    Article  MATH  Google Scholar 

  17. Tarassenko L (1998) A guide to neural computing applications. Arnold/NCAF, London

    Google Scholar 

Download references

Acknowledgements

This research was done with the financial support of the Comunidad Autónoma de Madrid under Project GR/MAT/0507/2004.

Author information

Authors and Affiliations

  1. Computer Science Department, University Carlos III of Madrid, Avda. de la Universidad 30, 28911, Leganés, Madrid, Spain

    A. García-Crespo & B. Ruiz-Mezcua

  2. Department of Continuum Mechanics and Structural Analysis, University Carlos III of Madrid, Avda. de la Universidad 30, 28911, Leganés, Madrid, Spain

    D. Fernández-Fdz & R. Zaera

  3. Research Institute “Pedro Juan de Lastanosa”, University Carlos III of Madrid, Avda. de la Universidad 30, 28911, Leganés, Madrid, Spain

    A. García-Crespo, B. Ruiz-Mezcua & R. Zaera

Authors
  1. A. García-Crespo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. Ruiz-Mezcua
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. Fernández-Fdz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R. Zaera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Zaera.

Rights and permissions

Reprints and permissions

About this article

Cite this article

García-Crespo, A., Ruiz-Mezcua, B., Fernández-Fdz, D. et al. Prediction of the response under impact of steel armours using a multilayer perceptron. Neural Comput & Applic 16, 147–154 (2007). https://doi.org/10.1007/s00521-006-0050-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-006-0050-1

Keywords

Search

Navigation