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The applicability of neural network model to predict flow stress for carbon steels

Phaniraj, Prabhakar Madakasira and Lahiri, Ashok Kumar (2003) The applicability of neural network model to predict flow stress for carbon steels. In: Journal of Materials Processing Technology, 141 (2). pp. 219-227.

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A number of semi-empirical models are available in literature to predict flow stress of steel during hot deformation. In recent years, neural networks have also been used. Quantitative assessment of these models shows that the prediction errors range from 2 to 60% of the mean flow stress, when used over a range of strain rates $(2-120 s^-^1)$, temperatures (900–1100 °C) and strains until 0.8. A neural network model, which can be used to predict flow stress for carbon steels, ranging from 0.03 to 0.34%C, is proposed. The network is able to simulate the flow stress behavior with an average error of 3.7% of the mean flow stress using strain, strain rate, temperature and carbon equivalent as inputs. The network is able to interpolate not only over the domain of strain rates and temperatures but also over the domain of carbon equivalents in which it is trained.

Item Type: Journal Article
Publication: Journal of Materials Processing Technology
Publisher: Elsevier
Additional Information: Copyright of this article belongs to Elsevier.
Keywords: Neural network;Hot working;Carbon steels;Flow stress
Department/Centre: Division of Mechanical Sciences > Materials Engineering (formerly Metallurgy)
Date Deposited: 23 May 2007
Last Modified: 19 Sep 2010 04:38
URI: http://eprints.iisc.ac.in/id/eprint/11049

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