Journal of Failure Analysis and Prevention

, Volume 16, Issue 5, pp 931–937 | Cite as

Computer Code for Materials Diagnosis Using Monte Carlo Method and Neural Networks

  • Hocine Bendjama
  • Djallel Mahdi
Technical Article---Peer-Reviewed


Non-destructive testing (NDT) is a highly valuable technique in evaluation and evolution of materials and products. X-ray imaging is an important NDT technique that is used widely in the metal industry in order to control the quality of materials. Sometimes it may be difficult to get a measurement. The simulation of X-ray imaging is often performed using computer codes. This paper presents a new simulation method for materials diagnosis. The simulation is based primarily on the X-ray attenuation law and it is performed using a combination between Monte Carlo method and multi-layer perceptron neural network. The main goal of the proposed method is to obtain more detailed information about the state of the materials.


Non-destructive testing X-ray imaging Materials diagnosis Monte Carlo Neural network 


  1. 1.
    L. Cartz, Non destructive Testing (ASM International, Materials Park, 1995)Google Scholar
  2. 2.
    J.M. Dinten, P. Dziopa and A. Koenig, X-rays image analysis for defects detection and characterization in metallic samples. Proceedings of the first IEEE International Conference on Image Processing, Texas, USA, pp. 321–325 (1994)Google Scholar
  3. 3.
    I. Kazantsev, I. Lemahieu, G. Salov, R. Denys, Statistical detection of defects in radiographic images in nondestructive testing. Signal Process. 82(5), 791–801 (2002)CrossRefGoogle Scholar
  4. 4.
    S.M. Anouncia, R. Saravanan, Non destructive testing using radiographic images—a survey. Insight 48(10), 592–597 (2006)CrossRefGoogle Scholar
  5. 5.
    J. Dutreix, A. Desgrez, B. Bok, J.M. Vinot, Biophysique des radiations, 2nd edn. (Masson, Paris, 1980)Google Scholar
  6. 6.
    G.F. Knoll, Radiation Detection and Measurement, 2nd edn. (Wiley, New York, 1989)Google Scholar
  7. 7.
    P. Duvauchelle, N. Freud, V. Kaftandjian, D. Babot, A computer code to simulate X-ray imaging techniques. Nucl. Instrum. Methods B. 170(1/2), 245–258 (2000)CrossRefGoogle Scholar
  8. 8.
    N. Freud, P. Duvauchelle, D. Babot, New developments in virtual X-Ray imaging: fast simulation using a deterministic approach. Rev. QNDE 22, 553–560 (2003)Google Scholar
  9. 9.
    N. Li, H.X. Zhao, S.H. Cho, J.G. Choi, M.H. Kim, A fast algorithm for voxel-based deterministic simulation of X-ray imaging. Comput. Phys. Commun. 178(7), 518–523 (2008)CrossRefGoogle Scholar
  10. 10.
    A. Brunetti, M. Sanchez del Rio, B. Golosio, A. Simionovici, A. Somogyi, A library for X-ray–matter interaction cross sections for X-ray fluorescence applications. Spectrochim. Acta B. 59, 1725–1731 (2004)CrossRefGoogle Scholar
  11. 11.
    T. Schoonjans, A. Brunetti, B. Golosio, M.S. Del-Rio, V.A. Solé, C. Ferrero, L. Vincze, The xraylib library for X-ray–matter interactions. Recent developments. Spectrochim. Acta B. 66(11–12), 776–784 (2011)CrossRefGoogle Scholar
  12. 12.
    B. Golosio, T. Schoonjans, A. Brunetti, P. Oliva, G.L. Masala, Monte Carlo simulation of X-ray imaging and spectroscopy experiments using quadric geometry and variance reduction techniques. Comput. Phys. Commun. 185, 1044–1052 (2014)CrossRefGoogle Scholar
  13. 13.
    M. Kiunke, R. Schielein, K. Dremel, S. Zabler, F. Sukowski, S. Kasperl, Monte Carlo X-ray scattering studies in the MeV regime. Proceedings of the 11th European Conference on Non-Destructive Testing, Prague, Czech Republic (2014)Google Scholar
  14. 14.
    R.Y. Rubinstein, D.P. Kroese, Simulation and the Monte Carlo Method, 2nd edn. (Wiley, New York, 2008)Google Scholar
  15. 15.
    S. Mark, S. Mordechai (eds.), Application of Monte Carlo Method in Science and Engineering (InTech, Rijeka, 2011)Google Scholar
  16. 16.
    M. Terrissol, Méthode de simulation du transport d’électrons d’énergies comprises entre 10 eV et 30 keV. Ph.D. Thesis, Paul Sabattier University, 1978Google Scholar
  17. 17.
    D. Djamai, H. Oudira, A. Saifi, Application d’un modèle hybride à l’étude des dommages radio-induits par un faisceau d’électrons sur la molécule d’ADN dans son environnement. Radioprotection. 43(3), 357–387 (2008)CrossRefGoogle Scholar
  18. 18.
    M. Nørgaard, O. Ravn, N.K. Poulsen, L.K. Hansen, Neural Networks for Modelling and Control of Dynamic systems (Springer, New York, 2000)CrossRefGoogle Scholar
  19. 19.
    H. Bendjama, S. Bouhouche, M.S. Boucherit, M. Mansour, Vibration signal analysis using Wavelet-PCA-NN technique for fault diagnosis in rotating machinery. Mediterr. J. Meas. Control 6(4), 145–154 (2010)Google Scholar
  20. 20.
    S. Kara, F. Dirgenali, A system to diagnose atherosclerosis via wavelet transforms, principal component analysis and artificial neural networks. Expert Syst. Appl. 32, 632–640 (2007)CrossRefGoogle Scholar
  21. 21.
    S.V. Nalinaksh, D.S. Kumar, Artificial neural network design for fault identification in rotor-bearing system. Mech. Mach. Theory 36, 157–175 (2001)CrossRefGoogle Scholar
  22. 22.
    M. Kalkat, S. Yildirim, I. Uzmay, Rotor dynamics analysis of rotating machine systems using artificial neural networks. Int. J. Rotating Mach. 9, 255–262 (2003)CrossRefGoogle Scholar
  23. 23.
    J. Allen, A. Murray, Development of a neural network screening aid for diagnosing lower limb peripheral vascular disease from photoelectric plethysmography pulse waveforms. Physiol. Meas. 14, 13–22 (1993)CrossRefGoogle Scholar
  24. 24.
    W.G. Baxt, Use of an ANN for data analysis in clinical decision making, the diagnosis of acute coronary occlusion. Neural Comput. 2(4), 480–489 (1990)CrossRefGoogle Scholar

Copyright information

© ASM International 2016

Authors and Affiliations

  1. 1.Research Center in Industrial Technologies CRTICheraga, AlgiersAlgeria
  2. 2.Department of HydraulicUniversity of M’silaM’silaAlgeria

Personalised recommendations