NDLI: Analytical modeling of tensile strength of functionally graded steels

Content Provider	Springer Nature Link
Author	Nazari, Ali
Copyright Year	2012
Abstract	In the present study, the tensile strength of ferritic and austenitic functionally graded steel produced by electroslag remelting has been modeled by artificial neural networks. 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, they were acquired. Finally, by applying the rule of mixtures, tensile strength of functionally graded steels configuration was found through a numerical method. The obtained results from the proposed model are in good agreement with those acquired from the experiments.
Starting Page	787
Ending Page	799
Page Count	13
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	23
Issue Number	3-4
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2012-06-29
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Functionally graded steel Microhardness, tensile strength Artificial neural networks ESR Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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