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Optimization of Stress-Strain Curves of WC-Co Two-Phase Materials by Artificial Neural Networks Method

  • Rabah TaoucheEmail author
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

In this study, an artificial neural networks method was used to optimize and predict the stress-strain curves as a function of second phase volume fraction of WC-Co two-phase materials deformed in compression and in tension. In order to train the artificial neural network, a set of different volume fractions of Co having different stress-strain curves of the WC-Co two-phase materials was used. A maximum of stress corresponding to each curve was obtained and used as a base to predict the theoretical stress-strain curve for no corresponding second phase volume fractions. The results of this study show that there is a good agreement between experimental and optimized stress-strain curves and the artificial neural networks method created is capable of successfully predicting the evolution of the stress-strain curves of WC-Co two-phase materials as a function of second phase volume fraction.

Keywords

Artificial Neural Networks Two-phase materials Stress-strain WC-Co 

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References

  1. 1.
    Mileiko, S.T.: The tensile strength and ductility of continuous composites. J. Mater Sci. 4, 974–977 (1969)CrossRefGoogle Scholar
  2. 2.
    Araki, K., Takada, Y., Nakoka, K.: Work hardening of continuously annealed dual phase steels. Trans. ISIJ 17, 710–717 (1977)Google Scholar
  3. 3.
    Durand, L., Thomas de Montpreville, C.: Etude du Comportement Mécanique des Matériaux Biphasés au moyen de la Méthode des Eléments Finis. Res Mechanica 29, 257–285 (1990)Google Scholar
  4. 4.
    Durand, L., Pastor, P.: Finite element method applied to tensile deformation of an equivolume two-phase alloy. Materials Characterization 29, 39–47 (1992)CrossRefGoogle Scholar
  5. 5.
    Berveiller, M., Zaoui, A.: A simplified self-consistent scheme for the plasticity of two-phase metals. Res. Mech. Lett. 1(3), 119–124 (1981)Google Scholar
  6. 6.
    Durand, L., Altibelli, A.: Application d’un Modèle Autocohérent à la Traction de Matériaux Biphasés à Grains Allongés. Rev. Mét CIT/SGM 90(12), 1593–1600 (1993)Google Scholar
  7. 7.
    Zhang, Z., Friedrich, K.: Artificial neural networks applied to polymer composites: a review. Composites Science and Technology 63, 2029–2044 (2003)CrossRefGoogle Scholar
  8. 8.
    Bahrami, A., Mousavi Anijdan, S.H., Ekrami, A.: Prediction of mechanical properties of DP steels using neural network model. Journal of Alloys and Compounds 392, 177–182 (2005)CrossRefGoogle Scholar
  9. 9.
    Koker, R., Altinkok, 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
  10. 10.
    Yescas, M.A.: Bhadeshia HKDH, Mackay DJ Estimation of the amount of retained austenite in austempered ductile irons using neural networks. Materials Science and Engineering A 311, 162–173 (2001)CrossRefGoogle Scholar
  11. 11.
    Doi, H., Fujiwara, Y., Miyake, K.: Mechanism of plastic deformation and dislocation damping of cemented carbides. Trans. Metall Soc. A I M E 245, 1457 (1969)Google Scholar
  12. 12.
    Nishimatsu, C., Gurland, J.: Experimental survey of the deformation of hard-ductile two-phase alloy system WC-Co. Trans. Am. Soc. Metals 52, 469 (1960)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Département des Sciences de la Matière, Faculté des SciencesUniversité du 20 août 1955 SkikdaSkikdaAlgeria

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