Treffer: Application of artificial neural networks for analytical modeling of Charpy impact energy of functionally graded steels

In the present study, the Charpy impact energy of ferritic and austenitic functionally graded steel produced by electroslag remelting has been modeled in crack divider configuration. To produce functionally graded steels, two slices of plain carbon steel and austenitic stainless steels were spot welded and used as electroslag remelting electrode. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with artificial neural networks. To build the model for graded ferritic and austenitic steels, training, testing and validation using, respectively, 174 and 120 experimental data were conducted. According to the input parameters, in the neural networks model, the Vickers microhardness of each layer was predicted. A good fit equation that correlates the Vickers microhardness of each layer to its corresponding chemical composition was achieved by the optimized network for both ferritic and austenitic graded steels. Afterward, the Vickers microhardness of each layer in functionally graded steels was related to the yield stress of the corresponding layer and by assuming Holloman relation for stress―strain curve of each layer, the area under each stress―strain curve was acquired. Finally, by applying the rule of mixtures, Charpy impact energy of functionally graded steels in crack divider configuration was found through numerical method. The obtained results from the proposed model are in good agreement with those acquired from the experiments.