Optimization of Stress-Strain Curves of WC-Co Two-Phase Materials by Artificial Neural Networks Method
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.
KeywordsArtificial Neural Networks Two-phase materials Stress-strain WC-Co
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