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Neural Computing and Applications

, Volume 31, Supplement 2, pp 707–721 | Cite as

Modeling the correlation between Charpy impact energy and chemical composition of functionally graded steels by artificial neural networks

  • Ali NazariEmail author
Original Article
  • 321 Downloads

Abstract

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. 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 Charpy impact energy of the corresponding layer. 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.

Keywords

Chemical composition Microhardness Charpy impact energy Crack divider Artificial neural networks ESR Ferritic FGS Austenitic FGS 

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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Department of Materials Engineering, Saveh BranchIslamic Azad UniversitySavehIran

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