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)


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.


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


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© 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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